title: Big Data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce

abstract: Abstract We live in a world were data are generated from a myriad of sources, and it is really cheap to collect and storage such data. However, the real benefit is not related to the data itself, but with the algorithms that are capable of processing such data in a tolerable elapse time, and to extract valuable knowledge from it. Therefore, the use of Big Data Analytics tools provide very significant advantages to both industry and academia. The MapReduce programming framework can be stressed as the main paradigm related with such tools. It is mainly identified by carrying out a distributed execution for the sake of providing a high degree of scalability, together with a fault-tolerant scheme. In every MapReduce algorithm, first local models are learned with a subset of the original data within the so-called Map tasks. Then, the Reduce task is devoted to fuse the partial outputs generated by each Map. The ways of designing such fusion of information/models may have a strong impact in the quality of the final system. In this work, we will enumerate and analyze two alternative methodologies that may be found both in the specialized literature and in standard Machine Learning libraries for Big Data. Our main objective is to provide an introduction of the characteristics of these methodologies, as well as giving some guidelines for the design of novel algorithms in this field of research. Finally, a short experimental study will allow us to contrast the scalability issues for each type of process fusion in MapReduce for Big Data Analytics.


title: Apprenticeship learning via inverse reinforcement learning

abstract: We consider learning in a Markov decision process where we are not explicitly given a reward function, but where instead we can observe an expert demonstrating the task that we want to learn to perform. This setting is useful in applications (such as the task of driving) where it may be difficult to write down an explicit reward function specifying exactly how different desiderata should be traded off. We think of the expert as trying to maximize a reward function that is expressible as a linear combination of known features, and give an algorithm for learning the task demonstrated by the expert. Our algorithm is based on using "inverse reinforcement learning" to try to recover the unknown reward function. We show that our algorithm terminates in a small number of iterations, and that even though we may never recover the expert's reward function, the policy output by the algorithm will attain performance close to that of the expert, where here performance is measured with respect to the expert's unknown reward function.


title: Evaluating implicit measures to improve web search

abstract: Of growing interest in the area of improving the search experience is the collection of implicit user behavior measures (implicit measures) as indications of user interest and user satisfaction. Rather than having to submit explicit user feedback, which can be costly in time and resources and alter the pattern of use within the search experience, some research has explored the collection of implicit measures as an efficient and useful alternative to collecting explicit measure of interest from users.This research article describes a recent study with two main objectives. The first was to test whether there is an association between explicit ratings of user satisfaction and implicit measures of user interest. The second was to understand what implicit measures were most strongly associated with user satisfaction. The domain of interest was Web search. We developed an instrumented browser to collect a variety of measures of user activity and also to ask for explicit judgments of the relevance of individual pages visited and entire search sessions. The data was collected in a workplace setting to improve the generalizability of the results.Results were analyzed using traditional methods (e.g., Bayesian modeling and decision trees) as well as a new usage behavior pattern analysis (“gene analysis”). We found that there was an association between implicit measures of user activity and the user's explicit satisfaction ratings. The best models for individual pages combined clickthrough, time spent on the search result page, and how a user exited a result or ended a search session (exit type/end action). Behavioral patterns (through the gene analysis) can also be used to predict user satisfaction for search sessions.


title: Online learning with kernels

abstract: Kernel-based algorithms such as support vector machines have achieved considerable success in various problems in batch setting, where all of the training data is available in advance. Support vector machines combine the so-called kernel trick with the large margin idea. There has been little use of these methods in an online setting suitable for real-time applications. In this paper, we consider online learning in a reproducing kernel Hilbert space. By considering classical stochastic gradient descent within a feature space and the use of some straightforward tricks, we develop simple and computationally efficient algorithms for a wide range of problems such as classification, regression, and novelty detection. In addition to allowing the exploitation of the kernel trick in an online setting, we examine the value of large margins for classification in the online setting with a drifting target. We derive worst-case loss bounds, and moreover, we show the convergence of the hypothesis to the minimizer of the regularized risk functional. We present some experimental results that support the theory as well as illustrating the power of the new algorithms for online novelty detection.


title: Short term re-identification of Automatic Teller Machine (ATM) users via face and body appearance features

abstract: This paper describes a novel biometric scenario, where a person is authenticated at an ATM, and has to be re-identified from a camera within a very short time period, under very challenging illumination and pose conditions, and using data from a single session. The application scenario is the automatic retraction of forgotten card or cash at an ATM, which happens frequently, and causes inconvenience to users and loss of profits for the banks. We propose a multimodal authentication system that operates under the constraints imposed by this applications scenario, and implement face recognition and color based body appearance recognition to create a system that improves ATM behavior in case of forgotten card or cash by re-identifying the user from an embedded ATM camera. We focus on the scenario and the platform, and report tests with the proposed system under challenging conditions, obtained from ATMs placed in the field.


title: Effect of Pencil Grasp on the Speed and Legibility of Handwriting in Children

abstract: OBJECTIVE. Pencil grasps other than the dynamic tripod may be functional for handwriting. This study examined the impact of grasp on handwriting speed and legibility. METHOD. We videotaped 120 typically developing fourth-grade students while they performed a writing task. We categorized the grasps they used and evaluated their writing for speed and legibility using a handwriting assessment. Using linear regression analysis, we examined the relationship between grasp and handwriting. RESULTS. We documented six categories of pencil grasp: four mature grasp patterns, one immature grasp pattern, and one alternating grasp pattern. Multiple linear regression results revealed no significant effect for mature grasp on either legibility or speed. CONCLUSION. Pencil grasp patterns did not influence handwriting speed or legibility in this sample of typically developing children. This finding adds to the mounting body of evidence that alternative grasps may be acceptable for fast and legible handwriting.


title: Bag-of-colors for improved image search

abstract: This paper investigates the use of color information when used within a state-of-the-art large scale image search system. We introduce a simple yet effective and efficient color signature generation procedure. It is used either to produce global or local descriptors. As a global descriptor, it outperforms several state-of-the-art color description methods, in particular the bag-of-words method based on color SIFT. As a local descriptor, our signature is used jointly with SIFT descriptors (no color) to provide complementary information. This significantly improves the recognition rate, outperforming the state of the art on two image search benchmarks. We provide an open source package of our signature (http://www.kooaba.com/en/learnmore/labs/).


title: Privacy and security for online social networks: challenges and opportunities

abstract: Online social networks such as Facebook, Myspace, and Twitter have experienced exponential growth in recent years. These OSNs offer attractive means of online social interactions and communications, but also raise privacy and security concerns. In this article we discuss the design issues for the security and privacy of OSNs. We find there are inherent design conflicts between these and the traditional design goals of OSNs such as usability and sociability. We present the unique security and privacy design challenges brought by the core functionalities of OSNs and highlight some opportunities of utilizing social network theory to mitigate these design conflicts.


title: SoK: Exploiting Network Printers

abstract: The idea of a paperless office has been dreamed of for more than three decades. However, nowadays printers are still one of the most essential devices for daily work and common Internet users. Instead of removing them, printers evolved from simple devices into complex network computer systems, installed directly into company networks, and carrying considerable confidential data in their print jobs. This makes them to an attractive attack target. In this paper we conduct a large scale analysis of printer attacks and systematize our knowledge by providing a general methodology for security analyses of printers. Based on our methodology, we implemented an open-source tool called PRinter Exploitation Toolkit (PRET). We used PRET to evaluate 20 printer models from different vendors and found all of them to be vulnerable to at least one of the tested attacks. These attacks included, for example, simple DoS attacks or skilled attacks, extracting print jobs and system files. On top of our systematic analysis we reveal novel insights that enable attacks from the Internet by using advanced cross-site printing techniques, combined with printer CORS spoofing. Finally, we show how to apply our attacks to systems beyond typical printers like Google Cloud Print or document processing websites.


title: Multimodal Human Activity Recognition for Industrial Manufacturing Processes in Robotic Workcells

abstract: We present an approach for monitoring and interpreting human activities based on a novel multimodal vision-based interface, aiming at improving the efficiency of human-robot interaction (HRI) in industrial environments. Multi-modality is an important concept in this design, where we combine inputs from several state-of-the-art sensors to provide a variety of information, e.g. skeleton and fingertip poses. Based on typical industrial workflows, we derived multiple levels of human activity labels, including large-scale activities (e.g. assembly) and simpler sub-activities (e.g. hand gestures), creating a duration- and complexity-based hierarchy. We train supervised generative classifiers for each activity level and combine the output of this stage with a trained Hierarchical Hidden Markov Model (HHMM), which models not only the temporal aspects between the activities on the same level, but also the hierarchical relationships between the levels.


Signal Processing Algorithm of STC Waveforms for the Phased array MIMO Radar : Overview on Target Localization


title: Amortised MAP Inference for Image Super-resolution

abstract: Image super-resolution (SR) is an underdetermined inverse problem, where a large number of plausible high resolution images can explain the same downsampled image. Most current single image SR methods use empirical risk minimisation, often with a pixel-wise mean squared error (MSE) loss. ::: However, the outputs from such methods tend to be blurry, over-smoothed and generally appear implausible. A more desirable approach would employ Maximum a Posteriori (MAP) inference, preferring solutions that always have a high probability under the image prior, and thus appear more plausible. Direct MAP estimation for SR is non-trivial, as it requires us to build a model for the image prior from samples. Here we introduce new methods for \emph{amortised MAP inference} whereby we calculate the MAP estimate directly using a convolutional neural network. We first introduce a novel neural network architecture that performs a projection to the affine subspace of valid SR solutions ensuring that the high resolution output of the network is always consistent with the low resolution input. We show that, using this architecture, the amortised MAP inference problem reduces to minimising the cross-entropy between two distributions, similar to training generative models. We propose three methods to solve this optimisation problem: (1) Generative Adversarial Networks (GAN) (2) denoiser-guided SR which backpropagates gradient-estimates from denoising to train the network, and (3) a baseline method using a maximum-likelihood-trained image prior. Our experiments show that the GAN based approach performs best on real image data. Lastly, we establish a connection between GANs and amortised variational inference as in e.g. variational autoencoders.


title: Bootstrapping Single-channel Source Separation via Unsupervised Spatial Clustering on Stereo Mixtures

abstract: Separating an audio scene into isolated sources is a fundamental problem in computer audition, analogous to image segmentation in visual scene analysis. Source separation systems based on deep learning are currently the most successful approaches for solving the underdetermined separation problem, where there are more sources than channels. Such systems are normally trained on sound mixtures where the ground truth decomposition is already known. In this work, we use an unsupervised spatial source separation on stereo mixtures which generates initial decompositions of mixtures to train a deep learning source separation model. These estimated decompositions vary greatly in quality across the training mixtures. To overcome this, we weight the data during training using a confidence measure that assesses which mixtures or parts of mixtures are well-separated by the unsupervised algorithm. Once trained, the model can be applied to separate single-channel mixtures, where no source direction information is available. The idea is to use simple, low-level processing to separate sources in an unsupervised fashion, identify easy conditions, and then use that knowledge to bootstrap a (self-)supervised source separation model for difficult conditions. We also explore using the two approaches in an ensemble.


title: Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network

abstract: Exploring contextual information in the local region is important for shape understanding and analysis. Existing studies often employ hand-crafted or explicit ways to encode contextual information of local regions. However, it is hard to capture fine-grained contextual information in hand-crafted or explicit manners, such as the correlation between different areas in a local region, which limits the discriminative ability of learned features. To resolve this issue, we propose a novel deep learning model for 3D point clouds, named Point2Sequence, to learn 3D shape features by capturing fine-grained contextual information in a novel implicit way. Point2Sequence employs a novel sequence learning model for point clouds to capture the correlations by aggregating multi-scale areas of each local region with attention. Specifically, Point2Sequence first learns the feature of each area scale in a local region. Then, it captures the correlation between area scales in the process of aggregating all area scales using a recurrent neural network (RNN) based encoder-decoder structure, where an attention mechanism is proposed to highlight the importance of different area scales. Experimental results show that Point2Sequence achieves state-of-the-art performance in shape classification and segmentation tasks.


title: Energy-Efficient Uplink Power Allocation in Multi-Cell MU-Massive-MIMO Systems

abstract: In this paper, we investigate a joint uplink pilot and data power allocation strategy for energy-efficient communications under pilot contamination in multi-cell multi-user massive multiple-input multiple-output (MU-Massive-MIMO) systems. Assuming the maximum-ratio combining detection and a large number of antennas at the base station, the system energy efficiency (EE) is maximized over the individual power assignment between pilot and data transmissions of each user, subject to the per-user signal to interference-plus-noise ratio and per-user power constraints. To decentralize the optimization, game theory has to be applied, in which each cell competes against neighboring cells to maximize its own EE via fractional programming. The existence and uniqueness of Nash equilibrium of the game are discussed. Numerical results of the proposed algorithm are compared with the uplink power control scheme, which illustrates the significant advantage in EE by applying the per-user power allocation in multi-cell MU-Massive-MIMO.


title: Adaptive Linked Data-Driven Web Components: Building Flexible and Reusable Semantic Web Interfaces

abstract: Due to the increasing amount of Linked Data openly published on the Web, user-facing Linked Data Applications LDAs are gaining momentum. One of the major entrance barriers for Web developers to contribute to this wave of LDAs is the required knowledge of Semantic Web SW technologies such as the RDF data model and SPARQL query language. This paper presents an adaptive component-based approach together with its open source implementation for creating flexible and reusable SW interfaces driven by Linked Data. Linked Data-driven LD-R Web components abstract the complexity of the underlying SW technologies in order to allow reuse of existing Web components in LDAs, enabling Web developers who are not experts in SW to develop interfaces that view, edit and browse Linked Data. In addition to the modularity provided by the LD-R components, the proposed RDF-based configuration method allows application assemblers to reshape their user interface for different use cases, by either reusing existing shared configurations or by creating their proprietary configurations.


title: Identifying the weaknesses of UML class diagrams during data model comprehension

abstract: In this paper we present an experiment and two replications aimed at comparing the support provided by ER and UML class diagrams during comprehension activities by focusing on the single building blocks of the two notations. This kind of analysis can be used to identify weakness in a notation and/or justify the need of preferring ER or UML for data modeling. The results reveal that UML class diagrams are generally more comprehensible than ER diagrams, even if the former has some weaknesses related to three building blocks, i.e., multivalue attribute, composite attribute, and weak entity. These findings suggest that a UML class diagram extension should be considered to overcome these weaknesses and improve the comprehensibility of the notation.


title: The heart communicates with the kidney exclusively through the guanylyl cyclase-A receptor: acute handling of sodium and water in response to volume expansion

abstract: Abstract ::: Disruption of guanylyl cyclase-A (GC-A) results in mice displaying an elevated blood pressure, which is not altered by high or low dietary salt. However, atrial natriuretic peptide (ANP), a proposed ligand for GC-A, has been suggested as critical for the maintenance of normal blood pressure during high salt intake. In this report, we show that infusion of ANP results in substantial natriuresis and diuresis in wild-type mice but fails to cause significant changes in sodium excretion or urine output in GC-A-deficient mice. ANP, therefore, appears to signal through GC-A in the kidney. Other natriuretic/diuretic factors could be released from the heart. Therefore, acute volume expansion was used as a means to cause release of granules from the atrium of the heart. That granule release occurred was confirmed by measurements of plasma ANP concentrations, which were markedly elevated in both wild-type and GC-A-null mice. After volume expansion, urine output as well as urinary sodium and cyclic GMP excretion increased rapidly and markedly in wild-type mice, but the rapid increases were abolished in GC-A-deficient animals. These results strongly suggest that natriuretic/diuretic factors released from the heart function exclusively through GC-A.


title: The systematics of Phytophthora sojae and P. megasperma.

abstract: Isolates of Phytophthora from alfalfa and soybean and the type isolate of Phytophthora megasperma Drechsler from hollyhock were compared morphologically, for temperature response, and pathogenically. Highly pathogenic, host-specific isolates from soybean and alfalfa, with relatively small oogonia, were identified as P. sojae Kaufmann and Gerdemann and divided into two formae speciales: f.sp. glycines and f.sp. medicaginis, respectively. Less pathogenic isolates from alfalfa with large oogonia were identified as P. megasperma and were compared favorably with Drechsler's original isolate and description. Isolates of P. sojae differed in their cardinal temperatures from those of P. megasperma. We consider P. sojae to be distinct from P. megasperma and propose that this taxon be reintroduced to the literature.


title: 3D Printing of Highly Stretchable, Shape-Memory, and Self-Healing Elastomer toward Novel 4D Printing

abstract: The three-dimensional (3D) printing of flexible and stretchable materials with smart functions such as shape memory (SM) and self-healing (SH) is highly desirable for the development of future 4D printing technology for myriad applications, such as soft actuators, deployable smart medical devices, and flexible electronics. Here, we report a novel ink that can be used for the 3D printing of highly stretchable, SM, and SH elastomer via UV-light-assisted direct-ink-write printing. An ink containing urethane diacrylate and a linear semicrystalline polymer is developed for the 3D printing of a semi-interpenetrating polymer network elastomer that can be stretched by up to 600%. The 3D-printed complex structures show interesting functional properties, such as high strain SM and SM -assisted SH capability. We demonstrate that such a 3D-printed SM elastomer has the potential application for biomedical devices, such as vascular repair devices. This research paves a new way for the further development of novel 4D pr...


title: Histamine Derived from Probiotic Lactobacillus reuteri Suppresses TNF via Modulation of PKA and ERK Signaling

abstract: Beneficial microbes and probiotic species, such as Lactobacillus reuteri, produce biologically active compounds that can modulate host mucosal immunity. Previously, immunomodulatory factors secreted by L. reuteri ATCC PTA 6475 were unknown. A combined metabolomics and bacterial genetics strategy was utilized to identify small compound(s) produced by L. reuteri that were TNF-inhibitory. Hydrophilic interaction liquid chromatography-high performance liquid chromatography (HILIC-HPLC) separation isolated TNF-inhibitory compounds, and HILIC-HPLC fraction composition was determined by NMR and mass spectrometry analyses. Histamine was identified and quantified in TNF-inhibitory HILIC-HPLC fractions. Histamine is produced from L-histidine via histidine decarboxylase by some fermentative bacteria including lactobacilli. Targeted mutagenesis of each gene present in the histidine decarboxylase gene cluster in L. reuteri 6475 demonstrated the involvement of histidine decarboxylase pyruvoyl type A (hdcA), histidine/histamine antiporter (hdcP), and hdcB in production of the TNF-inhibitory factor. The mechanism of TNF inhibition by L. reuteri-derived histamine was investigated using Toll-like receptor 2 (TLR2)-activated human monocytoid cells. Bacterial histamine suppressed TNF production via activation of the H2 receptor. Histamine from L. reuteri 6475 stimulated increased levels of cAMP, which inhibited downstream MEK/ERK MAPK signaling via protein kinase A (PKA) and resulted in suppression of TNF production by transcriptional regulation. In summary, a component of the gut microbiome, L. reuteri, is able to convert a dietary component, L-histidine, into an immunoregulatory signal, histamine, which suppresses pro-inflammatory TNF production. The identification of bacterial bioactive metabolites and their corresponding mechanisms of action with respect to immunomodulation may lead to improved anti-inflammatory strategies for chronic immune-mediated diseases.


title: Zipf ’ s law and the Internet

abstract: Zipf's law governs many features of the Internet. Observations of Zipf distributions, while interesting in and of themselves, have strong implications for the design and function of the Internet. The connectivity of Internet routers influences the robustness of the network while the distribution in the number of email contacts affects the spread of email viruses. Even web caching strategies are formulated to account for a Zipf distribution in the number of requests for webpages.


title: Fixing a Broken ELBO

abstract: Recent work in unsupervised representation learning has focused on learning deep directed latent-variable models. Fitting these models by maximizing the marginal likelihood or evidence is typically intractable, thus a common approximation is to maximize the evidence lower bound (ELBO) instead. However, maximum likelihood training (whether exact or approximate) does not necessarily result in a good latent representation, as we demonstrate both theoretically and empirically. In particular, we derive variational lower and upper bounds on the mutual information between the input and the latent variable, and use these bounds to derive a rate-distortion curve that characterizes the tradeoff between compression and reconstruction accuracy. Using this framework, we demonstrate that there is a family of models with identical ELBO, but different quantitative and qualitative characteristics. Our framework also suggests a simple new method to ensure that latent variable models with powerful stochastic decoders do not ignore their latent code.


title: Fast Nonparametric Clustering of Structured Time-Series

abstract: In this publication, we combine two Bayesian nonparametric models: the Gaussian Process (GP) and the Dirichlet Process (DP). Our innovation in the GP model is to introduce a variation on the GP prior which enables us to model structured time-series data, i.e., data containing groups where we wish to model inter- and intra-group variability. Our innovation in the DP model is an implementation of a new fast collapsed variational inference procedure which enables us to optimize our variational approximation significantly faster than standard VB approaches. In a biological time series application we show how our model better captures salient features of the data, leading to better consistency with existing biological classifications, while the associated inference algorithm provides a significant speed-up over EM-based variational inference.


title: A Personalized Time-Sequence-Based Book Recommendation Algorithm for Digital Libraries

abstract: Book recommendations are of great significance in colleges and universities. Although current recommendation approaches have made significant achievements, these approaches do not consider college students’ similar learning trajectories in the same major. In order to recommend books more accurately, mining the knowledge system is very crucial for college students in the same major. This paper proposes a personalized book recommendation algorithm that is based on the time sequential collaborative filtering recommendation, combined with students’ learning trajectories. In order to recommend books effectively, our algorithm leverages space distance. In this algorithm, we consider two important characteristics: the time sequence information of borrowing books and the circulation times of books. Our experimental results demonstrate that our book recommendation algorithm is in accordance with the college students’ demand for professional learning.


title: Analysing the critical factors influencing consumers' e-impulse buying behavior

abstract: The benefits of using e-commerce can lead to various kinds of hedonic consumer behavior, such as e-impulse buying behavior. The importance of impulse buying can be seen from the huge sales it generates which can account from 40% to 80% of all purchases depending on product category. Therefore, with the growing of online shopping, a deeper understanding of impulse buying on Internet is becoming increasingly necessary. The purpose of this study is to examine three dimensions, i.e., individual, product attributes, and web browsing activities as antecedents of consumers impulse buying behavior. The structural equation modeling with SmartPLS 3.2 was conducted to analyze the 124 respondents. The result of this study show that key factors influencing consumers' e-impulsive buying behavior are mostly from intrinsic factors, such as their shopping enjoyment tendency, impulsive buying tendency, and their personality, especially consumers with high on extraversion and low on conscientiousness trait Product attributes unfortunately did not have significant effect on impulse buying behavior. Futhermore, from web browsing activities, this study show that hedonic web browsing have a high possibility to make a real purchase, which can lead to impulse buying.


title: Feasibility of Wrist-Worn, Real-Time Hand, and Surface Gesture Recognition via sEMG and IMU Sensing

abstract: While most wearable gesture recognition approaches focus on the forearm or fingers, the wrist may be a more suitable location for practical use. We present the design and validation of a real-time gesture recognition wristband based on surface electromyography and inertial measurement unit sensing fusion, which can recognize 8 air gestures and 4 surface gestures with 2 distinct force levels. Ten healthy subjects performed an initial gesture recognition experiment, followed by a second experiment 1 h later and a third experiment 1 day later. Classification accuracies for the initial experiment were 92.6% and 88.8% for air and surface gestures, respectively, and there were no changes in accuracy results during testing 1 h. and 1 day later ( $p$ $>$ 0.05). These results demonstrate the feasibility of wrist-based gesture recognition paving the way for potential future integration in to a smart watch or other wrist-worn wearable for intuitive human computer interaction.


title: Multimodal Differential Network for Visual Question Generation

abstract: Generating natural questions from an image is a semantic task that requires using visual and language modality to learn multimodal representations. Images can have multiple visual and language contexts that are relevant for generating questions namely places, captions, and tags. In this paper, we propose the use of exemplars for obtaining the relevant context. We obtain this by using a Multimodal Differential Network to produce natural and engaging questions. The generated questions show a remarkable similarity to the natural questions as validated by a human study. Further, we observe that the proposed approach substantially improves over state-of-the-art benchmarks on the quantitative metrics (BLEU, METEOR, ROUGE, and CIDEr).


title: A 63,000 Q-factor relaxation oscillator with switched-capacitor integrated error feedback

abstract: There is a growing interest in implementing on-chip reference clock generators for low-cost low-power area-efficient SoCs, such as implantable biomedical devices and microcomputers. Relaxation oscillators are suitable candidates to generate such reference clocks due to their compact size, low power consumption and wide frequency tuning range. However, the poor phase noise performance and large long-term variation are two major problems that limit their application.


title: Real-Time Moving Object Segmentation and Classification From HEVC Compressed Surveillance Video

abstract: Moving object segmentation and classification from compressed video plays an important role in intelligent video surveillance. Compared with H.264/AVC, High Efficiency Video Coding (HEVC) introduces a host of new coding features that can be further exploited for moving object segmentation and classification. In this paper, we present a real-time approach to segment and classify moving objects using unique features directly extracted from the HEVC compressed domain for video surveillance. In the proposed method, first, motion vector (MV) interpolation for intra-coded prediction unit (PU) and MV outlier removal are employed for preprocessing. Second, blocks with nonzero MVs are clustered into the connected foreground regions using the four-connectivity component labeling algorithm. Third, object region tracking based on temporal consistency is applied to the connected foreground regions to remove the noise regions. The boundary of moving object region is further refined by the coding unit size and PU size. Finally, a person–vehicle classification model using bag of spatial–temporal HEVC syntax words is trained to classify the moving objects, either persons or vehicles. The experimental results demonstrate that the proposed method provides solid performance and can classify moving persons and vehicles accurately.


title: A 80W high gain and broadband Doherty power amplifier for 4/5G wireless communication systems

abstract: This paper presents a 80W high gain and broadband Doherty power amplifier (DPA) with symmetrical devices, employing Wolfspeed's CGHV27030S GaN HEMT. A novel architecture is used to eliminate the complex interaction of varying second harmonic impedances caused by active load modulation and provide high efficiency at output back-off (OBO) region and saturation point. Under a 10% duty cycle pulse excitation from 3.35–3.50 GHz, experimental results show the proposed DPA delivers 49.1–49.5 dBm output power with a drain efficiency (DE) of 50.2%–55.1% at 8 dB OBO and achieves a gain of 14.6–14.9 dB at an output power of 41 dBm. When extend the bandwidth to 3.3–3.6 GHz, the DPA can attain a measured DE higher than 40.9% at an OBO of 8 dB with a saturated power of 48.5–49.5 dBm. For a 2-carrier 40-MHz long-term evolution (LTE) signal with a peak-to-average power ratio (PAPR) of 8 dB, the adjacent channel leakage ratio (ACLR) is −30 dBc at 41 dBm average output power at 3.45 GHz.


title: Reduction of plasma IL-6 but not TNF-α by methotrexate in patients with early rheumatoid arthritis: a potential biomarker for radiographic progression

abstract: Objective ::: To determine the effect of methotrexate (MTX) on plasma levels of interleukin (IL)-6 and tumor necrosis factor (TNF)-α and to investigate their associations with clinical and radiographic responses in patients with early rheumatoid arthritis (RA).


title: ACCessory: password inference using accelerometers on smartphones

abstract: We show that accelerometer readings are a powerful side channel that can be used to extract entire sequences of entered text on a smart-phone touchscreen keyboard. This possibility is a concern for two main reasons. First, unauthorized access to one's keystrokes is a serious invasion of privacy as consumers increasingly use smartphones for sensitive transactions. Second, unlike many other sensors found on smartphones, the accelerometer does not require special privileges to access on current smartphone OSes. We show that accelerometer measurements can be used to extract 6-character passwords in as few as 4.5 trials (median).


title: Segmenting Retinal Blood Vessels With Deep Neural Networks

abstract: The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of vessels, relatively low contrast, and potential presence of pathologies like microaneurysms and hemorrhages. Many algorithms, both unsupervised and supervised, have been proposed for this purpose in the past. We propose a supervised segmentation technique that uses a deep neural network trained on a large (up to 400 $\thinspace$ 000) sample of examples preprocessed with global contrast normalization, zero-phase whitening, and augmented using geometric transformations and gamma corrections. Several variants of the method are considered, including structured prediction, where a network classifies multiple pixels simultaneously. When applied to standard benchmarks of fundus imaging, the DRIVE, STARE, and CHASE databases, the networks significantly outperform the previous algorithms on the area under ROC curve measure (up to $ > {0.99}$ ) and accuracy of classification (up to $ > {0.97}$ ). The method is also resistant to the phenomenon of central vessel reflex, sensitive in detection of fine vessels ( ${\rm sensitivity} > {0.87}$ ), and fares well on pathological cases.


title: Exploring Spark-SQL-Based Entity Resolution Using the Persistence Capability

abstract: Entity Resolution (ER) is a task to identify records that refer to the same real-world entities. A naive way to solve ER tasks is to calculate the similarity of the Cartesian product of all records, which is called pair-wise ER and leads to quadratic time complexity. Faced with an exploding data volume, pair-wise ER is challenged to achieve high efficiency and scalability. To tackle this challenge, parallel computing is proposed for speeding up the ER process. Due to the difficulty of distributed programming, big data processing frameworks are often used as tools to ease the realization of parallel ER, supporting data partitioning, workload balancing, and fault tolerance. However, the efficiency and scalability of parallel ER is also influenced by the adopted framework. In the area of parallel ER, the adoption of Apache Spark, a general framework supporting in-memory computation, still is not widely studied. Furthermore, though Apache Spark provides both low-level (RDD-based) and high-level APIs (Datasets-based), to date, only RDD-based APIs have been adopted in parallel ER research. In this paper, we have implemented a Spark-SQL-based ER process and explored its persistence capability to see the performance benefits. We have evaluated its speedup and compared its efficiency to Spark-RDD-based ER. We observed that different persistence options have a large impact on the efficiency of Spark-SQL-based ER, requiring a careful consideration for choosing it. By adopting the best persistence option, the efficiency of our Spark-SQL-based ER implementation is improved up to 3 times on different datasets, over a baseline without any persistence option or with misconfigured persistence.


title: Deep neural network marketplace recommenders in online experiments

abstract: Recommendations are broadly used in marketplaces to match users with items relevant to their interests and needs. To understand user intent and tailor recommendations to their needs, we use deep learning to explore various heterogeneous data available in marketplaces. This paper focuses on the challenge of measuring recommender performance and summarizes the online experiment results with several promising types of deep neural network recommenders - hybrid item representation models combining features from user engagement and content, sequence-based models, and multi-armed bandit models that optimize user engagement by re-ranking proposals from multiple submodels. The recommenders are currently running in production at the leading Norwegian marketplace FINN.no and serves over one million visitors everyday.


title: Cyclic redundancy code (CRC) polynomial selection for embedded networks

abstract: Cyclic redundancy codes (CRCs) provide a first line of defense against data corruption in many networks. Unfortunately, many commonly used CRC polynomials provide significantly less error detection capability than they might. An exhaustive exploration reveals that most previously published CRC polynomials are either inferior to alternatives or are only good choices for particular message lengths. Unfortunately these shortcomings and limitations often seem to be overlooked. This paper describes a polynomial selection process for embedded network applications and proposes a set of good general-purpose polynomials. A set of 35 new polynomials in addition to 13 previously published polynomials provides good performance for 3- to 16-bit CRCs for data word lengths up to 2048 bits.


title: Ku-band MMIC phase shifter using a parallel resonator with 0.18-/spl mu/m CMOS technology

abstract: A digital 5-bit phase shifter at Ku-band is presented, which is implemented with 0.18-mum RFCMOS technology. n-MOSFET switches and top metal microstrip lines with a first-metal ground allow the phase shifter to have small insertion losses. The proposed 90deg phase shifter utilizing a parallel resonator exhibits broad-band characteristics. All of the circuit components are derived to obtain a minimum phase variation at the operation frequency band. A bridged-T type phase shifter is also analyzed in view of parallel resonance using an ideal equivalent-circuit model. The conditions of the circuit elements are derived in an analytic form, which are used to obtain the broad-band phase characteristics. The fabricated 5-bit phase shifter demonstrates an overall rms phase error less than 12deg from 9 to 15 GHz. Insertion losses of 14.5 dB plusmn 0.5 dB and return losses less than 14 dB are obtained for 32 states at 12 GHz. The proposed 90deg phase shifter has performed a phase shift of 92.3degplusmn3.2deg over 9-15 GHz


title: A review of models for introducing computational thinking, computer science and computing in K-12 education

abstract: Computer science is becoming ever increasingly important to our society. Computer science content has, however, not traditionally been considered a natural part of curricula for primary and secondary education. Computer science has traditionally been primarily a university level discipline and there are no widely accepted general standards for what computer science at K-12 level entails. Also, as the interest in this area is rather new, the amount of research conducted in the field is still limited. In this paper we review how 10 different countries have approached introducing computer science into their K-12 education. The countries are Australia, England, Estonia, Finland, New Zealand, Norway, Sweden, South Korea, Poland and USA. The studied countries either emphasize digital competencies together with programming or the broader subject of computer science or computing. Computational thinking is rarely mentioned explicitly, but the ideas are often included in some form. The most common model is to make computer science content compulsory in primary school and elective in secondary school. A few countries have made it compulsory in both, while some countries have only introduced it in secondary school.


title: A conceptual model for video games and interactive media

abstract: In this article, we describe a conceptual model for video games and interactive media. Existing conceptual models such as the Functional Requirements for Bibliographic Records FRBR are not adequate to represent the unique descriptive attributes, levels of variance, and relationships among video games. Previous video game-specific models tend to focus on the development of video games and their technical aspects. Our model instead attempts to reflect how users such as game players, collectors, and scholars understand video games and the relationships among them. We specifically consider use cases of gamers, with future intentions of using this conceptual model as a foundation for developing a union catalog for various libraries and museums. In the process of developing the model, we encountered many challenges, including conceptual overlap with and divergence from FRBR, entity scoping, complex relationships among entities, and the question of how to model additional content for game expansion. Future work will focus on making this model interoperable with existing ontologies as well as further understanding and description of content and relationships.


title: The use of autologous PRP gel as an aid in the management of acute trauma wounds

abstract: SUMMARY Autologous platelet-rich plasma (PRP) gel is increasingly used in the treatment of a variety of soft and bony tissue defects, such as accelerating bone formation and in the management of chronic non-healing wounds. We performed this study to assess the benefits of using autologous PRP gel in the treatment of acute limb soft tissue wounds. 59 patients with acute wounds (open fractures, closed fractures with skin necrosis and friction burns) were randomised into two groups. Group A (32 patients) were treated with conventional dressings and Group B (27 patients) were managed with local application of PRP gel. Gustillo grade IIIb or IIIc open fractures were not included in this study, as these injuries required coverage with flap. The clinical endpoints were the healing rate and/or the time required to bring about adequate tissue regeneration in order to undergo reconstructive plastic surgery. The rate of wound healing rate was significantly faster in Group B at week 1, 2 and 3 (p = 0.003, p < 0.001 and p < 0.001, respectively). The mean time to plastic reconstruction in Group B was 21.26 days, S.D. = 1.35 vs 40.6 days in Group A, S.D. = 5.27 (p < 0.001). This study has shown that PRP gel treatment can be a valuable and effective aid in the management of acute trauma wounds.


title: AmbientGAN: Generative models from lossy measurements

abstract: Generative models provide a way to model structure in complex distributions and have been shown to be useful for many tasks of practical interest. However, current techniques for training generative models require access to fully-observed samples. In many settings, it is expensive or even impossible to obtain fully-observed samples, but economical to obtain partial, noisy observations. We consider the task of learning an implicit generative model given only lossy measurements of samples from the distribution of interest. We show that the true underlying distribution can be provably recovered even in the presence of per-sample information loss for a class of measurement models. Based on this, we propose a new method of training Generative Adversarial Networks (GANs) which we call AmbientGAN. On three benchmark datasets, and for various measurement models, we demonstrate substantial qualitative and quantitative improvements. Generative models trained with our method can obtain $2$-$4$x higher inception scores than the baselines.


title: Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks

abstract: In this paper, a complete and rigorous mathematical model for secondary surveillance radar systematic errors (biases) is developed. The model takes into account the physical effects systematically affecting the measurement processes. The azimuth biases are calculated from the physical error of the antenna calibration and the errors of the angle determination dispositive. Distance bias is calculated from the delay of the signal produced by the refractivity index of the atmosphere, and from clock errors, while the altitude bias is calculated taking into account the atmosphere conditions (pressure and temperature). It will be shown, using simulated and real data, that adapting a classical bias estimation process to use the complete parametrized model results in improved accuracy in the bias estimation.


title: Line-based rendering with truchet-like tiles

abstract: We explore the use of Truchet-like tiles (tiles with half area shaded) for single-tone (monochrome) line-based rendering of maps. We borrow concepts from information theory to develop some qualitative and quantitative measures, and use them to discuss four rendering styles which use Truchet-like tiles. We first present a style based on the original Truchet tiles, then we review the tile-based approach used by Inglis and Kaplan for Op Art rendering of 2-color maps, and extend the concept to 3- and 4-color input maps. We present two more concepts capable of rendering maps with up to 6 colors. We highlight some relationships between the four rendering styles, and utilize these relationships to generate Op Art/labyrinth renderings from input maps of up to 6 colors.


Going Online While Purchasing Offline : an Explorative Analysis of Omnichannel Shopping Behaviour in Retail Settings


title: A First Analysis of Flow Field Hysteresis in a Pump Impeller

abstract: The measured pump pressure discharge characteristic for a high specific speed radial pump (ω s = 1.7) reveals distinct discontinuities in part load operation. These pressure discontinuities occur at different threshold volume fluxes when increasing or decreasing the pump discharge and make up a hysteresis loop. The pump impeller characteristic was evaluated experimentally and numerically by taking the difference between the integrated impeller outlet and impeller inlet total pressure. The experimental and numerical characteristics agree well including the volume flux location and magnitude of the pressure discontinuities in the hysteresis loop. For volume fluxes within the hysteresis loop two stable well converged flows were calculated numerically. The numerical calculations were made on coarse and fine grids using commercially available software with and without the impeller clearance leakage flow. Further experimental and numerical comparisons are made at the impeller inlet/outlet with emphasis on the changing flow field in the hysteresis loop flow regime and its coupling to the onset of reverse flow zones. This combined application of numerical and experimental tools provides insight for the hysteresis flow field of a pump impeller characteristic.


title: A monolithically-integrated optical transmitter and receiver in a zero-change 45nm SOI process

abstract: An optical transmitter and receiver with monolithically-integrated photonic devices and circuits are demonstrated together for the first time in a commercial 45nm SOI process, without any process changes. The transmitter features an interleaved-junction carrier-depletion ring modulator and operates at 3.5Gb/s with an 8dB extinction ratio and combined circuit and device energy cost of 70fJ/bit. The optical receiver connects to an integrated SiGe detector designed for 1180nm wavelength and performs at 2.5Gb/s with 15μA sensitivity and energy cost of 220fJ/bit.


title: Raw Waveform-based Audio Classification Using Sample-level CNN Architectures

abstract: Music, speech, and acoustic scene sound are often handled separately in the audio domain because of their different signal characteristics. However, as the image domain grows rapidly by versatile image classification models, it is necessary to study extensible classification models in the audio domain as well. In this study, we approach this problem using two types of sample-level deep convolutional neural networks that take raw waveforms as input and uses filters with small granularity. One is a basic model that consists of convolution and pooling layers. The other is an improved model that additionally has residual connections, squeeze-and-excitation modules and multi-level concatenation. We show that the sample-level models reach state-of-the-art performance levels for the three different categories of sound. Also, we visualize the filters along layers and compare the characteristics of learned filters.


title: Clustering distributed data streams in peer-to-peer environments

abstract: This paper describes a technique for clustering homogeneously distributed data in a peer-to-peer environment like sensor networks. The proposed technique is based on the principles of the K-Means algorithm. It works in a localized asynchronous manner by communicating with the neighboring nodes. The paper offers extensive theoretical analysis of the algorithm that bounds the error in the distributed clustering process compared to the centralized approach that requires downloading all the observed data to a single site. Experimental results show that, in contrast to the case when all the data is transmitted to a central location for application of the conventional clustering algorithm, the communication cost (an important consideration in sensor networks which are typically equipped with limited battery power) of the proposed approach is significantly smaller. At the same time, the accuracy of the obtained centroids is high and the number of samples which are incorrectly labeled is also small.


title: A ranking approach to keyphrase extraction

abstract: This paper addresses the issue of automatically extracting keyphrases from a document. Previously, this problem was formalized as classification and learning methods for classification were utilized. This paper points out that it is more essential to cast the problem as ranking and employ a learning to rank method to perform the task. Specifically, it employs Ranking SVM, a state-of-art method of learning to rank, in keyphrase extraction. Experimental results on three datasets show that Ranking SVM significantly outperforms the baseline methods of SVM and Naive Bayes, indicating that it is better to exploit learning to rank techniques in keyphrase extraction.


title: Distribution Locational Marginal Pricing for Optimal Electric Vehicle Charging Management

abstract: AC optimal power flow (ACOPF) is typically used for operational dispatch decisions. However, DC optimal power flow (DCOPF) based on linearization of the ACOPF is widely used for locational marginal price (LMP) calculation in market operation and settlements due to its efficiency and reasonable accuracy. The DCOPF is considered to be sufficient in many cases. Especially for calculating LMPs of distribution systems, the DCOPF is a good option considering the large number of nodes in distribution systems. In industry, DCOPF has been employed by several software tools for chronological LMP simulation and forecasting.


title: Serotonin 1A and Serotonin 4 Receptors: Essential Mediators of the Neurogenic and Behavioral Actions of Antidepressants

abstract: Selective serotonin reuptake inhibitors are the mostly widely used treatment for major depressive disorders and also are prescribed for several anxiety disorders. However, similar to most antidepressants, selective serotonin reuptake inhibitors suffer from two major problems: They only show beneficial effects after 2 to 4 weeks and only about 33% of patients show remission to first-line treatment. Thus, there is a considerable need for development of more effective antidepressants. There is a growing body of evidence supporting critical roles of 5-HT1A and 5-HT4 receptor subtypes in mediating successful depression treatments. In addition, appropriate activation of these receptors may be associated with a faster onset of the therapeutic response. This review will examine the known roles of 5-HT1A and 5-HT4 receptors in mediating both the pathophysiology of depression and anxiety and the treatment of these mood disorders. At the end of the review, the role of these receptors in the regulation of adult hippo...


Zipf's and Benford's laws in Twitter hashtags


title: The nature of feelings: evolutionary and neurobiological origins

abstract: Homeostatic maintenance of the state of the body within an optimal range is essential for survival. Damasio and Carvalho discuss how feelings derived from body states enhance the effectiveness of the corrective response and suggest a cellular mechanism for how feelings might be generated.


title: Blobworld: A System for Region-Based Image Indexing and Retrieval

abstract: Blobworld is a system for image retrieval based on finding coherent image regions which roughly correspond to objects. Each image is automatically segmented into regions ("blobs") with associated color and texture descriptors. Queryingi s based on the attributes of one or two regions of interest, rather than a description of the entire image. In order to make large-scale retrieval feasible, we index the blob descriptions usinga tree. Because indexing in the high-dimensional feature space is computationally prohibitive, we use a lower-rank approximation to the high-dimensional distance. Experiments show encouraging results for both queryinga nd indexing.


title: UIMA Ruta: Rapid development of rule-based information extraction applications

abstract: Rule-based information extraction is an important approach for processing the increasingly available amount of unstructured data. The manual creation of rule-based applications is a time-consuming and tedious task, which requires qualified knowledge engineers. The costs of this process can be reduced by providing a suitable rule language and extensive tooling support. This paper presents UIMA Ruta, a tool for rule-based information extraction and text processing applications. The system was designed with focus on rapid development. The rule language and its matching paradigm facilitate the quick specification of comprehensible extraction knowledge. They support a compact representation while still providing a high level of expressiveness. These advantages are supplemented by the development environment UIMA Ruta Workbench. It provides, in addition to extensive editing support, essential assistance for explanation of rule execution, introspection, automatic validation, and rule induction. UIMA Ruta is a useful tool for academia and industry due to its open source license. We compare UIMA Ruta to related rule-based systems especially concerning the compactness of the rule representation, the expressiveness, and the provided tooling support. The competitiveness of the runtime performance is shown in relation to a popular and freely-available system. A selection of case studies implemented with UIMA Ruta illustrates the usefulness of the system in real-world scenarios.


title: Grid Array Antennas With Subarrays and Multiple Feeds for 60-GHz Radios

abstract: A grid array antenna is presented in this paper with sub grid arrays and multiple feed points, showing enhanced radiation characteristics and sufficient design flexibility. For instance, the grid array antenna can be easily designed as a linearly- or circularly-polarized, unbalanced or balanced antenna. A design example is given for a linearly-polarized unbalanced grid array antenna in Ferro A6M low temperature co-fired ceramic technology for 60-GHz radios to operate from 57 to 66 GHz (≈ 14.6% at 61.5 GHz ). It consists of 4 sub grid arrays and 4 feed points that are connected to a single-ended 50-Ω source by a quarter-wave matched T-junction network. The simulated results indicate that the grid array antenna has the maximum gain of 17.7 dBi at 59 GHz , an impedance bandwidth (|S11| ≤ -10ndB) nearly from 56 to 67.5 GHz (or 18.7%), a 3-dB gain bandwidth from 55.4 to 66 GHz (or 17.2%), and a vertical beam bandwidth in the broadside direction from 57 to 66 GHz (14.6%). The measured results are compared with the simulated ones. Discrepancies and their causes are identified with a tolerance analysis on the fabrication process.


title: Full-3D Edge Tracking with a Particle Filter

abstract: This paper demonstrates a real-time, full-3D edge tracker based on a particle filter. In contrast to previous methods this system is capable of tracking complex self-occluding three-dimensional structures. The system exploits graphics hardware in a novel manner, allowing it not only to perform hidden line removal for each particle but also to evaluate pose likelihoods directly on the graphics card. This approach allows video-rate filtering with hundreds of particles on a standard workstation.


title: Use Generalized Representations, But Do Not Forget Surface Features

abstract: Only a year ago, all state-of-the-art coreference resolvers were using an extensive amount of surface features. Recently, there was a paradigm shift towards using word embeddings and deep neural networks, where the use of surface features is very limited. In this paper, we show that a simple SVM model with surface features outperforms more complex neural models for detecting anaphoric mentions. Our analysis suggests that using generalized representations and surface features have different strength that should be both taken into account for improving coreference resolution.


title: Robot social presence and gender: do females view robots differently than males?

abstract: Social-psychological processes in humans will play an important role in long-term human-robot interactions. This study investigates people's perceptions of social presence in robots during (relatively) short interactions. Findings indicate that males tend to think of the robot as more human-like and accordingly show some evidence of "social facilitation" on an arithmetic task as well as more socially desirable responding on a survey administered by a robot. In contrast, females saw the robot as more machine-like, exhibited less socially desirable responding to the robot's survey, and were not socially facilitated by the robot while engaged in the arithmetic tasks. Various alternative accounts of these findings are explored and the implications of these results for future work are discussed.


title: Unsupervised Text Segmentation Using Semantic Relatedness Graphs

abstract: Segmenting text into semantically coherent ::: fragments improves readability of text ::: and facilitates tasks like text summarization ::: and passage retrieval. In this paper, ::: we present a novel unsupervised algorithm ::: for linear text segmentation (TS) ::: that exploits word embeddings and a measure ::: of semantic relatedness of short texts ::: to construct a semantic relatedness graph ::: of the document. Semantically coherent ::: segments are then derived from maximal ::: cliques of the relatedness graph. The algorithm ::: performs competitively on a standard ::: synthetic dataset and outperforms the ::: best-performing method on a real-world ::: (i.e., non-artificial) dataset of political manifestos.


title: A novel freeze protection strategy for shallow buried sewer pipes: temperature modelling and field investigation

abstract: The burial of sewer and water pipes below the maximum ground frost depth can be very costly and laborious in regions with cold winters. If a freeze protection measure is applied, the utility lines ...


title: An adaptation of the theory of interpersonal behaviour to the study of telemedicine adoption by physicians

abstract: Physicians' acceptance of telemedicine constitutes a prerequisite for its diffusion on a national scale. Based upon the Theory of Interpersonal Behavior, this study was aimed at assessing the predictors of physicians' intention to use telemedicine in their clinical practice. All of the physicians involved in the RQTE, the extended provincial telemedicine network of Quebec (Canada) were mailed a questionnaire to identify the psychosocial determinants of their intention to adopt telemedicine. Confirmatory factor analysis (CFA) was performed to assess the measurement model and structural equation modelling (SEM) was applied to test the theoretical model. The adapted theoretical model explained 81% (P<0.001) of variance in physicians' intention to use telehealth. The main predictors of intentions were a composite normative factor, comprising personal as well as social norms (beta=1.08; P<0.001) and self identity (beta=-0.33; P<0.001). Thus, physicians who perceived professional and social responsibilities regarding adoption of telehealth in their clinical practice had stronger intention to use this technology. However, it is likely that personal identity had a suppression effect in the regression equation, indicating that physicians' intention to use telemedicine was better predicted if their self-perception as telemedicine users was considered. These results have several implications at the theoretical and practical levels that are discussed in this paper.


title: Understanding Convolutional Neural Network Training with Information Theory

abstract: Using information theoretic concepts to understand and explore the inner organization of deep neural networks (DNNs) remains a big challenge. Recently, the concept of an information plane began to shed light on the analysis of multilayer perceptrons (MLPs). We provided an in-depth insight into stacked autoencoders (SAEs) using a novel matrix-based Renyi's {\alpha}-entropy functional, enabling for the first time the analysis of the dynamics of learning using information flow in real-world scenario involving complex network architecture and large data. Despite the great potential of these past works, there are several open questions when it comes to applying information theoretic concepts to understand convolutional neural networks (CNNs). These include for instance the accurate estimation of information quantities among multiple variables, and the many different training methodologies. By extending the novel matrix-based Renyi's {\alpha}-entropy functional to a multivariate scenario, this paper presents a systematic method to analyze CNNs training using information theory. Our results validate two fundamental data processing inequalities in CNNs, and also have direct impacts on previous work concerning the training and design of CNNs.


title: Relation of threatened egotism to violence and aggression: The dark side of high self-esteem.

abstract: Conventional wisdom has regarded low self-esteem as an important cause of violence, but the opposite view is theoretically viable. An interdisciplinary review of evidence about aggression, crime, and violence contradicted the view that low self-esteem is an important cause. Instead, violence appears to be most commonly a result of threatened egotismwthat is, highly favorable views of self that are disputed by some person or circumstance. Inflated, unstable, or tentative beliefs in the self's superiority may be most prone to encountering threats and hence to causing violence. The mediating process may involve directing anger outward as a way of avoiding a downward revision of the selfconcept. Only a minority of human violence can be understood as rational, instrumental behavior aimed at securing or protecting material rewards. The pragmatic futility of most violence has been widely recognized: Wars harm both sides, most crimes yield little financial gain, terrorism and assassination almost never bring about the desired political changes, most rapes fail to bring sexual pleasure, torture rarely elicits accurate or useful information, and most murderers soon regret their actions as pointless and selfdefeating (Ford, 1985; Gottfiedson & Hirschi, 1990; Groth, 1979; Keegan, 1993; Sampson & Laub, 1993; .Scm'ry, 1985). What drives people to commit violent and oppressive actions that so often are tangential or even contrary to the rational pursuit of material self-interest? This article reviews literature relevant to the hypothesis that one main source of such violence is threatened egotism, particularly when it consists of favorable self-appraisals that may be inflated or ill-founded and that are confronted with an external evaluation that disputes them. The focus on egotism (i.e., favorable self-appraisals) as one cause of violent aggression runs contrary to an entrenched body of wisdom that has long pointed to low self-esteem as the root of violence and other antisocial behavior. We shall examine the arguments for the low self-esteem view and treat it as a rival hypothesis to our emphasis on high self-esteem. Clearly, there


title: Fully Printed Halide Perovskite Light-Emitting Diodes with Silver Nanowire Electrodes

abstract: Printed organometal halide perovskite light-emitting diodes (LEDs) are reported that have indium tin oxide (ITO) or carbon nanotubes (CNTs) as the transparent anode, a printed composite film consisting of methylammonium lead tribromide (Br-Pero) and poly(ethylene oxide) (PEO) as the emissive layer, and printed silver nanowires as the cathode. The fabrication can be carried out in ambient air without humidity control. The devices on ITO/glass have a low turn-on voltage of 2.6 V, a maximum luminance intensity of 21014 cd m–2, and a maximum external quantum efficiency (EQE) of 1.1%, surpassing previous reported perovskite LEDs. The devices on CNTs/polymer were able to be strained to 5 mm radius of curvature without affecting device properties.


title: Representing and querying disease networks using graph databases

abstract: BackgroundSystems biology experiments generate large volumes of data of multiple modalities and this information presents a challenge for integration due to a mix of complexity together with rich semantics. Here, we describe how graph databases provide a powerful framework for storage, querying and envisioning of biological data.ResultsWe show how graph databases are well suited for the representation of biological information, which is typically highly connected, semi-structured and unpredictable. We outline an application case that uses the Neo4j graph database for building and querying a prototype network to provide biological context to asthma related genes.ConclusionsOur study suggests that graph databases provide a flexible solution for the integration of multiple types of biological data and facilitate exploratory data mining to support hypothesis generation.


title: Minimizing Energy Consumption of Wheeled Mobile Robots via Optimal Motion Planning

abstract: This paper presents a new optimal motion planning aiming to minimize the energy consumption of a wheeled mobile robot in robot applications. A model that can be used to formulate the energy consumption for kinetic energy transformation and for overcoming traction resistance is developed first. This model will provide a base for minimizing the robot energy consumption through a proper motion planning. To design the robot path, the A* algorithm is employed to generate an energy-efficient path where a new energy-related criterion is utilized in the cost function. To achieve a smooth trajectory along the generated path, the appropriate arrival time and velocity at the defined waypoints are selected for minimum energy consumption. Simulations and experiments are performed to demonstrate the energy-saving efficiency of the proposed motion planning approach.


title: ConcurTaskTrees: A Diagrammatic Notation for Specifying Task Models

abstract: In this paper we discuss a notation to describe task models, which can specify a wide range of temporal relationships among ta~ks. It is a compact and graphical notation, immediate both to use and understand. Its logical structure and the related automatic tool make it suitable for designing even large sized applications.


title: Optical properties of human skin

abstract: A survey of the literature is presented that provides an analysis of the optical properties of human skin, with particular regard to their applications in medicine. Included is a description of the primary interactions of light with skin and how these are commonly estimated using radiative transfer theory (RTT). This is followed by analysis of measured RTT coefficients available in the literature. Orders of magnitude differences are found within published absorption and reduced-scattering coefficients. Causes for these discrepancies are discussed in detail, including contrasts between data acquired in vitro and in vivo. An analysis of the phase functions applied in skin optics, along with the remaining optical coefficients (anisotropy factors and refractive indices) is also included. The survey concludes that further work in the field is necessary to establish a definitive range of realistic coefficients for clinically normal skin.


Direct charge measurement in Floating Gate transistors of Flash EEPROM using Scanning Electron Microscopy


title: Cutting Pattern Identification for Coal Mining Shearer through a Swarm Intelligence–Based Variable Translation Wavelet Neural Network

abstract: As a sound signal has the advantages of non-contacted measurement, compact structure, and low power consumption, it has resulted in much attention in many fields. In this paper, the sound signal of the coal mining shearer is analyzed to realize the accurate online cutting pattern identification and guarantee the safety quality of the working face. The original acoustic signal is first collected through an industrial microphone and decomposed by adaptive ensemble empirical mode decomposition (EEMD). A 13-dimensional set composed by the normalized energy of each level is extracted as the feature vector in the next step. Then, a swarm intelligence optimization algorithm inspired by bat foraging behavior is applied to determine key parameters of the traditional variable translation wavelet neural network (VTWNN). Moreover, a disturbance coefficient is introduced into the basic bat algorithm (BA) to overcome the disadvantage of easily falling into local extremum and limited exploration ability. The VTWNN optimized by the modified BA (VTWNN-MBA) is used as the cutting pattern recognizer. Finally, a simulation example, with an accuracy of 95.25%, and a series of comparisons are conducted to prove the effectiveness and superiority of the proposed method.


title: Breathing with the pelvic floor? Correlation of pelvic floor muscle function and expiratory flows in healthy young nulliparous women

abstract: Introduction and hypothesis ::: The aim of this cross-sectional study was to determine correlations between pelvic floor muscle (PFM) function and expiratory function in healthy young nulliparous women.


title: Energy efficiency maxima for wireless communications: 5G, IoT, and massive MIMO

abstract: Maximizing the operating energy efficiency of any wireless communication link requires a global optimization not only across the entire system block diagram, but also including the signal modulation and aspects of the link operating protocol. Achieving this global optimization is first examined for the transmitter, receiver, and baseband circuitry. Then the important aspects of signal modulation necessary to access these circuit optimizations, with examples from existing systems along with proposed signals for "5G" including Internet of Things (IoT) and massive MIMO, are presented. This is followed by the correspondingly important protocol aspects needed for link efficiency. Operating temperature consequences of signal modulation selection, and its corresponding PA efficiency ceiling, are explored for the envisioned massive-MIMO antenna arrays. A realization of this global energy efficiency optimization in actual circuitry with measurements completes this paper.


Chapter 8 Location-Based Social Networks: Users


title: Taking Identity Seriously: Dilemmas for Education Policy and Practice.

abstract: If we are to fully understand and adequately respond to the multicultural question in European education, it is necessary to develop rich empirical descriptions and theoretically rigorous explanations of policy processes and effects. For example, we need to be able to characterise and explain the differentiated ways in which education policies and practices do or do not recognise, support or undermine diverse cultural identities and do or do not reproduce various kinds of educational and social inequality. But we also need to be able to produce some kind of account of what ought to be going on. The latter involves confronting a number of important questions: Why does identity matter? What is ethically entailed by — and what are the limits to — recognising and supporting diverse cultural identities? In what ways are the various currents of multiculturalism an adequate response to these complex normative questions? In this article, the authors begin to respond to these questions by mapping out some of the d...


title: Capless LDO Regulator Achieving −76 dB PSR and 96.3 fs FOM

abstract: The performance of switching devices such as display driver ICs is degraded by large power supply noise at switching frequencies from a few hundreds of kilohertz to a few megahertz. In order to minimize the power supply noise, a low-dropout (LDO) regulator with higher power supply rejection (PSR) is essential. In this brief, a capless LDO regulator with a negative capacitance circuit and voltage damper is proposed for enhancing PSR and figure of merit (FOM), respectively, in switching devices. The proposed LDO regulator is fabricated in a $0.18~\boldsymbol {\mu }\text{m}$ CMOS. Measurement results show that the proposed LDO regulator achieves −76 dB PSR at 1 MHz and 96.3 fs FOM with a total on-chip capacitance of as small as 12.7 pF.


title: Robust nonlinear flight control of a high-performance aircraft

abstract: This paper considers probabilistic robust control of nonlinear uncertain systems. A combination of stochastic robustness and dynamic inversion is proposed for general systems that have a feedback-linearizable nominal system. In this paper, the stochastic robust nonlinear control approach is applied to a highly nonlinear complex aircraft model, the high-incidence research model (HIRM). The model addresses a high-angle-of-attack enhanced manual control problem. The aim of the flight control system is to give good handling qualities across the specified flight envelope without the use of gain scheduling and also to provide robustness to modeling uncertainties. The proposed stochastic robust nonlinear control explores the direct design of nonlinear flight control logic. Therefore, the final design accounts for all significant nonlinearities in the aircraft's high-fidelity simulation model. The controller parameters are designed to minimize the probability of violating design specifications, which provides the design with good robustness in stability and performance subject to modeling uncertainties. The present design compares favorably with earlier controllers that were generated for a benchmark design competition.


title: A survey of dimensionality reduction techniques

abstract: Experimental life sciences like biology or chemistry have seen in the recent decades an explosion of the data available from experiments. Laboratory instruments become more and more complex and report hundreds or thousands measurements for a single experiment and therefore the statistical methods face challenging tasks when dealing with such high dimensional data. However, much of the data is highly redundant and can be efficiently brought down to a much smaller number of variables without a significant loss of information. The mathematical procedures making possible this reduction are called dimensionality reduction techniques; they have widely been developed by fields like Statistics or Machine Learning, and are currently a hot research topic. In this review we categorize the plethora of dimension reduction techniques available and give the mathematical insight behind them.


title: Usage, barriers and measurement of social media marketing: an exploratory investigation of small and medium B2B brands

abstract: Previous research has established the benefits of branding for business-to-business (B2B) organizations. Various tools can be used to support B2B brands, including the internet and other interactive technologies. Yet research on how organizations use Social Networking Sites (SNS) to achieve brand objectives remains limited. This study addresses the gap by focusing on B2B SMEs and their social networking practices, particularly, usage, perceived barriers, and the measurement of effectiveness of SNS as a marketing tool. Findings from a mail survey show that over a quarter of B2B SMEs in the UK are currently using SNS to achieve brand objectives, the most popular of which is to attract new customers. On the other hand, the most significant barrier is the lack of perceived relevance for particular sectors. Notably, the overwhelming majority of users do not adopt any metrics to assess SNS effectiveness. Almost half of the sample of SMEs that currently use SNS have indicated their intention to increase their marketing spending on this channel, highlighting the growing importance of SNS in a B2B context.


title: Big Data Framework for Analyzing Patents to Support Strategic R&D Planning

abstract: Patents are important technical documents that are widely used to support strategic research and development (RaD) planning. With the rapid evolution of Information Technology (IT), many researchers have attempted to analyze patent data using IT-based methods such as data mining. More recently, advancements in big data technologies have increased their suitability for analyzing large-scale patent data. In this paper, we propose a big data framework to process and analyze large-scale patent data. The proposed framework consists of four layers: an aggregator layer, a storage layer, an analysis layer, and an application layer. These layers are designed to collect patent data, store the collected data, analyze the data, and present the results. The primary objectives of the proposed framework are to provide a patent analysis service platform based on big data technologies, and to support strategic RaD planning for organizations. To demonstrate the feasibility of the proposed approach, we implement a Hadoop cluster based on the proposed framework and conduct a case study to analyze patents in the Quantum Dot technology field.


title: Designing Hardware for Machine Learning: The Important Role Played by Circuit Designers

abstract: Machine learning is becoming increasingly important in this era of big data. It enables us to extract meaningful information from the overwhelming amount of data being generated and collected every day. This information can be used to analyze and understand the data to identify trends (e.g., surveillance and portable/wearable electronics) or to take immediate action (e.g., robotics/drones, self-driving cars, and smart Internet of Things). In many applications, embedded processing near the sensor is preferred over the cloud due to privacy or latency concerns or limitations in the communication bandwidth. However, sensor devices often have stringent constraints on energy consumption and cost in addition to throughput and accuracy requirements. Circuit designers can play an important role in addressing these challenges by developing energy-efficient platforms to perform the necessary processing for machine learning. In this article, we will give a short overview of the key concepts in machine learning, discuss its challenges particularly in the embedded space, and highlight various opportunities where circuit designers can help to address these challenges.


title: Multifunctional Fluorescent-Magnetic Polymeric Colloidal Particles: Preparations and Bioanalytical Applications.

abstract: Fluorescent-magnetic particles (FMPs) play important roles in modern materials, especially as nanoscale devices in the biomedical field. The interesting features of FMPs are attributed to their dual detection ability, i.e., fluorescent and magnetic modes. Functionalization of FMPs can be performed using several types of polymers, allowing their use in various applications. The synergistic potentials for unique multifunctional, multilevel targeting nanoscale devices as well as combination therapies make them particularly attractive for biomedical applications. However, the synthesis of FMPs is challenging and must be further developed. In this review article, we summarized the most recent representative works on polymer-based FMP systems that have been applied particularly in the bioanalytical field.


Synchronous Firefly Algorithm for Cluster Head Selection in WSN


title: Risk factors for pressure ulcers.

abstract: BACKGROUND ::: Pressure ulcers (PrUs) remain a common problem in all healthcare settings. The aim of this study was to assess the presence of risk factors in patients with PrUs. ::: ::: ::: METHODS ::: The study included 32 immobilized inpatients with PrUs and 30 immobilized inpatients without PrUs as a control group, who were being followed up at the Ankara Physical Therapy and Rehabilitation Hospital, Ankara, Turkey. Patients were given physical examinations and assessed for medical history, as well as for associated diseases, the duration of PrUs, the presence of stool and urinary incontinence, the use of pressure-reducing bed surface, and change of position. Routine biochemical and hematologic blood examinations were performed in all of the patients involved in the study. ::: ::: ::: RESULTS ::: When risk factors for PrUs were assessed, 81.2% of the patients with PrUs had stool and urine incontinence, 46.8% had been smoking, 46.8% had hypoalbuminemia, 15.6% had been drinking alcohol, and 9.3% had diabetes; 18.7% had been using a pressure-reducing bed surface before the ulcer appeared, and 40.6% started to use a pressure-reducing bed surface after the ulceration occurred; and 59.3% were repositioned periodically. PrUs were found to develop earlier in patients who were smoking and who had anemia. A significant relation also was found between the grade of the ulcer and the frequency of change of position in bed. Smoking and urinary/stool incontinence were found to be significantly present more frequently in the patient group than the control group. Stool incontinence and the absence of the use of a pressure-reducing bed were also found to be risk factors in the patient group when compared with the control group. ::: ::: ::: CONCLUSIONS ::: The authors believe that stool and urinary incontinence, smoking, anemia, not using a pressure-reducing bed surface, and infrequent change of position in bed are considerable risk factors for the development of PrUs. Immobilized patients should be assessed for these risk factors, and measures should be taken to prevent PrU development.


title: Deep Scattering Spectrum with deep neural networks

abstract: State-of-the-art convolutional neural networks (CNNs) typically use a log-mel spectral representation of the speech signal. However, this representation is limited by the spectro-temporal resolution afforded by log-mel filter-banks. A novel technique known as Deep Scattering Spectrum (DSS) addresses this limitation and preserves higher resolution information, while ensuring time warp stability, through the cascaded application of the wavelet-modulus operator. The first order scatter is equivalent to log-mel features and standard CNN modeling techniques can directly be used with these features. However the higher order scatter, which preserves the higher resolution information, presents new challenges in modelling. This paper explores how to effectively use DSS features with CNN acoustic models. Specifically, we identify the effective normalization, neural network topology and regularization techniques to effectively model higher order scatter. The use of these higher order scatter features, in conjunction with CNNs, results in relative improvement of 7% compared to log-mel features on TIMIT, providing a phonetic error rate (PER) of 17.4%, one of the lowest reported PERs to date on this task.


title: Semantic segmentation of microscopic images of H&E stained prostatic tissue using CNN

abstract: There is a need for an automatic Gleason scoring system that can be used for prostate cancer diagnosis. Today the diagnoses are determined by pathologists manually, which is both a complex and a time-consuming task. To reduce the pathologists' workload, but also to reduce variations between different pathologists, an automatic classification system would be of great use. Some previous works have aimed for this, but still more work needs to be done. It is probable that such a tool would benefit from having access to individually segmented, pathologically relevant objects from the images. Therefore, we have developed an algorithm for semantic segmentation of the microscopic images of H&E stained prostate tissue into Background, Stroma, Epithelial Cytoplasm and Nuclei. This algorithm is based on deep learning, or more specifically a convolutional neural network. The network design is inspired by architectures that previously have been proved successful in different applications. It consists of a contracting and an expanding part, which are symmetrical. We have reached an accuracy of 80 %, as measured by the mean of the intersection over union, for segmentation into four classes. Previous works have only investigated nuclei segmentation, and our network performed similar but for the more challenging task of four class segmentation.


title: Lung Cancer Screening Using Adaptive Memory-Augmented Recurrent Networks

abstract: In this paper, we investigate the effectiveness of deep learning techniques for lung nodule classification in computed tomography scans. Using less than 10,000 training examples, our deep networks perform two times better than a standard radiology software. Visualization of the networks' neurons reveals semantically meaningful features that are consistent with the clinical knowledge and radiologists' perception. Our paper also proposes a novel framework for rapidly adapting deep networks to the radiologists' feedback, or change in the data due to the shift in sensor's resolution or patient population. The classification accuracy of our approach remains above 80% while popular deep networks' accuracy is around chance. Finally, we provide in-depth analysis of our framework by asking a radiologist to examine important networks' features and perform blind re-labeling of networks' mistakes.


title: Gaze angle estimate and correction in iris recognition

abstract: Conventional iris recognition using a full frontal iris image has reached a very high accuracy rate. In this paper, we focus on processing off-angle iris images. Previous research has shown that it is possible to correct off-angle iris images, but knowledge of the angle was needed. Very little work has focused on iris angle estimation which can be used for angle correction. In this paper, we describe a two-phase angle estimation based on the geometric features of the ellipse. Angle correction is accomplished by projective transformation. Evaluation of this angle estimation and correction method includes a 3D eyeball simulator, and performance test on the West Virginia University Off-Angle Dataset.


title: Mutual-coupling based phased-array calibration: A robust and versatile approach

abstract: The transmit and receive modules of a large phased array are often calibrated for amplitude and phase variations by an internal calibration network and an offline characterization of the complete array in an anechoic chamber. Such a solution is less obvious in view of current trends towards integration and modularity. An alternative to surpass network and offline characterization is mutual-coupling based calibration. However, existing stepwise procedures for resolving the module transfer characteristics from (active) coupling measurements can be subject to propagating errors originating from e.g. measurement noise, manufacturing differences, and defective modules. In this paper we propose an alternative approach, which is more versatile and robust than a stepwise procedure, and we test this approach for two different array architectures with simulated coupling data.


title: Sequence Synopsis: Optimize Visual Summary of Temporal Event Data

abstract: Event sequences analysis plays an important role in many application domains such as customer behavior analysis, electronic health record analysis and vehicle fault diagnosis. Real-world event sequence data is often noisy and complex with high event cardinality, making it a challenging task to construct concise yet comprehensive overviews for such data. In this paper, we propose a novel visualization technique based on the minimum description length (MDL) principle to construct a coarse-level overview of event sequence data while balancing the information loss in it. The method addresses a fundamental trade-off in visualization design: reducing visual clutter vs. increasing the information content in a visualization. The method enables simultaneous sequence clustering and pattern extraction and is highly tolerant to noises such as missing or additional events in the data. Based on this approach we propose a visual analytics framework with multiple levels-of-detail to facilitate interactive data exploration. We demonstrate the usability and effectiveness of our approach through case studies with two real-world datasets. One dataset showcases a new application domain for event sequence visualization, i.e., fault development path analysis in vehicles for predictive maintenance. We also discuss the strengths and limitations of the proposed method based on user feedback.


title: Assembly algorithms for next-generation sequencing data

abstract: The emergence of next-generation sequencing platforms led to resurgence of research in whole-genome shotgun assembly algorithms and software. DNA sequencing data from the Roche 454, Illumina/Solexa, and ABI SOLiD platforms typically present shorter read lengths, higher coverage, and different error profiles compared with Sanger sequencing data. Since 2005, several assembly software packages have been created or revised specifically for de novo assembly of next-generation sequencing data. This review summarizes and compares the published descriptions of packages named SSAKE, SHARCGS, VCAKE, Newbler, Celera Assembler, Euler, Velvet, ABySS, AllPaths, and SOAPdenovo. More generally, it compares the two standard methods known as the de Bruijn graph approach and the overlap/layout/consensus approach to assembly.


Intelligence-Augmented Rat Cyborgs in Maze Solving


title: Identification of inpainted satellite images using evalutionary artificial neural network (EANN) and k-nearest neighbor(KNN) algorithm

abstract: Now a day's recognition of satellite image authenticity has received too much attention due to the invention of various remote sensing image inpainting algorithms. Satellite image forgery can be referred as a technique in which fake satellite image is generated by the creation and alternation of new image contents. This paper proposes an algorithm for the identification of inpainted remote sensing images. The proposed algorithm is the connection of two major processes: feature extraction followed by use of classifiers based on k-nearest neighbor's algorithm (KNN) and evolutionary artificial neural networks algorithm (EANN) separately. The proposed algorithm can efficiently identify whether a satellite image is inpainted or not. The experimental results reveal that the proposed method has better performance with faster speed for various kind of satellite inpainting images.


title: Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning

abstract: Learning how to act when there are many available actions in each state is a challenging task for Reinforcement Learning (RL) agents, especially when many of the actions are redundant or irrelevant. In such cases, it is sometimes easier to learn which actions not to take. In this work, we propose the Action-Elimination Deep Q-Network (AE-DQN) architecture that combines a Deep RL algorithm with an Action Elimination Network (AEN) that eliminates sub-optimal actions. The AEN is trained to predict invalid actions, supervised by an external elimination signal provided by the environment. Simulations demonstrate a considerable speedup and added robustness over vanilla DQN in text-based games with over a thousand discrete actions.


title: Data-Dependent Generalization Bounds for Multi-Class Classification

abstract: In this paper, we study data-dependent generalization error bounds that exhibit a mild dependency on the number of classes, making them suitable for multi-class learning with a large number of label classes. The bounds generally hold for empirical multi-class risk minimization algorithms using an arbitrary norm as the regularizer. Key to our analysis is new structural results for multi-class Gaussian complexities and empirical $\ell _\infty $ -norm covering numbers, which exploit the Lipschitz continuity of the loss function with respect to the $\ell _{2}$ - and $\ell _\infty $ -norm, respectively. We establish data-dependent error bounds in terms of the complexities of a linear function class defined on a finite set induced by training examples, for which we show tight lower and upper bounds. We apply the results to several prominent multi-class learning machines and show a tighter dependency on the number of classes than the state of the art. For instance, for the multi-class support vector machine of Crammer and Singer (2002), we obtain a data-dependent bound with a logarithmic dependency, which is a significant improvement of the previous square-root dependency. The experimental results are reported to verify the effectiveness of our theoretical findings.


title: Reactivity to saccharin and quinine solutions following amygdaloid or septal lesions in rats

abstract: The effect of septal or amygdaloid lesions on the preference behavior of rats to a variety of saccharin and quinine solutions was tested in a series of four experiments with one- and two-bottle preference techniques. Two-bottle tests revealed marked lesion effects only at high saccharin concentrations and no effect of lesions on the rejection of quinine solutions. Single-bottle tests also failed to reveal lesion effects on saccharin consumption. A control experiment suggested that long periods of access to test solutions and gradual increases in concentration produced adaptation effects which obscured the lesion effects. It is suggested that changes in reactivity to various solutions following amygdaloid or septal lesions may be restricted to test procedures which involve brief exposure to test solutions or few changes in concentration.