Miracle-dz/UniFAR-FTUs huggingface dataset:
Androgen receptor exon 1 CAG repeat length and breast cancer in women before age forty years.

BACKGROUND We conducted a population-based, case-control-family study to determine whether androgen receptor (AR) exon 1 polymorphic CAG repeat length (CAGn) was a risk factor for early-onset breast cancer in the Australian population. METHODS Case subjects under 40 years of age at diagnosis of a first primary breast cancer and age-matched control subjects were interviewed to assess family history and other risk factors. AR CAGn length was determined for 368 case subjects and 284 control subjects. Distributions in the two groups were compared by linear and logistic regression, allowing adjustment for measured risk factors. All statistical tests were two-tailed. RESULTS When analyzed as either a continuous or a dichotomous variable, there was no association between CAG, length and breast cancer risk, before or after adjustment for risk factors. Mean (95% confidence interval [CI]) CAGn lengths were 22.0 (21.8-22.2) for case subjects and 22.0 (21.7-22.3) for control subjects (P = .9). The frequency (95% CI) of alleles with 22 or more CAGn repeats was 0.531 (0.494-0.568) for case subjects and 0.507 (0.465-0.549) for control subjects (P = .4). After adjustment, the average effect on log OR (odds ratio) per allele was 0.16 (95% CI = -0.03 to 0.40; P = .2), and the effect of any allele was equivalent to an OR of 1.40 (95% CI = 0.94-2.09; P = .1). Stratification by family history also failed to reveal any association. Similar results were obtained when alleles were defined by other cutoff points. CONCLUSION We found no evidence for an association between AR exon 1 CAGn length and breast cancer risk in women under the age of 40, despite having 80% power to detect modest effects.
CIS-positive: A Combination of Convolutional Neural Networks and Support Vector Machines for Sentiment Analysis in Twitter

This paper describes our automatic sentiment analysis system – CIS-positive – for SemEval 2015 Task 10 “Sentiment Analysis in Twitter”, subtask B “Message Polarity Classification”. In this system, we propose to normalize the Twitter data in a way that maximizes the coverage of sentiment lexicons and minimizes distracting elements. Furthermore, we integrate the output of Convolutional Neural Networks into Support Vector Machines for the polarity classification. Our system achieves a macro F1 score of the positive and negative class of 59.57 on the SemEval 2015 test data.
Fractal topologies for efficient solar energy harvesting systems

When constructing efficient solar energy harvesting systems, it is important to connect the photovoltaic (PV) elements in the configuration which maximizes the absorbed solar energy. This undoubtedly gives the best possible efficiency for the system. The usage of fractal topologies to arrange the PV elements spatially is suggested. Our goal is to find the best possible configuration for the same number of elements. Carbon nanotubes (CNTs) diodes are chosen to be the PV elements. The interaction between electromagnetic waves and the CNT array is simulated using a Multi-physics SW package in order to calculate the amount of absorbed energy.
Fast traffic sign detection and recognition under changing lighting conditions

In this work a system for traffic-sign detection and classification is shown. It is intended for both prohibition and obligation circular signs and for advertising triangular ones. The system is divided into three stages: first, detection, using the Hough transform from the information of the edges of the image; second, classification, using a neural network, and third, tracking, making use of a Kalman filter, which provides the system with memory. Some results are presented, obtained by real images recorded by only one camera placed on board a conventional vehicle, in sunny days, and also cloudy, rainy ones or at night, in order to show the reliability and robustness of the system. The average processing time is 30 ms per frame, what makes the system a good approach to work in real time conditions
Joint relay selection and channel allocation in cooperative communication: A game theoretic learning solution

This article investigates the issue of joint relay selection and channel allocation in LTE relay networks, where the available channels of each node are restricted and heterogeneous. We decompose the issue into two subproblems: one is the channel selection of relay to mitigate the interference among relay nodes and another is joint relay and channel selection of source node to maximize the total capacity. The first subproblem is formulated as a global interaction game and a learning algorithm is proposed to achieve the Nash equilibrium. Based on the result of the first subproblem, the second one is formulated as a congestion game with player-specific payoff function. We propose a distributed solution to maximize the total capacity. Simulation results show that our approach can obtain a large total capacity and a high fairness index.
Continuum Robots : An Approach to Safer Robots

There is a growing need for robots that can not only function in close proximity to human beings but also physically interact with them safely. This calls for a paradigm shift in the way robots are designed. Towards this end, we propose the use of continuum robots to improve safety in physical human robot interaction (pHRI). Continuum robots have been looked at in the past for applications which involve access to convoluted spaces, such as in non-invasive surgery, but not in the context of safe pHRI. We study the key issue of compliance distribution in soft systems and its effect on safety and performance of a manipulator. We also study the analytical and experimental kinematics of a prototype manipulator with distributed compliance: a 2 DoF continuum type arm, the Tubot.
Image Classification Using Segmentation Graph

Introducing a multiregion graph cut image partitioning through kernel mapping of the image data. The proposed func- tional contains two terms: an original kernel-induced term which evaluates the deviation of the mapped image data within each region from the piecewise constant model and a regularization term expressed as a function of the region indices. Using a com- mon kernel function, the objective functional minimization is carried out by iterations of two consecutive steps: 1) minimization with respect to the image segmentation by graph cuts 2) minimization with respect to the regions parameters via fixed point computation
A hard real-time robot communication approach based on preemptive CSMA/CA

In the cooperative interaction between multiple complex robots, preemption capability in real-time communication is critical. However, the applications of universal approaches have the limitation in robot communications. In this paper, an improved approach is presented based on preemptive CSMA/CA (Carrier Sense Multiple Access/Collision Avoidance) method to boost the real-time capability in the priority-based transmission. By analyzing the influencing factors of real-time capability in multi-robot communication, the improved approach of preemptive CSMA/CA is presented, the corresponding protocol is designed in robot network application, and then the node module is implemented based on ARM processor. Finally, experimental scheme and platform are designed, and the result establishes that the improved preemptive approach is better than CSMA/CD (Carrier Sense Multiple Access/Collision Detect) in priority-based transmission.
A Regularized Correntropy Framework for Robust Pattern Recognition

This letter proposes a new multiple linear regression model using regularized correntropy for robust pattern recognition. First, we motivate the use of correntropy to improve the robustness of the classical mean square error (MSE) criterion that is sensitive to outliers. Then an l1 regularization scheme is imposed on the correntropy to learn robust and sparse representations. Based on the half-quadratic optimization technique, we propose a novel algorithm to solve the nonlinear optimization problem. Second, we develop a new correntropy-based classifier based on the learned regularization scheme for robust object recognition. Extensive experiments over several applications confirm that the correntropy-based l1 regularization can improve recognition accuracy and receiver operator characteristic curves under noise corruption and occlusion.
Low-cost robot arm with 3-DOF counterbalance mechanism

Low-cost but high performance robot arms are required for widespread use of service robots. Most robot arms use expensive motors and speed reducers to provide torques sufficient to support the robot mass and payload. If the gravitational torques due to the robot mass, which is usually much greater than the payload, can be compensated by some means, the robot would need much smaller torques, which can be delivered by cheap actuator modules. To this end, we propose a counterbalance mechanism which can completely counterbalance the gravitational torques due to the robot mass. Since most 6 DOF robots have 3 pitch joints, which are subject to gravitational torques due to the robot mass, we propose a 3 DOF counterbalance mechanism based on the double parallelogram mechanism, in which reference planes are provided to each joint for proper counterbalancing. A 5 DOF counterbalance robot arm was built to demonstrate the performance of the proposed mechanism. Simulation and experimental results showed that the proposed mechanism had effectively decreased the torque required to support the robot mass, thus allowing the prospective use of low-cost motors and speed reducers for high performance root arms.
2 past Research: Information Retrieval Models

My research interests lie in Machine Learning (ML) and its ap plications to Information Retrieval (IR), Text analysis and Data Mining (DM). These applied areas have historically been driven by empirical approaches. While these approaches have been quite successful in terms of performan ce, o e of their major drawbacks is their lack of easy interpretability. It is my firm belief that machine learning approaches are a key towards solving some of the complex problems in text analysis and IR for the following reasons: ( i) ML techniques are theoretically well grounded, making them easily interpretable, (ii) the theoretical framework f ML techniques acts as a guidance for future researchers to build extensions to existing models and (iii) the theoret ical foundation also makes the models generalizable and portable across different tasks and domains. In line with this philosophy, my research has thus far been ge ared towards advancing the state-of-the-art technology in the above mentioned areas using theoretically well motiv ated, machine learning based approaches. While staying within the confines of this broad objective, I consciously ma de n effort not to limit myself to any particular school of thought and retained the flexibility to build models in div erse frameworks such as discriminative and generative, supervised and unsupervised approaches. I started out my research career as a graduate student in IR. I describe several theoretical contributions I made to this area in section 2. Not content with the knowledge I gained in m y graduate study, I accepted a post doctoral fellowship in the Machine Learning department of Carnegie Mellon Unive rsity (CMU), with a strong desire to sharpen my skills in Machine Learning. In section 3, I discuss my current resea rch t CMU on topic modeling for data mining from large document collections. Section 4 describes my future r es arch directions and section 5 concludes the statement with a discussion on my philosophy on other matters pertaini ng to a successful research career.
An Agent-Based Approach to Assess Drivers' Interaction with Pre-Trip Information Systems

Abstract This article reports on the practical use of a multi-agent microsimulation framework to address the issue of assessing drivers' responses to pretrip information systems. The population of drivers is represented as a community of autonomous agents, and travel demand results from the decision-making deliberation performed by each individual of the population as regards route and departure time. A simple simulation scenario was devised, where pretrip information was made available to users on an individual basis so that its effects at the aggregate level could be observed. The simulation results show that the overall performance of the system is very likely affected by exogenous information, and these results are ascribed to demand formation and network topology. The expressiveness offered by cognitive approaches based on predicate logics, such as the one used in this research, appears to be a promising approximation to fostering more complex behavior modelling, allowing us to represent many of the mental aspects involved in the deliberation process.
Young Persons and Stateelevel Unemployment: the Role of the Housing Market Young Persons and Stateelevel Unemployment: the Role of the Housing Market

A provocative paper by Robert Shimer (2001) nds that within individual states, a higher youth share of population tends to lower the state's unemployment rate, in contrast to nearly three decades of conventional wisdom regarding the demographic determinants of unemployment. Shimer suggests that this empirical result can be explained by an externality that results from the introduction of on{the{job search into a standard matching model of the labor market. I argue that Shimer's empirical fact has a simpler explanation: Young persons raise the demand for housing, which increases state{level labor demand and lowers unemployment. Empirical work that accounts for the construction channel shows that Shimer's empirical nding points to the importance of the demographic demand for housing in state{level economies, not search{based externalities. Address. Department of Economics, Littauer Center, Cambridge MA, 02138 Young Persons and State{Level Unemployment: The Role of the Housing Market Christopher L. Foote
A Review on Wireless Grid Computing

Grid Computing is a concept, a network, a work in progress, part hype and part reality, and it is increasingly capturing the attention of the computing community. It uses clusters of personal computers, servers or other machines. They link together to tackle complex calculations. In part, grid computing lets companies harness their unused computing power, or processing cycles, to create a type of supercomputer. Wireless grids extend the capability of grid computing to wireless devices. The number of users using laptops, PDAs, cell phones, and other wireless devices is increasing leading to more networked wireless devices, and creating a vast collective potential of unexploited resources. Wireless grid computing with its model of coordinated resource sharing may provide a way to utilize such resources that are normally distributed throughout a grid. We may have Gridnet in the future as we have Internet today. This paper presents a state-of-the-art review of wireless grid computing. while the resources of these devices can be processor, memory, bandwidth, code repositories, softwares, etc. (5). Generally wireless devices are characterized by reduced CPU performance, small secondary storage, heightened battery consumption sensitivity, and unreliable low-bandwidth communication. But these devices also may include such additions as cameras, microphones, bar code and RFID readers, GPS receivers and satellite receiver/ transmitters as well as a wide variety of special purpose sensors. Among the many types of sensors currently available are those that measure temperature, air pressure and humidity, those that detect movement, and those that measure radiation and particulate levels. In general characteristics of wireless grid can be summarized as follows: • No centralized control. • Consists of small, low powered devices. • Includes heterogeneous resources, applications and interfaces.
A Fourier transform-based method for convertible bonds in a jump diffusion setting with stochastic interest rates

The paper proposes a fast Fourier transform (FFT) pricing algorithm for convertible bonds in a framework which comprises firm value, evolving as an exponential jump diffusion, and correlated stochastic interest rates movements. This is a novel numerical technique for the convertible bonds literature and aims at fixing dimensionality and convergence limitations previously reported for popular grid-based, lattice and Monte Carlo methods. We define the firm’s optimal call policy and investigate its impact on the computed convertible bond prices. We illustrate the performance of the numerical scheme and highlight the effects originated by the inclusion of jumps.
Voicing-Character Estimation of Speech Spectra: Application to Noise Robust Speech Recognition

This paper presents a novel method for estimating the voicing-character of speech spectra, demonstrates its employment in noise robust ASR and proposes a modified calculation of filter-bank energies. The proposed voicing-character estimation is based on calculation of a similarity between the shape of the signal short-term magnitude spectra around spectral peaks and spectra of the frame-analysis window. The similarity is weighted by the signal magnitude spectra to reflect the filter-bank analysis typically used in feature extraction for speech recognition. The experimental results show less than 5% false-acceptance and false-rejection errors in detection of voiced filter-bank channels in speech signal corrupted by white noise at 10 dB local SNR. The recognition results obtained by a missing-feature based ASR system using features estimated as voiced by the proposed method are very similar to using oracle voicing-label obtained by full a-priori knowledge of noise. The employment of features obtained by a modified calculation of filter-bank energies shows further improvements in the recognition accuracy
Addressing Scalability and Consistency Issues in Hybrid File System for BPRAM and NAND Flash

Recently, a number of work suggested to use ByteAddressable NVRAM(BPRAM) to address the drawbacks of NAND Flash based file system. These works use BPRAM to reduce write latency of NAND Flash, or to reduce the mount latency of log-structured file system for NAND Flash. Our work is aligned with the preceding works in that we develop a hybrid file system technique for NAND Flash and BPRAM. In this work, we address the scalability issue of BPRAM in a hybrid file system. Given the current state of art device technology, e.g. NAND Flash and FRAM, we believe that BPRAM size and NAND Flash size should be 1:1000 in hierarchical storage system and file system structure should be designed to satisfy this storage hierarchy. In our hybrid file system, BPRAM is responsible for harboring metadata in the file system. In the current widely used file system for NAND Flash, there are 16 Byte metadata for 512 Bytes of data (BPRAM to NAND size ratio is 3:100). To address the discrepancy in scalability between BPRAM and NAND Flash, we develop mount related compact metadata data structure for BPRAM andhybrid compressiontechnique which is specifically tailored for compressing file metadata for log-structured file system. We develop a flexible yet efficient data structure to represent a set of compressed metadata while effectively coping with size variability caused by compression. As a result, we dramatically reduce the file metadata size. We implement the file system in PXA320 core with Linux 2.6. The file system mount time and file system recovery time decrease by order of magnitude.
Maintenance of Informative Rule Sets for Predictions 1

An Informative Rule Set (IRS) is the smallest subset of an association rule set such that it has the same prediction sequence by confidence priority [9]. The problem of maintenance of IRS is a process by which, given a transaction database and its IRS, when the database receives insertion, deletion, or modification, we wish to maintain the IRS as efficiently as possible. Based on the Fast UPdating technique (FUP) [5] for the updating of discovered association rules, we propose here two algorithms to update the discovered IRS when the database is updated by insertion and deletion respectively. Numerical comparisons with the non-incremental informative rule set approach show that our proposed techniques require less computation time, due to less database scanning and less number of candidate rules generated.
Nutritional screening in surgical patients of a teaching hospital from Southern Brazil: the impact of nutritional risk in clinical outcomes

ABSTRACT Objective: To assess the prevalence of nutritional risk in surgical patients of a teaching hospital and its associated factors. Methods: A cross-sectional study with secondary data of surgical ward patients of the Hospital Escola da Universidade Federal de Pelotas, from April to October, 2010. Patients were evaluated up to 36 hours after admission using the Malnutrition Screening Tool. Results: The study included 565 patients, with a mean age of 52.8±15.6 years, and the majority (51%) was female. More than 30% of the patients presented with an average or high nutritional risk, and 7% of them were at high risk. Associated with the greater risk were aging, cancer surgery, and mortality. The length of hospital stay showed a linear increase according to nutritional risk. Conclusion: The Malnutrition Screening Tool is a simple and effective tool for nutritional screening that does not require anthropometric measurements. In this study, average or high nutritional risk was prevalent in one third of the sample, and was related to increased mortality, hospital stay, cancer, and aging. Nutritional care outpatients’ protocols could be used prior to elective surgery to reduce the nutritional risk of these patients, improving clinical outcomes and reducing length and costs of hospital stay.
Traditional Oriental Herbal Medicine for Children and Adolescents with ADHD: A Systematic Review

Objective. To evaluate the efficacy of traditional Oriental herbal medicines (TOHM) for children and adolescents with ADHD. Methods. Randomized clinical trials published from January 1, 1990, to December 31, 2010, in English, Chinese, Japanese, or Korean language which evaluated the use of TOHM on ADHD subjects of 18 years old or below, diagnosed based on DSM-IV, were searched from MEDLINE, EMBASE, PsyINFO, Cochrane Library, and 10 other databases. Results. Twelve studies involving 1189 subjects met the inclusion criteria. In general, the included studies claimed that TOHM has similar efficacy to methylphenidate and at the same time has fewer side effects compared to methylphenidate. Some studies also suggested that the effect of TOHM sustained better than methylphenidate. However, solid conclusions could not be drawn because the included studies were not of high quality. Risk of bias issues such as randomization, allocation, concealment and blinding were not addressed in most of the studies, and the risk of publication bias could not be ruled out. Conclusion. Currently, there is not strong evidence to say that TOHM is effective in treating the core symptoms of ADHD.