Miracle-dz/UniFAR-FTUs huggingface dataset:
Successful Regeneration After Experimental Stroke by Granulocyte-Colony Stimulating Factor Is Not Further Enhanced by Constraint-Induced Movement Therapy Either in Concurrent or in Sequential Combination Therapy

Background and Purpose— Both application of granulocyte-colony stimulating factor (G-CSF) and constraint-induced movement therapy (CIMT) have been shown to improve outcome after experimental stroke. The aim of the present study was to determine whether concurrent or sequential combination of both therapies will further enhance therapeutic benefit and whether specific modifications in the abundance of various neurotransmitter receptors do occur. Methods— Adult male Wistar rats were subjected to photothrombotic ischemia and assigned to the following treatment groups (n=20 each): (1) ischemic control (saline); (2) CIMT (CIMT between poststroke Days 2 and 11; (3) G-CSF (10 &mgr;g/kg G-CSF daily between poststroke Days 2 and 11; (4) combined concurrent group (CIMT plus 10 &mgr;g/kg G-CSF daily between poststroke Days 2 and 11; and (5) combined sequential group (CIMT between poststroke Days 2 and 11 and 10 &mgr;g/kg G-CSF daily between poststroke Days 12 and 21, respectively). Rats were functionally tested before and up to 4 weeks after ischemia. Quantitative receptor autography was performed for N-methyl-D-aspartate, AMPA, and GABAA receptors. Results— Significant improvement of functional outcome was seen in all groups treated with G-CSF alone and in either combination with CIMT, whereas CIMT alone failed to enhance recovery. Infarct sizes and remaining cortical tissue did not differ in the various treatment groups. Failure of significant benefit in the CIMT group was associated with a shift toward inhibition in perilesional and remote cortical regions. Conclusions— Our findings disclose G-CSF as the major player for enhanced recovery after experimental stroke, preventing a shift toward inhibition as seen in the CIMT group.
What is trust in a recommender for software development?

Many recommendation systems have been built to aid software developers. Few have been evaluated using human-based evaluation strategies. In studying situations where recommendation systems have been used or might be used, we have observed that issues related to trust are important to a developer's acceptance of recommendations. In this position paper, we outline the trust issues that we have identified and suggest some mechanisms for promoting trust in recommendation systems aimed at software developers.
Fairness analysis with cost impact for Brasilia's Flight Information Region using reinforcement learning approach

To analyze fairness between passengers and airlines considering financial cost, the management of the adaptation for air traffic flow in a heuristic and dynamically manner is studied in this research. Multi-Agent theory with reinforcement learning approach is used as a basic methodology integrated with a system of Decision Support System Applied to Tactical Air Traffic Flow Management (SISCONFLUX). The objective to develop this model is to increase the safety preserve and reduce the air traffic congestions. Reward structure with evaluation functions of financial cost and delay's impact is proposed for related flights using real data from Brasilia's Flight Information Region (FIR-BS). With the developed model, the experimental results show that the time delay is 25% less than the results computed only by Graph Theory with the same data, and fairness considering financial cost factor can be used together with congestion scenario in the air traffic management without affecting safety and flow factors.
Optimization of Self-Organizing Maps Ensemble in Prediction

The knowledge discovery process encounters the difficulties to analyze large amount of data. Indeed, some theoretical problems related to high dimensional spaces then appear and degrade the predictive capacity of algorithms. In this paper, we propose a new methodology to get a better representation and prediction of huge datasets. For that purpose, an ensemble approach is used to overcome problems related to high dimensional spaces. Self-Organized Map, which allows both a fast learning and a navigation through the data is used like base classifiers to learn several feature subspaces. A genetic algorithm optimizes diversity of the ensemble thanks to an adapted error measure. The experimentations show that this measure helps to construct a concise ensemble keeping representation capabilities. Furthermore, this approach is competitive in prediction with Boosting and Random Forests.
Serial changes in highly sensitive troponin I assay and early diagnosis of myocardial infarction.

CONTEXT Introduction of highly sensitive troponin assays into clinical practice has substantially improved the evaluation of patients with chest pain. OBJECTIVE To evaluate the diagnostic performance of a highly sensitive troponin I (hsTnI) assay compared with a contemporary troponin I (cTnI) assay and their serial changes in the diagnosis of acute myocardial infarction (AMI). DESIGN, SETTING, AND PATIENTS A total of 1818 patients with suspected acute coronary syndrome were consecutively enrolled at the chest pain units of the University Heart Center Hamburg, the University Medical Center Mainz, and the Federal Armed Forces Hospital Koblenz, all in Germany, from 2007 to 2008. Twelve biomarkers including hsTnI (level of detection, 3.4 pg/mL) and cTnI (level of detection, 10 pg/mL) were measured on admission and after 3 and 6 hours. MAIN OUTCOME MEASURES Diagnostic performance for AMI of baseline and serial changes in hsTnI and cTnI results at 3 hours after admission to the emergency department. RESULTS Of the 1818 patients, 413 (22.7%) were diagnosed as having AMI. For discrimination of AMI, the area under the receiver operating characteristic (ROC) curve was 0.96 (95% CI, 0.95-0.97) for hsTnI on admission and 0.92 (95% CI, 0.90-0.94) for cTnI on admission. Both were superior to the other evaluated diagnostic biomarkers. The use of hsTnI at admission (with the diagnostic cutoff value at the 99th percentile of 30 pg/mL) had a sensitivity of 82.3% and a negative predictive value (for ruling out AMI) of 94.7%. The use of cTnI (with the diagnostic cutoff value at the 99th percentile of 32 pg/mL) at admission had a sensitivity of 79.4% and a negative predictive value of 94.0%. Using levels obtained at 3 hours after admission, the sensitivity was 98.2% and the negative predictive value was 99.4% for both hsTnI and cTnI assays. Combining the 99th percentile cutoff at admission with the serial change in troponin concentration within 3 hours, the positive predictive value (for ruling in AMI) for hsTnI increased from 75.1% at admission to 95.8% after 3 hours, and for cTnI increased from 80.9% at admission to 96.1% after 3 hours. CONCLUSIONS Among patients with suspected acute coronary syndrome, hsTnI or cTnI determination 3 hours after admission may facilitate early rule-out of AMI. A serial change in hsTnI or cTnI levels from admission (using the 99th percentile diagnostic cutoff value) to 3 hours after admission may facilitate an early diagnosis of AMI.
Gate reminder: a design case of a smart reminder

In this paper, we present the design case for Gate Reminder, a family shared home appliance located at the front door area that represents informative messages, reminding users of things they need to take and know before leaving home. For this project, we built a working prototype and conducted a number of usability and user experience evaluations. In the paper we describe (1) why we chose reminder as our research topic (2) what we found from the early phases of user research for the Gate Reminder (3) what design requirements and decisions we have established from our user study (4) how we designed the working prototype based on our design decisions and (5) what we have learned from our user experience evaluation. The requirements for effective reminding, the usability challenges in ubicomp application, issues in the current prototype and future developments will be presented throughout the paper as well.
A LOW COST FINGERPRINT RECOGNITION SYSTEM BASED ON WAVELET TRANSFORM

__________________________________________________________________________________________ Abstract— Fingerprint technique is used for identification and verification purposes as it develops a low cost and fast computing system. There are many approaches for fingerprint recognition. The two basic approaches for fingerprint identification are minutiae based approach and Pattern recognition approach. Now a day’s wavelet based approach is most preferred due to its time-frequency. We are presenting here this new Wavelet based approach and compare the results by Traditional DFT , Traditional FFT, FRIRV techniques. Keywords— Fingerprints, Wavelets Transform, Pattern, Texture. __________________________________________________________________________________________
Locomotion control of a snake-like robot based on velocity disturbance

This paper presents a novel method based on the velocity disturbance to control the locomotion of a snake-like robot. The inputs are joint torques which are derived from the dynamics equations. The traditional dynamics equations of a snake-like robot are complex, so a simplified dynamics equations are firstly derived based on the differential geometry theory. Then the velocity disturbance is introduced to the simplified dynamics equation and the control method is proposed. The locomotion gait of the snake-like robot is decided by the velocity disturbance. So controlling the velocity disturbance can control the locomotion of the snake-like robot. This method can also avoid the singular posture of the snake-like robot. The numerical simulation results present that the proposed method can control the locomotion of the snake-like robot.
The research of spacial beam focusing in near-field from a plane array based on amplitude weighting

In the near-field assumption, we present a method to realize the beam focusing by weighting the amplitude of the radiation of array element, and derive the formula for calculating the array element amplitude weighs with the two-dimensional array as simulation model. Suppose that the array focus on one point in the near-field, namely the focus function is a impulse function, we verified that it can realize the focus on one point in the near-field by the method of the amplitude weighting through the simulation, and we also simulate the focus method which are controlled by both the amplitude and phase, the experiment prove that we can get a great focus result and the directivity is good. The cost and complexity of beam generator are also reduced greatly by the control of array amplitude.
Processor Tagged Descriptors: A Data Structure for Compiling for Distributed-Memory Multicomputers

The computation partitioning, communication analysis, and optimization phases performed during compilation for distributed-memory multicomputers require an eecient way of describing distributed sets of iterations and regions of data. Processor Tagged Descriptors (PTDs) provide these capabilities through a single set representation parameterized by the processor location for each dimension of a virtual mesh. A uniform representation is maintained for every processor in the mesh, whether it is a boundary or an interior node. As a result, operations on the sets are very eecient because the eeect on all processors in a dimension can be captured in a single symbolic operation. In addition, PTDs are easily extended to an arbitrary number of dimensions, necessary for describing iteration sets in multiply nested loops as well as sections of multidimensional arrays. Using the symbolic features of PTDs it is also possible to generate code for variable numbers of processors, thereby allowing a compiled program to run unchanged on varying sized machines. The PARADIGM (PARAllelizing compiler for DIstributed-memory General-purpose Multicomputers) project at the University of Illinois utilizes PTDs to provide an automated means to parallelize serial programs for execution on distributed-memory multicomputers.
Invisible or Clichéd: How Are Women Represented in Business Cases?

Women represent just less than 50% of undergraduate business graduates and 36% of MBA graduates. Despite their strong presence in management education programs, women are noticeably absent from business case studies—a key pedagogical tool for instruction within management education programs worldwide. While case studies inform students about business processes, decision making, strategy, and leadership and management challenges, they also promote unintentional learning about gender. We argue that case studies contain a “hidden curriculum” that presents and reinforces implicit assumptions and stereotypes about women’s fitness to lead. Using NVivo 11 software to analyze the content of written cases, we examine the presence, absence, and representation of female and male protagonists in a sample of business cases published by a large business school case publisher. The findings offer comparative insights into the proportion of cases featuring female protagonists, the representation of women and men in leadership roles, and the characterizations of the female and male protagonists. Women protagonists were absent in more than 80% of cases, and when present, were portrayed as less visionary, risk taking, agentic, certain, and more emotional, cautious, and quality and detail oriented than men.
Formalizing cardinality-based feature models and their specialization

Feature modeling is an important approach to capture the commonalities and variabilities in system families and product lines. Cardinality-based feature modeling integrates a number of existing extensions of the original feature-modeling notation from Feature-Oriented Domain Analysis. Staged configuration is a process that allows the incremental configuration of cardinality-based feature models. It can be achieved by performing a step-wise specialization of the feature model. In this article, we argue that cardinality-based feature models can be interpreted as a special class of context-free grammars. We make this precise by specifying a translation from a feature model into a context-free grammar. Consequently, we provide a semantic interpretation for cardinality-based feature models by assigning an appropriate semantics to the language recognized by the corresponding grammar. Finally, we give an account on how feature model specialization can be formalized as transformations on the grammar equivalent of feature models. Copyright © 2005 John Wiley & Sons, Ltd.
Kernel generalized nonlinear discriminant analysis algorithm for pattern recognition

Linear discriminant analysis (LDA) is a very effective tool used for dimensionality reduction and feature extraction in pattern recognition. However, the LDA is inadequate to describe complex and nonlinear patterns. To solve this problem, kernel nonlinear discriminant analysis (K-NDA) has been proposed. Although successful in many cases, classic K-NDA also suffers from the small sample size problem (SSSP) and loses some significant discriminatory information as same as classic LDA. In this paper, a novel K-NDA, i.e., the kernel generalized nonlinear discriminant analysis (KG-NDA) algorithm is introduced to effectively overcome these problems and it also views the optimal discriminant vectors as a global transform in the feature space to some extent. It not only deals with the nonlinear problem, but also effectively solves the SSSP. The KG-NDA is applied to the experiments on face recognition and the results tested on two popular databases demonstrate that this method is very effective.
An experiment in medical information retrieval

This work represents a feasibility study of effective conceptual information retrieval for medical / health care information. We focused on the OHSUMED collection because it supports the algorithms chosen in the study. Much of the focus has been on adopting the method of combining Latent Semantic Indexing (LSI) [3] with neural network learning, which showed substantial effectiveness in the task of classification. A related technique, that of using LSI in a straight-forward way has also been studied. Our conclusion is that the learning approach, which worked well for classification, has not done significantly better than randomly, because of inadequate training data. However, the LSI method has done better than expected, and better than many other algorithms. The precision obtained using the LSI method is in the neighborhood of 40%. We conclude with a few lessons learned and future research directions.
Automatic Identification of Functional Clusters in fMRI Data using Spatial Information

In independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data, extracting a large number of maximally independent components provides a more refined functional segmentation of brain. However, such segmentation does not per se establish the relationships among different brain networks, and also selecting and classifying components can be challenging. In this work, we present a multidimensional ICA (MICA) scheme to achieve automatic component clustering. In this MICA framework, stable components are hierarchically grouped into clusters based on spatial information and higher-order statistics, instead of typically used temporal information and second-order correlation. The final cluster membership is determined using a statistical hypothesis testing method. The experimental results from both simulated and real fMRI data sets show that the use of only spatial information with higher-order statistics leads to physiologically meaningful dependence structure of brain networks, which is consistently identified across various ICA model orders and algorithms. In addition, we observe that components related to artifacts, including cerebrospinal fluid (CSF), arteries, and large draining veins, demonstrate a higher degree of dependence among them and encouragingly distinguished from other components of interest using our MICA approach.
Real-Time Camera Pose Estimation for Virtual Reality Navigation

In this paper, we present a method for using a mobile device (in our case, the Motorola Droid) as both a navigational interface and a display device for navigating virtual worlds. Using the camera as our input, we extract meaningful corners and calculate corner correspondences between successive frames. From these correspondences, we calculate a homography transform, and then decompose this into rotation and translation vectors. Finally, we use these vectors to properly re-project a virtual world on the screen. Various methods for speeding up the process are investigated in order to make the entire process fast enough for real-time use.
Novel cluster-based probability model for texture synthesis, classification, and compression

We present a new probabilistic modeling technique for high-dimensional vector sources, and consider its application to the problems of texture synthesis, classification, and compression. Our model combines kernel estimation with clustering, to obtain a semiparametric probability mass function estimate which summarizes -- rather than contains -- the training data. Because the model is cluster based, it is inferable from a limited set of training data, despite the model's high dimensionality. Moreover, its functional form allows recursive implementation that avoids exponential growth in required memory as the number of dimensions increases. Experimental results are presented for each of the three applications considered.
Hybrid CramÉr–Rao Bounds for Crustal Displacement Field Estimators in SAR Interferometry

This letter focuses on the performance achievable by spaceborne synthetic aperture radar interferometry (InSAR) in the estimation of line-of-sight crustal deformations from acquisitions over a distributed scatterer. Our model is suited for exploiting the hybrid Cramer-Rao bound (HCRB), where the unknowns are both deterministic parameters and stochastic variables. We take into account both target decorrelation and atmospheric phase screen (APS). This approach leads to a viable evaluation of InSAR performance as a function of system configuration, target decorrelation, and APS variance.
Shared scans on main memory column stores

Column stores and shared scans have been found to be effective techniques in order to improve performance for many workloads. Another recent hardware trend makes it possible to keep most data in main memory. This paper builds upon these trends and explores how to implement shared scans on column stores in main memory efficiently. In particular, this paper proposes new approaches to avoid unnecessary work and to best implement position lists in such a query processing architecture. Performance experiments with real workloads from the travel industry show the advantages of combining column stores and shared scans in main memory over traditional database architectures.
Understanding motives for aggression in forensic psychiatric patients: a preliminary study

ABSTRACT The primary aim of this study was to explore motivations underpinning aggression among men detained within conditions of high security. Thirty men residing at a high secure psychiatric hospital completed self-report measures, including the Aggression Motivation Questionnaire, Revised EXPAGG and Barratt Impulsiveness Scale-IIr. The Historical items of the Historical, Clinical and Risk-Management (HCR-20) and the Psychopathy Checklist-Screening Version were rated. A subsample of participants agreed to complete a functional assessment on an aggressive incident that had occurred during their placement (n = 9). Increased psychopathy and impulsivity, and the presence of historical risk items were predicted to associate with higher levels of both aggression motivation and beliefs supportive of aggression. Young age at first violent incident and personality disorder related positively to aggression motivation. Thematic analysis conducted on the functional assessments identified social recognition, emotion regulation, communication and protection as functions underpinning aggression. Results are discussed with regards to their implication for violence treatment and assessment, with a focus on motivation recommended.
Complete genome sequence and comparative genomic analysis of an emerging human pathogen, serotype V Streptococcus agalactiae

The 2,160,267 bp genome sequence of Streptococcus agalactiae, the leading cause of bacterial sepsis, pneumonia, and meningitis in neonates in the U.S. and Europe, is predicted to encode 2,175 genes. Genome comparisons among S. agalactiae, Streptococcus pneumoniae, Streptococcus pyogenes, and the other completely sequenced genomes identified genes specific to the streptococci and to S. agalactiae. These in silico analyses, combined with comparative genome hybridization experiments between the sequenced serotype V strain 2603 V/R and 19 S. agalactiae strains from several serotypes using whole-genome microarrays, revealed the genetic heterogeneity among S. agalactiae strains, even of the same serotype, and provided insights into the evolution of virulence mechanisms.
Using Sense Clustering for the Disambiguation of Words (pp. 23-28)

Clustering methods have been extensively used in the solution of many Information Processing tasks in order to capture unknown object categories. This paper presents an approach to Word Sense Disambiguation based on clustering. The underlying idea is that the clustering of word senses provides a useful way to discover semantically related senses. We evaluate our proposal regarding both fine- and coarse-grained disambiguation. Experimental results over Senseval-3 all-words, SemCor 2.0 and SemEval-2007 corpora are presented. Promising values of precision and recall are obtained. Index Terms—Word sense disambiguation, clustering.