title: WEARPG: Movement-Based Tabletop Role-Playing Game with Arm-Worn Devices and an Augmented Die

abstract: Augmenting tabletop role-playing games (TTRPG) with computers took much attention of researchers recently. Nevertheless, these efforts mostly remained as functional augmentations. We believe that integrating new gameplay styles to this genre is still an underexplored area. Drawing upon the previous studies, we believe that wearables which support movement-based gameplay can be a good step taken in this direction as previous studies claim that wearables can strengthen the link to the imaginary worlds which is critical for TTRPG experience while the movement-based play can increase the player engagement. However, previous studies did not investigate these concepts with an implemented technology. Therefore, to get a better understanding of how wearables can alter the TTRPG experience, we designed a new RPG game system and developed the Elemental Gauntlet and the Luck Stone which enable movement-based game play in TTRPG context. Our preliminary results showed that, movement-based play and wearable props strengthen the identification feeling with the fictional character and resulted in a better immersion to the imaginary world of the game.


title: Nurses' attitudes toward emotional, sexual, and physical abusers of children with disabilities.

abstract: Some studies have linked birth defects, disability, and chronic illness with an increased incidence of child abuse. Nurses who are involved with disabled children face a challenge in preventing child abuse and intervening in cases in which abuse has occurred. Nurse must become aware of the risk factors and signs of abuse, understand the role of their personal attitudes toward abuse and abusers, and develop the skills to intervene effectively and deal with abusers. Thus, the authors of this article undertook a study to ascertain nurses' attitudes about emotional, sexual, and physical abuse of children with disabilities and to determine if nurses' anticipated level of comfort differed when dealing with abusers of children with disabilities in contrast to abusers of children without disabilities.


title: Single Phase Application of Space Vector Pulse Width Modulation for Shunt Active Power Filters

abstract: The most important part of the active power filters is generating of gate signal for inverters. This paper presents Single Phase Application of Space Vector Pulse Width Modulation for shunt active power filters. In conventional SVPWM, all of the phase's currents are controlled together, but in this method each of phase currents is controlled independently from the measured currents of other phases. In another word, this method prevents from influence of other phase's errors in the control of considered phase. In this method, the implementation of control logic will be simpler than the conventional SVPWM. For showing the performance of proposed method a typical system has been simulated by MATLAB/SIMULINK. At last, the results of proposed method are compared with the conventional SVPWM. The results show that proposed method have better performance in generating of the compensation current in active power filter.


title: Geometric design of the LineScout, a teleoperated robot for power line inspection and maintenance

abstract: Power line inspection and maintenance are fields of application where robotics has yet to be introduced. However, as in many other hostile environments, substantial benefits in terms of workers safety and quality of inspection results could arise. This paper describes one of the latest initiatives toward that goal by presenting the design of a teleoperated robot called the "LineScout". This moving platform has the capacity to cross obstacles found on the line. Special care was taken early in the design phase to account for the context of power utility operation and to customize the geometric parameters to the specific features of the electric power network. The final prototype incorporates several key mechanical design features, which are briefly described. The LineScout is now validated, fully tested and ready to undertake pilot projects.


title: Architecture Modeling and Analysis of Security in Android Systems

abstract: Software architecture modeling is important for analyzing system quality attributes, particularly security. However, such analyses often assume that the architecture is completely known in advance. In many modern domains, especially those that use plugin-based frameworks, it is not possible to have such a complete model because the software system continuously changes. The Android mobile operating system is one such framework, where users can install and uninstall apps at run time. We need ways to model and analyze such architectures that strike a balance between supporting the dynamism of the underlying platforms and enabling analysis, particularly throughout a system’s lifetime. In this paper, we describe a formal architecture style that captures the modifiable architectures of Android systems, and that supports security analysis as a system evolves. We illustrate the use of the style with two security analyses: a predicate-based approach defined over architectural structure that can detect some common security vulnerabilities, and inter-app permission leakage determined by model checking. We also show how the evolving architecture of an Android device can be obtained by analysis of the apps on a device, and provide some performance evaluation that indicates that the architecture can be amenable for use throughout the system’s lifetime.


title: Moses: Open Source Toolkit for Statistical Machine Translation

abstract: We describe an open-source toolkit for statistical machine translation whose novel contributions are (a) support for linguistically motivated factors, (b) confusion network decoding, and (c) efficient data formats for translation models and language models. In addition to the SMT decoder, the toolkit also includes a wide variety of tools for training, tuning and applying the system to many translation tasks.


title: Conditional Infomax Learning : An Integrated Framework for Feature Extraction and Fusion

abstract: The paper introduces a new framework for feature learning in classification motivated by information theory. We first systematically study the information structure and present a novel perspective revealing the two key factors in information utilization: class-relevance and redundancy. We derive a new information decomposition model where a novel concept called class-relevant redundancy is introduced. Subsequently a new algorithm called Conditional Informative Feature Extraction is formulated, which maximizes the joint class-relevant information by explicitly reducing the class-relevant redundancies among features. To address the computational difficulties in information-based optimization, we incorporate Parzen window estimation into the discrete approximation of the objective function and propose a Local Active Region method which substantially increases the optimization efficiency. To effectively utilize the extracted feature set, we propose a Bayesian MAP formulation for feature fusion, which unifies Laplacian Sparse Prior and Multivariate Logistic Regression to learn a fusion rule with good generalization capability. Realizing the inefficiency caused by separate treatment of the extraction stage and the fusion stage, we further develop an improved design of the framework to coordinate the two stages by introducing a feedback from the fusion stage to the extraction stage, which significantly enhances the learning efficiency. The results of the comparative experiments show remarkable improvements achieved by our framework.


title: Cracked infinite cylinder with two rigid inclusions under axisymmetric tension

abstract: Abstract This paper considers the problem of an axisymmetric infinite cylinder with a ring shaped crack at z = 0 and two ring-shaped rigid inclusions with negligible thickness at z = ± L . The cylinder is under the action of uniformly distributed axial tension applied at infinity and its lateral surface is free of traction. It is assumed that the material of the cylinder is linearly elastic and isotropic. Crack surfaces are free and the constant displacements are continuous along the rigid inclusions while the stresses have jumps. Formulation of the mixed boundary value problem under consideration is reduced to three singular integral equations in terms of the derivative of the crack surface displacement and the stress jumps on the rigid inclusions. These equations, together with the single-valuedness condition for the displacements around the crack and the equilibrium equations along the inclusions, are converted to a system of linear algebraic equations, which is solved numerically. Stress intensity factors are calculated and presented in graphical form.


title: Person Re-Identification by Deep Joint Learning of Multi-Loss Classification

abstract: Existing person re-identification (re-id) methods rely mostly on either localised or global feature representation alone. This ignores their joint benefit and mutual complementary effects. In this work, we show the advantages of jointly learning local and global features in a Convolutional Neural Network (CNN) by aiming to discover correlated local and global features in different context. Specifically, we formulate a method for joint learning of local and global feature selection losses designed to optimise person re-id when using only generic matching metrics such as the L2 distance. We design a novel CNN architecture for Jointly Learning Multi-Loss (JLML) of local and global discriminative feature optimisation subject concurrently to the same re-id labelled information. Extensive comparative evaluations demonstrate the advantages of this new JLML model for person re-id over a wide range of state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03, Market-1501).


title: Measuring similarity of graph nodes by neighbor matching

abstract: The problem of measuring similarity of graph nodes is important in a range of practical problems. There is a number of proposed measures, usually based on iterative calculation of similarity and the principle that two nodes are as similar as their neighbors are. In our work, we propose one novel method of that sort, with a refined concept of similarity of nodes that involves matching their neighbors. We prove convergence of the proposed method and show that it has some additional desirable properties that, to our knowledge, the existing methods lack. In addition, we construct a measure of similarity of whole graphs based on the similarities of nodes. We illustrate the proposed method on several specific problems and empirically compare it to other methods.


title: Sketch-based Geometric Monitoring of Distributed Stream Queries

abstract: Emerging large-scale monitoring applications rely on continuous tracking of complex data-analysis queries over collections of massive, physically-distributed data streams. Thus, in addition to the space- and time-efficiency requirements of conventional stream processing (at each remote monitor site), effective solutions also need to guarantee communication efficiency (over the underlying communication network). The complexity of the monitored query adds to the difficulty of the problem -- this is especially true for nonlinear queries (e.g., joins), where no obvious solutions exist for distributing the monitor condition across sites. The recently proposed geometric method offers a generic methodology for splitting an arbitrary (non-linear) global threshold-monitoring task into a collection of local site constraints; still, the approach relies on maintaining the complete stream(s) at each site, thus raising serious efficiency concerns for massive data streams. In this paper, we propose novel algorithms for efficiently tracking a broad class of complex aggregate queries in such distributed-streams settings. Our tracking schemes rely on a novel combination of the geometric method with compact sketch summaries of local data streams, and maintain approximate answers with provable error guarantees, while optimizing space and processing costs at each remote site and communication cost across the network. One of our key technical insights for the effective use of the geometric method lies in exploiting a much lower-dimensional space for monitoring the sketch-based estimation query. Due to the complex, highly nonlinear nature of these estimates, efficiently monitoring the local geometric constraints poses challenging algorithmic issues for which we propose novel solutions. Experimental results on real-life data streams verify the effectiveness of our approach.


title: Membership questions for timed and hybrid automata

abstract: Timed and hybrid automata are extensions of finite state machines for formal modeling of embedded systems with both discrete and continuous components. Reachability problems for these automata are well studied and have been implemented in verification tools. For the purpose of effective error reporting and testing, we consider the membership problems for such automata. We consider different types of membership problems depending on whether the path (i.e. edge sequence), or the trace (i.e. event sequence), or the timed trace (i.e. timestamped event sequence), is specified. We give comprehensive results regarding the complexity of these membership questions for different types of automata, such as timed automata and linear hybrid automata, with and without /spl epsiv/ transitions. In particular we give an efficient O(n/spl middot/m/sup 2/) algorithm for generating timestamps corresponding to a path of length n in a timed automaton with m clocks. This algorithm is implemented in the verifier COSPAN to improve its diagnostic feedback during timing verification. Second, we show that for automata without /spl epsiv/ transitions, the membership question is NP complete for different types of automata whether or not the timestamps are specified along with the trace. Third, we show that for automata with /spl epsiv/ transitions, the membership question is as hard as the reachability question even for timed traces: it is PSPACE complete for timed automata, and undecidable for slight generalizations.


TRACKING CONTROL FOR A TWO-LINK PLANAR RIGID ROBOT MANIPULATOR


title: Comparison of Self-Pierce Riveting, Resistance Spot Welding and Spot Friction Joining for Aluminium Automotive Sheet

abstract: This work compares three aluminium sheet joining processes to determine their capability, efficiency and cost for mass production applications in automotive structures and closures. The joining processes investigated are Resistance Spot Welding (RSW), SelfPierce Riveting (SPR) and Spot Friction Joining (SFJ). Quantitative comparisons have been made on the basis of tensile strength (shear and peel), process time, equipment price and running cost. RSW is the most commonly employed joining method for steel sheet in the automotive industry. Its principle benefits are high speed and low cost operation, plus the ability to weld a wide range of joint configurations with the same gun. The main process limitations for aluminium are weld consistency and electrode-life, though recent work has shown that both of these can be largely overcome with regular electrode polishing


title: Convergent Iterative Closest-Point Algorithm to Accomodate Anisotropic and Inhomogenous Localization Error

abstract: Since its introduction in the early 1990s, the Iterative Closest Point (ICP) algorithm has become one of the most well-known methods for geometric alignment of 3D models. Given two roughly aligned shapes represented by two point sets, the algorithm iteratively establishes point correspondences given the current alignment of the data and computes a rigid transformation accordingly. From a statistical point of view, however, it implicitly assumes that the points are observed with isotropic Gaussian noise. In this paper, we show that this assumption may lead to errors and generalize the ICP such that it can account for anisotropic and inhomogenous localization errors. We 1) provide a formal description of the algorithm, 2) extend it to registration of partially overlapping surfaces, 3) prove its convergence, 4) derive the required covariance matrices for a set of selected applications, and 5) present means for optimizing the runtime. An evaluation on publicly available surface meshes as well as on a set of meshes extracted from medical imaging data shows a dramatic increase in accuracy compared to the original ICP, especially in the case of partial surface registration. As point-based surface registration is a central component in various applications, the potential impact of the proposed method is high.


title: The effect of hydrophilic chain length and iRGD on drug delivery from poly(ε-caprolactone)-poly(N-vinylpyrrolidone) nanoparticles.

abstract: Poly(ε-caprolactone)-b-Poly(N-vinylpyrrolidone) (PCL-b-PVP) copolymers with different PVP block length were synthesized by xanthate-mediated reverse addition fragment transfer polymerization (RAFT) and the xanthate chain transfer agent on chain end was readily translated to hydroxy or aldehyde for conjugating various functional moieties, such as fluorescent dye, biotin hydrazine and tumor homing peptide iRGD. Thus, PCL-PVP nanoparticles were prepared by these functionalized PCL-b-PVP copolymers. Furthermore, paclitaxel-loaded PCL-PVP nanoparticles with satisfactory drug loading content (15%) and encapsulation efficiency (>90%) were obtained and used in vitro and in vivo antitumor examination. It was demonstrated that the length of PVP block had a significant influence on cytotoxicity, anti-BSA adsorption, circulation time, stealth behavior, biodistribution and antitumor activity for the nanoparticles. iRGD on PCL-PVP nanoparticle surface facilitated the nanoparticles to accumulate in tumor site and enhanced their penetration in tumor tissues, both of which improved the efficacy of paclitaxel-loaded nanoparticles in impeding tumor growth and prolonging the life time of H22 tumor-bearing mice.


Clinical benefit with cisplatin, hydroxyurea and 5-fluorouracil/ leucovorin in advanced pancreatic adenocarcinoma


title: Antiangiogenic therapy in oncology: current status and future directions

abstract: Angiogenesis, the formation of new blood vessels from pre-existing vessels, has been validated as a target in several tumour types through randomised trials, incorporating vascular endothelial growth factor (VEGF) pathway inhibitors into the therapeutic armoury. Although some tumours such as renal cell carcinoma, ovarian and cervical cancers, and pancreatic neuroendocrine tumours are sensitive to these drugs, others such as prostate cancer, pancreatic adenocarcinoma, and melanoma are resistant. Even when drugs have yielded significant results, improvements in progression-free survival, and, in some cases, overall survival, are modest. Thus, a crucial issue in development of these drugs is the search for predictive biomarkers-tests that predict which patients will, and will not, benefit before initiation of therapy. Development of biomarkers is important because of the need to balance efficacy, toxicity, and cost. Novel combinations of these drugs with other antiangiogenics or other classes of drugs are being developed, and the appreciation that these drugs have immunomodulatory and other modes of action will lead to combination regimens that capitalise on these newly understood mechanisms.


title: Mineralogical and Petrographic Characteristics of Indium and REE-Bearing Accessory Phases in the Kymi Granite Stock, Southern Finland

abstract: The Wiborg rapakivi batholith (1.64 Ga) in ::: southeastern Finland with documented occurrences of REE, indium and Zn-Cu-Pb sulphide mineralization was studied. Hydrothermal greisen and quartz ::: vein type Fe-Sn and Zn-Cu-Pb are found in the Kymi granite stock as intrusions. They are enriched with indium and rare earth ::: elements, with roquesite (CuInS2) being a major indium- carrier, whereas monazite (Ce), allanite (Ce), bastnaesite ::: (Ce), xenotime-(Y) and thorite are the main REE carriers. Combination of ::: optical and field emission scanning electron microscopy (FE-SEM) and electron ::: probe microanalysis (EPMA) were used to study the indium and REE-bearing ::: mineral assemblages. EPMA of roquesite found in galena had a composition of ::: 26.16% S, 0.02% Fe, 25.06% Cu, 0.03% Zn, 1.06% As, 0.31% Sb and 47.14% In. ::: Substitution reaction Pb2+S2-Cu+In3+S2- is the cause of the incorporation of indium ::: in the galena structure. The majority of the LREE are carried by monazite, ::: bastnaesite and allanite, and the HREE by xenotime and zircon. There is a ::: partial solid solution between monazite and xenotime with minor or trace ::: amounts of LREE in xenotime grains (6.0 wt%). ::: LREE (>95 mol% LREE) and less than 5 mol% HREE + Y reflects the enrichment of chondrite-normalized REE of the monazite grains of the Kymi granite stock. The xenotime grains (small and irregular) main composition contains 71 - 76 mol% YPO4, 16 - 27 mol% HREE, and 6 - 8 mol% LREE. It is believed that indium and ::: REE-mineralization presence is due to the combination of magmatic and postmagmatic ::: processes, particularly at later stages by fluid fractionation.


title: A constrained latent variable model

abstract: Latent variable models provide valuable compact representations for learning and inference in many computer vision tasks. However, most existing models cannot directly encode prior knowledge about the specific problem at hand. In this paper, we introduce a constrained latent variable model whose generated output inherently accounts for such knowledge. To this end, we propose an approach that explicitly imposes equality and inequality constraints on the model's output during learning, thus avoiding the computational burden of having to account for these constraints at inference. Our learning mechanism can exploit non-linear kernels, while only involving sequential closed-form updates of the model parameters. We demonstrate the effectiveness of our constrained latent variable model on the problem of non-rigid 3D reconstruction from monocular images, and show that it yields qualitative and quantitative improvements over several baselines.


title: Multifaceted Feature Visualization: Uncovering the Different Types of Features Learned By Each Neuron in Deep Neural Networks

abstract: We can better understand deep neural networks by identifying which features each of their neurons have learned to detect. To do so, researchers have created Deep Visualization techniques including activation maximization, which synthetically generates inputs (e.g. images) that maximally activate each neuron. A limitation of current techniques is that they assume each neuron detects only one type of feature, but we know that neurons can be multifaceted, in that they fire in response to many different types of features: for example, a grocery store class neuron must activate either for rows of produce or for a storefront. Previous activation maximization techniques constructed images without regard for the multiple different facets of a neuron, creating inappropriate mixes of colors, parts of objects, scales, orientations, etc. Here, we introduce an algorithm that explicitly uncovers the multiple facets of each neuron by producing a synthetic visualization of each of the types of images that activate a neuron. We also introduce regularization methods that produce state-of-the-art results in terms of the interpretability of images obtained by activation maximization. By separately synthesizing each type of image a neuron fires in response to, the visualizations have more appropriate colors and coherent global structure. Multifaceted feature visualization thus provides a clearer and more comprehensive description of the role of each neuron.


title: Light-weight wireless power transfer for mid-air charging of drones

abstract: Recent developments in high frequency inductive wireless power transfer (WPT) mean that the technology has reached a point where powering small autonomous drones has become feasible. Fundamentally, drones can only carry very limited payloads and thus require light-weight WPT receiver solutions. The key to achieving light weight is operating the WPT system at high frequency: this allows both the coils and the electronics to achieve very high gravametric power densities. When operated in the MHz region, the WPT coils can be manufactured without the need for ferrite, because the low coupling factor can be offset by very high coil Q factors. To make efficient MHz power conversion circuits, wide band-gap semiconductors, including Silicon Carbide (SiC) and Gallium Nitride (GaN) have provided a step change. For powering a drone, these devices are integrated into soft-switching resonant inverter and rectifier topologies and are able to operate efficiently at tens of MHz and hundreds of watts. In addition, recently discovered designs that make these inverters tolerant to load variations and have inherent voltage or current regulation features enable an WPT system to operate effectively as the separation distance and alignment between transmitter and receiver changes, which is critical when charging a flying drone. It will be shown that combining all these individual developments has enabled the charging of a 10 W micro-drone whilst hovering in the vicinity of a charging pad.


title: Combination of Plant Growth Regulators, Maltose, and Partial Desiccation Treatment Enhance Somatic Embryogenesis in Selected Malaysian Rice Cultivar

abstract: The development of efficient tissue culture protocol for somatic embryo would facilitate the genetic modification breeding program. The callus induction and regeneration were studied by using different parameters i.e., auxins, cytokinins, and desiccation treatment. Scanning electron microscopy and histological analysis were performed to identify the embryogenic callus for regeneration. The callus percentage results showed that MS (Murashige and Skoog) basal medium supplemented with 3 mg/L 2, 4-D and 30g/L maltose were the optimal callus induction medium for MR220 (80%) and MR220-CL2 (95%). The morphology of the embryogenic callus was confirmed by the SEM (Scanning Electron Microscopy) (presence of extracellular matrix surface network) and later by histological analysis. Finally, MS media supplemented with 0.5 mg/L NAA (Naphthalene Acetic Acid), 2 mg/L kin, and 1 mg/L BAP were selected as the optimum regeneration media treatment while callus desiccated for 48 h was proved to produce more plantlets in MR220 (60%) and MR220-CL2 (73.33%) compared to control treatment (without desiccation). The protocol presented here showed the necessity for the inclusion of partial desiccation as an important step in the tissue culture protocol of Malaysian indica rice genotypes in order to enhance their regeneration potential.


title: The essence of entanglement

abstract: Entanglement, according to Erwin Schroedinger the essence of quantum mechanics, is at the heart of the Einstein-Podolsky-Rosen paradox and of the so called quantum-nonlocality - the fact that a local realistic explanation of quantum mechanics is not possible as quantitatively expressed by violation of Bell's inequalities. Even as entanglement gains increasing importance in most quantum information processing protocols, its conceptual foundation is still widely debated. Among the open questions are: What is the conceptual meaning of quantum entanglement? What are the most general constraints imposed by local realism? Which general quantum states violate these constraints? Developing Schroedinger's ideas in an information-theoretic context we suggest that a natural understanding of quantum entanglement results when one accepts (1) that the amount of information per elementary system is finite and (2) that the information in a composite system resides more in the correlations than in properties of individuals. The quantitative formulation of these ideas leads to a rather natural criterion of quantum entanglement. Independently, extending Bell's original ideas, we obtain a single general Bell inequality that summarizes all possible constraints imposed by local realism on the correlations for a multi-particle system. Violation of the general Bell inequality results in an independent general criterion for quantum entanglement. Most importantly, the two criteria agree in essence, though the two approaches are conceptually very different. This concurrence strongly supports the information-theoretic interpretation of quantum entanglement and of quantum physics in general.


title: A Survey of Algebraic Properties Used in Cryptographic Protocols ?

abstract: Cryptographic protocols are successfully analyzed using formal methods. However, formal approaches usually consider the encryption schemes as black boxes and assume that an adversary cannot learn anything from an encrypted message except if he has the key. Such an assumption is too strong in general since some attacks exploit in a clever way the interaction between protocol rules and properties of cryptographic operators. Moreover, the executability of some protocols relies explicitly on some algebraic properties of cryptographic primitives such as commutative encryption. We give a list of some relevant algebraic properties of cryptographic operators, and for each of them, we provide examples of protocols or attacks using these properties. We also give an overview of the existing methods in formal approaches for analyzing cryptographic protocols.


title: A mobile payment mechanism with anonymity for cloud computing

abstract: In recent years, traditional transactions have been replaced by electronic transactions. To protect the security of the electronic transactions, various electronic payment (e-payment) mechanisms have been proposed. However, we find the previous e-payment mechanisms do not provide the non-repudiation requirement in the client side. Thus, a malicious client can easily deny the transaction and the merchant may not get the payment. In addition, these mechanisms have large computation and communication costs so they cannot be applied to the mobile payment for cloud computing. To solve the above problems, we propose a new mobile payment mechanism with anonymity for cloud computing in this paper. The proposed mechanism not only reduces the computation cost but also provides the non-repudiation requirement in the client side. Compared with the related works, the proposed mechanism is securer, fairer, and more efficient. Therefore, the proposed mobile payment mechanism is more suitable and practical for the cloud computing. The client can transact anonymously to protect the payment privacy.The client's bank generates the digital signature as the payment proof.The new payment scheme can eliminate the redundant symmetric keys between the client and the merchant.


title: Arabic Named Entity Recognition: A Feature-Driven Study

abstract: The named entity recognition task aims at identifying and classifying named entities within an open-domain text. This task has been garnering significant attention recently as it has been shown to help improve the performance of many natural language processing applications. In this paper, we investigate the impact of using different sets of features in three discriminative machine learning frameworks, namely, support vector machines, maximum entropy and conditional random fields for the task of named entity recognition. Our language of interest is Arabic. We explore lexical, contextual and morphological features and nine data-sets of different genres and annotations. We measure the impact of the different features in isolation and incrementally combine them in order to evaluate the robustness to noise of each approach. We achieve the highest performance using a combination of 15 features in conditional random fields using broadcast news data (Fbeta = 1=83.34).


title: Visual Tracking Using Neuromorphic Asynchronous Event-Based Cameras

abstract: This letter presents a novel computationally efficient and robust pattern tracking method based on a time-encoded, frame-free visual data. Recent interdisciplinary developments, combining inputs from engineering and biology, have yielded a novel type of camera that encodes visual information into a continuous stream of asynchronous, temporal events. These events encode temporal contrast and intensity locally in space and time. We show that the sparse yet accurately timed information is well suited as a computational input for object tracking. In this letter, visual data processing is performed for each incoming event at the time it arrives. The method provides a continuous and iterative estimation of the geometric transformation between the model and the events representing the tracked object. It can handle isometry, similarities, and affine distortions and allows for unprecedented real-time performance at equivalent frame rates in the kilohertz range on a standard PC. Furthermore, by using the dimension of time that is currently underexploited by most artificial vision systems, the method we present is able to solve ambiguous cases of object occlusions that classical frame-based techniques handle poorly.


title: Hardware Accelerated Per-Pixel Displacement Mapping

abstract: In this paper we present an algorithm capable of rendering a displacement mapped triangle mesh interactively on latest GPUs. The algorithm uses only pixel shaders and does not rely on adaptively adding geometry. All sampling of the displacement map takes place in the pixel shader and bi- or trilinear ltering can be applied to it, and at the same time as the calculations are done per pixel in the shader, the algorithm has automatic level of detail control. The triangles of the base mesh are extruded along the respective normal directions and then the resulting prisms are rendered by casting rays inside and intersecting them with the displaced surface. Two different implementations are discussed in detail.


title: Cutaneous horn: a potentially malignant entity

abstract: A cutaneous horn is a conical, dense, hyperkeratotic protrusion that often appears similar to the horn of an animal. It is a morphologic designation referring to an unusually cohesive keratinized material, not a true pathologic diagnosis. Cutaneous horns occur in association with, or as a response to, a wide variety of underlying benign, pre-malignant, and malignant cutaneous diseases. The most important common concern is distinguishing a hyperkeratotic actinic keratosis from a cutaneous squamous cell carcinoma. Keratoacanthoma is another cause, as illustrated herein as a projective cutaneous tumor with a fingernail-like appearance. The treatment of choice for cutaneous horns is shave excision with subsequent histopathologic evaluation to rule out underlying malignancy and to guide potential further therapy.


Modeling Temporal Progression of Emotional Status in Mental Health Forum: A Recurrent Neural Net Approach.


title: Data-Centric Computing Frontiers: A Survey On Processing-In-Memory

abstract: A major shift from compute-centric to data-centric computing systems can be perceived, as novel big data workloads like cognitive computing and machine learning strongly enforce embarrassingly parallel and highly efficient processor architectures. With Moore's law having surrendered, innovative architectural concepts as well as technologies are urgently required, to enable a path for tackling exascale and beyond -- even though current computing systems face the inevitable instruction-level parallelism, power, memory, and bandwidth walls. As part of any computing system, the general perception of memories depicts unreliability, power hungriness and slowness, resulting in a future prospective bottleneck. The latter being an outcome of a pin limitation derived by packaging constraints, an unexploited tremendous row bandwidth is determinable, which off-chip diminishes to a bare minimum. Building upon a shift towards data-centric computing systems, the near-memory processing concept seems to be most promising, since power efficiency and computing performance increase by co-locating tasks on bandwidth-rich in-memory processing units, whereas data motion mitigates by the avoidance of entire memory hierarchies. By considering the umbrella of near-data processing as the urgent required breakthrough for future computing systems, this survey presents its derivations with a special emphasis on Processing-In-Memory (PIM), highlighting historical achievements in technology as well as architecture while depicting its advantages and obstacles.


title: A safety/security risk analysis approach of industrial control systems: a cyber bowtie - combining new version of attack tree with bowtie analysis

abstract: The introduction of connected systems and digital technology in process industries creates new cyber-security vulnerabilities that can be exploited by sophisticated threats and lead to undesirable safety accidents. Thus, identifying these vulnerabilities during risk analysis becomes an important part for effective industrial risk evaluation. However, nowadays, safety and security are analyzed separately when they should not be. This is because a security threat can lead to the same dangerous phenomenon as a safety incident. In this paper, a new method that considers safety and security together during industrial risk analysis is proposed. This approach combines bowtie analysis, commonly used for safety analysis, with a new extended version of attack tree analysis, introduced for security analysis of industrial control systems. The combined use of bowtie and attack tree provides an exhaustive representation of risk scenarios in terms of safety and security. We then propose an approach for evaluating the risk level based on two-term likelihood parts, one for safety and one for security. The application of this approach is demonstrated using the case study of a risk scenario in a chemical facility.


title: Fusion of neural networks with fuzzy logic and genetic algorithm

abstract: Combining multiple estimators has appeared as one of hot research topics in several areas including artificial neural networks. This paper presents three methods to work out the problem based on so-called softcomputing techniques, toward a unified framework of hybrid softcomputing techniques. The first method based on fuzzy logic nonlinearly combines objective evidences, in the form of network outputs, with subjective evaluation of the reliability of the individual neural networks. The second method based on genetic algorithm gives us an effective vehicle to determine the optimal weight parameters that are multiplied by the network outputs. Finally, we have proposed a hybrid synergistic method of fuzzy logic and genetic algorithm to optimally combine neural networks. The experimental results with the recognition problem of totally unconstrained handwritten digits show that the performance could be improved significantly with the proposed softcomputing techniques.


title: Gridding aeromagnetic data using inverse interpolation

abstract: SUMMARY ::: ::: We regard the gridding of aeromagnetic data as a geophysical estimation problem and propose to use the inverse interpolation method for gridding aeromagnetic data. The gridding method we present is based on Tikhonov's regularization theory by setting up the data fitting goal and the model smoothing goal. The interpolation operator in the data fitting goal is chosen to be a Gaussian weighted operator. The smoothing filter in the model smoothing goal is chosen to be a Laplacian operator. The gridding equation is iteratively solved by using the preconditioning conjugate gradient algorithm, the preconditioning operator, which is the inverse of the Laplacian operator. The helix transform technique and Wilson's spectral factorization algorithm are used to make the filtering and inverse filtering of the Laplacian operator fast and stable. Gridding experiments were conducted on synthetic total field magnetic anomaly and real aeromagnetic data from a survey in South China and show that the method produced smooth and accurate results without obvious aliasing artefacts.


title: Egocentric Spatial Memory Network

abstract: Inspired by neurophysiological discoveries of navigation cells in the mammalian brain, we introduce the first deep neural network architecture for modeling Egocentric Spatial Memory (ESM). It learns to estimate the pose of the agent and progressively construct top-down 2D global maps from egocentric views in a spatially extended environment. During the exploration, our proposed ESM network model updates belief of the global map based on local observations using a recurrent neural network. It also augments the local mapping with a novel external memory to encode and store latent representations of the visited places based on their corresponding locations in the egocentric coordinate. This enables the agents to perform loop closure and mapping correction. This work contributes in the following aspects: first, our proposed ESM network provides an accurate mapping ability which is vitally important for embodied agents to navigate to goal locations. In the experiments, we demonstrate the functionalities of the ESM network in random walks in complicated 3D mazes by comparing with several competitive baselines and state-of-the-art Simultaneous Localization and Mapping (SLAM) algorithms. Secondly, we faithfully hypothesize the functionality and the working mechanism of navigation cells in the brain. Comprehensive analysis of our model suggests the essential role of individual modules in our proposed architecture and demonstrates efficiency of communications among these modules. We hope this work would advance research in the collaboration and communications over both fields of computer science and computational neuroscience.


title: A comparison between semi-supervised and supervised text mining techniques on detecting irony in greek political tweets

abstract: The present work describes a classification schema for irony detection in Greek political tweets. Our hypothesis states that humorous political tweets could predict actual election results. The irony detection concept is based on subjective perceptions, so only relying on human-annotator driven labor might not be the best route. The proposed approach relies on limited labeled training data, thus a semi-supervised approach is followed, where collective-learning algorithms take both labeled and unlabeled data into consideration. We compare the semi-supervised results with the supervised ones from a previous research of ours. The hypothesis is evaluated via a correlation study between the irony that a party receives on Twitter, its respective actual election results during the Greek parliamentary elections of May 2012, and the difference between these results and the ones of the preceding elections of 2009.


title: Information Security Management Standards: Problems and Solutions.

abstract: International information security management guidelines play a key role in managing and certifying organizational IS. We analyzed BS7799, BS ISO/IEC17799: 2000, GASPP/GAISP, and the SSE-CMM to determine and compare how these guidelines are validated, and how widely they can be applied. First, we found that BS7799, BS ISO/IEC17799: 2000, GASPP/GAISP and the SSE-CMM were generic or universal in scope; consequently they do not pay enough attention to the differences between organizations and the fact that their security requirements are different. Second, we noted that these guidelines were validated by appeal to common practice and authority and that this was not a sound basis for important international information security guidelines. To address these shortcomings, we believe that information security management guidelines should be seen as a library of material on information security management for practitioners.


title: “Neural” computation of decisions in optimization problems

abstract: Highly-interconnected networks of nonlinear analog neurons are shown to be extremely effective in computing. The networks can rapidly provide a collectively-computed solution (a digital output) to a problem on the basis of analog input information. The problems to be solved must be formulated in terms of desired optima, often subject to constraints. The general principles involved in constructing networks to solve specific problems are discussed. Results of computer simulations of a network designed to solve a difficult but well-defined optimization problem-the Traveling-Salesman Problem-are presented and used to illustrate the computational power of the networks. Good solutions to this problem are collectively computed within an elapsed time of only a few neural time constants. The effectiveness of the computation involves both the nonlinear analog response of the neurons and the large connectivity among them. Dedicated networks of biological or microelectronic neurons could provide the computational capabilities described for a wide class of problems having combinatorial complexity. The power and speed naturally displayed by such collective networks may contribute to the effectiveness of biological information processing.


title: Static secure page allocation for light-weight dynamic information flow tracking

abstract: Dynamic information flow tracking (DIFT) is an effective security countermeasure for both low-level memory corruptions and high-level semantic attacks. However, many software approaches suffer large performance degradation, and hardware approaches have high logic and storage overhead. We propose a flexible and light-weight hardware/software co-design approach to perform DIFT based on secure page allocation. Instead of associating every data with a taint tag, we aggregate data according to their taints, i.e., putting data with different attributes in separate memory pages. Our approach is a compiler-aided process with architecture support. The implementation and analysis show that the memory overhead is little, and our approach can protect critical information, including return address, indirect jump address, and system call IDs, from being overwritten by malicious users.


Node classification in signed social networks


title: Shadow detection and radiometric restoration in satellite high resolution images

abstract: In this paper a new transformation which enables us to detect boundaries of cast shadows in high resolution satellite images is introduced. The transformation is based on color invariant indices. Different radiometric restoration techniques such as gamma correction, linear-correlation correction and histogram matching are introduced in order to restore the brightness of detected shadow area


title: Semi-supervised Relational Topic Model for Weakly Annotated Image Recognition in Social Media

abstract: In this paper, we address the problem of recognizing images with weakly annotated text tags. Most previous work either cannot be applied to the scenarios where the tags are loosely related to the images, or simply take a pre-fusion at the feature level or a post-fusion at the decision level to combine the visual and textual content. Instead, we first encode the text tags as the relations among the images, and then propose a semi-supervised relational topic model (ss-RTM) to explicitly model the image content and their relations. In such way, we can efficiently leverage the loosely related tags, and build an intermediate level representation for a collection of weakly annotated images. The intermediate level representation can be regarded as a mid-level fusion of the visual and textual content, which is able to explicitly model their intrinsic relationships. Moreover, image category labels are also modeled in the ss-RTM, and recognition can be conducted without training an additional discriminative classifier. Our extensive experiments on social multimedia datasets (images+tags) demonstrated the advantages of the proposed model.


Nonisolated Bidirectional Soft Switching SEPIC / ZETA Converter with Reduced Ripple Currents 1 Nonisolated Bidirectional Soft Switching SEPIC / ZETA Converter with Reduced Ripple Currents


title: Innovative soft robots based on electro-rheological fluids

abstract: Electro-rheological (ER) fluids are smart fluids which can transform into solid-like phase by applying an electric field. This process is reversible and can be strategically used to build fluidic components for innovative soft robots capable of soft locomotion. In this work, we show the potential applications of ER fluids to build valves that simplify design of fluidic based soft robots. We propose the design and development of a composite ER valve, aimed at controlling the flexibility of soft robots bodies by controlling the ER fluid flow. We present how an ad hoc number of such soft components can be embodied in a simple crawling soft robot (Wormbot); in a locomotion mechanism capable of forward motion through rotation; and, in a tendon driven continuum arm. All these embodiments show how simplification of the hydraulic circuits relies on the simple structure of ER valves. Finally, we address preliminary experiments to characterize the behavior of Wormbot in terms of actuation forces.


title: A 500 W push-pull dc-dc power converter with a 30 MHz switching frequency

abstract: DESIGNERS of power conversion circuits are under relentless pressure to increase power density while maintaining high efficiency. Increased switching frequency is a primary path to higher power density. Prior work has shown that the use of switching frequencies in the VHF band (30 MHz-300 MHz) are a viable path to the achievement of gains in power density. A promising topology for VHF operation is the voltage-fed Class EF 2 (Class Φ 2 ) inverter based topology, where the use of controlled impedance at the switching frequency and its 2nd and 3rd harmonics provides both full soft switching and substantially reduced voltage stress compared to topologies such as Class E. However, such converters contain multiple resonant elements, and the tuning of the converter can be complicated due in part to the interaction of said elements. It is proposed that a push-pull version of the Class EF 2 inverter can alleviate some of these difficulties. In particular, it is shown that odd and even frequency components can be independently tuned without interaction, and furthermore that center-tapped inductors may be used to reduce the total volume occupied by said inductors. The benefits include simplified design and increased power density. Evidence is presented in the form of a push-pull Class EF 2 (Class Φ 2 ) unregulated 500 W prototype dc-dc converter with a 30 MHz switching frequency, an input voltage 150 VDC, and an output voltage of 65 VDC. This converter has an efficiency of > 81% under nominal conditions, including gate drive power.


title: A Separation Principle for Control in the Age of Deep Learning

abstract: We review the problem of defining and inferring a "state" for a control system based on complex, high-dimensional, highly uncertain measurement streams such as videos. Such a state, or representation, should contain all and only the information needed for control, and discount nuisance variability in the data. It should also have finite complexity, ideally modulated depending on available resources. This representation is what we want to store in memory in lieu of the data, as it "separates" the control task from the measurement process. For the trivial case with no dynamics, a representation can be inferred by minimizing the Information Bottleneck Lagrangian in a function class realized by deep neural networks. The resulting representation has much higher dimension than the data, already in the millions, but it is smaller in the sense of information content, retaining only what is needed for the task. This process also yields representations that are invariant to nuisance factors and having maximally independent components. We extend these ideas to the dynamic case, where the representation is the posterior density of the task variable given the measurements up to the current time, which is in general much simpler than the prediction density maintained by the classical Bayesian filter. Again this can be finitely-parametrized using a deep neural network, and already some applications are beginning to emerge. No explicit assumption of Markovianity is needed; instead, complexity trades off approximation of an optimal representation, including the degree of Markovianity.


Algorithms and Representations for Reinforcement Learning


title: Mumford and Shah Model and Its Applications to Image Segmentation and Image Restoration

abstract: We present in this chapter an overview of the Mumford and Shah model for image segmentation. We discuss its various formulations, some of its properties, the mathematical framework, and several approximations. We also present numerical algorithms and segmentation results using the Ambrosio–Tortorelli phase-field approximations on one hand, and using the level set formulations on the other hand. Several applications of the Mumford–Shah problem to image restoration are also presented. . Introduction: Description of theMumford and Shah Model An important problem in image analysis and computer vision is the segmentation one, that aims to partition a given image into its constituent objects, or to find boundaries of such objects. This chapter is devoted to the description, analysis, approximations, and applications of the classical Mumford and Shah functional proposed for image segmentation. In [–], David Mumford and Jayant Shah have formulated an energy minimization problem that allows to compute optimal piecewise-smooth or piecewise-constant approximations u of a given initial image g. Since then, their model has been analyzed and considered in depth by many authors, by studying properties of minimizers, approximations, and applications to image segmentation, image partition, image restoration, and more generally to image analysis and computer vision. We denote by Ω ⊂ Rd the image domain (an interval if d = , or a rectangle in the plane if d = ). More generally, we assume that Ω is open, bounded, and connected. Let g : Ω → R be a given gray-scale image (a signal in one dimension, a planar image in two dimensions, or a volumetric image in three dimensions). It is natural and without losing any generality to assume that g is a bounded function in Ω, g ∈ L(Ω). As formulated byMumford and Shah [], the segmentation problem in image analysis and computer vision consists in computing a decomposition Ω = Ω ∪Ω ∪ . . . ∪ Ωn ∪ K of the domain of the image g such that (a) The image g varies smoothly and/or slowly within each Ω i . (b) The image g varies discontinuously and/or rapidly across most of the boundary K between different Ω i . From the point of view of approximation theory, the segmentation problem may be restated as seeking ways to define and compute optimal approximations of a general function g(x) by piecewise-smooth functions u(x), i.e., functions u whose restrictions ui to the pieces Ω i of a decomposition of the domain Ω are continuous or differentiable.   Mumford and ShahModel and its Applications to Image Segmentation and Image Restoration In what follows, Ω i will be disjoint connected open subsets of a domain Ω, each one with a piecewise-smooth boundary, and K will be a closed set, as the union of boundaries of Ω i inside Ω, thus Ω = Ω ∪Ω ∪ . . . ∪ Ωn ∪ K, K = Ω ∩ (∂Ω ∪ . . . ∪ ∂Ωn). The functional E to be minimized for image segmentation is defined by [–], E(u,K) = μ ∫ Ω (u − g)dx + ∫ Ω/K ∣∇u∣dx + ∣K∣, (.) where u : Ω → R is continuous or even differentiable inside each Ω i (or u ∈ H(Ω i)) and may be discontinuous across K. Here, ∣K∣ stands for the total surface measure of the hypersurface K (the counting measure if d = , the length measure if d = , the area measure if d = ). Later, we will define ∣K∣ by Hd−(K), the d −  dimensional Hausdorff measure in Rd . As explained by Mumford and Shah, dropping any of these three terms in (> .), inf E = : without the first, take u = , K = /; without the second, take u = g, K = /; without the third, take for example, in the discrete case K to be the boundary of all pixels of the image g, each Ω i be a pixel and u to be the average (value) of g over each pixel. The presence of all three terms leads to nontrivial solutions u, and an optimal pair (u,K) can be seen as a cartoon of the actual image g, providing a simplification of g. An important particular case is obtained when we restrict E to piecewise-constant functions u, i.e., u = constant ci on each open set Ω i . Multiplying E by μ−, we have μ−E(u,K) = ∑ i ∫ Ω i (g − ci)dx + ∣K∣, where  = /μ. It is easy to verify that this is minimized in the variables ci by setting ci = meanΩ i (g) = ∫Ω i g(x)dx ∣Ω i ∣ , where ∣Ω i ∣ denotes here the Lebesgue measure of Ω i (e.g., area if d = , volume if d = ), so it is sufficient to minimize E(K) = ∑ i ∫ Ω i (g −meanΩ i g) dx + ∣K∣. It is possible to interpret E as the limit functional of E as μ →  []. Finally, the Mumford and Shah model can also be seen as a deterministic refinement of Geman and Geman’s image restoration model []. . Background: The First Variation In order to better understand, analyze, and use the minimization problem (> .), it is useful to compute its first variation with respect to each of the unknowns. Mumford and Shah Model and its Applications to Image Segmentation and Image Restoration   We first recall the definition of Sobolev functions u ∈ W ,(U) [], necessary to properly define a minimizer u when K is fixed. Definition  LetU ⊂ Rd be an open set. We denote byW ,(U) (or by H(U)) the set of functions u ∈ L(Ω), whose first-order distributional partial derivatives belong to L(U). This means that there are functions u, . . . ,ud ∈ L(U) such that ∫ U u(x) ∂φ ∂xi (x)dx = − ∫ U ui(x)φ(x)dx for  ≤ i ≤ d and for all functions φ ∈ C∞c (U). We may denote by ∂u ∂xi the distributional derivative ui of u and by∇u = ( ∂u ∂x , . . . , ∂u ∂xd ) its distributional gradient. In what follows, we denote by ∣∇u∣(x) the Euclidean norm of the gradient vector at x. H(U) = W ,(U) becomes a Banach space endowed with the norm ∥u∥W ,(U) = ∫ U udx + d ∑ i= ∫ U ( ∂u ∂xi )  dx] / . .. Minimizing in uwith K Fixed Let us assume first that K is fixed, as a closed subset of the open and bounded set Ω ⊂ Rd , and denote by E(u) = μ ∫ Ω/K (u − g)dx + ∫ Ω/K ∣∇u∣dx, for u ∈ W ,(Ω/K), where Ω/K is open and bounded, and g ∈ L(Ω/K). We have the following classical results obtained as a consequence of the standard method of calculus of variations. Proposition  There is a unique minimizer of the problem inf u∈W ,(Ω/K) E(u). (.) Proof [] First, we note that  ≤ inf E < +∞, since we can choose u ≡  and E(u) = μ ∫Ω/K g  (x)dx < +∞. Thus, we can denote by m = inf u E(u) and let {uj} j≥ ∈ W ,(Ω/K) be a minimizing sequence such that lim j→∞ E(uj) = m. Recall that for u, v ∈ L,


title: An Instrument to Measure Adherence to Weight Loss Programs: The Compliance Praxis Survey-Diet (COMPASS-Diet)

abstract: Adherence to behavioral weight loss strategies is important for weight loss success. We aimed to examine the reliability and validity of a newly developed compliance praxis-diet (COMPASS-diet) survey with participants in a 10-week dietary intervention program. During the third of five sessions, participants of the “slim-without-diet” weight loss program (n = 253) completed the COMPASS-diet survey and provided data on demographic and clinical characteristics, and general self-efficacy. Group facilitators completed the COMPASS-diet-other scale estimating participants’ likely adherence from their perspective. We calculated internal consistency, convergent validity, and predictive value for objectively measured weight loss. Mean COMPASS-diet-self score was 82.4 (SD 14.2) and COMPASS-diet-other score 80.9 (SD 13.6) (possible range 12–108), with lowest scores in the normative behavior subscale. Cronbach alpha scores of the COMPASS-diet-self and -other scale were good (0.82 and 0.78, respectively). COMPASS-diet-self scores (r = 0.31) correlated more highly with general self-efficacy compared to COMPASS-diet-other scores (r = 0.04) providing evidence for validity. In multivariable analysis adjusted for age and gender, both the COMPASS-diet-self (F = 10.8, p < 0.001, r2 = 0.23) and other (F = 5.5, p < 0.001, r2 = 0.19) scales were significantly associated with weight loss achieved at program conclusion. COMPASS-diet surveys will allow group facilitators or trainers to identify patients who need additional support for optimal weight loss.


title: An Enhanced Control Algorithm for Improving the Light-Load Efficiency of Noninverting Synchronous Buck–Boost Converters

abstract: A loss reduction algorithm at light loads is needed to increase the overall efficiency of noninverting synchronous buck–boost dc–dc converters. Inductor current ripple generates most of the total loss at light loads. Thus, a ripple current reduction algorithm is an effective method of increasing light-load efficiency. This letter proposes a control algorithm for increasing the light-load efficiency by controlling the inductor current ripple. To achieve the optimal control of the inductor current, a maximum efficiency tracking scheme is implemented. Experimental results verify that the proposed algorithm significantly improves the light-load efficiency; at a 10% load condition, the efficiency is approximately 3.19% higher than that of the conventional algorithm.


title: CHINESE CONSUMER PERCEIVED RISK AND RISK RELIEVERS IN E-SHOPPING FOR CLOTHING

abstract: Online perceived risk is an important issue in e-commerce. As China has a large Internet shopper population and online consumer spending continues to increase, better understanding Chinese online shoppers’ perceived risk and risk reduction strategies becomes particularly relevant. However, research in the Chinese context is limited. Given this reality, the purpose of this study is to (1) identify and rank Chinese consumer online perceived risk; (2) investigate consumer preferences for methods of reducing risk; and (3) present a cluster analysis of e-shoppers based on their perceived risk score. Managerially, the study intends to provide e-marketers and e-retailers with an overview of risk as perceived by Chinese online shoppers and their risk reduction strategies. It also aims to demonstrate for managers the impact of this awareness on their competitiveness by illustrating how consumer types are related to different perceived risk dimensions and risk reliever strategies on the Internet. Some results from this study are consistent with previous studies, but it is interesting to note that certain findings are different. These differences might be explained by the specificity of the Chinese Internet shopping environment and Chinese culture. Cluster analysis regrouped the Chinese e-shoppers into five groups based on their perceived risk.


title: Aphallia: experience with 3 cases

abstract: Abstract In view of the rarity of the lesion, we describe 3 cases of aphallia associated with genitourinary and nongenitourinary anomalies. One case was associated with an anorectal malformation, whereas 2 had a normal anal opening. One patient with a normal anal opening had an associated rectal-urethro-fistula. Development of the phallus, clinical presentation of aphallia, associated anomalies, and an outline of the management is discussed along with a review of the literature.


Planar -Band 4 4 Nolen Matrix in SIW Technology


title: A single-chip encrypted wireless 12-lead ECG smart shirt for continuous health monitoring

abstract: An electrocardiography SoC is integrated into a form-fitting textile along with flexible electrodes, battery and antenna. Clinically standard 12-lead ECG is recorded from this “smart shirt.” The data is encrypted and wirelessly transmitted via an on-chip ISM band radio and flexible antenna allowing secure, continuous cardiac monitoring on a smartphone while dissipating less than 1mW.


title: NFLB dropout: Improve generalization ability by dropping out the best -A biologically inspired adaptive dropout method for unsupervised learning

abstract: Generalization ability is widely acknowledged as one of the most important criteria to evaluate the quality of unsupervised models. The objective of our research is to find a better dropout method to improve the generalization ability of convolutional deep belief network (CDBN), an unsupervised learning model for vision tasks. In this paper, the phenomenon of low feature diversity during the training process is investigated. The attention mechanism of human visual system is more focused on rare events and depresses well-known facts. Inspired by this mechanism, No Feature Left Behind Dropout (NFLB Dropout), an adaptive dropout method is firstly proposed to automatically adjust the dropout rate feature-wisely. In the proposed method, the algorithm drops well-trained features and keeps poorly-trained ones with a high probability during training iterations. In addition, we apply two approximations of the quality of features, which are inspired by theory of saliency and optimization. Compared with the model trained by standard dropout, experiment results show that our NFLB Dropout method improves not only the accuracy but the convergence speed as well.


title: A Scalable Approach for Outlier Detection in Edge Streams Using Sketch-based Approximations.

abstract: Dynamic graphs are a powerful way to model an evolving set of objects and their ongoing interactions. A broad spectrum of systems, such as information, communication, and social, are naturally represented by dynamic graphs. Outlier (or anomaly) detection in dynamic graphs can provide unique insights into the relationships of objects and identify novel or emerging relationships. To date, outlier detection in dynamic graphs has been studied in the context of graph streams, focusing on the analysis and comparison of entire graph objects. However, the volume and velocity of data are necessitating a transition from outlier detection in the context of graph streams to outlier detection in the context of edge streams–where the stream consists of individual graph edges instead of entire graph objects. In this paper, we propose the first approach for outlier detection in edge streams. We first describe a highlevel model for outlier detection based on global and local structural properties of a stream. We propose a novel application of the Count-Min sketch for approximating these properties, and prove probabilistic error bounds on our edge outlier scoring functions. Our sketch-based implementation provides a scalable solution, having constant time updates and constant space requirements. Experiments on synthetic and real world datasets demonstrate our method’s scalability, effectiveness for discovering outliers, and the effects of approximation.


title: A Variable Speed Wind Turbine Control Strategy to Meet Wind Farm Grid Code Requirements

abstract: This paper presents a new operational strategy for a small scale wind farm which is composed of both fixed and variable speed wind turbine generator systems (WTGS). Fixed speed wind generators suffer greatly from meeting the requirements of new wind farm grid code, because they are largely dependent on reactive power. Integration of flexible ac transmission systems (FACTS) devices is a solution to overcome that problem, though it definitely increases the overall cost. Therefore, in this paper, we focuses on a new wind farm topology, where series or parallel connected fixed speed WTGSs are installed with variable speed wind turbine (VSWT) driven permanent magnet synchronous generators (PMSG). VSWT-PMSG uses a fully controlled frequency converter for grid interfacing and it has abilities to control its reactive power as well as to provide maximum power to the grid. Suitable control strategy is developed in this paper for the multilevel frequency converter of VSWT-PMSG. A real grid code defined in the power system is considered to analyze the low voltage ride through (LVRT) characteristic of both fixed and variable speed WTGSs. Moreover, dynamic performance of the system is also evaluated using real wind speed data. Simulation results clearly show that the proposed topology can be a cost effective solution to augment the LVRT requirement as well as to minimize voltage fluctuation of both fixed and variable speed WTGSs.


title: Systematic literature review and improved model for mitigating bullwhip effect in low shelf life food supply chain

abstract: Bullwhip effect in the supply chain distribution network is a phenomenon that is highly avoided because it can lead to high operational costs. It drew the attention of researchers to examine ways to minimize the bullwhip effect. Bullwhip effect occurs because of incorrect company planning in pursuit of customer demand. Bullwhip effect occurs due to increased amplitude of demand variance towards upper supply chain level. If the product handled is a perishable product it will make the bullwhip effect more sensitive. The purpose of this systematic literature review is to map out some of the variables used in constructing mathematical models to minimize the bullwhip effect on food supply chains that have perishable product characteristics. The result of this systematic literature review is that the authors propose an appropriate optimization model that will be applied in the food supply chain sales on the train in Indonesian railways in the next research.


title: Hardware Trojan Attacks on Neural Networks

abstract: With the rising popularity of machine learning and the ever increasing demand for computational power, there is a growing need for hardware optimized implementations of neural networks and other machine learning models. As the technology evolves, it is also plausible that machine learning or artificial intelligence will soon become consumer electronic products and military equipment, in the form of well-trained models. Unfortunately, the modern fabless business model of manufacturing hardware, while economic, leads to deficiencies in security through the supply chain. In this paper, we illuminate these security issues by introducing hardware Trojan attacks on neural networks, expanding the current taxonomy of neural network security to incorporate attacks of this nature. To aid in this, we develop a novel framework for inserting malicious hardware Trojans in the implementation of a neural network classifier. We evaluate the capabilities of the adversary in this setting by implementing the attack algorithm on convolutional neural networks while controlling a variety of parameters available to the adversary. Our experimental results show that the proposed algorithm could effectively classify a selected input trigger as a specified class on the MNIST dataset by injecting hardware Trojans into $0.03\%$, on average, of neurons in the 5th hidden layer of arbitrary 7-layer convolutional neural networks, while undetectable under the test data. Finally, we discuss the potential defenses to protect neural networks against hardware Trojan attacks.


title: An evaluation of several fusion algorithms for anti-tank landmine detection and discrimination

abstract: Many algorithms have been proposed for detecting anti-tank landmines and discriminating between mines and clutter objects using data generated by a ground penetrating radar (GPR) sensor. Our extensive testing of some of these algorithms has indicated that their performances are strongly dependent upon a variety of factors that are correlated with geographical and environmental conditions. It is typically the case that one algorithm may perform well in one setting and not so well in another. Thus, fusion methods that take advantage of the stronger algorithms for a given setting without suffering from the effects of weaker algorithms in the same setting are needed to improve the robustness of the detection system. In this paper, we discuss, test, and compare seven different fusion methods: Bayesian, distance-based, Dempster-Shafer, Borda count, decision template, Choquet integral, and context-dependent fusion. We present the results of a cross validation experiment that uses a diverse data set together with results of eight detection and discrimination algorithms. These algorithms are the top ranked algorithms after extensive testing. The data set was acquired from multiple collections from four outdoor sites at different locations using the NIITEK GPR system. This collection covers over 41,807m^2 of ground and includes 1593 anti-tank mine encounters.


title: Recurrent convolutional neural network for object recognition

abstract: In recent years, the convolutional neural network (CNN) has achieved great success in many computer vision tasks. Partially inspired by neuroscience, CNN shares many properties with the visual system of the brain. A prominent difference is that CNN is typically a feed-forward architecture while in the visual system recurrent connections are abundant. Inspired by this fact, we propose a recurrent CNN (RCNN) for object recognition by incorporating recurrent connections into each convolutional layer. Though the input is static, the activities of RCNN units evolve over time so that the activity of each unit is modulated by the activities of its neighboring units. This property enhances the ability of the model to integrate the context information, which is important for object recognition. Like other recurrent neural networks, unfolding the RCNN through time can result in an arbitrarily deep network with a fixed number of parameters. Furthermore, the unfolded network has multiple paths, which can facilitate the learning process. The model is tested on four benchmark object recognition datasets: CIFAR-10, CIFAR-100, MNIST and SVHN. With fewer trainable parameters, RCNN outperforms the state-of-the-art models on all of these datasets. Increasing the number of parameters leads to even better performance. These results demonstrate the advantage of the recurrent structure over purely feed-forward structure for object recognition.


title: Characterizing and visualizing physical world accessibility at scale using crowdsourcing, computer vision, and machine learning

abstract: Poorly maintained sidewalks and street intersections pose considerable accessibility challenges for people with mobility-impairments [13,14]. According to the most recent U.S. Census (2010), roughly 30.6 million adults have physical disabilities that affect their ambulatory activities [22]. Of these, nearly half report using an assistive aid such as a wheelchair (3.6 million) or a cane, crutches, or walker (11.6 million) [22]. Despite comprehensive civil rights legislation for Americans with Disabilities (e.g., [25,26]), many city streets, sidewalks, and businesses in the U.S. remain inaccessible. The problem is not just that street-level accessibility fundamentally affects where and how people travel in cities, but also that there are few, if any, mechanisms to determine accessible areas of a city a priori. Indeed, in a recent report, the National Council on Disability noted that they could not find comprehensive information on the "degree to which sidewalks are accessible" across the US [15]. This lack of information can have a significant negative impact on the independence and mobility of citizens [13,16] For example, in our own initial formative interviews with wheelchair users, we uncovered a prevailing view about navigating to new areas of a city: "I usually don't go where I don't know [about accessible routes]" (Interviewee 3, congenital polyneuropathy). Our overarching research vision is to transform the way in which street-level accessibility information is collected and used to support new types of assistive map-based tools.


title: Mining Subjective Knowledge from Customer Reviews: A Specific Case of Irony Detection

abstract: The research described in this work focuses on identifying key components for the task of irony detection. By means of analyzing a set of customer reviews, which are considered as ironic both in social and mass media, we try to find hints about how to deal with this task from a computational point of view. Our objective is to gather a set of discriminating elements to represent irony. In particular, the kind of irony expressed in such reviews. To this end, we built a freely available data set with ironic reviews collected from Amazon. Such reviews were posted on the basis of an online viral effect; i.e. contents whose effect triggers a chain reaction on people. The findings were assessed employing three classifiers. The results show interesting hints regarding the patterns and, especially, regarding the implications for sentiment analysis.


title: Design and evaluation of two-wheeled balancing robot chassis: Case study for Lego bricks

abstract: Two-wheeled balancing robot is one of interesting nonlinear plant. The goal is to control the robot so that it can move with only two wheels. This paper elaborates the design and evaluation of its chassis. The chassis is constructed with Lego Mindstorm NXT and controlled by the AVR ATMega16 microcontroller. The system uses MX2125 accelerometer sensor from Parallax to evaluate the robot chassis. Two chassis design, i.e. model A and B are developed. From the experiments, it is shown that robot chassis design must address the mechanically stable issue. Therefore chassis model A is discarded. PD controller is recommended for this chassis model B. The chassis also considers the flexibility to mount such a load. Mounting load under the axis helps to stabilize the chassis. This makes the controller design easier. The load position opens the further research topic. Using Lego Mindstorm is very helpful in design and evaluation of mechatronics set up.


title: Prediction of pitch and yaw head movements via recurrent neural networks

abstract: In virtual-environment (VE) applications, where virtual objects are presented in a head-mounted display, virtual images must be continuously stabilized in space against the user's head motion. Latencies in head-motion compensation cause virtual objects to swim around instead of being stable in space. This results in an unnatural feel, disorientation, and simulation sickness in addition to errors in fitting/matching of virtual and real objects. Visual update delays are a critical technical obstacle for implementation of head-mounted displays in a wide variety of applications. To address this problem, we propose to use machine learning techniques to define a forward model of head movement based on angular velocity information. In particular, we utilize recurrent neural network to capture the temporal pattern of pitch and yaw motion. Our results demonstrate an ability to predict head motion up to 40 ms. ahead thus eliminating the main source of latencies. The accuracy of the system is tested for conditions akin to those encountered in virtual environments. These results demonstrate successful generalization by the learning system.


title: An Ensemble Sentiment Classification System of Twitter Data for Airline Services Analysis

abstract: In airline service industry, it is difficult to collect data about customers' feedback by questionnaires, but Twitter provides a sound data source for them to do customer sentiment analysis. However, little research has been done in the domain of Twitter sentiment classification about airline services. In this paper, an ensemble sentiment classification strategy was applied based on Majority Vote principle of multiple classification methods, including Naive Bayes, SVM, Bayesian Network, C4.5 Decision Tree and Random Forest algorithms. In our experiments, six individual classification approaches, and the proposed ensemble approach were all trained and tested using the same dataset of 12864 tweets, in which 10 fold evaluation is used to validate the classifiers. The results show that the proposed ensemble approach outperforms these individual classifiers in this airline service Twitter dataset. Based on our observations, the ensemble approach could improve the overall accuracy in twitter sentiment classification for other services as well.


RFF: A Robust, FF-Based MDP Planning Algorithm for Generating Policies with Low Probability of Failure


title: HIGHLY SELECTIVE TRANSPORT OF AG+ BY A MACROBICYCLIC HOST CONTAINING A BIPYRIDINE MOIETY

abstract: Abstract A macrocyclic polyether bridged by 2,2′-bipyridine group was synthesized and found to exhibit a very high Ag + ion selectivity compared to heavy metal ions in transport through an organic liquid membrane.


title: Modeling and Significance of Fringe Capacitance in Nonclassical CMOS Devices With Gate–Source/Drain Underlap

abstract: Parasitic gate-source/drain (G-S/D) fringe capacitance in nonclassical nanoscale CMOS devices, e.g., double-gate (DG) MOSFETs, is shown, using two-dimensional numerical simulations, to be very significant, gate bias-dependent, and substantially reduced by a well-designed G-S/D underlap. Analytical modeling of the outer and inner components of the fringe capacitance is developed and verified by the numerical simulations; a BOX-fringe component is modeled for single-gate fully depleted silicon-on-insulator MOSFETs. With the new modeling implemented in UFDG, our process/physics-based generic compact model for DG MOSFETs, UFDG/Spice3 shows how nanoscale DG CMOS speed is severely affected by the fringe capacitance and how this effect can be moderated by an optimal underlap, which yields a good tradeoff between the parasitic capacitance and the S/D resistance


title: Rapid, massive and unphysiological breast enlargement

abstract: Two cases of gigantomastia are presented, one is non-gravid and the other occurred during pregnancy. This is probably more common than has been reported but the cause remains unknown. The medical and surgical treatment of the disorder is presented.


title: Automatic basis function construction for approximate dynamic programming and reinforcement learning

abstract: We address the problem of automatically constructing basis functions for linear approximation of the value function of a Markov Decision Process (MDP). Our work builds on results by Bertsekas and Castanon (1989) who proposed a method for automatically aggregating states to speed up value iteration. We propose to use neighborhood component analysis (Goldberger et al., 2005), a dimensionality reduction technique created for supervised learning, in order to map a high-dimensional state space to a low-dimensional space, based on the Bellman error, or on the temporal difference (TD) error. We then place basis function in the lower-dimensional space. These are added as new features for the linear function approximator. This approach is applied to a high-dimensional inventory control problem.


title: Feasibility of Fog Computing Deployment based on Docker Containerization over RaspberryPi

abstract: Fog computing is strongly emerging as a relevant and interest-attracting paradigm+technology for both the academic and industrial communities. However, architecture and methodological approaches are still prevalent in the literature, while few research activities have specifically targeted so far the issues of practical feasibility, cost-effectiveness, and efficiency of fog solutions over easily-deployable environments. In this perspective, this paper originally presents i) our fog-oriented framework for Internet-of-Things applications based on innovative scalability extensions of the open-source Kura gateway and ii) its Docker-based containerization over challenging and resource-limited fog nodes, i.e., RaspberryPi devices. Our practical experience and experimental work show the feasibility of using even extremely constrained nodes as fog gateways; the reported results demonstrate that good scalability and limited overhead can be coupled, via proper configuration tuning and implementation optimizations, with the significant advantages of containerization in terms of flexibility and easy deployment, also when working on top of existing, off-the-shelf, and limited-cost gateway nodes.


title: Millimeter-Wave Substrate Integrated Waveguide Multibeam Antenna Based on the Parabolic Reflector Principle

abstract: A novel substrate integrated waveguide (SIW) multibeam antenna based on the parabolic reflector principle is proposed and implemented at 37.5 GHz, which takes the advantages of low loss, low profile, high gain, and mass producible. A prototype of the proposed antenna with seven input ports generating a corresponding number of output beams is fabricated in a standard PCB process. The measurements are in good agreement with the simulations, and the results demonstrate that this type of printed multibeam antenna is a good choice for communications applications where mobility and high gain are simultaneously required.


title: CU2CL: A CUDA-to-OpenCL Translator for Multi- and Many-Core Architectures

abstract: The use of graphics processing units (GPUs) in high-performance parallel computing continues to become more prevalent, often as part of a heterogeneous system. For years, CUDA has been the de facto programming environment for nearly all general-purpose GPU (GPGPU) applications. In spite of this, the framework is available only on NVIDIA GPUs, traditionally requiring reimplementation in other frameworks in order to utilize additional multi- or many-core devices. On the other hand, OpenCL provides an open and vendor-neutral programming environment and runtime system. With implementations available for CPUs, GPUs, and other types of accelerators, OpenCL therefore holds the promise of a "write once, run anywhere" ecosystem for heterogeneous computing. Given the many similarities between CUDA and OpenCL, manually porting a CUDA application to OpenCL is typically straightforward, albeit tedious and error-prone. In response to this issue, we created CU2CL, an automated CUDA-to-OpenCL source-to-source translator that possesses a novel design and clever reuse of the Clang compiler framework. Currently, the CU2CL translator covers the primary constructs found in CUDA runtime API, and we have successfully translated many applications from the CUDA SDK and Rodinia benchmark suite. The performance of the automatically translated applications via CU2CL is on par with their manually ported counterparts.


title: Neural Models for Information Retrieval

abstract: Neural ranking models for information retrieval (IR) use shallow or deep neural networks to rank search results in response to a query. Traditional learning to rank models employ machine learning techniques over hand-crafted IR features. By contrast, neural models learn representations of language from raw text that can bridge the gap between query and document vocabulary. Unlike classical IR models, these new machine learning based approaches are data-hungry, requiring large scale training data before they can be deployed. This tutorial introduces basic concepts and intuitions behind neural IR models, and places them in the context of traditional retrieval models. We begin by introducing fundamental concepts of IR and different neural and non-neural approaches to learning vector representations of text. We then review shallow neural IR methods that employ pre-trained neural term embeddings without learning the IR task end-to-end. We introduce deep neural networks next, discussing popular deep architectures. Finally, we review the current DNN models for information retrieval. We conclude with a discussion on potential future directions for neural IR.


title: Power Management of an Ultracapacitor/Battery Hybrid Energy Storage System in an HEV

abstract: To overcome the power delivery limitations of batteries and energy storage limitations of ultracapacitors, hybrid energy storage systems, which combine the two energy sources, have been proposed. A comprehensive review of the state of the art is presented. In addition, a method of optimizing the operation of a battery/ultracapacitor hybrid energy storage system (HESS) is presented. The goal is to set the state of charge of the ultracapacitor and the battery in a way which ensures that the available power and energy is sufficient to supply the drivetrain. By utilizing an algorithm where the states of charge of both systems are tightly controlled, we allow for the overall system size to reduce since more power is available from a smaller energy storage system.


title: Auricular acupuncture in the treatment of cocaine/crack abuse: a review of the efficacy, the use of the National Acupuncture Detoxification Association protocol, and the selection of sham points.

abstract: BACKGROUND ::: The United Kingdom has had a significant increase in addiction to and use of cocaine among 16-29-year olds from 6% in 1998 to 10% in 2000. In 2000, the United Kingdom had the highest recorded consumption of "recent use" cocaine in Europe, with 3.3% of young adults. Acupuncture is quick, inexpensive, and relatively safe, and may establish itself as an important addiction service in the future. ::: ::: ::: AIM ::: To select investigations that meet the inclusion criteria and critically appraise them in order to answer the question: "Is acupuncture effective in the treatment of cocaine addiction?" The focus shall then be directed toward the use of the National Acupuncture Detoxification Association (NADA) protocol as the intervention and the selection of sham points for the control group. ::: ::: ::: DATA SOURCES ::: The ARRC database was accessed from Trina Ward (M. Phil. student) at Thames Valley University. AMED, MEDLINE and Embase were also accessed along with "hand" searching methods at the British Library. ::: ::: ::: INCLUSION AND EXCLUSION CRITERIA ::: People addicted to either cocaine or crack cocaine as their main addiction, needle-acupuncture, single-double-blinded process, randomized subjects, a reference group incorporating a form of sham points. ::: ::: ::: EXCLUSION CRITERIA ::: use of moxibustion, laser acupuncture, transcutaneous electrical nerve stimulation (TENS) electroacupuncture or conditions that did not meet the inclusion criteria. ::: ::: ::: QUALITY ASSESSMENT ::: The criteria set by ter Riet, Kleijnen and Knipschild (in 1990); Hammerschlag and Morris (in 1990); Koes, Bouter and van der Heijden (in 1995), were modified into one set of criteria consisting of 27 different values. ::: ::: ::: RESULTS ::: Six randomized controlled trials (RCTs) met the inclusion criteria and were included in this review. All studies scored over 60 points indicating a relatively adequate methodology quality. The mean was 75 and the standard deviation was 6.80. A linear regression analysis did not yield a statistically significant association (n = 6, p = 0.11). ::: ::: ::: CONCLUSIONS ::: This review could not confirm that acupuncture was an effective treatment for cocaine abuse. The NADA protocol of five treatment points still offers the acupuncturist the best possible combination of acupuncture points based upon Traditional Chinese Medicine. Throughout all the clinical trials reviewed, no side-effects of acupuncture were noted. This paper calls for the full set of 5 treatment points as laid out by the NADA to be included as the treatment intervention. Points on the helix, other than the liver yang points, should be selected as sham points for the control group.


title: Scientific Workflows: Moving Across Paradigms

abstract: Modern scientific collaborations have opened up the opportunity to solve complex problems that require both multidisciplinary expertise and large-scale computational experiments. These experiments typically consist of a sequence of processing steps that need to be executed on selected computing platforms. Execution poses a challenge, however, due to (1) the complexity and diversity of applications, (2) the diversity of analysis goals, (3) the heterogeneity of computing platforms, and (4) the volume and distribution of data. A common strategy to make these in silico experiments more manageable is to model them as workflows and to use a workflow management system to organize their execution. This article looks at the overall challenge posed by a new order of scientific experiments and the systems they need to be run on, and examines how this challenge can be addressed by workflows and workflow management systems. It proposes a taxonomy of workflow management system (WMS) characteristics, including aspects previously overlooked. This frames a review of prevalent WMSs used by the scientific community, elucidates their evolution to handle the challenges arising with the emergence of the “fourth paradigm,” and identifies research needed to maintain progress in this area.


title: MRR: an unsupervised algorithm to rank reviews by relevance

abstract: The automatic detection of relevant reviews plays a major role in tasks such as opinion summarization, opinion-based recommendation, and opinion retrieval. Supervised approaches for ranking reviews by relevance rely on the existence of a significant, domain-dependent training data set. In this work, we propose MRR (Most Relevant Reviews), a new unsupervised algorithm that identifies relevant revisions based on the concept of graph centrality. The intuition behind MRR is that central reviews highlight aspects of a product that many other reviews frequently mention, with similar opinions, as expressed in terms of ratings. MRR constructs a graph where nodes represent reviews, which are connected by edges when a minimum similarity between a pair of reviews is observed, and then employs PageRank to compute the centrality. The minimum similarity is graph-specific, and takes into account how reviews are written in specific domains. The similarity function does not require extensive pre-processing, thus reducing the computational cost. Using reviews from books and electronics products, our approach has outperformed the two unsupervised baselines and shown a comparable performance with two supervised regression models in a specific setting. MRR has also achieved a significantly superior run-time performance in a comparison with the unsupervised baselines.


Improved Neural Network-based Multi-label Classification with Better Initialization Leveraging Label Co-occurrence


title: Antimicrobial Resistance Pattern in Enterococcus faecalis Strains Isolated From Expressed Prostatic Secretions of Patients With Chronic Bacterial Prostatitis

abstract: Purpose: Enterococcus faecalis is one of the most common pathogens linked to chronic bacterial prostatitis (CBP). Owing to a limited number of previous studies addressing this topic, we aimed to determine the drug resistance patterns of E. faecalis strains isolated from CBP patients. Materials and Methods: One thousand twenty-one patients visited a single hospital owing to chronic prostatitis for 5 years. Culture specimens were obtained by use of a modified Meares-Stamey method. The minimal inhibitory concentrations of the antimicrobials were assessed by use of the Vitek II microbial identification system as suggested by the Clinical and Laboratory Standards Institute. Results: Forty-one samples from 41 patients who had significant E. faecalis loads for defining CBP were included in this study. The E. faecalis strains in our study were resistant to penicillin (9.7%), ampicillin (0%), ampicillin/sulbactam (0%), nitrofurantoin (0%), imipenem (0%), vancomycin (0%), teicoplanin (0%), quinupristin/dalfopristin (100%), ciprofloxacin (9.7%), levofloxacin (4.8%), norfloxacin (26.8%), erythromycin (95%), gentamicin (46.3%), tetracycline (97.5%), and trimethoprim/sulfamethoxazole (31.5%), respectively. Conclusions: Fluoroquinolones have been the preferred antibiotics for treating CBP. Because of their low rate of drug resistance, fluoroquinolones are suitable therapeutic agents for E. faecalis strains causing CBP in Korea. Even though tetracycline, erythromycin, and trimethoprim/sulfamethoxazole have been prescribed as an empirical antimicrobial therapy for chronic prostatitis, we cannot recommend these drugs for treatment of E. faecalis isolates because of the high rates of drug resistance.


title: Design and Evaluation of 2-DOF Compliant Forceps With Force-Sensing Capability for Minimally Invasive Robot Surgery

abstract: In this paper, a novel concept of two-degree-of-freedom (2-DOF) compliant forceps is suggested for the measure of pulling and grasp forces at the tip of surgical instrument for minimally invasive surgery robot. For the design of the compliant forceps, the required compliance characteristics are first defined using a simple spring model with one linear and one torsional springs. This model may be directly realized as the compliant forceps. However, for the compact realization of the mechanism, we synthesize the spring model with two torsional springs that has equivalent compliance characteristics to the linear-torsional spring model. Then, each of the synthesized torsional springs is realized physically by means of a flexure hinge. From this design approach, direct measurement of the pulling and grasp forces is possible at the forceps, and measuring sensitivity can be adjusted in the synthesis process. The validity of the design is evaluated by finite element analysis. Further, from the measured values of bending strains of two flexure hinges, a method to compute the decoupled pulling and grasp forces is presented via the theory of screws. Finally, force-sensing performance of the proposed compliant forceps is verified from the experiments of the prototype using some weights and load cells.


title: The W3C PROV family of specifications for modelling provenance metadata

abstract: Provenance, a form of structured metadata designed to record the origin or source of information, can be instrumental in deciding whether information is to be trusted, how it can be integrated with other diverse information sources, and how to establish attribution of information to authors throughout its history. The PROV set of specifications, produced by the World Wide Web Consortium (W3C), is designed to promote the publication of provenance information on the Web, and offers a basis for interoperability across diverse provenance management systems. The PROV provenance model is deliberately generic and domain-agnostic, but extension mechanisms are available and can be exploited for modelling specific domains. This tutorial provides an account of these specifications. Starting from intuitive and informal examples that present idiomatic provenance patterns, it progressively introduces the relational model of provenance along with the constraints model for validation of provenance documents, and concludes with example applications that show the extension points in use.