Miracle-dz/UniFAR-FTUs huggingface dataset:
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information

Recent pretraining models in Chinese neglect two important aspects specific to the Chinese language: glyph and pinyin, which carry significant syntax and semantic information for language understanding. In this work, we propose ChineseBERT, which incorporates both the glyph and pinyin information of Chinese characters into language model pretraining. The glyph embedding is obtained based on different fonts of a Chinese character, being able to capture character semantics from the visual features, and the pinyin embedding characterizes the pronunciation of Chinese characters, which handles the highly prevalent heteronym phenomenon in Chinese (the same character has different pronunciations with different meanings). Pretrained on large-scale unlabeled Chinese corpus, the proposed ChineseBERT model yields significant performance boost over baseline models with fewer training steps. The proposed model achieves new SOTA performances on a wide range of Chinese NLP tasks, including machine reading comprehension, natural language inference, text classification, sentence pair matching, and competitive performances in named entity recognition and word segmentation.
Relational VAE: A Continuous Latent Variable Model for Graph Structured Data

Graph Networks (GNs) enable the fusion of prior knowledge and relational reasoning with flexible function approximations. In this work, a general GN-based model is proposed which takes full advantage of the relational modeling capabilities of GNs and extends these to probabilistic modeling with Variational Bayes (VB). To that end, we combine complementary pre-existing approaches on VB for graph data and propose an approach that relies on graph-structured latent and conditioning variables. It is demonstrated that Neural Processes can also be viewed through the lens of the proposed model. We show applications on the problem of structured probability density modeling for simulated and real wind farm monitoring data, as well as on the meta-learning of simulated Gaussian Process data. We release the source code, along with the simulated datasets.
Entanglement spreading after local and extended excitations in a free-fermion chain

We study the time evolution of entanglement created by local or extended excitations upon the ground state of a free-fermion chain. A single particle or hole excitation produces a single bit of excess entropy for large times and subsystem lengths. In case of a double hole, some of the coherence between the excitations is preserved and the excess entropy becomes additive only for large hole separations. In contrast, the coherence is always lost for particle–hole excitations. Multiple hole excitations on a completely filled chain are also investigated. We find that for an extended contiguous hole the excess entropy scales logarithmically with the size, whereas the increase is linear for finite separations between the holes.
What can linear interpolation of neural network loss landscapes tell us?

Studying neural network loss landscapes provides insights into the nature of the underlying optimization problems. Unfortunately, loss landscapes are notoriously difficult to visualize in a human-comprehensible fashion. One common way to address this problem is to plot linear slices of the landscape, for example from the initial state of the network to the final state after optimization. On the basis of this analysis, prior work has drawn broader conclusions about the difficulty of the optimization problem. In this paper, we put inferences of this kind to the test, systematically evaluating how linear interpolation and final performance vary when altering the data, choice of initialization, and other optimizer and architecture design choices. Further, we use linear interpolation to study the role played by individual layers and substructures of the network. We find that certain layers are more sensitive to the choice of initialization, but that the shape of the linear path is not indicative of the changes in test accuracy of the model. Our results cast doubt on the broader intuition that the presence or absence of barriers when interpolating necessarily relates to the success of optimization.
Zero-shot Learning with Class Description Regularization

The purpose of generative Zero-shot learning (ZSL) is to learning from seen classes, transfer the learned knowledge, and create samples of unseen classes from the description of these unseen categories. To achieve better ZSL accuracies, models need to better understand the descriptions of unseen classes. We introduce a novel form of regularization that encourages generative ZSL models to pay more attention to the description of each category. Our empirical results demonstrate improvements over the performance of multiple state-of-the-art models on the task of generalized zero-shot recognition and classification when trained on textual description-based datasets like CUB and NABirds and attribute-based datasets like AWA2, aPY and SUN.
Evaluation of Thematic Coherence in Microblogs

Collecting together microblogs representing opinions about the same topics within the same timeframe is useful to a number of different tasks and practitioners. A major question is how to evaluate the quality of such thematic clusters. Here we create a corpus of microblog clusters from three different domains and time windows and define the task of evaluating thematic coherence. We provide annotation guidelines and human annotations of thematic coherence by journalist experts. We subsequently investigate the efficacy of different automated evaluation metrics for the task. We consider a range of metrics including surface level metrics, ones for topic model coherence and text generation metrics (TGMs). While surface level metrics perform well, outperforming topic coherence metrics, they are not as consistent as TGMs. TGMs are more reliable than all other metrics considered for capturing thematic coherence in microblog clusters due to being less sensitive to the effect of time windows.
Weakly Supervised Temporal Adjacent Network for Language Grounding

Temporal language grounding (TLG) is a fundamental and challenging problem for vision and language understanding. Existing methods mainly focus on fully supervised setting with temporal boundary labels for training, which, however, suffers expensive cost of annotation. In this work, we are dedicated to weakly supervised TLG, where multiple description sentences are given to an untrimmed video without temporal boundary labels. In this task, it is critical to learn a strong cross-modal semantic alignment between sentence semantics and visual content. To this end, we introduce a novel weakly supervised temporal adjacent network (WSTAN) for temporal language grounding. Specifically, WSTAN learns cross-modal semantic alignment by exploiting temporal adjacent network in a multiple instance learning (MIL) paradigm, with a whole description paragraph as input. Moreover, we integrate a complementary branch into the framework, which explicitly refines the predictions with pseudo supervision from the MIL stage. An additional self-discriminating loss is devised on both the MIL branch and the complementary branch, aiming to enhance semantic discrimination by self-supervising. Extensive experiments are conducted on three widely used benchmark datasets, i.e., ActivityNet-Captions, Charades-STA, and DiDeMo, and the results demonstrate the effectiveness of our approach.
No Detectable Kilonova Counterpart is Expected for O3 Neutron Star–Black Hole Candidates

We analyze the tidal disruption probability of potential neutron star–black hole (NSBH) merger gravitational-wave (GW) events, including GW190426_152155, GW190814, GW200105_162426, and GW200115_042309, detected during the third observing run of the LIGO/Virgo Collaboration and the detectability of kilonova emission in connection with these events. The posterior distributions of GW190814 and GW200105_162426 show that they must be plunging events, and hence no kilonova signal is expected from these events. With the stiffest NS equation of state allowed by the constraint of GW170817 taken into account, the probability that GW190426_152155 and GW200115_042309 can make tidal disruption is ∼24% and ∼3%, respectively. However, the predicted kilonova brightness is too faint to be detected for present follow-up search campaigns, which explains the lack of electromagnetic (EM) counterpart detection after triggers of these GW events. Based on the best-constrained population synthesis simulation results, we find that disrupted events account for only ≲20% of cosmological NSBH mergers, since most of the primary BHs could have low spins. The associated kilonovae for those disrupted events will still be difficult for LSST to discover after GW triggers in the future because of their low brightness and larger distances. For future GW-triggered multimessenger observations, potential short-duration gamma-ray bursts and afterglows are more probable EM counterparts of NSBH GW events.
Understanding Adversarial Attacks on Observations in Deep Reinforcement Learning

Deep reinforcement learning models are vulnerable to adversarial attacks that can decrease a victim's cumulative expected reward by manipulating the victim's observations. Despite the efficiency of previous optimization-based methods for generating adversarial noise in supervised learning, such methods might not be able to achieve the lowest cumulative reward since they do not explore the environmental dynamics in general. In this paper, we provide a framework to better understand the existing methods by reformulating the problem of adversarial attacks on reinforcement learning in the function space. Our reformulation generates an optimal adversary in the function space of the targeted attacks, repelling them via a generic two-stage framework. In the first stage, we train a deceptive policy by hacking the environment, and discover a set of trajectories routing to the lowest reward or the worst-case performance. Next, the adversary misleads the victim to imitate the deceptive policy by perturbing the observations. Compared to existing approaches, we theoretically show that our adversary is stronger under an appropriate noise level. Extensive experiments demonstrate our method's superiority in terms of efficiency and effectiveness, achieving the state-of-the-art performance in both Atari and MuJoCo environments.
A Conditional Splitting Framework for Efficient Constituency Parsing

We introduce a generic seq2seq parsing framework that casts constituency parsing problems (syntactic and discourse parsing) into a series of conditional splitting decisions. Our parsing model estimates the conditional probability distribution of possible splitting points in a given text span and supports efficient top-down decoding, which is linear in number of nodes. The conditional splitting formulation together with efficient beam search inference facilitate structural consistency without relying on expensive structured inference. Crucially, for discourse analysis we show that in our formulation, discourse segmentation can be framed as a special case of parsing which allows us to perform discourse parsing without requiring segmentation as a pre-requisite. Experiments show that our model achieves good results on the standard syntactic parsing tasks under settings with/without pre-trained representations and rivals state-of-the-art (SoTA) methods that are more computationally expensive than ours. In discourse parsing, our method outperforms SoTA by a good margin.
Magnetoelectric effects in superconductors due to spin-orbit scattering: Nonlinear 
σ
-model description

We suggest a generalization of nonlinear $\sigma$-model for diffusive superconducting systems to account for magnetoelectric effects due to spin-orbit scattering. In the leading orders of spin-orbit strength and gradient expansion it includes two additional terms responsible for the spin-Hall effect and the spin-current swapping. First, assuming a delta-correlated disorder we derive the new terms from the Keldysh path integral representation of the generating functional. Then we argue phenomenologically that they exhaust all invariants allowed in the effective action to the leading order in the spin-orbit coupling (SOC). Finally, the results are confirmed by a direct derivation of the saddle-point (Usadel) equation from the quantum kinetic equations in the presence of randomly distributed impurities with SOC. At this point we correct a recent derivation of the Usadel equation that includes magneto-electric effects and does not resort to the Born approximation.
Javanese Turmeric (Curcuma xanthorrhiza Roxb.): Ethnobotany, Phytochemistry, Biotechnology, and Pharmacological Activities

Curcuma xanthorrhiza Roxb. , locally famed as Temulawak, has been extensively utilized in Indonesia as medicinal and nutritional plants since immemorial time. The rhizome of this plant is an important ingredient for jamu formulation (Indonesian traditional medicine). C. xanthorrhiza is traditionally used to treat several ailments such as lack of appetite, stomach disorder, liver illness, constipation, bloody diarrhea, dysentery, arthritis, children's fevers, hypotriglyceridaemia, hemorrhoids, vaginal discharge, rheumatism, and skin eruptions. To date, over 40 active compounds, including terpenoids, curcuminoids, and other phenolic compounds, have been isolated and identified from C. xanthorrhiza Roxb. Some pharmacological tests reported that C. xanthorrhiza Roxb. has antioxidant, antimicrobial, anti-inflammatory, anticancer and antitumor, antidiabetic, and skincare and hepatoprotective properties. Efforts for biotechnologically production of C. xanthorrhiza have also been conducted, resulting in some micropropagation protocols of this plant. The current review focuses on the botanical description and distribution, ethnomedicinal uses, production and conservation status, phytochemical properties, and pharmacological activities of C. xanthorrhiza Roxb. to provide accurate and reliable data for future researches and commercialization purposes.
Mediators of extracellular matrix degradation and inflammation: A new team of possible biomarkers for oral squamous cell carcinoma stage

Oral cancer represents one of the most common types of cancer worldwide, with oral squamous cell carcinoma (OSCC) being the most frequently diagnosed. Cytokines play a crucial role in inflammation, apoptosis and metastasis. Interleukin (IL)-8 promotes the direct migration of inflammatory cells. IL-6 induces tumor cell proliferation, increases expression of invasiveness and angiogenetic factors or matrix metalloproteinases (MMPs), promoting metastasis. Tissue inhibitor of metalloproteinases (TIMPs) blocks the action of MMPs controlling extracellular matrix degradation and inhibiting metastasis. The aim of our study was to analyze the existence of correlations between inflammation markers (IL-6 and IL-8) and extracellular degradation protection markers such as TIMP-1 in OSCC tumors. Our study included 20 patients (12 females and 8 males) diagnosed with OSCC, recruited from January to April, 2020. IL-8, IL-6 and TIMP-1 levels were measured in the tumor cell lysates by ELISA technique, using relevant assay kits. Our results showed a positive and significant correlation between IL-6 and IL-8 (P=0.005, R=0.517) indicating that high IL-8 levels can be associated with high IL-6 levels. We also found a significant and high negative correlation (P<0.001, R=-0.673) between IL-6 and TIMP-1 and a significant and high negative correlation (P<0.001, R=-0.684) between IL-8 and TIMP-1 indicating that high levels of IL-8 and IL-6 are significantly associated with lower levels of TIMP-1. In conclusion, our study confirms the available literature data on IL-6 and IL-8 as potential markers for oral cancers such as OSCC and affect the tumor microenvironment by decreasing TIMPs. All three biomarkers included in this study have the potential to be used as detection or prognostic factors for oral cancer.
A generalized pustular psoriasis flare after CoronaVac COVID-19 vaccination: Case report

CoronoVac is a non-viable vaccine for severe acute respiratory syndrome coronavirus 2 (SARSCoV-2). Nowadays, there has been vaccination program for at-risk groups and older adults in Turkey. We here present 72-year-old male psoriasis patient who developed generalized pustular psoriasis flare after administration of CoronoVac. The COVID-PCR test was negative and investigations for flare etiology were all normal. He was first (to the best of our knowledge) psoriasis patient who developed an erythrodermic flare after the first dose of CoronaVac vaccine.
Mobile learning acceptance in social distancing during the COVID‐19 outbreak: The mediation effect of hedonic motivation

Abstract M‐learning is a trending field in educational organizations, companies, and also for individual study. However, in some regions the ampleness of the phenomenon is not quantifiable or comparable due to the lack of an adequate framework and reliable metrics. Our research intends to make a little light by assessing the degree of m‐learning adoption in students at a moment when face‐to‐face education moved suddenly online due to the COVID‐19 outbreak's rapid and unpredictable spread. A new model relying on the Unified Theory of Acceptance and Use of Technology (UTAUT) was built to investigate and explain relationships between constructs. It reveals the key factors affecting technology adoption by considering hedonic motivation a mediator instead of an exogenous variable as in UTAUT2. Based on an analysis of 311 higher education learners, the way how performance expectancy, effort expectancy, social influence, and facilitating conditions influence directly or indirectly the behavioral intention is researched. The analysis was conducted employing partial least squares structural equation modeling. The strongest relationship is between hedonic motivation and behavioral intention followed by the one between perceived effectiveness and hedonic motivation. Age, gender, and experience moderate the model's relationships. Research contributes to theory development by successfully adjusting the original UTAUT model. Results indicate that universities may offer learners an enjoyable m‐learning experience by activating social support groups and inserting gameplay elements into the learning system.
Characteristics, Classification, and Application of Stem Cells Derived from Human Teeth

Since mesenchymal stem cells derived from human teeth are characterized as having the properties of excellent proliferation, multilineage differentiation, and immune regulation. Dental stem cells exhibit fibroblast-like microscopic appearance and express mesenchymal markers, embryonic markers, and vascular markers but do not express hematopoietic markers. Dental stem cells are a mixed population with different sensitive markers, characteristics, and therapeutic effects. Single or combined surface markers are not only helpful for understanding the subpopulation of mixed stem cell populations according to cell function but also for improving the stable treatment effect of dental stem cells. Focusing on the discovery and characterization of stem cells isolated from human teeth over the past 20 years, this review outlines the effect of marker sorting on cell proliferation and differentiation ability and the assessment of the clinical application potential. Classified dental stem cells from markers and functional molecules can solve the problem of heterogeneity and ensure the efficacy of cell therapy strategies including dentistry, neurologic diseases, bone repair, and tissue engineering.
Efficiency of Treatment Plant and Drinking Water Quality Assessment from Source to Household, Gondar City, Northwest Ethiopia

Introduction Access to safe drinking water is essential to health, and it is a basic human right. However, drinking water treatment plant efficiency and its water quality are not well investigated in low-income countries including Ethiopia. Methods A laboratory-based cross-sectional study was conducted among 75 water samples. Data analysis was carried out using SPSS version 22 to generate descriptive statistics, and one-way ANOVA was used to test statistically significant difference. Results Physicochemical qualities of the water samples from tap water sources were found to be pH (6.88 ± 0.05), turbidity (5.15 ± 0.006 NTU), electrical conductivity (170.6 ± 0.1 μS/cm), residual chlorine (0.19 ± 0.003 mg/L), and fluoride (1.17 ± 0.009 mg/L). The removal efficiency of turbidity, total hardness, and nitrate was found to be 94.4%, 52.3%, and 88.7%, respectively. Removal efficiency of the treatment plant for total coliforms up to 91.6% (15 ± 0.26 CFU/100 mL in tap water) and faecal coliforms up to 99% (1.51 ± 0.03 CFU/100 mL in tap water) was recorded. Parameters of pH, temperature, and faecal coliform were statistically significant different at p < 0.05 in tap water source. The overall efficiency of the treatment plant (68.5%) and the water quality index (76) were recorded. Conclusion Based on the results, some of the investigated parameters of water quality (turbidity, residual chlorine, total coliform, and faecal coliform) were found to be not within the permissible limits of WHO guideline values for drinking water quality. The water quality index of the water samples was categorized under good water quality. To adequately treat drinking water and improve the treatment plant, adequate preliminary treatments like screening to reduce the incoming organic loading, proper chlorination of the drinking water system, and frequent monitoring and maintenance of the treatment plant system are required.
Antimicrobial Resistance of Escherichia coli from Broilers, Pigs, and Cattle in the Greater Kumasi Metropolis, Ghana

Globally, resistance to antimicrobial drugs in food animals is on the rise. Escherichia coli of livestock, though commensal in nature, serves as reservoir for antimicrobial resistance genes with the potential of disseminating them. This study sought to examine the antimicrobial resistance profiles of Escherichia coli in broilers, pigs, and cattle in the Kumasi Metropolis and undertake molecular characterisation of the resistances. Faecal E. coli isolates (n = 48) were obtained from 10 broiler farms, (n = 43) from 15 pig farms, and (n = 42) from cattle from the Kumasi Abattoir using standard bacteriological techniques. The Kirby–Bauer disc diffusion method was employed in testing the sensitivities of 133 E. coli isolates to 15 antimicrobials. All 48 isolates from broilers presented no resistance to amoxicillin/clavulanic acid and ceftiofur. A 100% resistance to meropenem was observed in pig and cattle isolates. Multidrug resistance (MDR) across animal groups was 95.8% (n = 46), 95.3% (n = 41), and 64.3% (n = 27) for broilers, pigs, and cattle, respectively. Twenty-eight isolates presenting phenotypic resistance to aminopenicillins and cephalosporins were screened for the presence of extended-spectrum beta-lactamase (ESBL) genes by PCR. One isolate from poultry and another from cattle tested positive for the blaCTX-M ESBL gene. There were no positives for the blaTEM and blaSHV ESBL genes. Commensal E. coli of food animal origin represents an important reservoir of antimicrobial resistance that transfers resistance to pathogenic and nonpathogenic microbes affecting humans and animals. There is an urgent need to institute routine surveillance for the establishment of the mechanisms and molecular orientation of resistance in these organisms.