title: Deep Structured Scene Parsing by Learning with Image Descriptions

abstract: This paper addresses a fundamental problem of scene understanding: How to parse the scene image into a structured configuration (i.e., a semantic object hierarchy with object interaction relations) that finely accords with human perception. We propose a deep architecture consisting of two networks: i) a convolutional neural network (CNN) extracting the image representation for pixelwise object labeling and ii) a recursive neural network (RNN) discovering the hierarchical object structure and the inter-object relations. Rather than relying on elaborative user annotations (e.g., manually labeling semantic maps and relations), we train our deep model in a weakly-supervised manner by leveraging the descriptive sentences of the training images. Specifically, we decompose each sentence into a semantic tree consisting of nouns and verb phrases, and facilitate these trees discovering the configurations of the training images. Once these scene configurations are determined, then the parameters of both the CNN and RNN are updated accordingly by back propagation. The entire model training is accomplished through an Expectation-Maximization method. Extensive experiments suggest that our model is capable of producing meaningful and structured scene configurations and achieving more favorable scene labeling performance on PASCAL VOC 2012 over other state-of-the-art weakly-supervised methods.


title: To Trust Or Not To Trust A Classifier

abstract: Knowing when a classifier's prediction can be trusted is useful in many applications and critical for safely using AI. While the bulk of the effort in machine learning research has been towards improving classifier performance, understanding when a classifier's predictions should and should not be trusted has received far less attention. The standard approach is to use the classifier's discriminant or confidence score; however, we show there exists an alternative that is more effective in many situations. We propose a new score, called the trust score, which measures the agreement between the classifier and a modified nearest-neighbor classifier on the testing example. We show empirically that high (low) trust scores produce surprisingly high precision at identifying correctly (incorrectly) classified examples, consistently outperforming the classifier's confidence score as well as many other baselines. Further, under some mild distributional assumptions, we show that if the trust score for an example is high (low), the classifier will likely agree (disagree) with the Bayes-optimal classifier. Our guarantees consist of non-asymptotic rates of statistical consistency under various nonparametric settings and build on recent developments in topological data analysis.


title: Research Trend Visualization by MeSH Terms from PubMed

abstract: Motivation: PubMed is a primary source of biomedical information comprising search tool function and the biomedical literature from MEDLINE which is the US National Library of Medicine premier bibliographic database, life science journals and online books. Complimentary tools to PubMed have been developed to help the users search for literature and acquire knowledge. However, these tools are insufficient to overcome the difficulties of the users due to the proliferation of biomedical literature. A new method is needed for searching the knowledge in biomedical field. Methods: A new method is proposed in this study for visualizing the recent research trends based on the retrieved documents corresponding to a search query given by the user. The Medical Subject Headings (MeSH) are used as the primary analytical element. MeSH terms are extracted from the literature and the correlations between them are calculated. A MeSH network, called MeSH Net, is generated as the final result based on the Pathfinder Network algorithm. Results: A case study for the verification of proposed method was carried out on a research area defined by the search query (immunotherapy and cancer and “tumor microenvironment”). The MeSH Net generated by the method is in good agreement with the actual research activities in the research area (immunotherapy). Conclusion: A prototype application generating MeSH Net was developed. The application, which could be used as a “guide map for travelers”, allows the users to quickly and easily acquire the knowledge of research trends. Combination of PubMed and MeSH Net is expected to be an effective complementary system for the researchers in biomedical field experiencing difficulties with search and information analysis.


title: Deep fruit detection in orchards

abstract: An accurate and reliable image based fruit detection system is critical for supporting higher level agriculture tasks such as yield mapping and robotic harvesting. This paper presents the use of a state-of-the-art object detection framework, Faster R-CNN, in the context of fruit detection in orchards, including mangoes, almonds and apples. Ablation studies are presented to better understand the practical deployment of the detection network, including how much training data is required to capture variability in the dataset. Data augmentation techniques are shown to yield significant performance gains, resulting in a greater than two-fold reduction in the number of training images required. In contrast, transferring knowledge between orchards contributed to negligible performance gain over initialising the Deep Convolutional Neural Network directly from ImageNet features. Finally, to operate over orchard data containing between 100-1000 fruit per image, a tiling approach is introduced for the Faster R-CNN framework. The study has resulted in the best yet detection performance for these orchards relative to previous works, with an F1-score of>0.9 achieved for apples and mangoes.


title: Selective Growth of Metal Sulfide, Metal, and Metal-Alloy on 2D CdS Nanoplates

abstract: Two-dimensional CdS-based hybrid nanostructures are intriguing materials with an application prospect in different fields such as sensing (i.e., photoresistors) and solar energy harvesting (photocatalysis, photovoltaics, and so forth). We report herein a colloidal synthetic path for interfacing metal and semiconductor with 2D CdS nanoplates. Selective growth of Au, Pt, and a PtNi alloy as well as Cu2–xS semiconductor is achieved on CdS nanoplates using controlled reduction of metallic precursors and thermal decomposition of a metal-sulfide single-source precursor using standard organic-phase colloidal chemistry.


title: Semantic-Geographic Trajectory Pattern Mining Based on a New Similarity Measurement

abstract: Trajectory pattern mining is becoming increasingly popular because of the development of ubiquitous computing technology. Trajectory data contain abundant semantic and geographic information that reflects people’s movement patterns, i.e., who is performing a certain type of activity when and where. However, the variety and complexity of people’s movement activity and the large size of trajectory datasets make it difficult to mine valuable trajectory patterns. Moreover, most existing trajectory similarity measurements only consider a portion of the information contained in trajectory data. The patterns obtained cannot be interpreted well in terms of both semantic meaning and geographic distributions. As a result, these patterns cannot be used accurately for recommendation systems or other applications. This paper introduces a novel concept of the semantic-geographic pattern that considers both semantic and geographic meaning simultaneously. A flexible density-based clustering algorithm with a new trajectory similarity measurement called semantic intensity is used to mine these semantic-geographic patterns. Comparative experiments on check-in data from the Sina Weibo service demonstrate that semantic intensity can effectively measure both semantic and geographic similarities among trajectories. The resulting patterns are more accurate and easy to interpret.


title: Maximal oxygen uptake "classical" versus "contemporary" viewpoints.

abstract: In two articles Timothy Noakes proposes a new physiological model in which skeletal muscle recruitment is regulated by a central "govenor," specifically to prevent the development of a progressive ...


title: Learning to Predict Consequences as a Method of Knowledge Transfer in Reinforcement Learning

abstract: The reinforcement learning (RL) paradigm allows agents to solve tasks through trial-and-error learning. To be capable of efficient, long-term learning, RL agents should be able to apply knowledge gained in the past to new tasks they may encounter in the future. The ability to predict actions’ consequences may facilitate such knowledge transfer. We consider here domains where an RL agent has access to two kinds of information: agent-centric information with constant semantics across tasks, and environment-centric information, which is necessary to solve the task, but with semantics that differ between tasks. For example, in robot navigation, environment-centric information may include the robot’s geographic location, while agent-centric information may include sensor readings of various nearby obstacles. We propose that these situations provide an opportunity for a very natural style of knowledge transfer, in which the agent learns to predict actions’ environmental consequences using agent-centric information. These predictions contain important information about the affordances and dangers present in a novel environment, and can effectively transfer knowledge from agent-centric to environment-centric learning systems. Using several example problems including spatial navigation and network routing, we show that our knowledge transfer approach can allow faster and lower cost learning than existing alternatives.


title: Stochastic Neural Networks with Monotonic Activation Functions

abstract: We propose a Laplace approximation that creates a stochastic unit from any smooth monotonic activation function, using only Gaussian noise. This paper investigates the application of this stochastic approximation in training a family of Restricted Boltzmann Machines (RBM) that are closely linked to Bregman divergences. This family, that we call exponential family RBM (Exp-RBM), is a subset of the exponential family Harmoniums that expresses family members through a choice of smooth monotonic non-linearity for each neuron. Using contrastive divergence along with our Gaussian approximation, we show that Exp-RBM can learn useful representations using novel stochastic units.


title: Evolution, Detection and Analysis of Malware for Smart Devices

abstract: Smart devices equipped with powerful sensing, computing and networking capabilities have proliferated lately, ranging from popular smartphones and tablets to Internet appliances, smart TVs, and others that will soon appear (e.g., watches, glasses, and clothes). One key feature of such devices is their ability to incorporate third-party apps from a variety of markets. This poses strong security and privacy issues to users and infrastructure operators, particularly through software of malicious (or dubious) nature that can easily get access to the services provided by the device and collect sensory data and personal information. Malware in current smart devices —mostly smartphones and tablets— have rocketed in the last few years, in some cases supported by sophisticated techniques purposely designed to overcome security architectures currently in use by such devices. Even though important advances have been made on malware detection in traditional personal computers during the last decades, adopting and adapting those techniques to smart devices is a challenging problem. For example, power consumption is one major constraint that makes unaffordable to run traditional detection engines on the device, while externalized (i.e., cloud-based) techniques rise many privacy concerns. This article examines the problem of malware in smart devices and recent progress made in detection techniques. We first present a detailed analysis on how malware has evolved over the last years for the most popular platforms. We identify exhibited behaviors, pursued goals, infection and distribution strategies, etc. and provide numerous examples through case studies of the most relevant specimens. We next survey, classify and discuss efforts made on detecting both malware and other suspicious software (grayware), concentrating on the 20 most relevant techniques proposed between 2010 and 2013. Based on the conclusions extracted from this study, we finally provide constructive discussion on open research problems and areas where we believe that more work is needed.


title: Noisy input-output system identification approach for time delay estimation

abstract: An unbiased impulse response estimation approach is applied to time delay estimation between noisy signals received at two spacially separated sensors. The estimated delay is modeled by an adaptive finite impulse response filter whose coefficients are updated according to a modified least mean square (LMS) scheme. Simulation results show that the proposed algorithm outperforms the conventional LMS time delay estimator for autoregressive signals as well as real speech data.


title: A Survey on Ensemble Learning for Data Stream Classification

abstract: Ensemble-based methods are among the most widely used techniques for data stream classification. Their popularity is attributable to their good performance in comparison to strong single learners while being relatively easy to deploy in real-world applications. Ensemble algorithms are especially useful for data stream learning as they can be integrated with drift detection algorithms and incorporate dynamic updates, such as selective removal or addition of classifiers. This work proposes a taxonomy for data stream ensemble learning as derived from reviewing over 60 algorithms. Important aspects such as combination, diversity, and dynamic updates, are thoroughly discussed. Additional contributions include a listing of popular open-source tools and a discussion about current data stream research challenges and how they relate to ensemble learning (big data streams, concept evolution, feature drifts, temporal dependencies, and others).


Telemanipulation of Snake-Like Robots for Minimally Invasive Surgery of the Upper Airway


title: Classification of Security Threats in Information Systems

abstract: Abstract Information systems are frequently exposed to various types of threats which can cause different types of damages that might lead to significant financial losses. Information security damages can range from small losses to entire information system destruction. The effects of various threats vary considerably: some affect the confidentiality or integrity of data while others affect the availability of a system. Currently, organizations are struggling to understand what the threats to their information assets are and how to obtain the necessary means to combat them which continues to pose a challenge. To improve our understanding of security threats, we propose a security threat classification model which allows us to study the threats class impact instead of a threat impact as a threat varies over time. This paper addresses different criteria of information system security risks classification and gives a review of most threats classification models. We define a hybrid model for information system security threat classification in order to propose a classification architecture that supports all threat classification principles and helps organizations implement their information security strategies.


title: Experimental perspectives on learning from imbalanced data

abstract: We present a comprehensive suite of experimentation on the subject of learning from imbalanced data. When classes are imbalanced, many learning algorithms can suffer from the perspective of reduced performance. Can data sampling be used to improve the performance of learners built from imbalanced data? Is the effectiveness of sampling related to the type of learner? Do the results change if the objective is to optimize different performance metrics? We address these and other issues in this work, showing that sampling in many cases will improve classifier performance.


REFRAMING SUPPLY CHAIN MANAGEMENT : A SERVICE-DOMINANT


title: A Probabilistic Generative Model for Mining Cybercriminal Network from Online Social Media: A Review

abstract: Social media has been increasingly utilized as an area of sharing and gathering of information. Data mining is the process of analyzing data from different context and summarizes them into useful information. It allows the users to analyze the data, categorize them and identifies the relationship inferred in them. Text mining often referred to as text data mining can be used to derive information from text. Text analysis can be used in information retrieval, information extraction, pattern recognition, frequency distribution and data mining techniques. An application of this is to scan a set of documents in natural language for predictive classification purposes. Recent researches shows that the number of crimes are increasing through social media that may cause tremendous loss to organizations. Existing security methods are weak in cyber crime forensics and predictions. The contribution of this paper is to mine cybercriminal network which can reveal both implicit and explicit meanings among cybercriminal based on their conversation messages.


title: A Real-Time EMG Pattern Recognition System Based on Linear-Nonlinear Feature Projection for a Multifunction Myoelectric Hand

abstract: This paper proposes a novel real-time electromyogram (EMG) pattern recognition for the control of a multifunction myoelectric hand from four channel EMG signals. To extract a feature vector from the EMG signal, we use a wavelet packet transform that is a generalized version of wavelet transform. For dimensionality reduction and nonlinear mapping of the features, we also propose a linear-nonlinear feature projection composed of principal components analysis (PCA) and a self-organizing feature map (SOFM). The dimensionality reduction by PCA simplifies the structure of the classifier and reduces processing time for the pattern recognition. The nonlinear mapping by SOFM transforms the PCA-reduced features into a new feature space with high class separability. Finally, a multilayer perceptron (MLP) is used as the classifier. Using an analysis of class separability by feature projections, we show that the recognition accuracy depends more on the class separability of the projected features than on the MLP's class separation ability. Consequently, the proposed linear-nonlinear projection method improves class separability and recognition accuracy. We implement a real-time control system for a multifunction virtual hand. Our experimental results show that all processes, including virtual hand control, are completed within 125 ms, and the proposed method is applicable to real-time myoelectric hand control without an operational time delay


title: An O(NP) sequence comparison algorithm

abstract: Abstract Let A and B be two sequences of length M and N respectively, where without loss of generality N ⩾ M , and let D be the length of a shortest edit script (consisting of insertions and deletions) between them. A parameter related to D is the number of deletions in such a script, P= 1 2 D− 1 2 (N−M) . We present an algorithm for finding a shortest edit distance of A and B whose worst-case running time is O( NP ) and whose expected running time is O( N + PD ). The algorithm is simple and is very efficient whenever A is similar to a subsequence of B . It is nearly twice as fast as the O( ND ) algorithm of Myers, and much more efficient when A and B differ substantially in length.


title: A Transformerless Bipolar/Unipolar High-Voltage Pulse Generator With Low-Voltage Components for Water Treatment Applications

abstract: Pulsed electric field is a commonly used and effective disinfection method in drinking water treatment applications. In this paper, a transformerless unipolar/bipolar high-voltage pulse generator is presented with well-regulated pulsed output voltage for drinking water treatment via underwater pulsed corona discharge. The proposed high-voltage pulse generator is fed from a relatively low-voltage dc supply. Then, capacitor–diode voltage multiplier centrally fed from a dc–dc boost converter is used to elevate the input voltage to a proper high-voltage level. Finally, a modular multilevel converter is employed to chop the elevated voltage into unipolar or bipolar pulsed output voltage (based on the user selection). The suggested arrangement provides a high-voltage gain with relatively low-voltage components, i.e., neither high-voltage nor series-connected components are needed in the proposed approach. A comparison between the proposed approach and the other Marx-Based pulse generators has been held to show the merits of the proposed approach. A detailed design of the generator passive components and semiconductor devices has been presented. Simulation and experimental results are presented for the unipolar as well as the bipolar pulsed output to validate the proposed concept.


title: From Isolation to Involvement: Adapting Machine Creativity Software to Support Human-Computer Co-Creation

abstract: This paper investigates how to transform machine creativity systems into interactive tools that support human-computer co-creation. We use three case studies to identify common issues in this transformation, under the perspective of User-Centered Design. We also analyse the interactivity and creative behavior of the three platforms in terms of Wiggins’ formalization of creativity as a search. We arrive at the conclusion that adapting creative software for supporting human-computer cocreation requires redesigning some major aspects of the software, which guides our on-going project of building an interactive poetry composition tool.


title: Brightness-Based Convolutional Neural Network for Thermal Image Enhancement

abstract: In this paper, we propose a convolutional neural network for thermal image enhancement by incorporating the brightness domain with a residual-learning technique, which improves the performance of enhancement and speed of convergence. Typically, the training domain uses the same domain as that of the target image; however, we evaluated several domains to determine the most suitable one for the network. In the analyses, we first compared the performance of networks that were trained by the corresponding regions of color-based and aligned infrared-based images, respectively, including thermal, far, and near spectra. Then, four RGB-based domains, namely, gray, lightness, intensity, and brightness were evaluated. Finally, the proposed network architecture was determined by considering the residual and brightness domains. The results of the analyses indicated that the brightness domain was the best training domain for enhancing the thermal images. The experimental results confirm that the proposed network, which can be trained in approximately one hour, outperforms the conventional learning-based approaches for thermal image enhancement, in terms of several image quality metrics and a qualitative evaluation. Furthermore, the results demonstrate that the brightness domain is effective as the training domain and can be used to increase the performance of existing networks.


title: Multilayer perceptron, fuzzy sets, and classification

abstract: A fuzzy neural network model based on the multilayer perceptron, using the backpropagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and other related models. >


title: OntoNotes: A Unified Relational Semantic Representation

abstract: The OntoNotes project is creating a corpus of large-scale, accurate, and integrated annotation of multiple levels of the shallow semantic structure in text. Such rich, integrated annotation covering many levels will allow for richer, cross-level models enabling significantly better automatic semantic analysis. At the same time, it demands a robust, efficient, scalable mechanism for storing and accessing these complex inter-dependent annotations. We describe a relational database representation that captures both the inter- and intra-layer dependencies and provide details of an object-oriented API for efficient, multi-tiered access to this data.


title: Different design approaches of surface mounted high performance PMSG

abstract: In this paper, the standard equations of PMSG have been utilized to develop model generator basic dimensions; where such primary design has been found unable to deliver the exact required rated power. Hence to reach such required rated output power different approaches depend on improved performance and reduced cost criterion are suggested and studied. Three different dimension changing techniques are studied for the highest efficiency and power to volume ratio through linear analysis, and then compared to finite element model (FEM) analysis results. As a conclusion a modified starting design formula is suggested for designing of high performance PMSGs.


title: Learning Dynamic Guidance for Depth Image Enhancement

abstract: The depth images acquired by consumer depth sensors (e.g., Kinect and ToF) usually are of low resolution and insufficient quality. One natural solution is to incorporate with high resolution RGB camera for exploiting their statistical correlation. However, most existing methods are intuitive and limited in characterizing the complex and dynamic dependency between intensity and depth images. To address these limitations, we propose a weighted analysis representation model for guided depth image enhancement, which advances the conventional methods in two aspects: (i) task driven learning and (ii) dynamic guidance. First, we generalize the analysis representation model by including a guided weight function for dependency modeling. And the task-driven learning formulation is introduced to obtain the optimized guidance tailored to specific enhancement task. Second, the depth image is gradually enhanced along with the iterations, and thus the guidance should also be dynamically adjusted to account for the updating of depth image. To this end, stage-wise parameters are learned for dynamic guidance. Experiments on guided depth image upsampling and noisy depth image restoration validate the effectiveness of our method.


title: ParlAI: A Dialog Research Software Platform

abstract: We introduce ParlAI (pronounced "par-lay"), an open-source software platform for dialog research implemented in Python, available at this http URL. Its goal is to provide a unified framework for sharing, training and testing of dialog models, integration of Amazon Mechanical Turk for data collection, human evaluation, and online/reinforcement learning; and a repository of machine learning models for comparing with others' models, and improving upon existing architectures. Over 20 tasks are supported in the first release, including popular datasets such as SQuAD, bAbI tasks, MCTest, WikiQA, QACNN, QADailyMail, CBT, bAbI Dialog, Ubuntu, OpenSubtitles and VQA. Several models are integrated, including neural models such as memory networks, seq2seq and attentive LSTMs.


title: Intimacy expectations in same-sex friendships: a prototype interaction-pattern model.

abstract: A prototype interaction-pattern model of intimacy expectations was proposed. The central tenet of this model was that people develop knowledge of the patterns of relating that are likely to produce intimacy in a same-sex friendship. Further, it was posited that these interaction patterns are structured as prototypes, such that some patterns of relating are regarded as more likely to create a sense of intimacy than others. Support for this model was found in 6 studies. Interaction patterns depicting self-disclosure, emotional support, and the like were considered more prototypical of intimacy expectations than patterns depicting shared activities and practical support. Regarding gender, women rated intimacy interaction patterns higher than did men, particularly prototypical patterns. However, women and men agreed that prototypical interaction patterns were more indicative of intimacy in a friendship than nonprototypical patterns. Implications for the controversy over whether women's friendships are more intimate than men's are discussed.


title: Orchestrating Caching, Transcoding and Request Routing for Adaptive Video Streaming Over ICN

abstract: Information-centric networking (ICN) has been touted as a revolutionary solution for the future of the Internet, which will be dominated by video traffic. This work investigates the challenge of distributing video content of adaptive bitrate (ABR) over ICN. In particular, we use the in-network caching capability of ICN routers to serve users; in addition, with the help of named function, we enable ICN routers to transcode videos to lower-bitrate versions to improve the cache hit ratio. Mathematically, we formulate this design challenge into a constrained optimization problem, which aims to maximize the cache hit ratio for service providers and minimize the service delay for endusers. We design a two-step iterative algorithm to find the optimum. First, given a content management scheme, we minimize the service delay via optimally configuring the routing scheme. Second, we maximize the cache hits for a given routing policy. Finally, we rigorously prove its convergence. Through extensive simulations, we verify the convergence and the performance gains over other algorithms. We also find that more resources should be allocated to ICN routers with a heavier request rate, and the routing scheme favors the shortest path to schedule more traffic.


title: Calibration-free rolling shutter removal

abstract: We present a novel algorithm for efficient removal of rolling shutter distortions in uncalibrated streaming videos. Our proposed method is calibration free as it does not need any knowledge of the camera used, nor does it require calibration using specially recorded calibration sequences. Our algorithm can perform rolling shutter removal under varying focal lengths, as in videos from CMOS cameras equipped with an optical zoom. We evaluate our approach across a broad range of cameras and video sequences demonstrating robustness, scaleability, and repeatability. We also conducted a user study, which demonstrates preference for the output of our algorithm over other state-of-the art methods. Our algorithm is computationally efficient, easy to parallelize, and robust to challenging artifacts introduced by various cameras with differing technologies.


Temporal information extraction from clinical text


title: A social diffusion model of misinformation and disinformation for understanding human information behaviour.

abstract: Introduction. People enjoy sharing information, even when they do not believe it. Thus, misinformation (inaccurate information) and disinformation (deceptive information) diffuse throughout social networks, as misinforming and disinforming are varieties of information behaviour. Social media have made such diffusion easier and faster. Many information behaviour models, however, suggest a normative model of information as true, accurate, complete, despite the ubiquity of misinformation and disinformation. Analysis. Misinformation and disinformation are defined and we show how they extend the concept of information through their informativeness. Table 1 summarizes the features of information, misinformation, and disinformation. Figure 1 illustrates the social diffusion process by which misinforming and disinforming function as types of information behaviour. Conclusion. Misinformation and disinformation are closely linked to information literacy, especially in terms of how they are diffused and shared and how people use both cues to credibility and cues to deception to make judgements. Misinformation and disinformation present both challenges and opportunities for individuals, businesses, and governments. Future work in immersive, 3D virtual worlds takes a naturalistic approach to understand the principal elements of cues to misinformation and disinformation.


title: Gemcitabine : metabolism, mechanisms of action, and self-potentiation

abstract: Abstract Gemcitabine (dFdC) is a new anticancer nucleoside that is an analog of deoxycytidine. It is a pro-drug and, once transported into the cell, must be phosphorylated by deoxycytidine kinase to an active form. Both gemcitabine diphosphate (dFdCTP) and gemcitabine triphosphate (dFdCTP) inhibit processes required for DNA synthesis. Incorporation of dFdCTP into DNA is most likely the major mechanism by which gemcitabine causes cell death. After incorporation of gemcitabine nucleotide on the end of the elongating DNA strand, one more deoxynucleotide is added and thereafter, the DNA polymerases are unable to proceed. This action ("masked termination") apparently locks the drug into DNA as the proofreading enzymes are unable to remove gemcitabine from this position. Furthermore, the unique actions that gemcitabine metabolites exert on cellular regulatory processes serve to enhance the overall inhibitory activities on cell growth. This interaction is termed "self-potentiation" and is evidenced in very few other anticancer drugs.


title: Effect of Hormones on Pancreatic Macromolecular Transport

abstract: The role of subcellular organelles in the synthesis, transport, and secretion of pancreatic digestive proteins has been well documented. This study was designed to examine effects of pentagastrin, secretin, and acute and chronic administration of cholecystokinin-pancreozymin (CCK-PZ) on pancreatic macromolecular transport and secretion. Pooled rat pancreas slices were pulse-labeled with L-[14-C]phenylalanine and migration of 14-C-labeled proteins studied by "chase" incubation for 15, 30, 60, and 120 min. After in vitro incubation of control and drug-treated pancreatic slices, subcellular particles were isolated by differential centrifugation; Specific activity of radioactive labeled proteins was determined in subcellular fractions involved in transport and secretion of digestive proteins. These studies indicate that pentagastrin and acute and chronic stimulation with CCK-PZ did not alter the rate of transport of labeled proteins from ribosomes to the zymogen granules. Pentagastrin and CCK-PZ stimulated secretion of labeled proteins from the zymogen granules into the medium in the concentrations used as evidenced by significant increase in the amount of labeled proteins in the medium and significant decrease in the specific activity of the zymogen granule fractions after 60- and 120-min periods of incubation. Secretin did not alter the rate of transport or secretion of the pulse-labeled proteins.


title: The Profit Benefits of Category Management

abstract: The practice of category management (i.e., empolying a single decision maker who is responsible for a firm's product line) has been advocated as a superior alternative to brand management (i.e., in...


title: Visual recognition of pointing gestures for human – robot interaction

abstract: In this paper, we present an approach for recognizing pointing gestures in the context of human-robot interaction. In order to obtain input features for gesture recognition, we perform visual tracking of head, hands and head orientation. Given the images provided by a calibrated stereo camera, color and disparity information are integrated into a multi-hypothesis tracking framework in order to find the 3D-positions of the respective body parts. Based on the hands' motion, an HMM-based classifier is trained to detect pointing gestures. We show experimentally that the gesture recognition performance can be improved significantly by using information about head orientation as an additional feature. Our system aims at applications in the field of human-robot interaction, where it is important to do run-on recognition in real-time, to allow for robot egomotion and not to rely on manual initialization.


title: Large-volume liposuction and extensive abdominoplasty: a feasible alternative for improving body shape.

abstract: Forty-two female patients underwent liposuction of large volumes and extensive abdominoplasty during an 18-month period, with an average follow-up period of 9 months. Fifty-two percent of the patients underwent a third surgical procedure, which was basically aesthetic. The average age was 40 years; weight varied between 51 kg and 113 kg, with only 11 percent of the patients at their ideal weight. The tumescent technique was used for performing liposuction, which was done on the loins, trochanters, lumbar, upper scapular, and upper lateral abdominal regions, avoiding liposuction of the abdominal flap in all cases except for one. The abdominoplasty was extensive with detachment to the xiphoid process without detaching liposuctioned areas. Volumes of 1600 ml to 11,200 ml were obtained through liposuction, with an average of 4230 cc, and minimal blood loss. Fatty dermal tissue was removed through abdominoplasty and it varied between 400 g and 5000 g with an average of 1300 g. Only the two heaviest patients required an autologous blood unit, because they underwent breast reduction at the same time. All the other patients were managed exclusively with intravenous crystalloids solutions. No major complications arose, and only five patients had minor complications. Based on these results, we consider it to be feasible to combine the liposuctioning of large volumes with abdominoplasty to improve body shape, even with the addition of a third surgical procedure. Nevertheless, it is essential to use the tumescent technique for liposuction to reduce bleeding to a minimum and to allow us to perform other procedures. (Plast. Reconstr. Surg. 102: 1698, 1998.)


title: Security, Privacy and Trust Issues in Smart Environments

abstract: Recent advances in networking, handheld computing and sensor technologies have driven forward research towards the realisation of Mark Weiser’s dream of calm and ubiquitous computing (variously called pervasive computing, ambient computing, active spaces, the disappearing computer or context-aware computing). In turn, this has led to the emergence of smart environments as one significant facet of research in this domain.


Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn't


title: “Making my own luck”: Serendipity strategies and how to support them in digital information environments

abstract: Serendipity occurs when unexpected circumstances and an “aha” moment of insight result in a valuable, unanticipated outcome. Designing digital information environments to support serendipity can not only provide users with new knowledge, but also propel them in directions they might not otherwise have traveled in—surprising and delighting them along the way. As serendipity involves unexpected circumstances it cannot be directly controlled, but it can be potentially influenced. However, to the best of our knowledge, no previous work has focused on providing a rich empirical understanding of how it might be influenced. We interviewed 14 creative professionals to identify their self-reported strategies aimed at increasing the likelihood of serendipity. These strategies form a framework for examining ways existing digital environments support serendipity and for considering how future environments can create opportunities for it. This is a new way of thinking about how to design for serendipity; by supporting the strategies found to increase its likelihood rather than attempting to support serendipity as a discrete phenomenon, digital environments not only have the potential to help users experience serendipity but also encourage them to adopt the strategies necessary to experience it more often.


title: Forensic analysis of social networking applications on mobile devices

abstract: The increased use of social networking applications on smartphones makes these devices a goldmine for forensic investigators. Potential evidence can be held on these devices and recovered with the right tools and examination methods. This paper focuses on conducting forensic analyses on three widely used social networking applications on smartphones: Facebook, Twitter, and MySpace. The tests were conducted on three popular smartphones: BlackBerrys, iPhones, and Android phones. The tests consisted of installing the social networking applications on each device, conducting common user activities through each application, acquiring a forensically sound logical image of each device, and performing manual forensic analysis on each acquired logical image. The forensic analyses were aimed at determining whether activities conducted through these applications were stored on the device’s internal memory. If so, the extent, significance, and location of the data that could be found and retrieved from the logical image of each device were determined. The results show that no traces could be recovered from BlackBerry devices. However, iPhones and Android phones store a significant amount of valuable data that could be recovered and used by forensic investigators.


Earnings Quality : Evidence from the Field


title: Language encodes geographical information

abstract: Population counts and longitude and latitude coordinates were estimated for the 50 largest cities in the United States by computational linguistic techniques and by human participants. The mathematical technique Latent Semantic Analysis applied to newspaper texts produced similarity ratings between the 50 cities that allowed for a multidimensional scaling (MDS) of these cities. MDS coordinates correlated with the actual longitude and latitude of these cities, showing that cities that are located together share similar semantic contexts. This finding was replicated using a first-order co-occurrence algorithm. The computational estimates of geographical location as well as population were akin to human estimates. These findings show that language encodes geographical information that language users in turn may use in their understanding of language and the world.


title: Addiction to the Internet and Online Gaming

abstract: As computer and Internet use become a staple of everyday life, the potential for overuse is introduced, which may lead to addiction. Research on Internet addiction has shown that users can become addicted to it. Addiction to the Internet shares some of the negative aspects of substance addiction and has been shown to lead to consequences such as failing school, family, and relationship problems.


title: PSYCHOTHERAPIST MINDFULNESS AND THE PSYCHOTHERAPY PROCESS

abstract: A psychotherapist’s ability to relate to his or her patients is essential for decreasing patient suffering and promoting patient growth. However, the psychotherapy field has identified few effective means for training psychotherapists in this ability. In this conceptual article, we propose that mindfulness practice may be a means for training psychotherapists to better relate to their patients. We posit that mindfulness is a means of self-attunement that increases one’s ability to attune to others (in this case, patients) and that this interpersonal attunement ultimately helps patients achieve greater self-attunement that, in turn, fosters decreased symptom severity, greater well-being, and better interpersonal relationships.


title: Optimal Design and Control of a Wheel Motor for Electric Passenger Cars

abstract: An optimal design and control technology of a wheel motor for electric passenger cars is proposed. The objectives are to maximize the driving torque, efficiency, rated speed, and to minimize weight of the motor under various constraints of size, materials, and power sources. A sandwich-type of disc motor is then obtained with a rotor embedded with NdFeB magnets and two plates of stators. The optimal driving current waveform is proved to be the same as that of the back electromotive force. This dedicated wheel motor is applicable to pure or hybrid electric vehicles as a promising solution to the direct-driven electric vehicle.


title: CMU OAQA at TREC 2015 LiveQA: Discovering the Right Answer with Clues

abstract: Abstract : In this paper, we present CMUs automatic, web-based, real-time question answering (QA) system that was evaluated in the TREC 2015 LiveQA Challenge.This system answers real-user questions freshly submitted to the Yahoo! Answers website that have not been previously answered by humans. Given the title and body of the question, we generated multiple sets of keyword queries and retrieved a collection of web pages based on those queries. Then we extracted answer candidates from web pages in the form of answer passages and their associated clue. Finally, we combined both IR- and NLP-based relevance models to rank and select answer candidates. In the TREC 2015 LiveQA evaluations, human assessors gave our system an average score of 1.081 on a three-point scale, the highest average score achieved by a system in the competition (the second-best score was .677, and the average score was .465 for the 21 systems evaluated).


title: Ascertaining the validity of individual protocols from Web-based personality inventories

abstract: Abstract The research described in this article estimated the relative incidence of protocols invalidated by linguistic incompetence, inattentiveness, and intentional misrepresentation in Web-based versus paper-and-pencil personality measures. Estimates of protocol invalidity were derived from a sample of 23,994 protocols produced by individuals who completed an on-line version of the 300-item IPIP representation of the NEO-PI-R ( Goldberg, 1999 ). Approximately 3.8% of the protocols were judged to be products of repeat participants, many of whom apparently resubmitted after changing some of their answers. Among non-duplicate protocols, about 3.5% came from individuals who apparently selected a response option repeatedly without reading the item, compared to .9% in a sample of paper-and-pencil protocols. The missing response rate was 1.2%, which is 2–10 times higher than the rate found in several samples of paper-and-pencil inventories of comparable length. Two measures of response consistency indicated that perhaps 1% of the protocols were invalid due to linguistic incompetence or inattentive responding, but that Web participants were as consistent as individuals responding to a paper-and-pencil inventory. Inconsistency did not affect factorial structure and was found to be related positively to neuroticism and negatively to openness to experience. Intentional misrepresentation was not studied directly, but arguments for a low incidence of misrepresentation are presented. Methods for preventing, detecting, and handling invalid response patterns are discussed. Suggested for future research are studies that assess the moderating effects of linguistic incompetence, inattentiveness, and intentional misrepresentation on agreement between self-report and acquaintance judgments about personality.


title: Algorithm for Face Matching Using Normalized Cross-Correlation

abstract:  Abstract— Face matching is the most important and crucial procedure in face recognition. It is difficult to achieve robust face matching under a wide variety of different image capturing conditions, such as lighting changes, head-pose or view-angle variations, expression variations, etc. Robust face matching is essential to the development of an illumination insensitive face recognition system. This paper proposes a face matching algorithm that allows a template called extracted face of person which is the Region of Interest from one image and start search for matching with the different image of same person taken at different times, from different viewpoints, or by different sensors using Normalized Cross-Correlation (NCC). The algorithm is implemented in MATLAB. The experimental results show that developed algorithm is robust for similarity measure.


title: An Inquiry-based Mobile Learning Approach to Enhancing Social Science Learning Effectiveness

abstract: Introduction Mobile learning entails the kind of learning in which learners use mobile devices with digital content inside, to learn in "anytime, anywhere" situations. Such devices include PDAs (Personal Digital Assistants), laptop computers, cellular phones with wireless communication capabilities, as well as customized hardware (Liu & Hwang, 2010; Shih, Chu, Hwang, & Kinshuk, in press). To avoid students aimlessly wandering around, instructors need to carefully arrange the learning environment and design an interactive learning model, along with meaningful learning content provided in time. In the past decade, mobile learning has been used in a variety of educational fields in both informal and formal settings, such as bird-watching (e.g. Chen, Kao, & Sheu, 2003), chemistry experiments (e.g. Hwang et al., 2009), math (e.g. Zurita & Nussbaum, 2004), language learning (e.g. Cui & Bull, 2005), environmental investigation (e.g. Rogers et al., 2005; Lai et al., 2007; Chu, Hwang, Tsai, & Tseng, 2010), and butterfly ecology learning (e.g. Chu et al., 2008; Hwang, Kuo, Yin, & Chuang, 2010; Hwang, Shih, & Chu, in press). Most of the existing research has been carried out for science, language, or technique-oriented content that trains students to master certain structural or systematic knowledge or skills, while the use of mobile learning technology for social science learning is seldom reported. Moreover, researchers have indicated that, although mobile learning seems to be innovative and interesting to the students, their learning performance could be disappointing without proper learning strategies or tools (Chu et al., 2010; Hwang, Chu, Shih, Huang, & Tsai, 2010). Unfortunately, so far few scholars have applied mobile learning practices in the social science subjects, not to mention proposing effective strategies or tools to facilitate social science learning. To cope with this problem, this research aims to develop a mobile learning system for supporting exploration activities in authentic learning environments. Moreover, to evaluate the effectiveness of this innovative approach, a mobile learning activity for a social science course is conducted by guiding elementary students in southern Taiwan to learn in a local temple with PDAs. The project is targeted, as social science, to include geographical, historical, and cultural issues since learning about the temple in the students' hometown requires overall content learning about these aspects. The students are situated in a learning scenario that combines both the real world (a local temple) and the virtual world (the PDA learning system with access to digital materials) to extend their learning experience. A series of educational activities between the field and the digital system are designed to demonstrate the practices of mobile learning, which emphasizes learning that happens close to real life. To achieve this objective, an inquiry-based approach is proposed for developing the mobile learning system to facilitate students' field studies; in the meantime, the cognitive loads of the students were taken into account when designing the user interface. Furthermore, both qualitative and quantitative analysis methods were used to evaluate the learning performance of the students. Literature Review Inquiry-based learning Inquiry-based learning is a concept which encourages teachers to allow learners to get in touch with authentic situations, and to explore and solve problems that are analogs to real life (Feletti, 1993; Li & Lim, 2008). By means of exploration, investigation, and observation, students become involved in more rigorous social interactions as well as higher level thinking. In inquiries, students not only develop a deeper understanding of the subject, but also 'learn how to learn' (Price, 2001). Lim (2004) stated that online inquiry learning ensures students' positive participation, on-task behavior, and rich collaboration, as well as empowering students' ownership and self-directed learning by increasing their involvement and responsibility for their own learning. …


title: Security Games Applied to Real-World: Research Contributions and Challenges

abstract: The goal of this chapter is to introduce a challenging real-world problem for researchers in multiagent systems and beyond, where our collective efforts may have a significant impact on activities in the real-world. The challenge is in applying game theory for security: our goal is not only to introduce the problem, but also to provide exemplars of initial successes of deployed systems in this problem arena. Furthermore, we present key ideas and algorithms for solving and understanding the characteristics large-scale real-world security games, and then present some key open research challenges in this area.


title: Dynamic topic models

abstract: A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics. Variational approximations based on Kalman filters and nonparametric wavelet regression are developed to carry out approximate posterior inference over the latent topics. In addition to giving quantitative, predictive models of a sequential corpus, dynamic topic models provide a qualitative window into the contents of a large document collection. The models are demonstrated by analyzing the OCR'ed archives of the journal Science from 1880 through 2000.


title: Dementia: timely diagnosis and early intervention

abstract: #### The bottom line ::: ::: Dementia describes a clinical syndrome that encompasses difficulties in memory, language, and behaviour that leads to impairments in activities of daily living. Alzheimer’s disease is the most common subtype of dementia, followed by vascular dementia, mixed dementia, and dementia with Lewy bodies. Because the global population is rapidly ageing, dementia has become a concern worldwide1; the illness places considerable burden on individuals and their families and also on health and social care provision. ::: ::: #### Sources and selection criteria ::: ::: We searched for articles through Medline, PubMed, and the Cochrane database of systematic reviews from January 2006 to December 2014—the period after publication of the current UK national dementia guidance9—using the search terms “dementia”, “Alzheimer’s”, “carer”, and “caregiver”. Additional searches were carried out for specific subsections—for example, “pharmacologic treatment” and “non-pharmacologic interventions/strategies/treatment”. Where possible, we focused on systematic reviews, meta-analyses, and high quality randomised controlled trials. We included only articles in English and excluded those published in non-peer reviewed journals. Recommendations in this review are derived from the most recent international and UK national …


title: Electromagnetic Performance of Novel Synchronous Machines With Permanent Magnets in Stator Yoke

abstract: Novel synchronous machines with doubly salient structure and permanent magnets (PMs) in stator yoke have been developed in this paper. The stator is constituted by T-shaped lamination segments sandwiched with circumferentially magnetized PMs with alternate polarity, while the rotor is identical to that of switched reluctance machines (SRMs). The stator pole number is multiples of six, which is the number of stator poles in a unit machine. Similar to variable flux reluctance machines (VFRMs), the rotor pole numbers in the novel machines are not restricted to those in SRMs. When the stator and rotor pole numbers differ by one (or the number of multiples), the novel synchronous machines show sinusoidal bipolar phase flux linkage and back electromotive force (EMF), which make the machines suitable for brushless ac operation. Moreover, two prototype machines with six-pole stator and five-pole, seven-pole rotors are designed and optimized by 2-D finite element analysis. It shows that, compared with VFRMs, the novel machines can produce ~70 % higher torque density with the same copper loss and machine size. Meanwhile, the proposed machines have negligible reluctance torque due to very low saliency ratio. Experimental results of back EFM, cogging torque, and average torque on the prototypes are provided to validate the analysis.


title: Fuzzy Computational Models of Trust and Distrust for Enhanced Recommendations

abstract: Trust models have received considerable attention in the recent past and have been employed in many of today's most successful recommender systems (RSs) for alleviating sparsity by enhancing their interuser connectivity obtained from historical preference data and to address the cold start problem. However, the incorporation of distrust in addition to trust for improved recommendations has not been analyzed fully because of the absence of publically available data sets containing gradual information about trust and distrust concepts. Our work in this paper is an attempt toward introducing recommendation strategies exploiting both the trust and the distrust in the RSs to further enhance their quality of recommendations through fuzzy computational models for trust and distrust. Since trust and distrust concepts are gradual phenomenon, therefore these can be represented more naturally by fuzzy logic using linguistic expressions. The contributions of this paper are three fold: First, we propose fuzzy computational models for both the trust and distrust concepts through similarity as well as knowledge factors based on linguistic expressions. Second, we suggest appropriate propagation and aggregation operators to deal with the data sparsity. Finally, we present a comparative analysis of proposed recommendation strategies utilizing both the trust and distrust concepts.


title: SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels

abstract: We present SOSELETO (SOurce SELEction for Target Optimization), a new method for exploiting a source dataset to solve a classification problem on a target dataset. SOSELETO is based on the following simple intuition: some source examples are more informative than others for the target problem. To capture this intuition, source samples are each given weights; these weights are solved for jointly with the source and target classification problems via a bilevel optimization scheme. The target therefore gets to choose the source samples which are most informative for its own classification task. Furthermore, the bilevel nature of the optimization acts as a kind of regularization on the target, mitigating overfitting. SOSELETO may be applied to both classic transfer learning, as well as the problem of training on datasets with noisy labels; we show state of the art results on both of these problems.


title: Guillain-Barre Syndrome

abstract: Guillain-Barre syndrome consists of a group of neuropathic conditions characterized by progressive weakness and diminished or absent myotatic reflexes. The estimated annual incidence in the United States is 1.65 to 1.79 per 100,000 persons. Guillain-Barre syndrome is believed to result from an aberrant immune response that attacks nerve tissue. This response may be triggered by surgery, immunizations, or infections. The most common form of the disease, acute inflammatory demyelinating polyradiculoneuropathy, presents as progressive motor weakness, usually beginning in the legs and advancing proximally. Symptoms typically peak within four weeks, then plateau before resolving. More than one-half of patients experience severe pain, and about two-thirds have autonomic symptoms, such as cardiac arrhythmias, blood pressure instability, or urinary retention. Advancing symptoms may compromise respiration and vital functions. Diagnosis is based on clinical features, cerebrospinal fluid testing, and nerve conduction studies. Cerebrospinal fluid testing shows increased protein levels but a normal white blood cell count. Nerve conduction studies show a slowing, or possible blockage, of conduction. Patients should be hospitalized for multidisciplinary supportive care and disease-modifying therapy. Supportive therapy includes controlling pain with nonsteroidal anti-inflammatory drugs, carbamazepine, or gabapentin; monitoring for respiratory and autonomic complications; and preventing venous thrombosis, skin breakdown, and deconditioning. Plasma exchange therapy has been shown to improve short-term and long-term outcomes, and intravenous immune globulin has been shown to hasten recovery in adults and children. Other therapies, including corticosteroids, have not demonstrated benefit. About 3 percent of patients with Guillain-Barre syndrome die. Neurologic problems persist in up to 20 percent of patients with the disease, and one-half of these patients are severely disabled.


title: Towards Massively Multi-user Augmented Reality on Handheld Devices

abstract: Augmented Reality (AR) can naturally complement mobile computing on wearable devices by providing an intuitive interface to a three-dimensional information space embedded within physical reality. Unfortunately, current wearable AR systems are relatively complex, expensive, fragile and heavy, rendering them unfit for large-scale deployment involving untrained users outside constrained laboratory environments. Consequently, the scale of collaborative multi-user experiments have not yet exceeded a handful of participants. In this paper, we present a system architecture for interactive, infrastructure-independent multi-user AR applications running on off-the-shelf handheld devices. We implemented a four-user interactive game installation as an evaluation setup to encourage playful engagement of participants in a cooperative task. Over the course of five weeks, more than five thousand visitors from a wide range of professional and socio-demographic backgrounds interacted with our system at four different locations.


title: Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped (RDF) Data

abstract: Keyword queries enjoy widespread usage as they represent an intuitive way of specifying information needs. Recently, answering keyword queries on graph-structured data has emerged as an important research topic. The prevalent approaches build on dedicated indexing techniques as well as search algorithms aiming at finding substructures that connect the data elements matching the keywords. In this paper, we introduce a novel keyword search paradigm for graph-structured data, focusing in particular on the RDF data model. Instead of computing answers directly as in previous approaches, we first compute queries from the keywords, allowing the user to choose the appropriate query, and finally, process the query using the underlying database engine. Thereby, the full range of database optimization techniques can be leveraged for query processing. For the computation of queries, we propose a novel algorithm for the exploration of top-k matching subgraphs. While related techniques search the best answer trees, our algorithm is guaranteed to compute all k subgraphs with lowest costs, including cyclic graphs. By performing exploration only on a summary data structure derived from the data graph, we achieve promising performance improvements compared to other approaches.


title: Genital Dermatology Atlas

abstract: Genital Anatomy ... 1 Ginat W. Mirowski and Libby Edwards Diagnostic and Therapeutic Procedures ... 9 Ginat W. Mirowski and Libby Edwards Red Plaques and Patches ... 18 Monique Pelisse and Libby Edwards Red Papules and Nodules ... 45 Christopher B. Bunker and Richard Staughton Yellow and Pustular Lesions ... 64 Betty Bellman Erosive and Vesiculobullous Diseases ... 81 Jenny Powell, Pauline Marren, and Fenella Wojnarowska Ulcers ... 117 Ted Rosen and Tricia J. Brown White Lesions ... 131 C. Marjorie Ridley and Sallie M. Neill Skin-Colored Lesions ... 149 Benjamin K. Fisher and Lynette J. Margesson Brown Lesions ... 170 Marni C. Wiseman and Erika Klemperer Genital Dermatology in the Immunosuppressed Patient ... 186 Jennifer L. Helton Pediatric Genital Disease ... 202 Annette M. Wagner Vaginitis and Balanitis ... 227 Libby Edwards Subject Index ... 245


title: NOVEL DRUGS FOR PARKINSON'S DISEASE

abstract: Therapeutic interventions can be divided into four broad classes: symptomatic treatments, which simply reduce or mask signs and symptoms; protective therapies, which retard or halt the underlying pathologic processes; curative strategies, in which normal tissue function is restored; and preventive interventions, in which causative risk factors are identified and eliminated. These four types of interventions are intentionally listed in this order, starting with the most fundamental to the most advanced. Symptomatic remedies represent the minimum in therapeutic success, whereas disease prevention is the therapeutic ideal. The article reviews ongoing efforts to perfect or develop new treatment strategies for Parkinson's disease (PD) within each class.


title: A 90-W Peak Power GaN Outphasing Amplifier With Optimum Input Signal Conditioning

abstract: A 90-W peak-power 2.14-GHz improved GaN outphasing amplifier with 50.5% average efficiency for wideband code division multiple access (W-CDMA) signals is presented. Independent control of the branch amplifiers by two in-phase/quadrature modulators enables optimum outphasing and input power leveling, yielding significant improvements in gain, efficiency, and linearity. In deep-power backoff operation, the outphasing angle of the branch amplifiers is kept constant below a certain power level. This results in class-B operation for the very low output power levels, yielding less reactive loading of the output stages, and therefore, improved efficiency in power backoff operation compared to the classical outphasing amplifiers. Based on these principles, the optimum design parameters and input signal conditioning are discussed. The resulting theoretical maximum achievable average efficiency for W-CDMA signals is presented. Experimental results support the foregoing theory and show high efficiency over a large bandwidth, while meeting the linearity specifications using low-cost low-complexity memoryless pre-distortion. These properties make this amplifier concept an interesting candidate for future multiband base-station implementations.


title: Large-scale model of mammalian thalamocortical systems

abstract: The understanding of the structural and dynamic complexity of mammalian brains is greatly facilitated by computer simulations. We present here a detailed large-scale thalamocortical model based on experimental measures in several mammalian species. The model spans three anatomical scales. (i) It is based on global (white-matter) thalamocortical anatomy obtained by means of diffusion tensor imaging (DTI) of a human brain. (ii) It includes multiple thalamic nuclei and six-layered cortical microcircuitry based on in vitro labeling and three-dimensional reconstruction of single neurons of cat visual cortex. (iii) It has 22 basic types of neurons with appropriate laminar distribution of their branching dendritic trees. The model simulates one million multicompartmental spiking neurons calibrated to reproduce known types of responses recorded in vitro in rats. It has almost half a billion synapses with appropriate receptor kinetics, short-term plasticity, and long-term dendritic spike-timing-dependent synaptic plasticity (dendritic STDP). The model exhibits behavioral regimes of normal brain activity that were not explicitly built-in but emerged spontaneously as the result of interactions among anatomical and dynamic processes. We describe spontaneous activity, sensitivity to changes in individual neurons, emergence of waves and rhythms, and functional connectivity on different scales.


title: Extraction of Heart Rate Variability from Smartphone Photoplethysmograms

abstract: Heart rate variability (HRV) is a useful clinical tool for autonomic function assessment and cardiovascular diseases diagnosis. It is traditionally calculated from a dedicated medical electrocardiograph (ECG). In this paper, we demonstrate that HRV can also be extracted from photoplethysmograms (PPG) obtained by the camera of a smartphone. Sixteen HRV parameters, including time-domain, frequency-domain, and nonlinear parameters, were calculated from PPG captured by a smartphone for 30 healthy subjects and were compared with those derived from ECG. The statistical results showed that 14 parameters (AVNN, SDNN, CV, RMSSD, SDSD, TP, VLF, LF, HF, LF/HF, nLF, nHF, SD1, and SD2) from PPG were highly correlated (r > 0.7, P < 0.001) with those from ECG, and 7 parameters (AVNN, TP, VLF, LF, HF, nLF, and nHF) from PPG were in good agreement with those from ECG within the acceptable limits. In addition, five different algorithms to detect the characteristic points of PPG wave were also investigated: peak point (PP), valley point (VP), maximum first derivative (M1D), maximum second derivative (M2D), and tangent intersection (TI). The results showed that M2D and TI algorithms had the best performance. These results suggest that the smartphone might be used for HRV measurement.


title: Graph Signal Denoising via Trilateral Filter on Graph Spectral Domain

abstract: This paper presents a graph signal denoising method with the trilateral filter defined in the graph spectral domain. The original trilateral filter (TF) is a data-dependent filter that is widely used as an edge-preserving smoothing method for image processing. However, because of the data-dependency, one cannot provide its frequency domain representation. To overcome this problem, we establish the graph spectral domain representation of the data-dependent filter, i.e., a spectral graph TF (SGTF). This representation enables us to design an effective graph signal denoising filter with a Tikhonov regularization. Moreover, for the proposed graph denoising filter, we provide a parameter optimization technique to search for a regularization parameter that approximately minimizes the mean squared error w.r.t. the unknown graph signal of interest. Comprehensive experimental results validate our graph signal processing-based approach for images and graph signals.


title: Multi-Watt Wideband MMICs in GaN and GaAs

abstract: A comparison is presented of 4 to 18 GHz MMIC power amplifiers implemented in AlGaN-GaN HEMT and GaAs PHEMT with common circuit design technology. Both GaN and GaAs MMICs were designed as non-uniform distributed power amplifiers and achieved approximately 4 Watts over the band. The circuit complexity of the GaAs circuit is much greater than for GaN, as shown by the relative transistor output peripheries of 14.4 mm and 2 mm. The authors believe that both the GaN and GaAs MMICs have higher power than any published result of comparable bandwidth.


title: Homomorphic filtering techniques for WCE image enhancement

abstract: This paper reveals that the Homomorphic filtering technique for contrast enhancement of wireless capsule endoscopy images. We investigate the DFT and DWT based Homomorphic filtering method employ on the contrast space to enhance the capsule endoscopy images. The experimental results show that this method can provide better results of the wireless capsule endoscopy images than the Perona-Malik diffusion (PM) method and the contrast-limited adaptive histogram equalization (CLAHE) so as to assist the inspection of WCE images.


Design of microstrip patch antenna for WLAN applications using Back to Back connection of Two E-Shapes.


title: Evaluating Bluetooth Low Energy Suitability for Time-Critical Industrial IoT Applications

abstract: In recent years, integration of wireless sensor networks in industrial environments has greatly increased. With this trend, new fields such as industrial IoT have arisen, which in turn have opened the doors to new possibilities that are shaping the future of industrial automation. In contrast to regular wireless networks, however, industrial applications of WSN are characterized for being time-critical systems with highly stringent requirements that challenge all available technologies. Because of its ultra-low energy properties, compatibility with most mobile units, reduced production costs, robustness and high throughput, Bluetooth low energy (BLE) is a potential candidate for these settings. This article explores the potential of BLE of meeting the real-time demands found in the domain of industrial process automation and industrial IoT. In order to evaluate the suitability of the protocol for these scenarios, the effect of adaptations in the retransmission scheme on the reliability and timeliness performance are thoroughly studied. Three retransmission schemes are evaluated and simulation results proved that by optimally modifying the BLE retransmission model, a maximum delay below 46 ms and a packet loss rate in the order of $$10^{-5}$$ ::: can be obtained, enabling BLE to fulfill the requirements of even the most demanding cases within the considered range of applications.


title: Quadratic Unconstrained Binary Optimization Problem Preprocessing: Theory and Empirical Analysis

abstract: The Quadratic Unconstrained Binary Optimization problem (QUBO) has become a unifying model for representing a wide range of combinatorial optimization problems, and for linking a variety of disciplines that face these problems. A new class of quantum annealing computer that maps QUBO onto a physical qubit network structure with specific size and edge density restrictions is generating a growing interest in ways to transform the underlying QUBO structure into an equivalent graph having fewer nodes and edges. In this article, we present rules for reducing the size of the QUBO matrix by identifying variables whose value at optimality can be predetermined. We verify that the reductions improve both solution quality and time to solution and, in the case of metaheuristic methods where optimal solutions cannot be guaranteed, the quality of solutions obtained within reasonable time limits. We discuss the general QUBO structural characteristics that can take advantage of these reduction techniques and perform careful experimental design and analysis to identify and quantify the specific characteristics most affecting reduction. The rules make it possible to dramatically improve solution times on a new set of problems using both the exact Cplex solver and a tabu search metaheuristic. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 70(2), 79–97 2017


title: Silver-Russell Syndrome – Part I: Clinical Characteristics and Genetic Background

abstract: Silver-Russell syndrome (SRS) is a rare, clinically and genetically heterogeneous entity, caused by (epi)genetic alternations. It is characterized by prenatal and postnatal growth retardation, relative macrocephaly, the triangular face and body asymmetry. About 40-60% of cases are caused by hypomethylation of 11p.15.5 Imprinting Centre Region 1 (ICR1) on the paternal chromosome, and maternal uniparental disomy for chromosome 7 (UPD(7)mat) is found in 5-10% of cases. There are suggested correlations between genotype and the phenotype. Psychomotor development may be delayed, usually mildly, with school difficulties and speech delay more common in patients with UPD(7)mat. Children with 11p15 hypomethylation are shorter and lighter at birth in comparison to children with UPD(7)mat, however further deceleration tends to be more apparent in the latter group. The onset of puberty tends to occur early, with acceleration of bone age, resulting in less apparent growth spurt. Failure to thrive and feeding problems are characteristic for the infant period, and further development of a child may be conditioned by additional congenital defects.


title: SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging

abstract: Convolutional Neural Networks (CNNs) are typically trained in the RGB color space. However, in medical imaging, we believe that pixel stain quantities offer a fundamental view of the interaction between tissues and stain chemicals. Since the optical density (OD) colorspace allows to compute pixel stain quantities from pixel RGB intensities using the Beer-Lambert’s law, we propose a stain deconvolutional layer, hereby named as SD-Layer, affixed at the front of CNN that performs two functions: (1) it transforms the input RGB microscopic images to Optical Density (OD) space and (2) this layer deconvolves OD image with the stain basis learned through backpropagation and provides tissue-specific stain absorption quantities as input to the following CNN layers. With the introduction of only nine additional learnable parameters in the proposed SD-Layer, we obtain a considerably improved performance on two standard CNN architectures: AlexNet and T-CNN. Using the T-CNN architecture prefixed with the proposed SD-Layer, we obtain 5-fold cross-validation accuracy of 93.2% in the problem of differentiating malignant immature White Blood Cells (WBCs) from normal immature WBCs for cancer detection.


title: How to generate and exchange secrets

abstract: In this paper we introduce a new tool for controlling the knowledge transfer process in cryptographic protocol design. It is applied to solve a general class of problems which include most of the two-party cryptographic problems in the literature. Specifically, we show how two parties A and B can interactively generate a random integer N = p¿q such that its secret, i.e., the prime factors (p, q), is hidden from either party individually but is recoverable jointly if desired. This can be utilized to give a protocol for two parties with private values i and j to compute any polynomially computable functions f(i,j) and g(i,j) with minimal knowledge transfer and a strong fairness property. As a special case, A and B can exchange a pair of secrets sA, sB, e.g. the factorization of an integer and a Hamiltonian circuit in a graph, in such a way that sA becomes computable by B when and only when sB becomes computable by A. All these results are proved assuming only that the problem of factoring large intergers is computationally intractable.


Multilevel Inverter Topology Survey Master of Science Thesis in Electric Power Engineering


Application of hierarchical linear models to assessing change.


title: Automatic movie ratings prediction using machine learning

abstract: Recommendation systems that model users and their interests are often used to improve various user services. Such systems are usually based on automatic prediction of user ratings of the items provided by the service. This paper presents an overview of some of the methods for automatic ratings prediction in the domain of movie ratings. The chosen methods are based on various approaches described in related papers. During the prediction process both the user and item features can be used. For the purpose of this paper, data was gathered from the publicly available movie database IMDb. The paper encompasses the implementation of the chosen methods and their evaluation using the gathered data. The results show an improvement in comparison to the chosen baseline methods.


title: Rational use of cognitive resources: levels of analysis between the computational and the algorithmic.

abstract: Marr’s levels of analysis—computational, algorithmic, and implementation—have served cognitive science well over the last 30 years. But the recent increase in the popularity of the computational level raises a new challenge: How do we begin to relate models at different levels of analysis? We propose that it is possible to define levels of analysis that lie between the computational and the algorithmic, providing a way to build a bridge between computational- and algorithmic-level models. The key idea is to push the notion of rationality, often used in defining computational-level models, deeper toward the algorithmic level. We offer a simple recipe for reverse-engineering the mind’s cognitive strategies by deriving optimal algorithms for a series of increasingly more realistic abstract computational architectures, which we call “resource-rational analysis.”


title: Learning Sparsely Used Overcomplete Dictionaries

abstract: We consider the problem of learning sparsely used overcomplete dictionaries, where each observation is a sparse combination of elements from an unknown overcomplete dictionary. We establish exact recovery when the dictionary elements are mutually incoherent. Our method consists of a clustering-based initialization step, which provides an a pproximate estimate of the true dictionary with guaranteed accuracy. This estimate is then refined via a n iterative algorithm with the following alternating steps: 1) estimation of the dictionary coef ficients for each observation through `1 minimization, given the dictionary estimate, and 2) estimation of the dictionary elements through least squares, given the coefficient estimates. We establis h that, under a set of sufficient conditions, our method converges at a linear rate to the true dictionary as well as the true coefficients for each observation.


title: Building Cloud-based Biometric Services

abstract: Over the next few years the amount of biometric data being at the disposal of various agencies and ::: authentication service providers is expected to grow significantly. Such quantities of data require not ::: only enormous amounts of storage but unprecedented processing power as well. To be able to face this ::: future challenges more and more people are looking towards cloud computing, which can address these ::: challenges quite effectively with its seemingly unlimited storage capacity, rapid data distribution and ::: parallel processing capabilities. Since the available literature on how to implement cloud-based ::: biometric services is extremely scarce, this paper capitalizes on the most important challenges ::: encountered during the development work on biometric services, presents the most important standards ::: and recommendations pertaining to biometric services in the cloud and ultimately, elaborates on the ::: potential value of cloud-based biometric solutions by presenting a few existing (commercial) examples. ::: In the final part of the paper, a case study on fingerprint recognition in the cloud and its integration into ::: the e-learning environment Moodle is presented.


title: FollowNet: Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learning

abstract: Understanding and following directions provided by humans can enable robots to navigate effectively in unknown situations. We present FollowNet, an end-to-end differentiable neural architecture for learning multi-modal navigation policies. FollowNet maps natural language instructions as well as visual and depth inputs to locomotion primitives. FollowNet processes instructions using an attention mechanism conditioned on its visual and depth input to focus on the relevant parts of the command while performing the navigation task. Deep reinforcement learning (RL) a sparse reward learns simultaneously the state representation, the attention function, and control policies. We evaluate our agent on a dataset of complex natural language directions that guide the agent through a rich and realistic dataset of simulated homes. We show that the FollowNet agent learns to execute previously unseen instructions described with a similar vocabulary, and successfully navigates along paths not encountered during training. The agent shows 30% improvement over a baseline model without the attention mechanism, with 52% success rate at novel instructions.


title: Youtube around the world: geographic popularity of videos

abstract: One of the most popular user activities on the Web is watching videos. Services like YouTube, Vimeo, and Hulu host and stream millions of videos, providing content that is on par with TV. While some of this content is popular all over the globe, some videos might be only watched in a confined, local region. In this work we study the relationship between popularity and locality of online YouTube videos. We investigate whether YouTube videos exhibit geographic locality of interest, with views arising from a confined spatial area rather than from a global one. Our analysis is done on a corpus of more than 20 millions YouTube videos, uploaded over one year from different regions. We find that about 50% of the videos have more than 70% of their views in a single region. By relating locality to viralness we show that social sharing generally widens the geographic reach of a video. If, however, a video cannot carry its social impulse over to other means of discovery, it gets stuck in a more confined geographic region. Finally, we analyze how the geographic properties of a video's views evolve on a daily basis during its lifetime, providing new insights on how the geographic reach of a video changes as its popularity peaks and then fades away. Our results demonstrate how, despite the global nature of the Web, online video consumption appears constrained by geographic locality of interest: this has a potential impact on a wide range of systems and applications, spanning from delivery networks to recommendation and discovery engines, providing new directions for future research.


title: HPCMalHunter: Behavioral malware detection using hardware performance counters and singular value decomposition

abstract: Malicious programs, also known as malware, often use code obfuscation techniques to make static analysis more difficult and to evade signature-based detection. To resolve this problem, various behavioral detection techniques have been proposed that focus on the run-time behaviors of programs in order to dynamically detect malicious ones. Most of these techniques describe the run-time behavior of a program on the basis of its data flow and/or its system call traces. Recent work in behavioral malware detection has shown promise in using hardware performance counters (HPCs), which are a set of special-purpose registers built into modern processors providing detailed information about hardware and software events. In this paper, we pursue this line of research by presenting HPCMalHunter, a novel approach for real-time behavioral malware detection. HPCMalHunter uses HPCs to collect a set of event vectors from the beginning of a program’s execution. It also uses the singular value decomposition (SVD) to reduce these event vectors and generate a behavioral vector for the program. By applying support vector machines (SVMs) to the feature vectors of different programs, it is able to identify malicious programs in real-time. Our results of experiments show that HPCMalHunter can detect malicious programs at the beginning of their execution with a high detection rate and a low false alarm rate. Keywords-behavioral malware detection; hardware-level detection; real-time detection; hardware performance counter; singular value decomposition


title: Waveform Simplification of Vehicle Acceleration Curve in Frontal Impact Test

abstract: In the frontal collision, the acceleration curve of the vehicle body is the most important factor on occupants protection, due to the characteristic of high frequency oscillation of crash curve, it is very difficult to be directly used to research on the relationships between crash pulse and occupants injury. In this paper, according to the characteristics of the vehicle frontal collision, the transmission path of the vehicle was divided into different stages and the acceleration curves were parametric modeled due to the different stages. The effects of two kinds of curves on the dummy injury were discussed by using the finite element model. The results show that the similarity between the simplified curve and the original curve was high, and the responses of dummies in the two curves were basically the same. The simplified curve can be used to replace the original curve for vehicle crash and dummy injury.


title: Power sandbox: power awareness redefined

abstract: Many apps benefit from knowing their power consumption and adapting their behaviors on the fly. To offer apps power knowledge at run time, an OS often meters system power and divides it among apps. Since the impacts of concurrent apps on system power are entangled, this approach not only makes it difficult to reason about power but also results in power side channels, a serious vulnerability. To this end, we introduce a new OS principal called power sandbox, which enables one app to observe the fine-grained power consumption of itself running in its vertical slice of the hardware/software stack. The observed power is insulated from the impacts of other apps. Our contribution is a set of lightweight kernel extensions that simultaneously i) enforce the power sandbox boundaries and ii) confine entailed performance loss to the sandboxed apps. Our experiences on two embedded platforms show that power sandboxes simplify reasoning about power, maintain fairness among apps, and minimize power side channels, thus facilitating construction of power-aware apps.


Realizing Strategic Value Through Center-Edge Digital Transformation in Consumer-Centric Industries


title: Predictive Duty Cycle Control of Three-Phase Active-Front-End Rectifiers

abstract: This paper proposed an online optimizing duty cycle control approach for three-phase active-front-end rectifiers, aiming to obtain the optimal control actions under different operating conditions. Similar to finite-control set model predictive control strategy, a cost function previously constructed based on the desired control performance is adopted here, which is essential for the solving process of the optimizing problem. On the other hand, differently, with respect to the proposed strategy, duty cycle signals are optimized, instead of possible switching states. The determination of optimal duty cycles is made by predicting the effect of duty cycles on instantaneous current variations and minimizing the cost function. Due to the adoption of behavior prediction, the proposed controller inherits the excellent dynamic characteristics of predictive controllers. Moreover, the application of optimal duty cycles determined by cost function minimization automatically ensures optimum operations of converters within each sampling period. Improved transient and steady-state features of the proposed strategy are confirmed by experimental validations and in-depth comparisons with linear controllers, deadbeat predictive controllers, and finite-control set model predictive control strategies.


title: Cognitive architecture and instructional design

abstract: Cognitive load theory has been designed to provide guidelines intended to assist in the presentation of information in a manner that encourages learner activities that optimize intellectual performance. The theory assumes a limited capacity working memory that includes partially independent subcomponents to deal with auditory/verbal material and visual/2- or 3-dimensional information as well as an effectively unlimited long-term memory, holding schemas that vary in their degree of automation. These structures and functions of human cognitive architecture have been used to design a variety of novel instructional procedures based on the assumption that working memory load should be reduced and schema construction encouraged. This paper reviews the theory and the instructional designs generated by it.


title: Rollin' Justin - Mobile platform with variable base

abstract: Research on humanoid robots for use in servicing tasks, e.g. fetching and delivery, attracts steadily more interest. With “Rollin' Justin” a mobile robotic system and research platform is presented that allows sophisticated control algorithms and dexterous manipulation. This video gives an overview of the mobile humanoid robotic system “Rollin' Justin” with special emphasis on mechanical design features, control issues and high-level system capabilities such as human robot interaction.


title: The behaviour markup language: recent developments and challenges

abstract: Since the beginning of the SAIBA effort to unify key interfaces in the multi-modal behavior generation process, the Behavior Markup Language (BML) has both gained ground as an important component in many projects worldwide, and continues to undergo further refinement. This paper reports on the progress made in the last year in further developing BML. It discusses some of the key challenges identified that the effort is facing, and reviews a number of projects that already are making use of BML or support its use.


title: Relational stacked denoising autoencoder for tag recommendation

abstract: Tag recommendation has become one of the most important ways of organizing and indexing online resources like articles, movies, and music. Since tagging information is usually very sparse, effective learning of the content representation for these resources is crucial to accurate tag recommendation. Recently, models proposed for tag recommendation, such as collaborative topic regression and its variants, have demonstrated promising accuracy. However, a limitation of these models is that, by using topic models like latent Dirichlet allocation as the key component, the learned representation may not be compact and effective enough. Moreover, since relational data exist as an auxiliary data source in many applications, it is desirable to incorporate such data into tag recommendation models. In this paper, we start with a deep learning model called stacked denoising autoencoder (SDAE) in an attempt to learn more effective content representation. We propose a probabilistic formulation for SDAE and then extend it to a relational SDAE (RSDAE) model. RSDAE jointly performs deep representation learning and relational learning in a principled way under a probabilistic framework. Experiments conducted on three real datasets show that both learning more effective representation and learning from relational data are beneficial steps to take to advance the state of the art.


title: New word analogy corpus for exploring embeddings of Czech words

abstract: The word embedding methods have been proven to be very useful in many tasks of NLP (Natural Language Processing). Much has been investigated about word embeddings of English words and phrases, but only little attention has been dedicated to other languages.


Oral erythromycin relieves dysphagia in a patient with known oesophageal hypomotility disorder who has had a Nissen's fundoplication.


title: Comparison between MOEA/D and NSGA-II on the Multiobjective Travelling Salesman Problem

abstract: Most multiobjective evolutionary algorithms are based on Pareto dominance for measuring the quality of solutions during their search, among them NSGA-II is well-known. A very few algorithms are based on decomposition and implicitly or explicitly try to optimize aggregations of the objectives. MOEA/D is a very recent such an algorithm. One of the major advantages of MOEA/D is that it is very easy to design local search operator within it using well-developed single-objective optimization algorithms. This chapter compares the performance of MOEA/D and NSGA-II on the multiobjective travelling salesman problem and studies the effect of local search on the performance of MOEA/D.


title: A Comparative Study of Data Mining Process Models (KDD, CRISP-DM and SEMMA)

abstract: Data Mining is about analyzing the huge amount data and extracting of information from it for different purposes. From the last few years the field of Data Mining becomes prominent and makes huge growth. There are different standard models for data mining. All these models are defined in sequential steps. These steps help in implementing the data mining tasks. In this paper we will compare these models and give brief understanding about them.


title: Implicit and Intuitive Grasp Posture Control for Wearable Robotic Fingers: A Data-Driven Method Using Partial Least Squares

abstract: Functionality of a human hand can be augmented with wearable robotic fingers to enable grasping and manipulation of objects with a single hand. Such technology will have applications in manufacturing and construction, as well as health care. This paper presents a method for controlling extra robotic fingers, termed “Supernumerary Robotic Fingers (SR Fingers),” in coordination with human fingers to grasp diverse objects. Two hypotheses are proposed and verified through experiments. One is that humans prefer grasp posture of their fingers and that of the SR Fingers to be highly correlated when working together, which is represented with a few principal components, resembling grasp synergy in neuromotor control. The other hypothesis is that SR Finger posture can be controlled to coordinate with human finger posture via grasp synergy of the hybrid human–robotic hand. Partial least squares regression is used for predicting a desired posture of the SR Fingers from the measurement of human fingers. This method is implemented on a pair of wrist-mounted SR Fingers. Experiments demonstrate that the prototype SR Fingers can assist the human user in performing single-handed grasping tasks without requiring explicit commands.