title: Convolutional Pose Machines

abstract: Pose Machines provide a sequential prediction framework for learning rich implicit spatial models. In this work we show a systematic design for how convolutional networks can be incorporated into the pose machine framework for learning image features and image-dependent spatial models for the task of pose estimation. The contribution of this paper is to implicitly model long-range dependencies between variables in structured prediction tasks such as articulated pose estimation. We achieve this by designing a sequential architecture composed of convolutional networks that directly operate on belief maps from previous stages, producing increasingly refined estimates for part locations, without the need for explicit graphical model-style inference. Our approach addresses the characteristic difficulty of vanishing gradients during training by providing a natural learning objective function that enforces intermediate supervision, thereby replenishing back-propagated gradients and conditioning the learning procedure. We demonstrate state-of-the-art performance and outperform competing methods on standard benchmarks including the MPII, LSP, and FLIC datasets.


title: Mondrian Forests for Large-Scale Regression when Uncertainty Matters

abstract: Many real-world regression problems demand a measure of the uncertainty associated with each prediction. Standard decision forests deliver efficient state-of-the-art predictive performance, but high-quality uncertainty estimates are lacking. Gaussian processes (GPs) deliver uncertainty estimates, but scaling GPs to large-scale data sets comes at the cost of approximating the uncertainty estimates. We extend Mondrian forests, first proposed by Lakshminarayanan et al. (2014) for classification problems, to the large-scale non-parametric regression setting. Using a novel hierarchical Gaussian prior that dovetails with the Mondrian forest framework, we obtain principled uncertainty estimates, while still retaining the computational advantages of decision forests. Through a combination of illustrative examples, real-world large-scale datasets, and Bayesian optimization benchmarks, we demonstrate that Mondrian forests outperform approximate GPs on large-scale regression tasks and deliver better-calibrated uncertainty assessments than decision-forest-based methods.


title: Entropy Search for Information-Efficient Global Optimization

abstract: Contemporary global optimization algorithms are based on local measures of utility, rather than a probability measure over location and value of the optimum. They thus attempt to collect low function values, not to learn about the optimum. The reason for the absence of probabilistic global optimizers is that the corresponding inference problem is intractable in several ways. This paper develops desiderata for probabilistic optimization algorithms, then presents a concrete algorithm which addresses each of the computational intractabilities with a sequence of approximations and explicitly adresses the decision problem of maximizing information gain from each evaluation.


title: Enhancing text clustering by leveraging Wikipedia semantics

abstract: Most traditional text clustering methods are based on "bag of words" (BOW) representation based on frequency statistics in a set of documents. BOW, however, ignores the important information on the semantic relationships between key terms. To overcome this problem, several methods have been proposed to enrich text representation with external resource in the past, such as WordNet. However, many of these approaches suffer from some limitations: 1) WordNet has limited coverage and has a lack of effective word-sense disambiguation ability; 2) Most of the text representation enrichment strategies, which append or replace document terms with their hypernym and synonym, are overly simple. In this paper, to overcome these deficiencies, we first propose a way to build a concept thesaurus based on the semantic relations (synonym, hypernym, and associative relation) extracted from Wikipedia. Then, we develop a unified framework to leverage these semantic relations in order to enhance traditional content similarity measure for text clustering. The experimental results on Reuters and OHSUMED datasets show that with the help of Wikipedia thesaurus, the clustering performance of our method is improved as compared to previous methods. In addition, with the optimized weights for hypernym, synonym, and associative concepts that are tuned with the help of a few labeled data users provided, the clustering performance can be further improved.


title: Dimensional ridge alterations following tooth extraction. An experimental study in the dog

abstract: Objective: To study dimensional alterations of the alveolar ridge that occurred following tooth extraction as well as processes of bone modelling and remodelling associated with such change. ::: ::: Material and Methods: Twelve mongrel dogs were included in the study. In both quadrants of the mandible incisions were made in the crevice region of the 3rd and 4th premolars. Minute buccal and lingual full thickness flaps were elevated. The four premolars were hemi-sected. The distal roots were removed. The extraction sites were covered with the mobilized gingival tissue. The extractions of the roots and the sacrifice of the dogs were staggered in such a manner that all dogs contributed with sockets representing 1, 2, 4 and 8 weeks of healing. The animals were sacrificed and tissue blocks containing the extraction socket were dissected, decalcified in EDTA, embedded in paraffin and cut in the buccal–lingual plane. The sections were stained in haematoxyline–eosine and examined in the microscope. ::: ::: Results: It was demonstrated that marked dimensional alterations occurred during the first 8 weeks following the extraction of mandibular premolars. Thus, in this interval there was a marked osteoclastic activity resulting in resorption of the crestal region of both the buccal and the lingual bone wall. The reduction of the height of the walls was more pronounced at the buccal than at the lingual aspect of the extraction socket. The height reduction was accompanied by a “horizontal” bone loss that was caused by osteoclasts present in lacunae on the surface of both the buccal and the lingual bone wall. ::: ::: Conclusions: The resorption of the buccal/lingual walls of the extraction site occurred in two overlapping phases. During phase 1, the bundle bone was resorbed and replaced with woven bone. Since the crest of the buccal bone wall was comprised solely of bundle this modelling resulted in substantial vertical reduction of the buccal crest. Phase 2 included resorption that occurred from the outer surfaces of both bone walls. The reason for this additional bone loss is presently not understood.


title: Comixify: Transform video into a comics

abstract: In this paper, we propose a solution to transform a video into a comics. We approach this task using a neural style algorithm based on Generative Adversarial Networks (GANs). Several recent works in the field of Neural Style Transfer showed that producing an image in the style of another image is feasible. In this paper, we build up on these works and extend the existing set of style transfer use cases with a working application of video comixification. To that end, we train an end-to-end solution that transforms input video into a comics in two stages. In the first stage, we propose a state-of-the-art keyframes extraction algorithm that selects a subset of frames from the video to provide the most comprehensive video context and we filter those frames using image aesthetic estimation engine. In the second stage, the style of selected keyframes is transferred into a comics. To provide the most aesthetically compelling results, we selected the most state-of-the art style transfer solution and based on that implement our own ComixGAN framework. The final contribution of our work is a Web-based working application of video comixification available at this http URL.


title: Choice of Software Development Methodologies: Do Organizational, Project, and Team Characteristics Matter?

abstract: Organizations can choose from software development methodologies ranging from traditional to agile approaches. Researchers surveyed project managers and other team members about their choice of methodologies. The results indicate that although agile methodologies such as Agile Unified Process and Scrum are more prevalent than 10 years ago, traditional methodologies, including the waterfall model, are still popular. Organizations are also taking a hybrid approach, using multiple methodologies on projects. Furthermore, their choice of methodologies is associated with certain organizational, project, and team characteristics.


title: Towards a Universal Grammar for Natural Language Processing

abstract: Universal Dependencies is a recent initiative to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. In this paper, I outline the motivation behind the initiative and explain how the basic design principles follow from these requirements. I then discuss the different components of the annotation standard, including principles for word segmentation, morphological annotation, and syntactic annotation. I conclude with some thoughts on the challenges that lie ahead.


title: The Effect of the Agency and Anthropomorphism on Users' Sense of Telepresence, Copresence, and Social Presence in Virtual Environments

abstract: We report on an experiment that examined the influence of anthropomorphism and perceived agency on presence, copresence, and social presence in a virtual environment. The experiment varied the level of anthropomorphism of the image of interactants: high anthropomorphism, low anthropomorphism, or no mage. Perceived agency was manipulated by telling the participants that the image was either an avatar controlled by a human, or an agent controlled by a computer. The results support the prediction that people respond socially to both human and computer-controlled entities, and that the existence of a virtual image increases telepresence. Participants interacting with the less-anthropomorphic image reported more copresence and social presence than those interacting with partners represented by either no mage at all or by a highly anthropomorphic image of the other, indicating that the more anthropomorphic images set up higher expectations that lead to reduced presence when these expectations were not met.


title: Self-Evaluation of Decision-Making: A General Bayesian Framework for Metacognitive Computation

abstract: People are often aware of their mistakes, and report levels of confidence in their choices that correlate with objective performance. These metacognitive assessments of decision quality are important for the guidance of behavior, particularly when external feedback is absent or sporadic. However, a computational framework that accounts for both confidence and error detection is lacking. In addition, accounts of dissociations between performance and metacognition have often relied on ad hoc assumptions, precluding a unified account of intact and impaired self-evaluation. Here we present a general Bayesian framework in which self-evaluation is cast as a “second-order” inference on a coupled but distinct decision system, computationally equivalent to inferring the performance of another actor. Second-order computation may ensue whenever there is a separation between internal states supporting decisions and confidence estimates over space and/or time. We contrast second-order computation against simpler first-order models in which the same internal state supports both decisions and confidence estimates. Through simulations we show that second-order computation provides a unified account of different types of self-evaluation often considered in separate literatures, such as confidence and error detection, and generates novel predictions about the contribution of one’s own actions to metacognitive judgments. In addition, the model provides insight into why subjects’ metacognition may sometimes be better or worse than task performance. We suggest that second-order computation may underpin self-evaluative judgments across a range of domains.


title: Did You Enjoy the Ride? Understanding Passenger Experience via Heterogeneous Network Embedding

abstract: Online taxicab platforms like DiDi and Uber have impacted hundreds of millions of users on their choices of traveling, but how do users feel about the ride-sharing services, and how to improve their experience? While current ride-sharing services have collected massive travel data, it remains challenging to develop data-driven techniques for modeling and predicting user ride experience. In this work, we aim to accurately predict passenger satisfaction over their rides and understand the key factors that lead to good/bad experiences. Based on in-depth analysis of large-scale travel data from a popular taxicab platform in China, we develop PHINE (Pattern-aware Heterogeneous Information Network Embedding) for data-driven user experience modeling. Our PHINE framework is novel in that it is composed of spatial-temporal node binding and grouping for addressing the inherent data variation, and pattern preservation based joint training for modeling the interactions among drivers, passengers, locations, and time. Extensive experiments on 12 real-world travel datasets demonstrate the effectiveness of PHINE over strong baseline methods. We have deployed PHINE in the DiDi Big Data Center, delivering high-quality predictions for passenger satisfaction on a daily basis.


New stigmatic vacuum ultraviolet monochromators


title: Multi-dimensional Graph Convolutional Networks

abstract: Convolutional neural networks (CNNs) leverage the great power in representation learning on regular grid data such as image and video. Recently, increasing attention has been paid on generalizing CNNs to graph or network data which is highly irregular. Some focus on graph-level representation learning while others aim to learn node-level representations. These methods have been shown to boost the performance of many graph-level tasks such as graph classification and node-level tasks such as node classification. Most of these methods have been designed for single-dimensional graphs where a pair of nodes can only be connected by one type of relation. However, many real-world graphs have multiple types of relations and they can be naturally modeled as multi-dimensional graphs with each type of relation as a dimension. Multi-dimensional graphs bring about richer interactions between dimensions, which poses tremendous challenges to the graph convolutional neural networks designed for single-dimensional graphs. In this paper, we study the problem of graph convolutional networks for multi-dimensional graphs and propose a multi-dimensional convolutional neural network model mGCN aiming to capture rich information in learning node-level representations for multi-dimensional graphs. Comprehensive experiments on real-world multi-dimensional graphs demonstrate the effectiveness of the proposed framework.


title: Structure and Network in the YouTube Core

abstract: In this paper, we present results of an empirical investigation into the social structure of YouTube, addressing friend relations and their correlation with tags applied to uploaded videos. Results indicate that YouTube producers are strongly linked to others producing similar content. Furthermore, there is a socially cohesive core of producers of mixed content, with smaller cohesive groups around Korean music video and anime music videos. Thus, social interaction on YouTube appears to be structured in ways similar to other social networking sites, but with greater semantic coherence around content. These results are explained in terms of the relationship of video producers to the tagging of uploaded content on the site.


title: 4D Ultrasound Tracking of Liver and its Verification for TIPS Guidance

abstract: In this work we describe a 4D registration method for on the fly stabilization of ultrasound volumes for improving image guidance for transjugular intrahepatic portosystemic shunt (TIPS) interventions. The purpose of the method is to enable a continuous visualization of the relevant anatomical planes (determined in a planning stage) in a free breathing patient during the intervention. This requires registration of the planning information to the interventional images, which is achieved in two steps. In the first step tracking is performed across the streaming input. An approximate transformation between the reference image and the incoming image is estimated by composing the intermediate transformations obtained from the tracking. In the second step a subsequent registration is performed between the reference image and the approximately transformed incoming image to account for the accumulation of error. The two step approach helps in reducing the search range and is robust under rotation. We additionally present an approach to initialize and verify the registration. Verification is required when the reference image (containing planning information) is acquired in the past and is not part of the (interventional) 4D ultrasound sequence. The verification score will help in invalidating the registration outcome, for instance, in the case of insufficient overlap or information between the registering images due to probe motion or loss of contact, respectively. We evaluate the method over thirteen 4D US sequences acquired from eight subjects. A graphics processing unit implementation runs the 4D tracking at 9 Hz with a mean registration error of 1.7 mm.


title: BIODIESEL PRODUCTION FROM ALGAE

abstract: Algae appear to be an emerging source of biomass for biodiesel that has the potential to completely displace fossil fuel. Two thirds of earth’s surface is covered with water, thus algae would truly be renewable option of great potential for global energy needs. This study discusses specific and comparative biodiesel quantitative potential of Cladophora sp., also highlighting its biomass (after oil extraction), pH and sediments (glycerine, water and pigments) quantitative properties. Comparison of Cladophora sp., with Oedogonium sp., and Spirogyra sp., (Hossain et al., 2008) shows that Cladophora sp., produce higher quantity of biodiesel than Spirogyra sp., whereas biomass and sediments were higher than the both algal specimens in comparison to the results obtained by earlier workers. No prominent difference in pH of biodiesel was found. In Pakistan this is a first step towards biodiesel production from algae. Results indicate that Cladophora sp., provide a reasonable quantity of biodiesel, its greater biomass after oil extraction and sediments make it a better option for biodiesel production than the comparing species.


Switchboard SWBD-DAMSL shallow-discourse-function annotation coders manual


title: Defining a degradation cost function for optimal control of a battery energy storage system

abstract: Optimal control of Battery Energy Storage Systems (BESSs) is challenging because it needs to consider benefits arising in power system operation as well as cost induced from BESS commitment. The presented approach relies on the methodology of Model Predictive Control (MPC) for optimal BESS operation. Variable and strongly usage dependent battery degradation costs constitute the bulk of the marginal costs for BESS operation. Battery degradation is usually modeled with nonlinear functional dependencies or an implicit cycle counting approach unsuited for an MPC implementation. In this paper an explicit cost function considering battery degradation is developed, which sufficiently captures the nonlinearities and is applicable for arbitrary battery load patterns. The resulting piece-wise affine cost function leads to a mixed-integer quadratic programming problem allowing a standard hybrid MPC formulation. As proof-of-concept, a peak shaving algorithm relying on the proposed cost function and on adaptive soft limits is developed and implemented on the Zurich 1MW BESS demonstration project, owned and operated by the utility of the Canton of Zurich (EKZ).


title: Motivation and Education: The Self-Determination Perspective

abstract: Self-determination theory (Deci & Ryan, 1985, 1991), when applied to the realm of education, is concerned primarily with promoting in students an interest in learning, a valuing of education, and a confidence in their own capacities and attributes. These outcomes are manifestations of being intrinsically motivated and internalizing values and regulatory processes. Research suggests that these processes result in high-quality learning and conceptual understanding, as well as enhanced personal growth and adjustment. In this article we also describe social-contextual factors that nurture intrinsic motivation and promote internalization, leading to the desired educational outcomes.


title: Equidistant prototypes embedding for single sample based face recognition with generic learning and incremental learning

abstract: Abstract We develop a parameter-free face recognition algorithm which is insensitive to large variations in lighting, expression, occlusion, and age using a single gallery sample per subject. We take advantage of the observation that equidistant prototypes embedding is an optimal embedding that maximizes the minimum one-against-the-rest margin between the classes. Rather than preserving the global or the local structure of the training data, our method, called linear regression analysis (LRA), applies a least-square regression technique to map gallery samples to the equally distant locations, regardless of the true structure of training data. Further, a novel generic learning method, which maps the intraclass facial differences of the generic faces to the zero vectors, is incorporated to enhance the generalization capability of LRA. Using this novel method, learning based on only a handful of generic classes can largely improve the face recognition performance, even when the generic data are collected from a different database and camera set-up. The incremental learning based on the Greville algorithm makes the mapping matrix efficiently updated from the newly coming gallery classes, training samples, or generic variations. Although it is fairly simple and parameter-free, LRA, combined with commonly used local descriptors, such as Gabor representation and local binary patterns, outperforms the state-of-the-art methods for several standard experiments on the Extended Yale B, CMU PIE, AR, and FERET databases.


title: A character-level sequence-to-sequence method for subtitle learning

abstract: This paper presents a character-level sequence-to-sequence learning method, RNNembed. Specifically, we embed a Recurrent Neural Network (RNN) into an encoder-decoder framework and generate character-level sequence representation as input. The dimension of input feature space can be significantly reduced as well as avoiding the need to handle unknown or rare words in sequences. In the language model, we improve the basic structure of a Gated Recurrent Unit (GRU) by adding an output gate, which is used for filtering out unimportant information involved in the attention scheme of the alignment model. Our proposed method was examined in a large-scale dataset on a task of English-to-Chinese translation. Experimental results demonstrate that the proposed approach achieves a translation performance comparable, or close, to conventional word-based and phrase-based systems.


title: Towards wireless emotional valence detection from EEG

abstract: Intelligent affective computers can have many medical and non-medical applications. However today's affective computers are limited in scope by their transferability to other application environments or that they monitor only one aspect of physiological emotion expression. Here, the use of a wireless EEG system, which can be implemented in a body area network, is used to investigate the potential of monitoring emotional valence in EEG, for application in real-life situations. The results show 82% accuracy for automatic classification of positive, negative and neutral valence based on film clip viewing, using features containing information on both the frequency content of the EEG and how this changes over time.


title: Conditional Neural Processes

abstract: Deep neural networks excel at function approximation, yet they are typically trained from scratch for each new function. On the other hand, Bayesian methods, such as Gaussian Processes (GPs), exploit prior knowledge to quickly infer the shape of a new function at test time. Yet GPs are computationally expensive, and it can be hard to design appropriate priors. In this paper we propose a family of neural models, Conditional Neural Processes (CNPs), that combine the benefits of both. CNPs are inspired by the flexibility of stochastic processes such as GPs, but are structured as neural networks and trained via gradient descent. CNPs make accurate predictions after observing only a handful of training data points, yet scale to complex functions and large datasets. We demonstrate the performance and versatility of the approach on a range of canonical machine learning tasks, including regression, classification and image completion.


title: Don't throw me away: Threats Caused by the Abandoned Internet Resources Used by Android Apps

abstract: This study aims to understand the threats caused by abandoned Internet resources used by Android apps. By abandoned, we mean Internet resources that support apps that were published and are still available on the mobile app marketplace, but have not been maintained and hence are at risk for abuse by an outsider. Internet resources include domain names and hard-coded IP addresses, which could be used for nefarious purposes, e.g., stealing sensitive private information, scamming and phishing, click fraud, and injecting malware distribution URL. As a result of the analysis of 1.1 M Android apps published in the official marketplace, we uncovered 3,628 of abandoned Internet resources associated with 7,331 available mobile apps. These resources are subject to hijack by outsiders. Of these apps, 13 apps have been installed more than a million of times, a measure of the breadth of the threat. Based on the findings of empirical experiments, we discuss potential threats caused by abandoned Internet resources and propose countermeasures against these threats.


title: Unsupervised Relation Extraction by Mining Wikipedia Texts Using Information from the Web

abstract: This paper presents an unsupervised relation extraction method for discovering and enhancing relations in which a specified concept in Wikipedia participates. Using respective characteristics of Wikipedia articles and Web corpus, we develop a clustering approach based on combinations of patterns: dependency patterns from dependency analysis of texts in Wikipedia, and surface patterns generated from highly redundant information related to the Web. Evaluations of the proposed approach on two different domains demonstrate the superiority of the pattern combination over existing approaches. Fundamentally, our method demonstrates how deep linguistic patterns contribute complementarily with Web surface patterns to the generation of various relations.


Sign of a Threat : The Effects of Warning Systems in Survival Horror Games


title: Quality of Context: What It Is And Why We Need It

abstract: When people interact with each other, they implicitly make use of context information while intuitively deducing and interpreting their actual situation. Compared to humans, IT infrastructures cannot easily take advantage of context information in interactions. Typically, context information has to be provided explicitly. Recently, cellular network operators have been showing interest in offering Context-Aware Services (CAS) in the future. For a service to be context-aware it must be able to use context information in order to adapt its behavior or the content it provides. Examples of CASs are restaurant finders, tour guides and dating services. These services will depend on the availability of context information which must be provided at the right time, in the right quality, and at the right place. The quality of this context information is neither identical to Quality of Service (QoS), nor to the quality of the underlying hardware components, i.e., Quality of Device (QoD). Rather, the precision, probability of correctness, trustworthiness, resolution, and up-to-dateness of context information form a new set of quality parameters which we call Quality of Context (QoC). In this paper, we will discuss what QoC is, what its most important parameters are and how QoC relates to QoS and QoD. These three notions of quality are unequal, but not unrelated. Based on several examples we will show the interdependence between them. We will argue that QoC as a new notion of quality is necessary to allow for the provisioning of CASs in an interorganizational manner.


title: 15 GHz SPDT switch design using 0.15 µm GaAs technology for microwave applications

abstract: In this paper, very low loss and high isolation single pole double throw (SPDT) switch design for microwave applications using pseudomorphic high-electron mobility transistor (pHEMT) is presented. The MMIC switch design is developed using a commercially available 0.15 mum GaAs pHEMT technology. At the operating frequency of 15 GHz, the SPDT switch has 1.89 dB insertion loss and 26.66 dB of isolation. It also demonstrates 28.8 dBm of input P1dB gain compression point (P1dB) and 25.9 dBm of output P1dB.


title: Optimization based controller design and implementation for the Atlas robot in the DARPA Robotics Challenge Finals

abstract: We describe the design and hardware implementation of our walking and manipulation controllers that are based on a cascade of online optimizations. A virtual force acting at the robot's center of mass (CoM) is estimated and used to compensated for modeling errors of the CoM and unplanned external forces. The proposed controllers have been implemented on the Atlas robot, a full size humanoid robot built by Boston Dynamics, and used in the DARPA Robotics Challenge Finals, which consisted of a wide variety of locomotion and manipulation tasks.


Extraction of Pharmacokinetic Evidence of Drug–Drug Interactions from the Literature


Architectural Innovation: The Reconfiguration of Existing Product Technologies and the Failure of Established Firms


title: Gaining insights into support vector machine pattern classifiers using projection-based tour methods

abstract: This paper discusses visual methods that can be used to understand and interpret the results of classification using support vector machines (SVM) on data with continuous real-valued variables. SVM induction algorithms build pattern classifiers by identifying a maximal margin separating hyperplane from training examples in high dimensional pattern spaces or spaces induced by suitable nonlinear kernel transformations over pattern spaces. SVM have been demonstrated to be quite effective in a number of practical pattern classification tasks. Since the separating hyperplane is defined in terms of more than two variables it is necessary to use visual techniques that can navigate the viewer through high-dimensional spaces. We demonstrate the use of projection-based tour methods to gain useful insights into SVM classifiers with linear kernels on 8-dimensional data.


Nucleosynthesis in Supernova Models. I. The Neutrino-Transport Model


title: CNN-Based Food Image Segmentation Without Pixel-Wise Annotation

abstract: We propose a CNN-based food image segmentation which requires no pixel-wise annotation. The proposed method consists of food region proposals by selective search and bounding box clustering, back propagation based saliency map estimation with the CNN model fine-tuned with the UEC-FOOD100 dataset, GrabCut guided by the estimated saliency maps and region integration by non-maximum suppression. In the experiments, the proposed method outperformed RCNN regarding food region detection as well as the PASCAL VOC detection task.


title: Density-Dependence as a Size-Independent Regulatory Mechanism

abstract: The growth function of populations is central in biomathematics. The main dogma is the existence of density-dependence mechanisms, which can be modelled with distinct functional forms that depend on the size of the Population. One important class of regulatory functions is the theta-logistic, which generalizes the logistic equation. Using this model as a motivation, this paper introduces a simple dynamical reformulation that generalizes many growth functions. The reformulation consists of two equations, one for population size, and one for the growth rate. Furthermore, the model shows that although population is density-dependent, the dynamics of the growth rate does not depend either on population size, nor on the carrying capacity. Actually, the growth equation is uncoupled from the population size equation, and the model has only two parameters, a Malthusian parameter rho and a competition coefficient theta. Distinct sign combinations of these parameters reproduce not only the family of theta-logistics, but also the van Bertalanffy, Gompertz and Potential Growth equations, among other possibilities. It is also shown that, except for two critical points, there is a general size-scaling relation that includes those appearing in the most important allometric theories, including the recently proposed Metabolic Theory of Ecology. With this model, several issues of general interest are discussed such as the growth of animal population, extinctions, cell growth and allometry, and the effect of environment over a population. (c) 2005 Elsevier Ltd. All rights reserved.


title: Carbon nanofibers (CNFs) supported cobalt- nickel sulfide (CoNi2S4) nanoparticles hybrid anode for high performance lithium ion capacitor

abstract: Lithium ion capacitors possess an ability to bridge the gap between lithium ion battery and supercapacitor. The main concern of fabricating lithium ion capacitors is poor rate capability and cyclic stability of the anode material which uses sluggish faradaic reactions to store an electric charge. Herein, we have fabricated high performance hybrid anode material based on carbon nanofibers (CNFs) and cobalt-nickel sulfide (CoNi2S4) nanoparticles via simple electrospinning and electrodeposition methods. Porous and high conducting CNF@CoNi2S4 electrode acts as an expressway network for electronic and ionic diffusion during charging-discharging processes. The effect of anode to cathode mass ratio on the performance has been studied by fabricating lithium ion capacitors with different mass ratios. The surface controlled contribution of CNF@CoNi2S4 electrode was 73% which demonstrates its excellent rate capability. Lithium ion capacitor fabricated with CNF@CoNi2S4 to AC mass ratio of 1:2.6 showed excellent energy density of 85.4 Wh kg−1 with the power density of 150 W kg−1. Also, even at the high power density of 15 kW kg−1, the cell provided the energy density of 35 Wh kg−1. This work offers a new strategy for designing high-performance hybrid anode with the combination of simple and cost effective approaches.


title: Leveraging uncertainty information from deep neural networks for disease detection

abstract: Deep learning (DL) has revolutionized the field of computer vision and image processing. In medical imaging, algorithmic solutions based on DL have been shown to achieve high performance on tasks that previously required medical experts. However, DL-based solutions for disease detection have been proposed without methods to quantify and control their uncertainty in a decision. In contrast, a physician knows whether she is uncertain about a case and will consult more experienced colleagues if needed. Here we evaluate drop-out based Bayesian uncertainty measures for DL in diagnosing diabetic retinopathy (DR) from fundus images and show that it captures uncertainty better than straightforward alternatives. Furthermore, we show that uncertainty informed decision referral can improve diagnostic performance. Experiments across different networks, tasks and datasets show robust generalization. Depending on network capacity and task/dataset difficulty, we surpass 85% sensitivity and 80% specificity as recommended by the NHS when referring 0−20% of the most uncertain decisions for further inspection. We analyse causes of uncertainty by relating intuitions from 2D visualizations to the high-dimensional image space. While uncertainty is sensitive to clinically relevant cases, sensitivity to unfamiliar data samples is task dependent, but can be rendered more robust.


title: Fairness in Machine Learning: Lessons from Political Philosophy

abstract: What does it mean for a machine learning model to be `fair', in terms which can be operationalised? Should fairness consist of ensuring everyone has an equal probability of obtaining some benefit, or should we aim instead to minimise the harms to the least advantaged? Can the relevant ideal be determined by reference to some alternative state of affairs in which a particular social pattern of discrimination does not exist? Various definitions proposed in recent literature make different assumptions about what terms like discrimination and fairness mean and how they can be defined in mathematical terms. Questions of discrimination, egalitarianism and justice are of significant interest to moral and political philosophers, who have expended significant efforts in formalising and defending these central concepts. It is therefore unsurprising that attempts to formalise `fairness' in machine learning contain echoes of these old philosophical debates. This paper draws on existing work in moral and political philosophy in order to elucidate emerging debates about fair machine learning.


title: A short survey of methods for voltage instability detection

abstract: This paper shortly surveys existing and proposed methods for voltage instability detection. The emphasis is on methods relying on real-time measurements as well as on long-term voltage instability. Methods are classified according to the required measurement configuration: local vs. wide-area, standard SCADA-type vs. synchronized phasor measurements, etc. In the various categories, some of the features are summarized, and what appears to the authors as advantages or limitations is shortly discussed. An important feature is the ability to anticipate instability. Some tracks for further work on the subject are also outlined.


title: Dominant and Complementary Emotion Recognition From Still Images of Faces

abstract: Emotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition.


title: DR-Net: Transmission Steered Single Image Dehazing Network with Weakly Supervised Refinement

abstract: Despite the recent progress in image dehazing, several problems remain largely unsolved such as robustness for varying scenes, the visual quality of reconstructed images, and effectiveness and flexibility for applications. To tackle these problems, we propose a new deep network architecture for single image dehazing called DR-Net. Our model consists of three main subnetworks: a transmission prediction network that predicts transmission map for the input image, a haze removal network that reconstructs latent image steered by the transmission map, and a refinement network that enhances the details and color properties of the dehazed result via weakly supervised learning. Compared to previous methods, our method advances in three aspects: (i) pure data-driven model; (ii) the end-to-end system; (iii) superior robustness, accuracy, and applicability. Extensive experiments demonstrate that our DR-Net outperforms the state-of-the-art methods on both synthetic and real images in qualitative and quantitative metrics. Additionally, the utility of DR-Net has been illustrated by its potential usage in several important computer vision tasks.


title: Mutational landscape and significance across 12 major cancer types

abstract: The Cancer Genome Atlas (TCGA) has used the latest sequencing and analysis methods to identify somatic variants across thousands of tumours. Here we present data and analytical results for point mutations and small insertions/deletions from 3,281 tumours across 12 tumour types as part of the TCGA Pan-Cancer effort. We illustrate the distributions of mutation frequencies, types and contexts across tumour types, and establish their links to tissues of origin, environmental/carcinogen influences, and DNA repair defects. Using the integrated data sets, we identified 127 significantly mutated genes from well-known (for example, mitogen-activated protein kinase, phosphatidylinositol-3-OH kinase, Wnt/β-catenin and receptor tyrosine kinase signalling pathways, and cell cycle control) and emerging (for example, histone, histone modification, splicing, metabolism and proteolysis) cellular processes in cancer. The average number of mutations in these significantly mutated genes varies across tumour types; most tumours have two to six, indicating that the number of driver mutations required during oncogenesis is relatively small. Mutations in transcriptional factors/regulators show tissue specificity, whereas histone modifiers are often mutated across several cancer types. Clinical association analysis identifies genes having a significant effect on survival, and investigations of mutations with respect to clonal/subclonal architecture delineate their temporal orders during tumorigenesis. Taken together, these results lay the groundwork for developing new diagnostics and individualizing cancer treatment.


title: Hybrid Iterative Space-Time Equalization for Multi-User mmW Massive MIMO Systems

abstract: The combination of millimeter wave (mmW) with massive MIMO is a promising approach to achieve the multi Gb/s required by future wireless systems. Fully digital architectures are not feasible due to hardware limitations, and thus, the design of signal processing techniques for hybrid analog-digital architectures is of paramount importance. In this paper, we propose a new hybrid iterative block space-time receiver structure for multiuser mmW massive MIMO systems. We consider low-complexity user terminals employing analog-only random precoding and a single RF chain. At the base station, a hybrid analog-digital equalizer/detector is designed to efficiently remove the multiuser interference. The analog and digital parts of the equalizer are jointly optimized using as a metric the average bit-error-rate. The specificities of the analog domain impose several constraints in the joint optimization. To efficiently handle these constraints, the analog part is selected from a dictionary based on the array response vectors. We also propose a simple, yet an accurate semi-analytical approach for obtaining the performance of the proposed hybrid receiver structure. The results show that the performance of the hybrid iterative equalizer is close to the fully digital counterpart after only a few iterations. Moreover, it clearly outperforms the linear receivers recently considered for hybrid mmW massive MIMO architectures.


title: Unsupervised Interpretable Pattern Discovery in Time Series Using Autoencoders

abstract: We study the use of feed-forward convolutional neural networks for the unsupervised problem of mining recurrent temporal patterns mixed in multivariate time series. Traditional convolutional autoencoders lack interpretability for two main reasons: the number of patterns corresponds to the manually-fixed number of convolution filters, and the patterns are often redundant and correlated. To recover clean patterns, we introduce different elements in the architecture, including an adaptive rectified linear unit function that improves patterns interpretability, and a group-lasso regularizer that helps automatically finding the relevant number of patterns. We illustrate the necessity of these elements on synthetic data and real data in the context of activity mining in videos.


title: Generic topology mapping strategies for large-scale parallel architectures

abstract: The steadily increasing number of nodes in high-performance computing systems and the technology and power constraints lead to sparse network topologies. Efficient mapping of application communication patterns to the network topology gains importance as systems grow to petascale and beyond. Such mapping is supported in parallel programming frameworks such as MPI, but is often not well implemented. We show that the topology mapping problem is NP-complete and analyze and compare different practical topology mapping heuristics. We demonstrate an efficient and fast new heuristic which is based on graph similarity and show its utility with application communication patterns on real topologies. Our mapping strategies support heterogeneous networks and show significant reduction of congestion on torus, fat-tree, and the PERCS network topologies, for irregular communication patterns. We also demonstrate that the benefit of topology mapping grows with the network size and show how our algorithms can be used in a practical setting to optimize communication performance. Our efficient topology mapping strategies are shown to reduce network congestion by up to 80%, reduce average dilation by up to 50%, and improve benchmarked communication performance by 18%.


title: Traffic speed prediction using deep learning method

abstract: Successful traffic speed prediction is of great importance for the benefits of both road users and traffic management agencies. To solve the problem, traffic scientists have developed a number of time-series speed prediction approaches, including traditional statistical models and machine learning techniques. However, existing methods are still unsatisfying due to the difficulty to reflect the stochastic traffic flow characteristics. Recently, various deep learning models have been introduced to the prediction field. In this paper, a deep learning method, the Deep Belief Network (DBN) model, is proposed for short-term traffic speed information prediction. The DBN model is trained in a greedy unsupervised method and fine-tuned by labeled data. Based on traffic speed data collected from one arterial in Beijing, China, the model is trained and tested for different prediction time horizons. From experiment analysis, it is concluded that the DBN can outperform Back Propagation Neural Network (BPNN) and Auto-Regressive Integrated Moving Average (ARIMA) for all time horizons. The advantages of DBN indicate that deep learning is promising in traffic research area.


title: Efficient Series Expansion for Matrix Inversion with Application to MMSE Equalization

abstract: In this paper, we describe an efficient approach to overcome the need for matrix inversion required in most wired applications encountered in practice. In particular, MMSE equalization based on series expansion to approximate the matrix inversion is addressed. By adjusting a scaling factor, the series expansion is directly optimized according to a fixed order with respect to a system performance criterion. In comparison with previous approaches, the resulting equalizer enables improved BER performance according to a fixed order, in addition to low complexity without the need for a complicated eigenvalue calculation procedure.


title: Deep learning hashing for mobile visual search

abstract: The proliferation of mobile devices is producing a new wave of mobile visual search applications that enable users to sense their surroundings with smart phones. As the particular challenges of mobile visual search, achieving high recognition bitrate becomes the consistent target of existed related works. In this paper, we explore to holistically exploit the deep learning-based hashing methods for more robust and instant mobile visual search. Firstly, we present a comprehensive survey of the existed deep learning based hashing methods, which showcases their remarkable power of automatic learning highly robust and compact binary code representation for visual search. Furthermore, in order to implement the deep learning hashing on computation and memory constrained mobile device, we investigate the deep learning optimization works to accelerate the computation and reduce the model size. Finally, we demonstrate a case study of deep learning hashing based mobile visual search system. The evaluations show that the proposed system can significantly improve 70% accuracy in MAP than traditional methods, and only needs less than one second computation time on the ordinary mobile phone. Finally, with the comprehensive study, we discuss the open issues and future research directions of deep learning hashing for mobile visual search.


title: Genetic diversity of Plasmodium falciparum isolates from uncomplicated malaria cases in Ghana over a decade

abstract: BackgroundGenotyping malaria parasites to assess their diversity in different geographic settings have become necessary for the selection of antigenic epitopes for vaccine development and for antimalarial drug efficacy or resistance investigations. This study describes the genetic diversity of Plasmodium falciparum isolates from uncomplicated malaria cases over a ten year period (2003–2013) in Ghana using the polymorphic antigenic marker, merozoite surface protein 2 (msp2).MethodsArchived filter paper blood blots from children aged nine years and below with uncomplicated malaria collected from nine sites in Ghana were typed for the presence of the markers. A total of 880 samples were genotyped for msp2 for the two major allelic families, FC27 and 3D7, using nested polymerase chain reaction (PCR). The allele frequencies and the multiplicity of infection were determined for the nine sites for five time points over a period of ten years, 2003–2004, 2005–2006, 2007–2008, 2010 and 2012–2013 malaria transmission seasons.ResultsThe number of different alleles detected for the msp2 gene by resolving PCR products on agarose gels was 14. Both of the major allelic families, 3D7 and FC27 were common in all population samples. The highest multiplicity of infection (MOI) was observed in isolates from Begoro (forest zone, rural site): 3.31 for the time point 2007–2008. A significant variation was observed among the sites in the MOIs detected per infection (Fisher's exact test, P < 0.001) for the 2007 isolates and also at each of the three sites with data for three different years, Hohoe, P = 0.03; Navrongo, P < 0.001; Cape Coast, P < 0.001. Overall, there was no significant difference between the MOIs of the three ecological zones over the years (P = 0.37) and between the time points when data from all sites were pooled (P = 0.40).ConclusionsThe diversity and variation between isolates detected using the msp2 gene in Ghanaian isolates were observed to be profound; however, there was homogeneity throughout the three ecological zones studied. This is indicative of gene flow between the parasite populations across the country probably due to human population movements (HPM).


title: Pairing and isospin symmetry in proton-rich nuclei

abstract: Unlike their lighter counterparts, most odd-odd N = Z nuclei with mass A > 40 have ground states with isospin T = 1, suggesting an increased role for the isovector pairing interaction. A simple SO(5) seniority-like model of this interaction reveals a striking and heretofore unnoticed interplay between like-particle and neutron-proton isovector pairing near N = Z that is reflected in the number of each kind of pair as a function of A and T. Large scale shell-model calculations exhibit the same trends, despite the simultaneous presence of isoscalar pairs, deformation, and other correlations.


title: PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attention

abstract: A point cloud is an agile 3D representation, efficiently modeling an object's surface geometry. However, these surface-centric properties also pose challenges on designing tools to recognize and synthesize point clouds. This work presents a novel autoregressive model, PointGrow, which generates realistic point cloud samples from scratch or conditioned on given semantic contexts. Our model operates recurrently, with each point sampled according to a conditional distribution given its previously-generated points. Since point cloud object shapes are typically encoded by long-range interpoint dependencies, we augment our model with dedicated self-attention modules to capture these relations. Extensive evaluation demonstrates that PointGrow achieves satisfying performance on both unconditional and conditional point cloud generation tasks, with respect to fidelity, diversity and semantic preservation. Further, conditional PointGrow learns a smooth manifold of given image conditions where 3D shape interpolation and arithmetic calculation can be performed inside.


title: SegNetRes-CRF: A Deep Convolutional Encoder-Decoder Architecture for Semantic Image Segmentation

abstract: Semantic segmentation is an essential task in computer vision that aims to label each image pixel. Several of the actual best approaches in this context are based on deep neural networks. For example, SegNet is a deep encoderdecoder architecture approach whose results were disruptive because it is fast and performs well. However, this architecture fails to finedelineating the edges between the objects of interest in the image. We propose some modifications in the SegNet-Basic architecture by using a post-processing segmentation layer (using Conditional Random Fields) and by transferring high resolution features combined to the decoder network. The proposed method was evaluated in the dataset CamVid. Moreover, it was compared with important variants of SegNet and showed to be able to improve the overall accuracy of SegNet-Basic by up to 17.5%.


Towards Discipline-Independent Argumentative Zoning : Evidence from Chemistry and Computational Linguistics


Opinion-aware Knowledge Graph for Political Ideology Detection


Feature-Rich Unsupervised Word Alignment Models


title: An Evaluation of Methods for Prioritizing Software Requirements

abstract: This article describes an evaluation of six different methods for prioritizing software requirements. Based on the quality requirements for a telephony system, the authors individually used all six methods on separate occasions to prioritize the requirements. The methods were then characterized according to a number of criteria from a user's perspective. We found the analytic hierarchy process to be the most promising method, although it may be problematic to scale-up. In an industrial follow-up study we used the analytic hierarchy process to further investigate its applicability. We found that the process is demanding but worth the effort because of its ability to provide reliable results, promote knowledge transfer and create consensus among project members.


title: Comparative Study of Sustainable Transportation Systems: A futuristic approach

abstract: Intelligent and Sustainable transportations are the trendy expressions in flow inquiries about on the Internet-of-Vehicles (10V). It is always a challenging factor to maintain sustainability in IoV. Not only pollution-free vehicular systems lead to sustainability, rather a road-safety/traffic-safety or preventing accidents/collisions will also result in sustainability in IoV. A short analysis is made on existing sustainable transportation methods in IoV to provide a suggestion to develop an efficient and sustainable transportation system. This paper ponders different feasible attributes, points of interest, and inconveniences in the current transportation frameworks. Before the end, a reasonable recommendation is accommodated a productive and economical transportation arranging, so ecofriendly condition and street/activity security are kept up, along these lines prompting the supportable transportation.


title: Web search/browse log mining: challenges, methods, and applications

abstract: Huge amounts of search and browse log data has been accumulated in various search engines. Such massive search/browse log data, on the one hand, provides great opportunities to mine the wisdom of crowds and improve Web search as well as online advertisement. On the other hand, designing effective and efficient algorithms and tools to clean, model, and process large scale log data presents great challenges. In this tutorial, we give a systematic survey on the applications, challenges, fundamental principles and state-of-the-art methods of mining large scale search and browse log data. We start with an introduction of search and browse log data and an overview of various log mining applications. Then, we focus on four popular areas of log mining applications, namely query understanding, document understanding, query-document matching, and user understanding. For each area, we review the major tasks, analyze the challenges, and exemplify several representative solutions. Finally, we discuss several new directions in search/browse log mining. The tutorial slides are available at the authors' homepages after the tutorial is presented.


title: Indoor Radio Propagation Model Based on Dominant Path

abstract: When there are bigger obstacles in the indoor environment such as elevator, the radio waves basically can not penetrate it. The contribution of received signal strength by transmission and reflection will be greatly reduced, and most of the time, the radio waves will reach the user by bypass diffraction. Therefore, the traditional path loss model is no longer applicable, and the improved model should be proposed. In this paper, we firstly proposed an indoor radio propagation model based on dominant path in which the received signal strength has nothing to do with the direct distance between user and access point, but is related to the length of dominant path. Then on the basis of dominant path model, the NLOS influence is considered in order to further improve the accuracy of dominant path model. Experimental results demonstrated that the proposed dominant path model can improve the accuracy of traditional path loss model remarkably.


title: Animal-Assisted Therapy: A Meta-Analysis

abstract: ABSTRACTAnimal-assisted therapy (AAT) has been practiced for many years and there is now increasing interest in demonstrating its efficacy through research. To date, no known quantitative review of AAT studies has been published; our study sought to fill this gap. We conducted a comprehensive search of articles reporting on AAT in which we reviewed 250 studies, 49 of which met our inclusion criteria and were submitted to meta-analytic procedures. Overall, AAT was associated with moderate effect sizes in improving outcomes in four areas: Autism-spectrum symptoms, medical difficulties, behavioral problems, and emotional well-being. Contrary to expectations, characteristics of participants and studies did not produce differential outcomes. AAT shows promise as an additive to established interventions and future research should investigate the conditions under which AAT can be most helpful.


Sentiment Analysis : An Examination of Cybersecurity Attitudes and Behavior Research-in-Progress


title: A 5.7-GHz low-power and high-gain 0.18-/spl mu/m CMOS double-balanced mixer for WLAN

abstract: This paper presents a low-power and high-gain 5.7 GHz CMOS double-balanced mixer for an IEEE 802.11a WLAN application. The RF input frequency of the mixer is 5.725 ~ 25 GHz, LO is 5.265 ~ 5.325 GHz, IF is at 480 MHz. The mixer is fabricated with the 0.18 mum 1P6M standard CMOS process. The die size is 0.627 times 0.649 mm2. The measurements are performed using an FR-4 PCB test fixture. The fabricated mixer exhibits a conversion gain of 11 dB, noise figure of 12.8 dB, and input P1dB -16.4 dBm. The DC consumption current is 2.0 mA /1.0 mA for the core/buffer amplifier at VDD = 1.8 V


title: Performance evaluation of scientific applications on modern parallel vector systems

abstract: Despite their dominance of high-end computing (HEC) through the 1980's, vector systems have been gradually replaced by microprocessor-based systems. However, while peak performance of microprocessors has grown exponentially, the gradual slide in sustained performance delivered to scientific applications has become a growing concern among HEC users. Recently, the Earth Simulator and Cray X1/X1E parallel vector processor systems have spawned renewed interest in vector technology for scientific applications. In this work, we compare the performance of two Lattice-Boltzmann applications and the Cactus astrophysics package on vector based systems including the Cray X1/X1E, Earth Simulator, and NEC SX-8, with commodity-based processor clusters including the IBM SP Power3, IBM Power5, Intel Itanium2, and AMD Opteron processors. We examine these important scientific applications to quantify the effective performance and investigate if efficiency benefits are applicable to a broader array of numerical methods.


title: Users' Awareness of Privacy on Online Social Networking Sites - Case Facebook

abstract: Online social networking offers a new, easy and inexpensive way to maintain already existing relationships and present oneself to others. However, the increasing number of actions in online services also gives a rise to privacy concerns and risks. In an attempt to understand the factors, especially privacy awareness, that influence users to disclose or protect information in online environment, we view privacy behavior from the perspectives of privacy protection and information disclosing. In our empirical study, we present results from a survey of 210 users of Facebook. Our results indicate, that most of our respondents, who seem to be active users of Facebook, disclose a considerable amount of private information. Contrary to their own belief, they are not too well aware of the visibility of their information to people they do not necessarily know. Furthermore, Facebook’s privacy policy and the terms of use were largely not known or understood by our respondents.


Basket-Sensitive Personalized Item Recommendation


title: Designing Learning by Teaching Agents: The Betty's Brain System

abstract: The idea that teaching others is a powerful way to learn is intuitively compelling and supported in the research literature. We have developed computer-based, domain-independent Teachable Agents that students can teach using a visual representation. The students query their agent to monitor their learning and problem solving behavior. This motivates the students to learn more so they can teach their agent to perform better. This paper presents a teachable agent called Betty's Brain that combines learning by teaching with self-regulated learning feedback to promote deep learning and understanding in science domains. A study conducted in a 5th grade science classroom compared three versions of the system: a version where the students were taught by an agent, a baseline learning by teaching version, and a learning by teaching version where students received feedback on self-regulated learning strategies and some domain content. In the other two systems, students received feedback primarily on domain content. Our results indicate that all three groups showed learning gains during a main study where students learnt about river ecosystems, but the two learning by teaching groups performed better than the group that was taught. These differences persisted in the transfer study, but the gap between the baseline learning by teaching and self-regulated learning group decreased. However, there are indications that self-regulated learning feedback better prepared students to learn in new domains, even when they no longer had access to the self-regulation environment.


title: Two-wheel self-balanced car based on Kalman filtering and PID algorithm

abstract: Self-balanced car is a typical Incomplete control system. Self-balanced car's body is a natural instability, so it has multivariable, nonlinear, strong coupling and other characteristics. The choice of inertial sensors has become one of the most important issues while designing a self-balanced car. But the sensor module is too expensive that is the reason of the high cost of the self-balanced car. A lower costed acceleration ADXL335 and angular velocity sensor ISZ-650 are chosen to make a sensor module much cheaper and the attitude is measured with Kalman filtering and PID algorithm. Finally a self-balanced car model is made to prove the feasibility of reducing costs.


title: Convection Heat Transfer and Flow Calculations Suitable for Analytical Modelling of Electric Machines

abstract: Motor-CAD is a commercial software package dedicated to the optimisation of motor cooling. Its solver is based on thermal lumped circuit analysis. This provides near instantaneous calculations speeds allowing `what-if' scenarios to be run in real time. The user inputs geometric data for the design they wish to simulate using the graphical radial and cross-section editors. Materials to be used in the machine and the cooling type to be modelled (TENV, TEFC, Liquid Cooling, etc) are selected. All thermal parameters such as conduction, radiation and convection thermal resistances are then calculated by the program and the thermal performance calculated. This article concentrates on the formulations used to predict the convection cooling and flow within the machine.


title: Combining Conjugate Direction Methods with Stochastic Approximation of Gradients

abstract: The method of conjugate directions provides a very eective way to optimize large, deterministic systems by gradient descent. In its standard form, however, it is not amenable to stochastic approximation of the gradient. Here we explore ideas from conjugate gradient in the stochastic (online) setting, using fast Hessian-gradient products to set up low-dimensional Krylov subspaces within individual mini-batches. In our benchmark experiments the resulting online learning algorithms converge orders of magnitude faster than ordinary stochastic gradient descent.


title: Expertness based cooperative Q-learning

abstract: By using other agents' experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rules for unseen situations. These benefits would be gained if the learning agent can extract proper rules from the other agents' knowledge for its own requirements. One possible way to do this is to have the learner assign some expertness values (intelligence level values) to the other agents and use their knowledge accordingly. Some criteria to measure the expertness of the reinforcement learning agents are introduced. Also, a new cooperative learning method, called weighted strategy sharing (WSS) is presented. In this method, each agent measures the expertness of its teammates and assigns a weight to their knowledge and learns from them accordingly. The presented methods are tested on two Hunter-Prey systems. We consider that the agents are all learning from each other and compare them with those who cooperate only with the more expert ones. Also, the effect of communication noise, as a source of uncertainty, on the cooperative learning method is studied. Moreover, the Q-table of one of the cooperative agents is changed randomly and its effects on the presented methods are examined.


title: Toward a Unified Ontology of Cloud Computing

abstract: Progress of research efforts in a novel technology is contingent on having a rigorous organization of its knowledge domain and a comprehensive understanding of all the relevant components of this technology and their relationships. Cloud computing is one contemporary technology in which the research community has recently embarked. Manifesting itself as the descendant of several other computing research areas such as service-oriented architecture, distributed and grid computing, and virtualization, cloud computing inherits their advancements and limitations. Towards the end-goal of a thorough comprehension of the field of cloud computing, and a more rapid adoption from the scientific community, we propose in this paper an ontology of this area which demonstrates a dissection of the cloud into five main layers, and illustrates their interrelations as well as their inter-dependency on preceding technologies. The contribution of this paper lies in being one of the first attempts to establish a detailed ontology of the cloud. Better comprehension of the technology would enable the community to design more efficient portals and gateways for the cloud, and facilitate the adoption of this novel computing approach in scientific environments. In turn, this will assist the scientific community to expedite its contributions and insights into this evolving computing field.


title: Priors for people tracking from small training sets

abstract: We advocate the use of scaled Gaussian process latent variable models (SGPLVM) to learn prior models of 3D human pose for 3D people tracking. The SGPLVM simultaneously optimizes a low-dimensional embedding of the high-dimensional pose data and a density function that both gives higher probability to points close to training data and provides a nonlinear probabilistic mapping from the low-dimensional latent space to the full-dimensional pose space. The SGPLVM is a natural choice when only small amounts of training data are available. We demonstrate our approach with two distinct motions, golfing and walking. We show that the SGPLVM sufficiently constrains the problem such that tracking can be accomplished with straightforward deterministic optimization.


title: Machine Learning on imbalanced data in Credit Risk

abstract: In Machine Learning, we often encounter instances of imbalanced data which occur whenever there is an unequal representation in the classification categories. New found interest in Machine Learning has made its usage ubiquitous. Its applications encompass a wide plethora of scenarios ranging from Business and Banking to Bioinformatics and Psychology. These problems are often characterized by imbalanced data, the presence of which often leads to inaccurate predictive models, since the distribution of testing data may differ from that of training data while learning, leading to misclassification of the response variable. The primary focus of the paper is on Credit Risk which is defined as the probability of defaulting on the loan or credit acquired from a banking or financial institution. The base risk is that of the loss of primary principal and interest, disruption of cash flows and increased collection costs. Loan Default is an uncommon phenomena, henceforth we obtain the imbalanced data. We've adopted the approach of Logistic Regression and Classification and Regression Trees (CART) with techniques such as undersampling, Prior Probabilities, Loss Matrix and Matrix Weighing to deal with imbalanced data.


title: RSVM: Reduced Support Vector Machines

abstract: Abstract An algorithm is proposed which generates a nonlinear kernel-based separating surface that requires as little as 1% of a large dataset for its explicit evaluation. To generate this nonlinear surface, the entire dataset is used as a constraint in an optimization problem with very few variables corresponding to the 1% of the data kept. The remainder of the data can be thrown away after solving the optimization problem. This is achieved by making use of a rectangular m×m kernel K(A, Ā) that greatly reduces the size of the quadratic program to be solved and simplifies the characterization of the nonlinear separating surface. Here, the m rows of A represent the original m data points while the m rows of Ā represent a greatly reduced m data points. Computational results indicate that test set correctness for the reduced support vector machine (RSVM), with a nonlinear separating surface that depends on a small randomly selected portion of the dataset, is better than that of a conventional support vector machine (SVM) with a nonlinear surface that explicitly depends on the entire dataset, and much better than a conventional SVM using a small random sample of the data. Computational times, as well as memory usage, are much smaller for RSVM than that of a conventional SVM using the entire dataset.


title: PV power system based MPPT Z-source inverter to supply a sensorless BLDC motor

abstract: This paper proposes a new PV power system feeding a sensorless brushless dc motor (BLDC) driving a water pump. A Z-source inverter (ZSI) is controlled to extract the maximum power from PV array and supplies the BLDC motor instantaneously. The BLDC motor is driven with variable reference speed dictated from instant maximum PV power by a hysteresis current control loop. Despite the conventional PV water pumping systems, employing a two stages converter, the proposed system enjoys one stage power conversion. Therefore, less number of power switches, less switching losses and lower cost are obtained. By proper designing of control system parameters and maximum power point tracking (MPPT) method for PV array, good steady state and transient performances have been achieved in response to different operation conditions for PV array. Sensorless drive with low torque ripple is provided for the BLDC motor. The system was simulated by PSCAD/EMTDC software for a PV array of 1 kW.


title: The research and development of 3D urban geographic information system with Unity3D

abstract: 3D urban geographic information system (GIS) is of great importance in urban planning, management and decision support. However, the system development is difficult from the infrastructure perspective or expensive based on the professional GIS platform. Therefore, this paper puts forward the development method of 3D urban GIS with the Unity3D game platform. The thought and technical framework are introduced firstly. 3D modeling, attribute data management and query, scene browsing and analysis methods are presented secondly. And a satisfactory result has been achieved through the development practice and proves that the method is feasible. This research also explores a new way in small 3D urban (county-level) GIS development and application.


Strategies for minimal residual disease detection: current perspectives


title: Unconstrained face verification using fisher vectors computed from frontalized faces

abstract: We present an algorithm for unconstrained face verification using Fisher vectors computed from frontalized off-frontal gallery and probe faces. In the training phase, we use the Labeled Faces in the Wild (LFW) dataset to learn the Fisher vector encoding and the joint Bayesian metric. Given an image containing the query face, we perform face detection and landmark localization followed by frontalization to normalize the effect of pose. We further extract dense SIFT features which are then encoded using the Fisher vector learnt during the training phase. The similarity scores are then computed using the learnt joint Bayesian metric. CMC curves and FAR/TAR numbers calculated for a subset of the IARPA JANUS challenge dataset are presented.


title: Channel-stacked NAND flash memory with layer selection by multi-level operation (LSM)

abstract: In this paper, the channel stacked array (CSTAR) NAND flash memory with layer selection by multi-level operation (LSM) of string select transistor (SST) is proposed and investigated to solve problems of conventional channel stacked array. In case of LSM architecture, the stacked layers can be distinguished by combinations of multi-level states of SST and string select line (SSL) bias. Due to the layer selection performed by the bias of SSL, the placement of bit lines and word lines is similar to the conventional planar structure, and proposed CSTAR with LSM has no island-type SSLs. As a result of the advantages of the proposed architecture, various issues of conventional channel stacked NAND flash memory array can be solved.


title: Depression, anxiety and substance use in medical students in the Republic of Macedonia.

abstract: OBJECTIVES ::: To determine the prevalence of depressive and high trait anxiety symptoms and substance use, including alcohol and nicotine, in first-year and second-year medical students in Skopje University Medical School, Republic of Macedonia. ::: ::: ::: BACKGROUND ::: It is important to investigate medical students because they are under significant pressure during early years of medical education, a period during which the attitudes and behaviors of physicians develop. ::: ::: ::: METHODS ::: A cross-sectional survey in classroom settings, using an anonymous self-administered questionnaire, was performed in 354 participants (181 first-year, 118 females and 63 males and 173 second-year medical students, 116 females and 57 males) aged 18 to 23 years. The Beck Depression Inventory (BDI) and Taylor Manifest Anxiety Scale (TMAS) were used to determine depressive and high trait anxiety symptoms. BDI scores 17 or higher were categorized as depressive and TMAS scores 16 or higher as high anxiety symptoms. A Student t-test was used for continuous data analysis. ::: ::: ::: RESULTS ::: Out of all participants 10.4% had BDI score 17 or higher and 65.5% had TMAS score 16 or higher. Alcohol was the most frequently used substance in both groups. Smoking prevalence was 25%. Benzodiazepines (diazepam, alprazolam) use was 13.1%. Illicit drug use was rare (1.1% in freshmen and 3.6% in juniors) in both groups. ::: ::: ::: CONCLUSIONS ::: High frequency of manifest high anxiety symptoms and depressive symptoms and benzodiazepine use among Macedonian junior medical students should be taken seriously and a student counseling service offering mental health assistance is necessary (Tab. 3, Ref. 23). Full Text (Free, PDF) www.bmj.sk.


title: Functional explanation and the function of explanation

abstract: Abstract Teleological explanations (TEs) account for the existence or properties of an entity in terms of a function: we have hearts because they pump blood, and telephones for communication. While many teleological explanations seem appropriate, others are clearly not warranted—for example, that rain exists for plants to grow. Five experiments explore the theoretical commitments that underlie teleological explanations. With the analysis of [Wright, L. (1976). Teleological Explanations . Berkeley, CA: University of California Press] from philosophy as a point of departure, we examine in Experiment 1 whether teleological explanations are interpreted causally, and confirm that TEs are only accepted when the function invoked in the explanation played a causal role in bringing about what is being explained. However, we also find that playing a causal role is not sufficient for all participants to accept TEs. Experiment 2 shows that this is not because participants fail to appreciate the causal structure of the scenarios used as stimuli. In Experiments 3–5 we show that the additional requirement for TE acceptance is that the process by which the function played a causal role must be general in the sense of conforming to a predictable pattern. These findings motivate a proposal, Explanation for Export, which suggests that a psychological function of explanation is to highlight information likely to subserve future prediction and intervention. We relate our proposal to normative accounts of explanation from philosophy of science, as well as to claims from psychology and artificial intelligence.


title: Data generation for testing and grading SQL queries

abstract: Correctness of SQL queries is usually tested by executing the queries on one or more datasets. Erroneous queries are often the results of small changes or mutations of the correct query. A mutation Q$$'$$? of a query Q is killed by a dataset D if Q(D) $$\ne $$? Q$$'$$?(D). Earlier work on the XData system showed how to generate datasets that kill all mutations in a class of mutations that included join type and comparison operation mutations. In this paper, we extend the XData data generation techniques to handle a wider variety of SQL queries and a much larger class of mutations. We have also built a system for grading SQL queries using the datasets generated by XData. We present a study of the effectiveness of the datasets generated by the extended XData approach, using a variety of queries including queries submitted by students as part of a database course. We show that the XData datasets outperform predefined datasets as well as manual grading done earlier by teaching assistants, while also avoiding the drudgery of manual correction. Thus, we believe that our techniques will be of great value to database course instructors and TAs, particularly to those of MOOCs. It will also be valuable to database application developers and testers for testing SQL queries.


title: Crowdfunding in Vietnam : the impact of project and founder quality on funding success

abstract: This study seeks to give an analytical understanding of crowdfunding in Vietnam by examining the impact of project quality and founder quality on the success of crowdfunding. Based on a sample of 122 crowdfunding projects on Vietnamese platforms, three factors are found to drive successful fundraising. In particular, the presence of more quality signals and provision of additional founder information are associated with a higher probability of success. These findings confirm the role of quality in project outcomes. By having more project and founder quality indicators, the information asymmetry problem becomes less severe and investors have more trust to invest in a project. Further analyses conducted separately for two types of crowdfunding show that the main results hold for the equity-based subsample. Meanwhile, in reward-based crowdfunding, the impact on success becomes weaker for project quality signals and insignificant for founder quality signals. The explanation for this difference lies in both the average amount of investment involved and the degree of riskiness of the specific crowdfunding type.


title: What happens next? event prediction using a compositional neural network model

abstract: We address the problem of automatically acquiring knowledge of event sequences from text, with the aim of providing a predictive model for use in narrative generation systems. We present a neural network model that simultaneously learns embeddings for words describing events, a function to compose the embeddings into a representation of the event, and a coherence function to predict the strength of association between two events. ::: ::: We introduce a new development of the narrative cloze evaluation task, better suited to a setting where rich information about events is available. We compare models that learn vector-space representations of the events denoted by verbs in chains centering on a single protagonist. We find that recent work on learning vector-space embeddings to capture word meaning can be effectively applied to this task, including simple incorporation of a verb's arguments in the representation by vector addition. These representations provide a good initialization for learning the richer, compositional model of events with a neural network, vastly outperforming a number of baselines and competitive alternatives.


title: Microwave Acoustic Wave Devices: Recent Advances on Architectures, Modeling, Materials, and Packaging

abstract: This paper reviews applications of acoustic wave devices in mobile communication. After a general and historical introduction to bulk acoustic wave (BAW) and surface acoustic wave (SAW) devices, a review is given on the architectures where acoustic wave devices are applied driving the requirements on the SAW and BAW components. Following this, we discuss the progress in technology important materials. Next, an overview on the modeling and characterization of SAW and BAW at high power levels is given. Finally an overview of packaging technologies and an outlook to future developments is provided. Finally, an outlook to future developments is provided.


title: Habitat suitability analysis for hippopotamus (H. amphibious) using GIS and remote sensing in Lake Tana and its environs, Ethiopia

abstract: This research was carried out from October 2013 to May 2014. Hippopotamus amphibious is a mammalian species distributed in different lakes and rivers where ecological requirements are fulfilled for its survival. Lake Tana and its environs are home to Hippopotamus amphibious. The species is identified as vulnerable worldwide due to habitat loss and poaching. However, despite its vulnerability, there is no research conducted regarding the species, and its environmental requirements in Ethiopia. Therefore, the main objective of this study was to carryout habitat suitability analysis and find out suitable habitat sites of hippopotamus within the Lake Tana and its environs using the integration of GIS and remote sensing techniques. The softwares such as, Arc GIS10.2, ERDAS IMAGINE2010, and Virtual satellite image downloader were used in this research. The data used were SPOT image of 2012 of the study area, bathymetric data of Lake Tana, DEM, Google Earth data and GCP. Running a suitability model requires estimation of weights by expertise for each individual criterion on GIS software. Thus, the habitats in Lake Tana and its environs ranging from most suitable to not suitable for hippopotamus were identified. It was shown that 50.88% of the areas under study was highly disturbed and became unsuitable to hippopotamus, 42.29% of the areas were moderately disturbed, and only 1.81% of the areas were revealed to be undisturbed. As the study result showed that in and around Lake Tana, a human factor was considered to be outweighing the physical factors to minimize the habitat for the aforementioned animal. The results revealed that only 22.54% of the study areas were identified as most suitable for the animal under study of which the large portions of the areas are located at the backside of settlements which are not easily accessible by the species, while 40.5% of the areas were found to be moderately suitable, and 36.96% were unsuitable habitats for hippopotamus. Based on the findings of the present study it was concluded that there was high interference of human being in the habitats of hippopotamus especially at the shores of the lake since the land were looked-for agricultural activities. Therefore, too much proximity of human activities in identified hippopotamus habitats have to be protected and conservation buffer surrounding the Lake has to be developed.


title: Conducted EMI simulation of switched mode power supply

abstract: This paper introduces an efficient method to predict the conducted EMI of switched mode power supply (SMPS) through time domain SPICE simulation, which can be used to design or optimize the filter circuit and quantify the suppression degree of the filter. The SMPS modelling method permits modelling of the SMPS as a noise signal source even when its internal structure is unknown. The method is verified by comparing predicted and measured noise signals.


title: Integration of Optimized Modulation Filter Sets Into Deep Neural Networks for Automatic Speech Recognition

abstract: Inspired by physiological studies on the human auditory system and by results from psychoacoustics, an amplitude modulation filter bank AMFB has been developed and successfully applied to feature extraction for automatic speech recognition ASR in earlier work. Here, we address the question as to which amplitude modulation AM frequency decomposition leads to optimal ASR performance by proposing a parameterized functional relationship between modulation center frequency and modulation bandwidth. Word error rates WERs of ASR experiments with 1551 different AMFBs are systematically evaluated and compared, resulting in the identification of a comparatively narrow range of optimal modulation frequency to modulation bandwidth characteristics. To integrate modulation processing with deep neural network DNN acoustic modeling, we propose merging of modulation filter coefficients with DNN weights prior to a final training step and an improved mean-variance normalization scheme for AMFBs. These modifications are shown to result in further reduction of WERs and are indicative of the proposed system's improved generalization ability, when compared across corpora of 100-960 h of data with mismatched training and test conditions. Analysis of DNN-learned temporal AM filtering properties is carried out and implications for the relevance of different modulation regions as well as the relation to psychoacoustic findings are discussed. ASR experiments with the proposed system demonstrate a high degree of robustness against extrinsic acoustic distortions, resulting in, e.g., an average WER of 9.79% on the Aurora-4 task.


title: A unified model of human semantic knowledge and its disorders

abstract: Chen et al. construct a model of the neural bases of semantic representation that unifies domain-specific (distinct systems represent different kinds of things) and domain-general (knowledge for all kinds is encoded in a single network) accounts.


title: HEAT-TRANSFER IN A TAYLOR VORTEX FLOW

abstract: The characteristics of heat transfer affected by Taylor vortex motion were investigated experimentally by the use of electrochemical method and theoretically with the aid of the nonlinear theory suggested by J. T. Stuart. The present non-linear theory, which considers the effect of the fundamentals of disturbances on heat transfer, especially in the wide gap problem, is also applicable to the heat-transfer problem in the range of 1<Re/Recr<2 for fluid with Pr=1. The local Nusselt numbers (Sherwood numbers) measured by means of electrochemical technique show a remarkable sinusoidal periodicity in the axial direction due to Taylor vortex motion, except for their cycloidal periodicity in a very short supercritical range of Reynolds number. The values of skin friction and heat-transfer coefficient calculated by the semiempirical modification of the equilibrium amplitude of supercritical disturbances agree quite well with measurements over an unexpectedly wide range of Reynolds number (2<Re/Recr<20) where higher-order disturbances should be taken into account.


title: A Multi-Task Learning Formulation for Survival Analysis

abstract: Predicting the occurrence of a particular event of interest at future time points is the primary goal of survival analysis. The presence of incomplete observations due to time limitations or loss of data traces is known as censoring which brings unique challenges in this domain and differentiates survival analysis from other standard regression methods. The popularly used survival analysis methods such as Cox proportional hazard model and parametric survival regression suffer from some strict assumptions and hypotheses that are not realistic in most of the real-world applications. To overcome the weaknesses of these two types of methods, in this paper, we reformulate the survival analysis problem as a multi-task learning problem and propose a new multi-task learning based formulation to predict the survival time by estimating the survival status at each time interval during the study duration. We propose an indicator matrix to enable the multi-task learning algorithm to handle censored instances and incorporate some of the important characteristics of survival problems such as non-negative non-increasing list structure into our model through max-heap projection. We employ the L2,1-norm penalty which enables the model to learn a shared representation across related tasks and hence select important features and alleviate over-fitting in high-dimensional feature spaces; thus, reducing the prediction error of each task. To efficiently handle the two non-smooth constraints, in this paper, we propose an optimization method which employs Alternating Direction Method of Multipliers (ADMM) algorithm to solve the proposed multi-task learning problem. We demonstrate the performance of the proposed method using real-world microarray gene expression high-dimensional benchmark datasets and show that our method outperforms state-of-the-art methods.


title: Age Differences in Stress, Coping, and Appraisal: Findings from the Normative Aging Study

abstract: Controversies exist concerning the influence of age on the stress and coping process, in part due to differences in methods across studies. We examined age differences in stress, appraisal, and coping, using both semistructured interview questions and a coping checklist in middle-aged, young-old, and old-old men. Despite extensive probing, nearly a quarter of the old-old reported having had no problems and they expended less coping effort even when they did have problems. The types of problems reported varied systematically with age. Middle-aged men were more likely to appraise their problems both as challenges and as annoyances than the older men. Different age patterns emerged from the coping interviews vs the checklists, but controlling for type of problem significantly attenuated age differences. However, there were no age differences in perceived stressfulness of the problem, appraisals of harm/loss, or helpless appraisals, number of emotions reported, or coping efficacy. One interpretation of these results is that the nature of stress changes with age, from episodic to chronic, which in turn affects appraisal and coping processes. T HE manner in which age influences the stress and coping process remains poorly understood (Folkman, Lazarus, Pimley, & Novacek, 1987). In general, debate focuses on whether young and old adults differ in the ways in which they experience and cope with stress. In part, this may be due to the fact that there are different components of the stress and coping process as well as different ways of assessing these components. For example, older people may (or may not) differ from younger people in either the amount of stress they report, the types of stressors reported, or the appraisal of how stressful a problem is, depending upon the type of measure used. Further, the use of coping strategies has variously been viewed as decreasing with age, increasing with age, or showing little or no age-related differences, again, depending on the conceptual frameworks and methodological techniques used (Aldwin, 1991). The purpose of this study was to examine age differences in the stress and coping process, contrasting in-depth interviews with more standardized measures in part to determine the extent of agerelated bias in self-report instruments.


title: Bounded verification of Ruby on Rails data models

abstract: The use of scripting languages to build web applications has increased programmer productivity, but at the cost of degrading dependability. In this paper we focus on a class of bugs that appear in web applications that are built based on the Model-View-Controller architecture. Our goal is to automatically discover data model errors in Ruby on Rails applications. To this end, we created an automatic translator that converts data model expressions in Ruby on Rails applications to formal specifications. In particular, our translator takes Active Records specifications (which are used to specify data models in Ruby on Rails applications) as input and generates a data model in Alloy language as output. We then use bounded verification techniques implemented in the Alloy Analyzer to look for errors in these formal data model specifications. We applied our approach to two open source web applications to demonstrate its feasibility.


title: Joint prediction in MST-style discourse parsing for argumentation mining

abstract: We introduce a new approach to argumentation mining that we applied to a parallel German/English corpus of short texts annotated with argumentation structure. We focus on structure prediction, which we break into a number of subtasks: relation identification, central claim identification, role classification, and function classification. Our new model jointly predicts different aspects of the structure by combining the different subtask predictions in the edge weights of an evidence graph; we then apply a standard MST decoding algorithm. This model not only outperforms two reasonable baselines and two datadriven models of global argument structure for the difficult subtask of relation identification, but also improves the results for central claim identification and function classification and it compares favorably to a complex mstparser pipeline.


title: Enhancing connected car adoption: Security and over the air update framework

abstract: A comprehensive security solution is no longer an option, and needs to be designed bottom-up into the car software. The architecture needs to be scalable and tiered, leveraging the proven technologies, processes and policies from the mature industries. The objective is to detect, defend and recover from any attack before harm comes to passengers, data and instrumentation. No matter how hardened security is there is always a need to patch any security vulnerabilities. This paper presents high level framework for security and over the air (OTA) framework.