title: Language Problems and ADHD Symptoms: How Specific Are the Links?

abstract: Symptoms of inattention and hyperactivity frequently co-occur with language difficulties in both clinical and community samples. We explore the specificity and strength of these associations in a heterogeneous sample of 254 children aged 5 to 15 years identified by education and health professionals as having problems with attention, learning and/or memory. Parents/carers rated pragmatic and structural communication skills and behaviour, and children completed standardised assessments of reading, spelling, vocabulary, and phonological awareness. A single dimension of behavioural difficulties including both hyperactivity and inattention captured behaviour problems. This was strongly and negatively associated with pragmatic communication skills. There was less evidence for a relationship between behaviour and language structure: behaviour ratings were more weakly associated with the use of structural language in communication, and there were no links with direct measures of literacy. These behaviour problems and pragmatic communication difficulties co-occur in this sample, but impairments in the more formal use of language that impact on literacy and structural communication skills are tied less strongly to behavioural difficulties. One interpretation is that impairments in executive function give rise to both behavioural and social communication problems, and additional or alternative deficits in other cognitive abilities impact on the development of structural language skills.


title: Australia's disabling income support system: tracing the history of the disability pension from 1908 to today

abstract: This article is based on a historical-comparative policy and discourse analysis of the principles underpinning the Australian disability income support system. It determines that these principles rely on a conception of disability that sustains a system of coercion and paternalism that perpetuates disability; this is referred to as disablism. The article examines the construction of disability in Australian income support across four major historical epochs spanning the period 1908-2007. Contextualisation of the policy trajectory and discourses of the contemporary disability pension regime for the time period 2008-now is also provided. The system was found to have perpetuated disablism through the generation of disability categories on the basis of normalcy and ableness as a condition of citizenship. Two major themes were found to have interacted with the ideology of disablism. The first theme - Commonwealth authority - set the tone for legitimising the regulation of disabled citizens. The second theme - conservative sanctioned paternalism and coercion - reflected the tensions between the paternalistic concern for income support provision while attempting to prevent idleness and welfare dependency. This article argues that a non-disabling provision based on social citizenship, rather than responsible or productive citizenship, counters the tendency for authoritarian and paternal approaches.


title: Robotic Exoskeletons: A Perspective for the Rehabilitation of Arm Coordination in Stroke Patients

abstract: Upper-limb impairment after stroke is caused by weakness, loss of individual joint control, spasticity, and abnormal synergies. Upper-limb movement frequently involves abnormal, stereotyped, and fixed synergies, likely related to the increased use of sub-cortical networks following the stroke. The flexible coordination of the shoulder and elbow joints is also disrupted. New methods for motor learning, based on the stimulation of activity-dependent neural plasticity have been developed. These include robots that can adaptively assist active movements and generate many movement repetitions. However, most of these robots only control the movement of the hand in space. The aim of the present text is to analyze the potential of robotic exoskeletons to specifically rehabilitate joint motion and particularly inter-joint coordination. First, a review of studies on upper-limb coordination in stroke patients is presented and the potential for recovery of coordination is examined. Second, issues relating to the mechanical design of exoskeletons and the transmission of constraints between the robotic and human limbs are discussed. The third section considers the development of different methods to control exoskeletons: existing rehabilitation devices and approaches to the control and rehabilitation of joint coordinations are then reviewed, along with preliminary clinical results available. Finally, perspectives and future strategies for the design of control mechanisms for rehabilitation exoskeletons are discussed.


title: Pulsar profile denoising using kernel regression based on maximum correntropy criterion

abstract: Abstract A pulsar profile denoising method using kernel regression based on maximum correntropy criterion is proposed. This method uses the kernel regression to reduce the human visual inspection inescapable in the current profile denoising methods and the reliance of the prior knowledge on the profile of interest. In order to cope with the non-Gaussian case that is common in a real application, the maximum correntropy criterion is introduced into the kernel regression to resist the impact of non-Gaussian noise. The performance of the prosed method is verified via simulation and real data. The results have shown that the proposed method outperforms the current signal denoising methods in a non-Gaussian environment and is readily to be applied.


title: To better stand on the shoulder of giants

abstract: Usually scientists breed research ideas inspired by previous publications, but they are unlikely to follow all publications in the unbounded literature collection. The volume of literature keeps on expanding extremely fast, whilst not all papers contribute equal impact to the academic society. Being aware of potentially influential literature would put one in an advanced position in choosing important research references. Hence, estimation of potential influence is of great significance. We study a challenging problem of identifying potentially influential literature. We examine a set of hypotheses on what are the fundamental characteristics for highly cited papers and find some interesting patterns. Based on these observations, we learn to identify potentially influential literature via Future Influence Prediction (FIP), which aims to estimate the future influence of literature. The system takes a series of features of a particular publication as input and produces as output the estimated citation counts of that article after a given time period. We consider several regression models to formulate the learning process and evaluate their performance based on the coefficient of determination (R2). Experimental results on a real-large data set show a mean average predictive performance of 83.6% measured in R^2. We apply the learned model to the application of bibliography recommendation and obtain prominent performance improvement in terms of Mean Average Precision (MAP).


title: A Semi-automatic Method for Efficient Detection of Stories on Social Media

abstract: Twitter has become one of the main sources of news for many people. As real-world events and emergencies unfold, Twitter is abuzz with hundreds of thousands of stories about the events. Some of these stories are harmless, while others could potentially be life-saving or sources of malicious rumors. Thus, it is critically important to be able to efficiently track stories that spread on Twitter during these events. In this paper, we present a novel semi-automatic tool that enables users to efficiently identify and track stories about real-world events on Twitter. We ran a user study with 25 participants, demonstrating that compared to more conventional methods, our tool can increase the speed and the accuracy with which users can track stories about real-world events.


title: Distributional Random Oversampling for Imbalanced Text Classification

abstract: The accuracy of many classification algorithms is known to suffer when the data are imbalanced (i.e., when the distribution of the examples across the classes is severely skewed). Many applications of binary text classification are of this type, with the positive examples of the class of interest far outnumbered by the negative examples. Oversampling (i.e., generating synthetic training examples of the minority class) is an often used strategy to counter this problem. We present a new oversampling method specifically designed for classifying data (such as text) for which the distributional hypothesis holds, according to which the meaning of a feature is somehow determined by its distribution in large corpora of data. Our Distributional Random Oversampling method generates new random minority-class synthetic documents by exploiting the distributional properties of the terms in the collection. We discuss results we have obtained on the Reuters-21578, OHSUMED-S, and RCV1-v2 datasets.


title: Vertical Nanowire Gate-All-Around p-type Tunneling Field-Effect Transistor With Si 0.8 Ge 0.2 /Si Heterojunction

abstract: We present a CMOS compatible p-type gate-all-around (GAA) vertical silicon nanowire tunneling field effect transistor (TFET) featuring Si0.8Ge0.2 source with silicon channel. Besides heterojunction on source side, the highly abrupt doping profile at source-to-channel junction is achieved by low temperature dopant segregation. The fabricated devices display subthreshold slope (SS) as low as 30mV/dec over one decade of drain current, which remains below 60 mV/dec over 3 decades. In addition, our TFET showed reasonable Ion/Ioff ratio of (10 4 ) and low drain induced barrier lowering (DIBL) of 40 mV/V.


title: Audio immersion in games — a case study using an online game with background music and sound effects

abstract: Immersion is term that is often used when describing how good a game is. However, there are different concepts of immersion and different opinions on what causes a player to become immersed. Previous studies have shown that immersion is an area of game development that has attracted great interest from gamers and researchers. Studies into areas such as player immersion have successfully shown that players can become immersed through graphics and audio features of games. Audio immersion is often overlooked in place of graphics and there is a general assumption that a game with high-quality graphics is more immersive than one with simple graphics.


title: High-Resolution Multifocal Pupillographic Objective Perimetry in Glaucoma

abstract: PURPOSE. The recent development of an objective and noninvasive perimetric technique using pupillary responses to sparse multifocal visual stimuli shows promise for the assessment of visual function in glaucoma. This study assesses the sensitivity and specificity of four variants of dichoptic multifocal pupillographic objective perimetry (mfPOP) with a highresolution, 40-region/field stimulus. METHODS. Nineteen normal subjects and 17 with open-angle glaucoma were tested with four 4-minute stimulus protocols, presented in eight segments of 30 seconds each. Achromatic multifocal stimuli comprised 40 test regions per eye arranged in a four-ring dartboard layout subtending 60° of visual field. Background luminance was 10 cd/m 2 with active stimulus regions displaying steady or flickered stimuli at 290 cd/m 2 . Stimulus durations were between 33 and 150 ms, mean intervals between presentations to each test region ranged from 1 to 16 seconds. Fixation was monitored in real time. RESULTS. Longer mean intervals and durations resulted in better diagnostic performance. Best results were obtained with 150-ms flickered stimuli and a discriminant function that incorporated both amplitude and width of responses: ROC area under the curve 0.86 0.05 (mean SE) across all visual field severities, (n 34) and 1.00 0.00 for moderate and severe fields (n 10). CONCLUSIONS. mfPOP produces separate information on response delay and afferent and efferent defects at every point in the field. The diagnostic accuracy of the 40-region, 150-ms stimulus is comparable to that of commonly used subjective perimeters and encourages further investigation of this technique. (Invest Ophthalmol Vis Sci. 2011;52:604‐610) DOI:10.1167/iovs.10-5737


title: Detecting cognitive impairments by agreeing on interpretations of linguistic features

abstract: Linguistic features have shown promising applications for detecting various cognitive impairments. To improve detection accuracies, increasing the amount of data or the number of linguistic features have been two applicable approaches. However, acquiring additional clinical data can be expensive, and hand-crafting features is burdensome. In this paper, we take a third approach, proposing Consensus Networks (CNs), a framework to classify after reaching agreements between modalities. We divide linguistic features into non-overlapping subsets according to their modalities, and let neural networks learn low-dimensional representations that agree with each other. These representations are passed into a classifier network. All neural networks are optimized iteratively. In this paper, we also present two methods that improve the performance of CNs. We then present ablation studies to illustrate the effectiveness of modality division. To understand further what happens in CNs, we visualize the representations during training. Overall, using all of the 413 linguistic features, our models significantly outperform traditional classifiers, which are used by the state-of-the-art papers.


title: LTPS-TFT Pixel Circuit to Compensate for OLED Luminance Degradation in Three-Dimensional AMOLED Display

abstract: A new low-temperature polysilicon thin-film-transistor pixel circuit for 3-D active-matrix organic light-emitting diode (OLED) display and its driving scheme that is based on simultaneous emission at a frame rate of 240 Hz are proposed. The proposed circuit can compensate for the threshold voltage deviation of TFTs and power-line IR-drops. Moreover, the degradation of OLED luminance is reduced using a feedback structure. The measurement results of the relationship between the luminance degradation and the threshold voltage shift of the OLED confirmed the stability of the luminance of the proposed circuit.


title: Unsupervised detection of music boundaries by time series structure features

abstract: Locating boundaries between coherent and/or repetitive segments of a time series is a challenging problem pervading many scientific domains. In this paper we propose an unsupervised method for boundary detection, combining three basic principles: novelty, homogeneity, and repetition. In particular, the method uses what we call structure features, a representation encapsulating both local and global properties of a time series. We demonstrate the usefulness of our approach in detecting music structure boundaries, a task that has received much attention in recent years and for which exist several benchmark datasets and publicly available annotations. We find our method to significantly outperform the best accuracies published so far. Importantly, our boundary approach is generic, thus being applicable to a wide range of time series beyond the music and audio domains.


title: Ball Localization for Robocup Soccer Using Convolutional Neural Networks

abstract: In RoboCup soccer, ball localization is an important and challenging task, especially since the last change of the rule which allows 50% of the ball’s surface to be of any color or pattern while the rest must remain white. Multi-color balls have changing color histograms and patterns in dependence of the current orientation and movement. This paper presents a neural approach using a convolutional neural network (CNN) to localize the ball in various scenes. CNNs were used in several image recognition tasks, particularly because of their capability to learn invariances in images. In this work we use CNNs to locate a ball by training two output layers, representing the x- and y-coordinates, with normal distributions fitted around the ball. Therefore the network not only locates the ball’s position but also provides an estimation of the noise. The architecture processes the whole image in full size, no sliding-window approach is used.


title: Indian Regional Movie Dataset for Recommender Systems

abstract: Indian regional movie dataset is the first database of regional Indian movies, users and their ratings. It consists of movies belonging to 18 different Indian regional languages and metadata of users with varying demographics. Through this dataset, the diversity of Indian regional cinema and its huge viewership is captured. We analyze the dataset that contains roughly 10K ratings of 919 users and 2,851 movies using some supervised and unsupervised collaborative filtering techniques like Probabilistic Matrix Factorization, Matrix Completion, Blind Compressed Sensing etc. The dataset consists of metadata information of users like age, occupation, home state and known languages. It also consists of metadata of movies like genre, language, release year and cast. India has a wide base of viewers which is evident by the large number of movies released every year and the huge box-office revenue. This dataset can be used for designing recommendation systems for Indian users and regional movies, which do not, yet, exist. The dataset can be downloaded from \href{https://goo.gl/EmTPv6}{this https URL}.


Assessment of the adaptive potential of the mother-infant system: the neonatal perception inventories.


title: Social Media Analytics: An Interdisciplinary Approach and Its Implications for Information Systems

abstract: In this contribution, we introduce “social media analytics” (SMA) as an emerging interdisciplinary research field that, in our view, will have a significant impact on social media-related future research from across different academic disciplines. Despite a number of challenges, we argue that SMA can provide other disciplines – including IS – with methodological foundations for research that focuses on social media. Furthermore, we believe that SMA can help IS research to develop decision-making or decision-aiding frameworks by tackling the issue of social media-related performance measurement, which has been challenging until now. Moreover, SMA can provide architectural designs and solution frameworks for new social media-based applications and information systems. Finally, we call for an interdisciplinary SMA research agenda as well as a significantly increased level of interdisciplinary research co-operation, which must aim to generate significant advancements in scientific methods for analyzing social media, as well as to answer research questions from across different disciplines. Copyright Springer Fachmedien Wiesbaden 2014


title: Control of Microgrids

abstract: The motivation to develop microgrids, as a particular form of active networks is explained and presented as an effective solution for the control of grids with high levels of distibuted energy resources. The operation, more in particular the voltage and frequency control, is discussed. Control concepts useful with microgrids are detailed and implemented. Besides technical control aspects, also economical ones are developed. Primary, secondary and tertiary control algorithms are designed operating in a completely distributed way. The theoretical concepts are tested in a extensive laboratory experiment implementing a realistic scenario by using a setup of four inverters able to communicate through an Internet connection.


title: ‘Technology Mining’ bibliometrics analysis: applying network analysis and cluster analysis

abstract: According to advances in text mining methods and tools, technology mining, or its brevity ‘tech mining’, is one of recent research areas progressively emerged in technology management area. Over past two decades, this area has been attractive for many scholars in business management, technology management, and computer science departments. The majority of tech mining applications is concentrated on analyzing patents which is also called patent mining by some scholars; moreover, there are some researchers reported tech mining applied to other types of technological documents like RD there is a jump in the number of publications after 2005 and a huge rise in the number of citation after 2012.


title: Long Short-Term Memory Neural Networks for Chinese Word Segmentation

abstract: Currently most of state-of-the-art methods for Chinese word segmentation are based on supervised learning, whose features aremostly extracted from a local context. Thesemethods cannot utilize the long distance information which is also crucial for word segmentation. In this paper, we propose a novel neural network model for Chinese word segmentation, which adopts the long short-term memory (LSTM) neural network to keep the previous important information inmemory cell and avoids the limit of window size of local context. Experiments on PKU, MSRA and CTB6 benchmark datasets show that our model outperforms the previous neural network models and state-of-the-art methods.


title: Wireless intrabody communication sensor node realized using PSoC microcontroller

abstract: The aim of this paper is the development of a frequency modulated signal generator and receiver for intrabody communication (IBC) using Programmable System-on-Chip (PSoC) microcontrollers by Cypress. The program is developed in Cypress PSoC Creator and PSoC Designer programs for the CY8C27643 — 24PVXI and CY8C5888LTI — LP097 microcontrollers. Based on the input received from the serial bus, the signal generator synthesizes a FSK signal using digital-to-analog conversion. The signal is transmitted between transmitter and receiver IBC electrodes placed on the human body. The received signal is then demodulated using low pass and band pass filters and a correlator and forwarded back to the computer using the serial bus.


title: Understanding Power and Rules of Thumb for Determining Sample Sizes

abstract: This article addresses the definition of power and its relationship to Type I and Type II errors. We discuss the relationship of sample size and power. Finally, we offer statistical rules of thumb guiding the selection of sample sizes large enough for sufficient power to detecting differences, associations, chi‐square, and factor analyses.


title: Assessing sentence scoring techniques for extractive text summarization

abstract: Abstract Text summarization is the process of automatically creating a shorter version of one or more text documents. It is an important way of finding relevant information in large text libraries or in the Internet. Essentially, text summarization techniques are classified as Extractive and Abstractive. Extractive techniques perform text summarization by selecting sentences of documents according to some criteria. Abstractive summaries attempt to improve the coherence among sentences by eliminating redundancies and clarifying the contest of sentences. In terms of extractive summarization, sentence scoring is the technique most used for extractive text summarization. This paper describes and performs a quantitative and qualitative assessment of 15 algorithms for sentence scoring available in the literature. Three different datasets (News, Blogs and Article contexts) were evaluated. In addition, directions to improve the sentence extraction results obtained are suggested.


title: A comprehensive review of permanent magnet transverse flux machines for direct drive applications

abstract: The use of direct drive machines in renewable and industrial applications are increasing at a rapid rate. Transverse flux machines (TFM) are ideally suited for direct drive applications due to their high torque density. In this paper, a comprehensive review of the permanent magnet (PM) TFMs for direct drive applications is presented. The paper introduces TFMs and their operating principle and then reviews the different type of TFMs proposed in the literature. The TFMs are categorized according to the number of stator sides, types of stator cores and magnet arrangement in the rotor. The review covers different design topologies, materials used for manufacturing, structural and thermal analysis, modeling and design optimization and cogging torque minimization in TFMs. The paper also reviews various applications and comparisons for TFMs that have been presented in the literature.


title: Central lobe epilepsy surgery – (functional) results and how to evaluate them

abstract: Abstract Object To evaluate whether central lobe epilepsy (CLE) surgery in the pericentral area implies inevitable function loss and to determine how postsurgical functional outcomes are perceived by the patient. Methods We included all 22 people with epilepsy (PWE) who received central lobe epilepsy (CLE) surgery in the pre- and postcentral gyri between 1995 and 2015 in the University Medical Center Utrecht. We determined function loss and followed-up on quality of life (AQoL-8D), mobility (Rivermead Mobility Index RMI) and self-evaluation of the surgery. To compare this with the literature, a systematic review was conducted, with specific regard for studies that included functional outcome. Results Our own cohort showed newly developed functional loss in 54.4% postoperatively. Follow-up questionnaires were returned by 11/19 PWE (the other 3 could not be contacted). The mean AQoL-8d score was 0.74 (SD 0.16) and the mean RMI score was 13.7 (SD 3.0). This mean AQoL-8d was slightly lower than the Western mean population scores (0.86 and 0.87 respectively). RMI scores and postoperative functional deficits were both significantly related to how well PWE scored on the AQoL-8d. 72.7% of the PWE became seizure free after surgery (Engel class 1A). All PWE were happy with the CLE surgery and would recommend this type of surgery to other PWE. Becoming seizure-free, gaining better functioning and having more energy were reported as the most important reasons. The literature provided 475 unique papers, of which 25 were selected for critical appraisal. Six studies were of adequate quality and provided sufficient information to extract results. Prevalence of postoperative neurological deficit varied between 0 and 50%. No information is given on patient’s perceptions. Conclusions About half of central lobe resections do not result in new neurological deficits. The patient’s perspective is important in CLE surgery, but neglected in the literature. PWE may report being satisfied with the results of surgery despite new deficits and impact on quality of life. Counseling in CLE surgery should take these findings into account. Neurologists and neurosurgeons should not by default refrain from CLE surgery and think a PWE will not accept a deficit.


title: Evaluating the Online Capabilities of Onset Detection Methods

abstract: In this paper, we evaluate various onset detection algorithms in terms of their online capabilities. Most methods use some kind of normalization over time, which renders them unusable for online tasks. We modified existing methods to enable online application and evaluated their performance on a large dataset consisting of 27,774 annotated onsets. We focus particularly on the incorporated preprocessing and peak detection methods. We show that, with the right choice of parameters, the maximum achievable performance is in the same range as that of offline algorithms, and that preprocessing can improve the results considerably. Furthermore, we propose a new onset detection method based on the common spectral flux and a new peak-picking method which outperforms traditional methods both online and offline and works with audio signals of various volume levels.


Journal of the Association for Information Systems Abstract Research Article Information Privacy Concerns: Linking Individual Perceptions with Institutional Privacy Assurances


Social Attitudes of AI Rebellion: A Framework.


title: Visual query expansion with or without geometry: Refining local descriptors by feature aggregation

abstract: This paper proposes a query expansion technique for image search that is faster and more precise than the existing ones. An enriched representation of the query is obtained by exploiting the binary representation offered by the Hamming Embedding image matching approach: The initial local descriptors are refined by aggregating those of the database, while new descriptors are produced from the images that are deemed relevant. The technique has two computational advantages over other query expansion techniques. First, the size of the enriched representation is comparable to that of the initial query. Second, the technique is effective even without using any geometry, in which case searching a database comprising 105k images typically takes 80 ms on a desktop machine. Overall, our technique significantly outperforms the visual query expansion state of the art on popular benchmarks. It is also the first query expansion technique shown effective on the UKB benchmark, which has few relevant images per query.


title: A fast and efficient sift detector using the mobile GPU

abstract: Emerging mobile applications, such as augmented reality, demand robust feature detection at high frame rates. We present an implementation of the popular Scale-Invariant Feature Transform (SIFT) feature detection algorithm that incorporates the powerful graphics processing unit (GPU) in mobile devices. Where the usual GPU methods are inefficient on mobile hardware, we propose a heterogeneous dataflow scheme. By methodically partitioning the computation, compressing the data for memory transfers, and taking into account the unique challenges that arise out of the mobile GPU, we are able to achieve a speedup of 4-7x over an optimized CPU version, and a 6.4x speedup over a published GPU implementation. Additionally, we reduce energy consumption by 87 percent per image. We achieve near-realtime detection without compromising the original algorithm.


title: Mechanical force antagonizes the inhibitory effects of RecX on RecA filament formation in Mycobacterium tuberculosis

abstract: Efficient bacterial recombinational DNA repair involves rapid cycles of RecA filament assembly and disassembly. The RecX protein plays a crucial inhibitory role in RecA filament formation and stability. As the broken ends of DNA are tethered during homologous search, RecA filaments assembled at the ends are likely subject to force. In this work, we investigated the interplay between RecX and force on RecA filament formation and stability. Using magnetic tweezers, at single molecular level, we found that Mycobacterium tuberculosis (Mt) RecX could catalyze stepwise de-polymerization of preformed MtRecA filament in the presence of ATP hydrolysis at low forces (<7 pN). However, applying larger forces antagonized the inhibitory effects of MtRecX, and a partially de-polymerized MtRecA filament could repolymerize in the presence of MtRecX, which cannot be explained by previous models. Theoretical analysis of force-dependent conformational free energies of naked ssDNA and RecA nucleoprotein filament suggests that mechanical force stabilizes RecA filament, which provides a possible mechanism for the observation. As the antagonizing effect of force on the inhibitory function of RecX takes place in a physiological range; these findings broadly suggest a potential mechanosensitive regulation during homologous recombination.


title: Unrealistic optimism about susceptibility to health problems

abstract: In this study, 100 college students compared their own chances of experiencing 45 different health- and life-threatening problems with the chances of their peers. They showed a significant optimistic bias for 34 of these hazards, consistently considering their own chances to be below average. Attempts to account for the amount of bias evoked by different hazards identified perceived controllability, lack of previous experience, and the belief that the problem appears during childhood as factors that tend to increase unrealistic optimism. The investigation also examined the importance of beliefs and emotions as determinants of self-reported interest in adopting precautions to reduce one's risk. It found that: (a) beliefs about risk likelihood, beliefs about risk severity, and worry about the risk all made independent contributions to interest in risk reduction; (b) unrealistic optimism undermined interest in risk reduction indirectly by decreasing worry; and (c) beliefs about risk likelihood and severity were not sufficient to explain the amount of worry expressed about different hazards.


title: Accumulation of plastic-derived chemicals in tissues of seabirds ingesting marine plastics

abstract: Abstract We analyzed polybrominated diphenyl ethers (PBDEs) in abdominal adipose of oceanic seabirds (short-tailed shearwaters, Puffinus tenuirostris) collected in northern North Pacific Ocean. In 3 of 12 birds, we detected higher-brominated congeners (viz., BDE209 and BDE183), which are not present in the natural prey (pelagic fish) of the birds. The same compounds were present in plastic found in the stomachs of the 3 birds. These data suggested the transfer of plastic-derived chemicals from ingested plastics to the tissues of marine-based organisms.


title: A McKinsey 7S Model-Based Framework for ERP Readiness Assessment

abstract: Implementing Enterprise Resource Planning ERP systems is a complex, lengthy and expensive process which usually faces serious challenges and failures. Thus, it is necessary to perform a readiness assessment at the initial stage of an ERP implementation project to identify weakness areas which may encounter the project with failure. This paper proposes a new framework for assessing readiness of an organization to implement the ERP project on the basis of McKinsey 7S model using confirmatory factor analysis. Through this method, the construct of ERP readiness is proposed based on 7 dimensions namely "structure", "strategy", "systems", "skills", "style/culture", "staff", and "shared values/ superordinate goals". Using the framework, the current situation of the organization can be determined and necessary changes can be made prior to system implementation. The proposed framework is then applied to 2 real Iranian banking cases and the advantages of the framework over available frameworks are illustrated.


title: Developmental traumatology part II: brain development

abstract: Abstract Background: Previous investigations suggest that maltreated children with a diagnosis of posttraumatic stress disorder (PTSD) evidence alterations of biological stress systems. Increased levels of catecholaminergic neurotransmitters and steroid hormones during traumatic experiences in childhood could conceivably adversely affect brain development. Methods: In this study, 44 maltreated children and adolescents with PTSD and 61 matched controls underwent comprehensive psychiatric and neuropsychological assessments and an anatomical magnetic resonance imaging (MRI) brain scan. Results: PTSD subjects had smaller intracranial and cerebral volumes than matched controls. The total midsagittal area of corpus callosum and middle and posterior regions remained smaller; while right, left, and total lateral ventricles were proportionally larger than controls, after adjustment for intracranial volume. Brain volume robustly and positively correlated with age of onset of PTSD trauma and negatively correlated with duration of abuse. Symptoms of intrusive thoughts, avoidance, hyperarousal or dissociation correlated positively with ventricular volume, and negatively with brain volume and total corpus callosum and regional measures. Significant gender by diagnosis effect revealed greater corpus callosum area reduction in maltreated males with PTSD and a trend for greater cerebral volume reduction than maltreated females with PTSD. The predicted decrease in hippocampal volume seen in adult PTSD was not seen in these subjects. Conclusions: These data suggest that the overwhelming stress of maltreatment experiences in childhood is associated with adverse brain development.


title: A versatile and modular capacitive tactile proximity sensor

abstract: In this work we discuss the design and realization of a novel capacitive tactile proximity sensor (CTPS) with applications in robotics and consumer-electronics. The current concept is an advancement with respect to a previous design developed at our lab. Experience in the development of applications based on the old sensor, such as manipulation and safe HRI, helped determining the requirements. The new sensor is capable of operating in a self- and mutual-capacitive proximity mode and in a mutual-capacitive tactile mode. The issue of having a trade-off between spatial resolution and sensing range in proximity mode due to the capacitive measurement principle is addressed. A circuit is designed to dynamically select or join electrodes involved in the proximity measurements, a technique which at the same time allows the implementation of the spatial resolution in the tactile mode. Also, all signal processing is done on board of the prototype sensor module, thus complete modularity is achieved.


title: 'Finding the person you really are … on the inside': Compassion focused therapy for adults with intellectual disabilities

abstract: This study utilized a mixed methods approach to examine the feasibility and acceptability of group compassion focused therapy for adults with intellectual disabilities (CFT-ID). Six participants with mild ID participated in six sessions of group CFT, specifically adapted for adults with ID. Session-by-session feasibility and acceptability measures suggested that participants understood the group content and process and experienced group sessions and experiential practices as helpful and enjoyable. Thematic analysis of focus groups identified three themes relating to (1) direct experiences of the group, (2) initial difficulties in being self-compassionate and (3) positive emotional changes. Pre- and post-group outcome measures indicated significant reductions in both self-criticism and unfavourable social comparisons. Results suggest that CFT can be adapted for individuals with ID and provide preliminary evidence that people with ID and psychological difficulties may experience a number of benefits from th...


title: An empirical study of tokenization strategies for biomedical information retrieval

abstract: Due to the great variation of biological names in biomedical text, appropriate tokenization is an important preprocessing step for biomedical information retrieval. Despite its importance, there has been little study on the evaluation of various tokenization strategies for biomedical text. In this work, we conducted a careful, systematic evaluation of a set of tokenization heuristics on all the available TREC biomedical text collections for ad hoc document retrieval, using two representative retrieval methods and a pseudo-relevance feedback method. We also studied the effect of stemming and stop word removal on the retrieval performance. As expected, our experiment results show that tokenization can significantly affect the retrieval accuracy; appropriate tokenization can improve the performance by up to 96%, measured by mean average precision (MAP). In particular, it is shown that different query types require different tokenization heuristics, stemming is effective only for certain queries, and stop word removal in general does not improve the retrieval performance on biomedical text.


title: Marked conservation of complementarity-determining region 3 of the beta-chain of TCRs recognizing a mycobacterial heat shock protein 60-derived peptide with strong sequence similarity to human heat shock protein 60.

abstract: The variable gene usage and sequence of human TCRs specific for a particular MHC/peptide combination have been investigated. The peptide comprises amino acids 456-466 of the 65-kDa Mycobacterium tuberculosis heat shock protein (hsp60), and is recognized in the context of HLA-DP. TCRs from both synovial fluid and peripheral blood (PB)-derived T cell clones used only five different V beta genes, three of which are closely related (V beta 6.7a, V beta 6.7b, and V beta 21.3). Among TCRs using these three genes there was marked conservation of the beta-chain sequence, whereby complementarity-determining region 3 (CDR3) contained an amino acid motif (*R*G*, amino acids 96-100) in association with either J beta 1.4 or J beta 2.5. These conclusions were strengthened by analysis of peptide-stimulated T cell lines that revealed not only TCR beta-chain sequences identical with those seen in T cell clones, but also additional beta-chains with similar CDR3 region sequences and J gene usage. In contrast, T cell lines derived by using IL-2 or a control peptide revealed variable usage of V beta and J beta genes; V beta 6.7a/b sequences from these lines and from freshly isolated PB did not contain the CDR3 motif noted in TCRs from Ag-specific T cells. We suggest that the remarkably limited diversity of TCRs noted in this study is a consequence of the similarity between the mycobacterial hsp60 peptide and the equivalent peptide from human hsp60, and reflects the trimming of the TCR repertoire required to maintain self-tolerance.


title: RevUP: Automatic Gap-Fill Question Generation from Educational Texts

abstract: This paper describes RevUP which deals with automatically generating gap-fill questions. RevUP consists of 3 parts: Sentence Selection, Gap Selection & Multiple Choice Distractor Selection. To select topicallyimportant sentences from texts, we propose a novel sentence ranking method based on topic distributions obtained from topic models. To select gap-phrases from each selected sentence, we collected human annotations, using the Amazon Mechanical Turk, on the relative relevance of candidate gaps. This data is used to train a discriminative classifier to predict the relevance of gaps, achieving an accuracy of 81.0%. Finally, we propose a novel method to choose distractors that are semantically similar to the gap-phrase and have contextual fit to the gap-fill question. By crowdsourcing the evaluation of our method through the Amazon Mechanical Turk, we found that 94% of the distractors selected were good. RevUP fills the semantic gap left open by previous work in this area, and represents a significant step towards automatically generating quality tests for teachers and self-motivated learners.


title: A compact dual-band half-mode SIW based semi-circular antenna

abstract: In this paper, a half mode SIW based semi-circular antenna with co-axial feed has been designed for dual band operation. The fundamental resonating frequency is found to operate at 4.66 GHz while the next higher mode is at 8.3 GHz. The antenna performance is optimized through parametric studies and the dual band antenna is designed using Arlon AD270 substrate. Simulation studies have been carried out to study the various antenna parameters.


title: The impact of machine learning techniques in the study of bipolar disorder: A systematic review

abstract: Abstract Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder. We systematically searched PubMed, Embase and Web of Science for articles published in any language up to January 2017. We found 757 abstracts and included 51 studies in our review. Most of the included studies used multiple levels of biological data to distinguish the diagnosis of bipolar disorder from other psychiatric disorders or healthy controls. We also found studies that assessed the prediction of clinical outcomes and studies using unsupervised machine learning to build more consistent clinical phenotypes of bipolar disorder. We concluded that given the clinical heterogeneity of samples of patients with BD, machine learning techniques may provide clinicians and researchers with important insights in fields such as diagnosis, personalized treatment and prognosis orientation.


title: Scripting a refactoring with Rascal and Eclipse

abstract: To facilitate experimentation with creating new, complex refactorings, we want to reuse existing transformation and analysis code as orchestrated parts of a larger refactoring: i.e., to script refactorings. The language we use to perform this scripting must be able to deal with the diversity of languages, tools, analyses, and transformations that arise in practice. To illustrate one solution to this problem, in this paper we describe, in detail, a specific refactoring script for switching from the Visitor design pattern to the Interpreter design pattern. This script, written in the meta-programming language Rascal, and targeting an interpreter written in Java, extracts facts from the interpreter code using the Eclipse JDT, performs the needed analysis in Rascal, and then transforms the interpreter code using a combination of Rascal code and existing JDT refactorings. Using this script we illustrate how a new, real and complex refactoring can be scripted in a few hundred lines of code and within a short timeframe. We believe the key to successfully building such refactorings is the ability to pair existing tools, focused on specific languages, with general-purpose meta-programming languages.


Automatic Soccer Player Tracking in Single Camera with Robust Occlusion Handling Using Attribute Matching


title: Study of PEV Charging on Residential Distribution Transformer Life

abstract: Due to the characteristics of electric power generation, transmission and distribution in the U.S., experts have identified local distribution as a likely part of the chain to be adversely affected by unregulated PEV (Plug-in Electric Vehicle) charging. This paper presents a study performed to assess the impact of PEV charging on a local residential distribution transformer.


Design Thinking implemented in Software Engineering Tools Proposing and Applying the Design Thinking Transformation Framework


title: Learning Human Pose Estimation Features with Convolutional Networks

abstract: Abstract: This paper introduces a new architecture for human pose estimation using a multi- layer convolutional network architecture and a modified learning technique that learns low-level features and higher-level weak spatial models. Unconstrained human pose estimation is one of the hardest problems in computer vision, and our new architecture and learning schema shows significant improvement over the current state-of-the-art results. The main contribution of this paper is showing, for the first time, that a specific variation of deep learning is able to outperform all existing traditional architectures on this task. The paper also discusses several lessons learned while researching alternatives, most notably, that it is possible to learn strong low-level feature detectors on features that might even just cover a few pixels in the image. Higher-level spatial models improve somewhat the overall result, but to a much lesser extent then expected. Many researchers previously argued that the kinematic structure and top-down information is crucial for this domain, but with our purely bottom up, and weak spatial model, we could improve other more complicated architectures that currently produce the best results. This mirrors what many other researchers, like those in the speech recognition, object recognition, and other domains have experienced.


title: DeepSearch: A Fast Image Search Framework for Mobile Devices

abstract: Content-based image retrieval (CBIR) is one of the most important applications of computer vision. In recent years, there have been many important advances in the development of CBIR systems, especially Convolutional Neural Networks (CNNs) and other deep-learning techniques. On the other hand, current CNN-based CBIR systems suffer from high computational complexity of CNNs. This problem becomes more severe as mobile applications become more and more popular. The current practice is to deploy the entire CBIR systems on the server side while the client side only serves as an image provider. This architecture can increase the computational burden on the server side, which needs to process thousands of requests per second. Moreover, sending images have the potential of personal information leakage. As the need of mobile search expands, concerns about privacy are growing. In this article, we propose a fast image search framework, named DeepSearch, which makes complex image search based on CNNs feasible on mobile phones. To implement the huge computation of CNN models, we present a tensor Block Term Decomposition (BTD) approach as well as a nonlinear response reconstruction method to accelerate the CNNs involving in object detection and feature extraction. The extensive experiments on the ImageNet dataset and Alibaba Large-scale Image Search Challenge dataset show that the proposed accelerating approach BTD can significantly speed up the CNN models and further makes CNN-based image search practical on common smart phones.


title: Indirect measures of gene flow and migration: FST not equal to 1/(4Nm + 1).

abstract: The difficulty of directly measuring gene flow has lead to the common use of indirect measures extrapolated from genetic frequency data. These measures are variants of FST, a standardized measure of the genetic variance among populations, and are used to solve for Nm, the number of migrants successfully entering a population per generation. Unfortunately, the mathematical model underlying this translation makes many biologically unrealistic assumptions; real populations are very likely to violate these assumptions, such that there is often limited quantitative information to be gained about dispersal from using gene frequency data. While studies of genetic structure per se are often worthwhile, and FST is an excellent measure of the extent of this population structure, it is rare that FST can be translated into an accurate estimate of Nm.


title: A practical algorithm for rendering interreflections with all-frequency BRDFs

abstract: Algorithms for rendering interreflection (or indirect illumination) effects often make assumptions about the frequency range of the materials' reflectance properties. For example, methods based on Virtual Point Lights (VPLs) perform well for diffuse and semi-glossy materials but not so for highly glossy or specular materials; the situation is reversed for methods based on ray tracing. In this article, we present a practical algorithm for rendering interreflection effects with all-frequency BRDFs. Our method builds upon a spherical Gaussian representation of the BRDF, based on which a novel mathematical development of the interreflection equation is made. This allows us to efficiently compute one-bounce interreflection from a triangle to a shading point, by using an analytic formula combined with a piecewise linear approximation. We show through evaluation that this method is accurate for a wide range of BRDFs. We further introduce a hierarchical integration method to handle complex scenes (i.e., many triangles) with bounded errors. Finally, we have implemented the present algorithm on the GPU, achieving rendering performance ranging from near interactive to a few seconds per frame for various scenes with different complexity.


title: Twenty-year-old stapled pouches for ulcerative colitis without evidence of rectal cancer: implications for surveillance strategy?

abstract: BACKGROUND ::: The risk of anal transition zone dysplasia/cancer after stapled IPAA for ulcerative colitis might be cumulative over time. ::: ::: ::: OBJECTIVE ::: The purpose of this work was to assess the long-term incidence and risk factors of anal transition zone dysplasia. ::: ::: ::: DESIGN ::: This was a retrospective study from a prospectively maintained database. ::: ::: ::: SETTINGS ::: The study was conducted at a tertiary referral center. ::: ::: ::: PATIENTS ::: Participants included those in our surveillance program of serial anal transition zone biopsies after stapled IPAA from 1986 to 1992. ::: ::: ::: MAIN OUTCOME MEASURES ::: Anal transition zone dysplasia was the main measured outcome. ::: ::: ::: RESULTS ::: Of 532 patients, 285 had 2 or more anal transition zone surveillance biopsies, including 73 with ≥20 years of regular follow-up. No adenocarcinoma was detected, and 15 patients died of unrelated causes after a median follow-up of 13.4 years (range, 2.9-19.5 years) without dysplasia. The estimated survival rates at 10, 15, and 20 years were 99.6% (95% CI, 96.9-99.9), 98.9% (95% CI, 95.7-99.7), and 92.6% (95% CI, 86.5-96.0). The estimated rates of anal transition zone dysplasia based on the 9 patients were 2.9% (95% CI, 1.5-5.7) and 3.4% (95% CI, 1.8-6.4) at 10 and 15 years. No new-onset dysplasia was identified beyond 125 months. Postoperative anal transition zone dysplasia was significantly associated with both preoperative and pathology findings of colorectal dysplasia (p < 0.001 for each) or cancer (p = 0.025 and p <0.001) and was managed expectantly or with mucosectomy (5 and 4 patients), depending on the number of positive biopsies and degree of dysplasia. Continued surveillance after detection of anal transition zone dysplasia showed no evidence of recurrent dysplasia during a median follow-up of 125 months (range, 9-256 months). ::: ::: ::: LIMITATIONS ::: Approximately half of the eligible patients were excluded from the analysis because of insufficient follow-up. ::: ::: ::: CONCLUSIONS ::: Long-term follow-up data corroborate the use of stapled IPAA for ulcerative colitis. Future studies should assess whether a less intensive surveillance strategy is safe 10 years after surgery.


title: Evaluating the Utility of Driving: Toward Automated Decision Making Under Uncertainty

abstract: The complexity of advanced driver-assistance systems (ADASs) is steadily increasing. While the first applications were based on mere warnings, current systems actively intervene in the driving process. Due to this development, such systems have to automatically choose between different action alternatives. From an algorithmic point of view, this requires automatic decision making on the basis of uncertain data. In this paper, the application of decision networks for this problem is proposed. It is demonstrated how this approach facilitates automatic maneuver decisions in a prototypical lane change assistance system. Furthermore, relevant research questions and unsolved problems related to this topic are identified.


title: Efficient Verification of Holograms Using Mobile Augmented Reality

abstract: Paper documents such as passports, visas and banknotes are frequently checked by inspection of security elements. In particular, optically variable devices such as holograms are important, but difficult to inspect. Augmented Reality can provide all relevant information on standard mobile devices. However, hologram verification on mobiles still takes long and provides lower accuracy than inspection by human individuals using appropriate reference information. We aim to address these drawbacks by automatic matching combined with a special parametrization of an efficient goal-oriented user interface which supports constrained navigation. We first evaluate a series of similarity measures for matching hologram patches to provide a sound basis for automatic decisions. Then a re-parametrized user interface is proposed based on observations of typical user behavior during document capture. These measures help to reduce capture time to approximately 15 s with better decisions regarding the evaluated samples than what can be achieved by untrained users.


title: The Role of Breast Size and Areolar Pigmentation in Perceptions of Women’s Sexual Attractiveness, Reproductive Health, Sexual Maturity, Maternal Nurturing Abilities, and Age

abstract: Women’s breast morphology is thought to have evolved via sexual selection as a signal of maturity, health, and fecundity. While research demonstrates that breast morphology is important in men’s judgments of women’s attractiveness, it remains to be determined how perceptions might differ when considering a larger suite of mate relevant attributes. Here, we tested how variation in breast size and areolar pigmentation affected perceptions of women’s sexual attractiveness, reproductive health, sexual maturity, maternal nurturing abilities, and age. Participants (100 men; 100 women) rated images of female torsos modeled to vary in breast size (very small, small, medium, and large) and areolar pigmentation (light, medium, and dark) for each of the five attributes listed above. Sexual attractiveness ratings increased linearly with breast size, but large breasts were not judged to be significantly more attractive than medium-sized breasts. Small and medium-sized breasts were rated as most attractive if they included light or medium colored areolae, whereas large breasts were more attractive if they had medium or dark areolae. Ratings for perceived age, sexual maturity, and nurturing ability also increased with breast size. Darkening the areolae reduced ratings of the reproductive health of medium and small breasts, whereas it increased ratings for large breasts. There were no significant sex differences in ratings of any of the perceptual measures. These results demonstrate that breast size and areolar pigmentation interact to determine ratings for a suite of sociosexual attributes, each of which may be relevant to mate choice in men and intra-sexual competition in women.


title: Bugs in the Freezer: Detecting Faults in Supermarket Refrigeration Systems Using Energy Signals

abstract: Refrigeration is a major component of supermarket energy consumption. Ensuring faultless operation of refrigeration systems is essential from both economic and sustainability perspectives. Present day industry practises of monitoring refrigeration systems to detect operational anomalies have several drawbacks: (i) Over-dependence on human skills; (ii) Limited help in identifying the root-cause of the anomaly; and (iii) Presumption about high degree of instrumentation - which prevents their usage in supermarkets in developing economies. Existing approaches in literature to detect anomalies in refrigeration systems either are done in controlled laboratory settings or assume the availability of sensory information other than energy. In this paper, we present an approach to detect anomalous behavior in the operation of refrigeration systems by monitoring their energy signals alone. We test the performance of our approach using data collected from refrigeration systems across 25 stores of a real world supermarket chain. We find that using energy signal, we can not only detect anomalies but also narrow down the possible root-cause of the anomaly to a reduced set. Further, using energy signal along with data collected from other sensors (if available) allows us to reduce the false positive rate while identifying the root-cause of the anomaly.


title: Named Entity Recognition in Bengali: A Multi-Engine Approach

abstract: This paper reports about a multi-engine approach for the development of a Named Entity Recognition (NER) system in Bengali by combining the classifiers such as Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector Machine (SVM) with the help of weighted voting techniques. The training set consists of approximately 272K wordforms, out of which 150K wordforms have been manually annotated with the four major named entity (NE) tags, namely Person name, Location name, Organization name and Miscellaneous name. An appropriate tag conversion routine has been defined in order to convert the 122K wordforms of the IJCNLP-08 NER Shared Task on South and South East Asian Languages (NERSSEAL)1 data into the desired forms. The individual classifiers make use of the different contextual information of the words along with the variety of features that are helpful to predict the various NE classes. Lexical context patterns, generated from an unlabeled corpus of 3 million wordforms in a semi-automatic way, have been used as the features of the classifiers in order to improve their performance. In addition, we propose a number of techniques to post-process the output of each classifier in order to reduce the errors and to improve the performance further. Finally, we use three weighted voting techniques to combine the individual models. Experimental results show the effectiveness of the proposed multi-engine approach with the overall Recall, Precision and F-Score values of 93.98%, 90.63% and 92.28%, respectively, which shows an improvement of 14.92% in F-Score over the best performing baseline SVM based system and an improvement http://ltrc.iiit.ac.in/ner-ssea-08


title: 3D Convolutional Neural Networks for Human Action Recognition

abstract: We consider the automated recognition of human actions in surveillance videos. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. Convolutional neural networks (CNNs) are a type of deep model that can act directly on the raw inputs. However, such models are currently limited to handling 2D inputs. In this paper, we develop a novel 3D CNN model for action recognition. This model extracts features from both the spatial and the temporal dimensions by performing 3D convolutions, thereby capturing the motion information encoded in multiple adjacent frames. The developed model generates multiple channels of information from the input frames, and the final feature representation combines information from all channels. To further boost the performance, we propose regularizing the outputs with high-level features and combining the predictions of a variety of different models. We apply the developed models to recognize human actions in the real-world environment of airport surveillance videos, and they achieve superior performance in comparison to baseline methods.


A SURVEY OF MACHINE LEARNING TECHNIQUES APPLIED FOR BREAST CANCER PREDICTION


title: Exploring Possible Reasons behind Low Student Retention Rates of Massive Online Open Courses: A Comparative Case Study from a Social Cognitive Perspective

abstract: Massive Open Online Courses (MOOCs) have been widely lauded by the press since its fairly recent inception. Besides its wide popularity among learners worldwide, the majority of MOOCs still present challenges with steep dropout rates in spite of their promising enrollment numbers. While enjoying various benefits MOOCs brings along, learners apparently face new challenges. This paper intends to explore possible reasons behind this phenomenon from a social cognitive perspective by analyzing and comparing the same subject content taught in both the traditional face-to-face setting and on a MOOC-based platform. Based on past research and theories including both the larger distance learning fields as well as recent MOOC-specific ones, three areas, namely, the lack of self-efficacy, self-regulation, and self-motivators are identified to help present an exploratory framework in interpreting findings of this study. Although far from all encompassing, this exploratory framework attempts to enhance our understanding of distinct challenges MOOC learners as well as MOOC designers face.


title: Robust Learning with Missing Data

abstract: This paper introduces a new method, called the robust Bayesian estimator (RBE), to learn conditional probability distributions from incomplete data sets. The intuition behind the RBE is that, when no information about the pattern of missing data is available, an incomplete database constrains the set of all possible estimates and this paper provides a characterization of these constraints. An experimental comparison with two popular methods to estimate conditional probability distributions from incomplete data—Gibbs sampling and the EM algorithm—shows a gain in robustness. An application of the RBE to quantify a naive Bayesian classifier from an incomplete data set illustrates its practical relevance.


title: Affective neural response to restricted interests in autism spectrum disorders

abstract: Background: Restricted interests are a class of repetitive behavior in autism spectrum disorders (ASD) whose intensity and narrow focus often contribute to significant interference with daily functioning. While numerous neuroimaging studies have investigated executive circuits as putative neural substrates of repetitive behavior, recent work implicates affective neural circuits in restricted interests. We sought to explore the role of affective neural circuits and determine how restricted interests are distinguished from hobbies or interests in typical development. Methods: We compared a group of children with ASD to a typically developing (TD) group of children with strong interests or hobbies, employing parent report, an operant behavioral task, and functional imaging with personalized stimuli based on individual interests. Results: While performance on the operant task was similar between the two groups, parent report of intensity and interference of interests was significantly higher in the ASD group. Both the ASD and TD groups showed increased BOLD response in widespread affective neural regions to the pictures of their own interest. When viewing pictures of other children’s interests, the TD group showed a similar pattern, whereas BOLD response in the ASD group was much more limited. Increased BOLD response in the insula and anterior cingulate cortex distinguished the ASD from the TD group, and parent report of the intensity and interference with daily life of the child’s restricted interest predicted insula response. Conclusions: While affective neural network response and operant behavior are comparable in typical and restricted interests, the narrowness of focus that clinically distinguishes restricted interests in ASD is reflected in more interference in daily life and aberrantly enhanced insula and anterior cingulate response to individuals’ own interests in the ASD group. These results further support the involvement of affective neural networks in repetitive behaviors in ASD. Keywords: Autism, restricted interests, reward, repetitive behavior, fMRI, insula, salience.


title: Adding Syntax To Dynamic Programming For Aligning Comparable Texts For The Generation Of Paraphrases

abstract: Multiple sequence alignment techniques have recently gained popularity in the Natural Language community, especially for tasks such as machine translation, text generation, and paraphrase identification. Prior work falls into two categories, depending on the type of input used: (a) parallel corpora (e.g., multiple translations of the same text) or (b) comparable texts (non-parallel but on the same topic). So far, only techniques based on parallel texts have successfully used syntactic information to guide alignments. In this paper, we describe an algorithm for incorporating syntactic features in the alignment process for non-parallel texts with the goal of generating novel paraphrases of existing texts. Our method uses dynamic programming with alignment decision based on the local syntactic similarity between two sentences. Our results show that syntactic alignment outrivals syntax-free methods by 20% in both grammaticality and fidelity when computed over the novel sentences generated by alignment-induced finite state automata.


title: BiSpectral Contactless hand based biometric identification device

abstract: Biometrics plays an increasingly important role in authentication and identification systems. The process of biometric recognition allows the identification of individuals based on the physical or behavioral characteristics. Among the most common biometric features used are fingerprint, iris, face, voice, signature and hand. Hand based biometric systems exhibit many desirable characteristics when working with low resolution sensors (which are most appropriate for civil and commercial applications), including low cost sensors, acceptable indentification performance, robustness to environmental conditions and individual anomalies, and high speed identification algorithms. For higher security applications such as forensics, high resolution images are more suitable (Jain et al, 2001) (Konga et al, 2009). Most hand-based biometric schemes in the literature are based on measuring the hand silhoutte as a distinctive personal attribute for an authentication task. First it was accomplished using guiding pegs mounted on a flat surface of the imaging device (SanchezReillo et al, 2000) (Jain et al, 1999). Although the guiding pegs provide consistent measuring positions, they cause some problems as well: 1) The pegs can deform the shape of a hand (Wong & Shi, 2002) and 2) The users must be well trained to cooperate with the system. Thus, peg-free hand geometry techniques were considered giving the hand some motion freedom (Bulatov et al, 2004). There are two main approaches for geometrical features extraction; those based on measure the finger lengths and widths at various positions, palm size, etc. and another based on represent the global hand shape (Oden et al, 2003) (Yoruk et al, 2006). Both approaches use the finger tip points and the finger valley points as the landmarksfor image alignment. The palm texture can be also used as biometric trait for personal identification. It can be used both by itself (Han et al, 2003) (Ribaric & Fratric, 2005) (Sun et al 2005) (Kong & Zhang, 2004) (Badrinath & Gupta, 2009) or combined with hand shape (Ribaric et al, 2003) (Li et al, 2006) (Kumar et al, 2006) (Kumar & Zhang, 2004) at score level or at representation level. Although fusion increases accuracy, it generally increases computation costs and template sizes and reduces user acceptance. Recently, perhaps due to hygiene consideration, contact-free hand biometric systems have been proposed. The two main issues to be dealt with in a contact-free system are hand segmentation and the projective distortions associated with the absence of the contact plane.


title: Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing

abstract: Bin Packing problems have been widely studied because of their broad applications in different domains. Known as a set of NP-hard problems, they have different variations and many heuristics have been proposed for obtaining approximate solutions. Specifically, for the 1D variable sized bin packing problem, the two key sets of optimization heuristics are the bin assignment and the bin allocation. Usually the performance of a single static optimization heuristic can not beat that of a dynamic one which is tailored for each bin packing instance. Building such an adaptive system requires modeling the relationship between bin features and packing perform profiles. The primary drawbacks of traditional AI machine learnings for this task are the natural limitations of feature engineering, such as the curse of dimensionality and feature selection quality. We introduce a deep learning approach to overcome the drawbacks by applying a large training data set, auto feature selection and fast, accurate labeling. We show in this paper how to build such a system by both theoretical formulation and engineering practices. Our prediction system achieves up to 89% training accuracy and 72% validation accuracy to select the best heuristic that can generate a better quality bin packing solution.


Cloud Computing Vs . Grid Computing


title: Functional domains and temperature-sensitive mutations in SPE-9, an EGF repeat-containing protein required for fertility in Caenorhabditis elegans

abstract: The spe-9 gene is required for fertility in Caenorhabditis elegans and encodes a sperm transmembrane protein with an extracellular domain (ECD) that contains 10 epidermal growth factor (EGF) repeats. Deletion analysis reveals that the EGF repeats and the transmembrane domain are required for fertilization. In contrast, the cytoplasmic region of SPE-9 is not essential for fertilization. Individual point mutations in all 10 EGF motifs uncover a differential sensitivity of these sequences to alteration. Some EGF repeats cannot tolerate mutation leading to a complete lack of fertility. Other EGF repeats can be mutated to create animals with temperature-sensitive (ts) fertility phenotypes. All ts mutations were generated by changing either conserved cysteine or glycine residues in the EGF motifs. For two endogenous ts alleles of spe-9, loss of function at nonpermissive temperatures is not due to protein mislocalization or degradation. Additionally, the proper localization of SPE-9 in sperm is not altered in a genetically interacting fertility mutant (spe-13) or a mutant that affects sperm vesicle-plasma membrane fusion (fer-1). Like the EGF repeats in the Notch/LIN-12/GLP-1 receptors and their ligands, the EGF repeats in SPE-9 may carry out different functions. Because EGF motifs are found in many proteins in different species, similar experimental strategies could be used to generate useful temperature-sensitive mutations in other EGF motif-containing molecules.


title: A Portable Image Overlay Projection Device for Computer-Aided Open Liver Surgery

abstract: Image overlay projection is a form of augmented reality that allows surgeons to view underlying anatomical structures directly on the patient surface. It improves intuitiveness of computer-aided surgery by removing the need for sight diversion between the patient and a display screen and has been reported to assist in 3-D understanding of anatomical structures and the identification of target and critical structures. Challenges in the development of image overlay technologies for surgery remain in the projection setup. Calibration, patient registration, view direction, and projection obstruction remain unsolved limitations to image overlay techniques. In this paper, we propose a novel, portable, and handheld-navigated image overlay device based on miniature laser projection technology that allows images of 3-D patient-specific models to be projected directly onto the organ surface intraoperatively without the need for intrusive hardware around the surgical site. The device can be integrated into a navigation system, thereby exploiting existing patient registration and model generation solutions. The position of the device is tracked by the navigation system's position sensor and used to project geometrically correct images from any position within the workspace of the navigation system. The projector was calibrated using modified camera calibration techniques and images for projection are rendered using a virtual camera defined by the projectors extrinsic parameters. Verification of the device's projection accuracy concluded a mean projection error of 1.3 mm. Visibility testing of the projection performed on pig liver tissue found the device suitable for the display of anatomical structures on the organ surface. The feasibility of use within the surgical workflow was assessed during open liver surgery. We show that the device could be quickly and unobtrusively deployed within the sterile environment.


title: A lineage of myeloid cells independent of Myb and hematopoietic stem cells.

abstract: Macrophages and dendritic cells (DCs) are key components of cellular immunity and are thought to originate and renew from hematopoietic stem cells (HSCs). However, some macrophages develop in the embryo before the appearance of definitive HSCs. We thus reinvestigated macrophage development. We found that the transcription factor Myb was required for development of HSCs and all CD11b(high) monocytes and macrophages, but was dispensable for yolk sac (YS) macrophages and for the development of YS-derived F4/80(bright) macrophages in several tissues, such as liver Kupffer cells, epidermal Langerhans cells, and microglia--cell populations that all can persist in adult mice independently of HSCs. These results define a lineage of tissue macrophages that derive from the YS and are genetically distinct from HSC progeny.


title: GUIDANCE LAW EVALUATION FOR MISSILE GUIDANCE SYSTEMS

abstract: In missile guidance system, to reduce the interception “miss distance,” it is important to choose a suitable guidance law and navigation constant. This paper investigates and compares the system behavior of guidance laws under different navigation constants. Based on use of the adjoint technique, miss distance sensitivity analyses which consider the system noise, target step maneuver, initial heading error and system parameters for different guidance laws and navigation constants are presented. Based on these analyses, some suggestions for choosing a suitable guidance law and navigation constant are given for the design of missile guidance systems. Also, a suggestion for the optimal escape time for pilots of fighter planes is given.


Influence of color category on visual search


title: Re-visiting the echo state property.

abstract: An echo state network (ESN) consists of a large, randomly connected neural network, the reservoir, which is driven by an input signal and projects to output units. During training, only the connections from the reservoir to these output units are learned. A key requisite for output-only training is the echo state property (ESP), which means that the effect of initial conditions should vanish as time passes. In this paper, we use analytical examples to show that a widely used criterion for the ESP, the spectral radius of the weight matrix being smaller than unity, is not sufficient to satisfy the echo state property. We obtain these examples by investigating local bifurcation properties of the standard ESNs. Moreover, we provide new sufficient conditions for the echo state property of standard sigmoid and leaky integrator ESNs. We furthermore suggest an improved technical definition of the echo state property, and discuss what practicians should (and should not) observe when they optimize their reservoirs for specific tasks.


title: Unsupervised Dependency Parsing with Transferring Distribution via Parallel Guidance and Entropy Regularization

abstract: We present a novel approach for inducing unsupervised dependency parsers for languages that have no labeled training data, but have translated text in a resourcerich language. We train probabilistic parsing models for resource-poor languages by transferring cross-lingual knowledge from resource-rich language with entropy regularization. Our method can be used as a purely monolingual dependency parser, requiring no human translations for the test data, thus making it applicable to a wide range of resource-poor languages. We perform experiments on three Data sets — Version 1.0 and version 2.0 of Google Universal Dependency Treebanks and Treebanks from CoNLL shared-tasks, across ten languages. We obtain stateof-the art performance of all the three data sets when compared with previously studied unsupervised and projected parsing systems.


title: Pose Invariant Activity Classification for Multi-floor Indoor Localization

abstract: Smartphone based indoor localization caught massive interest of the localization community in recent years. Combining pedestrian dead reckoning obtained using the phone's inertial sensors with the Graph SLAM (Simultaneous Localization and Mapping) algorithm is one of the most effective approaches to reconstruct the entire pedestrian trajectory given a set of visited landmarks during movement. A key to Graph SLAM-based localization is the detection of reliable landmarks, which are typically identified using visual cues or via NFC tags or QR codes. Alternatively, human activity can be classified to detect organic landmarks such as visits to stairs and elevators while in movement. We provide a novel human activity classification framework that is invariant to the pose of the smartphone. Pose invariant features allow robust observation no matter how a user puts the phone in the pocket. In addition, activity classification obtained by an SVM (Support Vector Machine) is used in a Bayesian framework with an HMM (Hidden Markov Model) that improves the activity inference based on temporal smoothness. Furthermore, the HMM jointly infers activity and floor information, thus providing multi-floor indoor localization. Our experiments show that the proposed framework detects landmarks accurately and enables multi-floor indoor localization from the pocket using Graph SLAM.


title: CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

abstract: When answering a question, people often draw upon their rich world knowledge in addition to some task-specific context. Recent work has focused primarily on answering questions based on some relevant document or content, and required very little general background. To investigate question answering with prior knowledge, we present CommonsenseQA: a difficult new dataset for commonsense question answering. To capture common sense beyond associations, each question discriminates between three target concepts that all share the same relationship to a single source drawn from ConceptNet (Speer et al., 2017). This constraint encourages crowd workers to author multiple-choice questions with complex semantics, in which all candidates relate to the subject in a similar way. We create 9,500 questions through this procedure and demonstrate the dataset's difficulty with a large number of strong baselines. Our best baseline, the OpenAI GPT (Radford et al., 2018), obtains 54.8% accuracy, well below human performance, which is 95.3%.


title: Coexistence of ZigBee-Based WBAN and WiFi for Health Telemonitoring Systems

abstract: The development of telemonitoring via wireless body area networks (WBANs) is an evolving direction in personalized medicine and home-based mobile health. A WBAN consists of small, intelligent medical sensors which collect physiological parameters such as electrocardiogram, electroencephalography, and blood pressure. The recorded physiological signals are sent to a coordinator via wireless technologies, and are then transmitted to a healthcare monitoring center. One of the most widely used wireless technologies in WBANs is ZigBee because it is targeted at applications that require a low data rate and long battery life. However, ZigBee-based WBANs face severe interference problems in the presence of WiFi networks. This problem is caused by the fact that most ZigBee channels overlap with WiFi channels, severely affecting the ability of healthcare monitoring systems to guarantee reliable delivery of physiological signals. To solve this problem, we have developed an algorithm that controls the load in WiFi networks to guarantee the delay requirement for physiological signals, especially for emergency messages, in environments with coexistence of ZigBee-based WBAN and WiFi. Since WiFi applications generate traffic with different delay requirements, we focus only on WiFi traffic that does not have stringent timing requirements. In this paper, therefore, we propose an adaptive load control algorithm for ZigBee-based WBAN/WiFi coexistence environments, with the aim of guaranteeing that the delay experienced by ZigBee sensors does not exceed a maximally tolerable period of time. Simulation results show that our proposed algorithm guarantees the delay performance of ZigBee-based WBANs by mitigating the effects of WiFi interference in various scenarios.


title: Low-Depth Quantum Simulation of Materials

abstract: Quantum simulation of the electronic structure problem is one of the most researched applications of quantum computing. The majority of quantum algorithms for this problem encode the wavefunction using N Gaussian orbitals, leading to Hamiltonians with O(N^4) second-quantized terms. We avoid this overhead and extend methods to condensed phase materials by utilizing a dual form of the plane wave basis which diagonalizes the potential operator, leading to a Hamiltonian representation with O(N^2) second-quantized terms. Using this representation, we can implement single Trotter steps of the Hamiltonians with linear gate depth on a planar lattice. Properties of the basis allow us to deploy Trotter- and Taylor-series-based simulations with respective circuit depths of O(N^(7/2)) and O(N^(8/3)) for fixed charge densities. Variational algorithms also require significantly fewer measurements in this basis, ameliorating a primary challenge of that approach. While our approach applies to the simulation of arbitrary electronic structure problems, the basis sets explored in this work will be most practical for treating periodic systems, such as crystalline materials, in the near term. We conclude with a proposal to simulate the uniform electron gas (jellium) using a low-depth variational ansatz realizable on near-term quantum devices. From these results, we identify simulations of low-density jellium as a promising first setting to explore quantum supremacy in electronic structure.


title: Locally Aligned Feature Transforms across Views

abstract: In this paper, we propose a new approach for matching images observed in different camera views with complex cross-view transforms and apply it to person re-identification. It jointly partitions the image spaces of two camera views into different configurations according to the similarity of cross-view transforms. The visual features of an image pair from different views are first locally aligned by being projected to a common feature space and then matched with softly assigned metrics which are locally optimized. The features optimal for recognizing identities are different from those for clustering cross-view transforms. They are jointly learned by utilizing sparsity-inducing norm and information theoretical regularization. This approach can be generalized to the settings where test images are from new camera views, not the same as those in the training set. Extensive experiments are conducted on public datasets and our own dataset. Comparisons with the state-of-the-art metric learning and person re-identification methods show the superior performance of our approach.


title: Character feature integration of Chinese calligraphy and font

abstract: A framework is proposed in this paper to effectively generate a new hybrid character type by means of integrating local ::: contour feature of Chinese calligraphy with structural feature of font in computer system. To explore traditional art ::: manifestation of calligraphy, multi-directional spatial filter is applied for local contour feature extraction. Then the ::: contour of character image is divided into sub-images. The sub-images in the identical position from various characters ::: are estimated by Gaussian distribution. According to its probability distribution, the dilation operator and erosion ::: operator are designed to adjust the boundary of font image. And then new Chinese character images are generated which ::: possess both contour feature of artistical calligraphy and elaborate structural feature of font. Experimental results ::: demonstrate the new characters are visually acceptable, and the proposed framework is an effective and efficient strategy ::: to automatically generate the new hybrid character of calligraphy and font.


title: Smart distributed subarray MIMO (DS-MIMO) microstrip antenna system with frequency agile for communication and radar applications

abstract: Recent technologies enable electronic and RF circuits in communication devices and radar to be miniaturized and become physically smaller in size. Antenna design has been one of the key limiting constraints to the development of small communication terminals and also in meeting next generation and radar requirements. Multiple antenna technologies (MATs) have gained much attention in the last few years because of the huge gain. MATs can enhance the reliability and the channel capacity levels. Furthermore, multiple antenna systems can have a big contribution to reduce the interference both in the uplink and the downlink. To increase the communication systems reliability, multiple antennas can be installed at the transmitter or/and at the receiver. The idea behind multiple antenna diversity is to supply the receiver by multiple versions of the same signal transmitted via independent channels. In modern communication transceiver and radar systems, primary aims are to direct high power RF signal from transmitter to antenna while preventing leakage of that large signal into more sensitive frontend of receiver. So, a Single-Pole Double-Throw (SPDT) Transmitter/Receiver (T/R) Switch plays an important role. In this paper, design of smart distributed subarray MIMO (DS-MIMO) microstrip antenna system with controller unit and frequency agile has been introduced and investigated. All the entire proposed antenna system has been evaluated using a commercial software. The final proposed design has been fabricated and the radiation characteristics have been illustrated using network analyzer to meet the requirements for communication and radar applications.


A whispered Mandarin corpus for speech technology applications.


title: Techniques to Detect Spammers in Twitter- A Survey

abstract: With the rapid growth of social networking sites for communicating, sharing, storing and managing significant information, it is attracting cybercriminals who misuse the Web to exploit vulnerabilities for their illicit benefits. Forged online accounts crack up every day. Impersonators, phishers, scammers and spammers crop up all the time in Online Social Networks (OSNs), and are harder to identify. Spammers are the users who send unsolicited messages to a large audience with the intention of advertising some product or to lure victims to click on malicious links or infecting user’s system just for the purpose of making money. A lot of research has been done to detect spam profiles in OSNs. In this paper we have reviewed the existing techniques for detecting spam users in Twitter social network. Features for the detection of spammers could be user based or content based or both. Current study provides an overview of the methods, features used, detection rate and their limitations (if any) for detecting spam profiles mainly in Twitter.


title: A Shifting Bloom Filter Framework for Set Queries

abstract: Set queries are fundamental operations in computer systems and applications. This paper addresses the fundamental problem of designing a probabilistic data structure that can quickly process set queries using a small amount of memory. We propose a Shifting Bloom Filter (ShBF) framework for representing and querying sets. We demonstrate the effectiveness of ShBF using three types of popular set queries: membership, association, and multiplicity queries. The key novelty of ShBF is on encoding the auxiliary information of a set element in a location offset. In contrast, prior BF based set data structures allocate additional memory to store auxiliary information. We conducted experiments using real-world network traces, and results show that ShBF significantly advances the state-of-the-art on all three types of set queries.


title: Efficient Adaptive-Support Association Rule Mining for Recommender Systems

abstract: Collaborative recommender systems allow personalization for e-commerce by exploiting similarities and dissimilarities among customers' preferences. We investigate the use of association rule mining as an underlying technology for collaborative recommender systems. Association rules have been used with success in other domains. However, most currently existing association rule mining algorithms were designed with market basket analysis in mind. Such algorithms are inefficient for collaborative recommendation because they mine many rules that are not relevant to a given user. Also, it is necessary to specify the minimum support of the mined rules in advance, often leading to either too many or too few ruless this negatively impacts the performance of the overall system. We describe a collaborative recommendation technique based on a new algorithm specifically designed to mine association rules for this purpose. Our algorithm does not require the minimum support to be specified in advance. Rather, a target range is given for the number of rules, and the algorithm adjusts the minimum support for each user in order to obtain a ruleset whose size is in the desired range. Rules are mined for a specific target user, reducing the time required for the mining process. We employ associations between users as well as associations between items in making recommendations. Experimental evaluation of a system based on our algorithm reveals performance that is significantly better than that of traditional correlation-based approaches.


title: Intelligent financial fraud detection: A comprehensive review

abstract: Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods involving manual detection are not only time consuming, expensive and inaccurate, but in the age of big data they are also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods. This paper presents a comprehensive review of financial fraud detection research using such data mining methods, with a particular focus on computational intelligence (CI)-based techniques. Over fifty scientific literature, primarily spanning the period 2004-2014, were analysed in this study; literature that reported empirical studies focussing specifically on CI-based financial fraud detection were considered in particular. Research gap was identified as none of the existing review articles addresses the association among fraud types, CI-based detection algorithms and their performance, as reported in the literature. We have presented a comprehensive classification as well as analysis of existing fraud detection literature based on key aspects such as detection algorithm used, fraud type investigated, and performance of the detection methods for specific financial fraud types. Some of the key issues and challenges associated with the current practices and potential future direction of research have also been identified.


title: Comparison of RNN Encoder-Decoder Models for Anomaly Detection

abstract: In this paper, we compare different types of Recurrent Neural Network (RNN) Encoder-Decoders in anomaly detection viewpoint. We focused on finding the model that can learn the same data more effectively. We compared multiple models under the same conditions, such as the number of parameters, optimizer, and learning rate. However, the difference is whether to predict the future sequence or restore the current sequence. We constructed the dataset with simple vectors and used them for the experiment. Finally, we experimentally confirmed that the model performs better when the model restores the current sequence, rather than predict the future sequence.


title: Tools for dynamics simulation of robots: a survey based on user feedback

abstract: The number of tools for dynamics simulation has grown in the last years. It is necessary for the robotics community to have elements to ponder which of the available tools is the best for their research. As a complement to an objective and quantitative comparison, difficult to obtain since not all the tools are open-source, an element of evaluation is user feedback. With this goal in mind, we created an online survey about the use of dynamical simulation in robotics. This paper reports the analysis of the participants' answers and a descriptive information fiche for the most relevant tools. We believe this report will be helpful for roboticists to choose the best simulation tool for their researches.


title: A One-Year Perspective on Exposed In-memory Key-Value Stores

abstract: Today's highly-scalable low-latency Web services rely on in-memory key-value stores. While they are essential to improve Web service performance they should not be exposed to the Internet. Security problems range from data leakage to remote code execution. In this paper we use a year long data set of exposed Redis and memcached instances to highlight the magnitude (about 200K) of the problem, document new transitive attacks, and explore misconfiguration patterns. We find that the number of exposed instances is constantly on the rise and that even severe problems only lead to temporal decreases. However, by correlating misconfiguration patterns we can explain significant changes in the number of exposed systems.


title: Non-specific low back pain

abstract: Non-specific low back pain has become a major public health problem worldwide. The lifetime prevalence of low back pain is reported to be as high as 84%, and the prevalence of chronic low back pain is about 23%, with 11-12% of the population being disabled by low back pain. Mechanical factors, such as lifting and carrying, probably do not have a major pathogenic role, but genetic constitution is important. History taking and clinical examination are included in most diagnostic guidelines, but the use of clinical imaging for diagnosis should be restricted. The mechanism of action of many treatments is unclear, and effect sizes of most treatments are low. Both patient preferences and clinical evidence should be taken into account for pain management, but generally self-management, with appropriate support, is recommended and surgery and overtreatment should be avoided.


title: Abstract Enhancing retinal image by the Contourlet transform

abstract: The evaluation of retinal images is widely used to help doctors diagnose many diseases, such as diabetes or hypertension. Due to the acquisition process, retinal images often have low grey level contrast and dynamic range. This problem may seriously affect the diagnostic procedure and its results. Here we present a new multi-scale method for retinal image contrast enhancement based on the Contourlet transform. The Contourlet transform has better performance in representing edges than wavelets for its anisotropy and directionality, and is therefore well-suited for multi-scale edge enhancement. We modify the Contourlet coefficients in corresponding subbands via a nonlinear function and take the noise into account for more precise reconstruction and better visualization. We compare this approach with enhancement based on the Wavelet transform, Histogram Equalization, Local Normalization and Linear Unsharp Masking. The application of this method on images from the DRIVE database showed that the proposed approach outperforms other enhancement methods on low contrast and dynamic range images, with an encouraging improvement, and might be helpful for vessel segmentation.