Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-frequency analysis of heart rate variability signals in patients with autonomic dysfunction

An imbalance in the autonomic nervous system has been suspected in patients with coronary artery disease during episodes of silent ischemia. Frequency analysis of the beat-to-beat heart rate variability (HRV) signals reveals a signature of autonomic regulation of the heart. We performed time-frequency analysis of HRV records containing silent ischemic episodes. In 14 out of 17 (82%) HRV data sets there was a loss of power during such intervals, as determined by S-T segment changes. Our studies suggest that if the frequency content of the signal is well preserved, there is less likelihood of ischemic events of any appreciable duration.
Post hoc confidence bounds on false positives using reference families

We follow a post-hoc, "user-agnostic" approach to false discovery control in a large-scale multiple testing framework, as introduced by Genovese and Wasserman (2006), Goeman and Solari (2011): the statistical guarantee on the number of correct rejections must hold for any set of candidate items, possibly selected by the user after having seen the data. To this end, we introduce a novel point of view based on a family of reference rejection sets and a suitable criterion, namely the joint-family-wise-error rate over that family (JER for short). First, we establish how to derive post hoc bounds from a given JER control and analyze some general properties of this approach. We then develop procedures for controlling the JER in the case where reference regions are p-value level sets. These procedures adapt to dependencies and to the unknown quantity of signal (via a step-down principle). We also show interesting connections to confidence envelopes of Meinshausen (2006); Genovese and Wasserman (2006), the closed testing based approach of Goeman and Solari (2011) and to the higher criticism of Donoho and Jin (2004). Our theoretical statements are supported by numerical experiments.
Factors associated with long-term post-traumatic amnesia*

Objective: To identify factors related to post-traumatic amnesia of long duration. Method: A prospective, longitudinal study, with 187 victims of blunt head trauma, age >14 years, seen at a referral hospital for trauma. Independent variables included: age, sex, severity of head injury, location and type of injury, number of brain lesions, and use of medication with central nervous system activity or corticosteroids. Results: The logistic regression model adjusted by the variable area of injury (intra / extra-axial) showed: an initial Glasgow Coma Scale of 3 (OR = 2.80) and use of phenytoin (OR = 2.60), midazolam (OR = 2.83) or both drugs (OR = 3.83). Conclusion: The use of midazolam and phenytoin, and the severity of head injury, were identified as
A new contextual based feature selection

The pre processing phase is essential in knowledge data discovery process. We study too particularly the data filtering in supervised context, and more precisely the feature selection. Our objective is to permit a better use of the data set. Most of filtering algorithm use myopic measures, and give bad results in the case of the features correlated part by part. Consequently in the first time, we build two new contextual criteria. In the second part we introduce those criteria in an algorithm similar to the greedy algorithm. The algorithm is tested on a set of benchmarks and the results were compared with five reference algorithms: Relief, CFS, Wrapper (C4.5), consistancySubsetEval and GainRatio. Our experiments have shown its ability to detect the semi-correlated features. We conduct extensive experiments by using our algorithm like pre processing data for decision tree, nearest neighbours and naive Bays classifiers.
Text Document Categorization by Machine Learning

of a thesis at the University of Miami. Thesis supervised by Dr. Moiez A. Tapia and Dr. Miroslav Kubat. No. of pages in text. (101) Because of the explosion of digital and online text information, automatic organization of documents has become a very important research area. There are mainly two machine learning approaches to enhance the organization task of the digital documents. One of them is the supervised approach, where predefined category labels are assigned to documents based on the likelihood suggested by a training set of labeled documents; and the other one is the unsupervised approach, where there is no need for human intervention or labeled documents at any point in the whole process. In this thesis, we concentrate on the supervised learning task which deals with document classification. One of the most important tasks of information retrieval is to induce classifiers capable of categorizing text documents. The same document can belong to two or more categories and this situation is referred by the term multilabel classification. Multi-label classification domains have been encountered in diverse fields. Most of the existing machine learning techniques which are in multi-label classification domains are extremely expensive since the documents
Compiler-Assisted and Profiling-Based Analysis for Fast and Efficient STT-MRAM On-Chip Cache Design

Spin Transfer Torque Magnetic Random Access Memory (STT-MRAM) is a promising candidate for large on-chip memories as a zero-leakage, high-density and non-volatile alternative to the present SRAM technology. Since memories are the dominating component of a System-on-Chip, the overall performance of the system is highly dependent on that memories. Nevertheless, the high write energy and latency of the emerging STT-MRAM are the most challenging design issues in a modern computing system. By relaxing the non-volatility of these devices, it is possible to reduce the write energy and latency costs, at the expense of reducing the retention time, which in turn may lead to loss of data. In this article, we propose a hybrid STT-MRAM design for caches with different retention capabilities. Then, based on the application requirements (i.e., execution time and memory access rate), program data layout is re-arranged at compilation time for achieving fast and energy-efficient hybrid STT-MRAM on-chip memory design with no reliability degradation. The application requirements have been defined at function granularity based on profiling and compiler-level analysis, which estimate the required retention time and memory access rate, respectively. Experimental results show that the proposed hybrid STT-MRAM cache combined with profiling-based and compiler-level analysis for the data re-arranging, on average, reduces the write energy per access by 49.7%. At system level, overall static and dynamic energy of the cache are reduced by 8.1% and 44%, respectively, whereas, the system performance has been improved up to 8.1%.
Circuit techniques in a 266-MHz MMX-enabled processor

The AMD-K6 MMX-enabled processor is plug-compatible with the industry-standard Socket 7 and is binary compatible with the existing base of legacy X86 software. The microarchitecture is based on an out-of-order, superscalar execution engine using speculative execution. High performance and compact die size are achieved by using self-resetting, self-timed and pulsed-latch circuit design techniques in custom blocks and placed-and-routed blocks of standard cells. The 162 sq. mm die is fabricated on a 0.35-/spl mu/m, five-layer metal process with local interconnect. It is assembled into a ceramic pin grid array (PGA) using C4 flip-chip mounting. The processor functions at clock speeds up to 266 MHz.
Secure Transmission via Joint Precoding Optimization for Downlink MISO NOMA

Non-orthogonal multiple access (NOMA) is a prospective technology for radio resource constrained future mobile networks. However, NOMA users far from a base station (BS) tend to be more susceptible to eavesdropping because they are allocated more transmit power. In this paper, we aim to jointly optimize the precoding vectors at the BS to ensure the legitimate security in a downlink multiple-input single-output (MISO) NOMA network. When the eavesdropping channel state information (CSI) is available at the BS, we can maximize the sum secrecy rate by joint precoding optimization. Owing to its non-convexity, the problem is converted into a convex one, which is solved by a second-order-cone-programming-based iterative algorithm. When the CSI of the eavesdropping channel is not available, we first consider the case that the secure user is not the farthest from the BS, and the transmit power of the farther users is maximized via joint precoding optimization to guarantee its security. Then, we consider the case when the farthest user from the BS requires secure transmission, and the modified successive interference cancellation order and joint precoding optimization can be adopted to ensure its security. A similar method can be exploited to solve the two non-convex problems when the CSI is unknown. Simulation results demonstrate that the proposed schemes can improve the security performance for MISO NOMA systems effectively, with and without eavesdropping CSI.
Kronik hepatit C genotip 1 ve 4 hastaların tedavisi sırasında tiroid bozuklukları prevalansı ve tiroid profili ile karaciğer enzimleri, serum activin-A ve follistatin arasındaki ilişki

Amac: Kronik hepatit C (KHC) ve Peg-interferon- alfa (Peg-INF-α) serum aktivinleri ve follistatini duzenler ve tiroid hastaliklari (TH) ile birliktedir. Bu calismanin amaci KHC\'nin indukledigi TH sikligini belirlemek ve karaciger hasari, serum activin-A ve follistatin\'in tiroid fonksiyonlari ve tiroid otoantikorlari ile iliskisini arastirmaktir. Yontemler: Bu calisma kesitsel olup, 3 gruba ayrilan 132 KHC\'li hastadan serumlari alinmistir: Tedavisiz 56 hasta, 24 haftalik Peg-INF-α tedavisi almis 30 hasta ve Peg-INF-α tedavisi tamamlanmis 46 hasta. Tiroid stimulan hormon (TSH), serbest tiroksin (FT4) ve tiroid otoantikorlari, serum activin-A ve follistatin duzeyleri ELISA yontemiyle olculdu. Bulgular: Hastalarin %15\'inde (n=20) tiroid hastaligi saptandi, bunlarin cogunlugu (%80) otoimmun tiroidit olup, kadinlarda daha sik (%70) idi. TSH reseptor antikorlari (TSHR-Abs), diger antikorlardan anlamli yuksek bulundu (p
The Study of Acousmatic Listening Behaviours

In 1989, after ten years of research, François Delalande presented for the first time the idea of ‘listening behaviours’, distinguishing several ways to listen to the same piece of music. The methods did not allow for many conclusions, but the approach started a new field of research, where diverse listening behaviours could serve as a basis for diverse, exclusive (or even contradictory) analyses of the same work. Furthermore, Delalande’s preliminary research based on an extract from Pierre Henry’s music is still one of the few instances of research into acousmatic music listening that addresses actual listeners. The last 20 years have seen the reproduction and extension of Delalande’s results, as well as a growing interest in listening in musicological circles. For this reason, it seemed necessary to review research about listening behaviours to evaluate their operational validity and to propose a robust theoretical framework for their study and description. This paper addresses this problem using the elicitation interview for the description and interpretation of listeners’ testimonies. Drawing on testimonies from five listeners about a single extract of acousmatic music, it seems that, for Delalande’s model to be able to describe various listening experiences, it should be further detailed and completed with attention management processes. Further investigation is needed to allow the model to include some listening behaviours which were not recurrent enough here to make sense of them.
Business Process Evaluation of Outpatient Services Using Process Mining

A business needs an evaluation to increase its services and adaptability to the environment changes. Business process evaluation is one of the several ways for business development. This paper reports an assessment of outpatient service process at RSUD Sukoharjo for BPJS Health insurance’s patient using process mining to get an objective process model. We implement the Inductive Miner infrequent approach and analyze the process model with conformance checking and performance analysis. Stakeholders can utilize the results of the evaluation to understand the real service condition and plan an action to improve their services. We can conclude that there is a bottleneck in the waiting time of the registration process with an average of 1.5-2 hours, a polyclinic treatment process with an average of 1-2 hours and pharmacy process with an average of 0.5-1 hours.
Let the Guitar Light Your Fire

* In September 1966, a former Ballard High School student, Suzette Gore, stopped in the choir room to say hello. In the several years since she had graduated from Ballard, she had learned to play folk guitar, and she was now writing her own songs and inventing her own guitar accompaniments. My students had often expressed interest in guitar. Suzette had some time to spare. We needed a guitar teacher. She was our answer.
Modeling and Simulation of a Team Game with Coloured Petri Nets

This paper work describes an example of how systems (economic, social, political, sporting, etc.) can be modeled by colored Petri nets. Here we present the modeling and the simulation of a team game. The network, which modeled the system, is described in detail. CPN-Tools are used for drawing a network colored Petri, in order to obtain the coverage graph that appears following evolution of a network, starting from a certain state of the system and to obtain a report that provides information about : boundedness properties, liveness properties and fairness properties of the modeling network.
WELLNESS AT WORK: WHAT INDIVIDUAL AND ORGANIZATIONAL FACTORS INFLUENCE PARTICIPATION IN AND ADHERENCE TO A WORKPLACE WEIGHT LOSS (WEIGHT WATCHERS) PROGRAM?

vi List of Abbreviations Used.vii Acknowledgements.viii Chapter 1: Introduction . 1 1.1 Workplace Wellness Programs.2 1.2 Participation and adherence.2 1.3 Purpose.3 1.4 Study Design.4 Chapter 2: Literature Review.5 2.
AN EVALUATION OF LANDSLIDE SUSCEPTIBILITY MAPPING USING REMOTE SENSING DATA AND MACHINE LEARNING ALGORITHMS IN IRAN

Abstract. Landslide is painstaking as one of the most prevalent and devastating forms of mass movement that affects man and his environment. The specific objective of this research paper is to investigate the application and performances of some selected machine learning algorithms (MLA) in landslide susceptibility mapping, in Dodangeh watershed, Iran. A 112 sample point of the past landslide, occurrence or inventory data was generated from the existing and field observations. In addition, fourteen landslide-conditioning parameters were derived from DEM and other topographic databases for the modelling process. These conditioning parameters include total curvature, profile curvature, plan curvature, slope, aspect, altitude, topographic wetness index (TWI), topographic roughness index (TRI), stream transport index (STI), stream power index (SPI), lithology, land use, distance to stream, distance to the fault. Meanwhile, factor analysis was employed to optimize the landslide conditioning parameters and the inventory data, by assessing the multi-collinearity effects and outlier detections respectively. The inventory data is divided into 70% (78) training dataset and 30% (34) test dataset for model validation. The receiver operating characteristics (ROC) curve or area under curve (AUC) value was used for assessing the model's performance. The findings reveal that TRI has 0.89 collinearity effect based on variance-inflated factor (VIF) and based on Gini factor optimization total curvature is not significant in the model development, therefore the two parameters are excluded from the modelling. All the selected MLAs (RF, BRT, and DT) shown promising performances on landslide susceptibility mapping in Dodangeh watershed, Iran. The ROC curve for training and validation for RF are 86% success rate and 83% prediction rate implies the best model performance compared to BRT and DT, with ROC curve of 72% and 70% prediction rate, respectively. In conclusion, RF could be the best algorithm for producing landslide susceptibility map, and such results could be adopted for the decision-making process to support land use planner for improving landslide risk assessment in similar environmental settings.
On the Difficulty to Design Arabic E-Learning System in Statistics

In this paper, we present a case study, which describe the development of the Statistic e-learning-course in Arabic language –``Arabic MM*STAT´´. The basic frame forthis E-book, the system MM*STAT was developed at the School for Business and Economics of Humboldt-Universitat zu Berlin. Arabic MM*STAT uses a HTML - based filing card structure. We discuss the difficulties of the implementation of such a system in to the standard WWW formats and present the solutions needed for Arab educational institutions and the Arabic user. Those solutions are consistent with the Arabic language, and include the modern trend in the e-learning environment.
SCnorm: A quantile-regression based approach for robust normalization of single-cell RNA-seq data

Normalization of RNA-sequencing data is essential for accurate downstream inference, but the assumptions upon which most methods are based do not hold in the single-cell setting. Consequently, applying existing normalization methods to single-cell RNA-seq data introduces artifacts that bias downstream analyses. To address this, we introduce SCnorm for accurate and efficient normalization of scRNA-seq data.
Vertex discard occlusion culling

Performing visibility determination in densely occluded environments is essential to avoid rendering unnecessary objects and achieve high frame rates. In this work we present an implementation of the image space Occlusion Culling algorithm done completely in GPU, avoiding the latency introduced by returning the visibility results to the CPU. Our algorithm utilizes the GPU rendering power to construct the Occlusion Map and then performs the image space visibility test by splitting the region of the screen space occludees into parallelizable blocks. Our implementation is especially applicable for lowend graphics hardware and the visibility results are accessible by GPU shaders. It can be applied with excellent results in scenes where pixel shaders alter the depth values of the pixels, without interfering with hardware Early-Z culling methods. We demonstrate the benefits and show the results of this method in real-time densely occluded scenes.
The Assessment of QT/QTc Interval Prolongation in Clinical Trials: A Regulatory Perspective

Excessive prolongation of the QT/QTc interval creates an electrophysiological environment that predisposes the myocardium to torsade de pointes, a polymorphic ventricular tachyarrhythmia that can progress to ventricular fibrillation and sudden cardiac death. Identification of QT/QTc prolongation liability is therefore an important objective of contemporary drug development programs. Nonclinical safety pharmacology studies are useful in early identification of this cardiac safety problem and in preliminary risk assessment. Specialized clinical pharmacology studies play a critical role in characterizing the magnitude, time course, dose dependency, and concentration relationship of drug-induced QT/QTc prolongation, as well as guiding the intensity of electrocardiogram (ECG) safety evaluations in subsequent trials. The reading, analysis, and interpretation of the ECG data acquired through these studies are complex issues that present many challenges to the pharmaceutical industry and regulatory authorities alike.
Blog Fingerprinting: Identifying Anonymous Posts Written by an Author of Interest Using Word and Character Frequency Analysis

Abstract : Internet blogs are an easily accessible means of global communications. Monitoring blogs for criminal and terrorist activity is a serious challenge, due to blogs' anonymous nature and the sheer volume of data. The intelligence community is often faced with more information than it can process. The need exists to develop methods for processing the massive amounts of data this media presents, without a significant increase in manpower. An automated tool capable of identifying posts written by an individual, given a sample of his writing, would allow law enforcement and intelligence agencies to gather evidence that would otherwise be overlooked due to manpower and time constraints. This research focuses on identifying blog posts written by a particular author, when we do not have a model of every potential author. Previous research either builds a distinct model for every possible author, or limits itself to large documents. Neither approach is appropriate for processing blog posts. Blog posts tend to be short documents, and building a distinct model of each author is unreasonable if you are looking for one author among millions. We address this problem by combining sample posts by other authors to create a model of an "average author. "
Reinventing Order in the Congo: How People Respond to State Failure in Kinshasa

1. Introduction: Reinventing Order 2. The Kinshasa Bargain 3. The Tap is on Strike: Water (Non-)Distribution and Supply Strategies 4. Food Security in Kinshasa: Coping with Adversity 5. The Miracle of Life in Kinshasa: New approaches to Public Health 6. The Diploma Paradox: University of Kinshasa between Crisis and Salvation 7. Acting on Behalf (and in spite) of the State: NGOs and Civil Society Associations in Kinshasa 8. Hidden Families, Single Mothers and Cibalabala: Economic Regress and Changing Household Composition in Kinshasa 9. When Kinois Take to the Streets 10. On Being Shege in Kinshasa: Children, the Occult and the Street 11. The Elusive Lupemba: Rumours About Fame and (Mis)Fortune in Kinshasa
A corpus approach to the history of Russian po delimitatives

This paper illustrates how enriched diachronic treebank data can shed new light on an old and vexed topic, even when that topic is primarily morphological and semantic in nature rather than syntactic. The topic is the rise of the Russian po delimitatives, a change seen as crucial in most accounts of the history of Russian aspect, since it represents a major step in generalising the derivational aspect system. Earlier accounts concur that the po delimitatives spread fairly recently, too recently for the development to be connected to the loss of the aorist tense, which also had delimitative readings with atelic verbs. Using treebank data from the Tromsø Old Russian and Old Church Slavonic Treebank, enriched with tags for derivational morphology and semantics, I show that the po delimitatives were not marginal even in the earliest Slavic sources, either in terms of frequency or semantics, and that they first complemented and then competed with the delimitative aorists. It can thus be claimed that the exotic po delimitatives grew organically out of the old Indo-European inflectional aspect system.
Approach of arbitrary clipping in volume rendering

This paper proposes a new clipping method that is capable of using arbitrary geometries in volume rendering. With the assistance of pre-computed outer contour mesh, the method adopts Constructive Solid Geometry (CSG) for clipping, and uses 3D texture for the rendering of clipping surface. The outer contour mesh defines the boundary of the volume data, and improves the efficiency of clipping and rendering. Furthermore, CSG-based clipping method protects the topology of geometries. This method computes the texture coordinate in vertex shader to implement the 3D texture mapping, and achieves high frame rates based on the powerful programming graphics hardware.