Miracle-dz/UniFAR-FTUs huggingface dataset:
Selective feature extraction via signal decomposition

In this article, a mathematical framework that jointly optimizes the parameters of classifier and feature extractor is presented. In this approach, feature extraction is formulated as a process of projecting the signals onto a smaller subspace in which the statistical properties of the signal can be efficiently modeled. An algorithm, called statistical matching pursuit (SMP), is proposed to learn from the training data the optimal projection dimensions and the extent of signal reduction. The algorithm is designed to achieve unconditional convergence and can be seamlessly incorporated into the expectation-maximization (EM) algorithm employed to train the classifier. Finally, we report some experimental results on speech recognition and elaborate the potential of the proposed method.
Tele-ICU: Efficacy and Cost-Effectiveness Approach of Remotely Managing the Critical Care

Tele-ICU has an off-site command center in which a critical care team (intensivists and critical care nurses) is connected with patients in distance intensive care units (ICUs) through a real-time audio, visual and electronic means and health information is exchanged. The aim of this paper is to review literature to explore the available studies related to efficacy and cost effectiveness of Tele-ICU applications and to study the possible barriers to broader adoption. While studies draw conclusions on cost based on the mortality and Length of Stay (LOS), actual cost was not reported. Another problem in the studies was the lack of consistent measurement, reporting and adjustment for patient severity. From the data available, Tele-ICU seems to be a promising path, especially in the United States where there is a limited number of board-certified intensivists.
Practical Results of ECC Side Channel Countermeasures on an ARM Cortex M3 Processor

This paper presents implementation results of several side channel countermeasures for protecting the scalar multiplication of ECC (Elliptic Curve Cryptography) implemented on an ARM Cortex M3 processor that is used in security sensitive wireless sensor nodes. Our implementation was done for the ECC curves P-256, brainpool256r1, and Ed25519. Investigated countermeasures include Double-And-Add Always, Montgomery Ladder, Scalar Randomization, Randomized Scalar Splitting, Coordinate Randomization, and Randomized Sliding Window. Practical side channel tests for SEMA (Simple Electromagnetic Analysis) and MESD (Multiple Exponent, Single Data) are included. Though more advanced side channel attacks are not evaluated, yet, our results show that an appropriate level of resistance against the most relevant attacks can be reached.
Towards Positive Test Takers' Reactions to Cognitive Ability Assessments: Development and Initial Validation of the Reasoning Ability at Work Test

The current diffusion of cognitive ability tests in the field of personnel selection is not reflecting their outstanding predictive validity. In the current study, we presume that this paradox is partly due to the fact that cognitive ability tests provide less face validity as compared to other selection tools. In an effort to overcome the gap between the unequivocal research findings and the current practice in the field, a reasoning test was developed with tasks being embedded in a job related context. Two studies were conducted to examine the psychometric properties and the face validity of this test. Results showed that psychometric properties were mediocre (reliability) to good (validity). In addition, the newly developed test provided higher face validity as compared to a matrix test and showed similar face validity as compared to a test aiming at assessing multiple facets of intelligence.
Diversity-Based Weighting Schemes for Clustering Ensembles

Clustering ensembles has been recently recognized as an emerging approach to provide more robust solutions to the data clustering problem. Current methods of clustering ensembles typically fall into instance-based, cluster-based, or hybrid approaches; however, most of such methods fail in discriminating among the various clusterings that participate to the ensemble. In this paper, we address the problem of weighting clustering ensembles by proposing general weighting approaches based on different implementations of the notion of diversity. We introduce three weighting schemes for clustering ensembles, called Single Weighting, Group Weighting and Dendrogram Weighting, which are independent of the particular method of clustering ensembles and designed to take into account correlations among the individual clustering solutions in different ways. We show how these schemes can be instantiated into any instance-based, cluster-based and hybrid clustering ensembles methods. Experiments have shown that the performance of the clustering ensembles algorithms increases when the proposed weighting schemes are employed.
EyeDroid: an open source mobile gaze tracker on Android for eyewear computers

In this paper we report on development and evaluation of a video-based mobile gaze tracker for eyewear computers. Unlike most of the previous work, our system performs all its processing workload on an Android device and sends the coordinates of the gaze point to an eyewear device through wireless connection. We propose a lightweight software architecture for Android to increase the efficiency of image processing needed for eye tracking. The evaluation of the system indicated an accuracy of 1.06 degrees and a battery lifetime of approximate 4.5 hours.
ROBUSTNESS OF PHONEME RECOGNITION USING SUPPORT VECTOR MACHINES

The robustness of phoneme recognition using support vector machines to additive noise is investigated for three kinds of speech representation. The representations considered are PLP, PLP with RASTA processing, and a high-dimensional principal component approximation of acoustic waveforms. While the classification in the PLP and PLP/RASTA domains attains superb accuracy on clean data, the classification in the high-dimensional space proves to be much more robust to additive noise.
Risk stratification of hospitalized patients on the wards.

Patients who suffer adverse events on the wards, such as cardiac arrest and death, often have vital sign abnormalities hours before the event. Early warning scores have been developed with the aim of identifying clinical deterioration early and have been recommended by the National Institute for Health and Clinical Excellence. In this review, we discuss recently developed and validated risk scores for use on the general inpatient wards. In addition, we compare newly developed systems with more established risk scores such as the Modified Early Warning Score and the criteria used in the Medical Early Response Intervention and Therapy (MERIT) trial in our database of > 59,000 ward admissions. In general we found the single-parameter systems, such as the MERIT criteria, to have the lowest predictive accuracy for adverse events, whereas the aggregate weighted scoring systems had the highest. The Cardiac Arrest Risk Triage (CART) score was best for predicting cardiac arrest, ICU transfer, and a composite outcome (area under the receiver operating characteristic curve [AUC], 0.83, 0.77, and 0.78, respectively), whereas the Standardized Early Warning Score, VitalPAC Early Warning Score, and CART score were similar for predicting mortality (AUC, 0.88). Selection of a risk score for a hospital or health-care system should be guided by available variables, calculation method, and system resources. Once implemented, ensuring high levels of adherence and tying them to specific levels of interventions, such as activation of a rapid response team, are necessary to allow for the greatest potential to improve patient outcomes.
Centrality-Based Approach for Supervised Term Weighting

The huge amount of text documents has made the manual organization of text data a tedious task. Automatic text classification helps to easily handle the large number of documents by organising them automatically into predefined classes. The effectiveness and efficiency of automatic text classification largely depends on the way text documents are represented. A text document is usually viewed as a bag of terms (or words) and represented as a vector using the vector space model where terms are assumed unordered and independent and term frequencies (or weights) are used in the representation. Graphs are another text representation scheme that considers the structure of terms in the text document which is important for natural language. Terms weighted on the basis of graph representation increase the performance of text classification. In this paper, we present a novel approach for graph-based supervised term weighting which considers information relevant for the classification task using node centrality in the co-occurrence graphs built from the labelled training documents. Our experimental evaluation of the proposed term weighting scheme on four benchmark datasets shows the scheme has consistently superior performance over the state-of-the-art term weighting methods for text classification.
Phrases and Feature Selection in E-Mail Classification

In this paper we study the effectiveness of using a phrase-based representation in e-mail classifi- cation, and the affect this approach has on a number of machine learning algorithms. We also evaluate various feature selection methods and reduction levels for the bag-of-words representation on several learning algo- rithms and corpora. The results show that the phrase- based representation and feature selection methods can be used to increase the performance of e-mail classi- fiers.
Notes on Boltzmann Machines

I. INTRODUCTION Boltzmann machines are probability distributions on high dimensional binary vectors which are analogous to Gaussian Markov Random Fields in that they are fully determined by first and second order moments. A key difference however is that augmenting Boltzmann machines with hidden variables enlarges the class of distributions that can be modeled, so that in principle it is possible to model distributions of arbitrary complexity [1]. (On the other hand, marginalizing over hidden variables in a Gaussian distribution merely gives another Gaussian.) Training Boltzmann machines still seems to be more of an art than a science, but a variational Bayes expectation maximization algorithm has been developed which deals with this problem in a reasonably efficient way for a class of sparsely connected Boltzmann machines that includes the deep Boltzmann machines studied in [2]. I will explain how this approach provides a principled framework for constructing complex Boltzmann machines incrementally and how it can be extended straightforwardly to handle higher order Boltzmann machines (e.g. Boltzmann machines defined by third order moments). The binary/Gaussian distinction is not an exclusive dichotomy: hybrid models containing both types of variable can be constructed. The term Gaussian-Bernoulli Boltzmann machine is usually used to refer to a model in which the vector of visible variables v is continuous and the vector of hidden variables h is binary. For each binary configuration h, the conditional distribution of v given h is Gaussian so writing
Biological and morphological studies of SJL-J strain reticulum cell neoplasms induced and transmitted serially in low-leukemia-strain mice.

Summary Reticulum cell neoplasms (RCN) were induced in 4 of 34 BALB/c low-leukemia-strain mice inoculated i.p. with supernatants of centrifuged extracts of spontaneous reticulum cell sarcomas of SJL/J strain mice. The extracts were obtained from tumorous organs and from tumorous spleen tissue culture derived from SJL/J strain mice. The RCN9s induced in BALB/c mice were transmitted serially to other low-leukemia-strain mice (BALB/c, C3H/f, and C3HeB/FeJ) by similarly prepared cell extracts and by cell-free filtrates of tumorous organs of the mice. The lesions induced included RCN types A and B (Dunn9s nomenclature) and heterotopic myelopoiesis, including some cases which histologically could be described as myeloid and erythroid leukemias and megakarocytosis. The incidence of induced RCN in mice increased to 92%, and the average latent period decreased to 90 days as serial transmission of the RCN progressed. Electron microscopic examination of lymph nodes, spleen, thymus gland, liver, kidneys, lungs, and bone marrow of mice with induced RCN revealed varying numbers of virus particles irrespective of the histological type of lesions. The virus particles were morphologically identical with murine leukemia type C virus particles and with virus particles observed in spontaneous RCN of SJL/J strain mice. Budding particles were observed in cells of all organs examined. Intracisternal type A virus particles were found in some organs. Type C virus particles were observed in cells of all examined tissue culture passages of tumorous spleen derived from an SJL/J strain mouse with spontaneous RCN. Budding virus particles were also observed in tissue culture, indicating that the particles multiply for prolonged periods in vitro. Biological activity of the virus produced in vitro was demonstrated by induction of RCN in BALB/c strain mice inoculated with supernatants from a centrifuged extract of tissue culture cells. Tissue culture from embryos of an apparently normal SJL/J strain mouse also contained type C virus particles. This indicates vertical transmission of the virus in mice of this strain. The observation of type C murine leukemia virus particles in organs of the low-leukemia-strain mice with induced RCN and myeloproliferative lesions and in tissue cultures of spontaneous RCN derived from SJL/J strain mice and the serial transmission of induced RCN by cell-free filtrate strongly support viral etiology of this type of neoplasia.
Optimization with Partial Differential Equations

We investigate numerical methods for solving large-scale saddle point systems which arise during the feedback control of flow problems. We focus on the Stokes equations that describe instationary, incompressible flows for low and moderate Reynolds numbers. After a mixed finite element discretization [23] we get a differential-algebraic system of differential index two [45]. To reduce this index, we follow the analytic ideas of Raymond [34, 35, 36] coupled with the projection idea of Heinkenschloss et al. [22]. Avoiding this explicit projection leads to solving a series of largescale saddle point systems. In this paper we construct iterative methods to solve such saddle point systems by deriving efficient preconditioners based on the approaches of Wathen et al. [19, 42]. In addition, the main results can be extended to the non-symmetric case of linearized Navier-Stokes equations. We conclude with numerical examples showcasing the performance of our preconditioned iterative saddle point solver.
University teacher preparation programmes as a quality enhancement mechanism: evaluating impact beyond individual teachers’ practice

Abstract Conceptually, practically and rhetorically teaching is at the core of quality in higher education. University teaching preparation programmes (TPPs) are regularly advocated to foster enhancement of teaching but there remains limited evidence to demonstrate their effectiveness or impact as a quality improvement mechanism. Support for such programmes is largely a matter of faith amongst their advocates. This paper presents an analysis of the impact of one initial TPP to add to the body of evidence on the efficacy of such programmes. The results reinforce other research indicating that such programmes do have beneficial effects on individual academics. The benefits extend to work groups and have value to the institution. However, the transfer of learning by academics to practice takes time and is mediated by many factors. Nevertheless, where institutional and local departmental cultures value teaching, TPPs provide a useful strategy for quality enhancement in higher education.
Diffusion properties of NAA in human corpus callosum as studied with diffusion tensor spectroscopy

In diffusion tensor imaging (DTI) the anisotropic movement of water is exploited to characterize microstructure. One confounding issue of DTI is the presence of intra‐ and extracellular components contributing to the measured diffusivity. This causes an ambiguity in determining the underlying cause of diffusion properties, particularly the fractional anisotropy (FA). In this study an intracellular constituent, N‐acetyl aspartate (NAA), was used to probe intracellular diffusion, while water molecules were used to probe the combined intra‐ and extracellular diffusion. NAA and water diffusion measurements were made in anterior and medial corpus callosum (CC) regions, which are referred to as R1 and R2, respectively. FA(NAA) was found to be greater than FA(Water) in both CC regions, thus indicating a higher degree of anisotropy within the intracellular space in comparison to the combined intra‐ and extracellular spaces. A decreasing trend in the FA of NAA and water was observed between R1 and R2, while the radial diffusivity (RD) for both molecules increased. The increase in RD(NAA) is particularly significant, thus explaining the more significant decrease in FA(NAA) between the two regions. It is suggested that diffusion tensor spectroscopy of NAA can potentially be used to further characterize microscopic anatomic organization in white matter. Magn Reson Med 58:1045–1053, 2007. © 2007 Wiley‐Liss, Inc.
Clinical response to azacitidine therapy depends on microRNA-29c (miR-29c) expression in older acute myeloid leukemia (AML) patients

Acute myeloid leukemia (AML) is a heterogeneous disease with different clinical course and prognosis. microRNA-29 (miR-29) family of non-coding small RNAs can play an important role in pathogenesis of AML, but also can influence response to therapy. The purpose of the study was to evaluate miR-29c expression in AML patients in relationship to clinical parameters and response to chemotherapy, including azacitidine. Materials and Methods miR-29c expression has been analyzed using RT-PCR in 95 bone marrow specimens from newly diagnosed AML patients in comparison to 20 healthy subject. Results We showed up-regulated miR-29c expression in AML patients which was linked also to higher risk of disease relapse after achieving complete remission. In subset of elderly AML patients treated with azacitidine, low miR-29c expression at diagnosis correlated with good response to therapy. Conclusions miR-29c is of prognostic value and influences response to azacitidine treatment in older AML patients.
Computational Characterization of Modes of Transcriptional Regulation of Nuclear Receptor Genes

Background Nuclear receptors are a large structural class of transcription factors that act with their co-regulators and repressors to maintain a variety of biological and physiological processes such as metabolism, development and reproduction. They are activated through the binding of small ligands, which can be replaced by drug molecules, making nuclear receptors promising drug targets. Transcriptional regulation of the genes that encode them is central to gaining a deeper understanding of the diversity of their biochemical and biophysical roles and their role in disease and therapy. Even though they share evolutionary history, nuclear receptor genes have fundamentally different expression patterns, ranging from ubiquitously expressed to tissue-specific and spatiotemporally complex. However, current understanding of regulation in nuclear receptor gene family is still nascent. Methodology/Principal Findings In this study, we investigate the relationship between long-range regulation of nuclear receptor family and their known functionality. Towards this goal, we identify the nuclear receptor genes that are potential targets based on counts of highly conserved non-coding elements. We validate our results using publicly available expression (RNA-seq) and histone modification (ChIP-seq) data from the ENCODE project. We find that nuclear receptor genes involved in developmental roles show strong evidence of long-range mechanism of transcription regulation with distinct cis-regulatory content they feature clusters of highly conserved non-coding elements distributed in regions spanning several Megabases, long and multiple CpG islands, bivalent promoter marks and statistically significant higher enrichment of enhancer mark around their gene loci. On the other hand nuclear receptor genes that are involved in tissue-specific roles lack these features, having simple transcriptional controls and a greater variety of mechanisms for producing paralogs. We further examine the combinatorial patterns of histone maps associated with dynamic functional elements in order to explore the regulatory landscape of the gene family. The results show that our proposed classification capturing long-range regulation is strongly indicative of the functional roles of the nuclear receptors compared to existing classifications. Conclusions/Significanc We present a new classification for nuclear receptor gene family capturing whether a nuclear receptor is a possible target of long-range regulation or not. We compare our classification to existing structural (mechanism of action) and homology-based classifications. Our results show that understanding long-range regulation of nuclear receptors can provide key insight into their functional roles as well as evolutionary history; and this strongly merits further study.
Criteria for Assessing Good Theory in Human Resource Development and Other Applied Disciplines from an Interpretive Perspective

Utilizing Patterson’s (1983) eight criteria for assessing theory in applied fields from a conventional (empirical-analytical) perspective, these criteria are evaluated and where applicable reconstructed from an interpretive (social constructivist) perspective of theory building research and assessment. Four additional criteria are proposed and described, namely, compellingness, saturation, prompt to action, and fittingness. The task of developing such criteria from different paradigms of inquiry while ensuring paradigmatic congruence holds particular challenges, some of which are discussed.
Timing analysis in presence of supply voltage and temperature variations

In the nanometer era, the physical verification of CMOS digital circuit becomes a complex task. Designers must account of numerous new factors that impose a drastic change in validation and physical verification methods. One of these major changes in timing verification to handle process variation lies in the progressive development of statistical static timing engines. However the statistical approach cannot capture accurately the deterministic variations of both the voltage and temperature variations. Therefore, we define a novel method, based on non-linear derating coefficients, to account of these environmental variations. Based on temperature and voltage drop CAD tool reports, this method allows computing the delay of logical paths considering the operating conditions of each cell.
Models and algorithms for efficient color image indexing

A robust color image retrieval algorithm is proposed, based on the hybrid graph representation, i.e. a dual graph which consists of a modified color adjacency graph (MCAG) and a spatial variance graph (SVG). The MCAG is proposed to enhance the indexing performance and the database capacity by increasing the feature dimension. In addition, the SVG is introduced in order to utilize the geometric statistics of the chromatic segment in the spatial domain. In the matching, we expand the histogram intersection into the graph intersection, in which graph matching is performed using simple matrix operations. Intensive discussions and experimental results are provided to evaluate the performance of the proposed algorithm. Experiments are carried out with M.J. Swain's et al.'s (1991) test images and the VIRAGE images. It is shown that the proposed algorithm provides high retrieval performance with tolerable computational complexity