title: Automatic fusion and classification using random forests and features extracted with deep learning

abstract: Fusion of different sensor modalities has proven very effective in numerous remote sensing applications. However, in order to benefit from fusion, advanced feature extraction mechanisms that rely on domain expertise are typically required. In this paper we present an automated feature extraction scheme based on deep learning. The feature extraction is unsupervised and hierarchical. Furthermore, computational efficiency (often a challenge for deep learning methods) is a primary goal in order to make certain that the method can be applied in large remote sensing datasets. Promising classification results show the applicability of the approach for both reducing the gap between naive feature extraction and methods relying on domain expertise, as well as further improving the performance of the latter in two challenging datasets.


Researching Privacy in Smart Homes : A Roadmap of Future Directions and Research Methods


title: Feature-Rich Named Entity Recognition for Bulgarian Using Conditional Random Fields

abstract: The paper presents a feature-rich approach to the automatic recognition and categorization of named entities (persons, organizations, locations, and miscellaneous) in news text for Bulgarian. We combine well-established features used for other languages with language-specific lexical, syntactic and morphological information. In particular, we make use of the rich tagset annotation of the BulTreeBank (680 morpho-syntactic tags), from which we derive suitable task-specific tagsets (local and nonlocal). We further add domain-specific gazetteers and additional unlabeled data, achieving F1=89.4%, which is comparable to the state-of-the-art results for English.


title: Random projections fuzzy c-means (RPFCM) for big data clustering

abstract: Many contemporary biomedical applications such as physiological monitoring, imaging, and sequencing produce large amounts of data that require new data processing and visualization algorithms. Algorithms such as principal component analysis (PCA), singular value decomposition and random projections (RP) have been proposed for dimensionality reduction. In this paper we propose a new random projection version of the fuzzy c-means (FCM) clustering algorithm denoted as RPFCM that has a different ensemble aggregation strategy than the one previously proposed, denoted as ensemble FCM (EFCM). RPFCM is more suitable than EFCM for big data sets (large number of points, n). We evaluate our method and compare it to EFCM on synthetic and real datasets.


Enhancing the Security of LTE Networks against Jamming Attacks


title: Describing Mathematics Departments: The Strengths and Limitations of Complexity Theory and Activity Theory

abstract: This paper draws on two studies of mathematics departments in 11-18 comprehensive maintained schools in England to compare and contrast the insights provided and questions raised by differing theoretical perspectives. In one study a mathematics department was viewed as a complex system and analysed accordingly. In the other activity theory was used to describe and analyse features of the departments involved. In both cases the departments involved were considered to be systems and it was the learning of the system rather than of individuals that was of interest. The affordances and limitations of the analytical perspectives are discussed.


title: Asymmetric-Circular Shaped Slotted Microstrip Antennas for Circular Polarization and RFID Applications

abstract: Novel asymmetric-circular shaped slotted microstrip patch antennas with slits are proposed for circularly polarized (CP) radiation and radio frequency identification (RFID) reader applications. A single-feed configuration based asymmetric-circular shaped slotted square microstrip patches are adopted to realize the compact circularly polarized microstrip antennas. The asymmetric-circular shaped slot(s) along the diagonal directions are embedded symmetrically onto a square microstrip patch for CP radiation and small antenna size. The CP radiation can be achieved by slightly asymmetric (unbalanced) patch along the diagonal directions by slot areas. Four symmetric-slits are also embedded symmetrically along the orthogonal directions of the asymmetric-circular shaped slotted patch to further reduce antenna size. The operating frequency of the antenna can be tuned by varying the slit length while keeping the CP radiation unchanged. The measured 3-dB axial-ratio (AR) bandwidth of around 6.0 MHz with 17.0 MHz impedance bandwidth is achieved for the antenna on a RO4003C substrate. The overall antenna size is 0.27λo × 0.27λo × 0.0137λo at 900 MHz.


title: National Survey on Distracted Driving Attitudes and Behaviors - 2015

abstract: The 2012 National Survey on Distracted Driving Attitudes and Behaviors (NSDDAB) is the second in a series of surveys on distracted driving that have provided data to help further the understanding of driving behavior and to contribute to the development of countermeasures and interventions to reduce distracted driving on the Nation’s roadways. Like the previous 2010 study, this survey yields national estimates of behavior and attitudes toward distracted driving in the United States. The present study differs from the earlier study in that it developed and used a driver typology based on the pattern of responses across multiple distracted driving behavior questions. Cluster analysis identified two distinct groups of drivers with similar overall behavioral tendencies and, among those categorized, 33% are distraction-prone and 67% are distraction-averse. Driver type is a powerful predictor of norms and attitudes towards distracted driving behavior and sanctions for distracted driving. This report details the findings from the 2012 NSDDAB, examining the data using the above mentioned driver typology as well as standard demographics. In the final chapter, results from the current study are compared to those of the 2010 NSDDAB.


title: Depicting urban boundaries from a mobility network of spatial interactions: a case study of Great Britain with geo-located Twitter data

abstract: ABSTRACT Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. However, it is not clear whether the boundaries truly reflect human interactions with urban space in intra- and interregional activities. Defining urban boundaries that consider socioeconomic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Specifically, we depicted the urban boundaries of Great Britain using a mobility network of Twitter user spatial interactions, which was inferred from over 69 million geo-located tweets. We define the non-administrative anthropographic boundaries in a hierarchical fashion based on different physical movement ranges of users derived from the collective mobility patterns of Twitter users in Great Britain. The results of strongly connected urban regions in the form of communities in the network space yield geographically cohesive, nonoverlapping urban areas, which provide a clear delineation of the non-administrative anthropographic urban boundaries of Great Britain. The method was applied to both national (Great Britain) and municipal scales (the London metropolis). While our results corresponded well with the administrative boundaries, many unexpected and interesting boundaries were identified. Importantly, as the depicted urban boundaries exhibited a strong instance of spatial proximity, we employed a gravity model to understand the distance decay effects in shaping the delineated urban boundaries. The model explains how geographical distances found in the mobility patterns affect the interaction intensity among different non-administrative anthropographic urban areas, which provides new insights into human spatial interactions with urban space.


title: A multi-strategy approach for information extraction of financial report documents

abstract: Information extraction studies have been conducted to improve the efficiency ansd accuracy of information retrieval. We developed information extraction techniques to extract name of company, period of document, currency, revenue, and number of employee information from financial report documents automatically. Different with other works, we applied a multi-strategy approach for developing extraction techniques. We separated information based on its similar characteristics before designing extraction techniques. We assumed that the difference of characteristics owned by each information induces the difference of strategy applied. First strategy is constructing extraction techniques using rule-based extraction method on information, which has good regularity on orthographic and layout features such as name of company, period of document and currency. Second strategy is applying machine learning-based extraction method on information, which has rich contextual and list look-up features such as revenue and number of employee. On the first strategy, rule patterns are defined by combining orthographic, layout, and limited contextual features. Defined rule patterns succeed to extract information and gain precision, recall, and F1-measure more than 0.98. On the second strategy, we conducted extraction task as classification task. First, we built classification models using Na?ve Bayes and Support Vector Machines algorithms. Then, we extracted the most informative features to train the classification models. The best classification model is used for extraction task. Contextual and list look-up features play important role in improving extraction performance. Second strategy succeed to extract revenue and number of employee information and gains precision, recall, and F-1 measure more than 0.93.


title: Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey

abstract: Glaucoma is the second leading cause of loss of vision in the world. Examining the head of optic nerve (cup-to-disc ratio) is very important for diagnosing glaucoma and for patient monitoring after diagnosis. Images of optic disc and optic cup are acquired by fundus camera as well as Optical Coherence Tomography. The optic disc and optic cup segmentation techniques are used to isolate the relevant parts of the retinal image and to calculate the cup-to-disc ratio. The main objective of this paper is to review segmentation methodologies and techniques for the disc and cup boundaries which are utilized to calculate the disc and cup geometrical parameters automatically and accurately to help the professionals in the glaucoma to have a wide view and more details about the optic nerve head structure using retinal fundus images. We provide a brief description of each technique, highlighting its classification and performance metrics. The current and future research directions are summarized and discussed.


title: A 14-nm 0.14-psrms Fractional-N Digital PLL With a 0.2-ps Resolution ADC-Assisted Coarse/Fine-Conversion Chopping TDC and TDC Nonlinearity Calibration

abstract: A digital fractional-N phase-locked loop (PLL) is presented. It achieves 137- and 142-fs rms jitter integrating from 10 kHz to 10 MHz and from 1 kHz to 10 MHz, respectively. With a frequency multiplication ratio of 207.0019231 [digitally controlled oscillator (DCO) frequency is 50 kHz away from an integer multiple of the 26-MHz reference clock], a −78.6-dBc fractional spur is achieved for an output clock that runs at half of the DCO frequency. Time-to-digital converter (TDC) chopping technique, TDC fine conversion through successive approximation register analog-to-digital converters (SARADCs), and TDC nonlinearity calibration improve integrated phase noise and fractional spurs. This design meets the performance requirement of the 256-QAM $4 \times 4$ MIMO LTE standard in 5-GHz ISM band and also the 5G cellular 64-QAM standard in 28-GHz band. This work, implemented in a 14-nm fin-shaped field effect transistor (FinFET) CMOS process, is integrated to a cellular RF integrated circuit supporting advanced carrier aggregation operation. This PLL draws 13.4 mW and occupies 0.257 mm2.


title: Future Networking Challenges: The Case of Mobile Augmented Reality

abstract: Mobile augmented reality (MAR) applications are gaining popularity due to the wide adoption of mobile and especially wearable devices. Such devices often present limited hardware capabilities while MAR applications often rely on computationally intensive computer vision algorithms with extreme latency requirements. To compensate for the lack of computing power, offloading data processing to a distant machine is often desired. However, if this process introduces new constrains in the application, especially in terms of latency and bandwidth. If current network infrastructures are not ready for such traffic, we envision that future wireless networks such as 5G will rapidly be saturated by resource hungry MAR applications. Moreover, due to the high variance of wireless networks, MAR applications should not rely only on the evolution of infrastructures. In this article, we analyze MAR applications and justify their need for accessing external infrastructure. After a review of the existing network infrastructures and protocols, we define guidelines for future real-time and multimedia transport protocols, with a focus on MAR offloading.


title: Male or Female? Brains are Intersex

abstract: The underlying assumption in popular and scientific publications on sex differences in the brain is that human brains can take one of two forms “male” or “female”, and that the differences between these two forms underlie differences between men and women in personality, cognition, emotion and behavior. Documented sex differences in brain structure are typically taken to support this dimorphic view of the brain. However, neuroanatomical data reveal that sex interacts with other factors in utero and throughout life to determine the structure of the brain, and that because these interactions are complex, the result is a multi-morphic, rather than a dimorphic, brain. More specifically, here I argue that human brains are composed of an ever-changing heterogeneous mosaic of “male” and “female” brain characteristics (rather than being all “male” or all “female”) that cannot be aligned on a continuum between a “male brain” and a “female brain”. I further suggest that sex differences in the direction of change in the brain mosaic following specific environmental events lead to sex differences in neuropsychiatric disorders.


title: Describing and forecasting video access patterns

abstract: Computer systems are increasingly driven by workloads that reflect large-scale social behavior, such as rapid changes in the popularity of media items like videos. Capacity planners and system designers must plan for rapid, massive changes in workloads when such social behavior is a factor. In this paper we make two contributions intended to assist in the design and provisioning of such systems.We analyze an extensive dataset consisting of the daily access counts of hundreds of thousands of YouTube videos. In this dataset, we find that there are two types of videos: those that show rapid changes in popularity, and those that are consistently popular over long time periods. We call these two types rarely-accessed and frequently-accessed videos, respectively. We observe that most of the videos in our data set clearly fall in one of these two types. In this work, we study the frequently-accessed videos by asking two questions: first, is there a relatively simple model that can describe its daily access patterns? And second, can we use this simple model to predict the number of accesses that a video will have in the near future, as a tool for capacity planning? To answer these questions we develop a framework for characterization and forecasting of access patterns. We show that for frequently-accessed videos, daily access patterns can be extracted via principal component analysis, and used efficiently for forecasting.


title: A Pilot Study and Randomized Controlled Trial of the Mindful Self‐Compassion Program

abstract: Objectives ::: The aim of these two studies was to evaluate the effectiveness of the Mindful Self-Compassion (MSC) program, an 8-week workshop designed to train people to be more self-compassionate. ::: ::: Methods ::: Study 1 was a pilot study that examined change scores in self-compassion, mindfulness, and various wellbeing outcomes among community adults (N = 21; mean [M] age = 51.26, 95% female). Study 2 was a randomized controlled trial that compared a treatment group (N = 25; M age = 51.21; 78% female) with a waitlist control group (N = 27; M age = 49.11; 82% female). ::: ::: Results ::: Study 1 found significant pre/post gains in self-compassion, mindfulness, and various wellbeing outcomes. Study 2 found that compared with the control group, intervention participants reported significantly larger increases in self-compassion, mindfulness, and wellbeing. Gains were maintained at 6-month and 1-year follow-ups. ::: ::: Conclusions ::: The MSC program appears to be effective at enhancing self-compassion, mindfulness, and wellbeing.


title: Causal Discovery with Continuous Additive Noise Models

abstract: We consider the problem of learning causal directed acyclic graphs from an observational joint distribution. One can use these graphs to predict the outcome of interventional experiments, from which data are often not available. We show that if the observational distribution follows a structural equation model with an additive noise structure, the directed acyclic graph becomes identifiable from the distribution under mild conditions. This constitutes an interesting alternative to traditional methods that assume faithfulness and identify only the Markov equivalence class of the graph, thus leaving some edges undirected. We provide practical algorithms for finitely many samples, RESIT (Regression with Subsequent Independence Test) and two methods based on an independence score. We prove that RESIT is correct in the population setting and provide an empirical evaluation.


title: A fingerprint sensor based on the feedback capacitive sensing scheme

abstract: This paper introduces a single-chip, 200/spl times/200-element sensor array implemented in a standard two-metal digital CMOS technology. The sensor is able to grab the fingerprint pattern without any use of optical and mechanical adaptors. Using this integrated sensor, the fingerprint is captured at a rate of 10 F/s by pressing the finger skin onto the chip surface. The fingerprint pattern is sampled by capacitive sensors that detect the electric field variation induced by the skin surface. Several design issues regarding the capacitive sensing problem are reported and the feedback capacitive sensing scheme (FCS) is introduced. More specifically, the problem of the charge injection in MOS switches has been revisited for charge amplifier design.


title: What Is Implicit Self-Esteem, and Does it Vary Across Cultures?

abstract: Implicit self-esteem (ISE), which is often defined as automatic self-evaluations, fuses research on unconscious processes with that on self-esteem. As ISE is viewed as immune to explicit control, it affords the testing of theoretical questions such as whether cultures vary in self-enhancement motivations. We provide a critical review and integration of the work on (a) the operationalization of ISE and (b) possible cultural variation in self-enhancement motivations. Although ISE measures do not often vary across cultures, recent meta-analyses and empirical studies question the validity of the most common way of defining ISE. We revive an alternative conceptualization that defines ISE in terms of how positively people evaluate objects that reflect upon themselves. This conceptualization suggests that ISE research should target alternative phenomena (e.g., minimal group effect, similarity-attraction effect, endowment effect) and it allows for a host of previous cross-cultural findings to bear on the question...


title: Tight Integration of New 5G Air Interface and LTE to Fulfill 5G Requirements

abstract: Integration of new radio technologies to legacy ones has always been an important feature in any wireless communication generation shift and it is envisioned that for the transition to 5G this will be even more critical. Different 5G research projects have acknowledged the demand for a new air interface that will be designed to operate in higher frequency bands (compared to the ones currently allocated to LTE) where propagation is more challenging and coverage is spotty. This paper proposes a radio access network (RAN) architecture that realizes a tight integration of this new air interface with LTE to enable cross-air interface optimizations such as common resource management, faster mobility and simultaneous multi-connectivity features. The tight integration also aims at leveraging both the better coverage of LTE and the higher capacity of the higher frequency bands. In order to realize this integration, a common protocol layer (also called integration layer) lying on the top of lower layer protocols specified per air interface is proposed. The paper analyzes the alternatives for this integration layer, having the LTE protocol stack specified by 3GPP as a reference model. A recommendation is issued and, based on that, potential multi-connectivity features are presented.


title: Stability criteria for switched and hybrid systems

abstract: The study of the stability properties of switched and hybrid systems gives rise to a number of interesting and challenging mathematical problems. The objective of this paper is to outline some of these problems, to review progress made in solving them in a number of diverse communities, and to review some problems that remain open. An important contribution of our work is to bring together material from several areas of research and to present results in a unified manner. We begin our review by relating the stability problem for switched linear systems and a class of linear differential inclusions. Closely related to the concept of stability are the notions of exponential growth rates and converse Lyapunov theorems, both of which are discussed in detail. In particular, results on common quadratic Lyapunov functions and piecewise linear Lyapunov functions are presented, as they represent constructive methods for proving stability and also represent problems in which significant progress has been made. We also comment on the inherent difficulty in determining stability of switched systems in general, which is exemplified by NP-hardness and undecidability results. We then proceed by considering the stability of switched systems in which there are constraints on the switching rules, through both dwell-time requirements and state-dependent switching laws. Also in this case the theory of Lyapunov functions and the existence of converse theorems are reviewed. We briefly comment on the classical Lur'e problem and on the theory of stability radii, both of which contain many of the features of switched systems and are rich sources of practical results on the topic. Finally we present a list of questions and open problems which provide motivation for continued research in this area.


title: An Evaluation of Digital Image Forgery Detection Approaches

abstract: With the headway of the advanced image handling software and altering tools, a computerized picture can be effectively controlled. The identification of image manipulation is vital in light of the fact that an image can be utilized as legitimate confirmation, in crime scene investigation, and in numerous different fields. The image forgery detection techniques intend to confirm the credibility of computerized pictures with no prior information about the original image. There are numerous routes for altering a picture, for example, resampling, splicing, and copy-move. In this paper, we have examined different type of image forgery and their detection techniques; mainly we focused on pixel based image forgery detection techniques.


title: Empiricism without magic: transformational abstraction in deep convolutional neural networks

abstract: In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain.


title: Using the h-index to rank influential information scientists

abstract: The authors apply a new bibliometric measure, the h-index (Hirsch, 2005), to the literature of information science. Faculty rankings based on raw citation counts are compared with those based on h-counts. There is a strong positive correlation between the two sets of rankings. It is shown how the h-index can be used to express the broad impact of a scholar’s research output over time in more nuanced fashion than straight citation counts.


title: Searching for foreign antigens as possible triggering factors of autoimmunity : Torque Teno virus DNA prevalence is elevated in sera of patients with bullous pemphigoid

abstract: Objective: The Torque Teno virus (TTV), a member of virus genus Anellovirus has been shown to be commonly present in humans, yet without detectable pathogenicity. Recent studies imply that TTV may contribute to provoke autoimmune progresses in systemic lupus erythematosus and idiopathic inflammatory myopathies. We aimed to study the presence of TTV in a group of patients with autoimmune bullous diseases with a further goal to identify long-lasting foreign antigen, such as TTV as possible triggers of skin-specific autoimmunity. ::: ::: Patients and methods: We performed in silico research to study similarities between known TTV sequences and antigens of bullous pemphigoid (BP), pemphigus vulgaris (PV) and dermatitis herpetiformis (DH). Basic Local Alignment Search Tool results showed matching regions for the major BP antigens BP180 and BP230, PV antigen desmoglein 3 and DH antigen transglutaminase 3 and disclosed overlapping, antigen-predicted sequences only for BP180 regions. We also assessed the prevalence of TTV in these disorders and compared them with the results from two healthy blood donor groups (group 1: sex- and age-matched for the general bullous group, n = 95; group 2: sex- and age-matched for BP, n = 50). Furthermore, we assayed lymphocytes from four TTV DNA and BP180 NC16A blot-positive BP patients and three controls in a standard lymphocyte transformation test with a TTV peptide from the conserved ORF(Open Reading Frame)1/N22 region. ::: ::: Results: We found that the detection rate of TTV was comparable with that in healthy controls in the group of PV (19/33); whereas detection rates in DH showed a slight, but not significant tendency for elevation (17/20). Contrary, the TTV prevalence in BP patients was significantly elevated (group 1: 36/40 vs group 2: 31/50, P < 0.032). Lymphocytes from all four virus-positive BP patients heavily reacted to TTV peptide while two of the three healthy controls have shown not to recognize the viral sequences. Only the TTV carrier healthy control had a minor reaction at lowest peptide concentration. The combined in silico, polymerse chain reaction and in vitro cell assay data of the present study indicate that a TTV persistence may contribute to the pathogenesis of BP.


title: Clonal Variation in Drug and Radiation Response among Glioma-Initiating Cells Is Linked to Proneural-Mesenchymal Transition

abstract: Intratumoral heterogeneity is a hallmark of glioblastoma multiforme and thought to negatively affect treatment efficacy. Here, we establish libraries of glioma-initiating cell (GIC) clones from pat ...


title: Learning to Globally Edit Images with Textual Description

abstract: We show how we can globally edit images using textual instructions: given a source image and a textual instruction for the edit, generate a new image transformed under this instruction. To tackle this novel problem, we develop three different trainable models based on RNN and Generative Adversarial Network (GAN). The models (bucket, filter bank, and end-to-end) differ in how much expert knowledge is encoded, with the most general version being purely end-to-end. To train these systems, we use Amazon Mechanical Turk to collect textual descriptions for around 2000 image pairs sampled from several datasets. Experimental results evaluated on our dataset validate our approaches. In addition, given that the filter bank model is a good compromise between generality and performance, we investigate it further by replacing RNN with Graph RNN, and show that Graph RNN improves performance. To the best of our knowledge, this is the first computational photography work on global image editing that is purely based on free-form textual instructions.


title: CNN-VWII: An Efficient Approach for Large-Scale Video Retrieval by Image Queries

abstract: Abstract This paper aims to solve the problem of large-scale video retrieval by a query image. Firstly, we define the problem of top-k image to video query. Then, we combine the merits of convolutional neural networks(CNN for short) and Bag of Visual Word(BoVW for short) module to design a model for video frames information extraction and representation. In order to meet the requirements of large-scale video retrieval, we propose a visual weighted inverted index(VWII for short) and related algorithm to improve the efficiency and accuracy of retrieval process. Comprehensive experiments show that our proposed technique achieves substantial improvements (up to an order of magnitude speed up) over the state-of-the-art techniques with similar accuracy.


title: Optimizing agent behavior over long time scales by transporting value

abstract: Humans prolifically engage in mental time travel. We dwell on past actions and experience satisfaction or regret. More than storytelling, these recollections change how we act in the future and endow us with a computationally important ability to link actions and consequences across spans of time, which helps address the problem of long-term credit assignment: the question of how to evaluate the utility of actions within a long-duration behavioral sequence. Existing approaches to credit assignment in AI cannot solve tasks with long delays between actions and consequences. Here, we introduce a paradigm where agents use recall of specific memories to credit past actions, allowing them to solve problems that are intractable for existing algorithms. This paradigm broadens the scope of problems that can be investigated in AI and offers a mechanistic account of behaviors that may inspire models in neuroscience, psychology, and behavioral economics. People are able to mentally time travel to distant memories and reflect on the consequences of those past events. Here, the authors show how a mechanism that connects learning from delayed rewards with memory retrieval can enable AI agents to discover links between past events to help decide better courses of action in the future.


title: SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-Identification

abstract: Holistic person re-identification (ReID) has received extensive study in the past few years and achieves impressive progress. However, persons are often occluded by obstacles or other persons in practical scenarios, which makes partial person re-identification non-trivial. In this paper, we propose a spatial-channel parallelism network (SCPNet) in which each channel in the ReID feature pays attention to a given spatial part of the body. The spatial-channel corresponding relationship supervises the network to learn discriminative feature for both holistic and partial person re-identification. The single model trained on four holistic ReID datasets achieves competitive accuracy on these four datasets, as well as outperforms the state-of-the-art methods on two partial ReID datasets without training.


title: Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks

abstract: Shot boundary detection (SBD) is an important component of many video analysis tasks, such as action recognition’ video indexing, summarization and editing. Previous work typically used a combination of low-level features like color histograms, in conjunction with simple models such as SVMs to predict shot changes. Instead, we propose to learn shot detection end-to-end, from pixels to final shot boundaries. For training such a model, we rely on our insight that all shot boundaries are generated. Thus, we create a dataset with one million frames and automatically generated transitions such as cuts, dissolves and fades. In order to efficiently analyze hours of videos, we propose a Convolutional Neural Network (CNN) which is fully convolutional in time, thus allowing to use a large temporal context without the need to repeatedly processing frames. With this architecture our method obtains state-of-the-art results on the RAI dataset, while running at an unprecedented speed of more than 120x real-time.


title: Mindfulness-Based Interventions: An Emerging Phenomenon

abstract: I offer an overview of the rapidly growing field of mindfulness-based interventions (MBIs). A working definition of mindfulness in this context includes the brahma viharas, sampajanna and appamada, and suggests a very particular mental state which is both wholesome and capable of clear and penetrating insight into the nature of reality. The practices in mindfulness-based stress reduction (MBSR) that apply mindfulness to the four foundations are outlined, along with a brief history of the program and the original intentions of the founder, Jon Kabat-Zinn. The growth and scope of these interventions are detailed with demographics provided by the Center for Mindfulness, an overview of salient research studies and a listing of the varied MBIs that have grown out of MBSR. The question of ethics is explored, and other challenges are raised including teacher qualification and clarifying the “outer limits,” or minimum requirements, of what constitutes an MBI. Current trends are explored, including the increasing number of cohort-specific interventions as well as the publication of books, articles, and workbooks by a new generation of MBI teachers. Together, they form an emerging picture of MBIs as their own new “lineage,” which look to MBSR as their inspiration and original source. The potential to bring benefit to new fields, such as government and the military, represent exciting opportunities for MBIs, along with the real potential to transform health care. Sufficient experience in the delivery of MBIs has been garnered to offer the greater contemplative community valuable resources such as secular language, best practices, and extensive research.


title: Gradient-Based Learning Applied to Document Recognition

abstract: Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.


title: Routing protocols in wireless mesh networks: challenges and design considerations

abstract: Wireless Mesh Networks (WMNs) are an emerging technology that could revolutionize the way wireless network access is provided. The interconnection of access points using wireless links exhibits great potential in addressing the "last mile" connectivity issue. To realize this vision, it is imperative to provide efficient resource management. Resource management encompasses a number of different issues, including routing. Although a profusion of routing mechanisms has been proposed for other wireless networks, the unique characteristics of WMNs (e.g., wireless backbone) suggest that WMNs demand a specific solution. To have a clear and precise focus on future research in WMN routing, the characteristics of WMNs that have a strong impact on routing must be identified. Then a set of criteria is defined against which the existing routing protocols from ad hoc, sensor, and WMNs can be evaluated and performance metrics identified. This will serve as the basis for deriving the key design features for routing in wireless mesh networks. Thus, this paper will help to guide and refocus future works in this area.


Controlled release technologies : methods, theory, and applications


title: Learning from positive and unlabeled amazon reviews: towards identifying trustworthy reviewers

abstract: On-line marketplaces have been growing in importance over the last few years. In such environments, reviews consist the main reputation mechanism for the available products. Hence, presenting high quality reviews is crucial in achieving a high level of customer satisfaction. Towards this direction, in this work, we introduce a new dimension of review quality, the reviewer's "trustfulness". We assume that voluntary information provided by Amazon reviewers, regarding whether they are the actual buyers of the product, signals the reliability of a review. Based on this information, we characterize a reviewer as trustworthy (positive instance) or of unknown "trustfulness" (unlabeled instance). Then, we build models that exploit reviewers' profile information and on-line behavior to rank them according to the probability of being trustworthy. Our results are very promising, since they provide evidence that our predictive models separate positive from unlabeled instances with very high accuracies.


title: Management of Complications Caused by Permanent Fillers in the Face: A Treatment Algorithm.

abstract: Background:Nonresorbable substances are still injected to enhance soft-tissue volumes and fill subcutaneous defects. Inflammatory reactions (often termed granulomas) to these materials can be functionally and socially disabling. Most therapeutic options used until now are nonspecific antiinflammator


title: Wearable Intelligent Systems for E-Health

abstract: Due to the increasingly aging population, there is a rising demand for assistive living technologies for the elderly to ensure their health and well-being. The elderly are mostly chronic patients who require frequent check-ups of multiple vital signs, some of which (e.g., blood pressure and blood glucose) vary greatly according to the daily activities that the elderly are involved in. Therefore, the development of novel wearable intelligent systems to effectively monitor the vital signs continuously over a 24 hour period is in some cases crucial for understanding the progression of chronic symptoms in the elderly. In this paper, recent development of Wearable Intelligent Systems for e-Health (WISEs) is reviewed, including breakthrough technologies and technical challenges that remain to be solved. A novel application of wearable technologies for transient cardiovascular monitoring during water drinking is also reported. In particular, our latest results found that heart rate increased by 9 bpm (P


title: Challenges in the Emulation of Large Scale Software Defined Networks

abstract: Researchers and practitioners alike have long relied upon emulation to evaluate the performance of networked applications and protocols without tying up production infrastructure or committing to expensive hardware purchases. As software defined networks (SDN) become more prevalent, it is increasingly useful to be able to accurately emulate their behavior. Tools like Mininet-HiFi have demonstrated the viability of small-scale SDN emulation, but emulated network size is limited by the hardware resources of a single machine. Unfortunately, there are many SDN applications that can only be evaluated meaningfully at scale. Traditionally, researchers have overcome similar challenges by scaling the capacity of the network links, the emulated workload, or both. We observe that shrinking a network arbitrarily to fit into a fixed set of emulation resources has fundamental limitations that impact the fidelity of results, especially in the context of SDN. In particular, we demonstrate cases where emulation yields false positives---poor network behavior that manifests only in emulation---and false negatives, where a real deployment would suffer issues not apparent in emulation. We discuss strategies that might be used to combat these effects and their limitations.


title: Non-Euclidean statistics for covariance matrices, with applications to diffusion tensor imaging

abstract: The statistical analysis of covariance matrix data is considered and, in particular, methodology is discussed which takes into account the non-Euclidean nature of the space of positive semi-definite symmetric matrices. The main motivation for the work is the analysis of diffusion tensors in medical image analysis. The primary focus is on estimation of a mean covariance matrix and, in particular, on the use of Procrustes size-and-shape space. Comparisons are made with other estimation techniques, including using the matrix logarithm, matrix square root and Cholesky decomposition. Applications to diffusion tensor imaging are considered and, in particular, a new measure of fractional anisotropy called Procrustes Anisotropy is discussed.


title: A 3.9-ps RMS Precision Time-to-Digital Converter Using Ones-Counter Encoding Scheme in a Kintex-7 FPGA

abstract: A 3.9-ps time-interval rms precision and 277-M events/second measurement throughput time-to-digital converter (TDC) is implemented in a Xilinx Kintex-7 field programmable gate array (FPGA). Unlike previous work, the TDC is achieved with a multichain tapped-delay line (TDL) followed by an ones-counter encoder. The four normal TDLs merged together make the TDC bins very small, so that the time precision can be significantly improved. The ones-counter encoder naturally applies global bubble error correction to the output of TDL, thus the TDC design is relatively simple even when using FPGAs made with current advanced process technology. The TDC implementation is a generally applicable method that can simultaneously achieve high time precision and high measurement throughput.


title: A Neural Attention Model for Abstractive Sentence Summarization

abstract: Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. Our method utilizes a local attention-based model that generates each word of the summary conditioned on the input sentence. While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data. The model shows significant performance gains on the DUC-2004 shared task compared with several strong baselines.


title: Fast alternating direction optimization methods

abstract: Alternating direction methods are a common tool for general mathematical programming and optimization. These methods have become particularly important in the field of variational image processing, which frequently requires the minimization of nondifferentiable objectives. This paper considers accelerated (i.e., fast) variants of two common alternating direction methods: the alternating direction method of multipliers (ADMM) and the alternating minimization algorithm (AMA). The proposed acceleration is of the form first proposed by Nesterov for gradient descent methods. In the case that the objective function is strongly convex, global convergence bounds are provided for both classical and accelerated variants of the methods. Numerical examples are presented to demonstrate the superior performance of the fast methods for a wide variety of problems.


title: HIV Infection and AIDS in the Deep South.

abstract: We examine epidemiological and demographic data documenting the HIV/AIDS epidemic in the Deep South region of the United States. These data document substantial increases in AIDS cases in the Deep South from 2000 to 2003. In contrast, other US regions are experiencing stable rates or small increases in new AIDS cases. Furthermore, the AIDS epidemic in the Deep South is more concentrated than in other regions among African Americans, women, and rural residents. The Deep South also has some of the highest levels of poverty and uninsured individuals, factors that complicate the prevention and treatment of HIV infection. Further research is needed to determine the cause of the disproportionate rise in AIDS incidence and to develop effective means of preventing HIV infection and providing care of those infected in this region.


title: Implementing enterprise content management: from evolution through strategy to contradictions out-of-the-box

abstract: This study examines a strategic development and implementation process of enterprise content management (ECM) in a large oil company. In view of the framework of four motors of development and change in organizations, this study represents a revelatory case of a hybrid development approach to ECM that involves the teleological, life-cycle, and dialectical motors of development. This is in contrast to the evolutionary development motor, which has prevailed in the hitherto reported content management research. The case study also complements process-based research on enterprise system implementations in general. We suggest that research and practice on large-scale ECM implementations should acknowledge all the four motors of change.


title: Reasoning-based vision recognition for agricultural humanoid robot toward tomato harvesting

abstract: We present a vision cognition framework for tomato harvesting humanoid robot based on geometrical and physical reasoning. Inspired from the natural human harvesting behaviour, our goal is to build a humanoid robot to pick tomatoes autonomously or with minimal human efforts. The proposed vision approach uses fusion of calibrated observation data from two RGB-D sensors installed on the head and the hand of the humanoid. We observe the natural human harvesting behaviour and equip our robot with similar grippers to follow the same picking processes for a specific fruit. In the vision approach, we mainly focus on modelling fruits in one branch and then estimating the pedicel direction of each fruit in a branch. Through pointcloud model segmentation, the primitive shape model of each fruit can be obtained and we consider a simple fact that crops in one branch should remain stable with respect to gravity and interaction forces from neighbouring crops in the branch. According to this assumption, a probabilistic model is created and the picking order in the branch is assigned under the evaluated geometrical structure. In the experiments, we tested harvesting of real tomatoes on actual branches and evaluated the successful harvesting rate.


title: Motivating Children to Learn Effectively: Exploring the Value of Intrinsic Integration in Educational Games

abstract: The concept of intrinsic motivation lies at the heart of the user engagement created by digital games. Yet despite this, educational software has traditionally attempted to harness games as extrinsic motivation by using them as a sugar coating for learning content. This article tests the concept of intrinsic integration as a way of creating a more productive relationship between educational games and their learning content. Two studies assessed this approach by designing and evaluating an educational game called Zombie Division to teach mathematics to 7- to 11-year-olds. Study 1 examined the learning gains of 58 children who played either the intrinsic, extrinsic, or control variants of Zombie Division for 2 hr, supported by their classroom teacher. Study 2 compared time on task for the intrinsic and extrinsic variants of the game when 16 children had free choice of which game to play. The results showed that children learned more from the intrinsic version of the game under fixed time limits and spent 7 ...


title: A review of evidence of health benefit from artificial neural networks in medical intervention

abstract: The purpose of this review is to assess the evidence of healthcare benefits involving the application of artificial neural networks to the clinical functions of diagnosis, prognosis and survival analysis, in the medical domains of oncology, critical care and cardiovascular medicine. The primary source of publications is PUBMED listings under Randomised Controlled Trials and Clinical Trials. The role of neural networks is introduced within the context of advances in medical decision support arising from parallel developments in statistics and artificial intelligence. This is followed by a survey of published Randomised Controlled Trials and Clinical Trials, leading to recommendations for good practice in the design and evaluation of neural networks for use in medical intervention.


title: Accelerating the discovery of unsupervised-shapelets

abstract: Over the past decade, time series clustering has become an increasingly important research topic in data mining community. Most existing methods for time series clustering rely on distances calculated from the entire raw data using the Euclidean distance or Dynamic Time Warping distance as the distance measure. However, the presence of significant noise, dropouts, or extraneous data can greatly limit the accuracy of clustering in this domain. Moreover, for most real world problems, we cannot expect objects from the same class to be equal in length. As a consequence, most work on time series clustering only considers the clustering of individual time series "behaviors," e.g., individual heart beats or individual gait cycles, and contrives the time series in some way to make them all equal in length. However, automatically formatting the data in such a way is often a harder problem than the clustering itself. In this work, we show that by using only some local patterns and deliberately ignoring the rest of the data, we can mitigate the above problems and cluster time series of different lengths, e.g., cluster one heartbeat with multiple heartbeats. To achieve this, we exploit and extend a recently introduced concept in time series data mining called shapelets. Unlike existing work, our work demonstrates the unintuitive fact that shapelets can be learned from unlabeled time series. We show, with extensive empirical evaluation in diverse domains, that our method is more accurate than existing methods. Moreover, in addition to accurate clustering results, we show that our work also has the potential to give insight into the domains to which it is applied. While a brute-force algorithm to discover shapelets in an unsupervised way could be untenably slow, we introduce two novel optimization procedures to significantly speed up the unsupervised-shapelet discovery process and allow it to be cast as an anytime algorithm.


title: INTRODUCTION: CRIME MAPPING AND CRIME PREVENTION

abstract: Crime maps have only recently begun to emerge as a significant tool in crime and justice. Until a decade ago, few criminal justice agencies had any capability for creating crime maps, and few investigators had the resources or patience to examine the spatial distribution of crime. Today, however, crime mapping is experiencing what might be termed an explosion of interest among both scholars and practitioners. This introduction begins by examining some early examples of mapping of crime, focusing in particular on factors that inhibited the widespread integration of mapping into crime prevention research and practice in the past. It then turns to innovations in mapping technologies and crime prevention theory that have recently brought crime mapping to the center of trends in crime prevention. The final section introduces the contributions that follow and discusses how they illustrate the many uses of mapping in crime prevention. It examines the pitfalls and problems that researchers and practitioners are likely to encounter in developing and analyzing maps, and the potential advances in crime mapping we might expect in coming decades.


title: Estimation of Safe Sensor Measurements of Autonomous System Under Attack

abstract: The introduction of automation in cyber-physical systems (CPS) has raised major safety and security concerns. One attack vector is the sensing unit whose measurements can be manipulated by an adversary through attacks such as denial of service and delay injection. To secure an autonomous CPS from such attacks, we use a challenge response authentication (CRA) technique for detection of attack in active sensors data and estimate safe measurements using the recursive least square algorithm. For demonstrating effectiveness of our proposed approach, a car-follower model is considered where the follower vehicle's radar sensor measurements are manipulated in an attempt to cause a collision.


title: Big-Data Architecture for Cyber Attack Graphs Representing Security Relationships in NoSQL Graph Databases

abstract: We introduce a new modeling framework for mapping vulnerability paths through networks and associating them with observed attacker activities. We merge a complex blend of network relationships and events, such as topology, firewall policies, host configurations, vulnerabilities, attack patterns, intrusion alerts, and logs. Our persistence layer includes Neo4j, a Not Only SQL (NoSQL) database optimized for graphs. We explore how the Neo4j property-graph data model supports analysis and queries within our problem domain. For interoperability with other tools, we employ standardized cyber- security data exchange language. We show that our approach supports the same kinds of graph analytics as an existing attack graph tool, through the application of the Neo4j Cypher query language. We then extend those analytics through a much richer model of the network environment and attacker/defender activities. Our work represents the first investigation of cyber attack graph analysis based on graph databases - an important class of NoSQL database.


title: Comparative Study of Fuel-Cell Vehicle Hybridization with Battery or Supercapacitor Storage Device

abstract: This paper studies the impact of fuel-cell (FC) performance and control strategies on the benefits of hybridization. One of the main weak points of the FC is slow dynamics dominated by a temperature and fuel-delivery system (pumps, valves, and, in some cases, a hydrogen reformer). As a result, fast load demand will cause a high voltage drop in a short time, which is recognized as a fuel-starvation phenomenon. Therefore, to employ an FC in vehicle applications, the electrical system must have at least an auxiliary power source to improve system performance when electrical loads demand high energy in a short time. The possibilities of using a supercapacitor or a battery bank as an auxiliary source with an FC main source are presented in detail. The studies of two hybrid power systems for vehicle applications, i.e., FC/battery and FC/supercapacitor hybrid power sources, are explained. Experimental results with small-scale devices (a polymer electrolyte membrane FC of 500 W, 40 A, and 13 V; a lead-acid battery module of 33 Ah and 48 V; and a supercapacitor module of 292 F, 500 A, and 30 V) in a laboratory authenticate that energy-storage devices can assist the FC to meet the vehicle power demand and help achieve better performance, as well as to substantiate the excellent control schemes during motor-drive cycles.


Comparative study of NoSQL databases for big data storage


title: Coping with sexual concerns after cancer: the use of flexible coping

abstract: Introduction ::: Although cancer treatment commonly has a negative impact on sexual functioning, sexual concerns are still largely undertreated in routine cancer care. The medical model that guides current approaches to sexual care in cancer does not adequately address key patient needs.


Efficient Representation and Derivation of fundamental Transformation of Relationships using Euler Angles and Quaternions


title: Automated Flower Classification over a Large Number of Classes

abstract: We investigate to what extent combinations of features can improve classification performance on a large dataset of similar classes. To this end we introduce a 103 class flower dataset. We compute four different features for the flowers, each describing different aspects, namely the local shape/texture, the shape of the boundary, the overall spatial distribution of petals, and the colour. We combine the features using a multiple kernel framework with a SVM classifier. The weights for each class are learnt using the method of Varma and Ray, which has achieved state of the art performance on other large dataset, such as Caltech 101/256. Our dataset has a similar challenge in the number of classes, but with the added difficulty of large between class similarity and small within class similarity. Results show that learning the optimum kernel combination of multiple features vastly improves the performance, from 55.1% for the best single feature to 72.8% for the combination of all features.


title: Adaptive Depth Control for Autonomous Underwater Vehicles Based on Feedforward Neural Networks

abstract: This paper studies the design and application of the neural network based adaptive control scheme for autonomous underwater vehicle’s (AUV’s) depth control system that is an uncertain nonlinear dynamical one with unknown nonlinearities. The unknown nonlinearity is approximated by a feedforward neural network whose parameters are adaptively adjusted online according to a set of parameter estimation laws for the purpose of driving the AUV to cruise at the preset depth. The Lyapunov synthesis approach is used to develop the adaptive control scheme. The overall control system can guarantee that the tracking error converges in the small neighborhood of zero and all adjustable parameters involved are uniformly bounded. Simulation examples are given to illustrate the design procedure and the applicability of the proposed method. The results indicate that the proposed method is suitable for practical applications.


title: AMBUBOT: Ambulance robot Automated External Defibrillator robotic ambulance

abstract: The purpose of this paper is to introduce a novel robot in order to overcome some difficulties in providing an Automated External Defibrillator (AED) device at the nearest location of victim suffering from sudden cardiac arrest in the shortest possible time before the advent of the ambulance. We designed and developed an ambulance robot, called it Ambubot, that brings along an AED to help lay rescuers for saving patients life in a sudden event of cardiac arrest. The first aid to the victim can be carried out once an incident alarm is transmitted to the Ambubot station by sensing via body-attached sensor and/or mobile phone application. Such applications transmit required information to the Ambubot center for further execution. Ambubot center is integrated with three independent servers namely database server, message controller, and GIS server. In addition, message controller server is connected with telecom's short message server for transmitting the message to family members of the patient. The Ambubot robots are located in Ambubot stations when several stations can be covered via single center where human operators are located. Different methods had been proposed for dispatching Ambubot to locate the victim namely tele-control, partially autonomous, and fully autonomous operations. While we have illustrated all those methods in this paper we present the conduction of the tele-control method to control the operation of the robot. In this method, not only Ambubot follows instructions of human operator till the robot reaches the location of victim and delivers the AED but also provides instruction to the people in the location for applying the AED hence the lay rescuers will dry the victim's chest and attach the AED pads by themselves while instructed and monitored by the human experts in the main center in real-time.


title: An Extensible Recommendation System for Health Research

abstract: A challenge for basic and clinical biomedical researchers is to determine the particular technologies that can play a role in their research. Core facilities house, centralize, and specialize in particular services and technologies important to biomedical research. At the University of North Carolina at Chapel Hill, over 70 core facilities are available for such use, many with multiple technologies and services. In this paper, we design an Extensible Recommendation System (ERS) to aid investigators in finding which core facilities may impact their research. Recommendation techniques are applied to enhance the process of recognition of technologies and collaborators in a modern biomedical research environment. The architecture of the system has two parts: a core foundation module and extensible module. The foundation module follows from a CMS framework called Plone that handles the basic functions like database queries, user login, etc. The extensible module has a few components such as search component, a recommendation component, etc. The extensible module is designed to be flexible, in that it can be extended by as many recommendation algorithms as we need. In this paper, we compare several recommender systems for biomedical core research facilities. Our simulation results show that the recommender system based on Graph-Theoretic performs best amongst few similar recommenders.


title: A survey of methods for mobile robot localization and mapping in dynamic indoor environments

abstract: The emergence of indoor applications of mobile robotics has led to the development of various algorithms for effective localization and mapping in the presence of moving obstacles. A mobile robot needs to simultaneously solve many problems like sensing, mapping, localization, path planning, obstacle detection and avoidance for a completely autonomous system. This paper surveys the different methods developed to handle dynamic changes in an indoor environment.


title: New methods for the assessment of Parkinson's disease (2005 to 2015): A systematic review

abstract: BACKGROUND ::: The past decade has witnessed a highly dynamic and growing expansion of novel methods aimed at improving the assessment of Parkinson's disease with technology (NAM-PD) in laboratory, clinical, and home environments. However, the current state of NAM-PD regarding their maturity, feasibility, and usefulness in assessing the main PD features has not been systematically evaluated. ::: ::: ::: METHODS ::: A systematic review of articles published in the field from 2005 to 2015 was performed. Of 9,503 publications identified in PubMed and the Web of Science, 848 full papers were evaluated, and 588 original articles were assessed to evaluate the technological, demographic, clinimetric, and technology transfer readiness parameters of NAM-PD. ::: ::: ::: RESULTS ::: Of the studies, 65% included fewer than 30 patients, < 50% employed a standard methodology to validate diagnostic tests, 8% confirmed their results in a different dataset, and 87% occurred in a clinic or lab. The axial features domain was the most frequently studied, followed by bradykinesia. Rigidity and nonmotor domains were rarely investigated. Only 6% of the systems reached a technology level that justified the hope of being included in clinical assessments in a useful time period. ::: ::: ::: CONCLUSIONS ::: This systematic evaluation provides an overview of the current options for quantitative assessment of PD and what can be expected in the near future. There is a particular need for standardized and collaborative studies to confirm the results of preliminary initiatives, assess domains that are currently underinvestigated, and better validate the existing and upcoming NAM-PD. © 2016 International Parkinson and Movement Disorder Society.


title: Elastomeric Conductive Composites Based on Carbon Nanotube Forests

abstract: Electrically conductive materials capable of substantial elastic stretch and bending are needed for such applications as smart clothing, flexible displays, stretchable circuits, strain gauges, implantable devices, high-stroke microelectromechanical systems, and dielectric elastomer actuators. A variety of approaches involving carbon nanotubes (CNTs) and elastic polymers have been suggested for the fabrication of conductive elastic composites. In particular, diverse active and passive electronic components have been embedded in rubber sheet by several research groups to obtain stretchable electronic devices. Sekitani et al. developed rubber-like conductive composites by mixing millimeter-long single-walled carbon nanotubes (SWNTs), an ionic liquid, and a fluorinated copolymer. The stretchability of the resulting composite was enhanced by creating perforated films with a net-shaped structure using a mechanical punching system. Cao et al. fabricated flexible electrodes by incorporating SWNTnetworks in plastics consisting of polyimide, polyurethane, and polyamic acid films. Although quite successful, these studies indicated that high loading of CNTs (or other conductive additive) was necessary to obtain a highly conducting composite. On the other hand, incorporation of high concentrations of CNTs into an elastic polymer increases the stiffness of the resulting composite and decreases its stretchability. In other words, the significant difference in the Young’s modulus of extremely rigid CNTs and the elastic polymer filler makes the creation of a highly stretchable conductive composites a challenging task. It is known that CNTs can be fabricated into macroscopic assemblies, such as mats (bucky paper), yarns, and fibers that possess useful electrical properties, and that these assemblies can be used for the fabrication of conductive polymer composites. While these assemblies are often more elastic than the individual CNTs, the achievable elastic strain range is still quite limited, normally less than 10%. We found that a combination of high stretchability and high electrical conductivity can be obtained for composites prepared from three-dimensional CNT structures, such as CNT forests (vertically aligned arrays of CNTs). Unlike previous methods involving casting CNT/ polymer dispersions as a film, our composites were prepared by the direct infiltration of multiwalled carbon nanotube (MWNT) forests with a polyurethane (PU) solution. Using this procedure, we obtained rubber-like forest/PU composites that combined high stretchability with high electrical conductivity. These composites provide highly reversible stress–strain behavior and little degradation of mechanical and electrical properties even when stretched over a wide strain range. The developed preparation procedure appears scalable for material fabrication on an industrial scale, though transition from present batchbased forest growth processes to continuous forest growth processes would be needed for applications that are price sensitive and depend on sheet weight, rather than the area of elastomeric sheet. The aligned arrays of MWNTs (MWNT forests) used in this study were grown on iron-catalyst-coated silicon wafers using a conventional chemical vapor deposition (CVD) method. Nanotubes in the forests typically had a diameter of about 10 nm; their length could be controlled across a wide range by changing the growth time and other fabrication conditions. The forest-covered area on the substrate used for the preparation of the composites typically had dimensions of about 50 100mm; the height of nanotubes in the forest was about 50mm as determined by the conventional optical microscopy. Since the nanotubes in the forests formed a three-dimensionally interconnected network, the forests were electrically conductive in all directions. The MWNT forests were infiltrated with a PU solution in N,N-dimethylformamide (DMF) using a simple drop-casting procedure, as shown in Figure 1a. The PU used was poly[4,40methylene-bis(phenyl isocyanate)-alt-1,4-butanediol/poly(butylene adipate)]. After evaporation of the solvent, we obtained about 250mm thick forest/PU composite sheets that could be peeled off the underlying Si wafer. Figure 1b shows a photograph of the MWNT/PU composite sheet taken at low magnification. One side of the prepared film facing the substrate (forest side) was black and conductive, and the other side (PU side) was white and insulating. The material was soft, flexible, and highly stretchable in the sheet plane. Figure 1c shows a SEM image of a cross-section of the composite sheet with the top ( 50mm in thickness) being the forest side and the bottom ( 200mm in thickness) being the PU side. A highmagnification image of the forest side is shown in the


title: A Hygroscopic Sensor Electrode for Fast Stabilized Non-Contact ECG Signal Acquisition

abstract: A capacitive electrocardiography (cECG) technique using a non-invasive ECG measuring technology that does not require direct contact between the sensor and the skin has attracted much interest. The system encounters several challenges when the sensor electrode and subject’s skin are weakly coupled. Because there is no direct physical contact between the subject and any grounding point, there is no discharge path for the built-up electrostatic charge. Subsequently, the electrostatic charge build-up can temporarily contaminate the ECG signal from being clearly visible; a stabilization period (3–15 min) is required for the measurement of a clean, stable ECG signal at low humidity levels (below 55% relative humidity). Therefore, to obtain a clear ECG signal without noise and to reduce the ECG signal stabilization time to within 2 min in a dry ambient environment, we have developed a fabric electrode with embedded polymer (FEEP). The designed hygroscopic FEEP has an embedded superabsorbent polymer layer. The principle of FEEP as a conductive electrode is to provide humidity to the capacitive coupling to ensure strong coupling and to allow for the measurement of a stable, clear biomedical signal. The evaluation results show that hygroscopic FEEP is capable of rapidly measuring high-accuracy ECG signals with a higher SNR ratio.


title: Clustering Text Data Streams

abstract: Clustering text data streams is an important issue in data mining community and has a number of applications such as news group filtering, text crawling, document organization and topic detection and tracing etc. However, most methods are similarity-based approaches and only use the TF*IDF scheme to represent the semantics of text data and often lead to poor clustering quality. Recently, researchers argue that semantic smoothing model is more efficient than the existing TF*IDF scheme for improving text clustering quality. However, the existing semantic smoothing model is not suitable for dynamic text data context. In this paper, we extend the semantic smoothing model into text data streams context firstly. Based on the extended model, we then present two online clustering algorithms OCTS and OCTSM for the clustering of massive text data streams. In both algorithms, we also present a new cluster statistics structure named cluster profile which can capture the semantics of text data streams dynamically and at the same time speed up the clustering process. Some efficient implementations for our algorithms are also given. Finally, we present a series of experimental results illustrating the effectiveness of our technique.


title: Gender Differences in Personality and Social Behavior

abstract: This article replaces the previous edition article by J.S. Hyde, volume 9, pp. 5989–5994, © 2001, Elsevier Ltd.


title: Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

abstract: We present a new question set, text corpus, and baselines assembled to encourage AI research in advanced question answering. Together, these constitute the AI2 Reasoning Challenge (ARC), which requires far more powerful knowledge and reasoning than previous challenges such as SQuAD or SNLI. The ARC question set is partitioned into a Challenge Set and an Easy Set, where the Challenge Set contains only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurence algorithm. The dataset contains only natural, grade-school science questions (authored for human tests), and is the largest public-domain set of this kind (7,787 questions). We test several baselines on the Challenge Set, including leading neural models from the SQuAD and SNLI tasks, and find that none are able to significantly outperform a random baseline, reflecting the difficult nature of this task. We are also releasing the ARC Corpus, a corpus of 14M science sentences relevant to the task, and implementations of the three neural baseline models tested. Can your model perform better? We pose ARC as a challenge to the community.


title: Review of Multi-Agent Algorithms for Collective Behavior: a Structural Taxonomy

abstract: In this paper, we review multi-agent collective behavior algorithms in the literature and classify them according to their underlying mathematical structure. For each mathematical technique, we identify the multi-agent coordination tasks it can be applied to, and we analyze its scalability, bandwidth use, and demonstrated maturity. We highlight how versatile techniques such as artificial potential functions can be used for applications ranging from low-level position control to high-level coordination and task allocation, we discuss possible reasons for the slow adoption of complex distributed coordination algorithms in the field, and we highlight areas for further research and development.


title: A performance comparison of dynamic Web technologies

abstract: Today, many Web sites dynamically generate responses "on the fly" when user requests are received. In this paper, we experimentally evaluate the impact of three different dynamic content technologies (Perl, PHP, and Java) on Web server performance. We quantify achievable performance first for static content serving, and then for dynamic content generation, considering cases both with and without database access. The results show that the overheads of dynamic content generation reduce the peak request rate supported by a Web server up to a factor of 8, depending on the workload characteristics and the technologies used. In general, our results show that Java server technologies typically outperform both Perl and PHP for dynamic content generation, though performance under overload conditions can be erratic for some implementations.


title: Incredible: is (almost) all web content trustworthy? analysis of psychological factors related to website credibility evaluation

abstract: This paper describes the results of a study conducted in February 2013 on Amazon Mechanical Turk aimed at identifying various determinants of credibility evaluations. 2046 adult participants evaluated credibility of websites with diversified trustworthiness reference index. We concentrated on psychological factors that lead to the characteristic positive bias observed in many working social feedback systems on the Internet. We have used International Personality Item Pool (IPIP) and measured the following traits: trust, conformity, risk taking, need for cognition and intellect. Results suggest that trustworthiness and risk taking are factors clearly differentiating people with respect to tendency to overestimate, underestimate and judge accordingly websites' credibility. Intuitively people characterized by high general trust tend to be more generous in their credibility evaluations. On the other hand, people who are more willing to take risk, tend to be more critical of the Internet content. The latter indicates that high credibility evaluations are being treated as a default option, and lower ratings require special conditions. Other, more detailed psychological patterns related to websites' credibility evaluations are described in full paper.


title: Hierarchical digital modulation classification using cumulants

abstract: A simple method, based on elementary fourth-order cumulants, is proposed for the classification of digital modulation schemes. These statistics are natural in this setting as they characterize the shape of the distribution of the noisy baseband I and Q samples. It is shown that cumulant-based classification is particularly effective when used in a hierarchical scheme, enabling separation into subclasses at low signal-to-noise ratio with small sample size. Thus, the method can be used as a preliminary classifier if desired. Computational complexity is order N, where N is the number of complex baseband data samples. This method is robust in the presence of carrier phase and frequency offsets and can be implemented recursively. Theoretical arguments are verified via extensive simulations and comparisons with existing approaches.


title: YOUNG CHILDREN, ATTACHMENT SECURITY, AND PARENTING SCHEDULES

abstract: The focus of a recent Family Court Review Special Issue on attachment, separation, and divorce included perspectives primarily from researchers in the Bowlby/Ainsworth tradition. A number of the authors made recommendations against overnight visits for very young children. I review conceptualizations and measures of attachment security as well as empirical findings. Currently the foundation to make general recommendations regarding overnight visits is inadequate. Caution is needed in using attachment measures in court evaluations and regarding restrictive legal presumptions and theoretical orientations. Custody and access evaluations and decisions need to apply scientifically sound principles to the individual characteristics of each case. ::: ::: Key Points for the Family Court Community: ::: •Simple “yes” or “no” answers are inadequate regarding the question of overnight visits for young children. ::: •Attachment security can change and is influenced by multiple factors over the life span. ::: •There are important scientific, ethical, and cost factors when considering the possible use of attachment security measures in custody evaluation. ::: •Scientific findings about how overnight visits affect young children are mixed and seem best explained by differences in parental characteristics and interactions. ::: •Each case deserves decision-making based on careful reasoning, informed by scientific findings and the relevant individual circumstances.


title: Disentangling by Factorising

abstract: We define and address the problem of unsupervised learning of disentangled representations on data generated from independent factors of variation. We propose FactorVAE, a method that disentangles by encouraging the distribution of representations to be factorial and hence independent across the dimensions. We show that it improves upon $\beta$-VAE by providing a better trade-off between disentanglement and reconstruction quality. Moreover, we highlight the problems of a commonly used disentanglement metric and introduce a new metric that does not suffer from them.


Quantum Programming Languages Survey and Bibliography


title: (Non-)Random Sequences from (Non-)Random Permutations—Analysis of RC4 Stream Cipher

abstract: RC4 has been the most popular stream cipher in the history of symmetric key cryptography. Its internal state contains a permutation over all possible bytes from 0 to 255, and it attempts to generate a pseudo-random sequence of bytes (called keystream) by extracting elements of this permutation. Over the last twenty years, numerous cryptanalytic results on RC4 stream cipher have been published, many of which are based on non-random (biased) events involving the secret key, the state variables, and the keystream of the cipher. ::: ::: Though biases based on the secret key are common in RC4 literature, none of the existing ones depends on the length of the secret key. In the first part of this paper, we investigate the effect of RC4 keylength on its keystream, and report significant biases involving the length of the secret key. In the process, we prove the two known empirical biases that were experimentally reported and used in recent attacks against WEP and WPA by Sepehrdad, Vaudenay and Vuagnoux in EUROCRYPT 2011. After our current work, there remains no bias in the literature of WEP and WPA attacks without a proof. ::: ::: In the second part of the paper, we present theoretical proofs of some significant initial-round empirical biases observed by Sepehrdad, Vaudenay and Vuagnoux in SAC 2010. ::: ::: In the third part, we present the derivation of the complete probability distribution of the first byte of RC4 keystream, a problem left open for a decade since the observation by Mironov in CRYPTO 2002. Further, the existence of positive biases towards zero for all the initial bytes 3 to 255 is proved and exploited towards a generalized broadcast attack on RC4. We also investigate for long-term non-randomness in the keystream, and prove a new long-term bias of RC4.


title: Visually Exploring Transportation Schedules

abstract: Public transportation schedules are designed by agencies to optimize service quality under multiple constraints. However, real service usually deviates from the plan. Therefore, transportation analysts need to identify, compare and explain both eventual and systemic performance issues that must be addressed so that better timetables can be created. The purely statistical tools commonly used by analysts pose many difficulties due to the large number of attributes at trip- and station-level for planned and real service. Also challenging is the need for models at multiple scales to search for patterns at different times and stations, since analysts do not know exactly where or when relevant patterns might emerge and need to compute statistical summaries for multiple attributes at different granularities. To aid in this analysis, we worked in close collaboration with a transportation expert to design TR-EX, a visual exploration tool developed to identify, inspect and compare spatio-temporal patterns for planned and real transportation service. TR-EX combines two new visual encodings inspired by Marey's Train Schedule: Trips Explorer for trip-level analysis of frequency, deviation and speed; and Stops Explorer for station-level study of delay, wait time, reliability and performance deficiencies such as bunching. To tackle overplotting and to provide a robust representation for a large numbers of trips and stops at multiple scales, the system supports variable kernel bandwidths to achieve the level of detail required by users for different tasks. We justify our design decisions based on specific analysis needs of transportation analysts. We provide anecdotal evidence of the efficacy of TR-EX through a series of case studies that explore NYC subway service, which illustrate how TR-EX can be used to confirm hypotheses and derive new insights through visual exploration.


title: A real-time matching system for large fingerprint databases

abstract: With the current rapid growth in multimedia technology, there is an imminent need for efficient techniques to search and query large image databases. Because of their unique and peculiar needs, image databases cannot be treated in a similar fashion to other types of digital libraries. The contextual dependencies present in images, and the complex nature of two-dimensional image data make the representation issues more difficult for image databases. An invariant representation of an image is still an open research issue. For these reasons, it is difficult to find a universal content-based retrieval technique. Current approaches based on shape, texture, and color for indexing image databases have met with limited success. Further, these techniques have not been adequately tested in the presence of noise and distortions. A given application domain offers stronger constraints for improving the retrieval performance. Fingerprint databases are characterized by their large size as well as noisy and distorted query images. Distortions are very common in fingerprint images due to elasticity of the skin. In this paper, a method of indexing large fingerprint image databases is presented. The approach integrates a number of domain-specific high-level features such as pattern class and ridge density at higher levels of the search. At the lowest level, it incorporates elastic structural feature-based matching for indexing the database. With a multilevel indexing approach, we have been able to reduce the search space. The search engine has also been implemented on Splash 2-a field programmable gate array (FPGA)-based array processor to obtain near-ASIC level speed of matching. Our approach has been tested on a locally collected test data and on NIST-9, a large fingerprint database available in the public domain.


title: Mining Contracts for Business Events and Temporal Constraints in Service Engagements

abstract: Contracts are legally binding descriptions of business service engagements. In particular, we consider business events as elements of a service engagement. Business events such as purchase, delivery, bill payment, and bank interest accrual not only correspond to essential processes but are also inherently temporally constrained. Identifying and understanding the events and their temporal relationships can help a business partner determine what to deliver and what to expect from others as it participates in the service engagement specified by a contract. However, contracts are expressed in unstructured text and their insights are buried therein. Our contributions are threefold. We develop a novel approach employing a hybrid of surface patterns, parsing, and classification to extract 1) business events and 2) their temporal constraints from contract text. We use topic modeling to 3) automatically organize the event terms into clusters. An evaluation on a real-life contract dataset demonstrates the viability and promise of our hybrid approach, yielding an F-measure of 0.89 in event extraction and 0.90 in temporal constraints extraction. The topic model yields event term clusters with an average match of 85 percent between two independent human annotations and an expert-assigned set of class labels for the clusters.


title: Proper ciliary assembly is critical for restricting Hedgehog signaling during early eye development in mice

abstract: Patterning of the vertebrate eye into optic stalk, retinal pigment epithelium (RPE) and neural retina (NR) territories relies on a number of signaling pathways, but how these signals are interpreted by optic progenitors is not well understood. The primary cilium is a microtubule-based organelle that is essential for Hedgehog (Hh) signaling, but it has also been implicated in the regulation of other signaling pathways. Here, we show that the optic primordium is ciliated during early eye development and that ciliogenesis is essential for proper patterning and morphogenesis of the mouse eye. Ift172 mutants fail to generate primary cilia and exhibit patterning defects that resemble those of Gli3 mutants, suggesting that cilia are required to restrict Hh activity during eye formation. Ift122 mutants, which produce cilia with abnormal morphology, generate optic vesicles that fail to invaginate to produce the optic cup. These mutants also lack formation of the lens, RPE and NR. Such phenotypic features are accompanied by strong, ectopic Hh pathway activity, evidenced by altered gene expression patterns. Removal of GLI2 from Ift122 mutants rescued several aspects of optic cup and lens morphogenesis as well as RPE and NR specification. Collectively, our data suggest that proper assembly of primary cilia is critical for restricting the Hedgehog pathway during eye formation in the mouse.


title: A Bayesian model of shape and appearance for subcortical brain segmentation

abstract: Abstract Automatic segmentation of subcortical structures in human brain MR images is an important but difficult task due to poor and variable intensity contrast. Clear, well-defined intensity features are absent in many places along typical structure boundaries and so extra information is required to achieve successful segmentation. A method is proposed here that uses manually labelled image data to provide anatomical training information. It utilises the principles of the Active Shape and Appearance Models but places them within a Bayesian framework, allowing probabilistic relationships between shape and intensity to be fully exploited. The model is trained for 15 different subcortical structures using 336 manually-labelled T1-weighted MR images. Using the Bayesian approach, conditional probabilities can be calculated easily and efficiently, avoiding technical problems of ill-conditioned covariance matrices, even with weak priors, and eliminating the need for fitting extra empirical scaling parameters, as is required in standard Active Appearance Models. Furthermore, differences in boundary vertex locations provide a direct, purely local measure of geometric change in structure between groups that, unlike voxel-based morphometry, is not dependent on tissue classification methods or arbitrary smoothing. In this paper the fully-automated segmentation method is presented and assessed both quantitatively, using Leave-One-Out testing on the 336 training images, and qualitatively, using an independent clinical dataset involving Alzheimer's disease. Median Dice overlaps between 0.7 and 0.9 are obtained with this method, which is comparable or better than other automated methods. An implementation of this method, called FIRST, is currently distributed with the freely-available FSL package.


title: Auto pricing Strategy for Real time bidding in advertising: A dynamic programming approach

abstract: Unlike the traditional advertising practices, in the process of Real time bidding (RTB), the impressions of a mobile application or a website are mapped to a particular advertiser through a real time auction, which triggers after an application is launched. As one of the key components of the RTB ecosystem, demand side platform (DSP) furnishes the advertisers a full pledge window to bid for available impressions. Due to fast growing market of mobile applications and websites, the selection of the most pertinent target audience to a particular advertiser, is not a simple human mediated process. Therefore, we propose an Auto Pricing Strategy (APS) approach to determine the applications to bid for and their respective bid prices from the advertisement agency perspective. We applied the APS on a real RTB bidding data and demonstrated how it outperforms the existing RTB approaches with a higher conversion rate for the same target spend.


title: Photometric Stereo via Computer Screen Lighting for Real-time Surface Reconstruction

abstract: We introduce a method which uses the light emitted by a computer screen to illuminate an object such as a human face from multiple directions, simultaneously capturing images with a webcam in order to perform photometric stereo. Dominant eigenvectors of the captured images provide surface normals, which are integrated into a 3D surface using Gauss-Seidel relaxation. The system runs at 10 frames per second on a consumer laptop computer.


title: Matching Demand with Supply in the Smart Grid using Agent-Based Multiunit Auction

abstract: Recent work has suggested reducing electricity generation cost by cutting the peak to average ratio (PAR) without reducing the total amount of the loads. However, most of these proposals rely on consumer's willingness to act. In this paper, we propose an approach to cut PAR explicitly from the supply side. The resulting cut loads are then distributed among consumers by the means of a multiunit auction which is done by an intelligent agent on behalf of the consumer. This approach is also in line with the future vision of the smart grid to have the demand side matched with the supply side. Experiments suggest that our approach reduces overall system cost and gives benefit to both consumers and the energy provider.


title: Gratitude and depressive symptoms: The role of positive reframing and positive emotion

abstract: Eight studies (N=2,973) tested the theory that gratitude is related to fewer depressive symptoms through positive reframing and positive emotion. Study 1 found a direct path between gratitude and depressive symptoms. Studies 2–5 demonstrated that positive reframing mediated the relationship between gratitude and depressive symptoms. Studies 6–7 showed that positive emotion mediated the relationship between gratitude and depressive symptoms. Study 8 found that positive reframing and positive emotion simultaneously mediated the relationship between gratitude and depressive symptoms. In sum, these eight studies demonstrate that gratitude is related to fewer depressive symptoms, with positive reframing and positive emotion serving as mechanisms that account for this relationship.


title: Incentives in BitTorrent induce free riding

abstract: We investigate the incentive mechanism of BitTorrent, which is a peer-to-peer file distribution system. As downloaders in BitTorrent are faced with the conflict between the eagerness to download and the unwillingness to upload, we relate this problem to the iterated prisoner's dilemma, which suggests guidelines to design a good incentive mechanism. Based on these guidelines, we propose a new, simple incentive mechanism. Our analysis and the experimental results using PlanetLab show that the original incentive mechanism of BitTorrent can induce free riding because it is not effective in rewarding and punishing downloaders properly. In contrast, a new mechanism proposed by us is shown to be more robust against free riders.


title: Cross-Space Affinity Learning with Its Application to Movie Recommendation

abstract: In this paper, we propose a novel cross-space affinity learning algorithm over different spaces with heterogeneous structures. Unlike most of affinity learning algorithms on the homogeneous space, we construct a cross-space tensor model to learn the affinity measures on heterogeneous spaces subject to a set of order constraints from the training pool. We further enhance the model with a factorization form which greatly reduces the number of parameters of the model with a controlled complexity. Moreover, from the practical perspective, we show the proposed factorized cross-space tensor model can be efficiently optimized by a series of simple quadratic optimization problems in an iterative manner. The proposed cross-space affinity learning algorithm can be applied to many real-world problems, which involve multiple heterogeneous data objects defined over different spaces. In this paper, we apply it into the recommendation system to measure the affinity between users and the product items, where a higher affinity means a higher rating of the user on the product. For an empirical evaluation, a widely used benchmark movie recommendation data set-MovieLens-is used to compare the proposed algorithm with other state-of-the-art recommendation algorithms and we show that very competitive results can be obtained.


title: Cybersecurity gets smart

abstract: Researchers aim to apply artificial intelligence and machine-learning methods to take cybersecurity to a new, higher, and better level.