Miracle-dz/UniFAR-FTUs huggingface dataset:
Wavelet Speech Enhancement Based on Nonnegative Matrix Factorization

For the state-of-the-art speech enhancement (SE) techniques, a spectrogram is usually preferred than the respective time-domain raw data, since it reveals more compact presentation together with conspicuous temporal information over a long time span. However, two problems can cause distortions in the conventional nonnegative matrix factorization (NMF)-based SE algorithms. One is related to the overlap-and-add operation used in the short-time Fourier transform (STFT)-based signal reconstruction, and the other is concerned with directly using the phase of the noisy speech as that of the enhanced speech in signal reconstruction. These two problems can cause information loss or discontinuity when comparing the clean signal with the reconstructed signal. To solve these two problems, we propose a novel SE method that adopts discrete wavelet packet transform (DWPT) and NMF. In brief, the DWPT is first applied to split a time-domain speech signal into a series of subband signals. Then, we exploit NMF to highlight the speech component for each subband. These enhanced subband signals are joined together via the inverse DWPT to reconstruct a noise-reduced signal in time domain. We evaluate the proposed DWPT-NMF-based SE method on the Mandarin hearing in noise test (MHINT) task. Experimental results show that this new method effectively enhances speech quality and intelligibility and outperforms the conventional STFT-NMF-based SE system.
haracterization of trial-to-trial fluctuations in local field potentials recorded in erebral cortex of awake behaving macaque avid

In analyzing neurophysiologic data, individual experimental trials are usually assumed to be statistically independent. However, many studies employing functional imaging and electrophysiology have shown that brain activity during behavioral tasks includes temporally correlated trial-to-trial fluctuations. This could lead to spurious results in statistical significance tests used to compare data from different interleaved behavioral conditions presented throughout an experiment. We characterize trial-
Phosphorylation Status of 72 kDa MMP-2 Determines Its Structure and Activity in Response to Peroxynitrite

Matrix metalloproteinase-2 (MMP-2) is a key intra- and extra-cellular protease which contributes to several oxidative stress related pathologies. A molecular understanding of 72 kDa MMP-2 activity, directly mediated by S-glutathiolation of its cysteine residues in the presence of peroxynitrite (ONOO−) and by phosphorylation of its serine and threonine residues, is essential to develop new generation inhibitors of intracellular MMP-2. Within its propeptide and collagen binding domains there is an interesting juxtaposition of predicted phosphorylation sites with nearby cysteine residues which form disulfide bonds. However, the combined effect of these two post-translational modifications on MMP-2 activity has not been studied. The activity of human recombinant 72 kDa MMP-2 (hrMMP-2) following in vitro treatments was measured by troponin I proteolysis assay and a kinetic activity assay using a fluorogenic peptide substrate. ONOO− treatment in the presence of 30 µM glutathione resulted in concentration-dependent changes in MMP-2 activity, with 0.1–1 µM increasing up to twofold and 100 µM attenuating its activity. Dephosphorylation of MMP-2 with alkaline phosphatase markedly increased its activity by sevenfold, either with or without ONOO−. Dephosphorylation of MMP-2 also affected the conformational structure of the enzyme as revealed by circular dichroism studies, suggesting an increase in the proportion of α-helices and a decrease in β-strands compared to the phosphorylated form of MMP-2. These results suggest that ONOO− activation (at low µM) and inactivation (at high µM) of 72 kDa MMP-2, in the presence or absence of glutathione, is also influenced by its phosphorylation status. These insights into the role of post-translational modifications in the structure and activity of 72 kDa MMP-2 will aid in the development of inhibitors specifically targeting intracellular MMP-2.
Reverse logistics for recycling: the customer service determinants

Customer service is a central concern in the logistics practice and a study topic in the forward logistics research. This article investigates the elements of customer service and their importance in reverse logistics for recycling. Since consumer is the first intervenient in any reverse system that aims to recycle household residues, the provision of an adequate customer service gains an increased importance. Applying multivariate statistical methods (exploratory factor analysis, confirmatory factor analysis and discriminant analysis) to the data from a sample of 267 Portuguese citizens, this study identifies the levels of customer service in this reverse logistics chain and evaluates their relative importance in achieving consumers’ participation. The study finds that, as in forward logistics, the customer service in reverse channels for recycling also has a hard and a soft level, being the former more important than the later. The results of this research suggest important guidelines to improve such a complex logistics service.
Single- and Dual-Process Models of Biased Contingency Detection

Abstract. Decades of research in causal and contingency learning show that people’s estimations of the degree of contingency between two events are easily biased by the relative probabilities of those two events. If two events co-occur frequently, then people tend to overestimate the strength of the contingency between them. Traditionally, these biases have been explained in terms of relatively simple single-process models of learning and reasoning. However, more recently some authors have found that these biases do not appear in all dependent variables and have proposed dual-process models to explain these dissociations between variables. In the present paper we review the evidence for dissociations supporting dual-process models and we point out important shortcomings of this literature. Some dissociations seem to be difficult to replicate or poorly generalizable and others can be attributed to methodological artifacts. Overall, we conclude that support for dual-process models of biased contingency detection is scarce and inconclusive.
An evaluation of Monte Carlo for nonlinear initial uncertainty propagation in Keplerian mechanics

This paper evaluates the performance of traditional Monte Carlo (FMC) for the nonlinear propagation of initial uncertainty in the two-body problem: an essential task in space situational awareness. This is done in light of a newly developed Markov chain Monte Carlo (MCMC) based particle approach that combines the benefits of MCMC sampling with the method of characteristics (MOC) for solving first order partial differential equations - in this case, the stochastic Liouville equation (SLE). The resulting MCMC-MOC ensemble is by construction, equivalent in measure to the true state probability density. Our recent results on the MCMC-MOC approach indicate that for systems with divergence-free dynamics, the FMC and MCMC-MOC ensembles are statistically consistent. Unfortunately, the unperturbed two-body problem (Keplerian motion) is one such system. In this paper, we demonstrate through simulation that the traditional MC propagated ensemble is indeed equivalent in measure to MCMC-MOC ensemble, which in turn is the true system measure by construction. As a result, it is not possible to improve upon FMC and its slow convergence rate for this problem.
Simulated Mobile Self-Location Using 3D Range Sensing and an A-Priori Map

Self-location is a critical component of unmanned ground vehicle operation. While GPS has met this need for many applications, there are others, indoors and outdoors, where GPS is not available or reliable. This paper discusses the investigation of a method to self-locate using an onboard 3D range sensor, when a 3D map of the environment is available. We have examined the performance in terms of what is needed for data coverage and terrain types. Localization is accomplished through surface fitting. Surface fitting is an important component of Simultaneous Localization and Mapping (SLAM) for 3D terrains. Three environments were chosen for testing, including a hilly desert scene, an indoor corridor scene, and a forest scene. A simulated 3D range sensor was used to scan models corresponding to these three environments. Various parameters were modified to identify strong and weak points in the scheme.
From Exemplar to Grammar: Integrating Analogy and Probability in Language Learning

We present a new model of language learning which is based on the following idea: if a language learner does not know which phrase-structure trees should be assigned to initial sentences, s/he allows (implicitly) for all possible trees and lets linguistic experience decide which is the ‘best’ tree for each sentence. The best tree is obtained by maximizing ‘structural analogy’ between a sentence and previous sentences, which is formalized by the most probable shortest combination of subtrees from all trees of previous sentences. Corpus-based experiments with this model on the Penn Treebank and the Childes database indicate that it can learn both exemplar-based and rulebased aspects of language, ranging from phrasal verbs to auxiliary fronting. By having learned the syntactic structures of sentences, we have also learned the grammar implicit in these structures, which can in turn be used to produce new sentences. We show that our model mimicks children’s language development from item-based constructions to abstract constructions, and that the model can simulate some of the errors made by children in producing complex questions.
Generalized hebbian algorithm for incremental latent semantic analysis

The Generalized Hebbian Algorithm is shown to be equivalent to Latent Semantic Analysis, and applicable to a range of LSAstyle tasks. GHA is a learning algorithm which converges on an approximation of the eigen decomposition of an unseen frequency matrix given observations presented in sequence. Use of GHA allows very large datasets to be processed.
Acoustic properties of different kinds of creaky voice

There is not one kind, but instead several kinds, of creaky voice, or creak. There is no single defining property shared by all kinds. Instead, each kind exhibits some properties but not others. Therefore different acoustic measures characterize different kinds of creak. This paper describes how various acoustic measures should pattern for each kind of creak.
A parameterized surface reflectivity model and estimation of bare-surface soil moisture with L-band radiometer

Soil moisture is an important parameter for hydrological and climatic investigations. Future satellite missions with L-band passive microwave radiometers will significantly increase the capability of monitoring Earth's soil moisture globally. Understanding the effects of surface roughness on microwave emission and developing quantitative bare-surface soil moisture retrieval algorithms is one of the essential components in many applications of geophysical properties in the complex Earth terrain by microwave remote sensing. We explore the use of the integral equation model (IEM) for modeling microwave emission. This model was validated using a three-dimensional Monte Carlo model. The results indicate that the IEM model can be used to simulate the surface emission quite well for a wide range of surface roughness conditions with high confidence. Several important characteristics of the effects of surface roughness on radiometer emission signals at L-band 1.4 GHz that have not been adequately addressed in the current semiempirical surface effective reflectivity models are demonstrated by using IEM-simulated data. Using an IEM-simulated database for a wide range of surface soil moisture and roughness properties, we developed a parameterized surface effective reflectivity model with three typically used correlation functions and an inversion model that puts different weights on the polarization measurements to minimize surface roughness effects and to estimate the surface dielectric properties directly from dual-polarization measurements. The inversion technique was validated with four years (1979-1982) of ground microwave radiometer experiment data over several bare-surface test sites at Beltsville, Maryland. The accuracies in random-mean-square error are within or about 3% for incidence angles from 20/spl deg/ to 50/spl deg/.
Route discovery and caching: A way to improve the scalability of Information-Centric Networking

Information Centric Networking (ICN) is a new paradigm in which the network layer provides users with content, instead of providing communication channels between hosts, and is aware of the name (or identifiers) of the contents. In this paper, we first describe what, we believe, are the main advantages and components of an ICN infrastructure; then we present an overall architecture for ICN, and then we focus on the main contribution of the paper, which is a route caching technique, designed to improve the scalability of the routing by name functionality.
Interactions between Germ Cells and Extracellular Matrix Glycoproteins during Migration and Gonad Assembly in the Mouse Embryo

Cells are known to bind to individual extracellular matrix glycoproteins in a complex and poorly understood way. Overall strength of adhesion is thought to be mediated by a combinatorial mechanism, involving adhesion of a cell to a variety of binding sites on the target glycoproteins. During migration in embryos, cells must alter their overall adhesiveness to the substrate to allow locomotion. The mechanism by which this is accomplished is not well understood. During early development, the cells destined to form the gametes, the primordial germ cells (PGCs), migrate from the developing hind gut to the site where the gonad will form. We have used whole-mount immunocytochemistry to study the changing distribution of three extracellular matrix glycoproteins, collagen IV, fibronectin, and laminin, during PGC migration and correlated this with quantitative assays of adhesiveness of PGCs to each of these. We show that PGCs change their strength of adhesion to each glycoprotein differentially during these stages. Furthermore, we show that PGCs interact with a discrete tract of laminin at the end of migration. Closer analysis of the adhesion of PGCs to laminin revealed that PGCs adhere particularly strongly to the E3 domain of laminin, and blocking experiments in vitro suggest that they adhere to this domain using a cell surface proteoglycan.
The Directed Steiner Network problem is tractable for a constant number of terminals

We consider the Directed Steiner Network (DSN) problem, also called the Point-to-Point Connection problem, where given a directed graph G and p pairs {(s/sub 1/,t/sub 1/), . , (s/sub p/,t/sub p/)} of nodes in the graph, one has to find the smallest subgraph H of G that contains paths from s/sub i/ to t/sub i/ for all i. The problem is NP-hard for general p, since the Directed Steiner Tree problem is a special case. Until now, the complexity was unknown for constant p/spl ges/3. We prove that the problem is polynomially solvable if p is any constant number, even if nodes and edges in G are weighted and the goal is to minimize the total weight of the subgraph H. In addition, we give an efficient algorithm for the Strongly Connected Steiner Subgraph problem for any constant p, where given a directed graph and p nodes in the graph, one has to compute the smallest strongly connected subgraph containing the p nodes.
Predicting students blood pressure by Artificial Neuron Network: Facebook predict students blood pressure

Emotion signifies the core value when a person comes in contact with multidimensional situation. Primary emotion has a capacity to observe when subject is contact with varying arising situation. Our research aims to present how primary emotion help to predict the human blood pressure (BP). Facebook is a Social Networking Sites (SNS) that provide emotionally rich environments and one of the most popular social sites for extracting information related to primary emotions. To extract the information of human emotion using Facebook, vector model is applied here. To predict the primary emotion using Facebook status, we adopt Artificial Neuron Network (ANN) since Facebook status updated by active users that helps to forecast the upcoming BP. The textual data is classified by vector model and ANN is used to predict human BP. The data set comprises of BP and human emotion gathered from Facebook updated post based on formal text from volunteer students at Hanyang University. The outcome shows that ANN can be prosperously applied for prediction of BP through primary emotion. The prediction result shows that there is 25% variation among the correlation coefficient variation of happy emotion instead of 18% of angry emotion.
Cancer Diagnosis Using Modified Fuzzy Network

in this study, a modified fuzzy c-means radial basis functions network is proposed. The main purposes of the suggested model are to diagnose the cancer diseases by using fuzzy rules with relatively small number of linguistic labels, reduce the similarity of the membership functions and preserve the meaning of the linguistic labels. The modified model is implemented and compared with adaptive neuro-fuzzy inference system (ANFIS). The both models are applied on "Wisconsin Breast Cancer" data set. Three rules are needed to obtain the classification rate 97% by using the modified model (3 out of 114 is classified wrongly). On the contrary, more rules are needed to get the same accuracy by using ANFIS. Moreover, the results indicate that the new model is more accurate than the state-of-art prediction methods. The suggested neuro-fuzzy inference system can be re-applied to many applications such as data approximation, human behavior representation, forecasting urban water demand and identifying DNA splice sites.
Amnestic syndrome of the medial temporal type identifies prodromal AD

Objective: To compare the power of tests assessing different cognitive domains for the identification of prodromal Alzheimer disease (AD) among patients with mild cognitive impairment (MCI). Background: Given the early involvement of the medial temporal lobe, a precocious and specific pattern of memory disorders might be expected for the identification of prodromal AD. Methods: A total of 251 patients with MCI were tested at baseline by a standardized neuropsychological battery, which included the Free and Cued Selective Recall Reminding Test (FCSRT) for verbal episodic memory; the Benton Visual Retention Test for visual memory; the Deno 100 and verbal fluency for language; a serial digit learning test and the double task of Baddeley for working memory; Wechsler Adult Intelligence Scale (WAIS) similarities for conceptual elaboration; and the Stroop test, the Trail Making test, and the WAIS digit symbol test for executive functions. The patients were followed at 6-month intervals for up to 3 years in order to identify those who converted to AD vs those who remained stable over time. Statistical analyses were based on receiver operating characteristic curve and Cox proportional hazards models. Results: A total of 59 subjects converted to AD dementia. The most sensitive and specific test for diagnosis of prodromal AD was the FCSRT. Significant cutoff for the diagnosis was 17/48 for free recall, 40/48 for total recall, and below 71% for index of sensitivity of cueing (% of efficacy of semantic cues for retrieval). Conclusions: The amnestic syndrome of the medial temporal type, defined by the Free and Cued Selective Recall Reminding Test, is able to distinguish patients at an early stage of Alzheimer disease from mild cognitive impairment non-converters. GLOSSARY: AD = Alzheimer disease; AUC = area under the curve; CDR = Clinical Dementia Rating; DSM-III-R = Diagnostic and Statistical Manual of Mental Disorders, 3rd ed., revised; FCSRT = Free and Cued Selective Recall Reminding Test; IADL = Instrumental Activities of Daily Living; MCI = mild cognitive impairment; MMSE = Mini-Mental State Examination; ROC = receiver operating characteristic; WAIS = Wechsler Adult Intelligence Scale.
SU-8 Composite Based "Lube-tape" for a Wide Range of Tribological Applications

In a previous work, we have developed a perflouropolyether (PFPE) lubricant droplet-filled SU-8 composite which promotes bonding between the molecules of SU-8 and PFPE and provides excellent boundary lubrication. The SU-8 + PFPE composite has enhanced the wear durability of SU-8 by more than four orders of magnitude. In this work, the same SU-8 + PFPE composite was used to fabricate a stand-alone laminate film called “Lube-tape”. It has integrated two layers of approximately 90 microns thickness each; the top layer is made of SU-8 + PFPE composite and the bottom layer of pristine SU-8. Thus, a single tape can have drastically contrasting high friction and low friction properties on its two surfaces. The composite side has the initial coefficient of friction ~7 times lower and the wear life more than four orders of magnitude than those of the pristine SU-8 side. This lube tape can be used on any load bearing surface to improve the tribological performance by simply pasting the pristine SU-8 side onto the substrate.
Accuracy of a Dual Path Platform (DPP) Assay for the Rapid Point-of-Care Diagnosis of Human Leptospirosis

Background Diagnosis of leptospirosis by the gold standard serologic assay, the microscopic agglutination test (MAT), requires paired sera and is not widely available. We developed a rapid assay using immunodominant Leptospira immunoglobulin-like (Lig) proteins in a Dual Path Platform (DPP). This study aimed to evaluate the assay's diagnostic performance in the setting of urban transmission. Methodology We determined test sensitivity using 446 acute and convalescent sera from MAT-confirmed case-patients with severe or mild leptospirosis in Brazil. We assessed test specificity using 677 sera from the following groups: healthy residents of a Brazilian slum with endemic transmission, febrile outpatients from the same slum, healthy blood donors, and patients with dengue, hepatitis A, and syphilis. Three operators independently interpreted visual results without knowing specimen status. Results The overall sensitivity for paired sera was 100% and 73% for severe and mild disease, respectively. In the acute phase, the assay achieved a sensitivity of 85% and 64% for severe and mild leptospirosis, respectively. Within seven days of illness onset, the assay achieved a sensitivity of 77% for severe disease and 60% for mild leptospirosis. Sensitivity of the DPP assay was similar to that for IgM-ELISA and increased with both duration of symptoms (chi-square regression P = 0.002) and agglutinating titer (Spearman ρ = 0.24, P<0.001). Specificity was ≥93% for dengue, hepatitis A, syphilis, febrile outpatients, and blood donors, while it was 86% for healthy slum residents. Inter-operator agreement ranged from very good to excellent (kappa: 0.82–0.94) and test-to-test reproducibility was also high (kappa: 0.89). Conclusions The DPP assay performed acceptably well for diagnosis of severe acute clinical leptospirosis and can be easily implemented in hospitals and health posts where leptospirosis is a major public health problem. However, test accuracy may need improvement for mild disease and early stage leptospirosis, particularly in regions with high transmission.
Optimal control and nonlinear filtering for nondegenerate diffusion processes

A linear parabolic partial differential equation describing the pathwise filter for a nondegenerate diffusion is changed, by an exponential substitution. into the dynamic programming equation of an optimal stochastic control problem. This substitution is applied to obtain results about the rate of decay as of solutions p(x,t)to the pathwise filter equation, and for solutions of the corresponding Zakai equation