title: Virtual autopsy using imaging: bridging radiologic and forensic sciences. A review of the Virtopsy and similar projects

abstract: The transdisciplinary research project Virtopsy is dedicated to implementing modern imaging techniques into forensic medicine and pathology in order to augment current examination techniques or even to offer alternative methods. Our project relies on three pillars: three-dimensional (3D) surface scanning for the documentation of body surfaces, and both multislice computed tomography (MSCT) and magnetic resonance imaging (MRI) to visualise the internal body. Three-dimensional surface scanning has delivered remarkable results in the past in the 3D documentation of patterned injuries and of objects of forensic interest as well as whole crime scenes. Imaging of the interior of corpses is performed using MSCT and/or MRI. MRI, in addition, is also well suited to the examination of surviving victims of assault, especially choking, and helps visualise internal injuries not seen at external examination of the victim. Apart from the accuracy and three-dimensionality that conventional documentations lack, these techniques allow for the re-examination of the corpse and the crime scene even decades later, after burial of the corpse and liberation of the crime scene. We believe that this virtual, non-invasive or minimally invasive approach will improve forensic medicine in the near future.


title: A 60 GHz linear wideband power amplifier using cascode neutralization in 28 nm CMOS

abstract: The rapid scaling of CMOS technology in the last decade has enabled the design of high speed and efficient digital CMOS circuits. However, the design of RF and mm-wave systems has become more challenging due to inaccuracies in modeling and increased losses in the active and passive devices. This paper presents the design of a 60 GHz linear wideband power amplifier (PA) in deeply scaled 28 nm CMOS technology. The PA utilizes cascode drain-source neutralization to improve stability and low-k transformer techniques to achieve high bandwidth. Using transmission line power combining, the PA delivers a saturated output power of 16.5 dBm with a peak power added efficiency (PAE) of 12.6%. The three stage PA achieves an overall bandwidth of 11 GHz with a peak gain of 24.4 dB.


title: An Autonomous Steering System for Wireless Charging Electric Vehicles to Minimize Power Degradation

abstract: Vehicles with wireless power transfer (WPT) is seen as one possible solution to overcome the limitations experienced in commercializing electric vehicles (EVs) due to limited battery power. However, one issue with vehicles with WPT is the power degradation that is experienced as misalignment occurs between the vehicle and the road. In order to overcome this problem, a system that can detect the misalignment between the coils and align the vehicle through autonomous steering is proposed in this paper. The concept of this system has been discussed, and simulation results validate that the proposed system's operational concept is feasible, and will be able to reduce power degradation from misalignment.


Fifty years of European history through the Lens of Computational Linguistics : the De Gasperi Project


title: Zero Norm Least Squares Proximal SVR

abstract: Least Squares Proximal Support Vector Regression (LSPSVR) requires only a single matrix inversion to obtain the Lagrange Multipliers as opposed to solving a Quadratic Programming Problem (QPP) for the conventional SVM optimization problem. However, like other least squares based methods, LSPSVR suffers from lack of sparseness. Most of the Lagrange multipliers are non-zero and thus the determination of the separating hyperplane requires a large number of data points. Large zero norm of Lagrange multipliers inevitably leads to a large kernel matrix that is inappropriate for fast regression on large datasets. This paper suggests how the LSPSVR formulation may be recast into one that also tries to minimize the zero norm of the vector of Lagrange multipliers, and in effect imposes sparseness. Experimental results on benchmark data show that a significant decrease in the number of support vectors can be achieved without a concomitant increase in the error.


title: Malicious Software Classification Using VGG16 Deep Neural Network’s Bottleneck Features

abstract: Malicious software (malware) has been extensively employed for illegal purposes and thousands of new samples are discovered every day. The ability to classify samples with similar characteristics into families makes possible to create mitigation strategies that work for a whole class of programs. In this paper, we present a malware family classification approach using VGG16 deep neural network’s bottleneck features. Malware samples are represented as byteplot grayscale images and the convolutional layers of a VGG16 deep neural network pre-trained on the ImageNet dataset is used for bottleneck features extraction. These features are used to train a SVM classifier for the malware family classification task. The experimental results on a dataset comprising 10,136 samples from 20 different families showed that our approach can effectively be used to classify malware families with an accuracy of 92.97%, outperforming similar approaches proposed in the literature which require feature engineering and considerable domain expertise.


title: On-Body Adhesive-Bandage-Like Antenna for Wireless Medical Telemetry Service

abstract: This paper presents a novel planar, via-free, printed antenna for wireless medical telemetry service (WMTS). The antenna structure is simple and looks like an adhesive bandage, and can be placed on human tissue. Depending on the application, the antenna can be used as a standalone antenna when it is fed by a coaxial cable, or it can be integrated with a single-chip sensor, with the chip placed in the center. Antenna parameters, such as reflection coefficients, radiation patterns, radiation efficiency, and the specific absorption rate, were evaluated in various scenarios to validate the proposed design. The measured radiation efficiency of the proposed adhesive-bandage-like antenna was 89.5% in free space and 44.7% when mounted on tissue-equivalent phantom. The antenna also retained its broad side radiation characteristics when it was bent at 90 °. The proposed antenna is a favorable candidate for use in wireless body area networks (WBANs). This paper presents the detailed design considerations of the proposed antenna.


title: Deep Learning to Detect Redundant Method Comments

abstract: Comments in software are critical for maintenance and reuse. But apart from prescriptive advice, there is little practical support or quantitative understanding of what makes a comment useful. In this paper, we introduce the task of identifying comments which are uninformative about the code they are meant to document. To address this problem, we introduce the notion of comment entailment from code, high entailment indicating that a comment's natural language semantics can be inferred directly from the code. Although not all entailed comments are low quality, comments that are too easily inferred, for example, comments that restate the code, are widely discouraged by authorities on software style. Based on this, we develop a tool called CRAIC which scores method-level comments for redundancy. Highly redundant comments can then be expanded or alternately removed by the developer. CRAIC uses deep language models to exploit large software corpora without requiring expensive manual annotations of entailment. We show that CRAIC can perform the comment entailment task with good agreement with human judgements. Our findings also have implications for documentation tools. For example, we find that common tags in Javadoc are at least two times more predictable from code than non-Javadoc sentences, suggesting that Javadoc tags are less informative than more free-form comments


title: Focused Hierarchical RNNs for Conditional Sequence Processing

abstract: Recurrent Neural Networks (RNNs) with attention mechanisms have obtained state-of-the-art results for many sequence processing tasks. Most of these models use a simple form of encoder with attention that looks over the entire sequence and assigns a weight to each token independently. We present a mechanism for focusing RNN encoders for sequence modelling tasks which allows them to attend to key parts of the input as needed. We formulate this using a multi-layer conditional sequence encoder that reads in one token at a time and makes a discrete decision on whether the token is relevant to the context or question being asked. The discrete gating mechanism takes in the context embedding and the current hidden state as inputs and controls information flow into the layer above. We train it using policy gradient methods. We evaluate this method on several types of tasks with different attributes. First, we evaluate the method on synthetic tasks which allow us to evaluate the model for its generalization ability and probe the behavior of the gates in more controlled settings. We then evaluate this approach on large scale Question Answering tasks including the challenging MS MARCO and SearchQA tasks. Our models shows consistent improvements for both tasks over prior work and our baselines. It has also shown to generalize significantly better on synthetic tasks as compared to the baselines.


title: Rethinking Blockchain Security: Position Paper

abstract: Blockchain technology has become almost as famous for incidents involving security breaches as for its innovative potential. We shed light on the prevalence and nature of these incidents through a database structured using the STIX format. Apart from OPSEC-related incidents, we find that the nature of many incidents is specific to blockchain technology. Two categories stand out: smart contracts, and techno-economic protocol incentives. For smart contracts, we propose to use recent advances in software testing to find flaws before deployment. For protocols, we propose the PRESTO framework that allows us to compare different protocols within a five-dimensional framework.


title: S3D: Stacking Segmental P3D for Action Quality Assessment

abstract: Action quality assessment is crucial in areas of sports, surgery and assembly line where action skills can be evaluated. In this paper, we propose the Segment-based P3D-fused network S3D built-upon ED-TCN and push the performance on the UNLV-Dive dataset by a significant margin. We verify that segment-aware training performs better than full-video training which turns out to focus on the water spray. We show that temporal segmentation can be embedded with few efforts.


title: Graph Convolutional Networks based Word Embeddings

abstract: Recently, word embeddings have been widely adopted across several NLP applications. However, most word embedding methods solely rely on linear context and do not provide a framework for incorporating word relationships like hypernym, nmod in a principled manner. In this paper, we propose WordGCN, a Graph Convolution based word representation learning approach which provides a framework for exploiting multiple types of word relationships. WordGCN operates at sentence as well as corpus level and allows to incorporate dependency parse based context in an efficient manner without increasing the vocabulary size. To the best of our knowledge, this is the first approach which effectively incorporates word relationships via Graph Convolutional Networks for learning word representations. Through extensive experiments on various intrinsic and extrinsic tasks, we demonstrate WordGCN's effectiveness over existing word embedding approaches. We make WordGCN's source code available to encourage reproducible research.


title: Corporal Punishment and Kids: How Do Parent Support and Gender Influence Child Adjustment?

abstract: Previous research on corporal punishment has failed to consider the interaction of parent support and parent gender in predicting child outcomes. The current study examined whether parental support moderated the effects of corporal punishment on child outcomes (i.e., depression and aggression), and more specifically, whether the gender of the supportive parent moderated the effects of punishment from the opposite-sex parent. Results differed depending on the gender of the punishing and supportive parents, suggesting that parental support can be a protective factor in child outcomes but only under certain conditions. Mother support moderated the effects of father punishment on child depression but not child aggression. High corporal punishment by father was related to more child depression at both high and low levels of mother support. High levels of mother support only seemed important (i.e., children were less depressed) at low levels of father corporal punishment. In contrast, father support moderated the relationship between mother corporal punishment and child aggression but not depression. Children with high father support showed less aggression across all levels of mother corporal punishment. At low levels of father support, child aggression increased as mother corporal punishment increased. For depression, mother corporal punishment was positively related to child depression regardless of level of father support. These findings suggest differential effects for mother and father support and have implications for the treatment and prevention of negative outcomes in children who are physically punished by their parents.


title: Mitigation of DDoS attacks in cloud computing

abstract: Cloud computing provided its users with the more convenient way to use the resources and with a model where the users are charged as per the usage of their resources. The model is known as “pay-as-per-usage”. Users can access the cloud services from anywhere at any time. They only need a working internet connection. Despite of all these benefits, cloud comes with some drawbacks also. The issue related to security of cloud is a biggest concern today. The attackers and hackers flood the network with attack packets and these are hard to identify as everything present on cloud is in virtual form. DDoS (Distributed Denial of Service) is a cloud-specific attack in which attack source is always more than one; multiple machines attacks on a user by sending packets with large data overhead. Such attacks make the resources unavailable to the user by overwhelming the network with unwanted traffic. In this research paper the main aim is to filter out the data packets with large data overhead to avoid DDoS attack and the Mean Time to Security Failure (MTSF) will be calculated so that an alternative dynamic plan can be adopted.


Analysis of the conformations of the HIV-1 protease from a large crystallographic data set


title: Multi-robot Task Allocation: A Review of the State-of-the-Art

abstract: Multi-robot systems (MRS) are a group of robots that are designed aiming to perform some collective behavior. By this collective behavior, some goals that are impossible for a single robot to achieve become feasible and attainable. There are several foreseen benefits of MRS compared to single robot systems such as the increased ability to resolve task complexity, increasing performance, reliability and simplicity in design. These benefits have attracted many researchers from academia and industry to investigate how to design and develop robust versatile MRS by solving a number of challenging problems such as complex task allocation, group formation, cooperative object detection and tracking, communication relaying and self-organization to name just a few. One of the most challenging problems of MRS is how to optimally assign a set of robots to a set of tasks in such a way that optimizes the overall system performance subject to a set of constraints. This problem is known as Multi-robot Task Allocation (MRTA) problem. MRTA is a complex problem especially when it comes to heterogeneous unreliable robots equipped with different capabilities that are required to perform various tasks with different requirements and constraints in an optimal way. This chapter provides a comprehensive review on challenging aspects of MRTA problem, recent approaches to tackle this problem and the future directions.


title: An analysis of the use and effect of questions in interactive learning-videos

abstract: This study focuses on the positioning of interactive questions within learning videos. It is attempted to show that the position of a question’s occurrence has an impact on the correctness rate of its answer and the learning success. As part of the study, the interactive learning videos in which the questions are placed are used as teaching materials with a class. The pupils have been working with the videos for around one month and some interesting results could be obtained. It is shown that questions which are asked too early in the videos are answered incorrectly more often than other questions. This manuscript also recommends an adequate positioning of the first question in learning videos. The new hypothesis that the length of intervals between popping up questions plays a minor role at rather short learning videos is constructed in this publication. Moreover, the positive impact on the long-term learning success of the participants of learning videos is determined.


title: Mining massive fine-grained behavior data to improve predictive analytics

abstract: Organizations increasingly have access to massive, fine-grained data on consumer behavior. Despite the hype over "big data," and the success of predictive analytics, only a few organizations have incorporated such finegrained data in a non-aggregated manner into their predictive analytics. This paper examines the use of massive, fine-grained data on consumer behavior—specifically payments to a very large set of particular merchants—to improve predictive models for targeted marketing. The paper details how using this different sort of data can substantially improve predictive performance, even in an application for which predictive analytics has been applied for years. One of the most striking results has important implications for managers considering the value of big data. Using a real-life data set of 21 million transactions by 1.2 million customers, as well as 289 other variables describing these customers, the results show that there is no appreciable improvement from moving to big data when using traditional structured data. However, in contrast, when using fine-grained behavior data, there continues to be substantial value to increasing the data size across the entire range of the analyses. This suggests that larger firms may have substantially more valuable data assets than smaller firms, when using their transaction data for targeted marketing.


title: Kinematic and dynamic analysis of an anatomically based knee joint.

abstract: This paper presents a knee-joint model to provide a better understanding on the interaction between natural joints and artificial mechanisms for design and control of rehabilitation exoskeletons. The anatomically based knee model relaxes several commonly made assumptions that approximate a human knee as engineering pin-joint in exoskeleton design. Based on published MRI data, we formulate the kinematics of a knee-joint and compare three mathematical approximations; one model bases on two sequential circles rolling a flat plane; and the other two are mathematically differentiable ellipses-based models with and without sliding at the contact. The ellipses-based model taking sliding contact into accounts shows that the rolling-sliding ratio of a knee-joint is not a constant but has an average value consistent with published measurements. This knee-joint kinematics leads to a physically more accurate contact-point trajectory than methods based on multiple circles or lines, and provides a basis to derive a knee-joint kinetic model upon which the effects of a planar exoskeleton mechanism on the internal joint forces and torque during flexion can be numerically investigated. Two different knee-joint kinetic models (pin-joint approximation and anatomically based model) are compared against a condition with no exoskeleton. The leg and exoskeleton form a closed kinematic chain that has a significant effect on the joint forces in the knee. Human knee is more tolerant than pin-joint in negotiating around a singularity but its internal forces increase with the exoskeleton mass-to-length ratio. An oversimplifying pin-joint approximation cannot capture the finite change in the knee forces due to the singularity effect.


title: Between-brain coherence during joint n-back task performance: A two-person functional near-infrared spectroscopy study

abstract: Abstract The present study aimed to step into two-person neuroscience by investigating the hemodynamic correlates of between-brain connectivity during joint task performance. To test this approach, wireless functional near-infrared spectroscopy (fNIRS) was used to record brain signals during performance of a dual n-back task simultaneously in paired players as compared to single players. Evaluating functional connectivity between the paired players’ brains using wavelet transform coherence (WTC) analysis revealed (1) a significant increase in between-brain coherence during joint task performance as compared to baseline condition. These patterns were observed in two frequency bands, i.e. in the heart rate (HR) frequency and in low-frequency oscillations (LFOs). (2) Averaged hemodynamic responses revealed larger responses in total hemoglobin concentration changes [tHb] for the paired players as compared to the single players; in addition, within the paired players groups joint task performance revealed larger changes in [tHb] as compared to a rest period and to a baseline condition. (3) No increase in behavioral performance was found in the paired players as compared to the single players. Our findings designate fNIRS as suitable tool for monitoring interpersonal performances between two subjects. The results show that two-person performance leads to relevant and significant effects, which are detectable using between-brain connectivity analysis. Using this approach can provide additional insight into interpersonal activation patterns not detectable using typical one-person experiments. Our study demonstrates the potential of simultaneously assessing cerebral hemodynamic responses for various two-person experimental paradigms and research areas where interpersonal performances are involved.


title: A refrigeration process and the production of liquefied natural gas

abstract: Utilizing a refrigeration cycle, a method for the production of liquefied natural gas, said method characterized by the following steps: i) pretreatment of the natural gas stream; ii) pretreatment of the gas stream obtained or the refrigeration cycle refrigerant stream in one or all of the cooling; and iii) the liquefied natural gas.


title: Smart Multi-Level Tool for Remote Patient Monitoring Based on a Wireless Sensor Network and Mobile Augmented Reality

abstract: Technological innovations in the field of disease prevention and maintenance of patient health have enabled the evolution of fields such as monitoring systems. One of the main advances is the development of real-time monitors that use intelligent and wireless communication technology. In this paper, a system is presented for the remote monitoring of the body temperature and heart rate of a patient by means of a wireless sensor network (WSN) and mobile augmented reality (MAR). The combination of a WSN and MAR provides a novel alternative to remotely measure body temperature and heart rate in real time during patient care. The system is composed of (1) hardware such as Arduino microcontrollers (in the patient nodes), personal computers (for the nurse server), smartphones (for the mobile nurse monitor and the virtual patient file) and sensors (to measure body temperature and heart rate), (2) a network layer using WiFly technology, and (3) software such as LabView, Android SDK, and DroidAR. The results obtained from tests show that the system can perform effectively within a range of 20 m and requires ten minutes to stabilize the temperature sensor to detect hyperthermia, hypothermia or normal body temperature conditions. Additionally, the heart rate sensor can detect conditions of tachycardia and bradycardia.


title: Performance thresholding in practical text classification

abstract: In practical classification, there is often a mix of learnable and unlearnable classes and only a classifier above a minimum performance threshold can be deployed. This problem is exacerbated if the training set is created by active learning. The bias of actively learned training sets makes it hard to determine whether a class has been learned. We give evidence that there is no general and efficient method for reducing the bias and correctly identifying classes that have been learned. However, we characterize a number of scenarios where active learning can succeed despite these difficulties.


title: Generating Impact-Based Summaries for Scientific Literature

abstract: In this paper, we present a study of a novel summarization problem, i.e., summarizing the impact of a scientic publication. Given a paper and its citation context, we study how to extract sentences that can represent the most inuential content of the paper. We propose language modeling methods for solving this problem, and study how to incorporate features such as authority and proximity to accurately estimate the impact language model. Experiment results on a SIGIR publication collection show that the proposed methods are effective for generating impact-based summaries.


title: A Study of Security Isolation Techniques

abstract: Security isolation is a foundation of computing systems that enables resilience to different forms of attacks. This article seeks to understand existing security isolation techniques by systematically classifying different approaches and analyzing their properties. We provide a hierarchical classification structure for grouping different security isolation techniques. At the top level, we consider two principal aspects: mechanism and policy. Each aspect is broken down into salient dimensions that describe key properties. We break the mechanism into two dimensions, enforcement location and isolation granularity, and break the policy aspect down into three dimensions: policy generation, policy configurability, and policy lifetime. We apply our classification to a set of representative articles that cover a breadth of security isolation techniques and discuss tradeoffs among different design choices and limitations of existing approaches.


title: When Are Tree Structures Necessary for Deep Learning of Representations?

abstract: Recursive neural models, which use syntactic parse trees to recursively generate representations bottom-up, are a popular architecture. However there have not been rigorous evaluations showing for exactly which tasks this syntax-based method is appropriate. In this paper, we benchmark recursive neural models against sequential recurrent neural models, enforcing applesto-apples comparison as much as possible. We investigate 4 tasks: (1) sentiment classification at the sentence level and phrase level; (2) matching questions to answerphrases; (3) discourse parsing; (4) semantic relation extraction. Our goal is to understand better when, and why, recursive models can outperform simpler models. We find that recursive models help mainly on tasks (like semantic relation extraction) that require longdistance connection modeling, particularly on very long sequences. We then introduce a method for allowing recurrent models to achieve similar performance: breaking long sentences into clause-like units at punctuation and processing them separately before combining. Our results thus help understand the limitations of both classes of models, and suggest directions for improving recurrent models.


title: Community detection methods can discover better structural clusters than ground-truth communities

abstract: Community detection emerged as an important exploratory task in complex networks analysis across many scientific domains. Many methods have been proposed to solve this problem, each one with its own mechanism and sometimes with a different notion of community. In this article, we bring most common methods in the literature together in a comparative approach and reveal their performances in both real-world networks and synthetic networks. Surprisingly, many of those methods discovered better communities than the declared ground-truth communities in terms of some topological goodness features, even on benchmarking networks with built-in communities. We illustrate different structural characteristics that these methods could identify in order to support users to choose an appropriate method according to their specific requirements on different structural qualities.


title: Colour Dynamic Photometric Stereo for Textured Surfaces

abstract: In this paper we present a novel method to apply photometric stereo on textured dynamic surfaces. We aim at exploiting the high accuracy of photometric stereo and reconstruct local surface orientation from illumination changes. The main difficulty derives from the fact that photometric stereo requires varying illumination while the object remains still, which makes it quite impractical to use for dynamic surfaces. Using coloured lights gives a clear solution to this problem; however, the system of equations is still ill-posed and it is ambiguous whether the change of an observed surface colour is due to the change of the surface gradient or of the surface reflectance. ::: ::: In order to separate surface orientation from reflectance, our method tracks texture changes over time and exploits surface reflectance's temporal constancy. This additional constraint allows us to reformulate the problem as an energy functional minimisation, solved by a standard quasi-Newton method. Our method is tested both on real and synthetic data, quantitatively evaluated and compared to a state-of-the-art method.


title: Why knowledge representation matters

abstract: A personal story: From philosophy to software.


title: Measuring and analyzing the characteristics of Napster and Gnutella hosts

abstract: The popularity of peer-to-peer multimedia file sharing applications such as Gnutella and Napster has created a flurry of recent research activity into peer-to-peer architectures. We believe that the proper evaluation of a peer-to-peer system must take into account the characteristics of the peers that choose to participate in it. Surprisingly, however, few of the peer-to-peer architectures currently being developed are evaluated with respect to such considerations. In this paper, we remedy this situation by performing a detailed measurement study of the two popular peer-to-peer file sharing systems, namely Napster and Gnutella. In particular, our measurement study seeks to characterize the population of end-user hosts that participate in these two systems. This characterization includes the bottleneck bandwidths between these, hosts and the Internet at large, IP-level latencies to send packets to these hosts, how often hosts connect and disconnect from the system, how many files hosts share and download, the degree of cooperation between the hosts, and several correlations between these characteristics. Our measurements show that there is significant heterogeneity and lack of cooperation across peers participating in these systems.


Deep Recurrent and Convolutional Neural Networks for Automated Behavior Classification


title: EEG, HRV and Psychological Correlates while Playing Bejeweled II: A Randomized Controlled Study.

abstract: Stress related medical disorders such as cardiovascular disease, diabetes, depression, and anxiety are serious medical issues that can cause disability and death. Interventions to prevent their development and exacerbation are needed. Casual video games (CVGs) are fun, easy to play, spontaneous and tremendously popular. People report that they play these games because they decrease their stress and improve their mood. This study tested this theory by comparing people playing Bejeweled II a popular CVG with control subjects measured under similar conditions. Electroencephalographic (EEG) changes after playing Bejeweled II were consistent with increased mood and corroborated with similar findings on psychological reports. Moreover, heart rate variability (HRV) changes consistent with autonomic nervous system relaxation or decreased physical stress were also recorded. It is concluded, therefore, that playing a CVG like Bejeweled II can increase mood and decrease stress. These finding have broad implications and include the potential development of prescriptive interventions using Bejeweled II to prevent and treat stress related medical disorders. Finally, these findings demonstrate a method using EEG, HRV and psychological correlates to understand the psychophysiological or cybernetic interconnection between participant and video game.


Community-Driven Development : Treating Poor People as Partners


title: Translating rodent behavioral repertoire to zebrafish (Danio rerio): Relevance for stress research

abstract: Abstract The current study provides a detailed description of the pattern of exploratory behaviors encountered in adult zebrafish when exposed to a novel/unfamiliar environment using the light/dark box and open field tests. We also document the impact of an acute stressor ( restraint stress ) given just prior the onset of behavioral testing. We report the following main findings: (1) zebrafish display anxiety-like behaviors including dark-avoidance (in light / dark box test) and thigmotaxis (in open field test), (2) upon exposure to a novel environment (first 2 min), zebrafish display place preference for the outer zone of the testing apparatus where they seek escape via the transparent wall, and exhibit high locomotor activity accompanied by high swimming speed, (3) thigmotaxis , behavioral hyperactivity, and swimming speed habituate (decrease) over time, (4) prior history of stress attenuates the natural tendency to engage in dark-avoidance behavior and thigmotaxis , reduces attempts to escape via the transparent wall, and greatly increased behavioral hyperactivity and swimming speed. Stress-induced patterns of behavior normalize to levels comparable to that of non-stressed controls by the end of the 5-min test session. Taken together, these findings suggest that novel environment can elicit anxiety-like behaviors in zebrafish such as dark-avoidance and thigmotaxis and the prior history of stress greatly affects patterns of exploration, defensive behaviors, and coping strategies in the light / dark box and open field tests. These findings are consistent with previous findings in rodents and support the usefulness of such behavioral paradigms in zebrafish.


title: A New Direction for Computer Architecture Research

abstract: In the past few years, two important trends have evolved that could change the shape of computing: multimedia applications and portable electronics. Together, these trends will lead to a personal mobile-computing environment, a small device carried all the time that incorporates the functions of the pager, cellular phone, laptop computer, PDA, digital camera, and video game. The microprocessor needed for these devices is actually a merged general-purpose processor and digital-signal processor, with the power budget of the latter. Yet for almost two decades, architecture research has focused on desktop or server machines. We are designing processors of the future with a heavy bias toward the past. To design successful processor architectures for the future, we first need to explore future applications and match their requirements in a scalable, cost-effective way. The authors describe Vector IRAM, an initial approach in this direction, and challenge others in the very successful computer architecture community to investigate architectures with a heavy bias for the future.


Sky and Ground Detection Using Convolutional Neural Networks


title: C.: "Games with Incomplete Information Played by 'Bayesian

abstract: (This article originally appeared in Management Science, November 1967, Volume 14, Number 3, pp. 159-182, published by The Institute of Management Sciences.) ::: ::: The paper develops a new theory for the analysis of games with incomplete information where the players are uncertain about some important parameters of the game situation, such as the payoff functions, the strategies available to various players, the information other players have about the game, etc. However, each player has a subjective probability distribution over the alternative possibilities. ::: ::: In most of the paper it is assumed that these probability distributions entertained by the different players are mutually "consistent," in the sense that they can be regarded as conditional probability distributions derived from a certain "basic probability distribution" over the parameters unknown to the various players. But later the theory is extended also to cases where the different players' subjective probability distributions fail to satisfy this consistency assumption. ::: ::: In cases where the consistency assumption holds, the original game can be replaced by a game where nature first conducts a lottery in accordance with the basic probability distribution, and the outcome of this lottery will decide which particular subgame will be played, i.e., what the actual values of the relevant parameters will be in the game. Yet, each player will receive only partial information about the outcome of the lottery, and about the values of these parameters. However, every player will know the "basic probability distribution" governing the lottery. Thus, technically, the resulting game will be a game with complete information. It is called the Bayes-equivalent of the original game. Part I of the paper describes the basic model and discusses various intuitive interpretations for the latter. Part II shows that the Nash equilibrium points of the Bayes-equivalent game yield "Bayesian equilibrium points" for the original game. Finally, Part III considers the main properties of the "basic probability distribution."


title: The role of entropy of review text sentiments on online WOM and movie box office sales

abstract: Examine the role of neutral review sentiments in WOM effects.By using classification of review text, we create an entropy variable of review text sentiment.Find that entropy of review text sentiment positively moderates the relationship between online WOM and product sales. Sentiments from online word-of-mouth (WOM) are often controversial, since individuals have different preferences toward the same products. Past studies have focused on online WOM effects by measuring WOM volume and valence. However, few studies have investigated how the entropy of the review text sentiment influences the relationship between online WOM and product sales. As WOM valence and volume are usually provided at an aggregated level, consumers often do not have enough information to make a decision. In this case, reading online review text has become an important process for consumers to make purchasing decisions. However, when consumers encounter too many positive review texts, they may doubt the credibility of online WOM itself. Thus, we analyzed the entropy of the review text sentiments by conducting text-mining techniques. We classified review text sentiment into positive, negative, and neutral categories and created an entropy variable. A high level of entropy in review texts indicates that sentiment from review texts are equally distributed, but not biased, towards positive or negative sentiment. We estimated our research model with the entropy variable in a panel dataset for 204 movies over a half-year period. The results suggest that the entropy level in the review texts has a positive moderating impact on the relationship between WOM (e.g., valence and volume) and movie box office sales. The findings imply that deleting negative reviews to enhance product sales may not help online retailers or related parties.


title: Parallel Strategies Selection

abstract: We consider the problem of selecting the best variable-value strategy for solving a given problem in constraint programming. We show that the recent Embarrassingly Parallel Search method (EPS) can be used for this purpose. EPS proposes to solve a problem by decomposing it in many subproblems and to give them on-demand to workers which run in parallel. Our method uses a sample of these subproblems for comparing strategies in order to select the most promising one to be used for solving the remaining subproblems. Each subproblem of the sample is solved with all the candidate strategies in parallel using a timeout that is twice the time of the best one. The selection of the strategy is then based on the Wilcoxon signed rank test. This test is able to deal with censored data caused by timeouts and makes no assumption on the solving time distribution. The experiments we performed on a set of classical benchmarks for satisfaction and optimization problems show that our method selects most of the time the best strategy. Our method also outperforms the portfolio approach consisting of running some strategies in parallel and is competitive with the multi armed bandit framework.


title: Impossibility results and lower bounds for consensus under link failures

abstract: We provide a suite of impossibility results and lower bounds for the required number of processes and rounds for synchronous consensus under transient link failures. Our results show that consensus can be solved even in the presence of $O(n^2)$ moving omission and/or arbitrary link failures per round, provided that both the number of affected outgoing and incoming links of every process is bounded. Providing a step further toward the weakest conditions under which consensus is solvable, our findings are applicable to a variety of dynamic phenomena such as transient communication failures and end-to-end delay variations. We also prove that our model surpasses alternative link failure modeling approaches in terms of assumption coverage.


title: Renal venous thrombosis in a newborn with prothrombotic risk factors.

abstract: Renal venous thrombosis (RVT) is a rare but a well recognized entity in children and neonates. The clinical signs of neonatal RVT include hypertension, enlarged kidney(s), hematuria, renal insufficiency, proteinuria, thrombocytopenia, or all. Persisting impairment of kidney function and hypertension are serious and common complications in patients with RVT. Risk factors for the development of RVT include maternal diabetes mellitus, pathologic states associated with thrombosis (e.g., shock, dehydration, perinatal asphyxia, polycythemia), and sepsis. Inherited prothrombotic abnormalities have been described in some reports of RVT. We report the case of a male newborn with left RVT and associated homozygosity for both factor V Leiden (G1691A) and methylenetetrahydrofolate reductase C677T mutations in addition to elevated serum lipoprotein (a). The patient was treated with heparin. We believe our case to be the first reported case in the English medical literature of such an association between neonatal RVT and homozygosity for both factor V Leiden and methylenetetrahydrofolate reductase. This case and other studies clearly demonstrate that neonatal RVT should be evaluated for thrombophilia conditions.


title: AdaDepth: Unsupervised Content Congruent Adaptation for Depth Estimation

abstract: Supervised deep learning methods have shown promising results for the task of monocular depth estimation; but acquiring ground truth is costly, and prone to noise as well as inaccuracies. While synthetic datasets have been used to circumvent above problems, the resultant models do not generalize well to natural scenes due to the inherent domain shift. Recent adversarial approaches for domain adaption have performed well in mitigating the differences between the source and target domains. But these methods are mostly limited to a classification setup and do not scale well for fully-convolutional architectures. In this work, we propose AdaDepth - an unsupervised domain adaptation strategy for the pixel-wise regression task of monocular depth estimation. The proposed approach is devoid of above limitations through a) adversarial learning and b) explicit imposition of content consistency on the adapted target representation. Our unsupervised approach performs competitively with other established approaches on depth estimation tasks and achieves state-of-the-art results in a semi-supervised setting.


title: Life role salience and subjective well-being among Macedonian employees: Does family-supportive organization perception moderate this relationship.

abstract: OBJECTIVES ::: As many studies have shown, one of the most important tendencies of employees nowadays is to achieve work- life balance. Organizations should develop various activities and create supportive climate, within the framework of which employees will have opportunities to realize aforementioned goals which in turn would increase work productivity and work motivation. The aim of this paper was to examine how subjective well-being (life satisfaction and exhaustion) is associated with commitment to work and family roles under the conditions of strongly and weakly perceived organizational support for family life among health care professionals, teachers and bankers in Macedonia. Marriage duration and the number of children were introduced as control variables. ::: ::: ::: MATERIAL AND METHODS ::: This cross-sectional study was conducted on a sample of 198 full-time employed doctors, nurses, teachers and bankers. Research variables were assessed using self-reported measures/ questionnaires. Hierarchical multiple linear regression was performed for data analysis. ::: ::: ::: RESULTS ::: It was revealed that occupational role commitment contributed to highly expressed life satisfaction, while exhaustion was predicted by marital role commitment. These relationships were stronger among surveyed employees who reported positive family-supportive organization perception, but tested moderation effect of this variable was not statistically significant. Findings demonstrated that family-supportive organization perception moderated association of the number of children with life satisfaction, that is, participants who perceived an organization as family-supportive and had more children were more satisfied with their life in general. ::: ::: ::: CONCLUSIONS ::: Results highlighted the importance of organizational orientation toward employees, their commitment to work and family roles, and their subjective well-being, as characteristics that might contribute to higher work engagement, success and family satisfaction. Int J Occup Med Environ Health 2018;31(3):281-291.


title: MedGAN: Medical Image Translation using GANs

abstract: Abstract Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications. However, a large portion of recent approaches offers individualized solutions based on specialized task-specific architectures or require refinement through non-end-to-end training. In this paper, we propose a new framework, named MedGAN, for medical image-to-image translation which operates on the image level in an end-to-end manner. MedGAN builds upon recent advances in the field of generative adversarial networks (GANs) by merging the adversarial framework with a new combination of non-adversarial losses. We utilize a discriminator network as a trainable feature extractor which penalizes the discrepancy between the translated medical images and the desired modalities. Moreover, style-transfer losses are utilized to match the textures and fine-structures of the desired target images to the translated images. Additionally, we present a new generator architecture, titled CasNet, which enhances the sharpness of the translated medical outputs through progressive refinement via encoder-decoder pairs. Without any application-specific modifications, we apply MedGAN on three different tasks: PET-CT translation, correction of MR motion artefacts and PET image denoising. Perceptual analysis by radiologists and quantitative evaluations illustrate that the MedGAN outperforms other existing translation approaches.


Moments in Time : submission to ActivityNet Challenge 2018


title: SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity

abstract: Foreground/background segmentation via change detection in video sequences is often used as a stepping stone in high-level analytics and applications. Despite the wide variety of methods that have been proposed for this problem, none has been able to fully address the complex nature of dynamic scenes in real surveillance tasks. In this paper, we present a universal pixel-level segmentation method that relies on spatiotemporal binary features as well as color information to detect changes. This allows camouflaged foreground objects to be detected more easily while most illumination variations are ignored. Besides, instead of using manually set, frame-wide constants to dictate model sensitivity and adaptation speed, we use pixel-level feedback loops to dynamically adjust our method’s internal parameters without user intervention. These adjustments are based on the continuous monitoring of model fidelity and local segmentation noise levels. This new approach enables us to outperform all 32 previously tested state-of-the-art methods on the 2012 and 2014 versions of the ChangeDetection.net dataset in terms of overall F-Measure. The use of local binary image descriptors for pixel-level modeling also facilitates high-speed parallel implementations: our own version, which used no low-level or architecture-specific instruction, reached real-time processing speed on a midlevel desktop CPU. A complete C++ implementation based on OpenCV is available online.


title: A Vision Enriched Intelligent Agent with Image Description Generation: (Demonstration)

abstract: In this paper, we present an intelligent conversational agent enriched with automatic image understanding and facial expression recognition using state-of-the-art machine learning techniques for the advancement of autonomous interaction with the elderly or infirm. The agent is developed to conduct health and emotion well-being monitoring for the elderly. It is not only capable of conducting question-answering via speech-based interaction, but also able to provide analysis of the user's surroundings, emotional states, hazards and fall actions via visual data. The agent is accessible from a web browser and can be communicated with via voice or text means, with a webcam required for the visual analysis functionality. The system has been evaluated with diverse real-life images to prove its efficiency.


Holonomic Control of a robot with an omni-directional drive .


title: Precision Battery Management System

abstract: The paper presents a new battery management system for a lithium ion battery pack for more efficient operation and sturdy. The new system contains an embedded microcontroller to track the energy content of cell battery, optimize the output current, and to provide extensive feedback of all the measurements taken. This system sends all data to a telemetry system so that the data can be relayed to a laptop via wireless signal. Two unique advance features of the BMS are its ability to optimize the battery pack energy and also to provide cell equalization. Since the BMS is used in an electric vehicle, very low power consumption is essential


title: Improved Distributed Principal Component Analysis

abstract: We study the distributed computing setting in which there are multiple servers, each holding a set of points, who wish to compute functions on the union of their point sets. A key task in this setting is Principal Component Analysis (PCA), in which the servers would like to compute a low dimensional subspace capturing as much of the variance of the union of their point sets as possible. Given a procedure for approximate PCA, one can use it to approximately solve problems such as k-means clustering and low rank approximation. The essential properties of an approximate distributed PCA algorithm are its communication cost and computational efficiency for a given desired accuracy in downstream applications. We give new algorithms and analyses for distributed PCA which lead to improved communication and computational costs for k-means clustering and related problems. Our empirical study on real world data shows a speedup of orders of magnitude, preserving communication with only a negligible degradation in solution quality. Some of these techniques we develop, such as a general transformation from a constant success probability subspace embedding to a high success probability subspace embedding with a dimension and sparsity independent of the success probability, may be of independent interest.


title: Story Generation from Sequence of Independent Short Descriptions

abstract: Existing Natural Language Generation (NLG) systems are weak AI systems and exhibit limited capabilities when language generation tasks demand higher levels of creativity, originality and brevity. Effective solutions or, at least evaluations of modern NLG paradigms for such creative tasks have been elusive, unfortunately. This paper introduces and addresses the task of coherent story generation from independent descriptions, describing a scene or an event. Towards this, we explore along two popular text-generation paradigms -- (1) Statistical Machine Translation (SMT), posing story generation as a translation problem and (2) Deep Learning, posing story generation as a sequence to sequence learning problem. In SMT, we chose two popular methods such as phrase based SMT (PB-SMT) and syntax based SMT (SYNTAX-SMT) to `translate' the incoherent input text into stories. We then implement a deep recurrent neural network (RNN) architecture that encodes sequence of variable length input descriptions to corresponding latent representations and decodes them to produce well formed comprehensive story like summaries. The efficacy of the suggested approaches is demonstrated on a publicly available dataset with the help of popular machine translation and summarization evaluation metrics.


title: IoT module improves smart environment reliability

abstract: Many mission critical infrastructure systems directly impact the longevity and the usefulness of the systems they support. Heating and cooling equipment (HVAC) are important support equipment for sensitive industrial systems including manufacturing, IT, high tech testing and measuring equipment. Therefore it is important to monitor functionality and health of this sensitive equipment. Moreover, it is critical to track failures in these systems as they are indicative of future outages of the supported systems. In this paper, we present an IoT based module that monitors the heating cooling element. The module can detect outages in HVAC equipment and notify operators about the sensitive system failures following these events. The module has been deployed to find that it detects the system failures can relay this information to operators to act timely. Therefore, improves the system reliability. Moreover, the module can support logging of HVAC equipment as well as supported sensitive equipment power usage to generate usage patterns and allow operators identify key trends and potential problems.


Sentiment Analysis of Twitter Feeds for the Prediction of Stock Market Movement


title: Not Without My Smartphone! Impacts of Smartphone Addiction on Smartphone Usage

abstract: The widespread diffusion of smartphones and their growing importance for private and business use have created new challenges for IS research. This study explores the potential negative effects of smartphone addiction on beliefs and implications for technology use. Using a quantitative survey linking smartphone addiction with technology acceptance, we investigate whether beliefs distorted by addiction, termed maladaptive cognitions, influence usage behavior and thereby potentially lead to smartphone over-use. We thereby assume that addicted users follow their own versions of rationality by acting on distorted beliefs. Based on our PLS-SEM results of 296 responses, we claim that beliefs are positively inflated by smartphone addiction in relation to perceived security, usefulness, and enjoyment, and that these beliefs ultimately bias a person’s smartphone usage behavior. Moreover, we discuss the relatively most important role of perceived enjoyment and conclude with implications.


title: Scaling Reinforcement Learning toward RoboCup Soccer

abstract: RoboCup simulated soccer presents many challenges to reinforcement learning methods, including a large state space, hidden and uncertain state, multiple agents, and long and variable delays in the effects of actions. We describe our application of episodic SMDP Sarsa(λ) with linear tile-coding function approximation and variable λ to learning higher-level decisions in a keepaway subtask of RoboCup soccer. In keepaway, one team, “the keepers,” tries to keep control of the ball for as long as possible despite the efforts of “the takers.” The keepers learn individually when to hold the ball and when to pass to a teammate, while the takers learn when to charge the ball-holder and when to cover possible passing lanes. Our agents learned policies that significantly out-performed a range of benchmark policies. We demonstrate the generality of our approach by applying it to a number of task variations including different field sizes and different numbers of players on each team.


title: Parameterized Diamond Tiling for Stencil Computations with Chapel parallel iterators

abstract: Stencil computations figure prominently in the core kernels of many scientific computations, such as partial differential equation solvers. Parallel scaling of stencil computations can be significantly improved on multicore processors using advanced tiling techniques that include the time dimension, such as diamond tiling. Such techniques are difficult to include in general purpose optimizing compilers because of the need for inter-procedural pointer and array data-flow analysis, plus the need to tune scheduling strategies and tile size parameters for each pairing of stencil computation and machine. Since a fully automatic solution is problematic, we propose to provide parameterized space and time tiling iterators through libraries. Ideally, the execution schedule or tiling code will be expressed orthogonally to the computation. This supports code reuse, easier tuning, and improved programmer productivity. Chapel iterators provide this capability implicitly. We present an advanced, parameterized tiling approach that we have implemented using Chapel parallel iterators. We show how such iterators can be used by programmers in stencil computations with multiple spatial dimensions. We also demonstrate that these new iterators provide better scaling than a traditional data parallel schedule.


title: A 6-bit active phase shifter for Ku-band phased arrays

abstract: This paper presents a 6-bit active phase shifter in 0.13-μm SiGe BiCMOS technology for Ku-band phased arrays based on a phase interpolation technique. An input Marchand balun and a RC poly phase filter are used for high I/Q accuracy wideband quadrature generation with high linearity and an integrated current-mode DAC is used for controlling the amplitude of I/Q path signal to achieve 6-bit phase resolution. The phase shifter operates in the 12–18 GHz range. The simulation results show an rms phase error less than 2.61°. The average insertion gain ranges from −11.16 dB to −20.39 dB with an rms gain error less than 0.4 dB for 64 phase states. The phase shifter achieves an input P1dB of 9.98 dBm at 16 GHz for 0°-phase state. The core area of the phase shifter is 1.1×0.9 mm2, with a total current consumption of 13.4 mA from a 3.3 V supply voltage.


title: Mobile Robotic Fabrication at 1:1 scale: the In situ Fabricator

abstract: This paper presents the concept of an In situ Fabricator, a mobile robot intended for on-site manufacturing, assembly and digital fabrication. We present an overview of a prototype system, its capabilities, and highlight the importance of high-performance control, estimation and planning algorithms for achieving desired construction goals. Next, we detail on two architectural application scenarios: first, building a full-size undulating brick wall, which required a number of repositioning and autonomous localisation manoeuvres. Second, the Mesh Mould concrete process, which shows that an In situ Fabricator in combination with an innovative digital fabrication tool can be used to enable completely novel building technologies. Subsequently, important limitations and disadvantages of our approach are discussed. Based on that, we identify the need for a new type of robotic actuator, which facilitates the design of novel full-scale construction robots. We provide brief insight into the development of this actuator and conclude the paper with an outlook on the next-generation In situ Fabricator, which is currently under development.


title: Control of double inverted pendulum (DIP) using fuzzy hybrid adaptive neuro controller

abstract: This paper presents a new methodological approach for selection of appropriate type and number of Membership function (MF's) for the effective control of Double Inverted Pendulum (DIP). A Matlab-Simulink model of the system is built using governing mathematical equations. The relation between error tolerance of successive approximations and the number of MF's for controllers is also shown. Stabilization is done using Fuzzy and Adaptive Neuro Fuzzy Inference System (ANFIS) controllers having triangular and gbell MF's respectively. The proposed ANFIS and fuzzy controller stabilizes DIP system within 2.5 and 3.0 seconds respectively. All the three controllers have shown almost zero amount of steady state error. Both the controllers gives excellent result which proves the validity of the proposed model. ANFIS controller provides better results as compared to fuzzy controller. Results for Settling time (s), Steady state error and Maximum overshoot (degrees) for each input and output are elaborated with the help of graphs and tables.


title: Dense Prediction on Sequences with Time-Dilated Convolutions for Speech Recognition

abstract: In computer vision pixelwise dense prediction is the task of predicting a label for each pixel in the image. Convolutional neural networks achieve good performance on this task, while being computationally efficient. In this paper we carry these ideas over to the problem of assigning a sequence of labels to a set of speech frames, a task commonly known as framewise classification. We show that dense prediction view of framewise classification offers several advantages and insights, including computational efficiency and the ability to apply batch normalization. When doing dense prediction we pay specific attention to strided pooling in time and introduce an asymmetric dilated convolution, called time-dilated convolution, that allows for efficient and elegant implementation of pooling in time. We show results using time-dilated convolutions in a very deep VGG-style CNN with batch normalization on the Hub5 Switchboard-2000 benchmark task. With a big n-gram language model, we achieve 7.7% WER which is the best single model single-pass performance reported so far.


title: Buffer minimization using max-coloring

abstract: Given a graph G = (V,E) and positive integral vertex weights w : V → N, the max-coloring problem seeks to find a proper vertex coloring of G whose color classes C 1, C 2, ...,C k , minimize Σk i = 1 max ν∈C i w(ν). This problem, restricted to interval graphs, arises whenever there is a need to design dedicated memory managers that provide better performance than the general purpose memory management of the operating system. Specifically, companies have tried to solve this problem in the design of memory managers for wireless protocol stacks such as GPRS or 3G.Though this problem seems similar to the wellknown dynamic storage allocation problem, we point out fundamental differences. We make a connection between max-coloring and on-line graph coloring and use this to devise a simple 2-approximation algorithm for max-coloring on interval graphs. We also show that a simple first-fit strategy, that is a natural choice for this problem, yields a 10-approximation algorithm. We show this result by proving that the first-fit algorithm for on-line coloring an interval graph G uses no more than 10.x(G) colors, significantly improving the bound of 26.x(G) by Kierstead and Qin (Discrete Math., 144, 1995). We also show that the max-coloring problem is NP-hard.


title: Neurocinematics: The neuroscience of film

abstract: This article describes a new method for assessing the effect of a given film on viewers' brain activity. Brain activity was measured using func- tional magnetic resonance imaging (fMRI) during free viewing of films, and inter-subject correlation analysis (ISC) was used to assess similarities in the spatiotemporal responses across viewers' brains during movie watching. Our results demonstrate that some films can exert considerable control over brain activity and eye movements. However, this was not the case for all types of motion picture sequences, and the level of control over viewers' brain activity differed as a function of movie content, editing, and directing style. We pro- pose that ISC may be useful to film studies by providing a quantitative neuro- scientific assessment of the impact of different styles of filmmaking on viewers' brains, and a valuable method for the film industry to better assess its prod- ucts. Finally, we suggest that this method brings together two separate and largely unrelated disciplines, cognitive neuroscience and film studies, and may open the way for a new interdisciplinary field of "neurocinematic" studies.


title: A New Box-Counting Method for Estimation of Image Fractal Dimension

abstract: Fractal dimension (FD) is an effective measure for complex objects. It is widely applied in the fields of image segmentation and shape recognition. This paper presents a new box-counting method for estimation of fractal dimension of images. Original ideas of the method came from the principles of differential box-counting (DBC) method. Improvements appear in the approach on partition of image plane and computation of the number of boxes covering the image. The computation results of fractal dimension of simulated images and Brodatz texture images demonstrate the new method is effective and produces a wider range in fractal dimension than the original DBC method.


title: An Iterative and Analytical Approach to Optimal Synthesis of a Multiplexer With a Star-Junction

abstract: This paper presents an iterative and analytical approach to optimal synthesis of a multiplexer with a star-junction. Two types of commonly used lumped-element junction models, namely, nonresonant node (NRN) type and resonant type, are considered and treated in a uniform way. A new circuit equivalence called phased-inverter to frequency-invariant reactance inverter transformation is introduced. It allows direct adoption of the optimal synthesis theory of a bandpass filter for synthesizing channel filters connected to a star-junction by converting the synthesized phase shift to the susceptance compensation at the junction. Since each channel filter is dealt with individually and alternately, when synthesizing a multiplexer with a high number of channels, good accuracy can still be maintained. Therefore, the approach can be used to synthesize a wide range of multiplexers. Illustrative examples of synthesizing a diplexer with a common resonant type of junction and a triplexer with an NRN type of junction are given to demonstrate the effectiveness of the proposed approach. A prototype of a coaxial resonator diplexer according to the synthesized circuit model is fabricated to validate the synthesized result. Excellent agreement is obtained.


title: Impact of surgeon experience on the rate of blood transfusion in off-pump coronary artery bypass

abstract: Background/Purpose Off-pump coronary artery bypass (OPCAB) reduces the rate of blood transfusion. No studies have focused on the effect of surgeon experience on the transfusion rate. We sought to assess the transfusion rate in OPCAB and to evaluate the effect of surgeon experience. Methods Retrospective review of 1055 consecutive patients undergoing OPCAB between 2000 and 2012. Patients were divided into tripartites by the year of operation (2000–2004, 2005–2008, and 2009–2012). Surgeon experience was evaluated with revascularization index and conversion rate. Results Mode of intervention was elective in 768, urgency in 185, and emergency in 102 patients (10%). Blood transfusion was associated with increased rates of hospital mortality and sternal wound/bloodstream infections. Revascularization index was 1.22 ± 0.29 per patient and increased over time, from 1.05 ± 0.21 in 2000–2004 to 1.39 ± 0.26 in 2009–2012. Conversion rate was 10% and decreased over time, from 17% in 2000–2004 to 6% in 2009–2012. The average rate of blood transfusion was 58% and decreased over time, from 74% in 2000–2004 to 41% in 2009–2012. Rate of red blood cell transfusion was 56% and decreased from 72% in 2000–2004 to 40% in 2009–2012. Rate of platelet transfusion was 21% and decreased from 25% in 2000–2004 to 15% in 2009–2012.The most significant decrease in the transfusion rate was observed in nonemergency cases. Conclusion Surgeon experience reduced the need of blood transfusion after OPCAB. Increasing surgeon experience was associated with a 33% reduction in blood transfusion rate.


title: System Outage Probability Analysis of Uplink Distributed Antenna Systems over a Composite Channel

abstract: This paper studies the system uplink outage probability in distributed antenna systems (DAS). Firstly, owing to the complexity of actual wireless environments, this paper establishes a composite channel model which takes path loss, lognormal shadowing and Rayleigh fading into account. Then, the probability density function (PDF) of the output signal-to-noise ratio (SNR) is derived. After that, an analytical expression of the uplink outage probability for the MS over a given position is obtained after employing the selective transmission (ST) scheme. Further, considering the distribution of MSs in the cell, a closed-form expression of the system outage probability is derived. Numerical results show that the closed-form expression of the system outage probability can provide sufficient precision for evaluating the outage performance of DAS.


I-MIBG Scintigraphy in the Localization of Pheochromocytoma and Paraganglioma


title: Modeling and Control of MM-UAV: Mobile Manipulating Unmanned Aerial Vehicle

abstract: Compared to autonomous ground vehicles, UAVs (unmanned aerial vehicles) have significant mobility advantages and the potential to operate in otherwise unreachable locations. Micro UAVs still suffer from one major drawback: they do not have the necessary payload capabilities to support high performance arms. This paper, however, investigates the key challenges in controlling a mobile manipulating UAV using a commercially available aircraft and a light-weight prototype 3-arm manipulator. Because of the overall instability of rotorcraft, we use a motion capture system to build an efficient autopilot. Our results indicate that we can accurately model and control our prototype system given significant disturbances when both moving the manipulators and interacting with the ground.


title: Near-optimal regret bounds for reinforcement learning

abstract: For undiscounted reinforcement learning in Markov decision processes (MDPs) we consider the total regret of a learning algorithm with respect to an optimal policy. In order to describe the transition structure of an MDP we propose a new parameter: An MDP has diameter D if for any pair of states s, s' there is a policy which moves from s to s' in at most D steps (on average). We present a reinforcement learning algorithm with total regret O(DS √AT) after T steps for any unknown MDP with S states, A actions per state, and diameter D. This bound holds with high probability. We also present a corresponding lower bound of Ω(√DSAT) on the total regret of any learning algorithm.


title: RFID Technology for Management and Tracking: e-Health Applications

abstract: Radio frequency identification (RFID) has become a key technology in the logistics and management industry, thanks to distinctive features such as the low cost of RFID tags, and the easiness of the RFID tags' deployment and integration within the items to be tracked. In consequence, RFID plays a fundamental role in the so-called digital factory or 4.0 Industry, aiming to increase the level of automatization of industrial processes. In addition, RFID has also been found to be of great help in improving the tracking of patients, medicines, and medical assets in hospitals, where the digitalization of these operations improves their efficiency and safety. This contribution reviews the state-of-the-art of RFID for e-Health applications, describing the contributions to improve medical services and discussing the limitations. In particular, it has been found that a lot of effort has been put into software development, but in most of the cases a detailed study of the physical layer (that is, the characterization of the RFID signals within the area where the system is deployed) is not properly conducted. This contribution describes a basic RFID system for tracking and managing assets in hospitals, aiming to provide additional details about implementation aspects that must be considered to ensure proper functionality of the system. Although the scope of the RFID system described in this contribution is restricted to a small area of the hospital, the architecture is fully scalable to cover the needs of the different medical services in the hospital. Ultra high-frequency (UHF) RFID technology is selected over the most extended near-field communication (NFC) and high-frequency (HF) RFID technology to minimize hardware infrastructure. In particular, UHF RFID also makes the coverage/reading area conformation easier by using different kinds of antennas. Information is stored in a database, which is accessed from end-user mobile devices (tablets, smartphones) where the position and status of the assets to be tracked are displayed.


title: Rapid Adaptation of Neural Machine Translation to New Languages

abstract: This paper examines the problem of adapting neural machine translation systems to new, low-resourced languages (LRLs) as effectively and rapidly as possible. We propose methods based on starting with massively multilingual"seed models", which can be trained ahead-of-time, and then continuing training on data related to the LRL. We contrast a number of strategies, leading to a novel, simple, yet effective method of"similar-language regularization", where we jointly train on both a LRL of interest and a similar high-resourced language to prevent over-fitting to small LRL data. Experiments demonstrate that massively multilingual models, even without any explicit adaptation, are surprisingly effective, achieving BLEU scores of up to 15.5 with no data from the LRL, and that the proposed similar-language regularization method improves over other adaptation methods by 1.7 BLEU points average over 4 LRL settings. Code to reproduce experiments at https://github.com/neubig/rapid-adaptation


title: Wearable Gait Lab System providing quantitative statistical support for human balance tests

abstract: Abstract Sensory devices have the potential to improve monitoring balance tests and providing quantitative supports for activity analysis. The Wearable Gait Lab system is proposed to monitor activities of feet and legs during balance tests. In this system, a wearable underfoot force sensing unit is used to record foot motions and plantar pressure data; a joint angular and EMG sensing unit is used to record leg motions and muscular data; and an android application is implemented to control all units, monitor the data recording process, and upload recorded data to cloud server, which allows health professionals to review it remotely. The system provides adequate data for balance ability analysis and simplifies the scoring processes in balance tests. The proposed system is evaluated with standard balance tests (Limits of Stability, Sit-To-Stand, and Rhythmic Weight Shift), whereas the data collected are analyzed with data mining techniques to verify the reliability of the designated process. Certain parameters are computed such as Center of Gravity (COG), weight transfer time, and sway velocities. The results show that the system is informational and reliable in the process of determining balance status during tests with additional advantages in high portability and efficient review communications.


title: An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data

abstract: Intelligent fault diagnosis is a promising tool to deal with mechanical big data due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In traditional intelligent diagnosis methods, however, the features are manually extracted depending on prior knowledge and diagnostic expertise. Such processes take advantage of human ingenuity but are time-consuming and labor-intensive. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed for intelligent diagnosis of machines. In the first learning stage of the method, sparse filtering, an unsupervised two-layer neural network, is used to directly learn features from mechanical vibration signals. In the second stage, softmax regression is employed to classify the health conditions based on the learned features. The proposed method is validated by a motor bearing dataset and a locomotive bearing dataset, respectively. The results show that the proposed method obtains fairly high diagnosis accuracies and is superior to the existing methods for the motor bearing dataset. Because of learning features adaptively, the proposed method reduces the need of human labor and makes intelligent fault diagnosis handle big data more easily.


3-D Reconstruction in Canonical Co-Ordinate Space From Arbitrarily Oriented 2-D Images


title: A Learning Machine: Part I

abstract: Machines would be more useful if they could learn to perform tasks for which they were not given precise methods. Difficulties that attend giving a machine this ability are discussed. It is proposed that the program of a stored-program computer be gradually improved by a learning procedure which tries many programs and chooses, from the instructions that may occupy a given location, the one most often associated with a successful result. An experimental test of this principle is described in detail. Preliminary results, which show limited success, are reported and interpreted. Further results and conclusions will appear in the second part of the paper.


title: Simulative assessments of IEEE 802.1 Ethernet AVB and Time-Triggered Ethernet for Advanced Driver Assistance Systems and in-car infotainment

abstract: Investigations into the usage of Ethernet in automobiles is in progress in academia, the car industry and companies producing automotive electronic devices. The interest in Ethernet is motivated by the high bandwidth and scalability provided. It is a well experienced technology with support for the Internet Protocol (IP) suite. Ethernet as in-car network is expected to breakthrough in Advanced Driver Assistance Systems (ADAS) involving cameras and in the multimedia domain. Both the IEEE Audio Video Bridging (AVB) standard and Time-Triggered Ethernet (TTE) are promising candidates. This paper presents a simulation study aimed to investigate the behavior of these technologies when supporting ADAS and multimedia traffic on star-based networks under varying workload. The performance under different operating conditions is presented and discussed.


title: Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation

abstract: In this paper, we study the heterogeneous domain adaptation (HDA) problem, in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. By introducing two different projection matrices, we first transform the data from two domains into a common subspace such that the similarity between samples across different domains can be measured. We then propose a new feature mapping function for each domain, which augments the transformed samples with their original features and zeros. Existing supervised learning methods ( e.g., SVM and SVR) can be readily employed by incorporating our newly proposed augmented feature representations for supervised HDA. As a showcase, we propose a novel method called Heterogeneous Feature Augmentation (HFA) based on SVM. We show that the proposed formulation can be equivalently derived as a standard Multiple Kernel Learning (MKL) problem, which is convex and thus the global solution can be guaranteed. To additionally utilize the unlabeled data in the target domain, we further propose the semi-supervised HFA (SHFA) which can simultaneously learn the target classifier as well as infer the labels of unlabeled target samples. Comprehensive experiments on three different applications clearly demonstrate that our SHFA and HFA outperform the existing HDA methods.


title: DeepIM: Deep Iterative Matching for 6D Pose Estimation

abstract: Estimating the 6D pose of objects from images is an important problem in various applications such as robot manipulation and virtual reality. While direct regression of images to object poses has limited accuracy, matching rendered images of an object against the input image can produce accurate results. In this work, we propose a novel deep neural network for 6D pose matching named DeepIM. Given an initial pose estimation, our network is able to iteratively refine the pose by matching the rendered image against the observed image. The network is trained to predict a relative pose transformation using an untangled representation of 3D location and 3D orientation and an iterative training process. Experiments on two commonly used benchmarks for 6D pose estimation demonstrate that DeepIM achieves large improvements over state-of-the-art methods. We furthermore show that DeepIM is able to match previously unseen objects.


title: A stigmergic approach to indoor localization using Bluetooth Low Energy beacons

abstract: Localization of people and devices is one of the main building blocks of context aware systems since the user position represents the core information for detecting user's activities, devices activations, proximity to points of interest, etc. While for outdoor scenarios Global Positioning System (GPS) constitutes a reliable and easily available technology, for indoor scenarios GPS is largely unavailable. In this paper we present a range-based indoor localization system that exploits the Received Signal Strength (RSS) of Bluetooth Low Energy (BLE) beacon packets broadcast by anchor nodes and received by a BLE-enabled device. The method used to infer the user's position is based on stigmergy. We exploit the stigmergic marking process to create an on-line probability map identifying the user's position in the indoor environment.


title: The performance of VIX option pricing models: Empirical evidence beyond simulation

abstract: We examine the pricing performance of VIX option models. Such models possess a wide‐range of underlying characteristics regarding the behavior of both the S&P500 index and the underlying VIX. Our tests employ three representative models for VIX options: Whaley ( 1993 ), Grunbichler and Longstaff ( 1996 ), Carr and Lee ( 2007 ), Lin and Chang ( 2009 ), who test four stochastic volatility models, as well as to previous simulation results of VIX option models. We find that no model has small pricing errors over the entire range of strike prices and times to expiration. In particular, out‐of‐the‐money VIX options are difficult to price, with Grunbichler and Longstaff's mean‐reverting model producing the smallest dollar errors in this category. Whaley's Black‐like option model produces the best results for in‐the‐money VIX options. However, the Whaley model does under/overprice out‐of‐the‐money call/put VIX options, which is opposite the behavior of stock index option pricing models. VIX options exhibit a volatility skew opposite the skew of index options. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark31:251–281, 2011


title: Fabrication of high strength nanostructured aluminium alloys by hydrostatic extrusion

abstract: In the present work, the possibility of obtaining high strength nanostructured aluminium alloys by hydrostatic extrusion has been shown. Billets of 2017 aluminium alloy were subjected to hydrostatic extrusion either immediately after water quenching or after subsequent ageing. The results have shown that processing by hydrostatic extrusion offers a possibility of grain refinement down to the nanometre scale in the age-hardenable aluminium alloy. However, the effectiveness of the process depends on its initial microstructure. The most promising results are obtained if the alloy is deformed in the as-quenched condition. The hydrostatically extruded aluminium alloy exhibits very high tensile strength with reasonable ductility. The stability of mechanical properties over a wide range of temperatures (from cryogenic to well above the ambient) is discussed. The results obtained are compared to similar 2XXX aluminium alloys processed by other severe plastic deformation methods.


title: Survey on audiovisual emotion recognition: Databases, features, and data fusion strategies

abstract: Emotion recognition is the ability to identify what people would think someone is feeling from moment to moment and understand the connection between his/her feelings and expressions. In today's world, human–computer interaction (HCI) interface undoubtedly plays an important role in our daily life. Toward harmonious HCI interface, automated analysis and recognition of human emotion has attracted increasing attention from the researchers in multidisciplinary research fields. In this paper, a survey on the theoretical and practical work offering new and broad views of the latest research in emotion recognition from bimodal information including facial and vocal expressions is provided. First, the currently available audiovisual emotion databases are described. Facial and vocal features and audiovisual bimodal data fusion methods for emotion recognition are then surveyed and discussed. Specifically, this survey also covers the recent emotion challenges in several conferences. Conclusions outline and address some of the existing emotion recognition issues.


title: Biometric Authentication using Human Footprint

abstract: The password based authorization scheme for all available security systems can effortlessly be hacked by the hacker or a malicious user. One might not be able to guarantee that the person who is using the password is authentic or not. Only biometric systems are one which make offered automated authentication. There are very exceptional chances of losing the biometric identity, only if the accident of an individual may persists. Footprint based biometric system has been evaluated so far. In this paper a number of approaches of footprint recognition have been deliberated. General Terms Biometric pattern recognition, Image processing.


title: Ontology for malware behavior: A core model proposal

abstract: The ubiquity of Internet-connected devices motivates attackers to create malicious programs (malware) to exploit users and their systems. Malware detection requires a deep understanding of their possible behaviors, one that is detailed enough to tell apart suspicious programs from benign, legitimate ones. A step to effectively address the malware problem leans toward the development of an ontology. Current efforts are based on an obsolete hierarchy of malware classes that defines a malware family by one single prevalent behavior (e.g., viruses infect other files, worms spread and exploit remote systems autonomously, Trojan horses disguise themselves as benign programs, and so on). In order to address the detection of modern, complex malware families whose infections involve sets of multiple exploit methods, we need an ontology broader enough to deal with these suspicious activities performed on the victim's system. In this paper, we propose a core model for a novel malware ontology that is based on their exhibited behavior, filling a gap in the field.


title: Deep Convolutional Neural Network for Inverse Problems in Imaging

abstract: In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyperparameter selection. The starting point of this paper is the observation that unrolled iterative methods have the form of a CNN (filtering followed by pointwise non-linearity) when the normal operator (<inline-formula> <tex-math notation="LaTeX">$H^{*}H$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$H^{*}$ </tex-math></inline-formula> is the adjoint of the forward imaging operator, <inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula>) of the forward model is a convolution. Based on this observation, we propose using direct inversion followed by a CNN to solve normal-convolutional inverse problems. The direct inversion encapsulates the physical model of the system, but leads to artifacts when the problem is ill posed; the CNN combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure. We demonstrate the performance of the proposed network in sparse-view reconstruction (down to 50 views) on parallel beam X-ray computed tomography in synthetic phantoms as well as in real experimental sinograms. The proposed network outperforms total variation-regularized iterative reconstruction for the more realistic phantoms and requires less than a second to reconstruct a <inline-formula> <tex-math notation="LaTeX">$512\times 512$ </tex-math></inline-formula> image on the GPU.


A systematic review to identify feature selection publications in multi-labeled data


title: M-HAJJ DSS: A mobile decision support system for Hajj pilgrims

abstract: Performing Hajj is one of the five pillars of Islam. Usually the pilgrims will attend a training before departing for Hajj as a preparation for the ritual process, in order to achieve a ‘mabrur’ Hajj. However, they probably encounter some problems related to Hajj ritual that require immediate decisions especially rituals that is related with ‘Dumm’ (penalty) imposition. We proposed a dynamic knowledge-based approach that can capture possible problems and solutions from the expert on mobile platform. It provides an interface that enables users to query both simple and advance questions. The system has been implemented using an artificial intelligence (AI) method of Case Based Reasoning (CBR) and Decision Tree.


title: Enabling instantaneous feedback with full-duplex backscatter

abstract: This paper introduces the first design that enables full-duplex communication on battery-free backscatter devices. Specifically, it gives receivers a way to provide low-rate feedback to the transmitter on the same frequency as that of the backscatter transmissions, using neither multiple antennas nor power-consuming cancellation hardware. Our design achieves this goal using only fully-passive analog components that consume near-zero power. We integrate our design with the backscatter network stack and demonstrate that it can minimize energy wastes that occur due to collisions and also correct for errors and changes in channel conditions at a granularity smaller than that of a packet. To show the feasibility of our design, we build a hardware prototype using off-the-shelf analog components. Our evaluation shows that our design cancels the self-interference down to the noise floor, while consuming only 0.25 μW and 0.54 μW of transmit and receive power, respectively.


title: Intelligent Tutoring Systems with Conversational Dialogue

abstract: Many of the intelligent tutoring systems that have been developed during the last 20 years have proven to be quite successful, particularly in the domains of mathematics, science, and technology. They produce significant learning gains beyond classroom environments. They are capable of engaging most students' attention and interest for hours. We have been working on a new generation of intelligent tutoring systems that hold mixed-initiative conversational dialogues with the learner. The tutoring systems present challenging problems and questions to the learner, the learner types in answers in English, and there is a lengthy multiturn dialogue as complete solutions or answers evolve. This article presents the tutoring systems that we have been developing. AutoTutor is a conversational agent, with a talking head, that helps college students learn about computer literacy. andes, atlas, and why2 help adults learn about physics. Instead of being mere information-delivery systems, our systems help students actively construct knowledge through conversations.


title: Meta-analysis of psychological treatments for posttraumatic stress disorder in adult survivors of childhood abuse

abstract: Posttraumatic stress disorder (PTSD) is highly prevalent in adult survivors of childhood sexual and/or physical abuse. However, intervention studies focusing on this group of patients are underrepresented in earlier meta-analyses on the efficacy of PTSD treatments. The current meta-analysis exclusively focused on studies evaluating the efficacy of psychological interventions for PTSD in adult survivors of childhood abuse. Sixteen randomized controlled trials meeting inclusion criteria could be identified that were subdivided into trauma-focused cognitive behavior therapy (CBT), non-trauma-focused CBT, eye movement desensitization and reprocessing, and other treatments (interpersonal, emotion-focused). Results showed that psychological interventions are efficacious for PTSD in adult survivors of childhood abuse, with an aggregated uncontrolled effect size of g = 1.24 (pre- vs. post-treatment), and aggregated controlled effect sizes of g = 0.72 (post-treatment, comparison to waitlist control conditions) and g = 0.50 (post-treatment, comparison with TAU/placebo control conditions), respectively. Effect sizes remained stable at follow-up. As the heterogeneity between studies was large, we examined the influence of two a priori specified moderator variables on treatment efficacy. Results showed that trauma-focused treatments were more efficacious than non-trauma-focused interventions, and that treatments including individual sessions yielded larger effect sizes than pure group treatments. As a whole, the findings are in line with earlier meta-analyses showing that the best effects can be achieved with individual trauma-focused treatments.


title: How quantum brain biology can rescue conscious free will

abstract: Conscious “free will” is problematic because (1) brain mechanisms causing consciousness are unknown, (2) measurable brain activity correlating with conscious perception apparently occurs too late for real-time conscious response, consciousness thus being considered “epiphenomenal illusion,” and (3) determinism, i.e., our actions and the world around us seem algorithmic and inevitable. The Penrose–Hameroff theory of “orchestrated objective reduction (Orch OR)” identifies discrete conscious moments with quantum computations in microtubules inside brain neurons, e.g., 40/s in concert with gamma synchrony EEG. Microtubules organize neuronal interiors and regulate synapses. In Orch OR, microtubule quantum computations occur in integration phases in dendrites and cell bodies of integrate-and-fire brain neurons connected and synchronized by gap junctions, allowing entanglement of microtubules among many neurons. Quantum computations in entangled microtubules terminate by Penrose “objective reduction (OR),” a proposal for quantum state reduction and conscious moments linked to fundamental spacetime geometry. Each OR reduction selects microtubule states which can trigger axonal firings, and control behavior. The quantum computations are “orchestrated” by synaptic inputs and memory (thus “Orch OR”). If correct, Orch OR can account for conscious causal agency, resolving problem 1. Regarding problem 2, Orch OR can cause temporal non-locality, sending quantum information backward in classical time, enabling conscious control of behavior. Three lines of evidence for brain backward time effects are presented. Regarding problem 3, Penrose OR (and Orch OR) invokes non-computable influences from information embedded in spacetime geometry, potentially avoiding algorithmic determinism. In summary, Orch OR can account for real-time conscious causal agency, avoiding the need for consciousness to be seen as epiphenomenal illusion. Orch OR can rescue conscious free will.


title: Cooperative control of multi-vehicle systems using cost graphs and optimization

abstract: We introduce a class of triangulated graphs for algebraic representation of formations that allows one to specify a mission cost for a group of vehicles. This representation plus the navigational information allows one to formally specify and solve tracking problems for groups of vehicles in formations using an optimization-based approach. The approach is illustrated using a collection of six underactuated vehicles that track a desired trajectory in formation.