Flicker-Free Electrolytic Capacitor-Less Universal Input Offline LED Driver With PFC

Recent developments in improving lighting efficiency and cost reduction of LEDs have made them suitable alternatives to the current lighting systems. In this paper, a novel offline structure is proposed to drive LEDs. The proposed circuit has a high-input power factor, high efficiency, a long lifetime, and it produces no flicker. To increase the lifetime of the converter, the proposed circuit does not include any electrolytic capacitors in the power stage. The proposed circuit consists of a transition mode flyback converter in order to improve power factor. Additionally, a buck converter is added to the third winding of the flyback transformer in order to create two parallel paths for the electrical power to feed the output load. DC power reaches the load through one stage (flyback) and ac power reaches the load through two stages of conversion (flyback + buck). Therefore, in the proposed one-and-a-half stage circuit, the efficiency is improved compared to a regular two-stage circuit. Although the proposed structure has some output current ripple, it is low enough (less than 8%) that the structure can be rendered flicker free, as shall be discussed. Principles of operation and design equations are presented as well as experimental results for a 700 mA/20 W universal input prototype.


Large air gap coupler for inductive charger

A novel magnetic coupler of large r r gap IS presented It IS developed for the eleehlo relucle s aumahc mduchve charger The ncw induchve coupler proposed hqe has sUmcient mung ~oducmnce ven rf I has a large a s gap Calculated exrung inductance 18 40 pH at 2 nuns of w d m g and 5 mm ay gap, whch apees well with measured value In order to assess the gmerahon of heat c w e d by the eddy cment, the magneuc flux densrhes in the lnduchve chaser and also a flat non plate, to wbch the inductive charger IS attached, Bre calculated The conyersion efXciency wth the coupler and a MOSFETs full-bndge inverier of IW lrHq IS 97% at 8 3 kW output


Implementing Remote Procedure Calls

Remote procedure calls (RPC) appear to be a useful paradig m for providing communication across a network between programs written in a high-level language. This paper describes a package providing a remote procedure call facility, the options that face the designer of such a package, and the decisions ~we made. We describe the overall structure of our RPC mechanism, our facilities for binding RPC clients, the transport level communication protocol, and some performance measurements. We include descriptioro~ of some optimizations used to achieve high performance and to minimize the load on server machines that have many clients.


Somatization vs . Psychologization of Emotional Distress : A Paradigmatic Example for Cultural Psychopathology

This paper describes the developing area of cultural psychopathology, an interdisciplinary field of study focusing on the ways in which cultural factors contribute to the experience and expression of psychological distress. We begin by outlining two approaches, often competing, in order to provide a background to some of the issues that complicate the field. The main section of the paper is devoted to a discussion of depression in Chinese culture as an example of the types of questions that can be studied. Here, we start with a review of the epidemiological literature, suggesting low rates of depression in China, and move to the most commonly cited explanation, namely that Chinese individuals with depression present this distress in a physical way. Different explanations of this phenomenon, known as somatization, are explored and reconceptualized according to an increasingly important model for cross-cultural psychologists: the cultural constitution of the self. We close by discussing some of the contributions, both theoretical and methodological, that can be made by cross-cultural psychologists to researchers in cultural psychopathology. Creative Commons License This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License. This article is available in Online Readings in Psychology and Culture: https://scholarworks.gvsu.edu/orpc/vol10/iss2/3


Privacy-Aware Tag Recommendation for Image Sharing

Image tags are very important for indexing, sharing, searching, and surfacing images with private content that needs protection. As the tags are at the sole discretion of users, they tend to be noisy and incomplete. In this paper, we present a privacy-aware approach to automatic image tagging, which aims at improving the quality of user annotations, while also preserving the images' original privacy sharing patterns. Precisely, we recommend potential tags for each target image by mining privacy-aware tags from the most similar images of the target image obtained from a large collection. Experimental results show that privacy-aware approach is able to predict accurate tags that can improve the performance of a downstream application on image privacy prediction. Crowd-sourcing predicted tags exhibit the quality of the recommended tags.


A study to evaluate the safety, tolerability, and efficacy of brodalumab in subjects with rheumatoid arthritis and an inadequate response to methotrexate.

OBJECTIVE
To evaluate the efficacy and safety of brodalumab, a human monoclonal antibody inhibitor of the interleukin 17 receptor, in subjects with rheumatoid arthritis (RA).

METHODS
Patients (n = 252) with inadequate response to methotrexate (MTX) were randomized to receive subcutaneous injections of brodalumab (70 mg, 140 mg, or 210 mg) or placebo. The primary endpoint was the American College of Rheumatology 50% response (ACR50) at Week 12.

RESULTS
Demographics and baseline characteristics were generally balanced among treatment groups. At Week 12, ACR50 occurred in 16% (70 mg), 16% (140 mg), 10% (210 mg), and 13% (placebo; all nonsignificant vs placebo) of subjects. No significant treatment effects were observed for the secondary endpoints, including ACR20, ACR70, and Disease Activity Score in 28 joints. Incidences of all adverse events (AE), including serious AE (SAE), were similar across treatment groups. A total of 7 subjects reported SAE during the study (2 in the placebo group and 5 in the brodalumab groups), none of which was treatment related. There was 1 death (cardiopulmonary failure) ∼1 week after the last dose in the 140 mg group.

CONCLUSION
Our study failed to find evidence of meaningful clinical efficacy with brodalumab treatment in subjects with RA who had an inadequate response to MTX. These preliminary results do not support further evaluation of brodalumab as a treatment for RA. Clinicaltrials.gov number: NCT00950989.


Alternative seating for young children with Autism Spectrum Disorder: effects on classroom behavior.

A single subject, withdrawal design was used to investigate the effects of therapy balls as seating on engagement and in-seat behavior of young children with Autism Spectrum Disorder (ASD). In addition, social validity was assessed to evaluate teachers' opinions regarding the intervention. During baseline and withdrawal (A phases) participants used their typical classroom seating device (chair, bench or carpet square). During the intervention (B phases) participants sat on therapy balls. Results indicated substantial improvements in engagement and in-seat behavior when participants were seated on therapy balls. Social validity findings indicated that the teachers' preferred the therapy balls. This study suggests therapy balls as classroom seating may facilitate engagement and in-seat behavior and create opportunities to provide effective instruction.


Automatic Analysis of Rhythmic Poetry with Applications to Generation and Translation

We employ statistical methods to analyze, generate, and translate rhythmic poetry. We first apply unsupervised learning to reveal word-stress patterns in a corpus of raw poetry. We then use these word-stress patterns, in addition to rhyme and discourse models, to generate English love poetry. Finally, we translate Italian poetry into English, choosing target realizations that conform to desired rhythmic patterns.


In vivo 3-dimensional analysis of scapular kinematics: comparison of dominant and nondominant shoulders.

BACKGROUND
Alterations in scapular motion frequently are seen in association with various shoulder disorders. It is common clinically to compare the pathological shoulder with the contralateral shoulder, in spite of arm dominance, to characterize the disorder. However, there have been few articles that test the underlying assumption that dominant and nondominant shoulders exhibit comparable dynamic kinematics. The purpose of this study was to compare the 3-dimensional (3-D) scapular kinematics of dominant and nondominant shoulders during dynamic scapular plane elevation using 3-D-2-D (2-dimensional) registration techniques.

MATERIALS AND METHODS
Twelve healthy males with a mean age of 32 years (range, 27-36) were enrolled in this study. Bilateral fluoroscopic images during scapular plane elevation and lowering were taken, and CT-derived 3-D bone models were matched with the silhouette of the bones in the fluoroscopic images using 3-D-2-D registration techniques. Angular values of the scapula and scapulohumeral rhythm were compared between dominant and nondominant shoulders with statistical analysis.

RESULTS
There was a significant difference in upward rotation angles between paired shoulders (P < .001), while significant differences were not found in the other angular values and scapulohumeral rhythm. The dominant scapulae were 10° more downwardly rotated at rest and 4° more upwardly rotated during elevation compared to the nondominant scapulae.

DISCUSSION/CONCLUSION
Scapular motion was not the same between dominant and nondominant arms in healthy subjects. The dominant scapula was rotated further downward at rest and reached greater upward rotation with abduction. These differences should be considered in clinical assessment of shoulder pathology.


A Low-Power Text-Dependent Speaker Verification System with Narrow-Band Feature Pre-Selection and Weighted Dynamic Time Warping

To fully enable voice interaction in wearable devices, a system requires low-power, customizable voice-authenticated wake-up. Existing speaker-verification (SV) methods have shortcomings relating to power consumption and noise susceptibility. To meet the application requirements, we propose a low-power, text-dependent SV system comprising a sparse spectral feature extraction front-end showing improved noise robustness and accuracy at low power, and a back-end running an improved dynamic time warping (DTW) algorithm that preserves signal envelope while reducing misalignments. Without background noise, the proposed system achieves an equal-errorrate (EER) of 1.1%, compared to 1.4% with a conventional Mel-frequency cepstral coefficients (MFCC)+DTW system and 2.6% with a Gaussian mixture universal background (GMMUBM) based system. At 3dB signal-to-noise ratio (SNR), the proposed system achieves an EER of 5.7%, compared to 13% with a conventional MFCC+DTW system and 6.8% with a GMM-UBM based system. The proposed system enables simple, low-power implementation such that the power consumption of the end-to-end system, which includes a voice activity detector, feature extraction front-end, and back-end decision unit, is under 380 μW.


Fast Method to Optimize RF Bumper Transparency for Wide-Band Automotive Radar

This article describes novel methods to enhance the RF integration of wideband automotive radars. The first part of the paper is introducing the context and the considered scenario. Whereas the second part is focusing on two solutions to improve the radar integration in car chassis environment. The aim is to have good RF performances on wideband operation (76–81 GHz) and on a wide range of angle of incidence for the radar having wide field of view (e.g. Blind spot radar, Pedestrian detection, …).


Unsupervised prediction of citation influences

Publication repositories contain an abundance of information about the evolution of scientific research areas. We address the problem of creating a visualization of a research area that describes the flow of topics between papers, quantifies the impact that papers have on each other, and helps to identify key contributions. To this end, we devise a probabilistic topic model that explains the generation of documents; the model incorporates the aspects of topical innovation and topical inheritance via citations. We evaluate the model's ability to predict the strength of influence of citations against manually rated citations.


Force to Rebalance Control of HRG and Suppression of Its Errors on the Basis of FPGA

A novel design of force to rebalance control for a hemispherical resonator gyro (HRG) based on FPGA is demonstrated in this paper. The proposed design takes advantage of the automatic gain control loop and phase lock loop configuration in the drive mode while making full use of the quadrature control loop and rebalance control loop in controlling the oscillating dynamics in the sense mode. First, the math model of HRG with inhomogeneous damping and frequency split is theoretically analyzed. In addition, the major drift mechanisms in the HRG are described and the methods that can suppress the gyro drift are mentioned. Based on the math model and drift mechanisms suppression method, four control loops are employed to realize the manipulation of the HRG by using a FPGA circuit. The reference-phase loop and amplitude control loop are used to maintain the vibration of primary mode at its natural frequency with constant amplitude. The frequency split is readily eliminated by the quadrature loop with a DC voltage feedback from the quadrature component of the node. The secondary mode response to the angle rate input is nullified by the rebalance control loop. In order to validate the effect of the digital control of HRG, experiments are carried out with a turntable. The experimental results show that the design is suitable for the control of HRG which has good linearity scale factor and bias stability.


Topic-Oriented Exploratory Search Based on an Indexing Network

An exploratory search may be driven by a user's curiosity or desire for specific information. When users investigate unfamiliar fields, they may want to learn more about a particular subject area to increase their knowledge rather than solve a specific problem. This work proposes a topic-oriented exploratory search method that provides browse guidance to users. It allows them to discover new associations and knowledge, and helps them find their interested information and knowledge. Since an exploratory search needs to judge the ability to discover new knowledge, the existing commonly used metrics fail to capture it. This paper thus defines a new set of criteria containing clarity, relevance, novelty, and diversity to analyze the effectiveness of an exploratory search. Experiments are designed to compare results from the proposed method and Google's “search related to ....” The results show that the proposed one is more suitable for learning new associations and discovering new knowledge with highly likely relevance to a query. This work concludes that it is more suitable than Google for an exploratory search.


Word Embedding based Correlation Model for Question/Answer Matching

The large scale of Q&A archives accumulated in community based question answering (CQA) servivces are important information and knowledge resource on the web. Question and answer matching task has been attached much importance to for its ability to reuse knowledge stored in these systems: it can be useful in enhancing user experience with recurrent questions. In this paper, a Word Embedding based Correlation (WEC) model is proposed by integrating advantages of both the translation model and word embedding. Given a random pair of words, WEC can score their co-occurrence probability in Q&A pairs, while it can also leverage the continuity and smoothness of continuous space word representation to deal with new pairs of words that are rare in the training parallel text. An experimental study on Yahoo! Answers dataset and Baidu Zhidao dataset shows this new method’s promising


OpenCL framework for ARM processors with NEON support

The state-of-the-art ARM processors provide multiple cores and SIMD instructions. OpenCL is a promising programming model for utilizing such parallel processing capability because of its SPMD programming model and built-in vector support. Moreover, it provides portability between multicore ARM processors and accelerators in embedded systems. In this paper, we introduce the design and implementation of an efficient OpenCL framework for multicore ARM processors. Computational tasks in a program are implemented as OpenCL kernels and run on all CPU cores in parallel by our OpenCL framework. Vector operations and built-in functions in OpenCL kernels are optimized using the NEON SIMD instruction set. We evaluate our OpenCL framework using 37 benchmark applications. The result shows that our approach is effective and promising.


LEARNING TO SOLVE CIRCUIT-SAT: AN UNSUPERVISED DIFFERENTIABLE APPROACH

Recent efforts to combine Representation Learning with Formal Methods, commonly known as Neuro-Symbolic Methods, have given rise to a new trend of applying rich neural architectures to solve classical combinatorial optimization problems. In this paper, we propose a neural framework that can learn to solve the Circuit Satisfiability problem. Our framework is built upon two fundamental contributions: a rich embedding architecture that encodes the problem structure, and an end-to-end differentiable training procedure that mimics Reinforcement Learning and trains the model directly toward solving the SAT problem. The experimental results show the superior out-of-sample generalization performance of our framework compared to the recently developed NeuroSAT method.


Interaction control of an UAV endowed with a manipulator

In this paper, we present the design, simulation and experimental validation of a control architecture for an unmanned aerial vehicle endowed with a manipulation system and interacting with a remote environment. The goal of this work is to show that the interaction control allows the manipulator to track a desired force, normal to a vertical wall, while still maintaining the possibility of moving on the wall. The control strategy has been implemented and validated in simulations and experiments on the manipulator standalone, i.e., attached to a fixed base, and on the manipulator attached to the aerial vehicle.


Negative pressure wave based pipeline Leak Detection: Challenges and algorithms

Negative pressure wave is a popular method to detect the occurrence and location of leak incidents in oil/gas pipeline. Three core technical challenges and related algorithm are discussed in this paper. The first is data quality. The balance between noise level and locating precision is discussed in filter design. The second one is dynamic slope in anomaly detection, whence a bi-SPC (Static Process Control) algorithms is proposed to make the threshold be adaptive. The third one is the false alarm caused normal working condition changes. Multiple-sensor paring algorithms is presented. With these algorithms, the robustness and locating precision of LDS (Leak Detection System) can be ensured.


Data mining techniques - for marketing, sales, and customer support

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An On-Chip Temperature Sensor With a Self-Discharging Diode in 32-nm SOI CMOS

We report a 39 μm × 27 μm on-chip temperature sensor which uses the temperature-dependent reverse-bias leakage current of a lateral silicon on insulator (SOI) CMOS p-n diode to monitor the thermal profile of a 32-nm microprocessor core. In this sensor, the diode junction capacitance is first charged to a fixed voltage. Subsequently, the diode capacitance is allowed to self-discharge through its temperature-dependent reverse-bias current. Next, by using a time-to-digital-converter circuit, the discharge voltage is converted to a temperature-dependent time pulse, and finally, its width is measured by using a digital counter. This compact temperature sensor demonstrates a 3σ measurement inaccuracy of ±1.95°C across the 5°C-100°C temperature range while consuming only 100 μW from a single 1.65-V supply.


2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text

The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks: a concept extraction task focused on the extraction of medical concepts from patient reports; an assertion classification task focused on assigning assertion types for medical problem concepts; and a relation classification task focused on assigning relation types that hold between medical problems, tests, and treatments. i2b2 and the VA provided an annotated reference standard corpus for the three tasks. Using this reference standard, 22 systems were developed for concept extraction, 21 for assertion classification, and 16 for relation classification. These systems showed that machine learning approaches could be augmented with rule-based systems to determine concepts, assertions, and relations. Depending on the task, the rule-based systems can either provide input for machine learning or post-process the output of machine learning. Ensembles of classifiers, information from unlabeled data, and external knowledge sources can help when the training data are inadequate.


Heterogenous Uncertainty Sampling for Supervised Learning

Uncertainty sampling methods iteratively request class labels for training instances whose classes are uncertain despite the previous labeled instances. These methods can greatly reduce the number of instances that an expert need label. One problem with this approach is that the classifier best suited for an application may be too expensive to train or use during the selection of instances. We test the use of one classifier (a highly efficient probabilistic one) to select examples for training another (the C4.5 rule induction program). Despite being chosen by this heterogeneous approach, the uncertainty samples yielded classifiers with lower error rates than random samples ten times larger.


Information-Based Objective Functions for Active Data Selection

Learning can be made more efficient if we can actively select particularly salient data points. Within a Bayesian learning framework, objective functions are discussed that measure the expected informativeness of candidate measurements. Three alternative specifications of what we want to gain information about lead to three different criteria for data selection. All these criteria depend on the assumption that the hypothesis space is correct, which may prove to be their main weakness.


The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data

BACKGROUND
Physiological noise is one of the major confounds for fMRI. A common class of correction methods model noise from peripheral measures, such as ECGs or pneumatic belts. However, physiological noise correction has not emerged as a standard preprocessing step for fMRI data yet due to: (1) the varying data quality of physiological recordings, (2) non-standardized peripheral data formats and (3) the lack of full automatization of processing and modeling physiology, required for large-cohort studies.

NEW METHODS
We introduce the PhysIO Toolbox for preprocessing of physiological recordings and model-based noise correction. It implements a variety of noise models, such as RETROICOR, respiratory volume per time and heart rate variability responses (RVT/HRV). The toolbox covers all intermediate steps - from flexible read-in of data formats to GLM regressor/contrast creation - without any manual intervention.

RESULTS
We demonstrate the workflow of the toolbox and its functionality for datasets from different vendors, recording devices, field strengths and subject populations. Automatization of physiological noise correction and performance evaluation are reported in a group study (N=35).

COMPARISON WITH EXISTING METHODS
The PhysIO Toolbox reproduces physiological noise patterns and correction efficacy of previously implemented noise models. It increases modeling robustness by outperforming vendor-provided peak detection methods for physiological cycles. Finally, the toolbox offers an integrated framework with full automatization, including performance monitoring, and flexibility with respect to the input data.

CONCLUSIONS
Through its platform-independent Matlab implementation, open-source distribution, and modular structure, the PhysIO Toolbox renders physiological noise correction an accessible preprocessing step for fMRI data.


Eratosthenes sieve based key-frame extraction technique for event summarization in videos

The rapid growth of video data demands both effective and efficient video summarization methods so that users are empowered to quickly browse and comprehend a large amount of video content. It is a herculean task to manage access to video content in real time where humongous amount of audiovisual recorded data is generated every second. In this paper we propose an Eratosthenes Sieve based key-frame extraction approach for video summarization (VS) which can work better for real-time applications. Here, Eratosthenes Sieve is used to generate sets of all Prime number frames and nonprime number frames up to total N frames of a video. k-means clustering procedure is employed on these sets to extract the key–frames quickly. Here, the challenge is to find the optimal set of clusters, achieved by employing Davies-Bouldin Index (DBI). DBI a cluster validation technique which allows users with free parameter based VS approach to choose the desired number of key-frames without incurring additional computational costs. Moreover, our proposed approach includes likes of both local and global perspective videos. The method strongly enhances clustering procedure performance trough engagement of Eratosthenes Sieve. Qualitative and quantitative evaluation and complexity computation are done in order to compare the performances of the proposed model and state-of-the-art models. Experimental results on two benchmark datasets with various types of videos exhibit that the proposed methods outperform the state-of-the-art models on F-measure.


Quantifying the total wetted surface area of the world fleet: a first step in determining the potential extent of ships’ biofouling

Ships’ hulls can transport aquatic nuisance species, but there is little quantitative information about the magnitude of vessel biofouling on a global or regional scale. There does not exist a robust method to estimate the wetted surface area (WSA) of a particular fleet of ships, especially across the diversity of possible vessel types. An estimate of the total WSA of ship arrivals into a port or region is essential to determine the potential scope of biofouling and to inform management strategies to reduce the future invasions. Multiple statistical models were developed so commonly available ships’ parameters could be used to estimate the WSA for a given set of fleet data. Using individual ship characteristics and publicly available data from  ~120,000 active commercial ships in the world fleet, the method results in a total global minimum WSA estimate of approximately 325 × 106 m2. The size of the global fleet employed here is greater than the commonly cited vessel number of approximately 80,000–90,000, as we include ships <100 gross tons. Over 190,000 vessels were initially identified, representing a WSA of 571 × 106 m2, but active status of only 120,000 vessels could be confirmed. Reliable data were unavailable on the operating status of many additional and especially smaller vessels. This approach, along with a contemporary and comprehensive estimate of global WSA, when combined with knowledge of the different operational profiles of ships that may reduce biofouling (port residence times, steaming speeds, extent of antifouling coatings, cleaning frequency, etc.), can inform current numerical models and risk assessments of bioinvasions.


Documenting Software Architectures: Views and Beyond

* This author is with the Software Engineering Institute at Carnegie Mellon University. ** This author is with the School of Computer Science at Carnegie Mellon University. Software architecture has emerged as a foundational concept for the successful development of large, complex systems. Signs that the field is maturing to become an engineering discipline include textbooks on the subject; the work in repeatable design, exemplified by architectural styles and patterns; robust methods for evaluating software architectures; international conferences devoted to it; and recognition of architecture as a professional practice. However, treatment of architecture to date has largely concentrated on its design, and to a lesser extent, its validation. But effectively documenting an architecture is as important as crafting it, because if the architecture is not understood (or worse, misunderstood) it cannot meet its goals as the unifying vision for system and software development. Three years ago, researchers at the Software Engineering Institute and the Carnegie Mellon School of Computer Science set out to answer the question: “How should you document an architecture so that others can successfully use it, maintain it, and build a system from it?” The result of that work is an approach we loosely call “views and beyond.” [1] Modern software architecture practice embraces the concept of architectural views. A view is a representation of a set of system elements and relations associated with them. Modern systems are more than complex enough to make it difficult to grasp them all at once. Instead, we restrict our attention at any one moment to one (or a small number) of the software system’s structures, which we represent as views. Some authors prescribe a fixed set of views with which to engineer and communicate an architecture. Rational’s Unified Process, for example, is based on Kruchten’s 4+1 view approach to software [4]. The Siemens Four Views model [3] is another example. A recent trend, however, is to recognize that architects should produce whatever views are useful for the system at hand. IEEE 1471 exemplifies this philosophy; it holds that an architecture description consists of a set of views, each of which conforms to a viewpoint, which in turn is a realization of the concerns of one or more stakeholders. This philosophy about views leads to the fundamental principle of the views-and-beyond approach:


VeTrack: Real Time Vehicle Tracking in Uninstrumented Indoor Environments

Although location awareness and turn-by-turn instructions are prevalent outdoors due to GPS, we are back into the darkness in uninstrumented indoor environments such as underground parking structures. We get confused, disoriented when driving in these mazes, and frequently forget where we parked, ending up circling back and forth upon return.In this paper, we propose VeTrack, a smartphone-only system that tracks the vehicle's location in real time using the phone's inertial sensors. It does not require any environment instrumentation or cloud backend. It uses a novel "shadow" tracing method to accurately estimate the vehicle's trajectories despite arbitrary phone/vehicle poses and frequent disturbances. We develop algorithms in a Sequential Monte Carlo framework to represent vehicle states probabilistically, and harness constraints by the garage map and detected landmarks to robustly infer the vehicle location. We also find landmark (e.g., speed bumps, turns) recognition methods reliable against noises, disturbances from bumpy rides and even hand-held movements. We implement a highly efficient prototype and conduct extensive experiments in multiple parking structures of different sizes and structures, with multiple vehicles and drivers. We find that VeTrack can estimate the vehicle's real time location with almost negligible latency, with error of 2-4 parking spaces at 80-percentile.


Prediction and Simulation of Human Mobility Following Natural Disasters

In recent decades, the frequency and intensity of natural disasters has increased significantly, and this trend is expected to continue. Therefore, understanding and predicting human behavior and mobility during a disaster will play a vital role in planning effective humanitarian relief, disaster management, and long-term societal reconstruction. However, such research is very difficult to perform owing to the uniqueness of various disasters and the unavailability of reliable and large-scale human mobility data. In this study, we collect big and heterogeneous data (e.g., GPS records of 1.6 million users1 over 3 years, data on earthquakes that have occurred in Japan over 4 years, news report data, and transportation network data) to study human mobility following natural disasters. An empirical analysis is conducted to explore the basic laws governing human mobility following disasters, and an effective human mobility model is developed to predict and simulate population movements. The experimental results demonstrate the efficiency of our model, and they suggest that human mobility following disasters can be significantly more predictable and be more easily simulated than previously thought.


Clarithromycin and Tetracycline Binding to Soil Humic Acid in the Absence and Presence of Calcium.

Numerous ionizable organic micropollutants contain positively charged moieties at pH values typical of environmental systems. Describing organic cation and zwitterion interaction with dissolved natural organic matter requires explicit consideration of the pH-dependent speciation of both sorbate and sorbent. We studied the pH-, ionic strength-, and concentration-dependent binding of relatively large, organic cations and zwitterions (viz., the antibiotics clarithromycin and tetracycline) to dissolved humic acid in the absence and presence of Ca(2+) and evaluated the ability of the NICA-Donnan model to describe the data. Clarithromycin interaction with dissolved humic acid was well described by the model including the competitive effect of Ca(2+) on clarithromycin binding over a wide range of solution conditions by considering only the binding of the cationic species to low proton-affinity sites in humic acid. Tetracycline possesses multiple ionizable moieties and forms complexes with Ca(2+). An excellent fit to experimental data was achieved by considering tetracycline cation interaction with both low and high proton-affinity sites of humic acid and zwitterion interaction with high proton-affinity sites. In contrast to clarithromycin, tetracycline binding to humic acid increased in the presence of Ca(2+), especially under alkaline conditions. Model calculations indicate that this increase is due to electrostatic interaction of positively charged tetracycline-Ca complexes with humic acid rather than due to the formation of ternary complexes, except at very low TC concentrations.


Survey of clustering algorithms for MANET

Many clustering schemes have been proposed for ad hoc networks. A systematic classification of these clustering schemes enables one to better understand and make improvements. In mobile ad hoc networks, the movement of the network nodes may quickly change the topology resulting in the increase of the overhead message in topology maintenance. Protocols try to keep the number of nodes in a cluster around a pre-defined threshold to facilitate the optimal operation of the medium access control protocol. The clusterhead election is invoked on-demand, and is aimed to reduce the computation and communication costs. A large variety of approaches for ad hoc clustering have been developed by researchers which focus on different performance metrics. This paper presents a survey of different clustering schemes.


Efficient reinforcement learning using Gaussian processes

IX


Learning Sparsely Used Overcomplete Dictionaries

We consider the problem of learning sparsely used overcomplete dictionaries, where each observation is a sparse combination of elements from an unknown overcomplete dictionary. We establish exact recovery when the dictionary elements are mutually incoherent. Our method consists of a clustering-based initialization step, which provides an approximate estimate of the true dictionary with guaranteed accuracy. This estimate is then refined via an iterative algorithm with the following alternating steps: 1) estimation of the dictionary coefficients for each observation through l1 minimization, given the dictionary estimate, and 2) estimation of the dictionary elements through least squares, given the coefficient estimates. We establish that, under a set of sufficient conditions, our method converges at a linear rate to the true dictionary as well as the true coefficients for each observation.


Fuzzy controlled fast charging system for lithium-ion batteries

A DSP is adopted to construct a fuzzy controlled lithium-ion battery charging system. By using this intelligent charging system, the data collection, calculation and peripheral circuit control are performed for the battery charging status. According to the lithium-ion battery charging specifications, two hours are required for battery charging. A fuzzy logic controller (FLC) is constructed by using the battery protection cell voltage and voltage difference among the batteries. They are used as the input variable to shorten the charging and equalizing time and assure that the battery will be operated within the safety voltage range.


A Particle Swarm Optimization ( PSO )-based Heuristic for Scheduling Work fl ow Applications in Cloud Computing Environments

Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. However, users are charged on a pay-per-use basis. User applications may incur large data retrieval and execution costs when they are scheduled taking into account only the ‘execution time’. In addition to optimizing execution time, the cost arising from data transfers between resources as well as execution costs must also be taken into account. In this paper, we present a particle swarm optimization (PSO) based scheduling heuristic for data intensive applications that takes into account both computation cost and data transmission cost. We experiment with a workflow application by varying its computation and communication costs. We analyze the cost savings when using PSO as compared to using existing ‘Best Resource Selection’ (BRS) algorithm. Our results show that we can achieve: a) as much as 3 times cost savings as compared to BRS, b) good distribution of workload onto resources, when using PSO based scheduling heuristic.


Community-Affiliation Graph Model for Overlapping Network Community Detection

One of the main organizing principles in real-world networks is that of network communities, where sets of nodes organize into densely linked clusters. Communities in networks often overlap as nodes can belong to multiple communities at once. Identifying such overlapping communities is crucial for the understanding the structure as well as the function of real-world networks. Even though community structure in networks has been widely studied in the past, practically all research makes an implicit assumption that overlaps between communities are less densely connected than the non-overlapping parts themselves. Here we validate this assumption on 6 large scale social, collaboration and information networks where nodes explicitly state their community memberships. By examining such ground-truth communities we find that the community overlaps are more densely connected than the non-overlapping parts, which is in sharp contrast to the conventional wisdom that community overlaps are more sparsely connected than the communities themselves. Practically all existing community detection methods fail to detect communities with dense overlaps. We propose Community-Affiliation Graph Model, a model-based community detection method that builds on bipartite node-community affiliation networks. Our method successfully captures overlapping, non-overlapping as well as hierarchically nested communities, and identifies relevant communities more accurately than the state-of-the-art methods in networks ranging from biological to social and information networks.


The head direction signal: origins and sensory-motor integration.

Navigation first requires accurate perception of one's spatial orientation within the environment, which consists of knowledge about location and directional heading. Cells within several limbic system areas of the mammalian brain discharge allocentrically as a function of the animal's directional heading, independent of the animal's location and ongoing behavior. These cells are referred to as head direction (HD) cells and are believed to encode the animal's perceived directional heading with respect to its environment. Although HD cells are found in several areas, the principal circuit for generating this signal originates in the dorsal tegmental nucleus and projects serially, with some reciprocal connections, to the lateral mammillary nucleus --> anterodorsal thalamus --> PoS, and terminates in the entorhinal cortex. HD cells receive multimodal information about landmarks and self-generated movements. Vestibular information appears critical for generating the directional signal, but motor/proprioceptive and landmark information are important for updating it.


A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm

Missing values in datasets should be extracted from the datasets or should be estimated before they are used for classification, association rules or clustering in the preprocessing stage of data mining. In this study, we utilize a fuzzy c-means clustering hybrid approach that combines support vector regression and a genetic algorithm. In this method, the fuzzy clustering parameters, cluster size and weighting factor are optimized and missing values are estimated. The proposed novel hybrid method yields sufficient and sensible imputation performance results. The results are compared with those of fuzzy c-means genetic algorithm imputation, support vector regression genetic algorithm imputation and zero imputation. 2013 Elsevier Inc. All rights reserved.


Task driven perceptual organization for extraction of rooftop polygons

A new method for extracting planar polygonal rooftops in monocular aerial imagery is proposed. Structural features are extracted and hierarchically related using perceptual grouping techniques. Top-down feature veri cation is used so that features, and links between the features, are veri ed with local information in the image and weighed in a graph. Cycles in the graph correspond to possible building rooftop hypotheses. Virtual features are hypothesized for the perceptual completion of partially occluded rooftops. Extraction of the \best" grouping of features into a building rooftop hypothesis is posed as a graph search problem. The maximally weighted, independent set of cycles in the graph is extracted as the nal set of roof


Didn't you see my message?: predicting attentiveness to mobile instant messages

Mobile instant messaging (e.g., via SMS or WhatsApp) often goes along with an expectation of high attentiveness, i.e., that the receiver will notice and read the message within a few minutes. Hence, existing instant messaging services for mobile phones share indicators of availability, such as the last time the user has been online. However, in this paper we not only provide evidence that these cues create social pressure, but that they are also weak predictors of attentiveness. As remedy, we propose to share a machine-computed prediction of whether the user will view a message within the next few minutes or not. For two weeks, we collected behavioral data from 24 users of mobile instant messaging services. By the means of machine-learning techniques, we identified that simple features extracted from the phone, such as the user's interaction with the notification center, the screen activity, the proximity sensor, and the ringer mode, are strong predictors of how quickly the user will attend to the messages. With seven automatically selected features our model predicts whether a phone user will view a message within a few minutes with 70.6% accuracy and a precision for fast attendance of 81.2%


Conjugated linoleic acid and disease prevention: a review of current knowledge.

Conjugated linoleic acid (CLA), a derivative of a fatty acid linoleic acid (LA), has been reported to decrease tumorigenesis in animals. CLA is unique because unlike most antioxidants which are components of plant products, it is present in food from animal sources such as dairy foods and meats. CLA concentrations in dairy products typically range from 2.9 to 8.92 mg/g fat of which the 9-cis, 11-trans isomer makes up to 73% to 93% of the total CLA. Low concentrations of CLA are found in human blood and tissues. In vitro results suggest that CLA is cytotoxic to MCF-7 cells and it inhibits the proliferation of human malignant melanoma and colorectal cancer cells. In animal studies, CLA has inhibited the development of mouse epidermal tumors, mouse forestomach cancer and rat mammary cancer. Hamsters fed CLA collectively had significantly reduced levels of plasma total cholesterol, non-high-density lipoprotein cholesterol, (combined very-low and low-density lipoprotein) and triglycerides with no effect on high-density lipoprotein cholesterol, as compared to controls. Dietary CLA modulated certain aspects of the immune defense but had no obvious effect on the growth of an established, aggressive mammary tumor in mice. It is now thought that CLA itself may not have anti-oxidant capabilities but may produce substances which protect cells from the detrimental effects of peroxides. There is, however, insufficient evidence from human epidemiological data, and very few of the animal studies have shown a dose-response relationship with the quantity of CLA feed and the extent of tumor growth. Further research with tumor models is needed to test the efficacy and utility of CLA in cancer and other disease prevention and form the basis of evaluating its effect in humans by observational studies and clinical trials.


Gore Galore: Literary Theory and Computer Games

Computer games have not been adequately theorized within the humanities. In this paper a brief history of computer games is presented as a starting point for developing a topology of games and a theory of computer games as rhetorical artifacts suitable for critical study. The paper addresses the question of why games should be treated seriously and suggests a theoretical approach based on Backhtin’s poetics of the novel where the experience of time and space (the chronotope) provides a framework of questions for discussing computer games.


Qualitative Methods in Research on Teaching

This chapter reviews basic issues of theory and method in approaches to research on teaching that are alternatively called ethnographic, qualitative, participant observational, case study, symbolic interactionist, phenomenological, constructivist, or interpretive. These approaches are all slightly different, but each bears strong family resemblance to the others. The set of related approaches is relatively new in the field of research on teaching. The approaches have emerged as significant in the decade of the 1960s in England and in the 1970s in the United States, Australia, New Zealand, and Germany. Because interest in these approaches is so recent, the previous editions of the Handbook of Research on Teaching do not contain a chapter devoted to participant observational research. Accordingly, this chapter attempts to describe r~s and their theoretical _nresup~s in considerable detail and does not attempt an exhaustive review of the rapidly growing literature in the field. Such a review will be appropriate for the next edition of this handbook. From this point on I will use the term interpretive to refer to the whole family of approaches to participant observational research. I adopt this term for three reasons: (a) It is more inclusive than many of the others (e.g., ethnography, case study); (b) W Blake


Breast disorders in pregnant and lactating women.

The breast undergoes extensive changes during pregnancy and lactation that can create diagnostic challenges. This article reviews the anatomy of the breast, breast changes associated with pregnancy and lactation, and breast imaging techniques for pregnant and lactating women. Various benign breast conditions in this patient population also are discussed, such as lactating adenomas, galactoceles, and granulomatous mastitis. Finally, pregnancy-associated breast cancer is presented, including its epidemiology, diagnosis, staging, treatment, and prognosis.


A traffic-aware street lighting scheme for Smart Cities using autonomous networked sensors

Street lighting is a ubiquitous utility, but sustaining its operation presents a heavy financial and environmental burden. Many schemes have been proposed which selectively dim lights to improve energy efficiency, but little consideration has been given to the usefulness of the resultant street lighting system. This paper proposes a real-time adaptive lighting scheme, which detects the presence of vehicles and pedestrians and dynamically adjusts their brightness to the optimal level. This improves the energy efficiency of street lighting and its usefulness; a streetlight utility model is presented to evaluate this. The proposed scheme is simulated using an environment modelling a road network, its users, and a networked communication system and considers a real streetlight topology from a residential area. The proposed scheme achieves similar or improved utility to existing schemes, while consuming as little as 1-2% of the energy required by conventional and state-of-the-art techniques.


Road Network Extraction and Intersection Detection From Aerial Images by Tracking Road Footprints

In this paper, a new two-step approach (detecting and pruning) for automatic extraction of road networks from aerial images is presented. The road detection step is based on shape classification of a local homogeneous region around a pixel. The local homogeneous region is enclosed by a polygon, called the footprint of the pixel. This step involves detecting road footprints, tracking roads, and growing a road tree. We use a spoke wheel operator to obtain the road footprint. We propose an automatic road seeding method based on rectangular approximations to road footprints and a toe-finding algorithm to classify footprints for growing a road tree. The road tree pruning step makes use of a Bayes decision model based on the area-to-perimeter ratio (the A/P ratio) of the footprint to prune the paths that leak into the surroundings. We introduce a lognormal distribution to characterize the conditional probability of A/P ratios of the footprints in the road tree and present an automatic method to estimate the parameters that are related to the Bayes decision model. Results are presented for various aerial images. Evaluation of the extracted road networks using representative aerial images shows that the completeness of our road tracker ranges from 84% to 94%, correctness is above 81%, and quality is from 82% to 92%.


UAV borne real-time road mapping system

Road information is useful in many fields. In this paper, a real-time mapping system is presented to acquire the image and determine the geometry of the road. These include designs of platform and instruments, data transmission, processing and archiving. Compare with the traditional platforms, the UAV (Unmanned Aerial Vehicle) platforms offer greater flexibility, shorter response time and is able to generate very high resolution data. It is inexpensive to operate, and its operation is not limited by air traffic constraints. With autonomous flight control system, it is easy to navigate the aircraft along the planned lines strictly. In this system, an unmanned fixed-wing aircraft with double engine and double generator is used, which helps to improve the reliability and capability of the platform. A digital three-axis stabilized platform is developed, which performs an important role in camera attitude controlling. To ensure there is no coverage hole, a data manage system is adopted: after an instantaneous inspection of data integrity, the data will be archived as they are received. If any anomaly is detected, the mission planning will be adapted to re-image the concerned area. Vehicle information extraction from image sequence is also described in this paper. The experiment showed the presented system worked well in real-time road mapping and vehicle information extraction.


In this paper, a unique approach for road extraction utilizing pixel spectral information for classification and image segmentation-derived object features was developed. In this approach, road extraction was performed in two steps. In the first step, support vector machine (SVM) was employed merely to classify the image into two groups of categories: a road group and a non-road group. For this classification, support vector machine (SVM) achieved higher accuracy than Gaussian maximum likelihood (GML). In the second step, the road group image was segmented into geometrically homogeneous objects using a region growing technique based on a similarity criterion, with higher weighting on shape factors over spectral criteria. A simple thresholding on the shape index and density features derived from these objects was performed to extract road features, which were further processed by thinning and vectorization to obtain road centerlines. The experiment showed the proposed approach worked well with images comprised by both rural and urban area features. Introduction Road information not only plays a central role in the transportation application, but also is an important data layer in Geographical Information Systems (GIS). Automated road extraction can save time and labor to a great degree in updating a road spatial database. Various road extraction approaches have been developed. Xiong (2001) grouped these methods into five categories: ridge finding, heuristic reasoning, dynamic programming, statistical inference, and map matching. In ridge finding, edge operators are performed on images to derive edge magnitude and direction, followed by a thresholding and thinning process to obtain ridge pixels (Nevatia and Babu, 1980; Treash and Amaratunga, 2000). Alternatively, gradient direction profile analysis can be performed to generate edge pixels (Gong and Wang, 1997). Ridge points are linked to produce the road segments. Heuristic reasoning is a knowledge-based method in which a series of pre-set rules on road characteristics such as shape index, the distance between image primitives, fragments trend, and contextual information are employed to detect and connect image primitives or antiparallel linear edges to road segments (McKeown, et al., 1985; Zhu and Yeh, 1986). In the dynamic programming method, roads are modeled with a set of mathematical equations on the derivatives of gray values and select characteristics of roads, such as smooth curves, homogeneous surface, narrow linear features, and relatively constant width. Dynamic programming is employed to solve the optimization problem Road Extraction Using SVM and Image Segmentation Mingjun Song and Daniel Civco (Gruen and Li, 1995). In the statistical inference method, linear features are modeled as a Markov point process or a geometric-stochastic model on the road width, direction, intensity and background intensity, and maximum a posteriori probability is used to estimate the road network (Barzohar and Cooper, 1996; Stoica, et al., 2000). In a map matching method, existing road maps are used as starting point to update the road network. In general, two steps are involved: first, a mapimage matching algorithm is employed to match the roads on the map to the image; second, new roads are searched based on the assumption that they are connected to existing roads (Stilla, 1995). Xiong’s classification on road extraction methods is only a generalization, and some other methods may combine different techniques. Active contour models, known as snakes, are also used in road extraction (Gruen and Li, 1997; Agouris, et al., 2001). A snake is a spline with minimized energy driven by internal spline and external image forces (Park, et al., 2001). In general, external image forces are represented by the gradient magnitude of an image, which attracts snakes to contours with strong edges. Internal forces are given by a continuity term and a curvature term expressed by the differences of adjacent snaxels, which are vertex nodes of the snake, with weights coming from training data, which control the shape and smoothness of the snakes. Through the optimization, the snake evolves from its initial position to desired position with minimized energy. Park and Kim (2001) used template matching to extract road segments in which a road template was formed around the road seed, and an adaptive least squares matching algorithm was used to detect a target window with similar transformation. This method assumes a small difference in brightness values between template and target windows. Most of these road extraction methods require some road seeds as starting points, which are in general provided by users, and road segments evolve under a certain model. Sometimes control points are needed to correct the evolution of roads (Zhao, et al., 2002). Further, these methods use black-and-white aerial photographs or the panchromatic band of high-resolution satellite images and therefore the geometric characteristics of roads alone play a critical role. Boggess (1993) used a classification method incorporating texture and neural networks to extract roads by classifying roads and other features from Landsat TM imagery, but obtained numerous false-inclusions. Roberts, et al. (2001) developed a spectral mixture library using hyperspectral images to extract roads, but the use of spectral information alone does not capture the spatial properties of these curvilinear features. P H OTO G R A M M E T R I C E N G I N E E R I N G & R E M OT E S E N S I N G December 2004 1 3 6 5 Center for Land use Education and Research, Department of Natural Resources Management and Engineering, The University of Connecticut U-4087, 1376 Storrs Road, Storrs, CT 06269-4087 (mingjun.song@uconn.edu, daniel.civco@ uconn.edu). Photogrammetric Engineering & Remote Sensing Vol. 70, No. 12, December 2004, pp. 1365–1371. 0099-1112/04/7012–1365/$3.00/0 © 2004 American Society for Photogrammetry and Remote Sensing LFX-536.qxd 11/9/04 16:13 Page 1365


Effect of temporal envelope smearing on speech reception.

The effect of smearing the temporal envelope on the speech-reception threshold (SRT) for sentences in noise and on phoneme identification was investigated for normal-hearing listeners. For this purpose, the speech signal was split up into a series of frequency bands (width of 1/4, 1/2, or 1 oct) and the amplitude envelope for each band was low-pass filtered at cutoff frequencies of 0, 1/2, 1, 2, 4, 8, 16, 32, or 64 Hz. Results for 36 subjects show (1) a severe reduction in sentence intelligibility for narrow processing bands at low cutoff frequencies (0-2 Hz); and (2) a marginal contribution of modulation frequencies above 16 Hz to the intelligibility of sentences (provided that lower modulation frequencies are completely present). For cutoff frequencies above 4 Hz, the SRT appears to be independent of the frequency bandwidth upon which envelope filtering takes place. Vowel and consonant identification with nonsense syllables were studied for cutoff frequencies of 0, 2, 4, 8, or 16 Hz in 1/4-oct bands. Results for 24 subjects indicate that consonants are more affected than vowels. Errors in vowel identification mainly consist of reduced recognition of diphthongs and of confusions between long and short vowels. In case of consonant recognition, stops appear to suffer most, with confusion patterns depending on the position in the syllable (initial, medial, or final).


A MapReduce Approach to NoSQL RDF Databases

In recent years, the increased need to house and process large volumes of data has prompted the need for distributed storage and querying systems. The growth of machine-readable RDF triples has prompted both industry and academia to develop new database systems, called “NoSQL,” with characteristics that differ from classical databases. Many of these systems compromise ACID properties for increased horizontal scalability and data availability. This thesis concerns the development and evaluation of a NoSQL triplestore. Triplestores are database management systems central to emerging technologies such as the Semantic Web and linked data. A triplestore comprises data storage using the RDF (resource description framework) data model and the execution of queries written in SPARQL. The triplestore developed here exploits an opensource stack comprising, Hadoop, HBase, and Hive. The evaluation spans several benchmarks, including the two most commonly used in triplestore evaluation, the Berlin SPARQL Benchmark, and the DBpedia benchmark, a query workload that operates an RDF representation of Wikipedia. Results reveal that the join algorithm used by the system plays a critical role in dictating query runtimes. Distributed graph databases must carefully optimize queries before generating MapReduce query plans as network traffic for large datasets can become prohibitive if the query is executed naively.


NOBLE – Flexible concept recognition for large-scale biomedical natural language processing

Natural language processing (NLP) applications are increasingly important in biomedical data analysis, knowledge engineering, and decision support. Concept recognition is an important component task for NLP pipelines, and can be either general-purpose or domain-specific. We describe a novel, flexible, and general-purpose concept recognition component for NLP pipelines, and compare its speed and accuracy against five commonly used alternatives on both a biological and clinical corpus. NOBLE Coder implements a general algorithm for matching terms to concepts from an arbitrary vocabulary set. The system’s matching options can be configured individually or in combination to yield specific system behavior for a variety of NLP tasks. The software is open source, freely available, and easily integrated into UIMA or GATE. We benchmarked speed and accuracy of the system against the CRAFT and ShARe corpora as reference standards and compared it to MMTx, MGrep, Concept Mapper, cTAKES Dictionary Lookup Annotator, and cTAKES Fast Dictionary Lookup Annotator. We describe key advantages of the NOBLE Coder system and associated tools, including its greedy algorithm, configurable matching strategies, and multiple terminology input formats. These features provide unique functionality when compared with existing alternatives, including state-of-the-art systems. On two benchmarking tasks, NOBLE’s performance exceeded commonly used alternatives, performing almost as well as the most advanced systems. Error analysis revealed differences in error profiles among systems. NOBLE Coder is comparable to other widely used concept recognition systems in terms of accuracy and speed. Advantages of NOBLE Coder include its interactive terminology builder tool, ease of configuration, and adaptability to various domains and tasks. NOBLE provides a term-to-concept matching system suitable for general concept recognition in biomedical NLP pipelines.


Analysis and optimization of software requirements prioritization techniques

Prioritizing requirements helps the project team to understand which requirements are most important and most urgent. Based on this finding a software engineer can decide what to develop/implement in the first release and what on the coming releases. Prioritization is also a useful activity for decision making in other phases of software engineering like development, testing, and implementation. There are a number of techniques available to prioritize the requirements with their associated strengths and limitations. In this paper we will examine state of the art techniques and analyze their applicability on software requirements domain. At the end we present a framework that will help the software engineer of how to perform prioritization process by combining existing techniques and approaches.


The academic social network

By means of their academic publications, authors form a social network. Instead of sharing casual thoughts and photos (as in Facebook), authors select co-authors and reference papers written by other authors. Thanks to various efforts (such as Microsoft Academic Search and DBLP), the data necessary for analyzing the academic social network is becoming more available on the Internet. What type of information and queries would be useful for users to discover, beyond the search queries already available from services such as Google Scholar? In this paper, we explore this question by defining a variety of ranking metrics on different entities—authors, publication venues, and institutions. We go beyond traditional metrics such as paper counts, citations, and h-index. Specifically, we define metrics such as influence, connections, and exposure for authors. An author gains influence by receiving more citations, but also citations from influential authors. An author increases his or her connections by co-authoring with other authors, and especially from other authors with high connections. An author receives exposure by publishing in selective venues where publications have received high citations in the past, and the selectivity of these venues also depends on the influence of the authors who publish there. We discuss the computation aspects of these metrics, and the similarity between different metrics. With additional information of author-institution relationships, we are able to study institution rankings based on the corresponding authors’ rankings for each type of metric as well as different domains. We are prepared to demonstrate these ideas with a web site ( http://pubstat.org ) built from millions of publications and authors.


Beamspace channel estimation for millimeter-wave massive MIMO systems with lens antenna array

By employing the lens antenna array, beamspace MIMO can utilize beam selection to reduce the number of required RF chains in mmWave massive MIMO systems without obvious performance loss. However, to achieve the capacity-approaching performance, beam selection requires the accurate information of beamspace channel of large size, which is challenging, especially when the number of RF chains is limited. To solve this problem, in this paper we propose a reliable support detection (SD)-based channel estimation scheme. Specifically, we propose to decompose the total beamspace channel estimation problem into a series of sub-problems, each of which only considers one sparse channel component. For each channel component, we first reliably detect its support by utilizing the structural characteristics of mmWave beamspace channel. Then, the influence of this channel component is removed from the total beamspace channel estimation problem. After the supports of all channel components have been detected, the nonzero elements of the sparse beamspace channel can be estimated with low pilot overhead. Simulation results show that the proposed SD-based channel estimation outperforms conventional schemes and enjoys satisfying accuracy, even in the low SNR region.


The Use of Wearable Inertial Motion Sensors in Human Lower Limb Biomechanics Studies: A Systematic Review

Wearable motion sensors consisting of accelerometers, gyroscopes and magnetic sensors are readily available nowadays. The small size and low production costs of motion sensors make them a very good tool for human motions analysis. However, data processing and accuracy of the collected data are important issues for research purposes. In this paper, we aim to review the literature related to usage of inertial sensors in human lower limb biomechanics studies. A systematic search was done in the following search engines: ISI Web of Knowledge, Medline, SportDiscus and IEEE Xplore. Thirty nine full papers and conference abstracts with related topics were included in this review. The type of sensor involved, data collection methods, study design, validation methods and its applications were reviewed.


An empirical study on the impact of static typing on software maintainability

Static type systems play an essential role in contemporary programming languages. Despite their importance, whether static type systems impact human software development capabilities remains open. One frequently mentioned argument in favor of static type systems is that they improve the maintainability of software systems—an often-used claim for which there is little empirical evidence. This paper describes an experiment that tests whether static type systems improve the maintainability of software systems, in terms of understanding undocumented code, fixing type errors, and fixing semantic errors. The results show rigorous empirical evidence that static types are indeed beneficial to these activities, except when fixing semantic errors. We further conduct an exploratory analysis of the data in order to understand possible reasons for the effect of type systems on the three kinds of tasks used in this experiment. From the exploratory analysis, we conclude that developers using a dynamic type system tend to look at different files more frequently when doing programming tasks—which is a potential reason for the observed differences in time.


A 12.77-MHz 31 ppm/°C On-Chip RC Relaxation Oscillator With Digital Compensation Technique

The design of a 12.77-MHz on-chip RC relaxation oscillator with digital compensation technique is presented. To maintain the frequency stability versus temperature and supply voltage variations, loop delay tuning by a digital feedback loop is developed in this system. In order to generate an on-chip reference for digital calibration, a replica comparator is added. The on-chip relaxation oscillator is fabricated in 0.18-μm CMOS process. The measured output frequency variation is 31 ppm/°C across -30 to 120 °C temperature range after compensation. The frequency variation over the supply voltage from 0.6 V to 1.1 V is ±0.5%/V. The measured total power consumption is 56.2 μW at 0.9-V supply voltage when the digital compensation blocks are enabled. After digital compensation, the compensation blocks can be shutdown for power saving, and the main oscillator consumes only 12.8 μW.


Learning Deep Face Representation

Face representation is a crucial step of face recognition systems. An optimal face representation should be discriminative, robust, compact, and very easyto-implement. While numerous hand-crafted and learning-based representations have been proposed, considerable room for improvement is still present. In this paper, we present a very easy-to-implement deep learning framework for face representation. Our method bases on a new structure of deep network (called Pyramid CNN). The proposed Pyramid CNN adopts a greedy-filter-and-down-sample operation, which enables the training procedure to be very fast and computationefficient. In addition, the structure of Pyramid CNN can naturally incorporate feature sharing across multi-scale face representations, increasing the discriminative ability of resulting representation. Our basic network is capable of achieving high recognition accuracy (85.8% on LFW benchmark) with only 8 dimension representation. When extended to feature-sharing Pyramid CNN, our system achieves the state-of-the-art performance (97.3%) on LFW benchmark. We also introduce a new benchmark of realistic face images on social network and validate our proposed representation has a good ability of generalization.


On the minimum node degree and connectivity of a wireless multihop network

This paper investigates two fundamental characteristics of a wireless multi -hop network: its minimum node degree and its k--connectivity. Both topology attributes depend on the spatial distribution of the nodes and their transmission range. Using typical modeling assumptions :--- :a random uniform distribution of the nodes and a simple link model :--- :we derive an analytical expression that enables the determination of the required range r0 that creates, for a given node density ρ, an almost surely k--connected network. Equivalently, if the maximum r0 of the nodes is given, we can find out how many nodes are needed to cover a certain area with a k--connected network. We also investigate these questions by various simulations and thereby verify our analytical expressions. Finally, the impact of mobility is discussed.The results of this paper are of practical value for researchers in this area, e.g., if they set the parameters in a network--level simulation of a mobile ad hoc network or if they design a wireless sensor network.


Imagination-Based Decision Making with Physical Models in Deep Neural Networks

Decision-making is challenging in continuous settings where complex sequences of events determine rewards, even when these event sequences are largely observable. In particular, traditional trial-and-error learning strategies may have a hard time associating continuous actions with their reward because of the size of the state space and the complexity of the reward function. Given a model of the world, a different strategy is to use imagination to exploit the knowledge embedded in that model. In this regime, the system directly optimizes the decision for each episode based on predictions from the model. We extend deep learning methods that have been previously used for model-free learning and apply them towards a model-based approach in which an expert is consulted multiple times in the agents’ imagination before it takes an action in the world. We show preliminary results on a difficult physical reasoning task where our model-based approach outperforms a model-free baseline, even when using an inaccurate expert.


Help seeking behavior and the Internet: A national survey

Health-related websites have the potential to powerfully influence the attitudes and behavior of consumers. Access to reliable disease information online has been linked to reduced anxiety, increased feelings of self-efficacy, and decreases in utilization of ambulatory care. Studies report that Internet health information seekers are more likely to have health concerns; adult seekers are more likely to rate themselves as having poor health status and adolescent seekers are more likely to demonstrate clinical impairment or depressive symptomatology compared to non-seekers. Although more and more Americans are using the Internet for healthcare information, little is known about how this information affects their health behaviors. The current study extends the literature by examining characteristics associated with help seeking, either from a healthcare provider or from peers, as a direct result of health information found online. Medical care seekers appear to be using the Internet to enhance their medical care; they report using the information online to diagnose a problem and feel more comfortable about their health provider's advice given the information found on the Internet. Support seekers tend to be of slightly lower income compared to non-support seekers. They are also significantly more likely to have searched for information about a loved one's medical or health condition, signaling that many of these consumers may be caretakers.


What is the function of the claustrum?

The claustrum is a thin, irregular, sheet-like neuronal structure hidden beneath the inner surface of the neocortex in the general region of the insula. Its function is enigmatic. Its anatomy is quite remarkable in that it receives input from almost all regions of cortex and projects back to almost all regions of cortex. We here briefly summarize what is known about the claustrum, speculate on its possible relationship to the processes that give rise to integrated conscious percepts, propose mechanisms that enable information to travel widely within the claustrum and discuss experiments to address these questions.


Distinct mechanisms regulate slow-muscle development

Vertebrate muscle development begins with the patterning of the paraxial mesoderm by inductive signals from midline tissues [1, 2]. Subsequent myotome growth occurs by the addition of new muscle fibers. We show that in zebrafish new slow-muscle fibers are first added at the end of the segmentation period in growth zones near the dorsal and ventral extremes of the myotome, and this muscle growth continues into larval life. In marine teleosts, this mechanism of growth has been termed stratified hyperplasia [3]. We have tested whether these added fibers require an embryonic architecture of muscle fibers to support their development and whether their fate is regulated by the same mechanisms that regulate embryonic muscle fates. Although Hedgehog signaling is required for the specification of adaxial-derived slow-muscle fibers in the embryo [4, 5], we show that in the absence of Hh signaling, stratified hyperplastic growth of slow muscle occurs at the correct time and place, despite the complete absence of embryonic slow-muscle fibers to serve as a scaffold for addition of these new slow-muscle fibers. We conclude that slow-muscle-stratified hyperplasia begins after the segmentation period during embryonic development and continues during the larval period. Furthermore, the mechanisms specifying the identity of these new slow-muscle fibers are different from those specifying the identity of adaxial-derived embryonic slow-muscle fibers. We propose that the independence of early, embryonic patterning mechanisms from later patterning mechanisms may be necessary for growth.


A practical approach for computing the diameter of a point set

We present an approximation algorithm for computing the diameter of a point-set in $d$-dimensions. The new algorithm is sensitive to the “hardness” of computing the diameter of the given input, and for most inputs it is able to compute the {\em exact} diameter extremely fast. The new algorithm is simple, robust, has good empirical performance, and can be implemented quickly. As such, it seems to be the algorithm of choice in practice for computing/approximating the diameter.


A Trigraph Based Centrality Approach Towards Text Summarization

As the electronic documents are increasing due to the revolution of information there is an urgent need for summarizing the text documents. From the previous works we observed that there is no generalized graph model for text summarization and low order ngrams could not preserve the contextual meaning. This paper focuses on an extractive based graphical approach for text summarization, based on trigrams and graph based centrality measure. Trigraph is generated and the centrality of the connected trigraph is taken to extract the important trigrams. A mapping is done between the original words and the trigrams to regain the link between the words. And after comparing the centrality from the graph, the summary is extracted. The ROUGE-SU4 F-measure obtained for the proposed approach is 0.036 which is significantly better than the previous approaches.


State of the Art in Example-based Texture Synthesis

Recent years have witnessed significant progress in example-based texture synthesis algorithms. Given an example texture, these methods produce a larger texture that is tailored to the user’s needs. In this state-of-the-art report, we aim to achieve three goals: (1) provide a tutorial that is easy to follow for readers who are not already familiar with the subject, (2) make a comprehensive survey and comparisons of different methods, and (3) sketch a vision for future work that can help motivate and guide readers that are interested in texture synthesis research. We cover fundamental algorithms as well as extensions and applications of texture synthesis.


The back squat: A proposed assessment of functional deficits and technical factors that limit performance.

Fundamental movement competency is essential for participation in physical activity and for mitigating the risk of injury, which are both key elements of health throughout life. The squat movement pattern is arguably one of the most primal and critical fundamental movements necessary to improve sport performance, to reduce injury risk and to support lifelong physical activity. Based on current evidence, this first (1 of 2) report deconstructs the technical performance of the back squat as a foundation training exercise and presents a novel dynamic screening tool that incorporates identification techniques for functional deficits that limit squat performance and injury resilience. The follow-up report will outline targeted corrective methodology for each of the functional deficits presented in the assessment tool.


Bioinformatics Curriculum Guidelines: Toward a Definition of Core Competencies

1 School of Electrical Engineering and Computer Science, Ohio University, Athens, Ohio, United States of America, 2 Bioinformatics and Research Computing, Whitehead Institute, Cambridge, Massachusetts, United States of America, 3 Department of Biological Sciences and School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America, 4 European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom, 5 School of Informatics and Computing, Indiana University, Bloomington, Indiana, United States of America, 6 School of Computer Science and Engineering, The University of New South Wales, Sydney, New South Wales, Australia, 7 The Genome Analysis Centre, Norwich Research Park, Norwich, United Kingdom


A Mixed Filtering Approach for Track Condition Monitoring Using Accelerometers on the Axle Box and Bogie

This paper describes a method of estimating irregularities in railway tracks using acceleration data measured from high-speed trains. Track irregularities are the main causes of the vibration of high-speed trains and thus should be carefully monitored to maintain the stability and ride quality of the trains. A mixed filtering approach is proposed for stable displacement estimation and waveband classification of the irregularities in the measured acceleration. Accelerometers are mounted on the axle box and the bogie of a high-speed train to measure the acceleration in the lateral and vertical directions. The estimated results are compared with those of a commercial track geometry measurement system. Finally, the performance of the proposed approach and the relationship between the mounted location of the accelerometers and the estimated track irregularities are discussed.


A Semantic Web Services Architecture

Formed in February 2003, the Semantic Web Services Initiative Architecture (SWSA) committee’s mission is to develop the necessary abstractions for an architecture that supports Semantic Web services. The resultant framework builds on the W3C Web Services Architecture working group report (and is motivated in part by Tim Berners-Lee’s vision for the Semantic Web1). Other groups developing Semantic Web services frameworks contributed to the discussions, including the Web Ontology Language for Services (OWL-S) consortium, the Web Service Modeling Ontology (WSMO; www.wsmo. org) group at the Digital Enterprise Research Institute (DERI), and the Managing End-to-End Operations-Semantics (METEOR-S; http://lsdis.cs.uga.edu/projects/meteor-s/) group at the University of Georgia.2,3 In this article, we describe the protocols exchanged between the interacting entities or agents that interpret and reason with semantic descriptions in the deployment of Semantic Web services. We focus specifically on those capabilities that extend the potential range of Web services; we also discuss security, reliability, and a flexible means of recovery from the problems that can occur in open and evolving environments. The SWSA architectural framework attempts to address five classes of Semantic Web agent requirements — dynamic service discovery, service engagement, service process enactment and management, community support services, and quality of service (QoS) — which we cover in detail here as well.


An Improved Algorithm for Harris Corner Detection

Corner points are formed from two or more edges and edges usually define the boundary between two different objects or parts of the same objects. In this novel we discuss the theory of the Harris corner detection and indicate its disadvantage. Then it proposes an improved algorithm of Harris detection algorithm based on the neighboring point eliminating method. It reduces the time of the detection, and makes the corners distributing more homogenous so that avoids too many corners stay together. Experimental results show that the algorithm can detect corner more equality distributing, and can be used in some fact applications such as image registration well. Key words-Corner point; Harris corner; neighboring point; homogenous distributing 1. Introduce Corner point is a maximum curvature point of the two-dimensional image brightness change or the edge of sharp curves in a image, which haves widely used in such as the camera calibration, virtual scene reconstruction, motion estimation, image registration and more computer vision tasks. These points keep important features of image and can also be effective in reducing the amount of data information, allow real-time processing possible. Now, there are two corner detection methods: based on the image edge method and based on the image gray scale method. The former always need to encode the image edge, which is heavily dependent on image segmentation and edge extraction, have large difficulty and calculate, and if the detected area haves some change, it will lead to the failure of the operation, therefore, this method is not used widely. The latter method detect corner by calculating the curvature and gradient, it avoid these disadvantage. In currently, it haves Moravec operator, Harris operator, Susan operator and so on. Experimental shows that Harris corner detection method haves the best result in above mentioned. But in fact applications, Harris algorithm must to give a proper threshold T, and the algorithm based on the provided threshold to detect the ideal corner. T always depend on the image, specifically it is difficult to determine under the different colors. And it gets the ideal T after user blind set T more times. In addition, the points which have the larger eigenvalues are concentrated in some regions cause the detect corners are non-uniform distribution; and if T dropped, even though the overall distribution of the corner towards reasonable, but it also leads to the points close together, have the clustering phenomenon, it will affect the later analysis and processing. 2. Harris Corner Detection Algorithm 2.1 Detection Theory Harris corner detection algorithm is based on the point feature extraction of signal. It makes the window ω (usually rectangular area) to move infinitesimal displacement to any direction, and the variation of gray can be defined as: − = + + 2 , , , , ] [ v u v y u x v u y x I I w E + + = v u v u y x O yY xX w , 2 2 2 , )] , ( [ 2 2 2 By Cxy Ax + + = T y x M y x ) , ( ) , ( = (1) Where X and Y are one-order gray-level gradient, they can be got by convolution with 978-1-4244-4131-0/09/$25.00 ©2009 IEEE


Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning

Cooling system plays a critical role in a modern data center (DC). Developing an optimal control policy for DC cooling system is a challenging task. The prevailing approaches often rely on approximating system models that are built upon the knowledge of mechanical cooling, electrical and thermal management, which is difficult to design and may lead to suboptimal or unstable performances. In this paper, we propose utilizing the large amount of monitoring data in DC to optimize the control policy. To do so, we cast the cooling control policy design into an energy cost minimization problem with temperature constraints, and tap it into the emerging deep reinforcement learning (DRL) framework. Specifically, we propose an end-toend cooling control algorithm (CCA) that is based on the actorcritic framework and an off-policy offline version of the deep deterministic policy gradient (DDPG) algorithm. In the proposed CCA, an evaluation network is trained to predict an energy cost counter penalized by the cooling status of the DC room, and a policy network is trained to predict optimized control settings when gave the current load and weather information. The proposed algorithm is evaluated on the EnergyPlus simulation platform and on a real data trace collected from the National Super Computing Centre (NSCC) of Singapore. Our results show that the proposed CCA can achieve about 11% cooling cost saving on the simulation platform compared with a manually configured baseline control algorithm. In the trace-based study, we propose a de-underestimation validation mechanism as we cannot directly test the algorithm on a real DC. Even though with DUE the results are conservative, we can still achieve about 15% cooling energy saving on the NSCC data trace if we set the inlet temperature threshold at 26.6 degree Celsius.


Sleep deprivation amplifies reactivity of brain reward networks, biasing the appraisal of positive emotional experiences.

Appropriate interpretation of pleasurable, rewarding experiences favors decisions that enhance survival. Conversely, dysfunctional affective brain processing can lead to life-threatening risk behaviors (e.g., addiction) and emotion imbalance (e.g., mood disorders). The state of sleep deprivation continues to be associated with maladaptive emotional regulation, leading to exaggerated neural and behavioral reactivity to negative, aversive experiences. However, such detrimental consequences are paradoxically aligned with the perplexing antidepressant benefit of sleep deprivation, elevating mood in a proportion of patients with major depression. Nevertheless, it remains unknown how sleep loss alters the dynamics of brain and behavioral reactivity to rewarding, positive emotional experiences. Using functional magnetic resonance imaging (fMRI), here we demonstrate that sleep deprivation amplifies reactivity throughout human mesolimbic reward brain networks in response to pleasure-evoking stimuli. In addition, this amplified reactivity was associated with enhanced connectivity in early primary visual processing pathways and extended limbic regions, yet with a reduction in coupling with medial frontal and orbitofrontal regions. These neural changes were accompanied by a biased increase in the number of emotional stimuli judged as pleasant in the sleep-deprived group, the extent of which exclusively correlated with activity in mesolimbic regions. Together, these data support a view that sleep deprivation not only is associated with enhanced reactivity toward negative stimuli, but imposes a bidirectional nature of affective imbalance, associated with amplified reward-relevant reactivity toward pleasure-evoking stimuli also. Such findings may offer a neural foundation on which to consider interactions between sleep loss and emotional reactivity in a variety of clinical mood disorders.


Factors contributing to attrition behavior in diabetes self-management programs: A mixed method approach

BACKGROUND
Diabetes self-management education is a critical component in diabetes care. Despite worldwide efforts to develop efficacious DSME programs, high attrition rates are often reported in clinical practice. The objective of this study was to examine factors that may contribute to attrition behavior in diabetes self-management programs.

METHODS
We conducted telephone interviews with individuals who had Type 2 diabetes (n = 267) and attended a diabetes education centre. Multivariable logistic regression was performed to identify factors associated with attrition behavior. Forty-four percent of participants (n = 118) withdrew prematurely from the program and were asked an open-ended question regarding their discontinuation of services. We used content analysis to code and generate themes, which were then organized under the Behavioral Model of Health Service Utilization.

RESULTS
Working full and part-time, being over 65 years of age, having a regular primary care physician or fewer diabetes symptoms were contributing factors to attrition behaviour in our multivariable logistic regression. The most common reasons given by participants for attrition from the program were conflict between their work schedules and the centre's hours of operation, patients' confidence in their own knowledge and ability when managing their diabetes, apathy towards diabetes education, distance to the centre, forgetfulness, regular physician consultation, low perceived seriousness of diabetes, and lack of familiarity with the centre and its services. There was considerable overlap between our quantitative and qualitative results.

CONCLUSION
Reducing attrition behaviour requires a range of strategies targeted towards delivering convenient and accessible services, familiarizing individuals with these services, increasing communication between centres and their patients, and creating better partnerships between centres and primary care physicians.


Bit-Cell Level Optimization for Non-volatile Memories Using Magnetic Tunnel Junctions and Spin-Transfer Torque Switching

Spin-transfer torque magnetic random access memories (STT-MRAM), using magnetic tunnel junctions (MTJ), is a resistive memory technology that has spurred significant research interest due to its potential for on-chip, high-density, high-speed, low-power, and non-volatile memory. However, due to conflicting read and write requirements, there is a need to develop optimization techniques for designing STT-MRAM bit-cells to minimize read and write failures. We propose an optimization technique that minimizes read and write failures by proper selection of bit-cell configuration and by proper access transistor sizing. A mixed-mode simulation framework was developed to evaluate the effectiveness of our optimization technique. Our simulation framework captures the transport physics in the MTJ using Non-Equilibrium Green's Function method and self-consistently solves the MTJ magnetization dynamics using Landau-Lifshitz-Gilbert equation augmented with the full Slonczewski spin-torque term. The electrical parameters of the MTJ are then encapsulated in a Verilog-A model and used in HSPICE to perform bit-cell level optimization. The optimization technique is applied to STT-MRAM bit-cells designed using 45 nm bulk and 45 nm silicon-on-insulator CMOS technologies. Finally, predictions are made for optimized STT-MRAM bit-cells designed in 16 nm predictive technology.


State of health monitoring of Li-ion batteries using dynamic resistance mapping and regression

The increase in speed and accuracy of computation methods and the advancement in sensor technologies has shifted the focus towards health monitoring systems of vehicles. Li-ion batteries which are used in electric vehicles, tend to get damaged and pose potential safety threats when ill-used. An adaptive control system called battery management system is needed to ensure the safety and life optimization of batteries in electric vehicles. This system takes care of both diagnosis and prognosis, giving the user an idea of current status as well as remaining lifetime of the battery. A novel algorithm based on dynamic resistance mapping is used to develop a data driven computational model for estimating the state of health of battery. A model with a correlation co-efficient of 99.45% is developed for state of health estimation to find the health of the battery during run time.


Metamorphic Testing: A Review of Challenges and Opportunities

Metamorphic testing is an approach to both test case generation and test result verification. A central element is a set of metamorphic relations, which are necessary properties of the target function or algorithm in relation to multiple inputs and their expected outputs. Since its first publication, we have witnessed a rapidly increasing body of work examining metamorphic testing from various perspectives, including metamorphic relation identification, test case generation, integration with other software engineering techniques, and the validation and evaluation of software systems. In this article, we review the current research of metamorphic testing and discuss the challenges yet to be addressed. We also present visions for further improvement of metamorphic testing and highlight opportunities for new research.


Adapted Neuro-Fuzzy Inference System on indirect approach TSK fuzzy rule base for stock market analysis

Nowadays because of the complicated nature of making decision in stock market and making real-time strategy for buying and selling stock via portfolio selection and maintenance, many research papers has involved stock price prediction issue. Low accuracy resulted by models may increase trade cost such as commission cost in more sequenced buy and sell signals because of insignificant alarms and otherwise bad diagnosis in price trend do not satisfy trader’s expectation and may involved him/her in irrecoverable cost. Therefore, in this paper, Neuro-Fuzzy Inference System adopted on a Takagi–Sugeno–Kang (TSK) type Fuzzy Rule Based System is developed for stock price prediction. The TSK fuzzy model applies the technical index as the input variables and the consequent part is a linear combination of the input variables. Fuzzy C-Mean clustering implemented for identifying number of rules. Initial membership function of the premise part approximately defined as Gaussian function. TSK parameters tuned by Adaptive NeroFuzzy Inference System (ANFIS). Proposed model is tested on the Tehran Stock Exchange Indexes (TEPIX). This index with high accuracy near by 97.8% has successfully forecasted with several experimental tests from different sectors. 2009 Published by Elsevier Ltd.