title: Technologies for augmented reality systems: realizing ultrasound-guided needle biopsies

abstract: We present a real-time stereoscopic video-see-through augmented reality (AR) system applied to the medical procedure known as ultrasound-guided needle biopsy of the breast. The AR system was used by a physician during procedures on breast models and during non-invasive examinations of human subjects. The system merges rendered live ultrasound data and geometric elements with stereo images of the patient acquired through head-mounted video cameras and presents these merged images to the physician in a head-mounted display. The physician sees a volume visualization of the ultrasound data directly under the ultrasound probe, properly registered within the patient and with the biopsy needle. Using this system, a physician successfully guided a needle into an artificial tumor within a training phantom of a human breast. We discuss the construction of the AR system and the issues and decisions which led to the system architecture and the design of the video see-through head-mounted display. We designed methods to properly resolve occlusion of the real and synthetic image elements. We developed techniques for realtime volume visualization of timeand position-varying ultrasound data. We devised a hybrid tracking system which achieves improved registration of synthetic and real imagery and we improved on previous techniques for calibration of a magnetic tracker. CR


title: A Novel Serial/Parallel Pole Climbing/Manipulating Robot: Design, Kinematic Analysis and Workspace Optimization with Genetic Algorithm

abstract: This paper introduces an optimized multi-task novel 4 DOF pole climbing/manipulating robot for construction works. The robot can travel along poles with bends, branches and step changes in cross section. It is also able to perform manipulation, repair, testing and maintenance tasks after reaching the target point on the pole. A hybrid serial/parallel mechanism, providing 2 translations and 2 rotations, have been designed as the main part of the mechanism. Optimization of this robot contains workspace optimization of the proposed mechanism and decreasing the total time of reaching the target point, has been established with genetic algorithm method.


title: Experimental quantum teleportation of a two-qubit composite system

abstract: Quantum teleportation1, a way to transfer the state of a quantum system from one location to another, is central to quantum communication2 and plays an important role in a number of quantum computation protocols3,4,5. Previous experimental demonstrations have been implemented with single photonic6,7,8,9,10,11 or ionic qubits12,13. However, teleportation of single qubits is insufficient for a large-scale realization of quantum communication and computation2,3,4,5. Here, we present the experimental realization of quantum teleportation of a two-qubit composite system. In the experiment, we develop and exploit a six-photon interferometer to teleport an arbitrary polarization state of two photons. The observed teleportation fidelities for different initial states are all well beyond the state estimation limit of 0.40 for a two-qubit system14. Not only does our six-photon interferometer provide an important step towards teleportation of a complex system, it will also enable future experimental investigations on a number of fundamental quantum communication and computation protocols3,15,16,17,18.


title: Area-efficient layout design of comparator using cascaded technique

abstract: In this paper a new design of cascaded comparator is described. Comparator is the basic building block of many arithmetic and logical units used in microprocessors and DSP. In the world of new emerging technology it has become essential to develop various new design concepts to reduce the power consumption and chip area. In this paper a semi-custom design of cascaded comparator has been presented and compared with the auto-generated layout on CMOS 90nm foundry technology. The proposed semi-custom design of cascaded comparator has showed an improvement of 35.65 % of total area as compared to auto-generated layout.


title: The secret lives of liberals and conservatives: Personality profiles, interaction styles, and things they leave behind

abstract: Although skeptics continue to doubt that most people are “ideological,” evidence suggests that meaningful left-right differences do exist and that they may be rooted in basic personality dispositions, that is, relatively stable individual differences in psychological needs, motives, and orientations toward the world. Seventy-five years of theory and research on personality and political orientation has produced a long list of dispositions, traits, and behaviors. Applying a theory of ideology as motivated social cognition and a “Big Five” framework, we find that two traits, Openness to New Experiences and Conscientiousness, parsimoniously capture many of the ways in which individual differences underlying political orientation have been conceptualized. In three studies we investigate the relationship between personality and political orientation using multiple domains and measurement techniques, including: self-reported personality assessment; nonverbal behavior in the context of social interaction; and personal possessions and the characteristics of living and working spaces. We obtained consistent and converging evidence that personality differences between liberals and conservatives are robust, replicable, and behaviorally significant, especially with respect to social (vs. economic) dimensions of ideology. In general, liberals are more open-minded, creative, curious, and novelty seeking, whereas conservatives are more orderly, conventional, and better organized.


title: Changing circumstances, disrupting habits

abstract: The present research investigated the mechanisms guiding habitual behavior, specifically, the stimulus cues that trigger habit performance. When usual contexts for performance change, habits cannot be cued by recurring stimuli, and performance should be disrupted. Thus, the exercising, newspaper reading, and TV watching habits of students transferring to a new university were found to survive the transfer only when aspects of the performance context did not change (e.g., participants continued to read the paper with others). In some cases, the disruption in habits also placed behavior under intentional control so that participants acted on their current intentions. Changes in circumstances also affected the favorability of intentions, but changes in intentions alone could not explain the disruption of habits. Furthermore, regardless of whether contexts changed, nonhabitual behavior was guided by intentions.


title: Psychopathy, Homicide, and the Courts Working the System

abstract: This study investigated the effects of psychopathy on homicidal postoffense behavior, denying the charges at court, appealing the lower court conviction, and final sentencing. A sample of 546 offenders prosecuted for a homicide and convicted in Finland during the 1995—2004 period was examined. Their postoffense behavior, self-reported reasons for the killing, charges, sentences, and psychopathic traits, as measured by the Psychopathy Checklist—Revised, were coded from official file information. Offenders with high scores were more likely than others to leave the crime scene without informing anyone of the killing, to deny the charges, to be convicted for involuntary manslaughter rather than manslaughter or murder, and to receive permission from the Supreme Court to appeal their lower court sentence. Given the risk that psychopathic offenders pose for violent crime, the finding that they are able to manipulate the criminal justice system is cause for concern.


title: Force sensing shell using a planar sensor for miniature legged robots

abstract: Mobile robot contact sensing can be useful for navigation and manipulation with small robots. In this work, we present a low-cost, 18.7 gram force-torque sensor for a 10 cm hexapedal millirobot. A planar array of photointerrupters, rigidly attached to the structure of the robot, is used to measure the six-axis movement of a shell attached to the robot with springs. The sensors measure the intensity of infrared light reflected off of a surface on the underside of the shell, which has been specially designed to enable the resolution of the forces and moments in the x-, y-, and z-axes applied to the shell. The sensor has a force sensitivity of 17 mN and torque sensitivity of 0.72 mN-m for a sampling rate of 100 Hz. The sensor can resolve a force equivalent to 2.9% of the combined robot and sensor weight of 581 mN.


title: Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

abstract: Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are used to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs.


title: A method to infer emotions from facial Action Units

abstract: We present a robust method to map detected facial Action Units (AUs) to six basic emotions. Automatic AU recognition is prone to errors due to illumination, tracking failures and occlusions. Hence, traditional rule based methods to map AUs to emotions are very sensitive to false positives and misses among the AUs. In our method, a set of chosen AUs are mapped to the six basic emotions using a learned statistical relationship and a suitable matching technique. Relationships between the AUs and emotions are captured as template strings comprising the most discriminative AUs for each emotion. The template strings are computed using a concept called discriminative power. The Longest Common Subsequence (LCS) distance, an approach for approximate string matching, is applied to calculate the closeness of a test string of AUs with the template strings, and hence infer the underlying emotions. LCS is found to be efficient in handling practical issues like erroneous AU detection and helps to reduce false predictions. The proposed method is tested with various databases like CK+, ISL, FACS, JAFFE, MindReading and many real-world video frames. We compare our performance with rule based techniques, and show clear improvement on both benchmark databases and real-world datasets.


title: AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images

abstract: The lack of publicly available ground-truth data has been identified as the major challenge for transferring recent developments in deep learning to the biomedical imaging domain. Though crowdsourcing has enabled annotation of large scale databases for real world images, its application for biomedical purposes requires a deeper understanding and hence, more precise definition of the actual annotation task. The fact that expert tasks are being outsourced to non-expert users may lead to noisy annotations introducing disagreement between users. Despite being a valuable resource for learning annotation models from crowdsourcing, conventional machine-learning methods may have difficulties dealing with noisy annotations during training. In this manuscript, we present a new concept for learning from crowds that handle data aggregation directly as part of the learning process of the convolutional neural network (CNN) via additional crowdsourcing layer (AggNet). Besides, we present an experimental study on learning from crowds designed to answer the following questions. 1) Can deep CNN be trained with data collected from crowdsourcing? 2) How to adapt the CNN to train on multiple types of annotation datasets (ground truth and crowd-based)? 3) How does the choice of annotation and aggregation affect the accuracy? Our experimental setup involved Annot8, a self-implemented web-platform based on Crowdflower API realizing image annotation tasks for a publicly available biomedical image database. Our results give valuable insights into the functionality of deep CNN learning from crowd annotations and prove the necessity of data aggregation integration.


title: Integrating Topic Models and Latent Factors for Recommendation

abstract: The research of personalized recommendation techniques today has mostly parted into two mainstream directions, i.e., the factorization-based approaches and topic models. Practically, they aim to benefit from the numerical ratings and textual reviews, correspondingly, which compose two major information sources in various real-world systems. However, although the two approaches are supposed to be correlated for their same goal of accurate recommendation, there still lacks a clear theoretical understanding of how their objective functions can be mathematically bridged to leverage the numerical ratings and textual reviews collectively, and why such a bridge is intuitively reasonable to match up their learning procedures for the rating prediction and top-N recommendation tasks, respectively. ::: In this work, we exposit with mathematical analysis that, the vector-level randomization functions to coordinate the optimization objectives of factorizational and topic models unfortunately do not exist at all, although they are usually pre-assumed and intuitively designed in the literature. Fortunately, we also point out that one can avoid the seeking of such a randomization function by optimizing a Joint Factorizational Topic (JFT) model directly. We apply our JFT model to restaurant recommendation, and study its performance in both normal and cross-city recommendation scenarios, where the latter is an extremely difficult task for its inherent cold-start nature. Experimental results on real-world datasets verified the appealing performance of our approach against previous methods, on both rating prediction and top-N recommendation tasks.


title: 3D all the way: Semantic segmentation of urban scenes from start to end in 3D

abstract: We propose a new approach for semantic segmentation of 3D city models. Starting from an SfM reconstruction of a street-side scene, we perform classification and facade splitting purely in 3D, obviating the need for slow image-based semantic segmentation methods. We show that a properly trained pure-3D approach produces high quality labelings, with significant speed benefits (20x faster) allowing us to analyze entire streets in a matter of minutes. Additionally, if speed is not of the essence, the 3D labeling can be combined with the results of a state-of-the-art 2D classifier, further boosting the performance. Further, we propose a novel facade separation based on semantic nuances between facades. Finally, inspired by the use of architectural principles for 2D facade labeling, we propose new 3D-specific principles and an efficient optimization scheme based on an integer quadratic programming formulation.


title: Ladder-Style DenseNets for Semantic Segmentation of Large Natural Images

abstract: Recent progress of deep image classification models provides a large potential to improve state-of-the-art performance in related computer vision tasks. However, the transition to semantic segmentation is hampered by strict memory limitations of contemporary GPUS. The extent of feature map caching required by convolutional backprop poses significant challenges even for moderately sized PASCAL images, while requiring careful architectural considerations when the source resolution is in the megapixel range. To address these concerns, we propose a DenseNet-based ladder-style architecture which is able to deliver high modelling power with very lean representations at the original resolution. The resulting fully convolutional models have few parameters, allow training at megapixel resolution on commodity hardware and display fair semantic segmentation performance even without ImageNet pre-training. We present experiments on Cityscapes and Pascal VOC 2012 datasets and report competitive results.


title: Partial discharge spectral response to variations in the supply voltage frequency

abstract: Partial discharge (PD) spectral response to variations in the supply voltage frequency was experimentally investigated through laboratory-based tests. The PD spectral content of each defect type responded uniquely to variations in the sinusoidal supply voltage frequency in the range 20 to 400 Hz. The findings are interpreted using the theory of space charge dynamics in PD mechanisms. Prospective diagnostic applications of the findings include PD recognition using supply voltage frequency sweeps. Knowledge on supply voltage frequency dependency of partial discharges also helps in comparing PD diagnostic test results obtained at different test voltage frequencies.


title: The efficacy of a structured group therapy intervention in improving communication and coping skills for adult cochlear implant recipients

abstract: This paper reports on an evaluation of a structured group therapy intervention for adult cochlear implant (CI) recipients designed to improve overall communication and coping skills. 33 adult CI recipients (14 males, 19 females; mean age 61; 1–14 years since CI) participated in a 2-day structured group therapy intervention with a follow-up session 4 weeks later. Measures were: communication behaviors (CPHI), assertiveness (Rathus), depression (DASS), and behavior during conversation (Dyalog). TCI personality traits were utilized as predictors of change. Repeated measures analyses showed that participants demonstrated significant improvements on measures of assertiveness, emotional well-being, and coping behaviors at 3 months post-intervention that persisted at a 12-month follow-up. Several personality traits predicted change. Although subjects had presumably adapted to their cochlear implants and had learned communication strategies in hearing rehabilitation programs, the improvements on several measures ...


title: PhilippineIndiosin the Service of Empire: Indigenous Soldiers and Contingent Loyalty, 1600–1700

abstract: This is the author accepted manuscript. The final version is available from Duke University Press via http://dx.doi.org/10.1215/00141801-3455363


What is Orthoceras?


title: Hierarchical Refined Local Associations for Robust Person Re-Identification

abstract: Person re-identification is the process to identify a person from images/ videos captured from different nonoverlapping cameras in an autonomous way. The biological vision scheme emphasis on local discriminative cues in addition to the global appearance of a person for re-identification. The local cues are helpful to identify a person even if viewed at different scales and with different backgrounds. To emphasize on local cues, in this paper we present a refined association scheme for local parts of the images. The proposed scheme eliminates the effects of scale differences and background noise for automated person re-identification. Our approach divides the image of a person in horizontal strips and vertical sub-patches. A hierarchical refined associations based network (HRAN) is introduced to establish the refined associations among local segments of given images. In the first phase, the associations are established among horizontal strips of two images. In the next phase, the vertical sub-patches of associated horizontal strips are aligned/ linked with each other. Background noise and scale differences between images are addressed effectively using the proposed two-step mechanism. The triplet loss is used to optimize the refined local associations among images. A different weighting scheme is used for local and global losses for optimization of proposed model. The evaluation results of proposed methodology on two publicly available large scale datasets Market-1501 and DukeMTMC-ReID verified the effectiveness of proposed refined alignment method.


Classification of VR Interaction Techniques, Based on User Intention


title: Integration of DC Microgrids as Virtual Synchronous Machines Into the AC Grid

abstract: A smart and autonomous integration concept for dc microgrids into the legacy ac grid is proposed based on the virtual synchronous machine (VSM) concept. It utilizes a dc–ac converter as a universal VSM-based interface (VSMBI) between the ac grid and various distributed energy resources (DER) connected on the dc side. The control strategy of it includes: 1) a frequency regulation improved from previous VSM works, which is suitable for the microgrid integration; 2) an improved dual droop control between the ac frequency and the dc side energy storages; 3) a power system stabilizer to enhance the system stability. Under this concept, the VSMBI integrates the DERs, loads and energy storages in the dc microgrid into a VSM. The VSMBI and the dc microgrid together will respond to short-term and long-term requirements of the grid frequency regulation, and achieve autonomous power management for the ac grid and the dc microgrid. It is therefore an important step forward in supporting high DER penetration. The concept, its design and small-signal analysis are presented in this paper. Its effectiveness and functions are verified by simulation and experimental results.


title: Blogging at work and the corporate attention economy

abstract: The attention economy motivates participation in peer-produced sites on the Web like YouTube and Wikipedia. However, this economy appears to break down at work. We studied a large internal corporate blogging community using log files and interviews and found that employees expected to receive attention when they contributed to blogs, but these expectations often went unmet. Like in the external blogosphere, a few people received most of the attention, and many people received little or none. Employees expressed frustration if they invested time and received little or no perceived return on investment. While many corporations are looking to adopt Web-based communication tools like blogs, wikis, and forums, these efforts will fail unless employees are motivated to participate and contribute content. We identify where the attention economy breaks down in a corporate blog community and suggest mechanisms for improvement.


title: Effect of cooling conditions on the design and operation of IPMSM

abstract: High power Interior Permanent Magnet Synchronous Machines (IPMSM) are used extensively in hybrid vehicle applications due to their high power density, wide speed range and better efficiency. In general, a high power IPMSM and its cooling is designed according to its rated power. Depending upon the application, specific power demand (other than rated) may arise to run the same machine at less or more than rated current. One may like to keep the same machine but change its cooling. Modification in machine cooling can compensate for the increase in machine losses due to its current change. The design of such a machine needs special attention because every machine designed for rated current may not be used continuously at current greater than that. In this paper, the operational range of a baseline machine design is presented and analyzed at less as well as more than rated current, using a cross saturated dq model. It is imperative to consider the demagnetization limit of permanent magnets in the analysis of machine operation. Different machine designs are presented and compared for torque-speed profile and efficiency at different current conditions with increased machine cooling. Machine design with increased width of permanent magnets provides more wider operating region at the cost of reduction in its efficiency for higher speeds.


title: One weird trick for parallelizing convolutional neural networks

abstract: I present a new way to parallelize the training of convolutional neural networks across multiple GPUs. The method scales significantly better than all alternatives when applied to modern convolutional neural networks.


title: Using Light-Activated Enzymes for Modulating Intracellular c-di-GMP Levels in Bacteria.

abstract: Signaling pathways involving second messenger c-di-GMP regulate various aspects of bacterial physiology and behavior. We describe the use of a red light-activated diguanylate cyclase (c-di-GMP synthase) and a blue light-activated c-di-GMP phosphodiesterase (hydrolase) for manipulating intracellular c-di-GMP levels in bacterial cells. We illustrate the application of these enzymes in regulating several c-di-GMP-dependent phenotypes, i.e., motility and biofilm phenotypes in E. coli and chemotactic behavior in the alphaproteobacterium Azospirillum brasilense. We expect these light-activated enzymes to be also useful in regulating c-di-GMP-dependent processes occurring at the fast timescale, for spatial control of bacterial populations, as well as for analyzing c-di-GMP-dependent phenomena at the single-cell level.


title: A New Early Termination Strategy for QC-LDPC Codes Based on the Layered Message Passing Decoding

abstract: Low-density parity-check (LDPC) codes are one of the most popular linear block codes have ever been used in the communication system. The standard two-phase message passing decoding (TPMPD) algorithm and the layered message-passing decoding (LMPD) algorithm are widely used in LDPC decoder. However, the convergence speed of LMPD algorithm is much faster than that of TPMPD algorithm, particularly in the condition of high signal noise ratio (SNR). In this paper, we propose an early termination criterion for quasi-cyclic low-density parity-check (QC-LDPC) codes based on LMPD algorithm with fast convergence speed. The strategy is to dynamically reprocesses the uncorrected related layers while skipping the corrected ones. Simulations show that 3% reduction in terms of iteration numbers can be achieved with no performance loss compared to the traditional termination strategies, such as hard decision aided (HDA) strategy and parity check equations (PCE) strategy. Furthermore, the optimized early termination strategy achieves low complexity in hardware implementation. nodes and updates all the a posteriori probability (APP) values for the CNP in next sub-iteration. Compared with TPMPD algorithm, LMPD algorithm achieves better decoding performance and faster convergence speed, especially in the high Signal Noise Ratio (SNR) condition. The early termination strategy for the LMPD algorithm is an important technique since it can reduce the hardware complexity, the number of sub-iterations, and the power consumption significantly in hardware implementation. There are various algorithms such as hard decision aided (HDA) (11) strategy, parity check equations (PCE) strategy and partial parity check equations (PPCE) strategy proposed for LDPC termination strategies. However the HDA strategy consumes large memory while the PPCE and PCE strategies have high computation complexity. In this paper, an optimized early termination criterion is proposed with lower hardware complexity, faster convergence speed, and almost the same decoding performance compared with the traditional strategies. The rest of this paper is organized as follows. Section II describes LDPC decoding algorithms and the traditional termination strategies. Section III proposes a new early termination strategy for QC-LDPC codes. Section IV optimizes the early termination strategy for hardware efficient implementation. Section V concludes the paper.


title: A distributed prevention scheme from malicious nodes in VANETs' routing protocols

abstract: Vehicular environments are vulnerable to attacks because of the continuous interactions between vehicles despite authentication techniques deployed by communication standards. In fact, an authenticated node with a certificate could initiate an attack while complying with implemented protocols if it has malicious intentions and benefit from this always on connection to threaten the network accuracy. Several mechanisms to counter these attacks were proposed but none of them is able to anticipate the behavior of nodes. In the present work, we target this problem by proposing a preventive mechanism able to predict the behavior of vehicles and prevent from attacks. We use Kalman filter to predict the future behavior of vehicles and classify them into three categories (white, gray and black) based on their expected trustworthiness. The main concern of this work is to prevent from the denial of service (DoS) attack. Results, given by the implementation of the proposed mechanism over an intersection-based routing protocol using ns3 simulator, prove its accuracy regarding the detection rate and a good impact on packets delivery ratio and end-to-end delay.


title: Building a Data Warehouse for Twitter Stream Exploration

abstract: In the recent year Twitter has evolved into an extremely popular social network and has revolutionized the ways of interacting and exchanging information on the Internet. By making its public stream available through a set of APIs Twitter has triggered a wave of research initiatives aimed at analysis and knowledge discovery from the data about its users and their messaging activities. While most of the projects and tools are tailored towards solving specific tasks, we pursue a goal of providing an application in dependent and universal analytical platform for supporting any kind of analysis and knowledge discovery. We employ the well established data warehousing technology with its underlying multidimensional data model, ETL routine for loading and consolidating data from different sources, OLAP functionality for exploring the data and data mining tools for more sophisticated analysis. In this work we describe the process of transforming the original stream into a set of related multidimensional cubes and demonstrate how the resulting data warehouse can be used for solving a variety of analytical tasks. We expect our proposed approach to be applicable for analyzing the data of other social networks as well.


title: Revisit of Minimum-area Enclosing Rectangle of a Convex Polygon

abstract: The problem of minimum-area enclosing rectangle of a convex polygon was first studied in [1] in 1975. We revis it this problem by providing a new complete proof via the elementary calculus and the method of rotating calipers [4], [5], [7] with transparent existence condition not revealed explicitly in [1] mainly based on geometric reasoning. The existence of minimum-area enclosing rectangle is mathematically due to monotonicy of area of enclosing rectangle with respect to the rotation angle defining its configuration relative to an initial enclosing rectangle.


title: Socialbots: voices from the fronts

abstract: The Social Mediator forum was created to bridge the gaps between the theory and practice of social media research and development. The articles are intended to promote greater awareness of new insights and experiences in the rapidly evolving domain of social media, some of which may influence perspectives and approaches in the more established areas of human-computer interaction. Each article in the forum is made up of several short contributions from people representing different perspectives on a particular topic. Previous installments of this forum have woven together diverse perspectives on the ways that social media is transforming relationships among different stakeholders in the realms of healthcare and government. The current article highlights some of the ways social robots (socialbots)---programs that operate autonomously on social networking sites---are transforming relationships within those sites, and how these transformations may more broadly influence relationships among people and organizations in the future. A recent article in Communications of the ACM called "The Social Life of Robots" reported that "researchers have started to explore the possibilities of 'social' machines capable of working together with minimal human supervision" [1]. That article illuminates recent developments involving interactions between humans and robots in the physical world; this article focuses on the interactions between humans and robots in the virtual world. Our authors are exploring and expanding the frontiers of designing, deploying, and analyzing the behavior and impact of robots operating in online social networks, and they have invited a number of other frontierspeople to share some of their insights, experiences, and future expectations for social robotics.


title: A Framework for Procedural Text Understanding

abstract: In this paper we propose a framework for procedural text understanding. Procedural texts are relatively clear without modality nor dependence on viewpoints, etc. and have many potential applications in artificial intelligence. Thus they are suitable as the first target of natural language understanding. As our framework we extend parsing technologies to connect important concepts in a text. Our framework first tokenizes the input text, a sequence of sentences, then recognizes important concepts like named entity recognition, and finally connect them like a sentence parser but dealing all the concepts in the text at once. We tested our framework on cooking recipe texts annotated with a directed acyclic graph as their meaning. We present experimental results and evaluate our framework.


title: Skeleton Boxes: Solving skeleton based action detection with a single deep convolutional neural network

abstract: Action recognition from well-segmented 3D skeleton video has been intensively studied. However, due to the difficulty in representing the 3D skeleton video and the lack of training data, action detection from streaming 3D skeleton video still lags far behind its recognition counterpart and image based object detection. In this paper, we propose a novel approach for this problem, which leverages both effective skeleton video encoding and deep regression based object detection from images. Our framework consists of two parts: skeleton-based video image mapping, which encodes a skeleton video to a color image in a temporal preserving way, and an end-to-end trainable fast skeleton action detector (Skeleton Boxes) based on image detection. Experimental results on the latest and largest PKU-MMD benchmark dataset demonstrate that our method outperforms the state-of-the-art methods with a large margin. We believe our idea would inspire and benefit future research in this important area.


Modern AI for games : RoboCode


UILU-ENG-99-7013 Agricultural Vehicle Navigation Using Multiple Guidance Sensors


title: A Classification Algorithm for Electroencephalography Signals by Self-Induced Emotional Stimuli

abstract: The aim of this paper is to propose a real-time classification algorithm for the low-amplitude electroencephalography (EEG) signals, such as those produced by remembering an unpleasant odor, to drive a brain–computer interface. The peculiarity of these EEG signals is that they require ad hoc signals preprocessing by wavelet decomposition, and the definition of a set of features able to characterize the signals and to discriminate among different conditions. The proposed method is completely parameterized, aiming at a multiclass classification and it might be considered in the framework of machine learning. It is a two stages algorithm. The first stage is offline and it is devoted to the determination of a suitable set of features and to the training of a classifier. The second stage, the real-time one, is to test the proposed method on new data. In order to avoid redundancy in the set of features, the principal components analysis is adapted to the specific EEG signal characteristics and it is applied; the classification is performed through the support vector machine. Experimental tests on ten subjects, demonstrating the good performance of the algorithm in terms of both accuracy and efficiency, are also reported and discussed.


title: A datasheet driven power MOSFET model and parameter extraction procedure for 1200V, 20A SiC MOSFETs

abstract: A compact model for SiC Power MOSFETs has been presented. The model has been validated with measurements from commercially available 1200V, 20A SiC power MOSFETs. The model features temperature scaling from 25°C to 225°C, which is the operating temperature for the new devices. In order to improve the user's experience with the model, a new datasheet driven parameter extraction strategy has been proposed. The parameter extraction strategy requires only the data normally given in device datasheets, so off-the-shelf devices can characterized quickly. The model includes charge conserving expressions for all non-linear capacitances of the power MOSFET. The SiC power MOSFET shows excellent performance over elevated temperatures, with small variation in on-state resistance over temperature.


title: Improving Robustness of Deep Neural Network Acoustic Models via Speech Separation and Joint Adaptive Training

abstract: Although deep neural network (DNN) acoustic models are known to be inherently noise robust, especially with matched training and testing data, the use of speech separation as a frontend and for deriving alternative feature representations has been shown to improve performance in challenging environments. We first present a supervised speech separation system that significantly improves automatic speech recognition (ASR) performance in realistic noise conditions. The system performs separation via ratio time-frequency masking; the ideal ratio mask (IRM) is estimated using DNNs. We then propose a framework that unifies separation and acoustic modeling via joint adaptive training. Since the modules for acoustic modeling and speech separation are implemented using DNNs, unification is done by introducing additional hidden layers with fixed weights and appropriate network architecture. On the CHiME-2 medium-large vocabulary ASR task, and with log mel spectral features as input to the acoustic model, an independently trained ratio masking frontend improves word error rates by 10.9% (relative) compared to the noisy baseline. In comparison, the jointly trained system improves performance by 14.4%. We also experiment with alternative feature representations to augment the standard log mel features, like the noise and speech estimates obtained from the separation module, and the standard feature set used for IRM estimation. Our best system obtains a word error rate of 15.4% (absolute), an improvement of 4.6 percentage points over the next best result on this corpus.


title: Clock tree optimization for electromagnetic compatibility (EMC)

abstract: Electromagnetic Interference (EMI) generated by electronic systems is increasing with operating frequency and shrinking process technologies. The clock distribution network is one of the major causes of on-chip EMI. In this paper, we discuss the EMI problem in clock tree design. Spectrum analysis shows that slew rate of clock signal is the main parameter determining the high-frequency spectral content distribution. This is the first work to consider maximum and minimum buffer slew rates in clock tree synthesis to reduce EMI. In this paper, we propose a dynamic programming algorithm to optimize the clock tree considering both traditional metrics and Electromagnetic Compatibility (EMC). Our experimental results show that slew can be controlled in a feasible range and high-frequency spectrum contents can be reduced without sacrificing the traditional metrics such as power and skew. With the efficient optimization and pruning method, the biggest benchmark is able to complete in four minutes.


Summarization of Articles using LexRank for Texts via Intermediate Embdedded Representations


title: The development of the social health scale for the elderly

abstract: With the elderly population comprising an increasing large proportion of society, a valid and reliable measure of social health in the elderly is indispensable for a comprehensive health assessment. The objective of this study is to develop a Social Health Scale for the Elderly (SHSE). A draft scale was generated based on a literature review and expert surveys. Pilot testing was conducted from December 14, 2015, to January 8, 2016. Some items were removed after assessment with five statistical analysis methods. Field testing began on November 6, 2016, and ended on January 20, 2017. After field testing, the reliability and validity of the scale were assessed and the norms in Hangzhou were calculated. In the two tests, 430 and 2404 subjects were included in the statistical analyses. The long form of the SHSE (SHSE-L) contained 25 items, 14 of which were also in the short form (SHSE-S). The internal consistency of the SHSE-L was acceptable. The test-rest reliability and inter-rater reliability were moderate, but the concurrent validity, construct validity, and convergent and discriminant validity were desirable in both versions. The standard and percentile rank norms in Hangzhou, China were developed based on the field testing data. The population-based social health of the Chinese elderly can be validly and reliably assessed with the SHSE.


title: Learning a Discriminative Null Space for Person Re-identification

abstract: Most existing person re-identification (re-id) methods focus on learning the optimal distance metrics across camera views. Typically a person's appearance is represented using features of thousands of dimensions, whilst only hundreds of training samples are available due to the difficulties in collecting matched training images. With the number of training samples much smaller than the feature dimension, the existing methods thus face the classic small sample size (SSS) problem and have to resort to dimensionality reduction techniques and/or matrix regularisation, which lead to loss of discriminative power. In this work, we propose to overcome the SSS problem in re-id distance metric learning by matching people in a discriminative null space of the training data. In this null space, images of the same person are collapsed into a single point thus minimising the within-class scatter to the extreme and maximising the relative between-class separation simultaneously. Importantly, it has a fixed dimension, a closed-form solution and is very efficient to compute. Extensive experiments carried out on five person re-identification benchmarks including VIPeR, PRID2011, CUHK01, CUHK03 and Market1501 show that such a simple approach beats the state-of-the-art alternatives, often by a big margin.


title: Communication optimization and code generation for distributed memory machines

abstract: This paper presents several algorithms to solve code generation and optimization problems specific to machines with distributed address spaces. Given a description of how the computation is to be partitioned across the processors in a machine, our algorithms produce an SPMD (single program multiple data) program to be run on each processor. Our compiler generated the necessary receive and send instructions, optimizes the communication by eliminating redundant communication and aggregating small messages into large messages, allocates space locally on each processor, and translates global data addresses to local addresses. Our techniques are based on an exact data-flow analysis on individual array element accesses. Unlike data dependence analysis, this analysis determines if two dynamic instances refer to the same value, and not just to the same location. Using this information, our compiler can handle more flexible data decompositions and find more opportunities for communication optimization than systems based on data dependence analysis. Our technique is based on a uniform framework, where data decompositions, computation decompositions and the data flow information are all represented as systems of linear inequalities. We show that the problems of communication code generation, local memory management, message aggregation and redundant data communication elimination can all be solved by projecting polyhedra represented by sets of inequalities onto lower dimensional spaces.


title: A comparative study of feature-ranking and feature-subset selection techniques for improved fault prediction

abstract: The quality of a fault prediction model depends on the software metrics that are used to build the prediction model. Feature selection represents a process of selecting a subset of relevant features that may lead to build improved prediction models. Feature selection techniques can be broadly categorized into two subcategories: feature-ranking and feature-subset selection. In this paper, we present a comparative investigation of seven feature-ranking techniques and eight feature-subset selection techniques for improved fault prediction. The performance of these feature selection techniques is evaluated using two popular machine-learning classifiers: Naive Bayes and Random Forest, over fourteen software project's fault-datasets obtained from the PROMISE data repository. The performances were measured using F-measure and AUC values. Our results demonstrated that feature-ranking techniques produced better results compared to feature-subset selection techniques. Among, the feature-ranking techniques used in the study, InfoGain and PCA techniques provided the best performance over all the datasets, while for feature-subset selection techniques ClassifierSubsetEval and Logistic Regression produced better results against their peers.


title: Random Walks for Image Segmentation

abstract: A novel method is proposed for performing multilabel, interactive image segmentation. Given a small number of pixels with user-defined (or predefined) labels, one can analytically and quickly determine the probability that a random walker starting at each unlabeled pixel will first reach one of the prelabeled pixels. By assigning each pixel to the label for which the greatest probability is calculated, a high-quality image segmentation may be obtained. Theoretical properties of this algorithm are developed along with the corresponding connections to discrete potential theory and electrical circuits. This algorithm is formulated in discrete space (i.e., on a graph) using combinatorial analogues of standard operators and principles from continuous potential theory, allowing it to be applied in arbitrary dimension on arbitrary graphs


Inductive Programming as Approach to Comprehensible Machine Learning


title: Static Free Space Detection with Laser Scanner using Occupancy Grid Maps

abstract: Drivable free space information is vital for autonomous vehicles that have to plan evasive maneuvers in real-time. In this paper, we present a new efficient method for environmental free space detection with laser scanner based on 2D occupancy grid maps (OGM) to be used for Advanced Driving Assistance Systems (ADAS) and Collision Avoidance Systems (CAS). Firstly, we introduce an enhanced inverse sensor model tailored for high-resolution laser scanners for building OGM. It compensates the unreflected beams and deals with the ray casting to grid cells accuracy and computational effort problems. Secondly, we introduce the 'vehicle on a circle for grid maps' map alignment algorithm that allows building more accurate local maps by avoiding the computationally expensive inaccurate operations of image sub-pixel shifting and rotation. The resulted grid map is more convenient for ADAS features than existing methods, as it allows using less memory sizes, and hence, results into a better real-time performance. Thirdly, we present an algorithm to detect what we call the 'in-sight edges'. These edges guarantee modeling the free space area with a single polygon of a fixed number of vertices regardless the driving situation and map complexity. The results from real world experiments show the effectiveness of our approach.


Neural Network based Extreme Classification and Similarity Models for Product Matching


title: Continual Learning in Generative Adversarial Nets

abstract: Developments in deep generative models have allowed for tractable learning of high-dimensional data distributions. While the employed learning procedures typically assume that training data is drawn i.i.d. from the distribution of interest, it may be desirable to model distinct distributions which are observed sequentially, such as when different classes are encountered over time. Although conditional variations of deep generative models permit multiple distributions to be modeled by a single network in a disentangled fashion, they are susceptible to catastrophic forgetting when the distributions are encountered sequentially. In this paper, we adapt recent work in reducing catastrophic forgetting to the task of training generative adversarial networks on a sequence of distinct distributions, enabling continual generative modeling.


title: Information integration in the enterprise

abstract: An oxidated silicon compound will react with a substituted organic compound in the presence of an alkali compound to produce organic alkali metal silicate products which may be used as molding powders, as adhesives, as coating agents and be reacted with polyisocyanates to produce insulating foams.


title: Multilinear Multitask Learning

abstract: Many real world datasets occur or can be arranged into multi-modal structures. With such datasets, the tasks to be learnt can be referenced by multiple indices. Current multitask learning frameworks are not designed to account for the preservation of this information. We propose the use of multilinear algebra as a natural way to model such a set of related tasks. We present two learning methods; one is an adapted convex relaxation method used in the context of tensor completion. The second method is based on the Tucker decomposition and on alternating minimization. Experiments on synthetic and real data indicate that the multilinear approaches provide a significant improvement over other multitask learning methods. Overall our second approach yields the best performance in all datasets.


title: An Empirical Study of the Reliability of UNIX Utilities

abstract: The following section describes the tools we built to test the utilities. These tools include the fuzz (random character) generator, ptyjig (to test interactive utilities), and scripts to automate the testing process. Next, we will describe the tests we performed, giving the types of input we presented to the utilities. Results from the tests will follow along with an analysis of the results, including identification and classification of the program bugs that caused the crashes. The final section presents concluding remarks, including suggestions for avoiding the types of problems detected by our study and some commentary on the bugs we found. We include an Appendix with the user manual pages for fuzz and ptyjig.


title: Synthetic cannabinoids: analysis and metabolites.

abstract: Cannabimimetics (commonly referred to as synthetic cannabinoids), a group of compounds encompassing a wide range of chemical structures, have been developed by scientists with the hope of achieving selectivity toward one or the other of the cannabinoid receptors CB1 and CB2. The goal was to have compounds that could possess high therapeutic activity without many side effects. However, underground laboratories have used the information generated by the scientific community to develop these compounds for illicit use as marijuana substitutes. This chapter reviews the different classes of these "synthetic cannabinoids" with particular emphasis on the methods used for their identification in the herbal products with which they are mixed and identification of their metabolites in biological specimens.


title: Visually Aligned Word Embeddings for Improving Zero-shot Learning

abstract: Zero-shot learning (ZSL) highly depends on a good semantic embedding to connect the seen and unseen classes. Recently, distributed word embeddings (DWE) pre-trained from large text corpus have become a popular choice to draw such a connection. Compared with human defined attributes, DWEs are more scalable and easier to obtain. However, they are designed to reflect semantic similarity rather than visual similarity and thus using them in ZSL often leads to inferior performance. To overcome this visual-semantic discrepancy, this work proposes an objective function to re-align the distributed word embeddings with visual information by learning a neural network to map it into a new representation called visually aligned word embedding (VAWE). Thus the neighbourhood structure of VAWEs becomes similar to that in the visual domain. Note that in this work we do not design a ZSL method that projects the visual features and semantic embeddings onto a shared space but just impose a requirement on the structure of the mapped word embeddings. This strategy allows the learned VAWE to generalize to various ZSL methods and visual features. As evaluated via four state-of-the-art ZSL methods on four benchmark datasets, the VAWE exhibit consistent performance improvement.


title: Change management : the real struggle for ERP systems practices

abstract: Enterprise Resource Planning (ERP) systems have become an essential ::: information systems infrastructure for large organisations. These ::: organisations are now looking for ways to leverage their ERP investment ::: by introducing new functionality. However no matter how many ::: implementations these companies have undertaken the same people ::: issues still provide barriers. This research looks at the change ::: management practices of Australian companies and identifies the main ::: success factors and barriers associated with implementing change ::: management strategies. The paper presents the results of a survey of 37 ::: major Australian organisations that have implemented an ERP system. ::: Many of these organisations have long histories of ERP usage and ::: multiple ERP implementations and upgrades. The main findings indicate ::: that the respondents considered change management crucial to successful ::: ERP implementations, yet their organisations did not perform change ::: management very well. The main success factor to change management ::: was provision of adequate resources with the main barrier being lack ::: of communication up and down the organization.


title: 3D City Map Reconstruction from UAV-Based Radio Measurements

abstract: This paper considers the problem of 3D city map reconstruction. The key novelty here lies in the sole exploitation of UAV-bound radio measurements as a way to recover the map data, i.e. no image of the city is taken or processed. The proposed approach relies on the unique ability for a UAV- to-ground communication system to detect and classify line-of-sight (LoS) vs. non line-of-sight (NLoS) channels towards ground users using machine learning tools. Once classification is carried out, the LoS vs. NLoS data is fed as input to a building position and height reconstruction algorithm. The map reconstruction quality is analyzed as a function of user density and UAV altitude, revealing the notion of an optimal height for the UAV which is predicted using an analytical model.


System Dynamics and Decision-Making in the Context of Dynamically Complex Multi-Dimensional Societal Issues


title: A Note on the Digiti Quinti Sign in Hemiplegic Migraine

abstract: The digiti quinti sign (DQS) was described as an indication of mild hemiparesis. It consists of a wider space between the fourth and fifth fingers at the affected side when the patient extends both arms horizontally to the front with the palms down. The three successively examined sporadic hemiplegic migraine patients presented herein disclosed no other neurological abnormality except the interictally present DQS on the same side of the motor deficits. This sign is perhaps clinically useful for the diagnosis of hemiplegic migraine.


title: GibbsNet: Iterative Adversarial Inference for Deep Graphical Models

abstract: Directed latent variable models that formulate the joint distribution as $p(x,z) = p(z) p(x \mid z)$ have the advantage of fast and exact sampling. However, these models have the weakness of needing to specify $p(z)$, often with a simple fixed prior that limits the expressiveness of the model. Undirected latent variable models discard the requirement that $p(z)$ be specified with a prior, yet sampling from them generally requires an iterative procedure such as blocked Gibbs-sampling that may require many steps to draw samples from the joint distribution $p(x, z)$. We propose a novel approach to learning the joint distribution between the data and a latent code which uses an adversarially learned iterative procedure to gradually refine the joint distribution, $p(x, z)$, to better match with the data distribution on each step. GibbsNet is the best of both worlds both in theory and in practice. Achieving the speed and simplicity of a directed latent variable model, it is guaranteed (assuming the adversarial game reaches the virtual training criteria global minimum) to produce samples from $p(x, z)$ with only a few sampling iterations. Achieving the expressiveness and flexibility of an undirected latent variable model, GibbsNet does away with the need for an explicit $p(z)$ and has the ability to do attribute prediction, class-conditional generation, and joint image-attribute modeling in a single model which is not trained for any of these specific tasks. We show empirically that GibbsNet is able to learn a more complex $p(z)$ and show that this leads to improved inpainting and iterative refinement of $p(x, z)$ for dozens of steps and stable generation without collapse for thousands of steps, despite being trained on only a few steps.


title: Nonlinear adaptive speed control for permanent magnet synchronous motors under the unbalanced resistances of phase windings

abstract: In this paper, we propose nonlinear adaptive speed control under three-phase voltage unbalance of permanent magnet synchronous motors. An adaptive control applied in a-b-c frame dynamics is proposed to compensate three-phase voltage unbalance by different values of resistance. At this method can estimate actual resistances. For velosity tracking, it is proven that the tracking error is globally asymptotically stable via Lyapunov theorem. The proposed method is performed to validate via the simulation.


title: Reflection and transmission of elastic waves at an elastic/porous solid saturated by two immiscible fluids

abstract: Wave propagation in a porous elastic medium saturated by two immiscible fluids is investigated. It is shown that there exist three dilatational waves and one transverse wave propagating with different velocities. It is found that the velocities of all the three longitudinal waves are influenced by the capillary pressure, while the velocity of transverse wave does not at all. The problem of reflection and refraction phenomena due to longitudinal and transverse wave incident obliquely at a plane interface between uniform elastic solid half-space and porous elastic half-space saturated by two immiscible fluids has been analyzed. The amplitude ratios of various reflected and refracted waves are found to be continuous functions of the angle of incidence. Expression of energy ratios of various reflected and refracted waves are derived in closed form. The amplitude ratios and energy ratios have been computed numerically for a particular model and the results obtained are depicted graphically. It is verified that during transmission there is no dissipation of energy at the interface. Some particular cases have also been reduced from the present formulation.


title: Deep Deconvolutional Networks for Scene Parsing

abstract: Scene parsing is an important and challenging prob- lem in computer vision. It requires labeling each pixel in an image with the category it belongs to. Tradition- ally, it has been approached with hand-engineered features from color information in images. Recently convolutional neural networks (CNNs), which automatically learn hierar- chies of features, have achieved record performance on the task. These approaches typically include a post-processing technique, such as superpixels, to produce the final label- ing. In this paper, we propose a novel network architecture that combines deep deconvolutional neural networks with CNNs. Our experiments show that deconvolutional neu- ral networks are capable of learning higher order image structure beyond edge primitives in comparison to CNNs. The new network architecture is employed for multi-patch training, introduced as part of this work. Multi-patch train- ing makes it possible to effectively learn spatial priors from scenes. The proposed approach yields state-of-the-art per- formance on four scene parsing datasets, namely Stanford Background, SIFT Flow, CamVid, and KITTI. In addition, our system has the added advantage of having a training system that can be completely automated end-to-end with- out requiring any post-processing.


title: Gambling Near-Misses Enhance Motivation to Gamble and Recruit Win-Related Brain Circuitry

abstract: “Near-miss” events, where unsuccessful outcomes are proximal to the jackpot, increase gambling propensity and may be associated with the addictiveness of gambling, but little is known about the neurocognitive mechanisms that underlie their potency. Using a simplified slot machine task, we measured behavioral and neural responses to gambling outcomes. Compared to “full-misses,” near-misses were experienced as less pleasant, but increased desire to play. This effect was restricted to trials where the subject had personal control over arranging their gamble. Near-miss outcomes recruited striatal and insula circuitry that also responded to monetary wins; in addition, near-miss-related activity in the rostral anterior cingulate cortex varied as a function of personal control. Insula activity to near-misses correlated with self-report ratings as well as a questionnaire measure of gambling propensity. These data indicate that near-misses invigorate gambling through the anomalous recruitment of reward circuitry, despite the objective lack of monetary reinforcement on these trials.


title: A dry electrode for EEG recording

abstract: Abstract This paper describes the design, fabrication and testing of a prototype dry surface electrode for EEG signal recording. The new dry electrode has the advantages of no need for skin preparation or conductive paste, potential for reduced sensitivity to motion artifacts and an enhanced signal-to-noise ratio. The electrode's sensing element is a 3 mm stainless steel disk which has a 2000 A (200 nm) thick nitride coating deposited onto one side. The back side of the disk is attached to an impedance converting amplifier. The prototype electrode was mounted on a copper plate attached to the scalp by a Velcro strap. The performance of this prototype dry electrode was compared to commercially available wet electrodes in 3 areas of electroencephalogram (EEG) recording: (1) spontaneous EEG, (2) sensory evoked potentials, and (3) cognitive evoked potentials. In addition to the raw EEG, the power spectra of the signals from both types of electrodes were also recorded. The results suggest that the dry electrode performs comparably to conventional electrodes for all types of EEG signals analysis. This new electrode may be useful for the production of high resolution surface maps of brain activity where a large number of electrodes or prolonged recordingtimes are required.


title: Single-Image Crowd Counting via Multi-Column Convolutional Neural Network

abstract: This paper aims to develop a method than can accurately estimate the crowd count from an individual image with arbitrary crowd density and arbitrary perspective. To this end, we have proposed a simple but effective Multi-column Convolutional Neural Network (MCNN) architecture to map the image to its crowd density map. The proposed MCNN allows the input image to be of arbitrary size or resolution. By utilizing filters with receptive fields of different sizes, the features learned by each column CNN are adaptive to variations in people/head size due to perspective effect or image resolution. Furthermore, the true density map is computed accurately based on geometry-adaptive kernels which do not need knowing the perspective map of the input image. Since exiting crowd counting datasets do not adequately cover all the challenging situations considered in our work, we have collected and labelled a large new dataset that includes 1198 images with about 330,000 heads annotated. On this challenging new dataset, as well as all existing datasets, we conduct extensive experiments to verify the effectiveness of the proposed model and method. In particular, with the proposed simple MCNN model, our method outperforms all existing methods. In addition, experiments show that our model, once trained on one dataset, can be readily transferred to a new dataset.


title: Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network

abstract: Depth estimation from a single image is a fundamental problem in computer vision. In this paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) to learn the affinity matrix for depth prediction. Specifically, we adopt an efficient linear propagation model, where the propagation is performed with a manner of recurrent convolutional operation, and the affinity among neighboring pixels is learned through a deep convolutional neural network (CNN). We apply the designed CSPN to two depth estimation tasks given a single image: (1) To refine the depth output from state-of-the-art (SOTA) existing methods; and (2) to convert sparse depth samples to a dense depth map by embedding the depth samples within the propagation procedure. The second task is inspired by the availability of LIDARs that provides sparse but accurate depth measurements. We experimented the proposed CSPN over two popular benchmarks for depth estimation, i.e. NYU v2 and KITTI, where we show that our proposed approach improves in not only quality (e.g., 30% more reduction in depth error), but also speed (e.g., 2 to 5 times faster) than prior SOTA methods.


title: Testing overconfidence bias in Pakistani stock market

abstract: Excessive trading phenomenon is contrary to the concept of traditional finance that is based on the rational expectation theorem and efficient market hypothesis. Therefore, this study is aimed at exploring the existence of overconfidence behavior in the stock market. The market-wide panel VAR model is used to investigate the lead–lag relationship between stock returns and turnover. Our results suggest that investors are overconfident in Pakistani stock market because turnover depends directly upon stock returns. The findings have important implications for investors and brokers for developing appropriate trading strategy.


title: Copula bayesian networks

abstract: We present the Copula Bayesian Network model for representing multivariate continuous distributions, while taking advantage of the relative ease of estimating univariate distributions. Using a novel copula-based reparameterization of a conditional density, joined with a graph that encodes independencies, our model offers great flexibility in modeling high-dimensional densities, while maintaining control over the form of the univariate marginals. We demonstrate the advantage of our framework for generalization over standard Bayesian networks as well as tree structured copula models for varied real-life domains that are of substantially higher dimension than those typically considered in the copula literature.


title: Artificial Intelligence techniques: An introduction to their use for modelling environmental systems

abstract: Knowledge-based or Artificial Intelligence techniques are used increasingly as alternatives to more classical techniques to model environmental systems. We review some of them and their environmental applicability, with examples and a reference list. The techniques covered are case-based reasoning, rule-based systems, artificial neural networks, fuzzy models, genetic algorithms, cellular automata, multi-agent systems, swarm intelligence, reinforcement learning and hybrid systems.


title: The Role of ICT to Achieve the UN Sustainable Development Goals (SDG)

abstract: This paper is aiming at illustrating the potential of ICT for achieving the Sustainable Development Goals which were declared by the United Nations in 2015 as binding for all nations of our planet addressing both developing and developed countries. ICT must play a significant role if the SDGs should be achieved as projected in 2030. The paper gives an overview of some of the existing efforts in this area and is written as an appeal to all professionals, scientists and IT-professional and their organization to take a holistic approach for all ICT-activities and projects to always include and monitor the effects of their work on the SDGs. The impacts of ICT on sustainability are twofold: On the one hand there might be negative effects on sustainability such as the generation of electronic waste, on the other hand ICT is definitely an enabler to more efficient resource usage, education and business operations which is critical success factor for achieving the SDGs.


title: Using Enterprise Architecture Standards in Managing Information Technology

abstract: Organizations increasingly need to build an enterprise-wide capability to leverage technology that is distributed in different business units. Some organizations establish enterprise architecture (EA) standards to enable greater compatibility of information technology (IT) components and integration of applications and data across the enterprise. Through a firm-level survey, we sought to answer two key questions about the use of EA standards: (1) How do different governance mechanisms affect the use of EA standards? and (2) To what extent does the use of EA standards help organizations to improve the sharing and integration of IT resources across the enterprise? We identified four key governance mechanisms for EA standards management and examined how each mechanism affected the use of EA standards. We also examined how the use of EA standards affects the management of IT infrastructure, applications, and data resources across business units. Our empirical results showed that the use of EA standards is effective in helping organizations to better manage their IT resources. Our study also provides detailed insights into how organizations can set up governance mechanisms to facilitate the use of EA standards in achieving enterprisewide goals.


title: The Essen Stroke Risk Score Predicts Recurrent Cardiovascular Events: A Validation Within the REduction of Atherothrombosis for Continued Health (REACH) Registry

abstract: In outpatients with previous stroke or transient ischemic attack, the ESRS accurately stratifies the risk of recurrent stroke or major vascular events. Patients with a high ESRS should be candidates for intensified secondary prevention strategies.


title: APPLICATION OF SILVER NANOPARTICLES IN VIRAL INHIBITION: A NEW HOPE FOR ANTIVIRALS

abstract: In modern era, viral infections constitute one of the main health problems. Chemically synthesized antiviral drugs have adverse side effects which are associated with other health complications. The emergence of new infectious diseases and increase in frequency of drug resistant viruses demand the most effective and novel therapeutic agents, one of which are nanoparticles. Nanoparticles excel as therapeutic agents due to their unique physiochemical properties and a universally applicable physical mode of action. Their large surface area to volume ratio offers them an edge as an antiviral agent. The review focuses on the mechanism of action of silver nanoparticles and their novel therapeutic applications against some viruses bearing potentially fatal consequences.


title: Occlusion Reasoning for Object Detectionunder Arbitrary Viewpoint

abstract: We present a unified occlusion model for object instance detection under arbitrary viewpoint. Whereas previous approaches primarily modeled local coherency of occlusions or attempted to learn the structure of occlusions from data, we propose to explicitly model occlusions by reasoning about 3D interactions of objects. Our approach accurately represents occlusions under arbitrary viewpoint without requiring additional training data, which can often be difficult to obtain. We validate our model by incorporating occlusion reasoning with the state-of-the-art LINE2D and Gradient Network methods for object instance detection and demonstrate significant improvement in recognizing texture-less objects under severe occlusions.


A Study on Expressions of Gratitude in Japanese and American English.


title: Smart operators in industry 4.0: A human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context

abstract: Abstract As the Industry 4.0 takes shape, human operators experience an increased complexity of their daily tasks: they are required to be highly flexible and to demonstrate adaptive capabilities in a very dynamic working environment. It calls for tools and approaches that could be easily embedded into everyday practices and able to combine complex methodologies with high usability requirements. In this perspective, the proposed research work is focused on the design and development of a practical solution, called Sophos-MS, able to integrate augmented reality contents and intelligent tutoring systems with cutting-edge fruition technologies for operators’ support in complex man-machine interactions. After establishing a reference methodological framework for the smart operator concept within the Industry 4.0 paradigm, the proposed solution is presented, along with its functional and non-function requirements. Such requirements are fulfilled through a structured design strategy whose main outcomes include a multi-layered modular solution, Sophos-MS, that relies on Augmented Reality contents and on an intelligent personal digital assistant with vocal interaction capabilities. The proposed approach has been deployed and its training potentials have been investigated with field experiments. The experimental campaign results have been firstly checked to ensure their statistical relevance and then analytically assessed in order to show that the proposed solution has a real impact on operators’ learning curves and can make the difference between who uses it and who does not.


title: Advances in Cryptographic Voting Systems

abstract: Democracy depends on the proper administration of popular elections. Voters should receive assurance that their intent was correctly captured and that all eligible votes were correctly tallied. The election system as a whole should ensure that, voter coercion is unlikely, even when voters are willing to be influenced. These conflicting requirements present a significant challenge: how can voters receive enough assurance to trust the election result, but not so much that they can prove to a potential coercer how they voted? ::: This dissertation explores cryptographic techniques for implementing verifiable, secret-ballot elections. We present the power of cryptographic voting, in particular its ability to successfully achieve both verifiability and ballot secrecy, a combination that cannot be achieved by other means. We review a large portion of the literature on cryptographic voting. We propose three novel technical ideas: (1) a simple and inexpensive paper-base cryptographic voting system with some interesting advantages over existing techniques; (2) a theoretical model of incoercibility for human voters with their inherent limited computational ability; and a new ballot casting system that fits the new definition, and (3) a new theoretical construct for shuffling encrypted votes in full view of public observers.


title: Influence of rotor structure on performance of permanent magnet assisted synchronous reluctance motor

abstract: The permanent magnet (PM) motor using the rare-earth PM is high performance. However, since these types of permanent magnets are expensive, less amount of magnet must be used to reduce costs or rare earth is not used. In the present paper, the performance of permanent magnet assisted synchronous reluctance motors (PMASynRMs) with ferrite magnets are examined. The suitable number of poles for high torque and high power performance for a PMASynRM and demagnetization characteristics of the PM are investigated. The rotor design that reduces the demagnetization effect is also clarified.


title: Leveraging Node Attributes for Incomplete Relational Data

abstract: Relational data are usually highly incomplete in practice, which inspires us to leverage side information to improve the performance of community detection and link prediction. This paper presents a Bayesian probabilistic approach that incorporates various kinds of node attributes encoded in binary form in relational models with Poisson likelihood. Our method works flexibly with both directed and undirected relational networks. The inference can be done by efficient Gibbs sampling which leverages sparsity of both networks and node attributes. Extensive experiments show that our models achieve the state-of-the-art link prediction results, especially with highly incomplete relational data.


title: There is no coherent evidence for a bilingual advantage in executive processing

abstract: Abstract Three studies compared bilinguals to monolinguals on 15 indicators of executive processing (EP). Most of the indicators compare a neutral or congruent baseline to a condition that should require EP. For each of the measures there was no main effect of group and a highly significant main effect of condition. The critical marker for a bilingual advantage, the Group × Condition interaction, was significant for only one indicator, but in a pattern indicative of a bilingual disadvantage. Tasks include antisaccade (Study 1), Simon (Studies 1–3), flanker (Study 3), and color-shape switching (Studies 1–3). The two groups performed identically on the Raven’s Advanced Matrices test (Study 3). Analyses on the combined data selecting subsets that are precisely matched on parent’s educational level or that include only highly fluent bilinguals reveal exactly the same pattern of results. A problem reconfirmed by the present study is that effects assumed to be indicators of a specific executive process in one task (e.g., inhibitory control in the flanker task) frequently do not predict individual differences in that same indicator on a related task (e.g., inhibitory control in the Simon task). The absence of consistent cross-task correlations undermines the interpretation that these are valid indicators of domain-general abilities. In a final discussion the underlying rationale for hypothesizing bilingual advantages in executive processing based on the special linguistic demands placed on bilinguals is interrogated.


title: NetScore: Towards Universal Metrics for Large-scale Performance Analysis of Deep Neural Networks for Practical On-Device Edge Usage

abstract: Much of the focus in the design of deep neural networks has been on improving accuracy, leading to more powerful yet highly complex network architectures that are difficult to deploy in practical scenarios, particularly on edge devices such as mobile and other consumer devices given their high computational and memory requirements. As a result, there has been a recent interest in the design of quantitative metrics for evaluating deep neural networks that accounts for more than just model accuracy as the sole indicator of network performance. In this study, we continue the conversation towards universal metrics for evaluating the performance of deep neural networks for practical on-device edge usage. In particular, we propose a new balanced metric called NetScore, which is designed specifically to provide a quantitative assessment of the balance between accuracy, computational complexity, and network architecture complexity of a deep neural network, which is important for on-device edge operation. In what is one of the largest comparative analysis between deep neural networks in literature, the NetScore metric, the top-1 accuracy metric, and the popular information density metric were compared across a diverse set of 60 different deep convolutional neural networks for image classification on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2012) dataset. The evaluation results across these three metrics for this diverse set of networks are presented in this study to act as a reference guide for practitioners in the field. The proposed NetScore metric, along with the other tested metrics, are by no means perfect, but the hope is to push the conversation towards better universal metrics for evaluating deep neural networks for use in practical on-device edge scenarios to help guide practitioners in model design for such scenarios.


title: Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge

abstract: Adversarial examples are inputs to machine learning models designed to cause the model to make a mistake. They are useful for understanding the shortcomings of machine learning models, interpreting their results, and for regularisation. In NLP, however, most example generation strategies produce input text by using known, pre-specified semantic transformations, requiring significant manual effort and in-depth understanding of the problem and domain. In this paper, we investigate the problem of automatically generating adversarial examples that violate a set of given First-Order Logic constraints in Natural Language Inference (NLI). We reduce the problem of identifying such adversarial examples to a combinatorial optimisation problem, by maximising a quantity measuring the degree of violation of such constraints and by using a language model for generating linguistically-plausible examples. Furthermore, we propose a method for adversarially regularising neural NLI models for incorporating background knowledge. Our results show that, while the proposed method does not always improve results on the SNLI and MultiNLI datasets, it significantly and consistently increases the predictive accuracy on adversarially-crafted datasets -- up to a 79.6% relative improvement -- while drastically reducing the number of background knowledge violations. Furthermore, we show that adversarial examples transfer among model architectures, and that the proposed adversarial training procedure improves the robustness of NLI models to adversarial examples.


title: Complications of Penis or Scrotum Enlargement Due to Injections with Permanent Filling Substances

abstract: For as long as people can remember, the size of the penis and the scrotum has been a source of worry to men. Through the ages and across cultures, all kinds of means have been used to increase the length and girth of this symbol of fertility, masculinity, and power. This tendency is currently still visible whenever, soon after each introduction of a new injectable filler material for the improvement of contour defects or wrinkles, such a “filler” is also being injected under the skin of the genitals to increase girth.


title: A particle swarm pattern search method for bound

abstract: In this paper we develop, analyze, and test a new algorithm for the global minimization of a function subject to simple bounds without the use of derivatives. The underlying algorithm is a pattern search method, more specifically a coordinate search method, which guarantees convergence to stationary points from arbitrary starting points. In the optional search phase of pattern search we apply a particle swarm scheme to globally explore the possible nonconvexity of the objective function. Our extensive numerical experiments showed that the resulting algorithm is highly competitive with other global optimization methods also based on function values.


title: Bayesian Co-clustering

abstract: In recent years, co-clustering has emerged as a powerful data mining tool that can analyze dyadic data connecting two entities. However, almost all existing co-clustering techniques are partitional, and allow individual rows and columns of a data matrix to belong to only one cluster. Several current applications, such as recommendation systems and market basket analysis, can substantially benefit from a mixed membership of rows and columns. In this paper, we present Bayesian co-clustering (BCC) models, that allow a mixed membership in row and column clusters. BCC maintains separate Dirichlet priors for rows and columns over the mixed membership and assumes each observation to be generated by an exponential family distribution corresponding to its row and column clusters. We propose a fast variational algorithm for inference and parameter estimation. The model is designed to naturally handle sparse matrices as the inference is done only based on the non-missing entries. In addition to finding a co-cluster structure in observations, the model outputs a low dimensional co-embedding, and accurately predicts missing values in the original matrix. We demonstrate the efficacy of the model through experiments on both simulated and real data.


title: SourceData: a semantic platform for curating and searching figures

abstract: Here we present SourceData (http://sourcedata.embo.org), a platform that allows researchers and publishers to share scientific figures and, when available, the underlying source data in a way that is machine-readable and findable. SourceData is unique in its focus on the core of scientific evidence—data presented in figures—and its capability to make papers searchable based on their data content and hence directly couple data to improved discoverability. SourceData aims at establishing a selfreinforcing data ‘ecosystem’ that bridges the conventional visual and narrative description of research findings with a machine-readable representation of data and hypotheses.


title: Towards an evolutionary framework for agile requirements elicitation

abstract: Numerous reports document difficulties experienced with the development of requirements in software projects. Specifically problems include: developers have limited access to stakeholders, don't fully understand the problem domain and as a consequence, requirements are not well understood. Agile Methods (AMs) encourage stakeholder involvement throughout development however considerable difficulty remains in accommodating continuous negotiation between multiple diverse stakeholders in a given domain. This paper reports on progress to date for developing an evolutionary framework to improve the facilitation of agile requirements elicitation. A potential solution is offered and an initial study indicates positive results.


title: An IoT Based Predictive Connected Car Maintenance Approach

abstract: Internet of Things (IoT) is fast emerging and becoming an almost basic necessity in general life. The concepts of using technology in our daily life is not new, but with the advancements in technology, the impact of technology in daily activities of a person can be seen in almost all the aspects of life. Today, all aspects of our daily life, be it health of a person, his location, movement, etc. can be monitored and analyzed using information captured from various connected devices. This paper discusses one such use case, which can be implemented by the automobile industry, using technological advancements in the areas of IoT and Analytics. ‘Connected Car’ is a terminology, often associated with cars and other passenger vehicles, which are capable of internet connectivity and sharing of various kinds of data with backend applications. The data being shared can be about the location and speed of the car, status of various parts/lubricants of the car, and if the car needs urgent service or not. Once data are transmitted to the backend services, various workflows can be created to take necessary actions, e.g. scheduling a service with the car service provider, or if large numbers of care are in the same location, then the traffic management system can take necessary action. ’Connected cars’ can also communicate with each other, and can send alerts to each other in certain scenarios like possible crash etc. This paper talks about how the concept of ‘connected cars’ can be used to perform ‘predictive car maintenance’. It also discusses how certain technology components, i.e., Eclipse Mosquito and Eclipse Paho can be used to implement a predictive connected car use case.


title: Fluorescence studies on concanavalin-A

abstract: Using the lectin-concanavalin-A, the tryptophan fluorescence as a function of pH was studied. The pH dependent, fluorescence intensity changes were significantly higher when excited at 305 nm, than when irradiated at 280nm. Only one tryptophanyl per monomer of concanavalin-A was available for oxidation by N-bromosuccinimide in the dimeric form at pH 4·9; no tryptophanyl could be oxidised in the demetallised dimer (pH 3·0) and native tetramer (pH 7·0). Based on this fluorescence data and the already known crystal structure data, it appears that tryptophanyl 88 in concanavalin-A may be selectively excited by 305 nm radiation


title: A 28-Gb/s 4-Tap FFE/15-Tap DFE Serial Link Transceiver in 32-nm SOI CMOS Technology

abstract: This paper presents a 28-Gb/s transceiver in 32-nm SOI CMOS technology for chip-to-chip communications over high-loss electrical channels such as backplanes. The equalization needed for such applications is provided by a 4-tap baud-spaced feed-forward equalizer (FFE) in the transmitter and a two-stage peaking amplifier and 15-tap decision-feedback equalizer (DFE) in the receiver. The transmitter employs a source-series terminated (SST) driver topology which doubles the speed of existing half-rate designs. The high-frequency boost provided by the peaking amplifier is enhanced by adopting a structure with capacitively coupled parallel input stages and active feedback. A capacitive level-shifting technique is introduced in the half-rate DFE which allows a single current-integrating summer to drive the four parallel paths used for speculating the first two DFE taps. Error-free signaling at 28 Gb/s is demonstrated with the transceiver over a channel with 35 dB loss at half-baud frequency. In a four-port core configuration, the power consumption at 28 Gb/s is 693 mW/lane.


title: TimeStream: reliable stream computation in the cloud

abstract: TimeStream is a distributed system designed specifically for low-latency continuous processing of big streaming data on a large cluster of commodity machines. The unique characteristics of this emerging application domain have led to a significantly different design from the popular MapReduce-style batch data processing. In particular, we advocate a powerful new abstraction called resilient substitution that caters to the specific needs in this new computation model to handle failure recovery and dynamic reconfiguration in response to load changes. Several real-world applications running on our prototype have been shown to scale robustly with low latency while at the same time maintaining the simple and concise declarative programming model. TimeStream handles an on-line advertising aggregation pipeline at a rate of 700,000 URLs per second with a 2-second delay, while performing sentiment analysis of Twitter data at a peak rate close to 10,000 tweets per second, with approximately 2-second delay.


title: Emerging topic detection for organizations from microblogs

abstract: Microblog services have emerged as an essential way to strengthen the communications among individuals and organizations. These services promote timely and active discussions and comments towards products, markets as well as public events, and have attracted a lot of attentions from organizations. In particular, emerging topics are of immediate concerns to organizations since they signal current concerns of, and feedback by their users. Two challenges must be tackled for effective emerging topic detection. One is the problem of real-time relevant data collection and the other is the ability to model the emerging characteristics of detected topics and identify them before they become hot topics. To tackle these challenges, we first design a novel scheme to crawl the relevant messages related to the designated organization by monitoring multi-aspects of microblog content, including users, the evolving keywords and their temporal sequence. We then develop an incremental clustering framework to detect new topics, and employ a range of content and temporal features to help in promptly detecting hot emerging topics. Extensive evaluations on a representative real-world dataset based on Twitter data demonstrate that our scheme is able to characterize emerging topics well and detect them before they become hot topics.


title: The Benefits of Financial Ratios' as the Indocators of Future Bankruptcy on the Economic Crisis

abstract: It is proved that financial ratios can predict future bankruptcy even on high uncertainty conditions such as an economic crisis. The research indicates that the accuracy of prediction is more increasing in line with a coming bankruptcy. The result of the research shows that four years before a corporate becomes bankrupt there have been significant differences of financial ratios between bankrupt company and sustained one. The ratios of liquidity, profitability, activity, and return on investment of sustained company are higher; while the leverage ratio is lower. The dominant influencing financial ratios toward a bankruptcy are liquidity and leverage ratios. The research finds that from ten tested ratios, Current Asset to current liabilities and total liabilities to total asset are the dominant financial ratios.


title: Deep 6-DOF Tracking

abstract: We present a temporal 6-DOF tracking method which leverages deep learning to achieve state-of-the-art performance on challenging datasets of real world capture. Our method is both more accurate and more robust to occlusions than the existing best performing approaches while maintaining real-time performance. To assess its efficacy, we evaluate our approach on several challenging RGBD sequences of real objects in a variety of conditions. Notably, we systematically evaluate robustness to occlusions through a series of sequences where the object to be tracked is increasingly occluded. Finally, our approach is purely data-driven and does not require any hand-designed features: robust tracking is automatically learned from data.


title: Real-time eye gaze direction classification using convolutional neural network

abstract: Estimation eye gaze direction is useful in various human-computer interaction tasks. Knowledge of gaze direction can give valuable information regarding users point of attention. Certain patterns of eye movements known as eye accessing cues are reported to be related to the cognitive processes in the human brain. We propose a real-time framework for the classification of eye gaze direction and estimation of eye accessing cues. In the first stage, the algorithm detects faces using a modified version of the Viola-Jones algorithm. A rough eye region is obtained using geometric relations and facial landmarks. The eye region obtained is used in the subsequent stage to classify the eye gaze direction. A convolutional neural network is employed in this work for the classification of eye gaze direction. The proposed algorithm was tested on Eye Chimera database and found to outperform state of the art methods. The computational complexity of the algorithm is very less in the testing phase. The algorithm achieved an average frame rate of 24 fps in the desktop environment.


title: Design and Implementation of the HPCS Graph Analysis Benchmark on Symmetric Multiprocessors

abstract: Graph theoretic problems are representative of fundamental computations in traditional and emerging scientific disciplines like scientific computing and computational biology, as well as applications in national security. We present our design and implementation of a graph theory application that supports the kernels from the Scalable Synthetic Compact Applications (SSCA) benchmark suite, developed under the DARPA High Productivity Computing Systems (HPCS) program. This synthetic benchmark consists of four kernels that require irregular access to a large, directed, weighted multi-graph. We have developed a parallel implementation of this benchmark in C using the POSIX thread library for commodity symmetric multiprocessors (SMPs). In this paper, we primarily discuss the data layout choices and algorithmic design issues for each kernel, and also present execution time and benchmark validation results.


title: Action control according to TEC (theory of event coding)

abstract: The theory of event coding (TEC) is a general framework explaining how perceived and produced events (stimuli and responses) are cognitively represented and how their representations interact to generate perception and action. This article discusses the implications of TEC for understanding the control of voluntary action and makes an attempt to apply, specify, and concretize the basic theoretical ideas in the light of the available research on action control. In particular, it is argued that the major control operations may take place long before a stimulus is encountered (the prepared-reflex principle), that stimulus-response translation may be more automatic than commonly thought, that action selection and execution are more interwoven than most approaches allow, and that the acquisition of action-contingent events (action effects) is likely to subserve both the selection and the evaluation of actions.