title: Any-Angle Pathfinding for Multiple Agents Based on SIPP Algorithm

abstract: The problem of finding conflict-free trajectories for multiple agents of identical circular shape, operating in shared 2D workspace, is addressed in the paper and decoupled, e.g., prioritized, approach is used to solve this problem. Agents' workspace is tessellated into the square grid on which any-angle moves are allowed, e.g. each agent can move into an arbitrary direction as long as this move follows the straight line segment whose endpoints are tied to the distinct grid elements. A novel any-angle planner based on Safe Interval Path Planning (SIPP) algorithm is proposed to find trajectories for an agent moving amidst dynamic obstacles (other agents) on a grid. This algorithm is then used as part of a prioritized multi-agent planner AA-SIPP(m). On the theoretical, side we show that AA-SIPP(m) is complete under well-defined conditions. On the experimental side, in simulation tests with up to 200 agents involved, we show that our planner finds much better solutions in terms of cost (up to 20%) compared to the planners relying on cardinal moves only.


title: Identifying categories of zones in scientific papers based on lexical and syntactical features

abstract: Scientific papers are continually increasing on the web and it is mandatory for the researchers to grasp on some powerful tools which are helpful in an efficient process of large amounts of data. Zone identification is a Natural Language Processing application which is to classify the sentences of scientific papers into a fixed set of zone categories. In this paper, we will propose an algorithm to identify some categories of zones in scientific papers. Regarding this, we make use of some significant lexical and syntactical features of the sentences standing for these categories in a particular way. In this respect, a sequence of sentences has been used. Experimental results show that these features are capable enough to identify the desired categories in a reasonable manner.


title: Internet of Things: a New Application for Intelligent Traffic Monitoring System

abstract: Constructing an intelligent traffic monitoring system firstly depends on automatic identification for vehicles. At present, automatic identification technology based on image and vehicle license plate is going to fall in the trap due to its low recognition rate and affection by adverse weather. Thus it is necessary to apply new technologies to solve this problem, and technologies based on Internet of Things provide a new approach for it. In this paper, we explored this issue and proposed a feasible scheme. At first, we took global unique EPC code as identity identification of vehicles in stead of vehicle license plate and utilized RFID reader to read EPC code by RF electromagnetic wave, which completely solve d the problem of no all-weather operations. Secondly, we obtained positioning information of vehicles by using GPS technology. Thirdly, because GPRS provides high-speed wireless IP services for mobile users, fully supports the TCP/IP, we t ook wireless GPRS scheme to transmit data of mobile objects. The realization of automatic detection and transmission of data provided a fundamental guarantee for cons tructing an intelligent traffic monitoring system. And then, we designed and discussed in turn its network architecture, data flow analysis, hardware logic structure , software flow, as well as its intelligent decision-making module. Research and design show that it is feasible and inexpensive to construct an intelligent traffic monitoring system based on Internet of Things, and the intelligent traffic monitoring system based on Internet of Things has a number of advantages such low cost, high reliability, never affected by adverse weather, all weather operations etc. Therefore, it will have a broad applying perspective.


Area , Power , and Latency Considerations of STT-MRAM to Substitute for Main Memory


title: A Novel Scheme for an Energy Efficient Internet of Things Based on Wireless Sensor Networks

abstract: One of the emerging networking standards that gap between the physical world and the cyber one is the Internet of Things. In the Internet of Things, smart objects communicate with each other, data are gathered and certain requests of users are satisfied by different queried data. The development of energy efficient schemes for the IoT is a challenging issue as the IoT becomes more complex due to its large scale the current techniques of wireless sensor networks cannot be applied directly to the IoT. To achieve the green networked IoT, this paper addresses energy efficiency issues by proposing a novel deployment scheme. This scheme, introduces: (1) a hierarchical network design; (2) a model for the energy efficient IoT; (3) a minimum energy consumption transmission algorithm to implement the optimal model. The simulation results show that the new scheme is more energy efficient and flexible than traditional WSN schemes and consequently it can be implemented for efficient communication in the IoT.


title: Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations

abstract: Exome sequencing on a large cohort of parent–child trios with sporadic autism spectrum disorders shows that de novo point mutations are mainly paternal in origin and positively correlate with paternal age, and identifies a highly interconnected network formed from the products of the most severe mutations.


title: PrIDE: A Protocol-Independent De-Duplication Engine for Packet Recording

abstract: Packet recording or capturing is one of the most useful tools for network forensics and surveillance. Since a storage system is of a limited size, de-duplication can be used to save disk space. In this article, we present a new scalable de-duplication engine for packet recording that can eliminate redundant contents over multiple packets. Unlike previous work, our proposed scheme is designed for packet-level de-duplication to support any kinds of network from the current Internet to emerging networks. We also present a new fast chunking method and a new indexing scheme that enable multiple engine instances to execute in parallel. We implement the de-duplication engine, and experimental results show that our proposed scheme can remove up to 65 percent of the packet contents in a real campus network. We also confirm that its throughput scalably increases with the number of CPU cores, which means that the proposed scheme can be implemented in a wide range of computing devices from small home gateways to high-end servers.


title: HFTools - An open source python package for microwave engineering

abstract: This paper presents HFTools an open source python package for microwave engineering. The focus of the development of the package at this time is fundamental tools, e.g. file IO, dataset object, array object, and multiport object. The basic HFTools package contains tools for reading data files in several common formats, e.g. touchstone, mdif, and citi. The package also contains tools for conversion of multi-port parameters. The paper presents the capabilities of the package by a series of short examples.


title: A constraint-based god-object method for haptic display

abstract: Haptic display is the process of applying forces to a human "observer" giving the sensation of touching and interacting with real physical objects. Touch is unique among the senses because it allows simultaneous exploration and manipulation of an environment. A haptic display system has three main components. The first is the haptic interface, or display device, generally some type of electro-mechanical system able to exert controllable forces on the user with one or more degrees of freedom. The second is the object model-a mathematical representation of the object containing its shape and other properties related to the way it feels. The third component, the haptic rendering algorithm, joins the first two components to compute, in real time, the model-based forces to give the user the sensation of touching the simulated objects. This paper focuses on a new haptic rendering algorithm for generating convincing interaction forces for objects modeled as rigid polyhedra. We create a virtual model of the haptic interface, called the god-object, which conforms to the virtual environment. The haptic interface can then be servo-ed to this virtual model. This algorithm is extensible to other functional descriptions and lays the groundwork for displaying not only shape information, but surface properties such as friction and compliance.


title: Semantic Image Inpainting with Deep Generative Models

abstract: Semantic image inpainting is a challenging task where large missing regions have to be filled based on the available visual data. Existing methods which extract information from only a single image generally produce unsatisfactory results due to the lack of high level context. In this paper, we propose a novel method for semantic image inpainting, which generates the missing content by conditioning on the available data. Given a trained generative model, we search for the closest encoding of the corrupted image in the latent image manifold using our context and prior losses. This encoding is then passed through the generative model to infer the missing content. In our method, inference is possible irrespective of how the missing content is structured, while the state-of-the-art learning based method requires specific information about the holes in the training phase. Experiments on three datasets show that our method successfully predicts information in large missing regions and achieves pixel-level photorealism, significantly outperforming the state-of-the-art methods.


title: Source Rock Occurrence in a Sequence Stratigraphic Framework: The Example of the Lias of the Paris Basin

abstract: The appraisal of the petroleum potential of a sedimentary basin requires a good evaluation of its source rocks. Sequence stratigraphy appears as a powerful tool for the study of basin-fill histories and is, at present, used for reservoir characterization purposes. Here, we demonstrate that this approach is also a powerful tool for predicting the organic matter distribution by providing a chronostratigraphic framework in which the role of the main parameters controlling its accumulation can be approached. The study was performed at the basin scale and covers a period of 25 m.y. where different orders of superimposed sequences were identified. It investigated the Lias (Lower Jurassic) of the Paris basin, an interval which is known as the bulk source rock for the oil pools in this basin. It used two methods, both applied on wireline logs: (1) the Carbolog method, which estimates the in-situ organic carbon content of the series, showed that the Liassic series was characterized by strong vertical and lateral variations of total organic carbon (TOC), and by the occurrence of several organic-rich intervals besides the well-known Schistes Carton; and (2) the "stacking pattern" method, which produced a consistent framework of three superimposed sequences which are in keeping with the global transgressive-regressive (T-R) Lias cycle. These are the genetic units (0.1 to 0.4 m.y.) of possible climatic origin, the genetic unit sets (0.6 to 1 m.y.) which might be of eustatic origin, and four minor T-R cycles (5 to 8 m.y.; i.e., the "stage scale") of clearly tectonic origin. The study showed a correlation between the distribution of the organic matter and the sequence stratigraphic framework, at the different sequence orders evidenced


title: Automated Facial Trait Judgment and Election Outcome Prediction: Social Dimensions of Face

abstract: The human face is a primary medium of human communication and a prominent source of information used to infer various attributes. In this paper, we study a fully automated system that can infer the perceived traits of a person from his face -- social dimensions, such as "intelligence," "honesty," and "competence" -- and how those traits can be used to predict the outcomes of real-world social events that involve long-term commitments, such as political elections, job hires, and marriage engagements. To this end, we propose a hierarchical model for enduring traits inferred from faces, incorporating high-level perceptions and intermediate-level attributes. We show that our trained model can successfully classify the outcomes of two important political events, only using the photographs of politicians' faces. Firstly, it classifies the winners of a series of recent U. S. elections with the accuracy of 67.9% (Governors) and 65.5% (Senators). We also reveal that the different political offices require different types of preferred traits. Secondly, our model can categorize the political party affiliations of politicians, i.e., Democrats vs. Republicans, with the accuracy of 62.6% (male) and 60.1% (female). To the best of our knowledge, our paper is the first to use automated visual trait analysis to predict the outcomes of real-world social events. This approach is more scalable and objective than the prior behavioral studies, and opens for a range of new applications.


title: An Experimental System for Advanced Heating, Ventilating and Air Conditioning (HVAC) Control

abstract: Abstract While having the potential to significantly improve heating, ventilating and air conditioning (HVAC) system performance, advanced (e.g., optimal, robust and various forms of adaptive) controllers have yet to be incorporated into commercial systems. Controllers consisting of distributed proportional-integral (PI) control loops continue to dominate commercial HVAC systems. Investigation into advanced HVAC controllers has largely been limited to proposals and simulations, with few controllers being tested on physical systems. While simulation can be insightful, the only true means for verifying the performance provided by HVAC controllers is by actually using them to control an HVAC system. The construction and modeling of an experimental system for testing advanced HVAC controllers, is the focus of this article. A simple HVAC system, intended for controlling the temperature and flow rate of the discharge air, was built using standard components. While only a portion of an overall HVAC system, it is representative of a typical hot water to air heating system. In this article, a single integrated environment is created that is used for data acquisition, controller design, simulation, and closed loop controller implementation and testing. This environment provides the power and flexibility needed for rapid prototyping of various controllers and control design methodologies.


title: An introduction to program comprehension for computer science educators

abstract: The area of program comprehension comprises a vast body of literature, with numerous conflicting models having been proposed. Models are typically grounded in experimental studies mostly involving experienced programmers. The question of how to relate this material to the teaching and learning of programming for novices has proven challenging for many researchers. In this critical review from a computer science educational perspective, the authors compare and contrast the way in which different models conceptualize program comprehension. This provides new insights into learning issues such as content, sequence, learning obstacles, effective learning tasks and teaching methods, as well as into the assessment of learning


title: Privacy preserving data mining — ‘A state of the art’

abstract: The process of extraction of interesting patterns or knowledge from the bulk of data refers to the data mining technique. “It is the process of discovering meaningful, new correlation patterns and trends through non-trivial extraction of implicit, previously unknown information from large amount of data stored in repositories using pattern recognition as well as statistical and mathematical techniques”. Due to the wide deployment of Internet and information technology, storage and processing of data technologies, the ever-growing privacy concern has been a major issue in data mining for information sharing. This gave rise to a new path in research, known as Privacy Preserving Data Mining (PPDM). The literature paper discusses various privacy preserving data mining algorithms and provide a wide analyses for the representative techniques for privacy preserving data mining along with their merits and demerits. The paper describes an overview of some of the well-known PPDM algorithms. Most of the algorithms are usually a modification of a well-known data-mining algorithm along with some privacy preserving techniques. This paper also focuses on the problems and directions for the future research here. The paper finally discusses the comparative analysis of some well-known privacy preservation techniques that are used. This paper is intended to be a summary and an overview of PPDM.


title: Changes in Dispositional Empathy in American College Students Over Time: A Meta-Analysis:

abstract: The current study examines changes over time in a commonly used measure of dispositional empathy. A cross-temporal meta-analysis was conducted on 72 samples of American college students who completed at least one of the four subscales (Empathic Concern, Perspective Taking, Fantasy, and Personal Distress) of the Interpersonal Reactivity Index (IRI) between 1979 and 2009 (total N = 13,737). Overall, the authors found changes in the most prototypically empathic subscales of the IRI: Empathic Concern was most sharply dropping, followed by Perspective Taking. The IRI Fantasy and Personal Distress subscales exhibited no changes over time. Additional analyses found that the declines in Perspective Taking and Empathic Concern are relatively recent phenomena and are most pronounced in samples from after 2000.


title: A general framework for motion segmentation: Independent, articulated, rigid, non-rigid, degenerate and nondegenerate

abstract: We cast the problem of motion segmentation of feature trajectories as linear manifold finding problems and propose a general framework for motion segmentation under affine projections which utilizes two properties of trajectory data: geometric constraint and locality. The geometric constraint states that the trajectories of the same motion lie in a low dimensional linear manifold and different motions result in different linear manifolds; locality, by which we mean in a transformed space a data and its neighbors tend to lie in the same linear manifold, provides a cue for efficient estimation of these manifolds. Our algorithm estimates a number of linear manifolds, whose dimensions are unknown beforehand, and segment the trajectories accordingly. It first transforms and normalizes the trajectories; secondly, for each trajectory it estimates a local linear manifold through local sampling; then it derives the affinity matrix based on principal subspace angles between these estimated linear manifolds; at last. spectral clustering is applied to the matrix and gives the segmentation result. Our algorithm is general without restriction on the number of linear manifolds and without prior knowledge of the dimensions of the linear manifolds. We demonstrate in our experiments that it can segment a wide range of motions including independent, articulated, rigid, non-rigid, degenerate, non-degenerate or any combination of them. In some highly challenging cases where other state-of-the-art motion segmentation algorithms may fail, our algorithm gives expected results.


title: A buck-boost based DC/AC converter for residential PV applications

abstract: The single stage architecture is often preferred to realize grid connected Photo-Voltaic generators for residential applications. In fact,by halving the power processing steps, the power losses can be considerably reduced if compared with those of a two stage configuration. However, single stage power conditioners are generally unable to boost the voltage of the PV modules, therefore, they feature a narrower input voltage range. A new approach to design a power conditioner for grid connected PV plants for residential applications is presented in this paper. It is based on the series connection between a buck-boost converter,and the PV array. According to such an approach, the buck-boost converter processes only a fraction of the inverter input power, while always operating in conditions of high efficiency. Experimental tests show that the proposed solution features an efficiency level very close to that of a single stage power conditioner, but owns a voltage boosting capability, similar to that of a two stage configuration.


title: Segmentation of Brain MRI Image - A Review

abstract: Automated brain tumor detection from MRI images is one of the most challenging task in today's modern Medical imaging research. Magnetic Resonance Images are used to produce images of soft tissue of human body. It is used to analyze the human organs without the need for surgery. Automatic detection requires brain image segmentation, which is the process of partitioning the image into distinct regions, is one of the most important and challenging aspect of computer aided clinical diagnostic tools. Noises present in the Brain MRI images are multiplicative noises and reductions of these noises are difficult task. The minute anatomical details should not be destroyed by the process of noise removal from clinical point of view. These makes accurate segmentation of brain images a challenge. However, accurate segmentation of the MRI images is very important and crucial for the exact diagnosis by computer aided clinical tools. A large variety of algorithms for segmentation of MRI images had been developed. In this paper, we present a review of the methods used in brain MRI image segmentation. The review covers imaging modalities, magnetic resonance imaging and methods for noise reduction and segmentation approaches. The paper concludes with a discussion on the upcoming trend of advanced researches in brain image segmentation.


title: Conditional Gradient Algorithms for Norm-Regularized Smooth Convex Optimization

abstract: Motivated by some applications in signal processing and machine learning, we consider two convex optimization problems where, given a cone $K$, a norm $\|\cdot\|$ and a smooth convex function $f$, we want either 1) to minimize the norm over the intersection of the cone and a level set of $f$, or 2) to minimize over the cone the sum of $f$ and a multiple of the norm. We focus on the case where (a) the dimension of the problem is too large to allow for interior point algorithms, (b) $\|\cdot\|$ is"too complicated"to allow for computationally cheap Bregman projections required in the first-order proximal gradient algorithms. On the other hand, we assume that {it is relatively easy to minimize linear forms over the intersection of $K$ and the unit $\|\cdot\|$-ball}. Motivating examples are given by the nuclear norm with $K$ being the entire space of matrices, or the positive semidefinite cone in the space of symmetric matrices, and the Total Variation norm on the space of 2D images. We discuss versions of the Conditional Gradient algorithm capable to handle our problems of interest, provide the related theoretical efficiency estimates and outline some applications.


title: Multilayer stacked coreless printed spiral winding inductor with wide frequency bandwidth

abstract: In this paper, three types of winding methods for the multi-layer stacked coreless printed spiral winding inductors are studied and evaluated for megahertz operation. They are traditional winding, alternating winding and partial alternating winding. The winding parasitic capacitances of different windings structures are estimated by distributed capacitance models. The electric field energy distributions in the inductors are derived from the voltage profiles to illustrate the relative magnitudes of winding parasitic capacitances. Calculated results show that partial alternating winding has a wide frequency bandwidth with reduced number of through-hole via. All calculated parameters are confirmed with measurements. The results provide useful information for the optimal design of coreless spiral or core-based planar inductors.


title: A Quality-Centric TCP-Friendly Congestion Control for Multimedia Transmission

abstract: In this paper, we propose a quality-centric congestion control for multimedia streaming over wired IP networks, which we refer to as media-TCP-friendly congestion control (MTCC). Our solution adapts the sending rate to both the network condition and the application characteristics by explicitly considering the distortion impacts, delay deadlines, and interdependencies of different video packet classes. The media-aware solution is able to provide differential services for transmitting various packet classes and thereby, further improves the multimedia streaming quality compared to the conventional network-aware congestion control. We use finite-horizon Markov decision process (FHMDP) to determine the optimal congestion control policy that maximizes the long-term multimedia quality, while adhering to the horizon-K TCP-friendliness constraint, which ensures long-term fairness with existing TCP applications. Moreover, the proposed MTCC is able to achieve quality-based fairness among multimedia users. We derive sufficient conditions for multiple multimedia users to achieve quality-based fairness using MTCC congestion control. Note that the proposed solution only modifies the adaptation mechanism of the TCP congestion window size at the sender, without changing the design at the receiver side (i.e., each current TCP receiver can correctly receive and process MTCC streams). Our simulation results show that MTCC achieves more than 3 dB improvement in terms of PSNR over the conventional TCP congestion control approaches, with the largest improvements observed for real-time streaming applications requiring stringent playback delays.


title: A study of open-source data mining tools for forecasting

abstract: This paper described five open-sources Data Mining (DM) tools which are Weka, RapidMiner, KEEL, Orange and Tanagra. The features and functionality of these DM tools can be benefited by educators and researchers. The DM algorithms embedded in the tools can be utilized for forecasting. Weka and RapidMiner have most of the desire characteristic for a fully-functional and flexible platform therefore their use can be recommended for most of DM tasks.


title: Sketches of thought: a study of the role of sketching in design problem-solving and its implications for the computational theory of the mind

abstract: Much of cognitive science is based on the Computational Theory of Mind hypothesis. The claim is that the mind is in part a computer and as such requires a representational medium--a language of thought--in which to represent information and to carry out computations. ::: But the Computational Theory of Mind is much more than a bland commitment to internal representations. It requires that the system of representation have some very stringent properties. In this dissertation it is demonstrated that, depending on which version of the computational theory one prefers one is necessarily committed to a mechanism with most or all of the following seven properties: (i) causally efficacious syntax, (ii) syntactic disjointness, (iii) syntactic differentiation, (iv) right causal connections, (v) unambiguity, (vi) semantic differentiation, and (vii) the maintenance criterion. These properties are referred to as the CTM-properties because they are entailed by the Computational Theory of Mind. A subset of these properties (ii, iii, v, vi) apply to symbol systems in general and are referred to as the CTM$\sp\prime$-properties. ::: The main thesis advanced is that, while it may be true that in certain cases the system of internal representation can be characterized as a language exhibiting the CTM-properties, there are also genuine cognitive situations where it needs to exhibit very un-CTM-like properties, in particular, properties associated with symbol systems of sketching. This leads to a dilemma which can only be solved by either circumscribing the domain of cognitive processes to be explained by a CTM-type symbol system or by reworking our notions of computation and representation. ::: Two problem solving studies involving expert designers are carried out and described. The first study identifies some of the cognitive processes involved in open-ended, ill-structured problem solving situations like design and notes a correlation between certain of these cognitive processes and certain external symbol systems used by designers. Some of these symbol systems possess the CTM$\sp\prime$-properties while others do not. The second study manipulates the external symbol systems subjects are allowed to use along these dimensions. It is predicted that this manipulation will hamper some cognitive functions and not others. The results indicate statistically significant differences along the lines predicted. (Abstract shortened with permission of author.)


title: SOAP Processing Performance and Enhancement

abstract: The web services (WS) technology provides a comprehensive solution for representing, discovering, and invoking services in a wide variety of environments, including Service Oriented Architectures (SOA ) and grid computing systems. At the core of WS technology lie a number of XML-based standards, such as the Simple Object Access Protocol (SOAP), that have successfully ensured WS extensibility, transparency, and interoperability. Nonetheless, there is an increasing demand to enhance WS performance, which is severely impaired by XML's verbosity. SOAP communications produce considerable network traffic, making them unfit for distributed, loosely coupled, and heterogeneous computing environments such as the open Internet. Also, they introduce higher latency and processing delays than other technologies, like Java RMI and CORBA. WS research has recently focused on SOAP performance enhancement. Many approaches build on the observation that SOAP message exchange usually involves highly similar messages (those created by the same implementation usually have the same structure, and those sent from a server to multiple clients tend to show similarities in structure and content). Similarity evaluation and differential encoding have thus emerged as SOAP performance enhancement techniques. The main idea is to identify the common parts of SOAP messages, to be processed only once, avoiding a large amount of overhead. Other approaches investigate nontraditional processor architectures, including micro- and macrolevel parallel processing solutions, so as to further increase the processing rates of SOAP/XML software toolkits. This survey paper provides a concise, yet comprehensive review of the research efforts aimed at SOAP performance enhancement. A unified view of the problem is provided, covering almost every phase of SOAP processing, ranging over message parsing, serialization, deserialization, compression, multicasting, security evaluation, and data/instruction-level processing.


title: Authentic Third-party Data Publication

abstract: Integrity critical databases, such as financial data used in high-value decisions, are frequently published over the Internet. Publishers of such data must satisfy the integrity, authenticity, and non-repudiation requirements of clients. Providing this protection over public networks is costly.


Lucid Dreaming: Psychophysiological Studies of Consciousness during REM Sleep


title: Collecting and Processing Arabic Facebook Comments for Sentiment Analysis

abstract: Social networks platforms such as Facebook are becoming one of the most powerful sources for information. The produced and shared data are important in volume, in velocity and in variety. Processing these data in the raw state to extract useful information can be a very difficult task and a big challenge. Furthermore, the Arabic language under its modern standard or dialectal shape is one of the languages producing an important quantity of data in social networks and the least analyzed. The characteristics and the specificity of the Arabic language present a big challenge for sentiment analysis, especially if this analysis is performed on Arabic Facebook comments. In this paper, we present a methodology that we have elaborated, for collecting and preprocessing Facebook comments written in Modern Standard Arabic (MSA) or in Moroccan Dialectal Arabic (MDA) for Sentiment Analysis (SA) using supervised classification methods. In this methodology, we have detailed the processing applied to the comments’ text as well as various schemes of features’ construction (words or groups of words) useful for supervised sentiments’ classification. This methodology was tested on comments written in MSA or in MDA collected from Facebook for the sentiment analysis on a political phenomenon. The experiments’ results obtained are promising and this encourages us to continue working on this topic.


title: Recovery of somatosensory deficits in acute stroke

abstract: Objectives – To study the recovery of somatosensory deficits after acute stroke. ::: ::: ::: ::: Material and methods – A detailed clinical examination of sensation, median nerve somatosensory evoked potentials (SEP), quantitative sensory tests (QST), and subjective evaluation were performed in five acute stroke patients at three control time points up to 12 months after the stroke. ::: ::: ::: ::: Results – The deficit recovered at least partially in all patients, mostly within 3 months after stroke. The improvement in warm and vibration detection thresholds occurred between 3 and 12 months. The SEP improved both by 3 and 12 months. ::: ::: ::: ::: Conclusion – The recovery of subjective sensory disturbance occurred in line with the improvement of the clinical sensory tests and QST. The most sensitive measure for somatosensory dysfunction at the early phase was graphesthesia. In our patients, initially normal SEP with a sensory deficit resulted in excellent clinical recovery, whereas initially absent SEP did not necessarily predict poor outcome.


title: Question asking during tutoring

abstract: Whereas it is well documented that student question asking is infrequent in classroom environments, there is little research on questioning processes during tutoring. The present study investigated the questions asked in tutoring sessions on research methods (college students) and algebra (7th graders). Student questions were approximately 240 times as frequent in tutoring settings as classroom settings, whereas tutor questions were only slightly more frequent than teacher questions. Questions were classified by (a) degree of specification, (b) content, and (c) question-generation mechanism to analyze their quality. Student achievement was positively correlated with the quality of student questions after students had some experience with tutoring, but the frequency of questions was not correlated with achievement. Students partially self-regulated their learning by identifying knowledge deficits and asking questions to repair them, but they need training to improve these skills. We identified some ways th...


title: Compensated-DNN: Energy Efficient Low-Precision Deep Neural Networks by Compensating Quantization Errors

abstract: Deep Neural Networks (DNNs) represent the state-of-the-art in many Artificial Intelligence (AI) tasks involving images, videos, text, and natural language. Their ubiquitous adoption is limited by the high computation and storage requirements of DNNs, especially for energy-constrained inference tasks at the edge using wearable and IoT devices. One promising approach to alleviate the computational challenges is implementing DNNs using low-precision fixed point (<16 bits) representation. However, the quantization error inherent in any Fixed Point (FxP) implementation limits the choice of bit-widths to maintain application-level accuracy. Prior efforts recommend increasing the network size and/or re-training the DNN to minimize loss due to quantization, albeit with limited success.Complementary to the above approaches, we present Compensated-DNN, wherein we propose to dynamically compensate the error introduced due to quantization during execution. To this end, we introduce a new fixed-point representation viz. Fixed Point with Error Compensation (FPEC). The bits in FPEC are split between computation bits vs. compensation bits. The computation bits use conventional FxP notation to represent the number at low-precision. On the other hand, the compensation bits (1 or 2 bits at most) explicitly capture an estimate (direction and magnitude) of the quantization error in the representation. For a given word length, since FPEC uses fewer computation bits compared to FxP representation, we achieve a near-quadratic improvement in energy in the multiply-and-accumulate (MAC) operations. The compensation bits are simultaneously used by a low-overhead sparse compensation scheme to estimate the error accrued during MAC operations, which is then added to the MAC output to minimize the impact of quantization. We build compensated-DNNs for 7 popular image recognition benchmarks with 0.05–20.5 million neurons and 0.01–15.5 billion connections. Based on gate-level analysis at 14nm technology, we achieve 2.65 × –4.88 × and 1.13 × –1.7 × improvement in energy compared to 16-bit and 8-bit FxP implementations respectively, while maintaining <0.5% loss in classification accuracy.


title: Toponym matching through deep neural networks

abstract: ABSTRACT Toponym matching, i.e. pairing strings that represent the same real-world location, is a fundamental problemfor several practical applications. The current state-of-the-art relies on string similarity metrics, either specifically developed for matching place names or integrated within methods that combine multiple metrics. However, these methods all rely on common sub-strings in order to establish similarity, and they do not effectively capture the character replacements involved in toponym changes due to transliterations or to changes in language and culture over time. In this article, we present a novel matching approach, leveraging a deep neural network to classify pairs of toponyms as either matching or nonmatching. The proposed network architecture uses recurrent nodes to build representations from the sequences of bytes that correspond to the strings that are to be matched. These representations are then combined and passed to feed-forward nodes, finally leading to a classification decision. We present the results of a wide-ranging evaluation on the performance of the proposed method, using a large dataset collected from the GeoNames gazetteer. These results show that the proposed method can significantly outperform individual similarity metrics from previous studies, as well as previous methods based on supervised machine learning for combining multiple metrics.


title: Intrinsic and Task-Evoked Network Architectures of the Human Brain

abstract: Summary Many functional network properties of the human brain have been identified during rest and task states, yet it remains unclear how the two relate. We identified a whole-brain network architecture present across dozens of task states that was highly similar to the resting-state network architecture. The most frequent functional connectivity strengths across tasks closely matched the strengths observed at rest, suggesting this is an "intrinsic," standard architecture of functional brain organization. Furthermore, a set of small but consistent changes common across tasks suggests the existence of a task-general network architecture distinguishing task states from rest. These results indicate the brain's functional network architecture during task performance is shaped primarily by an intrinsic network architecture that is also present during rest, and secondarily by evoked task-general and task-specific network changes. This establishes a strong relationship between resting-state functional connectivity and task-evoked functional connectivity—areas of neuroscientific inquiry typically considered separately.


title: Effect of CYP2D6 and CYP2C9 genotypes on fluoxetine and norfluoxetine plasma concentrations during steady-state conditions

abstract: ObjectivesCYP2D6 drug-metabolising enzyme has been shown to be involved in fluoxetine metabolism in vitro and in vivo. CYP2C9 has also been shown to influence the metabolism of fluoxetine in vitro; however, this relationship has not been studied in humans. The aim of the present study was to evaluate the influence of CYP2D6 and CYP2C9 genotypes on the plasma concentration of fluoxetine and norfluoxetine in psychiatric patients during steady-state conditions.MethodsWhite European psychiatric patients (n=64) receiving antidepressant monotherapy with fluoxetine were studied. CYP2D6 and CYP2C9 genotypes were determined by polymerase chain reaction-specific methods. The plasma concentrations of fluoxetine and its metabolite, norfluoxetine, were measured by high-performance liquid chromatography.ResultsThe dose-corrected plasma concentrations of fluoxetine were related (P<0.01, r=−0.36) to CYP2D6 genotypes (number of active genes). The fluoxetine/norfluoxetine ratio also correlated (P<0.01, r=−0.39) with the number of active CYP2D6 genes. Among patients with two CYP2D6 active genes, the dose-corrected plasma concentrations of fluoxetine and active moiety (fluoxetine plus norfluoxetine) were significantly (P<0.05) higher in the CYP2C9*1/*2 and CYP2C9*1/*3 genotype groups than in CYP2C9*1/*1. However, dose-corrected (C/D) plasma concentrations of fluoxetine, active moiety and fluoxetine/norfluoxetine ratios were not highly different in the individuals with two mutated alleles as compared with those heterozygous for *2 or *3.ConclusionThe present results show that CYP2D6 and potentially CYP2C9 genotypes seem to influence fluoxetine plasma concentration during steady-state conditions in patients.


EYE CONTROLLED WHEELCHAIR BASED ON ARDUINO CIRCUIT


title: Increasing consumption of ultra-processed foods and likely impact on human health: evidence from Brazil.

abstract: OBJECTIVE ::: To assess time trends in the contribution of processed foods to food purchases made by Brazilian households and to explore the potential impact on the overall quality of the diet. ::: ::: ::: DESIGN ::: Application of a new classification of foodstuffs based on extent and purpose of food processing to data collected by comparable probabilistic household budget surveys. The classification assigns foodstuffs to the following groups: unprocessed/minimally processed foods (Group 1); processed culinary ingredients (Group 2); or ultra-processed ready-to-eat or ready-to-heat food products (Group 3). ::: ::: ::: SETTING ::: Eleven metropolitan areas of Brazil. ::: ::: ::: SUBJECTS ::: Households; n 13,611 in 1987-8, n 16,014 in 1995-5 and n 13,848 in 2002-3. ::: ::: ::: RESULTS ::: Over the last three decades, the household consumption of Group 1 and Group 2 foods has been steadily replaced by consumption of Group 3 ultra-processed food products, both overall and in lower- and upper-income groups. In the 2002-3 survey, Group 3 items represented more than one-quarter of total energy (more than one-third for higher-income households). The overall nutrient profile of Group 3 items, compared with that of Group 1 and Group 2 items, revealed more added sugar, more saturated fat, more sodium, less fibre and much higher energy density. ::: ::: ::: CONCLUSIONS ::: The high energy density and the unfavourable nutrition profiling of Group 3 food products, and also their potential harmful effects on eating and drinking behaviours, indicate that governments and health authorities should use all possible methods, including legislation and statutory regulation, to halt and reverse the replacement of minimally processed foods and processed culinary ingredients by ultra-processed food products.


title: Toward Brief “Red Flags” for Autism Screening: The Short Autism Spectrum Quotient and the Short Quantitative Checklist in 1,000 Cases and 3,000 Controls

abstract: Objective Frontline health professionals need a "red flag" tool to aid their decision making about whether to make a referral for a full diagnostic assessment for an autism spectrum condition (ASC) in children and adults. The aim was to identify 10 items on the Autism Spectrum Quotient (AQ) (Adult, Adolescent, and Child versions) and on the Quantitative Checklist for Autism in Toddlers (Q-CHAT) with good test accuracy. Method: A case sample of more than 1,000 individuals with ASC (449 adults, 162 adolescents, 432 children and 126 toddlers) and a control sample of 3,000 controls (838 adults, 475 adolescents, 940 children, and 754 toddlers) with no ASC diagnosis participated. Case participants were recruited from the Autism Research Centre's database of volunteers. The control samples were recruited through a variety of sources. Participants completed full-length versions of the measures. The 10 best items were selected on each instrument to produce short versions. Results: At a cut-point of 6 on the AQ-10 adult, sensitivity was 0.88, specificity was 0.91, and positive predictive value (PPV) was 0.85. At a cut-point of 6 on the AQ-10 adolescent, sensitivity was 0.93, specificity was 0.95, and PPV was 0.86. At a cut-point of 6 on the AQ-10 child, sensitivity was 0.95, specificity was 0.97, and PPV was 0.94. At a cut-point of 3 on the Q-CHAT-10, sensitivity was 0.91, specificity was 0.89, and PPV was 0.58. Internal consistency was >0.85 on all measures. Conclusions: The short measures have potential to aid referral decision making for specialist assessment and should be further evaluated.


State of the art in Turkish Named Entity Recognition 1


title: Social Links from Latent Topics in Microblogs

abstract: Language use is overlaid on a network of social connections, which exerts an influence on both the topics of discussion and the ways that these topics can be expressed (Halliday, 1978). In the past, efforts to understand this relationship were stymied by a lack of data, but social media offers exciting new opportunities. By combining large linguistic corpora with explicit representations of social network structures, social media provides a new window into the interaction between language and society. Our long term goal is to develop joint sociolinguistic models that explain the social basis of linguistic variation.


title: Representation Learning of Drug and Disease Terms for Drug Repositioning

abstract: Drug repositioning (DR) refers to identification of novel indications for the approved drugs. The requirement of huge investment of time as well as money and risk of failure in clinical trials have led to surge in interest in drug repositioning. DR exploits two major aspects associated with drugs and diseases: existence of similarity among drugs and among diseases due to their shared involved genes or pathways or common biological effects. Existing methods of identifying drug-disease association majorly rely on the information available in the structured databases only. On the other hand, abundant information available in form of free texts in biomedical research articles are not being fully exploited. Word-embedding or obtaining vector representation of words from a large corpora of free texts using neural network methods have been shown to give significant performance for several natural language processing tasks. In this work we propose a novel way of representation learning to obtain features of drugs and diseases by combining complementary information available in unstructured texts and structured datasets. Next we use matrix completion approach on these feature vectors to learn projection matrix between drug and disease vector spaces. The proposed method has shown competitive performance with state-of-the-art methods. Further, the case studies on Alzheimer's and Hypertension diseases have shown that the predicted associations are matching with the existing knowledge.


title: Openfst: a general and efficient weighted finite-state transducer library

abstract: We describe OpenFst, an open-source library for weighted finite-state transducers (WFSTs). OpenFst consists of a C++ template library with efficient WFST representations and over twenty-five oper- ations for constructing, combining, optimizing, and searching them. At the shell-command level, there are corresponding transducer file repre- sentations and programs that operate on them. OpenFst is designed to be both very efficient in time and space and to scale to very large problems. This library has key applications speech, image, and natural language processing, pattern and string matching, and machine learning. We give an overview of the library, examples of its use, details of its design that allow customizing the labels, states, and weights and the lazy evaluation of many of its operations. Further information and a download of the OpenFst library can be obtained from http://www.openfst.org.


title: Effect of plant growth-promoting bacteria on growth and nutrient uptake of cotton and pea in a semi-arid region of Uzbekistan

abstract: Plant growth-promoting bacteria isolated from soil of the root zone of different agricultural crops were analysed for plant growth-promoting effects and improvement of nutrient uptake on cotton and pea in a semi-arid region of Uzbekistan. The investigations were carried out in pot experiments with Calcisol soil. After inoculation with effective bacterial strains Pseudomonas alcaligenes PsA15, P. denitrificans PsD6, Bacillus polymyxa BcP26 and Mycobacterium phlei MbP18, the root and shoot growth of cotton and pea significantly increased. Bacterial inoculation also resulted in significantly higher values for N, P and K contents of plant components. The growth-promoting bacteria produced the phytohormone auxin, were capable of nitrogenase activity and reacted antagonistically to plant pathogenic fungi. Furthermore, they were characterized as salt tolerant and temperature resistant.


title: Perception of lighting and shading for animated virtual characters

abstract: The design of lighting in Computer Graphics is directly derived from cinematography, and many digital artists follow the conventional wisdom on how lighting is set up to convey drama, appeal, or emotion. In this paper, we are interested in investigating the most commonly used lighting techniques to more formally determine their effect on our perception of animated virtual characters. Firstly, we commissioned a professional animator to create a sequence of dramatic emotional sentences for a typical CG cartoon character. Then, we rendered that character using a range of lighting directions, intensities, and shading techniques. Participants of our experiment rated the emotion, the intensity of the performance, and the appeal of the character. Our results provide new insights into how animated virtual characters are perceived, when viewed under different lighting conditions.


Effects of a Pulse Electric Field on Electrofusion of Giant Unilamellar Vesicle (GUV)-Jurkat Cell


title: What May Visualization Processes Optimize?

abstract: In this paper, we present an abstract model of visualization and inference processes, and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified four levels of typical visualization components, namely disseminative, observational, analytical and model-developmental visualization. We noticed a common phenomenon at different levels of visualization, that is, the transformation of data spaces (referred to as alphabets) usually corresponds to the reduction of maximal entropy along a workflow. Based on this observation, we establish an information-theoretic measure of cost-benefit ratio that may be used as a cost function for optimizing a data visualization process. To demonstrate the validity of this measure, we examined a number of successful visualization processes in the literature, and showed that the information-theoretic measure can mathematically explain the advantages of such processes over possible alternatives.


title: Train a 3D U-Net to segment cranial vasculature in CTA volume without manual annotation

abstract: Computed tomography angiography (CTA) is now applied as the gold standard in clinical diagnosis of cranial vascular diseases. Segmenting vasculature is a critical step of computer aided diagnosis. In this paper, we adopted a deep learning network architecture 3D U-Net to segment cranial vasculature from CTA images. Different from other traditional methods that require a large amount of manual annotation for network training, we adopted the incomplete vascular segmentation automatically obtained from time-of-flight magnetic resonance angiography (TOF-MRA) volume to train a segmentation network for CTA images. Our results showed that, by carefully tuning the network parameters, relatively complete cranial vascular segmentation can be achieved from CTA volume though the training truth is under-segmented. Our method does not require any human annotation.


title: Examining a hate speech corpus for hate speech detection and popularity prediction

abstract: As research on hate speech becomes more and more relevant every day, most of it is still focused on hate speech detection. By attempting to replicate a hate speech detection experiment performed on an existing Twitter corpus annotated for hate speech, we highlight some issues that arise from doing research in the field of hate speech, which is essentially still in its infancy. We take a critical look at the training corpus in order to understand its biases, while also using it to venture beyond hate speech detection and investigate whether it can be used to shed light on other facets of research, such as popularity of hate tweets.


title: To do or to have? That is the question

abstract: We live in a world of unprecedented abundance. Although few of us can live up to the advertising slogan that invites us to “have it all,” a growing swath of the population in developed countries has more discretionary income than ever before. We devote a considerable portion of our resources to the pursuit of “the good life”— one of contentment, pleasure, and happiness. For many of us, deciding how to invest our resources to maximize happiness is a challenge: We wonder whether we are as happy as we might be, given the resources at our disposal. We wonder whether more money, more leisure, or more stuff would make us happier. These queries may not apply to everyone, of course; individuals with severely limited resources may (rightfully) worry more about satisfying basic needs such as food, shelter, and clothing (Maslow, 1943). But for the fortunate majority in developed countries who enjoy a substantial measure of discretionary income, one can ask whether there is a simple, empirically grounded strategy to guide the allocation of resources in the pursuit of happiness. The thesis examined in this article is that happiness is advanced more by allocating discretionary income toward the acquisition of life experiences than toward the acquisition of material possessions. “The good life,” in other words, may be better lived by doing things than by having things.


title: A lower limb exoskeleton control system based on steady state visual evoked potentials

abstract: Objective. We have developed an asynchronous brain–machine interface (BMI)-based lower limb exoskeleton control system based on steady-state visual evoked potentials (SSVEPs). Approach. By decoding electroencephalography signals in real-time, users are able to walk forward, turn right, turn left, sit, and stand while wearing the exoskeleton. SSVEP stimulation is implemented with a visual stimulation unit, consisting of five light emitting diodes fixed to the exoskeleton. A canonical correlation analysis (CCA) method for the extraction of frequency information associated with the SSVEP was used in combination with k-nearest neighbors. Main results. Overall, 11 healthy subjects participated in the experiment to evaluate performance. To achieve the best classification, CCA was first calibrated in an offline experiment. In the subsequent online experiment, our results exhibit accuracies of 91.3 ± 5.73%, a response time of 3.28 ± 1.82 s, an information transfer rate of 32.9 ± 9.13 bits/min, and a completion time of 1100 ± 154.92 s for the experimental parcour studied. Significance. The ability to achieve such high quality BMI control indicates that an SSVEP-based lower limb exoskeleton for gait assistance is becoming feasible.


title: Automatic boolean query suggestion for professional search

abstract: In professional search environments, such as patent search or legal search, search tasks have unique characteristics: 1) users interactively issue several queries for a topic, and 2) users are willing to examine many retrieval results, i.e., there is typically an emphasis on recall. Recent surveys have also verified that professional searchers continue to have a strong preference for Boolean queries because they provide a record of what documents were searched. To support this type of professional search, we propose a novel Boolean query suggestion technique. Specifically, we generate Boolean queries by exploiting decision trees learned from pseudo-labeled documents and rank the suggested queries using query quality predictors. We evaluate our algorithm in simulated patent and medical search environments. Compared with a recent effective query generation system, we demonstrate that our technique is effective and general.


title: A Dependency Treebank of the Quran using traditional Arabic grammar

abstract: The Quran is a significant religious text, followed by the 1.5 billion believers of the Islamic faith worldwide. The text dates to 610–632 CE and is written in Quranic Arabic, the direct ancestor language of modern standard Arabic in use today. This paper presents the Quranic Arabic Dependency Treebank (QADT) and reports on the approaches and solutions used to apply Natural Language Processing to the unique and challenging language of the Quran. This project differs from other Arabic treebanks by providing a deep computational linguistic model based on historical traditional Arabic grammar($$$$). The treebank is part of the Quranic Arabic Corpus (http://corpus.quran.com), a popular free Arabic resource developed at the University of Leeds. Motivated by the importance of the Quran as a central religious text, we also report on how online collaborative annotation was used to bring together Quranic scholars and Arabic language experts to ensure a high level of accuracy for grammatical analysis of the entire Quran.


title: Building virtualized 5G networks using open source software

abstract: The upcoming 5G mobile networks will not only bring high data rates but also deliver flexibility and adaptability, which is conveyed by the virtualization of the mobile network. Unfortunately, virtualization of mobile networks is not well understood, and the work described in this paper aims at investigating and elucidating the particular matter. As a starting point, OpenAirInterface, an open source mobile communication software is selected for first virtualization and then cloudification. In addition to the descriptions of the virtualization and cloudification, the paper also provides key findings and lessons learned of the experiments.


title: Evaluation of machine learning techniques for network intrusion detection

abstract: Network traffic anomaly may indicate a possible intrusion in the network and therefore anomaly detection is important to detect and prevent the security attacks. The early research work in this area and commercially available Intrusion Detection Systems (IDS) are mostly signature-based. The problem of signature based method is that the database signature needs to be updated as new attack signatures become available and therefore it is not suitable for the real-time network anomaly detection. The recent trend in anomaly detection is based on machine learning classification techniques. We apply seven different machine learning techniques with information entropy calculation to Kyoto 2006+ data set and evaluate the performance of these techniques. Our findings show that, for this particular data set, most machine learning techniques provide higher than 90% precision, recall and accuracy. However, using area under the Receiver Operating Curve (ROC) metric, we find that Radial Basis Function (RBF) performs the best among the seven algorithms studied in this work.


title: User-defined Interface Gestures: Dataset and Analysis

abstract: We present a video-based gesture dataset and a methodology for annotating video-based gesture datasets. Our dataset consists of user-defined gestures generated by 18 participants from a previous investigation of gesture memorability. We design and use a crowd-sourced classification task to annotate the videos. The results are made available through a web-based visualization that allows researchers and designers to explore the dataset. Finally, we perform an additional descriptive analysis and quantitative modeling exercise that provide additional insights into the results of the original study. To facilitate the use of the presented methodology by other researchers we share the data, the source of the human intelligence tasks for crowdsourcing, a new taxonomy that integrates previous work, and the source code of the visualization tool.


Grid Map based Free Space Estimation using Stereo Vision


title: Individual Voltage Balancing Strategy for PWM Cascaded H-Bridge Converter-Based STATCOM

abstract: This paper presents a new control method for cascaded connected H-bridge converter-based static compensators. These converters have classically been commutated at fundamental line frequencies, but the evolution of power semiconductors has allowed the increase of switching frequencies and power ratings of these devices, permitting the use of pulsewidth modulation techniques. This paper mainly focuses on dc-bus voltage balancing problems and proposes a new control technique (individual voltage balancing strategy), which solves these balancing problems, maintaining the delivered reactive power equally distributed among all the H-bridges of the converter.


title: Infiltratie en herverdeling van water in grond

abstract: The flow of the liquid phase trough a soil can be predicted from pressure gradients. Different ways of predicting infiltration for irrigation of a basin were compared: numerical approximation; semianalytical; and analytical. A partly emperical equation was developed for description of rate of infiltration, after examination of existing equations. Under certain condions, infiltration was influenced by under or over pressure of the trapped gas phase and by swelling of clays. Complex models for redistribution were of little value in practice, since they could not be generalized and required too much physical data about the soil. A scheme was developed that grouped techniques for estimating physical properties of soil, according to cost and expertise required.


title: Design and Demonstration of High Power Density Inverter for Aircraft Applications

abstract: This paper presents a design methodology for a high power density converter, which emphasizes weight minimization. The design methodology considers various inverter topologies and semiconductor devices with application of cold plate cooling and LCL filter. Design for a high-power inverter is evaluated with demonstration of a 50 kVA 2-level 3-phase SiC inverter operating at 60 kHz switching frequency. The prototype achieves high gravimetric power density of 6.49 kW/kg.


title: Essential oils as alternatives to antibiotics in swine production

abstract: This review article summarizes the efficacy, feasibility and potential mechanisms of the application of essential oils as antibiotic alternatives in swine production. Although there are numerous studies demonstrating that essential oils have several properties, such as antimicrobial, antioxidative and anti-inflammatory effects, feed palatability enhancement and improvement in gut growth and health, there is still a need of further investigations to elucidate the mechanisms underlying their functions. In the past, the results has been inconsistent in both laboratory and field studies because of the varied product compositions, dosages, purities and growing stages and conditions of animals. The minimal inhibitory concentration (MIC) of essential oils needed for killing enteric pathogens may not ensure the optimal feed intake and the essential oils inclusion cost may be too high in swine production. With the lipophilic and volatile nature of essential oils, there is a challenge in effective delivery of essential oils within pig gut and this challenge can partially be resolved by microencapsulation and nanotechnology. The effects of essential oils on inflammation, oxidative stress, microbiome, gut chemosensing and bacterial quorum sensing (QS) have led to better production performance of animals fed essential oils in a number of studies. It has been demonstrated that essential oils have good potential as antibiotic alternatives in feeds for swine production. The combination of different essential oils and other compounds (synergistic effect) such as organic acids seems to be a promising approach to improve the efficacy and safety of essential oils in applications. High-throughput systems technologies have been developed recently, which will allow us to dissect the mechanisms underlying the functions of essential oils and facilitate the use of essential oils in swine production.


title: Locality-sensitive Hashing without False Negatives

abstract: We consider a new construction of locality-sensitive hash functions for Hamming space that is covering in the sense that is it guaranteed to produce a collision for every pair of vectors within a given radius $r$. The construction is efficient in the sense that the expected number of hash collisions between vectors at distance $cr$, for a given $c>1$, comes close to that of the best possible data independent LSH without the covering guarantee, namely, the seminal LSH construction of Indyk and Motwani (FOCS '98). The efficiency of the new construction essentially matches their bound if $cr = \log(n)/k$, where $n$ is the number of points in the data set and $k\in {\bf N}$, and differs from it by at most a factor $\ln(4)<1.4$ in the exponent for general values of $cr$. As a consequence, LSH-based similarity search in Hamming space can avoid the problem of false negatives at little or no cost in efficiency.


title: A Novel Dual-Broadband Dual-Polarized Electrical Downtilt Base Station Antenna for 2G/3G Applications

abstract: A novel dual-broadband dual-polarized base station antenna array with compact structure and low profile is proposed in this paper for the existing mobile communication system operating over 0.79–0.96 GHz (European Digital Dividend/CDMA/GSM) and 1.71–2.17 GHz (DCS/PCS/UMTS). The antenna array is mainly composed of five lower-band elements, ten upper-band elements, some U-shaped metal baffles, and a metal reflector with specific shape. In order to reduce the overall size of the antenna array, lower-band element is designed as octagon aperture shape that upper-band elements can be embedded in it. Two kinds of radiation elements (five for each kind of element) are applied in the antenna array as upper-band elements to achieve better radiation performance. The proposed antenna array achieves electrical downtilt (0°–14° and 0°–10° at lower frequency band and upper frequency band, respectively) by adjusting input amplitude and phase of each array element. Measured results demonstrate that the antenna array has good broadside radiation characteristics, including low voltage standing wave ratio (VSWR 28 dB), low backlobe level (>25 dB), high cross-polarization discrimination (>20 dB), and stable radiation pattern with horizontal half-power beamwidth (HPBW) 65° ± 5° at both frequency bands and all electrical downtilt angles. The peak gains of 15.1 and 17.3 dBi are obtained at lower and upper bands respectively. Owing to these advantages, the antenna array is suitable for existing 2G/3G applications in modern mobile communication systems.


title: Learning concept graphs from online educational data

abstract: This paper addresses an open challenge in educational data mining, i.e., the problem of automatically mapping online courses from different providers (universities, MOOCs, etc.) onto a universal space of concepts, and predicting latent prerequisite dependencies (directed links) among both concepts and courses. We propose a novel approach for inference within and across course-level and concept-level directed graphs. In the training phase, our system projects partially observed course-level prerequisite links onto directed concept-level links; in the testing phase, the induced concept-level links are used to infer the unknown course-level prerequisite links. Whereas courses may be specific to one institution, concepts are shared across different providers. The bi-directional mappings enable our system to perform interlingua-style transfer learning, e.g. treating the concept graph as the interlingua and transferring the prerequisite relations across universities via the interlingua. Experiments on our newly collected datasets of courses from MIT, Caltech, Princeton and CMU show promising results.


title: Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration

abstract: Image restoration is a long-standing problem in low-level computer vision with many interesting applications. We describe a flexible learning framework based on the concept of nonlinear reaction diffusion models for various image restoration problems. By embodying recent improvements in nonlinear diffusion models, we propose a dynamic nonlinear reaction diffusion model with time-dependent parameters ( i.e. , linear filters and influence functions). In contrast to previous nonlinear diffusion models, all the parameters, including the filters and the influence functions, are simultaneously learned from training data through a loss based approach. We call this approach TNRD— Trainable Nonlinear Reaction Diffusion . The TNRD approach is applicable for a variety of image restoration tasks by incorporating appropriate reaction force. We demonstrate its capabilities with three representative applications, Gaussian image denoising, single image super resolution and JPEG deblocking. Experiments show that our trained nonlinear diffusion models largely benefit from the training of the parameters and finally lead to the best reported performance on common test datasets for the tested applications. Our trained models preserve the structural simplicity of diffusion models and take only a small number of diffusion steps, thus are highly efficient. Moreover, they are also well-suited for parallel computation on GPUs, which makes the inference procedure extremely fast.


title: Semisupervised Multiview Distance Metric Learning for Cartoon Synthesis

abstract: In image processing, cartoon character classification, retrieval, and synthesis are critical, so that cartoonists can effectively and efficiently make cartoons by reusing existing cartoon data. To successfully achieve these tasks, it is essential to extract visual features that comprehensively represent cartoon characters and to construct an accurate distance metric to precisely measure the dissimilarities between cartoon characters. In this paper, we introduce three visual features, color histogram, shape context, and skeleton, to characterize the color, shape, and action, respectively, of a cartoon character. These three features are complementary to each other, and each feature set is regarded as a single view. However, it is improper to concatenate these three features into a long vector, because they have different physical properties, and simply concatenating them into a high-dimensional feature vector will suffer from the so-called curse of dimensionality. Hence, we propose a semisupervised multiview distance metric learning (SSM-DML). SSM-DML learns the multiview distance metrics from multiple feature sets and from the labels of unlabeled cartoon characters simultaneously, under the umbrella of graph-based semisupervised learning. SSM-DML discovers complementary characteristics of different feature sets through an alternating optimization-based iterative algorithm. Therefore, SSM-DML can simultaneously accomplish cartoon character classification and dissimilarity measurement. On the basis of SSM-DML, we develop a novel system that composes the modules of multiview cartoon character classification, multiview graph-based cartoon synthesis, and multiview retrieval-based cartoon synthesis. Experimental evaluations based on the three modules suggest the effectiveness of SSM-DML in cartoon applications.


title: Maternal uniparental disomy of chromosome 20: a novel imprinting disorder of growth failure

abstract: PURPOSE ::: Maternal uniparental disomy of chromosome 20 (UPD(20)mat) has been reported in only four patients, three of whom also had mosaicism for complete or partial trisomy of chromosome 20. We sought to evaluate the clinical significance of isolated UPD(20)mat in eight individuals. ::: ::: ::: METHODS ::: We evaluated phenotypic and genomic findings of a series of eight new patients with UPD(20)mat. ::: ::: ::: RESULTS ::: All eight individuals with UPD(20)mat had intrauterine growth restriction, short stature, and prominent feeding difficulties with failure to thrive. As a common feature, they often required gastric tube feeds. Genomic data in most patients are indicative of UPD as a result of trisomy rescue after meiosis II nondisjunction. ::: ::: ::: CONCLUSION ::: We describe the first natural history of the disorder and the results of therapeutic interventions, including the frequent requirement of direct gastric feedings only during the first few years of life, and propose that growth hormone supplementation is probably safe and effective for this condition. We suggest that UPD(20)mat can be regarded as a new imprinting disorder and its identification requires specialized molecular testing, which should be performed in patients with early-onset idiopathic isolated growth failure.Genet Med 18 4, 309-315.


title: Rate control for communication networks: shadow

abstract: This paper analyses the stability and fairness of two classes of rate control algorithm for communication networks. The algorithms provide natural generalisations to large-scale networks of simple additive increase/multiplicative decrease schemes, and are shown to be stable about a system optimum characterised by a proportional fairness criterion. Stability is established by showing that, with an appropriate formulation of the overall optimisation problem, the network's implicit objective function provides a Lyapunov function for the dynamical system defined by the rate control algorithm. The network's optimisation problem may be cast in primal or dual form: this leads naturally to two classes of algorithm, which may be interpreted in terms of either congestion indication feedback signals or explicit rates based on shadow prices. Both classes of algorithm may be generalised to include routing control, and provide natural implementations of proportionally fair pricing.


Multi-Region Ensemble Convolutional Neural Network for High Accuracy Age Estimation


title: Learning relational object categories using behavioral exploration and multimodal perception

abstract: This paper proposes a framework for learning human-provided category labels that describe individual objects, pairwise object relationships, as well as groups of objects. The framework was evaluated using an experiment in which the robot interactively explored 36 objects that varied by color, weight, and contents. The proposed method allowed the robot not only to learn categories describing individual objects, but also to learn categories describing pairs and groups of objects with high recognition accuracy. Furthermore, by grounding the category representations in its own sensorimotor repertoire, the robot was able to estimate how similar two categories are in terms of the behaviors and sensory modalities that are used to recognize them. Finally, this grounded measure of similarity enabled the robot to boost its recognition performance when learning a new category by relating it to a set of familiar categories.


title: Structure-Guided Statistical Textural Distinctiveness for Salient Region Detection in Natural Images

abstract: We propose a simple yet effective structure-guided statistical textural distinctiveness approach to salient region detection. Our method uses a multilayer approach to analyze the structural and textural characteristics of natural images as important features for salient region detection from a scale point of view. To represent the structural characteristics, we abstract the image using structured image elements and extract rotational-invariant neighborhood-based textural representations to characterize each element by an individual texture pattern. We then learn a set of representative texture atoms for sparse texture modeling and construct a statistical textural distinctiveness matrix to determine the distinctiveness between all representative texture atom pairs in each layer. Finally, we determine saliency maps for each layer based on the occurrence probability of the texture atoms and their respective statistical textural distinctiveness and fuse them to compute a final saliency map. Experimental results using four public data sets and a variety of performance evaluation metrics show that our approach provides promising results when compared with existing salient region detection approaches.


title: Acoustic-to-word model without OOV

abstract: Recently, the acoustic-to-word model based on the Connectionist Temporal Classification (CTC) criterion was shown as a natural end-to-end model directly targeting words as output units. However, this type of word-based CTC model suffers from the out-of-vocabulary (OOV) issue as it can only model limited number of words in the output layer and maps all the remaining words into an OOV output node. Therefore, such word-based CTC model can only recognize the frequent words modeled by the network output nodes. It also cannot easily handle the hot-words which emerge after the model is trained. In this study, we improve the acoustic-to-word model with a hybrid CTC model which can predict both words and characters at the same time. With a shared-hidden-layer structure and modular design, the alignments of words generated from the word-based CTC and the character-based CTC are synchronized. Whenever the acoustic-to-word model emits an OOV token, we back off that OOV segment to the word output generated from the character-based CTC, hence solving the OOV or hot-words issue. Evaluated on a Microsoft Cortana voice assistant task, the proposed model can reduce the errors introduced by the OOV output token in the acoustic-to-word model by 30%.


The Effects of Praise on Children ’ s Intrinsic Motivation : A Review and Synthesis


title: Distributed Localization Scheme for Mobile Sensor Networks

abstract: Localization is an essential and important research issue in wireless sensor networks (WSNs). Most localization schemes focus on static sensor networks. However, mobile sensors are required in some applications such that the sensed area can be enlarged. As such, a localization scheme designed for mobile sensor networks is necessary. In this paper, we propose a localization scheme to improve the localization accuracy of previous work. In this proposed scheme, the normal nodes without location information can estimate their own locations by gathering the positions of location-aware nodes (anchor nodes) and the one-hop normal nodes whose locations are estimated from the anchor nodes. In addition, we propose a scheme that predicts the moving direction of sensor nodes to increase localization accuracy. Simulation results show that the localization error in our proposed scheme is lower than the previous schemes in various mobility models and moving speeds.


title: Faults in linux: ten years later

abstract: In 2001, Chou et al. published a study of faults found by applying a static analyzer to Linux versions 1.0 through 2.4.1. A major result of their work was that the drivers directory contained up to 7 times more of certain kinds of faults than other directories. This result inspired a number of development and research efforts on improving the reliability of driver code. Today Linux is used in a much wider range of environments, provides a much wider range of services, and has adopted a new development and release model. What has been the impact of these changes on code quality? Are drivers still a major problem? To answer these questions, we have transported the experiments of Chou et al. to Linux versions 2.6.0 to 2.6.33, released between late 2003 and early 2010. We find that Linux has more than doubled in size during this period, but that the number of faults per line of code has been decreasing. And, even though drivers still accounts for a large part of the kernel code and contains the most faults, its fault rate is now below that of other directories, such as arch (HAL) and fs (file systems). These results can guide further development and research efforts. To enable others to continually update these results as Linux evolves, we define our experimental protocol and make our checkers and results available in a public archive.


title: Design optimization of a cable-driven two-DOF joint module with a flexible backbone

abstract: A cable-driven snake-like robot arm (CDSLRA) with a flexible backbone possesses a number of promising advantages over the conventional rigid-link manipulators, such as lightweight mechanical structure, large reachable workspace, and high maneuverability. In general, a CDSLRA is a hyper-redundant manipulator that consists of a large number of active segments. As the basic building block of a CDSLRA, a 2-DOF cable-driven joint module with a flexible backbone is proposed. This paper focuses on the kinematics, kinetostatics and design optimization of the 2-DOF flexible joint module. Based on the 2-DOF joint module design as well as its resultant motion characteristics, the concept of instantaneous screw axis is proposed to formulate the kinematic model of the 2-DOF joint module, in which the Product-Of-Exponentials (POE) formula is employed. In order to generate the feasible workspace subject to the positive tension constraint, the kinetostatics of the 2-DOF cable-driven joint module is addressed, where the stiffness resulting from both the driving cables and the flexible backbone are considered. A numerical orientation workspace evaluation method is proposed based on an equi-volumetric partition in its parametric space and the volume-element associated integral factor. A multi-performance index, which takes both the stiffness condition index and the workspace volume into account, is employed to optimize the geometric size of the joint module. The simulation results demonstrate the effectiveness of the proposed multi-performance optimization algorithm.


title: Intelligent video surveillance for monitoring fall detection of elderly in home environments

abstract: Video surveillance is an omnipresent topic when it comes to enhancing security and safety in the intelligent home environments. In this paper, we propose a novel method to detect various posture-based events in a typical elderly monitoring application in a home surveillance scenario. These events include normal daily life activities, abnormal behaviors and unusual events. Due to the fact that falling and its physical-psychological consequences in the elderly are a major health hazard, we monitor human activities with a particular interest to the problem of fall detection. Combination of best-fit approximated ellipse around the human body, projection histograms of the segmented silhouette and temporal changes of head position, would provide a useful cue for detection of different behaviors. Extracted feature vectors are fed to a MLP neural network for precise classification of motions and determination of fall event. Reliable recognition rate of experimental results underlines satisfactory performance of our system.


title: Face Recognition Using SIFT- PCA Feature Extraction and SVM Classifier

abstract: Face recognition is a biometric software application that can identify a specific individual in a digital image by analyzing and comparing pattern. In the proposed approach, Here main focus on the face recognition system. It will one of the new face recognition system based on an efficient design. This paper present proposed methodology of global thresholding technique, SIFT (Scale Invariant Feature Transform),PCA(Principal Component Analysis) and SVM (Support Vector Machine) classifier. SIFT is used for extract the feature from faces. This SIFT feature will possess strong robustness to the accessory, expression, pose, illumination variations. PCA is a standard technique use for dimensionality reduction in which face data can analyze and observation can be described by several inter-correlated dependent variables. The SVM classifiers are then applied to these extracted features to classify the input images. Global thresholding technique will used for detecting the face. So this proposed system will increases the face identification rate.


title: Effects of non-negligible cable mass on the static behavior of large workspace cable-driven parallel mechanisms

abstract: Cable-driven robots are currently extensively studied. Generally, for this type of manipulators, cables are considered to be massless and inextensible. But for large working volume applications, their mass cannot be neglected. Based on a well-known model which describes the profile of a cable under the action of its own weight, the inverse and forward kinematics of minimally constrained cable-driven manipulators can be numerically computed. This paper studies the effects of taking cable mass into account by comparison to classical massless cable model. It highlights the real effects of such a model on cable lengths to reach a given position. The effects on cable tensions are also studied.


title: Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks

abstract: We propose a method for lossy image compression based on recurrent, convolutional neural networks that outperforms BPG (4:2:0), WebP, JPEG2000, and JPEG as measured by MS-SSIM. We introduce three improvements over previous research that lead to this state-of-the-art result using a single model. First, we modify the recurrent architecture to improve spatial diffusion, which allows the network to more effectively capture and propagate image information through the network's hidden state. Second, in addition to lossless entropy coding, we use a spatially adaptive bit allocation algorithm to more efficiently use the limited number of bits to encode visually complex image regions. Finally, we show that training with a pixel-wise loss weighted by SSIM increases reconstruction quality according to multiple metrics. We evaluate our method on the Kodak and Tecnick image sets and compare against standard codecs as well as recently published methods based on deep neural networks.


title: High switching performance of 1.7kV, 50A SiC power MOSFET over Si IGBT for advanced power conversion applications

abstract: Silicon Carbide (SiC) has wider band gap compared to Silicon (Si) and hence MOSFET made in SiC has considerably lower drift region resistance, which is a significant resistive component in high-voltage power devices. Due to low on-state resistance combined with its inherently low switching loss, SiC MOSFET is an excellent candidate for high power converter design. With its lower power loss and operation capability at higher switching frequency, power converters based on SiC MOSFETs can offer much improved efficiency and compact size compared to those using Si IGBTs. In this paper, we report switching performance of a new 1.7kV, 50A SiC MOSFET; designed and developed by Cree, Inc. Hard-switching losses of the SiC MOSFETs with different circuit parameters and operating conditions are measured and compared with the 1.7kV, 50A Si IGBTs, using the same test setup. Switching performance of the 1.7kV SiC MOSFET and 1.7kV SiC Schottky diode connected in series are also evaluated under a zero current switching (ZCS) condition and important findings are reported.


title: A Study on Content Delivery Strategy for QoE Enhancement in P2P IPTV

abstract: Nowadays the peer-to-peer (P2P) systems have been widely deployed for the Internet television and live streaming. The real-time P2P service features advantages of scalability and heterogeneity in the existing network without modifying the underlay infrastructure. However, most of the common IPTV applications did not consider the user's surfing behavior which causes great burden to the IPTV streaming system. In addition, the high churn rate and insufficient upload capacity may lead to the unstable playback smoothness, and result in a poor quality of experience (QoE). In this paper, we propose a QoE-aware content delivery mechanism to distribute the important and instant data first. I-frame chunks near playback deadline are assigned the top priority. We discuss the comparison of overlay performance and demonstrate that the proposed scheme is workable via a series of experiments on OMNeT++.


The Fifth PASCAL Recognizing Textual Entailment Challenge


title: Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational Applications

abstract: The study of evaluation, affect, and subjectivity is a multidisciplinary enterprise, including sociology, psychology, economics, linguistics, and computer science. A number of excellent computational linguistics and linguistic surveys of the field exist. Most surveys, however, do not bring the two disciplines together to show how methods from linguistics can benefit computational sentiment analysis systems. In this survey, we show how incorporating linguistic insights, discourse information, and other contextual phenomena, in combination with the statistical exploitation of data, can result in an improvement over approaches that take advantage of only one of these perspectives. We first provide a comprehensive introduction to evaluative language from both a linguistic and computational perspective. We then argue that the standard computational definition of the concept of evaluative language neglects the dynamic nature of evaluation, in which the interpretation of a given evaluation depends on linguistic and extra-linguistic contextual factors. We thus propose a dynamic definition that incorporates update functions. The update functions allow for different contextual aspects to be incorporated into the calculation of sentiment for evaluative words or expressions, and can be applied at all levels of discourse. We explore each level and highlight which linguistic aspects contribute to accurate extraction of sentiment. We end the review by outlining what we believe the future directions of sentiment analysis are, and the role that discourse and contextual information need to play.


title: From markers to genome-based breeding in wheat

abstract: Key message ::: Recent technological advances in wheat genomics provide new opportunities to uncover genetic variation in traits of breeding interest and enable genome-based breeding to deliver wheat cultivars for the projected food requirements for 2050.


title: Bilateral brain processes for comprehending natural language

abstract: Comprehension of natural language – stories, conversations, text – is very simple for those doing the comprehending and very complex for cognitive neuroscientists. It also presents a paradox: the advantage of the left hemisphere (LH) for most language tasks is one of the best-established facts about the brain; yet, when it comes to comprehending complex, natural language, the right hemisphere (RH) might play an important role. Accumulated evidence from neuropsychology, neuroimaging, and neuroanatomy suggests at least three roughly separable (but highly interactive) components of semantic processing. Each process in turn has bilateral components, with the RH component performing coarser computations for the same general process. Examining asymmetrical brain and cognitive functions provides a unique opportunity for understanding the neural basis of complex cognition.


title: Enhancing community detection using a network weighting strategy

abstract: A community within a network is a group of vertices densely connected to each other but less connected to the vertices outside. The problem of detecting communities in large networks plays a key role in a wide range of research areas, e.g. Computer Science, Biology and Sociology. Most of the existing algorithms to find communities count on the topological features of the network and often do not scale well on large, real-life instances. In this article we propose a strategy to enhance existing community detection algorithms by adding a pre-processing step in which edges are weighted according to their centrality, w.r.t. the network topology. In our approach, the centrality of an edge reflects its contribute to making arbitrary graph transversals, i.e., spreading messages over the network, as short as possible. Our strategy is able to effectively complements information about network topology and it can be used as an additional tool to enhance community detection. The computation of edge centralities is carried out by performing multiple random walks of bounded length on the network. Our method makes the computation of edge centralities feasible also on large-scale networks. It has been tested in conjunction with three state-of-the-art community detection algorithms, namely the Louvain method, COPRA and OSLOM. Experimental results show that our method raises the accuracy of existing algorithms both on synthetic and real-life datasets.


title: Terrain RL Simulator

abstract: We provide $89$ challenging simulation environments that range in difficulty. The difficulty of solving a task is linked not only to the number of dimensions in the action space but also to the size and shape of the distribution of configurations the agent experiences. Therefore, we are releasing a number of simulation environments that include randomly generated terrain. The library also provides simple mechanisms to create new environments with different agent morphologies and the option to modify the distribution of generated terrain. We believe using these and other more complex simulations will help push the field closer to creating human-level intelligence.


title: 360-Degree Virtual-Reality Cameras for the Masses

abstract: To make VR cameras more accessible to the public, devices must be affordable, portable, reliable, high quality, and user friendly. In this article, we describe the challenges in meeting these goals and the techniques that Kandao--a VR startup company based in China--used to conquer them when designing its Obsidian cameras.


title: Balancing acts: walking the Agile tightrope

abstract: Self-organizing teams are one of the critical success factors on Agile projects - and yet, little is known about the self-organizing nature of Agile teams and the challenges they face in industrial practice. Based on a Grounded Theory study of 40 Agile practitioners across 16 software development organizations in New Zealand and India, we describe how self-organizing Agile teams perform balancing acts between (a) freedom and responsibility (b) cross-functionality and specialization, and (c) continuous learning and iteration pressure, in an effort to maintain their self-organizing nature. We discuss the relationship between these three balancing acts and the fundamental conditions of self-organizing teams - autonomy, cross-fertilization, and self-transcendence.


title: Solving the Periodic Timetabling Problem using a Genetic Algorithm

abstract: In railway operations, a timetable is established to determine the departure and arrival times for the trains or other rolling stock at the different stations or relevant points inside the rail network or a subset of this network. The elaboration of this timetable is done to respond to the commercial requirements for both passenger and freight traffic, but also it must respect a set of security and capacity constraints associated with the railway network, rolling stock and legislation. Combining these requirements and constraints, as well as the important number of trains and schedules to plan, makes the preparation of a feasible timetable a complex and time-consuming process, that normally takes several months to be completed. This article addresses the problem of generating periodic timetables, which means that the involved trains operate in a recurrent pattern. For instance, the trains belonging to the same train line, depart from some station every 15 minutes or one hour. To tackle the problem, we present a constraint-based model suitable for this kind of problem. Then, we propose a genetic algorithm, allowing a rapid generation of feasible periodic timetables. Finally, two case studies are presented, the first, describing a sub-set of the Netherlands rail network, and the second a large portion of the Nord-pas-de-Calais regional rail network, both of them are then solved using our algorithm and the results are presented and discussed.