title: Path Planning for Planetary Exploration Rovers and Its Evaluation based on Wheel Slip Dynamics

abstract: In this paper, a path planning and its evaluation method is described with taking into account wheel slip dynamics of lunar/planetary exploration rovers. The surface of the planetary body is largely covered with powdery soil. On such loose soil, the wheel slippage which will make the rover get stuck must be concerned. Since the slippage dynamically depends on the posture/velocity of vehicle, soil characteristics, and wheel-soil interactions, it becomes difficult issues to incorporate the wheel slip dynamics as a criterion into path-planning algorithms. To tackle the slippage problem, the authors develop the path-planning algorithm and the path-evaluation method based on the following approach. First, a path on a rough terrain is generated with the terrain-based criteria function. Subsequently, the dynamics simulation of a rover is carried out in which the rover is controlled to follow the candidate path. Finally, the path is properly evaluated based on the slip motion profiles calculated by the simulation. Demonstrations for the proposed technique are addressed along with a discussion on characteristics of the candidate path and the slip motion profile of the rover


title: Design and Control of a Variable Stiffness Actuator Based on Adjustable Moment Arm

abstract: For tasks that require robot-environment interaction, stiffness control is important to ensure stable contact motion and collision safety. The variable stiffness approach has been used to address this type of control. We propose a hybrid variable stiffness actuator (HVSA), which is a variable stiffness unit design. The proposed HVSA is composed of a hybrid control module based on an adjustable moment-arm mechanism, and a drive module with two motors. By controlling the relative motion of gears in the hybrid control module, position and stiffness of a joint can be simultaneously controlled. The HVSA provides a wide range of joint stiffness due to the nonlinearity provided by the adjustable moment arm. Furthermore, the rigid mode, which behaves as a conventional stiff joint, can be implemented to improve positioning accuracy when a robot handles a heavy object. In this paper, the mechanical design features and related analysis are explained. We show that the HVSA can provide a wide range of stiffness and rapid responses according to changes in the stiffness of a joint under varying loads by experiments. The effectiveness of the rigid mode is verified by some experiments on position tracking under high-load conditions.


title: A reinforcement learning formulation to the complex question answering problem

abstract: Reinforcement learning formulation for complex question answering.Abstract summaries used for small amount of supervision using reward scores.User interaction component incorporated to guide candidate sentence selection.Experiments reveal that systems trained with user interaction perform better.The reinforcement system is able to learn automatically and effectively. We use extractive multi-document summarization techniques to perform complex question answering and formulate it as a reinforcement learning problem. Given a set of complex questions, a list of relevant documents per question, and the corresponding human generated summaries (i.e. answers to the questions) as training data, the reinforcement learning module iteratively learns a number of feature weights in order to facilitate the automatic generation of summaries i.e. answers to previously unseen complex questions. A reward function is used to measure the similarities between the candidate (machine generated) summary sentences and the abstract summaries. In the training stage, the learner iteratively selects the important document sentences to be included in the candidate summary, analyzes the reward function and updates the related feature weights accordingly. The final weights are used to generate summaries as answers to unseen complex questions in the testing stage. Evaluation results show the effectiveness of our system. We also incorporate user interaction into the reinforcement learner to guide the candidate summary sentence selection process. Experiments reveal the positive impact of the user interaction component on the reinforcement learning framework.


title: Violence Detection in Video Using Spatio-Temporal Features

abstract: In this paper we presented a violence detector built on the concept of visual codebooks using linear support vector machines. It differs from the existing works of violence detection in what concern the data representation, as none has considered local spatio-temporal features with bags of visual words. An evaluation of the importance of local spatio-temporal features for characterizing the multimedia content is conducted through the cross-validation method. The results obtained confirm that motion patterns are crucial to distinguish violence from regular activities in comparison with visual descriptors that rely solely on the space domain.


title: PROTEIN IDENTIFICATION USING FOURIER TRANFORM INFRARED (FTIR)

abstract: The research was conducted to identify the presence of protein by indicating amide groups and measuring its level in food through specific groups of protein using FTIR (Fourier Transformed Infrared) method. The scanning process was conducted on wavenumber 400—4000 cm -1 . The determination of functional group was being done by comparing wavenumber of amide functional groups of the protein samples to existing standard. Protein level was measured by comparing absorbance of protein specific functional groups to absorbance of fatty acid functional groups. Result showed the FTIR spectrums of all samples were on 557-3381 cm -1 wavenumber range. The amides detected were Amide III, IV, and VI with absorbance between trace until 0.032%. The presence of protein can be detected in samples animal and vegetable cheese, butter, and milk through functional groups of amide III, IV, and VI were on 1240-1265 cm -1 , 713-721 cm -1 , and 551-586 cm -1 wavenumber respectively . Urine was detected through functional groups of amide III and IV were on 1639 cm -1 and 719 cm -1 wavenumber. The protein level of animal cheese, vegetable cheese, butter, and milk were 1.01%, 1.0%, 0.86%, and 1.55% respectively.


title: Chinese character CAPTCHA recognition based on convolution neural network

abstract: CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are increasingly used in many applications for machine and human identification. Compared with traditional English and digital characters based CAPTCHAs, Chinese characters contain more complicated characters which greatly enhance difficulty of automatic recognition. To solve that problem, we proposed a Convolution Neural Network (CNN) based approach. This approach greatly improves the recognition accuracy of Chinese Character CAPTCHAs with distortion, rotation and background noise. Our experiment results show that this approach achieves more than 95% accuracy for single character and 84% accuracy for three types of Chinese Character CAPTCHAs with four characters. This encouraging result indicates that deep neural network is useful in complicated structure perception of Chinese Character CAPTCHAs.


title: Design of an accurate pedestrian detection system using modified HOG and LSVM

abstract: This paper focuses on detecting a pedestrian in an image. This real time application aims for high detection accuracy as well as faster computation. For higher accuracy and detection rate Histogram of Oriented Gradients (HOG) algorithm is used. Further, Linear Support Vector Machine (LSVM) classification is used for faster and reliable classification. Since the HOG algorithm is compute expensive several modifications have been made in order to get the best results for real time application. We have used bilinear interpolation and L2-normalisation for more reliable output. Further since the data is linearly separable a LSVM is designed in Matlab. The proposed algorithm provides an accuracy of 93.27% with a high true positive rate of 92.27% and a minor false positive rate of 4%.


title: Stereoscopic First Person View System for Drone Navigation

abstract: Ground control of unmanned aerial vehicles (UAV) is key to the advancement of this technology for commercial purposes. Not only because handover situations will require humans to take control over the drones when autonomous systems fail. But also, because training deep neural network based control systems require extensive real world data, and capturing meaningful and diverse data is a bottleneck. This axiom is even more prominent for the case of unmanned flying robots where there is no simple solution to capture optimal navigation footage. In such scenarios, improving the ground control and developing better autonomous systems are two sides of the same coin. To improve the ground control experience, and thus the quality of the footage, we propose to upgrade on-boad teleoperation systems to a fully immersive setup that provides operators with a stereoscopic First Person View through a Virtual Reality (VR) Head Mounted Display (HMD). We tested users (n=7) by asking them to fly our drone on the field. Test flights showed that operators flying our system can take off, fly and land successfully while wearing VR headsets. Additionally, we ran two experiments with pre-recorded videos of the flights and walks to a wider set of participants (n=69 and n=20) to compare the proposed technology to the experience provided by current drone FPV solutions that only include monoscopic vision. Our immersive stereoscopic setup enables higher accuracy depth perception, which has clear implications for achieving better teleoperation and unmanned navigation. Our studies show comprehensive data on the impact of motion and simulator sickness in case of stereoscopic setup. We present the device specifications as well as the measures that improve teleoperation experience and reduce induced simulator sickness. Our approach provides higher perception fidelity during flights, which leads to a more precise better teleoperation and ultimately translates into better flight data for training deep UAV control policies.


title: Using Noun Phrase Heads to Extract Document Keyphrases

abstract: Automatically extracting keyphrases from documents is a task with many applications in information retrieval and natural language processing. Document retrieval can be biased towards documents containing relevant keyphrases; documents can be classified or categorized based on their keyphrases; automatic text summarization may extract sentences with high keyphrase scores. ::: ::: This paper describes a simple system for choosing noun phrases from a document as keyphrases. A noun phrase is chosen based on its length, its frequency and the frequency of its head noun. Noun phrases are extracted from a text using a base noun phrase skimmer and an off-the-shelf online dictionary. ::: ::: Experiments involving human judges reveal several interesting results: the simple noun phrase-based system performs roughly as well as a state-of-the-art, corpus-trained keyphrase extractor; ratings for individual keyphrases do not necessarily correlate with ratings for sets of keyphrases for a document; agreement among unbiased judges on the keyphrase rating task is poor.


title: Remote detection of internal corrosion in conductor cables of power transmission lines

abstract: This paper presents a prototype of equipment used to detect faults in conductor cables employed in power transmission lines. It is able to do visual inspection of the external condition of the conductors and to detect internal faults in its magnetic material core (ACSR cables). The equipment is composed by a mobile unit — a robot — which moves on the line conductor, and by a control unit — a notebook — communicating with each other through radio-frequency transceivers. The equipment design takes electromagnetic (EMI) and radio-frequency (RFI) interference into account, allowing it to be used in live lines, in no energized lines and in ground wires. To verify the equipment's behavior under different situations, several tests have been performed: in laboratory under both no-energized and energized conditions, and in a real live power transmission line. The results obtained are presented.


title: Predicting Counterfactuals from Large Historical Data and Small Randomized Trials

abstract: When a new treatment is considered for use, whether a pharmaceutical drug or a search engine ranking algorithm, a typical question that arises is, will its performance exceed that of the current treatment? The conventional way to answer this counterfactual question is to estimate the effect of the new treatment in comparison to that of the conventional treatment by running a controlled, randomized experiment. While this approach theoretically ensures an unbiased estimator, it suffers from several drawbacks, including the difficulty in finding representative experimental populations as well as the cost of running randomized trials. Moreover, such trials neglect the huge quantities of available control-condition data, which in principle can be utilized for the harder task of predicting individualized effects. ::: ::: In this paper, we propose a discriminative framework for predicting the outcomes of a new treatment from a large dataset of the control condition and data from a small (and possibly unrepresentative) randomized trial comparing new and old treatments. Our learning objective, which requires minimal assumptions on the treatments, models the relation between the outcomes of the different conditions. This allows us to not only estimate mean effects but also to generate individual predictions for examples outside the small randomized sample. ::: ::: We demonstrate the utility of our approach through experiments in three areas: search engine operation, treatments to diabetes patients, and market value estimation of houses. Our results demonstrate that our approach can reduce the number and size of the currently performed randomized controlled experiments, thus saving significant time, money and effort on the part of practitioners.


title: Convex subspace representation learning from multi-view data

abstract: Learning from multi-view data is important in many applications. In this paper, we propose a novel convex subspace representation learning method for unsupervised multi-view clustering. We first formulate the subspace learning with multiple views as a joint optimization problem with a common subspace representation matrix and a group sparsity inducing norm. By exploiting the properties of dual norms, we then show a convex min-max dual formulation with a sparsity inducing trace norm can be obtained. We develop a proximal bundle optimization algorithm to globally solve the minmax optimization problem. Our empirical study shows the proposed subspace representation learning method can effectively facilitate multi-view clustering and induce superior clustering results than alternative multiview clustering methods.


title: Low Throughput Networks for the IoT: Lessons learned from industrial implementations

abstract: The emerging Internet of Things (IoT) is getting closer to reality every day, with standards and protocols tailored to specific applications. Although some technologies follow a ‘make once, fit all’ approach, others are designed for specific applications such as smart-metering where the devices are often stationary with low jitter and throughput requirements. ETSI is already working towards a new standard for such applications, having specified a Specification Group to study what they call Low Throughput Networks (LTN) and propose a communications standard. In the meantime industrial solutions have entered the market such as, among others, Sigfox's and OnRamp Wireless technologies and LoRaWAN. Although this may lead to fragmentation of the IoT, they are worth examining to gain insights from their innovations and approaches to solving problems that are common in IoT technologies, and more specifically, security, energy management and resource constrains. This paper examines the wireless technologies of LoRaWan, Sigfox and OnRamp Wireless, the emerging leaders in IoT smart metering applications. These have so far not been studied in any peer-reviewed paper. Furthermore we discuss the suitability of Low Throughput Networks for certain applications and how the above technologies would work in such scenarios.


title: Nonlinear H-infinity control for the rotary pendulum

abstract: The rotary (Furuta's) pendulum is used to analyzed the performance of a new nonlinear optimal (H-infinity) control for underactuated robotic systems. After applying partial feedback linearization, the pendulum's dynamic model is first transformed to an equivalent form. The later description of the pendulum's dynamics undergoes approximate linearization which takes place round a temporary operating point (equilibrium) recomputed at each iteration of the control algorithm. The linearization makes use of Taylor series expansion of the state-space model of the system and of computation of the associated Jacobian matrices. For the approximately linearized model of the pendulum an H-infinity feedback controller is developed. Through the repetitive solution of an algebraic Riccati equation which is also performed at each step of the control method, the controller's gain is computed. The stability features of the control loop are proven with Lyapunov analysis. First it is shown that the control loop satisfies the H-infinity tracking performance condition. Next, under moderate conditions it is also shown that the global asymptotic stability of the control loop can be assured.


title: Rollback-recovery without checkpoints in distributed event processing systems

abstract: Reliability is of critical importance to many applications involving distributed event processing systems. Especially the use of stateful operators makes it challenging to provide efficient recovery from failures and to ensure consistent event streams. Even during failure-free execution, state-of-the-art methods for achieving reliability incur significant overhead at run-time concerning computational resources, event traffic, and event detection time. This paper proposes a novel method for rollback-recovery that allows for recovery from multiple simultaneous operator failures, but eliminates the need for persistent checkpoints. Thereby, the operator state is preserved in \emph{savepoints} at points in time when its execution solely depends on the state of incoming event streams which are reproducible by predecessor operators. We propose an expressive event processing model to determine savepoints and algorithms for their coordination in a distributed operator network. Evaluations show that very low overhead at failure-free execution in comparison to other approaches is achieved.


title: Decoding-History-Based Adaptive Control of Attention for Neural Machine Translation

abstract: Attention-based sequence-to-sequence model has proved successful in Neural Machine Translation (NMT). However, the attention without consideration of decoding history, which includes the past information in the decoder and the attention mechanism, often causes much repetition. To address this problem, we propose the decoding-history-based Adaptive Control of Attention (ACA) for the NMT model. ACA learns to control the attention by keeping track of the decoding history and the current information with a memory vector, so that the model can take the translated contents and the current information into consideration. Experiments on Chinese-English translation and the English-Vietnamese translation have demonstrated that our model significantly outperforms the strong baselines. The analysis shows that our model is capable of generating translation with less repetition and higher accuracy. The code will be available at this https URL


title: Comparative Analysis of Factors Influencing Career Choices among Senior Secondary School Students in Rivers State Nigeria

abstract: The study investigated factors influencing career choices among Senior Secondary School Students in Rivers State, Nigeria. Simple random sampling techniques were adopted to select four hundred (400) Senior Secondary School Student from five Secondary Schools in Rivers State. The “Comprehensive Career Choice Survey” (CCCS) was administered to the respondents to collect necessary data. The t-test statistics was used to test three null hypotheses at 0.05 level of significance. The findings revealed that there were significant differences among male and female secondary school students in their career choices in terms of: Prestige of a profession, gender parity, and parental influence. Based on the findings some recommendations were made: (1) Professional career counsellors should be consulted to assist students in planning and choosing their careers. (2) Professional career counsellors should include the students in the selecting process, considering interest, ability, skills and personality of the students.


title: Multimodal Multipart Learning for Action Recognition in Depth Videos

abstract: The articulated and complex nature of human actions makes the task of action recognition difficult. One approach to handle this complexity is dividing it to the kinetics of body parts and analyzing the actions based on these partial descriptors. We propose a joint sparse regression based learning method which utilizes the structured sparsity to model each action as a combination of multimodal features from a sparse set of body parts. To represent dynamics and appearance of parts, we employ a heterogeneous set of depth and skeleton based features. The proper structure of multimodal multipart features are formulated into the learning framework via the proposed hierarchical mixed norm, to regularize the structured features of each part and to apply sparsity between them, in favor of a group feature selection. Our experimental results expose the effectiveness of the proposed learning method in which it outperforms other methods in all three tested datasets while saturating one of them by achieving perfect accuracy.


title: 3D face recognition using stereoscopic vision

abstract: In this paper a new complete system for 3D face recognition is presented. 3D face recognition presents several advantages against 2D face recognition, as, for example, invariance to illumination conditions. The proposed system makes use of a stereo methodology, that does not require any expensive range sensors. The 3D image of the face is modelled using Multilevel Br-Splines coefficients, that are classified using Support Vector Machines. Preliminary experimental evaluation has produced encouraging results, making the proposed system a promising low cost 3D face recognition system.


K-nearest Neighbor Based Dbscan Clustering Algorithm for Image Segmentation


title: Design and fabrication of Schottky diodes in 1.2 µm CMOS process

abstract: The I–V curves for Schottky diodes with two different contact areas and geometries fabricated through 1.2 µm CMOS process are presented. These curves are described applying the analysis and practical layout design. It takes into account the resistance, capacitance and reverse breakdown voltage in the semiconductor structure and the dependence of these parameters to improve its operation. The described diodes are used for a charge pump circuit implementation.


title: The Co-ordinates of Design Fiction: Extrapolation, Irony, Ambiguity and Magic

abstract: This paper argues that design fiction is a powerful term in part because it is malleable. A wide range of differing design fictions are emerging and we pursue a spatial metaphor to provide a map based on literary approaches. Following Margaret Atwood we trace design fiction back to marvel and wonder tales such as the Arabian Nights through to the science fiction of the nineteenth and twentieth century. We suggest science, magic, ambiguity and irony as the cardinal points of design fiction. We then apply these four different approaches to design fiction to the concept of a divorce app for older people. We argue that currently design fiction is dominated by scientistic and ironic design fiction and suggest that magic and ambiguity are currently under explored.


title: Development of multi-coils full-bridge resonant inverter for dynamic wireless power transfer

abstract: This paper presents a multi-coils full-bridge resonant inverter for a dynamic wireless power transfer (DWPT). The key feature of the proposed circuit is to reduce the number of switching devices counts. A control strategy of proposed topology was also discussed. In order to verify the proposed circuit, a simulation and system prototype of the proposed dynamic wireless power transfer system has been built and implemented. Several simulations and experimental results were reported and discussed. The receiving power at different lateral offset between primary coil and secondary coil was also measured. The simulated and experimental results confirmed that the proposed system makes it possible to apply for the dynamic wireless charging applications.


title: Author ' s personal copy Credit card fraud detection : A fusion approach using Dempster – Shafer theory and Bayesian learning

abstract: We propose a novel approach for credit card fraud detection, which combines evidences from current as well as past behavior. The fraud detection system (FDS) consists of four components, namely, rule-based filter, Dempster-Shafer adder, transaction history database and Bayesian learner. In the rule-based component, we determine the suspicion level of each incoming transaction based on the extent of its deviation from good pattern. Dempster-Shafer's theory is used to combine multiple such evidences and an initial belief is computed. The transaction is classified as normal, abnormal or suspicious depending on this initial belief. Once a transaction is found to be suspicious, belief is further strengthened or weakened according to its similarity with fraudulent or genuine transaction history using Bayesian learning. Extensive simulation with stochastic models shows that fusion of different evidences has a very high positive impact on the performance of a credit card fraud detection system as compared to other methods.


title: Context-aware item-to-item recommendation within the factorization framework

abstract: Item-to-item recommendation -- when the most similar items sought to the actual item -- is an important recommendation scenario in practical recommender systems. One way to solve this task is to use the similarity between item feature vectors of factorization models. By doing so, one may transfer the well-known accuracy of factorization models observed at the personalized recommendations to the item-to-item case. This paper introduces context-awareness to item similarities in the factorization framework. Two levels of context-aware similarities are defined and applied to two context-aware implicit feedback based factorization methods (iTALS and iTALSx). We investigate the advantages and drawbacks of the approaches on four real life implicit feedback data sets and we characterize the conditions for their application. The results suggest that it is worth using contextual information for item-to-item recommendations in the factorization framework, however, one should carefully select the appropriate method to achieve similar accuracy gain than in the case of the more general item-to-user recommendation scenario.


title: Learning Exploration Strategies in Model-Based Reinforcement Learning

abstract: Reinforcement learning (RL) is a paradigm for learning sequential decision making tasks. However, typically the user must hand-tune exploration parameters for each different domain and/or algorithm that they are using. In this work, we present an algorithm called LEO for learning these exploration strategies on-line. This algorithm makes use of bandit-type algorithms to adaptively select exploration strategies based on the rewards received when following them. We show empirically that this method performs well across a set of five domains. In contrast, for a given algorithm, no set of parameters is best across all domains. Our results demonstrate that the LEO algorithm successfully learns the best exploration strategies on-line, increasing the received reward over static parameterizations of exploration and reducing the need for hand-tuning exploration parameters.


title: Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network

abstract: The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches. DCNNs have already provided superior performance in different modalities of medical imaging including breast cancer classification, segmentation, and detection. Breast cancer is one of the most common and dangerous cancers impacting women worldwide. In this paper, we have proposed a method for breast cancer classification with the Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model. The IRRCNN is a powerful DCNN model that combines the strength of the Inception Network (Inception-v4), the Residual Network (ResNet), and the Recurrent Convolutional Neural Network (RCNN). The IRRCNN shows superior performance against equivalent Inception Networks, Residual Networks, and RCNNs for object recognition tasks. In this paper, the IRRCNN approach is applied for breast cancer classification on two publicly available datasets including BreakHis and Breast Cancer (BC) classification challenge 2015. The experimental results are compared against the existing machine learning and deep learning–based approaches with respect to image-based, patch-based, image-level, and patient-level classification. The IRRCNN model provides superior classification performance in terms of sensitivity, area under the curve (AUC), the ROC curve, and global accuracy compared to existing approaches for both datasets.


title: Deep sequencing identifies novel regulatory variants in the distal promoter region of the dopamine-β-hydroxylase gene.

abstract: OBJECTIVE ::: Dopamine-β-hydroxylase (DBH), an enzyme that converts dopamine into norepinephrine, is a drug target in cardiovascular and neuropsychiatric disorders. We aimed to identify functional variants in this gene by deep sequencing and enzyme phenotyping in an Indian cohort. ::: ::: ::: MATERIALS AND METHODS ::: Targeted resequencing of 12 exons and 10 kb upstream sequences of DBH in healthy volunteers (n=50) was performed using the Ion Personal Genome Machine System. Enzyme quantity and activity in their sera samples were determined by ELISA and ultra performance liquid chromatography, respectively. The association of markers with phenotypes was determined using Matrix eQTL. Global P-values for haplotypes generated using UNPHASED 3.1.5 were graphed using GrASP v.082 beta. ::: ::: ::: RESULTS ::: Of the 49 variants identified, nine were novel (minor allele frequency≥0.01). Though individual markers associated with enzyme quantity did not withstand multiple corrections, a novel distal promoter block driven by rs113249250 (global P=1.5×10) was associated. Of the nine single nucleotide polymorphisms (SNPs) associated with enzyme activity, rs3025369, rs1076151 and rs1611115, all from the upstream region, withstood false discovery rate correction (false discovery rate=0.03, 0.03 and 2.9×10, respectively). Conditioning for rs1611115 identified rs1989787 also to affect activity. Importantly, we report an association of a novel haplotype block distal to rs1076151 driven by rs3025369 (global P=8.9×10) with enzyme activity. This regulatory SNP explained 4.9% of the total 46.1% of variance in DBH activity caused by associated SNPs. ::: ::: ::: CONCLUSION ::: This first study combining deep sequencing and enzyme phenotyping identified yet another regulatory SNP suggesting that regulatory variants may be central in the physiological or metabolic role of this gene of therapeutic and pharmacological relevance.


title: Assessing the accuracy of sentiment analysis of social media posts at small and medium-sized enterprises in Southern Germany

abstract: In recent years, small and medium-sized enterprises (SMEs) have increasingly adopted Social Media technologies with the purpose of fostering the bidirectional communication with customers or to facili-tate the collaboration between employees amongst each other. Thereby, customer posts in a company’s Social Media channels capture consumers’ current attitude towards product and service offerings or the enterprise as a whole. An automatic analysis of these posts does not only provide a firm with valuable knowledge on the customer relationship, but also frees up human resources in case the posts were screened by employees manually hitherto. However, posts in Social Media channels of SMEs are char-acterized by certain peculiarities such as regional slang or off-topic discussions amongst others. The study at hand investigates the impact of such characteristics on the accuracy of results received from an automatic sentiment analysis of corresponding posts. In this context, we revert to Social Media posts of five SMEs from southern Germany. The results show that an adaption of approaches used for senti-ment analysis to the specific language of customers and firms is mandatory for achieving a high level of accuracy.


title: The anatomical origin and course of the angular artery regarding its clinical implications.

abstract: BACKGROUND ::: The purposes of this study were to determine the morphological features and conceptualize the anatomical definition of the angular artery (AA) as an aid to practical operations in the clinical field. ::: ::: ::: MATERIALS AND METHODS ::: Thirty-one hemifaces from 17 Korean cadavers and 26 hemifaces from 13 Thai cadavers were dissected. ::: ::: ::: RESULTS ::: The topography of the AA was classified into 4 types according to its course: Type I (persistent pattern), in which the AA traverses the lateral side of the nose (11%); Type II (detouring pattern), in which the AA traverses the cheek and tear trough area (18%); Type III (alternative pattern), in which the AA traverses the medial canthal area through a branch of the ophthalmic artery (22.8%); and Type IV (latent pattern), in which the AA is absent (26.3%). ::: ::: ::: CONCLUSION ::: The findings of this study will contribute toward improved outcomes for cosmetic surgery involving the injection of facial filler by enhancing the understanding of AA anatomy.


title: Formation Control and Collision Avoidance for Multi-agent Non-holonomic Systems: Theory and Experiments

abstract: In this paper we present a theoretical and experimental result on the control of multi-agent non-holonomic systems. We design and implement a novel decentralized control scheme that achieves dynamic formation control and collision avoidance for a group of non-holonomic robots. First, we derive a feedback law using Lyapunov-type analysis that guarantees collision avoidance and tracking of a reference trajectory for a single robot. Then we extend this result to the case of multiple non-holonomic robots, and show how different multi-agent problems, such as formation control and leader-follower control, can be addressed in this framework. Finally, we combine the above results to address the problem of coordinated tracking for a group of agents. We give extensive experimental results that validate the effectiveness of our results in all three cases.


title: A hybrid hub-and-spoke postal logistics network with realistic restrictions: A case study of Korea Post

abstract: Abstract Postal logistics has a complex transportation network for efficient mail delivery. Therefore, a postal logistics network consists of various functional sites with a hybrid hub-and-spoke structure. More specifically, there are multiple Delivery & Pickup Stations (D&PSs), multiple Mail Processing Centers (MPCs), and one Exchange Center (EC). In this paper, we develop two mathematical models with realistic restrictions for Korea Post for the current postal logistics network by simultaneously considering locations and allocations. We propose an Integer Linear Programming (ILP) model for transportation network organization and vehicle operation and a Mixed Integer Linear Programming (MILP) model that considers potential ECs for decision making while simultaneously regarding the EC location, transportation network organization, and vehicle operation. We use modified real data from Korea Post. Additionally, we consider several scenarios for supporting EC decision makers. The proposed models and scenarios are very useful in decision making for postal logistics network designers and operators.


title: High Efficiency DC-DC Converters for Battery-Operated Systems with Energy Management

abstract: Dc-dc converters suitable for use in battery-powered electronic equipment with energy management are discussed. To realize the benefits of energy management, these converters must be capable of maintaining regulation of their output voltages at no load, while maintaining high efficiency. Variable frequency operation is shown to give an improvement in efficient operating range of approximately two orders of magnitude. Proportionality of the battery and load currents can be maintained over approximately five orders of magnitude, with minimum battery currents of less than 100μA and maximum load currents approaching 10A. Steady-state and small-signal ac characteristics of converters operating with variable-frequency control are tabulated, and an ac two-port model is described.


title: Anisotropic Ordering in 1T′ Molybdenum and Tungsten Ditelluride Layers Alloyed with Sulfur and Selenium

abstract: Alloying is an effective way to engineer the band-gap structure of two-dimensional transition-metal dichalcogenide materials. Molybdenum and tungsten ditelluride alloyed with sulfur or selenium layers (MX2xTe2(1–x), M = Mo, W and X = S, Se) have a large band-gap tunability from metallic to semiconducting due to the 2H-to-1T′ phase transition as controlled by the alloy concentrations, whereas the alloy atom distribution in these two phases remains elusive. Here, combining atomic resolution Z-contrast scanning transmission electron microscopy imaging and density functional theory (DFT), we discovered that anisotropic ordering occurs in the 1T′ phase, in sharp contrast to the isotropic alloy behavior in the 2H phase under similar alloy concentration. The anisotropic ordering is presumably due to the anisotropic bonding in the 1T′ phase, as further elaborated by DFT calculations. Our results reveal the atomic anisotropic alloyed behavior in 1T′ phase layered alloys regardless of their alloy concentration, shi...


Cardiac Stem Cells in the Postnatal Heart: Lessons from Development


title: Maternal Height as an Independent Risk Factor for Neonatal Size Among Adolescent Bengalees in Kolkata, India

abstract: BACKGROUND ::: Low Birth Weight is a major public health problem in developing countries. The causes of LBW are multifactorial including complication during pregnancy, genetic, environmental, social-cultural, demographic and nutritional variables. Comparison of anthropometric risk factors for neonatal size of adolescent mothers are lacking from West Bengal. Therefore, this study was undertaken to identify maternal anthropometric characteristics, which most strongly influence neonate weight and length among Bengalee mothers. ::: ::: ::: METHODS ::: A hospital based cross-sectional study was undertaken during 2004 in a Government general hospital in South Kolkata, India. A total of 76 adolescent (age<20years) pregnant women were enrolled from obstetric ward who were admitted for delivery. Due to 4 perinatal deaths; a total of 72 adolescent mother- baby pairs were included in this analysis. Anthropometric measurements were undertaken immediately after delivery following stabilization as well maternal body mass index (BMI) was calculated using standard formula. ::: ::: ::: RESULTS ::: The prevalence of low birth weight (LBW) in the present study was 52.8%. The results revealed that 30.6% of mothers were undernourished (BMI<19.8 kg/m(2)). It was noted that about 64% of undernourished mothers delivered LBW baby. Linear regression analyses of neonatal weight and length as dependent variables revealed that in both cases, maternal height had the most significant impact. It showed 12.9% (birth weight) and 16.1% (birth length) of variation. Moreover, the proportion of LBW was 75%, 52.3% and 25% among short (height ≤145 cm), average (146-155cm) and tall (>155cm) mothers (x(2)=6.855, p<0.01), respectively. Short mothers had 2.74 and 9.0 fold greater risk of delivering LBW baby than average and tall mothers. In contrast, mean birth weight and length of baby was lower in short mother than their counterparts. ::: ::: ::: CONCLUSION ::: This study revealed that maternal height had the strongest significant impact on neonate size. This strong association could have serious health implications for Bengalee adolescent mothers. However, since this is a preliminary finding, it needs validation using a larger sample of adolescent mothers.


title: Boosting video popularity through recommendation systems

abstract: While search engines are the major sources of content discovery on online content providers and e-commerce sites, their capability is limited since textual descriptions cannot fully describe the semantic of content such as videos. Recommendation systems are now widely used in online content providers and e-commerce sites and play an important role in discovering content. In this paper, we describe how one can boost the popularity of a video through the recommendation system in YouTube. We present a model that captures the view propagation between videos through the recommendation linkage and quantifies the influence that a video has on the popularity of another video. Furthermore, we identify that the similarity in titles and tags is an important factor in forming the recommendation linkage between videos. This suggests that one can manipulate the metadata of a video to boost its popularity.


title: Collaborative target tracking using distributed Kalman filtering on mobile sensor networks

abstract: In this paper, we introduce a theoretical framework for coupled distributed estimation and motion control of mobile sensor networks for collaborative target tracking. We use a Fisher Information theoretic metric for quality of sensed data. The mobile sensing agents seek to improve the information value of their sensed data while maintaining a safe-distance from other neighboring agents (i.e. perform information-driven flocking). We provide a formal stability analysis of continuous Kalman-Consensus filtering (KCF) algorithm on a mobile sensor network with a flocking-based mobility control model. The discrete-time counterpart of this coupled estimation and control algorithm is successfully applied to tracking of two types of targets with stochastic linear and nonlinear dynamics.


title: Nasogastric tubes and videofluoroscopic swallowing studies in children

abstract: Background ::: Videofluoroscopic swallowing study (VFSS) is considered to be the gold standard method in assessing the risk of aspiration. Not infrequently, children who undergo VFSS are on tube feeds.


title: Cumulated gain-based evaluation of IR techniques

abstract: Modern large retrieval environments tend to overwhelm their users by their large output. Since all documents are not of equal relevance to their users, highly relevant documents should be identified and ranked first for presentation. In order to develop IR techniques in this direction, it is necessary to develop evaluation approaches and methods that credit IR methods for their ability to retrieve highly relevant documents. This can be done by extending traditional evaluation methods, that is, recall and precision based on binary relevance judgments, to graded relevance judgments. Alternatively, novel measures based on graded relevance judgments may be developed. This article proposes several novel measures that compute the cumulative gain the user obtains by examining the retrieval result up to a given ranked position. The first one accumulates the relevance scores of retrieved documents along the ranked result list. The second one is similar but applies a discount factor to the relevance scores in order to devaluate late-retrieved documents. The third one computes the relative-to-the-ideal performance of IR techniques, based on the cumulative gain they are able to yield. These novel measures are defined and discussed and their use is demonstrated in a case study using TREC data: sample system run results for 20 queries in TREC-7. As a relevance base we used novel graded relevance judgments on a four-point scale. The test results indicate that the proposed measures credit IR methods for their ability to retrieve highly relevant documents and allow testing of statistical significance of effectiveness differences. The graphs based on the measures also provide insight into the performance IR techniques and allow interpretation, for example, from the user point of view.


title: Highly Linear mm-Wave CMOS Power Amplifier

abstract: A Ka-band highly linear power amplifier (PA) is implemented in 28-nm bulk CMOS technology. Using a deep class-AB PA topology with appropriate harmonic control circuit, highly linear and efficient PAs are designed at millimeter-wave band. This PA architecture provides a linear PA operation close to the saturated power. Also elaborated harmonic tuning and neutralization techniques are used to further improve the transistor gain and stability. A two-stack PA is designed for higher gain and output power than a common source (CS) PA. Additionally, average power tracking (APT) is applied to further reduce the power consumption at a low power operation and, hence, extend battery life. Both the PAs are tested with two different signals at 28.5 GHz; they are fully loaded long-term evolution (LTE) signal with 16-quadrature amplitude modulation (QAM), a 7.5-dB peak-to-average power ratio (PAPR), and a 20-MHz bandwidth (BW), and a wireless LAN (WLAN) signal with 64-QAM, a 10.8-dB PAPR, and an 80-MHz BW. The CS/two-stack PAs achieve power-added efficiency (PAE) of 27%/25%, error vector magnitude (EVM) of 5.17%/3.19%, and adjacent channel leakage ratio (ACLR $_{\mathrm{ E-UTRA}}$ ) of −33/−33 dBc, respectively, with an average output power of 11/14.6 dBm for the LTE signal. For the WLAN signal, the CS/2-stack PAs achieve the PAE of 16.5%/17.3%, and an EVM of 4.27%/4.21%, respectively, at an average output power of 6.8/11 dBm.


title: Origin and destination forecasting on dockless shared bicycle in a hybrid deep-learning algorithms

abstract: Nowadays, the dockless shared bicycle has a positive influence on people’s travel, thus it is useful to analyze the spatio-temporal features of shared bike. Due to the limitations of CNN or LSTM, the spatial correlation and time dependence is inferior to capture. In this paper, a combination of CNN and LSTM named CLTFP in deep learning model is applied to predict the travel distance and OD distribution of shared bicycles under different conditions of time and space. Experiments show that CLTFP has better performance to capture spatiotemporal correlations.


title: Mini-DOMAs as Political Process Failures: The Case for Heightened Scrutiny of State Anti-Gay Marriage Amendments

abstract: Lawsuits seeking gay and lesbian marriage equality are piling up — more than seventy separate cases in thirty-two jurisdictions as of this writing — and advocates are racing to the Supreme Court to see which case might settle the question for the whole nation. As these cases move up swiftly through the federal courts, it is becoming increasingly difficult to see how the Justices can avoid a final reckoning on the question of same-sex marriage during their October 2014 term.This Essay suggests that political process theory provides the most democratically legitimate justification for the Supreme Court to step in and resolve the question of same-sex marriage for the whole country, as it seems increasingly likely to do, at a time when a majority of states still outlaw the practice. In focusing on same-sex couples’ right to marry, courts and commentators have largely overlooked the history of how and why the mini-DOMAs were enacted. The history and circumstances surrounding the twenty-eight mini-DOMAs that are embedded in state constitutions deserve a more prominent examination in federal marriage litigation.Collectively, these laws represent a troubling failure of the political process. By strong-arming marriage discrimination into state constitutions — which typically are far more difficult to change than ordinary statutes — during a relatively brief period from 1998 to 2012, mini-DOMA proponents intended to freeze marriage discrimination in place and put it beyond the reach of ordinary democratic deliberation, future legislative reconsideration, and state judicial review. And so the remaining mini-DOMAs should receive searching, skeptical judicial review of their substance because they are the products of a constitutionally suspect lawmaking process.


The interaction of drought and nutrient stress in wheat


title: Concurrency and Automata on Infinite Sequences

abstract: The paper is concerned with ways in which fair concurrency can be modeled using notations for omega-regular languages - languages containing infinite sequences, whose recognizers a.re modified forms of Buchi or Muller-McNaughton automata. There are characterization of these languages in terms of recursion equation sets which involve both minimal and maximal fix point operators. The class of ω-regular languages is closed under a fair concurrency operator. A general method for proving/deciding equivalences between such languages is obtained, derived from Milner's notion of "simulation".


title: Deep convolutional activation features for large scale Brain Tumor histopathology image classification and segmentation

abstract: We propose a simple, efficient and effective method using deep convolutional activation features (CNNs) to achieve stat- of-the-art classification and segmentation for the MICCAI 2014 Brain Tumor Digital Pathology Challenge. Common traits of such medical image challenges are characterized by large image dimensions (up to the gigabyte size of an image), a limited amount of training data, and significant clinical feature representations. To tackle these challenges, we transfer the features extracted from CNNs trained with a very large general image database to the medical image challenge. In this paper, we used CNN activations trained by ImageNet to extract features (4096 neurons, 13.3% active). In addition, feature selection, feature pooling, and data augmentation are used in our work. Our system obtained 97.5% accuracy on classification and 84% accuracy on segmentation, demonstrating a significant performance gain over other participating teams.


title: The Characteristic Study of Data Remanence of SRAM

abstract: Security processors typically store secret key material in static RAM, from which if power is removed the device is thoroughly tampered with. This strategy is very security, but there is the data remanence problem in SRAM, which is a serious potential trouble for information system. To improve the security of information system, the characteristic of data remancence of SRAM has been studied by experiment. The critical temperature of data remanence of SRAM has been determined, the relationship between data remanence time and temperature has been built, the relationship between the characteristic of data remanence and electronic parameters has been analyzed, and the electronic characteristic parameter—standby current I_ ddsb has been extracted as well. All the study result is the base for advanced mechanism study and control of the data remanence of SRAM.


title: Parallelizing DBSCaN Algorithm Using MPI

abstract: The last years, huge bundles of information are extracted by computational systems and electronic devices. To exploit the derived amount of data, new innovative algorithms must be employed or the established ones maybe changed. One of the most fascinating and productive techniques, in order to locate and extract information from data repositories is clustering, and DBSCAN is a successful density based algorithm which clusters data according its characteristics. However, its main disadvantage is its severe computational complexity which proves the technique very inadequate to apply on big datasets. Although DBSCAN is a very well studied technique, a fully operational parallel version of it, has not been accepted yet by the scientific community. In this work, a three phase parallel version of DBSCAN is presented. The obtained experimental results are very promising and prove the correctness, the scalability, and the effectiveness of the technique.


title: PupilScreen: Using Smartphones to Assess Traumatic Brain Injury

abstract: Before a person suffering from a traumatic brain injury reaches a medical facility, measuring their pupillary light reflex (PLR) is one of the few quantitative measures a clinician can use to predict their outcome. We propose PupilScreen, a smartphone app and accompanying 3D-printed box that combines the repeatability, accuracy, and precision of a clinical device with the ubiquity and convenience of the penlight test that clinicians regularly use in emergency situations. The PupilScreen app stimulates the patient's eyes using the smartphone's flash and records the response using the camera. The PupilScreen box, akin to a head-mounted virtual reality display, controls the eyes' exposure to light. The recorded video is processed using convolutional neural networks that track the pupil diameter over time, allowing for the derivation of clinically relevant measures. We tested two different network architectures and found that a fully convolutional neural network was able to track pupil diameter with a median error of 0.30 mm. We also conducted a pilot clinical evaluation with six patients who had suffered a TBI and found that clinicians were almost perfect when separating unhealthy pupillary light reflexes from healthy ones using PupilScreen alone.


title: Organizational Discourse and New Organization Development Practices

abstract: A new ensemble of organization development (OD) practices have emerged that are based more on constructionist, post modern and new sciences premises than the assumptions of the early founders. These include practices associated with appreciative inquiry, large group interventions, changing mindsets and consciousness, addressing diversity and multicultural realities, and advancing new and different models of change. We propose that the emerging field of organizational discourse offers sympathetic concepts and research that could add additional insights and theoretical rigour to the New OD. In particular, studies of organizational discourse based upon social constructionist and critical perspectives offer compelling ideas and practices associated with the establishment of change concepts, the role of power and context in relation to organizational change, and specific discursive interventions designed to foster organizational change.


title: Building a player strategy model by analyzing replays of real-time strategy games

abstract: Developing computer-controlled groups to engage in combat, control the use of limited resources, and create units and buildings in real-time strategy (RTS) games is a novel application in game AI. However, tightly controlled online commercial game pose challenges to researchers interested in observing player activities, constructing player strategy models, and developing practical AI technology in them. Instead of setting up new programming environments or building a large amount of agentpsilas decision rules by playerpsilas experience for conducting real-time AI research, the authors use replays of the commercial RTS game StarCraft to evaluate human player behaviors and to construct an intelligent system to learn human-like decisions and behaviors. A case-based reasoning approach was applied for the purpose of training our system to learn and predict player strategies. Our analysis indicates that the proposed system is capable of learning and predicting individual player strategies, and that players provide evidence of their personal characteristics through their building construction order.


title: UAV-Enabled Intelligent Transportation Systems for the Smart City: Applications and Challenges

abstract: There could be no smart city without a reliable and efficient transportation system. This necessity makes the ITS a key component of any smart city concept. While legacy ITS technologies are deployed worldwide in smart cities, enabling the next generation of ITS relies on effective integration of connected and autonomous vehicles, the two technologies that are under wide field testing in many cities around the world. Even though these two emerging technologies are crucial in enabling fully automated transportation systems, there is still a significant need to automate other road and transportation components. To this end, due to their mobility, autonomous operation, and communication/processing capabilities, UAVs are envisaged in many ITS application domains. This article describes the possible ITS applications that can use UAVs, and highlights the potential and challenges for UAV-enabled ITS for next-generation smart cities.


title: Deep points consolidation

abstract: In this paper, we present a consolidation method that is based on a new representation of 3D point sets. The key idea is to augment each surface point into a deep point by associating it with an inner point that resides on the meso-skeleton, which consists of a mixture of skeletal curves and sheets. The deep points representation is a result of a joint optimization applied to both ends of the deep points. The optimization objective is to fairly distribute the end points across the surface and the meso-skeleton, such that the deep point orientations agree with the surface normals. The optimization converges where the inner points form a coherent meso-skeleton, and the surface points are consolidated with the missing regions completed. The strength of this new representation stems from the fact that it is comprised of both local and non-local geometric information. We demonstrate the advantages of the deep points consolidation technique by employing it to consolidate and complete noisy point-sampled geometry with large missing parts.


title: The AASM scoring manual: a critical appraisal.

abstract: PURPOSE OF REVIEW ::: Summarize recently published studies and critiques evaluating the effects of the American Academy of Sleep Medicine (AASM) Sleep Scoring Manual. ::: ::: ::: RECENT FINDINGS ::: Only a few retrospective studies have been published evaluating the new AASM Scoring Manual. These have shown that when scoring polysomnograms (PSGs) using the AASM rules compared to previous standards and guidelines: increased amount and percentage of sleep time in Non-Rapid Eye Movement Sleep (NREM) 1 (N1) and N3 sleep, and decreased NREM 2 (N2) sleep; improved interscorer reliability when scoring sleep stages in adults; large differences in apnea-hypopnea indexes (AHIs) using different hypopnea scoring definitions; and PSGs scored using the 'recommended' hypopnea definition in the new manual identified no significant sleep disordered breathing in 40% of lean individuals with symptomatic OSA (AHI ≥5/h by 1999 'Chicago' criteria) and a favorable response to treatment. ::: ::: ::: SUMMARY ::: Two years have passed since the AASM Scoring Manual was published, garnering less criticism than was feared by those who developed it. The improvement in interscorer reliability using the Manual is heartening since this goal shaped many of the choices made. The alternative hypopnea rule should be endorsed as a recommended option. The AASM Scoring Manual provides a foundation upon which we all can build rules and methods that quantify the complexity of sleep and its disorders. Multicenter validation and refinement of the Manual is encouraged.


title: Cost-effectiveness analysis of chromosomal microarray as a primary test for prenatal diagnosis in Hong Kong

abstract: Chromosomal microarray (CMA) has been shown to be cost-effective over karyotyping in invasive prenatal diagnosis for pregnancies with fetal ultrasound anomalies. Yet, information regarding preceding and subsequent tests must be considered as a whole before the true cost-effectiveness can emerge. Currently in Hong Kong, karyotyping is offered free as the standard prenatal test while genome-wide array comparative genome hybridization (aCGH), a form of CMA, is self-financed. A new algorithm was proposed to use aCGH following quantitative fluorescent polymerase chain reaction (QF-PCR) as primary test instead of karyotyping. This study aims to evaluate the cost-effectiveness of the proposed algorithm versus the current algorithm for prenatal diagnosis in Hong Kong. Between November 2014 and February 2016, 129 pregnant women who required invasive prenatal diagnosis at two public hospitals in Hong Kong were prospectively recruited. The proposed algorithm was performed for all participants in this demonstration study. For the cost-effectiveness analysis, cost and outcome (diagnostic rate) data were compared with that of a hypothetical scenario representing the current algorithm. Further analysis was performed to incorporate women’s willingness-to-pay for the aCGH test. Impact of government subsidies on the aCGH test was explored as a sensitivity analysis. The proposed algorithm dominated the current algorithm for prenatal diagnosis. Both algorithms were equally effective but the proposed algorithm was significantly cheaper (p ≤ 0.05). Taking into account women’s willingness-to-pay for an aCGH test, the proposed algorithm was more effective and less costly than the current algorithm. When the government subsidy reaches 100%, the maximum number of diagnoses could be made. By switching to the proposed algorithm, cost saving can be achieved whilst maximizing the diagnostic rate for invasive prenatal diagnosis. It is recommended to implement aCGH as a primary test following QF-PCR to replace the majority of karyotyping for prenatal diagnosis in Hong Kong.


title: Semantic Precision and Recall for Ontology Alignment Evaluation

abstract: In order to evaluate ontology matching algorithms it is necessary to confront them with test ontologies and to compare the results with some reference. The most prominent comparison criteria are precision and recall originating from information retrieval. Precision and recall are thought of as some degree of correction and completeness of results. However, when the objects to compare are semantically defined, like ontologies and alignments, it can happen that a fully correct alignment has low precision. This is due to the restricted set-theoretic foundation of these measures. Drawing on previous syntactic generalizations of precision and recall, semantically justified measures that satisfy maximal precision and maximal recall for correct and complete alignments is proposed. These new measures are compatible with classical precision and recall and can be computed.


title: Programmers' build errors: a case study (at google)

abstract: Building is an integral part of the software development process. However, little is known about the compiler errors that occur in this process. In this paper, we present an empirical study of 26.6 million builds produced during a period of nine months by thousands of developers. We describe the workflow through which those builds are generated, and we analyze failure frequency, compiler error types, and resolution efforts to fix those compiler errors. The results provide insights on how a large organization build process works, and pinpoints errors for which further developer support would be most effective.


title: Vibration Isolation for Active Suspensions With Performance Constraints and Actuator Saturation

abstract: This paper investigates the problem of vibration isolation for vehicle active suspension systems, where parameter uncertainties, external disturbances, actuator saturation, and performance constraints are considered in an unified framework. A constrained adaptive robust control technology is developed to not only stabilize the attitude of vehicle in the context of parameter uncertainties and external disturbances, but also cover the problems of actuator saturation and performance constraints. Furthermore, the performance analysis of the closed-loop systems is given, by means of rigorous mathematical derivations. Extensive comparative experimental results are obtained to illustrate the effectiveness of the proposed control law.


title: Multi-View Time Series Classification: A Discriminative Bilinear Projection Approach

abstract: By virtue of the increasingly large amount of various sensors, information about the same object can be collected from multiple views. These mutually enriched information can help many real-world applications, such as daily activity recognition in which both video cameras and on-body sensors are continuously collecting information. Such multivariate time series (m.t.s.) data from multiple views can lead to a significant improvement of classification tasks. However, the existing methods for time series data classification only focus on single-view data, and the benefits of mutual-support multiple views are not taken into account. In light of this challenge, we propose a novel approach, named Multi-view Discriminative Bilinear Projections (MDBP), for extracting discriminative features from multi-view m.t.s. data. First, MDBP keeps the original temporal structure of m.t.s. data, and projects m.t.s. from different views onto a shared latent subspace. Second, MDBP incorporates discriminative information by minimizing the within-class separability and maximizing the between-class separability of m.t.s. in the shared latent subspace. Moreover, a Laplacian regularization term is designed to preserve the temporal smoothness within m.t.s.. Extensive experiments on two real-world datasets demonstrate the effectiveness of our approach. Compared to the state-of-the-art multi-view learning and m.t.s. classification methods, our approach greatly improves the classification accuracy due to the full exploration of multi-view streaming data. Moreover, by using a feature fusion strategy, our approach further improves the classification accuracy by at least 10%.


title: Building a case-based diet recommendation system without a knowledge engineer

abstract: We present a new approach to the effective development of menu construction systems that allow to automatically construct a menu that is strongly tailored to the individual requirements and food preferences of a client. In hospitals and other health care institutions dietitians develop diets for clients which need to change their eating habits. Many clients have special needs in regards to their medical conditions, cultural backgrounds, or special levels of nutrient requirements for better recovery from diseases or surgery, etc. Existing computer support for this task is insufficient-many diets are not specifically tailored for the client's needs or require substantial time of a dietitian to be manually developed. Our approach is based on case-based reasoning, an artificial intelligence technique that finds increasing entry into industrial practice. Our approach goes beyond the traditional case-based reasoning (CBR) approach by allowing an incremental improvement of the system's competency during routine use of the system. The improvement of the system takes place through a direct expert user-system interaction while the expert is accomplishing their tasks of constructing a diet for a given client. Whenever the system performs unsatisfactorily, the expert will need to modify the system-produced diet 'manually', i.e. by entering the desired modifications into the system. Our implemented system, menu construction using an incremental knowledge acquisition system (MIKAS), asks the expert for simple explanations for each of the manual actions he/she takes and incorporates the explanations automatically into its knowledge base (KB) so that the system will perform these manually conducted actions automatically at the next occasion. We present MIKAS and discuss the results of our case study. While still being a prototype, the senior clinical dietitian involved in our evaluation studies judges the approach to have considerable potential to improve the daily routine of hospital dietitians as well as to improve the average quality of the dietary advice given to patients within the limited available time for dietary consultations. Our approach opens up a new avenue towards building highly specialised CBR systems in a more cost-effective way. Hence, our approach promises to allow a significantly more widespread development and practical deployment of CBR systems in a large variety of application domains including many medical applications.


title: An analysis of underground forums

abstract: Underground forums, where participants exchange information on abusive tactics and engage in the sale of illegal goods and services, are a form of online social network (OSN). However, unlike traditional OSNs such as Facebook, in underground forums the pattern of communications does not simply encode pre-existing social relationships, but instead captures the dynamic trust relationships forged between mutually distrustful parties. In this paper, we empirically characterize six different underground forums --- BlackHatWorld, Carders, HackSector, HackE1ite, Freehack, and L33tCrew --- examining the properties of the social networks formed within, the content of the goods and services being exchanged, and lastly, how individuals gain and lose trust in this setting.


title: Analytic modeling of SSD write performance

abstract: Solid state drives (SSDs) update data by writing a new copy, rather than overwriting old data, causing prior copies of the same data to be invalidated. These writes are performed in units of pages, while space is reclaimed in units of multi-page erase blocks, necessitating copying of any remaining valid pages in the block before reclamation. The efficiency of this cleaning process greatly affects performance under random workloads; in particular, in SSDs the write bottleneck is typically internal media throughput, and write amplification due to additional internal copying directly reduces application throughput. We present the first precise closed-form solution for write amplification under greedy cleaning for uniformly distributed random traffic, and validate its accuracy via simulation. In addition we also present the first models which predict performance degradation for both LRU cleaning and greedy cleaning under simple non-uniform traffic conditions; simulation results show the first model to be exact and the second to be accurate within 2%. We extend the LRU model to arbitrary combinations of random traffic, and demonstrate its use in predicting cleaning performance for real-world workloads. The utility of these analytic models lies in their amenability to optimization techniques not feasible in simulation. We examine the strategy of separating "hot" and "cold" data, showing that for our traffic model, such separation eliminates any loss in performance due to non-uniform traffic. We show how a system which separates hot and cold data may shift free space from cold to hot data in order to achieve improved performance, and how numeric methods may be used with our model to find the optimum operating point, which approaches a write amplification of 1.0 for increasingly skewed traffic. We examine online methods for achieving this optimal operating point, and show that a control strategy based on our model achieves near-optimal performance for a number of real-world block traces.


title: TidyFS : A Simple and Small Distributed File System

abstract: This paper describes TidyFS, a simple and small distributed file system that provides the abstractions necessary for data parallel computations on clusters. In recent years there has been an explosion of interest in computing using clusters of commodity, shared nothing computers. Frequently the primary I/O workload for such clusters is generated by a distributed execution engine such as MapReduce, Hadoop or Dryad, and is high-throughput, sequential, and read-mostly. Other large-scale distributed file systems have emerged to meet these workloads, notably the Google File System (GFS) and the Hadoop Distributed File System (HDFS). TidyFS differs from these earlier systems mostly by being simpler. The system avoids complex replication protocols and read/write code paths by exploiting properties of the workload such as the absence of concurrent writes to a file by multiple clients, and the existence of end-to-end fault tolerance in the execution engine. We describe the design of TidyFS and report some of our experiences operating the system over the past year for a community of a few dozen users. We note some advantages that stem from the system's simplicity and also enumerate lessons learned from our design choices that point out areas for future development.


title: A new parallel algorithm for connected components in dynamic graphs

abstract: Social networks, communication networks, business intelligence databases, and large scientific data sources now contain hundreds of millions elements with billions of relationships. The relationships in these massive datasets are changing at ever-faster rates. Through representing these datasets as dynamic and semantic graphs of vertices and edges, it is possible to characterize the structure of the relationships and to quickly respond to queries about how the elements in the set are connected. Statically computing analytics on snapshots of these dynamic graphs is frequently not fast enough to provide current and accurate information as the graph changes. This has led to the development of dynamic graph algorithms that can maintain analytic information without resorting to full static recomputation. In this work we present a novel parallel algorithm for tracking the connected components of a dynamic graph. Our approach has a low memory requirement of O(V) and is appropriate for all graph densities. On a graph with 512 million edges, we show that our new dynamic algorithm is up to 128X faster than well-known static algorithms and that our algorithm achieves a 14X parallel speedup on a x86 64-core shared-memory system. To the best of the authors' knowledge, this is the first parallel implementation of dynamic connected components that does not eventually require static recomputation.


title: An Antilock-Braking Systems (ABS) Control: A Technical Review

abstract: Many different control methods for ABS systems have been developed. These methods differ in their theoretical basis and performance under the changes of road conditions. The present review is a part of research project entitled “Intelligent Antilock Brake System Design for Road-Surfaces of Saudi Arabia” In the present paper we review the methods used in the design of ABS systems. We highlight the main difficulties and summarize the more recent developments in their control techniques. Intelligent control systems like fuzzy control can be used in ABS control to emulate the qualitative aspects of human knowledge with several advantages such as robustness, universal approximation theorem and rule-based algorithms.


title: Mechanical design and performance specifications of anthropomorphic prosthetic hands : A review

abstract: Persons with spinal cord injury (SCI) have a high prevalence of abnormalities in carbohydrate and lipid metabolism. These abnormalities cause adverse coronary heart disease (CHD) in patients with SCI. In this study, we performed a detailed analysis of the level-specific cardiometabolic risk factors in individuals with SCI and analyzed the association of injury level on these risk factors. This was a cross-sectional study of 162 patients with SCI, assessing the prevalence of diabetes mellitus, dyslipidemia, hypertension, obesity, and smoking. Fasting blood sugar (>100) was diagnosed in 27 patients (16.7%). Of the total patients, 36 (22.2%) had a total cholesterol (TC) level of >200. A triglyceride level of >150 was present in 56 patients (34.6%). Hypertension was present in 2.5% of the entire patient group. Body mass index (BMI), TC, and low-density lipoprotein cholesterol (LDL-C) were significantly higher in the paraplegia group than the tetraplegia group (24.44 +/- 4.23 vs 22.65 +/- 4.27, p = 0.01; 185.71 +/- 40.69 vs 163.28 +/- 37.92, p < 0.001; and 102.51 +/- 28.20 vs 89.15 +/- 22.35, p = 0.01, respectively). Patients with paraplegia may have increased hypertension, higher BMI, and increasing levels of serum LDL-C and TC than those with tetraplegia. Conventional risk factors for CHD should be identified and treated in individuals with SCI.


title: Neural‐Symbolic Machine Learning for Retrosynthesis and Reaction Prediction

abstract: Reaction prediction and retrosynthesis are the cornerstones of organic chemistry. Rule-based expert systems have been the most widespread approach to computationally solve these two related challenges to date. However, reaction rules often fail because they ignore the molecular context, which leads to reactivity conflicts. Herein, we report that deep neural networks can learn to resolve reactivity conflicts and to prioritize the most suitable transformation rules. We show that by training our model on 3.5 million reactions taken from the collective published knowledge of the entire discipline of chemistry, our model exhibits a top10-accuracy of 95 % in retrosynthesis and 97 % for reaction prediction on a validation set of almost 1 million reactions.


title: What is a Good Linear Element? Interpolation, Conditioning, and Quality Measures

abstract: When a mesh of simplicial elements (triangles or tetrahedra) is used to form a piecewise linear approximation of a function, the accuracy of the approximation depends on the sizes and shapes of the elements. In finite element methods, the conditioning of the stiffness matrices also depends on the sizes and shapes of the elements. This paper explains the mathematical connections between mesh geometry, interpolation errors, and stiffness matrix conditioning. These relationships are expressed by error bounds and element quality measures that determine the fitness of a triangle or tetrahedron for interpolation or for achieving low condition numbers. Unfortunately, the quality measures for these two purposes do not agree with each other; for instance, small angles are bad for matrix conditioning but not for interpolation. Several of the upper and lower bounds on interpolation errors and element stiffness matrix conditioning given here are tighter than those that have appeared in the literature before, so the quality measures are likely to be unusually precise indicators of element fitness.


title: Increased expression of fibroblast growth factor receptor 1 in endometriosis and its correlation with endometriosis-related dysmenorrhea and recurrence.

abstract: OBJECTIVE(S) ::: This study aims to identify a critical molecule that potentially participates in endometriosis pathogenesis and characterize its correlation with dysmenorrhea and recurrence. ::: ::: ::: STUDY DESIGN ::: We utilized a bioinformatics-based strategy to screen for candidate genes and fibroblast growth factor receptor 1(FGFR1) was chosen for further validation. FGFR1 expression was examined in specimens of ectopic and eutopic endometrium obtained from 48 patients with endometriosis and specimens of eutopic endometrium from 26 healthy control subjects using immunohistochemistry and Western blotting. In addition, FGFR shRNA treatment was applied in a nude mice endometriosis model to examine the functional role of FGFR1 in endometriosis formation in vivo. ::: ::: ::: RESULTS ::: FGFR1 was found commonly overexpressed in ectopic endometrium of endometriosis compared with either its eutopic counterpart or endometrium from normal patients (P < 0.05). FGFR shRNA treatment impaired endometriosis formation and alleviated endometriosis-related symptoms in vivo. FGFR1 expression in ectopic endometrium was correlated with dysmenorrhea severity (P < 0.05) and recurrence in endometriosis patients (P < 0.05). ::: ::: ::: CONCLUSION(S) ::: FGFR1 might be involved in endometriosis development, which could possibly serve as a novel therapeutic target and prognostic marker for this disease.


title: The role of predictability in the stress response of a cichlid fi sh

abstract: article i nfo Article history: In recent years there has been an increasing interest in the cognitive abilities of fish with implications for animal welfare and management of rearing operations. Although it is known that psychological factors can modulate the stress response in mammals, this aspect has seldom been investigated within stress in fish. In this study we investigate whether the perception (appraisal) that fish make of significant environmental events modifies their behavioural and physiological response. For this purpose we have used a predictable vs. unpredictable paradigm for positive (feeding) and negative (confinement) events using the cichlid fish Oreochromis mossambicus as a model species. Results show that there is a differential effect of predictability for the feeding and confinement events. In the confinement experiment, predictability involved more attention to the visual cue and lower cortisol. The feeding event triggered higher levels of anticipatory behaviour and a tendency for higher cortisol in the predictable group. Therefore, predictable negative events reduce the cortisol response. Predictable positive events may elicit an anticipatory response, and when there is a significant delay between the visual cue and the actual occurrence of the event, it may also contain elements that can be interpreted as a stress response. These findings demonstrate that fish can appraise relevant aspects of the environment, with welfare implications for housing, husbandry and experimental procedures.


title: Replication issues in syntax-based aspect extraction for opinion mining

abstract: Reproducing experiments is an important instrument to validate previous work and build upon existing approaches. It has been tackled numerous times in different areas of science. In this paper, we introduce an empirical replicability study of three well-known algorithms for syntactic centric aspect-based opinion mining. We show that reproducing results continues to be a difficult endeavor, mainly due to the lack of details regarding preprocessing and parameter setting, as well as due to the absence of available implementations that clarify these details. We consider these are important threats to validity of the research on the field, specifically when compared to other problems in NLP where public datasets and code availability are critical validity components. We conclude by encouraging code-based research, which we think has a key role in helping researchers to understand the meaning of the state-of-the-art better and to generate continuous advances.


title: Development of an end-point detector for parylene deposition process

abstract: Parylene is an emerging material for MEMS. It is an organic material that is grown by using the chemical vapor deposition method at room temperature. The deposition thickness is commonly controlled by the amount of solid-phase dimer loaded in a sublimation chamber. In a conventional deposition machine, the end point of the process is designated by the moment the dimer is exhausted. However, this end-of-process criterion does not offer precise, repeatable control of film thickness. We present the results of the development of an in situ end-point detector for a Parylene chemical vapor deposition process. The detector is based on the thermal transfer principle and can be implemented on commercial parylene deposition systems with minimal system modification. Such a sensor enables a user to stop the deposition when a targeted thickness is reached. The end point detector is very simple to implement on existing parylene deposition systems. A series of such sensors with different target deposition thickness would allow extraction of the actual deposition rate within a deposition run.


title: Illustrating a Computer Generated Narrative

abstract: This work describes a computer model that generates visual narratives. It is part of a research project on narrative generation. A visual narrative is defined as a sequence of pictorial-scenes; each scene contains characters, locations and symbols representing dramatic tensions. A computer generated plot is transformed into a visual narrative by converting each textual action into a pictorial scene. We present details of the composition process and explain how the graphic elements employed to produce a coherent narration are generated. We describe the questionnaire that we employed to evaluate the system, discuss the results and outline future developments.


title: Corresponding Security Level with the Risk Factors of Personally Identifiable Information through the Analytic Hierarchy Process

abstract: Since Taiwan government has announced the New Version of Personal Information Protection Act , People began to pay attention to their personal information and privacy. Many industries significantly increased their responsibilities and faced more serious challenges. In order to cope with the requirements of the new law, BS10012 specification can help enterprises to reduce impact of personal data protection law. Enterprises need to rethink policy objectives, and make risk assessment of personally identifiable information (PII). This study using analytic hierarchy process (AHP) to identify the security level of PII. Corresponding security level with the risk factors of personal information, so that find the suitable countermeasure to protect users PII. Rarely see research about risk assessment of PII by using AHP, therefore the feasibility of the application of AHP in this regard is worthwhile to explore.


title: Convergence of Online Mirror Descent

abstract: In this paper we consider online mirror descent (OMD) algorithms, a class of scalable online learning algorithms exploiting data geometric structures through mirror maps. Necessary and sufficient conditions are presented in terms of the step size sequence $\{\eta_t\}_{t}$ for the convergence of an OMD algorithm with respect to the expected Bregman distance induced by the mirror map. The condition is $\lim_{t\to\infty}\eta_t=0, \sum_{t=1}^{\infty}\eta_t=\infty$ in the case of positive variances. It is reduced to $\sum_{t=1}^{\infty}\eta_t=\infty$ in the case of zero variances for which the linear convergence may be achieved by taking a constant step size sequence. A sufficient condition on the almost sure convergence is also given. We establish tight error bounds under mild conditions on the mirror map, the loss function, and the regularizer. Our results are achieved by some novel analysis on the one-step progress of the OMD algorithm using smoothness and strong convexity of the mirror map and the loss function.


title: Boosting Image Forgery Detection using Resampling Features and Copy-move analysis

abstract: Author(s): Mohammed, TM; Bunk, J; Nataraj, L; Bappy, JH; Flenner, A; Manjunath, BS; Chandrasekaran, S; Roy-Chowdhury, AK; Peterson, LA | Editor(s): Alattar, AM; Memon, ND; Sharma, G


title: Query Aware Determinization of Uncertain Objects

abstract: This paper considers the problem of determinizing probabilistic data to enable such data to be stored in legacy systems that accept only deterministic input. Probabilistic data may be generated by automated data analysis/enrichment techniques such as entity resolution, information extraction, and speech processing. The legacy system may correspond to pre-existing web applications such as Flickr, Picasa, etc. The goal is to generate a deterministic representation of probabilistic data that optimizes the quality of the end-application built on deterministic data. We explore such a determinization problem in the context of two different data processing tasks -- triggers and selection queries. We show that approaches such as thresholding or top-1 selection traditionally used for determinization lead to suboptimal performance for such applications. Instead, we develop a query-aware strategy and show its advantages over existing solutions through a comprehensive empirical evaluation over real and synthetic datasets.


title: Design of a cybernetic hand for perception and action

abstract: Strong motivation for developing new prosthetic hand devices is provided by the fact that low functionality and controllability—in addition to poor cosmetic appearance—are the most important reasons why amputees do not regularly use their prosthetic hands. This paper presents the design of the CyberHand, a cybernetic anthropomorphic hand intended to provide amputees with functional hand replacement. Its design was bio-inspired in terms of its modular architecture, its physical appearance, kinematics, sensorization, and actuation, and its multilevel control system. Its underactuated mechanisms allow separate control of each digit as well as thumb–finger opposition and, accordingly, can generate a multitude of grasps. Its sensory system was designed to provide proprioceptive information as well as to emulate fundamental functional properties of human tactile mechanoreceptors of specific importance for grasp-and-hold tasks. The CyberHand control system presumes just a few efferent and afferent channels and was divided in two main layers: a high-level control that interprets the user’s intention (grasp selection and required force level) and can provide pertinent sensory feedback and a low-level control responsible for actuating specific grasps and applying the desired total force by taking advantage of the intelligent mechanics. The grasps made available by the high-level controller include those fundamental for activities of daily living: cylindrical, spherical, tridigital (tripod), and lateral grasps. The modular and flexible design of the CyberHand makes it suitable for incremental development of sensorization, interfacing, and control strategies and, as such, it will be a useful tool not only for clinical research but also for addressing neuroscientific hypotheses regarding sensorimotor control.


title: NG-DBSCAN: Scalable Density-Based Clustering for Arbitrary Data

abstract: We present NG-DBSCAN, an approximate density-based clustering algorithm that operates on arbitrary data and any symmetric distance measure. The distributed design of our algorithm makes it scalable to very large datasets; its approximate nature makes it fast, yet capable of producing high quality clustering results. We provide a detailed overview of the steps of NG-DBSCAN, together with their analysis. Our results, obtained through an extensive experimental campaign with real and synthetic data, substantiate our claims about NG-DBSCAN's performance and scalability.


title: A Survey on GPU-Based Implementation of Swarm Intelligence Algorithms

abstract: Inspired by the collective behavior of natural swarm, swarm intelligence algorithms (SIAs) have been developed and widely used for solving optimization problems. When applied to complex problems, a large number of fitness function evaluations are needed to obtain an acceptable solution. To tackle this vital issue, graphical processing units (GPUs) have been used to accelerate the optimization procedure of SIAs. Thanks to their inherent parallelism, SIAs are very suitable for parallel implementation under the GPU platform which have achieved a great success in recent years. This paper presents a comprehensive review of GPU-based parallel SIAs in accordance with a newly proposed taxonomy. Critical concerns for the efficient parallel implementation of SIAs are also described in detail. Moreover, novel criteria are also proposed to evaluate and compare the parallel implementation and algorithm performance universally. The rationality and practicability of the proposed optimization methodology and criteria are verified by careful case study. Finally, our opinions and perspectives on the trends and prospects on the relatively new research domain are also presented for future development.


title: 0.9-V Class-AB Miller OTA in 0.35- $\mu \text{m}$ CMOS With Threshold-Lowered Non-Tailed Differential Pair

abstract: This paper presents a CMOS operational transconductance amplifier (OTA), suitable for sub-1-V supply applications, whose (input) common-mode voltage can be set to ( $V_{\mathrm {\mathbf {DD}}}+V_{\mathrm {\mathbf {SS}}})$ /2 thanks to two combined techniques applied to the differential pair, namely, threshold voltage lowering and elimination of the tail current generator. Both techniques are implemented through a single common-mode feedback loop, which embeds the shared bulk terminal of the pair. In contrast to other low-voltage approaches employing bulk driving, the proposed OTA is driven from the gate terminals and exploits only MOS transistors in strong inversion. Therefore, effective values of dc gain, gain bandwidth, and noise are found, suitable for high-accuracy switched-capacitor applications. Using a standard 0.35- $\mu \text{m}$ technology with nominal MOS transistors threshold around 0.7 V, a 0.9-V OTA with 0.45-V analog ground was designed and successfully tested. The measured gain and unity gain frequency were 65 dB and 1 MHz with phase margin of 60° for a capacitive load of 10 pF.


title: Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video

abstract: It has recently been shown that only a small number of samples from a low-rank matrix are necessary to reconstruct the entire matrix. We bring this to bear on computer vision problems that utilize low-dimensional subspaces, demonstrating that subsampling can improve computation speed while still allowing for accurate subspace learning. We present GRASTA, Grassmannian Robust Adaptive Subspace Tracking Algorithm, an online algorithm for robust subspace estimation from randomly subsampled data. We consider the specific application of background and foreground separation in video, and we assess GRASTA on separation accuracy and computation time. In one benchmark video example [16], GRASTA achieves a separation rate of 46.3 frames per second, even when run in MATLAB on a personal laptop.


title: Future internet research and experimentation: the FIRE initiative

abstract: The research community worldwide has increasingly drawn its attention to the weaknesses of the current Internet. Many proposals are addressing the perceived problems, ranging from new enhanced protocols to fix specific problems up to the most radical proposal to redesign and deploy a fully new Internet. Most of the problems in the current Internet are rooted in the tremendous pace of increase of its use. As a consequence there was little time to address the deficiencies of the Internet from an architectural point of view. Within FP7, the European Commission has facilitated the creation of European expert groups around the theme FIRE "Future Internet Research and Experimentation". FIRE has two related dimensions: on one hand, promoting experimentally-driven long-term, visionary research on new paradigms and networking concepts and architectures for the future Internet; on the other hand, building a large-scale experimentation facility supporting both medium- and long-term research on networks and services by gradually federating existing and new testbeds for emerging or future Internet technologies. By addressing future challenges for the Internet such as mobility, scalability, security and privacy, this new experimentally-driven approach is challenging the mainstream perceptions for future Internet development. This new initiative is intended to complement the more industrially-driven approaches which are addressed under the FP7 Objective "The Network of the Future" within the FP7-ICT Workprogramme 2007-08. FIRE is focused on exploring new and radically better technological solutions for the future Internet, while preserving the "good" aspects of the current Internet, in terms of openness, freedom of expression and ubiquitous access. The FIRE activities are being launched in the 2nd ICT call, which closes in October 2007, under the FP7-ICT Objective 1.6 "New Paradigms and Experimental Facilities" (budget e40m). Projects are envisaged to start in early 2008.


title: Characterization and modeling of the LPT CSTBT-the 5/sup th/ generation IGBT

abstract: The 5/sup th/ generation IGBT device, carrier stored trench bipolar transistor (CSTBT), has been introduced in the market. This newer generation IGBT represents the current state of the art with its excellent electrical characteristics. In this work, the physics-based electro-thermal Leturcq-Palmer IGBT model, which has been proven robust by the validation with NPT and PT IGBTs, is used to simulate the behavior of the CSTBT. The hard switching experiments have been performed using inductive and resistive loads under different temperature conditions. The simulation results from the model are compared with the experimental results under different conditions to validate the model accuracy.


title: Tangible Query Interfaces: Physically Constrained Tokens for Manipulating Database Queries

abstract: We present a new approach for using physically constrained tokens to express, manipulate, and visualize parameterized database queries. This method extends tangible interfaces to enable interaction with large aggregates of information. We describe two interface prototypes that use physical tokens to represent database parameters. These tokens are manipulated upon physical constraints, which map compositions of tokens onto interpretations including database queries, views, and Boolean operations. We propose a framework for “token + constraint” interfaces, and compare one of our prototypes with a comparable graphical interface in a preliminary user study. † Current affiliation: Visualization Dept., Zuse Institute Berlin (ZIB), ullmer@zib.de †† Current affiliation: Computer Science Dept., Tufts University, jacob@cs.tufts.edu