title: SparseNN: An energy-efficient neural network accelerator exploiting input and output sparsity

abstract: Contemporary Deep Neural Network (DNN) contains millions of synaptic connections with tens to hundreds of layers. The large computation and memory requirements pose a challenge to the hardware design. In this work, we leverage the intrinsic activation sparsity of DNN to substantially reduce the execution cycles and the energy consumption. An end-to-end training algorithm is proposed to develop a lightweight run-time predictor for the output activation sparsity on the fly. From our experimental results, the computation overhead of the prediction phase can be reduced to less than 5% of the original feedforward phase with negligible accuracy loss. Furthermore, an energy-efficient hardware architecture, SparseNN, is proposed to exploit both the input and output sparsity. SparseNN is a scalable architecture with distributed memories and processing elements connected through a dedicated on-chip network. Compared with the state-of-the-art accelerators which only exploit the input sparsity, SparseNN can achieve a 10%-70% improvement in throughput and a power reduction of around 50%.


title: The Jacobi group and the squeezed states - some comments

abstract: The generalized coherent states attached to the Jacobi group realize the squeezed states. Imposing hermitian conjugacy to the generators of the Jacobi algebra, we find out the form of the weight function appearing in the scalar product. We show effectively the orthonormality of the base functions with respect to the scalar product. From the explicit form of the reproducing kernel, we find out the expression of the multiplier in a holomorphic representation of the Jacobi group.


title: Adaptive exploration for large-scale protein analysis in the molecular dynamics database

abstract: Molecular dynamics (MD) simulations generate detailed time-series data of all-atom motions. These simulations are leading users of the world's most powerful supercomputers, and are standard-bearers for a wide range of high-performance computing (HPC) methods. However, MD data exploration and analysis is in its infancy in terms of scalability, ease-of-use, and ultimately its ability to answer 'grand challenge' science questions. This demonstration introduces the Molecular Dynamics Database (MDDB) project at Johns Hopkins, to study the co-design of database methods for deep on-the-fly exploratory MD analyses with HPC simulations. Data exploration in MD suffers from a "human bottleneck", where the laborious administration of simulations leaves little room for domain experts to focus on tackling science questions. MDDB exploits the data-rich nature of MD simulations to provide adaptive control of the exploration process with machine learning techniques, specifically reinforcement learning (RL). We present MDDB's data and queries, architecture, and its use of RL methods. Our audience will co-operate with our steering algorithm and science partners, and witness MDDB's abilities to significantly reduce exploration times and direct computation resources to where they best address science questions.


title: The clinical impact of early immunological responses in human HER2-positive breast cancers on responsiveness to trastuzumab-based therapy.

abstract: 587Background: ‘TCHL’ an ICORG phase-II neo-adjuvant study in HER2+ve Breast cancer (BC) assessed the impact of TCH (Docetaxel, Carboplatin, Trastuzumab) versus TCHL (Docetaxel, Carboplatin, Trastuzumab, Lapatinib) in 78 patients. In these patients we determined the impact of total infiltrating lymphocytes (stromal and tumoral) (TIL) as a prognostic marker of pathological complete response (pCR). Methods: We determined TIL counts using immunohistochemical staining with Haemotoxalyin+Eosin, AE1/AE3 and CD45 in formalin fixed paraffin embedded (FFPE) baseline biopsy samples (n = 78) and in fresh frozen (FF) biopsies taken 20-days post cycle 1 (Day-20) of TCH/TCHL (n = 24). RNAseq analysis was performed in matched FF tumour biopsies taken pre-treatment and at Day-20 of treatment and analysed using ESTIMATE and CIBERSORT, to determine tumour content and specific immune cell subpopulations in each patient tumour sample. Results: Overall Mean TIL counts in the baseline pre-treatment biopsy samples were 33% high...


title: An Integrated Model of Influential Antecedents of Online Shopping Initial Trust: Empirical Evidence in a Low-Trust Environment

abstract: ABSTRACT This article explores the issue of what factors contribute to which dimensions of consumers' initial trust in online vendors in a relatively low-trust environment such as the People's Republic of China. The article has adopted the trust model proposed by McKnight and Chervany (2002) and explored the determinant factors of online shopping initial trust through building an integrated model. In our model, a multidimensional approach has been applied. Through hierarchical regression analysis, we find that distinct discrepancies do exist between influential antecedents of consumer initial trust in online vendors in a low-trust environment like China and those in a high-trust environment. Specifically, perceived corporate image, perceived security, and perceived reference power are influential antecedents of all three dimensions of trusting beliefs (except the effect of perceived security on ability and the effect of perceived reference power on benevolence), but perceived Web site quality is found to ...


title: Security as a service (SecaaS)—An overview

abstract: With cloud computing facilitating the migration of platforms, infrastructure and software to the cloud, security services are now following suit. Security as a service (SecaaS) is inherently a business model in which organizations can purchase on-demand security solutions to protect their data, applications, and systems, while taking advantage of what cloud computing has on offer. This chapter explores the evolution from traditional on-premise and managed security solutions to the SecaaS model, and evaluates the supporting and opposing arguments for the adoption of cloud security services. Furthermore, an overview is given for cloud security services categories with additional focus areas presented, and critical gaps identified in the literature.


World Knowledge for Reading Comprehension: Rare Entity Prediction with Hierarchical LSTMs Using External Descriptions


title: Functional map networks for analyzing and exploring large shape collections

abstract: The construction of networks of maps among shapes in a collection enables a variety of applications in data-driven geometry processing. A key task in network construction is to make the maps consistent with each other. This consistency constraint, when properly defined, leads not only to a concise representation of such networks, but more importantly, it serves as a strong regularizer for correcting and improving noisy initial maps computed between pairs of shapes in isolation. Up-to-now, however, the consistency constraint has only been fully formulated for point-based maps or for shape collections that are fully similar. In this paper, we introduce a framework for computing consistent functional maps within heterogeneous shape collections. In such collections not all shapes share the same structure --- different types of shared structure may be present within different (but possibly overlapping) sub-collections. Unlike point-based maps, functional maps can encode similarities at multiple levels of detail (points or parts), and thus are particularly suitable for coping with such diversity within a shape collection. We show how to rigorously formulate the consistency constraint in the functional map setting. The formulation leads to a powerful tool for computing consistent functional maps, and also for discovering shared structures, such as meaningful shape parts. We also show how to adapt the procedure for handling very large-scale shape collections. Experimental results on benchmark datasets show that the proposed framework significantly improves upon state-of-the-art data-driven techniques. We demonstrate the usefulness of the framework in shape co-segmentation and various shape exploration tasks.


title: Operations for Learning with Graphical Models

abstract: This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks frorn data. The paper concludes by sketching some implications for data analysis and summarizing how some popular algorithms fall within the framework presented. ::: ::: The main original contributions here are the decomposition techniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.


title: Learning-Based Testing of Distributed Microservice Architectures: Correctness and Fault Injection

abstract: We report on early results in a long term project to apply learning-based testing LBT to evaluate the functional correctness of distributed systems and their robustness to injected faults. We present a case study of a commercial product for counter-party credit risk implemented as a distributed microservice architecture. We describe the experimental set-up, as well as test results. From this experiment, we draw some conclusions about prospects for future research in learning-based testing.


title: A deep learning model integrating FCNNs and CRFs for brain tumor segmentation

abstract: Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis, treatment planning, and treatment outcome evaluation. Build upon successful deep learning techniques, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNNs) and Conditional Random Fields (CRFs) in a unified framework to obtain segmentation results with appearance and spatial consistency. We train a deep learning based segmentation model using 2D image patches and image slices in following steps: 1) training FCNNs using image patches; 2) training CRFs as Recurrent Neural Networks (CRF-RNN) using image slices with parameters of FCNNs fixed; and 3) fine-tuning the FCNNs and the CRF-RNN using image slices. Particularly, we train 3 segmentation models using 2D image patches and slices obtained in axial, coronal and sagittal views respectively, and combine them to segment brain tumors using a voting based fusion strategy. Our method could segment brain images slice-by-slice, much faster than those based on image patches. We have evaluated our method based on imaging data provided by the Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2013, BRATS 2015 and BRATS 2016. The experimental results have demonstrated that our method could build a segmentation model with Flair, T1c, and T2 scans and achieve competitive performance as those built with Flair, T1, T1c, and T2 scans.


title: Applied Software Project Management

abstract: "If you're looking for solid, easy-to-follow advice on estimation, requirements gathering, managing change, and more, you can stop now: this is the book for you." --Scott Berkun, Author of The Art of Project Management What makes software projects succeed? It takes more than a good idea and a team of talented programmers. A project manager needs to know how to guide the team through the entire software project. There are common pitfalls that plague all software projects and rookie mistakes that are made repeatedly--sometimes by the same people! Avoiding these pitfalls is not hard, but it is not necessarily intuitive. Luckily, there are tried and true techniques that can help any project manager. In Applied Software Project Management, Andrew Stellman and Jennifer Greene provide you with tools, techniques, and practices that you can use on your own projects right away. This book supplies you with the information you need to diagnose your team's situation and presents practical advice to help you achieve your goal of building better software. Topics include: Planning a software project Helping a team estimate its workload Building a schedule Gathering software requirements and creating use cases Improving programming with refactoring, unit testing, and version control Managing an outsourced project Testing software Jennifer Greene and Andrew Stellman have been building software together since 1998. Andrew comes from a programming background and has managed teams of requirements analysts, designers, and developers. Jennifer has a testing background and has managed teams of architects, developers, and testers. She has led multiple large-scale outsourced projects. Between the two of them, they have managed every aspect of software development. They have worked in a wide range of industries, including finance, telecommunications, media, nonprofit, entertainment, natural-language processing, science, and academia. For more information about them and this book, visit http://www.stellman-greene.com.


title: A Machine Reading System for Assembling Synthetic Paleontological Databases

abstract: Many aspects of macroevolutionary theory and our understanding of biotic responses to global environmental change derive from literature-based compilations of paleontological data. Existing manually assembled databases are, however, incomplete and difficult to assess and enhance with new data types. Here, we develop and validate the quality of a machine reading system, PaleoDeepDive, that automatically locates and extracts data from heterogeneous text, tables, and figures in publications. PaleoDeepDive performs comparably to humans in several complex data extraction and inference tasks and generates congruent synthetic results that describe the geological history of taxonomic diversity and genus-level rates of origination and extinction. Unlike traditional databases, PaleoDeepDive produces a probabilistic database that systematically improves as information is added. We show that the system can readily accommodate sophisticated data types, such as morphological data in biological illustrations and associated textual descriptions. Our machine reading approach to scientific data integration and synthesis brings within reach many questions that are currently underdetermined and does so in ways that may stimulate entirely new modes of inquiry.


title: Constants count: practical improvements to oblivious RAM

abstract: Oblivious RAM (ORAM) is a cryptographic primitive that hides memory access patterns as seen by untrusted storage. This paper proposes Ring ORAM, the most bandwidth-efficient ORAM scheme for the small client storage setting in both theory and practice. Ring ORAM is the first tree-based ORAM whose bandwidth is independent of the ORAM bucket size, a property that unlocks multiple performance improvements. First, Ring ORAM's overall bandwidth is 2.3× to 4× better than Path ORAM, the prior-art scheme for small client storage. Second, if memory can perform simple untrusted computation, Ring ORAM achieves constant online bandwidth (∼ 60× improvement over Path ORAM for practical parameters). As a case study, we show Ring ORAM speeds up program completion time in a secure processor by 1.5× relative to Path ORAM. On the theory side, Ring ORAM features a tighter and significantly simpler analysis than Path ORAM.


title: A microstrip differential power divider

abstract: A microstrip differential power divider, composed by one balanced-to-single-ended out-of-phase power divider and two Wilkinson power dividers, is proposed in this paper. The proposed power divider has compact size, and can achieve differential-mode power division, isolation between output ports and common-mode suppression. A prototype of the proposed power divider is fabricated, and the measured results consist well with the simulated ones.


title: Simultaneous Projector-Camera Self-Calibration for Three-Dimensional Reconstruction and Projection Mapping

abstract: Automatic calibration of structured-light systems, generally consisting of a projector and camera, is of great importance for a variety of practical applications. We propose a novel optimization approach for geometric calibration of a projector-camera system that estimates the intrinsic, extrinsic, and distortion parameters of both the camera and projector in an automatic fashion using structured light. Our approach benefits from a novel multifactor objective function that finds maximum-likelihood estimates from noisy point correspondences using constraints on focal lengths and resolves ambiguities estimating the fundamental matrix by enforcing epipolar geometry on the rectified noisy data. This new formulation allows estimation of all calibration parameters simultaneously and minimization is ensured by a greedy descent algorithm that decreases the cost function at each iteration. This provides more accurate parameter estimation, reconstruction accuracy, and robustness to noise and poor initialization compared to previous methods. Experimental results demonstrate the stability and robustness of our method, and show that the proposed solution outperforms a currently leading approach to an automatic geometric projector-camera calibration.


A Default First Order Family Weight Determination Procedure For WPDV Models


title: Mechanisms of facial emotion recognition in autism spectrum disorders: Insights from eye tracking and electroencephalography

abstract: While behavioural difficulties in facial emotion recognition (FER) have been observed in individuals with Autism Spectrum Disorder (ASD), behavioural studies alone are not suited to elucidate the s ...


title: Inferring Binary Relation Schemas for Open Information Extraction

abstract: This paper presents a framework to model the semantic representation of binary relations produced by open information extraction systems. For each binary relation, we infer a set of preferred types on the two arguments simultaneously, and generate a ranked list of type pairs which we call schemas. All inferred types are drawn from the Freebase type taxonomy, which are human readable. Our system collects 171,168 binary relations from ReVerb, and is able to produce top-ranking relation schemas with a mean reciprocal rank of 0.337.


title: Static repositioning in a bike-sharing system: models and solution approaches

abstract: Bike-sharing systems allow people to rent a bicycle at one of many automatic rental stations scattered around the city, use them for a short journey and return them at any station in the city. A crucial factor for the success of a bike- sharing system is its ability to meet the fluctuating demand for bicycles and for vacant lockers at each station. This is achieved by means of a repositioning oper- ation, which consists of removing bicycles from some stations and transferring them to other stations, using a dedicated fleet of trucks. Operating such a fleet in a large bike-sharing system is an intricate problem consisting of decisions regarding the routes that the vehicles should follow and the number of bicycles that should be removed or placed at each station on each visit of the vehicles. In this paper, we present our modeling approach to the problem that generalizes existing routing models in the literature. This is done by introducing a unique convex objective function as well as time-related considerations. We present two mixed integer linear program formulations, discuss the assumptions associated with each, strengthen them by several valid inequalities and dominance rules, and compare their perfor- mances through an extensive numerical study. The results indicate that one of the formulations is very effective in obtaining high quality solutions to real life instances of the problem consisting of up to 104 stations and two vehicles. Finally, we draw insights on the characteristics of good solutions.


title: Automatic 3d mesh generation for a domain with multiple materials

abstract: This paper describes an approach to construct unstructured tetrahedral and hexahedral meshes for a domain with multiple materials. In earlier works, we developed an octree-based isocontouring method to construct unstructured 3D meshes for a single-material domain. Based on this methodology, we introduce the notion of material change edge and use it to identify the interface between two or several materials. We then mesh each material region with conforming boundaries. Two kinds of surfaces, the boundary surface and the interface between two different material regions, are distinguished and meshed. Both material change edges and interior edges are analyzed to construct tetrahedral meshes, and interior grid points are analyzed for hexahedral mesh construction. Finally the edge-contraction and smoothing method is used to improve the quality of tetrahedral meshes, and a combination of pillowing, geometric flow and optimization techniques is used for hexahedral mesh quality improvement. The shrink set is defined automatically as the boundary of each material region. Several application results in different research fields are shown.


Feature Selection for Text Classification


title: Parallel simulation: parallel and distributed simulation systems

abstract: Originating from basic research conducted in the 1970's and 1980's, the parallel and distributed simulation field has matured over the last few decades. Today, operational systems have been fielded for applications such as military training, analysis of communication networks, and air traffic control systems, to mention a few. This tutorial gives an overview of technologies to distribute the execution of simulation programs over multiple computer systems. Particular emphasis is placed on synchronization (also called time management) algorithms as well as data distribution techniques.


title: Network-based approaches for analysis of complex biological systems.

abstract: Cells function and respond to changes in their environment by the coordinated activity of their molecular components, including mRNAs, proteins and metabolites. At the heart of proper cellular function are molecular networks connecting these components to process extra-cellular environmental signals and drive dynamic, context-specific cellular responses. Network-based computational approaches aim to systematically integrate measurements from high-throughput experiments to gain a global understanding of cellular function under changing environmental conditions. We provide an overview of recent methodological developments toward solving two major computational problems within this field in the past two years (2013-2015): network reconstruction and network-based interpretation. Looking forward, we envision development of methods that can predict phenotypes with high accuracy as well as provide biologically plausible mechanistic hypotheses.


title: Effects of hippotherapy on body functions, activities and participation in children with cerebral palsy based on ICF-CY assessments

abstract: AbstractPurpose: To evaluate the effects of hippotherapy on body functions, activities, and participation in children with CP of various functional levels by using the International Classification of Functioning, Disability and Health-Children and Youth (ICF-CY) checklist.Methods: Fourteen children with cerebral palsy (CP) (3–8 years of age) were recruited for a 36-week study composed of baseline, intervention, and withdrawal phases (12 weeks for each phase, ABA design). Hippotherapy was implemented for 30 min once weekly for 12 consecutive weeks during the intervention phase. Body Functions (b) and Activities and Participation (d) components of the ICF-CY checklist were used as outcome measures at the initial interview and at the end of each phase.Results: Over the 12 weeks of hippotherapy, significant improvements in ICF-CY qualifiers were found in neuromusculoskeletal and movement-related functions (b7), mobility (d4) and major life areas (d8) and, in particular, mobility of joint functions (b710), mus...


title: Black cattle body shape and temperature measurement using thermography and KINECT sensor

abstract: Black cattle body shape and temperature measurement system is introduced. It is important to evaluate the quality of Japanese black cattle periodically during their growth process. Not only the weight and size of cattle, but also the posture, shape, and temperature need to be tracked as primary evaluation criteria. In the present study, KINECT sensor and thermal camera obtains the body shape and its temperature. The whole system is calibrated to operate in a common coordinate system. Point cloud data are obtained from different angles and reconstructed in a computer. The thermal data are captured too. Both point cloud data and thermal information are combined by considering the orientation of the cow. The collected information is used to evaluate and estimate cattle conditions.


title: A review of techniques for sentiment analysis Of Twitter data

abstract: There has been a rapid increase in the use of social networking websites in the last few years. People most conveniently express their views and opinions on a wide array of topics via such websites. Sentiment analysis of such data which comprises of people's views is very important in order to gauge public opinion on a particular topic of interest. This paper reviews a number of techniques, both lexicon-based approaches as well as learning based methods that can be used for sentiment analysis of text. In order to adapt these techniques for sentiment analysis of data procured from one of the social networking websites, Twitter, a number of issues and challenges need to be addressed, which are put forward in this paper.


title: Modelling neural correlates of working memory: A coordinate-based meta-analysis

abstract: Abstract Working memory subsumes the capability to memorize, retrieve and utilize information for a limited period of time which is essential to many human behaviours. Moreover, impairments of working memory functions may be found in nearly all neurological and psychiatric diseases. To examine what brain regions are commonly and differently active during various working memory tasks, we performed a coordinate-based meta-analysis over 189 fMRI experiments on healthy subjects. The main effect yielded a widespread bilateral fronto-parietal network. Further meta-analyses revealed that several regions were sensitive to specific task components, e.g. Broca's region was selectively active during verbal tasks or ventral and dorsal premotor cortex were preferentially involved in memory for object identity and location, respectively. Moreover, the lateral prefrontal cortex showed a division in a rostral and a caudal part based on differential involvement in task set and load effects. Nevertheless, a consistent but more restricted “core” network emerged from conjunctions across analyses of specific task designs and contrasts. This “core” network appears to comprise the quintessence of regions, which are necessary during working memory tasks. It may be argued that the core regions form a distributed executive network with potentially generalized functions for focussing on competing representations in the brain. The present study demonstrates that meta-analyses are a powerful tool to integrate the data of functional imaging studies on a (broader) psychological construct, probing the consistency across various paradigms as well as the differential effects of different experimental implementations.


title: Multiple Radii DisC Diversity: Result Diversification Based on Dissimilarity and Coverage

abstract: Recently, result diversification has attracted a lot of attention as a means to improve the quality of results retrieved by user queries. In this article, we introduce a novel definition of diversity called DisC diversity. Given a tuning parameter r, which we call radius, we consider two items to be similar if their distance is smaller than or equal to r. A DisC diverse subset of a result contains items such that each item in the result is represented by a similar item in the diverse subset and the items in the diverse subset are dissimilar to each other. We show that locating a minimum DisC diverse subset is an NP-hard problem and provide algorithms for its approximation. We extend our definition to the multiple radii case, where each item is associated with a different radius based on its importance, relevance, or other factors. We also propose adapting DisC diverse subsets to a different degree of diversification by adjusting r, that is, increasing the radius (or zooming-out) and decreasing the radius (or zooming-in). We present efficient implementations of our algorithms based on the M-tree, a spatial index structure, and experimentally evaluate their performance.


title: Measurement error in geometric morphometrics

abstract: Geometric morphometrics—a set of methods for the statistical analysis of shape once saluted as a revolutionary advancement in the analysis of morphology —is now mature and routinely used in ecology and evolution. However, a factor often disregarded in empirical studies is the presence and the extent of measurement error. This is potentially a very serious issue because random measurement error can inflate the amount of variance and, since many statistical analyses are based on the amount of “explained” relative to “residual” variance, can result in loss of statistical power. On the other hand, systematic bias can affect statistical analyses by biasing the results (i.e. variation due to bias is incorporated in the analysis and treated as biologically-meaningful variation). Here, I briefly review common sources of error in geometric morphometrics. I then review the most commonly used methods to measure and account for both random and non-random measurement error, providing a worked example using a real dataset.


title: Multi-output RNN-LSTM for multiple speaker speech synthesis with α-interpolation model.

abstract: Deep Learning has been applied successfully to speech processing. In this paper we propose an architecture for speech synthesis using multiple speakers. Some hidden layers are shared by all the speakers, while there is a specific output layer for each speaker. Objective and perceptual experiments prove that this scheme produces much better results in comparison with sin- ::: gle speaker model. Moreover, we also tackle the problem of speaker interpolation by adding a new output layer (a-layer) on top of the multi-output branches. An identifying code is injected into the layer together with acoustic features of many speakers. Experiments show that the a-layer can effectively learn to interpolate the acoustic features between speakers.


title: Dante vision: In-air and touch gesture sensing for natural surface interaction with combined depth and thermal cameras

abstract: Researchers have paid considerable attention to natural user interfaces, especially sensing gestures and touches upon an un-instrumented surface from an overhead camera. We present a system that combines depth sensing from a Microsoft Kinect and temperature sensing from a thermal imaging camera to infer a variety of gestures and touches for controlling a natural user interface. The system, coined Dante, is capable of (1) inferring multiple touch points from multiple users (92.6% accuracy), (2) detecting and classifying each user using their depth and thermal footprint (87.7% accuracy), and (3) detecting touches on objects placed upon the table top (91.7% accuracy). The system can also classify the pressure of chording motions. The system is real time, with an average processing delay of 40 ms.


title: Detecting and Preventing Kernel Rootkit Attacks with Bus Snooping

abstract: To protect the integrity of operating system kernels, we present Vigilare system, a kernel integrity monitor that is architected to snoop the bus traffic of the host system from a separate independent hardware. This snoop-based monitoringenabled by the Vigilare system, overcomes the limitations of the snapshot-based monitoring employed in previous kernel integrity monitoring solutions. Being based on inspecting snapshots collected over a certain interval, the previous hardware-based monitoring solutions cannot detect transient attacks that can occur in between snapshots, and cannot protect the kernel against permanent damage. We implemented three prototypes of the Vigilare system by adding Snooper hardware connections module to the host system for bus snooping, and a snapshot-based monitor to be comared with, in order to evaluate the benefit of snoop-based monitoring. The prototypes of Vigilare system detected all the transient attacks and the second one protected the kernel with negligible performance degradation while the snapshot-based monitor could not detect all the attacks and induced considerable performance degradation as much as 10 percent in our tuned STREAM benchmark test.


title: Robust reconstruction of indoor scenes

abstract: We present an approach to indoor scene reconstruction from RGB-D video. The key idea is to combine geometric registration of scene fragments with robust global optimization based on line processes. Geometric registration is error-prone due to sensor noise, which leads to aliasing of geometric detail and inability to disambiguate different surfaces in the scene. The presented optimization approach disables erroneous geometric alignments even when they significantly outnumber correct ones. Experimental results demonstrate that the presented approach substantially increases the accuracy of reconstructed scene models.


title: The theory of planned behavior and Internet purchasing

abstract: Several opinion polls have found that many consumers resist making purchases via the Internet because of their concerns about the privacy of the personal information they provide to Internet merchants. Using the theory of planned behavior as its basis, this study investigated the relationships among beliefs about Internet privacy and trustworthiness, along with beliefs about perceived behavioral control and the expectations of important others, and online purchasing behavior. Data were collected from 193 college students. Analysis of the data indicates that beliefs about trustworthiness positively affect attitudes toward buying online, which in turn positively affect purchasing behavior. Beliefs about self‐efficacy regarding purchasing positively affect perceived behavioral control, which in turn affects online purchasing behavior. In short, respondents who believed in the trustworthiness of the Internet and in their own abilities to buy online were more likely to make Internet purchases than were those without such beliefs.


title: Cooperative Multi-Agent Planning: A Survey

abstract: Cooperative multi-agent planning (MAP) is a relatively recent research field that combines technologies, algorithms, and techniques developed by the Artificial Intelligence Planning and Multi-Agent Systems communities. While planning has been generally treated as a single-agent task, MAP generalizes this concept by considering multiple intelligent agents that work cooperatively to develop a course of action that satisfies the goals of the group. This article reviews the most relevant approaches to MAP, putting the focus on the solvers that took part in the 2015 Competition of Distributed and Multi-Agent Planning, and classifies them according to their key features and relative performance.


title: AN ADIABATIC APPROACH FOR LOW POWER FULL ADDER DESIGN

abstract: Over the past decade, several adiabatic logic styles have been reported. This paper deals with the design of a 1-bit full adder using several adiabatic logic styles, which are derived from static CMOS logic, without a large change. The full adders are designed using 180nm technology parameters provided by predictive technology and simulated using HSPICE. The full adders designed are compared in terms of average power consumption with different values of load capacitance, temperature and input frequency. The different designs of full adder are also compared on the basis of propagation delay exhibit by them. It is found that, full adders designed with adiabatic logic styles tends to consume very low power in comparison to full adder designed with static CMOS logic. Under certain operating conditions, one of adiabatic designs of full adder achieves upto 74% power saving in comparison to the full adder designed with static CMOS logic.


title: FrameWire: a tool for automatically extracting interaction logic from paper prototyping tests

abstract: Paper prototyping offers unique affordances for interface design. However, due to its spontaneous nature and the limitations of paper, it is difficult to distill and communicate a paper prototype design and its user test findings to a wide audience. To address these issues, we created FrameWire, a computer vision-based system that automatically extracts interaction flows from the video recording of paper prototype user tests. Based on the extracted logic, FrameWire offers two distinct benefits for designers: a structural view of the video recording that allows a designer or a stakeholder to easily distill and understand the design concept and user interaction behaviors, and automatic generation of interactive HTML-based prototypes that can be easily tested with a larger group of users as well as "walked through" by other stakeholders. The extraction is achieved by automatically aggregating video frame sequences into an interaction flow graph based on frame similarities and a designer-guided clustering process. The results of evaluating FrameWire with realistic paper prototyping tests show that our extraction approach is feasible and FrameWire is a promising tool for enhancing existing prototyping practice.


title: How much can we boost IQ and scholastic achievement

abstract: Arthur Jensen argues that the failure of recent compensatory education efforts to produce lasting effects on children's IQ and achievement suggests that the premises on which these efforts have been based should be reexamined.


title: Immunogenetic Investigations on Repeat Breeding Phenomenon in Purebred Arabian Mares

abstract: 2 Abstract: Investigations were carried out on 26 pure- bred Arabian mares to detect the genetic markers that may control the repeat breeding phenomenon in Arabian mares. Serum protein electrophoresis was carried out for genotyping blood protein loci and estimation of gene frequencies in both fertile and repeat breeder mares. Results indicated predominance of pr , A1 , F and E gene markers in repeat breeder mares and NF A H 2S F , Es and Gc gene markers in regular breeder mares. It was concluded that there is a tight relation between 2 BG F


Neurodegenerative disorder: Five groups of Questions to address. Routes to Therapy


title: Effects of Growth Hormone upon Erythropoiesis in the Hypophysectomized Rat.∗

abstract: Summary Administration of STH to hypophysectomized rats results in peripheral reticulocytosis and increases in the percentages of nucleated erythroid elements within the bone marrow. Considerable repair of the hypoplastic marrow is induced by this agent. A rise in peripheral erythrocytic values fails to occur probably because of a concomitant increase in plasma volume.


title: Application of the fuzzy multi-criteria decision-making method to identify nonlinear decision model

abstract: A large number of multi-criteria methods have been developed to deal with different kinds of problems. Most of them use a linear aggregation, what is the cause of many shortcomings in solving decision problems. This paper presents how to identify nonlinear multi-criteria decision-making models with using the new fuzzy method: the Characteristic Objects Method (COMET). In this approach, models are constructed on the basis of characteristic objects and fuzzy rules. Thereby, the COMET method is free of rank reversal phenomenon, which is one of the most commonly indicated shortcoming of the multi-criteria decisionmaking methods. This study introduces the concepts of characteristic objects and way of their determination. Subsequently, the paper presents approach to construct the fuzzy rule base and the entire model. Finally, the theoretical nonlinear problem is presented to verify the developed approach and to demonstrate its effectiveness.


title: Optically Invisible Antenna Integrated Within an OLED Touch Display Panel for IoT Applications

abstract: Future electronics and Internet of Things devices with the capability of high-speed wireless communication will increasingly rely on efficient and intelligent antennas. However, conventional wireless communication systems for small wireless electronics devices suffer from low radiation efficiencies of miniaturized antennas implemented within less-than ideal locations and real estates. Here, we introduce the original concept of utilizing the entire transparent region of high-resolution organic light-emitting diode (OLED) touch displays to render an antenna that is invisible to the human eye. A transverse magnetic resonant mode antenna is formulated based on transparent conductive polymers, which are precisely realized using mesoscale formation of electromagnetic boundary conditions. Simultaneously, these electromagnetic patterns achieve optical invisibility. We show that the transparent antenna film can be integrated inside an OLED touch display and feature efficient radiation properties at 2.4 GHz. The presented theory and measurement studies of the fabricated prototype for smartwatch application for Wi-Fi and Bluetooth connectivity inspire a broader range of integration of microwave and display technologies.


title: Fuzzy support vector machines

abstract: A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes. In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that different input points can make different contributions to the learning of decision surface. We call the proposed method fuzzy SVMs (FSVMs).


title: Combining high-fidelity human patient simulators with a standardized family member: a novel approach to teaching breaking bad news.

abstract: Our novel approach to teaching Breaking Bad News (BBN) involves having students actively participate in an unsuccessful resuscitation (mannequin) followed immediately by BBN to a standardized patient wife (SPW) portrayed by an actress. Thirty-nine 3(rd) year medical students completed a questionnaire and then were divided as follows: Group 1 (n=21) received little to no training prior to speaking with the SPW. Group 2 (n =18) received a lecture and practiced for 1 hour in small groups prior to the resuscitation and BBN. Both groups self assessed ability to BBN (p<.0002 & p<.00001), and ability to have a plan (p<.0004 & p <.0003) improved significantly over base line with greater improvement in group 2. Group 2 (pre-trained) students were rated superior by SPW's in several key areas. This novel approach to teaching BBN to 3(rd) year medical students was well received by the students and resulted in marked improvement of self assessed skills over baseline.


title: Learning Adversary-Resistant Deep Neural Networks

abstract: Deep neural networks (DNNs) have proven to be quite effective in a vast array of machine learning tasks, with recent examples in cyber security and autonomous vehicles. Despite the superior performance of DNNs in these applications, it has been recently shown that these models are susceptible to a particular type of attack that exploits a fundamental flaw in their design. This attack consists of generating particular synthetic examples referred to as adversarial samples. These samples are constructed by slightly manipulating real data-points in order to "fool" the original DNN model, forcing it to mis-classify previously correctly classified samples with high confidence. Addressing this flaw in the model is essential if DNNs are to be used in critical applications such as those in cyber security. ::: Previous work has provided various learning algorithms to enhance the robustness of DNN models, and they all fall into the tactic of "security through obscurity". This means security can be guaranteed only if one can obscure the learning algorithms from adversaries. Once the learning technique is disclosed, DNNs protected by these defense mechanisms are still susceptible to adversarial samples. In this work, we investigate this issue shared across previous research work and propose a generic approach to escalate a DNN's resistance to adversarial samples. More specifically, our approach integrates a data transformation module with a DNN, making it robust even if we reveal the underlying learning algorithm. To demonstrate the generality of our proposed approach and its potential for handling cyber security applications, we evaluate our method and several other existing solutions on datasets publicly available. Our results indicate that our approach typically provides superior classification performance and resistance in comparison with state-of-art solutions.


title: A Novel Wide-Temperature-Range, 3.9 ppm/$^{\circ}$C CMOS Bandgap Reference Circuit

abstract: This paper presents an innovative CMOS Bandgap Reference Generator topology that leads to an improved curvature compensation method over a very wide temperature range. The proposed design was implemented in a standard 0.35 μm CMOS process. The compensation is performed by using only poly-silicon resistors. This is achieved by using a second Op-amp that generates a CTAT current, which is subsequently used to enhance the curvature compensation method. The performance of the circuit was verified experimentally. Measured results have shown temperature coefficients as low as 3.9 ppm/°C over a temperature range of 165°C ( -15°C to 150°C ) and temperature coefficients as low as 13.7 ppm/°C over an extended temperature range of 200°C (-50°C to 150°C ). In addition the circuit demonstrated very good line regulation performance for a broad range of supply voltages. The measured line regulation at room temperature is 0.039% V.


title: PID control for improving P&O-MPPT performance of a grid-connected solar PV system with Ziegler-Nichols tuning method

abstract: This paper uses the Ziegler-Nichols (Z-N) tuning method approach combined with adjustments of the Proportional-Integral-Derivative (PID) gain parameters to achieve satisfactory improvement in the open-loop Perturb-and-Observe (P&O) maximum power point tracking (MPPT) performance of a grid-connected solar photovoltaic (PV) system. Various modes of the PID control produced varied effects and improvements in performance compared to the open-loop response. The P-controller reduced the open-loop overshoot by 62.28 % and the settling time by 166 ms, but introduced ripples. The PI-controller also reduced the open-loop overshoot by 70.99 % and reduced settling time by 106 ms, but increased the rise time slightly over that of the P-controller. The PID-controller virtually eliminated the overshoot, reducing it from 35.06 % to 0.51 %, and also increased the system response time by 236 ms. The superiority of the PID controller is thus confirmed as it virtually eliminated the overshoots, connoting a significant reduction in losses. The settling times were also reduced, signifying a much faster system response.


title: AGI Safety Literature Review

abstract: The development of Artificial General Intelligence (AGI) promises to be a major event. Along with its many potential benefits, it also raises serious safety concerns (Bostrom, 2014). The intention of this paper is to provide an easily accessible and up-to-date collection of references for the emerging field of AGI safety. A significant number of safety problems for AGI have been identified. We list these, and survey recent research on solving them. We also cover works on how best to think of AGI from the limited knowledge we have today, predictions for when AGI will first be created, and what will happen after its creation. Finally, we review the current public policy on AGI.


title: Automatic refinement of patent queries using concept importance predictors

abstract: Patent prior art queries are full patent applications which are much longer than standard web search topics. Such queries are composed of hundreds of terms and do not represent a focused information need. One way to make the queries more focused is to select a group of key terms as representatives. Existing works show that such a selection to reduce patent queries is a challenging task mainly because of the presence of ambiguous terms. Given this setup, we present a query modeling approach where we utilize patent-specific characteristics to generate more precise queries. We propose to automatically disambiguate query terms by employing noun phrases that are extracted using the global analysis of the patent collection. We further introduce a method for predicting whether expansion using noun phrases would improve the retrieval effectiveness. Our experiments show that we can obtain almost 20% improvement by performing query expansion using the true importance of the noun phrase queries. Based on this observation, we introduce various features that can be used to estimate the importance of the noun phrase query. We evaluated the effectiveness of the proposed method on the patent prior art search collection CLEF-IP 2010. Our experimental results indicate that the proposed features make good predictors of the noun phrase importance, and selective application of noun phrase queries using the importance predictors outperforms existing query generation methods.


title: Analysis of Firewall Policy Rules Using Data Mining Techniques

abstract: Firewall is the de facto core technology of today's network security and defense. However, the management of firewall rules has been proven to be complex, error-prone, costly and inefficient for many large-networked organizations. These firewall rules are mostly custom-designed and hand-written thus in constant need for tuning and validation, due to the dynamic nature of the traffic characteristics, ever-changing network environment and its market demands. One of the main problems that we address in this paper is that how much the firewall rules are useful, up-to-dated, well-organized or efficient to reflect the current characteristics of network traffics. In this paper, we present a set of techniques and algorithms to analysis and manage firewall policy rules: (1) Data Mining technique to deduce efficient firewall policy rules by mining its network traffic log based on its frequency, (2) Filtering-Rule Generalization (FRG) to reduce the number of policy rules by generalization, and (3) a technique to identify any decaying rule and a set of few dominant rules, to generate a new set of efficient firewall policy rules. The anomaly detection based on the mining exposes many hidden but not detectable by analyzing only the firewall policy rules, resulting in two new types of the anomalies. As a result of these mechanisms, network security administrators can automatically review and update the rules. We have developed a prototype system and demonstrated usefulness of our approaches.


title: Self-presentation and belonging on Facebook: How personality influences social media use and motivations

abstract: Abstract The present study examined the relationship between the Big Five and the use of Facebook to fulfill belonging and self-presentational needs. One hundred and eighty four undergraduates completed a survey assessing personality and Facebook behaviors and motivations. High agreeableness and neuroticism were the best predictors of belongingness-related behaviors and motivations. Extraversion was associated with more frequent use of Facebook to communicate with others. Self-presentational behaviors and motivations were best predicted by low conscientiousness and high neuroticism. Results suggest that conscientious individuals are cautious in their online self-presentation. Neuroticism, agreeableness, and extraversion were positively associated with the tendency to express one’s actual self. Neuroticism was positively associated with the expression of ideal and hidden self-aspects. The motivation to express these self-aspects mediated the relationship between neuroticism and self-disclosure.


title: Agent-Agnostic Human-in-the-Loop Reinforcement Learning

abstract: Providing Reinforcement Learning agents with expert advice can dramatically improve various aspects of learning. Prior work has developed teaching protocols that enable agents to learn efficiently in complex environments; many of these methods tailor the teacher's guidance to agents with a particular representation or underlying learning scheme, offering effective but specialized teaching procedures. In this work, we explore protocol programs, an agent-agnostic schema for Human-in-the-Loop Reinforcement Learning. Our goal is to incorporate the beneficial properties of a human teacher into Reinforcement Learning without making strong assumptions about the inner workings of the agent. We show how to represent existing approaches such as action pruning, reward shaping, and training in simulation as special cases of our schema and conduct preliminary experiments on simple domains.


title: An electronic travel aid for navigation of visually impaired persons

abstract: This paper presents an electronic travel aid for blind people to navigate safely and quickly, an obstacle detection system using ultrasonic sensors and USB camera based visual navigation has been considered. The proposed system detects the obstacles up to 300 cm via sonar and sends feedback (beep sound) to inform the person about its location. In addition to this, an USB webcam is connected with eBox 2300™ Embedded System for capturing the field of view of the user, which is used for finding the properties of the obstacle in particular, in the context of this work, locating a human being. Identification of human presence is based on face detection and cloth texture analysis. The major constraints for these algorithms to run on Embedded System are small image frame (160×120) having reduced faces, limited memory and very less processing time available to achieve real time image processing requirements. The algorithms are implemented in C++ using Visual Studio 5.0 IDE, which runs on Windows CE™ environment.


title: Scaling Textual Inference to the Web

abstract: Most Web-based Q/A systems work by finding pages that contain an explicit answer to a question. These systems are helpless if the answer has to be inferred from multiple sentences, possibly on different pages. To solve this problem, we introduce the Holmes system, which utilizes textual inference (TI) over tuples extracted from text. ::: ::: Whereas previous work on TI (e.g., the literature on textual entailment) has been applied to paragraph-sized texts, Holmes utilizes knowledge-based model construction to scale TI to a corpus of 117 million Web pages. Given only a few minutes, Holmes doubles recall for example queries in three disparate domains (geography, business, and nutrition). Importantly, Holmes's runtime is linear in the size of its input corpus due to a surprising property of many textual relations in the Web corpus---they are "approximately" functional in a well-defined sense.


title: Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces

abstract: A new heuristic approach for minimizing possibly nonlinear and non differentiable continuous space functions is presented. By means of an extensive testbed, which includes the De Jong functions, it will be demonstrated that the new method converges faster and with more certainty than Adaptive Simulated Annealing as well as the Annealed Nelder&Mead approach, both of which have a reputation for being very powerful. The new method requires few control variables, is robust, easy to use and lends itself very well to parallel computation. ________________________________________ 1)International Computer Science Institute, 1947 Center Street, Berkeley, CA 94704-1198, Suite 600, Fax: 510-643-7684. E-mail: storn@icsi.berkeley.edu. On leave from Siemens AG, ZFE T SN 2, OttoHahn-Ring 6, D-81739 Muenchen, Germany. Fax: 01149-636-44577, Email:rainer.storn@zfe.siemens.de. 2)836 Owl Circle, Vacaville, CA 95687, kprice@solano.community.net.


title: An efficient RFID mutual authentication scheme based on ECC.

abstract: Recently, Radio Frequency Identification (RFID) technique has been widely deployed in many applications, such as medical drugs management in hospitals and missing children searching in amusement parks. The applications basically can be classified into two types: non-public key cryptosystem (PKC)-based and PKC-based. However, many of them have been found to be flawed in the aspect of privacy problem. Therefore, many researchers tried to resolve this problem. They mainly investigated on how low-cost RFID tags can be used in large-scale systems. However, after analyses, we found those studies have some problems, such as suffering physical attack or de-synch attack. Hence, in this paper, we try to design an efficient RFID scheme based on Elliptic Curve Cryptography (ECC) to avoid these problems. After analyses, we conclude that our scheme not only can resist various kinds of attacks but also outperforms the other ECC based RFID schemes in security requirements, with needing only little extra elliptic curve point multiplications.


title: Creativity, emergence, and evolution in design

abstract: Abstract This paper commences by outlining notions of creativity before examining the role of emergence in creative design. Various process models of emergence are presented; these are based on notions of additive and substitutive variables resulting in additive and substitutive schemas. Frameworks for both representation and process for a computational model of creative design are presented. The representational framework is based on design prototypes whilst the process framework is based on an evolutionary model. The computational model brings both representation and process together.


title: Performance measurements for multithreaded programs

abstract: Multithreaded programming is an effective way to exploit concurrency, but it is difficult to debug and tune a highly threaded program. This paper describes a performance tool called Tmon for monitoring, analyzing and tuning the performance of multithreaded programs. The performance tool has two novel features: it uses "thread waiting time" as a measure and constructs thread waiting graphs to show thread dependencies and thus performance bottlenecks, and it identifies "semi-busy-waiting" points where CPU cycles are wasted in condition checking and context switching. We have implemented the Tmon tool and, as a case study, we have used it to measure and tune a heavily threaded file system. We used four workloads to tune different aspects of the file system. We were able to improve the file system bandwidth and throughput significantly. In one case, we were able to improve the bandwidth by two orders of magnitude.


title: The Troll-Trust Model for Ranking in Signed Networks

abstract: Signed social networks have become increasingly important in recent years because of the ability to model trust-based relationships in review sites like Slashdot, Epinions, and Wikipedia. As a result, many traditional network mining problems have been re-visited in the context of networks in which signs are associated with the links. Examples of such problems include community detection, link prediction, and low rank approximation. In this paper, we will examine the problem of ranking nodes in signed networks. In particular, we will design a ranking model, which has a clear physical interpretation in terms of the sign of the edges in the network. Specifically, we propose the Troll-Trust model that models the probability of trustworthiness of individual data sources as an interpretation for the underlying ranking values. We will show the advantages of this approach over a variety of baselines.


title: Bimodal multicast

abstract: There are many methods for making a multicast protocol “reliable.” At one end of the spectrum, a reliable multicast protocol might offer tomicity guarantees, such as all-or-nothing delivery, delivery ordering, and perhaps additional properties such as virtually synchronous addressing. At the other are protocols that use local repair to overcome transient packet loss in the network, offering “best effort” reliability. Yet none of this prior work has treated stability of multicast delivery as a basic reliability property, such as might be needed in an internet radio, television, or conferencing application. This article looks at reliability with a new goal: development of a multicast protocol which is reliable in a sense that can be rigorously quantified and includes throughput stability guarantees. We characterize this new protocol as a “bimodal multicast” in reference to its reliability model, which corresponds to a family of bimodal probability distributions. Here, we introduce the protocol, provide a theoretical analysis of its behavior, review experimental results, and discuss some candidate applications. These confirm that bimodal multicast is reliable, scalable, and that the protocol provides remarkably stable delivery throughput.


title: The neuromuscular differential diagnosis of joint hypermobility

abstract: Joint hypermobility is the defining feature of various inherited connective tissue disorders such as Marfan syndrome and various types of Ehlers-Danlos syndrome and these will generally be the first conditions to be considered by geneticists and pediatricians in the differential diagnosis of a patient presenting with such findings. However, several congenital and adult-onset inherited myopathies also present with joint hypermobility in the context of often only mild-to-moderate muscle weakness and should, therefore, be included in the differential diagnosis of joint hypermobility. In fact, on the molecular level disorders within both groups represent different ends of the same spectrum of inherited extracellular matrix (ECM) disorders. In this review we will summarize the measures of joint hypermobility, illustrate molecular mechanisms these groups of disorders have in common, and subsequently discuss the clinical features of: 1) the most common connective tissue disorders with myopathic or other neuromuscular features: Ehlers-Danlos syndrome, Marfan syndrome and Loeys-Dietz syndrome; 2) myopathy and connective tissue overlap disorders (muscle extracellular matrix (ECM) disorders), including collagen VI related dystrophies and FKBP14 related kyphoscoliotic type of Ehlers-Danlos syndrome; and 3) various (congenital) myopathies with prominent joint hypermobility including RYR1- and SEPN1-related myopathy. The aim of this review is to assist clinical geneticists and other clinicians with recognition of these disorders.


Psychoanalysis and the aggression of large groups.


title: Accuracy of Kinect’s skeleton tracking for upper body rehabilitation applications

abstract: AbstractGames and their use in rehabilitation have formed a new and rapidly growing area of research. A critical hardware component of rehabilitation programs is the input device that measures the patients’ movements. After Microsoft released Kinect, extensive research has been initiated on its applications as an input device for rehabilitation. However, since most of the works in this area rely on a qualitative determination of the joints’ movements rather than an accurate quantitative one, detailed analysis of patients’ movements is hindered. The aim of this article is to determine the accuracy of the Kinect’s joint tracking. To fulfill this task, a model of upper body was fabricated. The displacements of the joint centers were estimated by Kinect at different positions and were then compared with the actual ones from measurement. Moreover, the dependency of Kinect’s error on distance and joint type was measured and analyzed. Implications for RehabilitationIt measures and reports the accuracy of a senso...


title: A Three-Phase Current-Fed DC/DC Converter With Active Clamp for Low-DC Renewable Energy Sources

abstract: This paper focuses on a new three-phase high power current-fed DC/DC converter with an active clamp. A three-phase DC/DC converter with high efficiency and voltage boosting capability is designed for use in the interface between a low-voltage fuel-cell source and a high-voltage DC bus for inverters. Zero-voltage switching in all active switches is achieved through using a common active clamp branch, and zero current switching in the rectifier diodes is achieved through discontinuous current conduction in the secondary side. Further, the converter is capable of increased power transfer due to its three-phase power configuration, and it reduces the RMS current per phase, thus reducing conduction losses. Moreover, a delta-delta connection on the three-phase transformer provides parallel current paths and reduces conduction losses in the transformer windings. An efficiency of above 93% is achieved through both improvements in the switching and through reducing conduction losses. A high voltage ratio is achieved by combining inherent voltage boost characteristics of the current-fed converter and the transformer turns ratio. The proposed converter and three-phase PWM strategy is analyzed, simulated, and implemented in hardware. Experimental results are obtained on a 500-W prototype unit, with all of the design verified and analyzed.


title: Railway signalization and interlocking design via Automation Petri Nets

abstract: Signalization and interlocking systems play a vital role to provide safe transportation in railway systems [1]. Transportation on railways can be achieved confidently by the safety provided by these systems. Different methods including Petri Nets (PNs) can be used in designing such systems. In this study, interlocking and signalization design for a sample railway yard is achieved by Automation Petri Nets (APNs) which is an extended type of PNs. Once the model is obtained using APNs then it can easily be implemented into a programmable logic controller (PLC).


title: In vivo epiluminescence microscopy of pigmented skin lesions. I. Pattern analysis of pigmented skin lesions

abstract: The importance of recognizing early melanoma is generally accepted. Because not all pigmented skin lesions can be diagnosed correctly by their clinical appearance, additional criteria are required for the clinical diagnosis of such lesions. In vivo epiluminescence microscopy provides for a more detailed inspection of the surface of pigmented skin lesions, and, by using the oil immersion technic, which renders the epidermis translucent, opens a new dimension of skin morphology by including the dermoepidermal junction into the macroscopic evaluation of a lesion. In an epiluminescence microscopy study of more than 3000 pigmented skin lesions we have defined morphologic criteria that are not readily apparent to the naked eye but that are detected easily by epiluminescence microscopy and represent relatively reliable markers of benign and malignant pigmented skin lesions. These features include specific patterns, colors, and intensities of pigmentation, as well as the configuration, regularity, and other characteristics of both the margin and the surface of pigmented skin lesions. Pattern analysis of these features permits a distinction between different types of pigmented skin lesions and, in particular, between benign and malignant growth patterns. Epiluminescence microscopy is thus a valuable addition to the diagnostic armamentarium of pigmented skin lesions at a clinical level.


title: An Approach to Allocate Advertisement Slots for Banner Advertising

abstract: In the banner advertising scenario, an advertiser aims to reach the maximum number of potential visitors and a publisher tries to meet the requests of increased number of advertisers to maximize the revenue. In the literature, a model was introduced to extract the knowledge of coverage patterns from transactional database. In this paper, we propose an ad slots allocation approach by extending the notion of coverage patterns to select distinct sets of ad slots to meet the requests of multiple advertisers. The preliminary experimental results on a real world dataset show that the proposed approach meets the requests of increased number of advertisers when compared with the baseline approach of allocation.


title: Acute methamphetamine exposure induces long-lasting depressive-like behaviour in mice

abstract: Trabalho final de mestrado integrado em Medicina area cientifica de Farmacologia, apresentado a Faculdade de Medicina da Universidade de Coimbra


Off-Topic Spoken Response Detection Using Siamese Convolutional Neural Networks


title: Conversion of Petri net controllers for manufacturing systems into ladder logic diagrams

abstract: As automated manufacturing systems become more complex, the need for an effective design tool to produce both high level discrete event control system (DECS) and low level implementation, becomes increasingly more important. Petri nets represent the most effective method for both the design and implementation of DECSs. The conversion of such Petri nets into real-time applications has recently been greatly simplified through the advent of the token-passing logic (TPL) methodology. The technique has been developed for normal Petri nets, P-timed Petri nets, T-timed Petri nets and coloured Petri nets. In this paper the Petri net concepts are extended to deal with Petri net controllers, by including actuators and sensors as formal structures within the Petri net controller. The conversion of such a Petri net controller into ladder logic diagrams is also demonstrated by considering the control of a component sorting manufacturing system.


title: Planar reconfigurable antenna with integrated switching control circuitry

abstract: In this paper we propose a pattern reconfigurable planar antenna with integrated control circuitry, for flexible applications with Software Defined Radio (SDR) platforms. The antenna's layout is made by four folded microstrip elements placed symmetrically on a low cost FR-4 substrate. By using PIN diodes, the metallic elements can be switched to generate a single omnidirectional beam or six directional beams around the azimuth plane. The design includes MOSFET based control circuitry for switching the PIN diodes. The RF performance has been experimentally evaluated under different levels of control voltage. The radiation of omnidirectional and directional beams along with an integrated control board, allows for convenient utilization of the antenna in numerous SDR MIMO platforms.


title: An Electrolytic Capacitor-Less High Power Factor LED Driver Based on a “One-and-a-Half Stage” Forward-Flyback Topology

abstract: A “one-and-a-half stage” forward-flyback converter for electrolytic capacitor-less light-emitting diode (LED) driver with high power factor (PF), high efficiency, low output ripple current, and long lifetime has been proposed and studied in this paper. The basic topology of the proposed topology is a single-switch forward-flyback converter for achieving high PF. In addition, a buck converter is inserted between the forward subconverter and the load for creating two paralleled power transfer paths. The most of input energy directly reaches the load through flyback subconverter, and only about 1/4 of total energy is transferred to the load through forward subconverter and buck converter. Therefore, the proposed topology is a “one-and-a-half stage” converter and can achieve higher efficiency than the traditional two-stage topologies. At the same time, power decoupling can be realized and the electrolytic capacitor can be eliminated. Optimal control scheme, detailed analysis, and design considerations for this improved converter are presented. Finally, an experimental prototype for 28 V/700 mA LED driver was built up to verify the theoretical analysis.


Slug 2 Slug : A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation


title: Dimensional synthesis of a 3-translational-DOF in-parallel manipulator for a desired workspace

abstract: Abstract This paper deals with the synthesis and optimization of the workspace of a 3-translational-DOF in-parallel manipulator (3-T-P-M). Three variable length legs constitute the actuators of this manipulator, whereas two other kinematics' chains, with passive joints, are used to eliminate the three rotations of the platform with respect to the base. The workspace is made of the intersection of two workspaces. The first one is relative to the active legs, called here “active workspace”, and the second one is relative to the passive chains called here “passive workspace”. The influence of the link lengths of the passive chains is discussed. Then, for a desired active workspace, the dimensions of the passive chains are determined to yield the maximum size of the platform workspace, i.e., the active workspace is not altered by the presence of the passive kinematics' chains. The problem is presented first as a geometric one, in the case where the joint limits were not taken into account. Then, for the case where the joint limits were considered, the problem is presented as an analytical one. The solution was found analytically when the arm and the forearm, of the passive kinematics chain, have equal lengths. A numerical solution is then presented for the general case. The discussion is illustrated by examples showing the different cases that could arise.


title: Complications of Oropharyngeal Dysphagia: Aspiration Pneumonia

abstract: The incidence and prevalence of aspiration pneumonia (AP) are poorly defined. They increase in direct relation with age and underlying diseases. The pathogenesis of AP presumes the contribution of ris


title: An overview of vulnerability assessment and penetration testing techniques

abstract: All Internet facing systems and applications carry security risks. Security professionals across the globe generally address these security risks by Vulnerability Assessment and Penetration Testing (VAPT). The VAPT is an offensive way of defending the cyber assets of an organization. It consists of two major parts, namely Vulnerability Assessment (VA) and Penetration Testing (PT). Vulnerability assessment, includes the use of various automated tools and manual testing techniques to determine the security posture of the target system. In this step all the breach points and loopholes are found. These breach points/loopholes if found by an attacker can lead to heavy data loss and fraudulent intrusion activities. In Penetration testing the tester simulates the activities of a malicious attacker who tries to exploit the vulnerabilities of the target system. In this step the identified set of vulnerabilities in VA is used as input vector. This process of VAPT helps in assessing the effectiveness of the security measures that are present on the target system. In this paper we have described the entire process of VAPT, along with all the methodologies, models and standards. A shortlisted set of efficient and popular open source/free tools which are useful in conducting VAPT and the required list of precautions is given. A case study of a VAPT test conducted on a bank system using the shortlisted tools is also discussed.


title: Same Representation, Different Attentions: Shareable Sentence Representation Learning from Multiple Tasks

abstract: Distributed representation plays an important role in deep learning based natural language processing. However, the representation of a sentence often varies in different tasks, which is usually learned from scratch and suffers from the limited amounts of training data. In this paper, we claim that a good sentence representation should be invariant and can benefit the various subsequent tasks. To achieve this purpose, we propose a new scheme of information sharing for multi-task learning. More specifically, all tasks share the same sentence representation and each task can select the task-specific information from the shared sentence representation with attention mechanism. The query vector of each task's attention could be either static parameters or generated dynamically. We conduct extensive experiments on 16 different text classification tasks, which demonstrate the benefits of our architecture.


title: Secured outsourcing towards a cloud computing environment based on DNA cryptography

abstract: Cloud computing denotes an IT infrastructure where data and software are stored and processed remotely in a data center of a cloud provider, which are accessible via an Internet service. This new paradigm is increasingly reaching the ears of companies and has revolutionized the marketplace of today owing to several factors, in particular its cost-effective architectures covering transmission, storage and intensive data computing. However, like any new technology, the cloud computing technology brings new problems of security, which represents the main restrain on turning to this paradigm. For this reason, users are reluctant to resort to the cloud because of security and protection of private data as well as lack of trust in cloud service providers. The work in this paper allows the readers to familiarize themselves with the field of security in the cloud computing paradigm while suggesting our contribution in this context. The security schema we propose allowing a distant user to ensure a completely secure migration of all their data anywhere in the cloud through DNA cryptography. Carried out experiments showed that our security solution outperforms its competitors in terms of integrity and confidentiality of data.


title: Internet Addiction and its Psychosocial Risks (Depression, Anxiety, Stress and Loneliness) among Iranian Adolescents and Young Adults: A Structural Equation Model in a Cross-Sectional Study

abstract: Internet addiction has become an increasingly researched area in many Westernized countries. However, there has been little research in developing countries such as Iran, and when research has been conducted, it has typically utilized small samples. This study investigated the relationship of Internet addiction with stress, depression, anxiety, and loneliness in 1052 Iranian adolescents and young adults. The participants were randomly selected to complete a battery of psychometrically validated instruments including the Internet Addiction Test, Depression Anxiety Stress Scale, and the Loneliness Scale. Structural equation modeling and Pearson correlation coefficients were used to determine the relationship between Internet addiction and psychological impairments (depression, anxiety, stress and loneliness). Pearson correlation, path analysis, multivariate analysis of variance (MANOVA), and t-tests were used to analyze the data. Results showed that Internet addiction is a predictor of stress, depression, anxiety, and loneliness. Findings further indicated that addictive Internet use is gender sensitive and that the risk of Internet addiction is higher in males than in females. The results showed that male Internet addicts differed significantly from females in terms of depression, anxiety, stress, and loneliness. The implications of these results are discussed.


title: Reducing the progression of myopia with atropine: a long term cohort study of Olmsted County students.

abstract: BACKGROUND ::: Myopia is an important public health problem because it is common and is associated with increased risk for chorioretinal degeneration, retinal detachment, and other vision- threatening abnormalities. In animals, ocular elongation and myopia progression can be lessened with atropine treatment. This study provides information about progression of myopia and atropine therapy for myopia in humans. ::: ::: ::: METHODS ::: A total of 214 residents of Olmsted County, Minnesota (118 girls and 96 boys, median age, 11 years; range 6 to 15 years) received atropine for myopia from 1967 through 1974. Control subjects were matched by age, sex, refractive error, and date of baseline examination to 194 of those receiving atropine. Duration of treatment with atropine ranged from 18 weeks to 11.5 years (median 3.5 years). ::: ::: ::: RESULTS ::: Median followup from initial to last refraction in the atropine group (11.7 years) was similar to that in the control group (12.4 years). Photophobia and blurred vision were frequently reported, but no serious adverse effects were associated with atropine therapy. Mean myopia progression during atropine treatment adjusted for age and refractive error (0.05 diopters per year) was significantly less than that among control subjects (0.36 diopters per year)(P<.001). Final refractions standardized to the age of 20 years showed a greater mean level of myopia in the control group (3.78 diopters) than in the atropine group (2.79 diopters) (P<.001). ::: ::: ::: CONCLUSIONS ::: The data support the view that atropine therapy is associated with decreased progression of myopia and that beneficial effects remain after treatment has been discontinued.


title: ASP+POMDP: Integrating non-monotonic logic programming and probabilistic planning on robots

abstract: Mobile robots equipped with multiple sensors and deployed in real-world domains frequently find it difficult to process all sensor inputs, or to operate without any human input and domain knowledge. At the same time, robots cannot be equipped with all relevant domain knowledge in advance, and humans are unlikely to have the time and expertise to provide elaborate and accurate feedback. This paper presents a novel framework that addresses these challenges by integrating high-level logical inference with low-level probabilistic sequential decision-making. Specifically, Answer Set Programming (ASP), a non-monotonic logic programming paradigm, is used to represent, reason with and revise domain knowledge obtained from sensor inputs and high-level human feedback, while hierarchical partially observable Markov decision processes (POMDPs) are used to automatically adapt visual sensing and information processing to the task at hand. Furthermore, a psychophysics-inspired strategy is used to merge the output of logical inference with probabilistic beliefs. All algorithms are evaluated in simulation and on wheeled robots localizing target objects in indoor domains.


Power of the few vs. wisdom of the crowd: Wikipedia and the rise of the bourgeoisie


title: The state of the art in multiple object tracking under occlusion in video sequences

abstract: In this paper, we present a review of existing techniques and systems for tracking multiple occluding objects using one or more cameras. Following a formulation of the occlusion problem, we divide these techniques into two groups: mergesplit (MS) approaches and straight-through (ST) approaches. Then, we consider tracking in ball game applications, with emphasis on soccer. Based on this assessment of the state of the art, we identify what appear to be the most promising approaches for tracking in general and for soccer in


title: An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach

abstract: Reverse logistics consists of all operations related to the reuse of products. External suppliers are one of the important members of reverse logistics and closed loop supply chain (CLSC) networks. However in CLSC network configuration models, suppliers are assessed based on purchasing cost and other factors such as on-time delivery are ignored. In this research, a general closed loop supply chain network is examined that includes manufacturer, disassembly, refurbishing, and disposal sites. Meanwhile, it is managed by the manufacturer. We propose an integrated model which has two phases. In the first phase, a framework for supplier selection criteria in RL is proposed. Besides, a fuzzy method is designed to evaluate suppliers based on qualitative criteria. The output of this stage is the weight of each supplier according to each part. In the second phase, we propose a multi objective mixed-integer linear programming model to determine which suppliers and refurbishing sites should be selected (strategic decisions), and find out the optimal number of parts and products in CLSC network (tactical decisions). The objective functions maximize profit and weights of suppliers, and one of them minimizes defect rates. To our knowledge, this model is the first effort to consider supplier selection, order allocation, and CLSC network configuration, simultaneously. The mathematical programming model is validated through numerical analysis.


title: Cyber security impacts on all-PMU state estimator - a case study on co-simulation platform GECO

abstract: Traditional state estimators require longer scan time, leading to delayed, and inaccurate state estimation. Given the increased deployment trend of phasor measurement units (PMUs), it is expected that all-PMU state estimation will eventually replace traditional or mixed state estimators at the control centers of power utilities. Due to the repeated calibration of the voltage and current transformers at the measurement sites, and direct time-synchronized measurement of phasors, the estimated state by an all-PMU state estimator is not only accurate, but also available at a rapid rate, leading to the use of the system state for protection, stabilization, and even calibration of the measuring devices. However, due to high reliance on an advanced communication network infrastructure for the delivery of large amount of measurements in real-time, the cyber attack surface of the power system is increased. Deliberate cyber attacks or unintentional network failures can affect the state estimator leading to misoperations of the power system. In this paper, we study the cyber security impacts on the all-PMU state estimator, using a power system and data network co-simulation method. A linear state estimator for a model of the New England 39-bus system and the corresponding data network is built in a global event-driven co-simulation platform “GECO” which was developed and leveraged for our experimental setup. The co-simulation of PSLF (power system simulator) and NS-2 (network simulator) is run with injection of attacks on the network. The injected cyber attacks in the form of network failures or malicious data injection are simulated and their effects are observed. We also, observe the robustness of the all-PMU state estimator, when the number of affected measurements is below a threshold.


title: QMDP-Net: Deep Learning for Planning under Partial Observability

abstract: This paper introduces the QMDP-net, a neural network architecture for planning under partial observability. The QMDP-net combines the strengths of model-free learning and model-based planning. It is a recurrent policy network, but it represents a policy for a parameterized set of tasks by connecting a model with a planning algorithm that solves the model, thus embedding the solution structure of planning in a network learning architecture. The QMDP-net is fully differentiable and allows for end-to-end training. We train a QMDP-net on different tasks so that it can generalize to new ones in the parameterized task set and “transfer” to other similar tasks beyond the set. In preliminary experiments, QMDP-net showed strong performance on several robotic tasks in simulation. Interestingly, while QMDP-net encodes the QMDP algorithm, it sometimes outperforms the QMDP algorithm in the experiments, as a result of end-to-end learning.


title: A Fully Differential Potentiostat

abstract: Low-voltage, single-ended (SE) potentiostats are unable to detect many analytes of interest because the oxidation potentials of these analytes are greater than the voltage that the potentiostat can deliver to the electrodes. In this work, a fully-differential (FD) potentiostat is described which enables detection of a wide range of analytes using a supply voltage of 1.8 V. The FD potentiostat was implemented in TSMC's 0.18 mum CMOS process and has been verified experimentally. A theoretical discussion of the FD potentiostat is given and comparisons to SE potentiostats are provided. Biological and environmental analytes are chemically detected using the FD potentiostat.


title: Data mining techniques for cancer detection using serum proteomic profiling

abstract: Objective: Pathological changes in an organ or tissue may be reflected in proteomic patterns in serum. It is possible that unique serum proteomic patterns could be used to discriminate cancer samples from non-cancer ones. Due to the complexity of proteomic profiling, a higher order analysis such as data mining is needed to uncover the differences in complex proteomic patterns. The objectives of this paper are (1) to briefly review the application of data mining techniques in proteomics for cancer detection/diagnosis; (2) to explore a novel analytic method with different feature selection methods; (3) to compare the results obtained on different datasets and that reported by Petricoin et al. in terms of detection performance and selected proteomic patterns. Methods and material: Three serum SELDI MS data sets were used in this research to identify serum proteomic patterns that distinguish the serum of ovarian cancer cases from non-cancer controls. A support vector machine-based method is applied in this study, in which statistical testing and genetic algorithm-based methods are used for feature selection respectively. Leave-one-out cross validation with receiver operating characteristic (ROC) curve is used for evaluation and comparison of cancer detection performance. Results and conclusions: The results showed that (1) data mining techniques can be successfully applied to ovarian cancer detection with a reasonably high performance; (2) the classification using features selected by the genetic algorithm consistently outperformed those selected by statistical testing in terms of accuracy and robustness; (3) the discriminatory features (proteomic patterns) can be very different from one selection method to another. In other words, the pattern selection and its classification efficiency are highly classifier dependent. Therefore, when using data mining techniques, the discrimination of cancer from normal does not depend solely upon the identity and origination of cancer-related proteins.


title: The sources and consequences of mobile technostress in the workplace

abstract: In this study, we explore the phenomenon of mobile technostress: stress experienced by users of mobile information and communication technologies. We examine the impacts of mobile technostress on individuals’ job satisfaction. Based on the Transaction Based Model of stress and the existing literature on technostress, a conceptual model was proposed to understand this phenomenon. Two sources of mobile technostress have been identified: techno-overload and techno-insecurity. We hypothesize that techno-overload and techno-insecurity exert a negative impact on job satisfaction. The individual level mobile technostress inhibitors (i.e., self-efficacy) are identified as helping individuals reduce stress. We also hypothesize that self-efficacy has a positive impact on job satisfaction. Furthermore, the moderator effects of habit are also explored. We hypothesize that habit will negatively moderate the relationship between mobile technostress creators and job satisfaction, and positively moderate the relationship between mobile technostress inhibitors and job satisfaction. The methodological design as well as potential theoretical and practical implications has also been discussed.


title: Security and Privacy Issues in Cloud Computing Environment: A Survey Paper

abstract: Cloud computing is emerging as a powerful architecture to perform large-scale and complex computing. It extends the information technology (IT) capability by providing ondemand access to computer resources for dedicated use. The information security and privacy are the major concerns over the cloud from user perspective. This paper surveys and evaluates the architecture, data security and privacy issues in cloud computing like data confidentiality, integrity, authentication, trust, service level agreements and regulatory issues. The objective of this paper is to review comprehensively the current challenges of data security and privacy being faced by cloud computing and critically analyze these issues.


title: Ensemble of shape functions for 3D object classification

abstract: This work addresses the problem of real-time 3D shape based object class recognition, its scaling to many categories and the reliable perception of categories. A novel shape descriptor for partial point clouds based on shape functions is presented, capable of training on synthetic data and classifying objects from a depth sensor in a single partial view in a fast and robust manner. The classification task is stated as a 3D retrieval task finding the nearest neighbors from synthetically generated views of CAD-models to the sensed point cloud with a Kinect-style depth sensor. The presented shape descriptor shows that the combination of angle, point-distance and area shape functions gives a significant boost in recognition rate against the baseline descriptor and outperforms the state-of-the-art descriptors in our experimental evaluation on a publicly available dataset of real-world objects in table scene contexts with up to 200 categories.


Literal or idiomatic? Identifying the reading of single occurrences of German multiword expressions using word embeddings.


title: Preclinical pharmacokinetics, tissue distribution and excretion studies of a novel anti-candidal agent-thiosemicarbazide derivative of isoniazid (TSC-INH) by validated UPLC-MS/MS assay.

abstract: Abstract A simple and sensitive UPLC–MS/MS assay was developed and validated for rapid determination of thiosemicarbazide derivative of isoniazid (TSC-INH), a potent anti-candidal agent in rat plasma, tissues, urine and feces. All biological samples were prepared by protein precipitation method using celecoxib as an internal standard (IS). Chromatographic separation was achieved on Acquity BEH™ C 18 (50 × 2.1 mm, 1.7 μm) column using gradient mobile phase of acetonitrile and water (containing 0.1% formic acid) at flow rate of 0.3 mL/min. The MRM transitions were monitored at m / z 305.00 → 135.89 for TSC-INH and m / z 380.08 → 316.03 for IS in ESI negative mode. All validation parameter results were within the acceptable range described in guideline for bioanalytical method validation. The pharmacokinetic study showed that the compound TSC-INH was orally active with 66% absolute bioavailability in rats. It was rapidly absorbed with peak plasma concentration of 1985.92 ng/mL achieved within 1 h after single oral dose (10 mg/kg) administration. TSC-INH exhibited rapid distribution across the body with highest levels in liver and lungs. Penetration in brain tissues suggests that TSC-INH crossed the blood brain barrier. Only 5.23% of the orally administered drug was excreted as unconverted form in urine and feces implying that TSC-INH was metabolized extensively before excretion. With the preliminary knowledge of in vivo pharmacokinetics and disposition properties, this study will be beneficial for further development of compound TSC-INH in future studies.


title: Real-time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor

abstract: We present an approach for real-time, robust and accurate hand pose estimation from moving egocentric RGB-D cameras in cluttered real environments. Existing methods typically fail for hand-object interactions in cluttered scenes imaged from egocentric viewpoints-common for virtual or augmented reality applications. Our approach uses two subsequently applied Convolutional Neural Networks (CNNs) to localize the hand and regress 3D joint locations. Hand localization is achieved by using a CNN to estimate the 2D position of the hand center in the input, even in the presence of clutter and occlusions. The localized hand position, together with the corresponding input depth value, is used to generate a normalized cropped image that is fed into a second CNN to regress relative 3D hand joint locations in real time. For added accuracy, robustness and temporal stability, we refine the pose estimates using a kinematic pose tracking energy. To train the CNNs, we introduce a new photorealistic dataset that uses a merged reality approach to capture and synthesize large amounts of annotated data of natural hand interaction in cluttered scenes. Through quantitative and qualitative evaluation, we show that our method is robust to self-occlusion and occlusions by objects, particularly in moving egocentric perspectives.


title: A bottom up approach to category mapping and meaning change

abstract: In this article, we use an automated bottom-up approach to identify semantic categories in an entire corpus. We conduct an experiment using a word vector model to represent the meaning of words. The word vectors are then clustered, giving a bottom-up representation of semantic categories. Our main finding is that the likelihood of changes in a word’s meaning correlates with its position within its cluster.