title: A Sparse Coding Neural Network ASIC With On-Chip Learning for Feature Extraction and Encoding

abstract: Hardware-based computer vision accelerators will be an essential part of future mobile devices to meet the low power and real-time processing requirement. To realize a high energy efficiency and high throughput, the accelerator architecture can be massively parallelized and tailored to vision processing, which is an advantage over software-based solutions and general-purpose hardware. In this work, we present an ASIC that is designed to learn and extract features from images and videos. The ASIC contains 256 leaky integrate-and-fire neurons connected in a scalable two-layer network of 8 $\,\times\,$ 8 grids linked in a 4-stage ring. Sparse neuron activation and the relatively small grid keep the spike collision probability low to save access arbitration. The weight memory is divided into core memory and auxiliary memory, such that the auxiliary memory is only powered on for learning to save inference power. High-throughput inference is accomplished by the parallel operation of neurons. Efficient learning is implemented by passing parameter update messages, which is further simplified by an approximation technique. A 3.06 mm $^2$ 65 nm CMOS ASIC test chip is designed to achieve a maximum inference throughput of 1.24 Gpixel/s at 1.0 V and 310 MHz, and on-chip learning can be completed in seconds. To improve the power consumption and energy efficiency, core memory supply voltage can be reduced to 440 mV to take advantage of the error resilience of the algorithm, reducing the inference power to 6.67 mW for a 140 Mpixel/s throughput at 35 MHz.


title: The Porter stemming algorithm: then and now

abstract: Purpose – In 1980, Porter presented a simple algorithm for stemming English language words. This paper summarises the main features of the algorithm, and highlights its role not just in modern information retrieval research, but also in a range of related subject domains.Design/methodology/approach – Review of literature and research involving use of the Porter algorithm.Findings – The algorithm has been widely adopted and extended so that it has become the standard approach to word conflation for information retrieval in a wide range of languages.Orinality/value – The 1980 paper in Program by Porter describing his algorithm has been highly cited. This paper provides a context for the original paper as well as an overview of its subsequent use.


Wireless Sensor Based Remote Controlled Agriculture Monitoring System using ZigBee


title: Coherent detection and channel coding for bistatic scatter radio sensor networking

abstract: With low monetary cost and minimal energy consumption, communications by means of reflection and scatter radio have emerged as key enabler for low-cost, large-scale and dense ubiquitous wireless sensor network applications. This work maximizes scatter radio communication range by (a) proposing a novel coherent receiver of frequency-shift keying (FSK) modulation for the bistatic scatter radio channel (i.e., carrier emitter and receiver are dislocated) and (b) employing specific short block-length cyclic error-correcting codes. Despite the presence of three unknown channel links due to the bistatic setup and multiple unknown scatter radio-related parameters, the proposed receiver vastly improves BER performance compared to state-of-the-art bistatic scatter radio receivers. Experimental corroborating results are offered, with a commodity software-defined radio (SDR) reader, a custom scatter radio tag and omnidirectional antennas. Tag-to-reader ranges up to 150 meters are reported with as little as 20 milliWatt transmission power, offering range extension of approximately 10 additional meters compared to state-of-the-art bistatic receivers.


title: Chaotic Krill Herd algorithm

abstract: Recently, Gandomi and Alavi proposed a meta-heuristic optimization algorithm, called Krill Herd (KH). This paper introduces the chaos theory into the KH optimization process with the aim of accelerating its global convergence speed. Various chaotic maps are considered in the proposed chaotic KH (CKH) method to adjust the three main movements of the krill in the optimization process. Several test problems are utilized to evaluate the performance of CKH. The results show that the performance of CKH, with an appropriate chaotic map, is better than or comparable with the KH and other robust optimization approaches. (C) 2014 Elsevier Inc. All rights reserved.


title: Statin Use and Incident Frailty in Women Aged 65 Years or Older: Prospective Findings From the Women’s Health Initiative Observational Study

abstract: BACKGROUND ::: Inflammatory biomarkers have shown consistent associations with disability and frailty in older adults. Statin medications may reduce the incidence the frailty because of their anti-inflammatory effects. This study examines associations between current use, duration, and potency of statin medications and incident frailty in initially nonfrail women 65 years old or older. ::: ::: ::: METHODS ::: The authors conducted a prospective analysis of data from the Women's Health Initiative Observational Study (WHI-OS) conducted at 40 clinical centers in the United States. Eligible women were nonfrail and 65-79 years old at baseline (n = 25,378). Current statin use at baseline was ascertained through direct inspection of medicine containers during clinic visits. Frailty was ascertained through self-reported indicators and physical measurements at baseline and 3-year clinic contacts. Components of frailty included self-reported low physical function, exhaustion, low physical activity, and unintended weight loss. Multinomial logistic regression models were used to adjust for covariates predicting incident frailty. ::: ::: ::: RESULTS ::: Among the 25,378 eligible women, 3453 (13.6%) developed frailty by the 3-year follow-up contact. Current statin use had no association with incident frailty (multivariate-adjusted odds ratio [OR] = 1.00; 95% confidence interval [CI], 0.85-1.16). Duration and potency of statin use were also not significantly associated with incident frailty. Among low potency statin users, longer duration of use was associated with reduced risk of frailty (p for trend =.02). A similar pattern of results was observed when frailty was studied in the absence of intervening, incident cardiovascular events. ::: ::: ::: CONCLUSIONS ::: Overall, incidence of frailty was similar in current statin users and nonusers.


Dynamic Network Embedding by Modeling Triadic Closure Process


title: Grasper having tactile sensing function using acoustic reflection for laparoscopic surgery

abstract: Purpose: ::: In current minimally invasive surgery techniques, the tactile information available to the surgeon is limited. Improving tactile sensation could enhance the operability of surgical instruments. Considering surgical applications, requirements such as having electrical safety, a simple structure, and sterilization capability should be considered. The current study sought to develop a grasper that can measure grasping force at the tip, based on a previously proposed tactile sensing method using acoustic reflection. This method can satisfy the requirements for surgical applications because it has no electrical element within the part that is inserted into the patient’s body.


title: Leveraging Hierarchical Parametric Networks for Skeletal Joints Based Action Segmentation and Recognition

abstract: Over the last few years, with the immense popularity of the Kinect, there has been renewed interest in developing methods for human gesture and action recognition from 3D skeletal data. A number of approaches have been proposed to extract representative features from 3D skeletal data, most commonly hard wired geometric or bio-inspired shape context features. We propose a hierarchial dynamic framework that first extracts high level skeletal joints features and then uses the learned representation for estimating emission probability to infer action sequences. Currently gaussian mixture models are the dominant technique for modeling the emission distribution of hidden Markov models. We show that better action recognition using skeletal features can be achieved by replacing gaussian mixture models by deep neural networks that contain many layers of features to predict probability distributions over states of hidden Markov models. The framework can be easily extended to include a ergodic state to segment and recognize actions simultaneously.


title: A Continuous Learning Framework for Activity Recognition Using Deep Hybrid Feature Models

abstract: Most of the research on human activity recognition has focused on learning a static model, considering that all the training instances are labeled and present in advance, while in streaming videos new instances continuously arrive and are not labeled. Moreover, these methods generally use application- specific hand-engineered and static feature models, which are not suitable for continuous learning. Some recent approaches on activity recognition use deep-learning-based hierarchical feature models, but the large size of these networks constrain them from being used in continuous learning scenarios. In this work, we propose a continuous activity learning framework for streaming videos by intricately tying together deep hybrid feature models and active learning. This allows us to automatically select the most suitable features and take the advantage of incoming unlabeled instances to improve the existing model incrementally. Given the segmented activities from streaming videos, we learn features in an unsupervised manner using deep hybrid networks, which have the ability to take the advantage of both the local hand-engineered features and the deep model in an efficient way. Additionally, we use active learning to train the activity classifier using a reduced amount of manually labeled instances. Retraining the models with a huge amount of accumulated examples is computationally expensive and not suitable for continuous learning. Hence, we propose a method to select the best subset of these examples to update the models incrementally. We conduct rigorous experiments on four challenging human activity datasets to demonstrate the effectiveness of our framework.


title: Neural Networks of the Mouse Neocortex

abstract: Numerous studies have examined the neuronal inputs and outputs of many areas within the mammalian cerebral cortex, but how these areas are organized into neural networks that communicate across the entire cortex is unclear. Over 600 labeled neuronal pathways acquired from tracer injections placed across the entire mouse neocortex enabled us to generate a cortical connectivity atlas. A total of 240 intracortical connections were manually reconstructed within a common neuroanatomic framework, forming a cortico-cortical connectivity map that facilitates comparison of connections from different cortical targets. Connectivity matrices were generated to provide an overview of all intracortical connections and subnetwork clusterings. The connectivity matrices and cortical map revealed that the entire cortex is organized into four somatic sensorimotor, two medial, and two lateral subnetworks that display unique topologies and can interact through select cortical areas. Together, these data provide a resource that can be used to further investigate cortical networks and their corresponding functions.


title: Cole-Cole broadening in dielectric relaxation and strange kinetics.

abstract: We present a fresh appraisal of the Cole-Cole (CC) description of dielectric relaxation. While the approach is phenomenological, it demonstrates a fundamental connection between the parameters of the CC dispersion. Based on the fractal nature of the time set representing the interaction of the relaxing dipole with its encompassing matrix, and the Kirkwood-Froehlich correlation factor, a new 3D phase space linking together the kinetic and structural properties is proposed. The evolution of the relaxation process is represented in this phase space by a trajectory, which is determined by the variation of external macroscopic parameters. As an example, the validity of the approach is demonstrated on two porous silica glasses exhibiting a CC relaxation process.


title: Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization

abstract: Dynamic portfolio optimization is the process of sequentially allocating wealth to a collection of assets in some consecutive trading periods, based on investors' return-risk profile. Automating this process with machine learning remains a challenging problem. Here, we design a deep reinforcement learning (RL) architecture with an autonomous trading agent such that, investment decisions and actions are made periodically, based on a global objective, with autonomy. In particular, without relying on a purely model-free RL agent, we train our trading agent using a novel RL architecture consisting of an infused prediction module (IPM), a generative adversarial data augmentation module (DAM) and a behavior cloning module (BCM). Our model-based approach works with both on-policy or off-policy RL algorithms. We further design the back-testing and execution engine which interact with the RL agent in real time. Using historical {\em real} financial market data, we simulate trading with practical constraints, and demonstrate that our proposed model is robust, profitable and risk-sensitive, as compared to baseline trading strategies and model-free RL agents from prior work.


title: A Wide-Band Dual-Polarized Stacked Patch Antenna

abstract: A wideband dual-polarized slot-coupled stacked patch antenna operating in the UMTS (1920-2170 MHz), WLAN (2.4-2.484 GHz), and UMTS II (2500-2690 MHz) frequency bands is described. Measurements on a prototype of the proposed patch antenna confirm good performance in terms of both impedance matching and isolation


title: Ring Loss: Convex Feature Normalization for Face Recognition

abstract: We motivate and present Ring loss, a simple and elegant feature normalization approach for deep networks designed to augment standard loss functions such as Softmax. We argue that deep feature normalization is an important aspect of supervised classification problems where we require the model to represent each class in a multi-class problem equally well. The direct approach to feature normalization through the hard normalization operation results in a non-convex formulation. Instead, Ring loss applies soft normalization, where it gradually learns to constrain the norm to the scaled unit circle while preserving convexity leading to more robust features. We apply Ring loss to large-scale face recognition problems and present results on LFW, the challenging protocols of IJB-A Janus, Janus CS3 (a superset of IJB-A Janus), Celebrity Frontal-Profile (CFP) and MegaFace with 1 million distractors. Ring loss outperforms strong baselines, matches state-of-the-art performance on IJB-A Janus and outperforms all other results on the challenging Janus CS3 thereby achieving state-of-the-art. We also outperform strong baselines in handling extremely low resolution face matching.


title: Android Botnets: What URLs are Telling Us

abstract: Botnets have traditionally been seen as a threat to personal computers; however, the recent shift to mobile platforms resulted in a wave of new botnets. Due to its popularity, Android mobile Operating System became the most targeted platform. In spite of rising numbers, there is a significant gap in understanding the nature of mobile botnets and their communication characteristics. In this paper, we address this gap and provide a deep analysis of Command and Control (C&C) and built-in URLs of Android botnets detected since the first appearance of the Android platform. By combining both static and dynamic analyses with visualization, we uncover the relationships between the majority of the analyzed botnet families and offer an insight into each malicious infrastructure. As a part of this study we compile and offer to the research community a dataset containing 1929 samples representing 14 Android botnet families.


title: What's mine is yours: Pretrained CNNs for limited training sonar ATR

abstract: Finding mines in Sonar imagery is a significant problem with a great deal of relevance for seafaring military and commercial endeavors. Unfortunately, the lack of enormous Sonar image data sets has prevented automatic target recognition (ATR) algorithms from some of the same advances seen in other computer vision fields. Namely, the boom in convolutional neural nets (CNNs) which have been able to achieve incredible results - even surpassing human actors - has not been an easily feasible route for many practitioners of Sonar ATR. We demonstrate the power of one avenue to incorporating CNNs into Sonar ATR: transfer learning. We first show how well a straightforward, flexible CNN feature-extraction strategy can be used to obtain impressive if not state-of-the-art results. Secondly, we propose a way to utilize the powerful transfer learning approach towards multiple instance target detection and identification within a provided synthetic aperture Sonar data set.


title: Generating Synthetic Data to Match Data Mining Patterns

abstract: Synthetic data sets can be useful in a variety of situations, including repeatable regression testing and providing realistic - but not real - data to third parties for testing new software. Researchers, engineers, and software developers can test against a safe data set without affecting or even accessing the original data, insulating them from privacy and security concerns as well as letting them generate larger data sets than would be available using only real data. Practitioners use data mining technology to discover patterns in real data sets that aren't apparent at the outset. This article explores how to combine information derived from data mining applications with the descriptive ability of synthetic data generation software. Our goal is to demonstrate that at least some data mining techniques (in particular, a decision tree) can discover patterns that we can then use to inverse map into synthetic data sets. These synthetic data sets can be of any size and will faithfully exhibit the same (decision tree) patterns. Our work builds on two technologies: synthetic data definition language and predictive model markup language.


title: Visual loop closure detection with a compact image descriptor

abstract: In this paper, we present a method for visual loop closure detection using a compact image descriptor, Gabor-Gist. In contrast to the Bag-of-Words (BoW) approach, which is dominant in recent studies of the loop closure detection problem that derives an image descriptor from locally extracted keypoint descriptors, our method relies on a single efficient image descriptor of low dimension to describe and measure similarities among images. We employ PCA to transform a high dimensional Gabor-Gist descriptor to a lower dimensional form to improve both the computational efficiency of our method and the discriminative power of the image descriptor. In addition, we use a particle filter to exploit the correlation among images in a sequence captured by the robot in the process of identifying loop closure candidates. Our method is highly scalable due to the compactness of the image descriptor and the simplicity of particle filtering. To validate our method, we used the Oxford City dataset. Our experimental results show that for this dataset, high recall (up to 87%) can be obtained at 100% precision, with only a few particles.


title: Friction Models and Friction Compensation

abstract: This paper reviews friction phenomena and friction models of interest for automatic control. Particular emphasis is given to two recently developed dynamic friction models, the Bliman-Sorine model and the LuGre model. These models capture many frictional phenomena observed in laboratory experiments. The behaviors of the models in different situations are discussed in detail. Methods for friction compensation are presented and illustrated with results from practical experiments.


title: Hybrid Matching Algorithm for Personal Names

abstract: Companies acquire personal information from phone, World Wide Web, or email in order to sell or send an advertisement about their product. However, when this information is acquired, moved, copied, or edited, the data may lose its quality. Often, the use of data administrators or a tool that has limited capabilities to correct the mistyped information can cause many problems. Moreover, most of the correction techniques are particularly implemented for the words used in daily conversations. Since personal names have different characteristics compared to general text, a hybrid matching algorithm (PNRS) which employs phonetic encoding, string matching and statistical facts to provide a possible candidate for misspelled names is developed. At the end, the efficiency of the proposed algorithm is compared with other well known spelling correction techniques.


title: How transferable are features in deep neural networks?

abstract: Many deep neural networks trained on natural images exhibit a curious phenomenon in common: on the first layer they learn features similar to Gabor filters and color blobs. Such first-layer features appear not to be specific to a particular dataset or task, but general in that they are applicable to many datasets and tasks. Features must eventually transition from general to specific by the last layer of the network, but this transition has not been studied extensively. In this paper we experimentally quantify the generality versus specificity of neurons in each layer of a deep convolutional neural network and report a few surprising results. Transferability is negatively affected by two distinct issues: (1) the specialization of higher layer neurons to their original task at the expense of performance on the target task, which was expected, and (2) optimization difficulties related to splitting networks between co-adapted neurons, which was not expected. In an example network trained on ImageNet, we demonstrate that either of these two issues may dominate, depending on whether features are transferred from the bottom, middle, or top of the network. We also document that the transferability of features decreases as the distance between the base task and target task increases, but that transferring features even from distant tasks can be better than using random features. A final surprising result is that initializing a network with transferred features from almost any number of layers can produce a boost to generalization that lingers even after fine-tuning to the target dataset.


title: Character-level Convolutional Networks for Text Classification

abstract: This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.


title: Subjective and Objective Quality Assessment of Image: A Survey

abstract: With the increasing demand for image-based applications, the efficient and reliable evaluation of image quality has increased in importance. Measuring the image quality is of fundamental importance for numerous image processing applications, where the goal of image quality assessment (IQA) methods is to automatically evaluate the quality of images in agreement with human quality judgments. Numerous IQA methods have been proposed over the past years to fulfill this goal. In this paper, a survey of the quality assessment methods for conventional image signals, as well as the newly emerged ones, which includes the high dynamic range (HDR) and 3-D images, is presented. A comprehensive explanation of the subjective and objective IQA and their classification is provided. Six widely used subjective quality datasets, and performance measures are reviewed. Emphasis is given to the full-reference image quality assessment (FR-IQA) methods, and 9 often-used quality measures (including mean squared error (MSE), structural similarity index (SSIM), multi-scale structural similarity index (MS-SSIM), visual information fidelity (VIF), most apparent distortion (MAD), feature similarity measure (FSIM), feature similarity measure for color images (FSIMC), dynamic range independent measure (DRIM), and tone-mapped images quality index (TMQI)) are carefully described, and their performance and computation time on four subjective quality datasets are evaluated. Furthermore, a brief introduction to 3-D IQA is provided and the issues related to this area of research are reviewed.


title: RGB-D Salient Object Detection Based on Discriminative Cross-modal Transfer Learning

abstract: In this work, we propose to utilize Convolutional Neural Networks to boost the performance of depth-induced salient object detection by capturing the high-level representative features for depth modality. We formulate the depth-induced saliency detection as a CNN-based cross-modal transfer problem to bridge the gap between the "data-hungry" nature of CNNs and the unavailability of sufficient labeled training data in depth modality. In the proposed approach, we leverage the auxiliary data from the source modality effectively by training the RGB saliency detection network to obtain the task-specific pre-understanding layers for the target modality. Meanwhile, we exploit the depth-specific information by pre-training a modality classification network that encourages modal-specific representations during the optimizing course. Thus, it could make the feature representations of the RGB and depth modalities as discriminative as possible. These two modules are pre-trained independently and then stitched to initialize and optimize the eventual depth-induced saliency detection model. Experiments demonstrate the effectiveness of the proposed novel pre-training strategy as well as the significant and consistent improvements of the proposed approach over other state-of-the-art methods.


title: A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression

abstract: Stock price prediction has attracted much attention from both practitioners and researchers. However, most studies in this area ignored the non-stationary nature of stock price series. That is, stock price series do not exhibit identical statistical properties at each point of time. As a result, the relationships between stock price series and their predictors are quite dynamic. It is challenging for any single artificial technique to effectively address this problematic characteristics in stock price series. One potential solution is to hybridize different artificial techniques. Towards this end, this study employs a two-stage architecture for better stock price prediction. Specifically, the self-organizing map (SOM) is first used to decompose the whole input space into regions where data points with similar statistical distributions are grouped together, so as to contain and capture the non-stationary property of financial series. After decomposing heterogeneous data points into several homogenous regions, support vector regression (SVR) is applied to forecast financial indices. The proposed technique is empirically tested using stock price series from seven major financial markets. The results show that the performance of stock price prediction can be significantly enhanced by using the two-stage architecture in comparison with a single SVR model.


title: Asynchrony begets momentum, with an application to deep learning

abstract: Asynchronous methods are widely used in deep learning, but have limited theoretical justification when applied to non-convex problems. We show that running stochastic gradient descent (SGD) in an asynchronous manner can be viewed as adding a momentum-like term to the SGD iteration. Our result does not assume convexity of the objective function, so it is applicable to deep learning systems. We observe that a standard queuing model of asynchrony results in a form of momentum that is commonly used by deep learning practitioners. This forges a link between queuing theory and asynchrony in deep learning systems, which could be useful for systems builders. For convolutional neural networks, we experimentally validate that the degree of asynchrony directly correlates with the momentum, confirming our main result. An important implication is that tuning the momentum parameter is important when considering different levels of asynchrony. We assert that properly tuned momentum reduces the number of steps required for convergence. Finally, our theory suggests new ways of counteracting the adverse effects of asynchrony: a simple mechanism like using negative algorithmic momentum can improve performance under high asynchrony. Since asynchronous methods have better hardware efficiency, this result may shed light on when asynchronous execution is more efficient for deep learning systems.


title: Behavioral Consumers in Industrial Organization: An Overview

abstract: This paper overviews three primary branches of the industrial organization literature with behavioral consumers. The literature is organized according to whether consumers: (1) have non-standard preferences; (2) are overconfident or otherwise biased such that they systematically misweight different dimensions of price and other product attributes; or (3) fail to choose the best price due to suboptimal search, confusion comparing prices, or excessive inertia. The importance of consumer heterogeneity and equilibrium effects are also highlighted along with recent empirical work.


title: Tunneling Field-Effect Transistors (TFETs) With Subthreshold Swing (SS) Less Than 60 mV/dec

abstract: We have demonstrated a 70-nm n-channel tunneling field-effect transistor (TFET) which has a subthreshold swing (SS) of 52.8 mV/dec at room temperature. It is the first experimental result that shows a sub-60-mV/dec SS in the silicon-based TFETs. Based on simulation results, the gate oxide and silicon-on-insulator layer thicknesses were scaled down to 2 and 70 nm, respectively. However, the ON/ OFF current ratio of the TFET was still lower than that of the MOSFET. In order to increase the on current further, the following approaches can be considered: reduction of effective gate oxide thickness, increase in the steepness of the gradient of the source to channel doping profile, and utilization of a lower bandgap channel material


title: Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning

abstract: The Classification of medical images and illustrations in the literature aims to label a medical image according to the modality it was produced or label an illustration according to its production attributes. It is an essential and challenging research hotspot in the area of automated literature review, retrieval and mining. The significant intra-class variation and inter-class similarity caused by the diverse imaging modalities and various illustration types brings a great deal of difficulties to the problem. In this paper, we propose a synergic deep learning (SDL) model to address this issue. Specifically, a dual deep convolutional neural network with a synergic signal system is designed to mutually learn image representation. The synergic signal is used to verify whether the input image pair belongs to the same category and to give the corrective feedback if a synergic error exists. Our SDL model can be trained 'end to end'. In the test phase, the class label of an input can be predicted by averaging the likelihood probabilities obtained by two convolutional neural network components. Experimental results on the ImageCLEF2016 Subfigure Classification Challenge suggest that our proposed SDL model achieves the state-of-the art performance in this medical image classification problem and its accuracy is higher than that of the first place solution on the Challenge leader board so far.


title: Modulation of anxiety through blockade of anandamide hydrolysis

abstract: The psychoactive constituent of cannabis, Delta(9)-tetrahydrocannabinol, produces in humans subjective responses mediated by CB1 cannabinoid receptors, indicating that endogenous cannabinoids may contribute to the control of emotion. But the variable effects of Delta(9)-tetrahydrocannabinol obscure the interpretation of these results and limit the therapeutic potential of direct cannabinoid agonists. An alternative approach may be to develop drugs that amplify the effects of endogenous cannabinoids by preventing their inactivation. Here we describe a class of potent, selective and systemically active inhibitors of fatty acid amide hydrolase, the enzyme responsible for the degradation of the endogenous cannabinoid anandamide. Like clinically used anti-anxiety drugs, in rats the inhibitors exhibit benzodiazepine-like properties in the elevated zero-maze test and suppress isolation-induced vocalizations. These effects are accompanied by augmented brain levels of anandamide and are prevented by CB1 receptor blockade. Our results indicate that anandamide participates in the modulation of emotional states and point to fatty acid amide hydrolase inhibition as an innovative approach to anti-anxiety therapy.


title: Floating Points: A method for computing stipple drawings

abstract: We present a method for computer generated pen-and-ink illustrations by the simulation of stippling. In a stipple drawing, dots are used to represent tone and also material of surfaces. We create such drawings by generating an initial dot set which is then processed by a relaxation method based on Voronoi diagrams. The point patterns generated are approximations of Poisson disc distributions and can also be used for integrating functions or the positioning of objects. We provide an editor similar to paint systems for interactively creating stipple drawings. This makes it possible to create such drawings within a matter of hours, instead of days or even weeks when the drawing is done manually.


title: Information Sharing on Social Networking Websites: Antecedents and Consequences of Trust

abstract: Social networking websites (SNS) facilitate communication and information sharing between people. Media have reported significant concerns of SNS users regarding their privacy. SNS have tried to address these privacy concerns by introducing controlling features. This study investigates the issue of trust in SNS, its predictors, and its consequences. We suggest that three categories of factors should be considered for the study of social networking websites: (1) website characteristics, (2) environmental factors, and (3) personal factors. We find that trust in the website is a significant predictor of SNS information sharing. We also find that personal factors, such as privacy concerns and trust propensity and social presence, a website characteristic, are significant predictors of trust in SNS. The findings show similarities and differences between the context of e-commerce and SNS. We provide implications for research and practice as well as direction for future research.


title: Common microRNA–mRNA Interactions in Different Newcastle Disease Virus-Infected Chicken Embryonic Visceral Tissues

abstract: To investigate the roles and explore the altered expression of microRNAs (miRNAs) and mRNAs in chicken embryos in response to Newcastle disease virus (NDV) infection, deep sequencing was performed. Then, a conjoint analysis of small RNA-seq and mRNA-seq was performed to screen interactional miRNA⁻mRNA pairs during NDV infection. In total, 15 and 17 up- and downregulated miRNAs were identified that potentially targeted 4279 and 6080 mRNAs in NDV-infected chicken embryonic tissues, respectively; in addition, 595 upregulated and 480 downregulated mRNAs were identified. The conjoint analysis of the obtained data identified 1069 miRNA⁻mRNA pairs. Among these pairs, 130 pairs were related to immune or inflammatory responses. The relationship between gga-miR-203a and its target transglutaminase 2 (TGM2) was confirmed using a dual-luciferase reporter system and a real time quantitative polymerase chain reaction (RT-qPCR) assay. Overall, the discovery of miRNAs, mRNAs, and their potential pairing relationships, which may be involved in the regulation of NDV infection, will facilitate our understanding of the complex regulatory relationship between the host and the virus.


title: Isolation and characterization of Acanthamoeba strains from soil samples in Gran Canaria, Canary Islands, Spain

abstract: Free-living Amoebae of Acanthamoeba genus include non-pathogenic and pathogenic strains that are currently classified in 18 different genotypes, T1–T18. In this study, a survey was carried out to evaluate the presence of Acanthamoeba strains in soil samples collected between 2012 and 2013 in Gran Canaria Island, Canary Islands, Spain. Samples were inoculated onto non-nutrient agar (NNA) plates and were checked for the presence of Acanthamoeba. Identification of Acanthamoeba strains was based on the morphology of the cyst and trophozoite forms. Subsequently, positive samples were cloned for their molecular characterization at the genotype level by sequencing the DF3 region located in the 18S rDNA gene of Acanthamoeba as previously described. Sequencing results revealed the presence of T2, T5 and T4 genotypes within the studied samples. To the best of our knowledge, this is the first report demonstrating the presence of Acanthamoeba in Gran Canaria Island and the first study at the genotype level in the Canary Islands.


title: You Talking to Me? A Corpus and Algorithm for Conversation Disentanglement

abstract: When multiple conversations occur simultaneously, a listener must decide which conversation each utterance is part of in order to interpret and respond to it appropriately. We refer to this task as disentanglement. We present a corpus of Internet Relay Chat (IRC) dialogue in which the various conversations have been manually disentangled, and evaluate annotator reliability. This is, to our knowledge, the first such corpus for internet chat. We propose a graph-theoretic model for disentanglement, using discourse-based features which have not been previously applied to this task. The model’s predicted disentanglements are highly correlated with manual annotations.


An Automatic Hole Filling Method of Point Cloud for 3 D Scanning


Sacral Dimples


title: An algebra for fine-grained integration of XACML policies

abstract: Collaborative and distributed applications, such as dynamic coalitions and virtualized grid computing, often require integrating access control policies of collaborating parties. Such an integration must be able to support complex authorization specifications and the fine-grained integration requirements that the various parties may have. In this paper, we introduce an algebra for fine-grained integration of sophisticated policies. The algebra, which consists of three binary and two unary operations, is able to support the specification of a large variety of integration constraints. To assess the expressive power of our algebra, we introduce a notion of completeness and prove that our algebra is complete with respect to this notion. We then propose a framework that uses the algebra for the fine-grained integration of policies expressed in XACML. We also present a methodology for generating the actual integrated XACML policy, based on the notion of Multi-Terminal Binary Decision Diagrams.


title: Top-down modulation: bridging selective attention and working memory

abstract: Selective attention, the ability to focus our cognitive resources on information relevant to our goals, influences working memory (WM) performance. Indeed, attention and working memory are increasingly viewed as overlapping constructs. Here, we review recent evidence from human neurophysiological studies demonstrating that top-down modulation serves as a common neural mechanism underlying these two cognitive operations. The core features include activity modulation in stimulus-selective sensory cortices with concurrent engagement of prefrontal and parietal control regions that function as sources of top-down signals. Notably, top-down modulation is engaged during both stimulus-present and stimulus-absent stages of WM tasks; that is, expectation of an ensuing stimulus to be remembered, selection and encoding of stimuli, maintenance of relevant information in mind and memory retrieval.


title: Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised

abstract: We present a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision (e.g., in the form of product domain labels and user-provided ratings). Our method combines two weakly supervised components to identify salient opinions and form extractive summaries from multiple reviews: an aspect extractor trained under a multi-task objective, and a sentiment predictor based on multiple instance learning. We introduce an opinion summarization dataset that includes a training set of product reviews from six diverse domains and human-annotated development and test sets with gold standard aspect annotations, salience labels, and opinion summaries. Automatic evaluation shows significant improvements over baselines, and a large-scale study indicates that our opinion summaries are preferred by human judges according to multiple criteria.


title: Nice to know you: Positive emotions, self–other overlap, and complex understanding in the formation of a new relationship

abstract: Based on Fredrickson's ((1998). What good are positive emotions? Review of General Psychology, 2, 300–319.; (2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. American Psychologist, 56, 218–226) broaden-and-build theory and Aron and Aron's ((1986). Love as expansion of the self: Understanding attraction and satisfaction. New York: Hemisphere) self-expansion theory, it was hypothesized that positive emotions broaden people's feelings of self–other overlap in the beginning of a new relationship. In a prospective study of first-year college students, we found that, after 1 week in college, positive emotions predicted increased self–other overlap with new roommates, which in turn predicted a more complex understanding of the roommate. In addition, participants who experienced a high ratio of positive to negative emotions throughout the first month of college reported a greater increase in self–other overlap and complex understanding than participan...


title: Detailed Human Shape and Pose from Images

abstract: Much of the research on video-based human motion capture assumes the body shape is known a priori and is represented coarsely (e.g. using cylinders or superquadrics to model limbs). These body models stand in sharp contrast to the richly detailed 3D body models used by the graphics community. Here we propose a method for recovering such models directly from images. Specifically, we represent the body using a recently proposed triangulated mesh model called SCAPE which employs a low-dimensional, but detailed, parametric model of shape and pose-dependent deformations that is learned from a database of range scans of human bodies. Previous work showed that the parameters of the SCAPE model could be estimated from marker-based motion capture data. Here we go further to estimate the parameters directly from image data. We define a cost function between image observations and a hypothesized mesh and formulate the problem as optimization over the body shape and pose parameters using stochastic search. Our results show that such rich generative models enable the automatic recovery of detailed human shape and pose from images.


title: Generative and Discriminative Text Classification with Recurrent Neural Networks

abstract: We empirically characterize the performance of discriminative and generative LSTM models for text classification. We find that although RNN-based generative models are more powerful than their bag-of-words ancestors (e.g., they account for conditional dependencies across words in a document), they have higher asymptotic error rates than discriminatively trained RNN models. However we also find that generative models approach their asymptotic error rate more rapidly than their discriminative counterparts---the same pattern that Ng&Jordan (2001) proved holds for linear classification models that make more naive conditional independence assumptions. Building on this finding, we hypothesize that RNN-based generative classification models will be more robust to shifts in the data distribution. This hypothesis is confirmed in a series of experiments in zero-shot and continual learning settings that show that generative models substantially outperform discriminative models.


title: On Visual Hallmarks of Robustness to Adversarial Malware

abstract: A central challenge of adversarial learning is to interpret the resulting hardened model. In this contribution, we ask how robust generalization can be visually discerned and whether a concise view of the interactions between a hardened decision map and input samples is possible. We first provide a means of visually comparing a hardened model's loss behavior with respect to the adversarial variants generated during training versus loss behavior with respect to adversarial variants generated from other sources. This allows us to confirm that the association of observed flatness of a loss landscape with generalization that is seen with naturally trained models extends to adversarially hardened models and robust generalization. To complement these means of interpreting model parameter robustness we also use self-organizing maps to provide a visual means of superimposing adversarial and natural variants on a model's decision space, thus allowing the model's global robustness to be comprehensively examined.


title: Animation: Can it facilitate

abstract: Graphics have been used since ancient times to portray things that are inherently spatiovisual, like maps and building plans. More recently, graphics have been used to portray things that are metaphorically spatiovisual, like graphs and organizational charts. The assumption is that graphics can facilitate comprehension, learning, memory, communication and inference. Assumptions aside, research on static graphics has shown that only carefully designed and appropriate graphics prove to be beneficial for conveying complex systems. Effective graphics conform to the Congruence Principle according to which the content and format of the graphic should correspond to the content and format of the concepts to be conveyed. From this, it follows that animated graphics should be effective in portraying change over time. Yet the research on the efficacy of animated over static graphics is not encouraging. In cases where animated graphics seem superior to static ones, scrutiny reveals lack of equivalence between animated and static graphics in content or procedures; the animated graphics convey more information or involve interactivity. Animations of events may be ineffective because animations violate the second principle of good graphics, the Apprehension Principle, according to which graphics should be accurately perceived and appropriately conceived. Animations are often too complex or too fast to be accurately perceived. Moreover, many continuous events are conceived of as sequences of discrete steps. Judicious use of interactivity may overcome both these disadvantages. Animations may be more effective than comparable static graphics in situations other than conveying complex systems, for example, for real time reorientations in time and space.


title: Using High-Definition maps for precise urban vehicle localization

abstract: Global Navigation Satellite Systems (GNSS) based localization in the context of Intelligent Transportation Systems, Advanced Driver Assistance Systems and ultimately the development of autonomous vehicles often requires accuracy with integrity. Low-cost data fusion algorithms for positioning that integrate GNSS and additional in-vehicle sensor information are able to handle short term GNSS outages and can provide increased availability and accuracy. Challenging are non-line-of-sight effects that are often present in urban areas. As those can hardly be modeled accurately, an unobservable position bias is often introduced which violates the estimated confidence. Therefor, mass-market applicable localization algorithms that mitigate their influence are desired to allow GNSS-based positioning for safety critical applications. With the beginning market introduction of high-definition maps (HD maps) by major map providers, another inexpensive tool to enhance data fusion based localization algorithms is available. Those maps offer means to further improve the accuracy and integrity of vehicle position estimation, especially in challenging urban environments. In this paper, a particle filter based localization algorithm is presented that integrates HD maps, GNSS and vehicle odometry measurements to demonstrate the benefits of HD maps using real world measurements.


On Online Attention-Based Speech Recognition and Joint Mandarin Character-Pinyin Training.


title: Special issue on smart tourism: convergence of information technologies, experiences, and theories

abstract: The notion of smart tourism has been gaining currency recently, particularly as a strategic tool for tourism development. For instance, under the general framework of smart city initiatives, the European Commission (2015) has identified travel information and communication as one of the strategic objectives for improving human mobility and transportation. In emerging economies like China smart tourism is becoming an integral part of the national policy for economic development and overall improvement of service quality with the aide of information technology (CNTA 2015). Generally speaking, smart tourism aims to develop information and communication infrastructure and capabilities in order to improve management/ governance, facilitate service/product innovation, enhance the tourist experience, and, ultimately, improve the competitiveness of tourism firms and destinations (Gretzel et al. 2015; Hunter et al. 2015; Koo et al. 2015). Considering tourism is an important, and in many cases even primary, sector in national economies, smart tourism seems to offer a promising direction for sustainable tourism development and has the potential to impact tourist destinations and their representations in electronic markets at different levels. The notion of smart tourism was born against the backdrop of the recent development and diffusion of information technology that offers new modalities of communication, new ways for data collection, analysis and exchanges, and thus, new opportunities for value creation and management (Lazer et al. 2009). In the past decade or so, we have seen incredible growth of search technologies, online travel distribution channels, virtual tourism communities, and numerous forms of social media that allow travelers to make more convenient and smarter decisions (Sigala et al. 2012; Xiang et al. 2014). These technologies and systems are directly or indirectly connected to, or have impact on, various computerized reservation systems, which have long been integrated by the travel and tourism industry (Alt and Klein 2011; Karcher 1995; Mitev 1999). The widespread adoption of mobile technology, especially the smartphone, seems to have further accelerated this trend with the convergence of information search, communication, entertainment, social networking as well as mobility-related functionalities to assist travelers on the go (Wang and Xiang 2012). Furthermore, the growing capabilities of embedding and connecting a variety of RFID tags, sensors and mobile devices into our physical environment seems to lead to a more optimistic view that now is the time for technological innovations in terms of co-creation processes and the management of tourist experiences (Atzori et al. 2010; Taafe 2014). While it is a phenomenon of growing significance, scholarly work on smart tourism is lacking, both conceptually and empirically. In response to this emerging need, the journal of Electronic Markets presents a special issue that disseminates * Chulmo Koo helmetgu@khu.ac.kr


title: The spatial and temporal meanings of English prepositions can be independently impaired

abstract: Abstract English uses the same prepositions to describe both spatial and temporal relationships (e.g., at the corner , at 1:30 ), and other languages worldwide exhibit similar patterns. These space–time parallelisms have been explained by the Metaphoric Mapping Theory, which maintains that humans have a cognitive predisposition to structure temporal concepts in terms of spatial schemas through the application of a TIME IS SPACE metaphor. Evidence comes from (among other sources) historical investigations showing that languages consistently develop in such a way that expressions that originally have only spatial meanings are gradually extended to take on analogous temporal meanings. It is not clear, however, if the metaphor actively influences the way that modern adults process prepositional meanings during language use. To explore this question, a series of experiments was conducted with four brain-damaged subjects with left perisylvian lesions. Two subjects exhibited the following dissociation: they failed a test that assesses knowledge of the spatial meanings of prepositions, but passed a test that assesses knowledge of the corresponding temporal meanings of the same prepositions. This result suggests that understanding the temporal meanings of prepositions does not necessarily require establishing structural alignments with their spatial correlates. Two other subjects exhibited the opposite dissociation: they performed better on the spatial test than on the temporal test. Overall, these findings support the view that although the spatial and temporal meanings of prepositions are historically linked by virtue of the TIME IS SPACE metaphor, they can be (and may normally be) represented and processed independently of each other in the brains of modern adults.


title: A 3-D Split Manufacturing Approach to Trustworthy System Development

abstract: Securing the supply chain of integrated circuits is of utmost importance to computer security. In addition to counterfeit microelectronics, the theft or malicious modification of designs in the foundry can result in catastrophic damage to critical systems and large projects. In this letter, we describe a 3-D architecture that splits a design into two separate tiers: one tier that contains critical security functions is manufactured in a trusted foundry; another tier is manufactured in an unsecured foundry. We argue that a split manufacturing approach to hardware trust based on 3-D integration is viable and provides several advantages over other approaches.


Epidemiology and treatment of depression in patients with chronic medical illness


title: Bystander intervention in emergencies: diffusion of responsibility

abstract: Ss overheard an epileptic seizure. They believed either that they alone heard the emergency, or that 1 or 4 unseen others were also present. As predicted the presence of other bystanders reduced the individual's feelings of personal responsibility and lowered his speed of reporting (p < .01). In groups of size 3, males reported no faster than females, and females reported no slower when the 1 other bystander was a male rather than a female. In general, personality and background measures were not predictive of helping. Bystander inaction in real-life emergencies is often explained by "apathy," "alienation," and "anomie." This experiment suggests that the explanation may lie more in the bystander's response to other observers than in his indifference to the victim.


title: Apparent mental causation: Sources of the experience of will

abstract: The experience of willing an act arises from interpreting one's thought as the cause of the act. Conscious will is thus experienced as a function of the priority, consistency, and exclusivity of the thought about the action. The thought must occur before the action, be consistent with the action, and not be accompanied by other causes. An experiment illustrating the role of priority found that people can arrive at the mistaken belief that they have intentionally caused an action that in fact they were forced to perform when they are simply led to think about the action just before its occurrence.


title: Sensor integration in KIV brain model for decision making

abstract: KIV, a biologically inspired neural network with non-convergent dynamics is considered. This contribution builds on the previous studies concerning components of KIV and considers sensor integration. The method is demonstrated using a simple character recognition task. The significance of the results in the framework of biologically plausible sensor integration is discussed.


title: Tamperproof transmission of fingerprints using visual cryptography schemes

abstract: Abstract Visual Cryptography and biometrics have been identified as the two most important aspects of digital security. In this paper, we propose a method for the preparation and secure transmission of fingerprint images using visual cryptography scheme. Visual cryptography (VC) is a kind of secret image sharing scheme that uses the human visual system to perform the decryption computations. A visual cryptography scheme (VCS) allows confidential messages to be encrypted into k -out-of- n secret sharing schemes. Whenever the number of participants from the group ( n ) is larger than or equal to the predetermined threshold value ( k ) , the confidential message can be obtained by these participants. VCS is interesting because decryption can be done with no prior knowledge of cryptography and can be performed without any cryptographic computations. In this paper, the fingerprint image is broken up into n pieces, which individually yield no information about the image. These pieces of the images, called shares or shadows, may then be distributed among a group of n participants/dealers. By combining any k ( k ≤ n ) of these shares, the original fingerprint image can be recovered, but combining less than k of them will not reveal any information about the image. The scheme is perfectly secure and very easy to implement. To decode the encrypted information, i.e., to get the original information back, the shares are stacked and the secret image pops out. The only drawback of the VCS is the loss in contrast of reconstructed image. The proposed method for the reconstruction of fingerprint image is based on XNOR operation. This will enable one to obtain back perfect fingerprint image. Keywords: visual cryptography; visual secret sharing; biometric image; fingerprints.


title: The architecture of cognitive control in the human prefrontal cortex

abstract: The prefrontal cortex (PFC) subserves cognitive control: the ability to coordinate thoughts or actions in relation with internal goals. Its functional architecture, however, remains poorly understood. Using brain imaging in humans, we showed that the lateral PFC is organized as a cascade of executive processes from premotor to anterior PFC regions that control behavior according to stimuli, the present perceptual context, and the temporal episode in which stimuli occur, respectively. The results support an unified modular model of cognitive control that describes the overall functional organization of the human lateral PFC and has basic methodological and theoretical implications.


title: Next-Generation Machine Learning for Biological Networks

abstract: Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology.


title: Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder

abstract: Reparameterization of variational auto-encoders with continuous random variables is an effective method for reducing the variance of their gradient estimates. In the discrete case, one can perform reparametrization using the Gumbel-Max trick, but the resulting objective relies on an $\arg \max$ operation and is non-differentiable. In contrast to previous works which resort to \emph{softmax}-based relaxations, we propose to optimize it directly by applying the \emph{direct loss minimization} approach. Our proposal extends naturally to structured discrete latent variable models when evaluating the $\arg \max$ operation is tractable. We demonstrate empirically the effectiveness of the direct loss minimization technique in variational autoencoders with both unstructured and structured discrete latent variables.


title: A Self-adaptive Classifier for Efficient Text-stream Processing

abstract: A self-adaptive classifier for efficient text-stream processing is proposed. The proposed classifier adaptively speeds up its classification while processing a given text stream for various NLP tasks. The key idea behind the classifier is to reuse results for past classification problems to solve forthcoming classification problems. A set of classification problems commonly seen in a text stream is stored to reuse the classification results, while the set size is controlled by removing the least-frequently-used or least-recently-used classification problems. Experimental results with Twitter streams confirmed that the proposed classifier applied to a state-of-the-art base-phrase chunker and dependency parser speeds up its classification by factors of 3.2 and 5.7, respectively.


title: Why is sexual abuse declining? A survey of state child protection administrators*

abstract: Abstract Objectives: Cases of substantiated sexual abuse have declined approximately 39% nationwide from 1992 to 1999, according to estimates from the National Child Abuse and Neglect Data System (NCANDS). Despite the dramatic nature of the decline, little discussion of the trend has occurred at either the national or the state level. Aims of the research were to: (1) gather state-level information about possible sources of the sexual abuse decline, (2) identify child protection trends that might be contributing to the decline, and (3) assess the level of awareness of the decline in state child protection offices. Methods: Telephone interviews were conducted with child protection administrators in 43 states. Results: More than half of the officials in states with large declines were unaware of any discussion of the declines within their agency or in the public at large within their state. State officials cited a diverse array of possible causes for the decline, including: (1) increased evidentiary requirements to substantiate cases, (2) increased caseworker caution due to new legal rights for caregivers, and (3) increasing limitations on the types of cases that agencies accept for investigation. More than half also mentioned the effectiveness of prevention programs, increased prosecution, and public awareness campaigns, implying that a portion of the decline may result from a real decline in occurrence. Conclusions: Responses from child protection officials have inherent biases, but they are useful in generating hypotheses for further study in trying to account for the decline. The results suggest that further discussion and increased analysis of child maltreatment system data are important next steps.


Factors Affecting Housing Prices in Singapore


Visual domain-specific modelling : Benefits and experiences of using metaCASE tools


title: A Framework for Searching for General Artificial Intelligence

abstract: There is a significant lack of unified approaches to building generally intelligent machines. The majority of current artificial intelligence research operates within a very narrow field of focus, frequently without considering the importance of the 'big picture'. In this document, we seek to describe and unify principles that guide the basis of our development of general artificial intelligence. These principles revolve around the idea that intelligence is a tool for searching for general solutions to problems. We define intelligence as the ability to acquire skills that narrow this search, diversify it and help steer it to more promising areas. We also provide suggestions for studying, measuring, and testing the various skills and abilities that a human-level intelligent machine needs to acquire. The document aims to be both implementation agnostic, and to provide an analytic, systematic, and scalable way to generate hypotheses that we believe are needed to meet the necessary conditions in the search for general artificial intelligence. We believe that such a framework is an important stepping stone for bringing together definitions, highlighting open problems, connecting researchers willing to collaborate, and for unifying the arguably most significant search of this century.


title: Traumatic Penile Pain: A Case of Dorsal Vein Thrombophlebitis after Intercourse

abstract: Dorsal Vein thrombosis, also known as Mondor's disease of the penis, is a superficial thrombophlebitis first described in the literature by Falco in 1955. Mondor's disease refers to a superficial thrombophlebitis of any locale. Diagnosis can be made clinically with palpation of a mobile, cord-like thickening on dorsum of penis without associated evidence of inflammation, infection, or dermatologic changes. Bedside ultrasonography with color Doppler can aid in the diagnosis of penile thrombophlebitis by revealing a noncompressible superficial vessel with normal surrounding flow. The following case presentation details the etiology, diagnosis, and management of a particularly rare disease process.


title: On the Stability of the Passive Dynamics of Quadrupedal Running with a Bounding Gait

abstract: This paper examines the passive dynamics of quadrupedal bounding. First, an unexpected difference between local and global behavior of the forward speed versus touchdown angle in the self-stabilized Spring Loaded Inverted Pendulum (SLIP) model is exposed and discussed. Next, the stability properties of a simplified sagittal plane model of our Scout II quadrupedal robot are investigated. Despite its simplicity, this model captures the targeted steady state behavior of Scout II without dependence on the fine details of the robot structure. Two variations of the bounding gait, which are observed experimentally in Scout II, are considered. Surprisingly, numerical return map studies reveal that passive generation of a large variety of cyclic bounding motion is possible. Most strikingly, local stability analysis shows that the dynamics of the open loop passive system alone can confer stability to the motion! These results can be used in developing a general control methodology for legged robots, resulting from the synthesis of feedforward and feedback models that take advantage of the mechanical system, and might explain the success of simple, open loop bounding controllers on our experimental robot.


On Attacking Statistical Spam Filters


title: Effect of latency on presence in stressful virtual environments

abstract: Previous research has shown that even low end-to-end latency can have adverse effects on performance in virtual environments (VE). This paper reports on an experiment investigating the effect of latency on other metrics of VE effectiveness: physiological response, simulator sickness, and self-reported sense of presence. The VE used in the study includes two rooms: the first is normal and non-threatening; the second is designed to evoke a fear/stress response. Participants were assigned to either a low latency (/spl sim/50 ms) or high latency (/spl sim/90 ms) group. Participants in the low latency condition had a higher self-reported sense of presence and a statistically higher change in heart rate between the two rooms than did those in the high latency condition. There were no significant relationships between latency and simulator sickness.


title: Market Mechanisms in Online Peer-to-Peer Lending

abstract: Online peer-to-peer lending P2P lending has emerged as an appealing new channel of financing in recent years. A fundamental but largely unanswered question in this nascent industry is the choice of market mechanisms, i.e., how the supply and demand of funds are matched, and the terms price at which transactions will occur. Two of the most popular mechanisms are auctions where the "crowd" determines the price of the transaction through an auction process and posted prices where the platform determines the price. While P2P lending platforms typically use one or the other, there is little systematic research on the implications of such choices for market participants, transaction outcomes, and social welfare. We address this question both theoretically and empirically. We first develop a game-theoretic model that yields empirically testable hypotheses, taking into account the incentive of the platform. We then test these hypotheses by exploiting a regime change from auctions to posted prices on one of the largest P2P lending platforms. Consistent with our hypotheses, we find that under platform-mandated posted prices, loans are funded with higher probability, but the preset interest rates are higher than borrowers' starting interest rates and contract interest rates in auctions. More important, all else equal, loans funded under posted prices are more likely to default, thereby undermining lenders' returns on investment and their surplus. Although platform-mandated posted prices may be faster in originating loans, auctions that rely on the crowd to discover prices are not necessarily inferior in terms of overall social welfare. ::: ::: This paper was accepted by Chris Forman, information systems.


title: Real-time audio similarity comparison algorithm

abstract: Interference in the Air Traffic Control (ATC) communication band usually comes from FM broadcast stations. To identify sources of the interference, manually scanning a receiver to compare similarity with the sound from the interference is the only method available. This paper presents a similarity comparison method that will make automatic interference identifying possible. The target audios are divided into 3 bands. A subtraction result between two audios of the same band will be decreased if the two audios are similar. To get a final result, at least 2 similar results from 3 bands will be considered positive. The experimental results show 93.42% of the right positive from 380 comparisons and 0.38% of the false positive from 3420 comparisons.


title: High cerebral perfusion pressure improves low values of local brain tissue O2 tension (PtiO2) in focal lesions.

abstract: Arterial hypertension is widely applied to improve regional cerebral blood flow (rCBF). We measured local brain tissue O2 pressure (PtiO2) in low density lesions at computerized tomography (CT) of the head before and after manipulation of mean arterial pressure (MAP) in order to increase cerebral perfusion pressure (CPP). Nine patients, 7 subarachnoid hemorrhage (SAH), 1 severe head injury, 1 meningeoma, were included in our study. A flexible polarographic microcatheter for PtiO2 measurement was placed at the border of the low density area found at CT. PtiO2 was continuously measured for 615 hours. Hypoperfusion in low density areas was detected by perfusional single photon emission computed tomography (SPECT). We recorded 22 episodes of induced or spontaneous increase of MAP. Initial PtiO2 regularly improved after the CPP increase (r2 0.74 in induced episodes). Low PtiO2 showed a greater percent increase for unitary changes of CPP than normal-high PtiO2. Baseline PtiO2 below 20 mm Hg was associated with normal CPPs; 5 readings of PtiO2 below 20 mm Hg normalized when a higher CPP was obtained. Our results show that in ischemic areas PtiO2 is dependent on CPP suggesting both a derangement of pressure autoregulation and high regional cerebrovascular resistences (CVRs). Low PtiO2 was associated with normal CPP, thus indicating that CPP could be an inadequate estimate of rCBF in focal ischemic areas. Arterial hypertension, capable of increasing CPP above normal values, appeared useful in normalizing tissue oxygenation in ischemic areas.


title: Taking perspective into account in a communicative task

abstract: Previous neuroimaging studies of spatial perspective taking have tended not to activate the brain's mentalising network. We predicted that a task that requires the use of perspective taking in a communicative context would lead to the activation of mentalising regions. In the current task, participants followed auditory instructions to move objects in a set of shelves. A 2 × 2 factorial design was employed. In the Director factor, two directors (one female and one male) either stood behind or next to the shelves, or were replaced by symbolic cues. In the Object factor, participants needed to use the cues (position of the directors or symbolic cues) to select one of three possible objects, or only one object could be selected. Mere presence of the Directors was associated with activity in the superior dorsal medial prefrontal cortex (MPFC) and the superior/middle temporal sulci, extending into the extrastriate body area and the posterior superior temporal sulcus (pSTS), regions previously found to be responsive to human bodies and faces respectively. The interaction between the Director and Object factors, which requires participants to take into account the perspective of the director, led to additional recruitment of the superior dorsal MPFC, a region activated when thinking about dissimilar others' mental states, and the middle temporal gyri, extending into the left temporal pole. Our results show that using perspective taking in a communicative context, which requires participants to think not only about what the other person sees but also about his/her intentions, leads to the recruitment of superior dorsal MPFC and parts of the social brain network.


title: Considering music production and culture management as an emerging digital ecosystem

abstract: Digital music production processes trace a great amount of processes and objects. The important flow of these traces calls for a system to support their interpretation. We have studied and developed such a system in the digital music production context -- within the Gamelan research project -- towards musical object and process reconstitution. We present the results we obtained from combining trace engineering, knowledge modeling and knowledge engineering, based on the differential elaboration of a strongly-committed ontology, standard formats and common knowledge management tools. We conclude by discussing some hypotheses about trace-based knowledge management, digital music preservation and reconstitution, opening on to some considerations about artistic style and digital humanities aspects.


title: Prediction of individual brain maturity using fMRI.

abstract: Group functional connectivity magnetic resonance imaging (fcMRI) studies have documented reliable changes in human functional brain maturity over development. Here we show that support vector machine-based multivariate pattern analysis extracts sufficient information from fcMRI data to make accurate predictions about individuals' brain maturity across development. The use of only 5 minutes of resting-state fcMRI data from 238 scans of typically developing volunteers (ages 7 to 30 years) allowed prediction of individual brain maturity as a functional connectivity maturation index. The resultant functional maturation curve accounted for 55% of the sample variance and followed a nonlinear asymptotic growth curve shape. The greatest relative contribution to predicting individual brain maturity was made by the weakening of short-range functional connections between the adult brain's major functional networks.


title: Semantic Clustering : Identifying Topics in Source Code

abstract: Many of the existing approaches in Software Comprehension focus on program structure or external documentation. However, by analyzing formal information the informal semantics contained in the vocabulary of source code are overlooked. To understand software as a whole, we need to enrich software analysis with the developer knowledge hidden in the code naming. This paper proposes the use of information retrieval to exploit linguistic information found in source code, such as identifier names and comments. We introduce Semantic Clustering, a technique based on Latent Semantic Indexing and clustering to group source artifacts that use similar vocabulary. We call these groups semantic clusters and we interpret them as linguistic topics that reveal the intention of the code. We compare the topics to each other, identify links between them, provide automatically retrieved labels, and use a visualization to illustrate how they are distributed over the system. Our approach is language independent as it works at the level of identifier names. To validate our approach we applied it on several case studies, two of which we present in this paper. Note: Some of the visualizations presented make heavy use of colors. Please obtain a color copy of the article for better understanding.


title: Type synthesis of 3T1R 4-DOF parallel manipulators based on screw theory

abstract: 3T1R four-degrees-of-freedom (DOF) parallel manipulators (3T1R-PMs) are the parallel counterparts of the 4-DOF SCARA serial robots. In a 3T1R-PM, the moving platform can generate 3T1R motion (also called Schonflies motion), which refers to a rotation about any axis with a given direction in conjunction with 3-DOF translations. A method is proposed for the type synthesis of 3T1R-PMs based on screw theory. The wrench systems of a 3T1R parallel kinematic chain (3T1R-PKC) and its legs are first analyzed. A general procedure is then proposed for the type synthesis of 3T1R-PMs. The type synthesis of legs for 3T1R-PKCs, the type synthesis of 3T1R-PKCs, as well as the selection of actuated joints of 3T1R-PMs, are dealt with in sequence. 3T1R-PKCs with and without inactive joints are synthesized. The phenomenon of dependent joint groups in a 3T1R-PKC is revealed for the first time. Several 3T1R-PMs with identical type of legs are obtained.


title: Chunking based malayalam paraphrase identification using unfolding recursive autoencoders

abstract: Paraphrase Detection is the task of examining if two sentences convey the same meaning or not. Here, in this paper, we have chosen a sentence embedding by unsupervised RAE vectors for capturing syntactic as well as semantic information. The RAEs learn features from the nodes of the parse tree and chunk information along with unsupervised word embedding. These learnt features are used for measuring phrase wise similarity between two sentences. Since sentences are of varying length, we use dynamic pooling for getting a fixed sized representation for sentences. This fixed sized sentence representation is the input to the classifier. The DPIL (Detecting Paraphrases in Indian Languages) dataset is used for paraphrase identification here. Initially, paraphrase identification is defined as a 2-class problem and then later, it is extended to a 3-class problem. Word2vec and Glove embedding techniques producing 100, 200 and 300 dimensional vectors are used to check variation in accuracies. The baseline system accuracy obtained using word2vec for 2-class problem is 77.67% and the same for 3-class problem is 66.07%. Glove gave an accuracy of 77.33% for 2-class and 65.42% for 3-classproblem. The results are also compared with the existing open source word embedding and our system using Word2vec embedding is found to outperform better. This is a first attempt using chunking based approach for identification of Malayalam paraphrases.


title: Obstacle detection using optical flow

abstract: Motion has been examined in biology to be a critical component for obstacle avoidance and navigation. In particular, optical flow is a powerful motion cue that has been exploited in many biological systems for survival. In this paper, we investigate an obstacle detection system that uses optical flow to obtain range information to objects. Our experimental results demonstrate that optical flow is capable of providing good obstacle information but has obvious failure modes. We acknowledge that our optical flow system has certain disadvantages and cannot be solely used for navigation. Instead, we believe that optical flow is a critical visual subsystem used when moving at reason- ::: able speeds. When combined with other visual subsystems, considerable synergy can result.


title: Interactive Timetabling

abstract: Timetabling is a typical application of constraint programming whose task is to allocate activities to slots in available resources respecting various constraints like precedence and capacity. In this paper we present a basic concept, a constraint model, and the solving algorithms for interactive timetabling. Interactive timetabling combines automated timetabling (the machine allocates the activities) with user interaction (the user can interfere with the process of timetabling). Because the user can see how the timetabling proceeds and can intervene this process, we believe that such approach is more convenient than full automated timetabling which behaves like a black-box. The contribution of this paper is twofold: we present a generic model to describe timetabling (and scheduling in general) problems and we propose an interactive algorithm for solving such problems.


title: A hybrid cross-language name matching technique using novel modified Levenshtein Distance

abstract: Name matching is a key component in various applications in our life like record linkage and data mining applications. This process suffers from multiple complexities such as matching data from different languages or data written by people from different cultures. In this paper, we present a new modified Cross-Language Levenshtein Distance (CLLD) algorithm that supports matching names across different writing scripts and with many-to-many characters mapping. In addition, we present a hybrid cross-language name matching technique that uses phonetic matching technique mixed with our proposed CLLD algorithm to improve the overall f-measure and speed up the matching process. Our experiments demonstrate that this method substantially outperforms a number of well-known standard phonetic and approximate string similarity methods in terms of precision, recall, and f-measure.


title: PyElph - a software tool for gel images analysis and phylogenetics

abstract: BackgroundThis paper presents PyElph, a software tool which automatically extracts data from gel images, computes the molecular weights of the analyzed molecules or fragments, compares DNA patterns which result from experiments with molecular genetic markers and, also, generates phylogenetic trees computed by five clustering methods, using the information extracted from the analyzed gel image. The software can be successfully used for population genetics, phylogenetics, taxonomic studies and other applications which require gel image analysis. Researchers and students working in molecular biology and genetics would benefit greatly from the proposed software because it is free, open source, easy to use, has a friendly Graphical User Interface and does not depend on specific image acquisition devices like other commercial programs with similar functionalities do.ResultsPyElph software tool is entirely implemented in Python which is a very popular programming language among the bioinformatics community. It provides a very friendly Graphical User Interface which was designed in six steps that gradually lead to the results. The user is guided through the following steps: image loading and preparation, lane detection, band detection, molecular weights computation based on a molecular weight marker, band matching and finally, the computation and visualization of phylogenetic trees. A strong point of the software is the visualization component for the processed data. The Graphical User Interface provides operations for image manipulation and highlights lanes, bands and band matching in the analyzed gel image. All the data and images generated in each step can be saved. The software has been tested on several DNA patterns obtained from experiments with different genetic markers. Examples of genetic markers which can be analyzed using PyElph are RFLP (Restriction Fragment Length Polymorphism), AFLP (Amplified Fragment Length Polymorphism), RAPD (Random Amplification of Polymorphic DNA) and STR (Short Tandem Repeat). The similarity between the DNA sequences is computed and used to generate phylogenetic trees which are very useful for population genetics studies and taxonomic classification.ConclusionsPyElph decreases the effort and time spent processing data from gel images by providing an automatic step-by-step gel image analysis system with a friendly Graphical User Interface. The proposed free software tool is suitable for researchers and students which do not have access to expensive commercial software and image acquisition devices.


title: Learning multi-modal grounded linguistic semantics by playing I Spy

abstract: Grounded language learning bridges words like 'red' and 'square' with robot perception. The vast majority of existing work in this space limits robot perception to vision. In this paper, we build perceptual models that use haptic, auditory, and proprioceptive data acquired through robot exploratory behaviors to go beyond vision. Our system learns to ground natural language words describing objects using supervision from an interactive humanrobot "I Spy" game. In this game, the human and robot take turns describing one object among several, then trying to guess which object the other has described. All supervision labels were gathered from human participants physically present to play this game with a robot. We demonstrate that our multi-modal system for grounding natural language outperforms a traditional, vision-only grounding framework by comparing the two on the "I Spy" task. We also provide a qualitative analysis of the groundings learned in the game, visualizing what words are understood better with multimodal sensory information as well as identifying learned word meanings that correlate with physical object properties (e.g. 'small' negatively correlates with object weight).


title: Sub-nanosecond pulse generator for through-the-wall radar application

abstract: This paper presents a sub-nanosecond pulse generator intended for a transmitter of through-the-wall surveillance radar. The basis of the generator is a step recovery diode, which is used to sharpen the slow rise time edge of an input driving waveform. A unique pulse shaping technique is then applied to form an ultra-wideband Gaussian pulse. A simple transistor switching circuit was used to drive this Gaussian pulser, which transforms a TTL trigger signal to a driving pulse with the timing and amplitude parameters required by the step recovery diode. The maximum pulse repetition frequency of the generator is 20 MHz. High amplitude pulses are advantageous for obtaining a good radar range, especially when penetrating thick lossy walls. In order to increase the output power of the transmitter, the outputs of two identical generators were connected in parallel. The measurement results are presented, which show waveforms of the generated Gaussian pulses approximately 180 ps in width and over 32 V in amplitude.


title: A novel framework for cross-spectral iris matching

abstract: Previous work on iris recognition focused on either visible light (VL), near-infrared (NIR) imaging, or their fusion. However, limited numbers of works have investigated cross-spectral matching or compared the iris biometric performance under both VL and NIR spectrum using unregistered iris images taken from the same subject. To the best of our knowledge, this is the first work that proposes a framework for cross-spectral iris matching using unregistered iris images. To this end, three descriptors are proposed namely, Gabor-difference of Gaussian (G-DoG), Gabor-binarized statistical image feature (G-BSIF), and Gabor-multi-scale Weberface (G-MSW) to achieve robust cross-spectral iris matching. In addition, we explore the differences in iris recognition performance across the VL and NIR spectra. The experiments are carried out on the UTIRIS database which contains iris images acquired with both VL and NIR spectra for the same subject. Experimental and comparison results demonstrate that the proposed framework achieves state-of-the-art cross-spectral matching. In addition, the results indicate that the VL and NIR images provide complementary features for the iris pattern and their fusion improves notably the recognition performance.


Bag of Features Based Remote Sensing Image Classification Using RANSAC And SVM


Solar granulation: numerical simulation and resulting disc-center line profiles.


Neural Poetry Translation


title: SLAMCast: Large-Scale, Real-Time 3D Reconstruction and Streaming for Immersive Multi-Client Live Telepresence

abstract: Real-time 3D scene reconstruction from RGB-D sensor data, as well as the exploration of such data in VR/AR settings, has seen tremendous progress in recent years. The combination of both these components into telepresence systems, however, comes with significant technical challenges. All approaches proposed so far are extremely demanding on input and output devices, compute resources and transmission bandwidth, and they do not reach the level of immediacy required for applications such as remote collaboration. Here, we introduce what we believe is the first practical client-server system for real-time capture and many-user exploration of static 3D scenes. Our system is based on the observation that interactive frame rates are sufficient for capturing and reconstruction, and real-time performance is only required on the client site to achieve lag-free view updates when rendering the 3D model. Starting from this insight, we extend previous voxel block hashing frameworks by introducing a novel thread-safe GPU hash map data structure that is robust under massively concurrent retrieval, insertion and removal of entries on a thread level. We further propose a novel transmission scheme for volume data that is specifically targeted to Marching Cubes geometry reconstruction and enables a 90% reduction in bandwidth between server and exploration clients. The resulting system poses very moderate requirements on network bandwidth, latency and client-side computation, which enables it to rely entirely on consumer-grade hardware, including mobile devices. We demonstrate that our technique achieves state-of-the-art representation accuracy while providing, for any number of clients, an immersive and fluid lag-free viewing experience even during network outages.


title: Technical Evaluation of HoloLens for Multimedia: A First Look

abstract: A recently released cutting-edge AR device, Microsoft HoloLens, has attracted considerable attention with its advanced capabilities. In this article, we report the design and execution of a series of experiments to quantitatively evaluate HoloLens' performance in head localization, real environment reconstruction, spatial mapping, hologram visualization, and speech recognition.


title: Local minimum solution for the potential field method in multiple robot motion planning task

abstract: This work presents a new approach to solve the problem of local minimum which happens usually in the case of multiple robots navigation. The multi-robot path planning based on artificial potential field is among of the most popular method for trajectory planning. However, in some spatial-temporal coordinates, the algebraic sum of all the potentials generated by all the torques/forces is null. In such case, all the robots in the working area stopped and their speeds become null too. Purposely, this work solved the problem of local minimum in a multi-robot system which is validated by Matlab/Simulink simulation.


title: BitBlox: A Redesign of the Breadboard

abstract: Building physical computing projects can enable learners to integrate computing into a range of interests and disciplines. However, the electronic portion of these projects can be difficult. Students are learning new concepts as well as how to work with new tools. This influx of information can be difficult for students to retain in their working memory as they construct their circuits. In this paper, we introduce BitBlox, a set of modular, solderless Breadboards for prototyping circuits. BitBlox attempts to decrease the cognitive load on the user by reducing the complexity found in the standard Breadboard by bringing visibility to the underlying connections within its modules. We present a comparative classroom study integrating the Breadboard and BitBlox into two different high school classes. Our qualitative analysis focuses on student errors, strategies, and collaborative practices, highlighting important dynamics for designing hardware tools.


title: Religion, self-regulation, and self-control: Associations, explanations and implications

abstract: Many of the links of religiousness with health, well-being, and social behavior may be due to religion’s influences on self-control or self-regulation. Using Carver and Scheier’s (1998) theory of self-regulation as a framework for organizing the empirical research, the authors review evidence relevant to 6 propositions: (a) that religion can promote self-control; (b) that religion influences how goals are selected, pursued, and organized; (c) that religion facilitates self-monitoring; (d) that religion fosters the development of self-regulatory strength; (e) that religion prescribes and fosters proficiency in a suite of self-regulatory behaviors; and (f) that some of religion’s influences on health, well-being, and social behavior may result from religion’s influences on self-control and self-regulation. The authors conclude with suggestions for future research.


title: DRIVING CONSUMER ACCEPTANCE OF MOBILE MARKETING : A THEORETICAL FRAMEWORK AND EMPIRICAL STUDY

abstract: With the emergence of high speed wireless network technologies and the increasing market penetration of mobile phones the global advertising industry’s interest in using this medium as a means of marketing communication is rising. However, in spite of the increasing number of companies investing in mobile marketing campaigns, there is, as yet, little academic research on this topic and the nature and implications of this channel are not yet understood fully. This research attempts to help in bridging this gap; it investigates the factors that induce consumers to accept the mobile phone as a means of communicating promotional content. Underlying the research are a set of hypotheses that have been formulated to include determinants referring to consumer personality as well as determinants referring to the form of advertising. These hypotheses form the basis for this research and are empirically tested by means of a linear structural equation model. The empirical results (n = 1,028) identify entertainment value as well as information value as the strongest drivers of the acceptance of the mobile phone as an innovative medium for advertising content communication.


title: Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective

abstract: LTE in unlicensed spectrum using licensed assisted access LTE (LTE-LAA) is a promising approach to overcome the wireless spectrum scarcity. However, to reap the benefits of LTE-LAA, a fair coexistence mechanism with other incumbent WiFi deployments is required. In this paper, a novel deep learning approach is proposed for modeling the resource allocation problem of LTE-LAA small base stations (SBSs). The proposed approach enables multiple SBSs to proactively perform dynamic channel selection, carrier aggregation, and fractional spectrum access while guaranteeing fairness with existing WiFi networks and other LTE-LAA operators. Adopting a proactive coexistence mechanism enables future delay-tolerant LTE-LAA data demands to be served within a given prediction window ahead of their actual arrival time thus avoiding the underutilization of the unlicensed spectrum during off-peak hours while maximizing the total served LTE-LAA traffic load. To this end, a noncooperative game model is formulated in which SBSs are modeled as Homo Egualis agents that aim at predicting a sequence of future actions and thus achieving long-term equal weighted fairness with WLAN and other LTE-LAA operators over a given time horizon. The proposed deep learning algorithm is then shown to reach a mixed-strategy Nash equilibrium (NE), when it converges. Simulation results using real data traces show that the proposed scheme can yield up to 28% and 11% gains over a conventional reactive approach and a proportional fair coexistence mechanism, respectively. The results also show that the proposed framework prevents WiFi performance degradation for a densely deployed LTE-LAA network.