Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-pass feature enhancement based on generative-discriminative hybrid approach for noise robust speech recognition

This paper presents multi-pass feature enhancement technique that consists of three processing passes. In the proposed method, the first pass was described in our previous work, and consists of model-based feature enhancement realized by employing a generative-discriminative hybrid approach with Gaussian mixture models and deep neural networks (DNNs). As an extension of the previous work, the second pass of the proposed method utilizes DNNs retrained with iterative realignment and auxiliary features obtained from intermediate parameters of the first processing pass. In the third pass, we apply unsupervised DNN adaptation and system combination to the results of the second pass. Therefore, the proposed multi-pass technique realizes stepwise improvements in feature enhancement. For CHiME3 task evaluations, the proposed method provided noticeable improvements in noisy speech recognition accuracy compared with results obtained using the previous one-pass feature enhancement technique.
Optimal control to reduce emissions in gasoline engines: an iterative learning control approach for ECU calibration maps improvement

Control of emissions in gasoline engines has become more stringent in the last decades, especially in Europe, posing new and important problems in the control of complex nonlinear systems. In this work a preliminary investigation is conducted on the idea of exploiting Iterative Learning Control to optimize calibration maps that are commonly used in the Engine Control Unit of gasoline engines. In this spirit, starting from existing maps, we show how to refine them using a gradient-descent iterative learning control algorithm, considering additional constraints in the optimization problem. The outcome of this procedure is a control signal which can be integrated in a modified map. The performance of the proposed technique is validated on the provided training signal and cross-validated on different reference signals. Simulation results show the effectiveness of the approach.
The Necessity for Longitudinal Studies in Risk Perception Research

In their review of the risk perception and risk communication literature on flood risks, Kellens et al.(1) emphasize that flood hazards are a serious threat, and that they are likely to increase in the future. Researchers typically agree that it is important to better understand people’s risk perceptions and the factors that influence their willingness to adopt mitigation behaviors. (1–3) One important insight of Kellens’s review is the finding that a majority of the studies on flood risks are not structured around a theoretical framework. I completely agree that studies employing a good theoretical framework provide more insight than studies that are not connected to one. However, another very problematic aspect of many studies in the domain of natural hazards relates to the methods that they employ. Cross-sectional surveys are used in most studies examining risk perception and risk communication. In the review by Kellens et al., only two of 57 studies used an experimental design, and none used a longitudinal design. A similar focus on crosssectional designs has been found in another recent literature review, which examined factors influencing flood mitigation behavior. (2) Both reviews emphasize that in order to test causal relationships, more experimental research and longitudinal studies are needed. In the case of flooding, using cross-sectional
Listeriolysin O Is Necessary and Sufficient to Induce Autophagy during Listeria monocytogenes Infection

Background Recent studies have suggested that autophagy is utilized by cells as a protective mechanism against Listeria monocytogenes infection. Methodology/Principal Findings However we find autophagy has no measurable role in vacuolar escape and intracellular growth in primary cultured bone marrow derived macrophages (BMDMs) deficient for autophagy (atg5−/−). Nevertheless, we provide evidence that the pore forming activity of the cholesterol-dependent cytolysin listeriolysin O (LLO) can induce autophagy subsequent to infection by L. monocytogenes. Infection of BMDMs with L. monocytogenes induced microtubule-associated protein light chain 3 (LC3) lipidation, consistent with autophagy activation, whereas a mutant lacking LLO did not. Infection of BMDMs that express LC3-GFP demonstrated that wild-type L. monocytogenes was encapsulated by LC3-GFP, consistent with autophagy activation, whereas a mutant lacking LLO was not. Bacillus subtilis expressing either LLO or a related cytolysin, perfringolysin O (PFO), induced LC3 colocalization and LC3 lipidation. Further, LLO-containing liposomes also recruited LC3-GFP, indicating that LLO was sufficient to induce targeted autophagy in the absence of infection. The role of autophagy had variable effects depending on the cell type assayed. In atg5−/− mouse embryonic fibroblasts, L. monocytogenes had a primary vacuole escape defect. However, the bacteria escaped and grew normally in atg5−/− BMDMs. Conclusions/Significance We propose that membrane damage, such as that caused by LLO, triggers bacterial-targeted autophagy, although autophagy does not affect the fate of wild-type intracellular L. monocytogenes in primary BMDMs.
MEGA3: Integrated software for Molecular Evolutionary Genetics Analysis and sequence alignment

With its theoretical basis firmly established in molecular evolutionary and population genetics, the comparative DNA and protein sequence analysis plays a central role in reconstructing the evolutionary histories of species and multigene families, estimating rates of molecular evolution, and inferring the nature and extent of selective forces shaping the evolution of genes and genomes. The scope of these investigations has now expanded greatly owing to the development of high-throughput sequencing techniques and novel statistical and computational methods. These methods require easy-to-use computer programs. One such effort has been to produce Molecular Evolutionary Genetics Analysis (MEGA) software, with its focus on facilitating the exploration and analysis of the DNA and protein sequence variation from an evolutionary perspective. Currently in its third major release, MEGA3 contains facilities for automatic and manual sequence alignment, web-based mining of databases, inference of the phylogenetic trees, estimation of evolutionary distances and testing evolutionary hypotheses. This paper provides an overview of the statistical methods, computational tools, and visual exploration modules for data input and the results obtainable in MEGA.
Elements of Gameful Design Emerging from User Preferences

Several studies have developed models to explain player preferences. These models have been developed for digital games; however, they have been frequently applied in gameful design (i.e., designing non-game applications with game elements) without empirical validation of their fit to this different context. It is not clear if users experience game elements embedded in applications similarly to how players experience them in games. Consequently, we still lack a conceptual framework of design elements built specifically for a gamification context. To fill this gap, we propose a classification of eight groups of gameful design elements produced from an exploratory factor analysis based on participants' self-reported preferences. We describe the characteristics of the users who are more likely to enjoy each group of design elements in terms of their gender, age, gamification user type, and personality traits. Our main contribution is providing an overview of which design elements work best for what demographic clusters and how we can apply this knowledge to design effective gameful systems.
A distributed consensus protocol for clock synchronization in wireless sensor network

This paper describes a novel consensus-based protocol, referred as Average TimeSync (ATS), for synchronizing a wireless sensor. This algorithm is based on a class of popular distributed algorithms known as consensus, agreement, gossip or rendezvous whose main idea is averaging local information. The proposed algorithm has three main features. Firstly, it is fully distributed and therefore robust to node failure and to new node appearance. Secondly, it compensates for clock skew differences among nodes, thus maintaining the network synchronized for longer periods than using simple clock offset compensation. Finally, it is computationally lite as it involves only simple sum/product operations. The algorithm has been implemented and preliminary experimental results are provided.
Self-Organizing Schema Mappings in the GridVine Peer Data Management System

GridVine is a Peer Data Management System based on a decentralized access structure. Built following the principle of data independence, it separates a logical layer -- where data, schemas and mappings are managed -- from a physical layer consisting of a structured Peer-to-Peer network supporting efficient routing of messages and index load-balancing. Our system is totally decentralized, yet it fosters semantic interoperability through pairwise schema mappings and query reformulation. In this demonstration, we present a set of algorithms to automatically organize the network of schema mappings. We concentrate on three key functionalities: (1) the sharing of data, schemas and schema mappings in the network, (2) the dynamic creation and deprecation of mappings to foster global interoperability, and (3) the propagation of queries using the mappings. We illustrate these functionalities using bioinformatic schemas and data in a network running on several hundreds of peers simultaneously.
Evidence for frequency-dependent cortical plasticity in the human brain

Significance We extend the concept of frequency-dependent plasticity, thus far used to describe synaptic selective adaptation in response to stimulation at different frequencies, to the level of cortical networks. We demonstrate selective changes in perception, functional topography, and connectivity of the primary somatosensory cortex following tactile stimulation at different frequencies. Simulation of a network of oscillators incorporating Hebbian learning reproduced these changes and confirmed the influence of intrinsic cortical resonance on plasticity. We thus show that frequency-dependent plasticity extends to the cortical level and is influenced by cortical resonance, which is of potential importance for optimization of therapeutic stimulation approaches to augment learning and memory. Frequency-dependent plasticity (FDP) describes adaptation at the synapse in response to stimulation at different frequencies. Its consequence on the structure and function of cortical networks is unknown. We tested whether cortical “resonance,” favorable stimulation frequencies at which the sensory cortices respond maximally, influenced the impact of FDP on perception, functional topography, and connectivity of the primary somatosensory cortex using psychophysics and functional imaging (fMRI). We costimulated two digits on the hand synchronously at, above, or below the resonance frequency of the somatosensory cortex, and tested subjects’ accuracy and speed on tactile localization before and after costimulation. More errors and slower response times followed costimulation at above- or below-resonance, respectively. Response times were faster after at-resonance costimulation. In the fMRI, the cortical representations of the two digits costimulated above-resonance shifted closer, potentially accounting for the poorer performance. Costimulation at-resonance did not shift the digit regions, but increased the functional coupling between them, potentially accounting for the improved response time. To relate these results to synaptic plasticity, we simulated a network of oscillators incorporating Hebbian learning. Two neighboring patches embedded in a cortical sheet, mimicking the two digit regions, were costimulated at different frequencies. Network activation outside the stimulated patches was greatest at above-resonance frequencies, reproducing the spread of digit representations seen with fMRI. Connection strengths within the patches increased following at-resonance costimulation, reproducing the increased fMRI connectivity. We show that FDP extends to the cortical level and is influenced by cortical resonance.
Examining restricted and repetitive behaviors in young children with autism spectrum disorder during two observational contexts

This prospective study of the FIRST WORDS® Project examined restricted and repetitive behaviors in a sample of 55 toddlers at a mean age of 20 months who were later diagnosed with autism spectrum disorder. Restricted and repetitive behaviors were coded using the Repetitive Movement and Restricted Interest Scales in two video-recorded observation methods–structured sampling procedures in a clinic and naturalistic everyday activities at home. Measures of restricted and repetitive behaviors were higher in the clinic setting than in the home observation, especially for behaviors involving object use. Repetitive movements with objects in the clinic predicted nonverbal developmental scores and Autism Diagnostic Observation Schedule social affect scores at later follow-up. In contrast, repetitive movements with objects at home significantly predicted later Autism Diagnostic Observation Schedule restricted and repetitive behaviors scores. These results support the utility of the Repetitive Movement and Restricted Interest Scales to detect restricted and repetitive behaviors in toddlers and suggest that observations of restricted and repetitive behaviors in clinic and home settings may provide unique and important diagnostic information for improving early detection of autism spectrum disorder.
Entity Disambiguation Using a Markov-Logic Network

*Entity linking (EL) is the task of linking a textual named entity mention to a knowledge base entry. It is a difficult task involving many challenges, but the most crucial problem is entity ambiguity. Traditional EL approaches usually employ different constraints and filtering techniques to improve performance. However, these constraints are executed in several different stages and cannot be used interactively. In this paper, we propose several disambiguation formulae/features and employ a Markov logic network to model interweaved constraints found in one type of EL, gene mention linking. To assess our systems effectiveness in different applications, we adopt two evaluation schemes: article-wide and instance-based precision/recall/F-measure. Experimental results show that our system outperforms the baseline systems and state-of-the-art systems under both evaluation schemes.
The INCOME Approach for Conceptual Modelling and Prototyping of Information Systems

OJ Universitat Mannheim Fakultat fiir Mathematik und Informatik . D-6800 Mannheim West-Germany OOJ Universitat Karlsruhe (TH) Institut fiiI' Angewandte Informatik und Formale Beschreibungsverfahren D-7500 Karlsruhe West-Germany This paper surveys the main features of INCOME, which is an approach for conceptual modelling of information systems. INCOME supports the specification and prototyping of all static and dynamic system aspects which are regarded to be relevant for the design. Petri nets with different interpretations are used as uniform specification framework for object structures and system behaviour.
Universal O(congestion + dilation + log1+εN) local control packet switching algorithms

In 1988, Leighton, Maggs and Rao proved a much celebrated result: that for any network, given any collection of packets with a speciﬁed route for each packet, there exists an “optimal” schedule for all these packets. That is, there exists a schedule of the motion of the packets such that at each step, every edge is crossed by at most one packet, and all the packets are delivered to their destinations in O ( C + D ) steps, where C is the “congestion” (i.e., the maximum number of paths that share the same edge), and D is the “dila-tion” (i.e., the length of the longest path). The proof was non-constructive and relied on Lov´asz Local Lemma. In a followup paper, Leighton, Maggs, and Richa gave a centralized algorithm for ﬁnding the schedule. The original paper left open the question whether there exists a constructive distributed “on-line” algorithm with the same optimal performance. Last year, Rabani and Tardos presented a randomized local-control algorithm which with high probability delivers all packets in time O (cid:0) C + D · (cid:0) (log ∗ N ) O (log ∗ N ) (cid:1) + + (log N ) 6 (cid:1) . In this paper, we show a nearly optimal local control algo-rithm for this long-standing open problem. That is, we show a randomized local control algorithm which for any network topology delivers all the packets to their destinations in time O ( C + D +log 1+ (cid:15) N ) with high probability, where N is the size of the problem, and (cid:15) > 0 is arbitrary. Our result has implications to ATM (Asynchronous Transfer Mode) packet switching algorithms and other applications.
Network coding in undirected graphs is either very helpful or not helpful at all

While it is known that using network coding can significantly improve the throughput of directed networks, it is a notorious open problem whether coding yields any advantage over the multicommodity flow (MCF) rate in undirected networks. It was conjectured by Li and Li (2004) that the answer is "no". In this paper we show that even a small advantage over MCF can be amplified to yield a near-maximum possible gap. We prove that any undirected network with $k$ source-sink pairs that exhibits a $(1+\varepsilon)$ gap between its MCF rate and its network coding rate can be used to construct a family of graphs $G'$ whose gap is $\log(|G'|)^c$ for some constant $c < 1$. The resulting gap is close to the best currently known upper bound, $\log(|G'|)$, which follows from the connection between MCF and sparsest cuts. Our construction relies on a gap-amplifying graph tensor product that, given two graphs $G_1,G_2$ with small gaps, creates another graph $G$ with a gap that is equal to the product of the previous two, at the cost of increasing the size of the graph. We iterate this process to obtain a gap of $\log(|G'|)^c$ from any initial gap.
Interobserver Variability in Diagnosing High-Grade Neuroendocrine Carcinoma of the Lung and Comparing It with the Morphometric Analysis

Background Distinguishing small cell lung carcinoma (SCLC) and large cell neuroendocrine carcinoma (LCNEC) of the lung is difficult with little information about interobserver variability. Methods One hundred twenty-nine cases of resected SCLC and LCNEC were independently evaluated by four pathologists and classified according to the 2004 World Health Organization criteria. Agreement was regarded as "unanimous" if all four pathologists agreed on the classification. The kappa statistic was calculated to measure the degree of agreement between pathologists. We also measured cell size using image analysis, and receiver-operating-characteristic curve analysis was performed to evaluate cell size in predicting the diagnosis of high-grade neuroendocrine (NE) carcinomas in 66 cases. Results Unanimous agreement was achieved in 55.0% of 129 cases. The kappa values ranged from 0.35 to 0.81. Morphometric analysis reaffirmed that there was a continuous spectrum of cell size from SCLC to LCNEC and showed that tumors with cells falling in the middle size range were difficult to categorize and lacked unanimous agreement. Conclusions Our results provide an objective explanation for considerable interobserver variability in the diagnosis of high-grade pulmonary NE carcinomas. Further studies would need to define more stringent and objective definitions of cytologic and architectural characteristics to reliably distinguish between SCLC and LCNEC.
Effects of an Enriched Extract of Paeoniflorin, a Monoterpene Glycoside used in Chinese Herbal Medicine, on Cholesterol Metabolism in a Hyperlipidemic Rat Model

Background Paeoniflorin is a monoterpene glycoside extracted from the roots of Paeonia lactiflora and is used in Chinese herbal medicine to treat hyperlipidemia. The aim of this study was to evaluate the effects of an enriched extract of paeoniflorin on cholesterol levels, hemodynamics, and oxidative stress in a hyperlipidemic rat model. Material/Methods Male Sprague-Dawley rats were fed high-cholesterol diets and treated with three different doses of paeoniflorin for 12 weeks. The effects of paeoniflorin treatment were assessed on cholesterol levels, cholesterol metabolism, red blood cell vascular flow using hemorheology, antioxidant enzymes, and expression of the rate-limiting enzyme in the mevalonate pathway, 3-hydroxy-3-methylglutharyl-coenzyme A reductase (HMG-CoAR). Rat liver histology and immunohistochemical analysis were performed to evaluate the expression of nuclear factor erythroid 2–related factor 2 (Nrf2), cytochrome P450 7A1 (CYP7A1), and peroxisome proliferator-activated receptors (PPAR)-α. Protein expression HMG-CoAR, low-density lipoprotein receptor (LDLR), PPAR-α and CYP7A1 was measured by Western blotting. Antioxidant activity in rat liver was determined by measuring superoxide dismutase (SOD) and malondialdehyde (MDA). Results Serum and hepatic cholesterol, hepatic steatosis and the products of cholesterol metabolism were reduced by paeoniflorin treatment, which also reduced the activity of HMG-CoAR and upregulated the expression of LDLR, PPAR-α, and CYP7A1 expression, increased SOD, decreased MDA, and upregulated Nrf2 expression. Conclusions The findings of this study in a rat model of hyperlipidemia have shown that paeoniflorin regulates hepatic cholesterol synthesis and metabolism and may also protect the liver from oxidative stress.
Functional polymorphism of the promoter region of the prostacyclin synthase gene and severity of RSV infection in hospitalized children

Prostaglandin I2 (PGI2) protects against RSV‐induced illness in mice. A variable‐number tandem repeat (VNTR) polymorphism has been detected in the promoter region of the PGI2 synthase (PGIS) gene. We sought to determine if PGI2 concentrations or polymorphisms of the PGIS gene correlate with severity of RSV lower respiratory tract infections (LRTI) in human infants. VNTR polymorphisms were studied in 81 previously healthy children between birth and 12 months of age who were hospitalized for LRTI due to RSV and 98 healthy adult control subjects. The severity of RSV infection was quantified using a clinical scoring system, and infant urine samples were collected during the acute illness for measurement of the urinary metabolite of PGI2. There were no significant differences in the overall distribution of alleles and genotypes between infants with RSV LRTI and the control subjects. The severity of RSV infection significantly inversely correlated with urinary PGI2 metabolite concentrations. The urinary PGI2 metabolite concentration correlated with the number of VNTR. The presence of a genotype with a low number VNTR repeats significantly correlated with the most severe RSV LRTI, and genotypes with the highest number of VNTR correlated with the least severe RSV LRTI. A functional polymorphism in the promoter region of the PGIS gene is associated with both significant differences in urinary PGI2 concentrations during RSV LRTI, and severity of RSV infection in previously healthy infants. J. Med. Virol. 80:2015–2022, 2008. © 2008 Wiley‐Liss, Inc.