title: Parallax360: Stereoscopic 360° Scene Representation for Head-Motion Parallax

abstract: We propose a novel 360° scene representation for converting real scenes into stereoscopic 3D virtual reality content with head-motion parallax. Our image-based scene representation enables efficient synthesis of novel views with six degrees-of-freedom (6-DoF) by fusing motion fields at two scales: (1) disparity motion fields carry implicit depth information and are robustly estimated from multiple laterally displaced auxiliary viewpoints, and (2) pairwise motion fields enable real-time flow-based blending, which improves the visual fidelity of results by minimizing ghosting and view transition artifacts. Based on our scene representation, we present an end-to-end system that captures real scenes with a robotic camera arm, processes the recorded data, and finally renders the scene in a head-mounted display in real time (more than 40 Hz). Our approach is the first to support head-motion parallax when viewing real 360° scenes. We demonstrate compelling results that illustrate the enhanced visual experience - and hence sense of immersion-achieved with our approach compared to widely-used stereoscopic panoramas.


Consciousness and Understanding in the Chinese Room


title: Analytical Modeling of Surface PMSM Using a Combined Solution of Maxwell–s Equations and Magnetic Equivalent Circuit

abstract: This paper presents a complete analytical model for Surface Mounted Permanent Magnet Synchronous Machines (PMSMs). A new simple and fast technique was developed to obtain accurate results for the calculation of machine parameters (Electromotive Force (EMF), Torque, and Losses). This technique is based upon combined solution of two models. The first model generates an exact solution of Maxwell’s equations in the air gap area applied to a very simple geometry. The second model gives an accurate solution in the detailed parts with complex geometry, based on a Magnetic Equivalent Circuit (MEC) to obtain fast and accurate results in a simple way. The machine’s global quantities are then obtained and validated by the results of a Finite Element Model (FEM) for different loading conditions and geometries. Compared to FEM, the proposed combined solution has the advantage of flexibility in the geometrical machine parameters, a significantly less CPU time and an accuracy for the considered PMSM up to 4.85 % in the EMF, 4.41 % in the torque and 4.44 % in the iron losses. Finally, the relation between grid refinement in the MEC (coarse or fine grid of reluctances) and accuracy is pointed out, showing that the EMF can be accurately computed with a rather coarse grid, while accurate loss computation requires a fine grid.


title: Guided Face Cartoon Synthesis

abstract: In this paper, we propose a new method, called guided synthesis, to synthesize a face cartoon from a face photo. The guided synthesis is defined as a local linear model, which generates a cartoon image by incorporating the content of guidance images taken from the training set. Our synthesis operation is achieved based on four weight functions. The first is a photo-photo weight that aims to measure the similarity between an input photo patch and a training photo patch. The second is defined as a photo-cartoon weight, which is used to compute the likelihood by computing the similarity between a cartoon patch and an input photo patch. The third weight is defined in the synthesized photos, which is to set a smoothness constraint between neighboring synthesized patches. The final weight is designed to evaluate the similarity of a synthesized patch to an input patch based on the spatial distance. Experimental evaluation on a number of face photos demonstrates the good performance of the proposed method on the face cartoon synthesis.


title: Feeding the microbiota‐gut‐brain axis: diet, microbiome, and neuropsychiatry

abstract: The microbial population residing within the human gut represents one of the most densely populated microbial niche in the human body with growing evidence showing it playing a key role in the regulation of behavior and brain function. The bidirectional communication between the gut microbiota and the brain, the microbiota-gut-brain axis, occurs through various pathways including the vagus nerve, the immune system, neuroendocrine pathways, and bacteria-derived metabolites. This axis has been shown to influence neurotransmission and the behavior that are often associated with neuropsychiatric conditions. Therefore, research targeting the modulation of this gut microbiota as a novel therapy for the treatment of various neuropsychiatric conditions is gaining interest. Numerous factors have been highlighted to influence gut microbiota composition, including genetics, health status, mode of birth, and environment. However, it is diet composition and nutritional status that has repeatedly been shown to be one of the most critical modifiable factors regulating the gut microbiota at different time points across the lifespan and under various health conditions. Thus the microbiota is poised to play a key role in nutritional interventions for maintaining brain health.


Trajectory Analysis for Sport and Video Surveillance


title: Multi-aspect opinion polling from textual reviews

abstract: This paper presents an unsupervised approach to aspect-based opinion polling from raw textual reviews without explicit ratings. The key contribution of this paper is three-fold. First, a multi-aspect bootstrapping algorithm is proposed to learn from unlabeled data aspect-related terms of each aspect to be used for aspect identification. Second, an unsupervised segmentation model is proposed to address the challenge of identifying multiple single-aspect units in a multi-aspect sentence. Finally, an aspect-based opinion polling algorithm is presented. Experiments on real Chinese restaurant reviews show that our opinion polling method can achieve 75.5% precision performance.


title: Research on Anonymization and De-anonymization in the Bitcoin System

abstract: The Bitcoin system is an anonymous, decentralized crypto-currency. There are some deanonymizating techniques to cluster Bitcoin addresses and to map them to users' identifications in the two research directions of Analysis of Transaction Chain (ATC) and Analysis of Bitcoin Protocol and Network (ABPN). Nowadays, there are also some anonymization methods such as coin-mixing and transaction remote release (TRR) to cover the relationship between Bitcoin address and the user. This paper studies anonymization and de-anonymization technologies and proposes some directions for further research.


title: Identifying Diabetic Patients with High Risk of Readmission

abstract: Hospital readmissions are expensive and reflect the inadequacies in healthcare system. In the United States alone, treatment of readmitted diabetic patients exceeds 250 million dollars per year. Early identification of patients facing a high risk of readmission can enable healthcare providers to to conduct additional investigations and possibly prevent future readmissions. This not only improves the quality of care but also reduces the medical expenses on readmission. Machine learning methods have been leveraged on public health data to build a system for identifying diabetic patients facing a high risk of future readmission. Number of inpatient visits, discharge disposition and admission type were identified as strong predictors of readmission. Further, it was found that the number of laboratory tests and discharge disposition together predict whether the patient will be readmitted shortly after being discharged from the hospital (i.e. 30 days). These insights can help healthcare providers to improve inpatient diabetic care. Finally, the cost analysis suggests that \$252.76 million can be saved across 98,053 diabetic patient encounters by incorporating the proposed cost sensitive analysis model.


title: Synthesizing Training Images for Boosting Human 3D Pose Estimation

abstract: Human 3D pose estimation from a single image is a challenging task with numerous applications. Convolutional Neural Networks (CNNs) have recently achieved superior performance on the task of 2D pose estimation from a single image, by training on images with 2D annotations collected by crowd sourcing. This suggests that similar success could be achieved for direct estimation of 3D poses. However, 3D poses are much harder to annotate, and the lack of suitable annotated training images hinders attempts towards end-toend solutions. To address this issue, we opt to automatically synthesize training images with ground truth pose annotations. We find that pose space coverage and texture diversity are the key ingredients for the effectiveness of synthetic training data. We present a fully automatic, scalable approach that samples the human pose space for guiding the synthesis procedure and extracts clothing textures from real images. We demonstrate that CNNs trained with our synthetic images out-perform those trained with real photos on 3D pose estimation tasks.


title: The Stan Math Library: Reverse-Mode Automatic Differentiation in C++

abstract: As computational challenges in optimization and statistical inference grow ever harder, algorithms that utilize derivatives are becoming increasingly more important. The implementation of the derivatives that make these algorithms so powerful, however, is a substantial user burden and the practicality of these algorithms depends critically on tools like automatic differentiation that remove the implementation burden entirely. The Stan Math Library is a C++, reverse-mode automatic differentiation library designed to be usable, extensive and extensible, efficient, scalable, stable, portable, and redistributable in order to facilitate the construction and utilization of such algorithms. ::: Usability is achieved through a simple direct interface and a cleanly abstracted functional interface. The extensive built-in library includes functions for matrix operations, linear algebra, differential equation solving, and most common probability functions. Extensibility derives from a straightforward object-oriented framework for expressions, allowing users to easily create custom functions. Efficiency is achieved through a combination of custom memory management, subexpression caching, traits-based metaprogramming, and expression templates. Partial derivatives for compound functions are evaluated lazily for improved scalability. Stability is achieved by taking care with arithmetic precision in algebraic expressions and providing stable, compound functions where possible. For portability, the library is standards-compliant C++ (03) and has been tested for all major compilers for Windows, Mac OS X, and Linux.


title: Predicting conversion from MCI to AD with FDG-PET brain images at different prodromal stages

abstract: Early diagnosis of Alzheimer disease (AD), while still at the stage known as mild cognitive impairment (MCI), is important for the development of new treatments. However, brain degeneration in MCI evolves with time and differs from patient to patient, making early diagnosis a very challenging task. Despite these difficulties, many machine learning techniques have already been used for the diagnosis of MCI and for predicting MCI to AD conversion, but the MCI group used in previous works is usually very heterogeneous containing subjects at different stages. The goal of this paper is to investigate how the disease stage impacts on the ability of machine learning methodologies to predict conversion. After identifying the converters and estimating the time of conversion (TC) (using neuropsychological test scores), we devised 5 subgroups of MCI converters (MCI-C) based on their temporal distance to the conversion instant (0, 6, 12, 18 and 24 months before conversion). Next, we used the FDG-PET images of these subgroups and trained classifiers to distinguish between the MCI-C at different stages and stable non-converters (MCI-NC). Our results show that MCI to AD conversion can be predicted as early as 24 months prior to conversion and that the discriminative power of the machine learning methods decreases with the increasing temporal distance to the TC, as expected. These findings were consistent for all the tested classifiers. Our results also show that this decrease arises from a reduction in the information contained in the regions used for classification and by a decrease in the stability of the automatic selection procedure. HighlightsWe study the prediction of MCI to AD conversion using FDG-PET and several classifiers.We study how the disease stage impacts diagnostic performance.Results show a decrease in performance with the temporal distance to conversion.We achieve an accuracy of 85.1% at the time of conversion and 75% 24 months before it.


title: VIRTUAL REALITY

abstract: During the last decade the word virtual became one of the most exposed words in the English language. Today, we have virtual universities, virtual offices, virtual pets, virtual graveyards, virtual exhibitions, virtual wind tunnels, virtual actors, virtual studios, virtual museums, virtual doctors—and all because of virtual reality.


title: SecUp: Secure and Efficient Wireless Software Updates for Vehicles

abstract: Wireless software updates for vehicles are very beneficial for both customers and manufacturers, as they enable performance improvements and error correction without the need of vehicle recalls, as well as a reduction of warranty costs and continuous system upgrades over a vehicle's whole lifetime. However, adding a wireless interface to enable software updates over the air may also expose the vehicle to security threats and make it vulnerable to a variety of attacks. Hence, to protect the safety of the driver and passengers of a vehicle, a strong and comprehensive security concept is needed. In this paper, we propose SecUp: a novel security concept enabling efficient and trustworthy wireless software updates for vehicles. SecUp is based on a system-centric structured analysis enabling a secure system configuration. The concept uses, among others, symmetric and asymmetric keys securely stored on the devices in the network to prove the identity of the nodes and to ensure the integrity as well as the confidentiality of data. We evaluate the robustness of SecUp by employing an attacker-centric threat model and show that it is indeed applicable for efficient and trustworthy wireless software updates for vehicles.


Extending knowledge of AIML by using RDF


title: Net-Fli: On-the-fly Compression, Archiving and Indexing of Streaming Network Traffic

abstract: The ever-increasing number of intrusions in public and commercial networks has created the need for high-speed archival solutions that continuously store streaming network data to enable forensic analysis and auditing. However, "turning back the clock" for post-attack analyses is not a trivial task. The first major challenge is that the solution has to sustain data archiving under extremely high-speed insertion rates. Moreover, the archives created need to be stored in a format that is compressed but still amenable to indexing. The above requirements make general-purpose databases unsuitable for this task, and, thus, dedicated solutions are required. ::: ::: In this paper, we describe a prototype solution that satisfies all requirements for high-speed archival storage, indexing and data querying on network flow information. The superior performance of our approach is attributed to the on-the-fly compression and indexing scheme, which is based on compressed bitmap principles. Typical commercial solutions can currently process 20,000--60,000 flows per second. An evaluation of our prototype implementation on current commodity hardware using real-world traffic traces shows its ability to sustain insertion rates ranging from 500,000 to more than 1 million records per second. The system offers interactive query response times that enable administrators to perform complex analysis tasks on-the-fly. Our technique is directly amenable to parallel execution, allowing its application in domains that are challenged by large volumes of historical measurement data, such as network auditing, traffic behavior analysis and large-scale data visualization in service provider networks.


title: Observer-Based Adaptive Neural Network Trajectory Tracking Control for Remotely Operated Vehicle

abstract: This paper focuses on the adaptive trajectory tracking control for a remotely operated vehicle (ROV) with an unknown dynamic model and the unmeasured states. Unlike most previous trajectory tracking control approaches, in this paper, the velocity states and the angular velocity states in the body-fixed frame are unmeasured, and the thrust model is inaccurate. Obviously, it is more in line with the actual ROV systems. Since the dynamic model is unknown, a new local recurrent neural network (local RNN) structure with fast learning speed is proposed for online identification. To estimate the unmeasured states, an adaptive terminal sliding-mode state observer based on the local RNN is proposed, so that the finite-time convergence of the trajectory tracking error can be guaranteed. Considering the problem of inaccurate thrust model, an adaptive scale factor is introduced into thrust model, and the thruster control signal is considered as the input of the trajectory tracking system directly. Based on the local RNN output, the adaptive scale factor, and the state estimation values, an adaptive trajectory tracking control law is constructed. The stability of the trajectory tracking control system is analyzed by the Lyapunov theorem. The effectiveness of the proposed control scheme is illustrated by simulations.


title: Impact of Outlier Removal and Normalization Approach in Modified k-Means Clustering Algorithm

abstract: Clustering technique is mainly focus on pattern recognition for further organizational design analysis which finds groups of data objects such that objects in a group are similar to one another and dissimilar from the objects in the other group. It is important to preprocess data due to noisy data, errors, inconsistencies, outliers and lack of variable values. Different data preprocessing techniques like cleaning method, outlier detection, data integration and transformation can be carried out before clustering process to achieve successful analysis. Normalization is an important preprocessing step in Data Mining to standardize the values of all variables from dynamic range into specific range. Outliers can significantly affect data mining performance, so outlier detection and removal is an important task in wide variety of data mining applications. k-Means is one of the most well known clustering algorithms yet it suffers major shortcomings like initialize number of clusters and seed values preliminary and converges to local minima. This paper analyzed the performance of modified k-Means clustering algorithm with data preprocessing technique includes cleaning method, normalization approach and outlier detection with automatic initialization of seed values on datasets from UCI dataset repository.


title: Extracting Semantic Relations for Scholarly Knowledge Base Construction

abstract: The problem of information extraction from scientific articles, found as PDF documents in large digital repositories, is gaining more attention as the amount of research findings continues to grow. We propose a system to extract semantic relations among entities in scholarly articles by making use of external syntactic patterns and an iterative learner. While information extraction from scholarly documents have been studied before, it has been focused mainly on the abstract and keywords. Our method extracts semantic entities as concepts and instances along with their attributes from the fully body text of documents. We extract two types of relationships between concepts in the text using an iterative learning algorithm. External data sources from the web such as the Microsoft concept graph, as well as query logs, are utilized to evaluate the quality of the extracted concepts and relations. The concepts are used to construct a scientific taxonomy covering the research content of the documents. To evaluate the system we apply our approach on a set of 10k scholarly documents and conduct several evaluations to show the effectiveness of the proposed methods. We show that our system obtains a 23% improvement in precision over existing web IE tools when they are applied to scholarly documents.


title: Introducing qualitative research in psychology : adventures in theory and method

abstract: List of boxes chapter one: From recipes to adventures How, and what, can we know? Positivism Empiricism Hypothetico-deductivism Critique of the 'scientific method' Feminist critique of established epistemologies Social constructionism Epistemology and methodology Qualitative research Overview of the book Three epistemological questions Further reading chapter two: Qualitative research design General principles of qualitative research design The research question Choosing the 'right' method Semi-structured interviewing Participant observation Diaries Focus groups Further reading chapter three: Grounded Theory Basic principles of Grounded Theory An example of Grounded Theory Versions of Grounded Theory Limitations of Grounded Theory as a method for psychological research Three epistemological questions Further reading chapter four: Phenomenological methods Phenomenology The phenomenological method Phenomenology and psychology Descriptive phenomenology Interpretative phenomenology Interpretative Phenomenological Analysis Doing Interpretative Phenomenological Analysis Analysis of an individual case Worked example Integration of cases Interpretation Writing up An example of IPA Limitations of Interpretative Phenomenological Analysis Three epistemological questions Conclusion Further reading chapter five: Case studies Research methods for psychological case studies Types of design for case study research Procedural issues An example of case study research Limitations of case study research Three epistemological questions Conclusion Further reading chapter six: Discursive Psychology The 'turn to language' Discursive Psychology and Foucauldian Discourse Analysis Discursive Psychology An example of discourse analysis Limitations of discursive psychology Three epistemological questions Further reading chapter seven: Foucauldian Discourse Analysis Selecting texts for analysis Procedural guidelines for the analysis of discourse An illustration of the application of the six stages to an interview extract Limitations of Foucauldian Discourse Analysis Three epistemological questions Key differences between Discursive Psychology and Foucauldian Discourse Analysis Further reading chapter eight: Working with memories Narrative psychology Memory work Why memories? Data collection and analysis 'Saying sorry': an example of data analysis in Memory Work Limitations of Memory Work Three epistemological questions Conclusion Further reading chapter nine: Quality in qualitative research What constitutes 'good' qualitative research? Epistemology and evaluation Evaluation of the methods introduced in this book Some caveats Opportunities and limitations in qualitative research A word about technology 'What' and 'how' Further reading Appendix one: What is understood by 'dominance'? An interpretation through memories Goran Petronic Reflexive preface Abstract Introduction Method Participants Procedure Analysis of memories Comparison of memories Discussion References Appendix two: A qualitative study of the occurrence of abuse in one heterosexual and in one lesbian relationship Kris dew Valour Reflexive preface Abstract Introduction Method Reflexivity Results Discussion References Appendix Appendix three:The emotional experience of looking at art : an observation in the National Gallery Karolina Mornsjo Reflexive preface Abstract Introduction Method Setting Participants Ethical consideration The paintings Practical considerations Analysis Discussion References References Index


title: Real-time emotion recognition from facial images using Raspberry Pi II

abstract: In present day technology human-machine interaction is growing in demand and machine needs to understand human gestures and emotions. If a machine can identify human emotions, it can understand human behavior better, thus improving the task efficiency. Emotions can understand by text, vocal, verbal and facial expressions. Facial expressions play big role in judging emotions of a person. It is found that limited work is done in field of real time emotion recognition using facial images. In this paper, we propose a method for real time emotion recognition from facial image. In the proposed method we use three steps face detection using Haar cascade, features extraction using Active shape Model(ASM), (26 facial points extracted) and Adaboost classifier for classification of five emotions anger, disgust, happiness, neutral and surprise. The novelty of our proposed method lies in the implementation of emotion recognition at real time on Raspberry Pi II and an average accuracy of 94% is achieved at real time. The Raspberry Pi II when mounted on a mobile robot can recognize emotions dynamically in real time under social/service environments where emotion recognition plays a major role.


Color Image Segmentation: A State-of-the-Art Survey


title: A dual polarized cavity-backed aperture antenna for 5G mmW MIMO applications

abstract: A dual-polarized wideband aperture antenna operating in millimeter wave band (about 30GHz) for MIMO applications in 5G mobile communication systems is presented. Using the tapered aperture hybrid structure and the backed-cavity design on PCB technology, the antenna achieves dual-polarized and unidirectional broadside radiation. The proposed antenna also possesses a high port-to-port isolation of better than 26dB and a broad impedance bandwidth of 25%. This work further extends to a 4-element array with an isolation better than 18 dB under array arrangement. The antenna design were verified by simulation and measurement.


title: Space Vector PWM for PMSM simulation using Matlab Simulink

abstract: Space Vector PWM (SVPWM) model is often built based on high-level functions and verified based on the output of the inverter or the model of the electrical motor with best possible accuracy. However, SVPWM implementation on digital hardware such as Field Programmable Gate Array (FPGA) and Application-specific Integrated Circuit (ASIC) is constrained by the limited resources and computation accuracy in these digital hardware compared to the mathematical model. The paper proposed a method that utilizes Matlab Simulink and Fixed-Point Toolbox to construct hardware-amenable SVPWM model. Using the proposed model, it is possible to estimate the digital hardware resources used and analyze the accuracy of the system before the actual designing process takes place. The model has been simulated and verified with signal switching patterns and output signals from the model of the electrical motor. Based on functional comparisons, it was found that the outputs of the SVPWM model are almost identical to the digital hardware implementation.


title: The Role of Oxidative Stress in Hemolytic Anemia

abstract: The oxidative status of cells is determined by the balance between pro-oxidants and antioxidants. Pro-oxidants, referred to as reactive oxygen species (ROS), are classified into radicals and nonradicals. The radicals are highly reactive due to their tendency to accept or donate an electron and attain stability. When cells experience oxidative stress, ROS, which are generated in excess, may oxidize proteins, lipids and DNA - leading to cell death and organ damage. Oxidative stress is believed to aggravate the symptoms of many diseases, including hemolytic anemias. Oxidative stress was found in the beta-hemoglobinopathies (sickle cell anemia and thalassemia), glucose-6-phosphate dehydrogenase deficiency, hereditary spherocytosis, congenital dyserythropoietic anaemias and Paroxysmal Nocturnal Hemoglobinuria. Although oxidative stress is not the primary etiology of these diseases, oxidative damage to their erythroid cells plays a crucial role in hemolysis due to ineffective erythropoiesis in the bone marrow and short survival of red blood cells (RBC) in the circulation. Moreover, platelets and polymorphonuclear (PMN) white cells are also exposed to oxidative stress. As a result some patients develop thromboembolic phenomena and recurrent bacterial infections in addition to the chronic anemia. In this review we describe the role of oxidative stress and the potential therapeutic potential of anti-oxidants in various hemolytic anemias.


A New Normal-Form Theorem for Context-Free Phrase Structure Grammars


An improved algorithm for feature selection using fractal dimension


title: A New Decomposition Algorithm of DCT-IV/DST-IV for Realizing Fast IMDCT Computation

abstract: In this letter, a new decomposition algorithm of type-IV discrete cosine transform/type-IV discrete sine transform (DCT-IV/DST-IV) and architecture of a hardware accelerator for realizing fast inverse modified discrete cosine transform (IMDCT) computation are presented. The comparisons of computational complexity with some well-known algorithms show that the proposed algorithm possesses the advantages of higher computational efficiency and simpler hardware implementation. A real-time audio decoding experiment was performed to verify the efficiency of the proposed algorithm. Experimental results show more than one third of computational cycles are saved compared with one of reported fast algorithms for IMDCT computation.


title: Information-based functional brain mapping

abstract: The development of high-resolution neuroimaging and multielectrode electrophysiological recording provides neuroscientists with huge amounts of multivariate data. The complexity of the data creates a need for statistical summary, but the local averaging standardly applied to this end may obscure the effects of greatest neuroscientific interest. In neuroimaging, for example, brain mapping analysis has focused on the discovery of activation, i.e., of extended brain regions whose average activity changes across experimental conditions. Here we propose to ask a more general question of the data: Where in the brain does the activity pattern contain information about the experimental condition? To address this question, we propose scanning the imaged volume with a “searchlight,” whose contents are analyzed multivariately at each location in the brain.


title: Data Noising as Smoothing in Neural Network Language Models

abstract: Data noising is an effective technique for regularizing neural network models. While noising is widely adopted in application domains such as vision and speech, commonly used noising primitives have not been developed for discrete sequence-level settings such as language modeling. In this paper, we derive a connection between input noising in neural network language models and smoothing in $n$-gram models. Using this connection, we draw upon ideas from smoothing to develop effective noising schemes. We demonstrate performance gains when applying the proposed schemes to language modeling and machine translation. Finally, we provide empirical analysis validating the relationship between noising and smoothing.


title: Multiple camera types simultaneous stereo calibration

abstract: Calibration is a classical issue in computer vision needed to retrieve 3D information from image measurements. This work presents a calibration approach for hybrid stereo rig involving multiple central camera types (perspective, fisheye, catadioptric). The paper extends the method of monocular perspective camera calibration using virtual visual servoing. The simultaneous intrinsic and extrinsic calibration of central cameras rig, using different models for each camera, is developed. The presented approach is suitable for the calibration of rigs composed by N cameras modelled by N different models. Calibration results, compared with state of the art approaches, and a 3D plane estimation application, allowed by the calibration, show the effectiveness of the approach. A cross-platform software implementing this method is available1.


title: Partiqle: an elastic SQL engine over key-value stores

abstract: The demo features Partiqle, a SQL engine over key-value stores as a relational alternative for the recent procedural approaches to support OLTP workloads elastically. Based on our microsharding framework [12], it employs a declarative specification, called transaction classes, of constraints applied on the transactions in a workload. We demonstrate use of a transaction class in design and analysis of OLTP workloads. We then demonstrate live-scaling of our fully functioning system on a server cluster.


title: Fault Secure Encoder and Decoder for Memory Applications

abstract: We introduce a reliable memory system that can tolerate multiple transient errors in the memory words as well as transient errors in the encoder and decoder (corrector) circuitry. The key novel development is the fault-secure detector (FSD) error-correcting code (ECC) definition and associated circuitry that can detect errors in the received encoded vector despite experiencing multiple transient faults in its circuitry. The structure of the detector is general enough that it can be used for any ECC that follows our FSD-ECC definition. We prove that two known classes of Low-Density Parity-Check Codes have the FSD-ECC property: Euclidean Geometry and Projective Geometry codes. We identify a specific FSD-LDPC code that can tolerate up to 33 errors in each memory word or supporting logic that requires only 30% area overhead for memory blocks of 10 Kbits or larger. Larger codes can achieve even higher reliability and lower area overhead. We quantify the importance of protecting encoder and decoder (corrector) circuitry and illustrate a scenario where the system failure rate (FIT) is dominated by the failure rate of the encoder and decoder.


title: The effect of novelty on reinforcement learning

abstract: Abstract Recent research suggests that novelty has an influence on reward-related learning. Here, we showed that novel stimuli presented from a pre-familiarized category can accelerate or decelerate learning of the most rewarding category, depending on the condition. The extent of this influence depended on the individual trait of novelty seeking. Different reinforcement learning models were developed to quantify subjects’ choices. We introduced a bias parameter to model explorative behavior toward novel stimuli and characterize individual variation in novelty response. The theoretical framework allowed us to test different assumptions, concerning the motivational value of novelty. The best fitting-model combined all novelty components and had a significant positive correlation with both the experimentally measured novelty bias and the independent novelty-seeking trait. Altogether, we have not only shown that novelty by itself enhances behavioral responses underlying reward processing, but also that novelty has a direct influence on reward-dependent learning processes, consistently with computational predictions.


title: Decision Making, Risky Behavior, and Alcoholism

abstract: Abstract Alcoholism can be described as a disorder characterized by impulsive decision-making processes, wherein potential short-term appetitive outcomes of drinking (e.g., intoxication) are deemed more important than potential long-term aversive consequences of drinking (e.g., drunk-driving arrests). Separate but interrelated neurocognitive pathways to impulsive decision making exist – one reflected by weak “top-down” executive control over impulsive and compulsive urges to consume alcohol, the other reflected by a strong “bottom-up” appetitive drive in impulsive and compulsive urges to consume alcohol. We present behavioral evidence of poor executive control and strong appetitive drive and neural evidence describing differences in functional and organizational patterns in brain executive control and appetitive drive networks. We discuss how these behavioral and neural aspects of alcoholism are associated with impulsive decision making and risky behavior in alcoholics, and how these patterns differ at different stages of alcoholism dependence and recovery.


Low Cost , High Power Factor , Dimmable , Monolithic AC-Direct LED Driver with on-Chip Step-Dimmer for Outdoor Lighting Applications


title: TRESOR Runs Encryption Securely Outside RAM

abstract: Current disk encryption techniques store necessary keys in RAM and are therefore susceptible to attacks that target volatile memory, such as Firewire and cold boot attacks. We present TRESOR, a Linux kernel patch that implements the AES encryption algorithm and its key management solely on the microprocessor. Instead of using RAM, TRESOR ensures that all encryption states as well as the secret key and any part of it are only stored in processor registers throughout the operational time of the system, thereby substantially increasing its security. Our solution takes advantage of Intel's new AES-NI instruction set and exploits the x86 debug registers in a non-standard way, namely as cryptographic key storage. TRESOR is compatible with all modern Linux distributions, and its performance is on a par with that of standard AES implementations.


title: Mixed methods research designs in counseling psychology.

abstract: With the increased popularity of qualitative research, researchers in counseling psychology are expanding their methodologies to include mixed methods designs. These designs involve the collection, analysis, and integration of quantitative and qualitative data in a single or multiphase study. This article presents an overview of mixed methods research designs. It defines mixed methods research, discusses its origins and philosophical basis, advances steps and procedures used in these designs, and identifies 6 different types of designs. Important design features are illustrated using studies published in the counseling literature. Finally, the article ends with recommendations for designing, implementing, and reporting mixed methods studies in the literature and for discussing their viability and continued usefulness in the field of counseling psychology.


Context-aware Natural Language Generation for Spoken Dialogue Systems


Image Stitching using Watersheds and Graph Cuts


title: Another Look at LTL Model Checking

abstract: We show how LTL model checking can be reduced to CTL model checking with fairness constraints. Using this reduction, we also describe how to construct a {\em symbolic} LTL model checker that appears to be quite efficient in practice. In particular, we show how the SMV model checking system developed by McMillan [16] can be extended to permit LTL specifications. The results that we have obtained are quite surprising. For the specifications which can be expressed in both CTL and LTL, the LTL model checker required at most twice as much time and space as the CTL model checker. We also succeeded in verifying non-trivial LTL specifications. The amount of time and space that is required is quite reasonable. Based on the examples that we considered, it appears that efficient LTL model checking is possible when the specifications are not excessively complicated.


title: Making the case for mobile cognition: EEG and sports performance

abstract: Abstract In the high stakes world of International sport even the smallest change in performance can make the difference between success and failure, leading sports professionals to become increasingly interested in the potential benefits of neuroimaging. Here we describe evidence from EEG studies that either identify neural signals associated with expertise in sport, or employ neurofeedback to improve performance. Evidence for the validity of neurofeedback as a technique for enhancing sports performance remains limited. By contrast, progress in characterizing the neural correlates of sporting behavior is clear: frequency domain studies link expert performance to changes in alpha rhythms, whilst time-domain studies link expertise in response evaluation and motor output with modulations of P300 effects and readiness potentials. Despite early promise, however, findings have had relatively little impact for sports professionals, at least in part because there has been a mismatch between lab tasks and real sporting activity. After selectively reviewing existing findings and outlining limitations, we highlight developments in mobile EEG technology that offer new opportunities for sports neuroscience.


title: The combination of L-theanine and caffeine improves cognitive performance and increases subjective alertness

abstract: The non-proteinic amino acid L-theanine and caffeine, a methylxanthine derivative, are naturally occurring ingredients in tea. The present study investigated the effect of a combination of 97 mg Ltheanine and 40 mg caffeine as compared to placebo treatment on cognitive performance, alertness, blood pressure, and heart rate in a sample of young adults (n = 44). Cognitive performance, self-reported mood, blood pressure, and heart rate were measured before L-theanine and caffeine administration (i.e. at baseline) and 20 min and 70 min thereafter. The combination of moderate levels of L-theanine and caffeine significantly improved accuracy during task switching and self-reported alertness (both P < 0.01) and reduced self-reported tiredness (P < 0.05). There were no significant effects on other cognitive tasks, such as visual search, choice reaction times, or mental rotation. The present results suggest that 97 mg of L-theanine in combination with 40 mg of caffeine helps to focus attention during a demanding cognitive task.


title: Modeling and Characterization of the Implant Intra-Body Communication Based on Capacitive Coupling Using a Transfer Function Method

abstract: Implantable devices have important applications in biomedical sensor networks used for biomedical monitoring, diagnosis and treatment, etc. In this paper, an implant intra-body communication (IBC) method based on capacitive coupling has been proposed, and the modeling and characterization of this kind of IBC has been investigated. Firstly, the transfer function of the implant IBC based on capacitive coupling was derived. Secondly, the corresponding parameters of the transfer function are discussed. Finally, both measurements and simulations based on the proposed transfer function were carried out, while some important conclusions have been achieved, which indicate that the achieved transfer function and conclusions are able to help to achieve an implant communication method with the highly desirable characteristics of low power consumption, high data rate, high transmission quality, etc.


title: A Conditional Random Field Word Segmenter for Sighan Bakeoff 2005

abstract: We present a Chinese word segmentation system submitted to the closed track of Sighan bakeoff 2005. Our segmenter was built using a conditional random field sequence model that provides a framework to use a large number of linguistic features such as character identity, morphological and character reduplication features. Because our morphological features were extracted from the training corpora automatically, our system was not biased toward any particular variety of Mandarin. Thus, our system does not overfit the variety of Mandarin most familiar to the system's designers. Our final system achieved a F-score of 0.947 (AS), 0.943 (HK), 0.950 (PK) and 0.964 (MSR).


title: Big Data - Security and Privacy

abstract: The paper introduces a research agenda for security and privacy in big data. The paper discusses research challenges and directions concerning data confidentiality, privacy, and trustworthiness in the context of big data. Key research issues discussed in the paper include how to reconcile security with privacy, the notion of data ownership, and how to enforce access control in big data stores.


title: Animating non-humanoid characters with human motion data

abstract: This paper presents a method for generating animations of non-humanoid characters from human motion capture data. Characters considered in this work have proportion and/or topology significantly different from humans, but are expected to convey expressions and emotions through body language that are understandable to human viewers. Keyframing is most commonly used to animate such characters. Our method provides an alternative for animating non-humanoid characters that leverages motion data from a human subject performing in the style of the target character. The method consists of a statistical mapping function learned from a small set of corresponding key poses, and a physics-based optimization process to improve the physical realism. We demonstrate our approach on three characters and a variety of motions with emotional expressions.


title: A training algorithm for optimal margin classifiers

abstract: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjusted automatically to match the complexity of the problem. The solution is expressed as a linear combination of supporting patterns. These are the subset of training patterns that are closest to the decision boundary. Bounds on the generalization performance based on the leave-one-out method and the VC-dimension are given. Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.


Over the Hurdles: Barriers to Social Media Use in Extension Offices.


title: A compressed LSB steganography method

abstract: A least significant bit (LSB) Steganography is mainly characterized by its hiding capacity and imperceptibility which is measured by Peak Signal to Noise Ratio (PSNR). There is a trade-off between these two factors. In this paper, an improved LSB Steganography which uses modulus function for data hiding method has been proposed and implemented which is better than previous methods in imperceptibility keeping the hiding capacity same. The proposed Steganography method breaks the secret data into two components. Each component is hidden into cover image pixels using modulo function. Repetition in secret data component is also considered. Results show that the proposed method is better than existing methods unless there is high randomness in secret data.


title: On the Role of Sparse and Redundant Representations in Image Processing

abstract: Much of the progress made in image processing in the past decades can be attributed to better modeling of image content and a wise deployment of these models in relevant applications. This path of models spans from the simple l2-norm smoothness through robust, thus edge preserving, measures of smoothness (e.g. total variation), and until the very recent models that employ sparse and redundant representations. In this paper, we review the role of this recent model in image processing, its rationale, and models related to it. As it turns out, the field of image processing is one of the main beneficiaries from the recent progress made in the theory and practice of sparse and redundant representations. We discuss ways to employ these tools for various image-processing tasks and present several applications in which state-of-the-art results are obtained.


title: 765PDSAFETY OF RADIUM-223 DICHLORIDE (RA) WITH DOCETAXEL (D) IN PATIENTS (PTS) WITH BONE METASTASES (METS) FROM CASTRATION-RESISTANT PROSTATE CANCER (CRPC): A PHASE 1/2A CLINICAL TRIAL

abstract: M.J. Morris1, C. Higano2, H.I. Scher3, C. Sweeney4, E.S. Antonarakis5, D. Shevrin6, C.J. Ryan7, Y. Loriot8, K. Fizazi9, N. Pandit-Taskar10, J. E. Garcia-Vargas11, K. Lyseng12, M. Bloma12, A. Aksnes13, J.A. Carrasquillo10 Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA Medical Oncology, University of Washington, Seattle, WA, USA Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY, USA Lank Center for Genitourinary Oncology, Dana Farber Cancer Institute, Boston, MA, USA Oncology, Johns Hopkins Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, USA Medical Oncology, NorthShore University HealthSystem, Evanston, IL, USA Oncology, UCSF Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, USA Department of Cancer Medicine, Institut Gustave Roussey, Villejuif, FRANCE Department of Cancer Medicine, Institut Gustave Roussy, Villejuif, FRANCE Molecular Imaging and Therapy Service, Memorial Sloan-Kettering Cancer Center, New York, NY, USA Oncology Global Clinical Programs, Bayer HealthCare, Whippany, NJ, USA Clinical Department, Algeta ASA (Bayer), Oslo, NORWAY Clinical Research, Algeta ASA (Bayer), Oslo, NORWAY


title: A comparison between three proposed bridgeless Cuk rectifiers and conventional topology for power factor correction

abstract: Three new bridgeless single phase ac-dc power factor correction (PFC) rectifiers based on Cuk topology are proposed. The absence of an input diode bridge and the presence of only two semiconductor switches in the current flowing path during each switching cycle result in less conduction losses and an improved thermal management compared to the conventional Cuk PFC converter. The proposed topologies are designed to work in discontinuous conduction mode (DCM) to achieve almost unity power factor and low total harmonic distortion (THD) of input current. The DCM operation gives additional advantages such as: zero-current turn-on in the power switches, zero-current turn-off in the output diode, and reduces the complexity of the control circuitry. Performance comparisons between the proposed and conventional Cuk PFC rectifiers are performed based on circuit simulation. Experimental results for a 150W/48V dc at 100 V rms line voltage to evaluate the performance of the proposed bridgeless PFC rectifier are provided.


title: dDocent: a RADseq, variant-calling pipeline designed for population genomics of non-model organisms

abstract: Restriction-site associated DNA sequencing (RADseq) has become a powerful and useful approach for population genomics. Currently, no software exists that utilizes both paired-end reads from RADseq data to efficiently produce population-informative variant calls, especially for non-model organisms with large effective population sizes and high levels of genetic polymorphism. dDocent is an analysis pipeline with a user-friendly, command-line interface designed to process individually barcoded RADseq data (with double cut sites) into informative SNPs/Indels for population-level analyses. The pipeline, written in BASH, uses data reduction techniques and other stand-alone software packages to perform quality trimming and adapter removal, de novo assembly of RAD loci, read mapping, SNP and Indel calling, and baseline data filtering. Double-digest RAD data from population pairings of three different marine fishes were used to compare dDocent with Stacks, the first generally available, widely used pipeline for analysis of RADseq data. dDocent consistently identified more SNPs shared across greater numbers of individuals and with higher levels of coverage. This is due to the fact that dDocent quality trims instead of filtering, incorporates both forward and reverse reads (including reads with INDEL polymorphisms) in assembly, mapping, and SNP calling. The pipeline and a comprehensive user guide can be found at http://dDocent.wordpress.com.


title: Fisheye lens camera system calibration and localization error analysis

abstract: The paper presents a novel calibration method for fisheye lens. Five parameters, which fully reflect characters of fisheye lens, are proposed. Linear displacement platform is used to acquire precise sliding displacement between the target image and fisheye lens. Laser calibration method is designed to obtain the precise value of optical center. A convenient method, which is used to calculate the virtual focus of the fisheye lens, is proposed. To verify the result, indoor environment is built up to measure the localization error of omni-directional robot. Image including landmarks is acquired by fisheye lens and delivered to DSP (Digital Signal Processor) to futher process. Error analysis to localization of omni-directional robot is showed in the conclusion.


title: Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

abstract: Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name “deep patient”. We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.


Chapter 8 Robust Features in Deep Learning-Based Speech Recognition


title: CogNitive 5G networks: Comprehensive operator use cases with machine learning for management operations

abstract: This paper presents a novel cognitive management architecture developed within the H2020 CogNet project to manage 5G networks. We also present the instantiation of this architecture for two Operator use cases, namely ‘SLA enforcement’ and ‘Mobile Quality Predictor’. The SLA enforcement use case tackles the SLA management with machine learning techniques, precisely, LSTM (Long Short Term Memory). The second use case, Mobile Quality Predictor, proposes a framework using machine learning to enable an accurate bandwidth prediction for each mobile subscriber in real-time. A problem statement, stakeholders, an instantiation of the cognitive management architecture, a related work as well as an evaluation results are presented for each use case.


title: A Formal Semantic for UML 2.0 Activity Diagram based on Institution Theory

abstract: Giving a formal semantic to an UML Activity diagram (UML AD) is a hard task. The reason of this difficulty is the ambiguity and the absence of a precise formal semantic of such semi-formal formalism. A variety of semantics exist in the literature having tackled the aspects covered by this language. We can give as example denotational, functional and compositional semantics. To cope with the recent tendency which gave a heterogeneous semantic to UML diagrams, we aim to define an algebraic presentation of the semantic of UML AD. In this work, we define a formal semantic of UML 2.0 AD based on institution theory. For UML AD formalism, which is a graphical language, no precise formal semantic is given to it. We use the institution theory to define the intended semantic. Thus, the UML AD formalism will be defined in its own natural semantic.


title: Update on ventilator-associated pneumonia

abstract: Ventilator-associated pneumonia (VAP) is the most frequent life-threatening nosocomial infection in intensive care units. The diagnostic is difficult because radiological and clinical signs are inaccurate and could be associated with various respiratory diseases. The concept of infection-related ventilator-associated complication has been proposed as a surrogate of VAP to be used as a benchmark indicator of quality of care. Indeed, bundles of prevention measures are effective in decreasing the VAP rate. In case of VAP suspicion, respiratory secretions must be collected for bacteriological secretions before any new antimicrobials. Quantitative distal bacteriological exams may be preferable for a more reliable diagnosis and therefore a more appropriate use antimicrobials. To improve the prognosis, the treatment should be adequate as soon as possible but should avoid unnecessary broad-spectrum antimicrobials to limit antibiotic selection pressure. For empiric treatments, the selection of antimicrobials should consider the local prevalence of microorganisms along with their associated susceptibility profiles. Critically ill patients require high dosages of antimicrobials and more specifically continuous or prolonged infusions for beta-lactams. After patient stabilization, antimicrobials should be maintained for 7–8 days. The evaluation of VAP treatment based on 28-day mortality is being challenged by regulatory agencies, which are working on alternative surrogate endpoints and on trial design optimization.


title: Making privacy personal : Pro fi ling social network users to inform privacy education and nudging

abstract: Social Network Sites (SNSs) offer a plethora of privacy controls, but users rarely exploit all of these mechanisms, nor do they do so in the same manner. We demonstrate that SNS users instead adhere to one of a small set of distinct privacy management strategies that are partially related to their level of privacy feature awareness. Using advanced Factor Analysis methods on the self-reported privacy behaviors and feature awareness of 308 Facebook users, we extrapolate six distinct privacy management strategies, including: Privacy Maximizers, Selective Sharers, Privacy Balancers, Self-Censors, Time Savers/Consumers, and Privacy Minimalists and six classes of privacy proficiency based on feature awareness, ranging from Novices to Experts. We then cluster users on these dimensions to form six distinct behavioral profiles of privacy management strategies and six awareness profiles for privacy proficiency. We further analyze these privacy profiles to suggest opportunities for training and education, interface redesign, and new approaches for personalized privacy recommendations. We show that Facebook users' privacy behaviors and awareness are multi-dimensional.Feature awareness is a significant predictor of Facebook users' privacy behaviors.Six unique user profiles emerged to reveal different privacy management strategies.Six privacy proficiency profiles emerged from the dimensions of feature awareness.The privacy profiles can be used to personalize user education and nudging.


title: Chisel-Q: Designing quantum circuits with a scala embedded language

abstract: We introduce Chisel-Q, a high-level functional language for generating quantum circuits. Chisel-Q permits quantum computing algorithms to be constructed using the meta-language features of Scala and its embedded DSL Chisel. With Chisel-Q, designers of quantum computing algorithms gain access to high-level, modern language features and abstractions. We describe a synthesis flow that transforms Chisel-Q into an explicit quantum circuit in the Quantum Assembly Language (QASM) format. We also discuss several optimizations to reduce the generated hardware cost. The Chisel-Q tool includes resource and performance estimation which can be used to compare different implementations of the same functionality. We compare the output of the generic Chisel-Q synthesis flow with hand-tuned versions of well-known quantum circuits.


title: D$$^{2}$$CO: Fast and Robust Registration of 3D Textureless Objects Using the Directional Chamfer Distance

abstract: This paper introduces a robust and efficient vision based method for object detection and 3D pose estimation that exploits a novel edge-based registration algorithm we called Direct Directional Chamfer Optimization D$$^{2}$$CO. Our approach is able to handle textureless and partially occluded objects and does not require any off-line object learning step. Depth edges and visible patterns extracted from the 3D CAD model of the object are matched against edges detected in the current grey level image by means of a 3D distance transform represented by an image tensor, that encodes the minimum distance to an edge point in a joint direction/location space. D$$^{2}$$CO refines the object position employing a non-linear optimization procedure, where the cost being minimized is extracted directly from the 3D image tensor. Differently from other popular registration algorithms as ICP, that require to constantly update the correspondences between points, our approach does not require any iterative re-association step: the data association is implicitly optimized while inferring the object position. This enables D$$^{2}$$CO to obtain a considerable gain in speed over other registration algorithms while presenting a wider basin of convergence. We tested our system with a set of challenging untextured objects in presence of occlusions and cluttered background, showing accurate results and often outperforming other state-of-the-art methods.


title: Learning with Average Top-k Loss

abstract: In this work, we introduce the {\em average top-$k$} (\atk) loss as a new aggregate loss for supervised learning, which is the average over the $k$ largest individual losses over a training dataset. We show that the \atk loss is a natural generalization of the two widely used aggregate losses, namely the average loss and the maximum loss, but can combine their advantages and mitigate their drawbacks to better adapt to different data distributions. Furthermore, it remains a convex function over all individual losses, which can lead to convex optimization problems that can be solved effectively with conventional gradient-based methods. We provide an intuitive interpretation of the \atk loss based on its equivalent effect on the continuous individual loss functions, suggesting that it can reduce the penalty on correctly classified data. We further give a learning theory analysis of \matk learning on the classification calibration of the \atk loss and the error bounds of \atk-SVM. We demonstrate the applicability of minimum average top-$k$ learning for binary classification and regression using synthetic and real datasets.


title: Forensic Facial Reconstruction Using Mesh Template Deformation with Detail Transfer over HRBF

abstract: Forensic facial reconstruction is the application of anthropology, art and forensic science to recreate the face of an individual from his skull. It is usually done manually by a sculptor with clay and is considered a subjective technique as it relies upon an artistic interpretation of the skull features. In this work, we propose a computerized method based on anatomical rules that systematically generates the surface of the face through a HRBF deformation procedure over a mesh template. Our main contributions are a broader set of anatomical rules being applied over the soft tissue structures and a new deformation method that dissociates the details from the overall shape of the model.


Application mobility with Host Identity Protocol


title: Distributed autonomous control of concurrent combat tasks

abstract: This article discusses the use of decentralized model predictive control to manage a set of related combat tasks (e.g., the destruction of anti-aerial defenses in a given geographical area by a wing of uninhabited air vehicles (UAV)). The controller is meant to operate autonomously as it attempts to manage conditions in which success in all its objectives cannot be guaranteed at every step. It is also meant to work in a distributed fashion. This implies that complete knowledge of the state of each task is not needed at the central level. The approach presented suggests a way of aggregating battle information to be used at the higher levels of command. The aggregation used is based on combat models and physical concepts and does not rely on arbitrary valuations of assets and targets.


title: Toward Non-Intrusive Load Monitoring via Multi-Label Classification

abstract: Demand-side management technology is a key element of the proposed smart grid, which will help utilities make more efficient use of their generation assets by reducing consumers’ energy demand during peak load periods. However, although some modern appliances can respond to price signals from the utility companies, there is a vast stock of older appliances that cannot. For such appliances, utilities must infer what appliances are operating in a home, given only the power signals on the main feeder to the home (i.e., the home’s power consumption must be disaggregated into individual appliances). We report on an in-depth investigation of multi-label classification algorithms for disaggregating appliances in a power signal. A systematic review of this research topic shows that this class of algorithms has received little attention in the literature, even though it is arguably a more natural fit to the disaggregation problem than the traditional single-label classifiers used to date. We examine a multi-label meta-classification framework (RA ${k}$ EL), and a bespoke multi-label classification algorithm (ML ${k}$ NN), employing both time-domain and wavelet-domain feature sets. We test these classifiers on two real houses from the Reference Energy Disaggregation Dataset. We found that the multilabel algorithms are effective and competitive with published results on the datasets.


title: Embedding Watermarks into Deep Neural Networks

abstract: Significant progress has been made with deep neural networks recently. Sharing trained models of deep neural networks has been a very important in the rapid progress of research and development of these systems. At the same time, it is necessary to protect the rights to shared trained models. To this end, we propose to use digital watermarking technology to protect intellectual property and detect intellectual property infringement in the use of trained models. First, we formulate a new problem: embedding watermarks into deep neural networks. Second, we propose a general framework for embedding a watermark in model parameters, using a parameter regularizer. Our approach does not impair the performance of networks into which a watermark is placed because the watermark is embedded while training the host network. Finally, we perform comprehensive experiments to reveal the potential of watermarking deep neural networks as the basis of this new research effort. We show that our framework can embed a watermark during the training of a deep neural network from scratch, and during fine-tuning and distilling, without impairing its performance. The embedded watermark does not disappear even after fine-tuning or parameter pruning; the watermark remains complete even after 65% of parameters are pruned.


title: RésuMatcher: A personalized résumé-job matching system

abstract: Abstract Online jobs search through popular websites are quite beneficial having served for many years as a prominent tool for job seekers and employers alike. In spite of their valuable utility in linking employers with potential employees, the search process and technology utilized by job search websites have not kept pace with the rapid changes in computing capability and machine intelligence. The Information retrieval techniques utilized by these websites rely primarily on variants of manually entered search queries with some advanced similarity metrics for ranking search results. Advancements in machine intelligence techniques have enabled programmatic extraction of pertinent information about the job seeker and job postings without active user input. To this end, we developed a resume matching system, ResuMatcher, which intelligently extracts the qualifications and experience of a job seeker directly from his/her resume, and relevant information about the qualifications and experience requirements of job postings. Using a novel statistical similarity index, ResuMatcher returns results that are more relevant to the job seekers experience, academic, and technical qualifications, with minimal active user input. Our method provides up to a 34% improvement over existing information retrieval methods in the quality of search results. In addition however, ResuMatcher requires minimal active user input to search for jobs, compared to traditional manual search-based methods prevalent today. These improvements, we hypothesize, will lead to more relevant job search results and a better overall job search experience for job seekers. As an alternative to the fragmented organization-centric job application process, job recruitment websites offered the promise of simplifying and streamlining the job search process. However, these websites offer limited functionality using generic and simplistic information retrieval methods, which being non-domain lead to a poor and frustrating search experience. In this paper, we present ResuMatcher, a personalized job-resume matching system, which offers a novel statistical similarity index for ranking relevance between candidate resumes and a database of available jobs. In our experiments we show that our method offers a 37.44% improvement over existing information retrieval methods in the quality of matches returned.


title: Computing and Simplifying 2D and 3D Continuous Skeletons

abstract: Skeletons provide a synthetic and thin representation of objects. Therefore, they are useful for shape description. Recent papers have proposed to approximate the skeleton of continuous shapes using the Voronoi graph of boundary points. An original formulation is presented here, using the notion of polyballs (we call polyball any finite union of balls). A preliminary work shows that their skeletons consist of simple components (line segments in 2D and polygons in 3D). An efficient method for simplifying 3D continuous skeletons is also presented. The originality of our approach consists in simplifying the shape without modifying its topology and in including these modifications on the skeleton. Depending on the desired result, we propose two strategies which lead to either surfacical skeletons or wireframe skeletons. Two angular criteria are proposed that allow us to build a size-invariant hierarchy of simplified skeletons.


title: An Energy Framework for the Network Simulator 3 (ns-3)

abstract: The Network Simulator-3 (ns-3) is rapidly developing into a flexible and easy-to-use tool suitable for wireless network simulation. Since energy consumption is a key issue for wireless devices, wireless network researchers often need to investigate the energy consumption at a battery powered node or in the overall network, while running network simulations. This requires the underlying simulator to support energy consumption and energy source modeling. Currently however, ns-3 does not provide any support for modeling energy consumption or energy sources. In this paper, we introduce an integrated energy framework for ns-3, with models for energy source as well as energy consumption. We present the design and implementation of the overall framework and the specific models therein. Further, we show how the proposed framework can be used in ns-3 to simulate energy-aware protocols in a wireless network.


title: Patterns of call communication between group-housed zebra finches change during the breeding cycle

abstract: As the name implies, songbirds produce song, but they may also emit large numbers of shorter calls. Because calls are often given in social situations, they are difficult to record and to assign to the correct individual. Therefore, it is still unclear what information is communicated by these calls and how important they are. Zebra finches are highly vocal songbirds, with males singing and both females and males producing calls. In their natural habitat, Australia, these chatty birds pair for life and live in groups. To ensure successful breeding, zebra finches need to begin breeding activities as soon as the unpredictable environment allows. Therefore, even in captivity, they will readily breed when given nesting material. To find out about the role of zebra finch calls in relation to different environmental or social factors, Gill et al. brought together in groups female and male zebra finches that had not met before, and followed their individual calls during different breeding stages. This was done using a technique called microphone telemetry that involves placing tiny wireless microphones on the birds. The finches quickly formed breeding pairs, and when provided with nesting material, began building nests and laying eggs. While doing so, and especially when pairs began building nests, the birds changed how often they used certain calls and started using different call types; for example, they made more breeding-related ‘cackles’. Calls often featured precisely timed back-and-forth calling interactions, and, over time, were directed more and more towards their partner than other members of the group. Pairs that performed more of these call exchanges during nesting were more likely than others to lay a clutch of eggs. Overall, Gill et al. show that both the timing and types of calls used in pair communication are important for successful breeding. Future research could investigate the role of calls in group communication in more detail—possibly even in the wild—and how calling behaviour is reflected in the brain.


title: Outcome measures in chronic low back pain

abstract: The purpose of this prospective, single site cohort quasi-experimental study was to determine the responsiveness of the numerical rating scale (NRS), Roland-Morris disability questionnaire (RMDQ), Oswestry disability index (ODI), pain self-efficacy questionnaire (PSEQ) and the patient-specific functional scale (PSFS) in order to determine which would best measure clinically meaningful change in a chronic low back pain (LBP) population. Several patient-based outcome instruments are currently used to measure treatment effect in the chronic LBP population. However, there is a lack of consensus on what constitutes a "successful" outcome, how an important improvement/deterioration has been defined and which outcome measure(s) best captures the effectiveness of therapeutic interventions for the chronic LBP population. Sixty-three consecutive patients with chronic LBP referred to a back exercise and education class participated in this study; 48 of the 63 patients had complete data. Five questionnaires were administered initially and after the 5-week back class intervention. Also at 5 weeks, patients completed a global impression of change as a reflection of meaningful change in patient status. Score changes in the five different questionnaires were subjected to both distribution- and anchor-based methods: standard error of measurement (SEM) and receiver operating characteristic (ROC) curves to define clinical improvement. From these methods, the minimal clinically important difference (MCID) defined as the smallest difference that patients and clinicians perceive to be worthwhile is presented for each instrument. Based on the SEM, a point score change of 2.4 in the NRS, 5 in the RMDQ, 17 in the ODI, 11 on the PSEQ, and 1.4 on the PSFS corresponded to the MCID. Based on ROC curve analysis, a point score change of 4 points for both the NRS and RMDQ, 8 points for the ODI, 9 points for the PSEQ and 2 points for the PSFS corresponded to the MCID. The ROC analysis demonstrated that both the PSEQ and PSFS are responsive to clinically important change over time. The NRS was found to be least responsive. The exact value of the MCID is not a fixed value and is dependent on the assessment method used to calculate the score change. Based on ROC curve analysis the PSFS and PSEQ were more responsive than the other scales in measuring change in patients with chronic LBP following participation in a back class programme. However, due to the small sample size, the lack of observed worsening of symptoms over time, the single centre and intervention studied these results which need to be interpreted with caution.


title: AC Versus DC Distribution SystemsDid We Get it Right?

abstract: We presently enjoy a predominantly AC electrical distribution system, the engineering basis for which was designed over 100 years ago. While AC distribution systems have served us well, we should periodically pause to assess what opportunities we have accepted or been denied by the overwhelming predominance of AC electrical power distribution systems. What opportunities could be obtained by engineering DC distribution into at least portions of our present system? What advantages of the present AC distribution system should be recognized and protected? This paper will focus on distribution within premise and low-voltage distribution systems. Specifically, we will address the conversion efficiency costs of adopting various premise AC and DC distribution system topologies. According to a simple predictive model formulated in this paper, premise residential DC distribution will incur unfavorable total conversion efficiency compared with existing AC premise distribution. However, if a residence is supplied by a fuel cell or another DC generator, the total conversion efficiency within a residential DC distribution system could be similar to, or even better than, that for AC distribution.


title: Wideband coplanar waveguide RF probe pad to microstrip transitions without via holes

abstract: A novel via-less coplanar waveguide (CPW) to microstrip transition is discussed and design rules based on simulations and experimental results are presented. This transition demonstrates a maximum insertion loss of 1 dB over the frequency range from 10 GHz to 40 GHz with a value of 0.4 dB at 20 GHz. This transition could find a variety of applications due to its compatibility with RF systems-on-a chip, low loss performance, low cost and its ease of fabrication.


title: PoW-based distributed cryptography with no trusted setup

abstract: Motivated by the recent success of Bitcoin we study the question of constructing distributed cryptographic protocols in a fully peer-to-peer scenario under the assumption that the adversary has limited computing power and there is no trusted setup (like PKI, or an unpredictable beacon). We propose a formal model for this scenario and then we construct a broadcast protocol in it. This protocol is secure under the assumption that the honest parties have computing power that is some non-negligible fraction of computing power of the adversary (this fraction can be small, in particular it can be much less than 1 / 2), and a (rough) total bound on the computing power in the system is known.


title: A UAV search and rescue scenario with human body detection and geolocalization

abstract: The use of Unmanned Aerial Vehicles (UAVs) which can operate autonomously in dynamic and complex operational environments is becoming increasingly more common. The UAVTech Lab, is pursuing a long term research endeavour related to the development of future aviation systems which try and push the envelope in terms of using and integrating high-level deliberative or AI functionality with traditional reactive and control components in autonomous UAV systems. In order to carry on such research, one requires challenging mission scenarios which force such integration and development. In this paper, one of these challenging emergency services mission scenarios is presented. It involves search and rescue for injured civilians by UAVs. In leg I of the mission, UAVs scan designated areas and try to identify injured civilians. In leg II of the mission, an attempt is made to deliver medical and other supplies to identified victims. We show how far we have come in implementing and executing such a challenging mission in realistic urban scenarios.


title: Social power and information technology implementation: a contentious framing lens

abstract: Research on the organizational implementation of information technology (IT) and social power has favoured explanations based on issues of resource power and process power at the expense of matters of meaning power. As a result, although the existence and importance of meaning power is acknowledged, its distinctive practices and enacted outcomes remain relatively under-theorized and under-explored by IT researchers. This paper focused on unpacking the practices and outcomes associated with the exercise of meaning power within the IT implementation process. Our aim was to analyze the practices employed to construct meaning and enact a collective ‘definition of the situation’. We focused on framing and utilizing the signature matrix technique to represent and analyze the exercise of meaning power in practice. The paper developed and illustrated this conceptual framework using a case study of a conflictual IT implementation in a challenging public sector environment. We concluded by pointing out the situated nature of meaning power practices and the enacted outcomes. Our research extends the literature on IT and social power by offering an analytical framework distinctly suited to the analysis and deeper understanding of the meaning power properties.


title: Active calibration: alternative strategy and analysis

abstract: Almost all camera calibration techniques use a known calibrating pattern and a static camera. Techniques based on an active camera, which does not need any predefined patterns, are introduced. All that is required is a scene with some strong and stable edges. Two algorithms are presented and analyzed. It is shown that one strategy performs much better in the presence of noise, and thus is preferable in practical situations. Experimental results are shown, demonstrating the validity of the algorithms. >


title: Radar cross-section estimation of SAR images

abstract: We present an algorithm that is able to smooth out the speckle from many SAR images and which does not suffer from the drawbacks of multilooking. The algorithm is able to preserve the detail and resolution of the original image while producing a smooth, real-valued output. In many cases the quality of the smoothed image is sufficiently high that it may be used with standard optical post-processing algorithms. We use a global optimization method (simulated annealing) and single point gamma statistics to find the MAP solution for the radar cross- section. However, this method may also be regarded as an ideal adaptive filter that is both computationally efficient and highly parallelizable. Results are presented for airborne, ERS-1 and multi-temporal SAR images.


title: Scalable Person Re-identification on Supervised Smoothed Manifold

abstract: Most existing person re-identification algorithms either extract robust visual features or learn discriminative metrics for person images. However, the underlying manifold which those images reside on is rarely investigated. That arises a problem that the learned metric is not smooth with respect to the local geometry structure of the data manifold. In this paper, we study person re-identification with manifold-based affinity learning, which did not receive enough attention from this area. An unconventional manifold-preserving algorithm is proposed, which can 1) make best use of supervision from training data, whose label information is given as pairwise constraints, 2) scale up to large repositories with low on-line time complexity, and 3) be plunged into most existing algorithms, serving as a generic postprocessing procedure to further boost the identification accuracies. Extensive experimental results on five popular person re-identification benchmarks consistently demonstrate the effectiveness of our method. Especially, on the largest CUHK03 and Market-1501, our method outperforms the state-of-the-art alternatives by a large margin with high efficiency, which is more appropriate for practical applications.


title: A framework and algorithms for circular drawings of graphs

abstract: Abstract In this paper, we present a framework and two linear time algorithms for obtaining circular drawings of graphs. The first technique produces circular drawings of biconnected graphs and finds a zero crossing circular drawing if one exists. The second technique finds multiple embedding circle drawings. Techniques for the reduction of edge crossings are also discussed. Results of experimental studies are included.


title: LSCitter: building social machines by augmenting existing social networks with interaction models

abstract: We present LSCitter, an implemented framework for supporting human interaction on social networks with formal models of interaction, designed as a generic tool for creating social machines on existing infrastructure. Interaction models can be used to choreograph distributed systems, providing points of coordination and communication between multiple interacting actors. While existing social networks specify how interactions happen---who messages go to and when, the effects of carrying out actions---these are typically implicit, opaque and non user-editable. Treating interaction models as first class objects allows the creation of electronic institutions, on which users can then choose the kinds of interaction they wish to engage in, with protocols which are explicit, visible and modifiable. However, there is typically a cost to users to engage with these institutions. In this paper we introduce the notion of "shadow institutions", where actions on existing social networks are mapped onto formal interaction protocols, allowing participants access to computational intelligence in a seamless, zero-cost manner to carry out computation and store information.


Blockchain-Free Cryptocurrencies. A Rational Framework for Truly Decentralised Fast Transactions.


Web Tracking - A Literature Review on the State of Research


title: Fine-grained document clustering via ranking and its application to social media analytics

abstract: Extracting valuable insights from a large volume of unstructured data such as texts through clustering analysis is paramount to many big data applications. However, document clustering is challenged by the computational complexity of the underlying methods and the high dimensionality of data, especially when the number of required clusters is large. A fine-grained clustering solution is required to understand a data set that represents heterogeneous topics such as social media data. This paper presents the Fine-Grained document Clustering via Ranking (FGCR) approach which leverages the search engine capability of handling big data efficiently. Ranking scores from a search engine are used to calculate dynamic clusters’ representations called loci in an unsupervised learning setting. Clustering decisions are efficiently made based on an optimal selection from a small subset of loci instead of the entire cluster set as in the conventional centroid-based clustering. A comprehensive empirical study on several social media data sets shows that FGCR is able to produce insightful and accurate fine-grained solution. Moreover, it is magnitudes faster and requires less computational resources compared to other state-of-the-art document clustering approaches.


title: A 22-gb/s PAM-4 receiver in 90-nm CMOS SOI technology

abstract: We report a receiver for four-level pulse-amplitude modulated (PAM-4) encoded data signals, which was measured to receive data at 22 Gb/s with a bit error rate (BER) <10/sup -12/ at a maximum frequency deviation of 350 ppm and a 2/sup 7/-1 PRBS pattern. We propose a bit-sliced architecture for the data path, and a novel voltage shifting amplifier to introduce a programmable offset to the differential data signal. We present a novel method to characterize sampling latches and include them in the data path. A current-mode logic (CML) biasing scheme using programmable matched resistors limits the effect of process variations. The receiver also features a programmable signal termination, an analog equalizer and offset compensation for each sampling latch. The measured current consumption is 207 mA from a 1.1-V supply, and the active chip area is 0.12 mm/sup 2/.


title: HTCC: Haskell to Handel-C Compiler

abstract: Functional programming languages, such as Haskell, enable simple, concise, and correct-by-construction hardware development. HTCC compiles a subset of Haskell to Handel-C language with hardware output. Moreover, HTCC generates VHDL, Verilog, EDIF, and SystemC programs. The design of HTCC compiler includes lexical, syntax and semantic analyzers. HTCC automates a transformational derivation methodology to rapidly produce hardware that maps onto Field Programmable Gate Arrays (FPGAs). HTCC is generated using ANTLR compiler-compiler tool and supports an effective integrated development environment. This paper presents the design rationale and the implementation of HTCC. Several sample generations of first-class and higher-order functions are presented. In-addition, a compilation case-study is presented for the XTEA cipher. The investigation comprises a thorough evaluation and performance analysis. The targeted FPGAs include Cyclone II, Stratix IV, and Virtex-6 from Altera and Xilinx.


title: Using noise signature to optimize spike-sorting and to assess neuronal classification quality

abstract: We have developed a simple and expandable procedure for classification and validation of extracellular data based on a probabilistic model of data generation. This approach relies on an empirical characterization of the recording noise. We first use this noise characterization to optimize the clustering of recorded events into putative neurons. As a second step, we use the noise model again to assess the quality of each cluster by comparing the within-cluster variability to that of the noise. This second step can be performed independently of the clustering algorithm used, and it provides the user with quantitative as well as visual tests of the quality of the classification.


title: A new parameter estimation algorithm for an electrical analogue battery model

abstract: This paper describes a new parameter estimation algorithm for a well-recognized electrical analogue battery model. The limited bandwidth characteristic of the electrical analogue battery model is introduced and discussed, on which the new parameter estimation algorithm is built. While a real battery system is non-linear and time variant, a truncated representation of the system is provided by a commonly studied non-physical "electrical analogue" battery model. However, the limited bandwidth characteristic of the electrical analogue battery model is often overlooked. The proposed algorithm starts by assessing a desired battery application, followed by modeling the battery according to the application bandwidth, and then estimating the model parameters using the sequential quadratic programming method. This approach recognizes and makes use of the limited bandwidth of the battery model by reconciling the approximation with the limited bandwidth required by the simulation. This approach has been experimentally verified on a 6.8 Ah Ultralife UBBL10 Lithium-ion battery module. Since this electrical analogue battery model is independent of the battery chemistry it is applicable to Lithium-ion, Nickel-Metal-Hydride (NiMH), and Lead-acid batteries, among others.


title: Innovative Contactless Energy Transfer Accessory for Rotary Ultrasonic Machining and Its Circuit Compensation Based on Coil Turns

abstract: An innovative concentric rotary ultrasonic machining (RUM) device based on local induction is proposed. The device is used as a machine tool accessory that can add the RUM to any machining center. Rather than using a complete ring, a portion of a ring is used as the primary core in the proposed rotary transformer, which eliminates spindle speed limits. The simplified configuration provides increased safety and is convenient for material processing applications. Additionally, a circuit compensation method based on coil turns is proposed. It takes the whole circuit, reliability of electronic components, transmission efficiency, and output power into consideration. We use this method to choose the optimal number of coil turns and compensation elements required to achieve increased and safe power transmission performance. This paper also demonstrates the performance of the device through experimentation. Finally, practicability is discussed.


title: Mobile Technology Boosts the Effectiveness of Psychotherapy and Behavioral Interventions: A Meta-Analysis

abstract: We conducted a meta-analysis on the effects of mobile technology on treatment outcome for psychotherapy and other behavioral interventions. Our search of the literature resulted in 26 empirical articles describing 25 clinical trials testing the benefits of smartphone applications, personal digital assistants (PDAs), or text messaging systems either to supplement treatment or substitute for direct contact with a clinician. Overall, mobile technology use was associated with superior treatment outcome across all study designs and control conditions, effect size (ES) = .34, p < .0001. For the subset of 10 studies that looked specifically at the added benefit of mobile technology using a rigorous “Treatment” versus “Treatment + Mobile” design, effect sizes were only slightly more modest (ES = .27) and still significant (p < .05). Overall, the results support the role of mobile technology for the delivery of psychotherapy and other behavioral interventions.


title: The perception and practice of traditional medicine in the treatment of cancers and inflammations by the Hausa and Fulani tribes of Northern Nigeria.

abstract: A survey was conducted among Hausa and Fulani, two major tribes of Northern Nigeria to identify plants and methods used traditionally in the treatment of cancers and inflammatory diseases. The ecological zones that were considered include Zaria, Kaduna and Kano in the Northern part of Nigeria. The survey involves traditional healers, hunters, farmers and Fulani nomads. This survey has identified plants useful in the treatment of cancers. The plants were identified via taxonomic means and classified according to their habitats, families, genera. Evidently the plants span families and genera, the knowledge and values of the plants was evaluated with the aim of understanding the scientific basis for the use of the plants. The inventory provides the unique opportunity of capturing plants of common uses across the communities.


title: Emotion regulation deficits in eating disorders: A marker of eating pathology or general psychopathology?

abstract: Preliminary evidence indicates that individuals with eating disorders (ED) show emotion regulation (ER) difficulties. However, it is yet unclear whether different types of ED differ in their ER profile and whether certain ER difficulties are specific for ED or rather a transdiagnostic factor. Twenty women with anorexia nervosa (AN), 18 with bulimia nervosa (BN), 25 with binge eating disorder (BED), 15 with borderline personality disorder (BPD), 16 with major depressive disorder (MDD) and 42 female healthy controls (HC) were administered the Emotion Regulation Questionnaire, the Inventory of Cognitive Affect Regulation Strategies, the Difficulties in Emotion Regulation Scale and the Affect Intensity Measure. The ED groups reported significantly higher levels of emotion intensity, lower acceptance of emotions, less emotional awareness and clarity, more self-reported ER problems as well as decreased use of functional and increased use of dysfunctional emotion regulation strategies when compared to HC. No significant differences between the ED groups emerged for most ER variables. However, there were indications that the BED group may show a slightly more adaptive pattern of ER than the two other ED groups. As a whole, all clinical groups performed very similar on most ER variables and reported more difficulties regulating their emotions than HC. The findings suggest that ER difficulties are not linked to a particular diagnostic category. Instead, ER difficulties appear to be a transdiagnostic risk and/or maintenance factor rather than being disorder-specific.


title: 24-hour-restraint stress induces long-term depressive-like phenotypes in mice

abstract: There is an increasing risk of mental disorders, such as acute stress disorder (ASD), post-traumatic stress disorder (PTSD) and depression among survivors who were trapped in rubble during earthquake. Such long-term impaction of a single acute restraint stress has not been extensively explored. In this study, we subjected mice to 24-hour-restraint to simulate the trapping episode, and investigated the acute (2 days after the restraint) and long-term (35 days after the restraint) impacts. Surprisingly, we found that the mice displayed depression-like behaviors, decreased glucose uptake in brain and reduced adult hippocampal neurogenesis 35 days after the restraint. Differential expression profiling based on microarrays suggested that genes and pathways related to depression and other mental disorders were differentially expressed in both PFC and hippocampus. Furthermore, the depression-like phenotypes induced by 24-hour-restraint could be reversed by fluoxetine, a type of antidepressant drug. These findings demonstrated that a single severe stressful event could produce long-term depressive-like phenotypes. Moreover, the 24-hour-restraint stress mice could also be used for further studies on mood disorders. Language: en