Miracle-dz/UniFAR-FTUs huggingface dataset:
Compounding Geometric Operations for Knowledge Graph Completion

Geometric transformations including translation, rotation, and scaling are commonly used operations in image processing. Besides, some of them are successfully used in developing effective knowledge graph embedding (KGE). Inspired by the synergy, we propose a new KGE model by leveraging all three operations in this work. Since translation, rotation, and scaling operations are cascaded to form a composite one, the new model is named CompoundE. By casting CompoundE in the framework of group theory, we show that quite a few distanced-based KGE models are special cases of CompoundE. CompoundE extends the simple distance-based scoring functions to relation-dependent compound operations on head and/or tail entities. To demonstrate the effectiveness of CompoundE, we perform three prevalent KG prediction tasks including link prediction, path query answering, and entity typing, on a range of datasets. CompoundE outperforms extant models consistently, demonstrating its effectiveness and flexibility.
Syntax and Geometry of Information

This paper presents an information-theoretical model of syntactic generalization. We study syntactic generalization from the perspective of the capacity to disentangle semantic and structural information, emulating the human capacity to assign a grammaticality judgment to semantically nonsensical sentences. In order to isolate the structure, we propose to represent the probability distribution behind a corpus as the product of the probability of a semantic context and the probability of a structure, the latter being independent of the former. We further elaborate the notion of abstraction as a relaxation of the property of independence. It is based on the measure of structural and contextual information for a given representation. We test abstraction as an optimization objective on the task of inducing syntactic categories from natural language data and show that it significantly outperforms alternative methods. Furthermore, we find that when syntax-unaware optimization objectives succeed in the task, their success is mainly due to an implicit disentanglement process rather than to the model structure. On the other hand, syntactic categories can be deduced in a principled way from the independence between structure and context.
BREAK: Breaking the Dialogue State Tracking Barrier with Beam Search and Re-ranking

Despite the recent advances in dialogue state tracking (DST), the joint goal accuracy (JGA) of the existing methods on MultiWOZ 2.1 still remains merely 60%. In our preliminary error analysis, we find that beam search produces a pool of candidates that is likely to include the correct dialogue state. Motivated by this observation, we introduce a novel framework, called BREAK (Beam search and RE-rAnKing), that achieves outstanding performance on DST. BREAK performs DST in two stages: (i) generating k-best dialogue state candidates with beam search and (ii) re-ranking the candidates to select the correct dialogue state. This simple yet powerful framework shows state-of-the-art performance on all versions of MultiWOZ and M2M datasets. Most notably, we push the joint goal accuracy to 80-90% on MultiWOZ 2.1-2.4, which is an improvement of 23.6%, 26.3%, 21.7%, and 10.8% over the previous best-performing models, respectively. The data and code will be available at https://github.com/tony-won/DST-BREAK
Enhancing Event Causality Identification with Counterfactual Reasoning

Existing methods for event causality identification (ECI) focus on mining potential causal signals, i.e., causal context keywords and event pairs. However, causal signals are ambiguous, which may lead to the context-keywords bias and the event-pairs bias.To solve this issue, we propose the counterfactual reasoning that explicitly estimates the influence of context keywords and event pairs in training, so that we are able to eliminate the biases in inference.Experiments are conducted on two datasets, the result demonstrates the effectiveness of our method.
TemplateGEC: Improving Grammatical Error Correction with Detection Template

Grammatical error correction (GEC) can be divided into sequence-to-edit (Seq2Edit) and sequence-to-sequence (Seq2Seq) frameworks, both of which have their pros and cons. To utilize the strengths and make up for the shortcomings of these frameworks, this paper proposes a novel method, TemplateGEC, which capitalizes on the capabilities of both Seq2Edit and Seq2Seq frameworks in error detection and correction respectively. TemplateGEC utilizes the detection labels from a Seq2Edit model, to construct the template as the input. A Seq2Seq model is employed to enforce consistency between the predictions of different templates by utilizing consistency learning. Experimental results on the Chinese NLPCC18, English BEA19 and CoNLL14 benchmarks show the effectiveness and robustness of TemplateGEC.Further analysis reveals the potential of our method in performing human-in-the-loop GEC. Source code and scripts are available at https://github.com/li-aolong/TemplateGEC.
Cross-Domain Data Augmentation with Domain-Adaptive Language Modeling for Aspect-Based Sentiment Analysis

Cross-domain Aspect-Based Sentiment Analysis (ABSA) aims to leverage the useful knowledge from a source domain to identify aspect-sentiment pairs in sentences from a target domain. To tackle the task, several recent works explore a new unsupervised domain adaptation framework, i.e., Cross-Domain Data Augmentation (CDDA), aiming to directly generate much labeled target-domain data based on the labeled source-domain data. However, these CDDA methods still suffer from several issues: 1) preserving many source-specific attributes such as syntactic structures; 2) lack of fluency and coherence; 3) limiting the diversity of generated data. To address these issues, we propose a new cross-domain Data Augmentation approach based on Domain-Adaptive Language Modeling named DA^2LM, which contains three stages: 1) assigning pseudo labels to unlabeled target-domain data; 2) unifying the process of token generation and labeling with a Domain-Adaptive Language Model (DALM) to learn the shared context and annotation across domains; 3) using the trained DALM to generate labeled target-domain data. Experiments show that DA^2LM consistently outperforms previous feature adaptation and CDDA methods on both ABSA and Aspect Extraction tasks. The source code is publicly released at https://github.com/NUSTM/DALM.
What is the Real Intention behind this Question? Dataset Collection and Intention Classification

Asking and answering questions are inseparable parts of human social life. The primary purposes of asking questions are to gain knowledge or request help which has been the subject of question-answering studies. However, questions can also reflect negative intentions and include implicit offenses, such as highlighting one’s lack of knowledge or bolstering an alleged superior knowledge, which can lead to conflict in conversations; yet has been scarcely researched. This paper is the first study to introduce a dataset (Question Intention Dataset) that includes questions with positive/neutral and negative intentions and the underlying intention categories within each group. We further conduct a meta-analysis to highlight tacit and apparent intents. We also propose a classification method using Transformers augmented by TF-IDF-based features and report the results of several models for classifying the main intention categories. We aim to highlight the importance of taking intentions into account, especially implicit and negative ones, to gain insight into conflict-evoking questions and better understand human-human communication on the web for NLP applications.
Cross-lingual Science Journalism: Select, Simplify and Rewrite Summaries for Non-expert Readers

Automating Cross-lingual Science Journalism (CSJ) aims to generate popular science summaries from English scientific texts for non-expert readers in their local language. We introduce CSJ as a downstream task of text simplification and cross-lingual scientific summarization to facilitate science journalists’ work. We analyze the performance of possible existing solutions as baselines for the CSJ task. Based on these findings, we propose to combine the three components - SELECT, SIMPLIFY and REWRITE (SSR) to produce cross-lingual simplified science summaries for non-expert readers. Our empirical evaluation on the Wikipedia dataset shows that SSR significantly outperforms the baselines for the CSJ task and can serve as a strong baseline for future work. We also perform an ablation study investigating the impact of individual components of SSR. Further, we analyze the performance of SSR on a high-quality, real-world CSJ dataset with human evaluation and in-depth analysis, demonstrating the superior performance of SSR for CSJ.
IDRISI-RA: The First Arabic Location Mention Recognition Dataset of Disaster Tweets

Extracting geolocation information from social media data enables effective disaster management, as it helps response authorities; for example, in locating incidents for planning rescue activities, and affected people for evacuation. Nevertheless, geolocation extraction is greatly understudied for the low resource languages such as Arabic. To fill this gap, we introduce IDRISI-RA, the first publicly-available Arabic Location Mention Recognition (LMR) dataset that provides human- and automatically-labeled versions in order of thousands and millions of tweets, respectively. It contains both location mentions and their types (e.g., district, city). Our extensive analysis shows the decent geographical, domain, location granularity, temporal, and dialectical coverage of IDRISI-RA. Furthermore, we establish baselines using the standard Arabic NER models and build two simple, yet effective, LMR models. Our rigorous experiments confirm the need for developing specific models for Arabic LMR in the disaster domain. Moreover, experiments show the promising domain and geographical generalizability of IDRISI-RA under zero-shot learning.
Making Language Models Better Reasoners with Step-Aware Verifier

Few-shot learning is a challenging task that requires language models to generalize from limited examples. Large language models like GPT-3 and PaLM have made impressive progress in this area, but they still face difficulties in reasoning tasks such as GSM8K, a benchmark for arithmetic problems. To improve their reasoning skills, previous work has proposed to guide the language model with prompts that elicit a series of reasoning steps before giving the final answer, achieving a significant improvement on GSM8K from 17.9% to 58.1% in problem-solving rate. In this paper, we present DiVeRSe (Diverse Verifier on Reasoning Step), a novel approach that further enhances the reasoning capability of language models. DiVeRSe has three main components: first, it generates diverse prompts to explore different reasoning paths for the same question; second, it uses a verifier to filter out incorrect answers based on a weighted voting scheme; and third, it verifies each reasoning step individually instead of the whole chain. We evaluate DiVeRSe on the latest language model code-davinci-002 and show that it achieves new state-of-the-art results on six of eight reasoning benchmarks (e.g., GSM8K 74.4% to 83.2%).
Zshot: An Open-source Framework for Zero-Shot Named Entity Recognition and Relation Extraction

The Zero-Shot Learning (ZSL) task pertains to the identification of entities or relations in texts that were not seen during training. ZSL has emerged as a critical research area due to the scarcity of labeled data in specific domains, and its applications have grown significantly in recent years. With the advent of large pretrained language models, several novel methods have been proposed, resulting in substantial improvements in ZSL performance. There is a growing demand, both in the research community and industry, for a comprehensive ZSL framework that facilitates the development and accessibility of the latest methods and pretrained models.In this study, we propose a novel ZSL framework called Zshot that aims to address the aforementioned challenges. Our primary objective is to provide a platform that allows researchers to compare different state-of-the-art ZSL methods with standard benchmark datasets. Additionally, we have designed our framework to support the industry with readily available APIs for production under the standard SpaCy NLP pipeline. Our API is extendible and evaluable, moreover, we include numerous enhancements such as boosting the accuracy with pipeline ensembling and visualization utilities available as a SpaCy extension.
What does a Text Classifier Learn about Morality? An Explainable Method for Cross-Domain Comparison of Moral Rhetoric

Moral rhetoric influences our judgement. Although social scientists recognize moral expression as domain specific, there are no systematic methods for analyzing whether a text classifier learns the domain-specific expression of moral language or not. We propose Tomea, a method to compare a supervised classifier’s representation of moral rhetoric across domains. Tomea enables quantitative and qualitative comparisons of moral rhetoric via an interpretable exploration of similarities and differences across moral concepts and domains. We apply Tomea on moral narratives in thirty-five thousand tweets from seven domains. We extensively evaluate the method via a crowd study, a series of cross-domain moral classification comparisons, and a qualitative analysis of cross-domain moral expression.
Pipeline for modeling causal beliefs from natural language

We present a causal language analysis pipeline that leverages a Large Language Model to identify causal claims made in natural language documents, and aggregates claims across a corpus to produce a causal claim network. The pipeline then applies a clustering algorithm that groups causal claims based on their semantic topics. We demonstrate the pipeline by modeling causal belief systems surrounding the Covid-19 vaccine from tweets.
The Complexity of Multiple Inheritance in Complex Object Data Models

We specify a data model for complex objects, following closely recent approaches in the areas of object-oriented and deductive database research. The main extensions with respect to previous proposals are, firstly, the conjunction operator, which permits one to express multiple inheritance between types as a semantic property, and, secondly, a distinction between primitive and derived classes, the latter corresponding to database views. We sketch how such a data model can be exploited in schema design and specify an algorithm for computing specialization relationships between classes. Using results from formal analyses of knowledge representation languages, we note that the computational properties of interesting problems, such as detection of emptiness of a class interpretation and computation of a specialization ordering, appear to be computationally intractable from a theoretical point of view, but are feasible in almost all practical cases.
Approximate counting using Taylor's theorem: a survey

In this article we consider certain well-known polynomials associated with graphs including the independence polynomial and the chromatic polynomial. These polynomials count certain objects in graphs: independent sets in the case of the independence polynomial and proper colourings in the case of the chromatic polynomial. They also have interpretations as partition functions in statistical physics. The algorithmic problem of (approximately) computing these types of polynomials has been studied for close to 50 years, especially using Markov chain techniques. Around eight years ago, Barvinok devised a new algorithmic approach based on Taylor's theorem for computing the permanent of certain matrices, and the approach has been applied to various graph polynomials since then. This article is intended as a gentle introduction to the approach as well as a partial survey of associated techniques and results.
Heavy-traffic limits for parallel single-server queues with randomly split Hawkes arrival processes

We consider parallel single-server queues in heavy traffic with randomly split Hawkes arrival processes. The service times are assumed to be independent and identically distributed (i.i.d.) in each queue and are independent in different queues. In the critically loaded regime at each queue, it is shown that the diffusion-scaled queueing and workload processes converge to a multidimensional reflected Brownian motion in the non-negative orthant with orthonormal reflections. For the model with abandonment, we also show that the corresponding limit is a multidimensional reflected Ornstein–Uhlenbeck diffusion in the non-negative orthant.
Disentangling plasma interaction and induction signatures at Callisto: The Galileo C10 flyby

We apply a combination of data analysis and hybrid modeling to study Callisto's interaction with Jupiter's magnetosphere during the Galileo C10 flyby on 17 September 1997. This encounter took place while Callisto was located near the center of Jupiter's current sheet. Therefore, induction in Callisto's subsurface ocean and magnetospheric field line draping around the moon's ionosphere both made nonnegligible contributions to the observed magnetic perturbations. The induction signal during C10 was obscured by plasma currents to a significant degree, in contrast to previously studied Callisto flybys. Our analysis reveals that at large distances to Callisto, its magnetic environment was dominated by field line draping, leading to the formation of Alfvén wings. Closer to the surface and in Callisto's wake, Galileo encountered a quasi‐dipolar “core region” that was partially shielded from the plasma interaction and was dominated by the induced field. When exiting this core region, the spacecraft crossed a rotational discontinuity where the magnetic field vector rotated by approximately 50°. The hybrid model is able to quantitatively explain numerous key features of the observed magnetic signatures, especially the transitions between draping‐ and dipole‐dominated regimes along the C10 trajectory. The model also reproduces the electron number density enhancement by 3–4 orders of magnitude detected in Callisto's wake, requiring a substantial ionosphere to surround the moon during C10. For flybys with nonnegligible plasma currents, comprehensive knowledge of the incident flow conditions and properties of Callisto's atmosphere is required to refine existing constraints on the subsurface ocean (conductivity, thickness, and depth) based on magnetic field data. These findings are highly relevant for the upcoming JUpiter ICy moon Explorer (JUICE) mission, which will include multiple Callisto flybys.
Prospects of a superradiant laser based on a thermal or guided beam of 88Sr

The prospects of superradiant lasing on the 7.5-kHz-wide 1 S 0 - 3 P 1 transition in 88 Sr is explored by using numerical simulations of two systems based on realistic experimental numbers. One system uses the idea of demonstrating continuous superradiance in a simple, hot atom beam with high ﬂux, and the other system is based on using ultracold atoms in a dipole guide. For the hot-beam system we consider a range of atom beam parameters as well as the impact of a scheme to discard fast atoms along the cavity axis. We ﬁnd that the system achieves lasing above a ﬂux of 2 . 5 × 10 12 atoms / s and that it is capable of outputting hundreds of nanowatts and suppressing cavity noise by a factor of 20–30. The relativistic transverse Doppler shifts cause a shift in the lasing frequency on the order of 500 Hz. For the cold-atom beam we account for decoherence and thermal effects when using a repumping scheme for atoms conﬁned in a dipole guide. This is done by treating recoils and state-dependent forces acting on atoms in the dipole guide within the framework of the stochastic master equation. We ﬁnd that the output power is on the order of hundreds of picowatts; however, transverse Doppler shifts can be neglected, and cavity noise can be suppressed on the order of a factor of 50–100. Additionally, we show that both systems exhibit local insensitivity to ﬂuctuations in atomic ﬂux.
Utilising a Dataset for Two Text Classification Tasks : Sentiment Analysis and Author Identification

The Internet has many tools that connect people from different ends of the globe. Geographical boundaries do not hinder the connection between two people. Communication is easily facilitated, and news is easily spread. However, this connectivity can be exploited and used negatively. Bullying, hate speech, and fake news can all be spread across the internet, and the common denominator in combating these issues is Sentiment Analysis. Sentiment Analysis is the task of finding the sentiment (positivity or negativity) of a text. This process can be an essential feature for bullying detection, hate speech detection, and fake news detection models. However, there are many libraries and functions available to use, and choosing the optimal one that has the most accuracy to use as a feature for other Natural Language Processing (NLP) tasks is essential. During our search to find functions that calculate sentiment, otherwise known as pre-trained sentiment models, we find that most of the models are trained on polarised texts and datasets that already have sentiment annotated. Hence, we use a nonpolarised text form: news articles and we use a dataset that is not meant to be used with Sentiment Analysis. This helps us test different models. Through our experiments we find that VADER, Stanza, and Transformer’s distilBERT work with a good accuracy, and are ideal to use as the basis for calculating a sentiment feature whereas lexical-based models such as TextBlob and using SentiWordNet are less accurate in its calculations. The dataset we use is the Reuter 50 50 dataset that is meant to be used for another NLP task: Author Identification/Author Profiling. [60] Working with the dataset led to our second task: to create a model that works with minimal requirements for data and computational power but still gives a comparable accuracy to other deep learning methods of Author Identification. Author Identification is that task of being given a random text that could have been written by a group of suspect authors, and finding with rea-
Input and Output Coverage Needed in File System Testing

File systems need testing to discover bugs and to help ensure reliability. Many file system testing tools are evaluated based on their code coverage. We analyzed recently reported bugs in Ext4 and BtrFS and found a weak correlation between code coverage and test effectiveness: many bugs are missed because they depend on specific inputs, even though the code was covered by a test suite. Our position is that coverage of system call inputs and outputs is critically important for testing file systems. We thus suggest input and output coverage as criteria for file system testing, and show how they can improve the effectiveness of testing. We built a prototype called IOCov to evaluate the input and output coverage of file system testing tools. IOCov identified many untested cases (specific inputs and outputs or ranges thereof) for both CrashMonkey and xfstests. Additionally, we discuss a method and associated metrics to identify over- and under-testing using IOCov.
FSTL1 Promotes Alveolar epithelial cells Aging and Worsens Pulmonary Fibrosis by Affecting SENP1- Mediated DeSUMOylation

Wei Sun Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital Xiaoyu Yang Peking Union Medical College Hospital Chunyan Duan Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital Xiaoyan Jing Peking Union Medical College Hospital Lingyun Gao Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital Hui Huang Peking Union Medical College Hospital Lu Guo Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital Yang Yang Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital Zuojun Xu (  xuzjdoc@163.com ) Peking Union Medical College Hospital