Miracle-dz/UniFAR-FTUs huggingface dataset:
Conformer Space Neural Architecture Search for Multi-Task Audio Separation

Multi-task audio source separation aims to separate the audios collected from the complex environment into three fixed types of signal sources. Existing methods like EAD-Conformer usually take a manually designed model to process the separation. These networks may be sub-optimal since it is hard for humans to train and test all possible architectures. Especially, it is natu-ral to adopt different optimal sub-structures for decoding different types of signals, which, however, is very hard for humans to enumerate. In this paper, we quantitatively analyze the redundancy of the EAD-Conformer network and customize an effective and efficient search space. We propose an efficient K-path search method to search for the optimal architectures from the Conformer-based search space. We conduct a comprehensive search in terms of block numbers, head numbers, and channel numbers. Extensive experiments demonstrate that our searched architectures outperform existing methods in terms of efficiency and effectiveness.
Convergence theorem for kernel perceptron

The convergence of the kernel perceptron algorithm is examined. We first introduce the kernel perceptron algorithm which is an application of kernel methods to perceptron learning and also an extension of the algebraic perceptron algorithm to a general kernel function and a general learning coefficient. Although the naive perceptron is shown to converge, it is not clear whether the kernel perceptron algorithm converges or not. We prove that it converges when the learning coefficient is unity and derive the condition of the learning coefficient to converge for given examples.
On-the-fly ASR Corrections with Audio Exemplars

On-device end-to-end (E2E) models are required to handle long-tail vocabulary and a large number of acoustic conditions. With ﬁnite amount of training data some of these conditions and vocabulary words are unseen during training, which often leads to recognition errors. Text-based contextual biasing is intended to mitigate this problem, yet it works well only when sufﬁcient textual context is provided, and when the speech signal is well modeled by the ASR system. In this work, we propose to extend biasing to operate directly in the audio domain. We address a scenario where audio samples and the associated transcriptions are available, as is the case of manually corrected voice typing. We propose to directly compare incoming audio embeddings against a list of Audio Exemplars (AE), each associated with a text correction. We demonstrate the effectiveness of our approach by correcting the outputs of a production-quality RNNT model, which results in relative-WER reduction of 21.7% (one-shot) and 33.7% (multi-shot) on the Wiki-Names data set.
Validation of the cough phenotype TBQ among elderly Finnish subjects

Background Phenotypes can be utilised in the clinical management of disorders. Approaches to phenotype disorders have evolved from subjective expert opinion to data-driven methodologies. A previous cluster analysis among working-age subjects with cough revealed a phenotype TBQ (triggers, background disorders, quality-of-life impairment), which included 38% of the subjects with cough. The present study was carried out to validate this phenotyping among elderly, retired subjects with cough. Methods This was an observational cross-sectional study conducted via email among the members of the Finnish Pensioners’ Federation (n=26 205, 23.6% responded). The analysis included 1109 subjects with current cough (mean±sd age 72.9±5.3 years; 67.7% female). All filled in a comprehensive 86-item questionnaire including the Leicester Cough Questionnaire. Phenotypes were identified utilising k-means partitional clustering. Results Two clusters were identified. Cluster A included 75.2% of the subjects and cluster B 24.8% of the subjects. The three most important variables to separate the clusters were the number of cough triggers (mean±sd 2.47±2.34 versus 7.08±3.16, respectively; p<0.001), Leicester Cough Questionnaire physical domain (5.38±0.68 versus 4.21±0.81, respectively; p<0.001) and the number of cough background disorders (0.82±0.78 versus 1.99±0.89 respectively; p<0.001). Conclusion The phenotype TBQ could be identified also among elderly, retired subjects with cough, thus validating the previous phenotyping among working-age subjects. The main underlying pathophysiological feature separating the phenotype TBQ from the common cough phenotype is probably hypersensitivity of the cough reflex arc. The previously described cough phenotype TBQ (triggers, background disorders, quality-of-life impairment) and the common cough phenotype can be identified in elderly, retired subjects with current cough https://bit.ly/3cygezO
Gain-Scheduled Controller Design by Linear Programming with Application to a Double-Axis Positioning System

A linear programming approach is proposed to tune fixed-order linearly parameterized gain-scheduled controllers for stable SISO Linear Parameter Varying (LPV) plants. The method is based on the shaping of the open-loop transfer functions in the Nyquist diagram with constraints on the robustness margins and on the lower approximation of the crossover frequency. Two optimization problems are considered: optimization for robustness and optimization for performance. This method directly computes a gain-scheduled controller from a set of frequency-domain models in different operating points or from an LPV model and no interpolation is needed. In terms of closed-loop performance, this approach leads to extremely good results. However, closedloop stability is ensured only locally and for slow variations of the scheduling parameters. A stability analysis should be performed for fast variations of the scheduling parameters. An application to a high-precision double-axis positioning system illustrates the effectiveness of the proposed approach.
Statistical and clinical utility of multimodal dialogue-based speech and facial metrics for Parkinson's disease assessment

We present a framework for characterising the statistical and clinical relevance of speech and facial metrics in Parkinson’s disease (PD) extracted by a multimodal conversational platform. 38 people with PD (pPD) and 22 controls were recruited in an ongoing study and were asked to complete four interactive sessions, a week apart from each other. In each session, a virtual conversational agent, Tina, guided participants through a battery of standard tasks designed to elicit speech and facial behaviours. Speech and facial metrics were automatically extracted in real time, several of which showed statistically significant differences between pPD and controls. We explored which of these differences were greater than measurement error, a threshold defined as the minimally detectable change (MDC). Furthermore, we computed the minimal clinically important difference (MCID) with respect to the Communicative Partic-ipation Item Bank short form (CPIB-S) scale for these select metrics. Our results show that differences in metrics like duration and fundamental frequency (F0) of speech are captured beyond measurement error. We also discuss several confounding factors that need to be taken into consideration before making any clinical interpretation of changes in these metrics.
Self-Distillation Based on High-level Information Supervision for Compressing End-to-End ASR Model

Model compression of ASR aims to reduce the model parameters while bringing as little performance degradation as possible. Knowledge Distillation (KD) is an efficient model compression method that transfers the knowledge from a large teacher model to a smaller student model. However, most of the existing KD methods study how to fully utilize the teacher’s knowledge without paying attention to the student’s own knowledge. In this paper, we explore whether the high-level information of the model itself is helpful for low-level information. We first propose neighboring feature self-distillation (NFSD) approach to distill the knowledge from the adjacent deeper layer to the shallow one, which shows significant performance improvement. Therefore, we further propose attention-based feature self-distillation (AFSD) approach to exploit more high-level information. Specifically, AFSD fuses the knowledge from multiple deep layers with an attention mechanism and distills it to a shallow one. The experimental results on AISHELL-1 dataset show that 7.3% and 8.3% relative character error rate (CER) reduction can be achieved from NFSD and AFSD, respectively. In addition, our proposed two approaches can be easily combined with the general teacher-student knowledge distillation method to achieve 12.4% and 13.4% relative CER reduction compared with the baseline student model, respectively.
APLICACIONES DE LA ESTEREOLOGIA EN PSICOBIOLOGIA (II): LA PRECISION DE LAS ESTIMACIONES.

The applications of the stereology in Neuroscience have been improved by the recent advances in the precision of stereological estimates. Here we make a comparision between two methods of estimating the coefficients of error. The new formula described by Cruz-Orive (1999) incorporate the local or “nugget” effect in addition to the error due to the sections. The estimations derived from this formula are more precise in comparision with the previous formula described by Gundersen and Jensen (1987).
THE EVOLUTION OF Lyα-EMITTING GALAXIES BETWEEN z = 2.1 AND z = 3.1

We describe the results of a new, wide-field survey for z = 3.1 Lyα emitters (LAEs) in the Extended Chandra Deep Field South (ECDF-S). By using a nearly top-hat 5010 Å filter and complementary broadband photometry from the MUSYC survey, we identify a complete sample of 141 objects with monochromatic fluxes brighter than 2.4 × 10−17 erg cm−2 s−1 and observers-frame equivalent widths (EWs) greater than ∼80 Å (i.e., 20 Å in the rest frame of Lyα). The bright end of this data set is dominated by X-ray sources and foreground objects with Galaxy Evolution Explorer detections, but when these interlopers are removed, we are still left with a sample of 130 LAE candidates, 39 of which have spectroscopic confirmations. This sample overlaps the set of objects found in an earlier ECDF-S survey, but due to our filter's redder bandpass, it also includes 68 previously uncataloged sources. We confirm earlier measurements of the z = 3.1 LAE emission-line luminosity function and show that an apparent anticorrelation between EW and continuum brightness is likely due to the effect of correlated errors in our heteroskedastic data set. Finally, we compare the properties of z = 3.1 LAEs to LAEs found at z = 2.1. We show that in the ∼1 Gyr after z ∼ 3, the LAE luminosity function evolved significantly, with L* fading by ∼0.4 mag, the number density of sources with L > 1.5 × 1042 erg s−1 declining by ∼50%, and the EW scale length contracting from 70+7− 5 Å to 50+9− 6 Å. When combined with literature results, our observations demonstrate that over the redshift range z ∼ 0 to z ∼ 4, LAEs contain less than ∼10% of the star formation rate density of the universe.
Building Vietnamese Conversational Smart Home Dataset and Natural Language Understanding Model

Natural Language Understanding (NLU), which includes intent detection and slot tagging, plays an important role in any dialog system. This paper aims at building a first-ever conversational smart home dataset SmartNLU and NLU model for Vietnamese. Raw data were collected by asking participants provide or confirm the intents of and slot values in the user utterances that they sent or received in a smart home conversation until all were matched, using a Wizard-of-Oz set-up of a web tool. The data were then cleaned and processed to build templates of requests with empty slots. The entity strategy, which filled all slot values by the round-robin algorithm to templates, was empirically chosen to generate user utterances from collected templates, which made a total of 3,492/1,176/1,198 sentences correspond-ingly for the training/validating/test sets. The dataset has been released for a challenge carried out in AIHub, and published for the community. Several state-of-the-art joint NLU models were experimented on the released dataset. The proposed NLU model, which added PhoBERT to the DIET architecture of Rasa framework, gave the best results. The sentence accuracy of the DIET+PhoBERT was considerably higher than (i.e. 4.3% to 11.7%) the one of others.
Centenarian family and non-family companies. An exploratory analysis

Few companies are able to reach 100 years of existence, and even fewer do so without losing ownership control to the founders' families. Perhaps due to its scarcity, few quantitative data are available on this type of company. This paper analyzes, based on a sample of 120 Spanish centenary companies, the differences between family and non-family centenary companies, from a quantitative and exploratory perspective. Likewise, a comparison is made with a control sample of another 120 non-centenarian companies in order to draw conclusions. The analysis shows that there are more non-family centenary companies than family companies, although the latter obtain higher returns and have a more robust financial structure (less indebtedness and more liquidity) than the rest of the companies. Despite the limitations derived from the data used, the work contributes to the literature on business demography and on the longevity of family businesses.
A Low-Complexity Neural Normalized Min-Sum LDPC Decoding Algorithm Using Tensor-Train Decomposition

Compared with traditional low-density parity-check (LDPC) decoding algorithms, the current model-driven deep learning (DL)-based LDPC decoding algorithms face the disadvantage of high computational complexity. Based on the Neural Normalized Min-Sum (NNMS) algorithm, we propose a low-complexity model-driven DL-based LDPC decoding algorithm using Tensor-Train (TT) decomposition and syndrome loss function, called TT-NNMS+ algorithm. Our experiments show that the proposed TT-NNMS+ algorithm is more competitive than the NNMS algorithm in terms of bit error rate (BER) performance, memory requirement and computational complexity.
SOLO-SLAM: A Parallel Semantic SLAM Algorithm for Dynamic Scenes

Simultaneous localization and mapping (SLAM) is a core technology for mobile robots working in unknown environments. Most existing SLAM techniques can achieve good localization accuracy in static scenes, as they are designed based on the assumption that unknown scenes are rigid. However, real-world environments are dynamic, resulting in poor performance of SLAM algorithms. Thus, to optimize the performance of SLAM techniques, we propose a new parallel processing system, named SOLO-SLAM, based on the existing ORB-SLAM3 algorithm. By improving the semantic threads and designing a new dynamic point filtering strategy, SOLO-SLAM completes the tasks of semantic and SLAM threads in parallel, thereby effectively improving the real-time performance of SLAM systems. Additionally, we further enhance the filtering effect for dynamic points using a combination of regional dynamic degree and geometric constraints. The designed system adds a new semantic constraint based on semantic attributes of map points, which solves, to some extent, the problem of fewer optimization constraints caused by dynamic information filtering. Using the publicly available TUM dataset, SOLO-SLAM is compared with other state-of-the-art schemes. Our algorithm outperforms ORB-SLAM3 in accuracy (maximum improvement is 97.16%) and achieves better results than Dyna-SLAM with respect to time efficiency (maximum improvement is 90.07%).
Evaluation of Pulmonary Hypertension in Dogs with Heartworm Disease Using the Computed Tomographic Pulmonary Trunk to Aorta Diameter Ratio

Simple Summary Pulmonary hypertension (PH) is a consequence of proliferative pulmonary endoarteritis caused by the parasite Dirofilaria immitis (heartworm). To date, the diagnosis is carried out through echocardiographic determinations, with the right pulmonary artery distensibility (RPAD) index being the gold standard for diagnosis of PH caused by heartworm. Given the high importance and frequency of PH in dogs, the study of the pulmonary trunk to aorta ratio (PT:Ao) determined by computed tomography (CT) was proposed to assess its usefulness in the diagnosis of PH, compared to the gold standard method. The results show that the PT:Ao ratio was reliable for detecting and staging the severity of moderate to severe PH in dogs with heartworm. Abstract Dirofilaria immitis causes proliferative pulmonary endoarteritis that leads to the appearance of chronic precapillary pulmonary hypertension (PH) in dogs. Pulmonary trunk to aorta ratio (PT:Ao ratio) obtained by computed tomography (CT) was studied and the quantitative measure of the diameters of the pulmonary trunk (PT), the descending thoracic aorta (DAo) and ascending thoracic aorta (AAo) were evaluated for the determination of the presence of moderate to severe PH in 59 dogs. The diagnosis of PH was echocardiographically determined, based on the determination of the right pulmonary artery distensibility (RPAD) index (<29.5%), and compared with other parameters for estimating PH. The results showed a very high concordance: 0.976 (p-value 0.000) between the two CT methods (PT:DAo and PT:AAo) with an excellent intraclass correlation coefficient > 0.95. Moreover, cut-off values of ≥1.111 for PT:DAo, and ≥1.057 for PT:AAo were determined for dogs with an RPAD index < 29.5%, which suggests a cut-off value between healthy dogs and the presence of PH. As has been previously published, The PT:Ao ratios did not determine the presence of mild PH, so the measurements cannot be considered useful for the early diagnosis of PH in dogs with heartworm.
Heterocyclic Compounds as Dipeptidyl Peptidase-IV Inhibitors with Special Emphasis on Oxadiazoles as Potent Anti-Diabetic Agents

Dipeptidyl peptidase-IV (DPP-IV) inhibitors, often known as gliptins, have been used to treat type 2 diabetes mellitus (T2DM). They may be combined with other medications as an additional treatment or used alone as a monotherapy. In addition to insulin, sulfonylureas, thiazolidinediones, and metformin, these molecules appear as possible therapeutic options. Oxadiazole rings have been employed in numerous different ways during drug development efforts. It has been shown that including them in the pharmacophore increases the amount of ligand that may be bound. The exceptional hydrogen bond acceptor properties of oxadiazoles and the distinct hydrocarbon bonding potential of their regioisomers have been established. Beside their anti-diabetic effects, oxadiazoles display a wide range of pharmacological properties. In this study, we made the assumption that molecules containing oxadiazole rings may afford a different approach to the treatment of diabetes, not only for controlling glycemic levels but also for preventing atherosclerosis progression and other complications associated with diabetes. It was observed that oxadiazole fusion with benzothiazole, 5-(2,5,2-trifluoroethoxy) phenyl, β-homophenylalanine, 2-methyl-2-{5-(4-chlorophenyl), diamine-bridged bis-coumarinyl, 5-aryl-2-(6′-nitrobenzofuran-2′-yl), nitrobenzofuran, and/or oxindole leads to potential anti-diabetic activity.
Partial Incorporation of Nonlinear Resistive Field Grading Materials: A Strategy for Enhanced Field Reduction and Safety

In this study, we fabricate, characterize, and apply nonlinear resistive field grading (NLRFG) materials in vacuum recloser, cable joint, and outdoor cable termination models. The NLRFG materials are fabricated by dispersing various sizes and loadings of zinc oxide (ZnO) nanofillers in polymer matrices including epoxy resin and silicone rubber. The nonlinearly varying resistivity of these materials is characterized by measuring <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> curves. The <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> characteristics of the NLRFG materials are modeled and incorporated into a finite element analysis (FEA) platform. The electric field reducing the performance of the fabricated NLRFG composites is analyzed in FEA based on the actual designs of vacuum recloser, cable joint, and outdoor cable termination. Three different case studies are reported: 1) where regular epoxy or regular silicone rubber is used; 2) where NLRFG materials are used; and 3) where NLRFG materials are partially incorporated. The results suggest that 37%, 27%, and 29% reductions in the local maximum electric field are achieved when NLRFG materials are used in the vacuum recloser, cable joint, and outdoor cable termination, respectively. Power loss caused by leakage current in these models is also analyzed. The findings imply that the partial implementation of NLRFG materials not only could be more effective in terms of electric stress reduction but more safe than the full incorporation of them as it prohibits the formation of conductive paths across high-voltage and ground during overvoltage transients that can be caused by lightning strikes.
Small Scale Stochastic Dynamics For Particle Image Velocimetry Applications

Appointments 2018–present Associate Professor, Department of Mathematics, University of Utah, Salt Lake City, UT. 2010–2018 Assistant Professor, Department of Mathematics, University of Utah, Salt Lake City, UT. Spring 2018 Family Medical Leave, Department of Mathematics, University of Utah, Salt Lake City, UT. Fall 2016 Family Medical Leave, Department of Mathematics, University of Utah, Salt Lake City, UT. 2007–2010 Research Associate, Courant Institute of Mathematical Sciences, New York University, New York, NY. Mentor: Michael Shelley 2006–2007 Postdoctoral Fellow, Department of Mathematics, University of North Carolina, Chapel Hill, NC. Mentor: Gregory Forest
Word-wise Sparse Attention for Multimodal Sentiment Analysis

For multimodal sentiment analysis (MSA), the text-centric approach has been shown to be superior in performance, which adopts powerful text models (e.g., BERT) as backbone and studies how to effectively incorporate non-verbal modalities (i.e., audio and visual) to obtain more reﬁned and expressive word representations. In previous methods, the non-verbal information injected into a word representation only comes from a non-verbal segment corresponding to the time span of the word, ignoring the long-range dependencies across modalities. Meanwhile, these methods utilize the Softmax normalization function-based attention mechanism, which makes it difﬁcult to highlight the important information in non-verbal sequences. To this end, this paper proposes a non-verbal information injection method called Word-wise Sparse Attention (WSA) to capture the cross-modal long-range dependencies. When injecting the non-verbal information into a word, the word is used as the semantic anchor to search for the most relevant non-verbal information from holistic non-verbal sequences. Furthermore, an advanced Multimodal Adaptive Gating (MAG) mechanism is introduced to determine the amount of information injected from non-verbal modalities. We evaluate our method on the two publicly available multimodal sentiment datasets. Experimental results show that the proposed approach improves the baseline model consistently on all metrics.