title: When do people cooperate? The neuroeconomics of prosocial decision making

abstract: Understanding the roots of prosocial behavior is an interdisciplinary research endeavor that has generated an abundance of empirical data across many disciplines. This review integrates research findings from different fields into a theoretical framework that can account for when prosocial behavior is likely to occur. Specifically, we propose that the motivation to cooperate is generated by the reward system in the brain (extending from striatum to the ventromedial prefrontal cortex), and that it can be modulated by two neural networks: a cognitive control system (centered on the lateral prefrontal cortex) that processes extrinsic cooperative incentives, and/or a social cognition system (including the superior temporal sulcus, the anterior medial frontal cortex and the amygdala) that processes trust signals. The independent modulatory influence of incentives and trust on the decision to cooperate is substantiated by a growing body of neuroimaging data and reconciles the apparent paradox between economic versus social rationality in the literature, suggesting that we are in fact wired for both. Furthermore, the theoretical framework can account for substantial behavioral heterogeneity in prosocial behavior. Based on the existing data, we further postulate that self-regarding individuals (who are more likely to adopt an economically rational strategy) are more responsive to extrinsic cooperative incentives and therefore rely relatively more on cognitive control to make (un)cooperative decisions, whereas other-regarding individuals (who are more likely to adopt a socially rational strategy) are more sensitive to trust signals to avoid betrayal and recruit relatively more brain activity in the social cognition system.


title: Leveraging product adopter information from online reviews for product recommendation

abstract: The availability of the sheer volume of online product reviews makes it possible to derive implicit demographic information of product adopters from review documents. This paper proposes a novel approach to the extraction of product adopter mentions from online reviews. The extracted product adopters are the ncategorise into a number of different demographic user groups. The aggregated demographic information of many product adopters can be used to characterize both products and users, which can be incorporated into a recommendation method using weighted regularised matrix factorisation. Our experimental results on over 15 million reviews crawled from JINGDONG, the largest B2C e-commerce website in China, show the feasibility and effectiveness of our proposed frame work for product recommendation.


title: A Simple Surgical Technique for Correction of Macrotia with Poorly Defined Helical Fold

abstract: Macrotia is a relatively rare auricular deformity and few techniques for treatment have been described, most of which include skin and cartilage excisions. Effacement of the helical fold may accompany this deformity and should be reconstructed for aesthetic integrity. The authors present their ear reduction technique that achieves ear reduction and helical fold reconstruction through a posterior approach. The method is a simple procedure performed by incising and overlapping the cartilage and should be indicated for selected mild macrotia cases.


title: Technologies for the Energy-Efficient Data Center

abstract: Although semiconductor manufacturers have provided temporary relief with lower-power multi-core microprocessors, OEMs and data center operators continue to push the limits for individual rack power densities. It is not uncommon today for data center operators to deploy multiple 20 kW racks in a facility. Such rack densities are exacerbating the major issues of power and cooling in data centers. Data center operators are now forced to take a hard look at the efficiencies of their data centers. Malone and Belady (2006) have proposed three metrics, i.e., Power Usage Effectiveness (PUE), Data Center Efficiency (DCE), and the Energy-to-Acquisition Cost ratio (EAC), to help data center operators quickly quantify the efficiency of their data centers. In their paper, Malone and Belady present nominal values of PUE across a broad cross-section of data centers. PUE values are presented for data centers at four levels of optimization. One of these optimizations involves the use of Computational Fluid Dynamics (CFD). In the current paper, CFD is used to conduct an in-depth investigation of a liquid-cooled data center that would potentially be housed at the Pacific Northwest National Labs (PNNL). The boundary conditions used in the CFD model are based upon actual measurements on a rack of liquid-cooled servers housed at PNNL. The analysis shows that the liquid-cooled facility could achieve a PUE of 1.57 as compared to a PUE of 3.0 for a typical data center (the lower the PUE, the better, with values below 1.6 approaching ideal). The increase in data center efficiency is also translated into an increase in the amount of IT equipment that can be deployed. At a PUE of 1.57, the analysis shows that 91% more IT equipment can be deployed as compared to the typical data center. The paper will discuss the analysis of the PUE, and will also explore the impact of the raising data center efficiency via the use of multiple cooling technologies and CFD analysis. Complete results of the analyses will be presented in the paper.Copyright © 2007 by ASME


title: Build-to-last: strength to weight 3D printed objects

abstract: The emergence of low-cost 3D printers steers the investigation of new geometric problems that control the quality of the fabricated object. In this paper, we present a method to reduce the material cost and weight of a given object while providing a durable printed model that is resistant to impact and external forces. ::: We introduce a hollowing optimization algorithm based on the concept of honeycomb-cells structure. Honeycombs structures are known to be of minimal material cost while providing strength in tension. We utilize the Voronoi diagram to compute irregular honeycomb-like volume tessellations which define the inner structure. We formulate our problem as a strength--to--weight optimization and cast it as mutually finding an optimal interior tessellation and its maximal hollowing subject to relieve the interior stress. Thus, our system allows to build-to-last 3D printed objects with large control over their strength-to-weight ratio and easily model various interior structures. We demonstrate our method on a collection of 3D objects from different categories. Furthermore, we evaluate our method by printing our hollowed models and measure their stress and weights.


title: New concept of distributed vibration dampers

abstract: Vibration dampers and dynamic absorbers are widely used in various engineering problems. This paper presents a new concept of vibration control via a distributed damper composed of an elastic bar and a dissipative boundary conditioner in its configuration. The distributed inertia and stiffness of the elastic bar enables the damper to provide wide-band damping augmentation. Preliminary results show several advantages of the distributed damper over its lumped counterparts.


title: Fast on-line kernel density estimation for active object localization

abstract: A major goal of computer vision is to enable computers to interpret visual situations — abstract concepts (e.g., “a person walking a dog,” “a crowd waiting for a bus,” “a picnic”) whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity. In this paper, we propose a novel method for prior learning and active object localization for this kind of knowledge-driven search in static images. In our system, prior situation knowledge is captured by a set of flexible, kernel-based density estimations — a situation model — that represent the expected spatial structure of the given situation. These estimations are updated by information gained as the system searches for relevant objects, allowing the system to use context as it is discovered to narrow the search. More specifically, at any given time in a run on a test image, our system uses image features plus contextual information it has discovered to identify a small subset of training images — an importance cluster — that is deemed most similar to the given test image, given the context. This subset is used to generate an updated situation model in an on-line fashion, using an efficient multipole expansion technique. As a proof of concept, we apply our algorithm to a highly varied and challenging dataset consisting of instances of a “dogwalking” situation. Our results support the hypothesis that dynamically-rendered, context-based probabilistic models can support efficient object localization in visual situations. Moreover, our approach is general enough to be applied to diverse machine learning paradigms requiring interpretable, probabilistic representations generated from partially observed data.


title: ‘Some tactical problems in digital simulation’ for the next 10 years

abstract: In his influential 1963 paper ‘Some Tactical Problems in Digital Simulation’, Conway identified important issues that became the pillars of research in simulation analysis methodology. Naturally these ‘problems’ were a product of the applications of interest at the time, as well as the state of simulation and computing, much of which has changed dramatically. In light of those changes, we attempt to identify the tactical problems that might occupy simulation researchers for the next 10 years.


title: A machine learning framework for sport result prediction

abstract: Abstract Machine learning (ML) is one of the intelligent methodologies that have shown promising results in the domains of classification and prediction. One of the expanding areas necessitating good predictive accuracy is sport prediction, due to the large monetary amounts involved in betting. In addition, club managers and owners are striving for classification models so that they can understand and formulate strategies needed to win matches. These models are based on numerous factors involved in the games, such as the results of historical matches, player performance indicators, and opposition information. This paper provides a critical analysis of the literature in ML, focusing on the application of Artificial Neural Network (ANN) to sport results prediction. In doing so, we identify the learning methodologies utilised, data sources, appropriate means of model evaluation, and specific challenges of predicting sport results. This then leads us to propose a novel sport prediction framework through which ML can be used as a learning strategy. Our research will hopefully be informative and of use to those performing future research in this application area.


title: Auto-play: A Data Mining Approach to ODI Cricket Simulation and Prediction.

abstract: Cricket is a popular sport played by 16 countries, is the second most watched sport in the world after soccer, and enjoys a multi-million dollar industry. There is tremendous interest in simulating cricket and more importantly in predicting the outcome of games, particularly in their one-day international format. The complex rules governing the game, along with the numerous natural parameters affecting the outcome of a cricket match present significant challenges for accurate prediction. Multiple diverse parameters, including but not limited to cricketing skills and performances, match venues and even weather conditions can significantly affect the outcome of a game. The sheer number of parameters, along with their interdependence and variance create a non-trivial challenge to create an accurate quantitative model of a game Unlike other sports such as basketball and baseball which are well researched from a sports analytics perspective, for cricket, these tasks have yet to be investigated in depth. In this paper, we build a prediction system that takes in historical match data as well as the instantaneous state of a match, and predicts future match events culminating in a victory or loss. We model the game using a subset of match parameters, using a combination of linear regression and nearestneighbor clustering algorithms. We describe our model and algorithms and finally present quantitative results, demonstrating the performance of our algorithms in predicting the number of runs scored, one of the most important determinants of match outcome.


title: Zero-Shot Transfer VQA Dataset

abstract: Acquiring a large vocabulary is an important aspect of human intelligence. Onecommon approach for human to populating vocabulary is to learn words duringreading or listening, and then use them in writing or speaking. This ability totransfer from input to output is natural for human, but it is difficult for machines.Human spontaneously performs this knowledge transfer in complicated multimodaltasks, such as Visual Question Answering (VQA). In order to approach human-levelArtificial Intelligence, we hope to equip machines with such ability. Therefore, toaccelerate this research, we propose a newzero-shot transfer VQA(ZST-VQA)dataset by reorganizing the existing VQA v1.0 dataset in the way that duringtraining, some words appear only in one module (i.e. questions) but not in theother (i.e. answers). In this setting, an intelligent model should understand andlearn the concepts from one module (i.e. questions), and at test time, transfer themto the other (i.e. predict the concepts as answers). We conduct evaluation on thisnew dataset using three existing state-of-the-art VQA neural models. Experimentalresults show a significant drop in performance on this dataset, indicating existingmethods do not address the zero-shot transfer problem. Besides, our analysis findsthat this may be caused by the implicit bias learned during training.


title: Risk factors for obstetric anal sphincter injuries (OASIS) and the role of episiotomy: A retrospective series of 496 cases

abstract: Abstract Objective The objective of this study was to determine the prevalence and identify risk factors for obstetric anal sphincter injuries (OASIS), and to determine the prevalence of episiotomy and whether it is protective for the posterior perineum. Study design This is a retrospective case-control study carried out in a level 2 maternity unit and a level 3 maternity unit between 1 January 2006 and 31 December 2015. The sample population included all vaginal deliveries at term of a living singleton foetus in cephalic presentation. The case group comprised patients with an OASIS. The control group comprised patients without OASIS. Statistical analysis was subdivided into descriptive and inferential parts. Results 42,626 patients were included in the study of whom 496 were cases of OASIS, i.e. a rate of 1.2%. The overall episiotomy rate was 10.0%, which reflects a restrictive practice. Episiotomy doesn’t appear to be a statistically significant protective factor for OASIS (OR = 0.89–95%CI [0.68–1.16]). The principal independent risk factors for OASIS were nulliparity (ORa = 4.19–95%CI [3.03–5.84] - p Conclusion Episiotomy doesn’t appear to be a statistically significant protective factor on the perineal prognosis. A deeper understanding of the factors which promote OASIS and greater awareness of them would improve the perineal prognosis of parturient women.


title: ECO: Efficient Convolutional Network for Online Video Understanding

abstract: The state of the art in video understanding suffers from two problems: (1) The major part of reasoning is performed locally in the video, therefore, it misses important relationships within actions that span several seconds. (2) While there are local methods with fast per-frame processing, the processing of the whole video is not efficient and hampers fast video retrieval or online classification of long-term activities. In this paper, we introduce a network architecture (https://github.com/mzolfaghari/ECO-efficient-video-understanding) that takes long-term content into account and enables fast per-video processing at the same time. The architecture is based on merging long-term content already in the network rather than in a post-hoc fusion. Together with a sampling strategy, which exploits that neighboring frames are largely redundant, this yields high-quality action classification and video captioning at up to 230 videos per second, where each video can consist of a few hundred frames. The approach achieves competitive performance across all datasets while being 10\(\times \) to 80\(\times \) faster than state-of-the-art methods.


title: A survey on sentiment analysis of scientific citations

abstract: Sentiment analysis of scientific citations has received much attention in recent years because of the increased availability of scientific publications. Scholarly databases are valuable sources for publications and citation information where researchers can publish their ideas and results. Sentiment analysis of scientific citations aims to analyze the authors’ sentiments within scientific citations. During the last decade, some review papers have been published in the field of sentiment analysis. Despite the growth in the size of scholarly databases and researchers’ interests, no one as far as we know has carried out an in-depth survey in a specific area of sentiment analysis in scientific citations. This paper presents a comprehensive survey of sentiment analysis of scientific citations. In this review, the process of scientific citation sentiment analysis is introduced and recently proposed methods with the main challenges are presented, analyzed and discussed. Further, we present related fields such as citation function classification and citation recommendation that have recently gained enormous attention. Our contributions include identifying the most important challenges as well as the analysis and classification of recent methods used in scientific citation sentiment analysis. Moreover, it presents the normal process, and this includes citation context extraction, public data sources, and feature selection. We found that most of the papers use classical machine learning methods. However, due to limitations of performance and manual feature selection in machine learning, we believe that in the future hybrid and deep learning methods can possibly handle the problems of scientific citation sentiment analysis more efficiently and reliably.


title: The thalamus is more than just a relay

abstract: The lateral geniculate nucleus and pulvinar are examples of two different types of relay: the former is a first order relay, transmitting information from a subcortical source (retina), while the latter is mostly a higher order relay, transmitting information from layer 5 of one cortical area to another cortical area. First and higher order thalamic relays can also be recognized for much of the rest of thalamus, and most of thalamus seems to be comprised of higher order relays. Higher order relays seem especially important to general corticocortical communication, and this challenges and extends the conventional view that such communication is based on direct corticocortical connections.


title: Failing to deactivate: The association between brain activity during a working memory task and creativity

abstract: Abstract Working memory (WM) is an essential component for human higher order cognitive activities. Creativity has been essential to the development of human civilization. Previous studies from different fields have suggested creativity and capacity of WM have opposing characteristics possibly in terms of diffuse attention. However, despite a number of functional imaging studies on creativity, how creativity relates to brain activity during WM has never been investigated. In this functional magnetic resonance imaging (fMRI) study, we investigated this issue using an n-back WM paradigm and a psychometric measure of creativity (a divergent thinking test). A multiple regression analysis revealed that individual creativity was significantly and positively correlated with brain activity in the precuneus during the 2-back task (WM task), but not during the non-WM 0-back task. As the precuneus shows deactivation during cognitive tasks, our findings show that reduced task induced deactivation (TID) in the precuneus is associated with higher creativity measured by divergent thinking. The precuneus is included in the default mode network, which is deactivated during cognitive tasks. The magnitude of TID in the default mode network is considered to reflect the reallocation of cognitive resources from networks irrelevant to the performance of the task. Thus, our findings may indicate that individual creativity, as measured by the divergent thinking test, is related to the inefficient reallocation of attention, congruent with the idea that diffuse attention is associated with individual creativity.


title: Evaluating vad for automatic speech recognition

abstract: Voice activity detection (VAD) plays a crucial role in speech processing, especially in automatic speech recognition (ASR). It identifies the boundaries of the speech to be recognized and the boundary accuracies may significantly affect the recognition performance. Conventional VAD evaluation criteria are mostly based on frame-level accuracy of speech/non-speech classification, which may result in weak correlation between VAD and ASR performance. Even though some VAD evaluation criteria consider boundary effects, there has not been an effective overall criterion suitable for evaluating the effect of VAD on ASR. This paper proposes an integrated VAD evaluation criterion taking into account various boundary effects. Experiments on an English Switchboard task showed that, conventional frame accuracy based VAD criterion has weak and unstable correlation with word error rate while the proposed overall criterion is much more stably correlated to word error rate.


title: Chocolate and the brain: Neurobiological impact of cocoa flavanols on cognition and behavior

abstract: Abstract Cocoa products and chocolate have recently been recognized as a rich source of flavonoids, mainly flavanols, potent antioxidant and anti-inflammatory agents with established benefits for cardiovascular health but largely unproven effects on neurocognition and behavior. In this review, we focus on neuromodulatory and neuroprotective actions of cocoa flavanols in humans. The absorbed flavonoids penetrate and accumulate in the brain regions involved in learning and memory, especially the hippocampus. The neurobiological actions of flavanols are believed to occur in two major ways: (i) via direct interactions with cellular cascades yielding expression of neuroprotective and neuromodulatory proteins that promote neurogenesis, neuronal function and brain connectivity, and (ii) via blood-flow improvement and angiogenesis in the brain and sensory systems. Protective effects of long-term flavanol consumption on neurocognition and behavior, including age- and disease-related cognitive decline, were shown in animal models of normal aging, dementia, and stroke. A few human observational and intervention studies appear to corroborate these findings. Evidence on more immediate action of cocoa flavanols remains limited and inconclusive, but warrants further research. As an outline for future research on cocoa flavanol impact on human cognition, mood, and behavior, we underscore combination of functional neuroimaging with cognitive and behavioral measures of performance.


title: sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning

abstract: Abstract We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global potential energy surfaces (PES) for molecules with a few dozen atoms from a limited number of user-provided reference molecular conformations and the associated atomic forces. Here, we introduce a Python software package to reconstruct and evaluate custom sGDML force fields (FFs), without requiring in-depth knowledge about the details of the model. A user-friendly command-line interface offers assistance through the complete process of model creation, in an effort to make this novel machine learning approach accessible to broad practitioners. Our paper serves as a documentation, but also includes a practical application example of how to reconstruct and use a PBE0+MBD FF for paracetamol. Finally, we show how to interface sGDML with the FF simulation engines ASE (Larsen et al., 2017) and i-PI (Kapil et al., 2019) to run numerical experiments, including structure optimization, classical and path integral molecular dynamics and nudged elastic band calculations.


title: Fast and globally convergent pose estimation from video images

abstract: Determining the rigid transformation relating 2D images to known 3D geometry is a classical problem in photogrammetry and computer vision. Heretofore, the best methods for solving the problem have relied on iterative optimization methods which cannot be proven to converge and/or which do not effectively account for the orthonormal structure of rotation matrices. We show that the pose estimation problem can be formulated as that of minimizing an error metric based on collinearity in object (as opposed to image) space. Using object space collinearity error, we derive an iterative algorithm which directly computes orthogonal rotation matrices and which is globally convergent. Experimentally, we show that the method is computationally efficient, that it is no less accurate than the best currently employed optimization methods, and that it outperforms all tested methods in robustness to outliers.


title: A Low-Noise, Wide-BW 3.6GHz Digital ΔΣ Fractional-N Frequency Synthesizer with a Noise-Shaping Time-to-Digital Converter and Quantization Noise Cancellation

abstract: A digital fractional-N frequency synthesizer is presented that leverages a noise-shaping time-to-digital converter (TDC) and a simple quantization noise cancellation technique to achieve low phase noise with a wide PLL bandwidth of 500kHz. In contrast to previous cancellation techniques, this structure requires no analog components and is straightforward to implement with standard-cell digital logic.


title: Using Docker in high performance computing applications

abstract: Virtualization technology plays a vital role in cloud computing. In particular, benefits of virtualization are widely employed in high performance computing (HPC) applications. Recently, virtual machines (VMs) and Docker containers known as two virtualization platforms need to be explored for developing applications efficiently. We target a model for deploying distributed applications on Docker containers, among using well-known benchmarks to evaluate performance between VMs and containers. Based on their architecture, we propose benchmark scenarios to analyze the computing performance and the ability of data access on HPC system. Remarkably, Docker container has more advantages than virtual machine in terms of data intensive application and computing ability, especially the overhead of Docker is trivial. However, Docker architecture has some drawbacks in resource management. Our experiment and evaluation show how to deploy efficiently high performance computing applications on Docker containers and VMs.


title: 2012/8 Frontier-Based Performance Analysis Models for Supply Chain Management; State of the Art and Research Directions Per AGRELL

abstract: Effective supply chain management relies on information integration and implementation of best practice techniques across the chain. Supply chains are examples of complex multi-stage systems with temporal and causal interrelations, operating multi-input and multi-output production and services under utilization of fixed and variable resources. Acknowledging the lack of system's view, the need to identify system-wide and individual effects as well as incorporating a coherent set of performance metrics, the recent literature reports on an increasing, but yet limited, number of applications of frontier analysis models (e.g. DEA) for the performance assessment of supply chains or networks. The relevant models in this respect are multi-stage models with various assumptions on the intermediate outputs and inputs, enabling the derivation of metrics for technical and cost efficiencies for the system as well as the autonomous links. This paper reviews the state of the art in network DEA modeling, in particular two-stage models, along with a critical review of the advanced applications that are reported in terms of the consistency of the underlying assumptions and the results derived. Consolidating current work in this range using the unified notations and comparison of the properties of the presented models, the paper is closed with recommendations for future research in terms of both theory and application.


title: An End-To-End Log Management Framework for Distributed Systems

abstract: Logs have been widely employed to ensure the reliability of distributed systems, because logs are often the only data available that records system runtime information. Compared with logs generated by traditional standalone systems, distributed system logs are often large-scale and of great complexity, invalidating many existing log management methods. To address this problem, the paper describes and envisions an end-to-end log management framework for distributed systems. Specifically, this framework includes strategic logging placement, log collection, log parsing, interleaved logs mining, anomaly detection, and problem identification.


title: Fast and Accurate Matrix Completion via Truncated Nuclear Norm Regularization

abstract: Recovering a large matrix from a small subset of its entries is a challenging problem arising in many real applications, such as image inpainting and recommender systems. Many existing approaches formulate this problem as a general low-rank matrix approximation problem. Since the rank operator is nonconvex and discontinuous, most of the recent theoretical studies use the nuclear norm as a convex relaxation. One major limitation of the existing approaches based on nuclear norm minimization is that all the singular values are simultaneously minimized, and thus the rank may not be well approximated in practice. In this paper, we propose to achieve a better approximation to the rank of matrix by truncated nuclear norm, which is given by the nuclear norm subtracted by the sum of the largest few singular values. In addition, we develop a novel matrix completion algorithm by minimizing the Truncated Nuclear Norm. We further develop three efficient iterative procedures, TNNR-ADMM, TNNR-APGL, and TNNR-ADMMAP, to solve the optimization problem. TNNR-ADMM utilizes the alternating direction method of multipliers (ADMM), while TNNR-AGPL applies the accelerated proximal gradient line search method (APGL) for the final optimization. For TNNR-ADMMAP, we make use of an adaptive penalty according to a novel update rule for ADMM to achieve a faster convergence rate. Our empirical study shows encouraging results of the proposed algorithms in comparison to the state-of-the-art matrix completion algorithms on both synthetic and real visual datasets.


Beyond Algorithms: An HCI Perspective on Recommender Systems


title: Using GPUs for machine learning algorithms

abstract: Using dedicated hardware to do machine learning typically ends up in disaster because of cost, obsolescence, and poor software. The popularization of graphic processing units (GPUs), which are now available on every PC, provides an attractive alternative. We propose a generic 2-layer fully connected neural network GPU implementation which yields over 3/spl times/ speedup for both training and testing with respect to a 3 GHz P4 CPU.


Passage retrieval vs. document retrieval for factoid question answering


Optimization and Simulation of Electric Ship with Low Voltage AC / DC Hybrid Power System


title: Performance analysis of NVMe SSDs and their implication on real world databases

abstract: The storage subsystem has undergone tremendous innovation in order to keep up with the ever-increasing demand for throughput. Non Volatile Memory Express (NVMe) based solid state devices are the latest development in this domain, delivering unprecedented performance in terms of latency and peak bandwidth. NVMe drives are expected to be particularly beneficial for I/O intensive applications, with databases being one of the prominent use-cases. This paper provides the first, in-depth performance analysis of NVMe drives. Combining driver instrumentation with system monitoring tools, we present a breakdown of access times for I/O requests throughout the entire system. Furthermore, we present a detailed, quantitative analysis of all the factors contributing to the low-latency, high-throughput characteristics of NVMe drives, including the system software stack. Lastly, we characterize the performance of multiple cloud databases (both relational and NoSQL) on state-of-the-art NVMe drives, and compare that to their performance on enterprise-class SATA-based SSDs. We show that NVMe-backed database applications deliver up to 8× superior client-side performance over enterprise-class, SATA-based SSDs.


Local anesthesia in surgical practice


title: Bicycles and human riders backscattering at 77 GHz for automotive radar

abstract: Rapidly developing autonomous vehicle technologies aim to reduce roadway fatalities and promote driver comfort. Such autonomous vehicle are mostly empowered by optical and radar sensors. Collision avoidance systems are becoming a common a standard safety feature in newer or future cars. Some automotive radar sensors have been successfully used in parking, lane changing, and blind spot detection. Newer 76–78 GHz radars are being developed and tested by car makers for extending these safety features to pedestrian and bicyclist detection. However, using cost effective vehicular radars in reliably detecting pedestrian and bicyclist with low false alarm rate still poses challenging due to many possible variations of these targets as well as the presence of other road objects. To improve detection methods, real scenario testing in currently being implemented where hundreds of tests can be performed on mannequins which mimic pedestrians and bicyclists both optically and to and radar. To accomplish this task, the backscattering of targets must be studied and understood. This paper presents experimental study findings of radar characteristics of bicycles with and without riders at 77 GHz. The measured RCS patterns of common bicycles with and without riders are compared and analyzed.


title: Identifying opinion leaders in social networks with topic limitation

abstract: The social networks have been an important platform for people to share and exchange information in their daily life. There are some most critical users called opinion leaders who are always used to achieve the maximization of information transmission and suppress the diffusion of rumours in a short time. Many methods have been proposed by researches for these users. For identifying more accurately and efficiently, we make a further analysis for the real information spread and find that the information is commonly topic sensitive and opinion leaders are always topic limited. In the certain topic area, the users with higher authority in the topic area always play a more crucial role for the information spread. What’s more, in order to quantify the authority in certain topic area, we apply a series of rigorous definitions and topic model. Finally, comparing with the other widely used methods, the result shows the effective performance of our method.


title: Elements of Psychological Maturity and Its Influence on Antisocial and Criminal Behavior

abstract: Insights from developmental psychology provide a useful perspective from which to understand why young people might engage in higher levels of antisocial behavior than adults. They can also be useful in term of highlighting which aspects of development result in young people making less mature judgments. Yet despite this extensive body of knowledge, there is no psychological or legal definition of maturity that can be applied to legal decision making. Within a criminal context, investigations of adolescent immaturity have emphasized cognitive factors, influenced by the informed consent model which places a premium on factors such as "knowledge" and "competence", with an emphasis on cognitive functioning (e.g., thinking, reasoning, and understanding). This approach has been criticized for failing to adequately illustrate differences between adolescent and adult decision making. This paper reviews the notion of psychological maturity and comments on the importance of a broader assessment that includes psychosocial factors in the face of a growing trend in many jurisdictions to transfer juvenile offenders to the adult jurisdiction based on the severity of crime committed rather than their level of culpability.


title: The Yahoo! music dataset and KDDCup’11

abstract: KDD-Cup 2011 challenged the community to identify user tastes in music by leveraging Yahoo! Music user ratings. The competition hosted two tracks, which were based on two datasets sampled from the raw data, including hundreds of millions of ratings. The underlying ratings were given to four types of musical items: tracks, albums, artists, and genres, forming a four level hierarchical taxonomy. ::: ::: The challenge started on March 15, 2011 and ended on June 30, 2011 attracting 2389 participants, 2100 of which were active by the end of the competition. The popularity of the challenge is related to the fact that learning a large scale recommender systems is a generic problem, highly relevant to the industry. In addition, the contest drew interest by introducing a number of scientific and technical challenges including dataset size, hierarchical structure of items, high resolution timestamps of ratings, and a non-conventional ranking-based task. ::: ::: This paper provides the organizers' account of the contest, including: a detailed analysis of the datasets, discussion of the contest goals and actual conduct, and lessons learned throughout the contest.


Perspectives: Complex Adaptations and the Evolution of Evolvability


DETC 2011 / MESA-47864 DIGITAL FRACTIONAL ORDER SAVITZKY-GOLAY DIFFERENTIATOR AND ITS APPLICATION


title: Field Trial of Long-Reach and High-Splitting λ-Tunable TWDM-PON

abstract: This paper presents the world's first field trial of a 40-km reach and over 512-split symmetric-rate 40-Gbit/s λ-tunable time and wavelength division multiplexing passive optical network (TWDM-PON) that utilizes our newly developed high-speed λ-tunable optical network unit transceivers and automatic gain controlled semiconductor optical amplifier-based PON extender for repeater and central office use. The upstream signals for C-band and downstream signals for L+-band in our developed λ-tunable TWDM-PON comply with the wavelength plan of ITU-T G.989 series. In a field trial, we successfully demonstrate error free λ-tunable transmission for the 40-km reach and 1024-split (maximum number) symmetric-rate 40-Gbit/s λ-tunable TWDM-PON over installed standard single-mode fiber links, and advanced network functions such as dynamic load balancing, incremental system upgrade, optical subscriber unit protection, and OLT equipment power saving are also confirmed.


Datasets for Aspect-Based Sentiment Analysis in French.


title: A cross-layer communication module for the Internet of Things

abstract: The Internet of Things (IoT) is a novel networking paradigm which allows the communication among all sorts of physical objects over the Internet. The IoT defines a world-wide cyber-physical system with a plethora of applications in the fields of domotics, e-health, goods monitoring and logistics, among others. The use of cross-layer communication schemes to provide adaptive solutions for the IoT is motivated by the high heterogeneity in the hardware capabilities and the communication requirements among things. In this paper, a novel cross-layer module for the IoT is proposed to accurately capture both the high heterogeneity of the IoT and the impact of the Internet as part of the network architecture. The fundamental part of the module is a mathematical framework, which is developed to obtain the optimal routing paths and the communication parameters among things, by exploiting the interrelations among different layer functionalities in the IoT. Moreover, a cross-layer communication protocol is presented to implement this optimization framework in practical scenarios. The results show that the proposed solution can achieve a global communication optimum and outperforms existing layered solutions. The novel cross-layer module is a primary step towards providing efficient and reliable end-to-end communication in the IoT.


title: A 0.35μm CMOS 6-bit current steering DAC

abstract: This paper presents the design and experimental results of a high speed, low-power, thermometer coded and current steered 6-bit digital-to-analog converter (DAC). It is based on a hybrid architecture with a switched current matrix controlled by the four most significant digital bits, and a conventional 2-bit current source controlled by the two least significant bits. The DAC occupies 0.15 mm2 chip area in standard 0.35 μm Complementary metal-oxide-semiconductor (CMOS) technology. A spurious-free dynamic range (SFDR) of 25 dB has been measured over the complete Nyquist interval at sampling frequencies up to 800 MS/s with a power consumption of 165 mW at 3.3 V power supply.


title: Face-to-face collaborative learning supported by mobile phones

abstract: The use of handheld computers in educational contexts has increased considerably in recent years and their value as a teaching tool has been confirmed by many positive experiences, particular within collaborative learning systems (Mobile Computer Supported Collaborative Learning [MCSCL]). The cost of the devices has hindered widespread use in schools, however, and cell phones have emerged as an attractive alternative. To test their functionality as a platform for collaborative educational activities, the authors adapted an existing Personal Digital Assistant (PDA) application for use on cell phones equipped with Wi-Fi. This article examines the problems of developing applications for this alternative technology and reports on a usability analysis of a collaborative classroom activity for teaching physics. The results confirm the viability of the cell phone platform, taking due account of the device's processing, network and interface limitations. With an appropriate design, users quickly master the techno...


Object Recognition Based on ORB Descriptor for Markerless Augmented Reality


title: Smart Fault Location for Smart Grids

abstract: Fault location is an important application among intelligent monitoring and outage management tasks used for realization of self healing networks, one of the most attractive features of smart grids. The data gathered from various intelligent electronic devices (IEDs) installed throughout the power system could be utilized for smart approaches to locating faults in both transmission and distribution systems. This paper discusses issues associated with improving accuracy of fault location methods in smart grids using an abundance of IED data. Two examples of how the gathered data from different IEDs is used to improve fault location accuracy in transmission and distribution systems are discussed in detail.


Malicious JavaScript detection using machine learning


title: Hybrid prediction model for Type-2 diabetic patients

abstract: A wide range of computational methods and tools for data analysis are available. In this study we took advantage of those available technological advancements to develop prediction models for the prediction of a Type-2 Diabetic Patient. We aim to investigate how the diabetes incidents are affected by patients' characteristics and measurements. Efficient predictive modeling is required for medical researchers and practitioners. This study proposes Hybrid Prediction Model (HPM) which uses Simple K-means clustering algorithm aimed at validating chosen class label of given data (incorrectly classified instances are removed, i.e. pattern extracted from original data) and subsequently applying the classification algorithm to the result set. C4.5 algorithm is used to build the final classifier model by using the k-fold cross-validation method. The Pima Indians diabetes data was obtained from the University of California at Irvine (UCI) machine learning repository datasets. A wide range of different classification methods have been applied previously by various researchers in order to find the best performing algorithm on this dataset. The accuracies achieved have been in the range of 59.4-84.05%. However the proposed HPM obtained a classification accuracy of 92.38%. In order to evaluate the performance of the proposed method, sensitivity and specificity performance measures that are used commonly in medical classification studies were used.


title: A conversational intelligent tutoring system to automatically predict learning styles

abstract: This paper proposes a generic methodology and architecture for developing a novel conversational intelligent tutoring system (CITS) called Oscar that leads a tutoring conversation and dynamically predicts and adapts to a student's learning style. Oscar aims to mimic a human tutor by implicitly modelling the learning style during tutoring, and personalising the tutorial to boost confidence and improve the effectiveness of the learning experience. Learners can intuitively explore and discuss topics in natural language, helping to establish a deeper understanding of the topic. The Oscar CITS methodology and architecture are independent of the learning styles model and tutoring subject domain. Oscar CITS was implemented using the Index of Learning Styles (ILS) model (Felder & Silverman, 1988) to deliver an SQL tutorial. Empirical studies involving real students have validated the prediction of learning styles in a real-world teaching/learning environment. The results showed that all learning styles in the ILS model were successfully predicted from a natural language tutoring conversation, with an accuracy of 61-100%. Participants also found Oscar's tutoring helpful and achieved an average learning gain of 13%.


Prognostic Enhancements to Gas Turbine Diagnostic Systems 1


title: An Advanced External Compensation System for Active Matrix Organic Light-Emitting Diode Displays With Poly-Si Thin-Film Transistor Backplane

abstract: An advanced method for externally compensating the nonuniform electrical characteristics of polycrystalline silicon thin-film transistors (TFTs) and the degradation of organic light-emitting diode (OLED) devices is proposed, and the method is verified using a 14.1-in active matrix OLED (AMOLED) panel. The proposed method provides an effective solution for high-image-quality AMOLED displays by removing IR-drop and temperature effects during the sensing and displaying operations of the external compensation method. Experimental results show that the electrical characteristics of TFTs and OLEDs are successfully sensed, and that the stained image pattern due to the nonuniform luminance error and the differential aging of the OLED is removed. The luminance error range without compensation is from -6.1% to 9.0%, but it is from -1.1% to 1.2% using the external compensation at the luminance level of 120 cd/m2 in a 14.1-inch AMOLED panel.


title: A high power density power factor correction front end based on a 7-level flying capacitor multilevel converter

abstract: This paper presents a power factor correction (PFC) front end based on a 7-level flying capacitor multilevel (FCML) boost converter. The operation of the FCML converter is introduced to illustrate its benefits in terms of efficiency and power density. Compared to conventional 2-level boost converter design, the proposed 7-level FCML converter features the use of low-voltage-rating transistors and a high effective switching frequency. These characteristics of FCML converter lead to drastic reduction in the filter inductor size while maintaining high efficiency and small heat sink volume, and thus significantly improve the power density. On the other hand, the small inductance imposes unique challenges in the implementation of the PFC control. In this work, a digital control implementation is presented to overcome these challenges. A hardware prototype is designed for universal AC input (90 Vac to 265 Vac), 400 V DC output and 1.3 kW power rating, and its performance has been experimentally demonstrated.


title: Single Image Defogging Algorithm based on Dark Channel Priority

abstract: Based on dark channel priority, the paper proposes an improved defogging algorithm of single image which can defog the foggy images rapidly. The algorithm in the paper applies the method combining adaptive median filter and bilateral filter to figure out clear dark channel on the edge. And the algorithm is based on the physical model of foggy images to estimate transmission. Compared with the traditional algorithm, the estimated transmission is detailed and clear, and has no need to be optimized, which not only overcomes the disadvantages of traditional algorithm using plenty of time to optimize transmission, but also reduces the complexity of the algorithm. The experimental results indicate that the algorithm realizes rapid and high-quality defogging on single image


title: A Contour Completion Model for Augmenting Surface Reconstructions

abstract: The availability of commodity depth sensors such as Kinect has enabled development of methods which can densely reconstruct arbitrary scenes. While the results of these methods are accurate and visually appealing, they are quite often incomplete. This is either due to the fact that only part of the space was visible during the data capture process or due to the surfaces being occluded by other objects in the scene. In this paper, we address the problem of completing and refining such reconstructions. We propose a method for scene completion that can infer the layout of the complete room and the full extent of partially occluded objects. We propose a new probabilistic model, Contour Completion Random Fields, that allows us to complete the boundaries of occluded surfaces. We evaluate our method on synthetic and real world reconstructions of 3D scenes and show that it quantitatively and qualitatively outperforms standard methods. We created a large dataset of partial and complete reconstructions which we will make available to the community as a benchmark for the scene completion task. Finally, we demonstrate the practical utility of our algorithm via an augmented-reality application where objects interact with the completed reconstructions inferred by our method.


title: Design for Privacy in Ubiquitous Computing Environments

abstract: Current developments in information technology are leading to increasing capture and storage of information about people and their activities. This raises serious issues about the preservation of privacy. In this paper we examine why these issues are particularly important in the introduction of ubiquitous computing technology into the working environment. Certain problems with privacy are closely related to the ways in which the technology attenuates natural mechanisms of feedback and control over information released. We describe a framework for design for privacy in ubiquitous computing environments and conclude with an example of its application.


title: Control of Field Dodder (Cuscuta campestris) Parasitizing Tomato with ALS-Inhibiting Herbicides

abstract: Abstract Field dodder is a parasitic plant that attaches to the stems and leaves of broadleaf plants, including weeds, field crops, vegetables, and ornamentals, throughout most agricultural regions of the world. Effective field dodder control is extremely difficult to achieve, due to the nature of attachment and close association between host and parasite, which requires a highly effective and selective herbicide to destroy the parasite without crop damage. Previous studies have demonstrated the tolerance of certain tomato varieties to dodder parasitism. The aim of the present study was to evaluate the ability of sulfonylurea herbicides to control field dodder under greenhouse and field conditions. Two greenhouse studies and three field studies were conducted to evaluate the efficiency and crop selectivity of the sulfonylurea herbicides sulfosulfuron, rimsulfuron, halosulfuron, and flazasulfuron in controlling field dodder parasitizing tomato plants. Sulfosulfuron at 50 or 100 g ai ha−1 was effective and ...


title: Localized fault-tolerant event boundary detection in sensor networks

abstract: This paper targets the identification of faulty sensors and detection of the reach of events in sensor networks with faulty sensors. Typical applications include the detection of the transportation front line of a contamination and the diagnosis of network health. We propose and analyze two novel algorithms for faulty sensor identification and fault-tolerant event boundary detection. These algorithms are purely localized and thus scale well to large sensor networks. Their computational overhead is low, since only simple numerical operations are involved. Simulation results indicate that these algorithms can clearly detect the event boundary and can identify faulty sensors with a high accuracy and a low false alarm rate when as many as 20% sensors become faulty. Our work is exploratory in that the proposed algorithms can accept any kind of scalar values as inputs, a dramatic improvement over existing works that take only 0/1 decision predicates. Therefore, our algorithms are generic. They can be applied as long as the "events" can be modelled by numerical numbers. Though designed for sensor networks, our algorithms can be applied to the outlier detection and regional data analysis in spatial data mining.


title: Additive regularization of topic models

abstract: Probabilistic topic modeling of text collections has been recently developed mainly within the framework of graphical models and Bayesian inference. In this paper we introduce an alternative semi-probabilistic approach, which we call additive regularization of topic models (ARTM). Instead of building a purely probabilistic generative model of text we regularize an ill-posed problem of stochastic matrix factorization by maximizing a weighted sum of the log-likelihood and additional criteria. This approach enables us to combine probabilistic assumptions with linguistic and problem-specific requirements in a single multi-objective topic model. In the theoretical part of the work we derive the regularized EM-algorithm and provide a pool of regularizers, which can be applied together in any combination. We show that many models previously developed within Bayesian framework can be inferred easier within ARTM and in some cases generalized. In the experimental part we show that a combination of sparsing, smoothing, and decorrelation improves several quality measures at once with almost no loss of the likelihood.


title: A Novel Three-Way Clustering Algorithm for Mixed-Type Data

abstract: Large quantities of mixed-type data, containing categorical, ordinal and numerical attributes, have commonly existed in real world. In this paper, a mixed-type data clustering method, which could deal with the uncertain situation that a cluster may not have a definite cluster boundary, is proposed inspired by the theory of three-way decisions. Many existing studies represent a cluster with a single set based on a two-way strategy, which does not adequately show the fact that a cluster may not have a well-defined cluster boundary. In this paper, we represent a cluster with a pair of sets, i.e., the core region and fringe region. The three-way clustering is suitable for dealing with uncertainty because it shows intuitively which objects are fringe to the cluster. Then, new measurements of distance between mixed-type data are proposed for different types of attribute values by means of a weighted tree structure. The measurement considers the semantic of attributes, the number of attribute values and the occurrence frequency of attribute values. Finally, a three-way clustering algorithm for mixed-type data is proposed. The experimental results show that the proposed distance measure of mixed-type data is reasonable and effective, the proposed algorithm is in a better performance at the accuracy and the average adjusted rand index than the compared algorithms in most cases.


title: MASTRO: A Reasoner for Effective Ontology-Based Data Access

abstract: In this paper we present Mastro, a Java tool for ontologybased data access (OBDA) developed at Sapienza Universita di Roma. Mastro manages OBDA systems in which the ontology is specified in a logic of the DL-Lite family of Description Logics specifically tailored to ontology-based data access, and is connected to external data management systems through semantic mappings that associate SQL queries over the external data to the elements of the ontology. Advanced forms of integrity constraints, which turned out to be very useful in practical applications, are also enabled over the ontologies. Optimized algorithms for answering expressive queries are provided, as well as features for intensional reasoning and consistency checking. Mastro has been successfully used in several projects carried out in collaboration with important organizations, on which we briefly comment in this paper.


title: The Role of Self-compassion in Romantic Relationships

abstract: Self-compassion (SC) involves being kind to oneself when confronting personal inadequacies or situational difficulties, framing the imperfection of life in terms of common humanity, and being mindful of negative emotions so that one neither suppresses nor ruminates on them. The current study explored whether being self-compassionate is linked to healthier romantic relationship behavior, such as being more caring and supportive rather than controlling or verbally aggressive with partners. A total of 104 couples participated in the study, with self-reported SC levels being associated with partner reports of relationship behavior. Results indicated that self-compassionate individuals displayed more positive relationship behavior than those who lacked SC. SC was also a stronger predictor of positive relationship behavior than trait self-esteem (SE) or attachment style. Finally, partners were able to accurately report on each other's SC levels, suggesting that SC is an observable trait.


title: Application-Aware Local-Global Source Deduplication for Cloud Backup Services of Personal Storage

abstract: In personal computing devices that rely on a cloud storage environment for data backup, an imminent challenge facing source deduplication for cloud backup services is the low deduplication efficiency due to a combination of the resource-intensive nature of deduplication and the limited system resources. In this paper, we present ALG-Dedupe, an Application-aware Local-Global source deduplication scheme that improves data deduplication efficiency by exploiting application awareness, and further combines local and global duplicate detection to strike a good balance between cloud storage capacity saving and deduplication time reduction. We perform experiments via prototype implementation to demonstrate that our scheme can significantly improve deduplication efficiency over the state-of-the-art methods with low system overhead, resulting in shortened backup window, increased power efficiency and reduced cost for cloud backup services of personal storage.


Ad Hoc Retrieval Experiments Using WordNet and Automatically Constructed Thesauri


title: Eating Disorders in Children and Adolescents: State of the Art Review

abstract: Despite their high prevalence, associated morbidity and mortality, and available treatment options, eating disorders (EDs) continue to be underdiagnosed by pediatric professionals. Many adolescents go untreated, do not recover, or reach only partial recovery. Higher rates of EDs are seen now in younger children, boys, and minority groups; EDs are increasingly recognized in patients with previous histories of obesity. Medical complications are common in both full and subthreshold EDs and affect every organ system. No single cause of EDs has emerged, although neurobiological and genetic predispositions are emerging as important. Recent treatment paradigms acknowledge that they are not caused by families or chosen by patients. EDs present differently in pediatric populations, and providers should have a high index of suspicion using new Diagnostic and Statistical Manual, 5th edition diagnostic criteria because early intervention can affect prognosis. Outpatient family-based treatment focused on weight restoration, reducing blame, and empowering caregivers has emerged as particularly effective; cognitive behavioral therapy, individual therapy, and higher levels of care may also be appropriate. Pharmacotherapy is useful in specific contexts. Full weight restoration is critical, often involves high-calorie diets, and must allow for continued growth and development; weight maintenance is typically inappropriate in pediatric populations. Physical, nutritional, behavioral, and psychological health are all metrics of a full recovery, and pediatric EDs have a good prognosis with appropriate care. ED prevention efforts should work toward aligning with families and understanding the impact of antiobesity efforts. Primary care providers can be key players in treatment success.


title: Hardening Stratum, the Bitcoin Pool Mining Protocol

abstract: Stratum, the de-facto mining communication protocol used by blockchain based cryptocurrency systems, enables miners to reliably and efficiently fetch jobs from mining pool servers. In this paper we exploit Stratum's lack of encryption to develop passive and active attacks on Bitcoin's mining protocol, with important implications on the privacy, security and even safety of mining equipment owners. We introduce StraTap and ISP Log attacks, that infer miner earnings if given access to miner communications, or even their logs. We develop BiteCoin, an active attack that hijacks shares submitted by miners, and their associated payouts. We build BiteCoin on WireGhost, a tool we developed to hijack and surreptitiously maintain Stratum connections. Our attacks reveal that securing Stratum through pervasive encryption is not only undesirable (due to large overheads), but also ineffective: an adversary can predict miner earnings even when given access to only packet timestamps. Instead, we devise Bedrock, a minimalistic Stratum extension that protects the privacy and security of mining participants. We introduce and leverage the mining cookie concept, a secret that each miner shares with the pool and includes in its puzzle computations, and that prevents attackers from reconstructing or hijacking the puzzles. We have implemented our attacks and collected 138MB of Stratum protocol traffic from mining equipment in the US and Venezuela. We show that Bedrock is resilient to active attacks even when an adversary breaks the crypto constructs it uses. Bedrock imposes a daily overhead of 12.03s on a single pool server that handles mining traffic from 16,000 miners.


title: A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping

abstract: We study the problem of photo cropping, which aims to find a cropping window of an input image to preserve as much as possible its important parts while being aesthetically pleasant. Seeking a deep learning-based solution, we design a neural network that has two branches for attention box prediction (ABP) and aesthetics assessment (AA), respectively. Given the input image, the ABP network predicts an attention bounding box as an initial minimum cropping window, around which a set of cropping candidates are generated with little loss of important information. Then, the AA network is employed to select the final cropping window with the best aesthetic quality among the candidates. The two sub-networks are designed to share the same full-image convolutional feature map, and thus are computationally efficient. By leveraging attention prediction and aesthetics assessment, the cropping model produces high-quality cropping results, even with the limited availability of training data for photo cropping. The experimental results on benchmark datasets clearly validate the effectiveness of the proposed approach. In addition, our approach runs at 5 fps, outperforming most previous solutions. The code and results are available at: https://github.com/shenjianbing/DeepCropping.


title: Regulatory Mode and Risk-Taking: The Mediating Role of Anticipated Regret

abstract: We propose that decision maker’s regulatory mode affects risk-taking through anticipated regret. In the Study 1 either a locomotion or an assessment orientation were experimentally induced, and in the Studies 2 and 3 these different orientations were assessed as chronic individual differences. To assess risk-taking we used two behavioral measures of risk: BART and hot-CCT. The results show that experimentally induced assessment orientation–compared to locomotion–leads to decreased risk-taking through increased anticipated regret (Study 1). People chronically predisposed to be in the assessment state take less risk through increased anticipated regret (Study 2 and Study 3). Study 2 results also show a marginally non-significant indirect effect of chronic locomotion mode on BART through anticipated regret. Differently, Study 3 shows that people chronically predisposed to be in the locomotion state take greater risk through decreased anticipated regret, when play a dynamic risk task triggering stronger emotional arousal. Through all three studies, the average effect size for the relationship of assessment with anticipated regret was in the moderate-large range, whereas for risk-taking was in the moderate range. The average effect size for the relationship of locomotion with anticipated regret was in the moderate range, whereas for risk-taking was in the small-moderate range. These results increase our understanding of human behavior under conditions of risk obtaining novel insights into regulatory mode theory and decision science.


title: Inducing Task-Relevant Responses to Speech in the Sleeping Brain

abstract: Falling asleep leads to a loss of sensory awareness and to the inability to interact with the environment [1]. While this was traditionally thought as a consequence of the brain shutting down to external inputs, it is now acknowledged that incoming stimuli can still be processed, at least to some extent, during sleep [2]. For instance, sleeping participants can create novel sensory associations between tones and odors [3] or reactivate existing semantic associations, as evidenced by event-related potentials [4–7]. Yet, the extent to which the brain continues to process external stimuli remains largely unknown. In particular, it remains unclear whether sensory information can be processed in a flexible and task-dependent manner by the sleeping brain, all the way up to the preparation of relevant actions. Here, using semantic categorization and lexical decision tasks, we studied task-relevant responses triggered by spoken stimuli in the sleeping brain. Awake participants classified words as either animals or objects (experiment 1) or as either words or pseudowords (experiment 2) by pressing a button with their right or left hand, while transitioning toward sleep. The lateralized readiness potential (LRP), an electrophysiological index of response preparation, revealed that task-specific preparatory responses are preserved during sleep. These findings demonstrate that despite the absence of awareness and behavioral responsiveness, sleepers can still extract task-relevant information from external stimuli and covertly prepare for appropriate motor responses.


title: Digital inpainting based on the Mumford-Shah-Euler image model

abstract: Image inpainting is an image restoration problem, in which image models play a critical role, as demonstrated by Chan, Kang & Shen's [12] recent inpainting schemes based on the bounded variation and the elastica [11] image models. In this paper, we propose two novel inpainting models based on the Mumford–Shah image model [41], and its high order correction – the Mumford–Shah–Euler image model. We also present their efficient numerical realization based on the Γ -convergence approximations of Ambrosio & Tortorelli [2, 3] and De Giorgi [21].


title: Microstrip Ring Resonator for Soil Moisture Measurements

abstract: Development of a successful remote sensing instrument for soil moisture relies on the accurate knowledge of the soil dielectric constant (/spl epsiv//sub soil/) to its moisture content. Two existing methods for measurement of dielectric constant of soil at low and high frequencies are, respectively, the time domain reflectometry and the reflection coefficient measurement using an open-ended coaxial probe. The major shortcoming of these methods is the lack of accurate determination of the imaginary part of E/sub soil/. In this paper, a microstrip ring resonator is proposed for the accurate measurement of soil dielectric constant. In this technique the microstrip ring resonator is placed in contact with soil medium and the real and imaginary parts of E/sub soil/ are determined from the changes in the resonant frequency and the quality factor of the resonator, respectively. The solution of the electromagnetic problem is obtained using a hybrid approach based on the method of moments solution of the quasistatic formulation in conjunction with experimental data obtained from reference dielectric samples. Also, a simple inversion algorithm for E/sub soil/=E/sup '//sub r/-j/spl epsiv/E/sup "//sub r/ based on regression analysis is obtained. It is shown that the wide dynamic range of the measured quantities provides excellent accuracy in the dielectric constant measurement. A prototype microstrip ring resonator at L-band is designed and measurements of soil with different moisture contents are presented and compared with other approaches.


title: How visual fatigue and discomfort impact 3D-TV quality of experience: a comprehensive review of technological, psychophysical, and psychological factors

abstract: The quality of experience (QoE) of 3D contents is usually considered to be the combination of the perceived visual quality, the perceived depth quality, and lastly the visual fatigue and comfort. When either fatigue or discomfort are induced, studies tend to show that observers prefer to experience a 2D version of the contents. For this reason, providing a comfortable experience is a prerequisite for observers to actually consider the depth effect as a visualization improvement. In this paper, we propose a comprehensive review on visual fatigue and discomfort induced by the visualization of 3D stereoscopic contents, in the light of physiological and psychological processes enabling depth perception. First, we review the multitude of manifestations of visual fatigue and discomfort (near triad disorders, symptoms for discomfort), as well as means for detection and evaluation. We then discuss how, in 3D displays, ocular and cognitive conflicts with real world experience may cause fatigue and discomfort; these includes the accommodation–vergence conflict, the inadequacy between presented stimuli and observers depth of focus, and the cognitive integration of conflicting depth cues. We also discuss some limits for stereopsis that constrain our ability to perceive depth, and in particular the perception of planar and in-depth motion, the limited fusion range, and various stereopsis disorders. Finally, this paper discusses how the different aspects of fatigue and discomfort apply to 3D technologies and contents. We notably highlight the need for respecting a comfort zone and avoiding camera and rendering artifacts. We also discuss the influence of visual attention, exposure duration, and training. Conclusions provide guidance for best practices and future research.


title: The Role of Students' Attitudes and Motivation in Second Language Learning in Online Language Courses

abstract: This study investigated the role of students' motivation and attitudes in second language (L2) study within an online language course context (LOL). Students' attitudes and motivation were examined within a socioeducational framework (Gardner & MacIntyre, 1993) while learning contexts were examined based on Dorneyi's (1994) components of foreign language learning motivation. Students' learning behaviors and learning outcomes were used as predictor and criterion variables in a series of quantitative and qualitative analyses. The results showed that students tended to have relatively high anxiety about the LOL course at the beginning of the semester, perhaps due to their lack of familiarity with the specific LOL learning environment. However, students' motivation and attitudes toward L2 study were relatively positive and stable during the course. The findings provided some evidence that motivated students studied regularly and productively to take every opportunity to perfect their language skills. It was also found that each teacher idiosyncratically implemented the LOL course, thereby creating a unique class culture and affecting students' motivation and attitudes toward studying the L2 in the LOL context. The findings reinforced the importance of students' motivation and attitudes in L2 study and, equally important, the continuing critical role of the teacher in technology-enhanced teaching.


title: Software Engineering Project Courses with Industrial Clients

abstract: There is an acknowledged need for teaching realistic software development in project courses. The design space for such courses is wide, ranging from single-semester to two-semester courses, from single-client to multicustomer courses, from local to globally distributed courses, and from toy projects to projects with real clients. The challenge for a nontrivial project course is how to make the project complex enough to enrich students’ software engineering experience, yet realistic enough to have a teaching environment that does not unduly burden students or the instructor. We describe a methodology for project courses that is realizable for instructors, improves students’ skills, and leads to viable results for industry partners. In particular, recent advances in release management and collaboration workflows reduce the effort of students and instructors during delivery and increase the quality of the deliverables. To enable release and feedback management, we introduce Rugby, an agile process model based on Scrum that allows reacting to changing requirements. To improve early communication, we use Tornado, a scenario-based design approach that emphasizes the use of informal models for the interaction between clients and students. The combination of Rugby and Tornado allows students to deal with changing requirements, produce multiple releases, and obtain client feedback through the duration of the course. We describe our experience with more than 300 students working on 40 projects with external clients over a 4-year period. In the latest instance of our course, the students have produced more than 7000 builds with 600 releases for eleven clients. In an evaluation of the courses, we found that the introduction of Rugby and Tornado significantly increased students’ technical skills, especially with respect to software engineering, usability engineering, and configuration management, as well as their nontechnical skills such as communication with the client, teamwork, presentation, and demo management. Finally, we discuss how other instructors can adapt the course concept.


title: Design and Development of a Virtual Reality Application for Edutainment in Cultural Heritage

abstract: In this paper we describe part of the MediaEvo project on a Digital Heritage application aimed to serve as an edutainment tool. Visitors are able to virtually explore the reconstructed heritage environment of Otranto town and learn about the medieval world through a virtual experience. The project aims to identify the methodologies related to interactive learning and an approach oriented to the instructive paths which take advantage of multi-sensory media and their level of interaction. At the moment the MediaEvo project is in progress and in this paper the obtained results are shown.


title: Giving voice to office customers: Best practices in how office handles verbatim text feedback

abstract: Microsoft Office users submit hundreds of thousands of pieces of verbatim feedback per month. How can an engineer or manager in Office find the signal in this data to make business decisions? This paper presents an overview of the Office Customer Voice (OCV) system. OCV combines classification, on-demand clustering and other machine learning techniques with a rich web UI to solve this problem. In this paper, we describe the different types of feedback received. Next, we outline the architecture used to build OCV. We then detail the text processing, classification and clustering done to reason on the data. Finally, we present challenges, future plans, and best practices that may be relevant to other teams analyzing customer feedback. We argue that this multi-pronged approach to handling customer feedback presents a pattern that other organizations can use to mature their handling of customer feedback.


title: Visual Analytics for Ontology Matching Using Multi-linked Views

abstract: Ontology matching is the key to data integration on the Semantic Web. Advanced ontology matching systems incorporate a variety of algorithms. However, they do not always guarantee a complete and correct alignment (set of mappings). Hence, user involvement in the matching process is essential for complex ontologies. In this paper, we explore the power of multi-linked views, where actions in one view affect the display of the other views, thereby extending significantly the state of the art in ontology matching visualization in general and that of visual analytics for ontology matching in particular. A preliminary assessment of our approach that uses the ontologies of the OAEI Conference Track points to the effectiveness of our approach.


title: Personality & Emotional States: Understanding Users' Music Listening Needs

abstract: Music plays an important part in people’s lives to regulate their emotions throughout the day. We conducted an online user study to investigate how the emotional state relates to the use of emotionally laden music. We found among 359 participants that they in general prefer emotionally laden music that correspond with their emotional state. However, when looking at personality traits, different patterns occurred. We found that when in a negative emotional state, those who scored high on openness, extraversion, and agreeableness tend to cheer themselves up with happy music, while those who scored high on neuroticism tend to increase their worry with sad music. With our results we show general patterns of music usage, but also individual differences. Our results contribute to the improvement of applications such as recommender systems in order to provide tailored recommendations based on users’ personality and emotional state.


title: An RTP Payload Format for MIDI

abstract: This memo describes a Real-time Transport Protocol (RTP) payload ::: format for the MIDI (Musical Instrument Digital Interface) command ::: language. The format encodes all commands that may legally appear on a ::: MIDI 1.0 DIN cable. The format is suitable for interactive ::: applications (such as network musical performance) and content- ::: delivery applications (such as file streaming). The format may be used ::: over unicast and multicast UDP and TCP, and it defines tools for ::: graceful recovery from packet loss. Stream behavior, including the ::: MIDI rendering method, may be customized during session setup. The ::: format also serves as a mode for the mpeg4-generic format, to support ::: the MPEG 4 Audio Object Types for General MIDI, Downloadable Sounds ::: Level 2, and Structured Audio.


title: What Do We Understand About Convolutional Networks?

abstract: This document will review the most prominent proposals using multilayer convolutional architectures. Importantly, the various components of a typical convolutional network will be discussed through a review of different approaches that base their design decisions on biological findings and/or sound theoretical bases. In addition, the different attempts at understanding ConvNets via visualizations and empirical studies will be reviewed. The ultimate goal is to shed light on the role of each layer of processing involved in a ConvNet architecture, distill what we currently understand about ConvNets and highlight critical open problems.


title: The Staircase to Terrorism: A Psychological Exploration.

abstract: To foster a more in-depth understanding of the psychological processes leading to terrorism, the author conceptualizes the terrorist act as the final step on a narrowing staircase. Although the vast majority of people, even when feeling deprived and unfairly treated, remain on the ground floor, some individuals climb up and are eventually recruited into terrorist organizations. These individuals believe they have no effective voice in society, are encouraged by leaders to displace aggression onto out-groups, and become socialized to see terrorist organizations as legitimate and out-group members as evil. The current policy of focusing on individuals already at the top of the staircase brings only short-term gains. The best long-term policy against terrorism is prevention, which is made possible by nourishing contextualized democracy on the ground floor. Language: en


title: Healthy Schools-Healthy Kids: A controlled evaluation of a comprehensive universal eating disorder prevention program

abstract: This study was a controlled evaluation of a comprehensive school-based universal prevention program involving male and female students, parents, teachers, school administrators and local public health professionals. A total of 982 male and female Grades 6 and 7 middle school students (and 91 teachers/school administrators) completed self-report surveys at baseline on measures of body satisfaction, internalization of media ideals, size acceptance, disordered eating, weight-based teasing, weight loss and muscle-gaining behaviours, and perceptions of school climate (teachers only). Eighty-four percent of the students repeated the surveys immediately following the 8-month school-wide intervention and 71% again 6 months later. Repeated measures ANCOVAs revealed that participation in the Healthy Schools-Healthy Kids (HS-HK) program had a positive influence by reducing the internalization of media ideals among male and female students and by reducing disordered eating among female students. The program was also associated with reductions in weight-loss behaviours among the students, although this effect was lost by the 6-month follow-up. When the intervention students were sub-divided into low versus high-risk groups, the high-risk group appeared to benefit most from the intervention with significant reductions in internalization of media ideals, greater body satisfaction, and reduced disordered eating over time. There were no intervention effects for teachers. Challenges of engaging teachers in prevention are discussed.


title: Distributed Immutabilization of Secure Logs

abstract: Several applications require robust and tamper-proof logging systems, e.g. electronic voting or bank information systems. At Scytl we use a technology, called immutable logs, that we deploy in our electronic voting solutions. This technology ensures the integrity, authenticity and non-repudiation of the generated logs, thus in case of any event the auditors can use them to investigate the issue. As a security recommendation it is advisable to store and/or replicate the information logged in a location where the logger has no writing or modification permissions. Otherwise, if the logger gets compromised, the data previously generated could be truncated or altered using the same private keys. This approach is costly and does not protect against collusion between the logger and the entities that hold the replicated data. In order to tackle these issues, in this article we present a proposal and implementation to immutabilize integrity proofs of the secure logs within the Bitcoin’s blockchain. Due to the properties of the proposal, the integrity of the immutabilized logs is guaranteed without performing log data replication and even in case the logger gets latterly compromised.


title: Modeling and Simulation of Scalable Cloud Computing Environments and the CloudSim Toolkit: Challenges and Opportunities

abstract: Cloud computing aims to power the next generation data centers and enables application service providers to lease data center capabilities for deploying applications depending on user QoS (Quality of Service) requirements. Cloud applications have different composition, configuration, and deployment requirements. Quantifying the performance of resource allocation policies and application scheduling algorithms at finer details in Cloud computing environments for different application and service models under varying load, energy performance (power consumption, heat dissipation), and system size is a challenging problem to tackle. To simplify this process, in this paper we propose CloudSim: an extensible simulation toolkit that enables modelling and simulation of Cloud computing environments. The CloudSim toolkit supports modelling and creation of one or more virtual machines (VMs) on a simulated node of a Data Center, jobs, and their mapping to suitable VMs. It also allows simulation of multiple Data Centers to enable a study on federation and associated policies for migration of VMs for reliability and automatic scaling of applications.


title: Power Efficient Resource Allocation for Clouds Using Ant Colony Framework

abstract: Cloud computing is one of the rapidly improving technologies. It provides scalable resources needed for the ap- plications hosted on it. As cloud-based services become more dynamic, resource provisioning becomes more challenging. The QoS constrained resource allocation problem is considered in this paper, in which customers are willing to host their applications on the provider's cloud with a given SLA requirements for performance such as throughput and response time. Since, the data centers hosting the applications consume huge amounts of energy and cause huge operational costs, solutions that reduce energy consumption as well as operational costs are gaining importance. In this work, we propose an energy efficient mechanism that allocates the cloud resources to the applications without violating the given service level agreements(SLA) using Ant colony framework.


What constitutes “ style ” in authorship attribution ?


title: MT2DInvMatlab-A program in MATLAB and FORTRAN for two-dimensional magnetotelluric inversion

abstract: MT2DInvMatlab is an open-source MATLAB^(R) software package for two-dimensional (2D) inversion of magnetotelluric (MT) data; it is written in mixed languages of MATLAB and FORTRAN. MT2DInvMatlab uses the finite element method (FEM) to compute 2D MT model responses, and smoothness-constrained least-squares inversion with a spatially variable regularization parameter algorithm to stabilize the inversion process and provide a high-resolution optimal earth model. It is also able to include terrain effects in inversion by incorporating topography into a forward model. This program runs under the MATLAB environment so that users can utilize the existing general interface of MATLAB, while some specific functions are written in FORTRAN 90 to speed up computation and reuse pre-existing FORTRAN code in the MATLAB environment with minimal modification. This program has been tested using synthetic models, including one with variable topography, and on field data. The results were assessed by comparing inverse models obtained with MT2DInvMatlab and with a non-linear conjugate gradient (NLCG) algorithm. In both tests the new inversion software reconstructs the subsurface resistivity structure very closely and provides an improvement in both resolution and stability.


title: Clarifying the roles of homeostasis and allostasis in physiological regulation.

abstract: Homeostasis, the dominant explanatory framework for physiological regulation, has undergone significant revision in recent years, with contemporary models differing significantly from the original formulation. Allostasis, an alternative view of physiological regulation, goes beyond its homeostatic roots, offering novel insights relevant to our understanding and treatment of several chronic health conditions. Despite growing enthusiasm for allostasis, the concept remains diffuse, due in part to ambiguity as to how the term is understood and used, impeding meaningful translational and clinical research on allostasis. Here we provide a more focused understanding of homeostasis and allostasis by explaining how both play a role in physiological regulation, and a critical analysis of regulation suggests how homeostasis and allostasis can be distinguished. Rather than focusing on changes in the value of a regulated variable (e.g., body temperature, body adiposity, or reward), research investigating the activity and relationship among the multiple regulatory loops that influence the value of these regulated variables may be the key to distinguishing homeostasis and allostasis. The mechanisms underlying physiological regulation and dysregulation are likely to have important implications for health and disease.


Patterns of power: Religion and politics in American culture


title: Understanding how consumers categorise nutritional labels: A consumer derived typology for front-of-pack nutrition labelling

abstract: Significant ongoing debate exists amongst stakeholders as to the best front-of-pack labelling approach and emerging evidence suggests that the plethora of schemes may cause confusion for the consumer. To gain a better understanding of the relevant psychological phenomena and consumer perspectives surrounding FoP labelling schemes and their optimal development a Multiple Sort Procedure study involving free sorting of a range of nutritional labels presented on cards was performed in four countries (n=60). The underlying structure of the qualitative data generated was explored using Multiple Scalogram Analysis. Elicitation of categorisations from consumers has the potential to provide a very important perspective in this arena and results demonstrated that the amount of information contained within a nutrition label has high salience for consumers, as does the health utility of the label although a dichotomy exists in the affective evaluation of the labels containing varying degrees of information aggregation. Classification of exiting front-of-pack labelling systems on a proposed dimension of 'directiveness' leads to a better understanding of why some schemes may be more effective than others in particular situations or for particular consumers. Based on this research an enhanced hypothetical front-of-pack labelling scheme which combines both directive and non-directive elements is proposed.


title: Efficient mutation analysis by propagating and partitioning infected execution states

abstract: Mutation analysis evaluates a testing technique by measur- ing how well it detects seeded faults (mutants). Mutation analysis is hampered by inherent scalability problems — a test suite is executed for each of a large number of mutants. Despite numerous optimizations presented in the literature, this scalability issue remains, and this is one of the reasons why mutation analysis is hardly used in practice. Whereas most previous optimizations attempted to stati- cally reduce the number of executions or their computational overhead, this paper exploits information available only at run time to further reduce the number of executions. First, state infection conditions can reveal — with a single test execution of the unmutated program — which mutants would lead to a different state, thus avoiding unnecessary test executions. Second, determining whether an infected execution state propagates can further reduce the number of executions. Mutants that are embedded in compound expressions may infect the state locally without affecting the outcome of the compound expression. Third, those mutants that do infect the state can be partitioned based on the resulting infected state — if two mutants lead to the same infected state, only one needs to be executed as the result of the other can be inferred. We have implemented these optimizations in the Major mu- tation framework and empirically evaluated them on 14 open source programs. The optimizations reduced the mutation analysis time by 40% on average.


It Don't Mean a Thing (If It Ain't Got That Swing): Bateson's Epistemology and the Rhythms of Life


title: Diversity by proportionality: an election-based approach to search result diversification

abstract: This paper presents a different perspective on diversity in search results: diversity by proportionality. We consider a result list most diverse, with respect to some set of topics related to the query, when the number of documents it provides on each topic is proportional to the topic's popularity. Consequently, we propose a framework for optimizing proportionality for search result diversification, which is motivated by the problem of assigning seats to members of competing political parties. Our technique iteratively determines, for each position in the result ranked list, the topic that best maintains the overall proportionality. It then selects the best document on this topic for this position. We demonstrate empirically that our method significantly outperforms the top performing approach in the literature not only on our proposed metric for proportionality, but also on several standard diversity measures. This result indicates that promoting proportionality naturally leads to minimal redundancy, which is a goal of the current diversity approaches.


title: Labia Majora Augmentation with De-epithelialized Labial Rim (Minora) Flaps as an Auxiliary Procedure for Labia Minora Reduction

abstract: Background ::: Esthetic surgery of external female genitalia remains an uncommon procedure. This article describes a novel, de-epithelialized, labial rim flap technique for labia majora augmentation using de-epithelialized labia minora tissue otherwise to be excised as an adjunct to labia minora reduction.


title: Coherent receiver for AIS satellite detection

abstract: AIS is a universal shipborne Automatic Identification System recently introduced for traffic monitoring and safety at sea. Ships exchange navigational information to identify and localize vessels in a short range to avoid collisions. AIS signals use Gaussian Minimum Shift Keying (GMSK) for transmission.


What ’ s good for the goose is good for the GANder Comparing Generative Adversarial Networks for NLP


title: Times Series Regression Methods for the Evaluation of the Causes of Fluctuation in Fishery Stock Sizes

abstract: The present paper constitutes a preliminary report on the formulation end development of statistical approaches to partition the effects of fishing mortality, natural mortality and pollution mortality caused by habitat degradation on fishery stocks in estuarine waters. The selected approaches are illustrated using a data set involving striped bass in the Potomac river.


title: Efficient Computation and Model Selection for the Support Vector Regression

abstract: In this letter, we derive an algorithm that computes the entire solution path of the support vector regression (SVR). We also propose an unbiased estimate for the degrees of freedom of the SVR model, which allows convenient selection of the regularization parameter.


title: Leveraging 3D PCRAM technologies to reduce checkpoint overhead for future exascale systems

abstract: The scalability of future massively parallel processing (MPP) systems is challenged by high failure rates. Current hard disk drive (HDD) checkpointing results in overhead of 25% or more at the petascale. With a direct correlation between checkpoint frequencies and node counts, novel techniques that can take more frequent checkpoints with minimum overhead are critical to implement a reliable exascale system. In this work, we leverage the upcoming Phase-Change Random Access Memory (PCRAM) technology and propose a hybrid local/global checkpointing mechanism after a thorough analysis of MPP systems failure rates and failure sources. We propose three variants of PCRAM-based hybrid checkpointing schemes, DIMM+HDD, DIMM+DIMM, and 3D+3D, to reduce the checkpoint overhead and offer a smooth transition from the conventional pure HDD checkpoint to the ideal 3D PCRAM mechanism. The proposed pure 3D PCRAM-based mechanism can ultimately take checkpoints with overhead less than 4% on a projected exascale system.


title: Smart City Architecture: Vision and Challenges

abstract: The concept of smart city was born to provide improved quality of life to citizens. The key idea is to integrate information system services of each domain, such as health, education, transportation, power grid etc., of the city to provide public services to citizens efficiently and ubiquitously. These expectations induce massive challenges and requirements. This research is aimed to highlight key ICT (Information and Communication Technology) challenges related to adaptation of smart city. Realizing the significance of effective data collection, storage, retrieval, and efficient network resource provisioning, the research proposes a high level architecture for smart city. The proposed framework is based on a hierarchical model of data storage and defines how different stakeholders will be communicating and offering services to citizens. The architecture facilitates step by step implementation towards a smart city, integrating services, as they are developed in a timely manner.