title: Simultaneous reconstruction and segmentation with the Mumford-Shah functional for electron tomography

abstract: Electron micrography (EM) is an important method for determining the three-dimensional (3D) structure of macromolecular complexes and biological specimens. But there are several limitations such as poor signal-to-noise, limitation on range of tilt angles and sub-region subject to electron exposure, unintentional movements of the specimen, with EM systems that make the reconstruction procedure a severely ill-posed problem. A different choice of reconstruction method may lead to different results and create different additional artifacts in reconstructed images. In this paper, we combined the artifacts reduction strategy and the iterative reconstruction algorithm using a Mumford-Shah model. With the combined method, one can not only regularize the ill-posedness and provide segmentation simultaneously but also smooth additional artifacts due to the limited data. We applied the method to both simulated data from the Shepp-Logan phantom and cryo-specimen tomography. The results demonstrate the performance of the method in reducing the noise and artifacts while preserving and enhancing the edges in the reconstructed image.


title: The Generalized Loneliness Detector and Weak System Models for k-Set Agreement

abstract: This paper presents two weak partially synchronous system models Manti(n-k) and Msink(n-k), which are just strong enough for solving k-set agreement: We introduce the generalized (n-k)-loneliness failure detector L(k), which we first prove to be sufficient for solving k-set agreement, and show that L(k) but not L(k-1) can be implemented in both models. Manti(n-k) and Msink(n-k) are hence the first message passing models that lie between models where Ω (and therefore consensus) can be implemented and the purely asynchronous model. We also address k-set agreement in anonymous systems, that is, in systems where (unique) process identifiers are not available. Since our novel k -set agreement algorithm using L(k) also works in anonymous systems, it turns out that the loneliness failure detector L=L(n-1) introduced by Delporte et al. is also the weakest failure detector for set agreement in anonymous systems. Finally, we analyze the relationship between L(k) and other failure detectors suitable for solving k-set agreement.


title: Towards a sustainable fashion retail supply chain in Europe:Organisation and performance

abstract: Sustainability issues are particularly sensitive to the fashion supply chain, given current fierce competition, intensive resource use, and the exposure of penurious labour conditions in some regions. In this paper we discuss how the sustainability movement is impacting the fashion retail supply chain organization and its performance. We carried out a study with stakeholders of the fashion industry and we report on their views. We elaborate on the challenges and conflicts of the different dimensions of sustainability, and we discuss how to leverage both the internal and external organisations in the European supply chain.


title: Extended-spectrum beta-lactamases in Taiwan: epidemiology, detection, treatment and infection control.

abstract: Extended-spectrum beta-lactamases (ESBLs) efficiently hydrolyze extended-spectrum beta-lactams such as cefotaxime, ceftriaxone, ceftazidime, and aztreonam. ESBLs are most often plasmid-mediated. In Taiwan, the prevalence of ESBLs in bacteria has risen, ranging from 8.5 to 29.8% in Klebsiella pneumoniae and 1.5 to 16.7% in Escherichia coli isolates. The most prevalent types of ESBLs are SHV-5, SHV-12, CTX-M-3, and CTX-M-14 in isolates of K. pneumoniae and E. coli, with differences between institutions. SHV-12 and CTX-M-3 have been reported as the most common ESBLs in isolates of Enterobacter cloacae and Serratia marcescens, respectively. Molecular epidemiology studies suggest that the ESBL-encoding genes have been disseminated either by proliferation of epidemic strains or by transfer of plasmids carrying the resistance traits. The current ESBL screen guidelines of the Clinical and Laboratory Standards Institute (formerly National Committee for Clinical Laboratory Standards) are issued for E. coli, Klebsiella spp., and Proteus mirabilis. Owing to the lack of standard methods, it remains difficult to assure the presence of ESBL in an isolate co-harboring an AmpC beta-lactamase, particularly in cases where the latter is produced in larger amounts than the former. Empirical therapy with piperacillin-tazobactam to replace third-generation cephalosporins may help to reduce the occurrence of ESBLs in an institution with a high prevalence of ESBL producers. Carbapenems remain the drugs of choice for serious infections caused by ESBL-producing organisms. To retard the selection for carbapenem-resistant bacteria, 7-alpha-methoxy beta-lactams or fourth-generation cephalosporins can be therapeutic alternatives for mild-to-moderate infections provided that the pharmacokinetic and pharmacodynamic target can be easily achieved.


title: MetaCluster 4.0: A Novel Binning Algorithm for NGS Reads and Huge Number of Species

abstract: Abstract Next-generation sequencing (NGS) technologies allow the sequencing of microbial communities directly from the environment without prior culturing. The output of environmental DNA sequencing consists of many reads from genomes of different unknown species, making the clustering together reads from the same (or similar) species (also known as binning) a crucial step. The difficulties of the binning problem are due to the following four factors: (1) the lack of reference genomes; (2) uneven abundance ratio of species; (3) short NGS reads; and (4) a large number of species (can be more than a hundred). None of the existing binning tools can handle all four factors. No tools, including both AbundanceBin and MetaCluster 3.0, have demonstrated reasonable performance on a sample with more than 20 species. In this article, we introduce MetaCluster 4.0, an unsupervised binning algorithm that can accurately (with about 80% precision and sensitivity in all cases and at least 90% in some cases) and efficientl...


title: Space-time codes for high data rate wireless communication: Performance criterion and code construction

abstract: We consider the design of channel codes for improving the data rate and/or the reliability of communications over fading channels using multiple transmit antennas. Data is encoded by a channel code and the encoded data is split into n streams that are simultaneously transmitted using n transmit antennas. The received signal at each receive antenna is a linear superposition of the n transmitted signals perturbed by noise. We derive performance criteria for designing such codes under the assumption that the fading is slow and frequency nonselective. Performance is shown to be determined by matrices constructed from pairs of distinct code sequences. The minimum rank among these matrices quantifies the diversity gain, while the minimum determinant of these matrices quantifies the coding gain. The results are then extended to fast fading channels. The design criteria are used to design trellis codes for high data rate wireless communication. The encoding/decoding complexity of these codes is comparable to trellis codes employed in practice over Gaussian channels. The codes constructed here provide the best tradeoff between data rate, diversity advantage, and trellis complexity. Simulation results are provided for 4 and 8 PSK signal sets with data rates of 2 and 3 bits/symbol, demonstrating excellent performance that is within 2-3 dB of the outage capacity for these channels using only 64 state encoders.


title: Inflammatory reaction to hyaluronic acid: A newly described complication in vocal fold augmentation

abstract: OBJECTIVES/HYPOTHESIS ::: To establish the rate of inflammatory reaction to hyaluronic acid (HA) in vocal fold injection augmentation, determine the most common presenting signs and symptoms, and propose an etiology. ::: ::: ::: STUDY DESIGN ::: Retrospective chart review. ::: ::: ::: METHODS ::: Patients injected with HA over a 5-year period were reviewed to identify those who had a postoperative inflammatory reaction. Medical records were reviewed for patient demographic information, subjective complaints, Voice Handicap Index-10 (VHI-10) scores, medical intervention, and resolution time. Videolaryngostroboscopy examinations were also evaluated. ::: ::: ::: RESULTS ::: A total of 186 patients (245 vocal folds) were injected with HA over a 5-year period, with a postoperative inflammatory reaction rate of 3.8%. The most common complaints in these patients were odynophagia, dysphonia, and dyspnea with vocal fold erythema, edema, and loss of pliability on videolaryngostroboscopy. All patients were treated with corticosteroids. Return of vocal fold vibration ranged from 3 weeks to 26 months, with VHI-10 scores normalizing in 50% of patients. ::: ::: ::: CONCLUSIONS ::: This reaction may be a form of hypersensitivity related to small amounts of protein linked to HA. Alternatively, extravascular compression from the HA could lead to venous congestion of the vocal fold. The possibility of equipment contamination is also being investigated. Further studies are needed to determine the etiology and best treatment. ::: ::: ::: LEVEL OF EVIDENCE ::: 4 Laryngoscope, 2016 127:445-449, 2017.


title: AN IMAGE PROCESSING AND NEURAL NETWORK BASED APPROACH FOR DETECTION AND CLASSIFICATION OF PLANT LEAF DISEASES

abstract: In the present paper we propose and evaluate a framework for detection and classification of plant leaf/stem diseases using image processing and neural network technique. The images of plant leaves affected by four types of diseases namely early blight, late blight, powdery-mildew and septoria has been considered for study and evaluation of feasibility of the proposed method. The color transformation structures were obtained by converting images from RGB to HSI color space. The K- means clustering algorithm was used to divide images into clusters for demarcation of infected area of the leaves. After clustering, the set of color and texture features viz. moment, mean, variance, contrast, correlation and entropy were extracted based on Color Co-occurrence Method (CCM). A feed forward back propagation neural network was configured and trained using extracted set of features and subsequently utilized for detection of leaf diseases.


title: Conserved microtubule–actin interactions in cell movement and morphogenesis

abstract: Interactions between microtubules and actin are a basic phenomenon that underlies many fundamental processes in which dynamic cellular asymmetries need to be established and maintained. These are processes as diverse as cell motility, neuronal pathfinding, cellular wound healing, cell division and cortical flow. Microtubules and actin exhibit two mechanistic classes of interactions — regulatory and structural. These interactions comprise at least three conserved 'mechanochemical activity modules' that perform similar roles in these diverse cell functions.


title: Decisions and the evolution of memory: Multiple systems, multiple functions

abstract: Memory evolved to supply useful, timely information to the organism's decision-making systems. Therefore, decision rules, multiple memory systems, and the search engines that link them should have coevolved to mesh in a coadapted, functionally interlocking way. This adaptationist perspective suggested the scope hypothesis: When a generalization is retrieved from semantic memory, episodic memories that are inconsistent with it should be retrieved in tandem to place boundary conditions on the scope of the generalization. Using a priming paradigm and a decision task involving person memory, the authors tested and confirmed this hypothesis. The results support the view that priming is an evolved adaptation. They further show that dissociations between memory systems are not--and should not be--absolute: Independence exists for some tasks but not others.


title: Region growing method for the analysis of functional MRI data

abstract: Existing analytical techniques for functional magnetic resonance imaging (fMRI) data always need some specific assumptions on the time series. In this article, we present a new approach for fMRI activation detection, which can be implemented without any assumptions on the time series. Our method is based on a region growing method, which is very popular for image segmentation. A comparison of performance on fMRI activation detection is made between the proposed method and the deconvolution method and the fuzzy clustering method with receiver operating characteristic (ROC) methodology. In addition, we examine the effectiveness and usefulness of our method on real experimental data. Experimental results show that our method outperforms over the deconvolution method and the fuzzy clustering method on a number of aspects. These results suggest that our region growing method can serve as a reliable analysis of fMRI data.


title: APT: Accurate outdoor pedestrian tracking with smartphones

abstract: This paper presents APT, a localization system for outdoor pedestrians with smartphones. APT performs better than the built-in GPS module of the smartphone in terms of accuracy. This is achieved by introducing a robust dead reckoning algorithm and an error-tolerant algorithm for map matching. When the user is walking with the smartphone, the dead reckoning algorithm monitors steps and walking direction in real time. It then reports new steps and turns to the map-matching algorithm. Based on updated information, this algorithm adjusts the user's location on a map in an error-tolerant manner. If location ambiguity among several routes occurs after adjustments, the GPS module is queried to help eliminate this ambiguity. Evaluations in practice show that the error of our system is less than 1/2 that of GPS.


title: Unsupervised Domain Adaptation with Imbalanced Cross-Domain Data

abstract: We address a challenging unsupervised domain adaptation problem with imbalanced cross-domain data. For standard unsupervised domain adaptation, one typically obtains labeled data in the source domain and only observes unlabeled data in the target domain. However, most existing works do not consider the scenarios in which either the label numbers across domains are different, or the data in the source and/or target domains might be collected from multiple datasets. To address the aforementioned settings of imbalanced cross-domain data, we propose Closest Common Space Learning (CCSL) for associating such data with the capability of preserving label and structural information within and across domains. Experiments on multiple cross-domain visual classification tasks confirm that our method performs favorably against state-of-the-art approaches, especially when imbalanced cross-domain data are presented.


Answering Cloze-style Software Questions Using Stack Overflow


In Situ Nanoindentation and Scratch Testing Inside Scanning Electron Microscopes: Opportunities and Challenges


title: Robot Presence and Human Honesty: Experimental Evidence

abstract: Robots are predicted to serve in environments in which human honesty is important, such as the workplace, schools, and public institutions. Can the presence of a robot facilitate honest behavior? In this paper, we describe an experimental study evaluating the effects of robot social presence on people’s honesty. Participants completed a perceptual task, which is structured so as to allowthem to earn more money by not complying with the experiment instructions. We compare three conditions between subjects: Completing the task alone in a room; completing it with a non-monitoring human present; and completing it with a non-monitoring robot present. The robot is a new expressive social head capable of 4-DoF head movement and screen-based eye animation, specifically designed and built for this research. It was designed to convey social presence, but not monitoring. We find that people cheat in all three conditions, but cheat equally less when there is a human or a robot in the room, compared to when they are alone. We did not find differences in the perceived authority of the human and the robot, but did find that people felt significantly less guilty after cheating inthe presence of a robot as compared to a human. This has implications for the use of robots in monitoring and supervising tasks in environments in which honesty is key. Categories and Subject Descriptors H.1.2 [Models and Principles]: User/Machine Systems; J.4 [Computer Applications]: Social and Behavioral Sciences–- psychology. General Terms Experimentation, Human Factors.


COMPARING ARTIFICIAL INTELLIGENCE MUSIC AND HUMAN MUSIC: A CASE STUDY OF PROF. DAVID COPE’S EMMY SYSTEM


title: Exploring Deep Space: Learning Personalized Ranking in a Semantic Space

abstract: Recommender systems leverage both content and user interactions to generate recommendations that fit users' preferences. The recent surge of interest in deep learning presents new opportunities for exploiting these two sources of information. To recommend items we propose to first learn a user-independent high-dimensional semantic space in which items are positioned according to their substitutability, and then learn a user-specific transformation function to transform this space into a ranking according to the user's past preferences. An advantage of the proposed architecture is that it can be used to effectively recommend items using either content that describes the items or user-item ratings. We show that this approach significantly outperforms state-of-the-art recommender systems on the MovieLens 1M dataset.


title: Optimized Stock market prediction using ensemble learning

abstract: Researchers of various fields have always been interested in devising a fault-proof method for the prediction of stock market. Extensive research has been done using Machine Learning Algorithms like SVM, to successfully predict the stock activity in the market using machine learning algorithms mainly Support Vector Machine (SVM). In this paper a scenario in which a portfolio trading strategy is formulated using machine learning algorithms. The strategy will be considered profitable by judging its ability to identify stock indices accurately and consistently, proposing positive or negative returns and in the end it should use a learned model to produce a preferred portfolio allocation. Some of the Technical Indicators like Multiple Regression Analysis (MRA) and different data clustering techniques are used as input to train the system. The learned model is constructed as weighted support vector machine (SVM) classifier, Relevance Vector Machine, random forest classifiers and Multiple Layer Perceptron (MPL). The decision value would be chosen using a majority voting mechanism. The ensemble learning is augmented by a boosting meta-algorithm and feature selection is performed by a supervised Relief algorithm. Stocks listed in Istanbul Stock Exchange (ISE) in Turkey are used to evaluate the performance of the system. A comparison of results obtained using ensemble committee and those using other approaches would show that the ensemble approach has a lower error rate and generates fewer but compact rules.


Music everywhere - augmented reality piano improvisation learning system


title: A design of axial-gap switched reluctance motor for in-wheel direct-drive EV

abstract: Switched reluctance motors (SR Motors) attract attention as motor that use no rare earth materials. And it is a candidate technology for electric vehicle application. In addition, axial-gap structure has possibility of effective utilization of in-wheel flat motor space. This paper mainly discusses the design and the characteristics of axial-gap SR motors. This study focuses on the volumetric constrains of in-wheel drive system. First, the results of comparing the axialgap SR motors specification to the radial-gap one at same volume are shown that utilize the available active volume. By the results, a new flat volume axial-gap SR motor for in-wheel direct-drive EV is designed. Finally, a new support link structure is proposed for the large axial direction electromagnetic force of axial-gap SR motor.


title: Practical Secure Aggregation for Federated Learning on User-Held Data

abstract: Secure Aggregation protocols allow a collection of mutually distrust parties, each holding a private value, to collaboratively compute the sum of those values without revealing the values themselves. We consider training a deep neural network in the Federated Learning model, using distributed stochastic gradient descent across user-held training data on mobile devices, wherein Secure Aggregation protects each user's model gradient. We design a novel, communication-efficient Secure Aggregation protocol for high-dimensional data that tolerates up to 1/3 users failing to complete the protocol. For 16-bit input values, our protocol offers 1.73x communication expansion for $2^{10}$ users and $2^{20}$-dimensional vectors, and 1.98x expansion for $2^{14}$ users and $2^{24}$ dimensional vectors.


title: Monocular vision-based obstacle detection/avoidance for unmanned aerial vehicles

abstract: Robust real-time obstacle detection/avoidance is a challenging problem especially for micro and small aerial vehicles due to the limited number of the on-board sensors due to the battery constraint and low payload. Usually lightweight sensors such as CMOS camera are the best choice comparing with laser or radar sensors. For real-time applications, most studies focus on using stereo cameras to reconstruct a 3D model of the obstacles or to estimate their depth. Instead, in this paper, a method that mimics the human behavior of detecting the state of the approaching obstacles using single camera is proposed. During the flight, this method is able to detect the changes of the size area of the obstacles. First, the method detects the feature points of the obstacles, and then extracts the obstacles that has probability of getting close. In addition, by comparing the changes in the area ratios of the obstacle in the image sequence, the method can decide if it is obstacle or not. Finally, by estimating the obstacle 2D position in the image and combining with the tracked waypoints, the UAV can take the action of avoidance.


title: Improving contextual advertising matching by using Wikipedia thesaurus knowledge

abstract: As a prevalent type of Web advertising, contextual advertising refers to the placement of the most relevant commercial ads within the content of a Web page, to provide a better user experience and as a result increase the user's ad-click rate. However, due to the intrinsic problems of homonymy and polysemy, the low intersection of keywords, and a lack of sufficient semantics, traditional keyword matching techniques are not able to effectively handle contextual matching and retrieve relevant ads for the user, resulting in an unsatisfactory performance in ad selection. In this paper, we introduce a new contextual advertising approach to overcome these problems, which uses Wikipedia thesaurus knowledge to enrich the semantic expression of a target page (or an ad). First, we map each page into a keyword vector, upon which two additional feature vectors, the Wikipedia concept and category vector derived from the Wikipedia thesaurus structure, are then constructed. Second, to determine the relevant ads for a given page, we propose a linear similarity fusion mechanism, which combines the above three feature vectors in a unified manner. Last, we validate our approach using a set of real ads, real pages along with the external Wikipedia thesaurus. The experimental results show that our approach outperforms the conventional contextual advertising matching approaches and can substantially improve the performance of ad selection.


title: A Novel DC-Side Zero-Voltage Switching (ZVS) Three-Phase Boost PWM Rectifier Controlled by an Improved SVM Method

abstract: A novel active clamping zero-voltage switching three-phase boost pulsewidth modulation (PWM) rectifier is analyzed and a modified minimum-loss space vector modulation (SVM) strategy suitable for the novel zero-voltage switching (ZVS) rectifier is proposed in this paper. The topology of the novel ZVS rectifier only adds one auxiliary active switch, one resonant inductor, and one clamping capacitor to the traditional hard-switched three-phase boost PWM rectifier. With the proposed SVM strategy, the novel ZVS rectifier can achieve ZVS for all the main and auxiliary switches. In addition, the antiparallel diodes can be turned OFF softly, so the reverse recovery current is eliminated. Besides, the voltage stress of all the switches is equal to the dc-link voltage. The operation principle and soft-switching condition of the novel ZVS rectifier are analyzed. The design guidelines of the soft switched circuit parameters are described in detail. A DSP controlled 30 kW prototype is implemented to verify the theory.


title: A Grassroots Remote Sensing Toolkit Using Live Coding, Smartphones, Kites and Lightweight Drones

abstract: This manuscript describes the development of an android-based smartphone application for capturing aerial photographs and spatial metadata automatically, for use in grassroots mapping applications. The aim of the project was to exploit the plethora of on-board sensors within modern smartphones (accelerometer, GPS, compass, camera) to generate ready-to-use spatial data from lightweight aerial platforms such as drones or kites. A visual coding ‘scheme blocks’ framework was used to build the application (‘app’), so that users could customise their own data capture tools in the field. The paper reports on the coding framework, then shows the results of test flights from kites and lightweight drones and finally shows how open-source geospatial toolkits were used to generate geographical information system (GIS)-ready GeoTIFF images from the metadata stored by the app. Two Android smartphones were used in testing–a high specification OnePlus One handset and a lower cost Acer Liquid Z3 handset, to test the operational limits of the app on phones with different sensor sets. We demonstrate that best results were obtained when the phone was attached to a stable single line kite or to a gliding drone. Results show that engine or motor vibrations from powered aircraft required dampening to ensure capture of high quality images. We demonstrate how the products generated from the open-source processing workflow are easily used in GIS. The app can be downloaded freely from the Google store by searching for ‘UAV toolkit’ (UAV toolkit 2016), and used wherever an Android smartphone and aerial platform are available to deliver rapid spatial data (e.g. in supporting decision-making in humanitarian disaster-relief zones, in teaching or for grassroots remote sensing and democratic mapping).


title: Analysis of a Dual-Rotor, Toroidal-Winding, Axial-Flux Vernier Permanent Magnet Machine

abstract: In this paper, a vernier permanent magnet (VPM) machine with dual-rotor toroidal-winding axial-flux topology is analyzed. Through the combination of toroidal windings with the rotor-stator-rotor topology, the end winding length of the proposed machine is significantly reduced compared with that of the regular VPM machine. Based on the airgap permeance function, the back-EMF and torque expressions are derived and the nature of the machine is revealed. The influence of pole ratio (ratio of rotor pole pair number to stator pole pair number) and main geometric parameters, such as slot opening, magnet thickness, etc., on electromagnetic performance is then analytically investigated in detail. Both the quasi-three-dimensional (quasi-3D) finite element analysis (FEA) and 3-D FEA are applied to verify the derived equations. Finally, a prototype of the proposed machine has been manufactured and experimentally evaluated. The test results agree well with both theoretical and FEA analyses. With the current density of 5.8 A/mm 2 and slot fill factor of 0.35, the torque density of the proposed machine prototype can reach 31.9 kNm/m 3.


title: Informed Haar-Like Features Improve Pedestrian Detection

abstract: We propose a simple yet effective detector for pedestrian detection. The basic idea is to incorporate common sense and everyday knowledge into the design of simple and computationally efficient features. As pedestrians usually appear up-right in image or video data, the problem of pedestrian detection is considerably simpler than general purpose people detection. We therefore employ a statistical model of the up-right human body where the head, the upper body, and the lower body are treated as three distinct components. Our main contribution is to systematically design a pool of rectangular templates that are tailored to this shape model. As we incorporate different kinds of low-level measurements, the resulting multi-modal & multi-channel Haar-like features represent characteristic differences between parts of the human body yet are robust against variations in clothing or environmental settings. Our approach avoids exhaustive searches over all possible configurations of rectangle features and neither relies on random sampling. It thus marks a middle ground among recently published techniques and yields efficient low-dimensional yet highly discriminative features. Experimental results on the INRIA and Caltech pedestrian datasets show that our detector reaches state-of-the-art performance at low computational costs and that our features are robust against occlusions.


title: A session based personalized search using an ontological user profile

abstract: Within the information overload on the web and the diversity of the user interests, it is increasingly difficult for search engines to satisfy the user information needs. Personalized search tackles this problem by considering the user profile during the search. This paper describes a personalized search approach involving a semantic graph-based user profile issued from ontology. User profile refers to the user interest in a specific search session defined as a sequence of related queries. It is built using a score propagation that activates a set of semantically related concepts and maintained in the same search session using a graph-based merging scheme. We also define a session boundary recognition mechanism based on tracking changes in the dominant concepts held by the user profile relatively to a new submitted query using the Kendall rank correlation measure. Then, personalization is achieved by re-ranking the search results of related queries using the user profile. Our experimental evaluation is carried out using the HARD 2003 TREC collection and shows that our approach is effective.


title: Conditional Random Fields for Identifying Appropriate Types of Support for Propositions in Online User Comments

abstract: Park and Cardie (2014) proposed a novel task of automatically identifying appropriate types of support for propositions comprising online user comments, as an essential step toward automated analysis of the adequacy of supporting information. While multiclass Support Vector Machines (SVMs) proved to work reasonably well, they do not exploit the sequential nature of the problem: For instance, verifiable experiential propositions tend to appear together, because a personal narrative typically spans multiple propositions. According to our experiments, however, Conditional Random Fields (CRFs) degrade the overall performance, and we discuss potential fixes to this problem. Nonetheless, we observe that the F1 score with respect to the unverifiable proposition class is increased. Also, semi-supervised CRFs with posterior regularization trained on 75% labeled training data can closely match the performance of a supervised CRF trained on the same training data with the remaining 25% labeled as well.


title: STREMS: A Smart Real-Time Solution toward Enhancing EMS Prehospital Quality

abstract: Emergency medical service (EMS) systems are public services that provide quick response, transportation as well as appropriate emergency medical care to the emergent patient. For EMS, every second is critical. Unfortunately, current EMS systems have many challenges: lack effective communication between EMS providers and hospital professionals, less attention on care quality and limited resources of medical equipment and personnel. Motivated by this, in this paper, we explore the use of wearable sensing, smart mobile device as well as video technology to propose STREMS: an efficient smart real-time prehospital communication system for EMS. Specifically, we first introduce a cost-effective wearable physiological sensing solution to support multi-dimensional telemetry monitoring for an ambulance operating at as Basic Life Support, a type of EMS service level without sophisticated medical equipment or paramedics. Then we propose to build a cloud-based real-time data sharing platform, enabling automated streaming all gathered prehospital data (e.g., vital signs, EKG and image/short videos about accident scene) to the hospital prior to ambulance arrival, thus giving a more complete figure about the incoming patient. This can significantly decrease the handoff time and improve the efficiency at the hospital. Additionally, a live point to point video communication is proposed to support EMS telemedicine to enhance prehospital care quality through directly video conversation to assist EMS providers in consultation, triage, early medical examination and treatment. We implemented STREMS as an Android mobile app and evaluated its feasibility over the broadband cellular network in the city of Detroit. In a moving context, our results demonstrate STREMS can successfully deliver 100% of emergency data to the hospital in less than 1.5s, on average 0.75s for reporting a new case and 0.05s for health data. As the live video with 1280 × 720 pixel resolution, STREMS only works when the vehicle speed is less than 40MPH.


title: Impact of Frequency Ramp Nonlinearity, Phase Noise, and SNR on FMCW Radar Accuracy

abstract: One of the main disturbances in a frequency-modulated continuous wave radar system for range measurement is nonlinearity in the frequency ramp. The intermediate frequency (IF) signal and consequently the target range accuracy are dependent on the type of the nonlinearity present in the frequency ramp. Moreover, the type of frequency ramp nonlinearity cannot be directly specified, which makes the problem even more challenging. In this paper, the frequency ramp nonlinearity is investigated with the modified short-time Fourier transform method by using the short-time Chirp-Z transform method with high accuracy. The random and periodic nonlinearities are characterized and their sources are identified as phase noise and spurious. These types of frequency deviations are intentionally increased, and their influence on the linearity and the IF-signal is investigated. The dependence of target range estimation accuracy on the frequency ramp nonlinearity, phase noise, spurious, and signal-to-noise ratio in the IF-signal are described analytically and are verified on the basis of measurements.


title: Evolving Novel Cellular Automaton Seeds Using Compositional Pattern Producing Networks (CPPN)

abstract: The aim of this study is to evolve novel seeds for John Conway's Game of Life cellular automaton (CA) with Compositional Pattern Producing Networks (CPPNs), a variation of artificial neural networks known to evolve organic patterns when used to process visual data. CPPNs were evolved using both objective search (implemented with NeuroEvolution of Augmenting Topologies) and novelty search, which focuses on finding novel solutions rather than objectively "fitter" solutions. Objective search quickly evolved game of life solutions that converged to trivial combinations of previously known solutions. However, novelty search produced non-trivial symmetries and complex high period oscillators such as the period 15 pentadecathlon. Regardless, neither approach evolved purely novel or undocumented seeds. Despite this failure, the complex evolved solutions demonstrate that CPPNs can serve as a powerful encoding for cellular automata seeds. As such, these results stand as the first baseline for further exploration into encoding cellular automata using CPPN.


title: Mechanical design of a lightweight compliant and adaptable active ankle foot orthosis

abstract: The ankle joint is the main contributor in providing support to the body, ensuring forward progression and initiating the swing of the leg during the push-off phase of walking. However, its capability can be negatively altered by neuromuscular disorders. In these cases, an active ankle-foot orthosis (AAFO) can greatly enhance the rehabilitation of the affected patients. This paper presents the mechanical design of a lightweight and compliant AAFO, which can be used for rehabilitation purposes. The actuator is bidirectional, thus it can assist the ankle during both dorsiflexion and plantarflexion. With respect to most of the existing AAFOs, the presented one is adaptable to different subjects. The connections between the ankle actuator and the user's shank and foot are designed to fit the AAFO to different users and to align the human and the robot ankle joints, without the need of building customized versions of it. The implemented ankle actuator is a MACCEPA (Mechanically Adjustable Compliance and Controllable Equilibrium Position Actuator) and it can provide a peak torque of 25Nm. Due to a careful design of the actuator components, the total weight of the AAFO is only 1.7kg.


title: Uplink Non-Orthogonal Multiple Access for 5G Wireless Networks

abstract: Orthogonal Frequency Division Multiple Access (OFDMA) as well as other orthogonal multiple access techniques fail to achieve the system capacity limit in the uplink due to the exclusivity in resource allocation. This issue is more prominent when fairness among the users is considered in the system. Current Non-Orthogonal Multiple Access (NOMA) techniques introduce redundancy by coding/spreading to facilitate the users' signals separation at the receiver, which degrade the system spectral efficiency. Hence, in order to achieve higher capacity, more efficient NOMA schemes need to be developed. In this paper, we propose a NOMA scheme for uplink that removes the resource allocation exclusivity and allows more than one user to share the same subcarrier without any coding/spreading redundancy. Joint processing is implemented at the receiver to detect the users' signals. However, to control the receiver complexity, an upper limit on the number of users per subcarrier needs to be imposed. In addition, a novel subcarrier and power allocation algorithm is proposed for the new NOMA scheme that maximizes the users' sum-rate. The link-level performance evaluation has shown that the proposed scheme achieves bit error rate close to the single-user case. Numerical results show that the proposed NOMA scheme can significantly improve the system performance in terms of spectral efficiency and fairness comparing to OFDMA.


title: Skin lesion image segmentation using Delaunay Triangulation for melanoma detection

abstract: Developing automatic diagnostic tools for the early detection of skin cancer lesions in dermoscopic images can help to reduce melanoma-induced mortality. Image segmentation is a key step in the automated skin lesion diagnosis pipeline. In this paper, a fast and fully-automatic algorithm for skin lesion segmentation in dermoscopic images is presented. Delaunay Triangulation is used to extract a binary mask of the lesion region, without the need of any training stage. A quantitative experimental evaluation has been conducted on a publicly available database, by taking into account six well-known state-of-the-art segmentation methods for comparison. The results of the experimental analysis demonstrate that the proposed approach is highly accurate when dealing with benign lesions, while the segmentation accuracy significantly decreases when melanoma images are processed. This behavior led us to consider geometrical and color features extracted from the binary masks generated by our algorithm for classification, achieving promising results for melanoma detection.


title: Beneficial effects of Bacopa monnieri extract on opioid induced toxicity

abstract: The present study examined the hepatotoxicity and nephrotoxicity of morphine and illicit street heroin and their amelioration by a standardized methanolic extract of Bacopa monnieri (L.) (mBME) in rats. Morphine or street heroin was administered at a dose of 20 mg/kg for 14 and 21 days. mBME (40 mg/kg) or ascorbic acid (50 mg/kg) was administered two hours before morphine or street heroin. High performance liquid chromatography (HPLC) was used for the standardization of bacoside-A major components in mBME. The antioxidant potential of mBME was evaluated by 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging assay. Administration of morphine and street heroin resulted in marked elevation of serum alanine aminotransferase (ALT), aspartate aminotransferase (AST) and creatinine. Histopathological changes induced by morphine and street heroin after 14 days were of reversible nature while treatment for 21 days was associated with irreversible changes. Pretreatment with mBME or ascorbic acid restored the elevation of serum ALT, AST and creatinine and protected liver and kidneys from the toxicological influence of morphine and street heroin. HPLC analysis showed that mBME contained bacoside-A major components i.e. bacoside-A3 (37.5 μg/mg), bacopaside-II (4.62 μg/mg) and bacopasaponin-C (1.91 μg/mg). The EC50 for the DPPH free radical scavenging assay revealed that mBME possessed strong antioxidant potential. These results concluded that as compared to morphine, street heroin was associated with severe biochemical and histopathological changes in the liver and kidneys. Bacopa monnieri having strong antioxidant potential may provide a beneficial herbal remedy for the efficient management of opioid related hepatotoxicity and nephrotoxicity.


title: Brain Activity during Simulated Deception: An Event-Related Functional Magnetic Resonance Study

abstract: Abstract TheGuilty Knowledge Test (GKT) has been used extensively to model deception. An association between the brain evoked response potentials and lying on the GKT suggests that deception may be associated with changes in other measures of brain activity such as regional blood flow that could be anatomically localized with event-related functional magnetic resonance imaging (fMRI). Blood oxygenation level-dependent fMRI contrasts between deceptive and truthful responses were measured with a 4 Tesla scanner in 18 participants performing the GKT and analyzed using statistical parametric mapping. Increased activity in the anterior cingulate cortex (ACC), the superior frontal gyrus (SFG), and the left premotor, motor, and anterior parietal cortex was specifically associated with deceptive responses. The results indicate that: (a) cognitive differences between deception and truth have neural correlates detectable by fMRI, (b) inhibition of the truthful response may be a basic component of intentional deception, and (c) ACC and SFG are components of the basic neural circuitry for deception.


title: Unsupervised Construction Of Large Paraphrase Corpora: Exploiting Massively Parallel News Sources

abstract: We investigate unsupervised techniques for acquiring monolingual sentence-level paraphrases from a corpus of temporally and topically clustered news articles collected from thousands of web-based news sources. Two techniques are employed: (1) simple string edit distance, and (2) a heuristic strategy that pairs initial (presumably summary) sentences from different news stories in the same cluster. We evaluate both datasets using a word alignment algorithm and a metric borrowed from machine translation. Results show that edit distance data is cleaner and more easily-aligned than the heuristic data, with an overall alignment error rate (AER) of 11.58% on a similarly-extracted test set. On test data extracted by the heuristic strategy, however, performance of the two training sets is similar, with AERs of 13.2% and 14.7% respectively. Analysis of 100 pairs of sentences from each set reveals that the edit distance data lacks many of the complex lexical and syntactic alternations that characterize monolingual paraphrase. The summary sentences, while less readily alignable, retain more of the non-trivial alternations that are of greatest interest learning paraphrase relationships.


title: Trust-Based Service Management for Social Internet of Things Systems

abstract: A social internet of things (IoT) system can be viewed as a mix of traditional peer-to-peer networks and social networks, where “things” autonomously establish social relationships according to the owners’ social networks, and seek trusted “things” that can provide services needed when they come into contact with each other opportunistically. We propose and analyze the design notion of adaptive trust management for social IoT systems in which social relationships evolve dynamically among the owners of IoT devices. We reveal the design tradeoff between trust convergence versus trust fluctuation in our adaptive trust management protocol design. With our adaptive trust management protocol, a social IoT application can adaptively choose the best trust parameter settings in response to changing IoT social conditions such that not only trust assessment is accurate but also the application performance is maximized. We propose a table-lookup method to apply the analysis results dynamically and demonstrate the feasibility of our proposed adaptive trust management scheme with two real-world social IoT service composition applications.


title: A Study of Web Services Performance Prediction: A Client's Perspective

abstract: The Web service (WS) paradigm is an emerging approach to building Web applications, in which software designers typically build new WSs by leveraging existing, third-party WSs. Understanding performance characteristics of third party WSs is critical to the overall system performance. Although such performance evaluation can be done through testing of third party WSs, it is quite an expensive process. This is especially the case when testing at high workloads, because performance degradations are likely to occur, which may render the WS under testing unusable during the testsi¦ duration. Avoiding testing at high workloads by applying standard extrapolation approaches from data collected at low workloads (e.g., using regression analysis) results in a lack of accuracy. To address this challenge, in this paper, we propose a framework that utilizes the benefits of queueing models to guide the extrapolation process, while achieving accuracy in both regimes iV low and high workloads. Our extensive experiments show that our approach gives accurate results as compared to standard techniques (i.e., use of regression analysis alone).


title: Using Word Familiarities and Word Associations to Measure Corpus Representativeness

abstract: The definition of corpus representativeness used here assumes that a representative corpus should reflect as well as possible the average language use a native speaker encounters in everyday life over a longer period of time. As it is not practical to observe people's language input over years, we suggest to utilize two types of experimental data capturing two forms of human intuitions: Word familiarity norms and word association norms. If it is true that human language acquisition is corpus-based, such data should reflect people's perceived language input. Assuming so, we compute a representativeness score for a corpus by extracting word frequency and word association statistics from it and by comparing these statistics to the human data. The higher the similarity, the more representative the corpus should be for the language environments of the test persons. We present results for five different corpora and for truncated versions thereof. The results confirm the expectation that corpus size and corpus balance are crucial aspects for corpus representativeness.


title: Dynamic voltage balancing of series connected IGBTs using slope regulating and voltage clamping

abstract: Series connection of IGBTs is applied to increase the voltage rating of converters. The critical issue is to balance the dynamic voltage of each device in the series string. This paper investigates an auxiliary circuit as a solution involving slope regulating and voltage clamping. It adjusts the Miller capacitance and gate current with additional capacitors and feedback current from collector to gate, by which the gate voltage and then the collector-emitter voltage are controlled. Consequently, the differences between block voltages of IGBTs are restrained. A 15kV test rig is established and experiments are carried out successfully based on up to sixteen 1.7kV/800A IGBTs in series connection. It is proved that the proposed circuit has no limitation to the number of series connected devices and is simple, reliable and low-cost.


title: Inhibitory processes in adults with persistent childhood onset ADHD.

abstract: The theory that attention-deficit/hyperactivity disorder (ADHD) stems from a deficit in an executive behavioral inhibition process has been little studied in adults, where the validity of ADHD is in debate. This study examined, in high-functioning young adults with persistent ADHD and a control group, 2 leading measures of inhibitory control: the antisaccad task and the negative priming task. ADHD adults showed weakened ability to effortfully stop a refle ve or anticipated oculomotor response but had normal ability to automatically suppress irrelevant information. Results suggest that an inhibitory deficit in ADHD is confined to effortful inhibition of motor response, that antisaccade and negative priming tasks index distinct inhibition systems, and that persistence of ADHD symptoms into adulthood is associated with persistence of executive motor inhibition deficits.


title: A Full-Bridge Submodule-Based Modular Unipolar/Bipolar High-Voltage Pulse Generator With Sequential Charging of Capacitors

abstract: Repetitive high pulsed electric field (PEF) is an effective method to kill microorganisms and bacteria in water treatment applications. The PEF can be generated by applying high-power electromagnetic pulse across the sample to be treated. There are two main types of high-voltage pulse generators, namely, unipolar and bipolar. In this paper, a full-bridge submodule-based modular high-voltage pulse generator, having the ability to generate unipolar and bipolar high-voltage pulses with different shapes from a relatively low-voltage input dc supply, is proposed. In the proposed configuration, relatively low-voltage insulated gate bipolar transistors (IGBTs) are required to generate the high-voltage bipolar pulses. A thyristor rated at the level of the pulsed output voltage is required in the proposed configuration to bypass the load during the charging process of capacitors. In the proposed approach, a thyristor is used instead of the self-commutated high-voltage switch (e.g., series-connected IGBTs), as thyristors are available with high-voltage ratings and possess inherent reverse voltage blocking capability. A detailed illustration of the proposed configuration and its operational concept are introduced in this paper. Simulation and experimental results are presented to validate the proposed approach.


title: Morphological analysis of bead–string shaped and core–shell attapulgite@polystyrene (ATP@PS) particles via emulsion polymerization

abstract: A convenient method for the preparation of bead–string shaped and core–shell attapulgite@polystyrene (ATP@PS) composite particles through emulsion polymerization from needle-shaped attapulgite is reported. The pretreatment of attapulgite with cetyltrimethylammonium bromide (CTAB) in ultrasonic is essential for successful encapsulation and for creating strong interfacial interaction between attapulgite and polystyrene (PS). The different morphologies of the ATP@PS particles, bead–string shaped and core–shell shaped, obtained with different amounts of styrene charges, were characterized by transmission electron microscopy (TEM). Encapsulation mechanisms of the two morphologies were also developed. Copyright © 2007 John Wiley & Sons, Ltd.


title: On the measurability of information quality

abstract: The notion of information quality (IQ) has been investigated extensively in recent years. Much of this research has been aimed at conceptualizing IQ and its underlying dimensions (e.g., accuracy, completeness) and at developing instruments for measuring these quality dimensions. However, less attention has been given to the measurability of IQ. The objective of this study is to explore the extent to which a set of IQ dimensions—accuracy, completeness, objectivity, and representation—lend themselves to reliable measurement. By reliable measurement, we refer to the degree to which independent assessors are able to agree when rating objects on these various dimensions. Our study reveals that multiple assessors tend to agree more on certain dimensions (e.g., accuracy) while finding it more difficult to agree on others (e.g., completeness). We argue that differences in measurability stem from properties inherent to the quality dimension (i.e., the availability of heuristics that make the assessment more tangible) as well as on assessors' reliance on these cues. Implications for theory and practice are discussed. © 2011 Wiley Periodicals, Inc.


title: Study of Weak Signal Detection Based on Second FFT and Chaotic Oscillator

abstract: Due to the incapacity of conventional method, while detecting periodic signals buried in the noise and deciding accurate frequency, a novel method is presented. This method is an integration of second FFT, which is an additional DFT after PSD, and the chaotic oscillator. Second FFT can increase the detection ability of weak periodic signals and the chaotic oscillator can improve frequency precision. This method is simple, quick and convenient in hardware realization and instrument design. Furthermore, its effect is demonstrated by detecting a simulation signal and a communication signal. (Nature and Science. 2005;3(2):59-64).


The Role of Depression and Attachment Styles in Predicting Students’ Addiction to Cell Phones


title: Is experience the best teacher? A multilevel analysis of teacher characteristics and student achievement in low performing schools

abstract: The study investigated several teacher characteristics, with a focus on two measures of teaching experience, and their association with second grade student achievement gains in low performing, high poverty schools in a Mid-Atlantic state. Value-added models using three-level hierarchical linear modeling were used to analyze the data from 1,544 students, 154 teachers, and 53 schools. Results indicated that traditional teacher qualification characteristics such as licensing status and educational attainment were not statistically significant in producing student achievement gains. Total years of teaching experience was also not a significant predictor but a more specific measure, years of teaching experience at a particular grade level, was significantly associated with increased student reading achievement. We caution researchers and policymakers when interpreting results from studies that have used only a general measure of teacher experience as effects are possibly underestimated. Policy implications are discussed.


title: Gasification of municipal solid waste in the Plasma Gasification Melting process

abstract: A new waste-disposal technology named Plasma Gasification Melting (PGM) was developed. A pilot PGM reactor was constructed in northern Israel. The reactor is an updraft moving-bed gasifier, with plasma torches placed next to air nozzles to heat the incoming air to 6000°C. The inorganic substances of the feedstock are melted by the high-temperature air to form a vitrified slag in which undesirable materials such as heavy metals are trapped. The residual heat in the air supplies additional heat for the gasification process.


title: Personalized Travel Sequence Recommendation on Multi-Source Big Social Media

abstract: Big data increasingly benefit both research and industrial area such as health care, finance service and commercial recommendation. This paper presents a personalized travel sequence recommendation from both travelogues and community-contributed photos and the heterogeneous metadata (e.g., tags, geo-location, and date taken) associated with these photos. Unlike most existing travel recommendation approaches, our approach is not only personalized to user's travel interest but also able to recommend a travel sequence rather than individual Points of Interest (POIs). Topical package space including representative tags, the distributions of cost, visiting time and visiting season of each topic, is mined to bridge the vocabulary gap between user travel preference and travel routes. We take advantage of the complementary of two kinds of social media: travelogue and community-contributed photos. We map both user's and routes’ textual descriptions to the topical package space to get user topical package model and route topical package model (i.e., topical interest, cost, time and season). To recommend personalized POI sequence, first, famous routes are ranked according to the similarity between user package and route package. Then top ranked routes are further optimized by social similar users’ travel records. Representative images with viewpoint and seasonal diversity of POIs are shown to offer a more comprehensive impression. We evaluate our recommendation system on a collection of 7 million Flickr images uploaded by 7,387 users and 24,008 travelogues covering 864 travel POIs in nine famous cities, and show its effectiveness. We also contribute a new dataset with more than 200 K photos with heterogeneous metadata in nine famous cities.


title: Cadaveric Simulation of a Pes Cavus Foot

abstract: BACKGROUND ::: The pes cavus deformity has been well described in the literature; relative bony positions have been determined and specific muscle imbalances have been summarized. However, we are unaware of a cadaveric model that has been used to generate this foot pathology. The purpose of this study was to create such a model for future work on surgical and conservative treatment simulation. ::: ::: ::: MATERIALS AND METHODS ::: We used a custom designed, pneumatically actuated loading frame to apply forces to otherwise normal cadaveric feet while measuring bony motion as well as force beneath the foot. The dorsal tarsometatarsal and the dorsal intercuneiform ligaments were attenuated and three muscle imbalances, each similar to imbalances believed to cause the pes cavus deformity, were applied while bony motion and plantar forces were measured. ::: ::: ::: RESULTS ::: Only one of the muscle imbalances (overpull of the Achilles tendon, tibialis anterior, tibialis posterior, flexor hallucis longus and flexor digitorum longus) was successful at consistently generating the changes seen in pes cavus feet. This imbalance led to statistically significant changes including hindfoot inversion, talar dorsiflexion, medial midfoot plantar flexion and inversion, forefoot plantar flexion and adduction and an increase in force on the lateral mid- and forefoot. ::: ::: ::: CONCLUSION ::: We have created a cadaveric model that approximates the general changes of the pes cavus deformity compared to normal feet. These changes mirror the general patterns of deformity produced by several disease mechanisms. ::: ::: ::: CLINICAL RELEVANCE ::: Future work will entail increasing the severity of the model and exploring various pes cavus treatment strategies.


title: Syntax tree fingerprinting for source code similarity detection

abstract: Numerous approaches based on metrics, token sequence pattern-matching, abstract syntax tree (AST) or program dependency graph (PDG) analysis have already been proposed to highlight similarities in source code: in this paper we present a simple and scalable architecture based on AST fingerprinting. Thanks to a study of several hashing strategies reducing false-positive collisions, we propose a framework that efficiently indexes AST representations in a database, that quickly detects exact (w.r.t source code abstraction) clone clusters and that easily retrieves their corresponding ASTs. Our aim is to allow further processing of neighboring exact matches in order to identify the larger approximate matches, dealing with the common modification patterns seen in the intra-project copy-pastes and in the plagiarism cases.


title: Toward a comprehensive evidence map of overview of systematic review methods: paper 1—purpose, eligibility, search and data extraction

abstract: BACKGROUND ::: Overviews of systematic reviews attempt to systematically retrieve and summarise the results of multiple systematic reviews. Methods for conducting, interpreting and reporting overviews are in their infancy. To date, there has been no evidence map of the methods used in overviews, thus making it difficult to determine the gaps and priorities for methods research. Our objectives were to develop and populate a comprehensive framework of methods for conducting, interpreting and reporting overviews (stage I) and to create an evidence map by mapping studies that have evaluated overview methods to the framework (stage II). ::: ::: ::: METHODS ::: We searched methods collections (e.g. Cochrane Methodology Register, Meth4ReSyn library, AHRQ Effective Health Care Program) to identify eligible studies for both stages of this research. In stage I, cross-sectional studies, guidance documents and commentaries that described methods proposed for, or used in, overviews were used to develop and populate the framework of methods. Drafts and multiple iterations of the framework were discussed and refined by all authors. In stage II, we identified and described studies evaluating overview methods and mapped these evaluations to the framework. ::: ::: ::: RESULTS ::: In this paper, we present results for the four initial steps of conducting an overview: (a) specification of the purpose, objectives and scope, (b) specification of the eligibility criteria, (c) search methods and (d) data extraction. Twenty-nine studies mentioned or described methods relevant to one or more of these steps. In the developed framework, identified methods and approaches were grouped according to the steps an overview author would need to undertake. Fifteen studies evaluated identified methods, all of which mapped to the search methods step. These studies either reported the development and evaluation of a new search filter to retrieve systematic reviews or compared the performance of multiple filters. ::: ::: ::: CONCLUSION ::: Gaps in the evaluation of methods were found for the majority of steps in the framework. More empirical studies are needed to evaluate the methods outlined and provide a comprehensive evidence map. The framework is useful for planning these evaluations and for planning methods required to deal with challenges that arise when conducting an overview.


title: A single-chip x-band chirp radar MMIC with stretch processing

abstract: A single-chip X-band chirp radar transceiver with direct digital synthesis (DDS) for chirp generation is presented. The radar chip, including receiver, transmitter, quadrature DDS, phase-locked loop (PLL) and analog to digital converter (ADC), has been implemented in a 0.13μm BiCMOS technology. The stretch processing technique is employed to translate the time interval between the received and the transmitted chirp signals to a single tone at the baseband output with greatly reduced bandwidth, which allows for the use of a low-cost ADC with a reduced input bandwidth of 10MHz for digitizing the received RF chirp with a bandwidth of 150MHz. A Weaver receiver with a dc-offset is employed in order to use a single ADC for detecting the received quadrature signals with image rejection. A quadrature 1GHz DDS with an inverse sinc function for zero-order hold correction is implemented to provide the chirp signals for both receiver and transmitter. A wide-tuning PLL frequency synthesizer is integrated to generate the local oscillator (LO) signals as well as the clock signal for the DDS and ADC. The implemented radar-on-chip (RoC) MMIC occupies a die area of 3.5 × 2.5mm. With a 2.2V supply voltage for analog/RF and a 1.5V supply for digital, the chip consumes 326mW in the receive mode and 333mW in the transmit mode.


title: Autonomous cross-domain knowledge transfer in lifelong policy gradient reinforcement learning

abstract: Online multi-task learning is an important capability for lifelong learning agents, enabling them to acquire models for diverse tasks over time and rapidly learn new tasks by building upon prior experience. However, recent progress toward lifelong reinforcement learning (RL) has been limited to learning from within a single task domain. For truly versatile lifelong learning, the agent must be able to autonomously transfer knowledge between different task domains. A few methods for cross-domain transfer have been developed, but these methods are computationally inefficient for scenarios where the agent must learn tasks consecutively. ::: ::: In this paper, we develop the first cross-domain lifelong RL framework. Our approach efficiently optimizes a shared repository of transferable knowledge and learns projection matrices that specialize that knowledge to different task domains. We provide rigorous theoretical guarantees on the stability of this approach, and empirically evaluate its performance on diverse dynamical systems. Our results show that the proposed method can learn effectively from interleaved task domains and rapidly acquire high performance in new domains.


title: Using network science and text analytics to produce surveys in a scientific topic

abstract: The use of science to understand its own structure is becoming popular, but understanding the organization of knowledge areas is still limited because some patterns are only discoverable with proper computational treatment of large-scale datasets. In this paper, we introduce a framework to combine network-based methodologies and text analytics to construct the taxonomy of science fields. The methodology is illustrated with application to two topics: complex networks (CN) and photonic crystals (PC). We built citation networks using data from the Web of Science and used a community detection algorithm for partitioning to obtain science maps for the two topics. We also created an importance index for text analytics, which is employed to extract keywords that define the communities and, combined with network topology metrics, to generate dendrograms of relatedness among subtopics. Interesting patterns emerging from the analysis included identification of two well-defined communities in PC area, which is consistent with the known existence of two distinct communities of researchers in the area: telecommunication engineers and physicists. With the methodology, it was also possible to assess the interdisciplinary nature and time evolution of subtopics defined by the keywords. The automatic tools described here are potentially useful not only to provide an overview of scientific areas but also to assist scientists in performing systematic research on a specific topic.


title: Planar compact tablet monopole antenna for LTE/WWAN system

abstract: This work describes a compact tablet monopole antenna with eight-band LTE/WWAN operation by introducing a G-shaped driven monopole strip. The obtained impedance bandwidths across dual operating bands approach 264 / 1046 MHz at the LTE and WWAN bands, respectively. Given that the overall antenna size is only 35 × 10 × 0.8 mm3, the proposed planar monopole antenna provides more than 40% reduction in antenna size over that of the conventional ones. Additionally, the measured peak gains and antenna efficiencies are approximately 3.6 / 5.2 dBi and 67 / 70 % for the LTE/WWAN bands, respectively.


title: Multi-Label Collective Classification

abstract: Collective classification in relational data has become an important and active research topic in the last decade, where class labels for a group of linked instances are correlated and need to be predicted simultaneously. Collective classification has a wide variety of real world applications, e.g. hyperlinked document classification, social networks analysis and collaboration networks analysis. Current research on collective classification focuses on single-label settings, which assumes each instance can only be assigned with exactly one label among a finite set of candidate classes. However, in many real-world relational data, each instance can be assigned with a set of multiple labels simultaneously. In this paper, we study the problem of multi-label collective classification and propose a novel solution, called Icml (Iterative Classification of Multiple Labels), to effectively assign a set of multiple labels to each instance in the relational dataset. The proposed Icml model is able to capture the dependencies among the label sets for a group of related instances and the dependencies among the multiple labels within each label set simultaneously. Empirical studies on real-world tasks demonstrate that the proposed multi-label collective classification approach can effectively boost classification performances in multilabel relational datasets.


title: An integrated scheme for object-based video abstraction

abstract: In this paper, we present a novel scheme for object-based key-frame extraction facilitated by an efficient video object segmentation system. Key-frames are the subset of still images which best represent the content of a video sequence in an abstracted manner. Thus, key-frame based video abstraction transforms an entire video clip to a small number of representative images. The challenge is that the extraction of key-frames needs to be automated and context dependent so that they maintain the important contents of the video while remove all redundancy. Among various semantic primitives of video, objects of interest along with their actions and generated events can play an important role in some applications such as object-based video surveillance system. Furthermore, on-line processing combined with fast and robust video object segmentation is crucial for real-time applications to report unwanted action or event as soon as it happens. Experimental results on the proposed scheme for object-based video abstraction are presented.


title: A brief overview of the NEBULA future internet architecture

abstract: Nebula is a proposal for a Future Internet Architecture. It is based on the assumptions that: (1) cloud computing will comprise an increasing fraction of the application workload offered to an Internet, and (2) that access to cloud computing resources will demand new architectural features from a network. Features that we have identified include dependability, security, flexibility and extensibility, the entirety of which constitute resilience. Nebula provides resilient networking services using ultrareliable routers, an extensible control plane and use of multiple paths upon which arbitrary policies may be enforced. We report on a prototype system, Zodiac, that incorporates these latter two features.


title: RGSE: a regular property guided symbolic executor for Java

abstract: It is challenging to effectively check a regular property of a program. This paper presents RGSE, a regular property guided dynamic symbolic execution (DSE) engine, for finding a program path satisfying a regular property as soon as possible. The key idea is to evaluate the candidate branches based on the history and future information, and explore the branches along which the paths are more likely to satisfy the property in priority. We have applied RGSE to 16 real-world open source Java programs, totaling 270K lines of code. Compared with the state-of-the-art, RGSE achieves two orders of magnitude speedups for finding the first target path. RGSE can benefit many research topics of software testing and analysis, such as path-oriented test case generation, typestate bug finding, and performance tuning. The demo video is at: https://youtu.be/7zAhvRIdaUU, and RGSE can be accessed at: http://jrgse.github.io.


title: Machine learning assessments of soil drying for agricultural planning

abstract: Abstract The hydrologic processes of wetting and drying play a crucial role in agricultural activities involving heavy equipment on unpaved terrain. When soil conditions moisten, equipment can become mired, causing expensive delays. While experienced users may assess soil conditions before entering off-road areas, novice users or those who must remotely assess sites before traveling may have difficulty assessing conditions reliably. One means of assessing dryness is remotely-monitored in situ sensors. Unfortunately, land owners hesitate to place sensors due to monetary costs, complexity, and sometimes infeasibility of physical visits to remote locations. This work addresses these limitations by modeling the wetting/drying process through machine learning algorithms fed by hydrologic data – remotely assessing soil conditions using only publicly-accessible information. Classification trees, k-nearest-neighbors, and boosted perceptrons deliver statistical soil dryness estimates at a site located in Urbana, IL. The k-nearest-neighbor and boosted perceptron algorithms both performed with 91–94% accuracy, with most misclassifications falling within calculated margins of error. These analyses demonstrate that reasonably accurate predictions of current soil conditions are possible with only precipitation and potential evaporation data. These two values are measured throughout the continental United States and are likely to be available globally from satellite sensors in the near future. Through this type of approach, agricultural management decisions can be enabled remotely, without the time and expense of on-site visitations or extensive ground-based sensory grids.


title: Of cancer and cave fish

abstract: We propose that the drivers of carcinogenesis lie more in the adaptive changes that are enabled by local or systemic alterations of tissue architecture than in the genetic changes observed in cancer cells. A full understanding of cancer biology and therapy through a cataloguing of the cancer genome is unlikely unless it is integrated into an evolutionary and ecological context.


title: Deep Convolutional Ranking for Multilabel Image Annotation

abstract: Multilabel image annotation is one of the most important challenges in computer vision with many real-world applications. While existing work usually use conventional visual features for multilabel annotation, features based on Deep Neural Networks have shown potential to significantly boost performance. In this work, we propose to leverage the advantage of such features and analyze key components that lead to better performances. Specifically, we show that a significant performance gain could be obtained by combining convolutional architectures with approximate top-$k$ ranking objectives, as thye naturally fit the multilabel tagging problem. Our experiments on the NUS-WIDE dataset outperforms the conventional visual features by about 10%, obtaining the best reported performance in the literature.


title: Context-oriented programming: beyond layers

abstract: While many software systems today have to be aware of the context in which they are executing, there is still little support for structuring a program with respect to context. A first step towards better context-orientation was the introduction of method layers. This paper proposes two additional language concepts, namely the implicit activation of method layers, and the introduction of dynamic variables.


title: Mobile app-etite: Consumer attitudes towards and use of mobile technology in the context of eating behaviour

abstract: Understanding how consumers use mobile devices and mobile applications to support their eating behaviour, such as creating grocery lists, gathering meal ideas and ordering food from restaurants, is important for business, marketing and health professionals who seek to reach consumers through mobile technology. This study identified segments of users and non-users of food-related technology and described differences in their demographic characteristics, food-related app use and interest in food-related app functionality. Results revealed that 22 per cent of participants were highly engaged with technology and food, while just 12 per cent were disengaged from both. The remaining two-thirds of participants were evenly split between those who were engaged with technology and food generally but were ambivalent about food-related apps, and those who were utilitarian in their approach to food and disengaged from food-related apps. There were segment differences based on age such that younger adults (ages 18–34) were more likely to be engaged with technology and food than older adults (ages 55+). Technology and food-engaged segments reported the highest levels of use of select food-related apps, but even well-known apps were not highly used, indicating room for market expansion. Findings are discussed in the context of app development, digital advertising and nutritional health interventions.


title: Video abstraction: A systematic review and classification

abstract: The demand for various multimedia applications is rapidly increasing due to the recent advance in the computing and network infrastructure, together with the widespread use of digital video technology. Among the key elements for the success of these applications is how to effectively and efficiently manage and store a huge amount of audio visual information, while at the same time providing user-friendly access to the stored data. This has fueled a quickly evolving research area known as video abstraction. As the name implies, video abstraction is a mechanism for generating a short summary of a video, which can either be a sequence of stationary images (keyframes) or moving images (video skims). In terms of browsing and navigation, a good video abstract will enable the user to gain maximum information about the target video sequence in a specified time constraint or sufficient information in the minimum time. Over past years, various ideas and techniques have been proposed towards the effective abstraction of video contents. The purpose of this article is to provide a systematic classification of these works. We identify and detail, for each approach, the underlying components and how they are addressed in specific works.


title: Information fusion for wireless sensor networks: Methods, models, and classifications

abstract: Wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. The way these data are manipulated by the sensor nodes is a fundamental issue. Information fusion arises as a response to process data gathered by sensor nodes and benefits from their processing capability. By exploiting the synergy among the available data, information fusion techniques can reduce the amount of data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. In this work, we survey the current state-of-the-art of information fusion by presenting the known methods, algorithms, architectures, and models of information fusion, and discuss their applicability in the context of wireless sensor networks.


title: Listwise approach to learning to rank: theory and algorithm

abstract: This paper aims to conduct a study on the listwise approach to learning to rank. The listwise approach learns a ranking function by taking individual lists as instances and minimizing a loss function defined on the predicted list and the ground-truth list. Existing work on the approach mainly focused on the development of new algorithms; methods such as RankCosine and ListNet have been proposed and good performances by them have been observed. Unfortunately, the underlying theory was not sufficiently studied so far. To amend the problem, this paper proposes conducting theoretical analysis of learning to rank algorithms through investigations on the properties of the loss functions, including consistency, soundness, continuity, differentiability, convexity, and efficiency. A sufficient condition on consistency for ranking is given, which seems to be the first such result obtained in related research. The paper then conducts analysis on three loss functions: likelihood loss, cosine loss, and cross entropy loss. The latter two were used in RankCosine and ListNet. The use of the likelihood loss leads to the development of a new listwise method called ListMLE, whose loss function offers better properties, and also leads to better experimental results.


title: RGB-D object recognition and pose estimation based on pre-trained convolutional neural network features

abstract: Object recognition and pose estimation from RGB-D images are important tasks for manipulation robots which can be learned from examples. Creating and annotating datasets for learning is expensive, however. We address this problem with transfer learning from deep convolutional neural networks (CNN) that are pre-trained for image categorization and provide a rich, semantically meaningful feature set. We incorporate depth information, which the CNN was not trained with, by rendering objects from a canonical perspective and colorizing the depth channel according to distance from the object center. We evaluate our approach on the Washington RGB-D Objects dataset, where we find that the generated feature set naturally separates classes and instances well and retains pose manifolds. We outperform state-of-the-art on a number of subtasks and show that our approach can yield superior results when only little training data is available.


title: Exploiting Synchrony in Replicated State Machines

abstract: We present Timestamp Order Preserving (TOP), a replicated state machine (RSM) protocol that exploits the synchrony of networks to provide high performance. TOP uses physical timestamp of synchronized clock as a consistent total order to achieve consensus. It keeps estimating the bounds of network latency and offset of synchronized clock to deduce the commit time for each operation. It adopts speculative processing and reconciliation techniques to improve performance. To demonstrate its advantages, we implement a key-value data store that uses TOP for data replication. Through evaluations in a geo-deployed testbed, by comparing it with Primary-Copy and Quorum-Replication protocols, we demonstrate that TOP has a similar commit latency with a higher sustainable throughput. In addition, it processes operations in the order of submission timestamp, which provides a stricter form of consistency.


title: Double Deep Q-Learning for Optimal Execution

abstract: Optimal trade execution is an important problem faced by essentially all traders. Much research into optimal execution uses stringent model assumptions and applies continuous time stochastic control to solve them. Here, we instead take a model free approach and develop a variation of Deep Q-Learning to estimate the optimal actions of a trader. The model is a fully connected Neural Network trained using Experience Replay and Double DQN with input features given by the current state of the limit order book, other trading signals, and available execution actions, while the output is the Q-value function estimating the future rewards under an arbitrary action. We apply our model to nine different stocks and find that it outperforms the standard benchmark approach on most stocks using the measures of (i) mean and median out-performance, (ii) probability of out-performance, and (iii) gain-loss ratios.


title: Investigation Into IGBT dV/dt During Turn-Off and Its Temperature Dependence

abstract: In many power converter applications, particularly those with high variable loads, such as traction and wind power, condition monitoring of the power semiconductor devices in the converter is considered desirable. Monitoring the device junction temperature in such converters is an essential part of this process. In this paper, a method for measuring the insulated gate bipolar transistor (IGBT) junction temperature using the collector voltage dV/dt at turn-OFF is outlined. A theoretical closed-form expression for the dV/dt at turn-OFF is derived, closely agreeing with experimental measurements. The role of dV/dt in dynamic avalanche in high-voltage IGBTs is also discussed. Finally, the implications of the temperature dependence of the dV/dt are discussed, including implementation of such a temperature measurement technique.


A REVIEW ON IMAGE DENOISING METHODS


Cross-Domain Detection of Abusive Language Online


title: Dynamic Personalized Recommendation on Sparse Data

abstract: Recommendation techniques are very important in the fields of E-commerce and other web-based services. One of the main difficulties is dynamically providing high-quality recommendation on sparse data. In this paper, a novel dynamic personalized recommendation algorithm is proposed, in which information contained in both ratings and profile contents are utilized by exploring latent relations between ratings, a set of dynamic features are designed to describe user preferences in multiple phases, and finally, a recommendation is made by adaptively weighting the features. Experimental results on public data sets show that the proposed algorithm has satisfying performance.


Suicide and Depression Literacy Among Healthcare Profession Students in Tertiary Care Center in South India


title: Using Deep Learning Method for Classification: A Proposed Algorithm for the ISIC 2017 Skin Lesion Classification Challenge

abstract: Skin cancer, the most common human malignancy, is primarily diagnosed visually by physicians [1]. Classification with an automated method like CNN [2, 3] shows potential for challenging tasks [1]. By now, the deep convolutional neural networks are on par with human dermatologist [1]. This abstract is dedicated on developing a Deep Learning method for ISIC [5] 2017 Skin Lesion Detection Competition hosted at [6] to classify the dermatology pictures, which is aimed at improving the diagnostic accuracy rate and general level of the human health. The challenge falls into three sub-challenges, including Lesion Segmentation, Lesion Dermoscopic Feature Extraction and Lesion Classification. This project only participates in the Lesion Classification part. This algorithm is comprised of three steps: (1) original images preprocessing, (2) modelling the processed images using CNN [2, 3] in Caffe [4] framework, (3) predicting the test images and calculating the scores that represent the likelihood of corresponding classification. The models are built on the source images are using the Caffe [4] framework. The scores in prediction step are obtained by two different models from the source images.


title: A Case Study of Automated Face Recognition: The Boston Marathon Bombings Suspects

abstract: While face recognition can be a useful tool, helping authorities to narrow leads and confirm a criminal suspect's identity, the media tended to highlight the technology's limitations in the Boston Marathon bombings investigation. Consequently, we were motivated to reexamine the efficacy of unconstrained face recognition using this incident as a case study. We chose two automated face recognition systems known for their superior unconstrained matching performance based on tests conducted by the US National Institute of Standards and Technology: NEC's NeoFace v3.1 and Google-owned Pittsburgh Pattern Recognition v5.2.2. To conduct the experiment, we added six reference images of the Tsarnaevs taken from press releases and news articles following their identification to a background database of 1 million mug shots. Against this database, we searched for matches of the five face images of the brothers extracted by the FBI from surveillance, smartphone, or point-and-shoot camera footage before their identification.


title: Emotion and narrative fiction: Interactive influences before, during, and after reading

abstract: Emotions are central to the experience of literary narrative fiction. Affect and mood can influence what book people choose, based partly on whether their goal is to change or maintain their current emotional state. Once having chosen a book, the narrative itself acts to evoke and transform emotions, both directly through the events and characters depicted and through the cueing of emotionally valenced memories. Once evoked by the story, these emotions can in turn influence a person's experience of the narrative. Lastly, emotions experienced during reading may have consequences after closing the covers of a book. This article reviews the current state of empirical research for each of these stages, providing a snapshot of what is known about the interaction between emotions and literary narrative fiction. With this, we can begin to sketch the outlines of what remains to be discovered.


title: Efficiency and regulation of recruitment during colony emigration by the ant Temnothorax curvispinosus

abstract: Recruitment helps insect societies by bringing individuals to places where work needs to be done, but it also imposes energetic and opportunity costs. The net effect depends both on recruitment efficiency and on the ease with which insects can find work sites on their own. This study examined both of these factors for colony emigration by the ant Temnothorax curvispinosus. Emigrations were organized by a corps of active ants who transported the rest of the colony. These active ants either found new sites independently or followed tandem runs led by successful scouts. Although most tandem runs broke apart before reaching their target, even lost followers found the new site faster than did unguided searchers. When the new site was near the old nest, tandem runs were rare and summoned only a small proportion of the transporter corps. When the new site was instead distant and inconspicuous, tandem runs were common and brought roughly one third of the transporters. This pattern likely results from the quorum rule used by individual scouts to decide when to switch from tandem runs to transports. By monitoring how many nestmates have already found the nest, the ants ensure that the costs of recruitment are born only when necessary.


title: An Intelligent Software defined Network Controller for preventing Distributed Denial of Service Attack

abstract: Software Defined Network (SDN) architecture is a new and novel way of network management mechanism. In SDN, switches do not process the incoming packets like conventional network computing environment. They match for the incoming packets in the forwarding tables and if there is none it will be sent to the controller for processing which is the operating system of the SDN. A Distributed Denial of Service (DDoS) attack is a biggest threat to cyber security in SDN network. The attack will occur at the network layer or the application layer of the compromised systems that are connected to the network. In this paper a machine learning based intelligent method is proposed which can detect the incoming packets as infected or not. The different machine learning algorithms adopted for accomplishing the task are Naive Bayes, K-Nearest neighbor (KNN) and Support vector machine (SVM) to detect the anomalous behavior of the data traffic. These three algorithms are compared according to their performances and KNN is found to be the suitable one over other two. The performance measure is taken here is the detection rate of infected packets.


title: Video-based Emotion Recognition Using Deeply-Supervised Neural Networks

abstract: Emotion recognition (ER) based on natural facial images/videos has been studied for some years and considered a comparatively hot topic in the field of affective computing. However, it remains a challenge to perform ER in the wild, given the noises generated from head pose, face deformation, and illumination variation. To address this challenge, motivated by recent progress in Convolutional Neural Network (CNN), we develop a novel deeply supervised CNN (DSN) architecture, taking the multi-level and multi-scale features extracted from different convolutional layers to provide a more advanced representation of ER. By embedding a series of side-output layers, our DSN model provides class-wise supervision and integrates predictions from multiple layers. Finally, our team ranked 3rd at the EmotiW 2018 challenge with our model achieving an accuracy of 61.1%.


title: COPASI -- a COmplex PAthway SImulator

abstract: Motivation: Simulation and modeling is becoming a standard approach to understand complex biochemical processes. Therefore, there is a big need for software tools that allow access to diverse simulation and modeling methods as well as support for the usage of these methods. ::: ::: Results: Here, we present COPASI, a platform-independent and user-friendly biochemical simulator that offers several unique features. We discuss numerical issues with these features; in particular, the criteria to switch between stochastic and deterministic simulation methods, hybrid deterministic--stochastic methods, and the importance of random number generator numerical resolution in stochastic simulation. ::: ::: Availability: The complete software is available in binary (executable) for MS Windows, OS X, Linux (Intel) and Sun Solaris (SPARC), as well as the full source code under an open source license from http://www.copasi.org. ::: ::: Contact: mendes@vbi.vt.edu


title: Database Issues in Knowledge Discovery and Data Mining

abstract: In recent years both the number and the size of organisational databases have increased rapidly. However, although available processing power has also grown, the increase in stored data has not necessarily led to a corresponding increase in useful information and knowledge. This has led to a growing interest in the development of tools capable of harnessing the increased processing power available to better utilise the potential of stored data. The terms "Knowledge Discovery in Databases" and "Data Mining" have been adopted for a field of research dealing with the automatic discovery of knowledge implicit within databases. Data mining is useful in situations where the volume of data is either too large or too complicated for manual processing or, to a lesser extent, where human experts are unavailable to provide knowledge. The success already attained by a wide range of data mining applications has continued to prompt further investigation into alternative data mining techniques and the extension of data mining to new domains. This paper surveys, from the standpoint of the database systems community, current issues in data mining research by examining the architectural and process models adopted by knowledge discovery systems, the different types of discovered knowledge, the way knowledge discovery systems operate on different data types, various techniques for knowledge discovery and the ways in which discovered knowledge is used.


title: Using link structure to infer opinions in social networks

abstract: The emergence of online social networks in the past few years has generated an enormous amount of information about potentially any subject. Valuable data containing users' opinions and thoughts are available on those repositories and several sentiment analysis techniques have been proposed that address the problem of understanding the opinion orientation of the users' postings. In this paper, we take a different perspective to the problem through a user centric approach, which uses a graph to model users (and their postings) and applies link mining techniques to infer opinions of users. Preliminary experiments on a Twitter corpus have shown promising results.


title: Wait-free synchronization

abstract: A wait-free implementation of a concurrent data object is one that guarantees that any process can complete any operation in a finite number of steps, regardless of the execution speeds of the other processes. The problem of constructing a wait-free implementation of one data object from another lies at the heart of much recent work in concurrent algorithms, concurrent data structures, and multiprocessor architectures. First, we introduce a simple and general technique, based on reduction to a concensus protocol, for proving statements of the form, “there is no wait-free implementation of X by Y.” We derive a hierarchy of objects such that no object at one level has a wait-free implementation in terms of objects at lower levels. In particular, we show that atomic read/write registers, which have been the focus of much recent attention, are at the bottom of the hierarchy: thay cannot be used to construct wait-free implementations of many simple and familiar data types. Moreover, classical synchronization primitives such as test&set and fetch&add , while more powerful than read and write , are also computationally weak, as are the standard message-passing primitives. Second, nevertheless, we show that there do exist simple universal objects from which one can construct a wait-free implementation of any sequential object.


title: Agent-Based Modeling of Chronic Diseases: A Narrative Review and Future Research Directions

abstract: The United States is experiencing an epidemic of chronic disease. As the US population ages, health care providers and policy makers urgently need decision models that provide systematic, credible prediction regarding the prevention and treatment of chronic diseases to improve population health management and medical decision-making. Agent-based modeling is a promising systems science approach that can model complex interactions and processes related to chronic health conditions, such as adaptive behaviors, feedback loops, and contextual effects. This article introduces agent-based modeling by providing a narrative review of agent-based models of chronic disease and identifying the characteristics of various chronic health conditions that must be taken into account to build effective clinical- and policy-relevant models. We also identify barriers to adopting agent-based models to study chronic diseases. Finally, we discuss future research directions of agent-based modeling applied to problems related to specific chronic health conditions.