Miracle-dz/UniFAR-FTUs huggingface dataset:
Axion electrodynamics and magnetohydrodynamics

We formulate axion-electrodynamics and magnetohydrodynamics (MHD) in the cosmological context assuming weak gravity. The two formulations are made for a general scalar ﬁeld with general f ( φ )-coupling, and an axion as a massive scalar ﬁeld with φ 2 -coupling, with the helical electromagnetic ﬁeld. The α -dynamo term appears naturally from the helical coupling in the MHD formulation. In the presence of the electromagnetic coupling, however, the Schr¨odinger and hydrodynamic formulations of the coherently oscillating axion are not available for the conventional φ coupling; instead, φ 2 coupling allows successful formulations preserving the dark matter nature of the axion to nonlinear order. In the MHD formulation, direct couplings between the scalar and electromagnetic ﬁelds appear only for non-ideal MHD. We study gravitational and magnetic instabilities of the scalar ﬁeld and axion MHDs.
Toxic Comments Hunter : Score Severity of Toxic Comments

The detection and identification of toxic comments are conducive to creating a civilized and harmonious Internet environment. In this experiment, we collected various data sets related to toxic comments. Because of the characteristics of comment data, we perform data cleaning and feature extraction operations on it from different angles to obtain different toxic comment training sets. In terms of model construction, we used the training set to train the models based on TFIDF and finetuned the Bert model separately. Finally, we encapsulated the code into software to score toxic comments in real-time.
Edge Contraction and Forbidden Induced Graphs

A graph $G$ is $H$-free if any subset of $V(G)$ does not induce a subgraph of $G$ that is isomorphic to $H$. Given a graph $H$, we present sufficient and necessary conditions for a graph $G$ such that $G/e$ is $H$-free for any edge $e$ in $E(G)$. Thereafter, we use these conditions to characterize forests, claw-free, $2K_{2}$-free, $C_{4}$-free, $C_{5}$-free, split, and pseudo-split graphs.
Regularising for invariance to data augmentation improves supervised learning

Data augmentation is used in machine learning to make the classifier invariant to label-preserving transformations. Usually this invariance is only encouraged implicitly by including a single augmented input during training. However, several works have recently shown that using multiple augmentations per input can improve generalisation or can be used to incorporate invariances more explicitly. In this work, we first empirically compare these recently proposed objectives that differ in whether they rely on explicit or implicit regularisation and at what level of the predictor they encode the invariances. We show that the predictions of the best performing method are also the most similar when compared on different augmentations of the same input. Inspired by this observation, we propose an explicit regulariser that encourages this invariance on the level of individual model predictions. Through extensive experiments on CIFAR-100 and ImageNet we show that this explicit regulariser (i) improves generalisation and (ii) equalises performance differences between all considered objectives. Our results suggest that objectives that encourage invariance on the level of the neural network itself generalise better than those that achieve invariance by averaging predictions of non-invariant models.
SurvSet: An open-source time-to-event dataset repository

Time-to-event (T2E) analysis is a branch of statistics that models the duration of time it takes for an event to occur. Such events can include outcomes like death, unemployment, or product failure. Most modern machine learning (ML) algorithms, like decision trees and kernel methods, are supported for T2E modelling with data science software (python and R). To complement these developments, SurvSet is the first open-source T2E dataset repository designed for a rapid benchmarking of ML algorithms and statistical methods. The data in SurvSet have been consistently formatted so that a single preprocessing method will work for all datasets. SurvSet currently has 76 datasets which vary in dimensionality, time dependency, and background (the majority of which come from biomedicine). SurvSet is available on PyPI and can be installed with pip install SurvSet. R users can download the data directly from the corresponding git repository.
HintNet: Hierarchical Knowledge Transfer Networks for Traffic Accident Forecasting on Heterogeneous Spatio-Temporal Data

Traffic accident forecasting is a significant problem for transportation management and public safety. However, this problem is challenging due to the spatial heterogeneity of the environment and the sparsity of accidents in space and time. The occurrence of traffic accidents is affected by complex dependencies among spatial and temporal features. Recent traffic accident prediction methods have attempted to use deep learning models to improve accuracy. However, most of these methods either focus on small-scale and homogeneous areas such as populous cities or simply use sliding-window-based ensemble methods, which are inadequate to handle heterogeneity in large regions. To address these limitations, this paper proposes a novel Hierarchical Knowledge Transfer Network (HintNet) model to better capture irregular heterogeneity patterns. HintNet performs a multi-level spatial partitioning to separate sub-regions with different risks and learns a deep network model for each level using spatio-temporal and graph convolutions. Through knowledge transfer across levels, HintNet archives both higher accuracy and higher training efficiency. Extensive experiments on a real-world accident dataset from the state of Iowa demonstrate that HintNet outperforms the state-of-the-art methods on spatially heterogeneous and large-scale areas.
ALMA-IMF. III. Investigating the origin of stellar masses: Top-heavy core mass function in the W43-MM2&MM3 mini-starburst

The ALMA-IMF Large Program observed the W43-MM2-MM3 ridge, whose 1.3mm and 3mm ALMA 12m array continuum images reach a 2500au spatial resolution. We used both the best-sensitivity and the line-free ALMA-IMF images, reduced the noise with the multi-resolution segmentation technique MnGSeg, and derived the most complete and most robust core catalog possible. Using two different extraction software packages, getsf and GExt2D, we identified 200 compact sources, whose 100 common sources have on average fluxes consistent to within 30%. We filtered sources with non-negligible free-free contamination and corrected fluxes from line contamination, resulting in a W43-MM2-MM3 catalog of 205 getsf cores. With a median deconvolved FWHM size of 3400au, core masses range from 0.1Msun to 70Msun and the getsf catalog is 90% complete down to 0.8Msun. The high-mass end of the core mass function (CMF) of W43-MM2-MM3 is top-heavy compared to the canonical IMF. Fitting the cumulative CMF with a single power law of the form N(>logM)\propto M^a, we measured a=-0.95\pm0.04, compared to the canonical a=-1.35 Salpeter IMF slope. The slope of the CMF is robust with respect to map processing, extraction software package, and reasonable variations in the assumptions taken to estimate core masses. We explore several assumptions on how cores transfer their mass to stars and sub-fragment to predict the IMF resulting from the W43-MM2-MM3 CMF. In stark contrast to the commonly accepted paradigm, our result argues against the universality of the CMF shape. More robust functions of the star-formation efficiency and core sub-fragmentation are required to better predict the resulting IMF, here suggested to remain top-heavy at the end of the star-formation phase. If confirmed, the IMFs emerging from starburst events could inherit their top-heavy shape from their parental CMFs, challenging the IMF universality.
Building large multilingual conversational corpora for diversity-aware language science and technology

We present a pipeline and tools to build a large multilingual data sets of conversational speech that covers 66 languages and varieties from 32 phyla for the diversity-aware study of conversational speech in naturalistic settings. We describe compilation and format of the largely open-access resource based on language documentation projects and platforms and release an open-source tool ‘convo-parse’ to help building this type of resource. We conclude with outlining two applications of how massively multilingual data sets can inform interactional linguistics and speech recognition technology and thus contribute to broadening the empirical foundations of language sciences and technologies of the future.
Robustness of Exponential Stability of a Class of Switched Linear Systems with State Delays

This paper investigates the robustness of exponential stability of a class of switched systems described by linear functional differential equations under arbitrary switching. We will measure the stability robustness of such a system, subject to parameter affine perturbations of its constituent subsystems matrices, by introducing the notion of structured stability radius. The lower bounds and the upper bounds for this radius are established under the assumption that certain associated positive linear systems upper bounding the given subsystems have a common linear copositive Lyapunov function. In the case of switched positive linear systems with discrete multiple delays or distributed delay the obtained results yield some tractably computable bounds for the stability radius. Examples are given to illustrate the proposed method.
Precognition in Task-oriented Dialogue Understanding: Posterior Regularization by Future Context

Task-oriented dialogue systems have become overwhelmingly popular in recent researches. Dialogue understanding is widely used to comprehend users' intent, emotion and dialogue state in task-oriented dialogue systems. Most previous works on such discriminative tasks only models current query or historical conversations. Even if in some work the entire dialogue flow was modeled, it is not suitable for the real-world task-oriented conversations as the future contexts are not visible in such cases. In this paper, we propose to jointly model historical and future information through the posterior regularization method. More specifically, by modeling the current utterance and past contexts as prior, and the entire dialogue flow as posterior, we optimize the KL distance between these distributions to regularize our model during training. And only historical information is used for inference. Extensive experiments on two dialogue datasets validate the effectiveness of our proposed method, achieving superior results compared with all baseline models.
Selecting Sub-tables for Data Exploration

Data scientists frequently examine the raw content of large tables when exploring an unknown dataset. In such cases, small subsets of the full tables (sub-tables) that accurately capture table contents are useful. We present a framework which, given a large data table T, creates a sub-table of small, fixed dimensions by selecting a subset of T’s rows and projecting them over a subset of T’s columns. The question is: Which rows and columns should be selected to yield an informative sub-table? Our first contribution is an informativeness metric for sub-tables with two complementary dimensions: cell coverage, which measures how well the sub-table captures prominent data patterns in T, and diversity. We use association rules as the patterns captured by sub-tables, and show that computing optimal sub-tables directly using this metric is infeasible. We then develop an efficient algorithm that indirectly accounts for association rules using table embedding. The resulting framework produces sub-tables for the full table as well as for the results of queries over the table, enabling the user to quickly understand results and determine subsequent queries. Experimental results show that high-quality sub-tables can be efficiently computed, and verify the soundness of our metrics as well as the usefulness of selected sub-tables through user studies.
Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation

Token-level adaptive training approaches can alleviate the token imbalance problem and thus improve neural machine translation, through re-weighting the losses of different target tokens based on specific statistical metrics (e.g., token frequency or mutual information). Given that standard translation models make predictions on the condition of previous target contexts, we argue that the above statistical metrics ignore target context information and may assign inappropriate weights to target tokens. While one possible solution is to directly take target contexts into these statistical metrics, the target-context-aware statistical computing is extremely expensive, and the corresponding storage overhead is unrealistic. To solve the above issues, we propose a target-context-aware metric, named conditional bilingual mutual information (CBMI), which makes it feasible to supplement target context information for statistical metrics. Particularly, our CBMI can be formalized as the log quotient of the translation model probability and language model probability by decomposing the conditional joint distribution. Thus CBMI can be efficiently calculated during model training without any pre-specific statistical calculations and large storage overhead. Furthermore, we propose an effective adaptive training approach based on both the token- and sentence-level CBMI. Experimental results on WMT14 English-German and WMT19 Chinese-English tasks show our approach can significantly outperform the Transformer baseline and other related methods.
RNN Transducers for Nested Named Entity Recognition with constraints on alignment for long sequences

Popular solutions to Named Entity Recognition (NER) include conditional random fields, sequence-to-sequence models, or utilizing the question-answering framework. However, they are not suitable for nested and overlapping spans with large ontologies and for predicting the position of the entities. To fill this gap, we introduce a new model for NER task -- an RNN transducer (RNN-T). These models are trained using paired input and output sequences without explicitly specifying the alignment between them, similar to other seq-to-seq models. RNN-T models learn the alignment using a loss function that sums over all alignments. In NER tasks, however, the alignment between words and target labels are available from the human annotations. We propose a fixed alignment RNN-T model that utilizes the given alignment, while preserving the benefits of RNN-Ts such as modeling output dependencies. As a more general case, we also propose a constrained alignment model where users can specify a relaxation of the given input alignment and the model will learn an alignment within the given constraints. In other words, we propose a family of seq-to-seq models which can leverage alignments between input and target sequences when available. Through empirical experiments on a challenging real-world medical NER task with multiple nested ontologies, we demonstrate that our fixed alignment model outperforms the standard RNN-T model, improving F1-score from 0.70 to 0.74.
Questionnaire research on the evacuation behavior characteristics of different crowds in subway

In recent years,metro safety accident happens frequently,and the particularity and the vulnerability of the subway security work become increasingly prominent.In the subway station,as personnel intensive place,once accident happens,the behavior of passengers would be hard to control,which will make evacuation extremely difficult.Therefore,how to organize passengers to evacuate to safe area reasonably and rapidly is a significant problem for metro operation department to solve.Making a reasonable and effective emergency evacuation plan first needs to understand passengers' evacuation behavior characteristic,for example,route to choice,knowledge of safety,evacuation behavior and mental characteristics,and so on.In this paper,the safety consciousness and evacuation behavior characteristics were analyzed preliminarily through the questionnaire to different crowds under the subway emergencies.It provides reference for the subway evacuate research and establishing reasonable and effective emergency evacuation plan.
GPU Semiring Primitives for Sparse Neighborhood Methods

High-performance primitives for mathematical operations on sparse vectors must deal with the challenges of skewed degree distributions and limits on memory consumption that are typically not issues in dense operations. We demonstrate that a sparse semiring primitive can be flexible enough to support a wide range of critical distance measures while maintaining performance and memory efficiency on the GPU. We further show that this primitive is a foundational component for enabling many neighborhood-based information retrieval and machine learning algorithms to accept sparse input. To our knowledge, this is the first work aiming to unify the computation of several critical distance measures on the GPU under a single flexible design paradigm and we hope that it provides a good baseline for future research in this area. Our implementation is fully open source and publicly available as part of the RAFT library of GPU-accelerated machine learning primitives (https://github.com/rapidsai/raft).
Sofic Lie Algebras

We introduce and study soficity for Lie algebras, modelled after linear soficity in associative algebras. We introduce equivalent definitions of soficity, one involving metric ultraproducts and the other involving almost representations. We prove that any Lie algebra of subexponential growth is sofic. We also prove that a Lie algebra over a field of characteristic 0 is sofic if and only if its universal enveloping algebra is linearly sofic. Finally, we give explicit families of almost representations for the Witt and Virasoro algebras.
Thermal drag effect in quantum Hall circuits

We study the thermal drag between two mesoscopic quantum Hall (QH) circuits. Each circuit consists of Ohmic contact perfectly coupled to quantum Hall edge states. The drag is caused by strong capacitive coupling between Ohmic contacts. The non-equilibirum conditions and the electron-electron interaction are taken into account by using the non-equilibrium bosonization tech-nique. The thermal drag current in the passive circuit, the noise power of the corresponding heat current, and the Fano factor are calculated and analyzed.
Feeding What You Need by Understanding What You Learned

Machine Reading Comprehension (MRC) reveals the ability to understand a given text passage and answer questions based on it. Existing research works in MRC rely heavily on large-size models and corpus to improve the performance evaluated by metrics such as Exact Match (EM) and F_1. However, such a paradigm lacks sufficient interpretation to model capability and can not efficiently train a model with a large corpus. In this paper, we argue that a deep understanding of model capabilities and data properties can help us feed a model with appropriate training data based on its learning status. Specifically, we design an MRC capability assessment framework that assesses model capabilities in an explainable and multi-dimensional manner. Based on it, we further uncover and disentangle the connections between various data properties and model performance. Finally, to verify the effectiveness of the proposed MRC capability assessment framework, we incorporate it into a curriculum learning pipeline and devise a Capability Boundary Breakthrough Curriculum (CBBC) strategy, which performs a model capability-based training to maximize the data value and improve training efficiency. Extensive experiments demonstrate that our approach significantly improves performance, achieving up to an 11.22% / 8.71% improvement of EM / F_1 on MRC tasks.
ClueGraphSum: Let Key Clues Guide the Cross-Lingual Abstractive Summarization

Cross-Lingual Summarization (CLS) is the task to generate a summary in one language for an article in a different language. Previous studies on CLS mainly take pipeline methods or train the end-to-end model using the translated parallel data. However, the quality of generated cross-lingual summaries needs more further efforts to improve, and the model performance has never been evaluated on the hand-written CLS dataset. Therefore, we first propose a clue-guided cross-lingual abstractive summarization method to improve the quality of cross-lingual summaries, and then construct a novel hand-written CLS dataset for evaluation. Specifically, we extract keywords, named entities, etc. of the input article as key clues for summarization and then design a clue-guided algorithm to transform an article into a graph with less noisy sentences. One Graph encoder is built to learn sentence semantics and article structures and one Clue encoder is built to encode and translate key clues, ensuring the information of important parts are reserved in the generated summary. These two encoders are connected by one decoder to directly learn cross-lingual semantics. Experimental results show that our method has stronger robustness for longer inputs and substantially improves the performance over the strong baseline, achieving an improvement of 8.55 ROUGE-1 (English-to-Chinese summarization) and 2.13 MoverScore (Chinese-to-English summarization) scores over the existing SOTA.
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning

Complex heterogeneous dynamic networks like knowledge graphs are powerful constructs that can be used in modeling data provenance from computer systems. From a security perspective, these attributed graphs enable causality analysis and tracing for analyzing a myriad of cyberattacks. However, there is a paucity in systematic development of pipelines that transform system executions and provenance into usable graph representations for machine learning tasks. This lack of instrumentation severely inhibits scientific advancement in provenance graph machine learning by hindering reproducibility and limiting the availability of data that are critical for techniques like graph neural networks. To fulfill this need, we present Flurry, an end-to-end data pipeline which simulates cyberattacks, captures provenance data from these attacks at multiple system and application layers, converts audit logs from these attacks into data provenance graphs, and incorporates this data with a framework for training deep neural models that supports preconfigured or custom-designed models for analysis in real-world resilient systems. We showcase this pipeline by processing data from multiple system attacks and performing anomaly detection via graph classification using current benchmark graph representational learning frameworks. Flurry provides a fast, customizable, extensible, and transparent solution for providing this much needed data to cybersecurity professionals.
Improving the Energy Efficiency and Robustness of tinyML Computer Vision using Log-Gradient Input Images

This paper studies the merits of applying log-gradient input images to convolutional neural networks (CNNs) for tinyML computer vision (CV). We show that log gradients enable: (i) aggressive 1.5-bit quantization of first-layer inputs, (ii) potential CNN resource reductions, and (iii) inherent robustness to illumination changes (1.7% accuracy loss across 1/32.8 brightness variation vs. up to 10% for JPEG). We establish these results using the PASCAL RAW image data set and through a combination of experiments using neural architecture search and a fixed three-layer network. The latter reveal that training on log-gradient images leads to higher filter similarity, making the CNN more prunable. The combined benefits of aggressive first-layer quantization, CNN resource reductions, and operation without tight exposure control and image signal processing (ISP) are helpful for pushing tinyML CV toward its ultimate efficiency limits.