title: Regularity and Conformity: Location Prediction Using Heterogeneous Mobility Data

abstract: Mobility prediction enables appealing proactive experiences for location-aware services and offers essential intelligence to business and governments. Recent studies suggest that human mobility is highly regular and predictable. Additionally, social conformity theory indicates that people's movements are influenced by others. However, existing approaches for location prediction fail to organically combine both the regularity and conformity of human mobility in a unified model, and lack the capacity to incorporate heterogeneous mobility datasets to boost prediction performance. To address these challenges, in this paper we propose a hybrid predictive model integrating both the regularity and conformity of human mobility as well as their mutual reinforcement. In addition, we further elevate the predictive power of our model by learning location profiles from heterogeneous mobility datasets based on a gravity model. We evaluate the proposed model using several city-scale mobility datasets including location check-ins, GPS trajectories of taxis, and public transit data. The experimental results validate that our model significantly outperforms state-of-the-art approaches for mobility prediction in terms of multiple metrics such as accuracy and percentile rank. The results also suggest that the predictability of human mobility is time-varying, e.g., the overall predictability is higher on workdays than holidays while predicting users' unvisited locations is more challenging for workdays than holidays.


title: On the difficulty of training Recurrent Neural Networks

abstract: There are two widely known issues with properly training recurrent neural networks, the vanishing and the exploding gradient problems detailed in Bengio et al. (1994). In this paper we attempt to improve the understanding of the underlying issues by exploring these problems from an analytical, a geometric and a dynamical systems perspective. Our analysis is used to justify a simple yet effective solution. We propose a gradient norm clipping strategy to deal with exploding gradients and a soft constraint for the vanishing gradients problem. We validate empirically our hypothesis and proposed solutions in the experimental section.


Human Conceptions of Spaces: Implications for Geographic Information Systems


title: A mixed reality system for virtual glasses try-on

abstract: In this paper we present an augmented reality system for automatic try-on of 3D virtual eyeglasses. The user can select from various virtual models of eyeglasses for trying-on and the system will automatically fit the selected virtual glasses on the user's face. The user can see his/her face as in a mirror with the 3D virtual glasses fitted on it. We also propose a method for handling the occlusion problem, to display only those parts of the glasses that are not occluded by the face. This system can be used for online shopping, or short listing a large set of available models to a few before physical try-on at a retailer's site.


title: Morphological Analysis for Japanese Noisy Text based on Character-level and Word-level Normalization

abstract: Social media texts are often written in a non-standard style and include many lexical variants such as insertions, phonetic substitutions, abbreviations that mimic spoken language. The normalization of such a variety of non-standard tokens is one promising solution for handling noisy text. A normalization task is very difficult to conduct in Japanese morphological analysis because there are no explicit boundaries between words. To address this issue, in this paper we propose a novel method for normalizing and morphologically analyzing Japanese noisy text. We generate both character-level and word-level normalization candidates and use discriminative methods to formulate a cost function. Experimental results show that the proposed method achieves acceptable levels in both accuracy and recall for word segmentation, POS tagging, and normalization. These levels exceed those achieved with the conventional rule-based system.


title: Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation

abstract: The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence representation learning method that can integrate with any existing encoder-decoder dialog models for interpretable response generation. Building upon variational autoencoders (VAEs), we present two novel models, DI-VAE and DI-VST that improve VAEs and can discover interpretable semantics via either auto encoding or context predicting. Our methods have been validated on real-world dialog datasets to discover semantic representations and enhance encoder-decoder models with interpretable generation.


title: Air-gap data transmission using screen brightness modulation

abstract: Air-gap data is important for the security of computer systems. The injection of the computer virus is limited but possible, however data communication channel is necessary for the transmission of stolen data. This paper considers BFSK digital modulation applied to brightness changes of screen for unidirectional transmission of valuable data. Experimental validation and limitations of the proposed technique are provided.


title: Interaction control of an UAV endowed with a manipulator

abstract: In this paper, we present the design, simulation and experimental validation of a control architecture for an unmanned aerial vehicle endowed with a manipulation system and interacting with a remote environment. The goal of this work is to show that the interaction control allows the manipulator to track a desired force, normal to a vertical wall, while still maintaining the possibility of moving on the wall. The control strategy has been implemented and validated in simulations and experiments on the manipulator standalone, i.e., attached to a fixed base, and on the manipulator attached to the aerial vehicle.


title: The clipmap: a virtual mipmap

abstract: We describe the clipmap, a dynamic texture representation that efficiently caches textures of arbitrarily large size in a finite amount of physical memory for rendering at real-time rates. Further, we describe a software system for managing clipmaps that supports integration into demanding real-time applications. We show the scale and robustness of this integrated hardware/software architecture by reviewing an application virtualizing a 170 gigabyte texture at 60 Hertz. Finally, we suggest ways that other rendering systems may exploit the concepts underlying clipmaps to solve related problems. CR


title: Insulin Pump Therapy Guidelines for Successful Outcomes

abstract: Purpose Current forms of insulin delivery used in the treatment of diabetes mellitus (diabetes) include syringes, pens, and insulin pumps. Technical advantages of insulin pump therapy, or continuous subcutaneous insulin delivery (CSII), include precise and flexible insulin dosing. In the context of intensive diabetes management, insulin pumps can facilitate improved long-term glycemic control and reduce the risk for diabetes complications, with improved lifestyle flexibility for patients and their families. Comprehensive patient education, carbohydrate counting, and frequent self-monitoring of blood glucose or continuous glucose monitoring are necessary components of successful insulin pump therapy. Technological advances have increased the appeal of pump therapy to patients and clinicians. Physically, current insulin pumps are discreet, ergonomic, and water resistant. Meanwhile, software improvements have yielded smart pumps with features that support pump users in their daily diabetes management. Robust...


title: Deformable Shape Completion with Graph Convolutional Autoencoders

abstract: The availability of affordable and portable depth sensors has made scanning objects and people simpler than ever. However, dealing with occlusions and missing parts is still a significant challenge. The problem of reconstructing a (possibly non-rigidly moving) 3D object from a single or multiple partial scans has received increasing attention in recent years. In this work, we propose a novel learning-based method for the completion of partial shapes. Unlike the majority of existing approaches, our method focuses on objects that can undergo non-rigid deformations. The core of our method is a variational autoencoder with graph convolutional operations that learns a latent space for complete realistic shapes. At inference, we optimize to find the representation in this latent space that best fits the generated shape to the known partial input. The completed shape exhibits a realistic appearance on the unknown part. We show promising results towards the completion of synthetic and real scans of human body and face meshes exhibiting different styles of articulation and partiality.


title: Optimization algorithm using scrum process

abstract: Scrum process is methodology for software development. Members in a scrum team have self-organizing team by planning and sharing knowledge. This paper introduces optimization algorithm using the population as scrum team doing the scrum process to find an optimum solution. The proposed algorithm maintains the level of exploration and the exploitation search by specific of the scrum-team. The experiment has compared the proposed approach with GA and PSO by finding an optimal solution of five numerical functions. The experiment result indicates that the proposed algorithm provides the best solution and finds the result quickly.


title: Code clones in feature-oriented software product lines

abstract: Some limitations of object-oriented mechanisms are known to cause code clones (e.g., extension using inheritance). Novel programming paradigms such as feature-oriented programming (FOP) aim at alleviating these limitations. However, it is an open issue whether FOP is really able to avoid code clones or whether it even facilitates (FOP-related) clones. To address this issue, we conduct an empirical analysis on ten feature-oriented software product lines with respect to code cloning. We found that there is a considerable number of clones in feature-oriented software product lines and that a large fraction of these clones is FOP-related (i.e., caused by limitations of feature-oriented mechanisms). Based on our results, we initiate a discussion on the reasons for FOP-related clones and on how to cope with them. We show by means of examples how such clones can be removed by applying refactorings.


Machine Vision based Fruit Classification and Grading - A Review


title: Automating High-Precision X-Ray and Neutron Imaging Applications With Robotics

abstract: Los Alamos National Laboratory and the University of Texas at Austin recently implemented a robotically controlled nondestructive testing (NDT) system for X-ray and neutron imaging. This system is intended to address the need for accurate measurements for a variety of parts and, be able to track measurement geometry at every imaging location, and is designed for high-throughput applications. This system was deployed in a beam port at a nuclear research reactor and in an operational inspection X-ray bay. The nuclear research reactor system consisted of a precision industrial seven-axis robot, 1.1-MW TRIGA research reactor, and a scintillator-mirror-camera-based imaging system. The X-ray bay system incorporated the same robot, a 225-keV microfocus X-ray source, and a custom flat panel digital detector. The robotic positioning arm is programmable and allows imaging in multiple configurations, including planar, cylindrical, as well as other user defined geometries that provide enhanced engineering evaluation capability. The imaging acquisition device is coupled with the robot for automated image acquisition. The robot can achieve target positional repeatability within 17 $\mu \text{m}$ in the 3-D space. Flexible automation with nondestructive imaging saves costs, reduces dosage, adds imaging techniques, and achieves better quality results in less time. Specifics regarding the robotic system and imaging acquisition and evaluation processes are presented. This paper reviews the comprehensive testing and system evaluation to affirm the feasibility of robotic NDT, presents the system configuration, and reviews results for both X-ray and neutron radiography imaging applications. Note to Practitioners —While looking for ways to improve throughput and increase efficiency in nondestructive imaging applications, the NonDestructive Testing and Evaluation Group at the Los Alamos National Laboratory decided to take a look at automation opportunities. Digital radiography and computed tomography are time-consuming processes, making them ideal candidates for robotic solutions. Radiography applications often require several images to be acquired from different angles and a lot of time they have to be very precise so that the feature of interest is identifiable and the resulting image meets the client’s requirements. With the robot acting as the motion control system, the imaged part can be placed directly in the beam path and oriented in six degrees of freedom. The robot can achieve significantly higher levels of precision than a human and has the ability to adjust the part while the source is active. The system also reduces levels of radiation our staff is exposed to, as the robot is setup to handle radioactive and hazardous parts. Not only does the robot move parts more precisely and with higher resolution than humans, but it also adds additional flexibility in the type and nature of images that the lab can produce. Future work will involve using this system for advanced automated scans such as achieving evenly spaced views around a sphere autonomously, since this system has not yet been used for more advanced scans beyond helical scanning. A tightly linked feedback loop between the robot and imaging code in which the imaging code would autonomously communicate to the robot what additional views are needed to reduce imaging error can also be explored.


title: DC blocking capacitor interfacing for high speed communication buses

abstract: This paper studies integration aspects of DC blocking capacitors in differential high speed bus channels. The presence of a DC blocking capacitor in the signal path can present an impedance mismatched medium which can lead to signal degradation. Integrating the capacitor to minimize impedance mismatches becomes of high importance. The differential impedance of a micro-strip differential pair is evaluated in simulation under different physical design conditions. Additionally, test vehicle measurements for a variety of capacitor test sites with different characteristics including: package size, voiding scenarios under capacitor pads/packages and wiring layers for traces to the capacitors has been designed and fabricated. The test sites are measured up to 20GHz using a VNA and their results are discussed. Also, several of the test sites are simulated in order to validate the modelling method for future design analysis purposes.


title: Multi-oriented Text Detection with Fully Convolutional Networks

abstract: In this paper, we propose a novel approach for text detection in natural images. Both local and global cues are taken into account for localizing text lines in a coarse-to-fine procedure. First, a Fully Convolutional Network (FCN) model is trained to predict the salient map of text regions in a holistic manner. Then, text line hypotheses are estimated by combining the salient map and character components. Finally, another FCN classifier is used to predict the centroid of each character, in order to remove the false hypotheses. The framework is general for handling text in multiple orientations, languages and fonts. The proposed method consistently achieves the state-of-the-art performance on three text detection benchmarks: MSRA-TD500, ICDAR2015 and ICDAR2013.


title: Power-law stochastic neighbor embedding

abstract: Stochastic neighbor embedding (SNE) aims to transform the observations in high-dimensional space into a low-dimensional space which preserves neighbor identities by minimizing the Kullback-Leibler divergence of the pairwise distributions between two spaces where Gaussian distributions are assumed. Data visualization could be improved by adopting the t-SNE where Student t distribution is used in the low-dimensional space. However, data pairs in the latent space are forced to be squeezed due to the loss of dimensions. This study incorporates the power-law distribution into construction of the p-SNE. Such an unsupervised p-SNE increases the physical forces in neighbor embedding so that the neighbors in the low-dimensional space can be adjusted flexibly to reflect the neighboring in the high-dimensional space. The experiments on three learning tasks illustrate that the manifold or data structure using the proposed p-SNE is preserved in better shape than that using SNE and t-SNE.


title: Diagnostic studies of the fetus, placenta and maternal blood from 265 bovine abortions.

abstract: During 1981, the fetus, placenta and maternal serum were received from each of 265 bovine abortions. These specimens were examined using histopathological, histochemical, bacteriological, mycological, endocrinological, immunological, serological and virological techniques. The cause of abortion was identified in 98 (37%) cases. Of these diagnosed abortions 27 (28%) were due to infection with fungi, 17 (17%) to Salmonella spp, 11 (11%) to Campylobacter fetus and 10 (10%) to Corynebacterium pyogenes infection. Of the remaining 33 (34%) diagnosed abortions 8 were due to Leptospira sp, 5 due to protozoan (probably Sarcocystis) encephalitis and 20 due to miscellaneous bacterial infections (including Brucella abortus) and fetal deformities. Bovine virus diarrhoea virus was isolated in 5 (2%) abortions, however the significance of the isolations was not known. Infectious bovine rhinotracheitis virus and chlamydia were not identified as causal agents in any abortion. In 18 (7%) abortions no definite etiologic agent was identified despite pathological findings suggestive of infection. Fetal heart blood serology and immunoglobulin values were not reliable indicators of infectious abortion. Of the remaining 149 (56%) abortions, 17% had high maternal serum cortisol levels and twin fetuses occurred in 9%.


Plant Disease Diagnosis Based on Image Processing, Appropriate for Mobile Phone Implementation


title: Randomized Derivative-Free Optimization of Noisy Convex Functions

abstract: We propose STARS, a randomized derivative-free algorithm for unconstrained optimization when the function evaluations are contaminated with random noise. STARS takes dynamic, noise-adjusted smoothing step-sizes that minimize the least-squares error between the true directional derivative of a noisy function and its finite difference approximation. We provide a convergence rate analysis of STARS for solving convex problems with additive or multiplicative noise. Experimental results show that (1) STARS exhibits noise-invariant behavior with respect to different levels of stochastic noise; (2) the practical performance of STARS in terms of solution accuracy and convergence rate is significantly better than that indicated by the theoretical result; and (3) STARS outperforms a selection of randomized zero-order methods on both additive and multiplicative-noisy functions.


title: Predicting Reinforcement of Pitch Sequences via LSTM and TD

abstract: We examine the use of a recurrent neural network called Long Short-Term Memory (LSTM) with a prediction algorithm called temporal difference (TD) to predict the outcome of a music pitch sequence, while the sequence is being played. This is part of a larger system that will use this prediction in order to choose pitches to play. We describe our previous results using the LSTM network for musical tasks and then show its ability to predict a positive or negative outcome for a short musical task of chromatic lead-in to a chord tone. Then we describe its ability to predict positive outcomes when certain chord tones are played on the last beat of each bar of ii-V-I


title: Stacked U-Nets: A No-Frills Approach to Natural Image Segmentation

abstract: Many imaging tasks require global information about all pixels in an image. Conventional bottom-up classification networks globalize information by decreasing resolution; features are pooled and downsampled into a single output. But for semantic segmentation and object detection tasks, a network must provide higher-resolution pixel-level outputs. To globalize information while preserving resolution, many researchers propose the inclusion of sophisticated auxiliary blocks, but these come at the cost of a considerable increase in network size and computational cost. This paper proposes stacked u-nets (SUNets), which iteratively combine features from different resolution scales while maintaining resolution. SUNets leverage the information globalization power of u-nets in a deeper network architectures that is capable of handling the complexity of natural images. SUNets perform extremely well on semantic segmentation tasks using a small number of parameters.


title: Low-power logic styles: CMOS versus pass-transistor logic

abstract: Recently reported logic style comparisons based on full-adder circuits claimed complementary pass-transistor logic (CPL) to be much more power-efficient than complementary CMOS. However, new comparisons performed on more efficient CMOS circuit realizations and a wider range of different logic cells, as well as the use of realistic circuit arrangements demonstrate CMOS to be superior to CPL in most cases with respect to speed, area, power dissipation, and power-delay products. An implemented 32-b adder using complementary CMOS has a power-delay product of less than half that of the CPL version. Robustness with respect to voltage scaling and transistor sizing, as well as generality and ease-of-use, are additional advantages of CMOS logic gates, especially when cell-based design and logic synthesis are targeted. This paper shows that complementary CMOS is the logic style of choice for the implementation of arbitrary combinational circuits if low voltage, low power, and small power-delay products are of concern.


title: Linear Support Tensor Machine: Pedestrian Detection in Thermal Infrared Images

abstract: Pedestrian detection in thermal infrared images poses unique challenges because of the low resolution and noisy nature of the image. Here, we propose a mid-level attribute in the form of the multidimensional template, or tensor, using local steering kernel (LSK) as low-level descriptors for detecting pedestrians in far infrared images. LSK is specifically designed to deal with intrinsic image noise and pixel level uncertainty by capturing local image geometry succinctly instead of collecting local orientation statistics (e.g., histograms in histogram of oriented gradients). In order to learn the LSK tensor, we introduce a new image similarity kernel following the popular maximum margin framework of support vector machines facilitating a relatively short and simple training phase for building a rigid pedestrian detector. Tensor representation has several advantages, and indeed, LSK templates allow exact acceleration of the sluggish but de facto sliding window-based detection methodology with multichannel discrete Fourier transform, facilitating very fast and efficient pedestrian localization. The experimental studies on publicly available thermal infrared images justify our proposals and model assumptions. In addition, the proposed work also involves the release of our in-house annotations of pedestrians in more than 17 000 frames of OSU color thermal database for the purpose of sharing with the research community.


title: Can Children Catch Curiosity from a Social Robot?

abstract: Curiosity is key to learning, yet school children show wide variability in their eagerness to acquire information. Recent research suggests that other people have a strong influence on children’s exploratory behavior. Would a curious robot elicit children’s exploration and the desire to find out new things? In order to answer this question we designed a novel experimental paradigm in which a child plays an education tablet app with an autonomous social robot, which is portrayed as a younger peer. We manipulated the robot’s behavior to be either curiosity-driven or not and measured the child’s curiosity after the interaction. We show that some of the child’s curiosity measures are significantly higher after interacting with a curious robot, compared to a non-curious one, while others do not. These results suggest that interacting with an autonomous social curious robot can selectively guide and promote children’s curiosity. Categories and Subject Descriptors K.3.1 [Computers and Education]: Computer Uses in Education; I.2.9 [Artificial Intelligence]: Robotics


Guided Local Search


title: Trevis: a context tree visualization & analysis framework and its use for classifying performance failure reports

abstract: When developers profile their applications to identify performance problems, they normally use profilers producing calling context trees. A calling context tree (CCT) represents the caller-callee relationships of a program. A dynamically collected CCT provides method coverage information. The CCTs produced by profilers also include method hotness information. Trevis, our context tree visualization and analysis framework, allows users to visualize, compare, cluster, and intersect CCTs produced by profilers. We evaluate Trevis in the context of a novel profiling tool called FlyBy. FlyBy runs transparently on an end-user's computer and continuously samples the applications' call stack. When the user perceives the application as sluggish, she presses a "Was Slow!" button to tell FlyBy to file a performance failure report. The report contains the CCT based on the call stack samples FlyBy gathered over the last few seconds before the user pressed the button. We show how Trevis allows us to visualize and classify FlyBy bug reports.


title: A double-blind randomized study assessing safety and efficacy following one-year adjunctive treatment with bitopertin, a glycine reuptake inhibitor, in Japanese patients with schizophrenia

abstract: Bitopertin, a glycine reuptake inhibitor, was investigated as a novel treatment for schizophrenia. We report all the results of a double-blind randomized study assessing safety and efficacy following 52-week adjunctive treatment with bitopertin in Japanese patients with schizophrenia. This study enrolled Japanese outpatients with schizophrenia who met criteria for either “negative symptoms”, i.e., patients with persistent, predominant negative symptoms of schizophrenia even after long-term treatment with antipsychotics or “sub-optimally controlled symptoms”, i.e., patients with insufficiently improved symptoms of schizophrenia even after long-term treatment with antipsychotics, respectively. One hundred sixty-one patients were randomly assigned to receive 52-week treatments with bitopertin doses of 5, 10, or 20 mg/day at ratio of 1:5:5, where existing antipsychotics were concomitantly administered. Efficacy endpoints included Positive and Negative Syndrome Scale (PANSS), Clinical Global Impression (CGI), and Personal and Social Performance (PSP). The purpose of the present study is primarily to evaluate the safety, and secondarily to investigate the clinical efficacy of bitopertin. One hundred fourteen patients (71 %) completed 52-week treatment with bitopertin. Most of the adverse events were mild or moderate in their severity. The patients in the 20-mg group experienced more adverse events than the patients in the other two groups. Common dose-dependent adverse events were somnolence and insomnia associated with worsening schizophrenia. The blood hemoglobin levels gradually decreased from baseline in a dose-dependent manner, but there were no patients with the decrease below 10 g/dL that would have led to their discontinuation. All the efficacy endpoints gradually improved in all the treatment groups for both of the two symptoms, while there were no clear differences among the three dose groups. Altogether, bitopertin was found to be generally safe and well-tolerated for the treatment of patients with schizophrenia. All three bitopertin treated groups showed improvements in all the efficacy endpoints for both of the two symptoms, i.e., “negative symptoms” and “sub-optimally controlled symptoms”, throughout the duration of the study. Japan Pharmaceutical Information Center, number JapicCTI-111627 (registered on September 20, 2011)


title: Optimal Design of Energy-Efficient Multi-User MIMO Systems: Is Massive MIMO the Answer?

abstract: Assume that a multi-user multiple-input multiple-output (MIMO) system is designed from scratch to uniformly cover a given area with maximal energy efficiency (EE). What are the optimal number of antennas, active users, and transmit power? The aim of this paper is to answer this fundamental question. We consider jointly the uplink and downlink with different processing schemes at the base station and propose a new realistic power consumption model that reveals how the above parameters affect the EE. Closed-form expressions for the EE-optimal value of each parameter, when the other two are fixed, are provided for zero-forcing (ZF) processing in single-cell scenarios. These expressions prove how the parameters interact. For example, in sharp contrast to common belief, the transmit power is found to increase (not to decrease) with the number of antennas. This implies that energy-efficient systems can operate in high signal-to-noise ratio regimes in which interference-suppressing signal processing is mandatory. Numerical and analytical results show that the maximal EE is achieved by a massive MIMO setup wherein hundreds of antennas are deployed to serve a relatively large number of users using ZF processing. The numerical results show the same behavior under imperfect channel state information and in symmetric multi-cell scenarios.


title: A measure of similarity between graph vertices

abstract: We introduce a concept of similarity between vertices of directed graphs. Let G_A and G_B be two directed graphs. We define a similarity matrix whose (i, j)-th real entry expresses how similar vertex j (in G_A) is to vertex i (in G_B. The similarity matrix can be obtained as the limit of the normalized even iterates of a linear transformation. In the special case where G_A=G_B=G, the matrix is square and the (i, j)-th entry is the similarity score between the vertices i and j of G. We point out that Kleinberg's "hub and authority" method to identify web-pages relevant to a given query can be viewed as a special case of our definition in the case where one of the graphs has two vertices and a unique directed edge between them. In analogy to Kleinberg, we show that our similarity scores are given by the components of a dominant eigenvector of a non-negative matrix. Potential applications of our similarity concept are numerous. We illustrate an application for the automatic extraction of synonyms in a monolingual dictionary.


MANAGING HEALTH CARE SUPPLY CHAIN : TRENDS , ISSUES , AND SOLUTIONS FROM A LOGISTICS PERSPECTIVE


title: Knowledge-Based Question Answering as Machine Translation

abstract: A typical knowledge-based question answering (KB-QA) system faces two challenges: one is to transform natural language questions into their meaning representations (MRs); the other is to retrieve answers from knowledge bases (KBs) using generated MRs. Unlike previous methods which treat them in a cascaded manner, we present a translation-based approach to solve these two tasks in one unified framework. We translate questions to answers based on CYK parsing. Answers as translations of the span covered by each CYK cell are obtained by a question translation method, which first generates formal triple queries as MRs for the span based on question patterns and relation expressions, and then retrieves answers from a given KB based on triple queries generated. A linear model is defined over derivations, and minimum error rate training is used to tune feature weights based on a set of question-answer pairs. Compared to a KB-QA system using a state-of-the-art semantic parser, our method achieves better results.


title: Rabies and its Folk Drugs Remedies in Ethiopia: A Review

abstract: 2 Abstract: Rabies, the most fatal of all infectious diseases, remains a major public health problem and causes severe economic impact in many developing countries especially in Ethiopia regardless of the availability of effective vaccines for its treatment. It was assumed that 10,000 persons per annum died of rabies in Ethiopia in which most fatal cases reported in children under age of 14 years. Animal rabies highest occurrence was at cold season (June to September). Where rabies vaccines are physical inaccessible and economically unaffordable, folk drugs (traditional herbal medicine) were options for rabies treatment in different parts of Ethiopia. About twelve traditional antirabies plants in different ethnic groups of Ethiopia were reported by different investigators for the treatment of rabies in animals and humans. A few efficacy trials of these folk drugs were undertaken. Some health institute in Ethiopia assumed the usage of these antirabies folk drugs remedies associated with adverse fatal side effects and inability to cure rabies infected animals and humans.


title: A practical solution to the pervasive problems ofp values

abstract: In the field of psychology, the practice ofp value null-hypothesis testing is as widespread as ever. Despite this popularity, or perhaps because of it, most psychologists are not aware of the statistical peculiarities of thep value procedure. In particular,p values are based on data that were never observed, and these hypothetical data are themselves influenced by subjective intentions. Moreover,p values do not quantify statistical evidence. This article reviews thesep value problems and illustrates each problem with concrete examples. The three problems are familiar to statisticians but may be new to psychologists. A practical solution to thesep value problems is to adopt a model selection perspective and use the Bayesian information criterion (BIC) for statistical inference (Raftery, 1995). The BIC provides an approximation to a Bayesian hypothesis test, does not require the specification of priors, and can be easily calculated from SPSS output.


title: A high gain ultra-wideband low noise amplifier with 802.11a interference rejection

abstract: A high gain ultra-wideband (UWB) low noise amplifier (LNA) with notch filter for 5.8 GHz interference rejection is presented in this paper. For extending the bandwidth with a considerably gain flatness, the UWB LNA uses the stagger tuning technique of two stages with different resonant frequencies. The notch filter with extremely high quality factor provides high rejection ratio to eliminate the in-band interference from nearby wireless local area network systems. The proposed circuit was simulated with TSMC 0.18 μm 1P6M CMOS process technology with 1.8 V power supply. The maximum power gain is 15.43 dB. The minimum noise figure is 4.36 dB with full bandwidth of 3.1-10.6 GHz. The input reflection coefficient S11 and the output reflection coefficient S22 are both less than -10dB. The maximum interference rejection ratio (IRR) is over 44.7 dB at 5.83 GHz of interference. The chip size including I/O pads for further measurement is 0.877×0.985mm2.


title: Switching Control Technique of Phase-Shift-Controlled Full-Bridge Converter to Improve Efficiency Under Light-Load and Standby Conditions Without Additional Auxiliary Components

abstract: The main theme of this paper is to propose a switching method to reduce the loss of phase-shift-controlled full-bridge converter under light-load and standby conditions. It will be shown that the efficiency can be improved using the proposed switching control technique. The presented switching control technique controls the full-bridge converter by pulsewidth-modulated (PWM) switching mode under light-load condition and PWM switching with burst mode under standby condition. The transition point between phase-shift switching method and the proposed method is investigated and confirmed by experimental results. A field-programmable-gate-array-based digital-controlled experimental system has been set up. The specifications of the converter include: input voltage = 400 V, output voltage = 12 V, and output power = 400 W. Experimental results show that the efficiency improvement can be up to 26% under light-load condition and the standby power is less than 1 W. These results confirm the effectiveness of the proposed switching control technique.


title: Biologically vs. Logic Inspired Encoding of Facial Actions and Emotions in Video

abstract: Automatic facial expression analysis is an important aspect of human machine interaction as the face is an important communicative medium. We use our face to signal interest, disagreement, intentions or mood through subtle facial motions and expressions. Work on automatic facial expression analysis can roughly be divided into the recognition of prototypic facial expressions such as the six basic emotional states and the recognition of atomic facial muscle actions (action units, AUs). Detection of AUs rather than emotions makes facial expression detection independent of culture-dependent interpretation, reduces the dimensionality of the problem and reduces the amount of training data required. Classic psychological studies suggest that humans consciously map AUs onto the basic emotion categories using a finite number of rules. On the other hand, recent studies suggest that humans recognize emotions unconsciously with a process that is perhaps best modeled by artificial neural networks (ANNs). This paper investigates these two claims. A comparison is made between detection of emotions directly from features vs. a two-step approach where we first detect AUs and use the AUs as input to either a rulebase or an ANN to recognize emotions. The results suggest that the two-step approach is possible with a small loss of accuracy and that biologically inspired classification techniques outperform those that approach the classification problem from a logical perspective, suggesting that biologically inspired classifiers are more suitable for computer-based analysis of facial behavior than logic inspired methods


title: Terahertz Superconducting Hot-Electron Bolometer Mixers and Their Application in Radio Astronomy

abstract: We review the latest developments, research, and radioastronomy applications of hot-electron bolometer (HEB) mixers operated in the terahertz waveband. The physical principles of operation of terahertz HEB mixers are presented, their manufacturing from ultrathin NbN films, the main HEB-mixer parameters and their measurement techniques are discussed, and practical terahertz radioastronomy projects based on heterodyne receivers with HEB mixers are considered.


title: Large-Scale Hexagonal-Patterned Growth of Aligned ZnO Nanorods for Nano-optoelectronics and Nanosensor Arrays.

abstract: An effective approach is demonstrated for growing large-area, hexagonally patterned, aligned ZnO nanorods. The synthesis uses a catalyst template produced by a self-assembled monolayer of submicron spheres and guided vapor−liquid−solid (VLS) growth on a single crystal alumina substrate. The ZnO nanorods have uniform shape and length, align vertically on the substrate, and are distributed according to the pattern defined by the catalyst template. The nanorods grow along [0001] with side surfaces defined by {2110}. This approach opens the possibility of creating patterned one-dimensional nanostructures for applications as sensor arrays, piezoelectric antenna arrays, optoelectronic devices, and interconnects.


title: From Efficient Market Hypothesis To Behavioural Finance: Can Behavioural Finance Be The New Dominant Model For Investing?

abstract: The present paper reviews two fundamental investing paradigms, which have had a substantial impact on the manner investors tend to develop their own strategies. specifically, the study elaborates on efficient market hypothesis (emh), which, despite remaining most prominent and popular until the 1990s, is considered rather controversial and often disputed, and the theory of behavioural finance, which has increasingly been implemented in financial institutions. based on an extensive survey of behavioural finance and emh literature, the study demonstrates, despite any assertions, the inherent irrationality of the theory of efficient market, and discusses the potential reasons for its recent decline, arguing in favor of its replacement or co-existence with behavioural finance. in addition, the study highlights that the theory of behavioural finance, which endorses human behavioral and psychological attitudes, should become the theoretical framework for successful and profitable investing.


title: Unified motion control for dynamic quadrotor maneuvers demonstrated on slung load and rotor failure tasks

abstract: In recent years impressive results have been presented illustrating the potential of quadrotors to solve challenging tasks. Generally, the derivation of the controllers involve complex analytical manipulation of the dynamics and are very specific to the task at hand. In addition, most approaches construct a trajectory and then design a stabilizing controller in a separate step, whereas a fully optimal solution requires finding both simultaneously. In this paper, a generalized approach is presented using an iterative optimal control algorithm. A series of complex tasks are thus solved using the same algorithm without the need for manual manipulation of the system dynamics, heuristic simplifications, or manual trajectory generation. First, aggressive maneuvers are performed by requiring the quadrotor to pass with a slung load through a window not high enough for the load to pass while hanging straight down. Second, go-to-goal tasks with single and double rotor failure are demonstrated. The adaptability and applicability of this unified approach to such diverse tasks with a nonlinear, underactuated, constrained, and in the case of the slung load, hybrid quadrotor systems is thus shown.


title: A high-fat, ketogenic diet induces a unique metabolic state in mice

abstract: Ketogenic diets have been used as an approach to weight loss on the basis of the theoretical advantage of a low-carbohydrate, high-fat diet. To evaluate the physiological and metabolic effects of such diets on weight we studied mice consuming a very-low-carbohydrate, ketogenic diet (KD). This diet had profound effects on energy balance and gene expression. C57BL/6 mice animals were fed one of four diets: KD; a commonly used obesogenic high-fat, high-sucrose diet (HF); 66% caloric restriction (CR); and control chow (C). Mice on KD ate the same calories as mice on C and HF, but weight dropped and stabilized at 85% initial weight, similar to CR. This was consistent with increased energy expenditure seen in animals fed KD vs. those on C and CR. Microarray analysis of liver showed a unique pattern of gene expression in KD, with increased expression of genes in fatty acid oxidation pathways and reduction in lipid synthesis pathways. Animals made obese on HF and transitioned to KD lost all excess body weight, improved glucose tolerance, and increased energy expenditure. Analysis of key genes showed similar changes as those seen in lean animals placed directly on KD. Additionally, AMP kinase activity was increased, with a corresponding decrease in ACC activity. These data indicate that KD induces a unique metabolic state congruous with weight loss.


title: Parallel progressive photon mapping on GPUs

abstract: Accurate global illumination rendering using GPUs is gaining attention because of the highly parallel nature of global illumination algorithms. For example, computing the radiance of each pixel using path tracing is embarrassingly parallel. Some major commercial rendering software also started adopting global illumination on GPUs.


title: 10 Gbps implementation of TLS/SSL accelerator on FPGA

abstract: This paper proposes the one-chip architecture to mount all processes for TLS/SSL ciphered communication into one FPGA or ASIC, and shows the 10 Gbps implementation of low-power (23 W) TLS/SSL accelerator on 65 nm FPGA. The usage of FPGA/ASIC enables high efficient processing and low-power consumption by using parallel, optimized and pipelined processing. One-chip architecture achieves high throughput by using a switch to avoid the congestion in exchanging data between multiple processing-blocks. In this research, to reduce the circuit area in the one-chip architecture, high-efficient processing design (a parallel processing circuit shared with multiple data, and a circuit shared in transmitting and receiving) was used. In addition, to enhance the operating frequency, a switch downsized by sharing a port to exchange data with multiple blocks decreased the number of wires. By means of these designs, circuit area to implement all TLS/SSL processes was reduced to less than that of 65 nm FPGA used in this research, and 166 MHz operating frequency required to realize 10 Gbps throughput at 64-bit pipeline was achieved. In experimental evaluation using prototype, 23 W power consumption and 10 Gbps encryption throughput were achieved.


title: Towards a Biologically Plausible Backprop

abstract: This work contributes several new elements to the quest for a biologically plausible implementation of backprop in brains. We introduce a very general and abstract framework for machine learning, in which the quantities of interest are defined implicitly through an energy function. In this framework, only one kind of neural computation is involved both for the first phase (when the prediction is made) and the second phase (after the target is revealed), like the contrastive Hebbian learning algorithm in the continuous Hopfield model for example. Contrary to automatic differentiation in computational graphs (i.e. standard backprop), there is no need for special computation in the second phase of our framework. One advantage of our framework over contrastive Hebbian learning is that the second phase corresponds to only nudging the first-phase fixed point towards a configuration that reduces prediction error. In the case of a multi-layer supervised neural network, the output units are slightly nudged towards their target, and the perturbation introduced at the output layer propagates backward in the network. The signal 'back-propagated' during this second phase actually contains information about the error derivatives, which we use to implement a learning rule proved to perform gradient descent with respect to an objective cost function.


title: 3D Printing of Molecular Models

abstract: Physical models have a long and important role in exploring and communicating scientific and medical concepts and structures. One of the earliest physical scientific visualizations was the orrery, a mechanical model of the solar system, which first appeared in Greece around 150 BC. Early anatomical models, such as those made from Papier-Mache were developed in the early 19 th Century. The earliest molecular models were those produced in the mid 19 th century by chemists (such as Hoffman and Kekule) to visualize the nature of chemical composition. These models brought abstract or unseen structures into the context of human experience.


title: Comparative Effectiveness of Cancer Control and Survival after Robot‐Assisted versus Open Radical Prostatectomy

abstract: Purpose: Robot-assisted surgery has been rapidly adopted in the U.S. for prostate cancer. Its adoption has been driven by market forces and patient preference, and debate continues regarding whether it offers improved outcomes to justify the higher cost relative to open surgery. We examined the comparative effectiveness of robot-assisted vs open radical prostatectomy in cancer control and survival in a nationally representative population.Materials and Methods: This population based observational cohort study of patients with prostate cancer undergoing robot-assisted radical prostatectomy and open radical prostatectomy during 2003 to 2012 used data captured in the SEER (Surveillance, Epidemiology, and End Results)-Medicare linked database. Propensity score matching and time to event analysis were used to compare all cause mortality, prostate cancer specific mortality and use of additional treatment after surgery.Results: A total of 6,430 robot-assisted radical prostatectomies and 9,161 open radical prosta...


title: In vivo expansion of T reg cells with IL-2–mAb complexes: induction of resistance to EAE and long-term acceptance of islet allografts without immunosuppression

abstract: Via a transcription factor, Foxp3, immunoregulatory CD4+CD25+ T cells (T reg cells) play an important role in suppressing the function of other T cells. Adoptively transferring high numbers of T reg cells can reduce the intensity of the immune response, thereby providing an attractive prospect for inducing tolerance. Extending our previous findings, we describe an in vivo approach for inducing rapid expansion of T reg cells by injecting mice with interleukin (IL)-2 mixed with a particular IL-2 monoclonal antibody (mAb). Injection of these IL-2–IL-2 mAb complexes for a short period of 3 d induces a marked (>10-fold) increase in T reg cell numbers in many organs, including the liver and gut as well as the spleen and lymph nodes, and a modest increase in the thymus. The expanded T reg cells survive for 1–2 wk and are highly activated and display superior suppressive function. Pretreating with the IL-2–IL-2 mAb complexes renders the mice resistant to induction of experimental autoimmune encephalomyelitis; combined with rapamycin, the complexes can also be used to treat ongoing disease. In addition, pretreating mice with the complexes induces tolerance to fully major histocompatibility complex–incompatible pancreatic islets in the absence of immunosuppression. Tolerance is robust and the majority of grafts are accepted indefinitely. The approach described for T reg cell expansion has clinical potential for treating autoimmune disease and promoting organ transplantation.


title: HearThere: Networked Sensory Prosthetics Through Auditory Augmented Reality

abstract: In this paper we present a vision for scalable indoor and outdoor auditory augmented reality (AAR), as well as HearThere, a wearable device and infrastructure demonstrating the feasibility of that vision. HearThere preserves the spatial alignment between virtual audio sources and the user's environment, using head tracking and bone conduction headphones to achieve seamless mixing of real and virtual sounds. To scale between indoor, urban, and natural environments, our system supports multi-scale location tracking, using fine-grained (20-cm) Ultra-WideBand (UWB) radio tracking when in range of our infrastructure anchors and mobile GPS otherwise. In our tests, users were able to navigate through an AAR scene and pinpoint audio source locations down to 1m. We found that bone conduction is a viable technology for producing realistic spatial sound, and show that users' audio localization ability is considerably better in UWB coverage zones than with GPS alone. HearThere is a major step towards realizing our vision of networked sensory prosthetics, in which sensor networks serve as collective sensory extensions into the world around us. In our vision, AAR would be used to mix spatialized data sonification with distributed, livestreaming microphones. In this concept, HearThere promises a more expansive perceptual world, or umwelt, where sensor data becomes immediately attributable to extrinsic phenomena, externalized in the wearer's perception. We are motivated by two goals: first, to remedy a fractured state of attention caused by existing mobile and wearable technologies; and second, to bring the distant or often invisible processes underpinning a complex natural environment more directly into human consciousness.


title: Low Health Literacy and Health Outcomes: An Updated Systematic Review

abstract: BACKGROUND ::: Approximately 80 million Americans have limited health literacy, which puts them at greater risk for poorer access to care and poorer health outcomes. ::: ::: ::: PURPOSE ::: To update a 2004 systematic review and determine whether low health literacy is related to poorer use of health care, outcomes, costs, and disparities in health outcomes among persons of all ages. ::: ::: ::: DATA SOURCES ::: English-language articles identified through MEDLINE, CINAHL, PsycINFO, ERIC, and Cochrane Library databases and hand-searching (search dates for articles on health literacy, 2003 to 22 February 2011; for articles on numeracy, 1966 to 22 February 2011). ::: ::: ::: STUDY SELECTION ::: Two reviewers independently selected studies that compared outcomes by differences in directly measured health literacy or numeracy levels. ::: ::: ::: DATA EXTRACTION ::: One reviewer abstracted article information into evidence tables; a second reviewer checked information for accuracy. Two reviewers independently rated study quality by using predefined criteria, and the investigative team jointly graded the overall strength of evidence. ::: ::: ::: DATA SYNTHESIS ::: 96 relevant good- or fair-quality studies in 111 articles were identified: 98 articles on health literacy, 22 on numeracy, and 9 on both. Low health literacy was consistently associated with more hospitalizations; greater use of emergency care; lower receipt of mammography screening and influenza vaccine; poorer ability to demonstrate taking medications appropriately; poorer ability to interpret labels and health messages; and, among elderly persons, poorer overall health status and higher mortality rates. Poor health literacy partially explains racial disparities in some outcomes. Reviewers could not reach firm conclusions about the relationship between numeracy and health outcomes because of few studies or inconsistent results among studies. ::: ::: ::: LIMITATIONS ::: Searches were limited to articles published in English. No Medical Subject Heading terms exist for identifying relevant studies. No evidence concerning oral health literacy (speaking and listening skills) and outcomes was found. ::: ::: ::: CONCLUSION ::: Low health literacy is associated with poorer health outcomes and poorer use of health care services. ::: ::: ::: PRIMARY FUNDING SOURCE ::: Agency for Healthcare Research and Quality.


title: Harnessing the Wisdom of Crowds

abstract: We examine the negative information externality associated with herding on a crowd-based earnings forecast platform (Estimize.com). By tracking user viewing activities, we monitor the amount of information a user viewed before she makes an earnings forecast. We find the more public information viewed, the less weight she will put on her private information. While this improves the accuracy of each individual forecast, it reduces the accuracy of the consensus forecast since useful private information is prevented from entering the consensus. Predictable errors made by "influential users" early persist in the consensus forecast and result in return predictability at the earnings announcements. To address the endogeneity concerning the information acquisition choice, we collaborate with Estimize.com to run experiments where we restrict the information set on randomly selected stocks and users. The experiments confirm that "independent" forecasts lead to more accurate consensus and convince Estimize.com to switch to a "blind" platform from November 2015. Overall, our findings suggest that wisdom of crowds can be better harnessed by encouraging independent voice from the participants.


title: Broadband Micro-Coaxial Wilkinson Dividers

abstract: This paper presents several micro-coaxial broadband 2 : 1 Wilkinson power dividers operating from 2 to 22 GHz, a 11 : 1 bandwidth. Circuits are fabricated on silicon with PolyStrata technology, and are implemented with 650 mum times 400 mum air-supported micro-coaxial lines. The measured isolation between the output ports is greater than 11 dB and the return loss at each port is more than 13 dB over the entire bandwidth. The footprints of these dividers can be miniaturized due to the high isolation between adjacent coaxial lines and their tight bend radius. For higher power handling, larger lines with a cross section of 1050 mum times 850 mum are also demonstrated. The effect of mismatch at the output ports is investigated in order to find the power loss in the resistors.


title: Opportunistic error correction for mimo-ofdm: from theory to practice

abstract: Opportunistic error correction based on fountain codes is especially designed for the MIMOOFDM system. The key point of this new method is the tradeoff between the code rate of error correcting codes and the number of sub-carriers in the channel vector to be discarded. By transmitting one fountain-encoded packet over a single sub-carrier per antenna, the ADC is allowed to only take care of the sub-carriers with high energy in the channel vector. In such a case, the power in the ADC is reduced by quantizing the received signal coarsely. Correspondingly, this approach can afford higher level of noise floor than the joint coding scheme adopted by the current MIMO-OFDM system. In this paper, we evaluate its performance in the aspect of mitigating the noise and interference. At the same code rate, simulation results show that opportunistic error correction works better (i.e.requires lower SNR) than the FEC layers defined in the IEEE 802.11n standard. With respect to RCPC with interleaving, the SNR gained by opportunistic error correction decreases as the multiplexing gain increases. Furthermore, we evaluate their performance in the real world. This novel approach does not have the same SNR gain in practice as in the simulation, compared to the FEC layers in the IEEE 802.11n standard. Measurement results show that this new scheme survives in most of the channel conditions (i.e. 92%) with respect to RCPC with interleaving (i.e. 86%) and the LDPC code from the IEEE 802.11n standard (i.e. around 80%).


title: An introduction to computer science for non-majors using principles of computation

abstract: In this paper, the design and implementation of a novel introductory computer science course for non-majors is presented. This course focuses on the major contributions in computer science from the perspective of the process of computation. This course differs from most introductory courses in computer science in that it does not include programming using a computer programming language. Students focus on algorithms and the principle of computational thinking, and use a flowchart simulator to experiment with various short algorithms and build simple computer games without dealing with programming language syntax. Steadily increasing enrollments and interest from various departments on campus indicate that this course has become a successful addition to our introductory CS offerings.


title: Computational Models of Grid Cells

abstract: Grid cells are space-modulated neurons with periodic firing fields. In moving animals, the multiple firing fields of an individual grid cell form a triangular pattern tiling the entire space available to the animal. Collectively, grid cells are thought to provide a context-independent metric representation of the local environment. Since the discovery of grid cells in 2005, a number of models have been proposed to explain the formation of spatially repetitive firing patterns as well as the conversion of these signals to place signals one synapse downstream in the hippocampus. The present article reviews the most recent developments in our understanding of how grid patterns are generated, maintained, and transformed, with particular emphasis on second-generation computational models that have emerged during the past 2-3 years in response to criticism and new data.


title: Methods and uncertainties in bioclimatic envelope modelling under climate change

abstract: Potential impacts of projected climate change on biodiversity are often assessed using single-species bioclimatic 'envelope' models. Such models are a special case of species distribution models in which the current geographical distribution of species is related to climatic variables so to enable projections of distributions under future climate change scenarios. This work reviews a number of critical methodological issues that may lead to uncertainty in predictions from bioclimatic modelling. Particular attention is paid to recent developments of bioclimatic modelling that address some of these issues as well as to the topics where more progress needs to be made. Developing and applying bioclimatic models in a informative way requires good understanding of a wide range of methodologies, including the choice of modelling technique, model validation, collinearity, autocorrelation, biased sampling of explanatory variables, scaling and impacts of non- climatic factors. A key challenge for future research is integrating factors such as land cover, direct CO2 effects, biotic interactions and dispersal mechanisms into species-climate models. We conclude that, although bioclimatic envelope models have a number of important advantages, they need to be applied only when users of models have a thorough understanding of their limitations and uncertainties.


title: A Dynamic Model for Traffic Flow Prediction Using Improved DRN

abstract: Real-time traffic flow prediction can not only provide travelers with reliable traffic information so that it can save people's time, but also assist the traffic management agency to manage traffic system. It can greatly improve the efficiency of the transportation system. Traditional traffic flow prediction approaches usually need a large amount of data but still give poor performances. With the development of deep learning, researchers begin to pay attention to artificial neural networks (ANNs) such as RNN and LSTM. However, these ANNs are very time-consuming. In our research, we improve the Deep Residual Network and build a dynamic model which previous researchers hardly use. We firstly integrate the input and output of the $i^{th}$ layer to the input of the $i+1^{th}$ layer and prove that each layer will fit a simpler function so that the error rate will be much smaller. Then, we use the concept of online learning in our model to update pre-trained model during prediction. Our result shows that our model has higher accuracy than some state-of-the-art models. In addition, our dynamic model can perform better in practical applications.


title: A New Family of Software Anti-patterns: Linguistic Anti-patterns

abstract: Recent and past studies have shown that poor source code lexicon negatively affects software understand ability, maintainability, and, overall, quality. Besides a poor usage of lexicon and documentation, sometimes a software artifact description is misleading with respect to its implementation. Consequently, developers will spend more time and effort when understanding these software artifacts, or even make wrong assumptions when they use them. This paper introduces the definition of software linguistic antipatterns, and defines a family of them, i.e., those related to inconsistencies (i) between method signatures, documentation, and behavior and (ii) between attribute names, types, and comments. Whereas "design" antipatterns represent recurring, poor design choices, linguistic antipatterns represent recurring, poor naming and commenting choices. The paper provides a first catalogue of one family of linguistic antipatterns, showing real examples of such antipatterns and explaining what kind of misunderstanding they can cause. Also, the paper proposes a detector prototype for Java programs called LAPD (Linguistic Anti-Pattern Detector), and reports a study investigating the presence of linguistic antipatterns in four Java software projects.


title: Classifying mental gestures with in-ear EEG

abstract: While brain-computer interfaces (BCI) based on electroencephalography (EEG) have improved dramatically over the past five years, their inconvenient, head-worn form factor has challenged their wider adoption. In this paper, we investigate how EEG signals collected from the ear could be used for “gestural” control of a brain-computer interface (BCI). Specifically, we investigate the efficacy of a support vector classifier (SVC) in distinguishing between mental tasks, or gestures, recorded by a modified, consumer headset. We find that an SVC reaches acceptable BCI accuracy for nine of the subjects in our pool (n=12), and distinguishes at least one pair of gestures better than chance for all subjects. User surveys highlight the need for longer-term research on user attitudes toward in-ear EEG devices, for discreet, non-invasive BCIs.


title: Gaze-based password authentication through automatic clustering of gaze points

abstract: Researchers have proposed systems in which users utilize an eye tracker to enter passwords by merely looking at the proper symbols on the computer monitor in the appropriate order. This authentication method is immune to the practice of shoulder surfing: secretly observing the keystrokes of a legitimate user as he or she types a password on a keyboard. In this paper we describe the EyeDent system—in which users authenticate by looking at the symbols on an on-screen keyboard to enter their password. Existing eye-tracking based authentication systems require the user to dwell or press a trigger when looking at each symbol. Instead, in EyeDent, gaze points are automatically clustered to determine the user's selected symbols; this approach has the benefit of allowing users to authenticate at their natural speed, rather than with a fixed dwell time. Additionally, the absence of a visible trigger does not divulge the number of symbols in the password. Results from preliminary investigations indicate that quick (3 seconds for a 4 digit PIN) authentication is possible using this scheme, but more work is needed to account for calibration error, and to dynamically adapt system parameters to the characteristics of individual users.


title: Joint Subcarrier and CPU Time Allocation for Mobile Edge Computing

abstract: In mobile edge computing systems, mobile devices can offload compute-intensive tasks to a nearby cloudlet,so as to save energy and extend battery life. Unlike a fully-fledged cloud, a cloudlet is a small-scale datacenter deployed at a wireless access point, and thus is highly constrained by both radio and compute resources. We show in this paper that separately optimizing the allocation of either compute or radio resource, as most existing works did, is highly suboptimal: the congestion of compute resource leads to the waste of radio resource, and vice versa. To address this problem, we propose a joint scheduling algorithm that allocates both radio and compute resources coordinately. Specifically, we consider a cloudlet in an Orthogonal Frequency-Division Multiplexing Access (OFDMA) system with multiple mobile devices, where we study subcarrier allocation for task offloading and CPU time allocation for task execution in the cloudlet. Simulation results show that the proposed algorithm significantly outperforms per-resource optimization, accommodating more offloading requests while achieving salient energy saving.


title: CMP network-on-chip overlaid with multi-band RF-interconnect

abstract: In this paper, we explore the use of multi-band radio frequency interconnect (or RF-I) with signal propagation at the speed of light to provide shortcuts in a many core network-on-chip (NoC) mesh topology. We investigate the costs associated with this technology, and examine the latency and bandwidth benefits that it can provide. Assuming a 400 mm2 die, we demonstrate that in exchange for 0.13% of area overhead on the active layer, RF-I can provide an average 13% (max 18%) boost in application performance, corresponding to an average 22% (max 24%) reduction in packet latency. We observe that RF access points may become traffic bottlenecks when many packets try to use the RF at once, and conclude by proposing strategies that adapt RF-I utilization at runtime to actively combat this congestion.


Using Genetic Algorithm for network intrusion detection


title: Integrating Compact Printed Antennas Onto Small Diversity/MIMO Terminals

abstract: The integration of compact printed multielement antenna (MEA) systems on small diversity and multiple input multiple output (MIMO) terminal devices operating in the 5.2 GHz industrial, scientific and medical (ISM) band is presented. The investigated MEA systems comprise up to six printed elements (inverted F and Minkowski monopole antennas) and their performance is evaluated by means of the effective diversity gain (EDG) and the 1% outage MIMO capacity. The role of the propagation environment (both outdoor and indoor) on EDG is examined, proving that the uniform scenario is a good approximation to many real environments. The tradeoff study between system's performance and number of integrated antenna elements indicates that both diversity and MIMO performance saturate when placing more than five closely spaced elements. Even the least efficient 6-element system however, can be advantageously used as a reconfigurable 2-element array under the concept of receive antenna selection, since it provides significantly improved MIMO performance over a conventional 2-element fixed one. The paper concludes with a summary of useful guidelines for the MEA design optimization procedure that emanated from this study.


title: 3D Face Reconstruction with Geometry Details from a Single Image

abstract: 3D face reconstruction from a single image is a classical and challenging problem with wide applications in many areas. Inspired by recent works in face animation from RGB-D or monocular video inputs, we develop a novel method for reconstructing 3D faces from unconstrained 2D images using a coarse-to-fine optimization strategy. First, a smooth coarse 3D face is generated from an example-based bilinear face model by aligning the projection of 3D face landmarks with 2D landmarks detected from the input image. Afterward, using local corrective deformation fields, the coarse 3D face is refined using photometric consistency constraints, resulting in a medium face shape. Finally, a shape-from-shading method is applied on the medium face to recover fine geometric details. Our method outperforms the state-of-the-art approaches in terms of accuracy and detail recovery, which is demonstrated in extensive experiments using real-world models and publicly available data sets.


title: PlantGL: a Python-based geometric library for 3D plant modelling at different scales

abstract: In this paper, we present PlantGL, an open-source graphic toolkit for the creation, simulation and analysis of 3D virtual plants. This C++ geometric library is embedded in the Python language which makes it a powerful user-interactive platform for plant modeling in various biological application domains. PlantGL makes it possible to build and manipulate geometric models of plants or plant parts, ranging from tissues and organs to plant populations. Based on a scene graph augmented with primitives dedicated to plant representation, several methods are provided to create plant architectures from either field measurements or procedural algorithms. Because they are particularly useful in plant design and analysis, special attention has been paid to the definition and use of branching system envelopes. Several examples from different modelling applications illustrate how PlantGL can be used to construct, analyse or manipulate geometric models at different scales ranging from tissues to plant communities.


title: Dynamic Network Surgery for Efficient DNNs

abstract: Deep learning has become a ubiquitous technology to improve machine intelligence. However, most of the existing deep models are structurally very complex, making them difficult to be deployed on the mobile platforms with limited computational power. In this paper, we propose a novel network compression method called dynamic network surgery, which can remarkably reduce the network complexity by making on-the-fly connection pruning. Unlike the previous methods which accomplish this task in a greedy way, we properly incorporate connection splicing into the whole process to avoid incorrect pruning and make it as a continual network maintenance. The effectiveness of our method is proved with experiments. Without any accuracy loss, our method can efficiently compress the number of parameters in LeNet-5 and AlexNet by a factor of $\bm{108}\times$ and $\bm{17.7}\times$ respectively, proving that it outperforms the recent pruning method by considerable margins. Code and some models are available at this https URL


title: Visual discomfort caused by color asymmetry in 3D displays

abstract: Color asymmetry is a common phenomenon in 3D displays, which can cause serious visual discomfort. To ensure safe and comfortable stereo viewing, the color difference between the left and right eyes should not exceed a threshold value, named comfortable color difference limit (CCDL). In this paper, we have experimentally measured the CCDL for five sample color points which were selected from the 1976 CIE u'v' chromaticity diagram. By human observers viewing brief presentations of color asymmetry image pairs, a psychophysical experiment is conducted. As the color asymmetry image pairs, left and right circular patches are horizontally adjusted on image pixels with five levels of disparities: 0, ±60, ±120 arc minutes, along six color directions. The experimental results showed that CCDLs for each sample point varied with the level of disparity and color direction. The minimum of CCDL is 0.019Δu' v' , and the maximum of CCDL is 0.133 Δu' v'. The database collected in this study might help 3D system design and 3D content creation.


title: Tuning synchronization of integrate-and-fire oscillators through mobility

abstract: We analyze the emergence of synchronization in a population of moving integrate-and-fire oscillators. Oscillators, while moving on a plane, interact with their nearest neighbor upon firing time. We discover a non-monotonic dependence of the synchronization time on the velocity of the agents. Moreover, we find that mechanisms that drive synchronization are different for different dynamical regimes. We report the extreme situation where an interplay between the time-scales involved in the dynamical processes completely inhibit the achievement of a coherent state. We also provide estimators for the transitions between the different regimes.


title: Two types of dopamine neuron distinctly convey positive and negative motivational signals

abstract: The observation that midbrain dopamine neurons are activated by reward, or sensory stimuli predicting reward, has led to the hypothesis that they encode value-related signals and are inhibited by aversive events. This is now shown to be true for only a subset of dopamine neurons; by recording neuronal activity in monkeys, dopamine neurons are found to be more heterogeneous than this model would suggest.


title: Argument mining from speech: detecting claims in political debates

abstract: The automatic extraction of arguments from text, also known as argument mining, has recently become a hot topic in artificial intelligence. Current research has only focused on linguistic analysis. However, in many domains where communication may be also vocal or visual, paralinguistic features too may contribute to the transmission of the message that arguments intend to convey. For example, in political debates a crucial role is played by speech. The research question we address in this work is whether in such domains one can improve claim detection for argument mining, by employing features from text and speech in combination. To explore this hypothesis, we develop a machine learning classifier and train it on an original dataset based on the 2015 UK political elections debate.


title: Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers

abstract: In the deep neural network (DNN), the hidden layers can be considered as increasingly complex feature transformations and the final softmax layer as a log-linear classifier making use of the most abstract features computed in the hidden layers. While the loglinear classifier should be different for different languages, the feature transformations can be shared across languages. In this paper we propose a shared-hidden-layer multilingual DNN (SHL-MDNN), in which the hidden layers are made common across many languages while the softmax layers are made language dependent. We demonstrate that the SHL-MDNN can reduce errors by 3-5%, relatively, for all the languages decodable with the SHL-MDNN, over the monolingual DNNs trained using only the language specific data. Further, we show that the learned hidden layers sharing across languages can be transferred to improve recognition accuracy of new languages, with relative error reductions ranging from 6% to 28% against DNNs trained without exploiting the transferred hidden layers. It is particularly interesting that the error reduction can be achieved for the target language that is in different families of the languages used to learn the hidden layers.


title: The Fixed-Size Ordinally-Forgetting Encoding Method for Neural Network Language Models

abstract: In this paper, we propose the new fixedsize ordinally-forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence of words into a fixed-size representation. FOFE can model the word order in a sequence using a simple ordinally-forgetting mechanism according to the positions of words. In this work, we have applied FOFE to feedforward neural network language models (FNN-LMs). Experimental results have shown that without using any recurrent feedbacks, FOFE based FNNLMs can significantly outperform not only the standard fixed-input FNN-LMs but also the popular recurrent neural network (RNN) LMs.


title: Associations between Organochlorine Pesticides and Vitamin D Deficiency in the U.S. Population

abstract: Background ::: Recently low dose organochlorine (OC) pesticides have been strongly linked to various chronic diseases including diabetes and cardiovascular diseases. Both field and animal studies have suggested a possibility that persistent lipophilic chemicals like OC pesticides can cause vitamin D deficiency, but there have been no human studies of exposure to any chemical as a possible cause of vitamin D deficiency. This study was performed to examine if serum concentrations of OC pesticides were associated with serum concentrations of 25-hydroxyvitamin D (25(OH)D) in the U.S. general population. ::: Methodology/Principal Findings ::: Cross-sectional associations of serum OC pesticides with serum 25(OH)D were investigated in 1,275 subjects aged ≥20 in the National Health and Nutrition Examination Survey(NHANES), 2003–2004. We selected 7 OC pesticides detectable in ≥80% of participants. Among the 7 OC pesticides, p,p′-DDT (β = −0.022, P<0.01), p,p′-DDE (β = −0.018, P = 0.04), and β-hexachlorocyclohexane (β = −0.022, P = 0.02) showed significant inverse associations with serum 25(OH)D levels. When study subjects were stratified by age, race, and the presence of various chronic diseases, p,p′-DDT showed consistent inverse associations in all subgroups, although stronger associations tended to be observed among subjects with old age, white race, or chronic diseases. ::: Conclusion/Significance ::: The current study suggests that the background exposure to some OC pesticides leads to vitamin D deficiency in human. Considering the importance of vitamin D deficiency in the development of chronic diseases, chemical exposure as a possible cause of vitamin D deficiency should be evaluated in prospective and experimental studies.


title: Cuckoo optimized SVM for stock market prediction

abstract: Nowadays stock market is the one of the major sources of raising resources for India and is act as a key driver for economic growth of a country. The stock market forecasting is a very difficult and highly complicated task because it is affected by many factors such as economic conditions, investor's sentiments and political events etc. The stock market series are generally dynamic, nonparametric, noisy and chaotic by nature. Although many soft computing techniques have been widely used, Support Vector Machine (SVM) has gained its popularity and it has outperformed Artificial Neural Network (ANN) also. Even though SVM is novel and performs best in many applications, the practicality of SVM is impacted due to the problems of choosing suitable parameters of SVM (C, σ and e). The Cuckoo Search (CS) is based on the Swarm Intelligence optimization technique and is very simple to tune the parameters of SVM. The proposed hybrid CS-SVM technique has been proven to be able to generate better results when compared to ANN and SVM in the prediction of the stock price movement.


title: Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model

abstract: Objective ::: ::: To develop a new geometrical index that combines height, waist circumference (WC), and hip circumference (HC) and relate this index to total and visceral body fat. ::: ::: ::: ::: Design and Methods ::: ::: Subject data were pooled from three databases that contained demographic, anthropometric, dual energy X-ray absorptiometry (DXA) measured fat mass, and magnetic resonance imaging measured visceral adipose tissue (VAT) volume. Two elliptical models of the human body were developed. Body roundness was calculated from the model using a well-established constant arising from the theory. Regression models based on eccentricity and other variables were used to predict %body fat and %VAT. ::: ::: ::: ::: Results ::: ::: A body roundness index (BRI) was derived to quantify the individual body shape in a height-independent manner. Body roundness slightly improved predictions of %body fat and %VAT compared to the traditional metrics of body mass index (BMI), WC, or HC. On this basis, healthy body roundness ranges were established. An automated graphical program simulating study results was placed at http://www.pbrc.edu/bodyroundness. ::: ::: ::: ::: Conclusion ::: ::: BRI, a new shape measure, is a predictor of %body fat and %VAT and can be applied as a visual tool for health status evaluations.


title: Learning in a u-Museum: Developing a context-aware ubiquitous learning environment

abstract: Context-awareness techniques can support learners in learning without time or location constraints by using mobile devices and associated learning activities in a real learning environment. Enrichment of context-aware technologies has enabled students to learn in an environment that integrates learning resources from both the real world and the digital world. Although learning outside of the traditional classroom is an innovative teaching approach, the two main problems are the lack of proper learning strategies and the capacity to acquire knowledge on subjects effectively. To manage these problems, this study proposes a context-aware ubiquitous learning system (CAULS) based on radio-frequency identification (RFID), wireless network, embedded handheld device, and database technologies to detect and examine real-world learning behaviors of students. A case study of an aboriginal education course was conducted in classrooms and at the Atayal u-Museum in Taiwan. Participants included elementary school teachers and students. We also designed and used a questionnaire based on the Unified Theory of Acceptance and Use of Technology (UTAUT) theory to measure the willingness for adoption or usage of the proposed system. The experimental results demonstrated that this innovative approach can enhance their learning intention. Furthermore, the results of a posttest survey revealed that most students' testing scores improved significantly, further indicating the effectiveness of the CAULS.


Exploring Distributed Resource Allocation Techniques in the SLURM Job Management System


title: Research on Building Air Detecting System Based on ZigBee Wireless Sensor Network

abstract: According to the harm and influence of building air quality on human beings,a building air detecting system based on ZigBee wireless sensor network is designed,this system includes each module software and hardware design and can detect formaldehyde,benzene,sulfur dioxide,carbon dioxide,ammonia and nitrogen-oxygen compound and so on which are poisonous air harmful to human body,and data collection,processing,transmission,display and threshold warning are realized to provide safety information for households to avoid accidents.


title: Broadband and omnidirectional antireflection employing disordered GaN nanopillars

abstract: Disordered GaN nanopillars of three different heights: 300, 550, and 720 nm are fabricated, and demonstrate broad angular and spectral anti-reflective characteristics, up to an incident angle of 60° and for the wavelength range of λ=300–1800nm. An algorithm based on a rigorous coupled-wave analysis (RCWA) method is developed to investigate the correlations between the reflective characteristics and the structural properties of the nanopillars. The broadband and omnidirectional antireflection arises mainly from the refractive-index gradient provided by nanopillars. Calculations show excellent agreement with the measured reflectivities for both s- and p- polarizations.


title: A Mapping Study on Requirements Engineering in Agile Software Development

abstract: Agile software development (ASD) methods have gained popularity in the industry and been the subject of an increasing amount of academic research. Although requirements engineering (RE) in ASD has been studied, the overall understanding of RE in ASD as a phenomenon is still weak. We conducted a mapping study of RE in ASD to review the scientific literature. 28 articles on the topic were identified and analyzed. The results indicate that the definition of agile RE is vague. The proposed benefits from agile RE included lower process overheads, a better requirements understanding, a reduced tendency to over allocate development resources, responsiveness to change, rapid delivery of value, and improved customer relationships. The problematic areas of agile RE were the use of customer representatives, the user story requirements format, the prioritization of requirements, growing technical debt, tacit requirements knowledge, and imprecise effort estimation. We also report proposed solutions to the identified problems.


title: Social Sharing of Emotions on Facebook: Channel Differences, Satisfaction, and Replies

abstract: People often share emotions with others in order to manage their emotional experiences. We investigate how social media properties such as visibility and directedness affect how people share emotions in Facebook, and their satisfaction after doing so. 141 participants rated 1,628 of their own recent status updates, posts they made on others' timelines, and private messages they sent for intensity, valence, personal relevance, and overall satisfaction felt after sharing each message. For network-visible channels-status updates and posts on others' timelines-they also rated their satisfaction with replies they received. People shared differently between channels, with more intense and negative emotions in private messages. People felt more satisfied after sharing more positive emotions in all channels and after sharing more personally relevant emotions in network-visible channels. Finally, people's overall satisfaction after sharing emotions in network-visible channels is strongly tied to their reply satisfaction. Quality of replies, not just quantity, matters, suggesting the need for designs that help people receive valuable responses to their shared emotions.


title: Photocarcinogenicity of selected topically applied dermatological drugs: calcineurin inhibitors, corticosteroids, and vitamin D analogs

abstract: Topical therapies constitute the mainstay of dermatological treatments for skin disorders, such as atopic dermatitis, contact dermatitis, psoriasis, or acne. Since some of these diseases are often chronic, treatment duration may last for years and may even last the patient's entire lifetime. Obviously, such long-term therapy may raise safety concerns, which also include the potential photocarcinogenic effect. Most patients are exposed to ultraviolet radiation (UVR) during leisure, work, vacations, or in tanning beds. Additionally, the patients may receive UVR via UVB phototherapy or psoralens plus UVA radiation (PUVA). The use of immunosuppressant's, such as corticosteroids and calcineurin inhibitors, has markedly increased. Patients with skin diseases have benefited from both systemic and topical treatment of both new and established drugs. The issue of a black box warning by the US Food and Drug Administration has increased concerns about photocarcinogenesis, which raises the question: “Are these drugs safe?” ::: ::: This review focuses on the mechanism of action and photocarcinogenic potential of commonly used topical treatments, such as corticosteroids, calcineurin inhibitors, and vitamin D analogs.


title: On the benefits and pitfalls of extending a statically typed language JIT compiler for dynamic scripting languages

abstract: Whenever the need to compile a new dynamically typed language arises, an appealing option is to repurpose an existing statically typed language Just-In-Time (JIT) compiler (repurposed JIT compiler). Existing repurposed JIT compilers (RJIT compilers), however, have not yet delivered the hoped-for performance boosts. The performance of JVM languages, for instance, often lags behind standard interpreter implementations. Even more customized solutions that extend the internals of a JIT compiler for the target language compete poorly with those designed specifically for dynamically typed languages. Our own Fiorano JIT compiler is an example of this problem. As a state-of-the-art, RJIT compiler for Python, the Fiorano JIT compiler outperforms two other RJIT compilers (Unladen Swallow and Jython), but still shows a noticeable performance gap compared to PyPy, today's best performing Python JIT compiler. In this paper, we discuss techniques that have proved effective in the Fiorano JIT compiler as well as limitations of our current implementation. More importantly, this work offers the first in-depth look at benefits and limitations of the repurposed JIT compiler approach. We believe the most common pitfall of existing RJIT compilers is not focusing sufficiently on specialization, an abundant optimization opportunity unique to dynamically typed languages. Unfortunately, the lack of specialization cannot be overcome by applying traditional optimizations.


title: High Dynamic Range Imaging

abstract: While real scenes produce a wide range of brightness variations, current cameras use low dynamic range image detector that typically provide 256 levels of brightness data at each pixel. We propose methods to create High Dynamic Range images, the method to enhance the dynamic range of is based on capturing multiple exposure photographs of the scene. Even if there are few methods available for creating HDR images, HDR display technologies still lag behind. We present a tone mapping algorithm to display HDR images on Low Dynamic Range display devices. We also deal with histogram manipulation for painterly effect on output. Applications of HDR images are discussed in brief. An algorithm to create HDR images for still scenes in Color Filter Array (CFA) domain is proposed and successfully implemented.


title: Gut microbiota and attention deficit hyperactivity disorder: new perspectives for a challenging condition

abstract: A bidirectional communication between the gut and the brain (gut–brain axis) is well recognized with the gut microbiota viewed as a key regulator of this cross-talk. Currently, a body of preclinical and to a lesser extent epidemiological evidence supports the notion that host–microbe interactions play a key role in brain development and function and in the etiology of neurodevelopmental disorders. Early life events and shifts away from traditional lifestyles are known to impact gut microbiota composition and function and, thereby, may increase the risk of developing neurodevelopmental disorders. Attention deficit hyperactivity disorder (ADHD) is nowadays the most prevalent neurodevelopmental disorder. Despite many years of research its etiology is unclear and its diagnosis and treatment are still challenging. Different factors reported to be associated with the risk of developing ADHD and/or linked to different ADHD manifestations have also been linked to shifts in gut microbiota composition, suggesting a link between the microbiota and the disorder. Evidence from preliminary human studies also suggests that dietary components that modulate gut microbiota may also influence ADHD development or symptoms, although further studies are warranted to confirm this hypothesis. Here, we firstly review the potential mechanisms by which the gut microbiota may regulate the brain–gut axis and influence behavior and neurodevelopmental disorders. Secondly, we discuss the current knowledge about the different factors and dietary components reported to be associated with the risk of developing ADHD or its manifestations and with shifts in gut microbiota composition. Finally, we briefly highlight the need to progress our understanding regarding the role of the gut microbiota in ADHD, since this could open new avenues for early intervention and improved management of the disease.


title: Deep learning and conditional random fields‐based depth estimation and topographical reconstruction from conventional endoscopy

abstract: Colorectal cancer is the fourth leading cause of cancer deaths worldwide and the second leading cause in the United States. The risk of colorectal cancer can be mitigated by the identification and removal of premalignant lesions through optical colonoscopy. Unfortunately, conventional colonoscopy misses more than 20% of the polyps that should be removed, due in part to poor contrast of lesion topography. Imaging tissue topography during a colonoscopy is difficult because of the size constraints of the endoscope and the deforming mucosa. Most existing methods make geometric assumptions or incorporate a priori information, which limits accuracy and sensitivity. In this paper, we present a method that avoids these restrictions, using a joint deep convolutional neural network-conditional random field (CNN-CRF) framework. Estimated depth is used to reconstruct the topography of the surface of the colon from a single image. We train the unary and pairwise potential functions of a CRF in a CNN on synthetic data, generated by developing an endoscope camera model and rendering over 100,000 images of an anatomically-realistic colon. We validate our approach with real endoscopy images from a porcine colon, transferred to a synthetic-like domain, with ground truth from registered computed tomography measurements. The CNN-CRF approach estimates depths with a relative error of 0.152 for synthetic endoscopy images and 0.242 for real endoscopy images. We show that the estimated depth maps can be used for reconstructing the topography of the mucosa from conventional colonoscopy images. This approach can easily be integrated into existing endoscopy systems and provides a foundation for improving computer-aided detection algorithms for detection, segmentation and classification of lesions.


Automatically Identifying Good Conversations Online (Yes, They Do Exist!).


title: Statistical Edge Detection: Learning and Evaluating Edge Cues

abstract: We formulate edge detection as statistical inference. This statistical edge detection is data driven, unlike standard methods for edge detection which are model based. For any set of edge detection filters (implementing local edge cues), we use presegmented images to learn the probability distributions of filter responses conditioned on whether they are evaluated on or off an edge. Edge detection is formulated as a discrimination task specified by a likelihood ratio test on the filter responses. This approach emphasizes the necessity of modeling the image background (the off-edges). We represent the conditional probability distributions nonparametrically and illustrate them on two different data sets of 100 (Sowerby) and 50 (South Florida) images. Multiple edges cues, including chrominance and multiple-scale, are combined by using their joint distributions. Hence, this cue combination is optimal in the statistical sense. We evaluate the effectiveness of different visual cues using the Chernoff information and Receiver Operator Characteristic (ROC) curves. This shows that our approach gives quantitatively better results than the Canny edge detector when the image background contains significant clutter. In addition, it enables us to determine the effectiveness of different edge cues and gives quantitative measures for the advantages of multilevel processing, for the use of chrominance, and for the relative effectiveness of different detectors. Furthermore, we show that we can learn these conditional distributions on one data set and adapt them to the other with only slight degradation of performance without knowing the ground truth on the second data set. This shows that our results are not purely domain specific. We apply the same approach to the spatial grouping of edge cues and obtain analogies to nonmaximal suppression and hysteresis.