Miracle-dz/UniFAR-FTUs huggingface dataset:
Distance-aware DNNs for robust speech recognition

Distant speech recognition (DSR) remains to be an open challenge, even for the state-of-the-art deep neural network (DNN) models. Previous work has attempted to improve DNNs under constantly distant speech. However, in real applications, the speaker-microphone distance (SMD) can be quite dynamic, varying even within a single utterance. This paper investigates how to alleviate the impact of dynamic SMD on DNN models. Our solution is to incorporate the frame-level SMD information into DNN training. Generation of the SMD information relies on a universal extractor that is learned on a meeting corpus. We study the utility of different architectures in instantiating the SMD extractor. On our target acoustic modeling task, two approaches are proposed to build distance-aware DNN models using the SMD information: simple concatenation and distance adaptive training (DAT). Our experiments show that in the simplest case, incorporating the SMD descriptors improves word error rates of DNNs by 5.6% relative. Further optimizing SMD extraction and integration results in more gains.
Identification of Linear Parameter-Varying Systems Using Nonlinear Programming

This paper deals with the identification of a linear parameter-varying (LPV) syster whose parameter dependence can be written as a linear-fractional transformation (LFT). We formulate an output-error identification problem and present a parameter estimation scheme in which a prediction error-based cost function is minimized using nonlinear programming; its gradients and (approximate) Hessians can he computed using LPV filters and inne products, and identifiable model sets (i.e., local canonical forms) are obtained efficiently using a natural geometrical approach. Some computational issues and experiences are discussed, and a simple numerical example is provided fo r illustration.
Concentration in auditing: Evidence from entry, exit and market share mobility in Germany versus the Netherlands

This paper investigates the usefulness of a dynamic analysis of audit-market competition in terms of audit-market-share mobility, audit-firm entry, and audit-firm exit. These dynamic measures of market structure are compared between the more regulated German audit market and the more liberal Dutch audit market. Prior research on audit market structure has focused on static analyses in terms of seller concentration of single national audit markets. By using data on the number of auditors as the measure of audit-firm size, this paper covers all firms active in the two audit markets in the period 1970 to 1994. The results indicate that the more liberal Dutch audit market has the highest dynamic measures of market structure and the highest concentration. Hence, the results show that high concentration can go hand in hand with high-market-share mobility and high audit-firm entry and exit. The results for market-share mobility also hold for an analysis including only the largest audit firms. The paper therefore concludes, that compared with a static seller concentration analysis, an analysis of audit-market dynamics provides a better description of the degree of competition in audit markets.
Applications of Dynamic Programming to Agriculture, Forestry and Fisheries: Review and Prognosis

Farmers are faced with many decision problems in crop and livestock production which are multistage and stochastic. There have been many applications of dynamic programming (DP) to such decision problems, many primarily for illustrative purposes. It is argued that the advent of farmer access to computers will lead to on-farm use of DP. Applications of DP to forestry, fisheries and agricultural policy are also reviewed. The scope and limitations of DP are discussed, and the close relationship between DP and control theory is examined.
Present and prospective clinical therapeutic regimens for Alzheimer’s disease

Alzheimer’s disease (AD) is an incurable neurodegenerative disorder that produces cognitive impairments that increase in severity as the disease progresses. The clinical symptoms are related to the presence of neuritic plaques and neurofibrillary tangles in the cerebral cortex which represent the pathophysiological hallmarks of AD. The debilitating nature of the disease can result in clinical burden for the patient, emotional strain for those that care for patients with Alzheimer’s, and significant financial burden to society. The goals of current treatments, such as cholinesterase inhibitors and N-methyl-D-aspartate receptor antagonist, are to reduce the severity or slow the progression of cognitive symptoms. Although these treatments have demonstrated modest clinical benefit, they are unable to prevent, prohibit, or reverse the underlying pathophysiology of AD. Considerable progress has been made toward the development of disease-modifying treatments. Treatments currently under development mainly target the production, aggregation, and removal of existing amyloid β-peptide aggregates which are believed to instigate the overall development of the neuropathology. Additional strategies that target tau pathology are being studied to promote neural protection against AD pathology. The current research has continued to expand our knowledge toward the development of disease modifying Alzheimer’s therapies; however, no specific treatment strategy capable of demonstrating empirical efficacy and safety has yet to emerge.
Alu hypomethylation in smoke-exposed epithelia and oral squamous carcinoma.

BACKGROUND Alu elements are one of the most common repetitive sequences that now constitute more than 10% of the human genome and potential targets for epigenetic alterations. Correspondingly, methylation of these elements can result in a genome-wide event that may have an impact in cancer. However, studies investigating the genome-wide status of Alu methylation in cancer remain limited. OBJECTIVES Oral squamous cell carcinoma (OSCC) presents with high incidence in South-East Asia and thus the aim of this study was to evaluate the Alu methylation status in OSCCs and explore with the possibility of using this information for diagnostic screening. We evaluated Alu methylation status in a) normal oral mucosa compared to OSCC; b) peripheral blood mononuclear cells (PBMCs) of normal controls comparing to oral cancer patients; c) among oral epithelium of normal controls, smokers and oral cancer patients. MATERIALS AND METHODS Alu methylation was detected by combined bisulfite restriction analysis (COBRA) at 2 CpG sites. The amplified products were classified into three patterns; hypermethylation ((m)C(m)C), partial methylation (uC(m)C+(m)C(u)C), and hypomethylation ((u)C(u)C). RESULTS The results demonstrate that the %(m)C(m)C value is suitable for differentiating normal and cancer in oral tissues (p=0.0002), but is not significantly observe in PBMCs. In addition, a stepwise decrease in this value was observed in the oral epithelium from normal, light smoker, heavy smoker, low stage and high stage OSCC (p=0.0003). Furthermore, receiver operating characteristic (ROC) curve analyses demonstrated the potential of combined %mC or %(m)C(m)C values as markers for oral cancer detection with sensitivity and specificity of 86.7% and 56.7%, respectively. CONCLUSIONS Alu hypomethylation is likely to be associated with multistep oral carcinogenesis, and might be developed as a screening tool for oral cancer detection.
Cytometric fingerprints: evaluation of new tools for analyzing microbial community dynamics

Optical characteristics of individual bacterial cells of natural communities can be measured with flow cytometry (FCM) in high throughput. The resulting data are visualized in cytometric histograms. These histograms represent individual cytometric fingerprints of microbial communities, e.g., at certain time points or microenvironmental conditions. Up to now four tools for analyzing the variation in these cytometric fingerprints are available but have not yet been systematically compared regarding application: Dalmatian Plot, Cytometric Histogram Image Comparison (CHIC), Cytometric Barcoding (CyBar), and FlowFP. In this article these tools were evaluated concerning (i) the required experience of the operator in handling cytometric data sets, (ii) the detection level of changes, (iii) time demand for analysis, and (iv) software requirements. As an illustrative example, FCM was used to characterize the microbial community structure of electroactive microbial biofilms. Their cytometric fingerprints were determined, analyzed with all four tools, and correlated to experimental and functional parameters. The source of inoculum (four different types of wastewater samples) showed the strongest influence on the microbial community structure and biofilm performance while the choice of substrate (acetate or lactate) had no significant effect in the present study. All four evaluation tools were found suitable to monitor structural changes of natural microbial communities. The Dalmatian Plot was shown to be most sensitive to operator impact but nevertheless provided an overview on community shifts. CHIC, CyBar, and FlowFP showed less operator dependence and gave highly resolved information on community structure variation on different detection levels. In conclusion, experimental and productivity parameters correlated with the biofilm structures and practical process integration details were available from cytometric fingerprint analysis.
Semantic Features for Context Organization

In recent years the technological world has grown by incorporating billions of small sensing devices, collecting and sharing real-world information. As the number of such devices grows, it becomes increasingly difficult to manage all these new information sources. There is no uniform way to share, process and understand context information. In previous publications we discussed efficient ways to organize context information that is independent of structure and representation. However, our previous solution suffers from semantic sensitivity. In this paper we review semantic methods that can be used to minimize this issue, and propose an unsupervised semantic similarity solution that combines distributional profiles with public web services. Our solution was evaluated against Miller-Charles dataset, achieving a correlation of 0.6.
Automated 3D Modeling and Analysis of Metallic Materials Using Multiple LC-SEM Images

The scanning electron microscope (SEM) has been successfully used as an analysis tool for micro scale materials in the last few decades [1]. Recently, the large chamber (LC) SEM at Y-12 has demonstrated useful results in the analysis of large specimens at nano scales. This paper summarizes the research efforts conducted by UT and Y-12 in applying state of art 3D computer vision techniques to LC-SEM imaging, particularly 3D reconstruction and modeling [2]. In such a way, two or more LC-SEM images taken under appropriate condition (for example, with small changes in tilt angle), combined with knowledge of the imaging process, can be used to infer the 3D structure of the specimen automatically.
The importance of depression and alcohol use in coronary artery bypass graft surgery patients: risk factors for delirium and poorer quality of life

Objective To investigate whether depression, anxiety and stress increase the risk for delirium and poor quality of life (QOL) after coronary artery bypass (CABG) surgery. Methods A total of 180 CABG patients (mean age of 63.5 ± 10.1 years, 82.2% males) completed baseline and postoperative self-report questionnaires to assess distress and QOL. Incident delirium was diagnosed postoperatively with a structured clinical interview and patients were monitored every day post-operatively for confusion and disturbance in consciousness. Results Delirium developed in 63 persons (35% of sample). After adjustment for covariates, delirium was significantly associated with depression [odds ratio (OR): 1.08; 95% confidence interval (CI): 1.03–1.13, P = 0.003], anxiety (OR: 1.07; 95% CI: 1.02–1.13, P = 0.01) and stress (OR: 1.05; 95% CI: 1.00–1.09, P = 0.03). Preoperative depression scores were associated with poorer QOL including bodily pain (β = −0.39, P = 0.013), vitality (β = −0.32, P = 0.020), social functioning (β = −0.51, P ≤ 0.001), emotional role function (β = −0.44, P = 0.003) and general health (β = −0.33, P = 0.038). Among the covariates, harmful levels of alcohol use was consistently associated with poorer QOL. Conclusions Depression and harmful levels of alcohol use were consistently associated with poorer QOL whereas depression, anxiety and stress were associated with delirium risk. These findings point to further research examining depression and harmful levels of alcohol use in coronary heart disease populations undergoing coronary revascularization.
The impact of RNA binding motif protein 4-regulated splicing cascade on the progression and metabolism of colorectal cancer cells

Dysregulated splicing of pre-messenger (m)RNA is considered a molecular occasion of carcinogenesis. However, the underlying mechanism is complex and remains to be investigated. Herein, we report that the upregulated miR-92a reduced the RNA-binding motif 4 (RBM4) protein expression, leading to the imbalanced expression of the neuronal polypyrimidine tract-binding (nPTB) protein through alternative splicing-coupled nonsense mediated decay (NMD) mechanism. Increase in nPTB protein enhances the relative level of fibroblast growth factor receptor 2 IIIc (FGFR2) and pyruvate kinase M2 (PKM2) transcripts which contribute to the progression and metabolic signature of CRC cells. Expression profiles of RBM4 and downstream alternative splicing events are consistently observed in cancerous tissues compared to adjacent normal tissues. These results constitute a mechanistic understanding of RBM4 on repressing the carcinogenesis of colorectal cells.
Point Cloud Context Analysis for Rehabilitation Grasping Assistance

Controlling hand exoskeletons for assisting impaired patients in grasping tasks is challenging because it is difficult to infer user intent. We hypothesize that majority of daily grasping tasks fall into a small set of categories or modes which can be inferred through real-time analysis of environmental geometry from 3D point clouds. This paper presents a low-cost, realtime system for semantic image labeling of household scenes with the objective to inform and assist activities of daily living. The system consists of a miniature depth camera, an inertial measurement unit and a microprocessor. It is able to achieve 85% or higher accuracy at classification of predefined modes while processing complex 3D scenes at over 30 frames per second. Within each mode it can detect and localize graspable objects. Grasping points can be correctly estimated on average within 1 cm for simple object geometries. The system has potential applications in robotic-assisted rehabilitation as well as manual task assistance.
Interference-aware Fast Path Adaptation in Mesh Networks

Existing multihop wireless routing protocols are incapable of reacting quickly to transient degradation of link quality rendering them unsuitable for supporting real-time applications. This limitation is imposed on the metricbased routing protocols in order to achieve route stability and low protocol overhead. We design Deflect, a lightweight and opportunistic mechanism that works underneath the routing layer. Deflect enables fast local adaptation of end-to-end routes in response to sudden drops in link delivery ratio or node failures. Deflect nodes snoop on transmissions to monitor link quality, and deflect traffic on other nodes to circumvent a poor-quality link. The design of Deflect achieves the following goals: a) zero message overhead mechanism for monitoring link quality at a very short time scale; b) fast switching of traffic through a node in neighborhood of the poor link; c) transparency to end-to-end routing protocol. We show that Deflect can react to link quality degradation within a few hundred milliseconds, compared to tens of seconds for an end-to-end routing protocol. Experiments in presence of controlled interference with Deflect show that the average measurement error is less than 5%. Overall, Deflect is shown to provide significant improvement in packet loss and delay compared with traditional metric-based routing.
Vesicle Pools: Lessons from Adrenal Chromaffin Cells

The adrenal chromaffin cell serves as a model system to study fast Ca2+-dependent exocytosis. Membrane capacitance measurements in combination with Ca2+ uncaging offers a temporal resolution in the millisecond range and reveals that catecholamine release occurs in three distinct phases. Release of a readily releasable (RRP) and a slowly releasable (SRP) pool are followed by sustained release, due to maturation, and release of vesicles which were not release-ready at the start of the stimulus. Trains of depolarizations, a more physiological stimulus, induce release from a small immediately releasable pool of vesicles residing adjacent to calcium channels, as well as from the RRP. The SRP is poorly activated by depolarization. A sequential model, in which non-releasable docked vesicles are primed to a slowly releasable state, and then further mature to the readily releasable state, has been proposed. The docked state, dependent on membrane proximity, requires SNAP-25, synaptotagmin, and syntaxin. The ablation or modification of SNAP-25 and syntaxin, components of the SNARE complex, as well as of synaptotagmin, the calcium sensor, and modulators such complexins and Snapin alter the properties and/or magnitudes of different phases of release, and in particular can ablate the RRP. These results indicate that the composition of the SNARE complex and its interaction with modulatory molecules drives priming and provides a molecular basis for different pools of releasable vesicles.
Research on SR_UPF ground moving target tracking algorithm using road information

A kind of tracking algorithm using square root unscented particle filter is proposed to solve nonlinear problem caused by maneuverability of target and low measurement update rate in ground moving target tracking. Unscented transform is used to transfer state estimation and covariance matrix, where the covariance matrix has been extracted. From this, importance sampling used in particle filter can be gained. Then, the state estimation which is obtained through particle filter is projected to road according to road information if it is off road. Using one ground moving target indication sensor to track a target on road, simulation results show that the proposed algorithm can effectively solve nonlinear problem in ground moving target tracking. Compared with the conventional particle filter algorithm, the proposed algorithm not only advances the tracking precision, also reduces the needed number of particles which mean less run time will be taken. Another way, tracking performance can be further improved due to using of road information.
Stimulator Selection in SSVEP-Based Spatial Selective Attention Study

Steady-State Visual Evoked Potentials (SSVEPs) are widely used in spatial selective attention. In this process the two kinds of visual simulators, Light Emitting Diode (LED) and Liquid Crystal Display (LCD), are commonly used to evoke SSVEP. In this paper, the differences of SSVEP caused by these two stimulators in the study of spatial selective attention were investigated. Results indicated that LED could stimulate strong SSVEP component on occipital lobe, and the frequency of evoked SSVEP had high precision and wide range as compared to LCD. Moreover a significant difference between noticed and unnoticed frequencies in spectrum was observed whereas in LCD mode this difference was limited and selectable frequencies were also limited. Our experimental finding suggested that average classification accuracies among all the test subjects in our experiments were 0.938 and 0.853 in LED and LCD mode, respectively. These results indicate that LED simulator is appropriate for evoking the SSVEP for the study of spatial selective attention.
Timing Recovery in Magnetic Recording Systems

Timing recovery is a crucial component in a magnetic recording channel detector. Conventional timing recovery techniques are sufficient only when operating at high signalto-noise ratio (SNR). However, iterative error-correction codes allow reliable operation at very low SNR, where conventional techniques fail. In this paper, we summarize the timing recovery schemes based on a per-survivor processing technique that are capable of working at low SNR. Results indicate that they perform better than conventional schemes.
A machine vision based method for atmospheric circulation classification

Weather maps refer to meteorological data that characterize the atmospheric circulation in a region. The classification of weather maps into categories becomes an important task for understanding regional climate. Towards this goal, manual and semiautomatic techniques have been used, requiring manpower and supervision. In this paper, we propose a machine vision based method for the classification of weather maps into distinct classes. The chain code descriptor is applied to extract the feature of isobaric lines and we introduce the Double-Side Chain Code (DSCC) histogram for feature representation. Handling DSCC histograms as multidimensional vectors, the k-nearest neighbors (k-NN) algorithm classifies the objects to an appropriate number of classes, based on closest training set in the feature space. This method provides an automated and more ‘objective’ classification scheme, applying straightforward to the input weather map's image.
Measuring Team Knowledge: A Window to the Cognitive Underpinnings of Team Performance

The authors report an effort aimed at developing and evaluating measures of taskwork and teamwork team knowledge for teams in which members differ in knowledge backgrounds. These measures were used in a study with 36 teams to explore the cognitive underpinnings of team performance variations due to cross-training regime. The authors demonstrate that these measures are valid and provide team performance information that complements outcome and behavioral measures. Teams exposed to full cross-training acquired more taskwork and teamwork knowledge than control teams or teams exposed to a conceptual version of cross-training. Measures of team knowledge provide information regarding team task performance critical for system design and training programs.
A Multi-agent System Architecture for Modeling Multicultural Interaction

This paper presents an approach for managing a synthetic model of culture based on the cognitive processes that allow humans to display culturally biased behaviors. We present cognitive agents as the underlying methodology for simulating such processes, introducing their main capabilities and using them to simulate the main processes involved in culture management. We present the architecture of an agent with cultural background based on the OMAS cognitive agent architecture. As a study case, we give some examples showing how cultural conflicts in intercultural interactions are recognized.