title: High power density DC/DC converter using the close-coupled inductors

abstract: Among the specifications of eco-friendly electric vehicles, some will incorporate voltage converters in their systems to make full use of their power sources. The DC-DC converter using coupled inductors is known as one of converter types that may achieve higher power density. This paper focused on this circuit type especially using close-coupled inductors. Following, the elucidation of design guidelines for the close-coupled inductors theoretically, the superiority of the circuit in terms of reducing the size and losses in the magnetic components was demonstrated clearly through comparison with coupled inductors with looser coupling used in interleaved boost circuits and an inductor of boost chopper circuits. Finally, test results for a prototype of the proposed converter achieved a maximum power density of 24.7 kW/liter and a maximum efficiency of 98.4 % on an output of 42 kW (a maximum input current of 233 A) for a time rating of a few minutes.


title: Combination of donor splenocyte transfusion with blockade of γc signal synergizes to inhibit alloreactive T-cell proliferation and induces apoptosis.

abstract: BACKGROUND ::: The common γ chain (γc) plays a critical role in regulating proliferation, differentiation, and apoptosis of peripheral T-cells. It was previously confirmed that blocking the γc signal can successfully induce transplant tolerance in a murine model. Here we investigated the potential mechanism. ::: ::: ::: METHODS ::: Splenocytes from C57BL/6 mice were transfused into T-cell deficient Balb/c nude mice that were reconstituted with syngeneic wild-type T-cells labeled with 5-carboxyfluorescein diacetate succinimidyl ester (CFSE). After 24 hours, recipients received i.p. injection of mixture of anti-γc mAbs, or with isotype control IgG2a. The labeled T-cells were harvested from recipient spleens after 12 and 48 hours. T-cell proliferation and apoptosis were detected by flow cytometry. ::: ::: ::: RESULTS ::: T-cell proliferation was markedly inhibited and apoptotic T cells could be detected at 12 hours after the mAbs injection. Proliferation was inhibited at 48 hours, but the proportion of apoptotic T-cells was not more than at 12 hours. In the control group, however, T-cells actively proliferated and no significant apoptosis was detected at either time point. ::: ::: ::: CONCLUSIONS ::: The results suggested that blockade of γc signals can synergize with donor splenocyte transfusion and lead to inhibition of antigen-specific T-cell proliferation and induction of apoptotic T-cell death. This protocol may develop a novel approach to induce donor-specific tolerance.


title: TOSThreads: thread-safe and non-invasive preemption in TinyOS

abstract: Many threads packages have been proposed for programming wireless sensor platforms. However, many sensor network operating systems still choose to provide an event-driven model, due to efficiency concerns. We present TOS-Threads, a threads package for TinyOS that combines the ease of a threaded programming model with the efficiency of an event-based kernel. TOSThreads is backwards compatible with existing TinyOS code, supports an evolvable, thread-safe kernel API, and enables flexible application development through dynamic linking and loading. In TOS-Threads, TinyOS code runs at a higher priority than application threads and all kernel operations are invoked only via message passing, never directly, ensuring thread-safety while enabling maximal concurrency. The TOSThreads package is non-invasive; it does not require any large-scale changes to existing TinyOS code. We demonstrate that TOSThreads context switches and system calls introduce an overhead of less than 0.92% and that dynamic linking and loading takes as little as 90 ms for a representative sensing application. We compare different programming models built using TOSThreads, including standard C with blocking system calls and a reimplementation of Tenet. Additionally, we demonstrate that TOSThreads is able to run computationally intensive tasks without adversely affecting the timing of critical OS services.


title: Non-linear Metric Learning

abstract: In this paper, we introduce two novel metric learning algorithms, Χ2-LMNN and GB-LMNN, which are explicitly designed to be non-linear and easy-to-use. The two approaches achieve this goal in fundamentally different ways: Χ2-LMNN inherits the computational benefits of a linear mapping from linear metric learning, but uses a non-linear Χ2-distance to explicitly capture similarities within histogram data sets; GB-LMNN applies gradient-boosting to learn non-linear mappings directly in function space and takes advantage of this approach's robustness, speed, parallelizability and insensitivity towards the single additional hyper-parameter. On various benchmark data sets, we demonstrate these methods not only match the current state-of-the-art in terms of kNN classification error, but in the case of Χ2-LMNN, obtain best results in 19 out of 20 learning settings.


title: Identification of Optogenetically Activated Striatal Medium Spiny Neurons by Npas4 Expression

abstract: Optogenetics is a powerful neuromodulatory tool with many unique advantages to explore functions of neuronal circuits in physiology and diseases. Yet, interpretation of cellular and behavioral responses following in vivo optogenetic manipulation of brain activities in experimental animals often necessitates identification of photoactivated neurons with high spatial resolution. Although tracing expression of immediate early genes (IEGs) provides a convenient approach, neuronal activation is not always followed by specific induction of widely used neuronal activity markers like c-fos, Egr1 and Arc. In this study we performed unilateral optogenetic stimulation of the striatum in freely moving transgenic mice that expressed a channelrhodopsin-2 (ChR2) variant ChR2(C128S) in striatal medium spiny neurons (MSNs). We found that in vivo blue light stimulation significantly altered electrophysiological activity of striatal neurons and animal behaviors. To identify photoactivated neurons we then analyzed IEG expression patterns using in situ hybridization. Upon light illumination an induction of c-fos was not apparent whereas another neuronal IEG Npas4 was robustly induced in MSNs ipsilaterally. Our results demonstrate that tracing Npas4 mRNA expression following in vivo optogenetic modulation can be an effective tool for reliable and sensitive identification of activated MSNs in the mouse striatum.


CNN and CRF for Semantic Image Segmentation


title: Similarity of Semantic Relations

abstract: There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason:stone is analogous to the pair carpenter:wood. This article introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, and information retrieval. Recently the Vector Space Model (VSM) of information retrieval has been adapted to measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) The patterns are derived automatically from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data, and (3) automatically generated synonyms are used to explore variations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM.


title: Boosted decision trees for word recognition in handwritten document retrieval

abstract: Recognition and retrieval of historical handwritten material is an unsolved problem. We propose a novel approach to recognizing and retrieving handwritten manuscripts, based upon word image classification as a key step. Decision trees with normalized pixels as features form the basis of a highly accurate AdaBoost classifier, trained on a corpus of word images that have been resized and sampled at a pyramid of resolutions. To stem problems from the highly skewed distribution of class frequencies, word classes with very few training samples are augmented with stochastically altered versions of the originals. This increases recognition performance substantially. On a standard corpus of 20 pages of handwritten material from the George Washington collection the recognition performance shows a substantial improvement in performance over previous published results (75% vs 65%). Following word recognition, retrieval is done using a language model over the recognized words. Retrieval performance also shows substantially improved results over previously published results on this database. Recognition/retrieval results on a more challenging database of 100 pages from the George Washington collection are also presented.


title: Panoptic Segmentation with a Joint Semantic and Instance Segmentation Network

abstract: We present a single network method for panoptic segmentation. This method combines the predictions from a jointly trained semantic and instance segmentation network using heuristics. Joint training is the first step towards an end-to-end panoptic segmentation network and is faster and more memory efficient than training and predicting with two networks, as done in previous work. The architecture consists of a ResNet-50 feature extractor shared by the semantic segmentation and instance segmentation branch. For instance segmentation, a Mask R-CNN type of architecture is used, while the semantic segmentation branch is augmented with a Pyramid Pooling Module. Results for this method are submitted to the COCO and Mapillary Joint Recognition Challenge 2018. Our approach achieves a PQ score of 17.6 on the Mapillary Vistas validation set and 27.2 on the COCO test-dev set.


title: Smart Sensory Furniture Based on WSN for Ambient Assisted Living

abstract: Ubiquitous computing has been defined as “machines that fit the human environment instead of forcing humans to enter theirs.” An example of this type of approach is “Smart Sensory Furniture” (SSF) Project. SSF is an ambient assisted living system that allows inferring a potential dangerous action of an elderly person living alone at home. This inference is obtained by a specific sensory layer with sensor nodes fixed into furniture and a reasoning layer embedded in a PC that learns from the users’ behavioral patterns and advices when the system detects unusual patterns. This paper aims to explain the SSF sensory layer, which is a distributed signal processing system in a network of sensing objects massively distributed, physically coupled, wirelessly networked, and energy limited. A complete set of experimental test has been carried out. The results show the level of accuracy for each type of sensors and potential use. Finally, the power consumption was experimentally measured and the results show the low maintenance requirements of this solution. The complete system design is described and discussed, including the node mesh details, as well as the type of sensors and actuators and other aspects, such as integration issues and solutions.


title: The Resistance Perturbation Distance: A Metric for the Analysis of Dynamic Networks

abstract: To quantify the fundamental evolution of time-varying networks, and detect abnormal behavior, one needs a notion of temporal difference that captures significant organizational changes between two successive instants. In this work, we propose a family of distances that can be tuned to quantify structural changes occurring on a graph at different scales: from the local scale formed by the neighbors of each vertex, to the largest scale that quantifies the connections between clusters, or communities. Our approach results in the definition of a true distance, and not merely a notion of similarity. We propose fast (linear in the number of edges) randomized algorithms that can quickly compute an approximation to the graph metric. The third contribution involves a fast algorithm to increase the robustness of a network by optimally decreasing the Kirchhoff index. Finally, we conduct several experiments on synthetic graphs and real networks, and we demonstrate that we can detect configurational changes that are directly related to the hidden variables governing the evolution of dynamic networks.


title: Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning

abstract: We combine three methods which significantly improve the OCR accuracy of OCR models trained on early printed books: (1) The pretraining method utilizes the information stored in already existing models trained on a variety of typesets (mixed models) instead of starting the training from scratch. (2) Performing cross fold training on a single set of ground truth data (line images and their transcriptions) with a single OCR engine (OCRopus) produces a committee whose members then vote for the best outcome by also taking the top-N alternatives and their intrinsic confidence values into account. (3) Following the principle of maximal disagreement we select additional training lines which the voters disagree most on, expecting them to offer the highest information gain for a subsequent training (active learning). Evaluations on six early printed books yielded the following results: On average the combination of pretraining and voting improved the character accuracy by 46% when training five folds starting from the same mixed model. This number rose to 53% when using different models for pretraining, underlining the importance of diverse voters. Incorporating active learning improved the obtained results by another 16% on average (evaluated on three of the six books). Overall, the proposed methods lead to an average error rate of 2.5% when training on only 60 lines. Using a substantial ground truth pool of 1,000 lines brought the error rate down even further to less than 1% on average.


title: Automated Road Crack Detection Using Deep Convolutional Neural Networks

abstract: Effective and timely identification of cracks on the roads are crucial to propitiously repair and limit any further degradation. Till date, most crack detection methods follow a manual inspection approach as opposed to automatic image-based detection, making the overall procedure expensive and time-consuming. In this study, we propose an automated pavement distress analysis system based on the YOLO v2 deep learning framework. The system is trained using 7,240 images acquired from mobile cameras and tested on 1,813 road images. The detection and classification accuracy of the proposed distress analyzer is measured using the average F1 score obtained from the precision and recall values. Successful application of this study can help identify road anomalies in need of urgent repair, thereby facilitating a much better civil infrastructure monitoring system. The codes associated with the study including the trained model can be found in [11].


title: Theory of representation learning in cortical neural networks

abstract: Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same functional unit, the neuron, and develop through the same learning mechanism, synaptic plasticity. It motivates the conjecture of a unifying theory to explain cortical representational learning across sensory modalities. In this thesis we present theories and computational models of learning and optimization in neural networks, postulating functional properties of synaptic plasticity that support the apparent universal learning capacity of cortical networks. In the past decades, a variety of theories and models have been proposed to describe receptive field formation in sensory areas. They include normative models such as sparse coding, and bottom-up models such as spike-timing dependent plasticity. We bring together candidate explanations by demonstrating that in fact a single principle is sufficient to explain receptive field development. First, we show that many representative models of sensory development are in fact implementing variations of a common principle: nonlinear Hebbian learning. Second, we reveal that nonlinear Hebbian learning is sufficient for receptive field formation through sensory inputs. A surprising result is that our findings are independent of specific details, and allow for robust predictions of the learned receptive fields. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities. The Hebbian learning theory substantiates that synaptic plasticity can be interpreted as an optimization procedure, implementing stochastic gradient descent. In stochastic gradient descent inputs arrive sequentially, as in sensory streams. However, individual data samples have very little information about the correct learning signal, and it becomes a fundamental problem to know how many samples are required for reliable synaptic changes. Through estimation theory, we develop a novel adaptive learning rate model, that adapts the magnitude of synaptic changes based on the statistics of the learning signal, enabling an optimal use of data samples. Our model has a simple implementation and demonstrates improved learning speed, making this a promising candidate for large artificial neural network applications. The model also makes predictions on how cortical plasticity may modulate synaptic plasticity for optimal learning. The optimal sampling size for reliable learning allows us to estimate optimal learning times for a given model. We apply this theory to derive analytical bounds on times for the optimization of synaptic connections. First, we show this optimization problem to have exponentially many saddle-nodes, which lead to small gradients and slow learning. Second, we show that the number of input synapses to a neuron modulates the magnitude of the initial gradient, determining the duration of learning. Our final result reveals that the learning duration increases supra-linearly with the number of synapses, suggesting an effective limit on synaptic connections and receptive field sizes in developing neural networks.


title: DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks

abstract: The performance of deep neural networks is well-known to be sensitive to the setting of their hyperparameters. Recent advances in reverse-mode automatic differentiation allow for optimizing hyperparameters with gradients. The standard way of computing these gradients involves a forward and backward pass of computations. However, the backward pass usually needs to consume unaffordable memory to store all the intermediate variables to exactly reverse the forward training procedure. In this work we propose a simple but effective method, DrMAD, to distill the knowledge of the forward pass into a shortcut path, through which we approximately reverse the training trajectory. Experiments on several image benchmark datasets show that DrMAD is at least 45 times faster and consumes 100 times less memory compared to state-of-the-art methods for optimizing hyperparameters with minimal compromise to its effectiveness. To the best of our knowledge, DrMAD is the first research attempt to make it practical to automatically tune thousands of hyperparameters of deep neural networks. The code can be downloaded from this https URL


title: Robust one-time password authentication scheme using smart card for home network environment

abstract: Due to the exponential growth of the Internet users and wireless devices, interests on home networks have been enormously increased in recent days. In digital home networks, home services including remote access and control to home appliances as well as services offered by service providers are alluring. However, the remote control services cause digital home networks to have various security threats. Hence, for digital home networks, robust security services, especially remote user authentication, should be considered. This paper presents a robust and efficient authentication scheme based on strong-password approach to provide secure remote access in digital home network environments. The proposed scheme uses lightweight computation modules including hashed one-time password and hash-chaining technique along with low-cost smart card technology. It aims to satisfy several security requirements including stolen smart card attack and forward secrecy with lost smart card as well as functional requirements including no verification table and no time synchronization. Comparing with the existing representative schemes, it can be validated that the proposed scheme is more robust authentication mechanism having better security properties. We have conducted formal verification of the proposed scheme.


title: Guided GUI testing of android apps with minimal restart and approximate learning

abstract: Smartphones and tablets with rich graphical user interfaces (GUI) are becoming increasingly popular. Hundreds of thousands of specialized applications, called apps, are available for such mobile platforms. Manual testing is the most popular technique for testing graphical user interfaces of such apps. Manual testing is often tedious and error-prone. In this paper, we propose an automated technique, called Swift-Hand, for generating sequences of test inputs for Android apps. The technique uses machine learning to learn a model of the app during testing, uses the learned model to generate user inputs that visit unexplored states of the app, and uses the execution of the app on the generated inputs to refine the model. A key feature of the testing algorithm is that it avoids restarting the app, which is a significantly more expensive operation than executing the app on a sequence of inputs. An important insight behind our testing algorithm is that we do not need to learn a precise model of an app, which is often computationally intensive, if our goal is to simply guide test execution into unexplored parts of the state space. We have implemented our testing algorithm in a publicly available tool for Android apps written in Java. Our experimental results show that we can achieve significantly better coverage than traditional random testing and L*-based testing in a given time budget. Our algorithm also reaches peak coverage faster than both random and L*-based testing.


title: Wireless Energy Harvesting by Direct Voltage Multiplication on Lateral Waves From a Suspended Dielectric Layer

abstract: This paper explores the feasibility of wireless energy harvesting by direct voltage multiplication on lateral waves. Whilst free space is undoubtedly a known medium for wireless energy harvesting, space waves are too attenuated to support realistic transmission of wireless energy. A layer of thin stratified dielectric material suspended in mid-air can form a substantially less attenuated pathway, which efficiently supports propagation of wireless energy in the form of lateral waves. The conductivity of the suspended dielectric layer does not appear to be a critical factor rendering propagation of lateral waves impossible. In this paper, a mathematical model has been developed to simulate wireless energy harvesting over a suspended layer of stratified dielectric material. The model has been experimentally verified with the help of a novel open-ended voltage multiplier designed to harvest energy from ambient electromagnetic fields.


Nutrition and Nutritional Problems in Wild Animals


title: On Discrete Physics (Digital Philosophy/Digital Cosmology) and the Cellular Automaton: A Perfect Mathematical Deterministic Structure for Reality as a Huge Computational Simulation

abstract: In this paper we provide an analysis and overview of some notable definitions, works and thoughts concerning discrete physics (digital philosophy) that mainly suggest a finite and discrete characteristic for the physical world, as well as, of the cellular automaton, which could serve as the basis of a (or the only) perfect mathematical deterministic model for the physical reality. In particular and as a confirmation, in the reference [37] also has been shown that all the fundamental field equations of physics, mathematically and uniquely, could be derived - where it is assumed that certain physical quantities are discrete.


title: An Efficient Non-Negative Matrix-Factorization-Based Approach to Collaborative Filtering for Recommender Systems

abstract: Matrix-factorization (MF)-based approaches prove to be highly accurate and scalable in addressing collaborative filtering (CF) problems. During the MF process, the non-negativity, which ensures good representativeness of the learnt model, is critically important. However, current non-negative MF (NMF) models are mostly designed for problems in computer vision, while CF problems differ from them due to their extreme sparsity of the target rating-matrix. Currently available NMF-based CF models are based on matrix manipulation and lack practicability for industrial use. In this work, we focus on developing an NMF-based CF model with a single-element-based approach. The idea is to investigate the non-negative update process depending on each involved feature rather than on the whole feature matrices. With the non-negative single-element-based update rules, we subsequently integrate the Tikhonov regularizing terms, and propose the regularized single-element-based NMF (RSNMF) model. RSNMF is especially suitable for solving CF problems subject to the constraint of non-negativity. The experiments on large industrial datasets show high accuracy and low-computational complexity achieved by RSNMF.


A Study of Text Preprocessing Tools for Arabic Text Categorization


title: More Robust Hashing: Cuckoo Hashing with a Stash

abstract: Cuckoo hashing holds great potential as a high-performance hashing scheme for real applications. Up to this point, the greatest drawback of cuckoo hashing appears to be that there is a polynomially small but practically significant probability that a failure will occur during the insertion of an item, requiring an expensive rehashing of all items in the table. In this paper, we show that this failure probability can be dramatically reduced by the addition of a very small constant-sized stash. We demonstrate both analytically and through simulations that stashes of size equivalent to only three or four items yield tremendous improvements, enhancing cuckoo hashing's practical viability in both hardware and software. Our analysis naturally extends previous analyses of multiple cuckoo hashing variants, and the approach may prove useful in further related schemes.


title: Supraspinatus and infraspinatus weakness in overhead athletes with scapular dyskinesis: strength assessment before and after restoration of scapular musculature balance

abstract: A disturbance in scapulohumeral rhythm may cause negative biomechanic effects on rotator cuff (RC). Alteration in scapular motion and shoulder pain can influence RC strength. Purpose of this study was to assess supraspinatus and infraspinatus strength in 29 overhead athletes with scapular dyskinesis, before and after 3 and 6 months of rehabilitation aimed to restore scapular musculature balance. A passive posterior soft tissues stretching was prescribed to balance shoulder mobility. Scapular dyskinesis patterns were evaluated according to Kibler et al. Clinical assessment was performed with the empty can (EC) test and infraspinatus strength test (IST). Strength values were recorded by a dynamometer; scores for pain were assessed with VAS scale. Changes of shoulder IR were measured. The force values increased at 3 months (P < 0.01) and at 6 months (P < 0.01). Changes of glenohumeral IR and decrease in pain scores were found at both follow-up. Outcomes registered on pain and strength confirm the role of a proper scapular position for an optimal length–tension relationship of the RC muscles. These data should encourage those caring for athletes to consider restoring of scapular musculature balance as essential part of the athletic training.


Ten Years of WebTables


title: Rectangular Dielectric Resonator Antennas With Enhanced Gain

abstract: Rectangular dielectric resonator antennas were designed to operate at high-order modes to achieve enhanced gain. A simple model was developed to predict the radiation patterns of the higher-order modes. Prototypes designed at 11 GHz showed gains of up to 5 dB above those obtained by the fundamental mode.


title: Fair exchange in E-commerce

abstract: Many business transactions over the Internet involve the exchange of digital products between two parties - electronic mails, digital audio and video, electronic contract signing and digital signatures, to name a few. Often these transactions occur between players that do not trust each other. To facilitate such transactions, a number of secure protocols have been proposed. The main objective of these protocols is: either both the parties obtain each other's items or none do. Sometimes it is not possible to meet the above objective and researchers have aimed for a weaker objective: gather evidence during protocol execution using which an honest party can prove his case. Protocols which meet any of the two objectives are collectively termed fair exchange protocols. In this paper we review some of the work done on such fair exchange protocols and identify areas that still need to be addressed.


title: The Characteristics and Limits of Rapid Visual Categorization

abstract: Visual categorization appears both effortless and virtually instantaneous, the study by Thorpe et al. (1996) was the first to estimate the processing time necessary to perform fast visual categorization of animals in briefly flashed (20ms) natural photographs. They observed a large differential EEG activity between target and distrater correct trials that developed from 150 ms after stimulus onset. A value that was later shown to be even shorter in monkeys! With such strong processing time constraints, it was difficult to escape the conclusion that rapid visual categorization was relying on massively parallel, essentially feed-forward processing of visual information. Since 1996, we have conducted a large number of studies to determine the characteristics and limits of fast visual categorization. The present chapter will review some of the main results obtained. I will argue that rapid object categorizations in natural scenes can be done without focused attention and are most likely based on coarse and unconscious visual representations activated with the first available (magnocellular) visual information. Fast visual processing proved efficient for the categorization of large superordinate object or scene categories, but shows its limits when more detailed basic representations are required. Basic objects (dogs, cars) or scenes (mountain or sea landscapes) representations need additionnal processing time to be activated. A finding that is at odds with the widely accepted idea that such basic representations are at the entry level of the system. Interestingly, focused attention is still not required to perform such, more time consuming, basic categorizations. Finally we will show that object and context processing can interact very early in an ascending wave of visual information processing. We will discuss how such data could result from our experience with a highly structured and predictable surrounding world that shaped neuronal visual selectivity.


title: e-government: ITIL-based service management case study

abstract: E-government services require certain service levels to be achieved as they replace traditional channels. E-government also increases the dependence of government agencies on information technology based services. High quality services entail high performance, availability and scalability among other service characteristics. Strict measures are required to help e-governments evaluate the service level and assess the quality of the service. In this paper we introduce the IT Infrastructure Library (ITIL) framework - a set of best practices to achieve quality service and overcome difficulties associated with the growth of IT systems [17][21]. We conducted an in depth assessment and gap analysis for both of the service support and service delivery processes [16], in a government institution, which allowed us to assess its maturity level within the context of ITIL. We then proposed and modeled these processes in accordance to ITIL best practices and based upon agency aspirations and environment constraints.


title: The Orbitofrontal Cortex and Reward

abstract: The primate orbitofrontal cortex contains the secondary taste cortex, in which the reward value of taste is represented. It also contains the secondary and tertiary olfactory cortical areas, in which information about the identity and also about the reward value of odors is represented. The orbitofrontal cortex also receives information about the sight of objects and faces from the temporal lobe cortical visual areas, and neurons in it learn and reverse the visual stimulus to which they respond when the association of the visual stimulus with a primary reinforcing stimulus (such as a taste reward) is reversed. However, the orbitofrontal cortex is involved in representing negative reinforcers (punishers) too, such as aversive taste, and in rapid stimulus–reinforcement association learning for both positive and negative primary reinforcers. In complementary neuroimaging studies in humans it is being found that areas of the orbitofrontal cortex (and connected subgenual cingulate cortex) are activated by pleasant touch, by painful touch, by rewarding and aversive taste, and by odor. Damage to the orbitofrontal cortex in humans can impair the learning and reversal of stimulus– reinforcement associations, and thus the correction of behavioral responses when these are no longer appropriate because previous reinforcement contingencies change. This evidence thus shows that the orbitofrontal cortex is involved in decoding and representing some primary reinforcers such as taste and touch; in learning and reversing associations of visual and other stimuli to these primary reinforcers; and in controlling and correcting reward-related and punishment-related behavior, and thus in emotion. The prefrontal cortex is the cortex that receives projections from the mediodorsal nucleus of the thalamus and is situated in front of the motor and premotor cortices (areas 4 and 6) in the frontal lobe. Based on the divisions of the mediodorsal nucleus, the prefrontal cortex may be divided into three main regions (Fuster, 1997). First, the magnocellular, medial, part of the mediodorsal nucleus projects to the orbital (ventral) surface of the prefrontal cortex (which includes areas 13 and 12). This part of the prefrontal cortex is called the orbitofrontal cortex, and receives information from the ventral or object-processing visual stream, and taste, olfactory and somatosensory inputs. Second, the parvocellular, lateral, part of the mediodorsal nucleus projects to the dorsolateral prefrontal cortex. This part of the prefrontal cortex receives inputs from the parietal cortex and is involved in tasks such as spatial short-term memory tasks (Fuster, 1997; Rolls


title: A Distributed Requirements Management Framework for Legal Compliance and Accountability

abstract: Increasingly, new regulations are governing organizations and their information systems. Individuals responsible for ensuring legal compliance and accountability currently lack sufficient guidance and support to manage their legal obligations within relevant information systems. While software controls provide assurances that business processes adhere to specific requirements, such as those derived from government regulations, there is little support to manage these requirements and their relationships to various policies and regulations. We propose a requirements management framework that enables executives, business managers, software developers and auditors to distribute legal obligations across business units and/or personnel with different roles and technical capabilities. This framework improves accountability by integrating traceability throughout the policy and requirements lifecycle. We illustrate the framework within the context of a concrete healthcare scenario in which obligations incurred from the Health Insurance Portability and Accountability Act (HIPAA) are delegated and refined into software requirements. Additionally, we show how auditing mechanisms can be integrated into the framework and how auditors can certify that specific chains of delegation and refinement decisions comply with government regulations.


title: Automated segmentation of optic disc region on retinal fundus photographs: Comparison of contour modeling and pixel classification methods

abstract: The automatic determination of the optic disc area in retinal fundus images can be useful for calculation of the cup-to-disc (CD) ratio in the glaucoma screening. We compared three different methods that employed active contour model (ACM), fuzzy c-mean (FCM) clustering, and artificial neural network (ANN) for the segmentation of the optic disc regions. The results of these methods were evaluated using new databases that included the images captured by different camera systems. The average measures of overlap between the disc regions determined by an ophthalmologist and by using the ACM (0.88 and 0.87 for two test datasets) and ANN (0.88 and 0.89) methods were slightly higher than that by using FCM (0.86 and 0.86) method. These results on the unknown datasets were comparable with those of the resubstitution test; this indicates the generalizability of these methods. The differences in the vertical diameters, which are often used for CD ratio calculation, determined by the proposed methods and based on the ophthalmologist's outlines were even smaller than those in the case of the measure of overlap. The proposed methods can be useful for automatic determination of CD ratios.


Propaganda vs . education : a case study of hate radio in Rwanda


An Overview of Non-contact . . .


title: End-to-End Kernel Learning with Supervised Convolutional Kernel Networks

abstract: In this paper, we introduce a new image representation based on a multilayer kernel machine. Unlike traditional kernel methods where data representation is decoupled from the prediction task, we learn how to shape the kernel with supervision. We proceed by first proposing improvements of the recently-introduced convolutional kernel networks (CKNs) in the context of unsupervised learning; then, we derive backpropagation rules to take advantage of labeled training data. The resulting model is a new type of convolutional neural network, where optimizing the filters at each layer is equivalent to learning a linear subspace in a reproducing kernel Hilbert space (RKHS). We show that our method achieves reasonably competitive performance for image classification on some standard "deep learning" datasets such as CIFAR-10 and SVHN, and also for image super-resolution, demonstrating the applicability of our approach to a large variety of image-related tasks.


title: ReRAM Crossbar based Recurrent Neural Network for human activity detection

abstract: We present a programmable high-efficient Recurrent Neural Network (RNN) with Synapses design using Resistive Random Access Memory (ReRAM). The presented ReRAM-RNN employs crossbar ReRAM arrays as synapses. A fast synapses programming methodology is realized by CMOS-based neuron with in-built programming circuitry. The simulations are performed using experimentally verified physical resistive switching model, instead of only functional models, providing better estimate of system speed and power efficiency. Simulation results show that ReRAM-RNN can provide higher computation efficiency and/or more compact design than software realization of RNN, and dedicated CMOS based digital-and analog-RNN. We show that the efficiency improvement of ReRAM-based neural network design is more significant in feedback networks than in feedforward networks.


title: An Empirical Analysis of Bug Reports and Bug Fixing in Open Source Android Apps

abstract: Smartphone platforms and applications (apps) have gained tremendous popularity recently. Due to the novelty of the smartphone platform and tools, and the low barrier to entry for app distribution, apps are prone to errors, which affects user experience and requires frequent bug fixes. An essential step towards correcting this situation is understanding the nature of the bugs and bug-fixing processes associated with smartphone platforms and apps. However, prior empirical bug studies have focused mostly on desktop and server applications. Therefore, in this paper, we perform an in-depth empirical study on bugs in the Google Android smartphone platform and 24 widely-used open-source Android apps from diverse categories such as communication, tools, and media. Our analysis has three main thrusts. First, we define several metrics to understand the quality of bug reports and analyze the bug-fix process, including developer involvement. Second, we show how differences in bug life-cycles can affect the bug-fix process. Third, as Android devices carry significant amounts of security-sensitive information, we perform a study of Android security bugs. We found that, although contributor activity in these projects is generally high, developer involvement decreases in some projects, similarly, while bug-report quality is high, bug triaging is still a problem. Finally, we observe that in Android apps, security bug reports are of higher quality but get fixed slower than non-security bugs. We believe that the findings of our study could potentially benefit both developers and users of Android apps.


title: Traditional Chinese medicinal herbs for the treatment of idiopathic chronic fatigue and chronic fatigue syndrome.

abstract: BACKGROUND ::: Chronic fatigue is increasingly common. Conventional medical care is limited in treating chronic fatigue, leading some patients to use traditional Chinese medicine therapies, including herbal medicine. ::: ::: ::: OBJECTIVES ::: To assess the effectiveness of traditional Chinese medicine herbal products in treating idiopathic chronic fatigue and chronic fatigue syndrome. ::: ::: ::: SEARCH STRATEGY ::: The following databases were searched for terms related to traditional Chinese medicine, chronic fatigue, and clinical trials: CCDAN Controlled Trials Register (July 2009), MEDLINE (1966-2008), EMBASE (1980-2008), AMED (1985-2008), CINAHL (1982-2008), PSYCHINFO (1985-2008), CENTRAL (Issue 2 2008), the Chalmers Research Group PedCAM Database (2004), VIP Information (1989-2008), CNKI (1976-2008), OCLC Proceedings First (1992-2008), Conference Papers Index (1982-2008), and Dissertation Abstracts (1980-2008). Reference lists of included studies and review articles were examined and experts in the field were contacted for knowledge of additional studies. ::: ::: ::: SELECTION CRITERIA ::: Selection criteria included published or unpublished randomized controlled trials (RCTs) of participants diagnosed with idiopathic chronic fatigue or chronic fatigue syndrome comparing traditional Chinese medicinal herbs with placebo, conventional standard of care (SOC), or no treatment/wait lists. The outcome of interest was fatigue. ::: ::: ::: DATA COLLECTION AND ANALYSIS ::: 13 databases were searched for RCTs investigating TCM herbal products for the treatment of chronic fatigue. Over 2400 references were located. Studies were screened and assessed for inclusion criteria by two authors. ::: ::: ::: MAIN RESULTS ::: No studies that met all inclusion criteria were identified. ::: ::: ::: AUTHORS' CONCLUSIONS ::: Although studies examining the use of TCM herbal products for chronic fatigue were located, methodologic limitations resulted in the exclusion of all studies. Of note, many of the studies labelled as RCTs and conducted in China did not utilize rigorous randomization procedures. Improvements in methodology in future studies is required for meaningful synthesis of data.


title: Fuzzy multiple criteria decision making : Recent developments ∗

abstract: Abstract Multiple criteria decision making (MCDM) shows signs of becoming a maturing field. There are four quite distinct families of methods: (i) the outranking, (ii) the value and utility theory based, (iii) the multiple objective programming, and (iv) group decision and negotiation theory based methods. Fuzzy MCDM has basically been developed along the same lines, although with the help of fuzzy set theory a number of innovations have been made possible; the most important methods are reviewed and a novel approach — interdependence in MCDM — is introduced.


title: Big data challenge: a data management perspective

abstract: There is a trend that, virtually everyone, ranging from big Web companies to traditional enterprisers to physical science researchers to social scientists, is either already experiencing or anticipating unprecedented growth in the amount of data available in their world, as well as new opportunities and great untapped value. This paper reviews big data challenges from a data management respective. In particular, we discuss big data diversity, big data reduction, big data integration and cleaning, big data indexing and query, and finally big data analysis and mining. Our survey gives a brief overview about big-data-oriented research and problems.


title: Novel trends in automotive networks: A perspective on Ethernet and the IEEE Audio Video Bridging

abstract: Ethernet is going to play a major role in automotive communications, thus representing a significant paradigm shift in automotive networking. Ethernet technology will allow for multiple in-vehicle systems (such as, multimedia/infotainment, camera-based advanced driver assistance and on-board diagnostics) to simultaneously access information over a single unshielded twisted pair cable. The leading technology for automotive applications is the IEEE Audio Video Bridging (AVB), which offers several advantages, such as open specification, multiple sources of electronic components, high bandwidth, the compliance with the challenging EMC/EMI automotive requirements, and significant savings on cabling costs, thickness and weight. This paper surveys the state of the art on Ethernet-based automotive communications and especially on the IEEE AVB, with a particular focus on the way to provide support to the so-called scheduled traffic, that is a class of time-sensitive traffic (e.g., control traffic) that is transmitted according to a time schedule.


title: From image-level to pixel-level labeling with Convolutional Networks

abstract: We are interested in inferring object segmentation by leveraging only object class information, and by considering only minimal priors on the object segmentation task. This problem could be viewed as a kind of weakly supervised segmentation task, and naturally fits the Multiple Instance Learning (MIL) framework: every training image is known to have (or not) at least one pixel corresponding to the image class label, and the segmentation task can be rewritten as inferring the pixels belonging to the class of the object (given one image, and its object class). We propose a Convolutional Neural Network-based model, which is constrained during training to put more weight on pixels which are important for classifying the image. We show that at test time, the model has learned to discriminate the right pixels well enough, such that it performs very well on an existing segmentation benchmark, by adding only few smoothing priors. Our system is trained using a subset of the Imagenet dataset and the segmentation experiments are performed on the challenging Pascal VOC dataset (with no fine-tuning of the model on Pascal VOC). Our model beats the state of the art results in weakly supervised object segmentation task by a large margin. We also compare the performance of our model with state of the art fully-supervised segmentation approaches.


title: A new version of the flusser invariants set for pattern feature extraction

abstract: The choice of an appropriate feature extraction method is essential for the success of the following recognition/classification process. This paper proposes a design method for a new pattern descriptors set belonging to the Flusser moments family, which is invariant to the elementary geometric transformations and respectively, has an increased noise robustness. Experimental results based on a real video image database, confirm the basic properties of this new descriptors set.


title: Lambek vs. Lambek: Functorial Vector Space Semantics and String Diagrams for Lambek Calculus

abstract: Abstract The Distributional Compositional Categorical (DisCoCat) model is a mathematical framework that provides compositional semantics for meanings of natural language sentences. It consists of a computational procedure for constructing meanings of sentences, given their grammatical structure in terms of compositional type-logic, and given the empirically derived meanings of their words. For the particular case that the meaning of words is modelled within a distributional vector space model, its experimental predictions, derived from real large scale data, have outperformed other empirically validated methods that could build vectors for a full sentence. This success can be attributed to a conceptually motivated mathematical underpinning, something which the other methods lack, by integrating qualitative compositional type-logic and quantitative modelling of meaning within a category-theoretic mathematical framework. The type-logic used in the DisCoCat model is Lambekʼs pregroup grammar. Pregroup types form a posetal compact closed category, which can be passed, in a functorial manner, on to the compact closed structure of vector spaces, linear maps and tensor product. The diagrammatic versions of the equational reasoning in compact closed categories can be interpreted as the flow of word meanings within sentences. Pregroups simplify Lambekʼs previous type-logic, the Lambek calculus. The latter and its extensions have been extensively used to formalise and reason about various linguistic phenomena. Hence, the apparent reliance of the DisCoCat on pregroups has been seen as a shortcoming. This paper addresses this concern, by pointing out that one may as well realise a functorial passage from the original type-logic of Lambek, a monoidal bi-closed category, to vector spaces, or to any other model of meaning organised within a monoidal bi-closed category. The corresponding string diagram calculus, due to Baez and Stay, now depicts the flow of word meanings, and also reflects the structure of the parse trees of the Lambek calculus.


title: High-Power Terahertz Generation Using Large-Area Plasmonic Photoconductive Emitters

abstract: In this paper, we present a novel design of large-area photoconductive emitters which incorporates plasmonic contact electrodes to offer significantly higher optical-to-terahertz conversion efficiencies compared with conventional designs. Use of plasmonic contact electrodes enables a more efficient separation and acceleration of photocarriers, enhancing the effective dipole moment induced within the device active area in response to an incident optical pump. At an optical pump power level of 240 mW, we demonstrate broadband, pulsed terahertz radiation with radiation power levels as high as 3.8 mW over the 0.1–5-THz frequency range, exhibiting an order of magnitude higher optical-to-terahertz conversion efficiency compared with conventional designs.


title: Solution of a large-scale traveling-salesman problem

abstract: The RAND Corporation in the early 1950s contained “what may have been the most remarkable group of mathematicians working on optimization ever assembled” [6]: Arrow, Bellman, Dantzig, Flood, Ford, Fulkerson, Gale, Johnson, Nash, Orchard-Hays, Robinson, Shapley, Simon, Wagner, and other household names. Groups like this need their challenges. One of them appears to have been the traveling salesman problem (TSP) and particularly its instance of finding a shortest route through Washington, DC, and the 48 states [4, 7].


title: Wireless interfacing to cortical neural recording implants using 4-FSK modulation scheme

abstract: This paper used a 4-level Frequency Shift Keying (4-FSK) modulation scheme to enhance the density of wireless data transfer from implantable biomedical microsystems to the outside world. Modeling and simulation of the wireless channel for 4-FSK modulation in the case of a neural recording implant has been done. To realize the 4-FSK scheme, the modulator and demodulator circuits are proposed, designed and simulated in a 0.18-μm CMOS process, and in the 174–216 MHz frequency band at a data rate of 13.5 Mbps. Operated using a 1.8 V supply voltage, the modulator circuit consumes a power of 7.8 μW.


title: Inferring Parameters for an Elementary Step Model of DNA Structure Kinetics with Locally Context-Dependent Arrhenius Rates

abstract: Models of nucleic acid thermal stability are calibrated to a wide range of experimental observations, and typically predict equilibrium probabilities of nucleic acid secondary structures with reasonable accuracy. By comparison, a similar calibration and evaluation of nucleic acid kinetic models to a broad range of measurements has not been attempted so far. We introduce an Arrhenius model of interacting nucleic acid kinetics that relates the activation energy of a state transition with the immediate local environment of the affected base pair. Our model can be used in stochastic simulations to estimate kinetic properties and is consistent with existing thermodynamic models. We infer parameters for our model using an ensemble Markov chain Monte Carlo (MCMC) approach on a training dataset with 320 kinetic measurements of hairpin closing and opening, helix association and dissociation, bubble closing and toehold-mediated strand exchange. Our new model surpasses the performance of the previously established Metropolis model both on the training set and on a testing set of size 56 composed of toehold-mediated 3-way strand displacement with mismatches and hairpin opening and closing rates: reaction rates are predicted to within a factor of three for \(93.4\%\) and \(78.5\%\) of reactions for the training and testing sets, respectively.


title: On Mixing Fingerprints

abstract: This work explores the possibility of mixing two different fingerprints, pertaining to two different fingers, at the image level in order to generate a new fingerprint. To mix two fingerprints, each fingerprint pattern is decomposed into two different components, viz., the continuous and spiral components. After prealigning the components of each fingerprint, the continuous component of one fingerprint is combined with the spiral component of the other fingerprint. Experiments on the West Virginia University (WVU) and FVC2002 datasets show that mixing fingerprints has several benefits: (a) it can be used to generate virtual identities from two different fingers; (b) it can be used to obscure the information present in an individual's fingerprint image prior to storing it in a central database; and (c) it can be used to generate a cancelable fingerprint template, i.e., the template can be reset if the mixed fingerprint is compromised.


title: The Role of Gravity in Periorbital and Midfacial Aging

abstract: Background: With respect to the pathogenesis of periorbital and midfacial aging, gravity may play a greater role than volume loss. ::: ::: Objectives: The authors determined the effect of shifting from the upright to the supine position on specific attributes of facial appearance and ascertained whether facial appearance in the supine position bore any resemblance to its appearance in youth. ::: ::: Methods: Participants who showed signs of midface aging were positioned in the upright and supine positions, and photographs were obtained during smiling and repose. For each photograph, examiners graded the following anatomic parameters, using a standardized scale: brow position, tear trough length and depth, steatoblepharon, cheek volume, malar bags/festoons, and nasolabial folds. Some participants provided photographs of themselves taken 10 to 15 years earlier; these were compared with the study images. ::: ::: Results: Interobserver correlation was strong. When participants were transferred from upright to supine, all anatomic parameters examined became more youthful in appearance; findings were statistically significant. The grading of anatomic parameters of the earlier photographs most closely matched that of current supine photographs of the subjects smiling. ::: ::: Conclusions: In the supine position, as opposed to the upright position, participants with signs of midface aging appear to have much more volume in the periorbita and midface. For the subset of participants who provided photographs obtained 10 to 15 years earlier, the appearance of facial volume was similar between those images and the current supine photographs. This suggests that volume displacement due to gravitational forces plays an integral role in the morphogenesis of midface aging.


title: Memristor Applications for Programmable Analog ICs

abstract: This paper demonstrates that memristors can be used to implement programmable analog circuits, leveraging memristor's fine-resolution programmable resistance without causing perturbations due to parasitic components. Fine-resolution programmable resistance is achieved by varying the amount of flux across memristors. The resistance programming can be achieved by controlling the input pulsewidth and its frequency. For demonstration, a memristor is designed for a pulse-programmable midband differential gain amplifier with fine resolution.


title: A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking

abstract: Bayesian methods provide a rigorous general framework for dynamic state estimation problems. We describe the nonlinear/non-Gaussian tracking problem and its optimal Bayesian solution. Since the optimal solution is intractable, several different approximation strategies are then described. These approaches include the extended Kalman filter and particle filters. For a particular problem, if the assumptions of the Kalman filter hold, then no other algorithm can out-perform it. However, in a variety of real scenarios, the assumptions do not hold and approximate techniques must be employed. The extended Kalman filter approximates the models used for the dynamics and measurement process, in order to be able to approximate the probability density by a Gaussian. Particle filtering approximates the density directly as a finite number of samples. A number of different types of particle filter exist and some have been shown to outperform others when used for particular applications. However, when designing a particle filter for a particular application, it is the choice of importance density that is critical. These notes are of a tutorial nature and so, to facilitate easy implementation, 'pseudo-code' for algorithms are included at relevant points.


title: Blockchain as a Notarization Service for Data Sharing with Personal Data Store

abstract: Personal data such as electronic medical records and academic records are critical and sensitive private information. These personal information is usually hosted across many data-custodian systems. Personal Data Store (PDS) is a service that lets an individual store, manage and deploy their key personal data in a highly secure and structured way. It also gives the user a central point of control for their personal information. One of the inherent problems of digital records is that it can be easily forged. Therefore, the data-consumer(with whom the data is shared) often needs to verify the authenticity of the shared document/record by communicating with the document/certificate issuing authority (e.g., data custodian). However, this process is time consuming and inefficient. In recent time, blockchain has gained tremendous attention from both industry and academia for distributed recording and immutable transactions. Blockchain provides a shared, immutable and transparent history of transactions enabling the building of applications that incorporate trust, accountability and transparency. This provides a unique opportunity to develop a secure and trustable data sharing system using blockchain. However, blockchain is primarily proposed for publicly verifiable transactions and does not provide privacy to the individuals. In this paper, we propose a data sharing framework that will guarantee the authenticity of the shared data in real-time and provide transactional privacy in a blockchain network. We have implemented our framework in a prototype that ensures privacy, integrity, and fine-grained access control over the shared data. The proposed work can significantly reduce the turnaround time for data sharing, improve the decision making process and reduce the overall cost.


title: DroidPro: An AOTC-Based Bytecode-Hiding Scheme for Packing the Android Applications

abstract: Android is an open source mobile operating system represented by the Open Handset Alliance (OHA), developed by Google and other organizations since 2007, which has taken up most of the market share of smart devices. However, the applications on the platform are facing the increasingly serious security threat. Although the Android system itself provides a set of security mechanism to protect the safety of the system and applications, there are still many security risks. In order to hide the vulnerability of the applications and prevent the malicious users from tampering the apps, multiple anti-analysis methods have been applied by many Android packers to consolidate the apps. Bytecode-hiding is one of the most effective anti-analysis method, which can extract some bytecode from the Dex files and hide them from the vision of malicious analysts. Mostly, the hidden bytecode was encrypted, which can be recovered in runtime. But the conventional bytecode-hiding methods are always low-efficient and unsafe on some occasions, where the hidden bytecode can be recovered by the malicious analysts in some way. In this paper, we propose a bytecode-hiding scheme based on Ahead-Of-Time (AOT) compilation, called DroidPro, which can compile some chosen bytecode of Dex files of apps to native code in ahead-of-time that will be much harder to reverse. In our experiments, the apps packed by the packer associated with our bytecode-hiding scheme are more efficient and safer than other packers that use other bytecode-hiding schemes.


title: Design of an RGBW color VGA rolling and global shutter dynamic and active-pixel vision sensor

abstract: This paper reports the design of a color dynamic and active-pixel vision sensor (C-DAVIS) for robotic vision applications. The C-DAVIS combines monochrome eventgenerating dynamic vision sensor pixels and 5-transistor active pixels sensor (APS) pixels patterned with an RGBW color filter array. The C-DAVIS concurrently outputs rolling or global shutter RGBW coded VGA resolution frames and asynchronous monochrome QVGA resolution temporal contrast events. Hence the C-DAVIS is able to capture spatial details with color and track movements with high temporal resolution while keeping the data output sparse and fast. The C-DAVIS chip is fabricated in TowerJazz 0.18um CMOS image sensor technology. An RGBW 2×2-pixel unit measures 20um × 20um. The chip die measures 8mm × 6.2mm.


title: Learning (predictive) risk scores in the presence of censoring due to interventions

abstract: A large and diverse set of measurements are regularly collected during a patient's hospital stay to monitor their health status. Tools for integrating these measurements into severity scores, that accurately track changes in illness severity, can improve clinicians ability to provide timely interventions. Existing approaches for creating such scores either 1) rely on experts to fully specify the severity score, or 2) train a predictive score, using supervised learning, by regressing against a surrogate marker of severity such as the presence of downstream adverse events. The first approach does not extend to diseases where an accurate score cannot be elicited from experts. The second approach often produces scores that suffer from bias due to treatment-related censoring (Paxton, 2013). We propose a novel ranking based framework for disease severity score learning (DSSL). DSSL exploits the following key observation: while it is challenging for experts to quantify the disease severity at any given time, it is often easy to compare the disease severity at two different times. Extending existing ranking algorithms, DSSL learns a function that maps a vector of patient's measurements to a scalar severity score such that the resulting score is temporally smooth and consistent with the expert's ranking of pairs of disease states. We apply DSSL to the problem of learning a sepsis severity score using a large, real-world dataset. The learned scores significantly outperform state-of-the-art clinical scores in ranking patient states by severity and in early detection of future adverse events. We also show that the learned disease severity trajectories are consistent with clinical expectations of disease evolution. Further, using simulated datasets, we show that DSSL exhibits better generalization performance to changes in treatment patterns compared to the above approaches.


title: Structural Equations Modeling: Fit Indices, Sample Size, and Advanced Topics

abstract: This article is the second of two parts intended to serve as a primer for structural equations models for the behavioral researcher. The first article introduced the basics: the measurement model, the structural model, and the combined, full structural equations model. In this second article, advanced issues are addressed, including fit indices and sample size, moderators, longitudinal data, mediation, and so forth.


title: Heterogeneity of Dyggve-Melchior-Clausen dwarfism

abstract: Sibs with apparent Dyggve-Melchior-Clausen (DMC) dwarfism and normal intelligence are described. Three other familial and 3 sporadic cases with DMC dwarfism and normal intelligence are known. Twelve familial and 9 sporadic cases are known with the usual combination of DMC dwarfism and severe mental retardation. Since the two conditions appear to breed true they seem to be genetically different. We propose to name the former "Smith-McCort dwarfism" to clearly distinguish it from the DMC syndrome in which mental retardation is a constituent part. Both conditions are inherited as autosomal recessive traits. Spinal cord compression due to atlantoaxial instability is a serious and preventable complication of both disorders.


title: Angiotensin II enhances thrombosis development in renovascular hypertensive rats

abstract: There is an increased number of in vitro evidence that angiotensin II (Ang II) may promote thrombosis. However there are no in vivo experiments exploring the effect of Ang II on thrombus formation. In the present study we have investigated the influence of Ang II on venous thrombosis in renovascular hypertensive rats. Furthermore, we examined the role of AT1 receptor and Ang II metabolites: angiotensin III (Ang III) and angiotensin IV (Ang IV) in the mechanisms of Ang II action.The contribution of coagulation and fibrinolytic systems in the mode of Ang II action was also determined.Venous thrombosis was induced by ligation of vena cava. Ang II infused into rats developing ve nous thrombosis caused dose-dependent increase in thrombus weight, which was partially reversed by losartan, selective AT1 antagonist.Ang III did not influence the thrombus formation in hypertensive rats, while Ang IV caused a marked increase in thrombus weight only in one of the used doses. Our study shows that Ang II via AT1 receptor enhances thrombosis development. The prothrombotic effect of Ang II may partially depend on enhanced leukocytes adhesion to endothelial cells accompanied by accelerated fibrin formation and increased plasma level of PAI-1. Moreover,Ang II action is partially mediated by one of its metabolites – Ang IV.


title: Multi-Cell Multiuser Massive MIMO Networks: User Capacity Analysis and Pilot Design

abstract: We propose a novel pilot sequence design to mitigate pilot contamination in multi-cell multiuser massive multiple-input multiple-output networks. Our proposed design generates pilot sequences in the multi-cell network and devises power allocation at base stations (BSs) for downlink transmission. The pilot sequences together with the power allocation ensure that the user capacity of the network is achieved and the pre-defined signal-to-interference-plus-noise ratio (SINR) requirements of all users are met. To realize our design, we first derive new closed-form expressions for the user capacity and the user capacity region. Built upon these expressions, we then develop a new algorithm to obtain the required pilot sequences and power allocation. We further determine the minimum number of antennas required at the BSs to achieve certain SINR requirements of all users. The numerical results are presented to corroborate our analysis and to examine the impact of key parameters, such as the pilot sequence length and the total number of users, on the network performance. A pivotal conclusion is reached that our design achieves a larger user capacity region than the existing designs and needs less antennas at the BS to fulfill the pre-defined SINR requirements of all users in the network than the existing designs.


title: Constructive model-based analysis for safety assessment

abstract: The aerospace industry still uses fault trees to perform reliability analysis. This is because fault-tree modeling and analysis (FTA) seems easier to practical engineers when compared with Markov models, even though FTA provides a weaker form of analysis. In this paper, we propose an automatic strategy for generating Markov-based models and corresponding analysis formulations, according to ARP 4761, directly from Simulink diagrams annotated with failure information. The generated Markov-based models are expressed in the formal language PRISM, and the analysis is carried out by the PRISM model checker. The strategy is compositional and based on a comprehensive set of translation rules from Simulink to PRISM. We briefly address soundness and completeness of the rules and, to illustrate the application of the strategy, we apply it to a classical avionics case study: an actuator control system.


Generating Regular Expressions from Natural Language Specifications: Are We There Yet?


title: Direction Finding of ground based emitters from airborne platforms

abstract: In this paper we discuss and analyze the Direction Finding (DF) capability of an airborne Communication Electronic Support Measures (CESM) system employing ldquocorrelative interferometryrdquo in particular we underline the importance of elevation accuracy for airborne platforms.


title: Incidence of Lower Limbs Deep Vein Thrombosis After Open and Laparoscopic Gastric Bypass: A Prospective Study

abstract: Background ::: Lower limbs deep vein thrombosis (DVT) and pulmonary embolism (PE) are major causes of morbidity and mortality and are even higher in bariatric patients. The longer operative time and higher immobility in these patients increase the DVT risk. Although deaths after bariatric surgery have been reported, there is no consensus regarding the prophylaxis of DVT. This study’s objective is to determine the incidence of lower limbs DVT in patients submitted to Roux-en-Y-gastric bypass (RYGBP) under prophylaxis by enoxaparin.


title: Connecting the dots between news articles

abstract: The process of extracting useful knowledge from large datasets has become one of the most pressing problems in today's society. The problem spans entire sectors, from scientists to intelligence analysts and web users, all of whom are constantly struggling to keep up with the larger and larger amounts of content published every day. With this much data, it is often easy to miss the big picture. In this paper, we investigate methods for automatically connecting the dots -- providing a structured, easy way to navigate within a new topic and discover hidden connections. We focus on the news domain: given two news articles, our system automatically finds a coherent chain linking them together. For example, it can recover the chain of events starting with the decline of home prices (January 2007), and ending with the ongoing health-care debate. We formalize the characteristics of a good chain and provide an efficient algorithm (with theoretical guarantees) to connect two fixed endpoints. We incorporate user feedback into our framework, allowing the stories to be refined and personalized. Finally, we evaluate our algorithm over real news data. Our user studies demonstrate the algorithm's effectiveness in helping users understanding the news.


title: Proctalgia fugax, an evidence-based management pathway

abstract: Purpose ::: Proctalgia fugax (PF) is a benign anorectal condition which has been described in the literature since the nineteenth century commonly presenting to general surgeons. There is little high level evidence on the subject and its therapeutic modalities. We aimed through this systematic literature review to outline the definition and diagnostic criteria of this condition, the aetiology and differential diagnoses and describe the different treatment modalities that have been attempted and their success.


title: Novel intron 2 polymorphism in the melanophilin gene is in Hardy-Weinberg equilibrium and is not associated with coat color in goats

abstract: Pigmentation plays important ::: adaptation and physiological efficiency roles in animals. In the sequence of ::: a 648 bp fragment representing intron 1, exon 2, and part of intron 2 of the ::: MLPH mammalian pigmentation gene, we identified a novel g.469C> G mutation in intron 2, and ::: genotyped it in 266 Nigerian goats using PCR-RFLP analysis. The C allele had ::: frequencies of 0.9625, 0.9804 and 0.97405 ::: in West African Dwarf (WAD),Sahel(SH) and Red Sokoto (RS) breeds, respectively. The G allele was the highest in ::: WAD (0.0375), followed by RS (0.02595), and then SH (0.0196). Overall low FIS ::: and FST and high Nm values demonstrate little differentiation within and among ::: the goat breeds at this intronic locus. This g.469C> G polymorphism in MLPH gene is the first in ::: any goat breed and also first in Nigerian goats. Our results suggest that this ::: intronic SNP locus is maintained at Hardy-Weinberg equilibrium (P


title: Short-Term Wind Power Forecasting Using Artificial Neural Networks for Resource Scheduling in Microgrids

abstract: Use of wind power as one of renewable resources of energy has been growing quickly all over the world. Wind power generation is significantly vacillating due to the wind speed alteration. Therefore, assessment of the output power of this type of generators is always associated with some uncertainties. A precise wind power prediction aims to support the operation of large power systems or microgrids in the scope of the intraday resources scheduling model, namely with a time horizon of 5-10 minutes, and this can efficiently uphold transmission and distribution system operators to improve the power network control and management. A Shortterm ANN based wind power prediction model, for day-ahead energy management and scheduling in microgrids, is developed utilizing a real database of 12 months with 10 minutes time interval data from measured information of online supervisory control and data acquisition (SCADA) of Goldwind microgrid wind turbine system (Beijing, China) as well as Numerical Weather Prediction (NWP). The ANN predicted wind power has been compared with the actual power of Goldwind wind turbine system. The predicted wind power has shown acceptable agreement with the actual power output that has been recorded by the SCADA which confirms the robustness and accuracy of the ANN wind power forecasting model developed in this research paper.


title: MBBA: A Multi-Bandwidth Bus Arbiter for Hard Real-Time

abstract: Multi-core architectures are being increasingly used in embedded systems as they offer several advantages: improved hardware integration, low thermal dissipation and reduced energy consumption, while they make it possible to improve the computing power. In order to run real-time software on a multicore architecture, computing the Worst-Case Execution Time of every thread should be achievable. This notably involves bounding memory latencies by employing a predictable bus arbiter. However, state-of-the-art techniques prove to be irrelevant to schedule unbalanced workloads in which some threads require more bus bandwidth than the other ones. This paper proposes a new bus arbitration scheme that ensures that the shared bus latencies can be upper bounded. Compared to other schemes that make the bus latencies predictable, like the Round-Robin protocol, our approach defines several levels of bandwidth to meet requirements that may vary from one thread to another. Experimental results (WCET estimates) show that the worst-case bus latency is noticeably shortened, compared to Round-Robin, for the cores with highest priority that get the largest bandwidth. The relevance of the scheme is shown through an example workload composed of various benchmarks.


title: Fast Image Super-Resolution Based on In-Place Example Regression

abstract: We propose a fast regression model for practical single image super-resolution based on in-place examples, by leveraging two fundamental super-resolution approaches- learning from an external database and learning from self-examples. Our in-place self-similarity refines the recently proposed local self-similarity by proving that a patch in the upper scale image have good matches around its origin location in the lower scale image. Based on the in-place examples, a first-order approximation of the nonlinear mapping function from low-to high-resolution image patches is learned. Extensive experiments on benchmark and real-world images demonstrate that our algorithm can produce natural-looking results with sharp edges and preserved fine details, while the current state-of-the-art algorithms are prone to visual artifacts. Furthermore, our model can easily extend to deal with noise by combining the regression results on multiple in-place examples for robust estimation. The algorithm runs fast and is particularly useful for practical applications, where the input images typically contain diverse textures and they are potentially contaminated by noise or compression artifacts.


title: A Dynamic Model of Crowdfunding

abstract: Crowdfunding is quickly emerging as an alternative to traditional methods of funding new products. In a crowdfunding campaign, a seller solicits financial contributions from a crowd, usually in the form of pre-buying an unrealized product, and commits to producing the product if the total amount pledged is above a certain threshold. We provide a model of crowdfunding in which consumers arrive sequentially and make decisions about whether to pledge or not. Pledging is not costless, and hence consumers would prefer not to pledge if they think the campaign will not succeed. This can lead to cascades where a campaign fails to raise the required amount even though there are enough consumers who want the product. The paper introduces a novel stochastic process --- anticipating random walks --- to analyze this problem. The analysis helps explain why some campaigns fail and some do not, and provides guidelines about how sellers should design their campaigns in order to maximize their chances of success. More broadly, Anticipating Random Walks can also find application in settings where agents make decisions sequentially and these decisions are not just affected by past actions of others, but also by how they will impact the decisions of future actors as well.


title: Novel method to improve vascularization of tissue engineered constructs with biodegradable fibers.

abstract: Tissue engineered grafts lack adequate vascularization and suffer from poor perfusion in vivo curtailing clinical application. Improving vascularization in any tissue implants would hence increase their survivability and treatment efficacy. Many prevascularization strategies established to date involves the angiogenic induction of endothelial progenitor cells in thick tissue engineered scaffolds to obtain vascularization. These 3D scaffolds typically require a dynamic cell culturing system involving/needful of bioreactors to obtain vascularization in thick tissue engineered scaffolds. Herein, we developed a novel method to engineer a vessel network without bioreactor, where 3D blood vessels could be simply obtained in a 2D static cell culturing system. This network could be used to augment the prevascularization of tissue engineered grafts. Endothelial cells (HUVECs) were confluently cultured on resorbable electrospun poly (D, L-lactide-co-glycolide) microfibers of capillary dimensions. These cell encapsulated capillary fibers were further embedded in collagen with HUVECs and vascular endothelial growth factor. Green fluorescent protein and red fluorescent protein expressing HUVECs were used to label cells on fiber and in collagen respectively for visualization and monitoring of capillary network formation. Seeded HUVECs in the hybrid construct were subsequently cultured for 30 days before implantation. Vessel density was measured by the total tubule length per unit area at different time points. In vitro results indicated that the fibers provide contact guidance to form primary networks to direct more vessels branching of HUVECs in hybrid constructs and the vessel integrity of microvasculature was retained after fiber degradation. In addition, these preformed engineered capillaries could capably inosculate with de novo capillaries in collagen when combined, giving rise to a hybrid pre-vascularized scaffold of more extensive vessel network and interconnections, thereby markedly improved prevascularization. When implanted onto the dorsal skin of immune-deficient mice, vessels of hybrid pre-vascularized scaffold also rapidly anastomosed with mice vasculature within a day as confirmed with the immunostaining of endothelial cell markers CD31 and von Willebrand factor. This proof of concept study showed that artificial capillaries formed through contact guidance of endothelial cells on resorbable capillary sized microfibers can significantly enhance prevascularization in tissue engineered constructs intended for surgical implantation.


title: The Multilingual Paraphrase Database

abstract: We release a massive expansion of the paraphrase database (PPDB) that now includes a collection of paraphrases in 23 different languages. The resource is derived from large volumes of bilingual parallel data. Our collection is extracted and ranked using state of the art methods. The multilingual PPDB has over a billion paraphrase pairs in total, covering the following languages: Arabic, Bulgarian, Chinese, Czech, Dutch, Estonian, Finnish, French, German, Greek, Hungarian, Italian, Latvian, Lithuanian, Polish, Portugese, Romanian, Russian, Slovak, Slovenian, and Swedish.


title: Regression testing minimization, selection and prioritization: a survey

abstract: Regression testing is a testing activity that is performed to provide confidence that changes do not harm the existing behaviour of the software. Test suites tend to grow in size as software evolves, often making it too costly to execute entire test suites. A number of different approaches have been studied to maximize the value of the accrued test suite: minimization, selection and prioritization. Test suite minimization seeks to eliminate redundant test cases in order to reduce the number of tests to run. Test case selection seeks to identify the test cases that are relevant to some set of recent changes. Test case prioritization seeks to order test cases in such a way that early fault detection is maximized. This paper surveys each area of minimization, selection and prioritization technique and discusses open problems and potential directions for future research. Copyright © 2010 John Wiley & Sons, Ltd.


title: Collision free navigation of flying robots among moving obstacles

abstract: A non-holonomic under-actuated robot with a bounded control input travels in a dynamic unknown 3D environment with moving obstacles. We propose a 3D navigation strategy to reach a given final destination point while avoiding collisions with obstacles. Computer simulation results confirm the performance of the proposed guidance approach.


title: Measuring the Size of Large No-Limit Poker Games

abstract: In the eld of computational game theory, games are often compared in terms of their size. This can be measured in several ways, including the number of unique game states, the number of decision points, and the total number of legal actions over all decision points. These numbers are either known or estimated for a wide range of classic games such as chess and checkers. In the stochastic and imperfect information game of poker, these sizes are easily computed in \limit" games which restrict the players’ available actions, but until now had only been estimated for the more complicated \no-limit" variants. In this paper, we describe a simple algorithm for quickly computing the size of two-player no-limit poker games, provide an implementation of this algorithm, and present for the rst time precise counts of the number of game states, information sets, actions and terminal nodes in the no-limit poker games played in the Annual Computer Poker Competition.


title: Darknet and deepnet mining for proactive cybersecurity threat intelligence

abstract: In this paper, we present an operational system for cyber threat intelligence gathering from various social platforms on the Internet particularly sites on the darknet and deepnet. We focus our attention to collecting information from hacker forum discussions and marketplaces offering products and services focusing on malicious hacking. We have developed an operational system for obtaining information from these sites for the purposes of identifying emerging cyber threats. Currently, this system collects on average 305 high-quality cyber threat warnings each week. These threat warnings include information on newly developed malware and exploits that have not yet been deployed in a cyber-attack. This provides a significant service to cyber-defenders. The system is significantly augmented through the use of various data mining and machine learning techniques. With the use of machine learning models, we are able to recall 92% of products in marketplaces and 80% of discussions on forums relating to malicious hacking with high precision. We perform preliminary analysis on the data collected, demonstrating its application to aid a security expert for better threat analysis.


title: Learning by Example: Training Users with High-quality Query Suggestions

abstract: The queries submitted by users to search engines often poorly describe their information needs and represent a potential bottleneck in the system. In this paper we investigate to what extent it is possible to aid users in learning how to formulate better queries by providing examples of high-quality queries interactively during a number of search sessions. By means of several controlled user studies we collect quantitative and qualitative evidence that shows: (1) study participants are able to identify and abstract qualities of queries that make them highly effective, (2) after seeing high-quality example queries participants are able to themselves create queries that are highly effective, and, (3) those queries look similar to expert queries as defined in the literature. We conclude by discussing what the findings mean in the context of the design of interactive search systems.


title: An intent-based approach for network virtualization

abstract: Virtualizing resources for easy pooling and accounting, as well as for rapid provisioning and release, is essential for the effective management of modern data centers. Although the compute and storage resources can be virtualized quite effectively, a comprehensive solution for network virtualization has yet to be developed. Our analysis of the requirements for a comprehensive network virtualization solution identified two complimentary steps of ultimate importance. One is specifying the network-related requirements, another is carrying out the requirements of multiple independent tenants in an efficient and scalable manner. We introduce a novel intent-based modeling abstraction for specifying the network as a policy governed service and present an efficient network virtualization architecture, Distributed Overlay Virtual Ethernet network (DOVE), realizing the proposed abstraction. We describe the working prototype of DOVE architecture and report the results of the extensive simulation-based performance study, demonstrating the scalability and the efficiency of the solution.


title: Reporting usability defects: do reporters report what software developers need?

abstract: Reporting usability defects can be a challenging task, especially in convincing the software developers that the reported defect actually requires attention. Stronger evidence in the form of specific details is often needed. However, research to date in software defect reporting has not investigated the value of capturing different information based on defect type. We surveyed practitioners in both open source communities and industrial software organizations about their usability defect reporting practices to better understand information needs to address usability defect reporting issues. Our analysis of 147 responses show that reporters often provide observed result, expected result and steps to reproduce when describing usability defects, similar to the way other types of defects are reported. However, reporters rarely provide usability-related information. In fact, reporters ranked cause of the problem is the most difficult information to provide followed by usability principle, video recoding, UI event trace and title. Conversely, software developers consider cause of the problem as the most helpful information for them to fix usability defects. Our statistical analysis reveals a substantial gap between what reporters provide and what software developers need when fixing usability defects. We propose some remedies to resolve this gap.


title: Fuzzy evolutionary cellular learning automata model for text summarization

abstract: Abstract Text summarization is the automatic process of creating a short form of an original text. The main goal of an automatic text summarization system is production of a summary which satisfies the user's needs. In this paper, a new model for automatic text summarization is introduced which is based on fuzzy logic system, evolutionary algorithms and cellular learning automata. First, the most important features including word features, similarity measure, and the position and the length of a sentence are extracted. A linear combination of these features shows the importance of each sentence. To calculate similarity measure, a combined method based on artificial bee colony algorithm and cellular learning automata are used. In this method, joint n-grams among sentences are extracted by cellular learning automata and then an artificial bee colony algorithm classifies n-friends in order to extract data and optimize the similarity measure as fitness function. Moreover, a new approach is proposed to adjust the best weights of the text features using particle swarm optimization and genetic algorithm. This method discovers more important and less important text features and then assigns fair weights to them. At last, a fuzzy logic system is used to perform the final scoring. The results of the proposed approach were compared with the other methods including Msword, System19, System21, System28, System31, FSDH, FEOM, NetSum, CRF, SVM, DE, MA-SingleDocSum, Unified Rank and Manifold Ranking using ROUGE-l and ROUGE-2 measures on the DUC2002 dataset. The results show that proposed method outperforms the aforementioned methods.


title: A Predictive Investigation of Reputation as Mediator of the Political-Skill/Career-Success Relationship1

abstract: Career success is determined by a number of factors, including some combination of specific competencies and a performance record, along with network development, organizational politics, and reputation building. Theory and research suggest that employees’ political skill predicts their career success, and that this relationship is mediated by employees’ reputation in the workplace. These hypotheses were tested in a predictive study, collecting 2 waves of data from 135 career employees, covering a 1-year timeframe. Political skill at Time 1 predicted hierarchical position, income, and career satisfaction at Time 2. Furthermore, reputation mediated the relationships between political skill, hierarchical position, and career satisfaction. Contributions, implications, limitations, strengths, and future research directions are discussed. jasp_862 1..24


title: NVIS: an interactive visualization tool for neural networks

abstract: ABSTRACT This paper presents NVIS, an interactive graphical tool used to examine the weights, topology, and activations of a singleartificial neural network (ANN), as well as the genealogical relationships between members of a population of ANNs as theyevolve under an evolutionary algorithm. NVIS is unique in its depiction of nodal activation values, its usage of family treediagrams to indicate the origin of individual networks, and the degree of interactivity it allows the user while the learningprocess takes place. The authors have made use of these features to obtain insights into both the workings of single neuralnetworks and the evolutionary process, based upon which we consider NVIS to be an effective visualization tool of value todesigners, users, and students of ANNs.Keywords: neural networks, visualization, graphs, evolutionary computation 1. INTRODUCTION 1.1. Introduction to Artificial Neural NetworksArtificial neural networks are models of adaptive distributed computation with roots in neurobiology. ANNs have been usedwith great success in a variety of domains to solve problems involving function approximation or classification


title: Syntax as an Emergent Characteristic of the Evolution of Semantic Complexity

abstract: It is commonly argued that the rules of language, as distinct from its semantic features, are the characteristics which most clearly distinguish language from the communication systems of other species. A number of linguists (e.g., Chomsky 1972, 1980; Pinker 1994) have suggested that the universal features of grammar (UG) are unique human adaptations showing no evolutionary continuities with any other species. However, recent summaries of the substantive features of UG are quite remarkable in the very general nature of the features proposed. While the syntax of any given language can be quite complex, the specific rules vary so much between languages that the truly universal (i.e. innate) aspects of grammar are not complex at all. In fact, these features most closely resemble a set of general descriptions of our richly complex semantic cognition, and not a list of specific rules. General principles of the evolutionary process suggest that syntax is more properly understood as an emergent characteristic of the explosion of semantic complexity that occurred during hominid evolution. It is argued that grammatical rules used in given languages are likely to be simply conventionalized, invented features of language, and not the result of an innate, grammar-specific module. The grammatical and syntactic regularities that are found across languages occur simply because all languages attempt to communicate the same sorts of semantic information.