Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast and robust bootstrap method for testing hypotheses in the ICA model

Independent component analysis (ICA) is a widely used technique for extracting latent (unobserved) source signals from observed multidimensional measurements. In this paper we construct a fast and robust bootstrap (FRB) method for testing hypotheses on elements of the mixing matrix in the ICA model. The FRB method can be devised for estimators which are solutions to fixed-point (FP) equations. In this paper we develop FRB test for the widely popular FastICA estimator. The developed test can be used in real-world ICA analysis of high-dimensional data sets seen e.g. in big data analysis, as it avoids the common obstacles of conventional bootstrap such as immense computational cost and lack of robustness. Moreover, instability and convergence problems of the Fast ICA algorithm when applied to bootstrap data are prevented. Simulations and examples illustrate the usefulness and validity of the developed test.
Voltage stability assessment in semi-autonomous DC-grids with multiple power modules

Basic concepts with respect to stable operation of semi-autonomous low-voltage DC-grids are discussed. Design considerations on the output impedances of power electronic converters are analysed in order to avoid resonance issues within a DC-grid. With calculated or measured converter output impedances, potential resonance problems due to background harmonics between aggregated power modules and the DC-grid can be forecasted. Simulation results are included for verification of the proposed ideas.
The Gap between Architecture and Model: Strategies for Executive Control

One major limitation of current cognitive architectures is that models are typically constructed in an "empty" architecture, and that the knowledge specifications (typically production rules) are specific to the particular task. This means that general executive control strategies have to be implemented for each specific model, which means a lack of consistency and constraint. Alternatively, they are implemented as part of the architecture itself, which is often implausible, because strategies are learned and differ among individuals. The alternative is to assume executive control consists of strategies that can transfer from one task to another. The PRIMs theory (Taatgen 2013) provides a modeling framework for this transfer. The approach is discussed using the example of working memory control.
Support Vector Machine based Hybrid Classifiers and Rule Extraction thereof: Application to Bankruptcy Prediction in Banks

Support vector machines (SVMs) have proved to be a good alternative compared to other machine learning techniques specifically for classification problems. However just like artificial neural networks (ANN), SVMs are also black box in nature because of its inability to explain the knowledge learnt in the process of training, which is very crucial in some applications like medical diagnosis, security and bankruptcy prediction etc. In this chapter a novel hybrid approach for fuzzy rule extraction based on SVM is proposed. This approach handles rule-extraction as a learning task, which proceeds in two major steps. In the first step the authors use labeled training patterns to build an SVM model, which in turn yields the support vectors. In the second step extracted support vectors are used as input patterns to fuzzy rule based systems (FRBS) to generate fuzzy “if-then” rules. To study the effectiveness and validity of the extracted fuzzy rules, the hybrid SVM+FRBS is compared with other classification techniques like decision tree (DT), radial basis function network (RBF) and adaptive network based fuzzy inference system. To illustrate the effectiveness of the hybrid developed, the authors applied it to solve a bank bankruptcy prediction problem. The dataset used pertain to Spanish, Turkish and US banks. The quality of the extracted fuzzy rules is evaluated in terms of fidelity, coverage and comprehensibility.
Stochastic Model for the Generalized Subband Decomposition &#949;NLMS Algorithm with Gaussian Data

This paper proposes a stochastic model for the generalized subband decomposition normalized LMS (NLMS) algorithm. This algorithm is used as an alternative to the standard NLMS one, aiming to improve the convergence speed under correlated input data. Analytical models for the first and second moments of the filter weights are derived taking into account the time-varying nature of normalized step size. Moreover, in the model expressions a positive regularization parameter epsi is added to the power estimates, preventing division by zero during the power normalization process. Through simulation results, the accuracy of the proposed analytical model can be verified
Transistor-level sizing and timing verification of domino circuits in the Power PC/sup TM/ microprocessor

This paper describes a tool called Focus that is currently being used for the timing verification and siting of domino CMOS circuits in a Power PC/sup TM/ microprocessor. Domino CMOS circuits introduce more complex timing and sizing requirements compared to conventional static circuits. This paper shows how these requirements are addressed in Focus. Some case studies involving the application of Focus on production circuits are also described.
Momentum Effect and Market Conditions

The difference in firm-level momentum effects is investigated under bullish and bearish market conditions for Chinese real estate stocks, using panel data with a least-squares dummy variable. The results suggest that the momentum effect has different patterns in different market conditions for a shorter momentum horizon and that the momentum strategy can be only implemented successfully in a bullish market.
Effects of Newcastle Disease Virus Strains AF2240 and V4-UPM on Cytolysis and Apoptosis of Leukemia Cell Lines

Newcastle disease virus (NDV) is used as an antineoplastic agent in clinical tumor therapy. It has prompted much interest as an anticancer agent because it can replicate up to 10,000 times better in human cancer cells than in most normal cells. This study was carried out to determine the oncolytic potential of NDV strain AF2240 and V4-UPM on WEHI-3B leukemia cell line. Results from MTT cytotoxicity assay showed that the CD50 values for both strains were 2 and 8 HAU for AF2240 and V4-UPM, respectively. In addition, bromodeoxyuridine (BrdU) and trypan blue dye exclusion assays showed inhibition in cell proliferation after different periods. Increase in the cellular level of caspase-3 and detection of DNA laddering using agarose gel electrophoresis on treated cells with NDV confirmed that the mode of cell death was apoptosis. In addition, flow-cytometry analysis of cellular DNA content showed that the virus caused an increase in the sub-G1 region (apoptosis peaks). In conclusion, NDV strains AF2240 and V4-UPM caused cytolytic effects against WEHI-3B leukemic cell line.
A Decision Support System Based on Data Mining for Pediatric Cardiology Diagnosis

This paper presents a decision support system based on data mining for helping diagnose cardiac diseases on infants. The Maternal-Fetal Cardiac Unit of the Royal Portuguese Hospital of Recife (Brazil) has provided the data and characterized the problem with the objective of identifying patients who need surgical treatment for priority care. The solution development involved databases integration, data cleaning and preprocessing and data transformation to embed medical experts’ knowledge. Decision trees and induction of classification rules extracted the knowledge in explicit human language while the multi-layer perceptron neural network estimated the need of each patient for surgical treatment. The results in technical performance metrics were fine and the staff cardiologists assessed that the knowledge expressed by the induced rules was consistent with the medical understanding and that the success rate in surgical risk estimation recommended the solution as a valuable tool for supporting the patients diagnoses.
Towards a functional evaluation of manipulation performance in dexterous robotic hand design

Dexterous multifingered hands are the most complex and versatile variants of robotic end effectors. Compared to simpler grippers and underactuated hands, they should be more capable of grasping and, especially, manipulating different objects. This paper explores the relationship between kinematic design and manipulation performance of robotic hands. Some evaluation criteria frequently used by hand designers to verify kinematic configurations are revisited. The results from these criteria are scrutinized and compared with the evaluation of the manipulation workspace and the ranges of motion of inhand manipulation for a set of predefined objects. Simulations and actual manipulation experiments are carried out with different kinematic configurations on a modular dexterous hand. The results show some disconnection between perceived good designs through common evaluation criteria and their actual, realizable manipulation performance. This work finally gives some insight toward a more holistic approach to design hands that better address grasp and manipulation for the intended tasks and applications.
Sequence Search Algorithms for Single Pass Sequence Identification: Does One Size Fit All?

Bioinformatic tools have become essential to biologists in their quest to understand the vast quantities of sequence data, and now whole genomes, which are being produced at an ever increasing rate. Much of these sequence data are single-pass sequences, such as sample sequences from organisms closely related to other organisms of interest which have already been sequenced, or cDNAs or expressed sequence tags (ESTs). These single-pass sequences often contain errors, including frameshifts, which complicate the identification of homologues, especially at the protein level. Therefore, sequence searches with this type of data are often performed at the nucleotide level. The most commonly used sequence search algorithms for the identification of homologues are Washington University’s and the National Center for Biotechnology Information's (NCBI) versions of the BLAST suites of tools, which are to be found on websites all over the world. The work reported here examines the use of these tools for comparing sample sequence datasets to a known genome. It shows that care must be taken when choosing the parameters to use with the BLAST algorithms. NCBI’s version of gapped BLASTn gives much shorter, and sometimes different, top alignments to those found using Washington University’s version of BLASTn (which also allows for gaps), when both are used with their default parameters. Most of the differences in performance were found to be due to the choices of default parameters rather than underlying differences between the two algorithms. Washington University’s version, used with defaults, compares very favourably with the results obtained using the accurate but computationally intensive Smith–Waterman algorithm.
Automatically determining semantic relations in Chinese sentences

After parsing an important task is to determine the semantic structure of sentences. In this paper, we attempt to automatically annotate the Penn Chinese Treebank with semantic dependency structure. Initially a small portion of the Penn Chinese Treebank was manually annotated with headword and dependency relations. Two supervised machine learning algorithms with varying features were then used to learn the relations. Finally, a set of rules were created based on features of Chinese to solve some problem patterns that were found in the Penn Chinese Treebank dealing with ambiguous structures. The experimental results show that the algorithms and proposed approach are effective for determining semantic dependency structure automatically.
Graph-Based Semisupervised Learning for Acoustic Modeling in Automatic Speech Recognition

In this paper, we investigate how to apply graph-based semisupervised learning to acoustic modeling in speech recognition. Graph-based semisupervised learning is a widely used transductive semisupervised learning method in which labeled and unlabeled data are jointly represented as a weighted graph; the resulting graph structure is then used as a constraint during the classification of unlabeled data points. We investigate suitable graph-based learning algorithms for speech data and evaluate two different frameworks for integrating graph-based learning into state-of-the-art, deep neural network (DDN)-based speech recognition systems. The first framework utilizes graph-based learning in parallel with a DNN classifier within a lattice-rescoring framework, whereas the second framework relies on an embedding of graph neighborhood information into continuous space using an autoencoder. We demonstrate significant improvements in framelevel phonetic classification accuracy and consistent reductions in word error rate on large-vocabulary conversational speech recognition tasks.
Energy-Efficient Images

In this paper, a novel method is presented for producing energy-efficient images, i.e., images that consume less electrical energy on energy-adaptive displays, yet have the same or very similar perceptual quality to their original images. The proposed method relies on the fact that the energy consumption of pixels in modern energy-adaptive displays like OLED displays is directly proportional to the luminance of the pixels. Hence, in this paper, to reduce the energy consumption of an image, while at the same time preserving its perceptual quality, it is proposed to reduce the luminance of the pixels in the image by one just-noticeable-difference (JND) threshold. To determine the JND thresholds, an adaptive saliency-modulated JND (SJND) model is developed. In the proposed model, the JND thresholds of each block in the given image are elevated by two non-linear saliency modulation functions using the visual saliency of the block. The parameters of the saliency modulation functions are estimated through an adaptive optimization framework, which utilizes a state-of-the-art saliency-based objective image quality assessment method. To evaluate the proposed methods, a set of subjective experiments were conducted, and the real energy consumption of the produced energy-efficient images were measured by an accurate power monitor equipment on an OLED display. The obtained experimental results demonstrated that, on average, the proposed method is able to reduce the energy consumption by about 14.1% while preserving the perceptual quality of the displayed images.
A new variant vision on ECDSA

This paper describes a new variant level of signature scheme on ECDSA. In support of this scheme, a study of on a number of schemes was done. The number of scheme includes Lamport, Fail stop, Schnorr, and DSA etc. In this study three schemes, DSA, ECDSA and variant ECDSA are taken for a comparative study. From that comparison, the paper tries to develop a new scheme on ECDSA. Finally, It was found out that, variant signature on ECDSA is better than other schemes.
Interpreting the performance of HPF/Fortran 90D

We present a novel interpretive approach for accurate and cost effective performance prediction in a high performance computing environment, and describe the design of a source driven HPF/Fortran 90D performance prediction framework based on this approach. The performance prediction framework has been implemented as part of a HPF/Fortran 90D application development environment. A set of benchmarking kernels and application codes are used to validate the accuracy, utility, usability, and cost effectiveness of the performance prediction framework. The use of the framework for selecting appropriate compiler directives and for application performance debugging is demonstrated. <<ETX>>
Structural heterogeneity and protein composition of exosome‐like vesicles (prostasomes) in human semen

Human seminal fluid contains small exosome‐like vesicles called prostasomes. Prostasomes have been reported previously to play an important role in the process of fertilization by boosting survivability and motility of spermatozoa, in addition to modulating acrosomal reactivity. Prostasomes have also been reported to present with sizes varying from 50 to 500 nm and to have multilayered lipid membranes; however, the fine morphology of prostasomes has never been studied in detail.
Ionizing radiation-induced DNA injury and damage detection in patients with breast cancer

Abstract Breast cancer is the most common malignancy in women. Radiotherapy is frequently used in patients with breast cancer, but some patients may be more susceptible to ionizing radiation, and increased exposure to radiation sources may be associated to radiation adverse events. This susceptibility may be related to deficiencies in DNA repair mechanisms that are activated after cell-radiation, which causes DNA damage, particularly DNA double strand breaks. Some of these genetic susceptibilities in DNA-repair mechanisms are implicated in the etiology of hereditary breast/ovarian cancer (pathologic mutations in the BRCA 1 and 2 genes), but other less penetrant variants in genes involved in sporadic breast cancer have been described. These same genetic susceptibilities may be involved in negative radiotherapeutic outcomes. For these reasons, it is necessary to implement methods for detecting patients who are susceptible to radiotherapy-related adverse events. This review discusses mechanisms of DNA damage and repair, genes related to these functions, and the diagnosis methods designed and under research for detection of breast cancer patients with increased radiosensitivity.
Extension of the ” TIme-Frequency Ratio Of Mixtures ” blind source separation method to more than 2 channels

In a recent paper, we proposed a new blind source separation ( BSS) method, which uses timefrequency ( TF) information to extract two source signals from two linear i nstantaneous mixtures of these sources. In this new paper, we introduce an extension o f the latter method, intended for the general situation when mixtures of source signals are available. Unlike previously reported TF BSS methods, the proposed approach only requires slight dif ferences in theTF distributions of the considered signals: it mainly requests the sources to be ”vi sible”, i.e. to each occur alone in one local area of theTF plane. By usingTF ratios of mixed signals, it automatically determines these single-sourceTF areas and identifies the corresponding parts of the mixing ma trix. We present in detail the proposed method and give experimental results co ncerning mixtures of speech and music signals, thus showing that this approach yields very good pe rformance.
Bayesian Inference for NonstationarySpatial Covariance Structure viaSpatial

In geostatistics it is common practice to assume that the underlying spatial process is stationary and isotropic, that is the spatial distribution is unchanged when the origin of the index set is translated and the process is stationary under rotations about the origin. However in environmental problems, it is not very realistic to make such assumptions since local innuences in the correlation structure of the spatial process may be clearly found in the data. This paper proposes a Bayesian model wherein the main aim is to address the anisotropy problem. Following Sampson and Guttorp (1992), we deene the correlation function of the spatial process by reference to a latent space, denoted by D, where stationarity and isotropy hold. The space where the gauged monitoring sites lie is denoted by G. We adopt a Bayesian approach in which the mapping between G space and D space is represented by an unknown function d(:). A Gaussian process prior distribution is deened for d(:). Unlike the Sampson & Guttorp approach, the mapping of both gauged and ungauged sites is handled in a single framework, and predictive inferences take explicit account of uncertainty in the mapping. Monte Carlo Markov Chain (MCMC) methods are used to obtain samples from the posterior distributions. Three examples are discussed, two simulated data sets and the solar radiation data set also analysed by Sampson & Guttorp.
General Equilibrium, Capital and Macroeconomics: A Key to Recent Controversies in Equilibrium Theory

Preface 1. The Long-Period Method 2. The Problematic Relationship Between Neo-Walrasian Equilibrium Analyses and Real Economies 3. Long-Period Equilibria 4. Must Long-Period Equilibria Be Stationary? With Initial Observations on Investment 5. Walras, the Shift to Neo-Walrasian Equilibria, and Some Confusions 6. Reswitching and Reverse Capital Deepening 7. Capital Theory and Macroeconomics: The Theory of Aggregate Investment 8. Capital Theory and Macroeconomics: The Labour Demand Curve 9. Summary of the Critical Argument, and Sketch of an Alternative Approach References
Altered solar wind-magnetosphere interaction at low Mach numbers: Coronal mass ejections

[1] We illustrate some fundamental alterations of the solar wind-magnetosphere interaction that occur during low Mach number solar wind. We first show that low Mach number solar wind conditions are often characteristic of coronal mass ejections (CMEs) and magnetic clouds in particular. We then illustrate the pivotal role of the magnetosheath. This comes from the fact that low Mach number solar wind leads to the formation of a low thermal β magnetosheath downstream of the bow shock. This property influences magnetic forces and currents, in particular, and in turn alters magnetosheath-magnetosphere coupling. The implications of this unusual regime of interaction have generally been overlooked. Potentially affected phenomena include the following: (1) asymmetric magnetosheath flows (with substantial enhancements), (2) asymmetric magnetopause and magnetotail shapes, (3) changes in the development of the Kelvin-Helmholtz instability and giant spiral auroral features, (4) variations in the controlling factors of dayside magnetic reconnection, (5) cross polar cap potential saturation and Alfven wings, and (6) global sawtooth oscillations. Here we examine these phenomena, primarily by use of global magnetohydrodynamic simulations, and discuss the mechanisms that rule such an altered interaction. We emphasize the fact that all these effects tend to occur simultaneously so as to render the solar wind-magnetosphere interaction drastically different from the more typical high Mach number case. In addition to the more extensively studied inner magnetosphere and magnetotail processes, these effects may have important implications during CME-driven storms at Earth, as well as at other astronomical bodies such as Mercury.
Design & fabrication of turnstile antenna with feed network optimization for Leo satellites

In this paper design of a turnstile in UHF band (465 MHz) is carried out. Optimum values for its main physical parameters such as monopole lengths, monopoles distance and angles are obtained by using HFSSV10 software. Antenna feed network is designed with microstrip lines to achieve in to the 90 degrees phase difference between adjacent monopoles and small size, simultaneously. Obtained scattering parameters from antenna design are used with microstrip feed network in microwave office software to achieve the desired VSWR characteristic of antenna at 465 MHz. In next step, the line and stub lengths are optimized in order to achieve a return loss better than 20 dB in operation frequency. Variable lumped capacitors are used in designed feed network for frequency fine tuning. The designed antenna and feed network are fabricated and tested. The diagrams represent a very good agreement between simulation and measurement.