Miracle-dz/UniFAR-FTUs huggingface dataset:
How Phonotactics Affect Multilingual and Zero-Shot ASR Performance

The idea of combining multiple languages’ recordings to train a single automatic speech recognition (ASR) model brings the promise of the emergence of universal speech representation. Recently, a Transformer encoder-decoder model has been shown to leverage multilingual data well in IPA transcriptions of languages presented during training. However, the representations it learned were not successful in zero-shot transfer to unseen languages. Because that model lacks an explicit factorization of the acoustic model (AM) and language model (LM), it is unclear to what degree the performance suffered from differences in pronunciation or the mismatch in phono-tactics. To gain more insight into the factors limiting zero-shot ASR transfer, we replace the encoder-decoder with a hybrid ASR system consisting of a separate AM and LM. Then, we perform an extensive evaluation of monolingual, multilingual, and crosslingual (zero-shot) acoustic and language models on a set of 13 phonetically diverse languages. We show that the gain from modeling crosslingual phonotactics is limited, and imposing a too strong model can hurt the zero-shot transfer. Furthermore, we find that a multilingual LM hurts a multilingual ASR system’s performance, and retaining only the target language’s phonotactic data in LM training is preferable.
Modeling lactation curve for genetic evaluation of Murrah buffaloes

In this study, four lactation curve models viz. Gamma-type function (GF), Mixed log function (MLF), Exponential function (EF) and Polynomial regression function (PRF) were fitted to predict the weekly test-day milk yields pertaining to 961 Murrah buffaloes progeny of 101 sires spread over a period of 36 years (1977-2012) maintained at ICAR-NDRI, Karnal. The PRF was the best fit with highest R2 (99.3%) and lowest RMSE (0.3%) which was used subsequently to estimate the first lactation 305-day or less milk yield (FL305DMY) from weekly test-day milk yields. The sires were evaluated using four linear models viz. Least Squares(LSQ), Simple Regressed Least Squares (SRLS), Sire (BLUP-SM) and animal (BLUP-AM) models from actual as well as PRF predicted FL305DMYs. BLUP-AM was the most efficient, among the sire evaluation models, in predicting the breeding values. Sire evaluation on the basis of predicted data (using PRF) was similar to that on the basis of actual data indicating that the best fitted lactation curve function can be used as an alternative for early animal evaluation and reduction of generation interval.
Projection de la demande de lits de soins intensifs durant l’épidémie de COVID-19 au Canada

RÉSUMÉ CONTEXTE: La hausse des cas de maladie à coronavirus 2019 (COVID-19) au Canada peut créer une forte demande de soins hospitaliers et de soins intensifs. Nous avons évalué la mesure dans laquelle l’isolement volontaire des personnes présentant des symptômes légers retarde le sommet épidémique et réduit la demande de soins dans chaque province canadienne. MÉTHODES: Nous avons conçu un modèle de calcul et fait des simulations de la propagation de la COVID-19 dans chaque province. À partir des estimations des caractéristiques de la COVID-19, nous avons évalué la demande de lits d’hôpital et de lits de soins intensifs en l’absence d’isolement volontaire en supposant une moyenne de 2,5 cas secondaires, et avons comparé des scénarios en faisant varier le taux d’isolement volontaire des cas légers 24 heures après l’apparition des symptômes. RÉSULTATS: En l’absence d’isolement volontaire, l’épidémie atteindrait son sommet dans la première moitié de juin, et il faudrait en moyenne 569 jours-lits de soins intensifs par 10 000 habitants. Avec un taux d’isolement volontaire de 20 %, l’atteinte du sommet serait repoussée de 2 à 4 semaines, et la demande de lits diminuerait de 23,5 %; avec un taux de 40 %, le sommet serait repoussé de 2 à 4 semaines supplémentaires, et la demande de lits connaîtrait une baisse de 53,6 %. En fixant le taux d’occupation actuel des lits de soins intensifs à plus de 80 % et le taux d’isolement volontaire à 40 %, la demande de lits demeure supérieure au nombre de lits disponibles. INTERPRÉTATION: Au sommet de l’épidémie de COVID-19 au Canada, la demande de lits de soins intensifs excédera le nombre total de lits disponibles, même avec un taux d’isolement volontaire de 40 %. Nos résultats montrent que la situation sera difficile pour le système de santé et que l’isolement volontaire pourrait réduire la demande de soins hospitaliers et de soins intensifs.
Progressive Cerebral Small Vessel Disease Caused by an Autoimmune Response to Intravesical Bacille-Calmette-Guérin Treatment

Systemic BCGitis and autoimmune diseases are possible adverse events of intravesical Bacille Calmette-Guérin-(BCG)-instillations in the treatment of urothelioma cancer. We present the case of an 83-years-old male patient with rapid progressive symptoms of dementia up to mutism as well as tonic seizures. Immune-mediated cerebral small vessel disease was diagnosed and retraced to former instillations of BCG. Intense immunosuppressive treatment was performed and clinical restoration was achieved within several months. While cerebral vasculitis due to BCGitis has already been described before, this is to our knowledge the first case report to illustrate an immune-mediated small vessel disease after BCG-instillations. This should be considered in patients with rapidly progressive dementia-like symptoms treated with BCG, as an immunosuppressive treatment can be highly effective even at severe clinical stages.
基于BERT与柱搜索的中文释义生成(Chinese Definition Modeling Based on BERT and Beam Seach)

释义生成任务是指为一个目标词生成相应的释义。 前人研究中文释义生成任务时未考虑目标词的上下文,本文首次在中文释义生成任务中使用了目标词的上下文信息,并提出了一个基于BERT与柱搜索的释义生成模型。 本文构建了包含上下文的CWN中文数据集用于开展实验,除了BLEU指标之外,还使用语义相似度作为额外的自动评价指标,实验结果显示本文模型在中文CWN数据集和英文Oxford数据集上均有显著提升,人工评价结果也与自动评价结果一致。 最后,本文对生成实例进行了深入分析。
Profiling Alternative 3′ Untranslated Regions in Sorghum using RNA-seq Data

Sorghum is an important crop widely used for food, feed, and fuel. Transcriptome-wide studies of 3′ untranslated regions (3′UTR) using regular RNA-seq remain scarce in sorghum, while transcriptomes have been characterized extensively using Illumina short-read sequencing platforms for many sorghum varieties under various conditions or developmental contexts. 3′UTR is a critical regulatory component of genes, controlling the translation, transport, and stability of messenger RNAs. In the present study, we profiled the alternative 3′UTRs at the transcriptome level in three genetically related but phenotypically contrasting lines of sorghum: Rio, BTx406, and R9188. A total of 1,197 transcripts with alternative 3′UTRs were detected using RNA-seq data. Their categorization identified 612 high-confidence alternative 3′UTRs. Importantly, the high-confidence alternative 3′UTR genes significantly overlapped with the genesets that are associated with RNA N6-methyladenosine (m6A) modification, suggesting a clear indication between alternative 3′UTR and m6A methylation in sorghum. Moreover, taking advantage of sorghum genetics, we provided evidence of genotype specificity of alternative 3′UTR usage. In summary, our work exemplifies a transcriptome-wide profiling of alternative 3′UTRs using regular RNA-seq data in non-model crops and gains insights into alternative 3′UTRs and their genotype specificity.
Cortical Stimulation Induces Excitatory Postsynaptic Potentials of Inferior Colliculus Neurons in a Frequency-Specific Manner

Corticofugal modulation of auditory responses in subcortical nuclei has been extensively studied whereas corticofugal synaptic transmission must still be characterized. This study examined postsynaptic potentials of the corticocollicular system, i.e., the projections from the primary auditory cortex (AI) to the central nucleus of the inferior colliculus (ICc) of the midbrain, in anesthetized C57 mice. We used focal electrical stimulation at the microampere level to activate the AI (ESAI) and in vivo whole-cell current-clamp to record the membrane potentials of ICc neurons. Following the whole-cell patch-clamp recording of 88 ICc neurons, 42 ICc neurons showed ESAI-evoked changes in the membrane potentials. We found that the ESAI induced inhibitory postsynaptic potentials in 6 out of 42 ICc neurons but only when the stimulus current was 96 μA or higher. In the remaining 36 ICc neurons, excitatory postsynaptic potentials (EPSPs) were induced at a much lower stimulus current. The 36 ICc neurons exhibiting EPSPs were categorized into physiologically matched neurons (n = 12) when the characteristic frequencies of the stimulated AI and recorded ICc neurons were similar (≤1 kHz) and unmatched neurons (n = 24) when they were different (>1 kHz). Compared to unmatched neurons, matched neurons exhibited a significantly lower threshold of evoking noticeable EPSP, greater EPSP amplitude, and shorter EPSP latency. Our data allow us to propose that corticocollicular synaptic transmission is primarily excitatory and that synaptic efficacy is dependent on the relationship of the frequency tunings between AI and ICc neurons.
Control of Plant Viruses by CRISPR/Cas System-Mediated Adaptive Immunity

Plant diseases caused by invading plant viruses pose serious threats to agricultural production in the world, and the antiviral engineering initiated by molecular biotechnology has been an effective strategy to prevent and control plant viruses. Recent advances in clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) system-mediated DNA or RNA editing/interference in plants make them very attractive tools applicable to the plant protection field. Here, we review the development of CRISPR/Cas systems and summarize their applications in controlling different plant viruses by targeting viral sequences or host susceptibility genes. We list some potential recessive resistance genes that can be utilized in antiviral breeding and emphasize the importance and promise of recessive resistance gene-based antiviral breeding to generate transgene-free plants without developmental defects. Finally, we discuss the challenges and opportunities for the application of CRISPR/Cas techniques in the prevention and control of plant viruses in the field.
Cell Therapy With Human ESC-Derived Cardiac Cells: Clinical Perspectives

In the ongoing quest for the “ideal” cell type for heart repair, pluripotent stem cells (PSC) derived from either embryonic or reprogrammed somatic cells have emerged as attractive candidates because of their unique ability to give rise to lineage-specific cells and to transplant them at the desired stage of differentiation. The technical obstacles which have initially hindered their clinical use have now been largely overcome and several trials are under way which encompass several different diseases, including heart failure. So far, there have been no safety warning but it is still too early to draw definite conclusions regarding efficacy. In parallel, mechanistic studies suggest that the primary objective of “remuscularizing” the heart with PSC-derived cardiac cells can be challenged by their alternate use as ex vivo sources of a biologically active extracellular vesicle-enriched secretome equally able to improve heart function through harnessing endogenous repair pathways. The exclusive use of this secretome would combine the advantages of a large-scale production more akin to that of a biological medication, the likely avoidance of cell-associated immune and tumorigenicity risks and the possibility of intravenous infusions compatible with repeated dosing.
Remarkable Response of EGFR- and HER2-Amplified Metastatic Colon Cancer to Pyrotinib After Failed Multiline Treatments: A Case Report and Literature Review

Human epidermal growth factor receptor 2 (HER2) has been verified as a valuable biomarker and treatment target in metastatic colorectal cancer (mCRC). Pyrotinib, a novel irreversible HER2/epidermal growth factor receptor (EGFR) dual tyrosine kinase inhibitor, can efficiently inhibit the proliferation of HER2-positive cancer cells in many tumors. We report a case of a 40-year-old woman with both HER2- and EGFR-amplified metastatic colon cancer, who developed refractory disease resistant to multiline therapies (including trastuzumab with lapatinib) but achieved a remarkable response after pyrotinib treatment. For patients with HER2-positive mCRC, who have developed resistance to trastuzumab and lapatinib, pyrotinib is a promising new candidate, which can be used as salvage therapy.
Factuality Classification Using BERT Embeddings and Support Vector Machines

For any topic, its factuality can be defined as the category that determines the status of events with certainty of presentation of them. The first edition of the FACT task mainly focused on determination of the factuality of verb based events. The present edition is aimed at identifying noun based events and determine the factuality of all events be it verbs or nouns. We have participated in Subtask-1 of FACT 2020 task which is to automatically propose a factual tag for each event in the text. In this paper we have presented a method which extracts various features like BERT embeddings, Word2Vec embeddings and TF-IDF (Term Frequency-Inverse Document Frequency) scores of commonly recurring words, along with other manually extracted features as input features and passes them through a SVM (Support Vector Machine) classifier for classification purposes. Our system has achieved a f1-score of 36.6% and accuracy of 59.9% which is quite satisfactory relative to performance of other systems.
IXA-NER-RE at eHealth-KD Challenge 2020

The eHealth-KD 2020 set out this year an automatic extraction challenge on a coarse range of knowledge from health documents written in the Spanish Language. Our group has participated in all the proposed scenarios; the main one, the Named Entity Recognition (NER) subtask, the Relation Extraction (RE) sub-task, and the alternative domain obtaining very different results in each of them. The main task has been conceived as a pipeline of the NER and RE subtask, each of them independently developed from the other. The Name Entity Recognition task has been envisaged as a basic seq2seq system applying a general-purpose Language Model and static embeddings. Unlike the NER subtask, in the RE subtask several approaches were successfully explored; first, transfer learning methods as a way to measure the adaptation ability of pre-trained language models to both medical domain and Spanish language. Second, Matching the Blanks to tackle the problem of the reduced size of the training corpus by producing relation representations directly from non tagged text. As mentioned, the results in the different task were heterogeneous; while the result in NER is on the average (F1 0.66), with ample room for improvement, the result in RE has been outstanding, obtaining the first place in this task (F1 0.633) with more than 3 points over the next classified, demonstrating the soundness of the proposed techniques.
Towards Advanced Interfaces for Citizen Curation

The SPICE project builds on the growing trend for museums, rather than providing an authoritative view, to present multiple voices related to their collection and exhibitions. In SPICE, an approach we term citizen curation is proposed as a way of supporting visitors to share their own interpretations of museum objects and reflect on the variety of interpretations contributed by others. In order to capture a wide range of voices, interfaces will be designed specifically to engage minority groups that tend to be under-represented in cultural activities. To achieve this goal, the interface will need to be intuitive, aesthetic and accessible for different audiences. The paper presents the challenges we face and initial proposals for engaging visitors in citizen curation.
Increasing Usability of TLS Certificate Generation Process Using Secure Design

Based on the researches, usually users are not familiar with different security mechanisms of the systems and do not understand how to use them in everyday life. Special research field analyzing user behavior in the technological systems and in computer security world is called human computer interaction (HCI), is also known as usable security. TLS or Transport Layer Security is one of the most commonly used secure protocols today. It is used to protect data that travels between client and server using HTTPS. Data protection is critical issue, especially when users are sending sensitive information. TLS uses cryptographic algorithms to protect data transmitted via HTTPS protocol. In this paper we will offer balanced and more user-friendly generation process of one of the most frequently used security mechanism on websites Transport Layer Security (TLS) certificate for establishing HTTPS connection between client and server. Our system will check all the requirements of the website and its services to make users run more balanced systems. If system will found another approach with better value based on user requirements, client will be informed in understandable and easy to read form. As a result, user will get the best option for both security and usability sides in shortest period of time
Perceptual Collation Method for Multi-Level Color Halftone Image Based on Visual Characteristics

When printing a digital color image using a printing device, 24-bit RGB data are typically converted to CMYK halftone data by printer driver processing. The converted halftone pattern may change depending on the version of the operating system or printer driver. However, this change may be visually recognized as a difference in the printed image, which is problematic. Hence, a perceptual collation system that detects only differences in visually perceived images is required. Herein, we propose a perceptual collation method to automate the detection of differences in multilevel halftone images based on human assessment. We use the S-CIELAB metric to calculate the color difference by considering human visual characteristics. The proposed perceptual image collation is developed based on the color difference and its gradient features. The experimental results for 25 test image sets show an overall error rate of 8%, thereby verifying the effectiveness of the proposed method.
Image Statistics as Glossiness and Translucency Predictor in Photographs of Real-world Objects

We interpret our surrounding based on the visual stimuli, and perceive objects and materials around us to have various attributes, like color, glossiness, and translucency. We analyze the three-dimensional world based on the two-dimensional images detected by our retina. The state-of-the-art works conclude that the human visual system has a poor ability to fully understand and invert the complex optical nature of light and matter interaction. Some authors rather propose that the human brain calculates image statistics to perceive appearance, demonstrating correlation between perceptual attributes and various statistical metrics. However, the illustrated examples are usually unrealistic nearly-perfect stimuli, making real-life robustness of the ﬁndings questionable. In this study, we analyzed image statistics of photos of real world objects, and assessed the performance of statistical image metrics proposedly used by the human visual system. We identiﬁed very interesting trends, as well as limitations.
A Joint Model for Medical Named Entity Recognition and Normalization

Traditional pipeline models for medical named entity recognition and normalization (MER and MEN) suffer from error propagation. To tackle the error propagation problem, we propose a novel joint deep learning method for the 2020 IberLEF shared task on MER and MEN, where MER is regarded as a machine reading comprehension (MRC) problem and MEN as multiple sequence labeling problems corresponding to normalized hierarchical tumor codes. In the 2020 IberLEF shared task, our proposed joint model achieves an F1 score of 0.87 on MER and an F1 score of 0.825 on MEN, and significantly outperforms pipeline models for comparison.
UACh at MEX-A3T 2020: Detecting Aggressive Tweets by Incorporating Author and Message Context

In this paper we describe our participation in the Aggressiveness Detection Track at the third edition of MEX-A3T. We evaluate two strategies for text classification, a traditional classifier (Logistic Regression) and a classifier based on transformers (BETO). We also study the inclusion of social media metadata features to try to get context from authors and text messages.
GRU with Author Profiling Information to Detect Aggressiveness

This paper describes our participation for the Aggressiveness Identification Track in the third edition of MEX-A3T. The task focuses on the detection of aggressive tweets in Mexican Spanish. Our approach consists in the use of a Bidirectional Gated Recurrent Unit merged with author profiling derived features. The challenge results indicate that our proposal exceeds a Support Vector Machine baseline.
Clinical NER using Spanish BERT Embeddings

This paper presents an overview of transfer learning-based approach to the Named Entity Recognition (NER) sub-task from Cancer Text Mining Shared Task (CANTEMIST) conducted as a part of Iberian Languages Evaluation Forum (IberLEF) 2020. We explore the use of Bidirectional Encoder Representations from Transformers (BERT) based contextual embeddings trained on general domain Spanish text to extract tumor morphology from clinical reports written in Spanish. We achieve an F1 score of 73.4% on NER without using any feature engineered or rule-based approaches, and present our work as inspiration for further research on this task.
Study of diffusionless and diffusional transformations using in situ cooling and heating techniques in a scanning electron microscope

In situ electron microscopy can be an effective tool to investigate the underlying science of many transformation mechanisms in materials science. Useful utilization of these experimentations will provide greater insight into many of the existing theories, as microstructural changes can be visualized in real time under some applied constraints. In this study, we have investigated two basic phase transformation phenomena: diffusionless and diffusional mechanisms with the help of in situ cooling and heating techniques in scanning electron microscope (SEM). In situ cooling experiments have been carried out on secondary hardening ultra-high-strength steels to understand the diffusionless transformation of austenite to martensite. Nucleation and growth of the martensites have been observed with cooling in different steps to −194°C. Details of the formation of different variants of martensites in steel were studied with the help of orientation imaging microscopy. Diffusional transformations were studied in terms of oxidation of pure copper in SEM using in situ heating technique. Different heating cycles were adopted for different samples by in situ heating to a maximum temperature of 950°C for the oxidation study. Nucleation of copper oxides and subsequent growth of the copper oxides at different temperatures were studied systematically. Raman spectroscopy and orientation imaging were done to confirm the formation of oxides and their orientations. The thermal cycling phenomenon was replicated inside SEM with heating and cooling and it has been demonstrated how the nature of copper and its oxides changes with the thermal cycle. This article is part of a discussion meeting issue ‘Dynamic in situ microscopy relating structure and function’.