title: ImpAct: Immersive haptic stylus to enable direct touch and manipulation for surface computing

abstract: This article explores direct touch and manipulation techniques for surface computing environments using a specialized haptic force feedback stylus, called ImpAct, which can dynamically change its effective length and equipped with sensors to calculate its orientation in world coordinates. When a user pushes it against a touch screen, the physical stylus shrinks and a rendered projection of the stylus is drawn inside the screen, giving the illusion that it is submerged in the display device. Once the users can see the stylus immersed in the digital world below the screen, he or she can manipulate and interact with the virtual objects with active haptic sensations. In this article, ImpAct's functionality, design, and prototype applications are described in detail with relevance to the concept of direct touch, giving special attention to novel interaction scenarios and design challenges. Furthermore, a technical evaluation was done to study ImpAct's accuracy and controlability and the results presented. This article concludes by discussing ImpAct's current limitations and future perspectives as a direct touch and manipulation tool.


Swing-Pay : A Digital Card Module using NFC and Biometric Authentication for Peer-to-Peer Payment


Towards interactive robots in autism therapy


title: An Ensemble Learning Framework for Credit Card Fraud Detection Based on Training Set Partitioning and Clustering

abstract: The popularity of credit card has greatly facilitated the transactions between merchants and cardholders. However, credit card fraud has been derived, which results in losses of billions of euros every year. In recent years, machine learning and data mining technology have been widely used in fraud detection and achieved favorable performances. Most of these studies use the technology of under-sampling to deal with the high imbalance of credit card data. However, it will potentially discard some relevant training samples which will weaken the ability of the classifier. In this paper, we propose an ensemble learning framework based on training set partitioning and clustering. It turns out that the proposed framework not only ensures the integrity of the sample features, but also solves the high imbalance of the dataset. A main feature of our framework is that every base estimator can be trained in parallel. This improves the efficiency of the framework. We show the effectiveness of our proposed ensemble framework by experimental results on a real credit card transaction dataset.


How students read an e‐textbook in an engineering course


title: A compact passive retrodirective antenna array with SIW phase shifters integrated

abstract: A compact millimeter-wave passive retrodirective antenna (RDA) array with substrate integrated waveguide (SIW) phase shifters integrated is presented in this paper. The primary function of the passive RDA array is to enhance the monostatic radar cross section (RCS) of the metal plate. SIW phase shifters are utilized to realize the phase criterion of the passive RDA array instead of only changing the length of the transmission lines, resulting in a compact size and less scattering influence from the SIW structure. Alternating longitude SIW slots array antenna is adopted as radiation element and SIW transmission lines integrated with SIW phase shifters are used to form the feeding network. To validate the design method, a prototype passive RDA array with 8 radiation elements using the single-layer printed circuit board (PCB) process is designed and fabricated at 35 GHz. Experimental results agree well with simulation results, validating that the prototype RDA array can achieve an enhancement of more than 10 dB on the monostatic RCS compared to the metal plate with same size.


title: A Trust Based Framework for Secure Data Aggregation in Wireless Sensor Networks

abstract: In unattended and hostile environments, node compromise can become a disastrous threat to wireless sensor networks and introduce uncertainty in the aggregation results. A compromised node often tends to completely reveal its secrets to the adversary which in turn renders purely cryptography-based approaches vulnerable. How to secure the information aggregation process against compromised-node attacks and quantify the uncertainty existing in the aggregation results has become an important research issue. In this paper, we address this problem by proposing a trust based framework, which is rooted in sound statistics and some other distinct and yet closely coupled techniques. The trustworthiness (reputation) of each individual sensor node is evaluated by using an information theoretic concept, Kullback-Leibler (KL) distance, to identify the compromised nodes through an unsupervised learning algorithm. Upon aggregating, an opinion, a metric of the degree of belief, is generated to represent the uncertainty in the aggregation result. As the result is being disseminated and assembled through the routes to the sink, this opinion will be propagated and regulated by Josang's belief model. Following this model, the uncertainty within the data and aggregation results can be effectively quantified throughout the network. Simulation results demonstrate that our trust based framework provides a powerful mechanism for detecting compromised nodes and reasoning about the uncertainty in the network. It further can purge false data to accomplish robust aggregation in the presence of multiple compromised nodes


title: Protocols for secure computations

abstract: The author investigates the following problem: Suppose m people wish to compute the value of a function f(x1, x2, x3, ..., xm), which is an integer-valued function of m integer variables xi of bounded range. Assume initially person Pi knows the value of xi and no other x's. Is it possible for them to compute the value of f, by communicating among themselves, without unduly giving away any information about the values of their own variables? The author gives a precise formulation of this general problem and describe three ways of solving it by use of one-way functions (i.e., functions which are easy to evaluate but hard to invert). These results have applications to secret voting, private querying of database, oblivious negotiation, playing mental poker, etc.. He also discusses the complexity question "How many bits need to be exchanged for the computation," and describes methods to prevent participants from cheating. Finally, he studies the question "What cannot be accomplished with one-way functions."


title: Protein-Ligand Scoring with Convolutional Neural Networks

abstract: Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein–ligand binding and structural data enables the use of deep machine learning techniques for protein–ligand scoring. We describe convolutional neural network (CNN) scoring functions that take as input a comprehensive three-dimensional (3D) representation of a protein–ligand interaction. A CNN scoring function automatically learns the key features of protein–ligand interactions that correlate with binding. We train and optimize our CNN scoring functions to discriminate between correct and incorrect binding poses and known binders and nonbinders. We find that our CNN scoring function outperforms the AutoDock Vina scoring function when ranking poses both for pose prediction and virtual screening.


title: SideBySide: ad-hoc multi-user interaction with handheld projectors

abstract: We introduce SideBySide, a system designed for ad-hoc multi-user interaction with handheld projectors. SideBySide uses device-mounted cameras and hybrid visible/infrared light projectors to track multiple independent projected images in relation to one another. This is accomplished by projecting invisible fiducial markers in the near-infrared spectrum. Our system is completely self-contained and can be deployed as a handheld device without instrumentation of the environment. We present the design and implementation of our system including a hybrid handheld projector to project visible and infrared light, and techniques for tracking projected fiducial markers that move and overlap. We introduce a range of example applications that demonstrate the applicability of our system to real-world scenarios such as mobile content exchange, gaming, and education.


title: Analysis, control and design of a non-inverting buck-boost converter: A bump-less two-level T-S fuzzy PI control.

abstract: In this paper, a new modified fuzzy Two-Level Control Scheme (TLCS) is proposed to control a non-inverting buck-boost converter. Each level of fuzzy TLCS consists of a tuned fuzzy PI controller. In addition, a Takagi-Sugeno-Kang (TSK) fuzzy switch proposed to transfer the fuzzy PI controllers to each other in the control system. The major difficulty in designing fuzzy TLCS which degrades its performance is emerging unwanted drastic oscillations in the converter output voltage during replacing the controllers. Thereby, the fuzzy PI controllers in each level of TLCS structure are modified to eliminate these oscillations and improve the system performance. Some simulations and digital signal processor based experiments are conducted on a non-inverting buck-boost converter to support the effectiveness of the proposed TLCS in controlling the converter output voltage.


title: Design of High-Gain Lens Antenna by Gradient-Index Metamaterials Using Transformation Optics

abstract: Transformation of space coordinates can be used as a convenient tool for producing a controlled electromagnetic field pattern. In this paper, using a conformal transformation, the design procedure of a lens antenna with high gain and low sidelobes is presented which can be realized by isotropic graded refractive index (GRIN) materials. Applying proper simplifying techniques, the designed lens can be made by non-resonant metamaterials or non-magnetic dielectrics having wide frequency band and low loss. A graded photonic crystal (GPC) operating in metamaterial regime is used for this purpose. By placing the lens in a horn antenna, a highly directive beam is achieved. It is also shown that by lateral displacement of the lens medium, the beam direction can be controlled to some extent. A multibeam antenna in which each beam can be controlled independently is also presented. Simulation results are used to justify the design approach.


Model Selection Based on the Variational Bayes


title: Near-Data Processing for Differentiable Machine Learning Models

abstract: Near-data processing (NDP) refers to augmenting memory or storage with processing power. Despite its potential for acceleration computing and reducing power requirements, only limited progress has been made in popularizing NDP for various reasons. Recently, two major changes have occurred that have ignited renewed interest and caused a resurgence of NDP. The first is the success of machine learning (ML), which often demands a great deal of computation for training, requiring frequent transfers of big data. The second is the popularity of NAND flash-based solid-state drives (SSDs) containing multicore processors that can accommodate extra computation for data processing. In this paper, we evaluate the potential of NDP for ML using a new SSD platform that allows us to simulate instorage processing (ISP) of ML workloads. Our platform (named ISP-ML) is a full-fledged simulator of a realistic multi-channel SSD that can execute various ML algorithms using data stored in the SSD. To conduct a thorough performance analysis and an in-depth comparison with alternative techniques, we focus on a specific algorithm: stochastic gradient descent (SGD), which is the de facto standard for training differentiable models such as logistic regression and neural networks. We implement and compare three SGD variants (synchronous, Downpour, and elastic averaging) using ISP-ML, exploiting the multiple NAND channels to parallelize SGD. In addition, we compare the performance of ISP and that of conventional in-host processing, revealing the advantages of ISP. Based on the advantages and limitations identified through our experiments, we further discuss directions for future research on ISP for accelerating ML.


title: A novel photodetector using MOS tunneling structures

abstract: A metal/oxide/p-Si structure with ultrathin oxide is utilized as a photodetector. At positive gate bias, the dark current of the photodetector is limited by the thermal generation of minority carriers in the inversion layer. The high growth temperature (1000/spl deg/C) of the gate oxide can reduce the dark current to a level as low as 3 nA/cm/sup 2/. As biased in the inversion layer, the tunneling diode works in the deep depletion region with soft pinning of oxide voltage, instead of the pinning of surface potential, very different from the conventional MOS diode with thick oxide.


title: A Review on Green Purchase Behaviour Trend of Malaysian Consumers

abstract: The emergence of environmental problems and the increased awareness towards green purchase behaviour have received many responses by the stakeholders’ worldwide like from the government bodies, researchers, businesses, consumers and so on. Government’s bodies, for example, have responded by developing and introducing their own environmentally-linked policies to be implemented in their countries, which are intended to conserve and preserve the environment. Researchers, on the other hand, are continuously conducting extensive studies and publishing their findings on the issues to inform the public, while businesses that promote the selling of green products (or environmentally friendly products) in the marketplace have been increasing in number. Segments of green consumers have been observed to emerge and grow in size worldwide including Malaysia. This may be due to the increased number of green products introduced to consumers in the marketplace. Moreover, scholars from Malaysia also argued that, this trend is experiencing tremendous growth. Although there are responses from these stakeholders, especially consumers, who have had a positive impact on the environment, the trend of the green purchase behaviour by Malaysian consumers remains unobserved. Therefore, the authors aim to answer the questions concerning whether a trend can be observed in the green purchase behaviour of Malaysian consumers. The ability to observe the green purchase behaviour trend is useful, particularly for marketers and businesses that are selling or intending to sell green products within the country.


title: Measuring and avoiding side effects using relative reachability.

abstract: How can we design reinforcement learning agents that avoid causing unnecessary disruptions to their environment? We argue that current approaches to penalizing side effects can introduce bad incentives in tasks that require irreversible actions, and in environments that contain sources of change other than the agent. For example, some approaches give the agent an incentive to prevent any irreversible changes in the environment, including the actions of other agents. We introduce a general definition of side effects, based on relative reachability of states compared to a default state, that avoids these undesirable incentives. Using a set of gridworld experiments illustrating relevant scenarios, we empirically compare relative reachability to penalties based on existing definitions and show that it is the only penalty among those tested that produces the desired behavior in all the scenarios.


title: Enabling agricultural automation to optimize utilization of water, fertilizer and insecticides by implementing Internet of Things (IoT)

abstract: With the proliferation of smart devices, Internet can be extended into the physical realm of Internet-of-Things (IoT) by deploying them into a communicating-actuating network. In Ion, sensors and actuators blend seamlessly with the environment; collaborate globally with each other through internet to accomplish a specific task. Wireless Sensor Network (WSN) can be integrated into Ion to meet the challenges of seamless communication between any things (e.g., humans or objects). The potentialities of IoT can be brought to the benefit of society by developing novel applications in transportation and logistics, healthcare, agriculture, smart environment (home, office or plant). This research gives a framework of optimizing resources (water, fertilizers, insecticides and manual labour) in agriculture through the use of IoT. The issues involved in the implementation of applications are also investigated in the paper. This frame work is named as AgriTech.


title: Accelerating Biological Sequence Alignment Algorithm on GPU with CUDA

abstract: In this paper, we have used Compute Unified Device Architecture (CUDA) GPU to accelerate pair wise sequence alignment using the Smith-Waterman (SW) algorithm. Smith-Waterman(SW) is by far the best algorithm for its accuracy in similarity scoring. But the executing time of this algorithm is too long in sequence alignment. So we describe a multi-threaded parallel design and implementation of the Smith-Waterman (SW) on CUDA to reduce execution time. And according the architecure of CUDA, we have divided the computation of a whole pair wise sequence alignment scoring matrix into multiple sub-matrices, using 32 threads to process on submatrice, more over we optimized memory distribution scheme, and used reduction to find the maximum element of the alignment scoring matrix. We experiment the algorimthm on GeForce 9600 GT, connet to Windows xp 64-bit system. The results show this mplementation achieves more better performance than the other parallel implementation on the Graphics Processing Unit.


title: Intrinsic dimension estimation

abstract: The paper reviews state-of-the-art of the methods of Intrinsic Dimension (ID) Estimation.The paper defines the properties that an ideal ID estimator should have.The paper reviews, under the above mentioned framework, the major ID estimation methods underlining their advances and the open problems. Dimensionality reduction methods are preprocessing techniques used for coping with high dimensionality. They have the aim of projecting the original data set of dimensionality N, without information loss, onto a lower M-dimensional submanifold. Since the value of M is unknown, techniques that allow knowing in advance the value of M, called intrinsic dimension (ID), are quite useful. The aim of the paper is to review state-of-the-art of the methods of ID estimation, underlining the recent advances and the open problems.


title: Large deep neural networks for MS lesion segmentation

abstract: Multiple sclerosis (MS) is a multi-factorial autoimmune disorder, characterized by spatial and temporal dissemination of brain lesions that are visible in T2-weighted and Proton Density (PD) MRI. Assessment of lesion ::: burden and is useful for monitoring the course of the disease, and assessing correlates of clinical outcomes. ::: Although there are established semi-automated methods to measure lesion volume, most of them require ::: human interaction and editing, which are time consuming and limits the ability to analyze large sets of data ::: with high accuracy. The primary objective of this work is to improve existing segmentation algorithms and ::: accelerate the time consuming operation of identifying and validating MS lesions. ::: In this paper, a Deep Neural Network for MS Lesion Segmentation is implemented. The MS lesion samples ::: are extracted from the Partners Comprehensive Longitudinal Investigation of Multiple Sclerosis (CLIMB) study. ::: A set of 900 subjects with T2, PD and a manually corrected label map images were used to train a Deep Neural ::: Network and identify MS lesions. Initial tests using this network achieved a 90% accuracy rate. A secondary ::: goal was to enable this data repository for big data analysis by using this algorithm to segment the remaining ::: cases available in the CLIMB repository.


Topical corticosteroids used in dermatological disorders in tertiary care hospital : A prospective observational study


title: Fingerprint ridge orientation estimation based on neural network

abstract: Ridge orientation is one of the fundamental features of a fingerprint image. Orientation estimation serves for feature extraction and matching and is the base of fingerprint recognition. Most existing orientation estimation methods are based on the characteristic of pixel intensity in a block. This paper uses neural network to learn the ridge orientation. The trained network has the property of responding to true ridge orientation with a large value and responding to the false ridge orientation with a small value. When estimating fingerprint ridge orientation, the responded values to each orientation at each image block are used to compute the fingerprint orientation field. The proposed method turns out more robust than the existing method.


title: IBM Streams Processing Language: . . .

abstract: The IBM Streams Processing Language (SPL) is the programming language for IBM InfoSphere® Streams, a platform for analyzing Big Data in motion. By "Big Data in motion," we mean continuous data streams at high data-transfer rates. InfoSphere Streams processes such data with both high throughput and short response times. To meet these performance demands, it deploys each application on a cluster of commodity servers. SPL abstracts away the complexity of the distributed system, instead exposing a simple graph-of-operators view to the user. SPL has several innovations relative to prior streaming languages. For performance and code reuse, SPL provides a code-generation interface to C++ and Java®. To facilitate writing well-structured and concise applications, SPL provides higher-order composite operators that modularize stream sub-graphs. Finally, to enable static checking while exposing optimization opportunities, SPL provides a strong type system and user-defined operator models. This paper provides a language overview, describes the implementation including optimizations such as fusion, and explains the rationale behind the language design.


title: Robust license plate detection in the wild

abstract: License Plate Detection (LPD) is the pivotal step for License Plate Recognition. In this work, we explore and customize state-of-the-art detection approaches for exclusively handling the LPD in the wild. In-the-wild LPD considers license plates captured in challenging conditions caused by bad weathers, lighting, traffics, and other factors. As conventional methods failed to handle these inevitable conditions, we explore the latest deep learning based detectors, namely YOLO (You-Only-Look-Once) and its variant YOLO-9000 (referred here as YOLO-2), and customize them for effectively handling the LPD. The prime customizations include modification of the grid size and of the bounding box parameter estimation, and the composition of a more challenging AOLPE (Application-Oriented License Plate Extended) database for performance evaluation. The AOLPE database is an extended version of the AOLP database [1] with additional images taken under extreme but frequently-encountered conditions. As the original YOLO and YOLO-2 are not designed for the LPD, they failed to handle the LPD on the AOLPE without the customizations. This study can be one of the pioneering works that revise state-of-the-art real-time deep networks for handling the LPD. It also serves as a case study for those who wish to customize existing deep networks for detecting specific objects. In addition to a pioneering explorations of deep networks for handling the in-the-wild LPD, our contribution also includes the release of the AOLPE database and evaluation protocol for a novel benchmark for the LPD.


title: 3D point cloud processing using spin images for object detection

abstract: The goal of this paper is to propose a step-by-step methodology of 3D point cloud processing for object detection depth1. Since the use of point clouds are relatively new for object detection, we need to build a capable framework in order to handle an enormous amount data. The framework follows a system of methods to process 3D point cloud and allows autonomous systems to detect objects. We first propose a pipeline based on spin images for the descriptor stage that proves ineffective for static object detection. We then change to a method called Signature of Histogram of OrienTation (SHOT) and successfully achieve object detection. We also perform further experiments to retrieve real time data from Quanergy's M8 long range LiDAR sensors and acquire visualizations of processed 3D point cloud.


title: A 3D flash ADC structure for high-speed communication applications

abstract: A 5-bit highly-accurate, low-power, and high-performance three-dimensional (3D) flash analog to digital converter (ADC) is presented for communication system applications. This architecture implements very short vertical interconnections, namely through-silicon via (TSV) channels to improve dynamic performance, increase power efficiency, and decrease the silicon area. To validate the proposed 3D flash ADC design, the architecture is simulated in a 65 nm CMOS technology. The 3D TSV channels (i.e., TSV and μbumps) are modeled to generate S-parameters using a 3D EM solver tool (i.e., HFSS). The demonstrated results reveal that the whole structure achieves SFDR of 39.8 and power consumption of 5.4 at 400 MS/s sampling rate.


title: A tensor-based mutation operator for Neuroevolution of Augmenting Topologies (NEAT)

abstract: In Genetic Algorithms, the mutation operator is used to maintain genetic diversity in the population throughout the evolutionary process. Various kinds of mutation may occur over time, typically depending on a fixed probability value called mutation rate. In this work we make use of a novel data-science approach in order to adaptively generate mutation rates for each locus to the Neuroevolution of Augmenting Topologies (NEAT) algorithm. The trail of high quality candidate solutions obtained during the search process is represented as a third-order tensor; factorization of such a tensor reveals the latent relationship between solutions, determining the mutation probability which is likely to yield improvement at each locus. The single pole balancing problem is used as case study to analyze the effectiveness of the proposed approach. Results show that the tensor approach improves the performance of the standard NEAT algorithm for the case study.


title: Multi-level authentication system for smart home-security analysis and implementation

abstract: With the rapid increase in the demand for Internet of Things services, securing the information content delivered among various entities involved in the IoT architecture has become an important issue. Accordingly, the problem of Internet of Things security has become more and more challenging day-by-day. As IoT systems are very diverse in their requirements and implementations, here specifically focus on smart home systems. A smart home consists of diverse smart devices interconnected through Internet and therefore these low capacity devices and wireless networks, needs an adequate level of security when all working together. This paper presents a multi-level authentication system for smart home network and this authentication scheme is to make the network more secure. The proposed scheme provides various security properties which include data confidentiality and integrity, mutual authentication, forward security and privacy preservation and also provides prevention of various popular IoT attacks. In addition, when compared with other alternative schemes from the literature the proposed scheme acquires both computation and communication efficiency.


title: EVALUATION OF SLUDGE PRODUCED BY LIMESTONE NEUTRALIZATION OF AMD AT THE FRIENDSHIP HILL NATIONAL HISTORIC SITE

abstract: A pulsed limestone bed (PLB) treatment plant for remediation of acid mine drainage (AMD) has been tested at the Friendship Hill National Historic site in southwestern Pennsylvania. The plant performed well, neutralizing over 50 metric tons of acidity over a 14-month period, but the cost of disposal of the 450 metric tons of sludge generated was high, because the sludge was hauled from the site and disposed of in a commercial landfill to minimize the impact on park property. Although the sludge was found to be non-hazardous based on the Toxicity Characteristics Leaching Protocol (TCLP), hauling and disposal costs were still elevated, at $40 per m 3 . Since sludge handling and disposal was a significant fraction of the operating cost of the facility, a study of the sludge characteristics and possible alternate handling methods was undertaken. Samples of AMD influent, treated water and settled sludge were used in a series of small- scale treatment studies. These tests showed that the sludge volume produced by limestone neutralization was less than half of that for lime, sodium hydroxide or ammonia. The settling rate was also greater for the limestone-based sludge. Vacuum filtration tests demonstrated that the limestone sludge was more readily filtered than sludges generated with sodium hydroxide or lime and that solids contents of up to 28% could be achieved. Because of lower maintenance costs, pressure filtration using a plate and frame filter is recommended for future plant operations. In this case, solids contents of as high as 31% were realized. As an alternative to the operating costs of mechanical filtration, a settling/percolation process was tested. Percolation tests showed that solids content of the settled sludge could be increased from 8% to 25%. These results show that sludge disposal costs for the treatment plant could be decreased through installation of a settling or filtration process. Moreover, the PLB process offers saving not only in reagent costs, but also in sludge disposal costs, through decreased sludge volume generated, rapid settling rates and ready filterability.


title: DeepFam: deep learning based alignment‐free method for protein family modeling and prediction

abstract: Motivation A large number of newly sequenced proteins are generated by the next‐generation sequencing technologies and the biochemical function assignment of the proteins is an important task. However, biological experiments are too expensive to characterize such a large number of protein sequences, thus protein function prediction is primarily done by computational modeling methods, such as profile Hidden Markov Model (pHMM) and k‐mer based methods. Nevertheless, existing methods have some limitations; k‐mer based methods are not accurate enough to assign protein functions and pHMM is not fast enough to handle large number of protein sequences from numerous genome projects. Therefore, a more accurate and faster protein function prediction method is needed. Results In this paper, we introduce DeepFam, an alignment‐free method that can extract functional information directly from sequences without the need of multiple sequence alignments. In extensive experiments using the Clusters of Orthologous Groups (COGs) and G protein‐coupled receptor (GPCR) dataset, DeepFam achieved better performance in terms of accuracy and runtime for predicting functions of proteins compared to the state‐of‐the‐art methods, both alignment‐free and alignment‐based methods. Additionally, we showed that DeepFam has a power of capturing conserved regions to model protein families. In fact, DeepFam was able to detect conserved regions documented in the Prosite database while predicting functions of proteins. Our deep learning method will be useful in characterizing functions of the ever increasing protein sequences. Availability and implementation Codes are available at https://bhi‐kimlab.github.io/DeepFam.


title: Planar edge termination design and technology considerations for 1.7-kV 4H-SiC PiN diodes

abstract: This paper presents the design, fabrication, and comparison of different planar edge termination techniques on high-voltage 4H-SiC PiN diodes, including single- and double-junction termination extensions (JTE), floating guard rings, and a novel termination structure, the so-called "floating guard rings-assisted JTE." The influence of the anode metal edge location over different periphery regions on the breakdown voltage is also discussed, as well as the effect of a field plate and the passivation layer on the reverse characteristics. The terminations were studied by way of two-dimensional numerical device simulations and they are confirmed by fabricating and measuring 1.7-kV 4H-SiC aluminum implanted PiN diodes. It is shown that the novel termination structure provides the best results achieving the highest breakdown voltages with good production yield. The fabricated diodes also exhibited excellent forward current characteristics with a low on-state voltage drop of 3.0 V at 100 A/cm/sup 2/, thanks to the low specific contact resistivity achieved (/spl rho//sub C/=1/spl middot/10/sup -5/ /spl Omega/cm/sup 2/) after the high-temperature treatment of the anode contact. High-temperature reverse current-voltage measurements were also carried out and are presented and discussed.


title: Printable pneumatic artificial muscles for anatomy-based humanoid robots

abstract: We propose a printable, pneumatic artificial muscle with great flexibility in design that is suitable for bio-inspired machines, especially, anatomy-based musculoskeletal robots. This artificial muscle is based on the recently developed thermal fabrication technique of inflatable actuators on sheet materials. In contrast to the cylindrical shape of the McKibben artificial muscle, the proposed actuator has a free two-dimensional shape that is less than 0.1 mm thick. We investigated known morphologies of the muscles in anatomy, such as parallel/pennate muscles, and biceps/triceps muscles. A sample of a 120 mm long and 50 mm wide rectangular muscle with a weight of 1.2 g yielded a maximum output force of up to 35 N at a pressure of 40 kPa. The data showed that the actuator can mimic the properties of the pennate muscle, which has angled muscle fibers. We also implemented muscles with anatomy-based designs on a bionic arm.


title: Extending the mutation spectrum for Galloway-Mowat syndrome to include homozygous missense mutations in the WDR73 gene.

abstract: Galloway–Mowat syndrome is a rare autosomal-recessive disorder classically described as the combination of microcephaly and nephrotic syndrome. Recently, homozygous truncating mutations in WDR73 (WD repeat domain 73) were described in two of 31 unrelated families with Galloway–Mowat syndrome which was followed by a report of two sibs in an Egyptian consanguineous family. In this report, seven affecteds from four families showing biallelic missense mutations in WDR73 were identified by exome sequencing and confirmed to follow a recessive model of inheritance. Three-dimensional modeling predicted conformational alterations as a result of the mutation, supporting pathogenicity. An additional 13 families with microcephaly and renal phenotype were negative for WDR73 mutations. Missense mutations in the WDR73 gene are reported for the first time in Galloway–Mowat syndrome. A detailed phenotypic comparison of all reported WDR73-linked Galloway–Mowat syndrome patients with WDR73 negative patients showed that WDR73 mutations are limited to those with classical Galloway–Mowat syndrome features, in addition to cerebellar atrophy, thin corpus callosum, brain stem hypoplasia, occasional coarse face, late-onset and mostly slow progressive nephrotic syndrome, and frequent epilepsy. © 2016 Wiley Periodicals, Inc.


title: A ROS IMPLEMENTATION OF THE MONO-SLAM ALGORITHM

abstract: Computer vision approaches are increasingly used in mobile robotic systems, since they allow to obtain a very good representation of the environment by using low-power and cheap sensors. In particular it has been shown that they can compete with standard solutions based on laser range scanners when dealing with the problem of simultaneous localization and mapping (SLAM), where the robot has to explore an unknown environment while building a map of it and localizing in the same map. We present a package for simultaneous localization and mapping in ROS (Robot Operating System) using a monocular camera sensor only. Experimental results in real scenarios as well as on standard datasets show that the algorithm is able to track the trajectory of the robot and build a consistent map of small environments, while running in near real-time on a standard PC.


title: What makes consumers buy from Internet? A longitudinal study of online shopping

abstract: The objective of this study is to investigate the factors affecting online shopping. A model explaining the impact of different factors on online shopping intentions and behavior is developed based on the theory of planned behavior. The model is then tested empirically in a longitudinal study with two surveys. Data collected from 705 consumers indicate that subjective norms, attitude, and beliefs concerning the consequences of online shopping have significant effects on consumers' intentions to buy online. Behavioral control and intentions significantly influenced online shopping behavior. The results also provide strong support for the positive effects of personal innovativeness on attitude and intentions to shop online. The implications of the findings for theory and practice are discussed.


title: A need for closer examination of FASD by the criminal justice system: has the call been answered?

abstract: Individuals with FASD exhibit deficits in many domains that can include memory, learning, behavioural inhibition, executive functioning, interpersonal skills, and language. These deficits have serious implications for affected persons when they become engaged in the legal system. In 2004, Moore and Green reviewed case law and psychological literature which suggested that FASD-related deficits placed affected individuals at a significant disadvantage in the justice system. According to them, this disadvantage stemmed from the limited awareness and knowledge of FASD demonstrated by key players in the justice system, as well as the scarcity of effective interventions in place to rehabilitate affected defendants. The aim of the current paper is to assess the extent to which awareness of FASD-related issues in the Canadian justice system has advanced since the publication of Moore and Green’s conclusions. First, the deficits associated with FASD and their implications for the justice system are described. Next, recent case law and psychological evidence are reviewed as we consider issues of witness reliability and false confessions. The significance of FASD for sentencing, fitness to stand trial, and the Not Criminally Responsible by Reason of Mental Disorder defence are also briefly discussed. Finally, emerging system wide responses to FASD-related issues are presented. Overall, it appears that the call for closer examination of FASD by the justice system has been answered, but a need for increased education and awareness remains.


title: What is System Dynamics? A Paradigmatic Inquiry

abstract: There is not one single system dynamics approach. Apart from ’mainstream’ system dynamics, there are several distinctive practices in use. In this explorative paper, an attempt is made to reveal basic assumptions of dierent practices and to classify them in a paradigmatic classification framework on the basis of these basic assumptions. Revealing its basic assumptions might be important for increasing the acceptance and use of system dynamics. It will be shown here that mainstream system dynamics corresponds well with critical pluralism, that other approaches are more postpositivist, pragmatist or constructivist, and that still other paradigmatic approaches might potentially be interesting. The classification framework might be used furthermore to find (i) approaches that suit issues, circumstances, parties involved and goals, (ii) the basic assumptions of dierent approaches and hence the interpretation and use of the results, and (iii) consistent matches and mixes of system dynamics approaches with other method(ologie)s.


title: Local Convergence of SQP Methods for Mathematical Programs with Equilibrium Constraints

abstract: Recently, nonlinear programming solvers have been used to solve a range of mathematical programs with equilibrium constraints (MPECs). In particular, sequential quadratic programming (SQP) methods have been very successful. This paper examines the local convergence properties of SQP methods applied to MPECs. SQP is shown to converge superlinearly under reasonable assumptions near a strongly stationary point. A number of examples are presented that show that some of the assumptions are difficult to relax.


title: ParaDrop: a multi-tenant platform for dynamically installed third party services on home gateways

abstract: The landscape of computing capabilities within the home has seen a recent shift from persistent desktops to mobile platforms, which has led to the use of the cloud as the primary computing platform implemented by developers today. Cloud computing platforms, such as Amazon EC2 and Google App Engine, are popular for many reasons including their reliable, always on, and robust nature. The capabilities that centralized computing platforms provide are inherent to their implementation, and unmatched by previous platforms (e.g., Desktop applications). Thus, third-party developers have come to rely on cloud computing platforms to provide high quality services to their end-users.


Humor Detection in Yelp reviews


Social roles in electronic communities


title: Infinite Feature Selection

abstract: Filter-based feature selection has become crucial in many classification settings, especially object recognition, recently faced with feature learning strategies that originate thousands of cues. In this paper, we propose a feature selection method exploiting the convergence properties of power series of matrices, and introducing the concept of infinite feature selection (Inf-FS). Considering a selection of features as a path among feature distributions and letting these paths tend to an infinite number permits the investigation of the importance (relevance and redundancy) of a feature when injected into an arbitrary set of cues. Ranking the importance individuates candidate features, which turn out to be effective from a classification point of view, as proved by a thoroughly experimental section. The Inf-FS has been tested on thirteen diverse benchmarks, comparing against filters, embedded methods, and wrappers, in all the cases we achieve top performances, notably on the classification tasks of PASCAL VOC 2007-2012.


title: Explaining Recommendations: Satisfaction vs. Promotion

abstract: Recommender systems have become a popular technique for helping users select desirable books, movies, music and other items. Most research in the area has focused on developing and evaluating algorithms for efficiently producing accurate recommendations. However, the ability to effectively explain its recommendations to users is another important aspect of a recommender system. The only previous investigation of methods for explaining recommendations showed that certain styles of explanations were effective at convincing users to adopt recommendations (i.e. promotion) but failed to show that explanations actually helped users make more accurate decisions (i.e. satisfaction). We present two new methods for explaining recommendations of contentbased and/or collaborative systems and experimentally show that they actually improve user’s estimation of item quality.


title: READ–IT: Assessing Readability of Italian Texts with a View to Text Simplification

abstract: In this paper, we propose a new approach to readability assessment with a specific view to the task of text simplification: the intended audience includes people with low literacy skills and/or with mild cognitive impairment. READ-IT represents the first advanced readability assessment tool for what concerns Italian, which combines traditional raw text features with lexical, morpho-syntactic and syntactic information. In READ-IT readability assessment is carried out with respect to both documents and sentences where the latter represents an important novelty of the proposed approach creating the prerequisites for aligning the readability assessment step with the text simplification process. READ-IT shows a high accuracy in the document classification task and promising results in the sentence classification scenario.


title: Standing Out from the Crowd: Emotional Labor, Body Labor, and Temporal Labor in Ridesharing

abstract: CSCW researchers have become interested in crowd work as a new form of collaborative engagement, that is, as a new way in which people's actions are coordinated in order to achieve collective effects. We address this area but from a different perspective - that of the labor practices involved in taking crowd work as a form of work. Using empirical materials from a study of ride-sharing, we draw inspiration from studies of the immaterial forms of labor and alternate analyses of political economy that can cast a new light on the context of crowd labor that might matter for CSCW researchers.


title: Investment under Uncertainty, Debt, and Taxes

abstract: It is common practice in financial derivative valuation to use a discount factor based on the riskless debt rate. But, to what extent is this discount factor appropriate for cash flows emerging in capital budgeting? To answer this question, we introduce a framework for real asset valuation that considers both personal and corporate taxation. We first discuss broad circumstances under which personal taxes do not affect valuation. We show that the appropriate discount rate for equity-financed flows in a risk-neutral setting is an equity rate that differs from the riskless debt rate by a tax wedge due to the presence of personal taxation. We extend this result to the valuation of the interest tax shield for exogenous debt policy with default risk. Interest tax shields, which accrue at a net rate corresponding to the difference between the corporate tax rate and a tax rate related to the personal tax rates, can have either positive or negative values. We also provide an illustrative real options application of our valuation approach to the case of an option to delay investment in a project, showing that the application of Black and Scholes formula may be incorrect in presence of personal taxes. Copyright 2008 The Authors Journal compilation 2008 Banca Monte dei Paschi di Siena SpA.


title: ROS-based robot navigation and human interaction in indoor environment

abstract: This paper sets the basis of a robot collaborative application framework that is able to perform basic interactions with a human partner, namely patrol and monitor a designated area, followed by interaction with a human participant when this is detected. The application is developed using an adaptation of ROS to Corobot robot and image processing techniques that involve OpenCV face detection.


title: Hierarchical RNN Networks for Structured Semantic Web API Model Learning and Extraction

abstract: RESTful Web APIs have no description files like WSDL in traditional Web service. Although some REST API definition models have been arising recently, there is still lacking in structured description format for existing large mounts of Web APIs. Almost all Web APIs are documented in semi-structured web pages, and these documentation formats are various for different sites. It's hard for machine to read the semantics of Web APIs. In this paper, we have proposed a novel hierarchical recurrent neural network to convert REST API documentation to structured machine-readable description format -- the Swagger REST API specification. The network extracts the Swagger defined attributes of a REST API from HTML web pages without any feature engineering. With the extracted API specifications, we built an API repository to index, search and compose Web APIs. Experiment showed that the hierarchical RNN model performed well even with only a few training samples.


title: Towards a formalization of multi-touch gestures

abstract: Multi-touch is a technology which offers new styles of interaction compared to traditional input devices like keyboard and mouse. Users can quickly manipulate objects or execute commands by means of their fingers and hands. Current multi-touch frameworks offer a set of standard gestures that are easy to use when developing an application. In contrast, defining new gestures requires a lot of work involving low-level recognition of touch data. To address this problem, we contribute a discussion of strategies towards a formalization of gestural interaction on multi-touch surfaces. A test environment is presented, showing the applicability and benefit within multi-touch frameworks.


title: Continuous queries over append-only databases

abstract: In a database to which data is continually added, users may wish to issue a permanent query and be notified whenever data matches the query. If such continuous queries examine only single records, this can be implemented by examining each record as it arrives. This is very efficient because only the incoming record needs to be scanned. This simple approach does not work for queries involving joins or time. The Tapestry system allows users to issue such queries over a database of mail and bulletin board messages. The user issues a static query, such as “show me all messages that have been replied to by Jones,” as though the database were fixed and unchanging. Tapestry converts the query into an incremental query that efficiently finds new matches to the original query as new messages are added to the database. This paper describes the techniques used in Tapestry, which do not depend on triggers and thus be implemented on any commercial database that supports SQL. Although Tapestry is designed for filtering mail and news messages, its techniques are applicable to any append-only database.


title: Campus trajectory forecast based on human activity cycle and Markov method

abstract: Traditional Markov method used in trajectory prediction fails to capture the property of the moving objects. In this paper, a zoning method was discussed to extract the most popular areas in the campus. We presented a prediction model based on the students' activity cycle in campus. Markov method was applied in a periodically way to forecast the campus trajectory. Our forecast result was obtained by the weighted integration of different sub-models. Experimental results show that the optimized prediction gives us a satisfying forecast result.


title: Development of low-power high speed (10Gb/s) drivers in CMOS 130 nm technology

abstract: The aim of this work is to develop a dedicated low power transmitter interface for high-speed data transmission in CMOS 130 nm technology. Such interface is necessary in complex ASICs working at high frequencies and processing large amounts of data, in particular it is needed in advanced detector readout systems of particle physics experiments. New multichannel readout ASICs, capable to transmit data at high frequencies (>5Gb/s), with low jitter, and consuming very low power, are recently being intensively developed. A Current Mode Logic (CML) and Source-Series Terminated (SST) interfaces are natural candidates to drive the data out of the chip. A broadband extension techniques using inductors may be applied to extend the bandwidth of these drivers. Unfortunataly, inductors occupy very large area what limits their applications in ASICs. In this work the CML driver using inductive peaking and two SST drivers (with, without series peaking) were developed to achieve transmission speeds between 5–10 Gb/s, together with very low (<2 ps) jittter. We present and compare the schematic and post-layout simulations of the developed drivers together with all relevant parameters (speed, jitter, eye diagram). Prototype designs in CMOS 130 nm were already submitted and are now in fabrication.


title: A proximal algorithm for joint resource allocation and minimizing carbon footprint in geo-distributed fog computing

abstract: Large-scale Internet applications, such as content distribution networks, are deployed in a geographically distributed manner and emit massive amounts of carbon footprint at the data center. To provide uniform low access latencies, Cisco has introduced Fog computing as a new paradigm which can transform the network edge into a distributed computing infrastructure for applications. Fog nodes are geographically distributed and the deployment size at each location reflects the regional demand for the application. Thus, we need to control the fraction of user traffic to data center to maximize the social welfare. In this paper, we consider the emerging problem of joint resource allocation and minimizing carbon footprint problem for video streaming service in Fog computing. To solve the largescale optimization, we develop a distributed algorithm based on the proximal algorithm and alternating direction method of multipliers (ADMM). The numerical results show that our algorithm converges to near optimum within fifteen iterations, and is insensitive to step sizes.


title: A secure routing protocol for ad hoc networks

abstract: Most recent ad hoc network research has focused on providing routing services without considering security. We detail security threats against ad hoc routing protocols, specifically examining AODV and DSR. In light of these threats, we identify three different environments with distinct security requirements. We propose a solution to one, the managed-open scenario where no network infrastructure is pre-deployed, but a small amount of prior security coordination is expected. Our protocol, authenticated routing for ad hoc networks (ARAN), is based on certificates and successfully defeats all identified attacks.


title: A highly accurate constant voltage (CV) and constant current (CC) primary side controller for offline applications

abstract: This paper presents an innovative primary-sensing technique for constant voltage (CV) and constant current (CC) for flyback converter, eliminating the need for the opto-coupler, the secondary voltage reference, as well as the current sensor. The voltage regulation can achieve +/-2.5%, and current +/-5%. Furthermore, the control IC is integrated with high voltage (800V) MOSFET, which makes it suitable for low power battery chargers, LED applications, and auxiliary power supplies.


title: A randomized pilot study comparing safety and efficacy of irinotecan plus S-1 plus bevacizumab (IRIS+BV) and modified FOLFIRI plus BV (mFOLFIRI+BV) in patients (pts) with metastatic colorectal cancer (mCRC): The result of efficacy report of T-CORE0702.

abstract: e14001 Background: We conducted a randomized pilot study to compare the safety and efficacy of IRIS+BV and mFOLFIRI+BV against Japanese mCRC (T-CORE0702) and reported that IRIS+BV is a safe regimen with low hemtotoxicity and GI toxicity compared with mFOLFIRI+BV (ASCO GI 2011, abstract #496, S Kato et al.). This is the efficacy report of T-CORE0702. Methods: From July 2007 to March 2010, 60 pts with mCRC were randomized to compare 30 pts for IRIS+BV (CPT-11 150 mg/m2 infusion on day 1, S-1 80 mg/m2 orally on day 3-16, q3w) with 30 pts for mFOLFIRI+BV (CPT-11 150mg/m2). Tumor assessments were performed by every 6weeks. Tumor responses were assessed using RECIST 1.0 criteria by institutional evaluation and confirmed by blinded central monitoring. Results: Background of pts was well balanced among two arms. Among G3/4 nonhematological AEs, gastrointestinal toxicities were lower in IRIS+BV arm than mFOLFIRI+BV arm (anorexia: 3.5% vs. 17.9%, nausea: 0% vs. 7.1%, diarrhea: 6.9% vs. 14.3%, stomatitis: 0% vs. 3.6...


title: Evaluation of breakdown characteristics of oil-immersed transformers under non-standard lightning impulse waveforms - method for converting non-standard lightning impulse waveforms into standard lightning impulse waveforms

abstract: To lower the insulation specifications (specifically, the lightning impulse withstand voltage) of oil-immersed transformers and thus reduce equipment cost while maintaining high insulation reliability, it is required to identify the insulation characteristics under non-standard lightning impulse waveforms that are associated with actual surge waveforms in the field and quantitatively compare them with the characteristics under the standard lightning impulse waveform. In the previous research, field overvoltages in the lightning surge time region were analyzed, and four typical non-standard lightning impulse waveforms were defined. These four waveforms were used to measure the breakdown voltages and the partial discharge inception voltages on three models of the winding insulation elements of oil-immersed transformers. The average breakdown voltages were evaluated in terms of the overvoltage duration. This paper describes a method for converting of non-standard lightning impulse waveforms into standard lightning impulse waveforms with equivalent stress for the insulation. The constructed algorithm was applied to four examples representing two types of non-standard lightning waveforms. Due to the conversion into standard lightning impulse waveforms, the crest values were reduced by 14% to 26%. This seems to be a potential for reduction of lightning impulse insulation specifications of oil-immersed transformers.


title: Factors Influencing the Composition of the Intestinal Microbiota in Early Infancy

abstract: OBJECTIVE.The aim of this study was to examine the contribution of a broad range of external influences to the gut microbiotic composition in early infancy. METHODS.Fecal samples from 1032 infants at 1 month of age, who were recruited from the KOALA Birth Cohort Study in the Netherlands, were subjected to quantitative real-time polymerase chain reaction assays for the enumeration of bifidobacteria, Escherichia coli, Clostridium difficile, Bacteroides fragilis group, lactobacilli, and total bacterial counts. Information on potential determinants of the gut microbiotic composition was collected with repeated questionnaires. The associations between these factors and the selected gut bacteria were analyzed with univariate and multivariate analyses. RESULTS.Infants born through cesarean section had lower numbers of bifidobacteria and Bacteroides, whereas they were more often colonized with C difficile, compared with vaginally born infants. Exclusively formula-fed infants were more often colonized with E coli, C difficile, Bacteroides, and lactobacilli, compared with breastfed infants. Hospitalization and prematurity were associated with higher prevalence and counts of C difficile. Antibiotic use by the infant was associated with decreased numbers of bifidobacteria and Bacteroides. Infants with older siblings had slightly higher numbers of bifidobacteria, compared with infants without siblings. CONCLUSIONS.The most important determinants of the gut microbiotic composition in infants were the mode of delivery, type of infant feeding, gestational age, infant hospitalization, and antibiotic use by the infant. Term infants who were born vaginally at home and were breastfed exclusively seemed to have the most “beneficial” gut microbiota (highest numbers of bifidobacteria and lowest numbers of C difficile and E coli).


title: MODAClouds: a model-driven approach for the design and execution of applications on multiple clouds

abstract: Cloud computing is emerging as a major trend in the ICT industry. While most of the attention of the research community is focused on considering the perspective of the Cloud providers, offering mechanisms to support scaling of resources and interoperability and federation between Clouds, the perspective of developers and operators willing to choose the Cloud without being strictly bound to a specific solution is mostly neglected. We argue that Model-Driven Development can be helpful in this context as it would allow developers to design software systems in a cloud-agnostic way and to be supported by model transformation techniques into the process of instantiating the system into specific, possibly, multiple Clouds. The MODAClouds (MOdel-Driven Approach for the design and execution of applications on multiple Clouds) approach we present here is based on these principles and aims at supporting system developers and operators in exploiting multiple Clouds for the same system and in migrating (part of) their systems from Cloud to Cloud as needed. MODAClouds offers a quality-driven design, development and operation method and features a Decision Support System to enable risk analysis for the selection of Cloud providers and for the evaluation of the Cloud adoption impact on internal business processes. Furthermore, MODAClouds offers a run-time environment for observing the system under execution and for enabling a feedback loop with the design environment. This allows system developers to react to performance fluctuations and to re-deploy applications on different Clouds on the long term.


title: Isolation of N-Acetylneuraminic Acid and 4-Oxo-norleucine from a Polysaccharide Obtained from Citrobacter freundii

abstract: SUMMARY: A polysaccharide constituted primarily of a unit of N-acetylneuraminic acid, two of glucosamine and one of an acid labile nitrogenous component was extracted from Citrobacter freundii O5:H30. The material after purification gave a single peak in the ultracentrifuge. N-acetylneuraminic acid and 4-oxo-norleucine were isolated from hydrolysates of the polysaccharide. A similarly constituted polysaccharide was obtained from Salmonella dahlem. C. freundii O5:H30 and S. dahlem were shown previously to be serologically related to one another and the results of the present investigations indicate that a chemical relationship also exists between these micro-organisms. Moreover, it is suggestive that the serologically related S. djkarta which contains neuraminic acid is related chemically to S. dahlem and C. freundii O5:H30. In sum, it is concluded that derivatives of neuraminic acid exist in association with other amino sugars and amino acids in mucopolysaccharides of bacterial origin as well as in those derived from mammalian origin.


title: An efficient deep neural networks training framework for robust face recognition

abstract: In recent years, the triplet loss-based deep neural networks (DNN) are widely used in the task of face recognition and achieve the state-of-the-art performance. However, the complexity of training the triplet loss-based DNN is significantly high due to the difficulty in generating high-quality training samples. In this paper, we propose a novel DNN training framework to accelerate the training process of the triplet loss-based DNN and meanwhile to improve the performance of face recognition. More specifically, the proposed framework contains two stages: 1) The DNN initialization. A deep architecture based on the softmax loss function is designed to initialize the DNN. 2) The adaptive fine-tuning. Based on the trained model, a set of high-quality triplet samples is generated and used to fine-tune the network, where an adaptive triplet loss function is introduced to improve the discriminative ability of DNN. Experimental results show that, the model obtained by the proposed DNN training framework achieves 97.3% accuracy on the LFW benchmark with low training complexity, which verifies the efficiency and effectiveness of the proposed framework.


title: IRWRLDA: improved random walk with restart for lncRNA-disease association prediction

abstract: // Xing Chen 1 , Zhu-Hong You 2 , Gui-Ying Yan 3, 4 , Dun-Wei Gong 1 1 School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, 221116, China 2 School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China 3 Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China 4 National Center for Mathematics and Interdisciplinary Sciences, Chinese Academy of Sciences, Beijing, 100190, China Correspondence to: Xing Chen, email: xingchen@amss.ac.cn Zhu-Hong You, email: zhuhongyou@gmail.com Keywords: lncRNAs, disease, cancer, random walk with restart Received: January 20, 2016 Accepted: July 06, 2016 Published: August 09, 2016 ABSTRACT In recent years, accumulating evidences have shown that the dysregulations of lncRNAs are associated with a wide range of human diseases. It is necessary and feasible to analyze known lncRNA-disease associations, predict potential lncRNA-disease associations, and provide the most possible lncRNA-disease pairs for experimental validation. Considering the limitations of traditional Random Walk with Restart (RWR), the model of Improved Random Walk with Restart for LncRNA-Disease Association prediction (IRWRLDA) was developed to predict novel lncRNA-disease associations by integrating known lncRNA-disease associations, disease semantic similarity, and various lncRNA similarity measures. The novelty of IRWRLDA lies in the incorporation of lncRNA expression similarity and disease semantic similarity to set the initial probability vector of the RWR. Therefore, IRWRLDA could be applied to diseases without any known related lncRNAs. IRWRLDA significantly improved previous classical models with reliable AUCs of 0.7242 and 0.7872 in two known lncRNA-disease association datasets downloaded from the lncRNADisease database, respectively. Further case studies of colon cancer and leukemia were implemented for IRWRLDA and 60% of lncRNAs in the top 10 prediction lists have been confirmed by recent experimental reports.


title: Via-fence antennas on LTCC for radar applications at 122 GHz

abstract: As part of an ongoing pursuit of technologies for integrated millimeter-wave radar sensors, we present simulation and measurement results of two antennas in LTCC technology for the ISM band at 122 GHz on Hirai's CS71 substrate material. Both antennas share common design features, such as the feeding microstrip line, the H-shaped coupling aperture and the via-fence, but differ in their parameters, the parasitic element (a quadratic patch and three stripes) and therefore, their bandwidth and radiation pattern.


title: A novel booster antenna on flexible substrates for metal proximity NFC applications

abstract: In this article a novel booster antenna is proposed in order to increase the read range of a small (1.1×10−4 m2) metal mounted HF (13.56 MHz) RFID. The highly inductive coupling between the reader and booster, and between the RFID and booster is the main propagation method. The communication between the reader and RFID realized through the booster antenna. For this purpose two coils are connected in parallel to each other resonating at the same frequency. The greater coil is coupled to the reader antenna and the smaller coil is coupled to the RFID. The simulation and measurement results show the reliability of the proposed structure.


title: Active Learning in the Science Classroom

abstract: This study focused on active learning and how it affects student attitude and achievement in college level biology courses. A Biology Attitude Survey was administered at the beginning and middle of the semester to determine if student’s attitude towards biology improved from the active learning setting. The student’s attitude was also assessed from a classroom format survey containing questions about the benefits of undergraduate Learning Assistants and the active learning classroom format. These attitudinal measures were then compared with student’s achievement in biology by looking at their midterm grade, pre-test and post-test assessment scores. The ANOVA revealed that students with a high achievement in the course had a higher attitude towards biology.


title: Comparing Blockchain and Cloud Services for Business Process Execution

abstract: Blockchain is of rising importance as a technology for engineering applications in cross-organizational settings, avoiding reliance on central trusted third-parties. The use of blockchain, instead of traditional databases or services, is an architectural choice in the development of a software system. The costs of execution and storage are important non-functional qualities, but as yet very little has been done to study them for blockchain-based systems. We investigate the cost of using blockchain using business process execution as a lens. Specifically, we compare the cost for computation and storage of business process execution on blockchain vs. a popular cloud service. First, we capture the cost models for both alternatives. Second, we implemented and measured the cost of business process execution on blockchain and cloud services for an example business process model from the literature. We observe two orders of magnitude difference in this cost.


title: Wavelength-division-multiplexed passive optical network (WDM-PON) technologies for broadband access: a review (Invited)

abstract: Feature Issue on Optical Access Networks (OAN) The passive optical network (PON) is an optical fiber based network architecture, which can provide much higher bandwidth in the access network compared to traditional copper-based networks. Incorporating wavelength-division multiplexing (WDM) in a PON allows one to support much higher bandwidth compared to the standard PON, which operates in the


title: Long slot Van Atta array for far-field wireless power transfer

abstract: The design of a Van Atta array of long slots is presented for far-field wireless power transfer (WPT). The proposed array overcomes the scanning and bandwidth limitations of Van Atta arrays based on resonant elements for WPT applications. The proposed array consists of four slots with eight input ports, resulting in 32 access points. The Van Atta scheme is implemented by connecting pairs of input ports with coaxial cables. Measurements validate the design at a center frequency of 5.8 GHz. Monostatic RCS measurements show that the array has a −10 dB beamwidth of about 118° in the E-plane. The scanning performance of the array is preserved in the considered band: 4.8–6.2 GHz.


abstract: The proposed paper presents a Hybrid AC-DC microgrid to chop the process of multiple conversions in an individual AC microgrid or DC microgrid. The proposed hybrid microgrid compose of both AC microgrid and DC microgrid connected together by multilevel inverter. This paper concentrates on developing a control system for the hybrid AC-DC microgrid to manage power flows, which is more difficult than previously developed systems only for AC microgrid or DC microgrid. The objective is then to makeup a co-ordinated control system for power sharing within the sources in the hybrid AC-DC microgrid in proportion to the power ratings. The droop control technique is presented in this paper to control the hybrid microgrid. The droop control concept uses a suitable normalization technique for combining both the subgrids. The co-ordinated operation of DC and AC sources and multilevel inverter are investigated. Analysis of power sharing on hybrid AC-DC microgrid is done using MATLAB/SIMULINK.


title: Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow

abstract: We present a design study of the TensorFlow Graph Visualizer, part of the TensorFlow machine intelligence platform. This tool helps users understand complex machine learning architectures by visualizing their underlying dataflow graphs. The tool works by applying a series of graph transformations that enable standard layout techniques to produce a legible interactive diagram. To declutter the graph, we decouple non-critical nodes from the layout. To provide an overview, we build a clustered graph using the hierarchical structure annotated in the source code. To support exploration of nested structure on demand, we perform edge bundling to enable stable and responsive cluster expansion. Finally, we detect and highlight repeated structures to emphasize a model's modular composition. To demonstrate the utility of the visualizer, we describe example usage scenarios and report user feedback. Overall, users find the visualizer useful for understanding, debugging, and sharing the structures of their models.


Semi-Supervised Events Clustering in News Retrieval


title: Nonfunctional requirements: from elicitation to conceptual models

abstract: Nonfunctional requirements (NFRs) have been frequently neglected or forgotten in software design. They have been presented as a second or even third class type of requirement, frequently hidden inside notes. We tackle this problem by treating NFRs as first class requirements. We present a process to elicit NFRs, analyze their interdependencies, and trace them to functional conceptual models. We focus our attention on conceptual models expressed using UML (Unified Modeling Language). Extensions to UML are proposed to allow NFRs to be expressed. We show how to integrate NFRs into the class, sequence, and collaboration diagrams. We also show how use cases and scenarios can be adapted to deal with NFRs. This work was used in three case studies and their results suggest that by using our proposal we can improve the quality of the resulting conceptual models.


Chapter 10 Verification and Validation of Simulation Models


title: A review of bioinformatic pipeline frameworks

abstract: High-throughput bioinformatic analyses increasingly rely on pipeline frameworks to process sequence and metadata. Modern implementations of these frameworks differ on three key dimensions: using an implicit or explicit syntax, using a configuration, convention or class-based design paradigm and offering a command line or workbench interface. Here I survey and compare the design philosophies of several current pipeline frameworks. I provide practical recommendations based on analysis requirements and the user base.


title: Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

abstract: In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated in the existing deep-learning ecosystem to provide a tunable balance between performance, power consumption, and programmability. In this article, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a comparative study of their key characteristics, which include the supported applications, architectural choices, design space exploration methods, and achieved performance. Moreover, major challenges and objectives introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform evaluation methodology is proposed, aiming at the comprehensive, complete, and in-depth evaluation of CNN-to-FPGA toolflows.


title: Regulation of innate immune responses in the brain

abstract: In this Review article, Serge Rivest discusses how microglial cells (the resident innate immune cells of the central nervous system) are activated during infection and injury, whether microglial cell activation is neurodestructive or neuroprotective, and how targeting these cells could be a therapeutic approach for the treatment of Alzheimer's disease.


title: Double Q-learning

abstract: In some stochastic environments the well-known reinforcement learning algorithm Q-learning performs very poorly. This poor performance is caused by large overestimations of action values. These overestimations result from a positive bias that is introduced because Q-learning uses the maximum action value as an approximation for the maximum expected action value. We introduce an alternative way to approximate the maximum expected value for any set of random variables. The obtained double estimator method is shown to sometimes underestimate rather than overestimate the maximum expected value. We apply the double estimator to Q-learning to construct Double Q-learning, a new off-policy reinforcement learning algorithm. We show the new algorithm converges to the optimal policy and that it performs well in some settings in which Q-learning performs poorly due to its overestimation.


title: Some like it hot - visual guidance for preference prediction

abstract: For people first impressions of someone are of determining importance. They are hard to alter through further information. This begs the question if a computer can reach the same judgement. Earlier research has already pointed out that age, gender, and average attractiveness can be estimated with reasonable precision. We improve the state-of-the-art, but also predict - based on someone's known preferences - how much that particular person is attracted to a novel face. Our computational pipeline comprises a face detector, convolutional neural networks for the extraction of deep features, standard support vector regression for gender, age and facial beauty, and - as the main novelties - visual regularized collaborative filtering to infer inter-person preferences as well as a novel regression technique for handling visual queries without rating history. We validate the method using a very large dataset from a dating site as well as images from celebrities. Our experiments yield convincing results, i.e. we predict 76% of the ratings correctly solely based on an image, and reveal some sociologically relevant conclusions. We also validate our collaborative filtering solution on the standard MovieLens rating dataset, augmented with movie posters, to predict an individual's movie rating. We demonstrate our algorithms on howhot.io which went viral around the Internet with more than 50 million pictures evaluated in the first month.


title: Success Factors for Data Warehouse and Business Intelligence Systems

abstract: Data warehouse (DW) and business intelligence (BI) systems are among the most important IT-based systems in organizations. The decisions made using these systems can fundamentally affect an organization’s nature and performance. This paper reports the results of a case study that investigated the nature of DW/BI development. The theory lens used to frame and analyse the research was critical success factors (CSF). Ten CSF were identified from previous research on executive information systems, DW, and BI. This CSF set was used to analyse the failure of a corporate DW/BI project and the subsequent success of a smaller, functional BI system. To overcome previous criticism of the CSF approach, the CSF were analysed within the project’s organizational context and in terms of the dynamics of CSF over the life of the project. The case provides a number of lessons for organizations embarking on enterprise-scale DW/BI projects. The study shows that the augmented CSF approach is useful in understanding DW/BI development.


title: Electrical properties optimization of stripline for EMC testing of automotive parts

abstract: This paper is focused on optimization of electrical properties of 50 ohm stripline which was described in international standard for the automotive industry ISO 11452-5. The standard shows the dimension of the stripline but information about impedance matching is missing — and this information is crucial for a power amplifier connection, especially in terms of VSWR parameter. In this paper connection possibilities between stripline feeders and an input / output coaxial connector are shown, simulations results describe dependencies of the VSWR to the dimensions and shapes of the stripline feeder in the point of connection to the connector.


PUBIC BONE FRACTURE AND DISPLACEMENT DETECTION USING X-RAY IMAGES


title: A Probabilistic Collaborative Representation Based Approach for Pattern Classification

abstract: Conventional representation based classifiers, ranging from the classical nearest neighbor classifier and nearest subspace classifier to the recently developed sparse representation based classifier (SRC) and collaborative representation based classifier (CRC), are essentially distance based classifiers. Though SRC and CRC have shown interesting classification results, their intrinsic classification mechanism remains unclear. In this paper we propose a probabilistic collaborative representation framework, where the probability that a test sample belongs to the collaborative subspace of all classes can be well defined and computed. Consequently, we present a probabilistic collaborative representation based classifier (ProCRC), which jointly maximizes the likelihood that a test sample belongs to each of the multiple classes. The final classification is performed by checking which class has the maximum likelihood. The proposed ProCRC has a clear probabilistic interpretation, and it shows superior performance to many popular classifiers, including SRC, CRC and SVM. Coupled with the CNN features, it also leads to state-of-the-art classification results on a variety of challenging visual datasets.


title: Automatic analysis and sketch-based retrieval of architectural floor plans

abstract: The contribution of this article is twofold. First, we propose a sketch-based system, namely the a.SCatch system, for querying a floor plan repository. Second, for generating such a repository, a novel complete system for floor plan analysis is presented. The latter system extracts the semantics from existing floor plans. We introduce novel preprocessing methods, e.g., the differentiation between thick, medium, and thin lines and the removal of components outside the convex hull of the outer walls. Especially, the use of the convex hull increases the performance of the final system. The a.SCatch system enables the user to easily access knowledge from past projects. The user searches for semantically similar floor plans just by drawing parts of the new plan. An algorithm extracts the semantic structure sketched by the architect on DFKI's Touch &Write table. Finally, the extracted structures are compared using the graph matching, and the most similar one is retrieved. In our experiments for floor plan analysis on a reference data set we compare our approach to other approaches available in the literature. We show that our floor plan analysis system outperforms previous systems. Also the performance of the sketch recognition system is quite high. Overall, the performance of the floor plan analysis, as well as of the retrieval, is already good for the use in practice.


title: Personality and persuasive technology: An exploratory study on health-promoting mobile applications. Persuasive Technology

abstract: Though a variety of persuasive health applications have been designed with a preventive standpoint toward diseases in mind, many have been designed largely for a general audience. Designers of these technologies may achieve more success if applications consider an individual’s personality type. Our goal for this research was to explore the relationship between personality and persuasive technologies in the context of health-promoting mobile applications. We conducted an online survey with 240 participants using storyboards depicting eight different persuasive strategies, the Big Five Inventory for personality domains, and questions on perceptions of the persuasive technologies. Our results and analysis revealed a number of significant relationships between personality and the persuasive technologies we evaluated. The findings from this study can guide the development of persuasive technologies that can cater to individual personalities to improve the likelihood of their success.


N3Logic: A logical framework for the World Wide Web


title: Discovering dynamic brain networks from big data in rest and task

abstract: ABSTRACT Brain activity is a dynamic combination of the responses to sensory inputs and its own spontaneous processing. Consequently, such brain activity is continuously changing whether or not one is focusing on an externally imposed task. Previously, we have introduced an analysis method that allows us, using Hidden Markov Models (HMM), to model task or rest brain activity as a dynamic sequence of distinct brain networks, overcoming many of the limitations posed by sliding window approaches. Here, we present an advance that enables the HMM to handle very large amounts of data, making possible the inference of very reproducible and interpretable dynamic brain networks in a range of different datasets, including task, rest, MEG and fMRI, with potentially thousands of subjects. We anticipate that the generation of large and publicly available datasets from initiatives such as the Human Connectome Project and UK Biobank, in combination with computational methods that can work at this scale, will bring a breakthrough in our understanding of brain function in both health and disease.


title: Multi-terminal transport measurements of MoS2 using a van der Waals heterostructure device platform

abstract: Atomically thin two-dimensional semiconductors such as MoS2 hold great promise for electrical, optical and mechanical devices and display novel physical phenomena. However, the electron mobility of mono- and few-layer MoS2 has so far been substantially below theoretically predicted limits, which has hampered efforts to observe its intrinsic quantum transport behaviours. Potential sources of disorder and scattering include defects such as sulphur vacancies in the MoS2 itself as well as extrinsic sources such as charged impurities and remote optical phonons from oxide dielectrics. To reduce extrinsic scattering, we have developed here a van der Waals heterostructure device platform where MoS2 layers are fully encapsulated within hexagonal boron nitride and electrically contacted in a multi-terminal geometry using gate-tunable graphene electrodes. Magneto-transport measurements show dramatic improvements in performance, including a record-high Hall mobility reaching 34,000 cm(2) V(-1) s(-1) for six-layer MoS2 at low temperature, confirming that low-temperature performance in previous studies was limited by extrinsic interfacial impurities rather than bulk defects in the MoS2. We also observed Shubnikov-de Haas oscillations in high-mobility monolayer and few-layer MoS2. Modelling of potential scattering sources and quantum lifetime analysis indicate that a combination of short-range and long-range interfacial scattering limits the low-temperature mobility of MoS2.


title: Thermal imaging based elderly fall detection

abstract: Elderly fall detection is very special case of human action recognition from videos and has very practical application in old age home and nursing centers. Fall detection in its simplest form is a binary classification of fall event or other daily routine activities. Hence, the current trend of sophisticated techniques being developed for human action recognition, particularly with scenarios of large number of classes may not be required in elderly fall detection. However, other design considerations such as simplicity (ready to be deployed), privacy issues (not revealing the identity) are to focused and are the major contributions of this paper. The Spatio-Temporal Interest Points (STIP) and Fisher vector framework for human action recognition is established as baseline in this work. A novel optical flow based technique is proposed that yields better performance than the baseline. Further, a very economical thermal imaging based input modality is proposed. Along with the thermal images not revealing the identity of the persons, thermal images also aid human detection from backgrounds – a useful solution in computing the optical flow of human movements. The proposed solution is also validated on the KUL Simulated Fall dataset showing its generalization capability.


title: Dynamic Data Replication Scheme in the Cloud Computing Environment

abstract: In the cloud computing environment, data replication strategy (DRS) is used to improve data access. Related studies have proposed data replication strategies. The performances of these strategies are closely related to the users' access patterns, and work optimally for a particular data access pattern. However, as the data access patterns become more flexible and unpredictable, it is difficult to manage them with traditional replication strategies. Given this circumstance, this paper proposes an algorithm that detects changes in a user's data access pattern and dynamically applies an optimal replication strategy. The proposed algorithm has the advantage of maintaining an optimal performance by responding to various data access patterns. We tested the proposed algorithm and validated its effectiveness.


title: Detecting privacy leaks using corpus-based association rules

abstract: Detecting inferences in documents is critical for ensuring privacy when sharing information. In this paper, we propose a refined and practical model of inference detection using a reference corpus. Our model is inspired by association rule mining: inferences are based on word co-occurrences. Using the model and taking the Web as the reference corpus, we can find inferences and measure their strength through web-mining algorithms that leverage search engines such as Google or Yahoo!. Our model also includes the important case of private corpora, to model inference detection in enterprise settings in which there is a large private document repository. We find inferences in private corpora by using analogues of our Web-mining algorithms, relying on an index for the corpus rather than a Web search engine. We present results from two experiments. The first experiment demonstrates the performance of our techniques in identifying all the keywords that allow for inference of a particular topic (e.g. "HIV") with confidence above a certain threshold. The second experiment uses the public Enron e-mail dataset. We postulate a sensitive topic and use the Enron corpus and the Web together to find inferences for the topic. These experiments demonstrate that our techniques are practical, and that our model of inference based on word co-occurrence is well-suited to efficient inference detection.


title: Forming a Story: The Health Benefits of Narrative

abstract: Writing about important personal experiences in an emotional way for as little as 15 minutes over the course of three days brings about improvements in mental and physical health. This finding has been replicated across age, gender, culture, social class, and personality type. Using a text-analysis computer program, it was discovered that those who benefit maximally from writing tend to use a high number of positive-emotion words, a moderate amount of negative-emotion words, and increase their use of cognitive words over the days of writing. These findings suggest that the formation of a narrative is critical and is an indicator of good mental and physical health. Ongoing studies suggest that writing serves the function of organizing complex emotional experiences. Implications for these findings for psychotherapy are briefly discussed. © 1999 John Wiley & Sons, Inc. J Clin Psychol 55: 1243–1254, 1999.


title: Efficient human action recognition using histograms of motion gradients and VLAD with descriptor shape information

abstract: Feature extraction and encoding represent two of the most crucial steps in an action recognition system. For building a powerful action recognition pipeline it is important that both steps are efficient and in the same time provide reliable performance. This work proposes a new approach for feature extraction and encoding that allows us to obtain real-time frame rate processing for an action recognition system. The motion information represents an important source of information within the video. The common approach to extract the motion information is to compute the optical flow. However, the estimation of optical flow is very demanding in terms of computational cost, in many cases being the most significant processing step within the overall pipeline of the target video analysis application. In this work we propose an efficient approach to capture the motion information within the video. Our proposed descriptor, Histograms of Motion Gradients (HMG), is based on a simple temporal and spatial derivation, which captures the changes between two consecutive frames. For the encoding step a widely adopted method is the Vector of Locally Aggregated Descriptors (VLAD), which is an efficient encoding method, however, it considers only the difference between local descriptors and their centroids. In this work we propose Shape Difference VLAD (SD-VLAD), an encoding method which brings complementary information by using the shape information within the encoding process. We validated our proposed pipeline for action recognition on three challenging datasets UCF50, UCF101 and HMDB51, and we propose also a real-time framework for action recognition.


title: Efficient production of biodiesel from high free fatty acid-containing waste oils using various carbohydrate-derived solid acid catalysts.

abstract: In the present study, such carbohydrate-derived catalysts have been prepared from various carbohydrates such as d-glucose, sucrose, cellulose and starch. The catalytic and textural properties of the prepared catalysts have been investigated in detail and it was found that the starch-derived catalyst had the best catalytic performance. The carbohydrate-derived catalysts exhibited substantially higher catalytic activities for both esterification and transesterification compared to the two typical solid acid catalysts (sulphated zirconia and Niobic acid), and gave markedly enhanced yield of methyl esters in converting waste cooking oils containing 27.8wt% high free fatty acids (FFAs) to biodiesel. In addition, under the optimized reaction conditions, the starch-derived catalyst retained a remarkably high proportion (about 93%) of its original catalytic activity even after 50 cycles of successive re-use and thus displayed very excellent operational stability. Our results clearly indicate that the carbohydrate-derived catalysts, especially the starch-derived catalyst, are highly effective, recyclable, eco-friendly and promising solid acid catalysts that are highly suited to the production of biodiesel from waste oils containing high FFAs.


title: From ERPs to MVPA Using the Amsterdam Decoding and Modeling Toolbox (ADAM)

abstract: In recent years, time-resolved multivariate pattern analysis (MVPA) has gained much popularity in the analysis of electroencephalography (EEG) and magnetoencephalography (MEG) data. However, MVPA may appear daunting to those who have been applying traditional analyses using event-related potentials (ERPs) or event-related fields (ERFs). To ease this transition, we recently developed the Amsterdam Decoding and Modeling (ADAM) toolbox in MATLAB. ADAM is an entry-level toolbox that allows a direct comparison of ERP/ERF results to MVPA results using any dataset in standard EEGLAB or Fieldtrip format. The toolbox performs and visualizes multiple-comparison corrected group decoding and forward encoding results in a variety of ways, such as classifier performance across time, temporal generalization (time-by-time) matrices of classifier performance, channel tuning functions (CTFs) and topographical maps of (forward-transformed) classifier weights. All analyses can be performed directly on raw data or can be preceded by a time-frequency decomposition of the data in which case the analyses are performed separately on different frequency bands. The figures ADAM produces are publication-ready. In the current manuscript, we provide a cookbook in which we apply a decoding analysis to a publicly available MEG/EEG dataset involving the perception of famous, non-famous and scrambled faces. The manuscript covers the steps involved in single subject analysis and shows how to perform and visualize a subsequent group-level statistical analysis. The processing pipeline covers computation and visualization of group ERPs, ERP difference waves, as well as MVPA decoding results. It ends with a comparison of the differences and similarities between EEG and MEG decoding results. The manuscript has a level of description that allows application of these analyses to any dataset in EEGLAB or Fieldtrip format.


title: Bench-to-bedside review: Quorum sensing and the role of cell-to-cell communication during invasive bacterial infection

abstract: Bacteria communicate extensively with each other and employ a communal approach to facilitate survival in hostile environments. A hierarchy of cell-to-cell signaling pathways regulates bacterial growth, metabolism, biofilm formation, virulence expression, and a myriad of other essential functions in bacterial populations. The notion that bacteria can signal each other and coordinate their assault patterns against susceptible hosts is now well established. These signaling networks represent a previously unrecognized survival strategy by which bacterial pathogens evade antimicrobial defenses and overwhelm the host. These quorum sensing communication signals can transgress species barriers and even kingdom barriers. Quorum sensing molecules can regulate human transcriptional programs to the advantage of the pathogen. Human stress hormones and cytokines can be detected by bacterial quorum sensing systems. By this mechanism, the pathogen can detect the physiologically stressed host, providing an opportunity to invade when the patient is most vulnerable. These rather sophisticated, microbial communication systems may prove to be a liability to pathogens as they make convenient targets for therapeutic intervention in our continuing struggle to control microbial pathogens.