title: State/event-based LTL model checking under parametric generalized fairness

abstract: In modeling a concurrent system, fairness constraints are usually considered at a specific granularity level of the system, leading to many different variants of fairness: transition fairness, object/process fairness, actor fairness, etc. These different notions of fairness can be unified by making explicit their parametrization over the relevant entities in the system as universal quantification. We propose a state/event-based framework as well as an on-the-fly model checking algorithm to verify LTL properties under universally quantified parametric fairness assumptions, specified by generalized strong/weak fairness formulas. It enables verification of temporal properties under fairness conditions associated to dynamic entities such as new process creations. We have implemented our algorithm within the Maude system.


title: High Performance Ternary Multiplier Using CNTFET

abstract: Ternary logic is a promising alternative to the conventional binary logic in VLSI design as it provides the advantages of reduced interconnects, higher operating speeds and smaller chip area. This work presents a ternary multiplier using carbon nanotube field effect transistors (CNTFETs). The proposed designs use in-depth analysis of addition required for designing a two trit multiplier. Based on this analysis two efficient adders are proposed. These adders are used to optimize the multiplier design. The proposed circuits are extensively simulated using HSPICE to obtain power, delay and power delay product. The circuit performances are compared with designs reported in recent literature. This circuit demonstrates a power delay product improvement up to 16.8%, with lesser transistor count of 16%. So, the use of these circuits in complex arithmetic circuits will be advantageous.


title: Contextualized Videos: Combining Videos with Environment Models to Support Situational Understanding

abstract: Multiple spatially-related videos are increasingly used in security, communication, and other applications. Since it can be difficult to understand the spatial relationships between multiple videos in complex environments (e.g. to predict a person's path through a building), some visualization techniques, such as video texture projection, have been used to aid spatial understanding. In this paper, we identify and begin to characterize an overall class of visualization techniques that combine video with 3D spatial context. This set of techniques, which we call contextualized videos, forms a design palette which must be well understood so that designers can select and use appropriate techniques that address the requirements of particular spatial video tasks. In this paper, we first identify user tasks in video surveillance that are likely to benefit from contextualized videos and discuss the video, model, and navigation related dimensions of the contextualized video design space. We then describe our contextualized video testbed which allows us to explore this design space and compose various video visualizations for evaluation. Finally, we describe the results of our process to identify promising design patterns through user selection of visualization features from the design space, followed by user interviews.


title: Gap design for nonlinear ferrite cores to maximize inductance

abstract: Conventional design of ferrite-cored inductor employs air gaps to store magnetic energy. In this work, the gap length is allowed to be smaller than the conventional value so that the nonlinear ferrite material is biased in the region with low permeability and, hence, significant energy density. A peak in the inductance-gap relationship has thus been uncovered where the total energy stored in the gaps and the core is maximized. A reluctance model is formulated to explain the peaking behavior, and is verified experimentally. Curves of inductance versus gap length are generated to aid the design of swinging inductance and reduce the core size.


title: Search Based Path and Input Data Generation for Web Application Testing

abstract: Test case generation for web applications aims at ensuring full coverage of the navigation structure. Existing approaches resort to crawling and manual/random input generation, with or without a preliminary construction of the navigation model. However, crawlers might be unable to reach some parts of the web application and random input generation might not receive enough guidance to produce the inputs needed to cover a given path. In this paper, we take advantage of the navigation structure implicitly specified by developers when they write the page objects used for web testing and we define a novel set of genetic operators that support the joint generation of test inputs and feasible navigation paths. On a case study, our tool Subweb was able to achieve higher coverage of the navigation model than crawling based approaches, thanks to its intrinsic ability of generating inputs for feasible paths and of discarding likely infeasible paths.


title: Evaluating the Ability of LSTMs to Learn Context-Free Grammars

abstract: While long short-term memory (LSTM) neural net architectures are designed to capture sequence information, human language is generally composed of hierarchical structures. This raises the question as to whether LSTMs can learn hierarchical structures. We explore this question with a well-formed bracket prediction task using two types of brackets modeled by an LSTM. Demonstrating that such a system is learnable by an LSTM is the first step in demonstrating that the entire class of CFLs is also learnable. We observe that the model requires exponential memory in terms of the number of characters and embedded depth, where a sub-linear memory should suffice. Still, the model does more than memorize the training input. It learns how to distinguish between relevant and irrelevant information. On the other hand, we also observe that the model does not generalize well. We conclude that LSTMs do not learn the relevant underlying context-free rules, suggesting the good overall performance is attained rather by an efficient way of evaluating nuisance variables. LSTMs are a way to quickly reach good results for many natural language tasks, but to understand and generate natural language one has to investigate other concepts that can make more direct use of natural language's structural nature.


title: Learning to Embed Sentences Using Attentive Recursive Trees

abstract: Sentence embedding is an effective feature representation for most deep learning-based NLP tasks. One prevailing line of methods is using recursive latent tree-structured networks to embed sentences with task-specific structures. However, existing models have no explicit mechanism to emphasize task-informative words in the tree structure. To this end, we propose an Attentive Recursive Tree model (AR-Tree), where the words are dynamically located according to their importance in the task. Specifically, we construct the latent tree for a sentence in a proposed important-first strategy, and place more attentive words nearer to the root; thus, AR-Tree can inherently emphasize important words during the bottom-up composition of the sentence embedding. We propose an end-to-end reinforced training strategy for AR-Tree, which is demonstrated to consistently outperform, or be at least comparable to, the state-of-the-art sentence embedding methods on three sentence understanding tasks.


title: Evolutionary computation for feature selection in classification problems

abstract: Feature subset selection (FSS) has received a great deal of attention in statistics, machine learning, and data mining. Real world data analyzed by data mining algorithms can involve a large number of redundant or irrelevant features or simply too many features for a learning algorithm to handle them efficiently. Feature selection is becoming essential as databases grow in size and complexity. The selection process is expected to bring benefits in terms of better performing models, computational efficiency, and simpler more understandable models. Evolutionary computation (EC) encompasses a number of naturally inspired techniques such as genetic algorithms, genetic programming, ant colony optimization, or particle swarm optimization algorithms. Such techniques are well suited to feature selection because the representation of a feature subset is straightforward and the evaluation can also be easily accomplished through the use of wrapper or filter algorithms. Furthermore, the capability of such heuristic algorithms to efficiently search large search spaces is of great advantage to the feature selection problem. Here, we review the use of different EC paradigms for feature selection in classification problems. We discuss details of each implementation including representation, evaluation, and validation. The review enables us to uncover the best EC algorithms for FSS and to point at future research directions. WIREs Data Mining Knowl Discov 2013, 3:381–407. doi: 10.1002/widm.1106 ::: ::: ::: ::: Conflict of interest: The authors have declared no conflicts of interest for this article. ::: ::: ::: ::: For further resources related to this article, please visit the WIREs website.


title: No effect of genetic obesity and mazindol on imidazoline I2 binding sites in the brain of Zucker rats.

abstract: The density and affinity states of imidazoline I2 binding sites as well as the density of alpha 2-adrenoceptors were quantitated in the brain of lean and obese Zucker rats. No significant differences were obtained between Zucker phenotypes for these receptors in the cerebral cortex and hypothalamus. Moreover, chronic treatment with the anorexic imidazoline derivative, mazindol, did not alter the density of brain imidazoline I2 binding sites. It is concluded that this genetic model of obesity is not associated with abnormal imidazoline I2 binding sites.


title: A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving

abstract: 3D LiDAR scanners are playing an increasingly important role in autonomous driving as they can generate depth information of the environment. However, creating large 3D LiDAR point cloud datasets with point-level labels requires a significant amount of manual annotation. This jeopardizes the efficient development of supervised deep learning algorithms which are often data-hungry. We present a framework to rapidly create point clouds with accurate point-level labels from a computer game. The framework supports data collection from both auto-driving scenes and user-configured scenes. Point clouds from auto-driving scenes can be used as training data for deep learning algorithms, while point clouds from user-configured scenes can be used to systematically test the vulnerability of a neural network, and use the falsifying examples to make the neural network more robust through retraining. In addition, the scene images can be captured simultaneously in order for sensor fusion tasks, with a method proposed to do automatic calibration between the point clouds and captured scene images. We show a significant improvement in accuracy (+9%) in point cloud segmentation by augmenting the training dataset with the generated synthesized data. Our experiments also show by testing and retraining the network using point clouds from user-configured scenes, the weakness/blind spots of the neural network can be fixed.


Fine-Tuning for Neural Machine Translation with Limited Degradation across In-and Out-of-Domain Data


title: Correlation-based Intrinsic Evaluation of Word Vector Representations

abstract: We introduce QVEC-CCA--an intrinsic evaluation metric for word vector representations based on correlations of learned vectors with features extracted from linguistic resources. We show that QVEC-CCA scores are an effective proxy for a range of extrinsic semantic and syntactic tasks. We also show that the proposed evaluation obtains higher and more consistent correlations with downstream tasks, compared to existing approaches to intrinsic evaluation of word vectors that are based on word similarity.


title: Text categorization based on regularization extreme learning machine

abstract: This article proposes a novel approach for text categorization based on a regularization extreme learning machine (RELM) in which its weights can be obtained analytically, and a bias-variance trade-off could be achieved by adding a regularization term into the linear system of single-hidden layer feedforward neural networks. To fit the input scale of RELM, the latent semantic analysis was used to represent text for dimensionality reduction. Moreover, a classification algorithm based on RELM was developed including the uni-label (i.e., a document can only be assigned to a unique category) and multi-label (i.e., a document can be assigned to multiple categories simultaneously) situations. The experimental results in two benchmarks show that the proposed method can produce good performance in most cases, and it could learn faster than popular methods such as feedforward neural networks or support vector machine.


title: Mechanical versus clinical data combination in selection and admissions decisions: A meta-analysis.

abstract: In employee selection and academic admission decisions, holistic (clinical) data combination methods continue to be relied upon and preferred by practitioners in our field. This meta-analysis examined and compared the relative predictive power of mechanical methods versus holistic methods in predicting multiple work (advancement, supervisory ratings of performance, and training performance) and academic (grade point average) criteria. There was consistent and substantial loss of validity when data were combined holistically--even by experts who are knowledgeable about the jobs and organizations in question--across multiple criteria in work and academic settings. In predicting job performance, the difference between the validity of mechanical and holistic data combination methods translated into an improvement in prediction of more than 50%. Implications for evidence-based practice are discussed.


title: A Wide-Field-of-View Monocentric Light Field Camera

abstract: Light field (LF) capture and processing are important in an expanding range of computer vision applications, offering rich textural and depth information and simplification of conventionally complex tasks. Although LF cameras are commercially available, no existing device offers wide field-of-view (FOV) imaging. This is due in part to the limitations of fisheye lenses, for which a fundamentally constrained entrance pupil diameter severely limits depth sensitivity. In this work we describe a novel, compact optical design that couples a monocentric lens with multiple sensors using microlens arrays, allowing LF capture with an unprecedented FOV. Leveraging capabilities of the LF representation, we propose a novel method for efficiently coupling the spherical lens and planar sensors, replacing expensive and bulky fiber bundles. We construct a single-sensor LF camera prototype, rotating the sensor relative to a fixed main lens to emulate a wide-FOV multi-sensor scenario. Finally, we describe a processing toolchain, including a convenient spherical LF parameterization, and demonstrate depth estimation and post-capture refocus for indoor and outdoor panoramas with 15 x 15 x 1600 x 200 pixels (72 MPix) and a 138° FOV.


title: A Randomized, Controlled Clinical Trial of Honey-Impregnated Dressing for Treating Diabetic Foot Ulcer.

abstract: To investigate the effect of Beri-honey-impregnated dressing on diabetic foot ulcer and compare it with normal saline dressing.A randomized, controlled trial.Sughra Shafi Medical Complex, Narowal, Pakistan and Bhatti International Trust (BIT) Hospital, Affiliated with Central Park Medical College, Lahore, from February 2006 to February 2010.Patients with Wagner's grade 1 and 2 ulcers were enrolled. Those patients were divided in two groups; group A (n=179) treated with honey dressing and group B (n=169) treated with normal saline dressing. Outcome measures were calculated in terms of proportion of wounds completely healed (primary outcome), wound healing time, and deterioration of wounds. Patients were followed-up for a maximum of 120 days.One hundred and thirty six wounds (75.97%) out of 179 were completely healed with honey dressing and 97 (57.39%) out of 169 wtih saline dressing (p=0.001). The median wound healing time was 18.00 (6 - 120) days (Median with IQR) in group A and 29.00 (7 - 120) days (Median with IQR) in group B (p < 0.001).The present results showed that honey is an effective dressing agent instead of conventional dressings, in treating patients of diabetic foot ulcer.


title: Tubular linear permanent magnet motors: an overall comparison

abstract: This paper deals with tubular linear permanent magnet (TL-PM) motors, including different topologies, i.e. interior and surface-mounted PMs, slotted and slotless motors. After some preliminary analytical considerations, the performance of the TL-PM motor are highlighted, including specific force, maximum acceleration and cost. Not only the advantages are emphasised, but also the drawbacks such as detent forces and attractive radial forces due to eccentricity. Different models have been adopted in the various analyses, including 2D and 3D finite element analysis as necessary. In addition, a comparison between predicted and measured performance on a motor prototype are reported.


title: A whole-genome assembly of the domestic cow, Bos taurus

abstract: BackgroundThe genome of the domestic cow, Bos taurus, was sequenced using a mixture of hierarchical and whole-genome shotgun sequencing methods.ResultsWe have assembled the 35 million sequence reads and applied a variety of assembly improvement techniques, creating an assembly of 2.86 billion base pairs that has multiple improvements over previous assemblies: it is more complete, covering more of the genome; thousands of gaps have been closed; many erroneous inversions, deletions, and translocations have been corrected; and thousands of single-nucleotide errors have been corrected. Our evaluation using independent metrics demonstrates that the resulting assembly is substantially more accurate and complete than alternative versions.ConclusionsBy using independent mapping data and conserved synteny between the cow and human genomes, we were able to construct an assembly with excellent large-scale contiguity in which a large majority (approximately 91%) of the genome has been placed onto the 30 B. taurus chromosomes. We constructed a new cow-human synteny map that expands upon previous maps. We also identified for the first time a portion of the B. taurus Y chromosome.


title: Dynamic pricing and profit maximization for the cloud with geo-distributed data centers

abstract: Cloud providers often choose to operate datacenters over a large geographic span, in order that users may be served by resources in their proximity. Due to time and spatial diversities in utility prices and operational costs, different datacenters typically have disparate charges for the same services. Cloud users are free to choose the datacenters to run their jobs, based on a joint consideration of monetary charges and quality of service. A fundamental problem with significant economic implications is how the cloud should price its datacenter resources at different locations, such that its overall profit is maximized. The challenge escalates when dynamic resource pricing is allowed and long-term profit maximization is pursued. We design an efficient online algorithm for dynamic pricing of VM resources across datacenters in a geo-distributed cloud, together with job scheduling and server provisioning in each datacenter, to maximize the profit of the cloud provider over a long run. Theoretical analysis shows that our algorithm can schedule jobs within their respective deadlines, while achieving a time-average overall profit closely approaching the offline maximum, which is computed by assuming that perfect information on future job arrivals are freely available. Empirical studies further verify the efficacy of our online profit maximizing algorithm.


title: Arbuscular mycorrhizal symbiosis and active ingredients of medicinal plants: current research status and prospectives

abstract: Medicinal plants have been used world-wide for thousands of years and are widely recognized as having high healing but minor toxic side effects. The scarcity and increasing demand for medicinal plants and their products have promoted the development of artificial cultivation of medicinal plants. Currently, one of the prominent issues in medicinal cultivation systems is the unstable quality of the products. Arbuscular mycorrhiza (AM) affects secondary metabolism and the production of active ingredients of medicinal plants and thus influence the quality of herbal medicines. In this review, we have assembled, analyzed, and summarized the effects of AM symbioses on secondary metabolites of medicinal plants. We conclude that symbiosis of AM is conducive to favorable characteristics of medicinal plants, by improving the production and accumulation of important active ingredients of medicinal plants such as terpenes, phenols, and alkaloids, optimizing the composition of different active ingredients in medicinal plants and ultimately improving the quality of herbal materials. We are convinced that the AM symbiosis will benefit the cultivation of medicinal plants and improve the total yield and quality of herbal materials. Through this review, we hope to draw attention to the status and prospects of, and arouse more interest in, the research field of medicinal plants and mycorrhiza.


title: Price elasticity of the demand for soft drinks, other sugar-sweetened beverages and energy dense food in Chile

abstract: Abstract Background Chile is the second world’s largest per capita consumer of caloric beverages. Caloric beverages are associated with overweight, obesity and other chronic diseases. The objective of this study is to estimate the price elasticity of demand for soft drinks, other sugar-sweetened beverages and high-energy dense foods in urban areas in Chile in order to evaluate the potential response of households’ consumption to changes in prices. Methods We used microdata from the VII Family Budget Survey 2012–2013, which collects information on expenditures made by Chilean urban households on items such as beverages and foods. We estimated a Linear Approximation of an Almost Ideal Demand System Model to derive own and cross price elasticities of milk, coffee, tea and other infusions, plain water, soft drinks, other flavored beverages, sweet snacks, sugar and honey, and desserts. We considered the censored nature of the data and included the Inverse Mills Ratio in each equation of the demand system. We estimated a Quadratic Almost Ideal Demand System and a two-part model as sensitivity analysis. Results We found an own price-elasticity of −1.37 for soft drinks. This implies that a price increase of 10% is associated with a reduction in consumption of 13.7%. We found that the rest of food and beverages included in the demand system behave as substitutes for soft drinks. For instance, plain water showed a cross-price elasticity of 0.63: a 10% increase in price of soft drinks could lead to an increase of 6.3% of plain water. Own and cross price elasticities were similar between models. Conclusions The demand of soft drinks is price sensitive among Chilean households. An incentive system such as subsidies to non-sweetened beverages and tax to soft drinks could lead to increases in the substitutions for other healthier beverages.


title: The distribution of blow fly (Diptera: Calliphoridae) larval lengths and its implications for estimating post mortem intervals

abstract: The length or stage of development of blow fly (Diptera: Calliphoridae) larvae may be used to estimate a minimum postmortem interval, often by targeting the largest individuals of a species in the belief that they will be the oldest. However, natural variation in rate of development, and therefore length, implies that the size of the largest larva, as well as the number of larvae longer than any stated length, will be greater for larger cohorts. Length data from the blow flies Protophormia terraenovae and Lucilia sericata were collected from one field-based and two laboratory-based experiments. The field cohorts contained considerably more individuals than have been used for reference data collection in the literature. Cohorts were shown to have an approximately normal distribution. Summary statistics were derived from the collected data allowing the quantification of errors in development time which arise when different sized cohorts are compared through their largest larvae. These errors may be considerable and can lead to overestimation of postmortem intervals when making comparisons with reference data collected from smaller cohorts. This source of error has hitherto been overlooked in forensic entomology.


title: Learning a Single Convolutional Super-Resolution Network for Multiple Degradations

abstract: Recent years have witnessed the unprecedented success of deep convolutional neural networks (CNNs) in single image super-resolution (SISR). However, existing CNN-based SISR methods mostly assume that a low-resolution (LR) image is bicubicly downsampled from a high-resolution (HR) image, thus inevitably giving rise to poor performance when the true degradation does not follow this assumption. Moreover, they lack scalability in learning a single model to deal with multiple degradations. To address these issues, we propose a dimensionality stretching strategy that enables a single convolutional super-resolution network to take two key factors of the SISR degradation process, i.e., blur kernel and noise level, as input. Consequently, the proposed super-resolver can handle multiple and even spatially variant degradations, which significantly improves the practicability. Extensive experimental results on synthetic and real LR images show that the proposed convolutional super-resolution network not only can produce favorable results on multiple degradations but also is computationally efficient, providing a highly effective and scalable solution to practical SISR applications.


title: Fuzzy set approach for automatic tagging in evolving software

abstract: Software tagging has been shown to be an efficient, lightweight social computing mechanism to improve different social and technical aspects of software development. Despite the importance of tags, there exists limited support for automatic tagging for software artifacts, especially during the evolutionary process of software development. We conducted an empirical study on IBM Jazz's repository and found that there are several missing tags in artifacts and more precise tags are desirable. This paper introduces a novel, accurate, automatic tagging recommendation tool that is able to take into account users' feedbacks on tags, and is very efficient in coping with software evolution. The core technique is an automatic tagging algorithm that is based on fuzzy set theory. Our empirical evaluation on the real-world IBM Jazz project shows the usefulness and accuracy of our approach and tool.


title: Learning Bases of Activity for Facial Expression Recognition

abstract: The extraction of descriptive features from the sequences of faces is a fundamental problem in facial expression analysis. Facial expressions are represented by psychologists as a combination of elementary movements known as action units: each movement is localised and its intensity is specified with a score that is small when the movement is subtle and large when the movement is pronounced. Inspired by this approach, we propose a novel data-driven feature extraction framework that represents facial expression variations as a linear combination of localised basis functions, whose coefficients are proportional to movement intensity. We show that the linear basis functions of the proposed framework can be obtained by training a sparse linear model with Gabor phase shifts computed from facial videos. The proposed framework addresses generalisation issues that are not tackled by existing learnt representations, and achieves, with the same learning parameters, state-of-the-art results in recognising both posed expressions and spontaneous micro-expressions. This performance is confirmed even when the data used to train the model differ from test data in terms of the intensity of facial movements and frame rate.


title: Boredom Proneness: Its Relationship to Positive and Negative Affect:

abstract: 170 undergraduate students completed the Boredom Proneness Scale by Farmer and Sundberg and the Multiple Affect Adjective Checklist by Zuckerman and Lubin. Significant negative relationships were found between boredom proneness and negative affect scores (i.e., Depression, Hostility, Anxiety). Significant positive correlations also obtained between boredom proneness and positive affect (i.e., Positive Affect, Sensation Seeking). The correlations between boredom proneness “subscales” and positive and negative affect were congruent with those obtained using total boredom proneness scores. Implications for counseling are discussed.


title: RPC: An Efficient Classifier Ensemble Using Random Projections

abstract: We propose a classifier ensemble called RPC based on principles of rotation forest using random projections. Random projections project the original high dimensional data into lower dimensions while preserving the dataset's geometrical structure reducing classifier's complexity. Random projections are also an efficient dimensionality reduction tool, removing noisy features from dataset and representing the information using only small number of features. Training set for RPC is created by applying random projection on random subsets of the feature set. The randomness of random projection coupled with random sampling adds diversity to RPC. Initial evaluation using datasets from UCI machine learning repository shows that RPC performs equally well or better than Random Forest, Bagging and AdaBoost. We demonstrate that using dimensionality reduction with RPC we can dramatically reduce datasets dimensions without any loss in classification accuracy and significantly enhance computational performance. Finally, we experiment building RPC with different base learners.


title: A Retrieval-Based Dialogue System Utilizing Utterance and Context Embeddings

abstract: Finding semantically rich and computer-understandable representations for textual dialogues, utterances and words is crucial for dialogue systems (or conversational agents), as their performance mostly depends on understanding the context of conversations. Recent research aims at finding distributed vector representations (embeddings) for words, such that semantically similar words are relatively close within the vector-space. Encoding the "meaning" of text into vectors is a current trend, and text can range from words, phrases and documents to actual human-to-human conversations. In recent research approaches, responses have been generated utilizing a decoder architecture, given the vector representation of the current conversation. In this paper, the utilization of embeddings for answer retrieval is explored by using Locality-Sensitive Hashing Forest (LSH Forest), an Approximate Nearest Neighbor (ANN) model, to find similar conversations in a corpus and rank possible candidates. Experimental results on the well-known Ubuntu Corpus (in English) and a customer service chat dataset (in Dutch) show that, in combination with a candidate selection method, retrieval-based approaches outperform generative ones and reveal promising future research directions towards the usability of such a system.


title: High-speed parallel-prefix VLSI Ling adders

abstract: Parallel-prefix adders offer a highly efficient solution to the binary addition problem and are well-suited for VLSI implementations. A novel framework is introduced, which allows the design of parallel-prefix Ling adders. The proposed approach saves one-logic level of implementation compared to the parallel-prefix structures proposed for the traditional definition of carry lookahead equations and reduces the fanout requirements of the design. Experimental results reveal that the proposed adders achieve delay reductions of up to 14 percent when compared to the fastest parallel-prefix architectures presented for the traditional definition of carry equations.


title: Land Access and Household Food Security in Kpomassè District, Southern Benin: A Few Lessons for Smallholder Agriculture Interventions

abstract: Land remains a key asset in smallholder agriculture, and is expected to play a critical contribution to food security, still a major concern for decision makers. The objective of this study was to explore the relationship between land access mechanisms and the food security situation of households in Kpomasse district (southern Benin). A survey was conducted among 150 farmers selected randomly in six villages across the district. Data were collected on socio-demographic characteristics, access to land and food consumption patterns of the households. Data analysis encompassed a typology of households according to their access to land, by combining Hierarchical Cluster Analysis and Principal Component Analysis. The level of household food security was assessed by computing the food consumption score. Three types of producers were identified based on their access to land. These were typified as “renters”, “borrowers” and “heirs”, representing 44%, 21%, and 35% of the sample, respectively. The average food consumption score ranged between 51.9 and 57.4, showing a satisfactory food intake for all types of households. The study suggests that secure modes of access to land might improve the food security status of households through increased assets. Also, there is a need of capacity building for farmers, in order to address the critical issue of the impoverishment of soil, through fertility management programs. The issue of access to credit is also an important policy matter.


title: Reinforcement Learning Signal Predicts Social Conformity

abstract: We often change our decisions and judgments to conform with normative group behavior. However, the neural mechanisms of social conformity remain unclear. Here we show, using functional magnetic resonance imaging, that conformity is based on mechanisms that comply with principles of reinforcement learning. We found that individual judgments of facial attractiveness are adjusted in line with group opinion. Conflict with group opinion triggered a neuronal response in the rostral cingulate zone and the ventral striatum similar to the "prediction error" signal suggested by neuroscientific models of reinforcement learning. The amplitude of the conflict-related signal predicted subsequent conforming behavioral adjustments. Furthermore, the individual amplitude of the conflict-related signal in the ventral striatum correlated with differences in conforming behavior across subjects. These findings provide evidence that social group norms evoke conformity via learning mechanisms reflected in the activity of the rostral cingulate zone and ventral striatum.


title: Striving for the Moral Self: The Effects of Recalling Past Moral Actions on Future Moral Behavior

abstract: People’s desires to see themselves as moral actors can contribute to their striving for and achievement of a sense of self-completeness. The authors use self-completion theory to predict (and show) that recalling one’s own (im)moral behavior leads to compensatory rather than consistent moral action as a way of completing the moral self. In three studies, people who recalled their immoral behavior reported greater participation in moral activities (Study 1), reported stronger prosocial intentions (Study 2), and showed less cheating (Study 3) than people who recalled their moral behavior. These compensatory effects were related to the moral magnitude of the recalled event, but they did not emerge when people recalled their own positive or negative nonmoral behavior (Study 2) or others’ (im)moral behavior (Study 3). Thus, the authors extend self-completion theory to the moral domain and use it to integrate the research on moral cleansing (remunerative moral strivings) and moral licensing (relaxed moral striv...


title: Predicting breast cancer survivability : a comparison of three data mining methods

abstract: Objective:: The prediction of breast cancer survivability has been a challenging research problem for many researchers. Since the early dates of the related research, much advancement has been recorded in several related fields. For instance, thanks to innovative biomedical technologies, better explanatory prognostic factors are being measured and recorded; thanks to low cost computer hardware and software technologies, high volume better quality data is being collected and stored automatically; and finally thanks to better analytical methods, those voluminous data is being processed effectively and efficiently. Therefore, the main objective of this manuscript is to report on a research project where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability. Methods and material:: We used two popular data mining algorithms (artificial neural networks and decision trees) along with a most commonly used statistical method (logistic regression) to develop the prediction models using a large dataset (more than 200,000 cases). We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. Results:: The results indicated that the decision tree (C5) is the best predictor with 93.6% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), artificial neural networks came out to be the second with 91.2% accuracy and the logistic regression models came out to be the worst of the three with 89.2% accuracy. Conclusion:: The comparative study of multiple prediction models for breast cancer survivability using a large dataset along with a 10-fold cross-validation provided us with an insight into the relative prediction ability of different data mining methods. Using sensitivity analysis on neural network models provided us with the prioritized importance of the prognostic factors used in the study.


title: Locking without blocking: making lock based concurrent data structure algorithms nonblocking

abstract: Nonblocking algorithms for concurrent data structures guarantee that a data structure is always accessible. This is in contrast to blocking algorithms in which a slow or halted process can render part or all of the data structure inaccessible to other processes. This paper proposes a technique that can convert most existing lock-based blocking data structure algorithms into nonblocking algorithms with the same functionality. Our instruction-by-instruction transformation can be applied to any algorithm having the following properties: •Interprocess synchronization is established solely through the use of locks. •There is no possiblity of deadlock (e.g. because of a well-ordering among the lock requests). In contrast to a previous work, our transformation requires only a constant amount of overhead per operation and, in the absence of failures, it incurs no penalty in the amount of concurrency that was available in the original data structure. The techniques in this paper may obviate the need for a wholesale reinvention of techniques for nonblocking concurrent data structure algorithms.


title: Exact Model Counting of Query Expressions: Limitations of Propositional Methods

abstract: We prove exponential lower bounds on the running time of the state-of-the-art exact model counting algorithms—algorithms for exactly computing the number of satisfying assignments, or the satisfying probability, of Boolean formulas. These algorithms can be seen, either directly or indirectly, as building Decision-Decomposable Negation Normal Form (decision-DNNF) representations of the input Boolean formulas. Decision-DNNFs are a special case of d-DNNFs where d stands for deterministic. We show that any knowledge compilation representations from a class (called DLDDs in this article) that contain decision-DNNFs can be converted into equivalent Free Binary Decision Diagrams (FBDDs), also known as Read-Once Branching Programs, with only a quasi-polynomial increase in representation size. Leveraging known exponential lower bounds for FBDDs, we then obtain similar exponential lower bounds for decision-DNNFs, which imply exponential lower bounds for model-counting algorithms. We also separate the power of decision-DNNFs from d-DNNFs and a generalization of decision-DNNFs known as AND-FBDDs. We then prove new lower bounds for FBDDs that yield exponential lower bounds on the running time of these exact model counters when applied to the problem of query evaluation in tuple-independent probabilistic databases—computing the probability of an answer to a query given independent probabilities of the individual tuples in a database instance. This approach to the query evaluation problem, in which one first obtains the lineage for the query and database instance as a Boolean formula and then performs weighted model counting on the lineage, is known as grounded inference. A second approach, known as lifted inference or extensional query evaluation, exploits the high-level structure of the query as a first-order formula. Although it has been widely believed that lifted inference is strictly more powerful than grounded inference on the lineage alone, no formal separation has previously been shown for query evaluation. In this article, we show such a formal separation for the first time. In particular, we exhibit a family of database queries for which polynomial-time extensional query evaluation techniques were previously known but for which query evaluation via grounded inference using the state-of-the-art exact model counters requires exponential time.


title: Approximate Dynamic Programming for Ambulance Redeployment

abstract: We present an approximate dynamic programming approach for making ambulance redeployment decisions in an emergency medical service system. The primary decision is where we should redeploy idle ambulances so as to maximize the number of calls reached within a delay threshold. We begin by formulating this problem as a dynamic program. To deal with the high-dimensional and uncountable state space in the dynamic program, we construct approximations to the value function that are parameterized by a small number of parameters. We tune the parameters using simulated cost trajectories of the system. Computational experiments demonstrate the performance of the approach on emergency medical service systems in two metropolitan areas. We report practically significant improvements in performance relative to benchmark static policies.


title: Effective and Scalable Authorship Attribution Using Function Words

abstract: Techniques for identifying the author of an unattributed document can be applied to problems in information analysis and in academic scholarship. A range of methods have been proposed in the research literature, using a variety of features and machine learning approaches, but the methods have been tested on very different data and the results cannot be compared. It is not even clear whether the differences in performance are due to feature selection or other variables. In this paper we examine the use of a large publicly available collection of newswire articles as a benchmark for comparing authorship attribution methods. To demonstrate the value of having a benchmark, we experimentally compare several recent feature-based techniques for authorship attribution, and test how well these methods perform as the volume of data is increased. We show that the benchmark is able to clearly distinguish between different approaches, and that the scalability of the best methods based on using function words features is acceptable, with only moderate decline as the difficulty of the problem is increased.


A Comparison on the Classification of Short-text Documents Using Latent Dirichlet Allocation and Formal Concept Analysis


title: The PARSAX - full polarimetric FMCW radar with dual-orthogonal signals

abstract: The article describes the IRCTR PARSAX radar system - fully polarimetric FMCW radar with dual-orthogonal sounding signals, which has the possibility to measure all elements of the radar targets polarization scattering matrix simultaneously, in one sweep.


title: Basic Income: A Simple and Powerful Idea for the Twenty-First Century:

abstract: A basic income (or demogrant) is an income paid by a political community to all its members on an individual basis, without means test or work requirement. This article surveys the various forms the basic income proposal has taken and how they relate to kin ideas; synthesizes the central case for basic income, as a strategy against both poverty and unemployment; examines the question of whether and in what sense a universal basic income is affordable; and discusses the most promising next steps towards it, both in the North and in the South.


title: Overlapping communities in dynamic networks: their detection and mobile applications

abstract: Many practical problems on Mobile networking, such as routing strategies in MANETs, sensor reprogramming in WSNs and worm containment in online social networks (OSNs) share an ubiquitous, yet interesting feature in their organizations: community structure. Knowledge of this structure provides us not only crucial information about the network principles, but also key insights into designing more effective algorithms for practical problems enabled by Mobile networking. However, understanding this interesting feature is extremely challenging on dynamic networks where changes to their topologies are frequently introduced, and especially when network communities in reality usually overlap with each other. We focus on the following questions (1) Can we effectively detect the overlapping community structure in a dynamic network? (2) Can we quickly and adaptively update the network structure only based on its history without recomputing from scratch? (3) How does the detection of network communities help mobile applications? We propose AFOCS, a two-phase framework for not only detecting quickly but also tracing effectively the evolution of overlapped network communities in dynamic mobile networks. With the great advantages of the overlapping community structure, AFOCS significantly helps in reducing up to 7 times the infection rates in worm containment on OSNs, and up to 11 times overhead while maintaining good delivery time and ratio in forwarding strategies in MANETs.


title: ‘Splitting versus lumping’: Temple–Baraitser and Zimmermann–Laband Syndromes

abstract: KCNH1 mutations have recently been described in six individuals with Temple–Baraitser syndrome (TMBTS) and six individuals with Zimmermann–Laband syndrome (ZLS). TMBTS is characterized by intellectual disability (ID), epilepsy, dysmorphic facial features, broad thumbs and great toes with absent/hypoplastic nails. ZLS is characterized by facial dysmorphism including coarsening of the face and a large nose, gingival enlargement, ID, hypoplasia of terminal phalanges and nails and hypertrichosis. In this study, we present four additional unrelated individuals with de novo KCNH1 mutations from ID cohorts. We report on a novel recurrent pathogenic KCNH1 variant in three individuals and add a fourth individual with a previously TMBTS-associated KCNH1 variant. Neither TMBTS nor ZLS was suspected clinically. KCNH1 encodes a voltage-gated potassium channel, which is not only highly expressed in the central nervous system, but also seems to play an important role during development. Clinical evaluation of our mutation-positive individuals revealed that one of the main characteristics of TMBTS/ZLS, namely the pronounced nail hypoplasia of the great toes and thumbs, can be mild and develop over time. Clinical comparison of all published KCNH1 mutation-positive individuals revealed a similar facial but variable limb phenotype. KCNH1 mutation-positive individuals present with severe ID, neonatal hypotonia, hypertelorism, broad nasal tip, wide mouth, nail a/hypoplasia, a proximal implanted and long thumb and long great toes. In summary, we show that the phenotypic variability of individuals with KCNH1 mutations is more pronounced than previously expected, and we discuss whether KCNH1 mutations allow for “lumping” or for “splitting” of TMBTS and ZLS.


title: BioBERT: a pre-trained biomedical language representation model for biomedical text mining

abstract: Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. We make the pre-trained weights of BioBERT freely available at this https URL, and the source code for fine-tuning BioBERT available at this https URL.


Epistemic Closure and Skepticism


"It's Cool... 'cos you can't give us detentions and things, can you?!": Reflections on research with children


title: Significance of long chain polyunsaturated fatty acids in human health

abstract: In the last decades, the development of new technologies applied to lipidomics has revitalized the analysis of lipid profile alterations and the understanding of the underlying molecular mechanisms of lipid metabolism, together with their involvement in the occurrence of human disease. Of particular interest is the study of omega-3 and omega-6 long chain polyunsaturated fatty acids (LC-PUFAs), notably EPA (eicosapentaenoic acid, 20:5n-3), DHA (docosahexaenoic acid, 22:6n-3), and ARA (arachidonic acid, 20:4n-6), and their transformation into bioactive lipid mediators. In this sense, new families of PUFA-derived lipid mediators, including resolvins derived from EPA and DHA, and protectins and maresins derived from DHA, are being increasingly investigated because of their active role in the “return to homeostasis” process and resolution of inflammation. Recent findings reviewed in the present study highlight that the omega-6 fatty acid ARA appears increased, and omega-3 EPA and DHA decreased in most cancer tissues compared to normal ones, and that increments in omega-3 LC-PUFAs consumption and an omega-6/omega-3 ratio of 2–4:1, are associated with a reduced risk of breast, prostate, colon and renal cancers. Along with their lipid-lowering properties, omega-3 LC-PUFAs also exert cardioprotective functions, such as reducing platelet aggregation and inflammation, and controlling the presence of DHA in our body, especially in our liver and brain, which is crucial for optimal brain functionality. Considering that DHA is the principal omega-3 FA in cortical gray matter, the importance of DHA intake and its derived lipid mediators have been recently reported in patients with major depressive and bipolar disorders, Alzheimer disease, Parkinson’s disease, and amyotrophic lateral sclerosis. The present study reviews the relationships between major diseases occurring today in the Western world and LC-PUFAs. More specifically this review focuses on the dietary omega-3 LC-PUFAs and the omega-6/omega-3 balance, in a wide range of inflammation disorders, including autoimmune diseases. This review suggests that the current recommendations of consumption and/or supplementation of omega-3 FAs are specific to particular groups of age and physiological status, and still need more fine tuning for overall human health and well being.


title: Director: A User Interface Designed for Robot Operation with Shared Autonomy

abstract: Operating a high degree of freedom mobile manipulator, such as a humanoid, in a field scenario requires constant situational awareness, capable perception modules, and effective mechanisms for interactive motion planning and control. A well-designed operator interface presents the operator with enough context to quickly carry out a mission and the flexibility to handle unforeseen operating scenarios robustly. By contrast, an unintuitive user interface can increase the risk of catastrophic operator error by overwhelming the user with unnecessary information. With these principles in mind, we present the philosophy and design decisions behind Director-the open-source user interface developed by Team MIT to pilot the Atlas robot in the DARPA Robotics Challenge DRC. At the heart of Director is an integrated task execution system that specifies sequences of actions needed to achieve a substantive task, such as drilling a wall or climbing a staircase. These task sequences, developed a priori, make online queries to automated perception and planning algorithms with outputs that can be reviewed by the operator and executed by our whole-body controller. Our use of Director at the DRC resulted in efficient high-level task operation while being fully competitive with approaches focusing on teleoperation by highly trained operators. We discuss the primary interface elements that comprise Director, and we provide an analysis of its successful use at the DRC.


title: Classification of brain MRI with big data and deep 3D convolutional neural networks

abstract: Our ever-aging society faces the growing problem of neurodegenerative diseases, in particular dementia. Magnetic Resonance Imaging provides a unique tool for non-invasive investigation of these brain diseases. However, it is extremely difficult for neurologists to identify complex disease patterns from large amounts of three-dimensional images. In contrast, machine learning excels at automatic pattern recognition from large amounts of data. In particular, deep learning has achieved impressive results in image classification. Unfortunately, its application to medical image classification remains difficult. We consider two reasons for this difficulty: First, volumetric medical image data is considerably scarcer than natural images. Second, the complexity of 3D medical images is much higher compared to common 2D images. To address the problem of small data set size, we assemble the largest dataset ever used for training a deep 3D convolutional neural network to classify brain images as healthy (HC), mild cognitive impairment (MCI) or Alzheimers disease (AD). We use more than 20.000 images from subjects of these three classes, which is almost 9x the size of the previously largest data set. The problem of high dimensionality is addressed by using a deep 3D convolutional neural network, which is state-of-the-art in large-scale image classification. We exploit its ability to process the images directly, only with standard preprocessing, but without the need for elaborate feature engineering. Compared to other work, our workflow is considerably simpler, which increases clinical applicability. Accuracy is measured on the ADNI+AIBL data sets, and the independent CADDementia benchmark.


title: Tone Reproduction and Physically Based Spectral Rendering

abstract: The ultimate aim of realistic graphics is the creation of images that provoke the same responses that a viewer would have to a real scene. This STAR addresses two related key problem areas in this effort which are located at opposite ends of the rendering pipeline, namely the data structures used to describe light during the actual rendering process, and the issue of displaying such radiant intensities in a meaningful way. The interest in the first of these subproblems stems from the fact that it is common industry practice to use RGB colour values to describe light intensity and surface reflectancy. While viable in the context of methods that do not strive to achieve true realism, this approach has to be replaced by more physically accurate techniques if a prediction of nature is intended. The second subproblem is that while research into ways of rendering images provides us with better and faster methods, we do not necessarily see their full effect due to limitations of the display hardware. The low dynamic range of a standard computer monitor requires some form of mapping to produce images that are perceptually accurate. Tone reproduction operators attempt to replicate the effect of real-world luminance intensities. This STAR report will review the work to date on spectral rendering and tone reproduction techniques. It will include an investigation into the need for spectral imagery synthesis methods and accurate tone reproduction, and a discussion of major approaches to physically correct rendering and key tone mapping algorithms. The future of both spectral rendering and tone reproduction techniques will be considered, together with the implications of advances in display hardware.


Deep occupancy maps: a continuous mapping technique for dynamic environments


title: A sequential importance sampling algorithm for generating random graphs with prescribed degrees

abstract: Random graphs with a given degree sequence are a useful model capturing several features absent in the classical Erd˝os-Renyi model, such as dependent edges and non-binomial degrees. In this paper, we use a characterization due to Erd˝os and Gallai to develop a sequential algorithm for generating a random labeled graph with a given degree sequence. The algorithm is easy to implement and allows surprisingly ecient sequential importance sampling. Applications are given, in- cluding simulating a biological network and estimating the number of graphs with a given degree sequence. 1. Introduction. Random graphs with given vertex degrees have recently attracted great interest as a model for many real-world complex networks, including the World Wide Web, peer-to-peer networks, social networks, and biological networks. Newman (58) contains an excellent survey of these networks, with extensive references. A common approach to simulating these systems is to study (empirically or theoretically) the degrees of the vertices in instances of the network, and then to generate a random graph with the appropriate degrees. Graphs with prescribed degrees also appear in random matrix theory and string theory, which can call for large simulations based on random k-regular graphs. Throughout, we are concerned with generating simple graphs, i.e., no loops or multiple edges are allowed (the problem becomes considerably easier if loops and multiple edges are allowed). The main result of this paper is a new sequential importance sampling algorithm for generating random graphs with a given degree sequence. The idea is to build up the graph sequentially, at each stage choosing an edge from a list of candidates with probability proportional to the degrees. Most previously studied algorithms for this problem sometimes either get stuck or produce loops or multiple edges in the output, which is handled by starting over and trying again. Often for such algorithms, the probability of a restart being needed on a trial rapidly approaches 1 as the degree parameters grow, resulting in an enormous number of trials being needed on average to obtain a simple graph. A major advantage of our algorithm is that it never gets stuck. This is achieved using the Erd˝os- Gallai characterization, which is explained in Section 2, and a carefully chosen order of edge selection.


title: Exploring Human-like Attention Supervision in Visual Question Answering

abstract: Attention mechanisms have been widely applied in the Visual Question Answering (VQA) task, as they help to focus on the area-of-interest of both visual and textual information. To answer the questions correctly, the model needs to selectively target different areas of an image, which suggests that an attention-based model may benefit from an explicit attention supervision. In this work, we aim to address the problem of adding attention supervision to VQA models. Since there is a lack of human attention data, we first propose a Human Attention Network (HAN) to generate human-like attention maps, training on a recently released dataset called Human ATtention Dataset (VQA-HAT). Then, we apply the pre-trained HAN on the VQA v2.0 dataset to automatically produce the human-like attention maps for all image-question pairs. The generated human-like attention map dataset for the VQA v2.0 dataset is named as Human-Like ATtention (HLAT) dataset. Finally, we apply human-like attention supervision to an attention-based VQA model. The experiments show that adding human-like supervision yields a more accurate attention together with a better performance, showing a promising future for human-like attention supervision in VQA.


title: TDOA-Based Source Localization With Distance-Dependent Noises

abstract: This paper focuses on the problem of source localization using time-difference-of-arrival (TDOA) measurements in both 2-D and 3-D spaces. Different from existing studies where the variance of TDOA measurement noises is assumed to be independent of the associated source-to-sensor distances, we consider the more realistic model where the variance is a function of the source-to-sensor distances, which dramatically complicates TDOA-based source localization. After formulating the distance-dependent noise model, we prove that using the extra information about the source location in the functional variance improves the estimation accuracy of TDOA-based source localization, but contributes little under a sufficiently small noise level. Further, we theoretically analyze the problem of optimal sensor placement, and derive the necessary and sufficient conditions for optimizing localization performance under different circumstances. Then, a localization scheme based on the iteratively reweighted generalized least squares (IRGLS) method is proposed to efficiently exploit the extra source location information. Finally, a simulation analysis confirms our theoretical studies, and shows that the performance of the proposed localization scheme is comparable to the Cramer-Rao lower bound (CRLB) given moderate TDOA measurement noises.


title: Determination of Bloom's cognitive level of question items using artificial neural network

abstract: We propose a classification model for the cognitive level of question items in examinations based on Bloom's taxonomy. The model implements the artificial neural network approach, which is trained using the scaled conjugate gradient learning algorithm. Several data preprocessing techniques such as word extraction, stop word removal, stemming, and vector representation are applied to a feature set and then the content of a question item is transformed into a numeric form called a feature vector. Because of the poor scalability of neural networks on high-dimension input spaces, several feature reduction methods were investigated to reduce the dimensionality of the feature space. The experimental results indicate that the proposed model can enhance the convergence speed. The results also illustrate that document frequency is the most effective feature reduction method because it maintains the classification precision while enhancing the convergence speed.


title: Microplastics in the aquatic and terrestrial environment: sources (with a specific focus on personal care products), fate and effects

abstract: Due to the widespread use and durability of synthetic polymers, plastic debris occurs in the environment worldwide. In the present work, information on sources and fate of microplastic particles in the aquatic and terrestrial environment, and on their uptake and effects, mainly in aquatic organisms, is reviewed. Microplastics in the environment originate from a variety of sources. Quantitative information on the relevance of these sources is generally lacking, but first estimates indicate that abrasion and fragmentation of larger plastic items and materials containing synthetic polymers are likely to be most relevant. Microplastics are ingested and, mostly, excreted rapidly by numerous aquatic organisms. So far, there is no clear evidence of bioaccumulation or biomagnification. In laboratory studies, the ingestion of large amounts of microplastics mainly led to a lower food uptake and, consequently, reduced energy reserves and effects on other physiological functions. Based on the evaluated data, the lowest microplastic concentrations affecting marine organisms exposed via water are much higher than levels measured in marine water. In lugworms exposed via sediment, effects were observed at microplastic levels that were higher than those in subtidal sediments but in the same range as maximum levels in beach sediments. Hydrophobic contaminants are enriched on microplastics, but the available experimental results and modelling approaches indicate that the transfer of sorbed pollutants by microplastics is not likely to contribute significantly to bioaccumulation of these pollutants. Prior to being able to comprehensively assess possible environmental risks caused by microplastics a number of knowledge gaps need to be filled. However, in view of the persistence of microplastics in the environment, the high concentrations measured at some environmental sites and the prospective of strongly increasing concentrations, the release of plastics into the environment should be reduced in a broad and global effort regardless of a proof of an environmental risk.


title: Questioning the role of sparse coding in the brain

abstract: Coding principles are central to understanding the organization of brain circuitry. Sparse coding offers several advantages, but a near-consensus has developed that it only has beneficial properties, and these are partially unique to sparse coding. We find that these advantages come at the cost of several trade-offs, with the lower capacity for generalization being especially problematic, and the value of sparse coding as a measure and its experimental support are both questionable. Furthermore, silent synapses and inhibitory interneurons can permit learning speed and memory capacity that was previously ascribed to sparse coding only. Combining these properties without exaggerated sparse coding improves the capacity for generalization and facilitates learning of models of a complex and high-dimensional reality.


The intersubjective dynamics of trust, distrust, and manipulation


title: The effectiveness of protective clothing in the reduction of potential DNA contamination of the scene of crime

abstract: The use of ultra-sensitive low copy number (LCN) DNA typing allows the analysis of picogram amounts of DNA. Trace evidence accidentally left at a scene of crime (SOC) by the investigating team may be inadvertently collected and analysed, potentially leading to spurious evidence being introduced into the criminal investigation. A series of experiments were undertaken to determine the extent to which an investigator could contribute to any DNA contamination of a scene of crime under different simulated activities. Further, the degree to which any contamination was reduced by the use of commercially available protective clothing was demonstrated. Precautions that should routinely be taken at a scene of crime to reduce the risk of DNA contamination are recommended.


title: Succeeding through Disruption: Exploring the Factors Influencing the Adoption of Disruptive Technologies in the Mobile Telecommunications Industry in Zimbabwe

abstract: The research explored factors influencing the adoption of disruptive technologies in the mobile telecommunications industry in Zimbabwe. Data was gathered from the second biggest competitor in the industry with over 3 million subscribers. A survey was conducted by purposively selecting 70 respondents from a population of 3,000,000 (three million) active subscribers from the company’s database. A skip interval of 42,857 was used to randomly select the sample. Customer representatives were selected from the company’s five regional offices using a two-stage cluster sampling technique. Employee participants were purposively selected from the company’s head office. A pilot test was conducted to assess the reliability of the research instrument used. Self-administered questionnaires were used in the research. Research results were collected, recorded and analyzed. All T-tests conducted produced results with p= 0.001 (which was less than 0.05 at which the tests were conducted) indicating that internal company influences such as staff competence, availability of funding and the type of infrastructure help impede or accelerate the rate of adoption of disruptive technologies in companies. Future research should however look at organizational ambidexterity as well as exploitation and exploration paradigms in organizations in the telecommunications industry and their impact on the adoption of disruptive technologies. DOI: 10.5901/mjss.2014.v5n20p2550


title: Fast Image Dehazing Method Based on Linear Transformation

abstract: Images captured in hazy or foggy weather conditions are seriously degraded by the scattering of atmospheric particles, which directly influences the performance of outdoor computer vision systems. In this paper, a fast algorithm for single image dehazing is proposed based on linear transformation by assuming that a linear relationship exists in the minimum channel between the hazy image and the haze-free image. First, the principle of linear transformation is analyzed. Accordingly, the method of estimating a medium transmission map is detailed and the weakening strategies are introduced to solve the problem of the brightest areas of distortion. To accurately estimate the atmospheric light, an additional channel method is proposed based on quad-tree subdivision. In this method, average grays and gradients in the region are employed as assessment criteria. Finally, the haze-free image is obtained using the atmospheric scattering model. Numerous experimental results show that this algorithm can clearly and naturally recover the image, especially at the edges of sudden changes in the depth of field. It can, thus, achieve a good effect for single image dehazing. Furthermore, the algorithmic time complexity is a linear function of the image size. This has obvious advantages in running time by guaranteeing a balance between the running speed and the processing effect.


The Art of Multiple Sequence Alignment in R


title: Processing Dense Stereo Data Using Elevation Maps: Road Surface, Traffic Isle, and Obstacle Detection

abstract: A new approach for the detection of the road surface and obstacles is presented. The high accuracy of the method allows the detection of traffic isles as a distinct class. The 3-D data inferred from dense stereo are transformed into a rectangular digital elevation map (DEM). Two classifiers are proposed, namely, density based and road surface based. The density-based obstacle classifier marks DEM cells as road or obstacles, using the density of 3-D points as a criterion. A quadratic road surface model is initially fitted by a random sample consensus (RANSAC) approach to the region in front of the ego vehicle. A region growing-like process refines this primary solution, driven by the 3-D uncertainty model of the stereo sensor. A robust global solution for the road surface is obtained. The road surface is used for discrimination between road, traffic isle, and obstacle points. Fusion and error filtering is performed on the results of the two classifiers. The proposed real-time algorithm was evaluated in an urban scenario and can be used in complex applications from collision avoidance to path planning.


title: Personal data protection maturity model for the micro financial sector in Peru

abstract: The micro financial sector is a strategic element in the economy of developing countries since it facilitates the integration and development of all social classes and let the economic growth. In this point is the growth of data is high every day in sector like the micro financial, resulting from transactions and operations carried out with these companies on a daily basis. Appropriate management of the personal data privacy policies is therefore necessary because, otherwise, it will comply with personal data protection laws and regulations and let take quality information for decision-making and process improvement. The present study proposes a personal data protection maturity model based on international standards of privacy and information security, which also reveals personal data protection capabilities in organizations. Finally, the study proposes a diagnostic and tracing assessment tool that was carried out for five companies in the micro financial sector and the obtained results were analyzed to validate the model and to help in success of data protection initiatives.


title: Low-bit Quantization of Neural Networks for Efficient Inference

abstract: Recent machine learning methods use increasingly large deep neural networks to achieve state of the art results in various tasks. The gains in performance come at the cost of a substantial increase in computation and storage requirements. This makes real-time implementations on limited resources hardware a challenging task. One popular approach to address this challenge is to perform low-bit precision computations via neural network quantization. However, aggressive quantization generally entails a severe penalty in terms of accuracy, and often requires retraining of the network, or resorting to higher bit precision quantization. In this paper, we formalize the linear quantization task as a Minimum Mean Squared Error (MMSE) problem for both weights and activations, allowing low-bit precision inference without the need for full network retraining. The main contributions of our approach are the optimizations of the constrained MSE problem at each layer of the network, the hardware aware partitioning of the network parameters, and the use of multiple low precision quantized tensors for poorly approximated layers. The proposed approach allows 4 bits integer (INT4) quantization for deployment of pretrained models on limited hardware resources. Multiple experiments on various network architectures show that the suggested method yields state of the art results with minimal loss of tasks accuracy.


title: An Interactive Medical Image Segmentation Framework Using Iterative Refinement

abstract: Segmentation is often performed on medical images for identifying diseases in clinical evaluation. Hence it has become one of the major research areas. Conventional image segmentation techniques are unable to provide satisfactory segmentation results for medical images as they contain irregularities. They need to be pre-processed before segmentation. In order to obtain the most suitable method for medical image segmentation, we propose MIST (Medical Image Segmentation Tool), a two stage algorithm. The first stage automatically generates a binary marker image of the region of interest using mathematical morphology. This marker serves as the mask image for the second stage which uses GrabCut to yield an efficient segmented result. The obtained result can be further refined by user interaction, which can be done using the proposed Graphical User Interface (GUI). Experimental results show that the proposed method is accurate and provides satisfactory segmentation results with minimum user interaction on medical as well as natural images. Graphical abstractDisplay Omitted


title: Word Spotting and Recognition with Embedded Attributes

abstract: This paper addresses the problems of word spotting and word recognition on images. In word spotting, the goal is to find all instances of a query word in a dataset of images. In recognition, the goal is to recognize the content of the word image, usually aided by a dictionary or lexicon. We describe an approach in which both word images and text strings are embedded in a common vectorial subspace. This is achieved by a combination of label embedding and attributes learning, and a common subspace regression. In this subspace, images and strings that represent the same word are close together, allowing one to cast recognition and retrieval tasks as a nearest neighbor problem. Contrary to most other existing methods, our representation has a fixed length, is low dimensional, and is very fast to compute and, especially, to compare. We test our approach on four public datasets of both handwritten documents and natural images showing results comparable or better than the state-of-the-art on spotting and recognition tasks.


title: Trusted computing building blocks for embedded linux-based ARM trustzone platforms

abstract: Security is an emerging topic in the field of mobile and embedded platforms. The Trusted Computing Group (TCG) has outlined one possible approach to mobile platform security by recently extending their set of Trusted Computing specifications with Mobile Trusted Modules (MTMs). The MTM specification [13] published by the TCG is a platform independent approach to Trusted Computing explicitly allowing for a wide range of potential implementations. ARM follows a different approach to mobile platform security, by extending platforms with hardware supported ARM TrustZone security [3] mechanisms. This paper outlines an approach to merge TCG-style Trusted Computing concepts with ARM TrustZone technology in order to build an open Linux-based embedded trusted computing platform.


title: A Survey on Hypervisor-Based Monitoring: Approaches, Applications, and Evolutions

abstract: When designing computer monitoring systems, one goal has always been to have a complete view of the monitored target and at the same time stealthily protect the monitor itself. One way to achieve this is to use hypervisor-based, or more generally out of virtual machine (VM)-based, monitoring. There are, however, challenges that limit the use of this mechanism; the most significant of these is the semantic gap problem. Over the past decade, a considerable amount of research has been carried out to bridge the semantic gap and develop all kinds of out-of-VM monitoring techniques and applications. By tracing the evolution of out-of-VM security solutions, this article examines and classifies different approaches that have been proposed to overcome the semantic gap—the fundamental challenge in hypervisor-based monitoring—and how they have been used to develop various security applications. In particular, we review how the past approaches address different constraints, such as practicality, flexibility, coverage, and automation, while bridging the semantic gap; how they have developed different monitoring systems; and how the monitoring systems have been applied and deployed. In addition to systematizing all of the proposed techniques, we also discuss the remaining research problems and shed light on the future directions of hypervisor-based monitoring.


title: Adaptive Control of Hydraulic Actuators With LuGre Model-Based Friction Compensation

abstract: This paper concerns high-accuracy tracking control for hydraulic actuators with nonlinear friction compensation. Typically, LuGre model-based friction compensation has been widely employed in sundry industrial servomechanisms. However, due to the piecewise continuous property, it is difficult to be integrated with backstepping design, which needs the time derivation of the employed friction model. Hence, nonlinear model-based hydraulic control rarely sets foot in friction compensation with nondifferentiable friction models, such as LuGre model, Stribeck effects, although they can give excellent friction description and prediction. In this paper, a novel continuously differentiable nonlinear friction model is first derived by modifying the traditional piecewise continuous LuGre model, then an adaptive backstepping controller is proposed for precise tracking control of hydraulic systems to handle parametric uncertainties along with nonlinear friction compensation. In the formulated nonlinear hydraulic system model, friction parameters, servovalve null shift, and orifice-type internal leakage are all uniformly considered in the proposed controller. The controller theoretically guarantees asymptotic tracking performance in the presence of parametric uncertainties, and the robustness against unconsidered dynamics, as well as external disturbances, is also ensured via Lyapunov analysis. The effectiveness of the proposed controller is demonstrated via comparative experimental results.


title: Automatic Decoding of Facial Movements Reveals Deceptive Pain Expressions

abstract: Summary In highly social species such as humans, faces have evolved to convey rich information for social interaction, including expressions of emotions and pain [1–3]. Two motor pathways control facial movement [4–7]: a subcortical extrapyramidal motor system drives spontaneous facial expressions of felt emotions, and a cortical pyramidal motor system controls voluntary facial expressions. The pyramidal system enables humans to simulate facial expressions of emotions not actually experienced. Their simulation is so successful that they can deceive most observers [8–11]. However, machine vision may be able to distinguish deceptive facial signals from genuine facial signals by identifying the subtle differences between pyramidally and extrapyramidally driven movements. Here, we show that human observers could not discriminate real expressions of pain from faked expressions of pain better than chance, and after training human observers, we improved accuracy to a modest 55%. However, a computer vision system that automatically measures facial movements and performs pattern recognition on those movements attained 85% accuracy. The machine system's superiority is attributable to its ability to differentiate the dynamics of genuine expressions from faked expressions. Thus, by revealing the dynamics of facial action through machine vision systems, our approach has the potential to elucidate behavioral fingerprints of neural control systems involved in emotional signaling.


title: Class D Switching Power Amplifiers: Theory, Design, and Performance

abstract: Switching, or class D, amplifiers represent a source of untapped potential for efficient, high power amplification. They are mostly suited for mobile and portable applications. In an effort to better understand these amplifiers, this project examines the theory, design, and performance of these circuits. As a result, it was determined that Class D amplifiers are not as difficult to design and build as commonly believed. The output sound quality of a well-designed class D audio amplifier is comparable to many high fidelity conventional sound systems. D class amps typically have 90-95% efficiency at rated power compared to conventional audio amps, which have 65-70% efficiency ratings. Most importantly, these benefits are available in a smaller, more cost-effective package due to the switching amplifier?s high efficiency and capability for integration.


title: Decentralized Multi-Agent Optimization via Dual Decomposition

abstract: Abstract We study a distributed multi-agent optimization problem of minimizing the sum of convex objective functions. A new decentralized optimization algorithm is introduced, based on dual decomposition, together with the subgradient method for finding the optimal solution. The iterative algorithm is implemented on a multi-hop network and is designed to handle communication delays. The convergence of the algorithm is proved for communication networks with bounded delays. An explicit bound, which depends on the communication delays, on the convergence rate is given. A numerical comparison with a decentralized primal algorithm shows that the dual algorithm converges faster, and with less communication.


title: Performance Analysis in the Downlink of Asynchronous OFDM/FBMC Based Multi-Cellular Networks

abstract: This paper provides a theoretical performance evaluation of the downlink of asynchronous orthogonal frequency division multiplexing (OFDM) and filter bank based multicarrier (FBMC) cellular radio communication systems. An accurate derivation, for the interference caused by the timing synchronization errors in the neighboring cells, is developed. The multipath effects on the interfering and desired signal are also considered. Based on computing the moment generating functions of the interference power, exact expressions are derived for average error rates of OFDM and FBMC systems considering the frequency correlation fading in the case of block subcarrier assignment scheme.


title: Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment

abstract: Canonical correlation analysis (CCA) is a well established technique for identifying linear relationships among two variable sets. Kernel CCA (KCCA) is the most notable nonlinear extension but it lacks interpretability and robustness against irrelevant features. The aim of this article is to introduce two nonlinear CCA extensions that rely on the recently proposed Hilbert-Schmidt independence criterion and the centered kernel target alignment. These extensions determine linear projections that provide maximally dependent projected data pairs. The paper demonstrates that the use of linear projections allows removing irrelevant features, whilst extracting combinations of strongly associated features. This is exemplified through a simulation and the analysis of recorded data that are available in the literature.


title: MIHash: Online Hashing with Mutual Information

abstract: Learning-based hashing methods are widely used for nearest neighbor retrieval, and recently, online hashing methods have demonstrated good performance-complexity trade-offs by learning hash functions from streaming data. In this paper, we first address a key challenge for online hashing: the binary codes for indexed data must be recomputed to keep pace with updates to the hash functions. We propose an efficient quality measure for hash functions, based on an information-theoretic quantity, mutual information, and use it successfully as a criterion to eliminate unnecessary hash table updates. Next, we also show how to optimize the mutual information objective using stochastic gradient descent. We thus develop a novel hashing method, MIHash, that can be used in both online and batch settings. Experiments on image retrieval benchmarks (including a 2.5M image dataset) confirm the effectiveness of our formulation, both in reducing hash table recomputations and in learning high-quality hash functions.


title: Model Ensemble for Click Prediction in Bing Search Ads

abstract: Accurate estimation of the click-through rate (CTR) in sponsored ads significantly impacts the user search experience and businesses' revenue, even 0.1% of accuracy improvement would yield greater earnings in the hundreds of millions of dollars. CTR prediction is generally formulated as a supervised classification problem. In this paper, we share our experience and learning on model ensemble design and our innovation. Specifically, we present 8 ensemble methods and evaluate them on our production data. Boosting neural networks with gradient boosting decision trees turns out to be the best. With larger training data, there is a nearly 0.9% AUC improvement in offline testing and significant click yield gains in online traffic. In addition, we share our experience and learning on improving the quality of training.


title: Exploiting Document Content for Efficient Aggregation of Crowdsourcing Votes

abstract: The use of crowdsourcing for document relevance assessment has been found to be a viable alternative to corpus annotation by highly trained experts. The question of quality control is a recurring challenge that is often addressed by aggregating multiple individual assessments of the same topic-document pair from independent workers. In the past, such aggregation schemes have been weighted or filtered by estimates of worker reliability based on a multitude of behavioral features. In this paper, we propose an alternative approach by relying on document information. Inspired by the clustering hypothesis of information retrieval, we assume textually similar documents to show similar degrees of relevance towards a given topic. Following up on this intuition, we propagate crowd-generated relevance judgments to similar documents, effectively smoothing the distribution of relevance labels across the similarity space. Our experiments are based on TREC Crowdsourcing Track data and show that even simple aggregation methods utilizing document similarity information significantly improve over majority voting in terms of accuracy as well as cost efficiency.


title: Nonlinear Principal Component Analysis Using Autoassociative Neural Networks

abstract: Nonlinear principal component analysis is a novel technique for multivariate data analysis, similar to the well-known method of principal component analysis. NLPCA, like PCA, is used to identify and remove correlations among problem variables as an aid to dimensionality reduction, visualization, and exploratory data analysis. While PCA identifies only linear correlations between variables, NLPCA uncovers both linear and nonlinear correlations, without restriction on the character of the nonlinearities present in the data. NLPCA operates by training a feedforward neural network to perform the identity mapping, where the network inputs are reproduced at the output layer. The network contains an internal “bottleneck” layer (containing fewer nodes than input or output layers), which forces the network to develop a compact representation of the input data, and two additional hidden layers. The NLPCA method is demonstrated using time-dependent, simulated batch reaction data. Results show that NLPCA successfully reduces dimensionality and produces a feature space map resembling the actual distribution of the underlying system parameters.


title: Real-Time Load Reduction in Multimedia Big Data for Mobile Internet

abstract: In the age of multimedia big data, the popularity of mobile devices has been in an unprecedented growth, the speed of data increasing is faster than ever before, and Internet traffic is rapidly increasing, not only in volume but also in heterogeneity. Therefore, data processing and network overload have become two urgent problems. To address these problems, extensive papers have been published on image analysis using deep learning, but only a few works have exploited this approach for video analysis. In this article, a hybrid-stream model is proposed to solve these problems for video analysis. Functionality of this model covers Data Preprocessing, Data Classification, and Data-Load-Reduction Processing. Specifically, an improved Convolutional Neural Networks (CNN) classification algorithm is designed to evaluate the importance of each video frame and video clip to enhance classification precision. Then, a reliable keyframe extraction mechanism will recognize the importance of each frame or clip, and decide whether to abandon it automatically by a series of correlation operations. The model will reduce data load to a dynamic threshold changed by σ, control the input size of the video in mobile Internet, and thus reduce network overload. Through experimental simulations, we find that the size of processed video has been effectively reduced and the quality of experience (QoE) has not been lowered due to a suitably selected parameter η. The simulation also shows that the model has a steady performance and is powerful enough for continuously growing multimedia big data.


title: Application of Higher Order Spectra for the Identification of Diabetes Retinopathy Stages

abstract: Diabetic retinopathy (DR) is a condition where the retina is damaged due to fluid leaking from the blood vessels into the retina. In extreme cases, the patient will become blind. Therefore, early detection of diabetic retinopathy is crucial to prevent blindness. Various image processing techniques have been used to identify the different stages of diabetes retinopathy. The application of non-linear features of the higher-order spectra (HOS) was found to be efficient as it is more suitable for the detection of shapes. The aim of this work is to automatically identify the normal, mild DR, moderate DR, severe DR and prolific DR. The parameters are extracted from the raw images using the HOS techniques and fed to the support vector machine (SVM) classifier. This paper presents classification of five kinds of eye classes using SVM classifier. Our protocol uses, 300 subjects consisting of five different kinds of eye disease conditions. We demonstrate a sensitivity of 82% for the classifier with the specificity of 88%.


title: A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View

abstract: Machine learning is one of the most prevailing techniques in computer science, and it has been widely applied in image processing, natural language processing, pattern recognition, cybersecurity, and other fields. Regardless of successful applications of machine learning algorithms in many scenarios, e.g., facial recognition, malware detection, automatic driving, and intrusion detection, these algorithms and corresponding training data are vulnerable to a variety of security threats, inducing a significant performance decrease. Hence, it is vital to call for further attention regarding security threats and corresponding defensive techniques of machine learning, which motivates a comprehensive survey in this paper. Until now, researchers from academia and industry have found out many security threats against a variety of learning algorithms, including naive Bayes, logistic regression, decision tree, support vector machine (SVM), principle component analysis, clustering, and prevailing deep neural networks. Thus, we revisit existing security threats and give a systematic survey on them from two aspects, the training phase and the testing/inferring phase. After that, we categorize current defensive techniques of machine learning into four groups: security assessment mechanisms, countermeasures in the training phase, those in the testing or inferring phase, data security, and privacy. Finally, we provide five notable trends in the research on security threats and defensive techniques of machine learning, which are worth doing in-depth studies in future.


title: Characterization of contact offenders and child exploitation material trafficking on five peer-to-peer networks

abstract: We provide detailed measurement of the illegal trade in child exploitation material (CEM, also known as child pornography) from mid-2011 through 2014 on five popular peer-to-peer (P2P) file sharing networks. We characterize several observations: counts of peers trafficking in CEM; the proportion of arrested traffickers that were identified during the investigation as committing contact sexual offenses against children; trends in the trafficking of sexual images of sadistic acts and infants or toddlers; the relationship between such content and contact offenders; and survival rates of CEM. In the 5 P2P networks we examined, we estimate there were recently about 840,000 unique installations per month of P2P programs sharing CEM worldwide. We estimate that about 3 in 10,000 Internet users worldwide were sharing CEM in a given month; rates vary per country. We found an overall month-to-month decline in trafficking of CEM during our study. By surveying law enforcement we determined that 9.5% of persons arrested for P2P-based CEM trafficking on the studied networks were identified during the investigation as having sexually offended against children offline. Rates per network varied, ranging from 8% of arrests for CEM trafficking on Gnutella to 21% on BitTorrent. Within BitTorrent, where law enforcement applied their own measure of content severity, the rate of contact offenses among peers sharing the most-severe CEM (29%) was higher than those sharing the least-severe CEM (15%). Although the persistence of CEM on the networks varied, it generally survived for long periods of time; e.g., BitTorrent CEM had a survival rate near 100%.


JD D J ! . ! 3 1 1 7 RELATIONSHIP BETWEEN SUPPLY CHAIN MANAGEMENT AND OUTSOURCING


title: No Moore's Law for batteries

abstract: Fred Schlachter. ::: ::: ::: ::: The public has become accustomed to rapid progress in mobile phone technology, computers, and access to information; tablet computers, smart phones, and other powerful new devices are familiar to most people on the planet. ::: ::: These developments are due in part to the ongoing exponential increase in computer processing power, doubling approximately every 2 years for the past several decades. This pattern is usually called Moore’s Law and is named for Gordon Moore, a cofounder of Intel. The law is not a law like that for gravity; it is an empirical observation, which has become a self-fulfilling prophecy. Unfortunately, much of the public has come to expect that all technology does, will, or should follow such a law, which is not consistent with our everyday observations: For example, the maximum speed of cars, planes, or ships does not increase exponentially; maximum speed barely increases at all. ::: ::: Cars require a portable fuel, preferably one that is widely … ::: ::: [↵][1]1E-mail: fsschlachter{at}gmail.com. ::: ::: [1]: #xref-corresp-1-1