Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Both Dynamic-Shared and Dynamic-Specific Patterns for Chaotic Time-Series Prediction

In the real world, multivariate time series from the dynamical system are correlated with deterministic relationships. Analyzing them dividedly instead of utilizing the shared-pattern of the dynamical system is time consuming and cumbersome. Multitask learning (MTL) is an effective inductive bias method to utilize latent shared features and discover the structural relationships from related tasks. Base on this concept, we propose a novel MTL model for multivariate chaotic time-series prediction, which could learn both dynamic-shared and dynamic-specific patterns. We implement the dynamic analysis of multiple time series through a special network structure design. The model could disentangle the complex relationships among multivariate chaotic time series and derive the common evolutionary trend of the multivariate chaotic dynamical system by inductive bias. We also develop an efficient Crank—Nicolson-like curvilinear update algorithm based on the alternating direction method of multipliers (ADMM) for the nonconvex nonsmooth Stiefel optimization problem. Simulation results and analysis demonstrate the effectiveness on dynamic-shared pattern discovery and prediction performance.
Cognitive and affective outcomes of teaching about poisonous and venomous animals

ABSTRACT The use of live animals in instruction plays an important role in biological education, with the intention that attitudes and emotions should be positively influenced. To date, studies in this field have given mixed results, and there has certainly been a lack of studies on student attitudes and knowledge of poisonous and venomous animals. This study focuses on the effect of the use of poisonous and venomous animals in biology instruction. Two treatment groups of Slovenian seventh and eighth graders (N = 123; age 12.3 and 13.2 years, respectively) were compared. The experimental group (N = 82) was given the opportunity to observe with adequate risk assessment live animal species. The observation of live poisonous and venomous animals led to higher values on different attitudinal dimensions. After instruction, we found no differences between both treatment groups in knowledge. Interest in learning about the different poisonous and venomous animals increased significantly. The level of disgust decreased for the viper, spider, and scorpion. However, ‘fear’ seems to be more stable and less influenceable than ‘disgust’. We recommend that students should be offered as many first-hand experiences with live poisonous and venomous animals as possible mainly through informal learning environments, such as zoos.
A Robust and Cascaded Acoustic Echo Cancellation Based on Deep Learning

Acoustic echo cancellation (AEC) is used to cancel feedback between a loudspeaker and a microphone. Ideally, AEC is a linear problem and can be solved by adaptive ﬁltering. However, in practice, two important problems severely affect the performance of AEC, i.e. 1) double-talk problem and 2) nonlinear distortion mainly caused by loudspeakers and/or power ampli-ﬁers. Considering these two problems in AEC, we propose a novel cascaded AEC which integrates adaptive ﬁltering and deep learning. Speciﬁcally, two long short-term memory networks (LSTM) are employed for double-talk detection (DTD) and nonlinearity modeling, respectively. The adaptive ﬁltering is employed to remove the linear part of echo. Experimental results show that the proposed method outperforms conventional methods in terms of the objective evaluation metrics by a considerable margin in the matched scenario. Moreover, the proposed method has much better generalization ability in the unmatched scenarios, compared with end-to-end deep learning method.
Farmworkers—Always Essential, Always at Risk: An Interview With Jeannie Economos

In 1983, Central Florida farmworkers united to create the Farmworker Association of Florida after freezes to orange groves left them out of work. The group now has five offices in the state and more than 10,000 members. Jeannie Economos is the Coordinator of the Pesticide Safety and Environmental Health Project at the Farmworker Association of Florida. In an interview, she discusses the impact of racism, immigration, climate change and COVID-19 on the farmworker community.
Self-Attentive Similarity Measurement Strategies in Speaker Diarization

Speaker diarization can be described as the process of extracting sequential speaker embeddings from an audio stream and clustering them according to speaker identities. Nowadays, deep neural network based approaches like x-vector have been widely adopted for speaker embedding extraction. However, in the clustering back-end, probabilistic linear discriminant analysis (PLDA) is still the dominant algorithm for similarity measurement. PLDA works in a pair-wise and independent manner, which may ignore the positional correlation of adjacent speaker embeddings. To address this issue, our previous work proposed the long short-term memory (LSTM) based scoring model, followed by the spectral clustering algorithm. In this paper, we further propose two enhanced methods based on the self-attention mechanism, which no longer focuses on the lo-cal correlation but searches for similar speaker embeddings in the whole sequence. The ﬁrst approach achieves state-of-the-art performance on the DIHARD II Eval Set (18.44% DER after resegmentation), while the second one operates with higher efﬁciency.
Implementation of the Mini-Child Tourette Syndrome Impairment Scale: Relationships to Symptom Severity and Treatment Decisions

Functional impairment is an important factor in Tic Disorder treatment decisions. We evaluated the mini Child Tourette Syndrome Impairment Scale (mini-CTIM) for correlation with symptom severity and association with interventions. A total of 61 randomly selected tic encounters were retrospectively analyzed for mini-CTIM correlation with symptom severity scores and compared between patients who received treatment and those who did not. Regression models identified factors associated with treatment decisions. Mini-CTIM-tic scores correlated with tic severity and mini-CTIM-non-tic scores correlated with attention-deficit hyperactivity disorder (ADHD) severity. Tic treatment was associated with higher child, but not parent, mini-CTIM-tic scores. Regression models identified that comorbidity treatment was predicted by ADHD severity, obsessive compulsive disorder severity, and parent but not child mini-CTIM-non-tic scores. These findings suggest children have valuable insight into their tic-related impairment, but parent assessment is important for evaluating comorbidity-related impairment. The mini-CTIM may be a useful clinical tool for assessing tic-related impairment.
Calibration and Uncertainty in Neural Time-to-Event Modeling

Models for predicting the time of a future event are crucial for risk assessment, across a diverse range of applications. Existing time-to-event (survival) models have focused primarily on preserving pairwise ordering of estimated event times (i.e., relative risk). We propose neural time-to-event models that account for calibration and uncertainty while predicting accurate absolute event times. Specifically, an adversarial nonparametric model is introduced for estimating matched time-to-event distributions for probabilistically concentrated and accurate predictions. We also consider replacing the discriminator of the adversarial nonparametric model with a survival-function matching estimator that accounts for model calibration. The proposed estimator can be used as a means of estimating and comparing conditional survival distributions while accounting for the predictive uncertainty of probabilistic models. Extensive experiments show that the distribution matching methods outperform existing approaches in terms of both calibration and concentration of time-to-event distributions.
An Investigation of Cross-Cultural Semi-Supervised Learning for Continuous Affect Recognition

One of the keys for supervised learning techniques to succeed resides in the access to vast amounts of labelled training data. The process of data collection, however, is expensive, time-consuming, and application dependent. In the current digital era, data can be collected continuously. This continuity renders data annotation into an endless task, which potentially, in problems such as emotion recognition, requires annotators with different cultural backgrounds. Herein, we study the impact of utilising data from different cultures in a semi-supervised learning approach to label training material for the automatic recognition of arousal and valence. Speciﬁcally, we compare the performance of culture-speciﬁc affect recognition models trained with manual or cross-cultural automatic annotations. The experiments performed in this work use the dataset released for the Cross-cultural Emotion Sub-challenge of the Audio/Visual Emotion Challenge (AVEC) 2019. The results obtained convey that the cultures used for training impact on the system performance. Furthermore, in most of the scenarios assessed, affect recognition models trained with hybrid solutions, combining manual and automatic annotations, surpass the baseline model, which was exclusively trained with manual annotations.
Therapeutic effects of acupuncture in typical dry eye: a systematic review and meta‐analysis

Acupuncture is a treatment option for dry eye syndrome (DES), but its efficacy remains still controversial. We assessed the effectiveness of this treatment for typical DES without specific aetiologies. Eight databases up through June 2018 were searched for randomized clinical trials (RCTs) comparing treatments of acupuncture with artificial tears. The risk of bias was assessed using Cochrane criteria, and a random effects model was used for meta‐analyses on tear‐film breakup time (BUT), Schirmer test, corneal fluorescein staining (CFS), ocular surface disease index, visual analogue scale and score of symptoms (SOS). Subgroup and sensitivity analyses were conducted to explore the heterogeneity, and publication bias was assessed by funnel plot using Egger’s test. Twenty‐one RCTs in 19 studies (n = 1542 eyes) met our eligible criteria. The results demonstrated the superiority of acupuncture in improving the symptoms of BUT, Schirmer test, CFS and SOS, compared to artificial tears acting alone. The BUT and Schirmer test were also more improved in acupuncture combination with artificial tears than artificial tears alone. Further subgroup analyses suggest that acupuncture applied at 2.0–3.0 times per week for 21–30 days may be optimal for treating typical DES. This provides useful information for guiding acupuncture in the clinical trials.
Current attitudes of Canadian urologists towards surgical castration in the treatment of prostate cancer.

Prostate cancer is the most common non-cutaneous malignancy and the third leading cause of cancer death among men in North America.1 There is an approximate one in seven lifetime incidence within U.S. males. Most of these patients present with localized disease, however, approximately 1/3 of these men do progress to metastatic disease at some point within their lives.2,3 This has led to the current, and ongoing, research into novel treatments and regimens for metastatic prostate cancer (mPCa), with emerging evidence advocating for additional second-line androgen blocking agents to be used up front in addition to baseline androgen deprivation therapy (ADT). 4-6 Despite continued advancements in the management of mPCa, ADT still plays an important foundational component of treatment regimens. Currently, most cases of mPCa in North America are treated with pharmacological ADT rather than the historical alternative of surgical castration. The increased use of pharmacological ADT has likely contributed to dramatic increase in the cost of mPCa on a population level.7 Surgical castration remains an important treatment modality of mPCa across the world and is recommended as an alternative first-line ADT treatment in multiple practice guidelines.8,9 Additionally, a previous cost analysis by the same authors has identified the potential for significant cost savings through increased use of surgical castration in the treatment of mPCa.10 We have previously shown the average cost of medical ADT drugs alone over five years to be approximately $20 000 per patient. This does not include the cost of providing the injections or required travel for many patients and their family. The cost of a bilateral orchiectomy at our institution with general anesthesia can be performed for less than $5000. Canadian urologists have a leadership role within our public healthcare system. This requires us to be stewards to the system and use resources efficiently. In fact, this responsibility has been enshrined in the CanMEDS Physician Competency Framework published by the Royal College of Physicians and Surgeons of Canada.11 Given the equal treatment effect of surgical castration in mPCa and the potential for significant cost savings, it is important to identify barriers preventing wider use of this treatment. Here, we aimed to identify current practice patterns and attitudes of urologists practicing in Canada towards surgical castration in the treatment of PCa.
Red blood cell transfusion does not increase risk of venous or arterial thrombosis during hospitalization

Previous observational studies suggest associations between red blood cell (RBC) transfusion and risk for arterial or venous thrombosis. We determined the association between thrombosis and RBC transfusion in hospitalized patients using the Recipient Database from the National Heart Lung and Blood Institute (NHLBI) Recipient Epidemiology and Donor Evaluation Study‐III. A thrombotic event was a hospitalization with an arterial or venous thrombosis ICD‐9 code and administration of a therapeutic anticoagulant or antiplatelet agent. Patients with history of thrombosis or a thrombosis within 24 hours of admission were excluded. A proportional hazards regression model with time‐dependent covariates was calculated. Estimates were adjusted for age, sex, hospital, smoking, medical comorbidities, and surgical procedures. Of 657 412 inpatient admissions, 67 176 (10.2%) received at least one RBC transfusion. Two percent (12927) of patients experienced a thrombosis. Of these, 2587 developed thrombosis after RBC transfusion. In unadjusted analyses, RBC transfusion was associated with an increased thrombosis risk [HR = 1.3 (95% CI 1.23‐1.36)]. After adjustment for surgical procedures, age, sex, hospital, and comorbidities, no association between RBC transfusion on risk of venous and arterial thrombosis was found [HR 1.0 (95% CI: 0.96‐1.05)]. Thus, RBC transfusion does not appear to be an important risk factor for thrombosis in most hospitalized patients.
Age-Related Considerations in Cardio-Oncology

Cardio-Oncology has blossomed as a new field in cardiovascular medicine, in large part due to new therapies, which may have cardiovascular sequelae. Despite this, anthracyclines still serve as cornerstone therapy for most pediatric cancers, several solid tumors and hematological malignancies. Cardiotoxicity is the main limiting concern with anthracyclines, and this is particularly an issue in patients in extremes of age (both young and old patients). Pediatric hearts are susceptible for cardiotoxicity, while in older patients, concomitant risk factors may contribute to lower threshold for cardiotoxic effects. With increasing patient survival, a significant increase in elderly cancer patients and long-term cardiotoxicity effects of anthracyclines, a better mechanistic understanding of age-dependent processes—that define cardiotoxicity—is needed. This review sheds light on how age affects underlying molecular pathways of anthracycline-associated cardiotoxicity and aims to provide preventive strategies that can be used in clinical practice.
Exploring Text and Audio Embeddings for Multi-Dimension Elderly Emotion Recognition

This paper investigates the use of audio and text embed-dings for the classiﬁcation of emotion dimensions within the scope of the Elderly Emotion Sub-Challenge of the INTER-SPEECH 2020 Computational Paralinguistics Challenge. We explore speaker and time dependencies on the expression of emotions through the combination of well-known acoustic-prosodic features and speaker embeddings extracted for different time scales. We consider text information input through transformer language embeddings, both isolated and in combination with acoustic features. The combination of acoustic and text information is explored in early and late fusion schemes. Overall, early fusion of systems trained on top of hand-crafted acoustic-prosodic features (eGeMAPS and ComParE), acoustic model feature embeddings (x-vectors), and text feature embeddings provide the best classiﬁcation results in development for both Arousal and Valence. The combination of modalities allows us to reach a multi-dimension emotion classiﬁcation performance in the development challenge data set of up to 48.8% Unweighted Average Recall (UAR) and 61.0% UAR for Arousal and Valence, respectively. These results correspond to a 16.2% and a 8.7% relative UAR improvement.
Siamese Convolutional Neural Network Using Gaussian Probability Feature for Spoofing Speech Detection

The security and reliability of automatic speaker verification systems can be threatened by different types of spoofing attacks using speech synthetic, voice conversion, or replay. The 2-class Gaussian Mixture Model classifier for genuine and spoofed speech is usually used as the baseline in the ASVspoof challenge, which is designed to develop the generalized countermeasures with potential to detect varying and unforeseen spoofing attacks. In the scoring phase, the GMM accumulates the scores on all frames in a test speech independently, and does not consider the relationship between adjacent frames. We propose the 1-D Convolutional Neural Network whose input is the log-probabilities of the speech frames on the GMM components. The new model considers not only the score distribution of GMM components, but also the local relationship of frames. And the pooling is used to extract the speech global character. The Siamese CNN is also proposed, which is based on two GMMs trained on genuine and spoofed speech respectively. Experiments on the ASVspoof 2019 challenge logical and physical access scenarios show that the proposed model can improve performance greatly compared with the baseline systems.
Clinical Impact of Tumor-Infiltrating Lymphocytes and PD-L1-Positive Cells as Prognostic and Predictive Biomarkers in Urological Malignancies and Retroperitoneal Sarcoma

Simple Summary Two host-dependent biological characteristics, “avoiding immune destruction” and “tumor-promoting inflammation” have been added to cancer hallmarks in 2011. The interaction and cross-talk among tumor cells and several immune cells in a tumor microenvironment are dynamic and complex processes. The purpose of this review is to discuss the prognostic impact of tumor-infiltrating lymphocytes and predictive biomarkers for immune checkpoint inhibitors in four urological solid tumors, the urothelial carcinoma, renal cell carcinoma, prostate cancer, and retroperitoneal sarcoma, through summarizing the findings of observation studies and clinical trials. Abstract Over the past decade, an “immunotherapy tsunami”, more specifically that involving immune checkpoint inhibitors (ICIs), has overtaken the oncological field. The interaction and cross-talk among tumor cells and several immune cells in the tumor microenvironment are dynamic and complex processes. As immune contexture can vary widely across different types of primary tumors and tumor microenvironments, there is still a significant lack of clinically available definitive biomarkers to predict patient response to ICIs, especially in urogenital malignancies. An increasing body of evidence evaluating urological malignancies has proven that tumor-infiltrating lymphocytes (TILs) are a double-edged sword in cancer. There is an urgent need to shed light on the functional heterogeneity in the tumor-infiltrating immune system and to explore its prognostic impact following surgery and other treatments. Notably, we emphasized the difference in the immunological profile among urothelial carcinomas arising from different primary origins, the bladder, renal pelvis, and ureter. Significant differences in the density of FOXP3-positive TILs, CD204-positive tumor-infiltrating macrophages, PD-L1-positive cells, and colony-stimulating factors were observed. This review discusses two topics: (i) the prognostic impact of TILs and (ii) predictive biomarkers for ICIs, to shed light on lymphocyte migration in four solid tumors, the urothelial carcinoma, renal cell carcinoma, prostate cancer, and retroperitoneal sarcoma.
Mentoring-Reverse Mentoring for Unsupervised Multi-Channel Speech Source Separation

Mentoring-reverse mentoring , which is a novel knowledge transfer framework for unsupervised learning, is introduced in multi-channel speech source separation. This framework aims to improve two different systems, which are referred to as a senior and a junior system, by mentoring each other. The senior system, which is composed of a neural separator and a statistical blind source separation (BSS) model, generates a pseudo-target signal. The junior system, which is composed of a neural separator and a post-ﬁlter, was constructed using teacher-student learning with the pseudo-target signal generated from the senior system i.e, imitating the output from the senior system (mentoring step). Then, the senior system can be improved by propagating the shared neural separator of the grown-up junior system to the senior system (reverse mentoring step). Since the improved neural separator can give better initial parameters for the statistical BSS model, the senior system can yield more accurate pseudo-target signals, leading to iterative improvement of the pseudo-target signal generator and the neural separator. Experimental comparisons conducted under the condition where mixture-clean parallel data are not available demonstrated that the proposed mentoring-reverse mentoring framework yielded improvements in speech source separation over the existing un-supervised source separation methods.
Natural tree shade increases milk stability of lactating dairy cows during the summer in the subtropics

Abstract This research communication addresses the hypothesis that, during the summer in the subtropics, natural tree shade helps to improve milk functional characteristics such as stability and acidity. Sixteen Holstein lactating cows were enrolled. The study consisted of three periods (pre-stress, heat stress and post-stress) based on allocating grazing cows into two treatments (with and without access to shade during the Heat Stress period). Overall THI during the trial was (mean ± se) 76.0 ± 3.4. Access to shade prevented the heat stress-related decrease in milk stability both in the ethanol and in the coagulation time test, as well as maintained milk acidity within an acceptable range (14 to 18°D).