Miracle-dz/UniFAR-FTUs huggingface dataset:
Myocardial infarction detection and classification — A new multi-scale deep feature learning approach

This paper presents an efficient detection and classification algorithm of multiple-class myocardial infarction (MI) (i.e., prior and acute), which is one of mortality diseases for humans. However, feature extraction is one of the challenges in MI classification as the extracted features may not be optimized for class separation. To this end, we propose a new deep feature learning based MI detection and classification approach. It seeks to learn a representation of the extracted features that optimize the classification performance. Moreover, to further enhance the feature learning process, we incorporate multi-scale discrete wavelet transformation into the feature learning process to facilitate the extraction of MI features at specific frequency resolutions/scales. Finally, softmax regression is employed to build a multi-class classifier based on the learned optimal representation of the features. Experimental results using public ECG datasets obtained from the PTB diagnostic database show that the proposed approach can achieve better performance than other state-of-the-art approaches in terms of sensitivity and specificity. The effectiveness and good performance of the proposed approach may serve as an attractive alternative to MI classification or other related applications.
Reinforcement Learning in Games Autonomous Learning Systems Seminar

The field of reinforcement learning covers a considerably large variety of applications and possibilities of extensions. This report summarizes the basic concepts behind reinforcement learning and some of its applications in the field of game theory. The concept will be further discussed regarding the work of Tesauro with his TD-Gammon Program in 1995 [4] and the concept of Q-Learning in multi agent system as it was presented by Hu and Wellman in 2003 [1] where they combined the concept of Q-Learning with the Nash Equilibrium Points as an approach to reach better overall results in multiple agent environments instead of focussing on the individual per-agent reward. Some restrictions in what the Nash Q-Learning approach archieves will be discussed based on the examples which Hu and Wellman provided in their article.
Time efficient ranking system on map reduce framework

The dimension of World Wide Web (The Internet) is in billions in terms of web pages and increasing rapidly. With the diversity of web pages available on the web, the high degree relevant information retrieval becomes a major issue. Such huge number of pages not only make the computation complex but also raises the issues of fault tolerance and time complexity. Computing ranking for such large number of web graph on a particular system, makes it prone to system failure and time taking. The present work proposes a distributed ranking system to attain fault tolerance and speedy calculation of Pagerank vector. The computation of rank vector is performed by implementing Pagerank on Mapreduce framework. The pagerank vector is calculated via spectral analysis to make the computation even faster and the results are compared to traditional pagerank scores.
Delay-optimal computation task scheduling for mobile-edge computing systems

Mobile-edge computing (MEC) emerges as a promising paradigm to improve the quality of computation experience for mobile devices. Nevertheless, the design of computation task scheduling policies for MEC systems inevitably encounters a challenging two-timescale stochastic optimization problem. Specifically, in the larger timescale, whether to execute a task locally at the mobile device or to offload a task to the MEC server for cloud computing should be decided, while in the smaller timescale, the transmission policy for the task input data should adapt to the channel side information. In this paper, we adopt a Markov decision process approach to handle this problem, where the computation tasks are scheduled based on the queueing state of the task buffer, the execution state of the local processing unit, as well as the state of the transmission unit. By analyzing the average delay of each task and the average power consumption at the mobile device, we formulate a power-constrained delay minimization problem, and propose an efficient one-dimensional search algorithm to find the optimal task scheduling policy. Simulation results are provided to demonstrate the capability of the proposed optimal stochastic task scheduling policy in achieving a shorter average execution delay compared to the baseline policies.
System Level Delay Modeling for Network-on-Chip

To evaluate the performance of delay for Network-on-Chip (NoC) at system-level design as early as possible, a delay estimation methodology based on M/G/1 queuing theory is proposed in the paper. In this methodology, the influence of the finite buffer resources and virtual channels in NoC router on the packet latency is taken into account, and the backward algorithm is used to calculate the network average packet latency. Several experiments with different traffic loads are carried out to evaluate the efficacy of this delay model. Experimental results show that a significant improvement in evaluation speed is achieved with the proposed power model as compared with the RTL-level simulator, while maintaining a minor average error of approximately 8.0% and achieving a speedup of 250 times. As an application, the proposed model has been successfully used to evaluate the performance of different core mappings for MPEG4 decoder in system-level design.
Text Document Clustering Based on the Modifying Relations

Text document clustering plays an important role in the modern knowledge management. This paper addresses the task of developing an effective and efficient method of clustering the text document. To meet this requirement, we first extract the modifying relations (MR) from the sentences and then organize them as feature set for representing the document. A novel similarity measure is proposed on the basis of MR-vectors in this paper. We use agglomerative hierarchical clustering algorithm in the experimental work and compare the results with other previous studies.
1 Program Understanding with Code Visualization

Software is invisible, disappearing into files on disks. The invisible nature of software contributes to low programmer productivity by hiding system complexity, particularly for large team-oriented projects. The data regarding the components of a software system consists of a large amount of information such as version history, number of lines, defect density, and complexity measures. The ability to quickly grasp a comprehensive view of the evolution and dependencies of such information is the key to making informed decisions about future developments of the system. Code Visualization provides techniques and tools to understand the overall software and its complexity. It also contributes to identify any design defects that may be present in software. In the current paper, we present our study that we conducted to comprehend different techniques that aim to ease the task of code understanding. We present these techniques for our case study software and provide a comparative study of the studied visualization
UV-B radiation affects flavonoids and fungal colonisation in Fagopyrum esculentum and F. tataricum

In the present study, we have evaluated the effects of increased UV-B radiation that simulates 17% ozone depletion, on fungal colonisation and concentrations of rutin, catechin and quercetin in common buckwheat (Fagopyrum esculentum) and tartary buckwheat (Fagopyrum tataricum). Induced root growth and reduced shoot:root ratios were seen in both of these buckwheat species after enhanced UV-B radiation. There was specific induction of shoot quercetin concentrations in UV-B-treated common buckwheat, whereas there were no specific responses for flavonoid metabolism in tartary buckwheat. Root colonisation with arbuscular mycorrhizal fungi significantly reduced catechin concentrations in common buckwheat roots, and induced rutin concentrations in tartary buckwheat, but did not affect shoot concentrations of the measured phenolics. Specific UV-B-related reductions in the density of microsclerotia were observed in tartary buckwheat, indicating a mechanism that potentially affects fungus-plant interactions. The data support the hypothesis that responses to enhanced UV-B radiation can be influenced by the plant pre-adaptation properties and related changes in flavonoid metabolism.
Time-related heart vegetative balance characteristics in healthy subjects

Changes of the heart autonomic balance between morning (8 to 9 h) and afternoon (14 - 15 h) measurements were studied in 22 healthy subjects. The selection of these two daytime periods was substantiated by the established higher risk of cardiovascular incidents in the morning and the relative balance of the vegetative nervous system in the afternoon hours. The changes were analyzed by RR-variability indices from ECG recordings in resting state and with vegetative nervous system stimulation by handgrip test and Valsalva maneuver. The authors introduced an indicator for time-related autonomic balance changes. The indicator evaluates the power of each RR-variability index to respond to changes in the autonomic control, in comparisons between resting state and stimulation data in the morning and afternoon measurements.
Characterization of capture actions in video sequences

This paper deals with automatic characterization of video capture actions like scale change (zoom) or translation (traveling) in a video sequence. The overall objective is to enhance the scripted quality while capturing a video sequence by professionals or non professionals. Ultimately, the capture actions would use a predefined video template, progressively filled by the cameraman and automatically checked against the template scenario by the acquisition system. We focused on the detection of the zoom action. The characteristics of zoom are detected by analyzing pairs of frames of the target sequence. The principle of the method resides in choosing characteristic points into a pair of images, matching them in order to estimate the plane projective transformation using RANSAC method. Once the transformation model has been found, it allows to extract relevant information regarding the scale and the translation factor, necessary to verify the type and also the quality of the realized zoom sequence. We present several experiments that show very promising results.
Omnidirectional Depth Estimation by a Single Perspective Camera

Omnidirectional depth recovery in a novel omnidirectional stereo vision system was addressed. The stereo vision based on a common perspective camera coupled with two hyperbolic mirrors. Given two images of a same point acquired the system, the depth is calculated by means of triangulation. The calculation process aims at having corresponding incident rays intersecting at the same 3D point. As the hyperbolic mirrors ensure a single viewpoint (SVP) the incident light rays are easily found from the points of the image. The two hyperbolic mirrors are aligned coaxially and separately, share one focus that coincides with the camera center. So the geometry of the system naturally ensures matched epipolar lines in the two images of the scene. The separation between the two mirrors provides a large baseline and eventually leads to a precise result. The geometrical properties are derived and the experiments with real images are presented
Any Domain Parsing: Automatic Domain Adaptation for Natural Language Parsing

Current efforts in syntactic parsing are largely data-driven. These methods require labeled examples of syntactic structures to learn statistical patterns governing these structures. Labeled data typically requires expert annotators which makes it both time consuming and costly to produce. Furthermore, once training data has been created for one textual domain, portability to similar domains is limited. This domain-dependence has inspired a large body of work since syntactic parsing aims to capture syntactic patterns across an entire language rather than just a specific domain. The simplest approach to this task is to assume that the target domain is essentially the same as the source domain. No additional knowledge about the target domain is included. A more realistic approach assumes that only raw text from the target domain is available. This assumption lends itself well to semi-supervised learning methods since these utilize both labeled and unlabeled examples. This dissertation focuses on a family of semi-supervised methods called self-training. Self-training creates semi-supervised learners from existing supervised learners with minimal effort. We first show results on self-training for constituency parsing within a single domain. While self-training has failed here in the past, we present a simple modification which allows it to succeed, producing state-of-the-art results for English constituency parsing. Next, we show how self-training is beneficial when parsing across domains and helps further when raw text is available from the target domain. One of the remaining issues is that one must choose a training corpus appropriate for the target domain or performance may be severely impaired. Humans can do this in some situations, but this strategy becomes less practical as we approach larger data sets. We present a technique, Any Domain Parsing, which automatically detects useful source domains and mixes them together to produce a customized parsing model. The resulting models perform almost as well as the best seen parsing models (oracle) for each target domain. As a result, we have a fully automatic syntactic constituency parser which can produce high-quality parses for all types of text, regardless of domain.
Fast nonrigid mesh registration with a data-driven deformation prior

We propose an algorithm for non-rigidly registering a 3D template mesh with a dense point cloud, using a morphable shape model to control the deformation of the template mesh. A cost function involving nonrigid shape as well as rigid pose is proposed. Registration is performed by minimizing a first-order approximation of the cost function in the Iterative Closest Points framework. We show how a complex shape model, consisting of multiple PCA models for individual regions of the template, can be seamlessly integrated in the parameter estimation scheme. An appropriate Tikhonov regularization is introduced to guarantee the smoothness of the full mesh despite the splitting into local models. The proposed algorithm is compared to a recent generic nonrigid registration scheme. We show that the data-driven approach is faster, as the linear systems to be solved in the iterations are significantly smaller when a model is available. Also, we show that simultaneous optimization of pose and shape yields better registration results than shape alone.
Implementing Income Verification in Australia

The recent credit crunch and global recession has forced Governments to consider implementing compulsory income verification for loan applicants. This paper analyses the different methods used in UK and US for income verification and highlights the issues in implementing each method. Finally, each of the methods is assessed in looking at applying a method in Australia. The paper concludes that a modelling approach to income verification will work for the most people
Surfaces of Revolution with Constant Mean Curvature in Hyperbolic 3-Space

In this paper, we construct surfaces of revolution with constant mean curvature H = c and minimal surfaces of revolution in hyperbolic 3-space H 3 (ic 2 ) of constant sectional curvature ic 2 . It is shown that surfaces of revolution with constant mean curvature H = c in H 3 (ic 2 ) tend toward the catenoid, the minimal surface of revolution in Euclidean 3-space E 3 as c ! 0. Minimal surfaces of revolution in H 3 (ic 2 ) also tend toward the catenoid in E 3 as c ! 0.
The diabetes network internet-based physical activity intervention: a randomized pilot study.

OBJECTIVE Because of other competing priorities, physical activity (PA) is seldom addressed in a consistent way in either primary care or diabetes education. This 8-week pilot study evaluated the short-term benefits of an Internet-based supplement to usual care that focused on providing support for sedentary patients with type 2 diabetes to increase their PA levels. RESEARCH DESIGN AND METHODS A total of 78 type 2 diabetic patients (53% female, average age 52.3 years) were randomized to the Diabetes Network (D-Net) Active Lives PA Intervention or an Internet information-only condition. The intervention condition received goal-setting and personalized feedback, identified and developed strategies to overcome barriers, received and could post messages to an on-line "personal coach," and were invited to participate in peer group support areas. Key outcomes included minutes of PA per week and depressive symptomatology. RESULTS There was an overall moderate improvement in PA levels within both intervention and control conditions, but there was no significant improvement in regard to condition effects. There was substantial variability in both site use and outcomes within the intervention and control conditions. Internal analyses revealed that among intervention participants, those who used the site more regularly derived significantly greater benefits, whereas those in the control condition derived no similar benefits with increased program use. CONCLUSIONS Internet-based self-management interventions for PA and other regimen areas have great potential to enhance the care of diabetes and other chronic conditions. We conclude that greater attention should be focused on methods to sustain involvement with Internet-based intervention health promotion programs over time.
Neurophysiological Estimation of Interruptibility: Demonstrating Feasibility in a Field Context

Inappropriately timed interruptions from task-relevant electronic devices have been shown to have a negative impact on accuracy and efficiency in difficult task contexts. Research has also shown that these risks can be minimized by timing interruptions appropriately based on estimates of a user’s cognitive workload. The work reported here examines the potential for using body-worn electrophysiological sensors to assess cognitive workload in challenging field environments. Analysis of electroencephalogram (EEG) data gathered from a high fidelity military training exercise reveals that neurophysiological signals can provide the basis for accurate estimation of workload in harsh operational contexts.
Stress-Energy-Momentum Tensors in Lagrangian Field Theory. Part 2. Gravitational Superpotential

Our investigation of differential conservation laws in Lagrangian field theory is based on the first variational formula which provides the canonical decomposition of the Lie derivative of a Lagrangian density by a projectable vector field on a bundle (Part 1: gr-qc/9510061). If a Lagrangian density is invariant under a certain class of bundle isomorphisms, its Lie derivative by the associated vector fields vanishes and the corresponding differential conservation laws take place. If these vector fields depend on derivatives of parameters of bundle transformations, the conserved current reduces to a superpotential. This Part of the work is devoted to gravitational superpotentials. The invariance of a gravitational Lagrangian density under general covariant transformations leads to the stress-energy-momentum conservation law where the energy-momentum flow of gravity reduces to the corresponding generalized Komar superpotential. The associated energy-momentum (pseudo) tensor can be defined and calculated on solutions of metric and affine-metric gravitational models.
Ab initio identification of transcription start sites in the Rhesus macaque genome by histone modification and RNA-Seq

Rhesus macaque is a widely used primate model organism. Its genome annotations are however still largely comparative computational predictions derived mainly from human genes, which precludes studies on the macaque-specific genes, gene isoforms or their regulations. Here we took advantage of histone H3 lysine 4 trimethylation (H3K4me3)’s ability to mark transcription start sites (TSSs) and the recently developed ChIP-Seq and RNA-Seq technology to survey the transcript structures. We generated 14 013 757 sequence tags by H3K4me3 ChIP-Seq and obtained 17 322 358 paired end reads for mRNA, and 10 698 419 short reads for sRNA from the macaque brain. By integrating these data with genomic sequence features and extending and improving a state-of-the-art TSS prediction algorithm, we ab initio predicted and verified 17 933 of previously electronically annotated TSSs at 500-bp resolution. We also predicted approximately 10 000 novel TSSs. These provide an important rich resource for close examination of the species-specific transcript structures and transcription regulations in the Rhesus macaque genome. Our approach exemplifies a relatively inexpensive way to generate a reasonably reliable TSS map for a large genome. It may serve as a guiding example for similar genome annotation efforts targeted at other model organisms.
On the Bottom Rung: A Profile of Americans in Low-Income Working Families. New Federalism: Issues and Options for States. Series A, No. A-42. Assessing the New Federalism: An Urban Institute Program To Assess Changing Social Policies.

economic expansion, moves millions of people off the welfare rolls into low-paying jobs, the policy world is considering the challenges facing low-income working families. These are the people who work hard and play by the rules, but still must struggle to make ends meet. Although many of them are not literally poor as measured by the official poverty line, they are near enough to the economic edge that an illness, a job layoff, or even a major car repair can have severe consequences for their well-being. This brief uses data from the 1997 National Survey of Americas Families1 (NSAF) to count the number of people who live in low-income working families and to describe their personal characteristics and the jobs they hold. We find that one in six nonelderly Americans lives in families in which the adults work, on average, at least half-time but whose incomes fall below twice, or 200 percent of, the federal poverty level (FPL)our threshold for a low-income family.2 Two-thirds of these Americans are part of families with children and with two or more adults present. The primary earner in these families typically works full-time, year-round. While the work effort of the primary earner in these families matches that of higherincome families, other adults in these families work less. However, even if all ablebodied, prime-age adults in these families worked full-time, simulation results suggest that only about 20 percent would work their familys way off the bottom rung of the economic ladder.3 The relatively low wages of this group make it difficult for them to rise above 200 percent of the FPL.