Miracle-dz/UniFAR-FTUs huggingface dataset:
PM10 Alters Trophoblast Cell Function and Modulates miR-125b-5p Expression

Air pollution is one of the largest global environmental health hazards that threaten premature mortality or morbidity. Particulate matter 10 (PM10) has been demonstrated to contribute to several human diseases via dysregulated miRNA expression. Trophoblast cells play a key role in implantation and placentation for a successful pregnancy. Nonetheless, the PM10 associated trophoblast cell functions during pregnancy and miRNA expression are still unknown. Our study showed that PM10 affected HTR-8/SVneo cell viability and also decreased cell proliferation, migration, and invasion. A high concentration of PM10 caused an increase in HTR-8/SVneo cell apoptosis. Treatment with PM10 induced inflammation through the upregulated IL-1β, IL-6, and TNF-α expression in trophoblast cells. In PM10-treated HTR-8/SVneo cells, miR-125b-5p expression was considerably increased and TXNRD1 was found to be negatively related to miR-125b-5p. Collectively, our findings revealed that PM10 could alter miR-125b-5p expression by targeting TXNRD1 and suppressing trophoblast cell functions. Additional investigations relating to the function of miR-125b-5p and its target on particulate pollution exposure in trophoblast are warranted for future biomarker or effective therapeutic approaches.
LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation

Standard multi-task benchmarks are essential for developing pretraining models that can generalize to various downstream tasks. Existing benchmarks for natural language processing (NLP) usually focus only on understanding or generating short texts. However, long text modeling requires many distinct abilities in contrast to short texts, such as the modeling of long-range discourse and commonsense relations, and the coherence and controllability of generation. The lack of standardized benchmarks makes it difficult to assess these abilities of a model and fairly compare different models, especially Chinese models. Therefore, we propose a story-centric benchmark named LOT for evaluating Chinese long text modeling, which aggregates two understanding tasks and two generation tasks. We construct new datasets for these tasks based on human-written Chinese stories with hundreds of words. Furthermore, we release an encoder-decoder-based Chinese long text pretraining model named LongLM with up to 1 billion parameters. We pretrain LongLM on 120G Chinese novels with two generative tasks including text infilling and conditional continuation. Extensive experiments show that LongLM outperforms similar-sized pretraining models substantially on both the understanding and generation tasks in LOT.
Ultrastable Shear-Jammed Granular Material

Dry granular materials such as sand, gravel, pills, or agricultural grains, can become rigid when compressed or sheared. Under isotropic compression, the material will reach a certain jamming density and then resist further compression. Shear jamming occurs when resistance to shear emerges in a system at a density lower than the jamming density. Although shear-jamming is prevalent in frictional granular materials, their stability properties are not well described by standard elasticity theory and thus call for experimental characterization. We report on experimental observations of changes in the mechanical properties of a shear-jammed granular material subjected to small-amplitude, quasi-static cyclic shear. We study a layer of plastic discs conﬁned to a shear cell, using photoelasticimetry to measure all inter-particle vector forces. For suﬃciently small cyclic shear amplitudes and large enough initial shear, the material evolves to an unexpected “ultra-stable” state in which all the particle positions and inter-particle contact forces remain unchanged after each complete shear cycle for thousands of cycles. The stress response of these states to small imposed shear is nearly elastic, in contrast to the original shear jammed state.
Active Learning for Human-in-the-Loop Customs Inspection

We study the human-in-the-loop customs inspection scenario, where an AI-assisted algorithm supports customs officers by recommending a set of imported goods to be inspected. If the inspected items are fraudulent, the officers can levy extra duties. The updated decisions are used as additional training data for successive iterations. Inspecting only the likely fraudulent items may lead to an immediate gain in revenue, yet it does not bring new insights for learning dynamic trade patterns. In contrast, including uncertain items in the inspection helps gradually acquire new knowledge that will be used as supplementary training resources to update the system. Based on multiyear customs declaration logs obtained from three countries, we demonstrate that some degree of exploration is necessary to cope with domain shifts in trade data. The results show that a hybrid strategy of jointly selecting likely fraudulent and uncertain items will eventually outperform the exploitation-only strategy.
Faster Rates of Private Stochastic Convex Optimization

In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) and provide excess population risks for some special classes of functions that are faster than the previous results of general convex and strongly convex functions. In the first part of the paper, we study the case where the population risk function satisfies the Tysbakov Noise Condition (TNC) with some parameter $\theta>1$. Specifically, we first show that under some mild assumptions on the loss functions, there is an algorithm whose output could achieve an upper bound of $\tilde{O}((\frac{1}{\sqrt{n}}+\frac{\sqrt{d\log \frac{1}{\delta}}}{n\epsilon})^\frac{\theta}{\theta-1})$ for $(\epsilon, \delta)$-DP when $\theta\geq 2$, here $n$ is the sample size and $d$ is the dimension of the space. Then we address the inefficiency issue, improve the upper bounds by $\text{Poly}(\log n)$ factors and extend to the case where $\theta\geq \bar{\theta}>1$ for some known $\bar{\theta}$. Next we show that the excess population risk of population functions satisfying TNC with parameter $\theta\geq 2$ is always lower bounded by $\Omega((\frac{d}{n\epsilon})^\frac{\theta}{\theta-1}) $ and $\Omega((\frac{\sqrt{d\log \frac{1}{\delta}}}{n\epsilon})^\frac{\theta}{\theta-1})$ for $\epsilon$-DP and $(\epsilon, \delta)$-DP, respectively. In the second part, we focus on a special case where the population risk function is strongly convex. Unlike the previous studies, here we assume the loss function is {\em non-negative} and {\em the optimal value of population risk is sufficiently small}. With these additional assumptions, we propose a new method whose output could achieve an upper bound of $O(\frac{d\log\frac{1}{\delta}}{n^2\epsilon^2}+\frac{1}{n^{\tau}})$ for any $\tau\geq 1$ in $(\epsilon,\delta)$-DP model if the sample size $n$ is sufficiently large.
Estimating the Generalization in Deep Neural Networks via Sparsity

Generalization is the key capability for deep neural networks (DNNs). However, it is challenging to give a reliable measure of the generalization ability of a DNN via only its nature. In this paper, we propose a novel method for estimating the generalization gap based on network sparsity. In our method, two key quantities are proposed first. They have close relationship with the generalization ability and can be calculated directly from the training results alone. Then a simple linear model involving two key quantities are constructed to give accurate estimation of the generalization gap. By training DNNs with a wide range of generalization gap on popular datasets, we show that our key quantities and linear model could be efficient tools for estimating the generalization gap of DNNs.
Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset

Automatic speech recognition (ASR) on low resource languages improves the access of linguistic minorities to technological advantages provided by artificial intelligence (AI). In this paper, we address the problem of data scarcity for the Hong Kong Cantonese language by creating a new Cantonese dataset. Our dataset, Multi-Domain Cantonese Corpus (MDCC), consists of 73.6 hours of clean read speech paired with transcripts, collected from Cantonese audiobooks from Hong Kong. It comprises philosophy, politics, education, culture, lifestyle and family domains, covering a wide range of topics. We also review all existing Cantonese datasets and analyze them according to their speech type, data source, total size and availability. We further conduct experiments with Fairseq S2T Transformer, a state-of-the-art ASR model, on the biggest existing dataset, Common Voice zh-HK, and our proposed MDCC, and the results show the effectiveness of our dataset. In addition, we create a powerful and robust Cantonese ASR model by applying multi-dataset learning on MDCC and Common Voice zh-HK.
Memory effects and static disorder reduce information in single-molecule signals

A key theoretical challenge posed by single-molecule studies is the inverse problem of deducing the underlying molecular dynamics from the time evolution of low-dimensional experimental observables. Toward this goal, a variety of low-dimensional models have been proposed as descriptions of single-molecule signals, including random walks with or without conformational memory and/or with static or dynamics disorder. Differentiating among different models presents a challenge, as many distinct physical scenarios lead to similar experimentally observable behaviors such as anomalous diffusion and nonexponential relaxation. Here we show that information-theory-based analysis of single-molecule time series, inspired by Shannon’s work studying the information content of printed English, can differentiate between Markov (memoryless) and non-Markov single-molecule signals and between static and dynamic disorder. In particular, non-Markov time series are more predictable and thus can be compressed and transmitted within shorter messages (i.e. have a lower entropy rate) than appropriately constructed Markov approximations, and we demonstrate that in practice the LZMA compression algorithm reliably differentiates between these entropy rates across several simulated dynamical models.
Pharmacomicrobiomics: Exploiting the Drug-Microbiota Interactions in Antihypertensive Treatment

Hypertension is a leading risk factor for cardiovascular diseases and can reduce life expectancy. Owing to the widespread use of antihypertensive drugs, patients with hypertension have improved blood pressure control over the past few decades. However, for a considerable part of the population, these drugs still cannot significantly improve their symptoms. In order to explore the reasons behind, pharmacomicrobiomics provide unique insights into the drug treatment of hypertension by investigating the effect of bidirectional interaction between gut microbiota and antihypertensive drugs. This review discusses the relationship between antihypertensive drugs and the gut microbiome, including changes in drug pharmacokinetics and gut microbiota composition. In addition, we highlight how our current knowledge of antihypertensive drug-microbiota interactions to develop gut microbiota-based personalized ways for disease management, including antihypertensive response biomarker, microbial-targeted therapies, probiotics therapy. Ultimately, a better understanding of the impact of pharmacomicrobiomics in the treatment of hypertension will provide important information for guiding rational clinical use and individualized use.
RNA-Seq Transcriptome Analysis Provides Candidate Genes for Resistance to Tomato Leaf Curl New Delhi Virus in Melon

Tomato leaf curl New Delhi virus (ToLCNDV) emerged in the Mediterranean Basin in 2012 as the first DNA bipartite begomovirus (Geminiviridae family), causing severe yield and economic losses in cucurbit crops. A major resistance locus was identified in the wild melon accession WM-7 (Cucumis melo kachri group), but the mechanisms involved in the resistant response remained unknown. In this work, we used RNA-sequencing to identify disease-associated genes that are differentially expressed in the course of ToLCNDV infection and could contribute to resistance. Transcriptomes of the resistant WM-7 genotype and the susceptible cultivar Piñonet Piel de Sapo (PS) (C. melo ibericus group) in ToLCNDV and mock inoculated plants were compared at four time points during infection (0, 3, 6, and 12 days post inoculation). Different gene expression patterns were observed over time in the resistant and susceptible genotypes in comparison to their respective controls. Differentially expressed genes (DEGs) in ToLCNDV-infected plants were classified using gene ontology (GO) terms, and genes of the categories transcription, DNA replication, and helicase activity were downregulated in WM-7 but upregulated in PS, suggesting that reduced activity of these functions reduces ToLCNDV replication and intercellular spread and thereby contributes to resistance. DEGs involved in the jasmonic acid signaling pathway, photosynthesis, RNA silencing, transmembrane, and sugar transporters entail adverse consequences for systemic infection in the resistant genotype, and lead to susceptibility in PS. The expression levels of selected candidate genes were validated by qRT-PCR to corroborate their differential expression upon ToLCNDV infection in resistant and susceptible melon. Furthermore, single nucleotide polymorphism (SNPs) with an effect on structural functionality of DEGs linked to the main QTLs for ToLCNDV resistance have been identified. The obtained results pinpoint cellular functions and candidate genes that are differentially expressed in a resistant and susceptible melon line in response to ToLCNDV, an information of great relevance for breeding ToLCNDV-resistant melon cultivars.
Identification of a transcriptional signature found in multiple models of ASD and related disorders

Epigenetic regulation plays a critical role in many neurodevelopmental disorders, including Autism Spectrum Disorder (ASD). In particular, many such disorders are the result of mutations in genes that encode chromatin modifying proteins. However, while these disorders share many features, it is unclear whether they also share gene expression disruptions resulting from the aberrant regulation of chromatin. We examined 5 chromatin modifiers that are all linked to ASD despite their different roles in regulating chromatin. Specifically, we depleted Ash1L, Chd8, Crebbp, Ehmtl, and Nsd1 in parallel in a highly controlled neuronal culture system. We then identified sets of shared genes, or transcriptional signatures, that are differentially expressed following loss of multiple ASD-linked chromatin modifiers. We examined the functions of genes within the transcriptional signatures and found an enrichment in many neurotransmitter transport genes and activity-dependent genes. In addition, these genes are enriched for specific chromatin features such as bivalent domains that allow for highly dynamic regulation of gene expression. The downregulated transcriptional signature is also observed within multiple mouse models of neurodevelopmental disorders that result in ASD, but not those only associated with intellectual disability. Finally, the downregulated transcriptional signature can distinguish between neurons generated from iPSCs derived from healthy donors and idiopathic ASD patients through RNA-deconvolution, demonstrating that this gene set is relevant to the human disorder. This work identifies a transcriptional signature that is found within many neurodevelopmental syndromes, helping to elucidate the link between epigenetic regulation and the underlying cellular mechanisms that result in ASD.
Intuitionistic fuzzy sets applied to color image processing

Segmentation of color images is a challenge which is in constant development as they provide more information than a gray-scale image. In particular, the analysis of biomedical images is specially helpful for many purposes. In this work, a method to segment leukocytes is presented. This method is based on the combination of the use of RGB color space, intuitionistic fuzzy sets and K-means. The results show a good performance of the proposed method achieving the segmentation of the objects of interest.
Pitfalls of local explainability in complex black-box models

Post hoc models are becoming popular as additional tools to evaluate the results of black-box models and to provide explanations of the predictions they give. In this paper the main concerns that Local Induced models raise in the pointwise explanation of heavily overparametrized black-box models are discussed in depth, highlighting some vulnerabilities, some underrated issues and giving some warnings on the potentially negative effect on user trust of this explainability framework.
UNet based future weather prediction on Weather4cast 2021 stage 2

This paper describes our experiments on Weather4cast 2021 challenge stage 2. We tried to predict future weather patterns in various geographic regions worldwide. Similar to our previous participation at the same challenge of stage 1, we utilized UNet to make a prediction on every pixel of the weather image. Multiple UNet models having diverse network structures were trained and prediction outputs were combined to make more accurate predictions. Evaluation results show competitive performance compared to the baseline persistence method.
Gerenciamento Hierá rquico de Ambientes Inteligentes Utilizando SNMP

Smart environments are designed to improve our daily lives by providing means to monitor a physical environment and react to the context in automatic and proactive ways. These environments are typically composed by a variety of sensors, actuators, and controllers interconnected by a network infrastructure. In order to allow interoperability with current Internet, such devices need to support a TCP/IP stack relying on IPv6 for addressing. Proper management of smart devices is required to allow a human operator to gather updated statistics and perform remote management over smart devices. In this paper, we investigate the use of SNMP, the de facto Internet network management protocol, in the management of smart environments. Since smart devices are typically resource constrained, we propose and develop an SNMP-based hierarchical management architecture to manage an environment composed of Arduino and Raspberry Pi boards. MIB extensions are developed and instrumented to support the proposed management architecture. Results demonstrate the feasibility of the SNMP protocol in managing smart devices without adding significant overheads. Resumo. Ambientes inteligentes são projetados para melhorar nossa vida diária oferecendo meios para monitorar um ambiente fı́sico e reagir ao contexto de forma automática e pró-ativa. Estes ambientes são tipicamente compostos de uma variedade de sensores, atuadores e controladores interconectados por uma infraestrutura de rede. Para permitir a interoperabilidade com a Internet atual, estes dispositivos precisam suportar uma pilha TCP/IP e utilizar IPv6 para endereçamento. O gerenciamento adequado de dispositivos inteligentes é necessário para permitir que um operador humano obtenha estatı́sticas atualizadas e realize o gerenciamento remoto desses dispositivos. Neste artigo, se investiga o uso do SNMP, o protocolo de gerenciamento de redes padrão da Internet, no gerenciamento de ambientes inteligentes. Uma vez que dispositivos inteligentes normalmente possuem recursos limitados, uma arquitetura de gerenciamento hierárquica baseada em SNMP é proposta e desenvolvida com o objetivo de gerenciar um ambiente composto de placas Arduino e Raspberry Pi. Extensões de MIB são desenvolvidas e instrumentadas para viabilizar a arquitetura de gerenciamento proposta. Resultados demonstram a viabilidade do protocolo SNMP no gerenciamento de dispositivos inteligentes sem adicionar sobrecarga significativa.
Positive-to-negative behavioural responses suggest hedonic evaluation in treefrog mate choice

Sexual competition hinges on the ability to impress other conspecifics, to drive them away or attract them. In such cases, the selective environment may be hedonic or affective in nature, as it consists of the evaluations of the individuals making the decisions. This may contribute to the power of sexual selection because evaluations may range from positive to negative rather than simply from positive to neutral. Selection due to mate choice may therefore be stronger than currently appreciated. Further, change in preferred mate types can occur simply by changes (flips) in the evaluation of similar display features, adding to the dynamism of sexual selection as well as its strength. We tested the hypothesis of positive-to-negative behavioural responses in mate choice with a playback experiment using two treefrog species with ‘mirror image’ structures in their advertisement and aggressive calls. Female treefrog responses ranged from approach to evasion, and the presence of an aversive stimulus tainted evaluation of an attractive stimulus. Further, females in the two species showed flips in approach/evasion of stimuli with comparable signal structure. These results suggest that hedonic evaluation may have an important role in mate choice and showcase how mechanistic analysis can help understand evolutionary processes.
A multi-modality approach for enhancing 4D flow magnetic resonance imaging via sparse representation

This work evaluates and applies a multi-modality approach to enhance blood flow measurements and haemodynamic analysis with phase-contrast magnetic resonance imaging (4D flow MRI) in cerebral aneurysms (CAs). Using a library of high-resolution velocity fields from patient-specific computational fluid dynamic simulations and in vitro particle tracking velocimetry measurements, the flow field of 4D flow MRI data is reconstructed as the sparse representation of the library. The method was evaluated with synthetic 4D flow MRI data in two CAs. The reconstruction enhanced the spatial resolution and velocity accuracy of the synthetic MRI data, leading to reliable pressure and wall shear stress (WSS) evaluation. The method was applied on in vivo 4D flow MRI data acquired in the same CAs. The reconstruction increased the velocity and WSS by 6–13% and 39–61%, respectively, suggesting that the accuracy of these quantities was improved since the raw MRI data underestimated the velocity and WSS by 10–20% and 40–50%, respectively. The computed pressure fields from the reconstructed data were consistent with the observed flow structures. The results suggest that using the sparse representation flow reconstruction with in vivo 4D flow MRI enhances blood flow measurement and haemodynamic analysis.