title: A robust minimax approach to classification

abstract: When constructing a classifier, the probability of correct classification of future data points should be maximized. We consider a binary classification problem where the mean and covariance matrix of each class are assumed to be known. No further assumptions are made with respect to the class-conditional distributions. Misclassification probabilities are then controlled in a worst-case setting: that is, under all possible choices of class-conditional densities with given mean and covariance matrix, we minimize the worst-case (maximum) probability of misclassification of future data points. For a linear decision boundary, this desideratum is translated in a very direct way into a (convex) second order cone optimization problem, with complexity similar to a support vector machine problem. The minimax problem can be interpreted geometrically as minimizing the maximum of the Mahalanobis distances to the two classes. We address the issue of robustness with respect to estimation errors (in the means and covariances of the classes) via a simple modification of the input data. We also show how to exploit Mercer kernels in this setting to obtain nonlinear decision boundaries, yielding a classifier which proves to be competitive with current methods, including support vector machines. An important feature of this method is that a worst-case bound on the probability of misclassification of future data is always obtained explicitly.


title: Decontamination of 16S rRNA gene amplicon sequence datasets based on bacterial load assessment by qPCR

abstract: BackgroundIdentification of unexpected taxa in 16S rRNA surveys of low-density microbiota, diluted mock communities and cultures demonstrated that a variable fraction of sequence reads originated from exogenous DNA. The sources of these contaminants are reagents used in DNA extraction, PCR, and next-generation sequencing library preparation, and human (skin, oral and respiratory) microbiota from the investigators.ResultsFor in silico removal of reagent contaminants, a pipeline was used which combines the relative abundance of operational taxonomic units (OTUs) in V3–4 16S rRNA gene amplicon datasets with bacterial DNA quantification based on qPCR targeting of the V3 segment of the 16S rRNA gene. Serially diluted cultures of Escherichia coli and Staphylococcus aureus were used for 16S rDNA profiling, and DNA from each of these species was used as a qPCR standard. OTUs assigned to Escherichia or Staphylococcus were virtually unaffected by the decontamination procedure, whereas OTUs from Pseudomonas, which is a major reagent contaminant, were completely or nearly completely removed. The decontamination procedure also attenuated the trend of increase in OTU richness in serially diluted cultures.ConclusionsRemoval of contaminant sequences derived from reagents based on use of qPCR data may improve taxonomic representation in samples with low DNA concentration. Using the described pipeline, OTUs derived from cross-contamination of negative extraction controls were not recognized as contaminants and not removed from the sample dataset.


title: Building Energy Management Systems: The Age of Intelligent and Adaptive Buildings

abstract: Building automation systems (BAS), or building control systems (BCS), typically consist of building energy management systems (BEMSs), physical security and access control, fire/life safety, and other systems (elevators, public announcements, and closed-circuit television). BEMSs control heating, ventilation, and air conditioning (HVAC) and lighting systems in buildings; more specifically, they control HVAC?s primary components such as air handling units (AHUs), chillers, and heating elements. BEMSs are essential components of modern buildings, tasked with seemingly contradicting requirements?minimizing energy consumption while maintaining occupants? comfort [1]. In the United States, about 40% of total energy consumption and 70% of electricity consumption are spent on buildings every year. These numbers are comparable to global statistics that about 30% of total energy consumption and 60% of electricity consumption are spent on buildings. Buildings are an integral part of global cyberphysical systems (smart cities) and evolve and interact with their surroundings (Figure 1) [2]. As buildings undergo years of exploitation, their thermal characteristics deteriorate, indoor spaces (especially in commercial buildings) get rearranged, and usage patterns change. In time, their inner (and outer) microclimates adjust to changes in surrounding buildings, overshadowing patterns, and city climates, not to mention building retrofitting [3], [4]. Thus, even in cases of ?ideally? designed BEMS/HVAC systems, because of ever-changing and uncertain indoor and outdoor environments, their performance frequently falls short of expectations. Unfortunately, the complexity of BEMSs, large amounts of constantly changing data, and evolving interrelations among sensor feeds make identifying these suboptimal behaviors difficult [1], [5]. Therefore, traditional data-mining algorithms and data-analysis tools are often inadequate.


title: Memory in Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity From Spatiotemporal Dynamics

abstract: Natural spatiotemporal processes can be highly non-stationary in many ways, e.g. the low-level non-stationarity such as spatial correlations or temporal dependencies of local pixel values; and the high-level variations such as the accumulation, deformation or dissipation of radar echoes in precipitation forecasting. From Cramer's Decomposition, any non-stationary process can be decomposed into deterministic, time-variant polynomials, plus a zero-mean stochastic term. By applying differencing operations appropriately, we may turn time-variant polynomials into a constant, making the deterministic component predictable. However, most previous recurrent neural networks for spatiotemporal prediction do not use the differential signals effectively, and their relatively simple state transition functions prevent them from learning too complicated variations in spacetime. We propose the Memory In Memory (MIM) networks and corresponding recurrent blocks for this purpose. The MIM blocks exploit the differential signals between adjacent recurrent states to model the non-stationary and approximately stationary properties in spatiotemporal dynamics with two cascaded, self-renewed memory modules. By stacking multiple MIM blocks, we could potentially handle higher-order non-stationarity. The MIM networks achieve the state-of-the-art results on four spatiotemporal prediction tasks across both synthetic and real-world datasets. We believe that the general idea of this work can be potentially applied to other time-series forecasting tasks.


title: A predictive model for identifying students with dropout profiles in online courses

abstract: Online education often deals with the problem related to the high students’ dropout rate during a course in many areas. There is huge amount of historical data about students in online courses. Hence, a relevant problem on this context is to examine those data, aiming at finding effective mechanisms to understand student profiles, identifying those students with characteristics to drop out at early stage in the course. In this paper, we address this problem by proposing predictive models to provide educational managers with the duty to identify students whom are in the dropout bound. Four classification algorithms with different classification methods were used during the evaluation, in order to find the model with the highest accuracy in prediction the profile of dropouts students. Data for model generation were obtained from two data sources available from University. The results showed the model generated by using SVM algorithm as the most accurate among those selected, with 92.03% of accuracy.


title: Mining The Web For Bilingual Text

abstract: STRAND (Resnik, 1998) is a language-independent system for automatic discovery of text in parallel translation on the World Wide Web. This paper extends the preliminary STRAND results by adding automatic language identification, scaling up by orders of magnitude, and formally evaluating performance. The most recent end-product is an automatically acquired parallel corpus comprising 2491 English-French document pairs, approximately 1.5 million words per language.


title: Word Segmentation for Urdu OCR System

abstract: This paper presents a technique for word segmentation for the Urdu OCR system. Word segmentation or word tokeniza� tion is a preliminary task for Urdu lan� guage processing. Several techniques are available for word segmentation in other languages. A methodology is pro� posed for word segmentation in this pa� per which determines the boundaries of words given a sequence of ligatures, based on collocation of ligatures and words in the corpus. Using this tech� nique, word identification rate of 96.10% is achieved, using trigram prob� abilities normalized over the number of ligatures and words in the sequence.


title: Energy harvesting for the implantable biomedical devices: issues and challenges

abstract: The development of implanted devices is essential because of their direct effect on the lives and safety of humanity. This paper presents the current issues and challenges related to all methods used to harvest energy for implantable biomedical devices. The advantages, disadvantages, and future trends of each method are discussed. The concept of harvesting energy from environmental sources and human body motion for implantable devices has gained a new relevance. In this review, the harvesting kinetic, electromagnetic, thermal and infrared radiant energies are discussed. Current issues and challenges related to the typical applications of these methods for energy harvesting are illustrated. Suggestions and discussion of the progress of research on implantable devices are also provided. This review is expected to increase research efforts to develop the battery-less implantable devices with reduced over hole size, low power, high efficiency, high data rate, and improved reliability and feasibility. Based on current literature, we believe that the inductive coupling link is the suitable method to be used to power the battery-less devices. Therefore, in this study, the power efficiency of the inductive coupling method is validated by MATLAB based on suggested values. By further researching and improvements, in the future the implantable and portable medical devices are expected to be free of batteries.


title: A Comparison of LSTMs and Attention Mechanisms for Forecasting Financial Time Series

abstract: While LSTMs show increasingly promising results for forecasting Financial Time Series (FTS), this paper seeks to assess if attention mechanisms can further improve performance. The hypothesis is that attention can help prevent long-term dependencies experienced by LSTM models. To test this hypothesis, the main contribution of this paper is the implementation of an LSTM with attention. Both the benchmark LSTM and the LSTM with attention were compared and both achieved reasonable performances of up to 60% on five stocks from Kaggle's Two Sigma dataset. This comparative analysis demonstrates that an LSTM with attention can indeed outperform standalone LSTMs but further investigation is required as issues do arise with such model architectures.


title: Machine learning for physical layer link adaptation in multiple-antenna wireless networks

abstract: Prototyping and experimentation are key to understanding the operation of wireless systems in practice. In this extended abstract we present an implementation of physical layer link adaptation, or data rate selection, through machine learning on Hydra: an IEEE 802.11n draft standard multihop wireless networking prototype. This implementation highlights both the utility of learning-based link adaptation in practical networks as well as the flexibility of Hydra.


title: Symmetry-based text line detection in natural scenes

abstract: Recently, a variety of real-world applications have triggered huge demand for techniques that can extract textual information from natural scenes. Therefore, scene text detection and recognition have become active research topics in computer vision. In this work, we investigate the problem of scene text detection from an alternative perspective and propose a novel algorithm for it. Different from traditional methods, which mainly make use of the properties of single characters or strokes, the proposed algorithm exploits the symmetry property of character groups and allows for direct extraction of text lines from natural images. The experiments on the latest ICDAR benchmarks demonstrate that the proposed algorithm achieves state-of-the-art performance. Moreover, compared to conventional approaches, the proposed algorithm shows stronger adaptability to texts in challenging scenarios.


title: Asymptotic analysis of stochastic programs, Annals of Operations Research 30

abstract: In this paper we discuss a general approach to studying asymptotic properties of statistical estimators in stochastic programming. The approach is based on an extended delta method and appears to be particularly suitable for deriving asymptotics of the optimal value of stochastic programs. Asymptotic analysis of the optimal value will be presented in detail. Asymptotic properties of the corresponding optimal solutions are briefly discussed.


title: Computing bounds on the expected payoff of Alternative Risk Transfer products

abstract: The demand for integrated risk management solutions and the need for new sources of capital have led to the development of innovative risk management products that mix the characteristics of traditional insurance and financial products. Such products, usually referred as Alternative Risk Transfer (ART) products include: (re)insurance contracts that bundle several risks under a single policy; multi-trigger products where the payment of benefits depends upon the occurrence of several events; and insurance linked securities that place insurance risks in the capital market. Pricing of these complex products usually requires tailor-made complex valuation methods that combine derivative pricing and actuarial science techniques for each product, as well as strong distributional assumptions on the ART’s underlying risk factors. We present here an alternative methodology to compute bounds on the price of ART products when there is limited information on the distribution of the underlying risk factors. In particular, we develop a general optimization-based method that computes upper and lower price bounds for different ART products using market data and possibly expert information about the underlying risk factors. These bounds are useful when the structure of the product is too complex to develop analytical or simulation valuation methods, or when the scarcity of data makes it difficult to make strong distributional assumptions on the risk factors. We illustrate our results by computing bounds on the price of a floating retention insurance contract, and a catastrophe equity put (CatEPut) option.


title: On community outliers and their efficient detection in information networks

abstract: Linked or networked data are ubiquitous in many applications. Examples include web data or hypertext documents connected via hyperlinks, social networks or user profiles connected via friend links, co-authorship and citation information, blog data, movie reviews and so on. In these datasets (called "information networks"), closely related objects that share the same properties or interests form a community. For example, a community in blogsphere could be users mostly interested in cell phone reviews and news. Outlier detection in information networks can reveal important anomalous and interesting behaviors that are not obvious if community information is ignored. An example could be a low-income person being friends with many rich people even though his income is not anomalously low when considered over the entire population. This paper first introduces the concept of community outliers (interesting points or rising stars for a more positive sense), and then shows that well-known baseline approaches without considering links or community information cannot find these community outliers. We propose an efficient solution by modeling networked data as a mixture model composed of multiple normal communities and a set of randomly generated outliers. The probabilistic model characterizes both data and links simultaneously by defining their joint distribution based on hidden Markov random fields (HMRF). Maximizing the data likelihood and the posterior of the model gives the solution to the outlier inference problem. We apply the model on both synthetic data and DBLP data sets, and the results demonstrate importance of this concept, as well as the effectiveness and efficiency of the proposed approach.


title: Characterizing web content, user interests, and search behavior by reading level and topic

abstract: A user's expertise or ability to understand a document on a given topic is an important aspect of that document's relevance. However, this aspect has not been well-explored in information retrieval systems, especially those at Web scale where the great diversity of content, users, and tasks presents an especially challenging search problem. To help improve our modeling and understanding of this diversity, we apply automatic text classifiers, based on reading difficulty and topic prediction, to estimate a novel type of profile for important entities in Web search -- users, websites, and queries. These profiles capture topic and reading level distributions, which we then use in conjunction with search log data to characterize and compare different entities. We find that reading level and topic distributions provide an important new representation of Web content and user interests, and that using both together is more effective than using either one separately. In particular we find that: 1) the reading level of Web content and the diversity of visitors to a website can vary greatly by topic; 2) the degree to which a user's profile matches with a site's profile is closely correlated with the user's preference of the website in search results, and 3) site or URL profiles can be used to predict 'expertness' whether a given site or URL is oriented toward expert vs. non-expert users. Our findings provide strong evidence in favor of jointly incorporating reading level and topic distribution metadata into a variety of critical tasks in Web information systems.


title: Sensing Urban Land-Use Patterns By Integrating Google Tensorflow And Scene-Classification Models

abstract: With the rapid progress of China's urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model's ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.


BroncoVote: Secure Voting System Using Ethereum’s Blockchain


title: The CAPIO 2017 Conversational Speech Recognition System

abstract: In this paper we show how we have achieved the state-of-the-art performance on the industry-standard NIST 2000 Hub5 English evaluation set. We explore densely connected LSTMs, inspired by the densely connected convolutional networks recently introduced for image classification tasks. We also propose an acoustic model adaptation scheme that simply averages the parameters of a seed neural network acoustic model and its adapted version. This method was applied with the CallHome training corpus and improved individual system performances by on average 6.1% (relative) against the CallHome portion of the evaluation set with no performance loss on the Switchboard portion. With RNN-LM rescoring and lattice combination on the 5 systems trained across three different phone sets, our 2017 speech recognition system has obtained 5.0% and 9.1% on Switchboard and CallHome, respectively, both of which are the best word error rates reported thus far. According to IBM in their latest work to compare human and machine transcriptions, our reported Switchboard word error rate can be considered to surpass the human parity (5.1%) of transcribing conversational telephone speech.


title: Uninterrupted operation of doubly-fed induction generator based wind turbine during network

abstract: The proportion of the capacity of the large on-grid wind turbines to transmission networks is increasing rapidly. During the light degree network fault, it is required that wind turbines have certain self-adaptive ability, the wind turbines can operate uninterruptedly and continue to supply the power to network to help the recovery of network fault. Voltage sags is the most common fault of network. This paper mainly discusses the method to improve the uninterrupted operation ability of doubly-fed induction generator (DFIG) based wind turbine under voltage sags. First the simulation model of DFIG system under the network fault is developed. The restricted factors of uninterrupted operation of DFIG under network fault are output current of rotor side PWM converter and voltage of DC line. Then the conventional vector control schemes are improved in order to reducing the current shock of rotor and maintaining constant voltage of DC line. Finally, the simulation of the improved vector control schemes are done, the simulation result verifies the control scheme is effective.


title: Unrelated umbilical cord blood transplant for beta-thalassemia major.

abstract: A 5-year-old boy with beta-thalassemia major received an unrelated umbilical cord blood transplantation (URD-UCBT). The URD-UCB was six antigen HLA matched. The infused cell dose was 7.5 x 10(7)/kg nucleated cells. Conditioning included busulfan 20 mg/kg, cyclophosphamide 200 mg/kg, fludarabine 150 mg/kg, thiotepa 6 mg/kg, and antithymocyte globulin 90 mg/kg. The post transplant complications were mild hepatic veno-occlusive disease, acute GVHD grade III, and CMV interstitial pneumonia. The subject has been ex-thalassemic for more than 20 months post transplant. The chronic GVHD was limited and could be controlled by methylprednisolone combined with mycophenolate. This is the first successful report of an unrelated umbilical cord blood transplantation for beta-thalassemia major from China.


Motivation and barriers to participation in virtual knowledge-sharing communities of practice


title: Survival of a Potamogeton pectinatus L. population under various light conditions in a shallow eutrophic lake (Lake Veluwe) in The Netherlands

abstract: In Lake Veluwe, an eutrophicated shallow lake in The Netherlands, light connditions significantly affected the total biomass production of a Potamogeton pectinatus L. vegetation. A negative correlation was recorded between the annual biomass production and the mean daily photon flux densities in an experimental setup in which the photon flux densities were experimentally manipulated (four different levels of artificial shading) and the photoperiod was relatively unaffected. At the lowest mean photon flux density, the maximum total biomass was reached earlier in the growing season (end of June) than at the other photon flux densities (mid-August). Tuber production started at the same time in all conditions (mid-June). The numbers of tubers produced per gram ash-free dry weight of above-ground biomass was highest for the conditions with the lowest mean daily photon flux density. However, the total number of tubers per square metre recorded at the end of the growing season was highest for the control (no artificial shading). ::: ::: It is concluded that tuber initiation occurs under long-day conditions and is not controlled by mean daily photon flux densities. Tuber growth is clearly related to differences in daily photosynthetic periods. Tubers compete with above-ground biomass for photosynthate. At the highest level of shading, the photosynthetic tissue was ultimately unable to sustain tuber growth and, as a consequence, net growth of the vegetation ceased. Regarding tuber numbers of P. pectinatus in late summer, it is concluded that the size of the tuber bank was negatively affected by shade in the experiments.


title: Effect of Botulinum Toxin A on the Autonomic Nervous System of the Rat Lower Urinary Tract

abstract: ABSTRACTPurpose: The magnitude and duration of the effects of botulinum toxin A on acetylcholine (ACh) and norepinephrine release from the bladder and urethra of rats were measured using a radiochemical method.Materials and Methods: Saline (sham treatment) or botulinum toxin A was injected into the bladder (50 μl.) or urethra (30 μl.) in separate groups of animals. The release of 3H-norepinephrine or 14C-choline was measured at 2 time points after injection (5 or 30 days).Results: The fractional release of ACh in botulinum toxin A treated animals was significantly inhibited at higher frequencies of electrical field stimulation (20 Hz.) but not at lower frequencies (2 Hz.) 5 days after injection. However, ACh release recovered to sham injected values 30 days after toxin injection. No significant differences in the fractional release of norepinephrine from sham injected or botulinum toxin A bladders were observed. In contrast, norepinephrine release from the urethra was inhibited by botulinum toxin A for at...


title: The “Horse” Inside: Seeking Causes Behind the Behaviors of Music Content Analysis Systems

abstract: Building systems that possess the sensitivity and intelligence to identify and describe high-level attributes in music audio signals continues to be an elusive goal, but one that surely has broad and deep implications for a wide variety of applications. Hundreds of papers have so far been published toward this goal, and great progress appears to have been made. Some systems produce remarkable accuracies at recognising high-level semantic concepts, such as music style, genre and mood. However, it might be that these numbers do not mean what they seem. In this paper, we take a state-of-the-art music content analysis system and investigate what causes it to achieve exceptionally high performance in a benchmark music audio dataset. We dissect the system to understand its operation, determine its sensitivities and limitations, and predict the kinds of knowledge it could and could not possess about music. We perform a series of experiments to illuminate what the system has actually learned to do, and to what extent it is performing the intended music listening task. Our results demonstrate how the initial manifestation of music intelligence in this state-of-the-art can be deceptive. Our work provides constructive directions toward developing music content analysis systems that can address the music information and creation needs of real-world users.


Apoptosis in the Human Neonatal Vascular Remodeling


title: When visual object-context features meet generic and specific semantic priors in image captioning

abstract: In this work, we propose a novel encoding-decoding based image captioning framework, which improves the performance by jointly exploring the visual object-context features, generic and specific semantic priors. In the encoding of RNN, we extract the semantic attributes, object-related and scene-related image features first, and then feed them sequentially to the encoder of RNN, which considers the rich general semantic and visual object-context representation of images. To incorporate the testing specific semantic priors in the decoding of RNN, we apply cross-modal retrieval to find the most similar captions of the testing image in the visual-semantic embedding space of VSE++. The BLEU-4 similarity is utilized to evaluate the similarity between the generated sentence and the retrieved captions, which incorporates the sentence-making priors to the testing-specific reference captions. The evaluation on benchmark dataset Microsoft COCO shows the superiority of our algorithm over the state-of-the-art approaches on standard evaluation metrics.


title: Movie2Comics: Towards a Lively Video Content Presentation

abstract: As a type of artwork, comics is prevalent and popular around the world. However, despite the availability of assistive software and tools, the creation of comics is still a labor-intensive and time-consuming process. This paper proposes a scheme that is able to automatically turn a movie clip to comics. Two principles are followed in the scheme: 1) optimizing the information preservation of the movie; and 2) generating outputs following the rules and the styles of comics. The scheme mainly contains three components: script-face mapping, descriptive picture extraction, and cartoonization. The script-face mapping utilizes face tracking and recognition techniques to accomplish the mapping between characters' faces and their scripts. The descriptive picture extraction then generates a sequence of frames for presentation. Finally, the cartoonization is accomplished via three steps: panel scaling, stylization, and comics layout design. Experiments are conducted on a set of movie clips and the results have demonstrated the usefulness and the effectiveness of the scheme.


title: A wirelessly powered log-based closed-loop deep brain stimulation SoC with two-way wireless telemetry for treatment of neurological disorders

abstract: A log-based closed-loop Deep Brain Stimulation system detects and processes low-frequency brain field signals to optimize stimulation parameters. The fully self-contained single-chip system incorporates LNAs, a log-ADC, digital log-filters, a log-DSP with a PI-controller, current stimulators, a two-way wireless transceiver, a clock generator, and an RF energy harvester. The 2×2mm2 180nm CMOS prototype consumes 468µW for recording and processing neural signals, stimulation, and for two-way wireless communication.


title: Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC): Rationale and design

abstract: Abstract Remission rates for Major Depressive Disorder (MDD) are low and unpredictable for any given antidepressant. No biological or clinical marker has demonstrated sufficient ability to match individuals to efficacious treatment. Biosignatures developed from the systematic exploration of multiple biological markers, which optimize treatment selection for individuals (moderators) and provide early indication of ultimate treatment response (mediators) are needed. The rationale and design of a multi-site, placebo-controlled randomized clinical trial of sertraline examining moderators and mediators of treatment response is described. The target sample is 300 participants with early onset (≤30 years) recurrent MDD. Non-responders to an 8-week trial are switched double blind to either bupropion (for sertraline non-responders) or sertraline (for placebo non-responders) for an additional 8 weeks. Clinical moderators include anxious depression, early trauma, gender, melancholic and atypical depression, anger attacks, Axis II disorder, hypersomnia/fatigue, and chronicity of depression. Biological moderator and mediators include cerebral cortical thickness, task-based fMRI (reward and emotion conflict), resting connectivity, diffusion tensor imaging (DTI), arterial spin labeling (ASL), electroencephalograpy (EEG), cortical evoked potentials, and behavioral/cognitive tasks evaluated at baseline and week 1, except DTI, assessed only at baseline. The study is designed to standardize assessment of biomarkers across multiple sites as well as institute replicable quality control methods, and to use advanced data analytic methods to integrate these markers. A Differential Depression Treatment Response Index (DTRI) will be developed. The data, including biological samples (DNA, RNA, and plasma collected before and during treatment), will become available in a public scientific repository. Clinical Trial Registration : Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC). Identifier : NCT01407094. URL : http://clinicaltrials.gov/show/NCT01407094 .


title: Defying Unjust Authority: An Exploratory Study

abstract: This research explores the psychological factors potentially involved in fostering disobedience to an unjust authority. Our paradigm was modeled after that of the Utrecht Studies on Obedience (Meeus and Raaijmakers European Journal of Social Psychology 16:311-324, 1986) in which participants are ordered to give each of 15 increasingly hostile comments to a participant/victim whenever he fails a trial. Although 30% of our sample followed commands to insult the other participant (confederate), the majority did refuse to do so at some point in the escalating hostility sequence. Our procedure utilized conditions known from prior research to increase the ratio of disobedience to obedience: proximity of teacher to learner plus remote authority. In order to better understand some of the cognitive and affective processes that may predict such defiant behaviour, we utilized a variety of measures, among them, behavioural observations, individual difference assessments, and in depth post-experimental interviews.


title: The effects of video on cognitive load and social presence in multimedia-learning

abstract: Two studies examined the use of video in multimedia learning environments. In Study 1, participants (N=26) viewed one of two versions of a computer-based multimedia presentation: video, which included a video of a lecture with synchronized slides, or no video, which included the slides but only an audio narration of the lecture. Learning, cognitive load and social presence were assessed, but a significant difference was found only for cognitive load, with video experiencing greater cognitive load, t (24)=2.45, p<.05. In Study 2, students (N=25) were randomly assigned to either video or no video condition. Background knowledge and visual/verbal learning preference were assessed before viewing the presentation, and learning, cognitive load, and social presence were assessed after viewing. No significant differences were found for learning or social presence. However, a significant visual/verbal learning preference by condition interaction was found for cognitive load, F (1,21)=4.51, p<.05: low visual-preference students experienced greater cognitive load in the video condition, while high visual-preference students experienced greater cognitive load in the no video condition.


Dynamic Modeling and Simulation of Compliant Legged Quadruped Robot


title: Combining Satellite Imagery and Open Data to Map Road Safety

abstract: Improving road safety is critical for the sustainable development of cities. A road safety map is a powerful tool that can help prevent future traffic accidents. However, accurate mapping requires accurate data collection, which is both expensive and labor intensive. Satellite imagery is increasingly becoming abundant, higher in resolution and affordable. Given the recent successes deep learning has achieved in the visual recognition field, we are interested in investigating whether it is possible to use deep learning to accurately predict road safety directly from raw satellite imagery. To this end, we propose a deep learning-based mapping approach that leverages open data to learn from raw satellite imagery robust deep models able to predict accurate city-scale road safety maps at an affordable cost. To empirically validate the proposed approach, we trained a deep model on satellite images obtained from over 647 thousand traffic-accident reports collected over a period of four years by the New York city Police Department. The best model predicted road safety from raw satellite imagery with an accuracy of 78%. We also used the New York city model to predict for the city of Denver a city-scale map indicating road safety in three levels. Compared to a map made from three years' worth of data collected by the Denver city Police Department, the map predicted from raw satellite imagery has an accuracy of 73%.


title: Human-centric justification of machine learning predictions

abstract: Human decision makers in many domains can make use of predictions made by machine learning models in their decision making process, but the usability of these predictions is limited if the human is unable to justify his or her trust in the prediction. We propose a novel approach to producing justifications that is geared towards users without machine learning expertise, focusing on domain knowledge and on human reasoning, and utilizing natural language generation. Through a task-based experiment, we show that our approach significantly helps humans to correctly decide whether or not predictions are accurate, and significantly increases their satisfaction with the justification.


title: The rise of low-cost sensing for managing air pollution in cities

abstract: Ever growing populations in cities are associated with a major increase in road vehicles and air pollution. The overall high levels of urban air pollution have been shown to be of a significant risk to city dwellers. However, the impacts of very high but temporally and spatially restricted pollution, and thus exposure, are still poorly understood. Conventional approaches to air quality monitoring are based on networks of static and sparse measurement stations. However, these are prohibitively expensive to capture tempo-spatial heterogeneity and identify pollution hotspots, which is required for the development of robust real-time strategies for exposure control. Current progress in developing low-cost micro-scale sensing technology is radically changing the conventional approach to allow real-time information in a capillary form. But the question remains whether there is value in the less accurate data they generate. This article illustrates the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.


title: SmartBoxes for interactive urban reconstruction

abstract: We introduce an interactive tool which enables a user to quickly assemble an architectural model directly over a 3D point cloud acquired from large-scale scanning of an urban scene. The user loosely defines and manipulates simple building blocks, which we call SmartBoxes, over the point samples. These boxes quickly snap to their proper locations to conform to common architectural structures. The key idea is that the building blocks are smart in the sense that their locations and sizes are automatically adjusted on-the-fly to fit well to the point data, while at the same time respecting contextual relations with nearby similar blocks. SmartBoxes are assembled through a discrete optimization to balance between two snapping forces defined respectively by a data-fitting term and a contextual term, which together assist the user in reconstructing the architectural model from a sparse and noisy point cloud. We show that a combination of the user's interactive guidance and high-level knowledge about the semantics of the underlying model, together with the snapping forces, allows the reconstruction of structures which are partially or even completely missing from the input.


title: The importance of gesture in children's spatial reasoning.

abstract: On average, men outperform women on mental rotation tasks. Even boys as young as 41U2 perform better than girls on simplified spatial transformation tasks. The goal of our study was to explore ways of improving 5-year-olds’ performance on a spatial transformation task and to examine the strategies children use to solve this task. We found that boys performed better than girls before training and that both boys and girls improved with training, whether they were given explicit instruction or just practice. Regardless of training condition, the more children gestured about moving the pieces when asked to explain how they solved the spatial transformation task, the better they performed on the task, with boys gesturing about movement significantly more (and performing better) than girls. Gesture thus provides useful information about children’s spatial strategies, raising the possibility that gesture training may be particularly effective in improving children’s mental rotation skills.


title: A survey on image segmentation

abstract: Abstract For the past decade, many image segmentation techniques have been proposed. These segmentation techniques can be categorized into three classes, (1) characteristic feature thresholding or clustering, (2) edge detection, and (3) region extraction. This survey summarizes some of these techniques. In the area of biomedical image segmentation, most proposed techniques fall into the categories of characteristic feature thresholding or clustering and edge detection.


title: Comparisons of Citations in Web of Science, Scopus, and Google Scholar for Articles Published in General Medical Journals

abstract: CONTEXT ::: Until recently, Web of Science was the only database available to track citation counts for published articles. Other databases are now available, but their relative performance has not been established. ::: ::: ::: OBJECTIVE ::: To compare the citation count profiles of articles published in general medical journals among the citation databases of Web of Science, Scopus, and Google Scholar. ::: ::: ::: DESIGN ::: Cohort study of 328 articles published in JAMA, Lancet, or the New England Journal of Medicine between October 1, 1999, and March 31, 2000. Total citation counts for each article up to June 2008 were retrieved from Web of Science, Scopus, and Google Scholar. Article characteristics were analyzed in linear regression models to determine interaction with the databases. ::: ::: ::: MAIN OUTCOME MEASURES ::: Number of citations received by an article since publication and article characteristics associated with citation in databases. ::: ::: ::: RESULTS ::: Google Scholar and Scopus retrieved more citations per article with a median of 160 (interquartile range [IQR], 83 to 324) and 149 (IQR, 78 to 289), respectively, than Web of Science (median, 122; IQR, 66 to 241) (P < .001 for both comparisons). Compared with Web of Science, Scopus retrieved more citations from non-English-language sources (median, 10.2% vs 4.1%) and reviews (30.8% vs 18.2%), and fewer citations from articles (57.2% vs 70.5%), editorials (2.1% vs 5.9%), and letters (0.8% vs 2.6%) (all P < .001). On a log(10)-transformed scale, fewer citations were found in Google Scholar to articles with declared industry funding (nonstandardized regression coefficient, -0.09; 95% confidence interval [CI], -0.15 to -0.03), reporting a study of a drug or medical device (-0.05; 95% CI, -0.11 to 0.01), or with group authorship (-0.29; 95% CI, -0.35 to -0.23). In multivariable analysis, group authorship was the only characteristic that differed among the databases; Google Scholar had significantly fewer citations to group-authored articles (-0.30; 95% CI, -0.36 to -0.23) compared with Web of Science. ::: ::: ::: CONCLUSION ::: Web of Science, Scopus, and Google Scholar produced quantitatively and qualitatively different citation counts for articles published in 3 general medical journals.


title: Combined Reinforcement Learning via Abstract Representations

abstract: In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this paper we propose a new way of explicitly bridging both approaches via a shared low-dimensional learned encoding of the environment, meant to capture summarizing abstractions. We show that the modularity brought by this approach leads to good generalization while being computationally efficient, with planning happening in a smaller latent state space. In addition, this approach recovers a sufficient low-dimensional representation of the environment, which opens up new strategies for interpretable AI, exploration and transfer learning.


title: Speech segmentation and word discovery: A computational perspective

abstract: Abstract The segmentation and word discovery problem arises because speech does not contain any reliable acoustic analog of the blank spaces between words of printed English. As a result, children must segment the utterances they hear in order to discover the sound patterns of individual words in their language. A number of computational models have been proposed to explain how children segment speech and discover words, including ten new models in the last five years. This paper reviews all proposed models and organizes them according to their fundamental segmentation strategies, their processing characteristics, and the ways in which they use memory. All proposed models are found to use one of three fundamental strategies: the utterance-boundary strategy, the predictability strategy, or the word-recognition strategy. Selected predictions of the models are explained, their performance in computer simulations is summarized, and behavioral evidence bearing on them is discussed. Finally, ideas about how these diverse models might be synthesized into one comprehensive model are offered.


title: Knowledge Economy Dimensions

abstract: The purpose of this paper is to present an analysis of the emergent knowledge economy and its dimensions. The knowledge economy is based primarily on the development of intangibles, and knowledge processing. The knowledge revolution is changing the way we think and work, and the knowledge worker reflects the nature of the new economic driving forces. The knowledge economy opens new directions, and offers unprecedented opportunities to produce and sell on a mass scale, reduce costs, and customize to the needs of consumers, all at the same time. The dimensions of this new economy are the following: economic and institutional regime, education and skills, information and communication infrastructure, and the innovation system. Based on these dimensions, the World Bank Institute developed two important indices: the Knowledge Index (KI), and the Knowledge Economy Index (KEI).


title: Effective Detection of SQL/XPath Injection Vulnerabilities in Web Services

abstract: This paper proposes a new automatic approach for the detection of SQL Injection and XPath Injection vulnerabilities, two of the most common and most critical types of vulnerabilities in web services. Although there are tools that allow testing web applications against security vulnerabilities, previous research shows that the effectiveness of those tools in web services environments is very poor. In our approach a representative workload is used to exercise the web service and a large set of SQL/XPath Injection attacks are applied to disclose vulnerabilities. Vulnerabilities are detected by comparing the structure of the SQL/XPath commands issued in the presence of attacks to the ones previously learned when running the workload in the absence of attacks. Experimental evaluation shows that our approach performs much better than known tools (including commercial ones), achieving extremely high detection coverage while maintaining the false positives rate very low.


title: Implementing NB-IoT in Software - Experiences Using the srsLTE Library

abstract: NB-IoT is the 3GPP standard for machine-to-machine communications, recently finalized within LTE release 13. This article gives a brief overview about this new LTE-based radio access technology and presents a implementation developed using the srsLTE software radio suite. We also carry out a performance study in which we compare a theoretical analysis with experimental results obtained in our testbed. Furthermore, we provide some interesting details and share our experience in exploring one of the worldwide first commercial NB-IoT deployments.


title: Unifying distillation and privileged information

abstract: Distillation (Hinton et al., 2015) and privileged information (Vapnik & Izmailov, 2015) are two techniques that enable machines to learn from other machines. This paper unifies these two techniques into generalized distillation, a framework to learn from multiple machines and data representations. We provide theoretical and causal insight about the inner workings of generalized distillation, extend it to unsupervised, semisupervised and multitask learning scenarios, and illustrate its efficacy on a variety of numerical simulations on both synthetic and real-world data.


title: Foundations of Assisted Cognition Systems

abstract: reasoning. However, such an integration requires us to bridge the gap between continuous sensor data and discrete, high-level representations. We will develop techniques that can bridge this gap by clustering a user’s data into discrete activity segments, which are path segments during which a user shows highly predictable behavior. Such segments can be learned for both indoor and outdoor environments using expectation maximization (EM) along with location data and an annotated map of the environment. Another challenge in location estimation is the fact that users frequently switch between environments with vastly different structure and sensor coverage. For example, a location system has to be able to track a person in her home, on her way to the coffee shop, and during her walk through the park. We will develop adaptive filters that are able to switch among or use simultaneously different map representations and different representations of uncertainty using discrete grid filters, particle filters, and / or Kalman filters whenever appropriate. 2.2 Behavior, Goal, and Plan Recognition Behavior recognition explains physical motion in terms of sets of meaningful activities that extend over time. Goal recognition relates behaviors to the performer’s intention to bring about some state of affairs. Behaviors and goals can be hierarchically organized into plans. Representing and reasoning about plans and goals requires us to advance the state of the art in probabilistic dynamic models in two directions: • Representing hierarchically structured complex activities with relative and absolute metric temporal constraints between substeps leads us to develop new algorithms for hierarchical statistical representations. We are focusing in particular on extensions to the expressive hidden semi-Markov models (HHSMM) formalism. As an example, to track a person preparing breakfast, the system needs to reason about the relative order and duration of subactivities such as setting the table, preparing tea, toasting bread, etc. • Many of the entities in our domain are inherently relational. For example, in order to determine how much time a person spends socializing (an important measure of health in the elderly) we need to reason about relationships over sets of individuals. Actions that may be performed in different ways at different times are most naturally captured by slot/filler relationships between an instance of the action and its parameters. Even for handling locations it can be useful to explicitly represent and reason about the functional relationship between an object and a position in space (e.g., “the place I left my car, wherever that is”). Our work on relational Markov models (RMMs) and dynamic probabilistic relational models (DPRMs) will allow us to represent and reason about such entities in a scalable manner; see Fig. 2(c) and Sections 3.3 and 4.3 The combination of hierarchical and relational probabilistic models provides a language and basic inference algorithms for probabilistic plan recognition. Fundamental research challenges remain, however, in defining the content of those models. In particular, Assisted Cognition systems need to be able to recognize when the user has made an error in performing (or failing to perform) a task and may require assistance. 2.3 Modeling Cognitive Impairments One general strategy for error detection we are developing is based on online model selection, which can be used to detect surprising or unusual behaviors. Part of this approach may involve creating error models that target the particular kinds of cognitive errors the user population may make, based on an underlying theory of behavioral impairments resulting from specific neurological deficits. Such models support precise error detection and intervention strategies.


title: Modeling and Simulation of Sine PWM VSI fed Induction Motor Drives

abstract: This study deals with simulation of Sine PWM VSI inverter fed induction motor drive. By varying the modulation index of SPWM then vary the voltage of VSI. This technique minimizes the lower order harmonics, heating and improves the response of VSI. The transient rotor current, electromagnetic torque and rotor speed of the Induction Motor will improve. The drive system is modeled using matlab simulink and the results are presented. Fixed AC is converted into DC and this DC is converted into variable voltage and variable frequency AC using Sine PWM inverter. The output of inverter is applied to the stator of induction motor. The FFT analysis shows that the current spectrum has reduced harmonics compared to the conventional system. The three phase induction motor, which is most widely used AC motor type in the domestic, industry, has been favored because of its self starting capability, low inertia, simple & rugged structure, suitable to all applications, low cost, low maintenance cost, less weight per watt, high reliability and high efficiency. Along with variable frequency AC inverters induction motors are used in many adjustable speed applications, which do not require fast dynamic response. There are many possible PWM techniques like sinusoidal PWM, selected harmonic elimination PWM, space vector PWM etc used for speed control of induction motor. The PWM inverters are very commonly used in adjustable AC motor drive loads, where one needs to feed the motor with variable voltage variable supply frequency. For wide variation in drive speed, the frequency of the applied AC voltage needs to be varied over a wide range. The applied voltage also needs to vary almost linearly with the frequency. The switches of the PWM inverters are turned on and off at significant higher frequencies than the fundamental frequency of the output voltage waveform. In sine PWM Inverter, the width of the pole voltage pulses over the output cycle, vary in a sinusoidal manner. The scheme, in its simplified form, involves comparison of a high frequency triangular carrier voltage with a sinusoidal modulating signal that represents the desired fundamental component of the pole voltage waveform. The peak magnitude of the modulating signal should remain limited to the peak magnitude of the carrier signal. The comparator output is then used to control the high side and low side switches of the particular pole. Some of the following constraints for slow varying sinusoidal voltage be considered as the modulating signal are 1) the peak magnitude of the sinusoidal signal is less than or equal to the peak magnitude of the carrier signal. This ensures that the instantaneous magnitude of the modulating signal never exceeds the peak magnitude of the carrier signal. 2) The frequency of the modulating signal is several orders lower than the frequency of the carrier signal. A typical figure will be 50 Hz for the modulating signal and 20 KHZ for the carrier signal. Under such high frequency ratio's the magnitude of the modulating signal will be virtually constant over any particular carrier signal time period. 3) A three phase sine-PWM inverter would require a balanced set of three sinusoidal modulating signals along with a triangular carrier signal of high frequency. For a variable voltage- variable frequency (VVVF) type inverter, a typical requirement for adjustable speed drives of AC motor, the magnitude as well as frequency of the fundamental component of the inverters output voltage


title: Psychological Needs and the Facilitation of Integrative Processes

abstract: ABSTRACT The assumption that there are innate integrative or actualizing tendencies underlying personality and social development is reexamined. Rather than viewing such processes as either nonexistent or as automatic, I argue that they are dynamic and dependent upon social-contextual supports Pertaining to basic human psychological needs. To develop this viewpoint, I conceptually link the notion of integrative tendencies to specific developmental processes, namely intrinsic motivation; internalization; and emotional integration. These processes are then shown to be facilitated by conditions that fulfill psychological needs for autonomy, competence, and relatedness, and forestalled within contexts that frustrate these needs. Interactions between psychological needs and contextual supports account, in part, for the domain and situational specificity of motivation, experience, and relative integration. The meaning of psychological needs (vs. wants) is directly considered, as are the relations between concepts of integration and autonomy and those of independence, individualism, efficacy, and cognitive models of “multiple selves.”


title: Active Learning for Structured Prediction from Partially Labeled Data

abstract: We propose a general purpose active learning algorithm for structured prediction, gathering labeled data for training a model that outputs a set of related labels for an image or video. Active learning starts with a limited initial training set, then iterates querying a user for labels on unlabeled data and retraining the model. We propose a novel algorithm for selecting data for labeling, choosing examples to maximize expected information gain based on belief propagation inference. This is a general purpose method and can be applied to a variety of tasks or models. As a specific example we demonstrate this framework for learning to recognize human actions and group activities in video sequences. Experiments show that our proposed algorithm outperforms previous active learning methods and can achieve accuracy comparable to fully supervised methods while utilizing significantly less labeled data.


title: IoT Planting: Watering system using mobile application for the elderly

abstract: Nowadays, Thailand has become an aged society. The elderly's mental health has decreased resulting from physiological changes. At present, technology has connect everything to the world of the Internet called the Internet of Things (IoT). IoT can be applied to many ways, which can improve the quality of life and reduce spaces between the elderly and technology. In this paper, we studied the situation of the elderly and proposed the IoT Planting which consists of gardening platform prototype with soil moisture, temperature, water sensor, grow light, and Android application for the elderly. An evaluation was performed by Technology Acceptance Model (TAM). The result represents that the elderly perceived the usefulness and found that it easy to use contrary to their expectations. Moreover, by reviewing their opinion, it was found out that they have a positive attitude toward our application and willingness to use it.


title: Implementation of an instruction dispatch unit applied to digital signal processors with VLIW architecture

abstract: Digital signal processor (DSP) is a particular kind of processor that is able to achieve a variety of digital signal processing algorithms rapidly. Nowadays, the hardware of the DSP of the high performance relies on the Very Long Instruction Words (VLIW) architecture, which takes advantage of instruction level parallelism and executes instructions in parallel to accomplish computing-intensive tasks. However, VLIW machines are strict not only in instruction dispatching but also with the control of the branch. It is essential to ensure the correct executions of the programs in every pipeline stage. In this paper, an instruction dispatch unit is proposed, which serves to VLIW processors. It is modeled and detailed described in Verilog HDL, emulated by VCS tools and synthesized by Design Compiler in 0.13 µm CMOS technology. The final result indicates that the dispatch unit works well at the clock rate of 400 MHz, as the critical path delay of the proposed design is 2.3 ns.


title: A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction

abstract: BACKGROUND ::: Direct prediction of the three-dimensional (3D) structures of proteins from one-dimensional (1D) sequences is a challenging problem. Significant structural characteristics such as solvent accessibility and contact number are essential for deriving restrains in modeling protein folding and protein 3D structure. Thus, accurately predicting these features is a critical step for 3D protein structure building. ::: ::: ::: RESULTS ::: In this study, we present DeepSacon, a computational method that can effectively predict protein solvent accessibility and contact number by using a deep neural network, which is built based on stacked autoencoder and a dropout method. The results demonstrate that our proposed DeepSacon achieves a significant improvement in the prediction quality compared with the state-of-the-art methods. We obtain 0.70 three-state accuracy for solvent accessibility, 0.33 15-state accuracy and 0.74 Pearson Correlation Coefficient (PCC) for the contact number on the 5729 monomeric soluble globular protein dataset. We also evaluate the performance on the CASP11 benchmark dataset, DeepSacon achieves 0.68 three-state accuracy and 0.69 PCC for solvent accessibility and contact number, respectively. ::: ::: ::: CONCLUSIONS ::: We have shown that DeepSacon can reliably predict solvent accessibility and contact number with stacked sparse autoencoder and a dropout approach.


abstract: The Advanced license plate recognition system for car parking consists of two section: Radio frequency Identification (RFID) based reserved car parking system and number plate recognition by using Open Computer Vision(CV). The system provides high security and there is no need for a guard. In this system only authorized vehicles are permitted to the parking area by checking some conditions. Authorized vehicles have a valid RFID tag number and standardized license plate. RFID is used to detect certain objects and access the data using certain devices such as transponders or RFID tags. RFID is a small wireless device based on an RFID reader and and an RFID tag. The technology requires some extent of cooperation of an RFID reader and an RFID tag. The number in license plate is recognised by using open CV. Open CV contains many programming functions used in the real time computer vision systems. The library consists of many optimized algorithms and C is considered as the backbone of open CV. Reserved car parking system offer convenience for vehicle users and efficient usage of space. This system save time, space and make the parking easier.


title: Single Image Super-Resolution via Adaptive High-Dimensional Non-Local Total Variation and Adaptive Geometric Feature

abstract: Single image super-resolution (SR) is very important in many computer vision systems. However, as a highly ill-posed problem, its performance mainly relies on the prior knowledge. Among these priors, the non-local total variation (NLTV) prior is very popular and has been thoroughly studied in recent years. Nevertheless, technical challenges remain. Because NLTV only exploits a fixed non-shifted target patch in the patch search process, a lack of similar patches is inevitable in some cases. Thus, the non-local similarity cannot be fully characterized, and the effectiveness of NLTV cannot be ensured. Based on the motivation that more accurate non-local similar patches can be found by using shifted target patches, a novel multishifted similar-patch search (MSPS) strategy is proposed. With this strategy, NLTV is extended as a newly proposed super-high-dimensional NLTV (SHNLTV) prior to fully exploit the underlying non-local similarity. However, as SHNLTV is very high-dimensional, applying it directly to SR is very difficult. To solve this problem, a novel statistics-based dimension reduction strategy is proposed and then applied to SHNLTV. Thus, SHNLTV becomes a more computationally effective prior that we call adaptive high-dimensional non-local total variation (AHNLTV). In AHNLTV, a novel joint weight strategy that fully exploits the potential of the MSPS-based non-local similarity is proposed. To further boost the performance of AHNLTV, the adaptive geometric duality (AGD) prior is also incorporated. Finally, an efficient split Bregman iteration-based algorithm is developed to solve the AHNLTV-AGD-driven minimization problem. Extensive experiments validate the proposed method achieves better results than many state-of-the-art SR methods in terms of both objective and subjective qualities.


title: Smart water grids for smart cities: A sustainable prototype demonstrator

abstract: In the framework of a smart city, information and communications technologies are applied to tackle urban living challenges, with the aim of improving the citizens' quality of life, through a more efficient use of limited resources. Urban sensing by wireless sensor networks allows the collection, processing, analysis, and dissemination of valuable information, that is used to design adequate policies matching the users' needs, and avoiding non-ecological wasting of resources. To face an increasing water demand, and ensure a safe access to clean water resources, a network of sensor nodes could be located along water pipes. The automatic monitoring of the water grid, and the smart metering of water consumptions could be enabled, to increase the users' awareness, and improve the efficiency of the infrastructure management. To this aim, the paper presents a prototype demonstrator of a smart water metering infrastructure, developed in cooperation with Telecom Italia Lab, and built upon self-powered nodes in a Wireless Metering Bus capillary network at 169 MHz. The main elements of the system are presented, and the attainable performance discussed, through a number of experimental results.


title: A Survey of Visibility for Walkthrough Applications

abstract: Visibility algorithms for walkthrough and related applications have grown into a significant area, spurred by the growth in the complexity of models and the need for highly interactive ways of navigating them. In this survey, we review the fundamental issues in visibility and conduct an overview of the visibility culling techniques developed in the last decade. The taxonomy we use distinguishes point-based methods from-region methods. Point-based methods are further subdivided into object and image-precision techniques, while from-region approaches can take advantage of the cell-and-portal structure of architectural environments or handle generic scenes.


title: Recessive dystrophic epidermolysis bullosa (RDEB) complicated by secondary hepatic amyloidosis

abstract: Epidermolysis bullosa (EB) consists of a heterogeneous group of autosomal dominant or recessive disorders, characterized by epithelial fragility.1 In dystrophic EB, patients have a genetic defect in the gene encoding collagen VII, COL7A1. Generalized severe recessive dystrophic EB (RDEB) is characterized by large, flaccid bullae present at birth with healing atrophic scars.2 RDEB is associated with a high mortality and morbidity. ::: ::: Although no definitive therapy exists, recent advances in medical care have significantly increased the survival of RDEB. This has exposed complications not previously observed, including secondary amyloidosis, which can have fatal outcomes. Secondary amyloidosis, a rare complication, has been scarcely reported in the literature to date, usually solely affecting renal function. We report a case of RDEB with amyloidosis, causing liver hypertrophy and caput medusa.


title: Using Activity Theory to Model Context Awareness

abstract: One of the cornerstones of any intelligent entity is the ability to understand how occurrences in the surrounding world influence its own behaviour. Different states, or situations, in its environment should be taken into account when reasoning or acting. When dealing with different situations, context is the key element used to infer possible actions and information needs. The activities of the perceiving agent and other entities are arguably one of the most important features of a situation; this is equally true whether the agent is artificial or not. ::: ::: This work proposes the use of Activity Theory to first model context and further on populate the model for assessing situations in a pervasive computing environment. Through the socio-technical perspective given by Activity Theory, the knowledge intensive context model, utilised in our ambient intelligent system, is designed.


title: Turing’s Responses to Two Objections

abstract: In this paper I argue that Turing's responses to the mathematical objection are straightforward, despite recent claims to the contrary. I then go on to show that by understanding the importance of learning machines for Turing as related not to the mathematical objection, but to Lady Lovelace's objection, we can better understand Turing's response to Lady Lovelace's objection. Finally, I argue that by understanding Turing's responses to these objections more clearly, we discover a hitherto unrecognized, substantive thesis in his philosophical thinking about the nature of mind.


title: Stress, anxiety & depression among medical undergraduate students & their socio-demographic correlates

abstract: Background & objectives: Presence of psychological morbidity in medical undergraduate students has been reported from various countries across the world. Indian studies to document this burden are very few. Therefore, the presence of depression, anxiety and stress among medical undergraduate students was assessed using a previously validated and standardized instrument, Depression Anxiety Stress Scale (DASS 42) and the associations with their socio-demographic and personal characteristics were identified. Methods: In a cross-sectional survey, a self-administered, pre-designed, pre-tested anonymous questionnaire including DASS 42 was used to collect information on basic socio-demographic (age, gender, semester) and personal characteristics (alcohol and tobacco use, academic performance). All students present on the day of survey were contacted for participation after obtaining informed written consent. Scores for each of the respondents over each of the sub-scales (Depression, Anxiety and Stress) were calculated as per the severity-rating index. Results: More than half of the respondents were affected by depression (51.3%), anxiety (66.9%) and stress (53%). Morbidity was found to be more in 5 th semester students rather than students of 2 nd semester. Females reported higher score as compared to their male counterparts. Perception of self assessment in academics was strongly associated with the higher score. Conclusions: A substantial proportion of medical undergraduate students was found to be depressed, anxious and stressed revealing a neglected area of the students' psychology requiring urgent attention. Student counselling services need to be made available and accessible to curb this morbidity.


Characteristics of Urease from Vibrio parahaemolyticus Possessing tdh and trh Genes Isolated in Korea


title: DNS graph mining for malicious domain detection

abstract: As a vital component of variety cyber attacks, malicious domain detection becomes a hot topic for cyber security. Several recent techniques are proposed to identify malicious domains through analysis of DNS data because much of global information in DNS data which cannot be affected by the attackers. The attackers always recycle resources, so they frequently change the domain — IP resolutions and create new domains to avoid detection. Therefore, multiple malicious domains are hosted by the same IPs and multiple IPs also host same malicious domains in simultaneously, which create intrinsic association among them. Hence, using the labeled domains which can be traced back from queries history of all domains to verify and figure out the association of them all. Graphs seem the best candidate to represent for this relationship and there are many algorithms developed on graph with high performance. A graph-based interface can be developed and transformed to the graph mining task of inferring graph node's reputation scores using improvements of the belief propagation algorithm. Then higher reputation scores the nodes reveal, the more malicious probabilities they infer. For demonstration, this paper proposes a malicious domain detection technique and evaluates on a real-world dataset. The dataset is collected from DNS data servers which will be used for building a DNS graph. The proposed technique achieves high performance in accuracy rates over 98.3%, precision and recall rates as: 99.1%, 98.6%. Especially, with a small set of labeled domains (legitimate and malicious domains), the technique can discover a large set of potential malicious domains. The results indicate that the method is strongly effective in detecting malicious domains.


title: Addressing Complex and Subjective Product-Related Queries with Customer Reviews

abstract: Online reviews are often our first port of call when considering products and purchases online. When evaluating a potential purchase, we may have a specific query in mind, e.g. `will this baby seat fit in the overhead compartment of a 747?' or `will I like this album if I liked Taylor Swift's 1989?'. To answer such questions we must either wade through huge volumes of consumer reviews hoping to find one that is relevant, or otherwise pose our question directly to the community via a Q/A system. ::: ::: In this paper we hope to fuse these two paradigms: given a large volume of previously answered queries about products, we hope to automatically learn whether a review of a product is relevant to a given query. We formulate this as a machine learning problem using a mixture-of-experts-type framework---here each review is an `expert' that gets to vote on the response to a particular query; simultaneously we learn a relevance function such that `relevant' reviews are those that vote correctly. At test time this learned relevance function allows us to surface reviews that are relevant to new queries on-demand. We evaluate our system, Moqa, on a novel corpus of 1.4 million questions (and answers) and 13 million reviews. We show quantitatively that it is effective at addressing both binary and open-ended queries, and qualitatively that it surfaces reviews that human evaluators consider to be relevant.


title: Real-Time Human Motion Capture with Multiple Depth Cameras

abstract: Commonly used human motion capture systems require intrusive attachment of markers that are visually tracked with multiple cameras. In this work we present an efficient and inexpensive solution to markerless motion capture using only a few Kinect sensors. Unlike the previous work on 3d pose estimation using a single depth camera, we relax constraints on the camera location and do not assume a co-operative user. We apply recent image segmentation techniques to depth images and use curriculum learning to train our system on purely synthetic data. Our method accurately localizes body parts without requiring an explicit shape model. The body joint locations are then recovered by combining evidence from multiple views in real-time. We also introduce a dataset of ~6 million synthetic depth frames for pose estimation from multiple cameras and exceed state-of-the-art results on the Berkeley MHAD dataset.


title: Complex Population Patterns of Eunica tatila Herrich-Schäffer (Lepidoptera: Nymphalidae), with Special Emphasis on Sexual Dimorphism

abstract: The species Eunica tatila (Herrich-Schäffer) is present in the Neotropical region and comprises three subspecies. In Mexico, only one subspecies is reported: E. t. tatila (Herrich-Schäffer). The Yucatan Peninsula, in southeastern Mexico, is located in a transitional geographical position, between southern Florida, the West Indies and Central America. It is part of a transitional region, important for the dispersion of insects from southern Florida via Cuba and the Yucatan Peninsula. Considering the possibility of the overlapping and delimitation of described subspecies, we sampled different populations in the Yucatan Peninsula to possibly assign a subspecies name and evaluate the magnitude of sexual dimorphism. We collected 591 individuals (♀284, ♂307) in conserved areas. The study of male genitalia led to the identification of Eunica tatila tatilista (Kaye) as a subspecies; however, hypandrium structure and wing pattern analysis suggest a mix of E. t. tatila and E. t. tatilista characteristics. The analysis of sexual dimorphism provided evidence of more complex wing morphs for females, with 12 patterns instead of four as previously described. Our results demonstrate the complexity of characterizing E. tatila and suggest that the Yucatan Peninsula is a transitional zone for subspecies of some butterflies.


title: Learning to efficiently rank on big data

abstract: Ranking in response to user queries is a central problem in information retrieval, data mining, and machine learning. In the era of "Big data", traditional effectiveness-centric ranking techniques tend to get more and more costly (requiring additional hardware and energy costs) to sustain reasonable ranking speed on large data. The mentality of combating big data by throwing in more hardware/machines will quickly become highly expensive since data is growing at an extremely fast rate oblivious to any cost concerns from us. "Learning to efficiently rank" offers a cost-effective solution to ranking on large data (e.g., billions of documents). That is, it addresses a critically important question -- whether it is possible to improve ranking effectiveness on large data without incurring (too much) additional cost?


title: A modular neural model of motor synergies

abstract: Animals such as reptiles, amphibians and mammals (including humans) are mechanically extremely complex. It has been estimated that the human body has between 500 and 1400 degrees of freedom! And yet, these animals can generate an infinite variety of very precise, complicated and goal-directed movements in continuously changing and uncertain environments. Understanding how this is achieved is of great interest to both biologists and engineers. There are essentially two questions that must be addressed: (1) What type of control strategy is used to handle the large number of degrees of freedom involved? and (2) How is this strategy instantiated in the substrate of neural and musculoskeletal elements comprising the animal bodies? The first question has been studied intensively for several decades, providing strong indications that, rather than using standard feedback control based on continuous tracking of desired trajectories, animals' movements emerge from the controlled combination of pre-configured movement primitives or synergies. These synergies represent coordinated activity patterns over groups of muscles, and can be triggered as a whole with controlled amplitude and temporal offset. Complex movements can thus be constructed from the appropriate combination of a relatively small number of synergies, greatly simplifying the control problem. Although experimental studies on animal movements have confirmed the existence of motor synergies, and their utility has been demonstrated in the control of fairly complex robots, their neural basis remains poorly understood. In this paper, we introduce a simple but plausible and general neural model for motor synergies based on the principle that these functional modules reflect the structural modularity of the underlying physical system. Using this model, we show that a small set of synergies selected through a redundancy-reduction principle can generate a rich motor repertoire in a model two-jointed arm system. We investigate the synergies generated by this model systematically with respect to various parameters, and compare them to those observed in experiments.


title: Validation of soil parameter identification for track-terrain interaction Dynamics

abstract: This paper considers a tracked vehicle traversing unknown terrain, and proposes an approach based on the Generalized Newton Raphson (GNR) method for identifying all the unknown soil parameters required for tractive force prediction. For the first time, the methodology, based on measurements of track slip, i, and tractive force, F, to find unknown soil parameters is developed. The tractive force is the force generated by a tracked vehicle to drive itself forwards. This tractive force depends to a large extent on certain soil parameters, namely soil cohesion (c), soil internal friction angle (phi), and soil shear deformation modulus (K). Accurately identifying parameters of the soil on which a tracked vehicle is moving will potentially lead to accurate traversability prediction, effective traction control, and precise trajectory tracking. The soil parameter identification algorithm is validated with the experimental data from Wong [3] and from in-house track- terrain interaction test rig showing good identification accuracy and fast execution speed. It is also shown to be relatively robust to initial condition. The identified soil parameters are, in turn, used to predict the tractive forces showing good agreement with all the experimental data. The technique presented in this paper is general and can be applied to any tracked vehicle.


title: Multi-variable LSTM neural network for autoregressive exogenous model

abstract: In this paper, we propose multi-variable LSTM capable of accurate forecasting and variable importance interpretation for time series with exogenous variables. Current attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance. To this end, the multi-variable LSTM equipped with tensorized hidden states is developed to learn hidden states for individual variables, which give rise to our mixture temporal and variable attention. Based on such attention mechanism, we infer and quantify variable importance. Extensive experiments using real datasets with Granger-causality test and the synthetic dataset with ground truth demonstrate the prediction performance and interpretability of multi-variable LSTM in comparison to a variety of baselines. It exhibits the prospect of multi-variable LSTM as an end-to-end framework for both forecasting and knowledge discovery.


title: Automated Irrigation System Using a Wireless Sensor Network and GPRS Module

abstract: An automated irrigation system was developed to optimize water use for agricultural crops. The system has a distributed wireless network of soil-moisture and temperature sensors placed in the root zone of the plants. In addition, a gateway unit handles sensor information, triggers actuators, and transmits data to a web application. An algorithm was developed with threshold values of temperature and soil moisture that was programmed into a microcontroller-based gateway to control water quantity. The system was powered by photovoltaic panels and had a duplex communication link based on a cellular-Internet interface that allowed for data inspection and irrigation scheduling to be programmed through a web page. The automated system was tested in a sage crop field for 136 days and water savings of up to 90% compared with traditional irrigation practices of the agricultural zone were achieved. Three replicas of the automated system have been used successfully in other places for 18 months. Because of its energy autonomy and low cost, the system has the potential to be useful in water limited geographically isolated areas.


title: An Optimized Floyd Algorithm for the Shortest Path Problem

abstract: There is too much iteration when us ing the traditional Floyd algorithm to calculat e the shortest path , that is to say, the computation of traditional Floyd algorithm is too large for an urban road network with a large number of nodes. As f or this disadvantage, the Floyd algorithm was improved and optimized further in this study; moreover , the improved Floyd algorithm was used to solve the shortest path problem in car navigation system, and it achieved good results. Two improvements were done: firstly, construct an iterative matrix for solving the shortest path, all the nodes in it were compared at first, and those nodes which have nothing to do with the results were removed, then search for the next node directly to reduce the frequency of iteration; Secondly, construct a serial number matrix for solving the shortest path, it was used to record the case of inserting node in the process of iteration. Finally, the frequency of iteration and the computational complexity were compared for both the traditional Floyd algorithm and the improved Floyd algorithm. The experimental results and analysis showed that the computational complexity of the improved Floyd algorithm has reduced to half of that of the traditional algorithm. What ’ s more, by checking the iterative matrix and the serial number matrix, the shortest path can be found out simply, intuitively and efficiently.


title: An empirical investigation of the factors that influence Internet user’s ability to correctly identify a phishing website

abstract: Purpose – The aim of this study is to report on the results of an empirical investigation of the various factors which have significant impacts on the Internet user’s ability to correctly identify a phishing website. Design/methodology/approach – The research participants were Internet users who have had at least some experience of financial transactions over the Internet. This study conducted a quantitative research with the help of a structured survey questionnaire along with three experimental tasks. A total of 621 valid samples were collected and the multiple regression analysis technique was used to deduce the answers to the research question. Findings – The results show that the model is useful and has explanatory power. And adjusted R2 computed as 0.927, means that 92.7 per cent of the variations in the Internet user’s ability to identify phishing website can be explained by the predictors selected for the model. Research limitations/implications – Future research should account for the Internet us...


title: Stress, Depression, and Neuroplasticity: A Convergence of Mechanisms

abstract: Increasing evidence demonstrates that neuroplasticity, a fundamental mechanism of neuronal adaptation, is disrupted in mood disorders and in animal models of stress. Here we provide an overview of the evidence that chronic stress, which can precipitate or exacerbate depression, disrupts neuroplasticity, while antidepressant treatment produces opposing effects and can enhance neuroplasticity. We discuss neuroplasticity at different levels: structural plasticity (such as plastic changes in spine and dendrite morphology as well as adult neurogenesis), functional synaptic plasticity, and the molecular and cellular mechanisms accompanying such changes. Together, these studies elucidate mechanisms that may contribute to the pathophysiology of depression. Greater appreciation of the convergence of mechanisms between stress, depression, and neuroplasticity is likely to lead to the identification of novel targets for more efficacious treatments.


abstract: Scand J Caring Sci; 2009; 23; 100–106The silent demand in the diagnostic phasePatients’ experience of going through the diagnostic phasein hospital is apt to be overlooked by nurses and physi-cians; most of their inner preparative work for receivingthe diagnosis is hidden because of the vulnerability of thesituation. This paper discusses ﬁndings from a groundedtheory study, of 18 in-depth interviews of 15 patientsgoing through medical investigation at a gastric ward in aNorwegian university hospital. The interviews were con-ducted in 2002–2003. The generated ‘Preparative WaitingTheory’, which accounts for how patients in the diagnosticphase work to prepare themselves for receiving a diagnosis,is discussed in relation to the Danish theologian and phi-losopher Logstrup’s writings about the ethical demand andlife utterances of trust, openness and the untouchablezone. The ethical demand from patients going throughmedical investigations is silent and radical, and thus chal-lenging for nurses and physicians to judge how to bestfulﬁl. Examples from the interviews are used to illustratepatients’ vulnerability as they prepare to face the conclu-sion about their case. To the extent trust develops betweenpatients and nurses/physicians, patients can reveal what isat stake in their lives without feeling exposed or embar-rassed. Because of the power nurses and physicians have inthe healthcare system, and because of laws, morals andconventions guiding practice, we have a professionalresponsibility to develop a culture that fosters the abilityand willingness to take care of our patients.Keywords: grounded theory, research in practice, ethics,interpersonal communication, nurse–patient interaction,adult nursing, diagnostic phase, hospitalisation, trust,vulnerability, power.Submitted 7 April 2007, Accepted 21 September 2007


title: JSUT corpus: free large-scale Japanese speech corpus for end-to-end speech synthesis

abstract: Thanks to improvements in machine learning techniques including deep learning, a free large-scale speech corpus that can be shared between academic institutions and commercial companies has an important role. However, such a corpus for Japanese speech synthesis does not exist. In this paper, we designed a novel Japanese speech corpus, named the "JSUT corpus," that is aimed at achieving end-to-end speech synthesis. The corpus consists of 10 hours of reading-style speech data and its transcription and covers all of the main pronunciations of daily-use Japanese characters. In this paper, we describe how we designed and analyzed the corpus. The corpus is freely available online.


title: SOAP2: an improved ultrafast tool for short read alignment

abstract: SOAP2 is a significantly improved version of the short oligonucleotide alignment program that both reduces computer memory usage and increases alignment speed at an unprecedented rate. We used a Burrows Wheeler Transformation (BWT) compression index to substitute the seed strategy for indexing the reference sequence in the main memory. We tested it on the whole human genome and found that this new algorithm reduced memory usage from 14.7 to 5.4 GB and improved alignment speed by 20-30 times. SOAP2 is compatible with both single- and paired-end reads. Additionally, this tool now supports multiple text and compressed file formats. A consensus builder has also been developed for consensus assembly and SNP detection from alignment of short reads on a reference genome.


title: Binary Rating Estimation with Graph Side Information

abstract: While rich experimental evidences show that one can better estimate users' unknown ratings with the aid of graph side information such as social graphs, its theoretical understanding is still lacking. In this work, we study the binary rating estimation problem to understand the fundamental value of graph side information. Considering a simple correlation model between a rating matrix and a graph, we characterize the sharp threshold on the number of observed entries required to recover the rating matrix (called the optimal sample complexity) as a function of the quality of graph side information (to be detailed). To the best of our knowledge, we are the first to reveal when and by how much the graph side information reduces sample complexity. Further, we propose a computationally efficient algorithm that achieves the limit. Our experimental results on synthetic and real-world data demonstrate the superiority of our algorithm over the state of the arts.


title: Best practices in business process redesign: use and impact

abstract: Purpose – This paper seeks to provide business process redesign (BPR) practitioners and academics with insight into the most popular heuristics to derive improved process designs.Design/methodology/approach – An online survey was carried out in the years 2003‐2004 among a wide range of experienced BPR practitioners in the UK and The Netherlands.Findings – The survey indicates that this “top ten” of best practices is indeed extensively used in practice. Moreover, indications for their business impact have been collected and classified.Research limitations/implications – The authors' estimations of best practices effectiveness differed from feedback obtained from respondents, possibly caused by the design of the survey instrument. This is food for further research.Practical implications – The presented framework can be used by practitioners to keep the various aspects of a redesign in perspective. The presented list of BPR best practices is directly applicable to derive new process designs.Originality/value...


title: Summarization Evaluation for Text and Speech: Issues and Approaches

abstract: This paper surveys current text and speech summarization evaluation approaches. It discusses advantages and disadvantages of these, with the goal of identifying summarization techniques most suitable to speech summarization. Precision/recall schemes, as well as summary accuracy measures which incorporate weightings based on multiple human decisions, are suggested as particularly suitable in evaluating speech summaries.


title: The Emerging Meanings of Religiousness and Spirituality: Problems and Prospects

abstract: This article examines traditional and modern psychological characterizations of religiousness and spirituality. Three ways in which religiousness and spirituality are polarized by contemporary theorists are examined: organized religion versus personal spirituality; substantive religion versus functional spirituality; and negative religiousness versus positive spirituality. An alternative approach to understanding religiousness and spirituality is presented that integrates rather than polarizes these constructs, and sets boundaries to the discipline while acknowledging the diversity of religious and spiritual expressions. Directions for future investigations of these two constructs are presented.


title: Nesse, “An evolutionary life-history framework for understanding sex differences in human mortality rates

abstract: Sex differences in mortality rates stem from genetic, physiological, behavioral, and social causes that are best understood when integrated in an evolutionary life history framework. This paper investigates the Male-to-Female Mortality Ratio (M:F MR) from external and internal causes and across contexts to illustrate how sex differences shaped by sexual selection interact with the environment to yield a pattern with some consistency, but also with expected variations due to socioeconomic and other factors.


title: Real-Time Physics-Based 3D Biped Character Animation Using an Inverted Pendulum Model

abstract: We present a physics-based approach to generate 3D biped character animation that can react to dynamical environments in real time. Our approach utilizes an inverted pendulum model to online adjust the desired motion trajectory from the input motion capture data. This online adjustment produces a physically plausible motion trajectory adapted to dynamic environments, which is then used as the desired motion for the motion controllers to track in dynamics simulation. Rather than using Proportional-Derivative controllers whose parameters usually cannot be easily set, our motion tracking adopts a velocity-driven method which computes joint torques based on the desired joint angular velocities. Physically correct full-body motion of the 3D character is computed in dynamics simulation using the computed torques and dynamical model of the character. Our experiments demonstrate that tracking motion capture data with real-time response animation can be achieved easily. In addition, physically plausible motion style editing, automatic motion transition, and motion adaptation to different limb sizes can also be generated without difficulty.


title: Bandwidth Enhancement of Cavity-Backed Slot Antenna Using a Via-Hole Above the Slot

abstract: A novel technique for the bandwidth enhancement of a cavity-backed slot antenna is presented. A via-hole located above the slot creates an additional resonance at a higher frequency by shortening the effective length of the slot. The location of the via-hole can be changed to determine the second resonance frequency of the antenna. With proper placement of the via-hole, the bandwidth of cavity-backed slot antenna can be increased. The fabricated antenna has a 60% wider bandwidth than a cavity-backed slot antenna without a via-hole. The proposed antenna maintains high radiation efficiency and gain, which are characteristics of a conventional cavity-backed slot antenna. The proposed technique is especially useful for enhancing the bandwidth of a cavity-backed slot antenna in a limited area.