Miracle-dz/UniFAR-FTUs huggingface dataset:
Association between serum uric acid and carotid disease in patients with atherosclerotic acute ischemic stroke

Aim The role of serum uric acid in ischemic stroke is controversial. On the one hand, it has a role as neuroprotectant in acute phase, but on the other hand, it may promote atherosclerosis in carotid arteries. Our aim is to investigate the association of serum uric acid levels at admission of acute ischemic stroke patients with carotid disease. Methods Cross-sectional study of patients admitted due to acute ischemic stroke. Clinical and laboratory variables were recorded. The carotid disease was defined based on the findings of carotid echography intima to media thickness and stenosis. Patients were grouped according to these findings. Robust statistical methods were applied into analysis. Results A total of 245 patients were recruited through a stroke registry. Their values of serum uric acid were related to both carotid intima to media thickness and stenosis showing a positive relationship between serum uric acid levels and intima to media thickness by Pearson correlation (p < 0.05). Similarly, after adjusting for all potential confounders, eGFR, glucose, age and serum uric acid levels, (OR 1.26 (95% CI 1.04 −1.52, p¼0.01), were identified as independent predictors for having a intima to media thickness ≥1 mm. Similarly, by grouping patients in tertiles of the serum uric acid distribution, we found a predominantly greater carotid disease in the tertile with the highest levels of serum uric acid (p < 0.005). Conclusion Our study supports the hypotheses that serum uric acid levels have different roles in the case of ischemic stroke. Its effects on the vascular wall contribute to the development of atherosclerosis and carotid disease.
Trends and Outcomes Associated With Using Long-Acting Opioids During Delivery Hospitalizations.

OBJECTIVE To assess trends in use of long-acting opioids during delivery hospitalizations. METHODS The Perspective database, an administrative inpatient database that includes medication receipt, was analyzed to evaluate patterns of long-acting opioid use during delivery hospitalizations from January 2006 through March 2015. Medications evaluated included methadone, formulations including buprenorphine and extended-release formulations of oxycodone, morphine, fentanyl, and other opioids. Temporal trends in use of these medications were determined. Unadjusted and adjusted models evaluating the role of demographic and hospital factors were created evaluating both use of these medications and risk for severe morbidity. Risk for severe morbidity was determined based on Centers for Disease Control and Prevention criteria. RESULTS Our analysis included 2,994,630 delivery hospitalizations meeting study criteria. Over the entire study period, use of long-acting opioids increased significantly from 457 to 844 per 100,000 deliveries. Although buprenorphine and methadone use increased, use of other long-acting opioids decreased. In 2006, methadone and buprenorphine accounted for less than one third of all long-acting opioids used during delivery hospitalizations. By 2015, buprenorphine and methadone represented 73.5% of long-acting opioids used. In adjusted and unadjusted models, risk for severe morbidity was significantly lower with buprenorphine or methadone compared with other long-acting opioids. Restricting the cohort to only women with drug abuse or dependence, risk for severe morbidity was lower with methadone and buprenorphine than without any long-acting opioids. CONCLUSION Increased use of methadone and buprenorphine in this study supports the feasibility of use of these medications during pregnancy and uptake of clinical recommendations for women with opioid use disorder. Use of methadone and buprenorphine is associated with decreased maternal morbidity, although causation cannot be presumed from this study model.
Naturalness Improvement Algorithm for Reconstructed Glossectomy Patient's Speech Using Spectral Differential Modification in Voice Conversion

In this paper, we propose an algorithm to improve the naturalness of the reconstructed glossectomy patient's speech that is generated by voice conversion to enhance the intelligibility of speech uttered by patients with a wide glossectomy. While existing VC algorithms make it possible to improve intelligibility and naturalness, the result is still not satisfying. To solve the continuing problems, we propose to directly modify the speech waveforms using a spectrum differential. The motivation is that glossectomy patients mainly have problems in their vocal tract, not in their vocal cords. The proposed algorithm requires no source parameter extractions for speech synthesis, so there are no errors in source parameter extractions and we are able to make the best use of the original source characteristics. In terms of spectrum conversion, we evaluate with both GMM and DNN. Subjective evaluations show that our algorithm can synthesize more natural speech than the vocoder-based method. Judging from observations of the spectrogram, power in high-frequency bands of fricatives and stops is reconstructed to be similar to that of natural speech.
Real-time Single-channel Dereverberation and Separation with Time-domain Audio Separation Network

We investigate the recently proposed Time-domain Audio Separation Network (TasNet) in the task of real-time single-channel speech dereverberation. Unlike systems that take time-frequency representation of the audio as input, TasNet learns an adaptive front-end in replacement of the time-frequency representation by a time-domain convolutional non-negative au-toencoder. We show that by formulating the dereverberation problem as a denoising problem where the direct path is separated from the reverberations, a TasNet denoising autoencoder can outperform a deep LSTM baseline on log-power magnitude spectrogram input in both causal and non-causal settings. We further show that adjusting the stride size in the convolutional autoencoder helps both the dereverberation and separation performance.
Role Play Dialogue Aware Language Models Based on Conditional Hierarchical Recurrent Encoder-Decoder

We propose role play dialogue-aware language models (RPDA-LMs) that can leverage interactive contexts in role play multi-turn dialogues for estimating the generative probability of words. Our motivation is to improve automatic speech recognition (ASR) performance in role play dialogues such as contact center dialogues and service center dialogues. Although long short-term memory recurrent neural network based language models (LSTM-RNN-LMs) can capture long-range contexts within an utterance, they cannot utilize sequential interactive information between speakers in multi-turn dialogues. Our idea is to explicitly leverage speakers’ roles of individual utterances, which are often available in role play dialogues, for neural language modeling. The RPDA-LMs are represented as a generative model conditioned by a role sequence of a target role play dialogue. We compose the RPDA-LMs by extending hierarchical recurrent encoder-decoder modeling so as to handle the role information. Our ASR evaluation in a contact center dialogue demonstrates that RPDA-LMs outperform LSTM-RNN-LMs and document-context LMs in terms of perplexity and word error rate. In addition, we verify the effectiveness of explicitly taking interactive contexts into consideration.
A Multistage Training Framework for Acoustic-to-Word Model

Acoustic-to-word (A2W) prediction model based on Connec-tionist Temporal Classiﬁcation (CTC) criterion has gained increasing interest in recent studies. Although previous studies have shown that A2W system could achieve competitive Word Error Rate (WER), there is still performance gap compared with the conventional speech recognition system when the amount of training data is not exceptionally large. In this study, we empirically investigate advanced model initializations and training strategies to achieve competitive speech recognition performance on 300 hour subset of the Switchboard task (SWB-300Hr). We ﬁrst investigate the use of hierarchical CTC pretraining for improved model initialization. We also explore curriculum training strategy to gradually increase the target vocabulary size from 10k to 20k. Finally, joint CTC and Cross Entropy (CE) training techniques are studied to further improve the performance of A2W system. The combination of hierarchical-CTC model initialization, curriculum training and joint CTC-CE training translates to a relative of 12.1% reduction in WER. Our ﬁnal A2W system evaluated on Hub5-2000 test sets achieves a WER of 11.4/20.8 for Switchboard and Call-Home parts without using language model and complex de-coder.
Local Image Descriptors with Statistical Losses

We present a novel regularization technique for learning local feature descriptors based on statistical information extracted from batches of training samples. With the proposed regularization term, we learn a descriptor distribution in Euclidean space that aims at minimizing the overlap between the distributions of positive pairs and that of negative pairs. The proposed method is able to improve the performance of pairwise and triplet losses with various deep convolution network architectures. This improvement is demonstrated through two different types of architectures, able to obtain state-of-the-art results on the reference benchmark for local feature matching.
Multiple Concurrent Sound Source Tracking Based on Observation-Guided Adaptive Particle Filter

Particle ﬁlter (PF) has been proved to be an effective tool to track sound sources. In traditional PF, a pre-deﬁned dynamic model is used to model source motion, which tends to be mis-matched due to the uncertainty of source motion. Besides, non-stationary interferences pose a severe challenge to source tracking. To this end, an observation-guided adaptive particle ﬁlter (OAPF) is proposed for multiple concurrent sound source tracking. Firstly, sensor signals are processed in the time-frequency domain to obtain the direction of arrival (DOA) observations of sources. Then, by updating particle states with these DOA observations, angular distances between particles and observations are reduced to guide particles to directions of sources. Third-ly, particle weights are updated by an interference-adaptive likelihood function to reduce the impacts of interferences. At last, with the updated particles and the corresponding weights, OAPF is utilized to determine the ﬁnal DOAs of sources. Experimental results demonstrate that our method achieves favorable performance for multiple concurrent sound source tracking in noisy environments.
Cross-Corpora Convolutional Deep Neural Network Dereverberation Preprocessing for Speaker Verification and Speech Enhancement

Deep neural network (DNN) dereverberation preprocessing has been shown to be a viable strategy for speech enhancement and increasing the accuracy of automatic speech recognition and automatic speaker verification. In this paper, an improved DNN technique based on convolutional neural networks is presented and compared to existing methods for speech enhancement and speaker verification in the presence of reverberation. This new technique is first shown to enhance speech quality as compared to other existing methods. Then, a more thorough set of experiments is presented that assesses cross-corpora speaker verification performance on data that contains real reverberation and noise. A discussion of the applicability and generalizability of such techniques is given.
Object Geolocation Using MRF Based Multi-Sensor Fusion

Abundant image and sensory data collected over the last decades represents an invaluable source of information for cataloging and monitoring of the environment. Fusion of heterogeneous data sources is a challenging but promising tool to efficiently leverage such information. In this work we propose a pipeline for automatic detection and geolocation of recurring stationary objects deployed on fusion scenario of street level imagery and LiDAR point cloud data. The objects are geolocated coherently using a fusion procedure formalized as a Markov random field problem. This allows us to efficiently combine information from object segmentation, triangulation, monocular depth estimation and position matching with LiDAR data. The proposed fusion approach produces object mappings robust to scenes reporting multiple object instances. We introduce a new challenging dataset of over 200 traffic lights in Dublin city centre and demonstrate high performance of the proposed methodology and its capacity to perform multi -sensor data fusion.
A Two-Stage Approach to Noisy Cochannel Speech Separation with Gated Residual Networks

Cochannel speech separation is the task of separating two speech signals from a single mixture. The task becomes even more challenging if the speech mixture is further corrupted by background noise. In this study, we focus on a gender-dependent scenario, where target speech is from a male speaker and interfering speech from a female speaker. We pro-pose a two-stage separation strategy to address this problem in a noise-independent way. In the proposed system, denoising and cochannel separation are performed successively by two modules, which are based on a newly-introduced convolutional neural network for speech separation. The evaluation results demonstrate that the proposed system substantially outperforms one-stage baselines in terms of objective intelligibility and perceptual quality.
Comparison of an End-to-end Trainable Dialogue System with a Modular Statistical Dialogue System

This paper presents a comparison of two dialogue systems: one is end-to-end trainable and the other uses a more traditional, modular architecture. End-to-end trainable dialogue systems recently attracted a lot of attention because they offer several advantages over traditional systems. One of them is the avoidance to train each system module independently, by creating a single network architecture which maps an input to the corresponding output without the need for intermediate representations. While the end-to-end system investigated here had been tested in a text-in/out scenario it remained an open question how the sys-tem would perform in a speech-in/out scenario, with noisy input from a speech recognizer and output speech generated by a speech synthesizer. To evaluate this, both dialogue systems were trained on the same corpus, including human-human dialogues in the Cam-bridge restaurant domain, and then compared in both scenarios by human evaluation. The results show, that in both interfaces the end-to-end system receives signiﬁcantly higher ratings on all metrics than the traditional modular system, an indication that it enables users to reach their goals faster and experience both a more natural system response and a better comprehension by the dialogue system.
Analysis of L2 Learners' Progress of Distinguishing Mandarin Tone 2 and Tone 3

Many studies have shown the effectiveness of perceptual training to improve L2 learners’ ability to distinguish Mandarin tones. In this paper, we quantiﬁed learners’ perceptual char-acteristics on discriminating the most difﬁcult tone pair, Tone 2 and 3 in Mandarin before and after training. L2 learners’ categorical perception is measured by ﬁtting a sigmoid curve to the identiﬁcation responses with average F0 height as the acoustic dimension. The boundary location of the two tones in L2 learners’ perception space is signiﬁcantly improved to a higher F0 height after training. Regression analysis indicated that δF 0 and δt of the initial fall of the concave F0 shape are the key acoustic features for native speakers in discrimination. L2 learners rely on not only the initial fall but also the δF 0 of the ﬁnal rise to discriminate the tones. A detailed analysis using cognitive measurements reports an increasing attention on the initial fall of the F0 contour for L2 learners after perceptual training. These results conﬁrmed that directing the attention to key acoustic features is essential for L2 learners to improve their categorical perception of novel speech contrasts.
URBAN-i: From urban scenes to mapping slums, transport modes, and pedestrians in cities using deep learning and computer vision

In recent years, deep learning and computer vision have been applied to solve complex problems across many domains. In urban studies, these technologies have been instrumental in the development of smart cities and autonomous vehicles. However, a knowledge gap is present when it comes to informal urban regions in less developed countries. How can deep learning and artificial intelligence untangle the complexities of informality to advance urban modelling? In this paper, we introduce a framework for multipurpose realistic-dynamic urban modelling using deep convolutional neural networks. The purpose of the framework is twofold: (1) to sense and detect informality and slums in urban scenes from aerial and street-level images and (2) to detect pedestrian and transport modes. The model has been trained on images of urban scenes in cities across the globe. The framework shows strong validation performance in the identification of planned and unplanned regions, despite broad variations in the classified images. The algorithms of the URBAN-i model are coded in Python and the trained models can be applied to images of any urban setting, including informal settlements and slum regions.
The CSU-K Rule-Based System for the 2nd Edition Spoken CALL Shared Task

This paper presents the set-up and results of the rule-based Cooperative State University Karlsruhe (CSU-K) system for the 2nd edition of the shared spoken CALL ESL task. The data was collected from Swiss teenage students using a speech-enabled online tool for English conversation practice. The tool should eventually be able to judge student input with respect to syntactic and semantic correctness. The tasks consisted of training data of a German text prompt with the associated audio ﬁle containing an English language response by the students. In the second edition of the task, 6.698 utterances were provided in addition to the 2017 task. The contribution of this paper is a further look at how rule-based systems can be employed for these sorts of tasks. Meaning and grammar are treated separately in order to classify the language as correct. A number of experts were constructed to deal separately with different POS such as nouns, adjectives, verb usage and pronouns or determin-ers. Distance measurements derived from Doc2Vec were then employed between utterance and prompt responses. A D-value of 10.08 is reported on the ﬁnal 2nd Edition evaluation test ﬁles.
The Dirt on Clean Eating: A Cross Sectional Analysis of Dietary Intake, Restrained Eating and Opinions about Clean Eating among Women

Clean eating is understood in broad terms to be an approach to eating which promotes the exclusion of processed foods. Social media and websites which promote clean eating are becoming increasingly popular as sources of nutrition information. Currently, there is a lack of knowledge regarding women’s opinions about clean eating sites and their influence on eating behaviour. The aim of the present study was to investigate differences in dietary intake, dietary restraint and opinions about clean eating between women who had, and women who had never adhered to dietary advice from clean eating sites. Using a cross-sectional survey design, women (n = 762) ranging in age from 17–55 completed a self-report questionnaire on eating behaviour and beliefs about clean eating. Findings showed that 25.5% of the sample adhered to dietary advice from a clean eating site sometimes, often or very often. A significantly higher proportion of women who had adhered to dietary advice from clean eating sites met dietary guidelines for the consumption of fruit, meats and alternatives compared to women who had seldom or never adhered. Adherers also had significantly higher levels of restrained eating and were more positive about clean eating in general in comparison to those who seldom or never adhered. Results provide new information about exposure to clean eating sites and how they may influence women’s eating practices. These preliminary findings suggest additional studies are required to better understand the influence of clean eating sites, particularly with regard to whether the information on such sites are from reputable sources and to what degree their recommendations may be problematic for individuals with eating concerns.
Gabor Binary Layer in Convolutional Neural Networks

Convolutional neural networks(CNNs) have achieved overwhelming success in image recognition. For all the architectures of CNNs, the low-level features extracted from the input is essential for the whole model because it determines the upper bound of accuracy. However, due to the random initialization and end-to-end training mechanism, there is no guarantee that the learned convolutional layer is an excellent low-level feature extractor. Thus, we propose Gabor binary layer(GBL) as a more efficient alternative to the first convolutional layer in CNNs. The design principle of GBL is motivated by local Gabor binary patterns. The GBL is comprised of a module of pre-defined Gabor convolutional filters in different orientations and shapes, and a module of fixed randomly generated binary convolutional filters to encode the Gabor features. By replacing the first layer of Resnet-56, Resnet-110, and LBCNN with the GBL, we achieve better performances on SVHN, CIFAR-10 and CIFAR-100 than that of the original models. Results show that the proposed GBL outperforms the standard convolutional layer for extracting low-level features, and thus the GBL can easily improve the performances of CNNs on image recognition.
Slow Binocular Rivalry as a Potential Endophenotype of Schizophrenia

Objectives: Binocular rivalry is a typical example of bistable perception that arises when two monocular images are simultaneously presented to each eye. Binocular rivalry is a heritable perceptual cognitive function that is impaired in patients with schizophrenia (SZ). Despite its potential suitability as a visual endophenotype, binocular rivalry has hardly been studied in the unaffected siblings of schizophrenia (SIB). There is also little research about whether binocular rivalry is a potential visual endophenotype between SZ and SIB. Methods: In our cross-sectional study, we included 40 SZ and their unaffected SIBs, as well as 40 age- and sex-matched healthy controls (HC). All subjects underwent the binocular rivalry test, the Positive and Negative Syndrome Scale (PANSS) and a battery of cognitive neuropsychological assessments evaluating attention, memory and executive function domains. Results: Our results demonstrate that the switching rate in SZ was significantly slower than in HC (p < 0.001), and compared to the SIB, the mean alternation rates were significantly different (p < 0.01). Moreover, there was a significant difference in mean switching rate between the SIB and the HC (p < 0.001). There was no significant correlation between the alternation rate of binocular rivalry and these cognitive tasks and the PANSS scores. Conclusion: The present study shows that SZ and SIB both exhibit changes in binocular rivalry, with SIB exhibiting intermediate performance compared with that of SZ and the HC. This supports the claim that the switching rate for SZ differs from that of SIB and suggests that binocular rivalry may qualify as a visual endophenotype for SZ.
Word Emphasis Prediction for Expressive Text to Speech

Word emphasis prediction is an important part of expressive prosody generation in modern Text-To-Speech (TTS) systems. We present a method for predicting emphasized words for expressive TTS, based on a Deep Neural Network (DNN). We show that the presented method outperforms machine learning methods based on hand-crafted features in terms of objective metrics such as precision and recall. Using a listening test, we further demonstrate that the contribution of the predicted emphasized words to the expressiveness of the synthesized speech is subjectively perceivable