Miracle-dz/UniFAR-FTUs huggingface dataset:
Formation of subgalactic objects within two-component dark matter

We consider the Einstein--de Sitter universe dominated by two kinds of collisionless relics: neutrinos as hot dark matter, and X particles having negligible thermal velocity as cold dark matter (CDM). We assume . cap omega. /sub v/>. cap omega. /sub c/> or =. cap omega. /sub b/ = 0.05, which is referred to as the hybrid model. On scales larger than that of neutrino collisionless damping, neutrino fluctuations develop with time, producing large-scale neutrino overdense (NOD) and underdense (NUD) regions which evolve consequently to superclusters and huge voids, respectively. We calculate the growth of fluctuations of CDM and induced fluctuations of baryons on subgalactic scales in the NOD and NUD regions from the recombination epoch to the present. CDM fluctuations on subgalactic scales can grow faster in the NOD region than in the NUD region. It is shown that dwarf galaxies are preferably formed in NOD regions, induced by CDM potential wells; and subgalactic baryon fluctuations in NUD regions are subject to UV heating by QSOs before collapse and converted to expanding clouds unclustered in voids. These clouds are favorable candidates for Ly.cap alpha. absorbers with just the same neutral hydrogen column density as observed, irrespective of . cap omega. /sub c/. This scenario for the formation ofmore » subgalactic objects is successful if 0.15 or =. cap omega. /sub v/> or =0.6, assuming the constant curvature spectrum. It is another characteristic of the hybrid model that ordinary galaxies, including QSOs, are formed early from aggregations of dwarfs; and some galaxies can be born in voids in an environment of few dwarfs after the birth of galaxies in NOD regions. « less
The danger of feeling sorry for oneself: How coworker incivility diminishes job performance through perceived organizational isolation among self-pitying employees

The study examined how employees’ experience of resource-draining coworker incivility might undermine their job performance, with a focus on how this harmful process might be explained by perceptions of organizational isolation and moderated by susceptibility to self-pity. Three-wave survey data, collected among employees and their supervisors in various industries, indicated that an important reason that employees’ exposure to rude coworker treatment escalated into diminished performance outcomes was a belief that the employing organization was the source of their sense of abandonment. As a mediator, perceived organizational isolation exerted an especially prominent effect among employees who had a general tendency to pity themselves in difficult circumstances. Organizations accordingly can contain the risk that disrespectful coworker relationships translate into tarnished performance by discouraging employees to feel bad for themselves in the face of work-related hardships. JEL Classification: M50
A dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets

Structure-based generative chemistry aims to explore much bigger chemical space to design a ligand with high binding affinity to the target proteins; it is a critical step in de novo computer-aided drug discovery. Traditional in silico methods suffer from calculation inefficiency and the performances of existing machine learning methods could be bottlenecked by the auto-regressive sampling strategy. To address these concerns, we herein have developed a novel conditional deep generative model, PMDM, for 3D molecule generation fitting specified target proteins. PMDM incorporates a dual equivariant diffusion model framework to leverage the local and global molecular dynamics to generate 3D molecules in a one-shot fashion. By considering the conditioned protein semantic information and spatial information, PMDM is able to generate chemically and conformationally valid molecules which suitably fit pocket holes. We have conducted comprehensive experiments to demonstrate that PMDM can generate drug-like, synthesis-accessible, novel, and high-binding affinity molecules targeting specific proteins, outperforming the state-of-the-art (SOTA) models in terms of multiple evaluation metrics. In addition, we perform chemical space analysis for generated molecules and lead compound optimization for SARS-CoV-2 main protease (Mpro) by only utilizing three atoms as the seed fragment. The experimental results implicate that the structures of generated molecules are rational compared to the reference molecules, and PMDM can generate massive bioactive molecules highly binding to the targeted proteins which are not included in the training set.
Can a Fish Learn to Ride a Bicycle? Sensorimotor Adaptation to Destabilizing Dynamics in Weakly Electric Fish

Animals (including humans) can readily learn to compensate for destabilizing dynamics, such as balancing an object or riding a bicycle, but it remains unclear how the nervous system learns to compensate for such destabilizing dynamics, and what the beneﬁts of the newly learned control policies are. We examined how the weakly electric glass knifeﬁsh, Eigenmannia virescens , retunes its control system in the face of novel, destabilizing dynamics. Using a real-time feedback system, we measured ﬁsh swimming movements as they tracked a moving refuge, and fed the swimming movements back through novel dynamics to alter the motion of the very refuge they were tracking, creating an artiﬁcially destabilized reaﬀerent loop. We discovered that ﬁsh learned to retune their sensorimotor controllers as the artiﬁcially destabilizing feedback was gradually introduced. This retuned controller reduced sensitivity of the sensorimotor system to low-frequency disturbances, such as would arise from turbulence or motor noise. When the artiﬁcial feedback was extinguished, ﬁsh exhibited a clear after eﬀect, retaining their learned sensorimotor controllers for several minutes before washing out. This study sheds light on how the nervous system adapts to changing closed-loop dynamics and how those changes improve performance, and the after eﬀects suggest a plasticity-based mechanism reminiscent of cerebellar learning.
A common tolerance mechanism of bacterial biofilms to antibiotics

A deeper mechanistic understanding of antibiotics tolerance exhibited by bacterial biofilms is crucial for clinical treatment and drug development. Here, we found a common binary distribution for various antibiotics in biofilms, which was determined by glucose distribution. Antibiotic penetration would be accompanied by glucose leakage. Surprisingly, the biofilm periphery exhibited constant glucose consumption and antibiotics patterns after drug treatment, while planktonic bacteria did not. Spatial multi-omics revealed that activation of lipid metabolism was responsible for continuous glucose consumption, which was channeled to thicken the cell membrane and subsequently weakened antibiotic penetration, leading to a low drug concentration in the biofilm interior - a common mechanism underlying biofilm drug tolerance. We further revealed that the biofilm-specific activation of lipid metabolism was derived from slower drug penetration and longer response time of biofilms to antibiotics, owing to lower energy metabolism and membrane potential in biofilms compared to planktonic bacteria.
A Precision Liquid Nitrogen Boil‐Off Calorimeter and Its Application to Surface Energy Measurements

A liquid nitrogen boil‐off calorimeter has been developed and utilized for surface energy measurements. The calorimeter is an all metal, multiwalled chamber which incorporates a means of precisely manostating the calorimetric fluid. The calorimeter also incorporates means for reducing the background pressures in the reaction vessel to 10−8 Torr. This calorimeter was designed to accept heat inputs in the range from 50 to 200 J. An achieved measurement precision of 0.05% in energy permits discrimination of small differences in energy states of the reactions occurring within the calorimeter. The calorimeter has been used to determine the surface energy of a metal film formed by evaporation from a wire.
Th1 Cells Alter the Inflammatory Signature of IL-6 by Channeling STAT Transcription Factors to Alu-like Retroelements

Cytokines that signal via STAT1 and STAT3 transcription factors instruct decisions affecting tissue homeostasis, anti-microbial host defense, and inflammation-induced tissue injury. To understand the coordination of these activities, we applied RNA-seq, ChIP-seq, and ATAC-seq to identify the transcriptional output of STAT1 and STAT3 in peritoneal tissues during acute resolving inflammation and inflammation primed to drive fibrosis. Bioinformatics focussed on the transcriptional signature of the immuno-modulatory cytokine IL-6 in both settings and examined how pro-fibrotic IFNγ-secreting CD4+ T-cells altered the interpretation of STAT1 and STAT3 cytokine cues. In resolving inflammation, STAT1 and STAT3 cooperated to drive stromal gene expression affecting anti-microbial immunity and tissue homeostasis. The introduction of IFNγ-secreting CD4+ T-cells altered this transcriptional program and channeled STAT1 and STAT3 to a previously latent GAS motif in Alu-like elements. STAT1 and STAT3 binding to this conserved sequence revealed evidence of reciprocal cross-regulation and gene signatures relevant to pathophysiology. Thus, we propose that effector T-cells re-tune the transcriptional output of IL-6 by shaping a regulatory interplay between STAT1 and STAT3 in inflammation.
Pseudo-Relevance for Enhancing Document Representation

This paper studies how to enhance the document representation for the bi-encoder approach in dense document retrieval. The bi-encoder, separately encoding a query and a document as a single vector, is favored for high efficiency in large-scale information retrieval, compared to more effective but complex architectures. To combine the strength of the two, the multi-vector representation of documents for bi-encoder, such as ColBERT preserving all token embeddings, has been widely adopted. Our contribution is to reduce the size of the multi-vector representation, without compromising the effectiveness, supervised by query logs. Our proposed solution decreases the latency and the memory footprint, up to 8- and 3-fold, validated on MSMARCO and real-world search query logs.
SEEN: Structured Event Enhancement Network for Explainable Need Detection of Information Recall Assistance

When recalling life experiences, people often forget or confuse life events, which necessitates information recall services. Previous work on information recall focuses on providing such assistance reactively, i.e., by retrieving the life event of a given query. Proactively detecting the need for information recall services is rarely discussed. In this paper, we use a human-annotated life experience retelling dataset to detect the right time to trigger the information recall service. We propose a pilot model—structured event enhancement network (SEEN) that detects life event inconsistency, additional information in life events, and forgotten events. A fusing mechanism is also proposed to incorporate event graphs of stories and enhance the textual representations. To explain the need detection results, SEEN simultaneously provides support evidence by selecting the related nodes from the event graph. Experimental results show that SEEN achieves promising performance in detecting information needs. In addition, the extracted evidence can be served as complementary information to remind users what events they may want to recall.
aiML at the FinNLP-2022 ERAI Task: Combining Classification and Regression Tasks for Financial Opinion Mining

Identifying posts of high financial quality from opinions is of extraordinary significance for investors. Hence, this paper focuses on evaluating the rationales of amateur investors (ERAI) in a shared task, and we present our solutions. The pairwise comparison task aims at extracting the post that will trigger higher MPP and ML values from pairs of posts. The goal of the unsupervised ranking task is to find the top 10% of posts with higher MPP and ML values. We initially model the shared task as text classification and regression problems. We then propose a multi-learning approach applied by financial domain pre-trained models and multiple linear classifiers for factor combinations to integrate better relationships and information between training data. The official results have proved that our method achieves 48.28% and 52.87% for MPP and ML accuracy on pairwise tasks, 14.02% and -4.17% regarding unsupervised ranking tasks for MPP and ML. Our source code is available.
What Was Your Name Again? Interrogating Generative Conversational Models For Factual Consistency Evaluation

Generative conversational agents are known to suffer from problems like inconsistency and hallucination, and a big challenge in studying these issues remains evaluation: they are not properly reflected in common text generation metrics like perplexity or BLEU, and alternative implicit methods like semantic similarity or NLI labels can be misguided when few specific tokens are decisive. In this work we propose ConsisTest; a factual consistency benchmark including both WH and Y/N questions based on PersonaChat, along with a hybrid evaluation pipeline which aims to get the best of symbolic and sub-symbolic methods. Using these and focusing on pretrained generative models like BART, we provide detailed statistics and analysis on how the model’s consistency is affected by variations in question and context.
Global Span Selection for Named Entity Recognition

Named Entity Recognition (NER) is an important task in Natural Language Processing with applications in many domains. In this paper, we describe a novel approach to named entity recognition, in which we output a set of spans (i.e., segmentations) by maximizing a global score. During training, we optimize our model by maximizing the probability of the gold segmentation. During inference, we use dynamic programming to select the best segmentation under a linear time complexity. We prove that our approach outperforms CRF and semi-CRF models for Named Entity Recognition. We will make our code publicly available.
Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence

Multi-intent detection and slot filling joint model attracts more and more attention since it can handle multi-intent utterances, which is closer to complex real-world scenarios. Most existing joint models rely entirely on the training procedure to obtain the implicit correlation between intents and slots. However, they ignore the fact that leveraging the rich global knowledge in the corpus can determine the intuitive and explicit correlation between intents and slots. In this paper, we aim to make full use of the statistical co-occurrence frequency between intents and slots as prior knowledge to enhance joint multiple intent detection and slot filling. To be specific, an intent-slot co-occurrence graph is constructed based on the entire training corpus to globally discover correlation between intents and slots. Based on the global intent-slot co-occurrence, we propose a novel graph neural network to model the interaction between the two subtasks. Experimental results on two public multi-intent datasets demonstrate that our approach outperforms the state-of-the-art models.
Directions for NLP Practices Applied to Online Hate Speech Detection

Addressing hate speech in online spaces has been conceptualized as a classification task that uses Natural Language Processing (NLP) techniques. Through this conceptualization, the hate speech detection task has relied on common conventions and practices from NLP. For instance, inter-annotator agreement is conceptualized as a way to measure dataset quality and certain metrics and benchmarks are used to assure model generalization. However, hate speech is a deeply complex and situated concept that eludes such static and disembodied practices. In this position paper, we critically reflect on these methodologies for hate speech detection, we argue that many conventions in NLP are poorly suited for the problem and encourage researchers to develop methods that are more appropriate for the task.
A Generative Model for End-to-End Argument Mining with Reconstructed Positional Encoding and Constrained Pointer Mechanism

Argument mining (AM) is a challenging task as it requires recognizing the complex argumentation structures involving multiple subtasks.To handle all subtasks of AM in an end-to-end fashion, previous works generally transform AM into a dependency parsing task.However, such methods largely require complex pre- and post-processing to realize the task transformation.In this paper, we investigate the end-to-end AM task from a novel perspective by proposing a generative framework, in which the expected outputs of AM are framed as a simple target sequence. Then, we employ a pre-trained sequence-to-sequence language model with a constrained pointer mechanism (CPM) to model the clues for all the subtasks of AM in the light of the target sequence. Furthermore, we devise a reconstructed positional encoding (RPE) to alleviate the order biases induced by the autoregressive generation paradigm.Experimental results show that our proposed framework achieves new state-of-the-art performance on two AM benchmarks.
Findings of the Word-Level AutoCompletion Shared Task in WMT 2022

Recent years have witnessed rapid advancements in machine translation, but the state-of-the-art machine translation system still can not satisfy the high requirements in some rigorous translation scenarios. Computer-aided translation (CAT) provides a promising solution to yield a high-quality translation with a guarantee. Unfortunately, due to the lack of popular benchmarks, the research on CAT is not well developed compared with machine translation. In this year, we hold a new shared task called Word-level AutoCompletion (WLAC) for CAT in WMT. Specifically, we introduce some resources to train a WLAC model, and particularly we collect data from CAT systems as a part of test data for this shared task. In addition, we employ both automatic and human evaluations to measure the performance of the submitted systems, and our final evaluation results reveal some findings for the WLAC task.
Identifying Condescending Language: A Tale of Two Distinct Phenomena?

Patronizing and condescending language is characterized, among others, by its subtle nature. It thus seems reasonable to assume that detecting condescending language in text would be harder than detecting more explicitly harmful language, such as hate speech. However, the results of a SemEval-2022 Task devoted to this topic paint a different picture, with the top-performing systems achieving remarkably strong results. In this paper, we analyse the surprising effectiveness of standard text classification methods in more detail. In particular, we highlight the presence of two rather different types of condescending language in the dataset from the SemEval task. Some inputs are condescending because of the way they talk about a particular subject, i.e. condescending language in this case is a linguistic phenomenon, which can, in principle, be learned from training examples. However, other inputs are condescending because of the nature of what is said, rather than the way in which it is expressed, e.g. by emphasizing stereotypes about a given community. In such cases, our ability to detect condescending language, with current methods, largely depends on the presence of similar examples in the training data.
DRLK: Dynamic Hierarchical Reasoning with Language Model and Knowledge Graph for Question Answering

In recent years, Graph Neural Network (GNN) approaches with enhanced knowledge graphs (KG) perform well in question answering (QA) tasks. One critical challenge is how to effectively utilize interactions between the QA context and KG. However, existing work only adopts the identical QA context representation to interact with multiple layers of KG, which results in a restricted interaction. In this paper, we propose DRLK (Dynamic Hierarchical Reasoning with Language Model and Knowledge Graphs), a novel model that utilizes dynamic hierarchical interactions between the QA context and KG for reasoning. DRLK extracts dynamic hierarchical features in the QA context, and performs inter-layer and intra-layer interactions on each iteration, allowing the KG representation to be grounded with the hierarchical features of the QA context. We conduct extensive experiments on four benchmark datasets in medical QA and commonsense reasoning. The experimental results demonstrate that DRLK achieves state-of-the-art performances on two benchmark datasets and performs competitively on the others.
A Hybrid Knowledge and Transformer-Based Model for Event Detection with Automatic Self-Attention Threshold, Layer and Head Selection

Event and argument role detection are frequently conceived as separate tasks. In this work we conceive both processes as one taskin a hybrid event detection approach. Its main component is based on automatic keyword extraction (AKE) using the self-attention mechanism of a BERT transformer model. As a bottleneck for AKE is defining the threshold of the attention values, we propose a novel method for automatic self-attention thresholdselection. It is fueled by core event information, or simply the verb and its arguments as the backbone of an event. These are outputted by a knowledge-based syntactic parser. In a secondstep the event core is enriched with other semantically salient words provided by the transformer model. Furthermore, we propose an automatic self-attention layer and head selectionmechanism, by analyzing which self-attention cells in the BERT transformer contribute most to the hybrid event detection and which linguistic tasks they represent. This approach was integrated in a pipeline event extraction approachand outperforms three state of the art multi-task event extraction methods.
Generating Coherent Narratives with Subtopic Planning to Answer How-to Questions

Answering how-to questions remains a major challenge in question answering research. A vast number of narrow, long-tail questions cannot be readily answered using a search engine. Moreover, there is little to no annotated data available to develop such systems. This paper makes a first attempt at generating coherent, long-form answers for how-to questions. We propose new architectures, consisting of passage retrieval, subtopic planning and narrative generation, to consolidate multiple relevant passages into a coherent, explanatory answer. Our subtopic planning module aims to produce a set of relevant, diverse subtopics that serve as the backbone for answer generation to improve topic coherence. We present extensive experiments on a WikiHow dataset repurposed for long-form question answering. Empirical results demonstrate that generating narratives to answer how-to questions is a challenging task. Nevertheless, our architecture incorporated with subtopic planning can produce high-quality, diverse narratives evaluated using automatic metrics and human assessment.