title: Improving Gloss Vector Semantic Relatedness Measure by Integrating Pointwise Mutual Information: Optimizing Second-Order Co-occurrence Vectors Computed from Biomedical Corpus and UMLS

abstract: Methods of semantic relatedness are essential for wide range of tasks such as information retrieval and text mining. This paper, concerned with these automated methods, attempts to improve Gloss Vector semantic relatedness measure for more reliable estimation of relatedness between two input concepts. Generally, this measure by considering frequency cut-off for big rams tries to remove low and high frequency words which usually do not end up being significant features. However, this naive cutting approach can lead to loss of valuable information. By employing point wise mutual information (PMI) as a measure of association between features, we will try to enforce the foregoing elimination step in a statistical fashion. Applying both approaches to the biomedical domain, using MEDLINE as corpus, MeSH as thesaurus, and available reference standard of 311 concept pairs manually rated for semantic relatedness, we will show that PMI for removing insignificant features is more effective approach than frequency cut-off.


title: Generalized Residual Vector Quantization and Aggregating Tree for Large Scale Search

abstract: Vector quantization is an essential tool for tasks involving large scale data, for example, large scale similarity search, which is crucial for content-based information retrieval and analysis. In this paper, we propose a novel vector quantization framework that iteratively minimizes quantization error. First, we provide a detailed review on a relevant vector quantization method named residual vector quantization (RVQ). Next, we propose generalized residual vector quantization (GRVQ) to further improve over RVQ. Many vector quantization methods can be viewed as special cases of our proposed method. To enable GRVQ on billion scale data, we introduce a nonexhaustive search scheme named aggregating tree (A-Tree) for high dimensional data that uses GRVQ encodings to build a radix tree and perform the nearest neighbor search by beam search. To search accurately and efficiently, VQ-encodings should satisfy locally aggregating encoding criterion: For any node of the corresponding A-Tree, neighboring vectors should aggregate in fewer subtrees to make beam search efficient. We show that the proposed GRVQ encodings best satisfy the suggested criterion, and the joint use of GRVQ and A-Tree shows significantly better performances on billion scale datasets. Our methods are validated on several standard benchmark datasets. Experimental results and empirical analysis show the superior efficiency and effectiveness of our proposed methods compared to the state-of-the-art for large scale search.


title: A Temptation to Stalk: The Impact of Curiosity on User Acceptance of Social Networking Sites

abstract: Social networking sites occupy increasing fields of daily life and act as important communication channels today. But recent research also discusses the dark side of these sites, which expresses in form of stress, envy, addiction or even depression. Nevertheless, there must be a reason why people use social networking sites, even though they face related risks. One reason is human curiosity that tempts users to behave like this. The research on hand presents the impact of curiosity on user acceptance of social networking sites, which is theorized and empirically evaluated by using the technology acceptance model and a quantitative study among Facebook users. It further reveals that especially two types of human curiosity, epistemic and interpersonal curiosity, influence perceived usefulness and perceived enjoyment, and with it technology acceptance.


title: Modeling trap assisted recombination in Dye Sensitized Solar Cells

abstract: Despite the evidences on the significance of TiO2/dye/electrolyte interface states on the performance of Dye Sensitized Solar Cell (DSSC), no previous model has incorporated interfacial trap-assisted charge transfer processes to model recombination rate in DSSCs. A new charge transport model for DSSC is presented in this paper based on physics of electron capture, electron emission, oxidation and reduction processes mediated by the deep interfacial trap states. The model suggests that recombination in DSSC is due to trapping of conduction band electrons by deep defect states followed by reduction process at the interface. The model has been investigated through simulated quantum efficiency, dark and illuminated IV characteristics. The simulated results based on this model are in good accord with the experimental results.


title: Analysis and classification of the upper lip aesthetic unit.

abstract: BACKGROUND ::: Disharmonies of the upper lip aesthetic unit generally stem from tall ergotrids and/or thin lips. Comprehension and correction of such defects has been stifled by a lack of metrics and organized systems of diagnosis and treatment. ::: ::: ::: METHODS ::: The philtral-labial score was devised to better analyze the upper lip region. Measurements were made with Adobe Photoshop CS6, and computations were performed on a standard calculator. A retrospective medical records review identified 908 patients of the senior authors' (P.R. and S.W.H.) practices who underwent perioral rejuvenation between January 1, 2001, and July 31, 2012. Two hundred patients were randomly selected and assessed for disharmonies in three surveys that sequentially built on data points provided. ::: ::: ::: RESULTS ::: When preoperative anteroposterior and lateral photographs, dental show measurements, and philtral-labial scores were available, diagnostic concordance between the authors approached 100 percent. Pattern analysis resulted in a classification system (labral classification system), designating patients as either type 0 (no defects), type 1 (thin upper lip), type 2 (long philtrum), or type 3 (both) defects. Characteristic dental show values, philtral-labial scores, and suggested treatments were paired with each type. ::: ::: ::: CONCLUSIONS ::: The labral classification system and its associated analytical tools serve as useful references in consultation, simplify discussion of patients with upper lip defects, furnish a practical alternative to complex algorithms, enable documentation of changes, and facilitate analysis of large sample sizes. When implemented judiciously, the tools described in this article will help surgeons confidently address upper lip problems by streamlining accurate diagnosis and guiding proper treatment.


title: BATON: A Balanced Tree Structure for Peer-to-Peer Networks

abstract: We propose a balanced tree structure overlay on a peer-to-peer network capable of supporting both exact queries and range queries efficiently. In spite of the tree structure causing distinctions to be made between nodes at different levels in the tree, we show that the load at each node is approximately equal. In spite of the tree structure providing precisely one path between any pair of nodes, we show that sideways routing tables maintained at each node provide sufficient fault tolerance to permit efficient repair. Specifically, in a network with N nodes, we guarantee that both exact queries and range queries can be answered in O(log N) steps and also that update operations (to both data and network) have an amortized cost of O(log N). An experimental assessment validates the practicality of our proposal.


title: Accurate heart-rate estimation from face videos using quality-based fusion

abstract: Estimating heart rate (HR) accurately using face videos acquired from a low cost camera in contactless manner is of paramount importance for many real-world applications. Such existing systems perform spuriously due to change in camera parameters, respiration, facial expressions and environmental factors. This paper mitigates the issues for accurate HR estimation. The face video consisting of frontal, profile or multiple faces is divided into multiple overlapping fragments to determine HR estimates. The HR estimates are fused using quality-based fusion which aims to minimize illumination and face deformations. Experimental results demonstrate that the proposed system exhibit better performance than the state of the art systems and establishes the efficacy of the quality-based fusion in HR estimation.


title: Framing Labels and Immigration Policy Attitudes in the Iowa Caucuses: “Trying to Out-Tancredo Tancredo”

abstract: We use an experiment built into a series of surveys of Iowa voters during the 2008 Iowa Caucus campaign to test the effect of differing group framing labels on immigration policy preferences. We find that certain framing labels matter, but only among Republican partisans for whom the immigration issue is important. We also find that issue importance produces more conservative policy preferences for Democrats as well as Republicans. We examine and discuss these results as well as their implications for the immigration debate, the interaction between issue salience and policy preferences, and the theory of political framing in general.


title: Trifo-VIO: Robust and Efficient Stereo Visual Inertial Odometry Using Points and Lines

abstract: In this paper, we present the Trifo Visual Inertial Odometry (Trifo-VIO), a tightly-coupled filtering-based stereo VIO system using both points and lines. Line features help improve system robustness in challenging scenarios when point features cannot be reliably detected or tracked, e.g. low-texture environment or lighting change. In addition, we propose a novel lightweight filtering-based loop closing technique to reduce accumulated drift without global bundle adjustment or pose graph optimization. We formulate loop closure as EKF updates to optimally relocate the current sliding window maintained by the filter to past keyframes. We also present the Trifo Ironsides dataset, a new visual-inertial dataset, featuring high-quality synchronized stereo camera and IMU data from the Ironsides sensor [3] with various motion types and textures and millimeter-accuracy groundtruth. To validate the performance of the proposed system, we conduct extensive comparison with state-of-the-art approaches (OKVIS, VINS-MONO and S-MSCKF) using both the public EuRoC dataset and the Trifo Ironsides dataset.


title: A practical concurrent binary search tree

abstract: We propose a concurrent relaxed balance AVL tree algorithm that is fast, scales well, and tolerates contention. It is based on optimistic techniques adapted from software transactional memory, but takes advantage of specific knowledge of the the algorithm to reduce overheads and avoid unnecessary retries. We extend our algorithm with a fast linearizable clone operation, which can be used for consistent iteration of the tree. Experimental evidence shows that our algorithm outperforms a highly tuned concurrent skip list for many access patterns, with an average of 39% higher single-threaded throughput and 32% higher multi-threaded throughput over a range of contention levels and operation mixes.


Two Decades of Recommender Systems at Amazon


title: Investigation of Rectenna Array Configurations for Enhanced RF Power Harvesting

abstract: RF power harvesting enables controllable and simultaneous wireless power delivery to many RF devices. Devices built with this unique technology can be sealed, embedded within structures, or made mobile, thus eliminating additional service for a battery. A key component of this technology is the "rectenna," which is composed of antennas and rectifying circuitry to convert RF energy into dc power. Typically, multiple rectenna elements are used to generate the dc power for reliable device operation. This letter compares two different rectenna architectures for maximum RF-to-dc power conversion efficiency. A simple rectenna design ex ample containing a 2 × 2 planar antenna array will be presented to demonstrate such RF power harvesting technology. The parameter, Rectenna Topology Indicator (RTI), is introduced for performance comparison.


title: Smart home implementation based on Internet and WiFi technology

abstract: Smart home has become more and more popular in recent years. It aims at helping people manage the home appliance freely and build an autonomous environment in home or work area. This paper introduces a wireless solution based on Internet protocol to manage the smart home units easily. Based on this approach, we design a smart home system with the implementation of related software and hardware. People can use smart phones or tablets to control or monitor the home appliances both locally and remotely. We come up with the concept of Smart Units and Home Proxy. Low cost WiFi module is used to build Smart Units. The combination of remote server and home proxy is a new scheme for remote control in which XMPP is used. The remote server can act as a service provider and provide service for different homes and offices.


title: Small signal modeling of dual-edge PWM modulator with fixed clock frequency

abstract: In this paper, the describing function approach is used to model a dual-edge PWM modulator with a fixed clock frequency. A detailed PWM modulator circuit configuration is introduced with the derivation of the model. Good matching is observed when comparing the analytical model and SIMPLIS simulation. System level analysis demonstrates the advantages of the proposed model versus a conventional average model. The proposed model enables a more accurate compensator design for predicting the dynamic characteristics of the entire system.


title: How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance

abstract: Past research in the area of information systems acceptance has primarily focused on initial adoption under the implicit assumption that IS usage is mainly determined by intention. While plausible in the case of initial IS adoption, this assumption may not be as readily applicable to continued IS usage behavior since it ignores that frequently performed behaviors tend to become habitual and thus automatic over time. ::: ::: This paper is a step forward in defining and incorporating the "habit" construct into IS research. Specifically, the purpose of this study is to explore the role of habit and its antecedents in the context of continued IS usage. ::: ::: Building on previous work in other disciplines, we define habit in the context of IS usage as the extent to which people tend to perform behaviors (use IS) automatically because of learning. Using recent work on the continued usage of IS (IS continuance), we have developed a model suggesting that continued IS usage is not only a consequence of intention, but also of habit. In particular, in our research model, we propose IS habit to moderate the influence of intention such that its importance in determining behavior decreases as the behavior in question takes on a more habitual nature. Integrating past research on habit and IS continuance further, we suggest how antecedents of behavior/behavioral intention as identified by IS continuance research relate to drivers of habitualization. ::: ::: We empirically tested the model in the context of voluntary continued WWW usage. Our results support the argument that habit acts as a moderating variable of the relationship between intentions and IS continuance behavior, which may put a boundary condition on the explanatory power of intentions in the context of continued IS usage. The data also support that satisfaction, frequency of past behavior, and comprehensiveness of usage are key to habit formation and thus relevant in the context of IS continuance behavior. Implications of these findings are discussed and managerial guidelines presented.


title: Digital Nerve Grafts in Replanted Digits

abstract: In most cases where digits have been amputated by avulsion and the digital nerve extensively injured, primary nerve suture cannot be performed. For such a digit when replanted, secondary nerve grafting was done under the operating microscope using a lateral femoral cutaneous nerve. Two years after operation, two-point discrimination of these cases proved to be 10.5 mm on average. And in the case of the transplanted three strand cable graft, using a lateral femoral cutaneous nerve yielded the most favourable results. The replacement was superior to the side receiving primary crushed nerve suture. Sensory nerve action potentials indicate that the regenerated nerve does not necessarily mature sufficiently. The recovery of sensitivity even to this degree however was sufficient for the replanted digits to be useful in all cases.


A Workflow Instance-based Model-checking Approach to Analysing Organisational Controls in a Loan Origination Process


title: Review: a review of culture in information systems research: toward a theory of information technology culture conflict

abstract: An understanding of culture is important to the study of information technologies in that culture at various levels, including national, organizational, and group, can influence the successful implementation and use of information technology. Culture also plays a role in managerial processes that may directly, or indirectly, influence IT. Culture is a challenging variable to research, in part because of the multiple divergent definitions and measures of culture. Notwithstanding, a wide body of literature has emerged that sheds light on the relationship of IT and culture. This paper sets out to provide a review of this literature in order to lend insights into our understanding of the linkages between IT and culture. We begin by conceptualizing culture and laying the groundwork for a values-based approach to the examination of IT and culture. Using this approach, we then provide a comprehensive review of the organizational and cross-cultural IT literature that conceptually links these two traditionally separate streams of research. From our analysis, we develop six themes of IT-culture research emphasizing culture's impact on IT, IT's impact on culture, and IT culture. Building upon these themes, we then develop a theory of IT, values, and conflict. Based upon the theory, we develop propositions concerning three types of cultural conflict and the results of these conflicts. Ultimately, the theory suggests that the reconciliation of these conflicts results in a reorientation of values. We conclude with the particular research challenges posed in this line of inquiry.


title: Emotional and uncontrolled eating styles and chocolate chip cookie consumption. A controlled trial of the effects of positive mood enhancement

abstract: The study tested the effects of positive mood enhancement on chocolate chip cookie consumption in the context of emotional and uncontrolled eating styles. The relationship between emotional eating style and chocolate chip cookie intake was assumed to be mediated by uncontrolled eating style. Further, it was hypothesized that the effectiveness of the positive mood enhancement may be more salient among those who have effective control of their eating. In this experimental study, respondents (N = 106, 70% women, aged 16–45 years old) were assigned by means of cluster randomization to the control or positive mood enhancement condition (a comedy movie clip). Compared to the control condition, positive mood enhancement resulted in consuming on average 53.86 kcal less. Relationships between emotional eating style and cookie intake were mediated by uncontrolled eating. Moderated mediation analysis indicated that the effect of a mediator (uncontrolled eating) on cookie intake was moderated by the group assignment. Positive mood enhancement resulted in eating on average 3.3 cookies less among individuals with a more controlled eating style. By contrast, among those who presented uncontrolled eating, positive mood enhancement led to consuming an average of 1.7 cookies more.


title: Invisible Hand: A Privacy Preserving Mobile Crowd Sensing Framework Based on Economic Models

abstract: Privacy issues are strongly impeding the development of mobile crowd sensing (MCS) applications. Under the current MCS framework, processes including bidding, task assignment, and sensed data uploading are all potentially risky for participants. As an effort toward this issue, we propose a framework that enhances the location privacy of MCS applications by reducing the bidding and assignment steps in the MCS cycle. Meanwhile, to reduce the unnecessary privacy loss while maintaining the required quality of service (QoS), economic theory is used to help both the service provider and participants to decide their strategies. We propose schemes based on both the Monopoly and Oligopoly models. In the former case, the participants cooperate to gain exclusive control of the supply of crowd sensing data, while the latter case is a state of limited competition. The parameters in different schemes are analyzed, and the strengths and weaknesses of both schemes are discussed. Additionally, the proposed schemes are evaluated by extensive simulations, and the results are discussed in detail.


title: Performance catastrophes in sport: A test of the hysteresis hypothesis

abstract: An experiment is reported which tests Fazey and Hardy's (1988) catastrophe model of anxiety and performance. Eight experienced crown green bowlers performed a bowling task under conditions of high and low cognitive anxiety. On each of these occasions, physiological arousal (measured by heart rate) was manipulated by means of physical work in such a way that the subjects were tested with physiological arousal increasing and decreasing. A repeated‐measures three‐factor ANOVA was used to test the hysteresis hypothesis that the performance x heart rate graph would follow a different path for heart rate increasing compared with heart rate decreasing in the high cognitive anxiety condition, but not in the low cognitive anxiety condition. The ANOVA revealed the predicted three‐way interaction of cognitive anxiety, heart rate, and the direction of change in heart rate upon performance, with follow‐up tests indicating that the interaction was due to hysteresis occurring in the high cognitive anxiety condition but ...


title: Enabling Flexibility in Process-Aware Information Systems

abstract: Traditionally, process-ware information systems (PAISs) have focused on the support of predictable and repetitive business processes. Even though respective processes are suited to be fully pre-specified in a process model, flexibility is required to support dynamic process adaptations in case of exceptions. Flexibility is also needed to accommodate the need for evolving business processes and to cope with business process variability. Furthermore, PAISs are increasingly used to support less structured processes which can often be characterized as knowledgeintensive. Processes of this category are neither fully predictable nor repetitive, and therefore cannot be fully pre-specified at build-time. The (partial) unpredictability of these processes also demands a certain amount of looseness. This chapter deals with the flexibility needs of both pre-specified and loosely-specified processes and elicitates requirements for flexible process support in a PAIS. In addition, the chapter discusses PAIS features needed to accommodate flexibility needs in practice like, for example, traceability, business compliance and user support.


Motivation Behind Volunteerism


title: Groove Radio: A Bayesian Hierarchical Model for Personalized Playlist Generation

abstract: This paper describes an algorithm designed for Microsoft's Groove music service, which serves millions of users world wide. We consider the problem of automatically generating personalized music playlists based on queries containing a ``seed'' artist and the listener's user ID. Playlist generation may be informed by a number of information sources including: user specific listening patterns, domain knowledge encoded in a taxonomy, acoustic features of audio tracks, and overall popularity of tracks and artists. The importance assigned to each of these information sources may vary depending on the specific combination of user and seed~artist. The paper presents a method based on a variational Bayes solution for learning the parameters of a model containing a four-level hierarchy of global preferences, genres, sub-genres and artists. The proposed model further incorporates a personalization component for user-specific preferences. Empirical evaluations on both proprietary and public datasets demonstrate the effectiveness of the algorithm and showcase the contribution of each of its components.


title: Wideband Pattern-Reconfigurable Antenna Using Pair of Radial Radiators on Truncated Ground With Switchable Director and Reflector

abstract: A compact wideband pattern-reconfigurable antenna that is based on multiple radiators and switchable director/reflector is presented. The antenna consists of two quarter-wavelength radial radiators placed on two sides of a central feeder at the top layer. The bottom layer of the antenna includes a truncated quarter-wavelength ground plane and two quarter-wavelength parallel strips surrounding the radiators. Four p-i-n diodes, which are interlocked in pair, are used to control the radiation direction by selecting the radiator at the top layer and switching the strips at the bottom layer between being a director or reflector. Thus, the antenna achieves four-state pattern reconfigurability in the azimuthal plane. The antenna has a bandwidth of 1.88–2.64 GHz (34% fractional bandwidth) and is capable of steering the beam in the four directions 0°, 45°, 135°, and 180° in the azimuth plane. The antenna achieves a stable measured gain of more than 2.8 dBi across the band with a peak gain of 3.7 dBi. It has a low profile of 0.48λ e × 0.64λ e × 0.02λ e (λ e guided wavelength at 1.88 GHz) on a low-cost FR4 substrate. With the covered band, the antenna can be used in compact handheld and portable devices, indoor or short-range communications, WiMAX and WLAN.


title: Indirect-Rotor-Field-Oriented-Control of a Double-Star Induction Machine using the RST controller

abstract: This paper focuses to apply the RST controller on the indirect-rotor-field-oriented-control (IRFOC) of a double star induction machine (DSIM). After fixing the reference models, some algebraic conditions permits to design the current and speed controllers. This step consists to find three polynomials R, S, T of the controllers by solving the Diophante equation. The RST controllers can ensure robustness against uncertainties of mechanical and electrical parameters of the motor. It also offers further degrees of freedom upon the choice of the dynamic of the system. Simulations and analysis will be developed to prove the effectiveness of the study.


title: Wearable impedance analyzer based on AD5933

abstract: In this paper a system for measuring impedance, based on AD5933 circuit is presented. The impedance measuring range is between 9 Ω and 18 MΩ for a 1 kHz ÷ 100 kHz frequency range. Possibilities of expanding this range of measurement are also presented in the paper. The system calibration is automatic and the relative error of the impedance modulus measurement is in the range ±2%. Measured impedance main parameters are shown locally on an OLED display but can also be stored in an SD memory card for further processing. The system is portable, modular and adaptable to a large number of applications.


title: A low power AES-GCM authenticated encryption core in 65nm SOTB CMOS process

abstract: This paper presents a low power AES-GCM authenticated encryption IP core which combines an improved four-parallel architecture, an advanced 65nm SOTB CMOS technology and a low complexity clock gating technique. As a result, the power consumption of the proposed AES-GCM core is only 8.9mW which is lower than other AES-GCM IP cores presented in literature. The detail implementation results are also presented and discussed.


title: Theory of Cognitive Relativity: A Promising Paradigm for True AI

abstract: The rise of deep learning has brought artificial intelligence (AI) to the forefront. The ultimate goal of AI is to realize machines with human mind and consciousness, but existing achievements mainly simulate intelligent behavior on computer platforms. These achievements all belong to weak AI rather than strong AI. How to achieve strong AI is not known yet in the field of intelligence science. Currently, this field is calling for a new paradigm, especially Theory of Cognitive Relativity (TCR). The TCR aims to summarize a simple and elegant set of first principles about the nature of intelligence, at least including the Principle of World's Relativity and the Principle of Symbol's Relativity. The Principle of World's Relativity states that the subjective world an intelligent agent can observe is strongly constrained by the way it perceives the objective world. The Principle of Symbol's Relativity states that an intelligent agent can use any physical symbol system to express what it observes in its subjective world. The two principles are derived from scientific facts and life experience. Thought experiments show that they are important to understand high-level intelligence and necessary to establish a scientific theory of mind and consciousness. Rather than brain-like intelligence, the TCR indeed advocates a promising change in direction to realize true AI, i.e. artificial general intelligence or artificial consciousness, particularly different from humans' and animals'. Furthermore, a TCR creed has been presented and extended to reveal the secrets of consciousness and to guide realization of conscious machines. In the sense that true AI could be diversely implemented in a brain-different way, the TCR would probably drive an intelligence revolution in combination with some additional first principles.


title: Inferring Point Clouds from Single Monocular Images by Depth Intermediation

abstract: In this paper, we propose a framework for generating 3D point cloud of an object from a single-view RGB image. Most previous work predict the 3D point coordinates from single RGB images directly. We decompose this problem into depth estimation from single images and point completion from partial point clouds. ::: Our method sequentially predicts the depth maps and then infers the complete 3D object point clouds based on the predicted partial point clouds. We explicitly impose the camera model geometrical constraint in our pipeline and enforce the alignment of the generated point clouds and estimated depth maps. ::: Experimental results for the single image 3D object reconstruction task show that the proposed method outperforms state-of-the-art methods. Both the qualitative and quantitative results demonstrate the generality and suitability of our method.


title: Active deep Q-learning with demonstration

abstract: Reinforcement learning (RL) is a machine learning technique aiming to learn how to take actions in an environment to maximize some kind of reward. Recent research has shown that although the learning efficiency of RL can be improved with expert demonstration, it usually takes considerable efforts to obtain enough demonstration. The efforts prevent training decent RL agents with expert demonstration in practice. In this work, we propose Active Reinforcement Learning with Demonstration, a new framework to streamline RL in terms of demonstration efforts by allowing the RL agent to query for demonstration actively during training. Under the framework, we propose Active deep Q-Network, a novel query strategy based on a classical RL algorithm called deep Q-network (DQN). The proposed algorithm dynamically estimates the uncertainty of recent states and utilizes the queried demonstration data by optimizing a supervised loss in addition to the usual DQN loss. We propose two methods of estimating the uncertainty based on two state-of-the-art DQN models, namely the divergence of bootstrapped DQN and the variance of noisy DQN. The empirical results validate that both methods not only learn faster than other passive expert demonstration methods with the same amount of demonstration and but also reach super-expert level of performance across four different tasks.


title: Super-Resolution Power and Robustness of Compressive Sensing for Spectral Estimation With Application to Spaceborne Tomographic SAR

abstract: We address the problem of resolving two closely spaced complex-valued points from N irregular Fourier do- main samples. Although this is a generic super-resolution (SR) problem, our target application is SAR tomography (TomoSAR), where typically the number of acquisitions is N = 10 - 100 and SNR = 0-10 dB. As the TomoSAR algorithm, we introduce "Scale-down by LI norm Minimization, Model selection, and Estimation Reconstruction" (SL1MMER), which is a spectral estimation algorithm based on compressive sensing, model order selection, and final maximum likelihood parameter estimation. We investigate the limits of SLIMMER concerning the following questions. How accurately can the positions of two closely spaced scatterers be estimated? What is the closest distance of two scat- terers such that they can be separated with a detection rate of 50% by assuming a uniformly distributed phase difference? How many acquisitions N are required for a robust estimation (i.e., for separating two scatterers spaced by one Rayleigh resolution unit with a probability of 90%)? For all of these questions, we provide numerical results, simulations, and analytical approxima- tions. Although we take TomoSAR as the preferred application, the SLIMMER algorithm and our results on SR are generally applicable to sparse spectral estimation, including SR SAR focus- ing of point-like objects. Our results are approximately applicable to nonlinear least-squares estimation, and hence, although it is derived experimentally, they can be considered as a fundamental bound for SR of spectral estimators. We show that SR factors are in the range of 1.5-25 for the aforementioned parameter ranges of N and SNR.


Post-WIMP user interfaces


title: Multi-Objective Approach for Energy-Aware Workflow Scheduling in Cloud Computing Environments

abstract: We address the problem of scheduling workflow applications on heterogeneous computing systems like cloud computing infrastructures. In general, the cloud workflow scheduling is a complex optimization problem which requires considering different criteria so as to meet a large number of QoS (Quality of Service) requirements. Traditional research in workflow scheduling mainly focuses on the optimization constrained by time or cost without paying attention to energy consumption. The main contribution of this study is to propose a new approach for multi-objective workflow scheduling in clouds, and present the hybrid PSO algorithm to optimize the scheduling performance. Our method is based on the Dynamic Voltage and Frequency Scaling (DVFS) technique to minimize energy consumption. This technique allows processors to operate in different voltage supply levels by sacrificing clock frequencies. This multiple voltage involves a compromise between the quality of schedules and energy. Simulation results on synthetic and real-world scientific applications highlight the robust performance of the proposed approach.


EmpiriST: AIPHES - Robust Tokenization and POS-Tagging for Different Genres


title: Silicon carbide power electronics for electric vehicles

abstract: The success of electric vehicle drives heavily depends on the maximization of energy conversion efficiency. Losses in power electronic converters increase system size, weight and cost, raise energy demand and limit the operating distance range. Advances in semiconductor technology can remedy these problems and silicon carbide devices are of special interest in this context. This material enables manufacturing high-voltage devices with lower on-state voltage drop and shorter switching times, thus reducing both static and dynamic power loss. In this paper, recent achievements in silicon carbide technology as well as their applications in electrical vehicles have been reviewed.


INTRODUCTION


title: Healthcare utilization in older patients using personal emergency response systems: an analysis of electronic health records and medical alert data

abstract: BACKGROUND ::: Personal Emergency Response Systems (PERS) are traditionally used as fall alert systems for older adults, a population that contributes an overwhelming proportion of healthcare costs in the United States. Previous studies focused mainly on qualitative evaluations of PERS without a longitudinal quantitative evaluation of healthcare utilization in users. To address this gap and better understand the needs of older patients on PERS, we analyzed longitudinal healthcare utilization trends in patients using PERS through the home care management service of a large healthcare organization. ::: ::: ::: METHODS ::: Retrospective, longitudinal analyses of healthcare and PERS utilization records of older patients over a 5-years period from 2011-2015. The primary outcome was to characterize the healthcare utilization of PERS patients. This outcome was assessed by 30-, 90-, and 180-day readmission rates, frequency of principal admitting diagnoses, and prevalence of conditions leading to potentially avoidable admissions based on Centers for Medicare and Medicaid Services classification criteria. ::: ::: ::: RESULTS ::: The overall 30-day readmission rate was 14.2%, 90-days readmission rate was 34.4%, and 180-days readmission rate was 42.2%. While 30-day readmission rates did not increase significantly (p = 0.16) over the study period, 90-days (p = 0.03) and 180-days (p = 0.04) readmission rates did increase significantly. The top 5 most frequent principal diagnoses for inpatient admissions included congestive heart failure (5.7%), chronic obstructive pulmonary disease (4.6%), dysrhythmias (4.3%), septicemia (4.1%), and pneumonia (4.1%). Additionally, 21% of all admissions were due to conditions leading to potentially avoidable admissions in either institutional or non-institutional settings (16% in institutional settings only). ::: ::: ::: CONCLUSIONS ::: Chronic medical conditions account for the majority of healthcare utilization in older patients using PERS. Results suggest that PERS data combined with electronic medical records data can provide useful insights that can be used to improve health outcomes in older patients.


title: Query-by-Example : a data base language

abstract: Discussed is a high-level data base management language that provides the user with a convenient and unified interface to query, update, define, and control a data base. ::: ::: When the user performs an operation against the data base, he fills in an example of a solution to that operation in skeleton tables that can be associated with actual tables in the data base. The system is currently being used experimentally for various applications.


title: Contextual and Variational Contrast Enhancement

abstract: This paper proposes an algorithm that enhances the contrast of an input image using interpixel contextual information. The algorithm uses a 2-D histogram of the input image constructed using a mutual relationship between each pixel and its neighboring pixels. A smooth 2-D target histogram is obtained by minimizing the sum of Frobenius norms of the differences from the input histogram and the uniformly distributed histogram. The enhancement is achieved by mapping the diagonal elements of the input histogram to the diagonal elements of the target histogram. Experimental results show that the algorithm produces better or comparable enhanced images than four state-of-the-art algorithms.


title: Mechanical fault detection and classification using pattern recognition based on bispectrum algorithm

abstract: Higher order spectral analysis of vibration signals is an efficient tool in condition monitoring and fault detection and diagnosis of rotating machinery. In this paper, features extracted from vibration bispectrum are used in fault classification of critical rotating components in the AH-64D helicopter tail rotor drive train system. Different classifiers are used to compare the performance of the proposed algorithm based on bispectrum to the traditional algorithms based on linear auto- and cross-power spectral analysis techniques. Principal component analysis (PCA) is used to reduce the size of features extracted from vibration bispectrum and linear spectral analysis, then the reduced set is used to train different classifiers. Using different criteria such as accuracy, precision, sensitivity, F score, true alarm, and error classification accuracy (ECA), the performance of the proposed algorithm is evaluated and compared against similar classification algorithms. The proposed method is verified using real-world data collected from a dedicated AH-64D helicopter drive-train research test bed at the CPM center, University of South Carolina. The proposed algorithm increases the accuracy of fault detection to 96.88%, precession to 95.83%, sensitivity to 95.83%.


title: Function-Based Categorization of Online Product Information Types

abstract: Hardly any in-depth knowledge is currently available on how different types of product-relevant information influence online consumer trust and purchase decision. To address this research gap, we apply a generic function-based information typology to systematically classify the large variety of online product information and plan for a focused comparison of their functional roles and differential effects on online consumer decision making. The ultimate goal is to provide a theoretical basis for guiding and prioritizing online information organization and provision, which becomes increasingly important in the current context of information overload. The paper briefly reviews information research in e-commerce, discusses the conceptual basis of applying the generic function-based topical relevance typology to analyze online product information, and uses a variety of product examples from Amazon.com to demonstrate the process. The paper concludes with preliminary findings from this pilot study.


title: Introducing secure provenance: problems and challenges

abstract: Data provenance summarizes the history of the ownership of the item, as well as the actions performed on it. While widely used in archives, art, and archeology, provenance is also very important in forensics, scientific computing, and legal proceedings involving data. Significant research has been conducted in this area, yet the security and privacy issues of provenance have not been explored. In this position paper, we define the secure provenance problem and argue that it is of vital importance in numerous applications. We then discuss a select few of the issues related to ensuring the privacy and integrity of provenance information.


title: High-speed video analysis of laboratory rats behaviors in forced swim test

abstract: The forced swim test (FST) is a method for measuring the pharmacological effects of antidepressants or changes in stress-evoked behavior in laboratory animals such as rats and mice. However, traditional manual or automatic measurement methods using video recorded at 30 fps (frames per second) have not always been accurate and objective because laboratory animals frequently make quick movements during the FST. In the present paper, a novel laboratory animal behavior quantification system is developed for the FST by introducing a high-speed vision system that can record images at a frame rate of 240 fps for a long time. The developed system can accurately discriminate immobility, climbing, and swimming behaviors of laboratory rats in the FST. We evaluate the effectiveness of the developed system by obtaining experimental results for a nine-minute FST using several laboratory rats.


title: Anxiety, processing efficiency, and cognitive performance: New developments from attentional control theory.

abstract: There have been many attempts to account theoretically for the effects of anxiety on cognitive performance. This article focuses on two theories based on insights from cognitive psychology. The more recent is the attentional control theory (Eysenck, Derakshan, Santos, & Calvo, 2007), which developed from the earlier processing efficiency theory (Eysenck & Calvo, 1992). Both theories assume there is a fundamental distinction between performance effectiveness (quality of performance) and processing efficiency (the relationship between performance effectiveness and use of processing resources), and that anxiety impairs processing efficiency more than perfor- mance effectiveness. Both theories also assume that anxiety impairs the efficiency of the central executive component of the working memory system. In addition, attentional control theory assumes that anxiety impairs the efficiency of two types of attentional control: (1) negative attentional control (involved in inhibiting attention to task-irrelevant stimuli); and (2) positive attentional control (involved in flexibly switching attention between and within tasks to maximize performance). Recent (including unpublished) research relevant to theoretical predictions from attentional control theory is discussed. In addition, future directions for theory and research in the area of anxiety and performance are presented.


title: Short-Term Load Forecasting using a Cluster of Neural Networks for the Greek Energy Market

abstract: In the context of the liberalization of the Greek Energy Market, load forecasting is essential in various system programming procedures. Short-term load forecasting extends from one to seven days, although in this paper a model is proposed for the next calendar day in step of sixty minutes. The objective is to design and implement a software-based short-term load forecasting model for the Greek interconnected transmission system that will show improved performance compared to previous methods. The proposed model introduces a categorization of the forecasted days along with a dedicated artificial neural network for each category. Appropriate input vectors are selected at the training process for each custom-built network.


title: Data quality: A statistical perspective

abstract: Abstract We present the old-but-new problem of data quality from a statistical perspective, in part with the goal of attracting more statisticians, especially academics, to become engaged in research on a rich set of exciting challenges. The data quality landscape is described, and its research foundations in computer science, total quality management and statistics are reviewed. Two case studies based on an EDA approach to data quality are used to motivate a set of research challenges for statistics that span theory, methodology and software tools.


title: Fine Grain Cross-VM Attacks on Xen and VMware

abstract: This work exposes further vulnerabilities in virtualized cloud servers by mounting Cross-VM cache attacks in Xen and VMware VMs targeting AES running in the victim VM. Even though there exists a rich literature on cache attacks on AES, so far only a single work, demonstrating a working attack on an ARM platform running a L4Re virtualization layer has been published. Here we show that AES in a number popular cryptographic libraries including OpenSSL, PolarSSL and Libgcrypt are vulnerable to Bernstein’s correlation attack when run in Xen and VMware (bare metal version) VMs, the most popular VMs used by cloud service providers (CSP) such as Amazon and Rackspace. We also show that the vulnerability persists even if the VMs are placed on different cores in the same machine. The results of this study shows that there is a great security risk to AES and (data encrypted under AES) on popular cloud services.


BNL-108233-2015-CP Unsupervised Feature Selection on Data Streams


title: Floating search algorithm for structure learning of Bayesian network classifiers

abstract: This paper presents a floating search approach for learning the network structure of Bayesian network classifiers. A Bayesian network classifier is used which in combination with the search algorithm allows simultaneous feature selection and determination of the structure of the classifier.The introduced search algorithm enables conditional exclusions of previously added attributes and/or arcs from the network classifier. Hence, this algorithm is able to correct the network structure by removing attributes and/or arcs between the nodes if they become superfluous at a later stage of the search. Classification results of selective unrestricted Bayesian network classifiers are compared to naive Bayes classifiers and tree augmented naive Bayes classifiers. Experiments on different data sets show that selective unrestricted Bayesian network classifiers achieve a better classification accuracy estimate in two domains compared to tree augmented naive Bayes classifiers, whereby in the remaining domains the performance is similar. However, the achieved network structure of selective unrestricted Bayesian network classifiers is simpler.


title: Human Body Detection and Geolocalization for UAV Search and Rescue Missions Using Color and Thermal Imagery

abstract: Recent advances in the field of Unmanned Aerial Vehicles (UAVs) make flying robots suitable platforms for carrying sensors and computer systems capable of performing advanced tasks. This paper presents a technique which allows detecting humans at a high frame rate on standard hardware onboard an autonomous UAV in a real-world outdoor environment using thermal and color imagery. Detected human positions are geolocated and a map of points of interest is built. Such a saliency map can, for example, be used to plan medical supply delivery during a disaster relief effort. The technique has been implemented and tested on-board the UAVTech1 autonomous unmanned helicopter platform as a part of a complete autonomous mission. The results of flight- tests are presented and performance and limitations of the technique are discussed.


title: A native Android application development based on watercolor stylized image

abstract: Now we can see the enormous increase of smart phones that are based on android platform. For this reason, the mobile application development based on android has boosted the developers' interest. This paper is mainly a description about a native android application development, the content includes not only the application functions to be achieved, but also involves the specific use of JNI. In order to realize the function of this application, the image stylized watercolor, In this paper we have come to our own programming algorithms including image graying, edge detection, darkening, pigment diffusion, image expansion after a series of studies and experiments. In addition, Java and C/C ++ call each other is another key problem which we also need to solve in the application development process.


title: Loop and Backbone Modifications of Peptide Nucleic Acid Improve G-Quadruplex Binding Selectivity

abstract: Targeting guanine (G) quadruplex structures is an exciting new strategy with potential for controlling gene expression and designing anticancer agents. Guanine-rich peptide nucleic acid (PNA) oligomers bind to homologous DNA and RNA to form hetero-G-quadruplexes but can also bind to complementary cytosine-rich sequences to form heteroduplexes. In this study, we incorporated backbone modifications into G-rich PNAs to improve the selectivity for quadruplex versus duplex formation. Incorporation of abasic sites as well as chiral modifications to the backbone were found to be effective strategies for improving selectivity as shown by UV-melting and surface plasmon resonance measurements. The enhanced selectivity is due primarily to decreased affinity for complementary sequences, since binding to the homologous DNA to form PNA-DNA heteroquadruplexes retains high affinity. The improved selectivity of these PNAs is an important step toward using PNAs for regulating gene expression by G-quadruplex formation.


NetCAT : Practical Cache Attacks from the Network


title: In-body experiences: embodiment, control, and trust in robot-mediated communication

abstract: Communication technologies are becoming increasingly diverse in form and functionality, making it important to identify which aspects of these technologies actually improve geographically distributed communication. Our study examines two potentially important aspects of communication technologies which appear in robot-mediated communication - physical embodiment and control of this embodiment. We studied the impact of physical embodiment and control upon interpersonal trust in a controlled laboratory experiment using three different videoconferencing settings: (1) a handheld tablet controlled by a local user, (2) an embodied system controlled by a local user, and (3) an embodied system controlled by a remote user (n = 29 dyads). We found that physical embodiment and control by the local user increased the amount of trust built between partners. These results suggest that both physical embodiment and control of the system influence interpersonal trust in mediated communication and have implications for future system designs.


Baseline: Strong, Extensible, Reproducible, Deep Learning Baselines for NLP


title: RFID 13.56MHz transponder IC frontend

abstract: This paper presents a new receiver frontend for 13.56 MHz carrier frequency smart card transponders. The designed frontend is compatible with ISO14443 and ISO15693 standards, and can be easily adapted for other ISO standards. The included asynchronous ASK demodulator has high sensitivity compared with standard diode demodulators. Amplifier gain and common mode are adjustable using a configuration register.


title: DOI: 10.1080/10635150490522304 Model Selection and Model Averaging in Phylogenetics: Advantages of Akaike Information Criterion and Bayesian Approaches Over Likelihood Ratio Tests

abstract: Model selection is a topic of special relevance in molecular phylogenetics that affects many, if not all, stages of phylogenetic inference. Here we discuss some fundamental concepts and techniques of model selection in the context of phylogenetics. We start by reviewing different aspects of the selection of substitution models in phylogenetics from a theoretical, philosophical and practical point of view, and summarize this comparison in table format. We argue that the most commonly implemented model selection approach, the hierarchical likelihood ratio test, is not the optimal strategy for model selection in phylogenetics, and that approaches like the Akaike Information Criterion (AIC) and Bayesian methods offer important advantages. In particular, the latter two methods are able to simultaneously compare multiple nested or nonnested models, assess model selection uncertainty, and allow for the estimation of phylogenies and model parameters using all available models (model-averaged inference or multimodel inference). We also describe how the relative importance of the different parameters included in substitution models can be depicted. To illustrate some of these points, we have applied AIC-based model averaging to 37 mitochondrial DNA sequences from the subgenus Ohomopterus(genus Carabus) ground beetles described by Sota and Vogler (2001).


title: Mapping payload development for MBARI's Dorado-class AUVs

abstract: The Monterey Bay Aquarium Research Institute (MBARI) is developing an autonomous seafloor mapping capability for deep ocean science applications. The MBARI Mapping AUV is a 0.53 m (21 in) diameter, 5.1 m (16.7 ft) long, Dorado-class vehicle designed to carry four mapping sonars. The primary sensor is a 200 kHz multibeam sonar producing swath bathymetry and sidescan. In addition, the vehicle carries 100 kHz and 410 kHz chirp sidescan sonars, and a 2-16 kHz sweep chirp subbottom profiler. Navigation and attitude data are obtained from an inertial navigation system (INS) incorporating a ring laser gyro and a 300 kHz Doppler velocity log (DVL). The vehicle also includes acoustic modem, ultra-short baseline navigation, and long-baseline navigation systems. A single cylindrical pressure housing contains all of the mapping sonar electronics, and the main vehicle control and acoustic communications electronics are housed in a separate glass ball. The Mapping AUV is powered by three 2 kWhr Li-polymer batteries, providing an expected mission duration of 12 hours at a typical speed of 1.5 m/s. The assembled package is rated to 6000 m depth, allowing MBARI to conduct high-resolution mapping of the deep-ocean seafloor. Initial at-sea testing commenced in May 2004 using the subbottom profiler and 100 kHz sidescan. The sonar package will also be mountable on ROV Ventana, allowing surveys at altitudes < 10 m at topographically challenging sites. The MBARI Seafloor Mapping team is now working towards integration of the multibeam sonar and towards achieving regular operations during 2005.


title: Autonomous Automobile Trajectory Tracking for Off-Road Driving: Controller Design, Experimental Validation and Racing

abstract: This paper presents a nonlinear control law for an automobile to autonomously track a trajectory, provided in real-time, on rapidly varying, off-road terrain. Existing methods can suffer from a lack of global stability, a lack of tracking accuracy, or a dependence on smooth road surfaces, any one of which could lead to the loss of the vehicle in autonomous off-road driving. This work treats automobile trajectory tracking in a new manner, by considering the orientation of the front wheels - not the vehicle's body - with respect to the desired trajectory, enabling collocated control of the system. A steering control law is designed using the kinematic equations of motion, for which global asymptotic stability is proven. This control law is then augmented to handle the dynamics of pneumatic tires and of the servo-actuated steering wheel. To control vehicle speed, the brake and throttle are actuated by a switching proportional integral (PI) controller. The complete control system consumes a negligible fraction of a computer's resources. It was implemented on a Volkswagen Touareg, "Stanley", the Stanford Racing Team's entry in the DARPA Grand Challenge 2005, a 132 mi autonomous off-road race. Experimental results from Stanley demonstrate the ability of the controller to track trajectories between obstacles, over steep and wavy terrain, through deep mud puddles, and along cliff edges, with a typical root mean square (RMS) crosstrack error of under 0.1 m. In the DARPA National Qualification Event 2005, Stanley was the only vehicle out of 40 competitors to not hit an obstacle or miss a gate, and in the DARPA Grand Challenge 2005 Stanley had the fastest course completion time.


title: ApproxEye: Enabling approximate computation reuse for microrobotic computer vision

abstract: Aiming at real-life problems, microrobotic systems have gained more and more attention. However, limited achievable performance of microrobotic system prevents it from carrying out complex tasks. Current research work propose customize designs for different applications and incorporate dedicated accelerator for high energy efficiency. However, not only such techniques require significant manual effort and expertise for specified applications, but also the accelerator itself dictates unnegligible amount of chip resources. So in this paper we propose ApproxEye, a partial approximate computation reuse framework to accelerate microrobotic computer vision. Leveraging computation locality, ApproxEye reuses previous “similar” computations to reduce redundant computations. To squeeze every piece of computation reuse opportunity, ApproxEye proposes to 1) heuristically define optimal reuse granularity and 2) apply adaptive reuse requirements for different computations. Moreover, to reduce latency of computation reuse, ApproxEye tailors a parallel implemented search scheme for approximate computation reuse. Experimental results show ApproxEye could effectively exploit the potential of computation reuse and achieve 57.05% speedup on average.


title: Quality of service for workflows and web service processes

abstract: Workflow management systems (WfMSs) have been used to support various types of business processes for more than a decade now. In workflows or Web processes for e-commerce and Web service applications, suppliers and customers define a binding agreement or contract between the two parties, specifying Quality of Service (QoS) items such as products or services to be delivered, deadlines, quality of products, and cost of services. The management of QoS metrics directly impacts the success of organizations participating in e-commerce. Therefore, when services or products are created or managed using workflows or Web processes, the underlying workflow engine must accept the specifications and be able to estimate, monitor, and control the QoS rendered to customers. In this paper, we present a predictive QoS model that makes it possible to compute the quality of service for workflows automatically based on atomic task QoS attributes. We also present the implementation of our QoS model for the METEOR workflow system. We describe the components that have been changed or added, and discuss how they interact to enable the management of QoS.


title: Love-related changes in the brain: a resting-state functional magnetic resonance imaging study

abstract: Romantic love is a motivational state associated with a desire to enter or maintain a close relationship with a specific other person. Studies with functional magnetic resonance imaging (fMRI) have found activation increases in brain regions involved in processing of reward, emotion, motivation when romantic lovers view photographs of their partners. However, not much is known on whether romantic love affects the brain’s functional architecture during rest. In the present study, resting state functional magnetic resonance imaging (rsfMRI) data was collected to compare the regional homogeneity (ReHo) and functional connectivity (FC) across a lover group (LG, N=34, currently intensely in love), ended-love group (ELG, N=34, romantic relationship ended recently), and single group (SG, N=32, never fallen in love).The results showed that:1) ReHo of the left dorsal anterior cingulate cortex (dACC) was significantly increased in the LG (in comparison to the ELG and the SG); 2) ReHo of the left dACC was positively correlated with length of time in love in the LG, and negatively correlated with the lovelorn duration since breakup in the ELG; 3) functional connectivity (FC) within the reward, motivation, and emotion network (dACC, insula, caudate, amygdala and nucleus accumbens) and the social cognition network (temporo-parietal junction (TPJ), posterior cingulate cortex (PCC), medial prefrontal cortex (MPFC), inferior parietal, precuneus and temporal lobe) was significantly increased in the LG (in comparison to the ELG and SG); 4) in most regions within both networks FC was positively correlated with the love duration in the LG but negatively correlated with the lovelorn duration in the ELG. This study provides first empirical evidence of love-related alterations of brain functional architecture. The results shed light on the underlying neural mechanisms of romantic love, and demonstrate the possibility of applying a resting state approach for investigating romantic love.


title: SLAM Family Receptors Distinguish Hematopoietic Stem and Progenitor Cells and Reveal Endothelial Niches for Stem Cells

abstract: Summary To improve our ability to identify hematopoietic stem cells (HSCs) and their localization in vivo, we compared the gene expression profiles of highly purified HSCs and non-self-renewing multipotent hematopoietic progenitors (MPPs). Cell surface receptors of the SLAM family, including CD150, CD244, and CD48, were differentially expressed among functionally distinct progenitors. HSCs were highly purified as CD150 + CD244 − CD48 − cells while MPPs were CD244 + CD150 − CD48 − and most restricted progenitors were CD48 + CD244 + CD150 − . The primitiveness of hematopoietic progenitors could thus be predicted based on the combination of SLAM family members they expressed. This is the first family of receptors whose combinatorial expression precisely distinguishes stem and progenitor cells. The ability to purify HSCs based on a simple combination of SLAM receptors allowed us to identify HSCs in tissue sections. Many HSCs were associated with sinusoidal endothelium in spleen and bone marrow, though some HSCs were associated with endosteum. HSCs thus occupy multiple niches, including sinusoidal endothelium in diverse tissues.


title: 0.3 V supply, 17 ppm/°C 3-transistor picowatt voltage reference

abstract: In this work a novel resistorless MOSFET 3-transistor voltage reference that operates in the picowatt range and occupies very small area is proposed. The circuit is based on a self-cascode structure that is biased in subthreshold condition using the leakage current provided by a reverse biased MOSFET diode. Its electrical behavior is analytically described and a design methodology is presented to allow the transistors sizing for optimal temperature compensation. Simulation results for a standard 130 nm CMOS process are presented to validated the proposed circuit topology. A reference voltage of 85 mV is obtained with a temperature coefficient (TC) of 17.4 ppm/°C and consuming only 7 pW under 0.3 V of power supply at room temperature. Monte Carlo analysis shows that the reference voltage σ/μ< 3.3% and that 90% of the samples present TC<50 ppm/°C without trimming.


title: Learning in non-stationary environments with class imbalance

abstract: Learning in non-stationary environments is an increasingly important problem in a wide variety of real-world applications. In non-stationary environments data arrives incrementally, however the underlying generating function may change over time. In addition to the environments being non-stationary, they also often exhibit class imbalance. That is one class (the majority class) vastly outnumbers the other class (the minority class). This combination of class imbalance with non-stationary environments poses significant and interesting practical problems for classification. To overcome these issues, we introduce a novel instance selection mechanism, as well as provide a modification to the Heuristic Updatable Weighted Random Subspaces (HUWRS) method for the class imbalance problem. We then compare our modifications of HUWRS (called HUWRS.IP) to other state of the art algorithms, concluding that HUWRS. IP often achieves vastly superior performance.


title: Automatic Feature Learning for Robust Shadow Detection

abstract: We present a practical framework to automatically detect shadows in real world scenes from a single photograph. Previous works on shadow detection put a lot of effort in designing shadow variant and invariant hand-crafted features. In contrast, our framework automatically learns the most relevant features in a supervised manner using multiple convolutional deep neural networks (ConvNets). The 7-layer network architecture of each ConvNet consists of alternating convolution and sub-sampling layers. The proposed framework learns features at the super-pixel level and along the object boundaries. In both cases, features are extracted using a context aware window centered at interest points. The predicted posteriors based on the learned features are fed to a conditional random field model to generate smooth shadow contours. Our proposed framework consistently performed better than the state-of-the-art on all major shadow databases collected under a variety of conditions.


title: Changes in Adult Attachment Styles in American College Students Over Time A Meta-Analysis

abstract: The current article examines changes over time in a commonly used measure of adult attachment style. A cross-temporal meta-analysis was conducted on 94 samples of American college students (total N = 25,243, between 1988 and 2011) who chose the most representative description of four possible attachment styles (Secure, Dismissing, Preoccupied, and Fearful) on the Relationship Questionnaire. The percentage of students with Secure attachment styles has decreased in recent years (1988: 48.98%; 2011: 41.62%), whereas the percentage of students with Insecure attachment styles (sum of Dismissing, Preoccupied, Fearful) has increased in recent years (1988: 51.02%; 2011: 58.38%). The percentage of students with Dismissing attachment styles has increased over time (1988: 11.93%; 2011: 18.62%), even after controlling for age, gender, race, and publication status. Positive views of others have declined across the same time period. We discuss possible implications and explanations for these changes.


title: Machine Learning for Document Structure Recognition

abstract: The backbone of the information age is digital information which may be searched, accessed, and transferred instantaneously. Therefore the digitization of paper documents is extremely interesting. This chapter describes approaches for document structure recognition detecting the hierarchy of physical components in images of documents, such as pages, paragraphs, and figures, and transforms this into a hierarchy of logical components, such as titles, authors, and sections. This structural information improves readability and is useful for indexing and retrieving information contained in documents. First we present a rule-based system segmenting the document image and estimating the logical role of these zones. It is extensively used for processing newspaper collections showing world-class performance. In the second part we introduce several machine learning approaches exploring large numbers of interrelated features. They can be adapted to geometrical models of the document structure, which may be set up as a linear sequence or a general graph. These advanced models require far more computational resources but show a better performance than simpler alternatives and might be used in future.


title: Amigo: proximity-based authentication of mobile devices

abstract: Securing interactions between devices that do not know each other a priori is an important and challenging task. We present Amigo, a technique to authenticate co-located devices using knowledge of their shared radio environment as proof of physical proximity. We present evaluation results that show that our technique is robust against a range of passive and active attacks. The key advantages of our technique are that it does not require any additional hardware to be present on the devices beyond the radios that are already used for communication, it does not require user involvement to verify the validity of the authentication process, and it is not vulnerable to eavesdropping.


title: Partition SRAM and RRAM based synaptic arrays for neuro-inspired computing

abstract: Memory array architectures have been proposed for on-chip acceleration of weighted sum and weight update in the neuro-inspired machine learning algorithms. As the learning algorithms usually operate on a large weight matrix size, an efficient mapping of a large weight matrix on the hardware accelerator may require partitioning the matrix into multiple sub-arrays. In this work, we built a circuit-level macro simulator to evaluate the performance of partitioning a 512×512 weight matrix into the SRAM and RRAM based accelerators. Generally, with more partitioning and finer granularity of the array architecture, the read/write latency and the dynamic read/write energy will decrease due to an increased computation parallelism at the expense of larger area and leakage power, as shown in the case of the SRAM accelerator. However, the RRAM accelerator does not improve the read latency and read energy beyond a certain partition point because the overhead due to multiple intermediate stages of adders and registers will dominate.


title: Scrum Metrics for Hyperproductive Teams: How They Fly like Fighter Aircraft

abstract: Scrum Teams use lightweight tools like Story Points, the Burn down chart, and Team Velocity. While essential, these tools alone provide insufficient information to maintain a high energy state that yields Hyper productivity. More data is required, but data collection itself can slow Teams. This effect must be avoided when productivity is the primary marker of success. Here we describe nine metrics that can develop and sustain Hyper productive Teams -- Velocity, Work Capacity, Focus Factor, Percentage of Adopted Work, Percentage of Found Work, Accuracy of Estimation, Accuracy of Forecast, Targeted Value Increase, Success at Scale, and the Win/Loss Record of the Team. The unique contribution of this paper is to demonstrate how a light touch and lightweight strategy can be used to compare Teams with different Story Point reference scales.


title: Review of optical flow technique for moving object detection

abstract: Object detection in a video is a challenging task in the field of image processing. Some applications of the domain are Human Machine Interaction (HMI), Security and Surveillance, Supplemented Authenticity, Traffic Monitoring on Roads, Medicinal Imaging etc. There happens to be a number of methods available for object detection. Each of the method has some constraints on the kind of application it has been used for. This paper presents one of such method which is termed as Optical Flow technique. This technique is found to be more robust and efficient for moving object detection and the same has been shown by an experiment in the paper. Applying optical flow to an image gives flow vectors of the points corresponding to the moving objects. Next part of marking the required moving object of interest counts to the post processing. Post processing is the legitimate contribution of the paper for moving object detection problem. This here is discussed as Blob Analysis. It is tested on datasets available online, real time videos and also on videos recorded manually. The results show that the moving objects are successfully detected using optical flow technique and the required post processing.


title: Applying GIS and Machine Learning Methods to Twitter Data for Multiscale Surveillance of Influenza

abstract: Traditional methods for monitoring influenza are haphazard and lack fine-grained details regarding the spatial and temporal dynamics of outbreaks. Twitter gives researchers and public health officials an opportunity to examine the spread of influenza in real-time and at multiple geographical scales. In this paper, we introduce an improved framework for monitoring influenza outbreaks using the social media platform Twitter. Relying upon techniques from geographic information science (GIS) and data mining, Twitter messages were collected, filtered, and analyzed for the thirty most populated cities in the United States during the 2013–2014 flu season. The results of this procedure are compared with national, regional, and local flu outbreak reports, revealing a statistically significant correlation between the two data sources. The main contribution of this paper is to introduce a comprehensive data mining process that enhances previous attempts to accurately identify tweets related to influenza. Additionally, geographical information systems allow us to target, filter, and normalize Twitter messages.


title: Relationships between mindfulness practice and levels of mindfulness, medical and psychological symptoms and well-being in a mindfulness-based stress reduction program

abstract: Relationships were investigated between home practice of mindfulness meditation exercises and levels of mindfulness, medical and psychological symptoms, perceived stress, and psychological well-being in a sample of 174 adults in a clinical Mindfulness-Based Stress Reduction (MBSR) program. This is an 8- session group program for individuals dealing with stress-related problems, illness, anxiety, and chronic pain. Participants completed measures of mindfulness, perceived stress, symptoms, and well-being at pre- and post-MBSR, and monitored their home practice time throughout the intervention. Results showed increases in mindfulness and well-being, and decreases in stress and symptoms, from pre- to post-MBSR. Time spent engaging in home practice of formal meditation exercises (body scan, yoga, sitting meditation) was significantly related to extent of improvement in most facets of mindfulness and several measures of symptoms and well-being. Increases in mindfulness were found to mediate the relationships between formal mindfulness practice and improvements in psychological functioning, suggesting that the practice of mindfulness meditation leads to increases in mindfulness, which in turn leads to symptom reduction and improved well-being


title: Relationship between general or specific immunoreactivity and prognosis in postoperative patients with hepatocellular carcinoma

abstract: The levels of a variety of immunological parameters were examined in 203 preoperative patients with hepatocellular carcinoma (HCC) at various stages (I–IV). The changes in the peripheral blood lymphocyte (PBL) count, the serum level of immunosuppressive acidic protein and the degree of the skin reaction to purified protein derivative were associated significantly with the stage of HCC progression. However, the percentages of lymphocyte subsets, mitogenic responsiveness of PBL and serum immunoglobulin concentration remained at the levels of stage I. Further study demonstrated that in patients undergoing hepatic artery ligation, there were statistically significant correlations between the PBL count, immunosuppressive acidic protein concentration and intensity of the skin reaction to purified protein derivative, assayed 1 month after surgery, and the prognosis. HCC-specific immunity was examined in 34 patients treated by hepatic resection or hepatic artery ligation using in vitro responses of PBL to HCC extracts (ATS test). This test was performed using culture medium containing added arginine. None of the PBL from the patients showed a positive response to allogeneic HCC extracts, but the PBL from 12 patients (9 hepatic resections, 3 hepatic artery ligations) were stimulated significantly (SI ⩾ 2.5) with autologous HCC extracts. In 7 of 9 hepatic resection patients who were positive in the ATS test, tumor recurrence was identified. Statistical analysis indicated that the ATS test result was significantly correlated with tumor recurrence in hepatic resection patients. Autologous-PBL-stimulating activities were isolated in a fraction at pH 8.3 and in fractions at pH 6.7–7.0 by chromatofocusing of the crude extract. Although identification of the HCC-specific antigen remains to be done, use of the above fractions may simplify the ATS test procedure and improve its sensitivity.


title: Information Security in Big Data: Privacy and Data Mining

abstract: The growing popularity and development of data mining technologies bring serious threat to the security of individual,'s sensitive information. An emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied in recent years. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. Current studies of PPDM mainly focus on how to reduce the privacy risk brought by data mining operations, while in fact, unwanted disclosure of sensitive information may also happen in the process of data collecting, data publishing, and information (i.e., the data mining results) delivering. In this paper, we view the privacy issues related to data mining from a wider perspective and investigate various approaches that can help to protect sensitive information. In particular, we identify four different types of users involved in data mining applications, namely, data provider, data collector, data miner, and decision maker. For each type of user, we discuss his privacy concerns and the methods that can be adopted to protect sensitive information. We briefly introduce the basics of related research topics, review state-of-the-art approaches, and present some preliminary thoughts on future research directions. Besides exploring the privacy-preserving approaches for each type of user, we also review the game theoretical approaches, which are proposed for analyzing the interactions among different users in a data mining scenario, each of whom has his own valuation on the sensitive information. By differentiating the responsibilities of different users with respect to security of sensitive information, we would like to provide some useful insights into the study of PPDM.


title: Penumbra: automatically identifying failure-relevant inputs using dynamic tainting

abstract: Most existing automated debugging techniques focus on reducing the amount of code to be inspected and tend to ignore an important component of software failures: the inputs that cause the failure to manifest. In this paper, we present a new technique based on dynamic tainting for automatically identifying subsets of a program's inputs that are relevant to a failure. The technique (1) marks program inputs when they enter the application, (2) tracks them as they propagate during execution, and (3) identifies, for an observed failure, the subset of inputs that are potentially relevant for debugging that failure. To investigate feasibility and usefulness of our technique, we created a prototype tool, PENUMBRA, and used it to evaluate our technique on several failures in real programs. Our results are promising, as they show that PENUMBRA can point developers to inputs that are actually relevant for investigating a failure and can be more practical than existing alternative approaches.


title: First trimester caesarean scar ectopic pregnancy evaluation using MRI.

abstract: AIM ::: To determine the features of caesarean scar ectopic pregnancy (CSP) by using magnetic resonance imaging (MRI) in the first trimester. ::: ::: ::: MATERIALS AND METHODS ::: Thirty-nine women with CSP in the first trimester were initially selected and underwent transvaginal ultrasound and MRI examinations. Thirty-five patients with CSP were finally included in this study. ::: ::: ::: RESULTS ::: The CSPs were categorized into three groups: type I, in which a thin-walled diverticulum is present at the caesarean section scar (CSS) defect and the gestational sac (GS) is embedded in the diverticulum; type II, in which a thin-walled diverticulum is present at the CSS defect and the GS is partially embedded in the diverticulum; type III, in which a niche is present in the CSS defect and the GS is mainly embedded in the isthmus. Types I, II, and III CSP occurred in 40, 46, and 14% of the women, respectively. There was no significant difference between the three types in the minimum thickness of the CSS defect. In types I and II, there was a positive correlation in the maximum inlet diameter of the CSS defect and the approximate area of the GS. ::: ::: ::: CONCLUSION ::: Using MRI, most CSPs present as a diverticulum at the CSS defect, and the CSS defect becomes weaker with the growth of the GS. MRI showed detailed features of the CSP.


title: Decentralized operation framework for hybrid AC/DC microgrid

abstract: This paper proposes a methodology to attain the optimal generation scheduling of hybrid AC/DC microgrids. The hybrid microgrid consists of AC and DC networks with respective generation and demand resources that are connected by bi-directional AC/DC converters. The proposed methodology leverages decentralized optimization framework that uses the dispatch of the AC/DC converter to facilitate the coordinated operation of AC and DC networks. The presented framework utilizes an iterative approach to determine the optimal operation schedule for the hybrid AC/DC microgrid using distinct optimization problems. The results illustrate the effectiveness of the presented approach for operation planning of hybrid AC/DC microgrids.


Dress Fashionably: Learn Fashion Collocation With Deep Mixed-Category Metric Learning


title: THE MULTI-COMPONENT MODEL OF WORKING MEMORY: EXPLORATIONS IN EXPERIMENTAL COGNITIVE PSYCHOLOGY

abstract: Abstract There are a number of ways one can hope to describe and explain cognitive abilities, each of them contributing a unique and valuable perspective. Cognitive psychology tries to develop and test functional accounts of cognitive systems that explain the capacities and properties of cognitive abilities as revealed by empirical data gathered by a range of behavioral experimental paradigms. Much of the research in the cognitive psychology of working memory has been strongly influenced by the multi-component model of working memory [Baddeley AD, Hitch GJ (1974) Working memory. In: Recent advances in learning and motivation, Vol. 8 (Bower GA, ed), pp 47–90. New York: Academic Press; Baddeley AD (1986) Working memory. Oxford, UK: Clarendon Press; Baddeley A. Working memory: Thought and action. Oxford: Oxford University Press, in press]. By expanding the notion of a passive short-term memory to an active system that provides the basis for complex cognitive abilities, the model has opened up numerous questions and new lines of research. In this paper we present the current revision of the multi-component model that encompasses a central executive, two unimodal storage systems: a phonological loop and a visuospatial sketchpad, and a further component, a multimodal store capable of integrating information into unitary episodic representations, termed episodic buffer. We review recent empirical data within experimental cognitive psychology that has shaped the development of the multicomponent model and the understanding of the capacities and properties of working memory. Research based largely on dual-task experimental designs and on neuropsychological evidence has yielded valuable information about the fractionation of working memory into independent stores and processes, the nature of representations in individual stores, the mechanisms of their maintenance and manipulation, the way the components of working memory relate to each other, and the role they play in other cognitive abilities. With many questions still open and new issues emerging, we believe that the multicomponent model will continue to stimulate research while providing a comprehensive functional description of working memory.


title: Automated test case generation for FBD programs implementing reactor protection system software

abstract: Automated and effective testing for function block diagram FBD programs has become an important issue, as FBD is increasingly used in implementing safety-critical systems. This work describes an automated test case generation technique for FBD programs and its associated tool-FBDTester. Given an FBD program and desired test coverage criteria, FBDTester generates test requirements and invokes the Satisfiability Modulo Theories solver iteratively to derive a set of test cases. An industrial case study using reactor protection system software shows that the automatically generated test suites detected at least 82% of the known faults, whereas manually generated test cases only detected approximately 35%. Mutation analysis revealed that the automatically generated test suites substantially outperformed manually generated ones. Although test sequence generation requires some manual effort in the current FBDTester, it is apparent that the proposed approach significantly improves the efficiency and the reliability of FBD testing. Copyright © 2014 John Wiley & Sons, Ltd.


title: Keyword and Keyphrase Extraction Using Centrality Measures on Collocation Networks

abstract: Keyword and keyphrase extraction is an important problem in natural language processing, with applications ranging from summarization to semantic search to document clustering. Graph-based approaches to keyword and keyphrase extraction avoid the problem of acquiring a large in-domain training corpus by applying variants of PageRank algorithm on a network of words. Although graph-based approaches are knowledge-lean and easily adoptable in online systems, it remains largely open whether they can benefit from centrality measures other than PageRank. In this paper, we experiment with an array of centrality measures on word and noun phrase collocation networks, and analyze their performance on four benchmark datasets. Not only are there centrality measures that perform as well as or better than PageRank, but they are much simpler (e.g., degree, strength, and neighborhood size). Furthermore, centrality-based methods give results that are competitive with and, in some cases, better than two strong unsupervised baselines.


abstract: © 2012 Savaneviciene and Stankeviciute, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Human Resource Management and Performance: From Practices Towards Sustainable Competitive Advantage


title: Compute Pairwise Manhattan Distance and Pearson Correlation Coefficient of Data Points with GPU

abstract: Graphics processing units (GPUs) are powerful computational devices tailored towards the needs of the 3-D gaming industry for high-performance, real-time graphics engines. Nvidia Corporation released a new generation of GPUs designed for general-purpose computing in 2006, and it released a GPU programming language called CUDA in 2007. The DNA microarray technology is a high throughput tool for assaying mRNA abundance in cell samples. In data analysis, scientists often apply hierarchical clustering of the genes, where a fundamental operation is to calculate all pairwise distances. If there are n genes, it takes O(n^2) time. In this work, GPUs and the CUDA language are used to calculate pairwise distances. For Manhattan distance, GPU/CUDA achieves a 40 to 90 times speed-up compared to the central processing unit implementation; for Pearson correlation coefficient, the speed-up is 28 to 38 times.


title: Research on identification of moving targets in space

abstract: In order to find man-made spacecraft, this paper proposes a method of visual target detection and identification. In this ::: method, because of the same relative position of the stars, by using image registration and intelligent frame difference ::: method, the non-moving stars are removed. By distinguishing of the different profiles of the natural and man-made star, ::: the man-made stars are separated from nature stars. Therefore, the goal of man-made satellites dynamic recognition is ::: achieved. Experimental results show that the proposed method compared with traditional methods, operations reduced by ::: about 30%, reliability has been improved. And it has practical significance in engineering practice.


title: Exploring product design quality control and assurance under both traditional and crowdsourcing-based design environments

abstract: Small and medium-sized enterprises face the challenges that they do not have enough employees and related resources to produce high-quality products with limited budget and time. The emergence of crowdsourcing provides an opportunity for them to improve their products by leveraging the wisdom of a large community of crowds, including their potential customers. With this new opportunity, product design could be conducted partially in a traditional design environment (in-house design) and partially in a crowdsourcing environment. This article focuses on product design stages to investigate what key factors affect product design quality and how it can be controlled and assured. First, we define the concept of product design quality and then identify its attributes and sub-attributes. Second, we separately survey key factors affecting product design quality in traditional and crowdsourcing-based design environments, quality control approaches/theories and quality assurance policies in traditional design environment. Third, a comparison of product design quality issues between the traditional and crowdsourcing-based design environments is progressed focusing on various aspects influencing product design activity quality. Finally, we discuss product design quality control approaches and quality assurance policies, quality control challenges and corresponding solutions in crowdsourcing-based design environment.


title: Attacks on Anonymization-Based Privacy-Preserving: A Survey for Data Mining and Data Publishing

abstract: Data mining is the extraction of vast interesting patterns or ::: knowledge from huge amount of data. The initial idea of privacy-preserving ::: data mining PPDM was to extend traditional data mining techniques to work with ::: the data modified to mask sensitive information. The key issues were how to ::: modify the data and how to recover the data mining result from the modified ::: data. Privacy-preserving data mining considers the problem of running data mining ::: algorithms on confidential data that is not supposed to be revealed even to the ::: party running the algorithm. In contrast, privacy-preserving ::: data publishing (PPDP) may not necessarily be tied to a specific data mining ::: task, and the data mining task may be unknown at the time of data publishing. ::: PPDP studies how to transform raw data into a version that is immunized against ::: privacy attacks but that still supports effective data mining tasks. Privacy-preserving ::: for both data mining (PPDM) and data publishing (PPDP) has become increasingly ::: popular because it allows sharing of privacy sensitive data for analysis ::: purposes. One well studied approach is the k-anonymity model [1] ::: which in turn led to other models such as confidence bounding, l-diversity, ::: t-closeness, (α,k)-anonymity, etc. In particular, all known mechanisms try to ::: minimize information loss and such an attempt provides a loophole for attacks. ::: The aim of this paper is to present a survey for most of the common attacks ::: techniques for anonymization-based PPDM & PPDP and explain their effects ::: on Data Privacy.


title: Intelligent robotic system for autonomous crack detection and caracterization in concrete tunnels

abstract: This paper presents a complete report for the finished ROBO-SPECT European FP7 project, funded under the ICT-2013.2.2 programme on Robotics use cases & Accompanying measures, describing a completely autonomous tunnel inspection robotic system. The document includes a brief introduction about the motivation of the project, defining advantages of robotic inspection over manual methods. The paper contains descriptions of the different components of the robot, including a mobile vehicle, extended crane, high precision robotic arm, computer vision system and ultrasonic sensors. Some details about the software implemented will be discussed as well. Finally, experimental evidence and details on validation in a real motorway tunnel with ongoing traffic will be provided.