Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors Influencing Quality of Life of Obese Students in Hangzhou, China

Objective To evaluate the quality of life (QOL) of overweight and obese middle or high school students and identify relevant factors influencing their QOL scores. Methods 716 students were recruited from 6 middle or high schools in Hangzhou, China. The Chinese version of the Youth Quality of Life Instrument–Weight Module (YQOL-W) was self administered. The YQOL-W scores were compared among different BMI groups, gender, educational status, annual household income, parental education and recruitment community using t test or one-way analysis of variance. The independent association of these variables with QOL among overweight and obese students was examined using multivariable linear regression modeling. Results Overweight and obese students reported lower total scores, self, social and environment scores than their normal weight peers (all P<0.001). The QOL of overweight and obese middle and high school students was associated with BMI value, gender, educational status, parental education, and recruitment community. Girls had lower total scores, self, social and environment domain scores than boys (all P<0.001); high school students had lower total and three domain scores than middle school students (all P<0.05). Students whose fathers had higher education reported higher total scores, self and social scores than students with less educated fathers (all P<0.05). Students whose mothers had higher education reported higher environment scores than students with less educated mothers (P = 0.01). Students from migrant communities reported significantly lower total scores, self and social scores than those from rural communities (all P<0.05), but comparable scores with those from urban communities (P>0.05). Students from migrant communities reported comparable environment scores with those from rural and urban communities (P>0.05). Conclusions Overweight and obesity have negative effects on students’ quality of life. Therefore weight specific QOL could be included in weight reduction interventions as a relevant outcome.
Implementation of a sensitive troponin I assay and risk of recurrent myocardial infarction and death in patients with suspected acute coronary syndrome.

CONTEXT Although troponin assays have become increasingly more sensitive, it is unclear whether further reductions in the threshold of detection for plasma troponin concentrations will improve clinical outcomes in patients with suspected acute coronary syndrome (ACS). OBJECTIVE To determine whether lowering the diagnostic threshold for myocardial infarction (MI) with a sensitive troponin assay could improve clinical outcomes. DESIGN, SETTING, AND PATIENTS All consecutive patients admitted with suspected ACS to the Royal Infirmary of Edinburgh, Edinburgh, Scotland, before (n = 1038; February 1-July 31, 2008, during the validation phase) and after (n = 1054; February 1-July 31, 2009, during the implementation phase) lowering the threshold of detection for myocardial necrosis from 0.20 to 0.05 ng/mL with a sensitive troponin I assay were stratified into 3 groups (<0.05 ng/mL, 0.05-0.19 ng/mL, and ≥0.20 ng/mL). During the validation phase, only concentrations above the original diagnostic threshold of 0.20 ng/mL were reported to clinicians. MAIN OUTCOME MEASURE Event-free survival (recurrent MI and death) at 1 year in patients grouped by plasma troponin concentrations. RESULTS Plasma troponin concentrations were less than 0.05 ng/mL in 1340 patients (64%), 0.05 to 0.19 ng/mL in 170 patients (8%), and 0.20 ng/mL or more in 582 patients (28%). During the validation phase, 39% of patients with plasma troponin concentrations of 0.05 to 0.19 ng/mL were dead or had recurrent MI at 1 year compared with 7% and 24% of those patients with troponin concentrations of less than 0.05 ng/mL (P < .001) or 0.20 ng/mL or more (P = .007), respectively. During the implementation phase, lowering the diagnostic threshold to 0.05 ng/mL was associated with a lower risk of death and recurrent MI (from 39% to 21%) in patients with troponin concentrations of 0.05 to 0.19 ng/mL (odds ratio, 0.42; 95% confidence interval, 0.24-0.84; P = .01). CONCLUSIONS In patients with suspected ACS, implementation of a sensitive troponin assay increased the diagnosis of MI and identified patients at high risk of recurrent MI and death. Lowering the diagnostic threshold of plasma troponin was associated with major reductions in morbidity and mortality.
Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses

A survey of pre/post-test data using the Halloun–Hestenes Mechanics Diagnostic test or more recent Force Concept Inventory is reported for 62 introductory physics courses enrolling a total number of students N=6542. A consistent analysis over diverse student populations in high schools, colleges, and universities is obtained if a rough measure of the average effectiveness of a course in promoting conceptual understanding is taken to be the average normalized gain 〈g〉. The latter is defined as the ratio of the actual average gain (%〈post〉−%〈pre〉) to the maximum possible average gain (100−%〈pre〉). Fourteen “traditional” (T) courses (N=2084) which made little or no use of interactive-engagement (IE) methods achieved an average gain 〈g〉T-ave=0.23±0.04 (std dev). In sharp contrast, 48 courses (N=4458) which made substantial use of IE methods achieved an average gain 〈g〉IE-ave=0.48±0.14 (std dev), almost two standard deviations of 〈g〉IE-ave above that of the traditional courses. Results for 30 (N=3259) of the a.
A biased random-key genetic algorithm for OSPF and DEFT routing to minimize network congestion

Interior gateway routing protocols like OSPF (Open Shortest Path First) and DEFT (Distributed Exponentially-Weighted Flow Splitting) send flow through forward links towards the destination node. OSPF routes only on shortest-weight paths, whereas DEFT sends flow on all forward links, but with an exponential penalty on longer paths. Finding suitable weights for these protocols is known as the weight setting problem. In this paper, we present a biased random-key genetic algorithm for the weight setting prob- lem using both protocols. The algorithm uses dynamic flow and dynamic shortest path computations. We report computational experiments that show that DEFT achieves less network congestion when compared with OSPF, while, however, yielding larger delays.
One Size Fits All? Part 2: Benchmarking Studies

Two years ago, some of us wrote a paper predicting the demise of “One Size Fits All (OSFA)” [Sto05a]. In that paper, we examined the stream processing and data warehouse markets and gave reasons for a substantial performance advantage to specialized architectures in both markets. Herein, we make three additional contributions. First, we present reasons why the same performance advantage is enjoyed by specialized implementations in the text processing market. Second, the major contribution of the paper is to show “apples to apples” performance numbers between commercial implementations of specialized architectures and relational DBMSs in both stream processing and data warehouses. Finally, we also show comparison numbers between an academic prototype of a specialized architecture for scientific and intelligence applications, a relational DBMS, and a widely used mathematical computation tool. In summary, there appear to be at least four markets where specialized architectures enjoy an overwhelming performance advantage. 1. The History of the OSFA Architecture
Power loading for CP-OFDM over frequency-selective fading channels

We compare cyclic prefix (CP) based single and multicarrier block transmission schemes in frequency-selective fading channels. Analytical comparison shows that at moderate-to-high signal-to-noise ratio (SNR), the uncoded error rate performance of multicarrier transmission is inferior to that of single carrier. We propose new minimum bit error rate (MBER) power loading algorithms for multicarrier transmission. It is also shown that a simpler approximate MBER (AMBER) power loading method has performance close to that of the optimum MBER scheme. Performance of a variety of methods is compared analytically and verified by simulations.
Quantitative Evaluation of Side-Channel Security

There have been enormous research efforts dedicated to new countermeasures against side-channel attacks. However, very few reports were published about how to quantitatively verify their effectiveness. This paper presents statistical approaches of quantitative evaluation in respect of time disarrangement and side-channel magnitude confusion. The statistical mean value of the cross correlation coefficients measures the effectiveness of time disarrangement. The difference between the highest two correlation factors out of the key guesses is assessed to evaluate the effectiveness of side-channel magnitude confusion. Experiment results demonstrate that the proposed evaluation approaches are accurate and feasible. Eliminating requirement of reference makes the evaluations applicable in objective assessment of various devices by any designers or evaluators.
The Haves and the Have-Nots: Leveraging Unlabelled Corpora for Sentiment Analysis

Expensive feature engineering based on WordNet senses has been shown to be useful for document level sentiment classification. A plausible reason for such a performance improvement is the reduction in data sparsity. However, such a reduction could be achieved with a lesser effort through the means of syntagma based word clustering. In this paper, the problem of data sparsity in sentiment analysis, both monolingual and cross-lingual, is addressed through the means of clustering. Experiments show that cluster based data sparsity reduction leads to performance better than sense based classification for sentiment analysis at document level. Similar idea is applied to Cross Lingual Sentiment Analysis (CLSA), and it is shown that reduction in data sparsity (after translation or bilingual-mapping) produces accuracy higher than Machine Translation based CLSA and sense based CLSA.
Semi-supervised method for gene expression data classification with Gaussian fields and harmonic functions

In real world applications, there are great many of DNA expressed microarray data, many supervised classification algorithms such as decision tree, KNN and SVM in the machine learning field have been introduced for microarray data classification. However, in real worlds, the labeled examples, especially gene expression data examples are often very difficult and expensive to obtain. The traditional supervised methods can not work well when lack of training examples. So in this paper, we propose to use the semi-supervised learning algorithms which learning with both labeled and unlabeled data to do classification for microarray data. We perform experiments on four public microarray data sets and the results showed the semi-supervised method holds a much higher classification accuracy than the supervised methods and is much more stable when the labeled examples are very few.
A rank-reduced LMMSE canceller for narrowband interference suppression in OFDM-based systems

We present a narrowband interference (NBI) canceller that suppresses spectral leakage in an orthogonal frequency-division multiplexing (OFDM)-based system caused by a narrowband (NB) signal. We assume that the spectrum of the NB signal is within the spectrum of the OFDM signal. This can be the case, e.g., on digital subscriber lines (DSL) and in new unlicensed frequency bands for radio transmission. The canceller makes linear minimum mean-square error estimates of the spectral leakage by measuring the NBI on a few modulated or unmodulated OFDM subcarriers. It uses a model of the NB signal's power spectral density as a priori information. Using a frequency invariant design, it is possible to cancel NBI from signals that are changing their frequency location with significantly reduced complexity overhead. The operational complexity of the canceller can be lowered by using the theory of optimal rank reduction and using the time-bandwidth product of the NB signal. Analytical performance evaluations, as well as Monte Carlo simulations, show that, without perfect a priori information, this canceller can suppress the spectral leakage from a strong NB signal (e.g., with equal power as the OFDM signal) to well below the background noise floor for typical applications where it causes negligible signal-to-noise ratio and symbol error rate degradation.
A cluster-based simulation of facet-based search

The recent increase of online video has challenged the research in the field of video information retrieval. Video search engines are becoming more and more interactive, helping the user to easily find what he or she is looking for. In this poster, we present a new approach of using an iterative clustering algorithm on text and visual features to simulate users creating new facets in a facet-based interface. Our experimental results prove the usefulness of such an approach.
A Domain-Decomposed Multi-Level Method for Adaptively Refined Cartesian Grids with Embedded Boundaries

The work presents a new method for on-the-fly domain decomposition technique for mapping grids and solution algorithms to parallel machines, and is applicable to both shared-memory and message-passing architectures. It will be demonstrated on the Cray T3E, HP Exemplar, and SGI Origin 2000. Computing time has been secured on all these platforms. The decomposition technique is an outgrowth of techniques used in computational physics for simulations of N-body problems and the event horizons of black holes, and has not been previously used by the CFD community. Since the technique offers on-the-fly partitioning, it offers a substantial increase in flexibility for computing in heterogeneous environments, where the number of available processors may not be known at the time of job submission. In addition, since it is dynamic it permits the job to be repartitioned without global communication in cases where additional processors become available after the simulation has begun, or in cases where dynamic mesh adaptation changes the mesh size during the course of a simulation. The platform for this partitioning strategy is a completely new Cartesian Euler solver tarcreted at parallel machines which may be used in conjunction with Ames' "Cart3D" arbitrary geometry simulation package.
A Neural Network Model for Real-Time Scheduling on Heterogeneous SoC Architectures

With increasing embedded application complexity, designers have proposed to introduce new hardware architectures based on heterogeneous processing units on a single chip. For these architectures, the scheduling service of a realtime operating system must be able to assign tasks on different execution resources. This paper presents a model of artificial neural networks used for real-time task scheduling to heterogeneous system-on-chip architectures. Our proposition is an adaptation of the Hopfield model and the main objective concerns the minimization of the neuron number to facilitate future hardware implementation of this service. In fact, to ensure rapid convergence and low complexity, this number must be dramatically reduced. So, we propose new constructing rules to design smaller neural network and we show, through simulations, that network stabilization is obtained without reinitialisation of the network.
Pseudo-Anchor Text Extraction for Vertical Search

Anchor text plays a special important role in improving the performance of general Web search. The importance of anchor text comes from the fact that it is fairly objective description for a Web page by potentially a large amount of other Web pages. Vertical search provides indexing and search functionality for objects in a certain domain, and is becoming an important supplement for general Web search. It is desired to utilize anchor text in vertical search as well to improve search performance. Vertical objects typically lack explicit URLs to accurately identify them. The anchor-text of a vertical object is also hard to acquire explicitly. This paper proposes concepts of pseudo-URL and pseudo-anchor-text for vertical objects, corresponding to the URL and anchor-text of a general Web page. For extracting and utilizing pseudo-anchor-text information of vertical objects, we focus on candidate anchor block accumulation and pseudo-anchor extraction in this paper. State-of-the-art data integration techniques are utilized to accumulate candidate anchor blocks belonging to same objects. Pseudo-anchor text for each object is extracted from its candidate anchor blocks using a machine learning based approach. A case study in academic search domain indicates that our approach is able to dramatically improve search performance.
Emerging collective intelligence in Othello players evolved by differential evolution

The evaluation function for game playing is very important. However, it is difficult to make a good evaluation function. In this study, we propose to play Othello using collective intelligence of players. The evaluation functions of the players are learned or optimized by Differential Evolution. The objective value is defined based on the total score of the games with a standard Othello player. In order to generate different types of players, the objective value is slightly changed by introducing the stability of each player. Each player can select a next move using the learned evaluation function. The collective intelligence player selects a move based on majority vote where the move voted by many players is selected. It is shown that the collective intelligence is effective to game players through computer simulation.
Generation Scheduling of Thermal Units Integrated with Wind-Battery System Using a Fuzzy Modified Differential Evolution Approach

This paper presents a fuzzy methodology for solving thermal unit commitment problem integrated with wind power system using differential evolution approach. Wind power facility is coupled with an equivalent battery to compensate with fre- quency and voltage fluctuations. Wind energy system is integrated with the system due to lower electric cost and positive effect on the environment. But due to the uncertainty of wind speed and hence wind power generation and load forecasting, a crisp opti- mization method may fail short providing the effective solution. Therefore, to solve the problem effectively, this model handles such imprecision by fuzzyfication. Then the unit commitment problem is solved by using a modified differential evolution approach. Trivial differential evolution method is modified to work with thermal scheduling problem which is a mixed-integer problem requiring discrete optimization. Several simulations are presented in order to demonstrate the effectiveness of the proposed method.
What is Cook's theorem?

In this paper, we make a preliminary interpretation of Cook's theorem presented in [1]. This interpretation reveals cognitive biases in the proof of Cook's theorem that arise from the attempt of constructing a formula in CNF to represent a computation of a nondeterministic Turing machine. Such cognitive biases are due to the lack of understanding about the essence of nondeterminism, and lead to the confusion between different levels of nondeterminism and determinism, thus cause the loss of nondeterminism from the NP-completeness theory. The work shows that Cook's theorem is the origin of the loss of nondeterminism in terms of the equivalence of the two definitions of NP, the one defining NP as the class of problems solvable by a nondeterministic Turing machine in polynomial time, and the other defining NP as the class of problems verifiable by a deterministic Turing machine in polynomial time. Therefore, we argue that fundamental difficulties in understanding P versus NP lie firstly at cognition level, then logic level.
A Novel Quality Evaluating Method for Over-Segmentation Approaches Using Real-Time Boundary Information

In this paper, we explore the segmentation performance of four state-of-the-art unsupervised over-segmentation methods by evaluating the quality of over-segments. To achieve this goal, we design a novel real-time evaluating framework, this method is integrating the classical goodness methods and our proposed new discrepancy method based on real-time boundary extraction, which is able to suppress the minor edges found within a homogeneous region, while being able to locate the important edges in real-time. We use it to qualitatively and quantitatively evaluate the quality of over-segments. Experiments are carried out both on the Berkeley Segmentation Dataset and real-scene images. The quality-evaluating results show that the EGIS outperforms the other three over-segmentation methods. The evaluating accuracy and efficiency of our proposed method is verified by the over-segmentation results.
Dynamics of Trimming the Content of Face Representations for Categorization in the Brain

To understand visual cognition, it is imperative to determine when, how and with what information the human brain categorizes the visual input. Visual categorization consistently involves at least an early and a late stage: the occipito-temporal N170 event related potential related to stimulus encoding and the parietal P300 involved in perceptual decisions. Here we sought to understand how the brain globally transforms its representations of face categories from their early encoding to the later decision stage over the 400 ms time window encompassing the N170 and P300 brain events. We applied classification image techniques to the behavioral and electroencephalographic data of three observers who categorized seven facial expressions of emotion and report two main findings: (1) over the 400 ms time course, processing of facial features initially spreads bilaterally across the left and right occipito-temporal regions to dynamically converge onto the centro-parietal region; (2) concurrently, information processing gradually shifts from encoding common face features across all spatial scales (e.g., the eyes) to representing only the finer scales of the diagnostic features that are richer in useful information for behavior (e.g., the wide opened eyes in ‘fear’; the detailed mouth in ‘happy’). Our findings suggest that the brain refines its diagnostic representations of visual categories over the first 400 ms of processing by trimming a thorough encoding of features over the N170, to leave only the detailed information important for perceptual decisions over the P300.
Results on uniform asymptotic stability of nonlinear time-varying switched systems

This paper presents two new stability criteria for nonlinear time-varying (NLTV) switched systems based on a slightly extended version of a newly developed result. The first one generalizes a result of Aeyels to NLTV switched systems, while the second one is a version of Krasovskii-LaSalle theorem in NLTV switched systems. A well-konwn example is revisited to illustrate the usefulness of the proposed results, especially in case that common quadratic Lyapunov functions are not easily found.