Miracle-dz/UniFAR-FTUs huggingface dataset:
Referring to what you know and do not know: Making Referring Expression Generation Models Generalize To Unseen Entities

Data-to-text Natural Language Generation (NLG) is the computational process of generating natural language in the form of text or voice from non-linguistic data. A core micro-planning task within NLG is referring expression generation (REG), which aims to automatically generate noun phrases to refer to entities mentioned as discourse unfolds. A limitation of novel REG models is not being able to generate referring expressions to entities not encountered during the training process. To solve this problem, we propose two extensions to NeuralREG, a state-of-the-art encoder-decoder REG model. The first is a copy mechanism, whereas the second consists of representing the gender and type of the referent as inputs to the model. Drawing on the results of automatic and human evaluation as well as an ablation study using the WebNLG corpus, we contend that our proposal contributes to the generation of more meaningful referring expressions to unseen entities than the original system and related work. Code and all produced data are publicly available.
Plume dynamics structure the spatiotemporal activity of glomerular networks in the mouse olfactory bulb

Although mice locate resources using turbulent airborne odor plumes, the stochasticity and intermittency of fluctuating plumes create challenges for interpreting odor cues in natural environments. Population activity within the olfactory bulb (OB), is thought to process this complex spatial and temporal information, but how plume dynamics impact odor representation in this early stage of the mouse olfactory system is not known. Limitations in odor detection technology have made it impossible to measure plume fluctuations while simultaneously recording from the mouse’s brain. Thus, previous studies have measured OB activity following controlled odor pulses of varying profiles or frequencies, but this approach only captures a subset of features found within olfactory plumes. Adequately sampling this feature space is difficult given a lack of knowledge regarding which features the brain extracts during exposure to natural olfactory scenes. Here we measured OB responses to naturally fluctuating odor plumes using a miniature, adapted odor sensor combined with wide-field GCaMP6f signaling from the dendrites of mitral and tufted (MT) cells imaged in olfactory glomeruli of head-fixed mice. We precisely tracked plume dynamics and imaged glomerular responses to this fluctuating input, while varying flow conditions across a range of ethologically-relevant values. We found that a consistent portion of MT activity in glomeruli follows odor concentration dynamics, and the strongest responding glomeruli are the best at following fluctuations within odor plumes. Further, the reliability and average response magnitude of glomerular populations of MT cells are affected by the flow condition in which the animal samples the plume, with the fidelity of plume following by MT cells increasing in conditions of higher flow velocity where odor dynamics result in intermittent whiffs of stronger concentration. Thus, the flow environment in which an animal encounters an odor has a large-scale impact on the temporal representation of an odor plume in the OB. Additionally, across flow conditions odor dynamics are a major driver of activity in many glomerular networks. Taken together, these data demonstrate that plume dynamics structure olfactory representations in the first stage of odor processing in the mouse olfactory system.
Bi2Pt(hP9) by Low-Temperature Reduction of Bi13Pt3I7: Reinvestigation of the Crystal Structure and Chemical Bonding Analysis

Bi2Pt(hP9) or γ-Bi2Pt is a high-temperature modification, which is metastable below 420 °C. We obtained Bi2Pt(hP9) by reducing the layered bismuth subiodide Bi13Pt3I7 with a high excess of n-butyllithium at 70 °C. The crystals endure the heterogeneous reaction and the enormous mass loss. X-ray diffraction on a small single-crystal revealed that Bi2Pt(hP9) crystallizes in the acentric trigonal space group P31m (no. 157) with a = 657.30(7) pm and c = 616.65(7) pm. Although structure and stacking of the layers of edge-sharing [PtBi6/3] octahedra resemble the 1H-polytype of CdI2, Pt–Pt bonding interactions cause distortions that introduce polarity to the structure. Quantum chemical calculations followed by real-space bonding analysis reveal polar covalent bonding between bismuth and platinum atoms that is not limited to nearest neighbor atoms but is essentially delocalized. The Pt–Pt bond strength in Bi2Pt(hP9) is not much weaker than in the element.
Affective Determinants of Physical Activity: A Conceptual Framework and Narrative Review

The literature on affective determinants of physical activity (PA) is growing rapidly. The present paper aims to provide greater clarity regarding the definition and distinctions among the various affect-related constructs that have been examined in relation to PA. Affective constructs are organized according to the Affect and Health Behavior Framework (AHBF), including: (1) affective response (e.g., how one feels in response to PA behavior) to PA; (2) incidental affect (e.g., how one feels throughout the day, unrelated to the target behavior); (3) affect processing (e.g., affective associations, implicit attitudes, remembered affect, anticipated affective response, and affective judgments); and (4) affectively charged motivational states (e.g., intrinsic motivation, fear, and hedonic motivation). After defining each category of affective construct, we provide examples of relevant research showing how each construct may relate to PA behavior. We conclude each section with a discussion of future directions for research.
Field Evaluation of the Interferon Gamma Assay for Diagnosis of Tuberculosis in Water Buffalo (Bubalus bubalis) Comparing Four Interpretative Criteria

Bovine tuberculosis (bTB) is a worldwide zoonosis that affects many species of domestic and wild animals. Mycobaterium bovis is the main cause of infection in water buffalo (Bubalus bubalis) and bovines and is of great concern for human health and for buffalo producers in Italy. The bTB eradication programme is based on slaughterhouse surveillance and intradermal skin tests. Other in vivo diagnostic methods such as the interferon-gamma (IFN-γ) assay have been developed and are widely used in cattle to accelerate the elimination of bTB positive animals. The present study is the first to assess the use and performance of IFN-γ assays, which is used as an ancillary test for bTB diagnosis in water buffalo, and presents the results of a field-evaluation of the assay from 2012 to 2019 during the buffalo bTB eradication programme in Italy. The study involved 489 buffaloes with a positive result to the single intradermal tuberculin test (SITT). The IFN-γ assays and single intradermal comparative tuberculin test were used as confirmation tests. Then, a total of 458 buffaloes, reared on officially tuberculosis-free (OTF) herds, that were confirmed bTB-free for at least the last 6 years were subjected to IFN-γ testing. Furthermore, to evaluate the IFN-γ test in an OTF herd with Paratuberculosis (PTB) infection, 103 buffaloes were subjected to SITT and IFN-γ test simultaneously. Four interpretative criteria were used, and the IFN-γ test showed high levels of accuracy, with sensitivity levels between 75.3% (CI 95% 71.2–79.0%) and 98.4% (CI 95% 96.7–99.4%) and specificity levels between 94.3% (CI 95% 91.2–96.50%) and 98.5% (CI 95% 96.9–99.4%), depending on the criterion used. Finally, in the OTF herd with PTB infection, in buffalo, the IFN-γ test displayed high specificity values according to all 4 interpretative criteria, with specificity levels between 96.7% (CI 95% 88.4–99.5%) and 100% (CI 95% 96.2–100%), while SITT specificity proved unsatisfactory, with a level of 45.3% (CI 95% 35.0–55.7%). Our results showed that the IFN-γ test in the buffalo species could reach high Sensitivity and Specificity values, and that the level of Sensitivity and Specificity could be chosen based on the interpretative criterion and the antigens used depending on the health status of the herd and the epidemiological context of the territory. The IFN-γ test and the use of different interpretative criteria proved to be useful to implement bTB diagnostic strategies in buffalo herds, with the possibility of a flexible use of the assay.
Evaluating Machine Learning Algorithms in Predicting Financial Restatements

The identification of financial statements which were willfully or accidentally misstated is important for all involved parties: Investors can expect improved returns, analysts preserve their reputation and auditors avoid costly litigation. In this paper, we chose six state-of-the-art machine learning methods which we analyze in their ability to detect misstatements. In addition to that we investigated the influence of a FeatureBoost algorithm, namely XG-Boost to all of the six machine learning methods. The underlying data is retrieved from Eikon, a financial database provided by Refinitiv (former provided by Thomson Reuters). In order to take out our experiments we chose about 9000 US-companies and 757 features per year over ten years. We offer six definitions of ground truth of which three can be calculated with the data extracted from the Eikon database. The other three definitions are created with the help of an external data source provided by Audit Analytics Europe. Our well structured results give an overview on the performance of current machine learning methods in order to identify misstatements.
Whole-Genome Sequencing: An Effective Strategy for Insertion Information Analysis of Foreign Genes in Transgenic Plants

Molecular characterization is a key step in the risk assessment of genetically modified organisms (GMOs) for regulatory approval. Herein, we describe a method for analyzing copy number, insertion loci, and flanking sequences through whole-genome sequencing (WGS) and bioinformatics. Comprehensive molecular characterization of G2-6 transgenic rice was performed using this pipeline. The results showed that one copy of the foreign gene was inserted into rice chromosome 8. There was no vector backbone insertion but an unexpected insertion and DNA rearrangement at the 3′ end of the T-DNA. We also obtained the 5′ and 3′ flanking sequences of the T-DNA. Our results suggested that the use of a combination of WGS and bioinformatics is an effective strategy for the molecular characterization of GMOs.
A Model of Aerobic and Anaerobic Metabolism of Hydrogen in the Extremophile Acidithiobacillus ferrooxidans

Hydrogen can serve as an electron donor for chemolithotrophic acidophiles, especially in the deep terrestrial subsurface and geothermal ecosystems. Nevertheless, the current knowledge of hydrogen utilization by mesophilic acidophiles is minimal. A multi-omics analysis was applied on Acidithiobacillus ferrooxidans growing on hydrogen, and a respiratory model was proposed. In the model, [NiFe] hydrogenases oxidize hydrogen to two protons and two electrons. The electrons are used to reduce membrane-soluble ubiquinone to ubiquinol. Genetically associated iron-sulfur proteins mediate electron relay from the hydrogenases to the ubiquinone pool. Under aerobic conditions, reduced ubiquinol transfers electrons to either cytochrome aa3 oxidase via cytochrome bc1 complex and cytochrome c4 or the alternate directly to cytochrome bd oxidase, resulting in proton efflux and reduction of oxygen. Under anaerobic conditions, reduced ubiquinol transfers electrons to outer membrane cytochrome c (ferrireductase) via cytochrome bc1 complex and a cascade of electron transporters (cytochrome c4, cytochrome c552, rusticyanin, and high potential iron-sulfur protein), resulting in proton efflux and reduction of ferric iron. The proton gradient generated by hydrogen oxidation maintains the membrane potential and allows the generation of ATP and NADH. These results further clarify the role of extremophiles in biogeochemical processes and their impact on the composition of the deep terrestrial subsurface.
Shaking Table Test on Dynamic Response of Bedding Rock Slopes With Weak Structural Plane Under Earthquake

Taking a bedding rock slope with weak structural plane as the prototype, a shaking table test with a similarity ratio of 1:10 is designed and carried out. By analyzing the acceleration and displacement responses at different positions of the slope, the seismic response and instability mechanism of rock bedding slope under different seismic amplitudes, frequencies, and durations are studied. Before the failure of the slope, the rock bedding slope shows an obvious “elevation effect” and “surface effect” under the action of Wenchuan Wolong earthquake wave with different amplitudes. With the increase of the amplitude of the input seismic wave, the elevation effect and the surface effect gradually weaken. When the amplitude of the seismic wave reaches 0.9 g, the rock bedding slope begins to show damage, which demonstrates that the difference of PGA amplification coefficients on both sides of the weak structural plane increases significantly. Compared with the Kobe seismic wave and Wenchuan Wolong seismic wave, the excellent frequency of EL Centro seismic wave is closer to the first-order natural frequency of slope model and produces resonance phenomenon, which leads to the elevation effect of PGA amplification coefficient more significantly. Through the analysis of the instability process of rock bedding slope, it can be found that the failure mechanism of the slope can be divided into two stages: the formation of sliding shear plane and the overall instability of the slope.
Embedding Semantic Taxonomies

A common step in developing an understanding of a vertical domain, e.g. shopping, dining, movies, medicine, etc., is curating a taxonomy of categories specific to the domain. These human created artifacts have been the subject of research in embeddings that attempt to encode aspects of the partial ordering property of taxonomies. We compare Box Embeddings, a natural containment representation of category taxonomies, to partial-order embeddings and a baseline Bayes Net, in the context of representing the Medical Subject Headings (MeSH) taxonomy given a set of 300K PubMed articles with subject labels from MeSH. We deeply explore the experimental properties of training box embeddings, including preparation of the training data, sampling ratios and class balance, initialization strategies, and propose a fix to the original box objective. We then present first results in using these techniques for representing a bipartite learning problem (i.e. collaborative filtering) in the presence of taxonomic relations within each partition, inferring disease (anatomical) locations from their use as subject labels in journal articles. Our box model substantially outperforms all baselines for taxonomic reconstruction and bipartite relationship experiments. This performance improvement is observed both in overall accuracy and the weighted spread by true taxonomic depth.
NUIG at SemEval-2020 Task 12: Pseudo Labelling for Offensive Content Classification

This work addresses the classification problem defined by sub-task A (English only) of the OffensEval 2020 challenge. We used a semi-supervised approach to classify given tweets into an offensive (OFF) or not-offensive (NOT) class. As the OffensEval 2020 dataset is loosely labelled with confidence scores given by unsupervised models, we used last year’s offensive language identification dataset (OLID) to label the OffensEval 2020 dataset. Our approach uses a pseudo-labelling method to annotate the current dataset. We trained four text classifiers on the OLID dataset and the classifier with the highest macro-averaged F1-score has been used to pseudo label the OffensEval 2020 dataset. The same model which performed best amongst four text classifiers on OLID dataset has been trained on the combined dataset of OLID and pseudo labelled OffensEval 2020. We evaluated the classifiers with precision, recall and macro-averaged F1-score as the primary evaluation metric on the OLID and OffensEval 2020 datasets. This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http://creativecommons.org/licenses/by/4.0/.
SOME: Reference-less Sub-Metrics Optimized for Manual Evaluations of Grammatical Error Correction

We propose a reference-less metric trained on manual evaluations of system outputs for grammatical error correction (GEC). Previous studies have shown that reference-less metrics are promising; however, existing metrics are not optimized for manual evaluations of the system outputs because no dataset of the system output exists with manual evaluation. This study manually evaluates outputs of GEC systems to optimize the metrics. Experimental results show that the proposed metric improves correlation with the manual evaluation in both system- and sentence-level meta-evaluation. Our dataset and metric will be made publicly available.
ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction

Distantly Supervised Relation Extraction (DSRE) has proven to be effective to find relational facts from texts, but it still suffers from two main problems: the wrong labeling problem and the long-tail problem. Most of the existing approaches address these two problems through flat classification, which lacks hierarchical information of relations. To leverage the informative relation hierarchies, we formulate DSRE as a hierarchical classification task and propose a novel hierarchical classification framework, which extracts the relation in a top-down manner. Specifically, in our proposed framework, 1) we use a hierarchically-refined representation method to achieve hierarchy-specific representation; 2) a top-down classification strategy is introduced instead of training a set of local classifiers. The experiments on NYT dataset demonstrate that our approach significantly outperforms other state-of-the-art approaches, especially for the long-tail problem.
SHIKEBLCU at SemEval-2020 Task 2: An External Knowledge-enhanced Matrix for Multilingual and Cross-Lingual Lexical Entailment

Lexical entailment recognition plays an important role in tasks like Question Answering and Machine Translation. As important branches of lexical entailment, predicting multilingual and cross-lingual lexical entailment (LE) are two subtasks of SemEval2020 Task2. In previous monolingual LE studies, researchers leverage external linguistic constraints to transform word embeddings for LE relation. In our system, we expand the number of external constraints in multiple languages to obtain more specialised multilingual word embeddings. For the cross-lingual subtask, we apply a bilingual word embeddings mapping method in the model. The mapping method takes specialised embeddings as inputs and is able to retain the embeddings’ LE features after operations. Our results for multilingual subtask are about 20% and 10% higher than the baseline in graded and binary prediction respectively.
distinct: a novel approach to differential distribution analyses

We present distinct, a general method for differential analysis of full distributions that is well suited to applications on single-cell data, such as single-cell RNA sequencing and high-dimensional flow or mass cytometry data. High-throughput single-cell data reveal an unprecedented view of cell identity and allow complex variations between conditions to be discovered; nonetheless, most methods for differential expression target differences in the mean and struggle to identify changes where the mean is only marginally affected. distinct is based on a hierarchical non-parametric permutation approach and, by comparing empirical cumulative distribution functions, identifies both differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean. We performed extensive bench-marks across both simulated and experimental datasets from single-cell RNA sequencing and mass cytometry data, where distinct shows favourable performance, identifies more differential patterns than competitors, and displays good control of false positive and false discovery rates. distinct is available as a Bioconductor R package.
Glucolipotoxicity-induced Mig6 desensitizes EGFR signaling and promotes pancreatic beta cell death

A loss of functional beta cell mass is a final etiological event in the development of frank type 2 diabetes (T2D). To preserve or expand beta cells and therefore treat/prevent T2D, growth factors have been considered therapeutically but have largely failed to achieve robust clinical success. The molecular mechanisms preventing the activation of mitogenic signaling pathways from maintaining functional beta cell mass during the development of T2D remain unknown. We speculated that endogenous negative effectors of mitogenic signaling cascades impede beta cell survival/expansion. Thus, we tested the hypothesis that a stress-inducible epidermal growth factor receptor (EGFR) inhibitor, Mitogen-inducible gene 6 (Mig6), regulates beta cell fate in a T2D milieu. To this end, we determined that: 1) glucolipotoxicity (GLT) induces Mig6, thereby blunting EGFR signaling cascades, and 2) Mig6 mediates molecular events regulating beta cell survival/death. We discovered that GLT impairs EGFR activation, and Mig6 is elevated in human islets from T2D donors as well as GLT-treated rodent islets and 832/13 INS-1 beta cells. Mig6 is essential for GLT-induced EGFR desensitization, as Mig6 suppression rescued the GLT-impaired EGFR and ERK1/2 activation. Further, Mig6 mediated EGFR but not insulin-like growth factor-1 receptor nor hepatocyte growth factor receptor activity in beta cells. Finally, we identified that elevated Mig6 augmented beta cell apoptosis, as Mig6 suppression reduced apoptosis during GLT. In conclusion, we established that T2D and GLT induce Mig6 in pancreatic beta cells. The elevated Mig6 desensitizes EGFR signaling and induces beta cell death. Our findings suggest that Mig6 could be a novel therapeutic target for T2D, as blocking Mig6 could possibly enhance mitogenic signaling cascades in a diabetic milieu to promote beta cell survival and prevent beta cell death.
SQL Generation via Machine Reading Comprehension

Text-to-SQL systems offers natural language interfaces to databases, which can automatically generates SQL queries given natural language questions. On the WikiSQL benchmark, state-of- the-art text-to-SQL systems typically take a slot-filling approach by building several specialized models for each type of slot. Despite being effective, such modularized systems are complex and also fall short in jointly learning for different slots. To solve these problems, this paper proposes a novel approach that formulates the task as a question answering problem, where different slots are predicted by a unified machine reading comprehension (MRC) model. For this purpose, we use a BERT-based MRC model, which can also benefit from intermediate training on other MRC datasets. The proposed method can achieve competitive results on WikiSQL, suggesting it being a promising direction for text-to-SQL.