Miracle-dz/UniFAR-FTUs huggingface dataset:
Preoperative radiomics model using gadobenate dimeglumine-enhanced magnetic resonance imaging for predicting β-catenin mutation in patients with hepatocellular carcinoma: A retrospective study

Objective To compare and evaluate radiomics models to preoperatively predict β-catenin mutation in patients with hepatocellular carcinoma (HCC). Methods Ninety-eight patients who underwent preoperative gadobenate dimeglumine (Gd-BOPTA)-enhanced MRI were retrospectively included. Volumes of interest were manually delineated on arterial phase, portal venous phase, delay phase, and hepatobiliary phase (HBP) images. Radiomics features extracted from different combinations of imaging phases were analyzed and validated. A linear support vector classifier was applied to develop different models. Results Among all 15 types of radiomics models, the model with the best performance was seen in the RHBP radiomics model. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity of the RHBP radiomics model in the training and validation cohorts were 0.86 (95% confidence interval [CI], 0.75–0.93), 0.75, 1.0, and 0.65 and 0.82 (95% CI, 0.63–0.93), 0.73, 0.67, and 0.76, respectively. The combined model integrated radiomics features in the RHBP radiomics model, and signatures in the clinical model did not improve further compared to the single HBP radiomics model with AUCs of 0.86 and 0.76. Good calibration for the best RHBP radiomics model was displayed in both cohorts; the decision curve showed that the net benefit could achieve 0.15. The most important radiomics features were low and high gray-level zone emphases based on gray-level size zone matrix with the same Shapley additive explanation values of 0.424. Conclusion The RHBP radiomics model may be used as an effective model indicative of HCCs with β-catenin mutation preoperatively and thus could guide personalized medicine.
Semi-supervised Acoustic and Language Modeling for Hindi ASR

This paper describes the submission made by our team to the Hindi Gram Vaani ASR challenge. This challenge involves building an ASR system for spontaneous telephonic recordings. The challenge is unique because of the small amount of labelled data available for model development. On top of that, the acoustic variabilities such as spontaneity of natural conversations, rich diversity of Hindi across India and varied backgrounds present in the corpus make it much more challenging. We participated in two of the three tracks where the first track involves 100 hours of labelled speech only and the second track involves 1000 hours of additional unlabelled corpus along with 100 hours of labelled speech. A Kaldi based hybrid model has been developed for the first and second track involving TDNN-F character based acoustic model, N-gram first pass decoding, RNN-LM re-scoring and system combinations. On the other hand, for the second track, an E2E conformer based system has been trained on representations obtained from a contrastive predictive coding (CPC) model. The results obtained for both the tracks are significantly better than the baseline results published by the challenge organizers on the development set consisting of 5 hours of audio.
Chunking Defense for Adversarial Attacks on ASR

While deep learning has lead to dramatic improvements in automatic speech recognition (ASR) systems in the past few years, it has also made them vulnerable to adversarial attacks. These attacks may be designed to either make ASR fail in producing the correct transcription or worse, output an adversary-chosen sentence. In this work, we propose a defense based on independently processing random or fixed size chunks of the speech input in the hope of “containing” the cumulative effect of the adversarial perturbations. This approach does not require any additional training of the ASR system, or any defensive pre-processing of the input. It can be easily applied to any ASR systems with little loss in performance under benign conditions, while improving adversarial robustness. We perform experiments on the Librispeech data set with different adversarial attack budgets, and show that the proposed defense achieves consistent improvement on two different ASR systems/models.
One Size Does Not Fit All: How Leadership Strategies Affect Teacher Satisfaction and Retention

Teacher retention rates continue to plummet at schools across the nation. A principal at Sunnyside Elementary watches firsthand as the revolving door of teachers increases significantly in her brief 3 years of leadership at this school. Once well-known and highly regarded at an award-winning school in the northeast, the principal struggles to delegate, communicate, and acclimate to a high-needs school in the southeastern United States. Yet the practices that worked so well in the northeast sends many of her teachers to search for greener pastures year after year. This case study examines leadership for retention, including communication, organizational climate, and relationship development.
PORTFOLIO OPTIMIZATION WITH MEAN-VARIANCE & MEAN-CVAR: EVIDENCE FROM PAKISTAN STOCK MARKET

The stock market in any country plays a significant role in economic development through saving mobilization and providing diversification opportunities. The investors and wealth managers always looked for those securities, investment to which leads towards maximum returns. The Markowitz portfolio optimization model is based on risk and reward trade off where the returns are measured with Mean and Variance is used as proxy of risk. The framework is criticized due to its normality of returns assumption and in case of non-elliptical distribution the optimization results may lead towards suboptimal portfolios. In emerging markets, returns are attributed to high volatility, low liquidity and increased market concentration. Such volatile phenomena signify the presence of downside risk. The expected shortfall or CVaR has reported ability to measure loss beyond VaR and regarded as coherent risk measure. This study investigated the impact on asset pricing of PSX stocks with Mean Variance and Mean-CVaR model by analyzing KSE100 index stocks for the time period 2009-2018. The empirical findings of the in sample performance of Mean-CVaR optimization for non-normal stocks indicated higher Sharpe ratio while for normal stocks return data portfolio selection and optimization with MV model SR is higher. The results of this study are useful for the investors and fund managers to understand PSX behavior and in selection of optimal portfolios.
Predicting E-learning Course Final Average-Grade using Machine Learning Techniques : A Case Study in Shaqra University

It is critical to understand the factors that may influence students’ performance in an e-learning course delivered through a Learning Management System (LMS). The conditions affecting students are unique to every e-learning course. With wide adoption of using Machine-learning for making decisions in many areas of research. In this research, we apply machine-learning algorithms using regression analysis to predict final average grades of an e-learning course based on number of factors: total activities, total time-spent on the LMS, number of course views, and number of enrolled students. We use deep learning, decision tree, linear regression, bayesian ridge regression, and random forest techniques.The results show that the deep learning model presents the best mean absolute error, mean squared error, and R-squared for predicting the courses’ final average grade. In addition, the results reveal that the relationships between various input course features (total activities, total time-spent on the LMS, number of course views, and number of enrolled students) and the e-learning course final average grade of students is weak and required considering more features.
HUBBLE SPACE TELESCOPE IMAGING OF DECOUPLED DUST CLOUDS IN THE RAM PRESSURE STRIPPED VIRGO SPIRALS NGC 4402 AND NGC 4522

We present the highest-resolution study to date of the interstellar medium (ISM) in galaxies undergoing ram pressure stripping, using Hubble Space Telescope BVI imaging of NGC 4522 and NGC 4402, Virgo Cluster spirals that are well known to be experiencing intracluster medium (ICM) ram pressure. We find that throughout most of both galaxies, the main dust lane has a fairly well-defined edge, with a population of giant molecular cloud (GMC) sized (tens- to hundreds-of-pc scale), isolated, highly extincting dust clouds located up to ∼1.5 kpc radially beyond it. Outside of these dense clouds, the area has little or no diffuse dust extinction, indicating that the clouds have decoupled from the lower-density ISM material that has already been stripped. Several of the dust clouds have elongated morphologies that indicate active ram pressure, including two large (kpc scale) filaments in NGC 4402 that are elongated in the projected ICM wind direction. We calculate a lower limit on the H i + H2 masses of these clouds based on their dust extinctions and find that a correction factor of ∼10 gives cloud masses consistent with those measured in CO for clouds of similar diameters, probably due to the complicating factors of foreground light, cloud substructure, and resolution limitations. Assuming that the clouds' actual masses are consistent with those of GMCs of similar diameters (∼104–105 M☉), we estimate that only a small fraction (∼1%–10%) of the original H i + H2 remains in the parts of the disks with decoupled clouds. Based on Hα images, a similar fraction of star formation persists in these regions, 2%–3% of the estimated pre-stripping star formation rate. We find that the decoupled cloud lifetimes may be up to 150–200 Myr.
Brain Deep Medullary Veins on 7T MRI in Dutch-Type Hereditary Cerebral Amyloid Angiopathy

Background: Deep medullary vein (DMV) changes occur in cerebral small vessel diseases (SVD) and in Alzheimer’s disease. Cerebral amyloid angiopathy (CAA) is a common SVD that has a high co-morbidity with Alzheimer’s disease. So far, DMVs have not been evaluated in CAA. Objective: To evaluate DMVs in Dutch-type hereditary CAA (D-CAA) mutation carriers and controls, in relation to MRI markers associated with D-CAA. Methods: Quantitative DMV parameters length, tortuosity, inhomogeneity, and density were quantified on 7 Tesla 3D susceptibility weighted MRI in pre-symptomatic D-CAA mutation carriers (n = 8), symptomatic D-CAA mutation carriers (n = 8), and controls (n = 25). Hemorrhagic MRI markers (cerebral microbleeds, intracerebral hemorrhages, cortical superficial siderosis, convexity subarachnoid hemorrhage), non-hemorrhagic MRI markers (white matter hyperintensities, enlarged perivascular spaces, lacunar infarcts, cortical microinfarcts), cortical grey matter perfusion, and diffusion tensor imaging parameters were assessed in D-CAA mutation carriers. Univariate general linear analysis was used to determine associations between DMV parameters and MRI markers. Results: Quantitative DMV parameters length, tortuosity, inhomogeneity, and density did not differ between pre-symptomatic D-CAA mutation carriers, symptomatic D-CAA mutation carriers, and controls. No associations were found between DMV parameters and MRI markers associated with D-CAA. Conclusion: This study indicates that vascular amyloid-β deposition does not affect DMV parameters. In patients with CAA, DMVs do not seem to play a role in the pathogenesis of MRI markers associated with CAA.
Low Resource Comparison of Attention-based and Hybrid ASR Exploiting wav2vec 2.0

Low resource speech recognition can potentially beneﬁt a lot from exploiting a pretrained model such as wav2vec 2.0. These pretrained models have learned useful representations in an unsupervised or self-supervised task, often leveraging a very large corpus of untranscribed speech. The pretrained models can then be used in various ways. In this work we compare two approaches which exploit wav2vec 2.0: an attention-based end-to-end model (AED), where the wav2vec 2.0 model is used in the model encoder, and a hybrid hidden Markov model (HMM/DNN) speech recognition system, where the wav2vec 2.0 model is used in the acoustic model. These approaches are compared in a very difﬁcult Northern S´ami task, as well as an easier, simulated low resource task in Finnish. We ﬁnd that the wav2vec 2.0 AED models can learn a working attention mechanism, but are still outperformed by wav2vec 2.0 HMM/DNN systems. Our best wav2vec 2.0 HMM/DNN recipe on 20 hours is competitive with an HMM/DNN system trained on 1600 hours.
The ABA-LANCL1/2 Hormone-Receptors System Protects H9c2 Cardiomyocytes from Hypoxia-Induced Mitochondrial Injury via an AMPK- and NO-Mediated Mechanism

Abscisic acid (ABA) regulates plant responses to stress, partly via NO. In mammals, ABA stimulates NO production by innate immune cells and keratinocytes, glucose uptake and mitochondrial respiration by skeletal myocytes and improves blood glucose homeostasis through its receptors LANCL1 and LANCL2. We hypothesized a role for the ABA-LANCL1/2 system in cardiomyocyte protection from hypoxia via NO. The effect of ABA and of the silencing or overexpression of LANCL1 and LANCL2 were investigated in H9c2 rat cardiomyoblasts under normoxia or hypoxia/reoxygenation. In H9c2, hypoxia induced ABA release, and ABA stimulated NO production. ABA increased the survival of H9c2 to hypoxia, and L-NAME, an inhibitor of NO synthase (NOS), abrogated this effect. ABA also increased glucose uptake and NADPH levels and increased phosphorylation of Akt, AMPK and eNOS. Overexpression or silencing of LANCL1/2 significantly increased or decreased, respectively, transcription, expression and phosphorylation of AMPK, Akt and eNOS; transcription of NAMPT, Sirt1 and the arginine transporter. The mitochondrial proton gradient and cell vitality increased in LANCL1/2-overexpressing vs. -silenced cells after hypoxia/reoxygenation, and L-NAME abrogated this difference. These results implicate the ABA-LANCL1/2 hormone-receptor system in NO-mediated cardiomyocyte protection against hypoxia.
Text aware Emotional Text-to-speech with BERT

Emotional text to speech is the idea of synthesizing emotional audio via a text-to-speech model. With neural text-to-speech, sentence-level naturalness has improved a lot and is almost at par with human speech, but the current approach to emotional text-to-speech models heavily relies on the user to input the expected emotion along with the text to synthesize the desired speech. In this work, we propose a novel text-aware emotional text-to-speech system that leverages a pre-trained BERT model to get a deep representation of the emotional context from the text both during training and inference. We show that our proposed method synthesizes emotional audio with emotion depending on the emotional context of the input text. We also show that our method outperforms baseline systems in varying the emotional intensity depending on the text.
Association between perceived discrimination and physical activity among adolescents

Abstract Perceived discrimination is a major producer of stress and social trauma and can have negative effects on the physical and mental health of those exposed to it. This study aims to analyze the direct and indirect association between perceived discrimination and physical activity, mediated by common mental disorders in adolescents. This is a cross-sectional study with 2,484 adolescents (aged 18 and 19 years old) from a cohort in São Luís, Maranhão. Structural equation models were used to verify the direct effect of perceived discrimination on physical activity (PA) and its indirect effect mediated by common mental disorders (CMD). The potential of sex to act as an effect-modifying variable was also tested. Discrimination was associated with higher levels of PA in women (SC - Standardized Coefficient direct =0.105, p-value=0.005), and indirectly associated with lower PA via CMT among men (SCindirect =-0.024, p-value=0.017) and women (SCindirect =-0.024, p-value=0.015). Our findings show the association between perceived discrimination and PA in this population of adolescents, attesting that its effects can alter health behaviors and that gender can modify the relation between these variables.
WA-Transformer: Window Attention-based Transformer with Two-stage Strategy for Multi-task Audio Source Separation

The standard Conformer adopts convolution layers to exploit lo-cal features. However, the one-dimensional convolution ignores the correlation of adjacent time-frequency features. In this paper, we design a two-dimensional window attention block with dilation, and then we propose a window attention-based Trans-former network (named WA-Transformer) for multi-task audio source separation. The proposed WA-Transformer adopts self-attention and window attention blocks to model global dependencies and local correlation in a parameter-efficient way. Besides, it follows a two-stage pipeline, in which the first stage separates the mixture and outputs the three types of audio signals, and the second stage performs signal compensation. Experiments demonstrate the effectiveness of WA-Transformer. WA-Transformer achieves 13.86 dB, 12.22 dB, 11.21 dB signal-to-distortion ratio improvement on speech, music, noise track, respectively, and advantages over several well-known models.
Enhancing Embeddings for Speech Classification in Noisy Conditions

Robustness against noise is critical for several speech applications in real-world environments. In general, to improve the robustness, a speech enhancement front-end is integrated as a pre-processing stage, often jointly trained with the network back-end to reduce the impact of distortions and artifacts on the performance. Recently, the use of speech representation computed using pre-trained models on large amounts of data, as Wav2Vec , has proved to be effective in a variety of speech processing and classification tasks. However, the performance of these models, although very robust, deteriorates in presence of environmental noise. In this paper, we investigate how enhancement can be applied in neural speech classification architectures employing pre-trained speech embeddings. We investigate two approaches: one applies time-domain enhancement prior to extracting the embeddings; the other employs a convolutional neural network to map the noisy embeddings to the corresponding clean ones. Exhaustive experiments on the Fluent Speech Commands and Google Speech Commands corpora, contaminated with noises from the Microsoft Scalable Noisy Speech Dataset, sheds light and provide insights about the most promising enhancement training approaches.
Physicochemical Properties and Sensory Attributes of Homemade Bakery Products from Two Local Varieties of Pineapple Compared with the Locally Available Market Products in Bangladesh

Pineapples (Ananas comosus L. Merr) contain beneficial properties and the object of the study is to compare the physical and chemical properties of two local varieties of pineapples and their prepared bakery products such as jelly and biscuit using flesh of pineapple as one of the raw materials and compare those with the locally available market products. Physico – chemical properties such as fruit weight, diameter, length, edible portion %, pH, Vitamin C, Titratable acidity, sugar content, total soluble solids, lipid content, crude protein, phenolic compound and sensory attributes (Firmness, sweetness, tartness, colour, and overall acceptability) of fresh pineapple fruit of Honey Queen (HQ) and Giant Kew (GK) were studied. Both the varieties contained high amount of moisture, GK 84.42% and HQ 83.95%. Further, HQ variety showed more than GK variety in almost all of the properties. The phenolic contents of the extracts as caffeic acid equivalents were found to be highest in methanol HQ (12.45%), GK (9.8%) while in water extract HQ (5.4%), and GK (5.3%). In terms of prepared bakery product from pineapple, Sample H (Home prepared) was more preferable than the locally market available brand in consideration of the physico-chemical properties that affected the sensory attributes acceptability having the overall acceptability score of 7 in a 9-point hedonic scale.
Model Compression by Iterative Pruning with Knowledge Distillation and Its Application to Speech Enhancement

Over the past decade, deep learning has demonstrated its effectiveness and keeps setting new records in a wide variety of tasks. However, good model performance usually leads to a huge amount of parameters and extremely high computational complexity which greatly limit the use cases of deep learning models, particularly in embedded systems. Therefore, model compression is getting more and more attention. In this paper, we propose a compression strategy based on iterative pruning and knowledge distillation. Specifically, in each iteration, we first utilize a pruning criterion to drop the weights which have less impact on performance. Then, the model before pruning is used as a teacher to fine-tune the student which is the model after pruning. After several iterations, we get the final compressed model. The proposed method is verified on gated convolutional recurrent network (GCRN) and long short-term memory (LSTM) for single channel speech enhancement tasks. Experimental results show that the proposed compression strategy can dramatically reduce the model size by 40x without significant performance degradation for GCRN.
Automatic Selection of Discriminative Features for Dementia Detection in Cantonese-Speaking People

Dementia is a severe cognitive impairment that affects the health of older adults and creates a burden on their families and caretakers. This paper analyzes diverse features extracted from spoken languages and selects the most discriminative features for dementia detection. The paper presents a deep learning-based feature ranking method called dual-net feature ranking (DFR). The proposed DFR utilizes a dual-net architecture, where two networks (called operator and selector) are alternatively and cooperatively trained to simultaneously perform feature selection and dementia detection. The DFR interprets the contribution of individual features to the predictions of the selector network using all of the selector’s parameters. The DFR was evaluated on the Cantonese JCCOCC-MoCA Elderly Speech Dataset. Results show that the DFR can signiﬁcantly reduce feature dimensionality while identifying small feature subsets with comparable or superior performance than the whole feature set. The selected features have been uploaded to https://github.com/kexquan/ AD-detection-Feature-selection .