title: Tracking the mind during reading: The influence of past, present, and future words on fixation durations.

abstract: Reading requires the orchestration of visual, attentional, language-related, and oculomotor processing constraints. This study replicates previous effects of frequency, predictability, and length of fixated words on fixation durations in natural reading and demonstrates new effects of these variables related to previous and next words. Results are based on fixation durations recorded from 222 persons, each reading 144 sentences. Such evidence for distributed processing of words across fixation durations challenges psycholinguistic immediacy-of-processing and eye-mind assumptions. Most of the time the mind processes several words in parallel at different perceptual and cognitive levels. Eye movements can help to unravel these processes. Reading is a fairly recent cultural invention. The perceptual, attentional, and oculomotor processes enabling this remarkable and complex human skill had been in place for a long time before the first sentence was read. Of course, reading also fundamentally presupposes language, reasoning, and memory processes. If we want to understand how internal processes of the mind and exter- nal stimuli play together in the generation of complex action, reading may serve as an ideal sample case, because, despite its complexity, it occurs in settings that are very amenable to exper- imental control. In addition, the measurement of eye movements yields high-resolution time series that have proven to be very sensitive to factors at all levels of the behavioral and cognitive hierarchy. Most important, we already know or can determine basic perceptual, attentional, and oculomotor constraints that any theory of reading and any computational model implementing such a theory at a behavioral microlevel must respect. Looking at the eyes, reading proceeds as an alternating sequence of fixations (lasting 150 to 300 ms) and saccades (30 ms). Infor- mation uptake is largely restricted to fixations. For example, fixation durations reliably decrease with the printed frequency of words and with their predictability from prior words of the sen- tence. Beyond these uncontroversial facts, however, much still needs to be learned about perceptual and attentional processes and properties of words that guide the eyes through a sentence. Starr and Rayner (2001, p. 156) highlighted the following three issues as particularly controversial: 1. The extent to which eye-movement behavior is affected by low-level oculomotor factors versus higher-level cog- nitive processes. 2. How much information is extracted from the right of fixations. 3. Whether readers process information from more than one word at a time. In this article, we report new empirical results relating to each of these issues. We also present a data-analytic framework within which these issues can be addressed simultaneously and propose a set of theoretical principles that account for a complex set of experimental observations. Basically, in our analysis of reading fixations we show that most of the time the mind processes several words in parallel at different perceptual and cognitive levels. Similar dynamical and parallel effects of perceptual and attentional modulation guide human gaze control during real-word scene perception and interpretation (e.g., Henderson, 2005). A future challenge is to use the potential of eye movements as prime indicators of these general cognitive and behavioral dynamics.


title: Understanding power transformer frequency response analysis signatures

abstract: This paper presents a comprehensive analysis of the effects of various faults on the FRA signatures of a transformer simulated by a high-frequency model. The faults were simulated through changes in the values of some of the electrical components in the model. It was found that radial displacement of a winding alters the FRA signature over the entire frequency range (10 Hz-1 MHz), whereas changes due to axial displacement occur only at frequencies above 200 kHz. A Table listing various transformer faults and the associated changes in the FRA signature was compiled and could be used in the formulation of standard codes for power transformer FRA signature interpretation.


title: Developing university-business cooperation through work-integrated learning

abstract: While university-business cooperation (UBC) is predominantly focused on commercialisation involving university staff and firms, the area of work-integrated learning (WIL) including students is increasing in prominence. WIL is an intrinsic component and catalyst for broader UBC. It has also been widely recognised as fundamental in the education arena to equip graduates with the required industry experience to innovate successfully. Despite its importance, the UBC literature places limited attention on WIL. Cooperation between university and business at the organisational level of relevant institutions as well as the individual level of participating managers and students is fundamental. Focusing on the student at the interface of UBC, this study contributes a framework for developing UBC through WIL. Managerial implications are discussed for universities and businesses engaged in UBC.


title: Quo Vadis, Dynamic Capabilities? A Content-Analytic Review of the Current State of Knowledge and Recommendations for Future Research

abstract: Although the dynamic capabilities perspective has become one of the most frequently used theoretical lenses in management research, critics have repeatedly voiced their frustration with this literature, particularly bemoaning the lack of empirical knowledge and the underspecification of the construct of dynamic capabilities. But research on dynamic capabilities has advanced considerably since its early years, in which most contributions to this literature were purely conceptual. A plethora of empirical studies as well as further theoretical elaborations have shed substantial light on a variety of specific, measurable factors connected to dynamic capabilities. Our article starts out by analyzing these studies to develop a meta-framework that specifies antecedents, dimensions, mechanisms, moderators, and outcomes of dynamic capabilities identified in the literature to date. This framework provides a comprehensive and systematic synthesis of the dynamic capabilities perspective that reflects the richness of the research while at the same time unifying it into a cohesive, overarching model. Such an analysis has not yet been undertaken; no comprehensive framework with this level of detail has previously been presented for dynamic capabilities. Our analysis shows where research has made the most progress and where gaps and unresolved tensions remain. Based on this analysis, we propose a forward-looking research agenda that outlines directions for future research.


title: A camera calibration technique based on OpenCV

abstract: Camera calibration is very improtant in 3D computer vision systems. In this paper, the camera model in OpenCV (open source computer vision library) is discussed, the non-linear distortion of the tangential and radial distortion aberration are considered. Based on these, we introduce the corner extraction of the Camera Calibration Toolbox for Matlab, and an arithmetic of camera calibration based on OpenCV is given. The proposed algorithm can achieve accurate results. It has features such as computational efficiency and good cross-platform portability. It is able to meet the needs of 3D computer vision systems. Experimental results show the feasibility of the proposed method.


Ethics and Customer Loyalty : Some Insights into Online Retailing Services


A Real-Time Atrial Fibrillation Detection Algorithm Based on the Instantaneous State of Heart Rate


title: Composite Focus Measure for High Quality Depth Maps

abstract: Depth from focus is a highly accessible method to estimate the 3D structure of everyday scenes. Today’s DSLR and mobile cameras facilitate the easy capture of multiple focused images of a scene. Focus measures (FMs) that estimate the amount of focus at each pixel form the basis of depth-from-focus methods. Several FMs have been proposed in the past and new ones will emerge in the future, each with their own strengths. We estimate a weighted combination of standard FMs that outperforms others on a wide range of scene types. The resulting composite focus measure consists of FMs that are in consensus with one another but not in chorus. Our two-stage pipeline first estimates fine depth at each pixel using the composite focus measure. A cost-volume propagation step then assigns depths from confident pixels to others. We can generate high quality depth maps using just the top five FMs from our composite focus measure. This is a positive step towards depth estimation of everyday scenes with no special equipment.


title: Predicting query performance

abstract: We develop a method for predicting query performance by computing the relative entropy between a query language model and the corresponding collection language model. The resulting clarity score measures the coherence of the language usage in documents whose models are likely to generate the query. We suggest that clarity scores measure the ambiguity of a query with respect to a collection of documents and show that they correlate positively with average precision in a variety of TREC test sets. Thus, the clarity score may be used to identify ineffective queries, on average, without relevance information. We develop an algorithm for automatically setting the clarity score threshold between predicted poorly-performing queries and acceptable queries and validate it using TREC data. In particular, we compare the automatic thresholds to optimum thresholds and also check how frequently results as good are achieved in sampling experiments that randomly assign queries to the two classes.


Vaccine Hesitancy and Refusal


title: A Robust Deep Model for Improved Classification of AD/MCI Patients

abstract: Accurate classification of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI), plays a critical role in possibly preventing progression of memory impairment and improving quality of life for AD patients. Among many research tasks, it is of a particular interest to identify noninvasive imaging biomarkers for AD diagnosis. In this paper, we present a robust deep learning system to identify different progression stages of AD patients based on MRI and PET scans. We utilized the dropout technique to improve classical deep learning by preventing its weight coadaptation, which is a typical cause of overfitting in deep learning. In addition, we incorporated stability selection, an adaptive learning factor, and a multitask learning strategy into the deep learning framework. We applied the proposed method to the ADNI dataset, and conducted experiments for AD and MCI conversion diagnosis. Experimental results showed that the dropout technique is very effective in AD diagnosis, improving the classification accuracies by 5.9% on average as compared to the classical deep learning methods.


title: foo.castr: visualising the future AI workforce

abstract: Organization of companies and their HR departments are becoming hugely affected by recent advancements in computational power and Artificial Intelligence, with this trend likely to dramatically rise in the next few years. This work presents foo.castr, a tool we are developing to visualise, communicate and facilitate the understanding of the impact of these advancements in the future of workforce. It builds upon the idea that particular tasks within job descriptions will be progressively taken by computers, forcing the shaping of human jobs. In its current version, foo.castr presents three different scenarios to help HR departments planning potential changes and disruptions brought by the adoption of Artificial Intelligence.


title: Exchange-enhanced Ultrastrong Magnon-Magnon Coupling in a Compensated Ferrimagnet

abstract: The ultrastrong coupling of (quasi-)particles has gained considerable attention due to its application potential and richness of the underlying physics. Coupling phenomena arising due to electromagnetic interactions are well explored. In magnetically ordered systems, the quantum-mechanical exchange-interaction should furthermore enable a fundamentally different coupling mechanism. Here, we report the observation of ultrastrong intralayer exchange-enhanced magnon-magnon coupling in a compensated ferrimagnet. We experimentally study the spin dynamics in a gadolinium iron garnet single crystal using broadband ferromagnetic resonance. Close to the ferrimagnetic compensation temperature, we observe ultrastrong coupling of clockwise and anticlockwise magnon modes. The magnon-magnon coupling strength reaches more than 30% of the mode frequency and can be tuned by varying the direction of the external magnetic field. We theoretically explain the observed phenomenon in terms of an exchange-enhanced mode-coupling mediated by a weak cubic anisotropy.


title: The Objective Measurement of Brace-Use Adherence in the Treatment of Idiopathic Clubfoot.

abstract: BACKGROUND ::: A successful outcome for the treatment of idiopathic clubfoot is believed to require adequate adherence to brace use. Previous studies have relied on parental reporting of brace application. We used temperature sensors to determine the adherence to the bracing protocol, the accuracy of parent-reported use, and differences in adherence between patients who experienced relapse of deformity and those who did not. ::: ::: ::: METHODS ::: Using wireless sensors attached to brace sandals, we monitored brace wear over a 3-month period in this cross-sectional study involving 48 patients in 4 age-based groups: 6 to 12 months (Group 1), >1 to 2 years (Group 2), >2 to 3 years (Group 3), and >3 to 4 years (Group 4). Parents were blinded to the purpose of the sensors. The mean number of hours of daily brace use as measured by the sensors was compared with the physician-recommended hours and parent-reported hours of brace use. ::: ::: ::: RESULTS ::: Sensors were retrieved from 44 of 48 patients. Overall, the median brace use recorded by the sensors was 62% (range, 5% to 125%) of that recommended by the physician, and 77% (range, 6% to 213%) of that reported by the parents. For Groups 1 to 3, the difference between the physician-recommended and measured number of hours of daily brace use was significant (p ≤ 0.002), and the difference between the parent-reported and measured number of hours of daily brace use was also significant (p ≤ 0.013). Eight (18%) of the 44 patients who completed the study experienced relapse during the period of monitoring; most importantly, the mean number of hours of brace wear for these patients, 5 hours per day (median, 4; and standard deviation [SD], 3 hours per day) was significantly lower than the 8 hours per day for those who did not experience relapse (median, 9; and SD, 5 hours per day) (p = 0.045). ::: ::: ::: CONCLUSIONS ::: The present study objectively quantified the number of daily hours of post-corrective brace wear for patients with clubfoot in varying age groups and provides an estimate of the number of hours required to avoid relapse. ::: ::: ::: LEVEL OF EVIDENCE ::: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.


Protein Family Classification with Neural Networks


title: Data-driven image stylization using graph-based filtering

abstract: In this work, we consider the problem of image abstraction and stylization. A graph-based framework is proposed to render real images into painterly-style ones and create a simple stylized format from color images. The goal is to abstract images by simplifying their visual content while preserving edges and emphasizing most of the perceptually important information. To this end, the low-contrast regions of an image are first smoothened using iterative graph filters in both the vertex and spectral domains. The abstracted luminance channel is quantized and sharpened using an iterative highpass graph filter in the spectral domain. The effectiveness of the proposed graph-based image stylization method is verified through simulations. It is shown that the proposed method can yield significantly better visual quality for stylized images as compared to other existing works.


title: Exploiting a semantic annotation tool for metaphor analysis

abstract: In this paper, we describe the development and exploitation of a corpus-based tool for the identification of metaphorical patterns in large datasets. The analysis of metaphor as a cognitive and cultural, rather than solely linguistic, phenomenon has become central as metaphor researchers working within ‘Cognitive Metaphor Theory’ have drawn attention to the presence of systematic and pervasive conventional metaphorical patterns in ‘ordinary’ language (e.g. I’m at a crossroads in my life). Cognitive Metaphor Theory suggests that these linguistic patterns reflect the existence of conventional conceptual metaphors, namely systematic cross-domain correspondences in conceptual structure (e.g. LIFE IS A JOURNEY). This theoretical approach, described further in section 2, has led to considerable advances in our understanding of metaphor both as a linguistic device and a cognitive model, and to our awareness of its role in many different genres and discourses. Although some recent research has incorporated corpus linguistic techniques into this framework for the analysis of metaphor, to date, such analyses have primarily involved the concordancing of pre-selected search strings (e.g. Deignan 2005). The method described in this paper represents an attempt to extend the limits of this form of analysis. In our approach, we have applied an existing semantic field annotation tool (USAS) developed at Lancaster University to aid metaphor researchers in searching corpora. We are able to filter all possible candidate semantic fields proposed by USAS to assist in finding possible ‘source’ (e.g. JOURNEY) and ‘target’ (e.g. LIFE) domains, and we can then go on to consider the potential metaphoricity of the expressions included under each possible source domain. This method thus enables us to identify open-ended sets of metaphorical expressions, which are not limited to predetermined search strings. In section 3, we present this emerging methodology for the computer-assisted analysis of metaphorical patterns in discourse. The semantic fields automatically annotated by USAS can be seen as roughly corresponding to the domains of metaphor theory. We have used USAS in combination with key word and domain techniques in Wmatrix (Rayson, 2003) to replicate earlier manual analyses, e.g. machine metaphors in Ken Kesey’s One Flew Over the Cuckoo’s Nest (Semino and Swindlehurst, 1996) and war, machine and organism metaphors in business magazines (Koller, 2004a). These studies are described in section 4.


title: Quantifying moral foundations from various topics on Twitter conversations

abstract: Moral foundations theory explains variations in moral behavior using innate moral foundations: Care, Fairness, Ingroup, Authority, and Purity, along with experimental supports. However, little is known about the roles of and relationships between those foundations in everyday moral situations. To address these, we quantify moral foundations from a large amount of online conversations (tweets) about moral topics on the social media site Twitter. We measure moral loadings using latent semantic analysis of tweets related to topics on abortion, homosexuality, immigration, religion, and immorality in general, showing how the five moral foundations function in spontaneous conversations about moral violating situations. The results indicate that although the five foundations are mutually related, Purity is the most distinctive foundation and Care is the most dominant foundation in everyday conversations on immorality. Our study shows a new possibility of natural language processing and social big data for moral psychology.


title: on-line at: www.actabp.pl A 2D-IR study of heat- and [ 13 C]urea-induced denaturation of

abstract: Two-dimensional infrared correlation spectroscopy (2D-IR) was applied to the study of urea- and heat-induced unfolding denaturation of sarcoplasmic reticulum Ca(2+)-ATPase (SR ATPase). Urea at 2-3 M causes reversible loss of SR ATPase activity, while higher concentrations induce irreversible denaturation. Heat-induced denaturation is a non-two-state process, with an "intermediate state" (at t approximately 45 degrees C) characterized by the presence of protein monomers, instead of the native oligomers. 2D-IR reveals that urea denaturation causes loss of the structural transition to the "intermediate state". Whenever the urea effect can be reversed, the transition to the "intermediate state" is re-established.


Stacked inductors and transformers in CMOS technology


title: Core Python Applications Programming

abstract: Already know Python but want to learn more? A lot more? Dive into a variety of topics used in practice for real-world applications. Covers regular expressions, Internet/network programming, GUIs, SQL/databases/ORMs, threading, and Web development.Learn about contemporary development trends such as Google+, Twitter, MongoDB, OAuth, Python 3 migration, and Java/Jython. Presents brand new material on Django, Google App Engine, CSV/JSON/XML, and Microsoft Office. Includes Python 2 and 3 code samples to get you started right away!Provides code snippets, interactive examples, and practical exercises to help build your Python skills. The Complete Developers Guide to Python Python is an agile, robust, and expressive programming language that continues to build momentum. It combines the power of compiled languages with the simplicity and rapid development of scripting languages. In Core Python Applications Programming, Third Edition, leading Python developer and corporate trainer Wesley Chun helps you take your Python knowledge to the next level. This book has everything you need to become a versatile Python developer. You will be introduced to multiple areas of application development and gain knowledge that can be immediately applied to projects, and you will find code samples in both Python 2 and 3, including migration tips if thats on your roadmap too. Some snippets will even run unmodified on 2.x or 3.x. Learn professional Python style, best practices, and good programming habits Build clients and servers using TCP, UDP, XML-RPC, and be exposed to higher-level libraries like SocketServer and Twisted Develop GUI applications using Tkinter and other available toolkits Improve application performance by writing extensions in C/C++, or enhance I/O-bound code with multithreading Discover SQL and relational databases, ORMs, and even non-relational (NonSQL) databases like MongoDB Learn the basics of Web programming, including Web clients and servers, plus CGI and WSGI Expose yourself to regular expressions and powerful text processing tools for creating and parsing CSV, JSON, and XML data Interface with popular Microsoft Office applications such as Excel, PowerPoint, and Outlook using COM client programming Dive deeper into Web development with the Django framework and cloud computing with Google App Engine Explore Java programming with Jython, the way to run Python code on the JVM Connect to Web services Yahoo! Finance to get stock quotes, or Yahoo! Mail, Gmail, and others to download or send e-mailJump into the social media craze by learning how to connect to the Twitter and Google+ networks Core Python Applications Programming, Third Edition, delivers Broad coverage of a variety of areas of development used in real-world applications today Powerful insights into current and best practices for the intermediate Python programmer Dozens of code examples, from quick snippets to full-fledged applications A variety of exercises at the end of every chapter to help hammer the concepts home


title: Temperature balancing in steam methane reforming furnace via an integrated CFD/data-based optimization approach

abstract: Abstract In this work, we introduce a furnace-balancing scheme that generates an optimal furnace-side feed distribution that has the potential to improve the thermal efficiency of a reformer. The furnace-balancing scheme is composed of four major components: data generation, model identification, a model-based furnace-balancing optimizer and a termination checker. Initially, a computational fluid dynamics (CFD) model of an industrial-scale reformer, developed in our previous work, is used for the data generation as the model has been confirmed to simulate the typical transport and chemical reaction phenomena observed during reformer operation, and the CFD simulation data is in good agreement with various sources in literature. Then, we propose a model identification process in which the algorithm is formulated based on the least squares regression method, basic knowledge of radiative heat transfer and the existing furnace-side flow pattern. Subsequently, we propose a model-based furnace-balancing optimizer that is formulated as an optimization problem within which the valve position distribution is the decision variable, and minimizing the sum of the weighted squared deviations of the outer reforming tube wall temperatures from a set-point value for all reforming tubes with a penalty term on the deviation of the valve positions from their fully open positions is the objective function. CFD simulation results provide evidence that the optimized furnace-side feed distribution created by the furnace-balancing scheme can reduce the severity of nonuniformity in the spatial distribution of furnace-side temperature in the combustion chamber even when the reformer is under the influence of common valve-related disturbances.


title: Facebook makes the heart grow fonder: relationship maintenance strategies among geographically dispersed and communication-restricted connections

abstract: The increasing ubiquity of information and communication technologies has dramatically impacted interpersonal communication and relationship maintenance processes. These technologies remove temporal and spatial constraints, enabling communication at a distance for low to no physical costs. Research has established that technologies such as email supplement other forms of communication in relationship maintenance, but to what extent do newer technologies--which contain a unique set of affordances--facilitate these processes? Furthermore, do SNS users engage in different practices through the site and obtain different relational benefits based on specific characteristics of the tie? Findings from a survey of adult Facebook users (N=415) indicate that geographically distant Facebook Friends, as well as those who rely on the site as their primary form of communication, engage in relationship maintenance strategies through the site to a greater extent and perceive the site to have a more positive impact on the quality of their relationships.


title: Drug addiction: the neurobiology of behaviour gone awry

abstract: Drug addiction manifests as a compulsive drive to take a drug despite serious adverse consequences. This aberrant behaviour has traditionally been viewed as bad 'choices' that are made voluntarily by the addict. However, recent studies have shown that repeated drug use leads to long-lasting changes in the brain that undermine voluntary control. This, combined with new knowledge of how environmental, genetic and developmental factors contribute to addiction, should bring about changes in our approach to the prevention and treatment of addiction.


title: Social Reward Questionnaire (SRQ): development and validation

abstract: Human beings seek out social interactions as a source of reward. To date, there have been limited attempts to identify different forms of social reward, and little is known about how the value of social rewards might vary between individuals. This study aimed to address both these issues by developing the Social Reward Questionnaire (SRQ), a measure of individual differences in the value of different social rewards. Exploratory factor analysis (EFA) was run on an initial set of 75 items (N = 305). Based on this analysis, confirmatory factor analysis (CFA) was then conducted on a second sample (N = 505) with a refined 23-item scale. This analysis was used to test a six-factor structure, which resulted in good model fit (CFI = 0.96, RSMEA = 0.07). The factors represent six subscales of social reward defined as follows: Admiration; Negative Social Potency; Passivity; Prosocial Interactions; Sexual Reward; and Sociability. All subscales demonstrated good test-retest reliability and internal consistency. Each subscale also showed a distinct pattern of associations with external correlates measuring personality traits, attitudes, and goals, thus demonstrating construct validity. Taken together, the findings suggest that the SRQ is a reliable, valid measure that can be used to assess individual differences in the value experienced from different social rewards.


title: Near-infrared LED based non-invasive blood glucose sensor

abstract: Diabetes is a metabolic pathological condition of concern, which affects vital organs of body if not diagnosed and treated on time. Regular monitoring of blood glucose is important to avoid complication of diabetes. Commonly used glucose measurement methods are invasive which generally involves finger puncturing. These methods are painful and frequent pricking cause calluses on the skin and have risk of spreading infectious diseases. Therefore there is need to develop a non-invasive monitoring system which can measure blood glucose continuously without much problem. The present work is focused on development of non-invasive blood glucose measurement sensor system using Near-infrared (NIR) technique. Initially in-vitro glucose measurement prototype is developed using continuous wave (CW) from NIR LED (940 nm) to check the sensitivity of the system for different glucose concentrations. Later a Sensor patch was designed using LED and a photodiode to observe diffused reflectance spectra of blood from the human forearm. Diffused reflectance spectra of the subjects obtained with this technique was also compared with commercially available invasive finger tip gluco-meter. The results are promising and show the potential of using NIR for glucose measurement.


title: Introduction to Physical Systems Modeling with Bond Graphs

abstract: Bond graphs are a domain-independent graphical description of dynamic behaviour of physical systems. This means that systems from different domains (cf. electrical, mechanical, hydraulical, acoustical, thermodymanical, material) are described in the same way. The basis is that bond graphs are based on energy and energy exchange. Analogies between domains are more than just equations being analogous: the used physical concepts are analogous.


abstract: This volume contains the Satellite Events proceedings of the 19th International Conference on Knowledge Engineering and Knowledge Management (EKAW 2014), held in Linkoping, Sweden, during November ...


title: Increased Aggression and Loneliness as Potential Effects of Pathological Video-Gaming among Adolescents

abstract: The present research was conducted to examine the impact of video games on changing the levels of aggression and loneliness in adolescents. It was hypothesized that the adolescent and male pathological gamers are more aggressive and lonely than non-pathological and female gamers. In this study three scales were used, the first one was to measure the pathological video gaming by Video Game Addiction Scale of Gentile (2006); the second scale was to measure loneliness by R-UCLA Scale of Russell (1996 ), the third scale was Aggression Scale of Pamela Orpinas (Orpinas, 2001). The sample consisted of 150 adolescents, divided into 76 male and 74 female, age ranged from 12 to 20 years. Results indicated a significant variation between males and females on Video Game Addiction scale. There is also a significant difference between males and females in the loneliness scale, and there is a significant difference between males and females on the aggression scale. It was concluded that pathological video gaming can induce aggression and create feelings of loneliness among adolescents.


title: A Low-Voltage PLL With a Supply-Noise Compensated Feedforward Ring VCO

abstract: A low-voltage phase-locked-loop (PLL) circuit with a supply-noise-compensated feedforward ring voltage-controlled oscillator (FRVCO) is demonstrated. The oscillation frequency fluctuation due to supply noise is compensated by adjusting the ratio of driving strength in feedforward and direct paths in FRVCO. A prototype 400-MHz PLL circuit operating at 0.65 V is fabricated with 180-nm standard CMOS process. Measurement results show that supply-noise compensation is successfully achieved. Our PLL consumes only 242.1 $\mu\text{W}$ .


title: Cache-oblivious algorithms and data structures

abstract: Frigo, Leiserson, Prokop and Ramachandran in 1999 introduced the ideal-cache model as a formal model of computation for developing algorithms in environments with multiple levels of caching, and coined the terminology of cache-oblivious algorithms. Cache-oblivious algorithms are described as standard RAM algorithms with only one memory level, i.e. without any knowledge about memory hierarchies, but are analyzed in the two-level I/O model of Aggarwal and Vitter for an arbitrary memory and block size and an optimal off-line cache replacement strategy. The result are algorithms that automatically apply to multi-level memory hierarchies. This paper gives an overview of the results achieved on cache-oblivious algorithms and data structures since the seminal paper by Frigo et al.


title: Quantifying Performance Requirements of Vehicle-to-Vehicle Communication Protocols for Rear-End Collision Avoidance

abstract: We derive analytical bounds for the maximum acceptable message delivery latency and the minimum required retransmission frequency of 802.11-based vehicle-to-vehicle (V2V) communication protocols for rear-end collision avoidance applications. Using a microscopic car-following model of highway traffic combined with a probabilistic two-ray ground propagation model of the V2V wireless channel, we numerically investigate variations in these bounds with mean vehicular velocity, road grip coefficient, V2V packet loss rate and wireless channel fluctuation. Our analysis provides new quantitative guidelines and analytical inputs for the design of adaptive V2V protocols, which are capable of maintaining high reliability and efficiency in the face of large variations in vehicular traffic and V2V network conditions.


title: Modeling intrusion detection system using hybrid intelligent systems

abstract: The process of monitoring the events occurring in a computer system or network and analyzing them for sign of intrusions is known as intrusion detection system (IDS). This paper presents two hybrid approaches for modeling IDS. Decision trees (DT) and support vector machines (SVM) are combined as a hierarchical hybrid intelligent system model (DT-SVM) and an ensemble approach combining the base classifiers. The hybrid intrusion detection model combines the individual base classifiers and other hybrid machine learning paradigms to maximize detection accuracy and minimize computational complexity. Empirical results illustrate that the proposed hybrid systems provide more accurate intrusion detection systems.


title: Do-it-yourself lighting design for product videography

abstract: The growth of online marketplaces for selling goods has increased the need for product photography by novice users and consumers. Additionally, the increased use of online media and large-screen billboards promotes the adoption of videos for advertising, going beyond just using still imagery. Lighting is a key distinction between professional and casual product videography. Professionals use specialized hardware setups, and bring expert skills to create good lighting that shows off the product's shape and material, while also producing aesthetically pleasing results. In this paper, we introduce a new do-it-yourself (DIY) approach to lighting design that lets novice users create studio quality product videography. We identify design principles to light products through emphasizing highlights, rim lighting, and contours. We devise a set of computational metrics to achieve these design goals. Our workflow is: the user acquires a video of the product by mounting a video camera on a tripod and using a tablet to light objects by waving the tablet around the object. We automatically analyze and split this acquired video into snippets that match our design principles. Finally, we present an interface that lets users easily select snippets with specific characteristics and then assembles them to produce a final pleasing video of the product. Alternatively, they can rely on our template mechanism to automatically assemble a video.


title: Phillips Cr/Silica Catalyst for Ethylene Polymerization

abstract: The Phillips Cr/silica catalyst, discovered by Hogan and Banks at the Phillips Petroleum Company in the early 1950s, is one of the most important industrial catalysts for polyethylene production. In contrast to its great commercial success during the past half-century, academic progress regarding a basic understanding of the nature of the active sites and polymerization mechanisms is lagging far behind. During the last decade, increasing research efforts have been performed on the Phillips catalyst through various approaches, including spectroscopic methods, polymerization kinetics, heterogeneous model catalysts, homogeneous model catalysts, and molecular modeling. Much deeper mechanistic understanding, together with successive catalyst innovations through modifications of the Phillips catalyst, has been achieved.


title: A Link Prediction Approach for Item Recommendation with Complex Number

abstract: Recommendation can be reduced to a sub-problem of link prediction, with specific nodes (users and items) and links (similar relations among users/items, and interactions between users and items). However, the previous link prediction algorithms need to be modified to suit the recommendation cases since they do not consider the separation of these two fundamental relations: similar or dissimilar and like or dislike. In this paper, we propose a novel and unified way to solve this problem, which models the relation duality using complex number. Under this representation, the previous works can directly reuse. In experiments with the Movie Lens dataset and the Android software website AppChina.com, the presented approach achieves significant performance improvement comparing with other popular recommendation algorithms both in accuracy and coverage. Besides, our results revealed some new findings. First, it is observed that the performance is improved when the user and item popularities are taken into account. Second, the item popularity plays a more important role than the user popularity does in final recommendation. Since its notable performance, we are working to apply it in a commercial setting, AppChina.com website, for application recommendation.


title: Fatigue resistance and crack propensity of large MOD composite resin restorations: Direct versus CAD/CAM inlays

abstract: Abstract Objectives To assess the influence of material/technique selection (direct vs. CAD/CAM inlays) for large MOD composite adhesive restorations and its effect on the crack propensity and in vitro accelerated fatigue resistance. Methods A standardized MOD slot-type tooth preparation was applied to 32 extracted maxillary molars (5 mm depth and 5 mm bucco-palatal width) including immediately sealed dentin for the inlay group. Fifteen teeth were restored with direct composite resin restoration (Miris2) and 17 teeth received milled inlays using Paradigm MZ100 block in the CEREC machine. All inlays were adhesively luted with a light curing composite resin (Filtek Z100). Enamel shrinkage-induced cracks were tracked with photography and transillumination. Cyclic isometric chewing (5 Hz) was simulated, starting with a load of 200 N (5000 cycles), followed by stages of 400, 600, 800, 1000, 1200 and 1400 N at a maximum of 30,000 cycles each. Samples were loaded until fracture or to a maximum of 185,000 cycles. Results Teeth restored with the direct technique fractured at an average load of 1213 N and two of them withstood all loading cycles (survival = 13%); with inlays, the survival rate was 100%. Most failures with Miris2 occurred above the CEJ and were re-restorable (67%), but generated more shrinkage-induced cracks (47% of the specimen vs. 7% for inlays). Significance CAD/CAM MZ100 inlays increased the accelerated fatigue resistance and decreased the crack propensity of large MOD restorations when compared to direct restorations. While both restorative techniques yielded excellent fatigue results at physiological masticatory loads, CAD/CAM inlays seem more indicated for high-load patients.


title: Machine Learning-Guided Etch Proximity Correction

abstract: Rule- and model-based methods of etch proximity correction (EPC) are widely used, but they are insufficiently accurate for technologies below 20 nm. Simple rules are no longer adequate for the complicated patterns in layouts; and models based on a few empirically determined parameters cannot reflect etching phenomena physically. We introduce machine learning to EPC: each segment of interest, together with its surroundings, is characterized by geometric and optical parameters, which are then submitted to an artificial neural network that predicts the etch bias. We have implemented this new approach to EPC using a commercial OPC tool, and applied it to a DRAM gate layer in 20-nm technology, achieving predictions that are 34% more accurate than model-based EPC.


SoMeWeTa: A Part-of-Speech Tagger for German Social Media and Web Texts.


title: Harvesting implicit group attitudes and beliefs from a demonstration web site

abstract: Respondents at an Internet site completed over 600,000 tasks between October 1998 and April 2000 measuring attitudes toward and stereotypes of social groups. Their responses demonstrated, on average, implicit preference for White over Black and young over old and stereotypic associations linking male terms with science and career and female terms with liberal arts and family. The main purpose was to provide a demonstration site at which respondents could experience their implicit attitudes and stereotypes toward social groups. Nevertheless, the data collected are rich in information regarding the operation of attitudes and stereotypes, most notably the strength of implicit attitudes, the association and dissociation between implicit and explicit attitudes, and the effects of group membership on attitudes and stereotypes.


title: Botulinum-A Toxin Application for the Treatment of Asymmetric Crying Facies

abstract: An infant with a face symmetrical at rest, but one corner of the mouth does not pull downward and outward symmetrically with the other while crying and smiling, is said to have an “asymmetric crying facies” (ACF). The etiology of this asymmetry is congenital absence or hypoplasia of the depressor anguli oris muscle (DAOM) at one corner of the mouth. Its associations with major congenital anomalies, most commonly in the cardiovascular system, have been reported. In this report, a six-year-old boy with asymmetric crying facies and its treatment with botulinumA toxin (BOTOX) is presented. To our knowledge, the treatment of ACF with BOTOX has not been reported yet.


title: Working Knowledge: How Organizations Manage What They Know

abstract: From the Publisher: ::: The definitive primer on knowledge management, this book will establish the enduring vocabulary and concepts and serve as the hands-on resource of choice for fast companies that recognize knowledge as the only sustainable source of competitive advantage. Drawing on their work with more than 30 knowledge-rich firms, the authors-experienced consultants with a track record of success-examine how all types of companies can effectively understand, analyze, measure, and manage their intellectual assets, turning corporate knowledge into market value. They consider such questions as: What key cultural and behavioral issues must managers address to use knowledge effectively?; What are the best ways to incorporate technology into knowledge work?; What does a successful knowledge project look like-and how do you know when it has succeeded? In the end, say the authors, the human qualities of knowledge-experience, intuition, and beliefs-are the most valuable and the most difficult to manage. Applying the insights of Working Knowledge is every manager's first step on that rewarding road to long-term success. A Library Journal Best Business Book of the Year. "For an entire company...to have knowledge, that information must be coordinated and made accessible. Thomas H. Davenport...and Laurence Prusak... offer an elegantly simple overview of the 'knowledge market' aimed at fulfilling that goal.... Working Knowledge provides practical advice about implementing a knowledge-management system....A solid dose of common sense for any company looking to acquire -- or maintain -- a competitive edge."--Upside, June 1998


title: Automated Pyramid Scoring of Summaries using Distributional Semantics

abstract: The pyramid method for content evaluation of automated summarizers produces scores that are shown to correlate well with manual scores used in educational assessment of students’ summaries. This motivates the development of a more accurate automated method to compute pyramid scores. Of three methods tested here, the one that performs best relies on latent semantics.


title: Reliability and economic analysis of offshore wind power systems - A comparison of internal grid topologies

abstract: Wind power has emerged the last decades as an important technology for low carbon emitting electricity production. The development was first onshore, but recently many wind farms have been developed offshore due to space availability, low noise and visual impacts. Offshore wind farms experiences higher winds resulting in higher production, but investment costs, and operation and maintenance costs are also much higher. More specifically, a failure may result in long downtime due to inaccessibility to the site during harsh conditions, and transportation costs are also higher since boats, and sometimes helicopters are necessary. ::: This study consists of a reliability and economic analysis of the internal grid of offshore wind power system. Different topologies are evaluated and compared against each other with the aim of presenting alternative layouts with increased level of reliability at an acceptable cost. The different layouts are compared in additional income over the life time of the wind farm considering the energy not supplied. ::: The results show that for the case study consisting of two arrays of ten 3MW wind turbines–three of the proposed alternative layouts presents a positive Net Present Value of the extra investment of 63k€, 165k€ and 201k€ respectively. It can be concluded that it can be beneficial to implement some degree of redundancy along with improvements in the protection system.


title: Simultaneous Real-Time Segmentation of Diversified Barcode Symbols in Complex Background

abstract: Traditional barcode localization methods based on image analysis are sensitive to the types of the target symbols and the environment they are applied to. To develop intelligent barcode reading system used in industry, a real-time region based barcode segmentation approach which is available for various types of linear and two-dimensional symbols is proposed. The two-stage approach consists of the target region connection part by orientation detection and morphological operation, and the target region detection part by efficient contour based connected component labeling. The result of target location, which is represented by the coordinates of the upper left corner and the lower right corner of its bounding box, is robust to the barcode orientation, noise, and uneven environment illumination. The segmentation method is proved to work well in the real-time barcode reading system. In the experiments, 100% of the DATAMATRIX codes, 99.3% of the Code39 symbols and 98.7% PDF417 codes are corrected segmented.


title: Generalized Multicast Multibeam Precoding for Satellite Communications

abstract: This paper deals with the problem of precoding in multibeam satellite systems. In contrast to general multiuser multiple-input-multiple-output cellular schemes, multibeam satellite architectures suffer from different challenges. First, satellite communications standards embed more than one user in each frame in order to increase the channel coding gain. This leads to the different so-called multigroup multicast models, whose optimization requires computationally complex operations. Second, when the data traffic is generated by several Earth stations (gateways), the precoding matrix must be distributively computed and meet additional payload restrictions. Third, since the feedback channel is adverse (large delay and quantization errors), the precoding must be able to deal with such uncertainties. In order to solve the aforementioned problems, we propose a two-stage precoding design in order to both limit the multibeam interference and to enhance the intra-beam minimum user signal power (i.e., the one that dictates the rate allocation per beam). A robust version of the proposed precoder based on a first perturbation model is presented. This mechanism behaves well when the channel state information is corrupted. Furthermore, we propose a per beam user grouping mechanism together with its robust version in order to increase the precoding gain. Finally, a method for dealing with the multiple gateway architecture is presented, which offers high throughputs with a low inter-gateway communication. The conceived designs are evaluated with a close-to-real beam pattern and the latest broadband communication standard for satellite communications.


title: A multi-exposure image fusion algorithm without ghost effect

abstract: This paper proposes a new multi-exposure fusion algorithm for HDR imaging. The advantage of the fusion based method is that it does not actually generate HDR images so that the compression to LDR is also not needed. However, the conventional exposure fusion suffers from ghost effects when there are moving object or hand trembling because the output is just a weighted sum of multiple exposure input images. In order to alleviate this problem, we propose two kinds of weights in addition to the conventional weights that reflect the contrast, saturation and well-exposedness. The first is the weight that removes the pixels that can cause ghost effects, based on the observation that pixels near the boundary of moving objects often violate the rule that more highly exposed pixel have higher intensity level. The second is to reduce the weight when the correlation between an exposed image and the reference image is low. Experimental results show that the proposed method removes the ghost effectively, and yield less artifacts than the conventional methods.


title: Towards a List of Heuristics to Evaluate Smartphone Apps Targeted at Older Adults: A Study with Apps that Aim at Promoting Health and Well-Being

abstract: This paper presents a list of heuristics to evaluate smartphone apps for older adults. It further verifies the usefulness of the proposed list through a study performed with two groups of five expert evaluators, who inspected two popular health and fitness smartphone apps -- Nike+ and Run keeper -- through heuristic evaluation. Additionally, the evaluators completed a post-evaluation survey to provide feedback to the researchers about the usefulness, strengths and gaps of the heuristics list. The results of the heuristic evaluations and post questionnaires demonstrate both a comprehensive application of the heuristics as well as an overall positive assessment of their quality and potential to identify usability issues of smartphone apps targeted at older adults.


title: Nepotistic Relationships in Twitter and their Impact on Rank Prestige Algorithms

abstract: Abstract Micro-blogging services such as Twitter allow anyone to publish anything, anytime. Needless to say, many of the available contents can be diminished as babble or spam. However, given the number and diversity of users, some valuable pieces of information should arise from the stream of tweets. Thus, such services can develop into valuable sources of up-to-date information (the so-called real-time web) provided a way to find the most relevant/trustworthy/authoritative users is available. Hence, this makes a highly pertinent question for which graph centrality methods can provide an answer. In this paper the author offers a comprehensive survey of feasible algorithms for ranking users in social networks, he examines their vulnerabilities to linking malpractice in such networks, and suggests an objective criterion against which to compare such algorithms. Additionally, he suggests a first step towards “desensitizing” prestige algorithms against cheating by spammers and other abusive users.


Learning to Sample Using Stein Discrepancy


title: Product recommendation and rating prediction based on multi-modal social networks

abstract: Online Social Rating Networks (SRNs) such as Epinions and Flixter, allow users to form several implicit social networks, through their daily interactions like co-commenting on the same products, or similarly co-rating products. The majority of earlier work in Rating Prediction and Recommendation of products (e.g. Collaborative Filtering) mainly takes into account ratings of users on products. However, in SRNs users can also built their explicit social network by adding each other as friends. In this paper, we propose Social-Union, a method which combines similarity matrices derived from heterogeneous (unipartite and bipartite) explicit or implicit SRNs. Moreover, we propose an effective weighting strategy of SRNs influence based on their structured density. We also generalize our model for combining multiple social networks. We perform an extensive experimental comparison of the proposed method against existing rating prediction and product recommendation algorithms, using synthetic and two real data sets (Epinions and Flixter). Our experimental results show that our Social-Union algorithm is more effective in predicting rating and recommending products in SRNs.


title: Measuring user satisfaction with knowledge management systems: scale development, purification, and initial test

abstract: The shift from a product-based to a knowledge-based economy has resulted in an increased demand for knowledge workers who are capable of higher-order thinking and reasoning to solve intricate problems in the workplace. This requires organizations to introduce knowledge management systems (KMS) for employees and has fueled predictions and speculations about what makes KMS effective. Unfortunately, there are very few empirical studies that examine this issue. Thus, this paper develops a validated instrument to measure user satisfaction as a surrogate measure of KMS effectiveness. Based on a survey of 147 respondents practicing mostly in four international semiconductor manufacturing companies in the Hsin-Chu Science-based Industrial Park in Taiwan suggests a 16-item instrument that measures four dimensions of user satisfaction with knowledge management systems (USKMS) is well-validated. The instrument and comprehensive model proposed in this paper would be valuable to researchers and practitioners interested in designing, implementing, and managing knowledge management systems.


title: MEC 2016: The Multimodal Emotion Recognition Challenge of CCPR 2016

abstract: Emotion recognition is a significant research filed of pattern recognition and artificial intelligence. The Multimodal Emotion Recognition Challenge (MEC) is a part of the 2016 Chinese Conference on Pattern Recognition (CCPR). The goal of this competition is to compare multimedia processing and machine learning methods for multimodal emotion recognition. The challenge also aims to provide a common benchmark data set, to bring together the audio and video emotion recognition communities, and to promote the research in multimodal emotion recognition. The data used in this challenge is the Chinese Natural Audio-Visual Emotion Database (CHEAVD), which is selected from Chinese movies and TV programs. The discrete emotion labels are annotated by four experienced assistants. Three sub-challenges are defined: audio, video and multimodal emotion recognition. This paper introduces the baseline audio, visual features, and the recognition results by Random Forests.


Some Reactions of the Trifluoromethyl Group in the Benzotrifluoride Series. I. Hydrolysis


title: What Objective Does Self-paced Learning Indeed Optimize?

abstract: Self-paced learning (SPL) is a recently raised methodology designed through simulating the learning principle of humans/animals. A variety of SPL realization schemes have been designed for different computer vision and pattern recognition tasks, and empirically substantiated to be effective in these applications. However, the investigation on its theoretical insight is still a blank. To this issue, this study attempts to provide some new theoretical understanding under the SPL scheme. Specifically, we prove that the solving strategy on SPL accords with a majorization minimization algorithm implemented on a latent objective function. Furthermore, we find that the loss function contained in this latent objective has a similar configuration with non-convex regularized penalty (NSPR) known in statistics and machine learning. Such connection inspires us discovering more intrinsic relationship between SPL regimes and NSPR forms, like SCAD, LOG and EXP. The robustness insight under SPL can then be finely explained. We also analyze the capability of SPL on its easy loss prior embedding property, and provide an insightful interpretation to the effectiveness mechanism under previous SPL variations. Besides, we design a group-partial-order loss prior, which is especially useful to weakly labeled large-scale data processing tasks. Through applying SPL with this loss prior to the FCVID dataset, which is currently one of the biggest manually annotated video dataset, our method achieves state-of-the-art performance beyond previous methods, which further helps supports the proposed theoretical arguments.


title: A new multi-modal dataset for human affect analysis

abstract: In this paper we present a new multi-modal dataset of spontaneous three way human interactions. Participants were recorded in an unconstrained environment at various locations during a sequence of debates in a video conference, Skype style arrangement. An additional depth modality was introduced, which permitted the capture of 3D information in addition to the video and audio signals. The dataset consists of 16 participants and is subdivided into 6 unique sections. The dataset was manually annotated on a continuously scale across 5 different affective dimensions including arousal, valence, agreement, content and interest. The annotation was performed by three human annotators with the ensemble average calculated for use in the dataset. The corpus enables the analysis of human affect during conversations in a real life scenario. We first briefly reviewed the existing affect dataset and the methodologies related to affect dataset construction, then we detailed how our unique dataset was constructed.


1 Multiobjective Combinatorial Optimization by Using Decomposition and Ant Colony


title: Computation of a face attractiveness index based on neoclassical canons, symmetry, and golden ratios

abstract: Analysis of attractiveness of faces has long been a topic of research. Literature has identified many different factors that can be related to attractiveness. In this research we analyze the role of symmetry, neoclassical canons, and golden ratio in the determination of attractiveness of a face. We focus on the geometry of a face and use actual faces for our analysis. We find there are some differences in the criteria used by males and females to determine attractiveness. The model we have developed to predict the attractiveness of a face using its geometry is accurate with low residual errors.


title: The Astrophysics of Ultrahigh Energy Cosmic Rays

abstract: The origin of the highest energy cosmic rays is still unknown. The discovery of their sources will reveal the workings of the most energetic astrophysical accelerators in the universe. Current observations show a spectrum consistent with an origin in extragalactic astrophysical sources. Candidate sources range from the birth of compact objects to explosions related to gamma-ray bursts or to events in active galaxies. We discuss the main effects of propagation from cosmologically distant sources including interactions with cosmic background radiation and magnetic fields. We examine possible acceleration mechanisms leading to a survey of candidate sources and their signatures. New questions arise from an observed hint of sky anisotropies and an unexpected evolution of composition indicators. Future observations may reach the necessary sensitivity to achieve charged particle astronomy and to observe ultrahigh energy photons and neutrinos, which will further illuminate the workings of the universe at these extreme energies. In addition to fostering a new understanding of high-energy astrophysical phenomena, the study of ultrahigh energy cosmic rays can constrain the structure of the Galactic and extragalactic magnetic fields as well as probe particle interactions at energies orders of magnitude higher than achieved in terrestrial accelerators.


title: Spectral Lens: Explainable Diagnostics, Tools and Discoveries in Directed, Weighted Graphs

abstract: How can we quickly explain large-scale directed and weighted graphs? We present Spectral Lens (SL) to analyze a variety of real-world networks with both negative and positive edge weights, like DBLP relationships, email communications and Bitcoin trust votings. SL offers value on three levels: (a) Diagnostics: Spectral Lens combines spectral properties from Singular Value Decomposition to create an SL-Dictionary (SLD) to enhance understanding of any directed, weighted graph. (b) Tools: the SL-Algorithm (SLA) automatically extracts the top groups of nodes with similar connectivity, finds groups of shared connectivity and detects suspicious behaviour in a graph. (c) Discoveries: Experiments on several real-world networks illustrate the effectiveness of SLA. Observations from synthetic and real-world networks reveal relations between spectral and graph properties. We show that SLA is highly scalable and linear on the size of a graph. Analyzing a graph with over 2 million edges takes less than 5 minutes. Overall, SL provides an easy-to-use tool for practitioners to explain a weighted and directed graph quickly, to understand its connectivity and identify regular and anomalous behaviors.


Adult brain tissue segmentation in multi-modal images with a HyperDenseNet


title: A novel noninvasive blood glucose monitor.

abstract: OBJECTIVE ::: To evaluate the precision and accuracy of a new advanced prototype of a noninvasive blood glucose monitor across a wide range of serum glucose concentrations. ::: ::: ::: RESEARCH DESIGN AND METHODS ::: An advanced handheld noninvasive glucose monitor prototype was calibrated and tested using patients recruited by the General Research Center of the University of Connecticut Health Center. The monitor, developed by Infratec, uses principles of thermal emission spectroscopy. The noninvasive measurement of tympanic membrane glucose concentration was calibrated to the serum glucose concentration using 432 paired measurements from 20 subjects with insulin-requiring diabetes. This calibration was subsequently tested (results of power analyses) in a blind fashion with 126 paired measurements from six diabetic subjects who require insulin. ::: ::: ::: RESULTS ::: In vivo measurements demonstrated the reproducibility of the methodology of the noninvasive glucose monitor. Based on the calibration model, predicted glucose concentrations for six subjects were as follows (for 126 data points): SD = 32 mg/dl, mean absolute relative error (%MARE) = 11.6, with a correlation coefficient of r = 0.87. Noninvasive glucose results were also compared with laboratory reference measurements using an error-in-variables method. Clark error grid analysis showed that 100% of the measurements fell within zones A and B (90% in zone A and 10% in zone B). The SD for all noninvasive measured concentrations was 27 mg/dl, %MARE was 8.6, and the correlation coefficient was r = 0.94. ::: ::: ::: CONCLUSIONS ::: This first independent clinical study of an advanced noninvasive blood glucose prototype based on thermal emission in the mid-infrared spectral region has demonstrated glucose measurements with clinically acceptable accuracy but without the necessity of individual daily calibration.


title: Batch versus interactive learning by demonstration

abstract: Agents that operate in human environments will need to be able to learn new skills from everyday people. Learning from demonstration (LfD) is a popular paradigm for this. Drawing from our interest in Socially Guided Machine Learning, we explore the impact of interactivity on learning from demonstration. We present findings from a study with human subjects showing people who are able to interact with the learning agent provide better demonstrations (in part) by adapting based on learner performance which results in improved learning performance. We also find that interactivity increases a sense of engagement and may encourage players to participate longer. Our exploration of interactivity sheds light on how best to obtain demonstrations for LfD applications.


title: The infarct-sparing effect of IB-MECA against myocardial ischemia/reperfusion injury in mice is mediated by sequential activation of adenosine A3 and A2A receptors

abstract: Conflicting results exist regarding the role of A3 adenosine receptors (A3ARs) in mediating cardioprotection during reperfusion following myocardial infarction. We hypothesized that the effects of the A3AR agonist IB-MECA to produce cardioprotection might involve activation of other adenosine receptor subtypes. C57Bl/6 (B6), A3AR KO, A2AAR KO, and A2AAR KO/WT bone marrow chimeric mice were assigned to 12 groups undergoing either hemodynamic studies or 45 min of LAD occlusion and 60 min of reperfusion. IB-MECA (100 μg/kg) or vehicle was administered by iv bolus 5 min before reperfusion. Radioligand binding assays showed that IB-MECA has high affinity for the mouse A3AR (K i = 0.17 ± 0.05 nM), but also can bind with lower affinity to the A1AR (9.0 ± 2.4nM) or the A2AAR (56.5 ± 10.2nM). IB-MECA caused bi-phasic hemodynamic changes, which were completely absent in A3AR KO mice and were modified by A2AAR blockade or deletion. IB-MECA stimulated histamine release, increased heart rate, and significantly reduced IF size in B6 mice from 61.5 ± 1.4 to 48.6 ± 2.4 % of risk region (RR; 21 % reduction, p < 0.05) but not in A3AR KO mice. Compared to B6, A3AR KO mice had significantly reduced IF size (p < 0.05). In B6/B6 bone marrow chimeras, IB-MECA caused a 47 % reduction of IF size (from 47.3 ± 3.9 to 24.7 ± 4.5, p < 0.05). However, no significant cardioprotective effect of IB-MECA was observed in A2AARKO/B6 mice, which lacked A2AARs only on their bone marrow-derived cells. Activation of A3ARs induces a bi-phasic hemodynamic response, which is partially mediated by activation of A2AARs. The cardioprotective effect of IB-MECA is due to the initial activation of A3AR followed by activation of A2AARs in bone marrow-derived cells.


title: Brain mechanisms of pain affect and pain modulation

abstract: Abstract Recent animal studies reveal ascending nociceptive and descending modulatory pathways that may contribute to the affective–motivational aspects of pain and play a critical role in the modulation of pain. In humans, a reliable pattern of cerebral activity occurs during the subjective experience of pain. Activity within the anterior cingulate cortex and possibly in other classical limbic structures, appears to be closely related to the subjective experience of pain unpleasantness and may reflect the regulation of endogenous mechanisms of pain modulation.


title: Traceability patterns: an approach to requirement-component traceability in agile software development

abstract: Requirements traceability, regardless of the process used to produce a software system, is an important quality attribute for the resulting software product. However, existing approaches to achieving requirements traceability have been specifically designed for traditional software development with formal requirements processes. With the wide industry adoption of agile development methodologies, where requirements are captured and communicated through informal channels, the applicability of existing traceability approaches to agile software projects has been restricted. In this paper, we introduce traceability patterns as a solution to requirement-component tractability in agile software development. In our approach, we achieve traceability through source code, which is the main development artifact in agile methodologies.


title: Evolved structure of language shows lineage-specific trends in word-order universals

abstract: to the generative account of parameter setting, we show that the evolution of only a few word-order features of languages are strongly correlated. Second, contrary to the Greenbergian generalizations, we show that most observed functional dependencies between traits are lineage-specific rather than universal tendencies. These findings support the view that—at least with respect to word order—cultural evolution is the primary factor that determines linguistic structure, with the current state of a linguistic system shaping and constraining future states. Human language is unique amongst animal communication systems not only for its structural complexity but also for its diversity at every level of structure and meaning. There are about 7,000 extant languages, some with just a dozen contrastive sounds, others with more than 100, some with complex patterns of word formation, others with simple words only, some with the verb at the beginning of the sentence, some in the middle, and some at the end. Understanding this diversity and the systematic constraints on it is the central goal of linguistics. The generative approach to linguistic variation has held that linguistic diversity can be explained by changes in parameter settings. Each of these parameters controls a number of specific linguistic traits. For example, the setting ‘heads first’ will cause a language both to place verbs before objects (‘kick the ball’), and prepositions before nouns (‘into the goal’) 1,7 . According to this account, language change occurs when child learners simplify or regularize by choosing parameter settings other than those of the parental generation. Across a few generations such changes might work through a population, effecting language change across all the associated traits. Language change should therefore be relatively fast, and the traits set by one parameter must co-vary 8 . In contrast, the statistical approach adopted by Greenbergian linguists samples languages to find empirically co-occurring traits. These cooccurring traits are expected to be statistical tendencies attributable to universal cognitive or systems biases. Among the most robust of these tendencies are the so-called ‘‘word-order universals’’ 3 linking the order of elements in a clause. Dryer has tested these generalizations on a worldwide sample of 625 languages and finds evidence for some of these expected linkages between word orders 9 . According to Dryer’s reformulation of the word-order universals, dominant verb–object ordering correlates with prepositions, as well as relative clauses and genitives after the noun, whereas dominant object–verb ordering predicts postpositions, relative clauses and genitives before the noun 4 . One general explanation for these observations is that languages tend to be consistent (‘harmonic’) in their order of the most important element or ‘head’ of a phrase relative to its ‘complement’ or ‘modifier’ 3 , and so if the verb is first before its object, the adposition (here preposition) precedes the noun, while if the verb is last after its object, the adposition follows the noun (a ‘postposition’). Other functionally motivated explanations emphasize consistent direction of branching within the syntactic structure of a sentence 9 or information structure and processing efficiency 5 . To demonstrate that these correlations reflect underlying cognitive or systems biases, the languages must be sampled in a way that controls for features linked only by direct inheritance from a common ancestor 10 . However, efforts to obtain a statistically independent sample of languages confront several practical problems. First, our knowledge of language relationships is incomplete: specialists disagree about highlevel groupings of languages and many languages are only tentatively assigned to language families. Second, a few large language families contain the bulk of global linguistic variation, making sampling purely from unrelated languages impractical. Some balance of related, unrelated and areally distributed languages has usually been aimed for in practice 11,12 . The approach we adopt here controls for shared inheritance by examining correlation in the evolution of traits within well-established family trees 13 . Drawing on the powerful methods developed in evolutionary biology, we can then track correlated changes during the historical processes of language evolution as languages split and diversify. Large language families, a problem for the sampling method described above, now become an essential resource, because they permit the identification of coupling between character state changes over long time


title: MINING STUDENTS' ACADEMIC PERFORMANCE

abstract: Data Mining techniques are widely used in educational field to find new hidden patterns from student’s data. The hidden patterns that are discovered can be used to understand the problem arise in the educational field. This paper surveys the three elements needed to make prediction on Students’ Academic Performances which are parameters, methods and tools. This paper also proposes a framework for predicting the performance of first year bachelor students in computer science course. Naive Bayes Classifier is used to extract patterns using the Data Mining Weka tool. The framework can be used as a basis for the system implementation and prediction of Students’ Academic Performance in Higher Learning Institutions.


title: Unsupervised Learning with Stein's Unbiased Risk Estimator

abstract: Learning from unlabeled and noisy data is one of the grand challenges of machine learning. As such, it has seen a flurry of research with new ideas proposed continuously. In this work, we revisit a classical idea: Stein's Unbiased Risk Estimator (SURE). We show that, in the context of image recovery, SURE and its generalizations can be used to train convolutional neural networks (CNNs) for a range of image denoising and recovery problems {\em without any ground truth data.} Specifically, our goal is to reconstruct an image $x$ from a {\em noisy} linear transformation (measurement) of the image. We consider two scenarios: one where no additional data is available and one where we have measurements of other images that are drawn from the same noisy distribution as $x$, but have no access to the clean images. Such is the case, for instance, in the context of medical imaging, microscopy, and astronomy, where noise-less ground truth data is rarely available. We show that in this situation, SURE can be used to estimate the mean-squared-error loss associated with an estimate of $x$. Using this estimate of the loss, we train networks to perform denoising and compressed sensing recovery. In addition, we also use the SURE framework to partially explain and improve upon an intriguing results presented by Ulyanov et al. in"Deep Image Prior": that a network initialized with random weights and fit to a single noisy image can effectively denoise that image.


title: Feature detection and tracking with the dynamic and active-pixel vision sensor (DAVIS)

abstract: Because standard cameras sample the scene at constant time intervals, they do not provide any information in the blind time between subsequent frames. However, for many high-speed robotic and vision applications, it is crucial to provide high-frequency measurement updates also during this blind time. This can be achieved using a novel vision sensor, called DAVIS, which combines a standard camera and an asynchronous event-based sensor in the same pixel array. The DAVIS encodes the visual content between two subsequent frames by an asynchronous stream of events that convey pixel-level brightness changes at microsecond resolution. We present the first algorithm to detect and track visual features using both the frames and the event data provided by the DAVIS. Features are first detected in the grayscale frames and then tracked asynchronously in the blind time between frames using the stream of events. To best take into account the hybrid characteristics of the DAVIS, features are built based on large, spatial contrast variations (i.e., visual edges), which are the source of most of the events generated by the sensor. An event-based algorithm is further presented to track the features using an iterative, geometric registration approach. The performance of the proposed method is evaluated on real data acquired by the DAVIS.


title: Holographic near-eye displays for virtual and augmented reality

abstract: We present novel designs for virtual and augmented reality near-eye displays based on phase-only holographic projection. Our approach is built on the principles of Fresnel holography and double phase amplitude encoding with additional hardware, phase correction factors, and spatial light modulator encodings to achieve full color, high contrast and low noise holograms with high resolution and true per-pixel focal control. We provide a GPU-accelerated implementation of all holographic computation that integrates with the standard graphics pipeline and enables real-time (≥90 Hz) calculation directly or through eye tracked approximations. A unified focus, aberration correction, and vision correction model, along with a user calibration process, accounts for any optical defects between the light source and retina. We use this optical correction ability not only to fix minor aberrations but to enable truly compact, eyeglasses-like displays with wide fields of view (80°) that would be inaccessible through conventional means. All functionality is evaluated across a series of hardware prototypes; we discuss remaining challenges to incorporate all features into a single device.


title: Mean Birds: Detecting Aggression and Bullying on Twitter

abstract: In recent years, bullying and aggression against social media users have grown significantly, causing serious consequences to victims of all demographics. Nowadays, cyberbullying affects more than half of young social media users worldwide, suffering from prolonged and/or coordinated digital harassment. Also, tools and technologies geared to understand and mitigate it are scarce and mostly ineffective. In this paper, we present a principled and scalable approach to detect bullying and aggressive behavior on Twitter. We propose a robust methodology for extracting text, user, and network-based attributes, studying the properties of bullies and aggressors, and what features distinguish them from regular users. We find that bullies post less, participate in fewer online communities, and are less popular than normal users. Aggressors are relatively popular and tend to include more negativity in their posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3 months, and show that machine learning classification algorithms can accurately detect users exhibiting bullying and aggressive behavior, with over 90% AUC.


title: Systematic Review of Yoga Interventions for Anxiety Reduction Among Children and Adolescents.

abstract: OBJECTIVE ::: Anxiety disorders are the most prevalent psychological disorders among children and youths. There is growing interest in intervention options for anxiety. Yoga is widely used in clinical, school, and community settings, but consolidated sources outlining its effectiveness in reducing anxiety are limited. ::: ::: ::: METHOD ::: This systematic review examined the evidence base (1990-2014) for yoga interventions addressing anxiety among children and adolescents (ages 3-18 yr). ::: ::: ::: RESULTS ::: We identified 2,147 references and found 80 articles that were eligible for full-text review. The final analysis included 16: 6 randomized controlled trials, 2 nonrandomized preintervention-postintervention control-group designs, 7 uncontrolled preintervention-postintervention studies, and 1 case study. ::: ::: ::: CONCLUSION ::: Nearly all studies indicated reduced anxiety after a yoga intervention. However, because of the wide variety of study populations, limitations in some study designs, and variable outcome measures, further research is needed to enhance the ability to generalize and apply yoga to reduce anxiety.


title: Time-frequency segmentation of bird song in noisy acoustic environments

abstract: Recent work in machine learning considers the problem of identifying bird species from an audio recording. Most methods require segmentation to isolate each syllable of bird call in input audio. Energy-based time-domain segmentation has been successfully applied to low-noise, single-bird recordings. However, audio from automated field recorders contains too much noise for such methods, so a more robust segmentation method is required. We propose a supervised time-frequency audio segmentation method using a Random Forest classifier, to extract syllables of bird call from a noisy signal. When applied to a test data set of 625 field-collected audio segments, our method isolates 93.6% of the acoustic energy of bird song with a false positive rate of 8.6%, outperforming energy thresholding.


title: An Interleaved Totem-Pole Power Factor Correction Converter

abstract: This paper explains about basic working principles, implementation problems, and experimental results of a new power factor correction (PFC) topology. In this proposed topology, a totem-pole boost PFC circuit that works under interleaved critical-conduction-mode (CRM) condition is presented. The circuit is developed towards high efficiency, high performance, lowcost, simple control scheme, and low conducted electro magnetic interference (EMI) PFC circuit. The application is targeted toward middle power applications that normally employs continuous or non-bridgeless interleaved boost converter.


title: Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning

abstract: Brain segmentation is a fundamental first step in neuroimage analysis. In the case of fetal MRI, it is particularly challenging and important due to the arbitrary orientation of the fetus, organs that surround the fetal head, and intermittent fetal motion. Several promising methods have been proposed but are limited in their performance in challenging cases and in realtime segmentation. We aimed to develop a fully automatic segmentation method that independently segments sections of the fetal brain in 2D fetal MRI slices in real-time. To this end, we developed and evaluated a deep fully convolutional neural network based on 2D U-net and autocontext, and compared it to two alternative fast methods based on 1) a voxelwise fully convolutional network and 2) a method based on SIFT features, random forest and conditional random field. We trained the networks with manual brain masks on 250 stacks of training images, and tested on 17 stacks of normal fetal brain images as well as 18 stacks of extremely challenging cases based on extreme motion, noise, and severely abnormal brain shape. Experimental results show that our U-net approach outperformed the other methods and achieved average Dice metrics of 96.52% and 78.83% in the normal and challenging test sets, respectively. With an unprecedented performance and a test run time of about 1 second, our network can be used to segment the fetal brain in real-time while fetal MRI slices are being acquired. This can enable real-time motion tracking, motion detection, and 3D reconstruction of fetal brain MRI.


ASLR on the Line: Practical Cache Attacks on the MMU


Predicting Publication Inclusion for Diagnostic Accuracy Test Reviews Using Random Forests and Topic Modelling


title: Composing Music with Grammar Argumented Neural Networks and Note-Level Encoding

abstract: Creating aesthetically pleasing pieces of art, including music, has been a long-term goal for artificial intelligence research. Despite recent successes of long-short term memory (LSTM) recurrent neural networks (RNNs) in sequential learning, LSTM neural networks have not, by themselves, been able to generate natural-sounding music conforming to music theory. To transcend this inadequacy, we put forward a novel method for music composition that combines the LSTM with Grammars motivated by music theory. The main tenets of music theory are encoded as grammar argumented (GA) filters on the training data, such that the machine can be trained to generate music inheriting the naturalness of human-composed pieces from the original dataset while adhering to the rules of music theory. Unlike previous approaches, pitches and durations are encoded as one semantic entity, which we refer to as note-level encoding. This allows easy implementation of music theory grammars, as well as closer emulation of the thinking pattern of a musician. Although the GA rules are applied to the training data and never directly to the LSTM music generation, our machine still composes music that possess high incidences of diatonic scale notes, small pitch intervals and chords, in deference to music theory.


title: Asymmetric Transfer Learning with Deep Gaussian Processes

abstract: We introduce a novel Gaussian process based Bayesian model for asymmetric transfer learning. We adopt a two-layer feed-forward deep Gaussian process as the task learner of source and target domains. The first layer projects the data onto a separate non-linear manifold for each task. We perform knowledge transfer by projecting the target data also onto the source domain and linearly combining its representations on the source and target domain manifolds. Our approach achieves the state-of-the-art in a benchmark real-world image categorization task, and improves on it in cross-tissue tumor detection from histopathology tissue slide images.


title: Online detection of malicious data access using DBMS auditing

abstract: This paper proposes a mechanism that allows concurrent detection of malicious data access through the online analysis of the Database Management Systems (DBMS) audit trail. The proposed mechanism uses a directed graph representing the profile of valid transactions to detect illegal accesses to data, which are seen as unauthorized sequences of Structured Query Language (SQL) commands. The paper proposes a generic algorithm that learns the graph representing the profile of the transactions executed by the users. This mechanism can be used to protect traditional database applications from data attacks as well as web based applications from SQL injection types of attacks. The proposed mechanism is generic and can be used in most commercial DBMS, adding concurrent detection of malicious data access to classical database security mechanisms. The paper presents a practical example of the implementation of the proposed mechanism using Oracle 10g. The Transaction Processing Performance Council benchmark C (TPC-C) and a real database installation were used to assess the detection mechanism and learning algorithm.


title: Methods and metrics for cold-start recommendations

abstract: We have developed a method for recommending items that combines content and collaborative data under a single probabilistic framework. We benchmark our algorithm against a naive Bayes classifier on the cold-start problem, where we wish to recommend items that no one in the community has yet rated. We systematically explore three testing methodologies using a publicly available data set, and explain how these methods apply to specific real-world applications. We advocate heuristic recommenders when benchmarking to give competent baseline performance. We introduce a new performance metric, the CROC curve, and demonstrate empirically that the various components of our testing strategy combine to obtain deeper understanding of the performance characteristics of recommender systems. Though the emphasis of our testing is on cold-start recommending, our methods for recommending and evaluation are general.


title: Human detection in surveillance videos and its applications - a review

abstract: Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. The first step of the detection process is to detect an object which is in motion. Object detection could be performed using background subtraction, optical flow and spatio-temporal filtering techniques. Once detected, a moving object could be classified as a human being using shape-based, texture-based or motion-based features. A comprehensive review with comparisons on available techniques for detecting human beings in surveillance videos is presented in this paper. The characteristics of few benchmark datasets as well as the future research directions on human detection have also been discussed.


title: A comparative study of differential evolution variants for global optimization

abstract: In this paper, we present an empirical comparison of some Differential Evolution variants to solve global optimization problems. The aim is to identify which one of them is more suitable to solve an optimization problem, depending on the problem's features and also to identify the variant with the best performance, regardless of the features of the problem to be solved. Eight variants were implemented and tested on 13 benchmark problems taken from the specialized literature. These variants vary in the type of recombination operator used and also in the way in which the mutation is computed. A set of statistical tests were performed in order to obtain more confidence on the validity of the results and to reinforce our discussion. The main aim is that this study can help both researchers and practitioners interested in using differential evolution as a global optimizer, since we expect that our conclusions can provide some insights regarding the advantages or limitations of each of the variants studied.


title: Morphometric analysis of three-dimensional networks of karst conduits

abstract: The main idiosyncrasy of a typical karst system is the presence of a three-dimensional network of conduits behaving as drains in the system and being responsible of both the quick response of karst springs to rainfall events and the complex distribution of solutes in the system. A morphometric analysis of the three-dimensional geometry of conduits provides quantitative measures that can be used in a range of applications. These morphometric parameters can be used as descriptors of the underground geomorphology, they provide information on speleogenesis processes, they can be correlated with karst denudation ratios, they can be used to control the simulation of realistic stochastic karst networks of conduits, and they can be correlated with hydrogeologic behaviour of the karst system. The main purpose of this paper is to define, describe and illustrate a range of morphometric indexes and morphometric functions that can be calculated nowadays because the availability of three-dimensional topographies provided by speleological work and the availability of the computational and graphical power provided by modern computers. Some of the morphometric parameters describe the existence of preferential directions of karstification, others describe the kartification along the vertical and the possible presence of inception horizons. Other indexes describe the shape complexity of the karstic network, whilst other indexes describe spatial variability of the conduit geometry, and other parameters give account of the connectivity of the three-dimensional network. The morphometric analysis is illustrated with a three-dimensional karstic network in Southern France.


title: Automatic foveation for video compression using a neurobiological model of visual attention

abstract: We evaluate the applicability of a biologically-motivated algorithm to select visually-salient regions of interest in video streams for multiply-foveated video compression. Regions are selected based on a nonlinear integration of low-level visual cues, mimicking processing in primate occipital, and posterior parietal cortex. A dynamic foveation filter then blurs every frame, increasingly with distance from salient locations. Sixty-three variants of the algorithm (varying number and shape of virtual foveas, maximum blur, and saliency competition) are evaluated against an outdoor video scene, using MPEG-1 and constant-quality MPEG-4 (DivX) encoding. Additional compression radios of 1.1 to 8.5 are achieved by foveation. Two variants of the algorithm are validated against eye fixations recorded from four to six human observers on a heterogeneous collection of 50 video clips (over 45 000 frames in total). Significantly higher overlap than expected by chance is found between human and algorithmic foveations. With both variants, foveated clips are, on average, approximately half the size of unfoveated clips, for both MPEG-1 and MPEG-4. These results suggest a general-purpose usefulness of the algorithm in improving compression ratios of unconstrained video.


title: Autoencoder with recurrent neural networks for video forgery detection

abstract: Video forgery detection is becoming an important issue in recent years, because modern editing software provide powerful and easy-to-use tools to manipulate videos. In this paper we propose to perform detection by means of deep learning, with an architecture based on autoencoders and recurrent neural networks. A training phase on a few pristine frames allows the autoencoder to learn an intrinsic model of the source. Then, forged material is singled out as anomalous, as it does not fit the learned model, and is encoded with a large reconstruction error. Recursive networks, implemented with the long short-term memory model, are used to exploit temporal dependencies. Preliminary results on forged videos show the potential of this approach.


title: Wireless body sensor networks for health-monitoring applications

abstract: Current wireless technologies, such as wireless body area networks and wireless personal area networks, provide promising applications in medical monitoring systems to measure specified physiological data and also provide location-based information, if required. With the increasing sophistication of wearable and implantable medical devices and their integration with wireless sensors, an ever-expanding range of therapeutic and diagnostic applications is being pursued by research and commercial organizations. This paper aims to provide a comprehensive review of recent developments in wireless sensor technology for monitoring behaviour related to human physiological responses. It presents background information on the use of wireless technology and sensors to develop a wireless physiological measurement system. A generic miniature platform and other available technologies for wireless sensors have been studied in terms of hardware and software structural requirements for a low-cost, low-power, non-invasive and unobtrusive system.