title: Distribution of serotypes and genotypes of Salmonella enterica species in French pig production.

abstract: The population of Salmonella found at various stages of pig production in France was characterised to analyse the distribution and spread of Salmonella in the pig production chain. We serotyped and genotyped by PFGE 174 isolates collected from breeding pigs from breeding farms, 163 collected from breeding pigs from production farms, and 325 collected from fattening pigs. Forty-seven serovars and 110 genotypes were identified. The major serovars were S Derby (263 isolates) and S Typhimurium (162 isolates). The percentage of S Derby isolates decreased slightly through the production system (44.3, 41.1 per cent and 36.5 per cent) and 79.1 per cent of the S Derby isolates were distributed in the five genotypes common to all three stages. The percentage of S Typhimurium isolates was high for slaughter pigs (40.8 per cent) and 43 of the 46 S Typhimurium genotypes were only identified at this stage. Distributions of S Derby and S Typhimurium between breeding and fattening pigs were different. S Derby was found throughout the pig production pyramid, suggesting that this serotype may be transmitted by the transfer of animals between herds. The presence of multiple S Typhimurium genotypes in fattening pigs suggests that there were many sources of contamination at this stage, with fattening pigs having higher levels of exposure and/or sensitivity to this serotype.


Evaluating Personalization and Persuasion in E-Commerce


title: Enduring effects for cognitive behavior therapy in the treatment of depression and anxiety

abstract: Recent studies suggest that cognitive and behavioral interventions have enduring effects that reduce risk for subsequent symptom return following treatment termination. These enduring effects have been most clearly demonstrated with respect to depression and the anxiety disorders. It remains unclear whether these effects are a consequence of the amelioration of the causal processes that generate risk or the introduction of compensatory strategies that offset them and whether these effects reflect the mobilization of cognitive or other mechanisms. No such enduring effects have been observed for the psychoactive medications, which appear to be largely palliative in nature. Other psychosocial interventions remain largely untested, although claims that they produce lasting change have long been made. Whether such enduring effects extend to other disorders remains to be seen, but the capacity to reduce risk following treatment termination is one of the major benefits provided by the cognitive and behavioral in...


title: On the risk of severe dengue during secondary infection: a systematic review coupled with mathematical modeling.

abstract: BACKGROUND & OBJECTIVES ::: The present study aimed to systematically quantify the well known risk of severe dengue during secondary infection in literature and to understand how epidemiological mechanisms of enhancement during the secondary infection influence the empirically estimated risk of severe dengue by means of mathematical modeling. ::: ::: ::: METHODS ::: Two conditional risks of severe dengue, i.e. symptomatic illness and dengue hemorrhagic fever (DHF) or dengue shock syndrome (DSS), given secondary infection were explored based on systematically searched prospective studies. A two-strain epidemiological model was employed to simulate the transmission dynamics of dengue and to identify the relevant data gaps in empirical observations. ::: ::: ::: RESULTS ::: Using the variance-based weighting, the pooled relative risk (RR) of symptomatic illness during secondary infection was estimated at 9.4 [95% confidence interval (CI): 6.1-14.4], and similarly, RR of DHF/DSS was estimated to be 23.7 (95% CI: 15.3-36.9). A variation in the RR of DHF/DSS was observed among prospective studies. Using the mathematical modeling technique, we identified the duration of cross-protective immunity as an important modulator of the time-dependent behaviour of the RR of severe dengue. Different epidemiological mechanisms of enhancement during secondary infection yielded different RR of severe dengue. ::: ::: ::: INTERPRETATION & CONCLUSION ::: Optimal design of prospective cohort study for dengue should be considered, accounting for the time-dependence in the RR during the course of dengue epidemic. It is critical to statistically infer the duration of cross-protective immunity and clarify how the enhancement influences the epidemiological dynamics during secondary infection.


title: IDK Cascades: Fast Deep Learning by Learning not to Overthink

abstract: Advances in deep learning have led to substantial increases in prediction accuracy but have been accompanied by increases in the cost of rendering predictions. We conjecture that fora majority of real-world inputs, the recent advances in deep learning have created models that effectively "overthink" on simple inputs. In this paper, we revisit the classic question of building model cascades that primarily leverage class asymmetry to reduce cost. We introduce the "I Don't Know"(IDK) prediction cascades framework, a general framework to systematically compose a set of pre-trained models to accelerate inference without a loss in prediction accuracy. We propose two search based methods for constructing cascades as well as a new cost-aware objective within this framework. The proposed IDK cascade framework can be easily adopted in the existing model serving systems without additional model re-training. We evaluate the proposed techniques on a range of benchmarks to demonstrate the effectiveness of the proposed framework.


title: Measuring benchmark similarity using inherent program characteristics

abstract: This paper proposes a methodology for measuring the similarity between programs based on their inherent microarchitecture-independent characteristics, and demonstrates two applications for it: 1) finding a representative subset of programs from benchmark suites and 2) studying the evolution of four generations of SPEC CPU benchmark suites. Using the proposed methodology, we find a representative subset of programs from three popular benchmark suites - SPEC CPU2000, MediaBench, and MiBench. We show that this subset of representative programs can be effectively used to estimate the average benchmark suite IPC, L1 data cache miss-rates, and speedup on 11 machines with different ISAs and microarchitectures - this enables one to save simulation time with little loss in accuracy. From our study of the similarity between the four generations of SPEC CPU benchmark suites, we find that, other than a dramatic increase in the dynamic instruction count and increasingly poor temporal data locality, the inherent program characteristics have more or less remained unchanged


title: Science Magazine

abstract: Machine learning has been used to beat a human competitor in a game of Go ( 1 ), a game that has long been viewed as the most challenging of board games for artificial intelligence. Research is now under way to investigate whether machine learning can be used to solve long outstanding problems in quantum science. On page 602 of this issue, Carleo and Troyer ( 2 ) use machine learning on one of quantum science's greatest challenges: the simulation of quantum many-body systems. Carleo and Troyer used an artificial neural network to represent the wave function of a quantum many-body system and to make the neural network "learn" what the ground state (or dynamics) of the system is. Their approach is found to perform better than the current state-of-the-art numerical simulation methods.


title: A 1.66-nW/kHz, 32.7-kHz, 99.5ppm/°C fully integrated current-mode RC oscillator for real-time clock applications with PVT stability

abstract: A compact and low-power current-mode RC oscillator (RCO) with process, voltage, and temperature (PVT) stability has been developed. The circuit employs a current-mode RCO architecture without using a conventional comparator based voltage-mode architecture. The current-mode architecture enables a compact RCO and faster switching speed to be achieved. By designing transistor sizes equally between one in a bias circuit and another in a voltage to current converter, the effect of process variation can be minimized. A prototype chip in a 0.18-µm CMOS process demonstrated that the RCO generates a stable clock frequency of 32.7 kHz with a small area of 0.19 mm2 and low-power dissipation of 54.2 nW at 0.85-V power supply, which achieves a figure of merit (FoM) of 1.66 nW/kHz. The measured temperature coefficient and line regulation were 99.5ppm/°C and 8.9ppm/mV, respectively.


title: A dual frequency OMT in the Ku band for TT&C applications

abstract: A mixed characterisation by means of the generalized admittance matrix and the generalized scattering matrices, obtained with mode matching is proposed to design dual-band orthomode transducer (OMT) components. Accurate and efficient fullwave analysis software based on this procedure has been developed. A dual frequency OMT in the Ku band with high performance has been fully designed with the developed software. The good agreement between the numerical and experimental results validates the design process.


Interior Design in Restaurants as a Factor Influencing Customer Satisfaction


title: Design of single-sided AC magnetic field generating coil for wireless power transfer

abstract: In this paper, wireless power tranfer coil with novel winding arrangement is proposed to reduce unwanted magnetic field leakage in electronics charging application. The new winding arrangement generates single-sided magnetic field to suppress magnetic field leakage horizontally and vertically. The shielding effectiveness and wireless power transfer efficiency were compared with conventional coil which simply has windings in single direction by 3D EM solver simulation.


title: Mitigation of Multipath Fading Effects to Improve Indoor RSSI Performance

abstract: Received signal strength indicator (RSSI) is not suitable for fine-grained indoor applications because of imprecise and unreliable readings caused by multipath fading. This paper presents a simple and robust mechanism to improve RSSI by mitigating multipath fading effects in an indoor environment. Signal analysis is provided to explain the rationale of this method. The proposed method provides instantaneous solution, thus it is compatible with time-series filters and can be cascaded as additional improvement. The contributions of this proposal are the ease of implementation and its excellent output performance. The results show that it is able to eliminate unwanted noisy components caused by multipath fading, and thus obtain more reliable and stable RSSI readings.


title: Path Integral Networks: End-to-End Differentiable Optimal Control

abstract: In this paper, we introduce Path Integral Networks (PI-Net), a recurrent network representation of the Path Integral optimal control algorithm. The network includes both system dynamics and cost models, used for optimal control based planning. PI-Net is fully differentiable, learning both dynamics and cost models end-to-end by back-propagation and stochastic gradient descent. Because of this, PI-Net can learn to plan. PI-Net has several advantages: it can generalize to unseen states thanks to planning, it can be applied to continuous control tasks, and it allows for a wide variety learning schemes, including imitation and reinforcement learning. Preliminary experiment results show that PI-Net, trained by imitation learning, can mimic control demonstrations for two simulated problems; a linear system and a pendulum swing-up problem. We also show that PI-Net is able to learn dynamics and cost models latent in the demonstrations.


title: Latent Class Models for Collaborative Filtering

abstract: This paper presents a statistical approach to collaborative filtering and investigates the use of latent class models for predicting individual choices and preferences based on observed preference behavior. Two models are discussed and compared: the aspect model, a probabilistic latent space model which models individual preferences as a convex combination of preference factors, and the two-sided clustering model, which simultaneously partitions persons and objects into clusters. We present EM algorithms for different variants of the aspect model and derive an approximate EM algorithm based on a variational principle for the two-sided clustering model. The benefits of the different models are experimentally investigated on a large movie data set.


title: Chianti: a tool for change impact analysis of java programs

abstract: This paper reports on the design and implementation of Chianti, a change impact analysis tool for Java that is implemented in the context of the Eclipse environment. Chianti analyzes two versions of an application and decomposes their difference into a set of atomic changes. Change impact is then reported in terms of affected (regression or unit) tests whose execution behavior may have been modified by the applied changes. For each affected test, Chianti also determines a set of affecting changes that were responsible for the test's modified behavior. This latter step of isolating the changes that induce the failure of one specific test from those changes that only affect other tests can be used as a debugging technique in situations where a test fails unexpectedly after a long editing session. We evaluated Chianti on a year (2002) of CVS data from M. Ernst's Daikon system, and found that, on average, 52% of Daikon's unit tests are affected. Furthermore, each affected unit test, on average, is affected by only 3.95% of the atomic changes. These findings suggest that our change impact analysis is a promising technique for assisting developers with program understanding and debugging.


title: To Docker or Not to Docker: A Security Perspective

abstract: The need for ever-shorter development cycles, continuous delivery, and cost savings in cloud-based infrastructures led to the rise of containers, which are more flexible than virtual machines and provide near-native performance. Among all container solutions, Docker, a complete packaging and software delivery tool, currently leads the market. This article gives an overview of the container ecosystem and discusses the Docker environment's security implications through realistic use cases. The authors define an adversary model, point out several vulnerabilities affecting current Docker usage, and discuss further research directions.


title: Suffix Bidirectional Long Short-Term Memory

abstract: Recurrent neural networks have become ubiquitous in computing representations of sequential data, especially textual data in natural language processing. In particular, Bidirectional LSTMs are at the heart of several neural models achieving state-of-the-art performance in a wide variety of tasks in NLP. We propose a general and effective improvement to the BiLSTM model which encodes each suffix and prefix of a sequence of tokens in both forward and reverse directions. We call our model Suffix BiLSTM or SuBiLSTM. Using an extensive set of experiments, we demonstrate that using SuBiLSTM instead of a BiLSTM in existing base models leads to improvements in performance in learning general sentence representations, text classification, textual entailment and named entity recognition. We achieve new state-of-the-art results for fine-grained sentiment classification and question classification using SuBiLSTM.


title: 11K Hands: Gender recognition and biometric identification using a large dataset of hand images

abstract: The human hand possesses distinctive features which can reveal gender information. In addition, the hand is considered one of the primary biometric traits used to identify a person. In this work, we propose a large dataset of human hand images with detailed ground-truth information for gender recognition and biometric identification. The proposed dataset comprises of 11,076 hand images (dorsal and palmar sides), from 190 subjects of different ages under the same lighting conditions. Using this dataset, a convolutional neural network (CNN) can be trained effectively for the gender recognition task. Based on this, we design a two-stream CNN to tackle the gender recognition problem. This trained model is then used as a feature extractor to feed a set of support vector machine classifiers for the biometric identification task. To facilitate access to the proposed dataset and replication of our experiments, the dataset, trained CNN models, and Matlab source code are available at (this https URL).


Quantum digital signatures


title: A meta-analysis of the effect of cognitive bias modification on anxiety and depression.

abstract: Cognitive biases have been theorized to play a critical role in the onset and maintenance of anxiety and depression. Cognitive bias modification (CBM), an experimental paradigm that uses training to induce maladaptive or adaptive cognitive biases, was developed to test these causal models. Although CBM has generated considerable interest in the past decade, both as an experimental paradigm and as a form of treatment, there have been no quantitative reviews of the effect of CBM on anxiety and depression. This meta-analysis of 45 studies (2,591 participants) assessed the effect of CBM on cognitive biases and on anxiety and depression. CBM had a medium effect on biases (g = 0.49) that was stronger for interpretation (g = 0.81) than for attention (g = 0.29) biases. CBM further had a small effect on anxiety and depression (g = 0.13), although this effect was reliable only when symptoms were assessed after participants experienced a stressor (g = 0.23). When anxiety and depression were examined separately, CBM significantly modified anxiety but not depression. There was a nonsignificant trend toward a larger effect for studies including multiple training sessions. These findings are broadly consistent with cognitive theories of anxiety and depression that propose an interactive effect of cognitive biases and stressors on these symptoms. However, the small effect sizes observed here suggest that this effect may be more modest than previously believed.


title: Evaluating the Effectiveness of Current Anti-ROP Defenses

abstract: Recently, many defenses against the offensive technique of return-oriented programming (ROP) have been developed. Prominently among them are kBouncer, ROPecker, and ROPGuard which all target legacy binary software while requiring no or only minimal binary code rewriting.


A Comparative Study on Shape Retrieval Using Fourier Descriptors with Different Shape Signatures


title: Synthesizing parallel graph programs via automated planning

abstract: We describe a system that uses automated planning to synthesize correct and efficient parallel graph programs from high-level algorithmic specifications. Automated planning allows us to use constraints to declaratively encode program transformations such as scheduling, implementation selection, and insertion of synchronization. Each plan emitted by the planner satisfies all constraints simultaneously, and corresponds to a composition of these transformations. In this way, we obtain an integrated compilation approach for a very challenging problem domain. We have used this system to synthesize parallel programs for four graph problems: triangle counting, maximal independent set computation, preflow-push maxflow, and connected components. Experiments on a variety of inputs show that the synthesized implementations perform competitively with hand-written, highly-tuned code.


title: Output voltage regulation of IPOS modular dual active bridge DC/DC converters using sliding mode control

abstract: In this paper, a novel output voltage control scheme for the input-parallel output-series (IPOS) modular dual active bridge (DAB) DC/DC converter is proposed, based on the hierarchical sliding mode control (HSMC). The proposed controller provides a balancing control of output voltages of individual modules as well as the overall DC bus voltage regulation even if there is a parameter mismatch. Also, the control performance is less sensitive to parameter perturbation by applying the sliding mode control theory. The validity of the proposed control method is verified by experimental results.


title: Cooperative Control to Enhance the Frequency Stability of

abstract: The issue of frequency stability of microgrids under islanded operation mode and mode transfer has attracted particular attention recently. In this paper, a cooperative frequency control method, which consists of a microgrid central control (MGCC) and microgrid local control (MGLC), is proposed to achieve a seamless transfer from grid-connected to islanded mode, and hence increase the frequency stability of islanded microgrids during both primary and secondary frequency control. A power deficiency prediction and distribution method is proposed in MGCC to effectively distribute and utilize the power and loads, and accomplish the cooperative control of all microgrid units. With regards to MGLC, a Hopfield fuzzy neural network control (HFNNC) is applied to make the corresponding frequency control of DFIG-SMES more adaptive. Meanwhile a state of capacity (SOC) control is utilized in battery energy storage (BES) to extend battery life. Simulation results indicate that the proposed frequency control approach can maintain the frequency stability of islanded microgrids even in emergency conditions.


title: Dimensions of Apathy in Parkinson's Disease: Exploratory Factor Analysis of the Apathy Scale

abstract: Apathy, an intrinsic disorder of motivation, can manifest in cognitive, affective, and behavioral domains. Recent attempts to formalize diagnostic criteria for apathy have advocated this triadic structure. The Apathy Scale (AS) is the only recommended measure for assessing apathy in Parkinson’s disease (PD) by a Movement Disorders task force. We tested the hypothesis that the AS distinctly measures the three proposed domains of apathy in PD. Participants were 226 non-demented PD patients. Apathy Scale data were examined with exploratory factor analysis (EFA). The three factor solution resembled the triadic structure of cognitive, behavioral, and affective domains; however, adjustments to the AS may be appropriate.


title: Topologies of single-phase inverters for small distributed power generators: an overview

abstract: This paper presents an overview of single-phase inverters developed for small distributed power generators. The functions of inverters in distributed power generation (DG) systems include dc-ac conversion, output power quality assurance, various protection mechanisms, and system controls. Unique requirements for small distributed power generation systems include low cost, high efficiency and tolerance for an extremely wide range of input voltage variations. These requirements have driven the inverter development toward simpler topologies and structures, lower component counts, and tighter modular design. Both single-stage and multiple-stage inverters have been developed for power conversion in DG systems. Single-stage inverters offer simple structure and low cost, but suffer from a limited range of input voltage variations and are often characterized by compromised system performance. On the other hand, multiple-stage inverters accept a wide range of input voltage variations, but suffer from high cost, complicated structure and low efficiency. Various circuit topologies are presented, compared, and evaluated against the requirements of power decoupling and dual-grounding, the capabilities for grid-connected or/and stand-alone operations, and specific DG applications in this paper, along with the identification of recent development trends of single-phase inverters for distributed power generators.


title: Music recommendation via heterogeneous information graph embedding

abstract: Traditional music recommendation techniques suffer from limited performance due to the sparsity of user-music interaction data, which is addressed by incorporating auxiliary information. In this paper, we study the problem of personalized music recommendation that takes different kinds of auxiliary information into consideration. To achieve this goal, a Heterogeneous Information Graph (HIG) is first constructed to encode different kinds of heterogeneous information, including the interactions between users and music pieces, music playing sequences, and the metadata of music pieces. Based on HIG, a Heterogeneous Information Graph Embedding method (HIGE) is proposed to learn the latent low-dimensional representations of music pieces. Then, we further develop a context-aware music recommendation method. Extensive experiments have been conducted on real-world datasets to compare the proposed method with other state-of-the-art recommendation methods. The results demonstrate that the proposed method significantly outperforms those baselines, especially on sparse datasets.


abstract: Despite deep reinforcement learning has recently achieved great successes, however in multiagent environments, a number of challenges still remain. Multiagent reinforcement learning (MARL) is commonly considered to suffer from the problem of non-stationary environments and exponentially increasing policy space. It would be even more challenging to learn effective policies in circumstances where the rewards are sparse and delayed over long trajectories. In this paper, we study Hierarchical Deep Multiagent Reinforcement Learning (hierarchical deep MARL) in cooperative multiagent problems with sparse and delayed rewards, where efficient multiagent learning methods are desperately needed. We decompose the original MARL problem into hierarchies and investigate how effective policies can be learned hierarchically in synchronous/asynchronous hierarchical MARL frameworks. Several hierarchical deep MARL architectures, i.e., Ind-hDQN, hCom and hQmix, are introduced for different learning paradigms. Moreover, to alleviate the issues of sparse experiences in high-level learning and non-stationarity in multiagent settings, we propose a new experience replay mechanism, named as Augmented Concurrent Experience Replay (ACER). We empirically demonstrate the effects and efficiency of our approaches in several classic Multiagent Trash Collection tasks, as well as in an extremely challenging team sports game, i.e., Fever Basketball Defense.


Data Oblivious ISA Extensions for Side Channel-Resistant and High Performance Computing


title: The straight line, the catenary, the brachistochrone, the circle, and Fermat

abstract: This paper shows that the well-known curve optimization problems which lead to the straight line, the catenary curve, the brachistochrone, and the circle, can all be handled using a unified formalism. Furthermore, from the general differential equation fulfilled by these geodesics, we can guess additional functions and the required metric. The parabola, for example, is a geodesic under a metric guessed in this way. Numerical solutions are found for the curves corresponding to geodesics in the various metrics using a ray-tracing approach based on Fermat's principle.


State-Space Constrained Model Predictive Control.


title: Impedance-Based Stability Criterion for Grid-Connected Inverters

abstract: Grid-connected inverters are known to become unstable when the grid impedance is high. Existing approaches to analyzing such instability are based on inverter control models that account for the grid impedance and the coupling with other grid-connected inverters. A new method to determine inverter-grid system stability using only the inverter output impedance and the grid impedance is developed in this paper. It will be shown that a grid-connected inverter will remain stable if the ratio between the grid impedance and the inverter output impedance satisfies the Nyquist stability criterion. This new impedance-based stability criterion is a generalization to the existing stability criterion for voltage-source systems, and can be applied to all current-source systems. A single-phase solar inverter is studied to demonstrate the application of the proposed method.


Metrics for Modeling Code-Switching Across Corpora


title: Comparing Postoperative Pain After Laparoscopic Cholecystectomy

abstract: To the Editor: In a recent article published by Guo et al.[1] who compared postoperative pain after traditional and single-incision laparoscopic cholecystectomy (SILC), it was showed that SILC resulted in a slightly less immediate postoperative pain, but pain scores at 24 hours, 7 days, and 1–6 months after surgery were not different between the two operations. Many things of this study were well done. The authors used a double-blinded, randomized, and controlled design. They chose a well-validated endpoint of postoperative pain assessment: Visual analog scale (VAS). They had a large number of patients with a consistent operation and attempted to control most of the known factors affecting postoperative pain following cholecystectomy, such as age, gender, body mass index, type of surgery, and surgical complications. Furthermore, they openly discussed the limitations of their work. All of these are strengths in the study design. We can learn from their example. In our view, however, there were several important issues in this study that were not well addressed. ::: ::: First, the study subjects were patients with symptomatic gallstones or polyps documented by imaging. Actually, chronic abdominal pain is highly prevalent among these patients and can often cause psychological disorders, such as neuroticism, depression, and anxiety. It was unclear whether preoperative pain intensity, duration of pain, and pain-related psychological morbidities were comparable between groups. It has been shown that a high preoperative gastrointestinal quality of life index score, episodic pain, and duration of pain of 1 year or less are associated with the postoperative absence of pain in patients undergoing cholecystectomy for uncomplicated symptomatic cholecystolithiasis.[2] Furthermore, preoperative neuroticism, depression, and anxiety can significantly affect the postoperative pain severity determined by VAS.[2,3] ::: ::: Second, the readers were not provided with detail of anesthesia and perioperative managements. The available literature indicates that preoperative single-dose dexamethasone, wound infiltration of local anesthetic, subhepatic infiltration of local anesthetic, and combined wound and intraperitoneal use of local anesthetic can significantly reduce pain scores after laparoscopic cholecystectomy.[4,5] For laparoscopic cholecystectomy, spinal anesthesia is associated with less early postoperative pain and lower incidence of postoperative complications than general anesthesia.[6] Moreover, we were very interested in knowing whether dose of opioid and analgesic drugs used during anesthesia were comparable between groups. When early postoperative pain between groups is compared, standardization of intraoperative use of opioid and analgesic drugs should be a crucial component of study design. For example, in patients scheduled for cholecystectomy under general anesthesia, intraoperative low-dose ketamine infusion provides good postoperative analgesia and reduces the need of opioid analgesics.[7] Therefore, in the absence of comparisons of opioid and analgesic drug dosages during anesthesia, the primary findings about early postoperative pain and their subsequent conclusions must be interpreted with caution, as they may have been obtained using incomplete methodology. ::: ::: Third, the authors did not specify what VAS was used in this study. In clinical practice, VASs at rest and during movement were often used for pain assessment after chest and abdominal operations, and they can provide different information for postoperative pain management. To ensure a good quality of postoperative recovery, a fast recovery of normal activity and a high patient satisfaction, it is recommended that postoperative VAS during movement should be maintained at 3 or less. ::: ::: Finally, a number of patients requiring nonnarcotic analgesic loxoprofen sodium for postoperative analgesia was not significantly different between groups, but the readers were not provided with the aim of postoperative analgesia. Thus, it was unclear how investigators determined postoperative analgesic requirements of a patient. Moreover, the authors calculated the sample size only based on mean VASs obtained in their initial study but did not take other variables of interest into account. Thus, we cannot exclude the possibility that this study is not powered to show a difference in postoperative analgesic requirement between groups. ::: ::: We argue that addressing the above factors would further clarify the transparency of this study and improve interpretation of their findings. ::: ::: Financial support and sponsorship ::: Nil. ::: ::: ::: Conflicts of interest ::: There are no conflicts of interest.


title: Entity Matching in Online Social Networks

abstract: In recent years, Online Social Networks (OSNs) have essentially become an integral part of our daily lives. There are hundreds of OSNs, each with its own focus and offers for particular services and functionalities. To take advantage of the full range of services and functionalities that OSNs offer, users often create several accounts on various OSNs using the same or different personal information. Retrieving all available data about an individual from several OSNs and merging it into one profile can be useful for many purposes. In this paper, we present a method for solving the Entity Resolution (ER), problem for matching user profiles across multiple OSNs. Our algorithm is able to match two user profiles from two different OSNs based on machine learning techniques, which uses features extracted from each one of the user profiles. Using supervised learning techniques and extracted features, we constructed different classifiers, which were then trained and used to rank the probability that two user profiles from two different OSNs belong to the same individual. These classifiers utilized 27 features of mainly three types: name based features (i.e., the Soundex value of two names), general user info based features (i.e., the cosine similarity between two user profiles), and social network topological based features (i.e., the number of mutual friends between two users' friends list). This experimental study uses real-life data collected from two popular OSNs, Facebook and Xing. The proposed algorithm was evaluated and its classification performance measured by AUC was 0.982 in identifying user profiles across two OSNs.


title: Are You Ready? How Health Professionals Can Comprehensively Conceptualize Readiness for Change

abstract: One important factor influencing the successful implementation of system-wide change is initial readiness. Readiness is defined as the degree to which those involved are individually and collectively primed, motivated, and technically capable of executing the change. We present a conceptual framework that highlights three broad areas to be considered if health-care professionals are to comprehensively evaluate readiness that includes psychological factors (i.e., characteristics of those being asked to change), structural factors (i.e., circumstances under which the change is occurring) as well as the level of analysis (i.e., individual and organizational levels). We also describe more specific dimensions within each of these broad categories that have both empirical and theoretical support, presenting several valid and reliable survey instruments that measure key dimensions of readiness quantitatively.


title: Measuring and modeling the perception of natural and unconstrained gaze in humans and machines

abstract: Humans are remarkably adept at interpreting the gaze direction of other individuals in their surroundings. This skill is at the core of the ability to engage in joint visual attention, which is essential for establishing social interactions. How accurate are humans in determining the gaze direction of others in lifelike scenes, when they can move their heads and eyes freely, and what are the sources of information for the underlying perceptual processes? These questions pose a challenge from both empirical and computational perspectives, due to the complexity of the visual input in real-life situations. Here we measure empirically human accuracy in perceiving the gaze direction of others in lifelike scenes, and study computationally the sources of information and representations underlying this cognitive capacity. We show that humans perform better in face-to-face conditions compared with recorded conditions, and that this advantage is not due to the availability of input dynamics. We further show that humans are still performing well when only the eyes-region is visible, rather than the whole face. We develop a computational model, which replicates the pattern of human performance, including the finding that the eyes-region contains on its own, the required information for estimating both head orientation and direction of gaze. Consistent with neurophysiological findings on task-specific face regions in the brain, the learned computational representations reproduce perceptual effects such as the Wollaston illusion, when trained to estimate direction of gaze, but not when trained to recognize objects or faces.


title: Effect of gate pulse variation on the performance of fifteen-level cascaded H-bridge voltage source inverter

abstract: This paper proposes a fifteen level H-Bridge cascaded multilevel inverter with fundamental frequency switching for low power applications such as solar powered power supplies, battery powered standby power supplies. The effect of the variation of gate pulse on the performance of the inverter for different conditions of gate pulse variation is studied and simulation results are presented. Experimental implementation of gate pulse generation on a low cost FPGA is presented. The results presented in this paper are useful in designing the inverter, determining the control range of the inverter for the reliable and stable closed loop and fault tolerant operation of the inverter.


title: Metric-Promoted Siamese Network for Gender Classification

abstract: Gender classification is a fundamental and important application in computer vision, and it has become a research hotspot. Real-world applications require gender classification in unconstrained conditions where traditional methods are not appropriate. This paper proposes a Deep Convolutional Neural Network for feature extraction together with fully-connected layers for metric learning. A Siamese network is built for similarity measuring to promote the performance of classification. Extensive experiments on several databases demonstrate that a significant improvement can be obtained for gender classification tasks in both constrained and unconstrained conditions.


title: Bayesian estimation of possible causal direction in the presence of latent confounders using a linear non-Gaussian acyclic structural equation model with individual-specific effects

abstract: We consider learning the possible causal direction of two observed variables in the presence of latent confounding variables. Several existing methods have been shown to consistently estimate causal direction assuming linear or some type of nonlinear relationship and no latent confounders. However, the estimation results could be distorted if either assumption is actually violated. In this paper, we first propose a new linear non-Gaussian acyclic structural equation model with individual-specific effects that allows latent confounders to be considered. We then propose an empirical Bayesian approach for estimating possible causal direction using the new model. We demonstrate the effectiveness of our method using artificial and real-world data.


title: A Distributed Architecture for Autonomous Robots

abstract: As autonomous robots become more complex in their behavior, more sophisticated software architectures are required to support the ever more sophisticated robotics software. These software architectures must support complex behaviors involving adaptation and learning, implemented, in particular, by neural networks. We present in this paper a neural based schema [2] software architecture for the development and execution of autonomous robots in both simulated and real worlds. This architecture has been developed in the context of adaptive robotic agents, ecological robots [6], cooperating and competing with each other in adapting to their environment. The architecture is the result of integrating a number of development and execution systems: NSL, a neural simulation language; ASL, an abstract schema language; and MissionLab, a schema-based mission-oriented simulation and robot system. This work contributes to modeling in Brain Theory (BT) and Cognitive Psychology, with applications in Distributed Artificial Intelligence (DAI), Autonomous Agents and Robotics. Areas: Robotics, Agent-oriented programming, Neural Nets


title: HyPer4: Using P4 to Virtualize the Programmable Data Plane

abstract: Through virtualization, single physical data planes can logically support multiple networking contexts. We propose HyPer4 as a portable virtualization solution. HyPer4 provides a general purpose program, written in the P4 dataplane programming language, that may be dynamically configured to adopt behavior that is functionally equivalent to other P4 programs. HyPer4 extends, through software, the following features to diverse P4-capable devices: the ability to logically store multiple programs and either run them in parallel (network slicing) or as hot-swappable snapshots; and virtual networking between programs (supporting program composition or multi-tenant service interaction). HyPer4 permits modifying the set of programs, as well as the virtual network connecting them, at runtime, without disrupting currently active programs. We show that realistic ASICs-based hardware would be capable of running HyPer4 today.


title: High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

abstract: We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic. In this work, we generate 2048x1024 visually appealing results with a novel adversarial loss, as well as new multi-scale generator and discriminator architectures. Furthermore, we extend our framework to interactive visual manipulation with two additional features. First, we incorporate object instance segmentation information, which enables object manipulations such as removing/adding objects and changing the object category. Second, we propose a method to generate diverse results given the same input, allowing users to edit the object appearance interactively. Human opinion studies demonstrate that our method significantly outperforms existing methods, advancing both the quality and the resolution of deep image synthesis and editing.


title: Deep Contextual Recurrent Residual Networks for Scene Labeling

abstract: Abstract Designed as extremely deep architectures, deep residual networks which provide a rich visual representation and offer robust convergence behaviors have recently achieved exceptional performance in numerous computer vision problems. Being directly applied to a scene labeling problem, however, they were limited to capture long-range contextual dependence, which is a critical aspect. To address this issue, we propose a novel approach, Contextual Recurrent Residual Networks (CRRN) which is able to simultaneously handle rich visual representation learning and long-range context modeling within a fully end-to-end deep network. Furthermore, our proposed end-to-end CRRN is completely trained from scratch, without using any pre-trained models in contrast to most existing methods usually fine-tuned from the state-of-the-art pre-trained models, e.g. VGG-16, ResNet, etc. The experiments are conducted on four challenging scene labeling datasets, i.e. SiftFlow, CamVid, Stanford background and SUN datasets, and compared against various state-of-the-art scene labeling methods.


title: Disentangling by Partitioning: A Representation Learning Framework for Multimodal Sensory Data

abstract: Multimodal sensory data resembles the form of information perceived by humans for learning, and are easy to obtain in large quantities. Compared to unimodal data, synchronization of concepts between modalities in such data provides supervision for disentangling the underlying explanatory factors of each modality. Previous work leveraging multimodal data has mainly focused on retaining only the modality-invariant factors while discarding the rest. In this paper, we present a partitioned variational autoencoder (PVAE) and several training objectives to learn disentangled representations, which encode not only the shared factors, but also modality-dependent ones, into separate latent variables. Specifically, PVAE integrates a variational inference framework and a multimodal generative model that partitions the explanatory factors and conditions only on the relevant subset of them for generation. We evaluate our model on two parallel speech/image datasets, and demonstrate its ability to learn disentangled representations by qualitatively exploring within-modality and cross-modality conditional generation with semantics and styles specified by examples. For quantitative analysis, we evaluate the classification accuracy of automatically discovered semantic units. Our PVAE can achieve over 99% accuracy on both modalities.


title: Metatrace: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning Control

abstract: Reinforcement learning (RL) has had many successes in both "deep" and "shallow" settings. In both cases, significant hyperparameter tuning is often required to achieve good performance. Furthermore, when nonlinear function approximation is used, non-stationarity in the state representation can lead to learning instability. A variety of techniques exist to combat this --- most notably large experience replay buffers or the use of multiple parallel actors. These techniques come at the cost of moving away from the online RL problem as it is traditionally formulated (i.e., a single agent learning online without maintaining a large database of training examples). Meta-learning can potentially help with both these issues by tuning hyperparameters online and allowing the algorithm to more robustly adjust to non-stationarity in a problem. This paper applies meta-gradient descent to derive a set of step-size tuning algorithms specifically for online RL control with eligibility traces. Our novel technique, Metatrace, makes use of an eligibility trace analogous to methods like $TD(\lambda)$. We explore tuning both a single scalar step-size and a separate step-size for each learned parameter. We evaluate Metatrace first for control with linear function approximation in the classic mountain car problem and then in a noisy, non-stationary version. Finally, we apply Metatrace for control with nonlinear function approximation in 5 games in the Arcade Learning Environment where we explore how it impacts learning speed and robustness to initial step-size choice. Results show that the meta-step-size parameter of Metatrace is easy to set, Metatrace can speed learning, and Metatrace can allow an RL algorithm to deal with non-stationarity in the learning task.


title: Perceptions of Alienation Among Students with Learning Disabilities in Inclusive and Resource Settings

abstract: This survey study was designed to investigate the perceptions of alienation among senior high school students identified with learning disabilities in fully-inclusive versus resource room placements. Seventy-six students from a rural high school (mean CA = 16.59; IQ = 91.76) were assessed with the Dean Alienation Scale to determine their perceptions of isolation, normlessness, and powerlessness in a school context. Results indicate that students with learning disabilities who received pullout academic support daily for 45 minutes reported significantly higher levels of alienation than students who were fully included in the regular classroom. Findings are discussed within the context of procedures educators may implement to reduce the perception of alienation among learning disabled students receiving resource services.


Automatic Car Number Plate Recognition System for Authorization


title: Support vector machine techniques for nonlinear equalization

abstract: The emerging machine learning technique called support vector machines is proposed as a method for performing nonlinear equalization in communication systems. The support vector machine has the advantage that a smaller number of parameters for the model can be identified in a manner that does not require the extent of prior information or heuristic assumptions that some previous techniques require. Furthermore, the optimization method of a support vector machine is quadratic programming, which is a well-studied and understood mathematical programming technique. Support vector machine simulations are carried out on nonlinear problems previously studied by other researchers using neural networks. This allows initial comparison against other techniques to determine the feasibility of using the proposed method for nonlinear detection. Results show that support vector machines perform as well as neural networks on the nonlinear problems investigated. A method is then proposed to introduce decision feedback processing to support vector machines to address the fact that intersymbol interference (ISI) data generates input vectors having temporal correlation, whereas a standard support vector machine assumes independent input vectors. Presenting the problem from the viewpoint of the pattern space illustrates the utility of a bank of support vector machines. This approach yields a nonlinear processing method that is somewhat different than the nonlinear decision feedback method whereby the linear feedback filter of the decision feedback equalizer is replaced by a Volterra filter. A simulation using a linear system shows that the proposed method performs equally to a conventional decision feedback equalizer for this problem.


title: The Game Imitation: Deep Supervised Convolutional Networks for Quick Video Game AI

abstract: We present a vision-only model for gaming AI which uses a late integration deep convolutional network architecture trained in a purely supervised imitation learning context. Although state-of-the-art deep learning models for video game tasks generally rely on more complex methods such as deep-Q learning, we show that a supervised model which requires substantially fewer resources and training time can already perform well at human reaction speeds on the N64 classic game Super Smash Bros. We frame our learning task as a 30-class classification problem, and our CNN model achieves 80% top-1 and 95% top-3 validation accuracy. With slight test-time fine-tuning, our model is also competitive during live simulation with the highest-level AI built into the game. We will further show evidence through network visualizations that the network is successfully leveraging temporal information during inference to aid in decision making. Our work demonstrates that supervised CNN models can provide good performance in challenging policy prediction tasks while being significantly simpler and more lightweight than alternatives.


title: Device-Free Human Activity Recognition Using Commercial WiFi Devices

abstract: Since human bodies are good reflectors of wireless signals, human activities can be recognized by monitoring changes in WiFi signals. However, existing WiFi-based human activity recognition systems do not build models that can quantify the correlation between WiFi signal dynamics and human activities. In this paper, we propose a Channel State Information (CSI)-based human Activity Recognition and Monitoring system (CARM). CARM is based on two theoretical models. First, we propose a CSI-speed model that quantifies the relation between CSI dynamics and human movement speeds. Second, we propose a CSI-activity model that quantifies the relation between human movement speeds and human activities. Based on these two models, we implemented the CARM on commercial WiFi devices. Our experimental results show that the CARM achieves recognition accuracy of 96% and is robust to environmental changes.


title: Automated Insider Threat Detection System Using User and Role-Based Profile Assessment

abstract: Organizations are experiencing an ever-growing concern of how to identify and defend against insider threats. Those who have authorized access to sensitive organizational data are placed in a position of power that could well be abused and could cause significant damage to an organization. This could range from financial theft and intellectual property theft to the destruction of property and business reputation. Traditional intrusion detection systems are neither designed nor capable of identifying those who act maliciously within an organization. In this paper, we describe an automated system that is capable of detecting insider threats within an organization. We define a tree-structure profiling approach that incorporates the details of activities conducted by each user and each job role and then use this to obtain a consistent representation of features that provide a rich description of the user's behavior. Deviation can be assessed based on the amount of variance that each user exhibits across multiple attributes, compared against their peers. We have performed experimentation using ten synthetic data-driven scenarios and found that the system can identify anomalous behavior that may be indicative of a potential threat. We also show how our detection system can be combined with visual analytics tools to support further investigation by an analyst.


title: Complex Fully Convolutional Neural Networks for MR Image Reconstruction

abstract: Undersampling the k-space data is widely adopted for acceleration of Magnetic Resonance Imaging (MRI). Current deep learning based approaches for supervised learning of MRI image reconstruction employ real-valued operations and representations by treating complex valued k-space/spatial-space as real values. In this paper, we propose complex dense fully convolutional neural network ($\mathbb{C}$DFNet) for learning to de-alias the reconstruction artifacts within undersampled MRI images. We fashioned a densely-connected fully convolutional block tailored for complex-valued inputs by introducing dedicated layers such as complex convolution, batch normalization, non-linearities etc. $\mathbb{C}$DFNet leverages the inherently complex-valued nature of input k-space and learns richer representations. We demonstrate improved perceptual quality and recovery of anatomical structures through $\mathbb{C}$DFNet in contrast to its real-valued counterparts.


title: "Factual"or"Emotional": Stylized Image Captioning with Adaptive Learning and Attention

abstract: Generating stylized captions for an image is an emerging topic in image captioning. Given an image as input, it requires the system to generate a caption that has a specific style (e.g., humorous, romantic, positive, and negative) while describing the image content semantically accurately. In this paper, we propose a novel stylized image captioning model that effectively takes both requirements into consideration. To this end, we first devise a new variant of LSTM, named style-factual LSTM, as the building block of our model. It uses two groups of matrices to capture the factual and stylized knowledge, respectively, and automatically learns the word-level weights of the two groups based on previous context. In addition, when we train the model to capture stylized elements, we propose an adaptive learning approach based on a reference factual model, it provides factual knowledge to the model as the model learns from stylized caption labels, and can adaptively compute how much information to supply at each time step. We evaluate our model on two stylized image captioning datasets, which contain humorous/romantic captions and positive/negative captions, respectively. Experiments shows that our proposed model outperforms the state-of-the-art approaches, without using extra ground truth supervision.


title: Simultaneous Authentication of Equals: A Secure, Password-Based Key Exchange for Mesh Networks

abstract: We propose a simple protocol for authentication using only a password. The result of the protocol is a cryptographically strong shared secret for securing other data - e.g. network communication. SAE is resistant to passive attack, active attack, and dictionary attack. It provides a secure alternative to using certificates or when a centralized authority is not available. It is a peer-to-peer protocol, has no asymmetry, and supports simultaneous initiation. It is therefore well-suited for use in mesh networks. It supports the ability to tradeoff speed for strength of the resulting shared key. SAE has been implemented for 802.11-based mesh networks and can easily be adapted to other wireless mesh technology.


title: Computing at the Mobile Edge: Designing Elastic Android Applications for Computation Offloading

abstract: Current centralized cloud datacenters provide scalable computation- and storage resources in a virtualized infrastructure and employ a use-based "pay-as-you-go" model. But current mobile devices and their resource-hungry applications (e.g., Speech-or face recognition) demand for these resources on the spot, though a mobile device's intrinsic characteristic is its limited availability of resources (e.g., CPU, storage, bandwidth, energy). Thus, mobile cloud computing (MCC) was introduced to overcome these limitations by transparently making accessible the apparently infinite cloud resources to the mobile devices and by allowing mobile applications to (elastically) expand into the cloud. However, MCC often relies on a stable and fast connection to the mobile devices' surrogate in the cloud, which is a rare case in mobile scenarios. Moreover, the increased latency and the limited bandwidth prevent the use of real-time applications like, e.g. Cloud gaming. Instead, mobile edge computing (MEC) or fog computing tries to provide the necessary resources at the logical edge of the network by including infrastructure components to create ad-hoc mobile clouds. However, this approach requires the replication and management of the applications' business logic in an untrusted, unreliable and constantly changing environment. Consequently, this paper presents a novel approach to allow mobile app developers to easily benefit from the features of MEC. In particular, we present a programming model and framework that directly fit the common app developers' mindset to design elastic and scalable edge-based mobile applications.


title: A routing strategy for vehicular ad hoc networks in city environments

abstract: Routing of data in a vehicular ad hoc network is a challenging task due to the high dynamics of such a network. Recently, it was shown for the case of highway traffic that position-based routing approaches can very well deal with the high mobility of network nodes. However, baseline position-based routing has difficulties to handle two-dimensional scenarios with obstacles (buildings) and voids as it is the case for city scenarios. In this paper we analyze a position-based routing approach that makes use of the navigational systems of vehicles. By means of simulation we compare this approach with non-position-based ad hoc routing strategies (dynamic source routing and ad-hoc on-demand distance vector routing). The simulation makes use of highly realistic vehicle movement patterns derived from Daimler-Chrysler's Videlio traffic simulator. While DSR's performance is limited due to problems with scalability and handling mobility, both AODV and the position-based approach show good performances with the position-based approach outperforming AODV.


title: Modified Microstrip Franklin Array Antenna for Automotive Short-Range Radar Application in Blind Spot Information System

abstract: A modified microstrip Franklin array antenna (MFAA) is proposed for short-range radar applications in vehicle blind spot information systems (BLIS). It is shown that the radiating performance [i.e., absolute gain value and half-power beamwidth (HPBW) angle] can be improved by increasing the number of radiators in the MFAA structure and assigning appropriate values to the antenna geometry parameters. The MFAA possesses a high absolute gain value (>10 dB), good directivity (HPBW 80°) in the H-plane at an operating frequency of 24 GHz. Moreover, the 10-dB impedance bandwidth of the proposed antenna is around 250 MHz. The MFAA is, thus, an ideal candidate for automotive BLIS applications.


title: Distance-Based Image Classification: Generalizing to New Classes at Near-Zero Cost

abstract: We study large-scale image classification methods that can incorporate new classes and training images continuously over time at negligible cost. To this end, we consider two distance-based classifiers, the k-nearest neighbor (k-NN) and nearest class mean (NCM) classifiers, and introduce a new metric learning approach for the latter. We also introduce an extension of the NCM classifier to allow for richer class representations. Experiments on the ImageNet 2010 challenge dataset, which contains over 106 training images of 1,000 classes, show that, surprisingly, the NCM classifier compares favorably to the more flexible k-NN classifier. Moreover, the NCM performance is comparable to that of linear SVMs which obtain current state-of-the-art performance. Experimentally, we study the generalization performance to classes that were not used to learn the metrics. Using a metric learned on 1,000 classes, we show results for the ImageNet-10K dataset which contains 10,000 classes, and obtain performance that is competitive with the current state-of-the-art while being orders of magnitude faster. Furthermore, we show how a zero-shot class prior based on the ImageNet hierarchy can improve performance when few training images are available.


title: Latency-aware composition of Virtual Functions in 5G

abstract: The adoption of the virtualization paradigm in both computing and networking domains portends a landscape of heterogeneous service capabilities and resources pervasively distributed and interconnected and deeply integrated through the 5G network infrastructure. In this service ecosystem, dynamic service demand can be flexibly and elastically accomplished by composing heterogeneous services provisioned over a distributed and virtualized resource infrastructure. Indeed, with the term Virtual Functions we refer to virtual computing as well as network service capabilities (e.g., routers and middlebox functions provided as Virtual Network Functions). In order to cope with the increasingly resource intensive demand, these virtual functions will be deployed in distributed clusters of small-scale datacenters typically located in current exchanges at the network edge and will supplement those deployed in traditional large cloud datacenters. In this work we formulate the problem of composing, computing and networking Virtual Functions to select those nodes along the path that minimizes the overall latency (i.e. network and processing latency) in the above mentioned scenario. The optimization problem is formulated as a Resource Constrained Shortest Path problem on an auxiliary layered graph accordingly defined. The layered structure of the graph ensures that the order of VFs specified in the request is preserved. Additional constraints can be also taken into account in the graph construction phase. Finally, we provide a use case preliminary evaluation of the proposed model.


title: DNA methylation in health and disease

abstract: DNA methylation has recently moved to centre stage in the aetiology of human neurodevelopmental syndromes such as the fragile X, ICF and Rett syndromes. These diseases result from the misregulation of genes that occurs with the loss of appropriate epigenetic controls during neuronal development. Recent advances have connected DNA methylation to chromatin-remodelling enzymes, and understanding this link will be central to the design of new therapeutic tools. Summary:


title: Korat: automated testing based on Java predicates

abstract: This paper presents Korat, a novel framework for automated testing of Java programs. Given a formal specification for a method, Korat uses the method precondition to automatically generate all (nonisomorphic) test cases up to a given small size. Korat then executes the method on each test case, and uses the method postcondition as a test oracle to check the correctness of each output.To generate test cases for a method, Korat constructs a Java predicate (i.e., a method that returns a boolean) from the method's pre-condition. The heart of Korat is a technique for automatic test case generation: given a predicate and a bound on the size of its inputs, Korat generates all (nonisomorphic) inputs for which the predicate returns true. Korat exhaustively explores the bounded input space of the predicate but does so efficiently by monitoring the predicate's executions and pruning large portions of the search space.This paper illustrates the use of Korat for testing several data structures, including some from the Java Collections Framework. The experimental results show that it is feasible to generate test cases from Java predicates, even when the search space for inputs is very large. This paper also compares Korat with a testing framework based on declarative specifications. Contrary to our initial expectation, the experiments show that Korat generates test cases much faster than the declarative framework.


title: An EMG-Based Control for an Upper-Limb Power-Assist Exoskeleton Robot

abstract: Many kinds of power-assist robots have been developed in order to assist self-rehabilitation and/or daily life motions of physically weak persons. Several kinds of control methods have been proposed to control the power-assist robots according to user's motion intention. In this paper, an electromyogram (EMG)-based impedance control method for an upper-limb power-assist exoskeleton robot is proposed to control the robot in accordance with the user's motion intention. The proposed method is simple, easy to design, humanlike, and adaptable to any user. A neurofuzzy matrix modifier is applied to make the controller adaptable to any users. Not only the characteristics of EMG signals but also the characteristics of human body are taken into account in the proposed method. The effectiveness of the proposed method was evaluated by the experiments.


title: Constructing deservingness: federal welfare reform, supplemental security income, and elderly immigrants.

abstract: On August 22, 1996, President Clinton signed the welfare reform law that ended eligibility for all immigrants to federal means tested entitlements. Poor elderly immigrants on Supplemental Security Income were specifically targeted. This article documents how the print media responded to these policy changes. The following are the major research questions: (1) How were older immigrants on Supplemental Security Income portrayed in the print media before and after federal welfare reform? (2) Who was involved in the print media coverage of older immigrants on Supplemental Security Income before and after federal welfare reform? (3) What types of statements were made by those involved in the print media coverage of older immigrants on Supplemental Security Income, before and after federal welfare reform? The approach used was an in-depth content analysis of newspaper articles from major U.S. newspapers. The findings demonstrate that older immigrants were constructed as "undeserving" in news articles prior to the passage of the federal welfare reform bill. However, after the passage of the federal welfare reform bill the coverage of older immigrants on Supplemental Security Income started to change, and older immigrants were portrayed as "deserving." In advancing aging policy for poor, vulnerable elderly, such as elderly immigrants, advocates, health and social service providers can play an influential role in bringing their voices to the print media.


title: Separation of fNIRS Signals into Functional and Systemic Components Based on Differences in Hemodynamic Modalities

abstract: In conventional functional near-infrared spectroscopy (fNIRS), systemic physiological fluctuations evoked by a body's motion and psychophysiological changes often contaminate fNIRS signals. We propose a novel method for separating functional and systemic signals based on their hemodynamic differences. Considering their physiological origins, we assumed a negative and positive linear relationship between oxy- and deoxyhemoglobin changes of functional and systemic signals, respectively. Their coefficients are determined by an empirical procedure. The proposed method was compared to conventional and multi-distance NIRS. The results were as follows: (1) Nonfunctional tasks evoked substantial oxyhemoglobin changes, and comparatively smaller deoxyhemoglobin changes, in the same direction by conventional NIRS. The systemic components estimated by the proposed method were similar to the above finding. The estimated functional components were very small. (2) During finger-tapping tasks, laterality in the functional component was more distinctive using our proposed method than that by conventional fNIRS. The systemic component indicated task-evoked changes, regardless of the finger used to perform the task. (3) For all tasks, the functional components were highly coincident with signals estimated by multi-distance NIRS. These results strongly suggest that the functional component obtained by the proposed method originates in the cerebral cortical layer. We believe that the proposed method could improve the reliability of fNIRS measurements without any modification in commercially available instruments.


title: OVERCONFIDENCE IN CURRENCY MARKETS

abstract: This paper tests the influential hypothesis, typically attributed to Friedman (1953), that irrational traders will be driven out of financial markets by trading losses. The paper’s main finding is that overconfident currency dealers are not driven out of the market. Traders with extensive experience are neither more nor less overconfident than their inexperienced colleagues. We first provide evidence that currency dealers are indeed overconfident, which is notable since they get daily trading practice and face intense financial incentives to accuracy.


title: Personality impressions based on facebook profiles

abstract: Although still largely the province of teenagers and college students, Online Social-Networking Websites (OSNWs) like MySpace and Facebook are increasingly used by people in the 2454 year age range and many employers now use them to check out prospective employees. For many people, these websites have changed the dynamics of how individuals become acquainted. Indeed, viewing an individual’s profile on MySpace or Facebook now features early in the process of getting to know others, often serving as the very first exposure. But how accurate are the impressions based on OSNW profiles? Our previous research on personal websites suggests OSNW profiles should provide more information about targets than most other sources, including actually meeting the person. Here we examine impressions based on 133 Facebook profiles, comparing them with how the targets see themselves and are seen by close acquaintances. As in our previous research, results show generally strong patterns of convergence, although the accuracy correlations vary considerably across traits. Findings are discussed with regard to the increasing role of technology-borne social information in everyday interpersonal interactions


title: Efficient Euclidean projections in linear time

abstract: We consider the problem of computing the Euclidean projection of a vector of length n onto a closed convex set including the l1 ball and the specialized polyhedra employed in (Shalev-Shwartz & Singer, 2006). These problems have played building block roles in solving several l1-norm based sparse learning problems. Existing methods have a worst-case time complexity of O(n log n). In this paper, we propose to cast both Euclidean projections as root finding problems associated with specific auxiliary functions, which can be solved in linear time via bisection. We further make use of the special structure of the auxiliary functions, and propose an improved bisection algorithm. Empirical studies demonstrate that the proposed algorithms are much more efficient than the competing ones for computing the projections.


title: An on-line learning algorithm for energy efficient delay constrained scheduling over a fading channel

abstract: In this paper, we consider the problem of energy efficient scheduling under average delay constraint for a single user fading channel. We propose a new approach for on-line implementation of the optimal packet scheduling algorithm. This approach is based on reformulating the value iteration equation by introducing a virtual state called post-decision state. The resultant value iteration equation becomes amenable to online implementation based on stochastic approximation. This approach has an advantage that an explicit knowledge of the probability distribution of the channel state as well as the arrivals is not required for the implementation. We prove that the on-line algorithm indeed converges to the optimal policy.


title: Response of sea urchin pluteus larvae (Echinodermata: Echinoidea) to reduced seawater pH: a comparison among a tropical, temperate, and a polar species

abstract: Ocean acidification, as a result of increased atmospheric CO2, is predicted to lower the pH of seawater to between pH 7.6 and 7.8 over the next 100 years. The greatest changes are expected in polar waters. Our research aimed to examine how echinoid larvae are affected by lower pH, and if effects are more pronounced in polar species. We examined the effects of lowered pH on larvae from tropical (Tripneustes gratilla), temperate (Pseudechinus huttoni, Evechinus chloroticus), and a polar species (Sterechinus neumayeri) in a series of laboratory experiments. Larvae were reared in a range of lower pH seawater (pH 6.0, 6.5, 7.0, 7.5, 7.7, 7.8 and ambient), adjusted by bubbling CO2 gas. The effect of pH on somatic and skeletal growth, calcification index, development and survival were quantified, while SEM examination of the larval skeleton provided information on the effects of seawater pH on the fine-scale skeletal morphology. Lowering pH resulted in a decrease in survival in all species, but only below pH 7.0. The size of larvae were reduced at lowered pH, but the external morphology (shape) was unaffected. Calcification of the larval skeleton was significantly reduced (13.8–36.9% lower) under lowered pH, with the exception of the Antarctic species, which showed no significant difference. SEM examination revealed a degradation of the larval skeletons of Pseudechinus and Evechinus when grown in reduced pH. Sterechinus and Tripneustes showed no apparent difference in the skeletal fine structure under lowered pH. The study confirms the need to look beyond mortality as a single endpoint when considering the effects of ocean acidification that may occur through the 21st century, and instead, look for a suite of more subtle changes, which may indirectly affect the functioning of larval stages.


title: A measurement study of insecure javascript practices on the web

abstract: JavaScript is an interpreted programming language most often used for enhancing webpage interactivity and functionality. It has powerful capabilities to interact with webpage documents and browser windows, however, it has also opened the door for many browser-based security attacks. Insecure engineering practices of using JavaScript may not directly lead to security breaches, but they can create new attack vectors and greatly increase the risks of browser-based attacks. In this article, we present the first measurement study on insecure practices of using JavaScript on the Web. Our focus is on the insecure practices of JavaScript inclusion and dynamic generation, and we examine their severity and nature on 6,805 unique websites. Our measurement results reveal that insecure JavaScript practices are common at various websites: (1) at least 66.4p of the measured websites manifest the insecure practices of including JavaScript files from external domains into the top-level documents of their webpages; (2) over 44.4p of the measured websites use the dangerous eval() function to dynamically generate and execute JavaScript code on their webpages; and (3) in JavaScript dynamic generation, using the document.write() method and the innerHTML property is much more popular than using the relatively secure technique of creating script elements via DOM methods. Our analysis indicates that safe alternatives to these insecure practices exist in common cases and ought to be adopted by website developers and administrators for reducing potential security risks.


title: Structured-light stereo: Comparative analysis and integration of structured-light and active stereo for measuring dynamic shape

abstract: Abstract In computer vision, two major active range imaging methods have been frequently employed for rapid and efficient shape recovery: (a) conventional active stereo vision and (b) conventional structured-light vision. This paper presents a comparative analysis and an integration of the two active approaches, namely, a structured-light stereo approach for the acquisition of dynamic shape. We first investigate the strengths and weaknesses of the two approaches in terms of accuracy, computational cost, field of view, depth of field, and color sensitivity. Based on this analysis, we propose a novel integrated method, the structured-light stereo, to recover dynamic shapes from a wider view with less occlusion by taking most of the benefits of the two approaches. The main idea is as follows. We first build a system composed of two cameras and a single projector (just a basic setup for conventional active stereo), and the projector projects a single “one-shot” color-stripe pattern. The next step is to estimate reliable correspondences between each camera and the projector via an accurate and efficient pattern decoding technique, and some remaining unresolved regions are explored by a stereo matching technique, which is less sensitive to object surface colors and defocus due to the projector's short depth of field, to estimate additional correspondences. We demonstrate the efficacy of the integrated method through experimental results.


title: Published on Brain Research, 2009, vol. 1253, pp. 117-128 Sentence comprehension and simulation of object temporary, canonical and stable affordances

abstract: Abstract Two experiments investigate the effects of language comprehension on affordances. Participants read a sentence composed by either an observation or an action verb (Look at/Grasp) followed by an object name. They had to decide whether the visual object following the sentence was the same as the one mentioned in the sentence. Objects graspable with either a precision or a power grip were presented in an orientation affording action (canonical) or not. Action sentences were faster than observation sentences, and power grip objects were faster than precision grip objects. Moreover, faster RTs were obtained when orientation afforded action. Results indicate that the simulation activated during language comprehension leads to the formation of a “motor prototype” of the object. This motor prototype encodes information on temporary/canonical and stable affordances (e.g., orientation, size), which can be possibly referred to different cognitive and neural systems (dorsal, ventral systems).


title: CORALINA: a universal method for the generation of gRNA libraries for CRISPR-based screening

abstract: BACKGROUND ::: The bacterial CRISPR system is fast becoming the most popular genetic and epigenetic engineering tool due to its universal applicability and adaptability. The desire to deploy CRISPR-based methods in a large variety of species and contexts has created an urgent need for the development of easy, time- and cost-effective methods enabling large-scale screening approaches. ::: ::: ::: RESULTS ::: Here we describe CORALINA (comprehensive gRNA library generation through controlled nuclease activity), a method for the generation of comprehensive gRNA libraries for CRISPR-based screens. CORALINA gRNA libraries can be derived from any source of DNA without the need of complex oligonucleotide synthesis. We show the utility of CORALINA for human and mouse genomic DNA, its reproducibility in covering the most relevant genomic features including regulatory, coding and non-coding sequences and confirm the functionality of CORALINA generated gRNAs. ::: ::: ::: CONCLUSIONS ::: The simplicity and cost-effectiveness make CORALINA suitable for any experimental system. The unprecedented sequence complexities obtainable with CORALINA libraries are a necessary pre-requisite for less biased large scale genomic and epigenomic screens.


title: Does the Tyndall Effect Describe the Blue Hue Periodically Observed in Subdermal Hyaluronic Acid Gel Placement?

abstract: BACKGROUND ::: The blue hue of skin overlying injected hyaluronic acid (HA) fillers in certain cases has been hypothesized in the literature as related to the Tyndall effect. This investigation aims to understand the relevant optical concepts and to discuss the plausibility of this assertion. ::: ::: ::: METHODS ::: Theoretic and physical aspects of relevant optical theories including the Tyndall effect, the Raleigh criterion and the Mie Solution are discussed, with simple examples. The physical properties of the system (both HA and subcutaneous tissue) are explored. Alternate concepts of dermal hue generation are discussed. ::: ::: ::: RESULTS ::: The Tyndall effect (and Rayleigh criterion) describe optical phenomenon that occur as light passes through colloidal solutions containing uniform spherical particles of sizes less than the length of a wavelength of visible light. HA fillers are complex, large, non-spherical, cross-linked hydrogels, and thus are not well characterized by these theories.Skin is a complex optical surface in which shorter wavelengths of light are selectively filtered at superficial depths. Light passing through to subdermal HA would have low blue light amplitude, minimizing what light could be preferentially scattered. Further, should blue hues be 'generated' subdermally, the same skin filters work in reverse, making the blue light poorly detectable by an external observer. ::: ::: ::: CONCLUSIONS ::: The Tyndall effect is unlikely to cause dermal hue changes in HA filler instillation. Optical and perceptual processes explaining superficial vein coloration may better describe subdermal HA hue changes. Vein coloration is thought to be related to three processes: the reflective properties of the skin, the absorptive properties of blood and the perceptive properties of an observer's eyes. Subdermal HA may simulate these phenomena by a number of undetermined, yet plausible mechanisms.


title: Animated Non-photorealistic Rendering in Multiple Styles

abstract: This paper presents an algorithm and its applications for artistic rendering of recorded video data following ideas of non-photorealistic rendering. The proposed algorithm does not only work on a variety of artistic rendering styles for static photography but it can also be applied to the creation of artistic videos. Cartoon-like and comic-like styles are the two artistic styles considered in this paper. For creating successfully an artistic video, three key challenges are addressed: temporal or colour consistency, stylistic flexibility, and scalability. Our work on addressing these challenges started with collecting samples of image and video data. Based on experimental results, we designed a method for video-based non-photorealistic rendering from those input data, either in cartoon-like or in comics-like style. The paper demonstrates the benefit of the designed video-based rendering framework by comparing its results with results obtained from existing Android apps.


title: The RNase III Enzyme DROSHA Is Essential for

abstract: DROSHA is a nuclear RNase III enzyme responsible for cleaving primary microRNAs (miRNAs) into precursor miRNAs and thus is essential for the biogenesis of canonical miRNAs. DICER is a cytoplasmic RNase III enzyme that not only cleaves precursor miRNAs to produce mature miRNAs but also dissects naturally formed/synthetic double-stranded RNAs to generate small interfering RNAs (siRNAs). To investigate the role of canonical miRNA and/or endogenous siRNA production in spermatogenesis, we generated Drosha or Dicer conditional knock-out (cKO) mouse lines by inactivating Drosha or Dicer exclusively in spermatogenic cells in postnatal testes using the Cre-loxp strategy. Both Drosha and Dicer cKO males were infertile due to disrupted spermatogenesis characterized by depletion of spermatocytes and spermatids leading to oligoteratozoospermia or azoospermia. The developmental course of spermatogenic disruptions was similar at morphological levels between Drosha and Dicer cKO males, but Drosha cKO testes appeared to be more severe in spermatogenic disruptions than Dicer cKO testes. Microarray analyses revealed transcriptomic differences between Drosha- and Dicer-null pachytene spermatocytes or round spermatids. Although levels of sex-linked mRNAs were mildly elevated, meiotic sex chromosome inactivation appeared to have occurred normally. Our data demonstrate that unlike DICER, which is required for the biogenesis of several small RNA species, DROSHA is essential mainly for the canonical miRNA production, and DROSHA-mediated miRNA production is essential for normal spermatogenesis and male fertility.


title: Octree-based fusion for realtime 3D reconstruction

abstract: This paper proposes an octree-based surface representation for KinectFusion, a realtime reconstruction technique of in-door scenes using a low-cost moving depth camera and a commodity graphics hardware. In KinectFusion, the scene is represented as a signed distance function (SDF) and stored as an uniform grid of voxels. Though the grid-based SDF is suitable for parallel computation in graphics hardware, most of the storage are wasted, because the geometry is very sparse in the scene volume. In order to reduce the memory cost and save the computation time, we represent the SDF in an octree, and developed several octree-based algorithms for reconstruction update and surface prediction that are suitable for parallel computation in graphics hardware. In the reconstruction update step, the octree nodes are adaptively split in breath-first order. To handle scenes with moving objects, the corresponding nodes are automatically detected and removed to avoid storage overflow. In the surface prediction step, an octree-based ray tracing method is adopted and parallelized for graphic hardware. To further reduce the computation time, the octree is organized into four layers, called top layer, branch layer, middle layer and data layer. The experiments showed that, the proposed method consumes only less than 10% memory of original KinectFusion method, and achieves faster performance. Consequently, it can reconstruct scenes with more than 10 times larger size than the original KinectFusion on the same hardware setup.


title: A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music

abstract: The Variational Autoencoder (VAE) has proven to be an effective model for producing semantically meaningful latent representations for natural data. However, it has thus far seen limited application to sequential data, and, as we demonstrate, existing recurrent VAE models have difficulty modeling sequences with long-term structure. To address this issue, we propose the use of a hierarchical decoder, which first outputs embeddings for subsequences of the input and then uses these embeddings to generate each subsequence independently. This structure encourages the model to utilize its latent code, thereby avoiding the"posterior collapse"problem, which remains an issue for recurrent VAEs. We apply this architecture to modeling sequences of musical notes and find that it exhibits dramatically better sampling, interpolation, and reconstruction performance than a"flat"baseline model. An implementation of our"MusicVAE"is available online at http://g.co/magenta/musicvae-code.


title: NoCloud: Exploring Network Disconnection through On-Device Data Analysis

abstract: Application developers often advocate uploading data to the cloud for analysis or storage, primarily due to concerns about the limited computational capability of ubiquitous devices. Today, however, many such devices can still effectively operate and execute complex algorithms without reliance on the cloud. The authors recommend prioritizing on-device analysis over uploading the data to another host, and if on-device analysis is not possible, favoring local network services over a cloud service.


title: Antecedents of the adoption of the new mobile payment systems: The moderating effect of age

abstract: Abstract The purpose of this study was to propose and test an integrative theoretical model that allows one to determine the relative importance of certain factors (i.e. external influences, ease of use, attitude, usefulness, trust, risk) for the adoption of a new mobile payment system advertised on new electronic environments, as well as to analyze the eventual moderating effect of the age of the consumer in the use of this tool. The empirical results show that the proposed behavioral model was appropriately adjusted, thus proving that the age of the user introduces significant differences in the proposed relationships between influences from third parties and ease of use of the payment system, between the perceived trust in the system and its ease of use, as well as between trust and a favorable attitude towards its use.


title: Real-Time Bidding Benchmarking with iPinYou Dataset

abstract: Being an emerging paradigm for display advertising, Real-Time Bidding (RTB) drives the focus of the bidding strategy from context to users' interest by computing a bid for each impression in real time. The data mining work and particularly the bidding strategy development becomes crucial in this performance-driven business. However, researchers in computational advertising area have been suffering from lack of publicly available benchmark datasets, which are essential to compare different algorithms and systems. Fortunately, a leading Chinese advertising technology company iPinYou decided to release the dataset used in its global RTB algorithm competition in 2013. The dataset includes logs of ad auctions, bids, impressions, clicks, and final conversions. These logs reflect the market environment as well as form a complete path of users' responses from advertisers' perspective. This dataset directly supports the experiments of some important research problems such as bid optimisation and CTR estimation. To the best of our knowledge, this is the first publicly available dataset on RTB display advertising. Thus, they are valuable for reproducible research and understanding the whole RTB ecosystem. In this paper, we first provide the detailed statistical analysis of this dataset. Then we introduce the research problem of bid optimisation in RTB and the simple yet comprehensive evaluation protocol. Besides, a series of benchmark experiments are also conducted, including both click-through rate (CTR) estimation and bid optimisation.


title: Innocent by association: early recognition of legitimate users

abstract: This paper presents the design and implementation of Souche, a system that recognizes legitimate users early in online services. This early recognition contributes to both usability and security. Souche leverages social connections established over time. Legitimate users help identify other legitimate users through an implicit vouching process, strategically controlled within vouching trees. Souche is lightweight and fully transparent to users. In our evaluation on a real dataset of several hundred million users, Souche can efficiently identify 85% of legitimate users early, while reducing the percentage of falsely admitted malicious users from 44% to 2.4%. Our evaluation further indicates that Souche is robust in the presence of compromised accounts. It is generally applicable to enhance usability and security for a wide class of online services.


Improving Docker Registry Design Based on Production Workload Analysis.


title: Extracting more concurrency from distributed transactions

abstract: Distributed storage systems run transactions across machines to ensure serializability. Traditional protocols for distributed transactions are based on two-phase locking (2PL) or optimistic concurrency control (OCC). 2PL serializes transactions as soon as they conflict and OCC resorts to aborts, leaving many opportunities for concurrency on the table. This paper presents ROCOCO, a novel concurrency control protocol for distributed transactions that outperforms 2PL and OCC by allowing more concurrency. ROCOCO executes a transaction as a collection of atomic pieces, each of which commonly involves only a single server. Servers first track dependencies between concurrent transactions without actually executing them. At commit time, a transaction's dependency information is sent to all servers so they can re-order conflicting pieces and execute them in a serializable order. ::: ::: We compare ROCOCO to OCC and 2PL using a scaled TPC-C benchmark. ROCOCO outperforms 2PL and OCC in workloads with varying degrees of contention. When the contention is high, ROCOCO's throughput is 130% and 347% higher than that of 2PL and OCC.


title: A survey on information security threats and solutions for Machine to Machine (M2M) communications

abstract: Although Machine to Machine (M2M) networks allow the development of new promising applications, the restricted resources of machines and devices in the M2M networks bring several constraints including energy, bandwidth, storage, and computation. Such constraints pose several challenges in the design of M2M networks. Furthermore, some elements that contributed to the rise of M2M applications have caused several new security threats and risks, typically due to the advancements in technology, increasing computing power, declining hardware costs, and freely available software tools.Due to the restricted capabilities of M2M devices, most of the recent research efforts on M2M have focused on computing, resource management, sensing, congestion control and controlling technologies. However, there are few studies on security aspects and there is a need to introduce the threats existing in M2M systems and corresponding solutions. Accordingly, in this paper, after presenting an overview of potential M2M applications, we present a survey of security threats against M2M networks and solutions to prevent or reduce their impact. Then, we investigate security-related challenges and open research issues in M2M networks to provide an insight for future research opportunities. Moreover, we discuss the oneM2M standard, one of the prominent standard initiatives for more secure and smoother M2M networks and the Internet of Things. A detailed review of information security threats and solutions for M2M communications.Research challenges and open research issues in M2M communications.A review of oneM2M standard.


title: A smoke detection algorithm based on K-means segmentation

abstract: The fire accident usually causes economical and ecological damage as well as cause danger to people's lives. Therefore, its early detection is must for controlling this damage. Also smoke is considered as main constituent of fire, thus an efficient smoke detection algorithm on sequences of frame obtained from static camera is proposed. It is based on computer vision based technology. This algorithm uses color feature of smoke & is comprised of following steps: reading the image, preprocessing, classify color pixels using k-means segmentation. This paper discusses mainly the segmentation problem. It adopts L*a*b* color space and k-means clustering algorithm to isolate the smoke from video sequences. Finally the K-means algorithm used is compared with the fuzzy c-means algorithm used previously.


title: Multi-agent systems and decentralized artificial superintelligence

abstract: Multi-agents systems communication is a technology, which provides a way for multiple interacting intelligent agents to communicate with each other and with environment. Multiple-agent systems are used to solve problems that are difficult for solving by individual agent. Multiple-agent communication technologies can be used for management and organization of computing fog and act as a global, distributed operating system. In present publication we suggest technology, which combines decentralized P2P BOINC general-purpose computing tasks distribution, multiple-agents communication protocol and smart-contract based rewards, powered by Ethereum blockchain. Such system can be used as distributed P2P computing power market, protected from any central authority. Such decentralized market can further be updated to system, which learns the most efficient way for software-hardware combinations usage and optimization. Once system learns to optimize software-hardware efficiency it can be updated to general-purpose distributed intelligence, which acts as combination of single-purpose AI.