Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of eigenvalue condition numbers for a class of randomized numerical methods for singular matrix pencils

The numerical solution of the generalized eigenvalue problem for a singular matrix pencil is challenging due to the discontinuity of its eigenvalues. Classically, such problems are addressed by first extracting the regular part through the staircase form and then applying a standard solver, such as the QZ algorithm, to that regular part. Recently, several novel approaches have been proposed to transform the singular pencil into a regular pencil by relatively simple randomized modifications. In this work, we analyze three such methods by Hochstenbach, Mehl, and Plestenjak that modify, project, or augment the pencil using random matrices. All three methods rely on the normal rank and do not alter the finite eigenvalues of the original pencil. We show that the eigenvalue condition numbers of the transformed pencils are unlikely to be much larger than the $\delta$-weak eigenvalue condition numbers, introduced by Lotz and Noferini, of the original pencil. This not only indicates favorable numerical stability but also reconfirms that these condition numbers are a reliable criterion for detecting simple finite eigenvalues. We also provide evidence that, from a numerical stability perspective, the use of complex instead of real random matrices is preferable even for real singular matrix pencils and real eigenvalues. As a side result, we provide sharp left tail bounds for a product of two independent random variables distributed with the generalized beta distribution of the first kind or Kumaraswamy distribution.
Toward Adversarially Robust Recommendation From Adaptive Fraudster Detection

The robustness of recommender systems under node injection attacks has garnered significant attention. Recently, GraphRfi, a Graph-Neural-Network-based (GNN-based) recommender system, was proposed and shown to effectively mitigate the impact of injected fake users. However, we demonstrate that GraphRfi remains vulnerable to attacks due to the supervised nature of its fraudster detection component, where obtaining clean labels is challenging in practice. In particular, we propose a powerful poisoning attack, MetaC, against both GNN-based and Martix-Faxtorization-based recommender systems. Furthermore, we analyze why GraphRfi fails under such an attack. Then, based on our insights obtained from vulnerability analysis, we design an adaptive fraudster detection module that explicitly considers label uncertainty. This module can serve as a plug-in for different recommender systems, resulting in a robust framework named Posterior-Detection Recommender (PDR). Comprehensive experiments show that our defense approach outperforms other benchmark methods under attacks. Overall, our research presents an effective framework for integrating fraudster detection into recommendation systems to achieve adversarial robustness.
Impact of Anomalous Active Regions on the Large-scale Magnetic Field of the Sun

One of the major sources of perturbation in the solar cycle amplitude is believed to be the emergence of anomalous active regions that do not obey Hale’s polarity law and Joy’s law of tilt angles. Anomalous regions containing high magnetic flux that disproportionately impact the polar field are sometimes referred to as “rogue regions. ” In this study, utilizing a surface flux transport model, we analyze the large-scale dipole moment buildup due to the emergence of anomalous active regions on the solar surface. Although these active regions comprise a small fraction of the total sunspot number, they can substantially influence the magnetic dipole moment buildup and subsequent solar cycle amplitude. Our numerical simulations demonstrate that the impact of “anti-Joy” regions on the solar cycle is similar to those of “anti-Hale” regions. We also find that the emergence time, emergence latitude, relative number, and flux distribution of anomalous regions influence the large-scale magnetic field dynamics in diverse ways. We establish that the results of our numerical study are consistent with the algebraic (analytic) approach to explaining the Sun’s dipole moment evolution. Our results are relevant for understanding how anomalous active regions modulate the Sun’s large-scale dipole moment buildup and its reversal timing within the framework of the Babcock–Leighton dynamo mechanism—now believed to be the primary source of solar cycle variations.
DUMB: A Benchmark for Smart Evaluation of Dutch Models

We introduce the Dutch Model Benchmark: DUMB. The benchmark includes a diverse set of datasets for low-, medium- and high-resource tasks. The total set of nine tasks includes four tasks that were previously not available in Dutch. Instead of relying on a mean score across tasks, we propose Relative Error Reduction (RER), which compares the DUMB performance of language models to a strong baseline which can be referred to in the future even when assessing different sets of language models. Through a comparison of 14 pre-trained language models (mono- and multi-lingual, of varying sizes), we assess the internal consistency of the benchmark tasks, as well as the factors that likely enable high performance. Our results indicate that current Dutch monolingual models under-perform and suggest training larger Dutch models with other architectures and pre-training objectives. At present, the highest performance is achieved by DeBERTaV3 (large), XLM-R (large) and mDeBERTaV3 (base). In addition to highlighting best strategies for training larger Dutch models, DUMB will foster further research on Dutch. A public leaderboard is available at https://dumbench.nl.
HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models

Large language models (LLMs), such as ChatGPT, are prone to generate hallucinations, i.e., content that conflicts with the source or cannot be verified by the factual knowledge. To understand what types of content and to which extent LLMs are apt to hallucinate, we introduce the Hallucination Evaluation benchmark for Large Language Models (HaluEval), a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing hallucination. To generate these samples, we propose a ChatGPT-based two-step framework, i.e., sampling-then-filtering. Besides, we also hire some human labelers to annotate the hallucinations in ChatGPT responses. The empirical results suggest that ChatGPT is likely to generate hallucinated content in specific topics by fabricating unverifiable information (i.e., about $19.5\%$ responses). Moreover, existing LLMs face great challenges in recognizing the hallucinations in texts. However, our experiments also prove that providing external knowledge or adding reasoning steps can help LLMs recognize hallucinations. Our benchmark can be accessed at https://github.com/RUCAIBox/HaluEval.
Learning Deep Time-index Models for Time Series Forecasting

Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historical-value models. Yet, despite the attractive properties of time-index models, such as being able to model the continuous nature of underlying time series dynamics, little attention has been given to them. Indeed, while naive deep time-index models are far more expressive than the manually predefined function representations of classical time-index models, they are inadequate for forecasting, being unable to generalize to unseen time steps due to the lack of inductive bias. In this paper, we propose DeepTime, a meta-optimization framework to learn deep time-index models which overcome these limitations, yielding an efficient and accurate forecasting model. Extensive experiments on real world datasets in the long sequence time-series forecasting setting demonstrate that our approach achieves competitive results with state-of-the-art methods, and is highly efficient. Code is available at https://github.com/salesforce/DeepTime.
Analysis of Media Writing Style Bias through Text-Embedding Networks

With the rise of phenomena like `fake news' and the growth of heavily-biased media ecosystems, there has been increased attention on understanding and evaluating media bias. Of particular note in the evaluation of media bias is writing style bias, which includes lexical bias and framing bias. We propose a novel approach to evaluating writing style bias that utilizes natural language similarity estimation and a network-based representation of the shared content between articles to perform bias characterization. Our proposed method presents a new means of evaluating writing style bias that does not rely on human experts or knowledge of a media producer's publication procedures. The results of experimentation on real-world vaccine mandate data demonstrates the utility of the technique and how the standard bias labeling procedures of only having one bias label for a media producer is insufficient to truly characterize the bias of that media producer.
Observational Properties of a Bright Type lax SN 2018cni and a Faint Type Iax SN 2020kyg

We present the optical photometric and spectroscopic analysis of two Type Iax supernovae (SNe), 2018cni and 2020kyg. SN 2018cni is a bright Type Iax SN (M V,peak = −17.81 ± 0.21 mag), whereas SN 2020kyg (M V,peak = −14.52 ± 0.21 mag) is a faint one. We derive 56Ni mass of 0.07 and 0.002 M ⊙ and ejecta mass of 0.48 and 0.14 M ⊙ for SNe 2018cni and 2020kyg, respectively. A combined study of the bright and faint Type Iax SNe in R/r-band reveals that the brighter objects tend to have a longer rise time. However, the correlation between the peak luminosity and decline rate shows that bright and faint Type Iax SNe exhibit distinct behavior. Comparison with standard deflagration models suggests that SN 2018cni is consistent with the deflagration of a CO white dwarf, whereas the properties of SN 2020kyg can be better explained by the deflagration of a hybrid CONe white dwarf. The spectral features of both the SNe point to the presence of similar chemical species but with different mass fractions. Our spectral modeling indicates stratification at the outer layers and mixed inner ejecta for both of the SNe.
Machine-Created Universal Language for Cross-lingual Transfer

There are two primary approaches to addressing cross-lingual transfer: multilingual pre-training, which implicitly aligns the hidden representations of various languages, and translate-test, which explicitly translates different languages into an intermediate language, such as English. Translate-test offers better interpretability compared to multilingual pre-training. However, it has lower performance than multilingual pre-training and struggles with word-level tasks due to translation altering word order. As a result, we propose a new Machine-created Universal Language (MUL) as an alternative intermediate language. MUL comprises a set of discrete symbols forming a universal vocabulary and a natural language to MUL translator for converting multiple natural languages to MUL. MUL unifies shared concepts from various languages into a single universal word, enhancing cross-language transfer. Additionally, MUL retains language-specific words and word order, allowing the model to be easily applied to word-level tasks. Our experiments demonstrate that translating into MUL yields improved performance compared to multilingual pre-training, and our analysis indicates that MUL possesses strong interpretability. The code is at: https://github.com/microsoft/Unicoder/tree/master/MCUL.
VideoLLM: Modeling Video Sequence with Large Language Models

With the exponential growth of video data, there is an urgent need for automated technology to analyze and comprehend video content. However, existing video understanding models are often task-specific and lack a comprehensive capability of handling diverse tasks. The success of large language models (LLMs) like GPT has demonstrated their impressive abilities in sequence causal reasoning. Building upon this insight, we propose a novel framework called VideoLLM that leverages the sequence reasoning capabilities of pre-trained LLMs from natural language processing (NLP) for video sequence understanding. VideoLLM incorporates a carefully designed Modality Encoder and Semantic Translator, which convert inputs from various modalities into a unified token sequence. This token sequence is then fed into a decoder-only LLM. Subsequently, with the aid of a simple task head, our VideoLLM yields an effective unified framework for different kinds of video understanding tasks. To evaluate the efficacy of VideoLLM, we conduct extensive experiments using multiple LLMs and fine-tuning methods. We evaluate our VideoLLM on eight tasks sourced from four different datasets. The experimental results demonstrate that the understanding and reasoning capabilities of LLMs can be effectively transferred to video understanding tasks. We release the code at https://github.com/cg1177/VideoLLM.
Hi-ResNet: A High-Resolution Remote Sensing Network for Semantic Segmentation

High-resolution remote sensing (HRS) semantic segmentation extracts key objects from high-resolution coverage areas. However, objects of the same category within HRS images generally show significant differences in scale and shape across diverse geographical environments, making it difficult to fit the data distribution. Additionally, a complex background environment causes similar appearances of objects of different categories, which precipitates a substantial number of objects into misclassification as background. These issues make existing learning algorithms sub-optimal. In this work, we solve the above-mentioned problems by proposing a High-resolution remote sensing network (Hi-ResNet) with efficient network structure designs, which consists of a funnel module, a multi-branch module with stacks of information aggregation (IA) blocks, and a feature refinement module, sequentially, and Class-agnostic Edge Aware (CEA) loss. Specifically, we propose a funnel module to downsample, which reduces the computational cost, and extract high-resolution semantic information from the initial input image. Secondly, we downsample the processed feature images into multi-resolution branches incrementally to capture image features at different scales and apply IA blocks, which capture key latent information by leveraging attention mechanisms, for effective feature aggregation, distinguishing image features of the same class with variant scales and shapes. Finally, our feature refinement module integrate the CEA loss function, which disambiguates inter-class objects with similar shapes and increases the data distribution distance for correct predictions. With effective pre-training strategies, we demonstrated the superiority of Hi-ResNet over state-of-the-art methods on three HRS segmentation benchmarks.
Discovering Universal Geometry in Embeddings with ICA

This study utilizes Independent Component Analysis (ICA) to unveil a consistent semantic structure within embeddings of words or images. Our approach extracts independent semantic components from the embeddings of a pre-trained model by leveraging anisotropic information that remains after the whitening process in Principal Component Analysis (PCA). We demonstrate that each embedding can be expressed as a composition of a few intrinsic interpretable axes and that these semantic axes remain consistent across different languages, algorithms, and modalities. The discovery of a universal semantic structure in the geometric patterns of embeddings enhances our understanding of the representations in embeddings.
Beyond Labels: Empowering Human with Natural Language Explanations through a Novel Active-Learning Architecture

Data annotation is a costly task; thus, researchers have proposed low-scenario learning techniques like Active-Learning (AL) to support human annotators; Yet, existing AL works focus only on the label , but overlook the natu-ral language explanation of a data point, despite that real-world humans (e.g., doctors) often need both the labels and the corresponding explanations at the same time. This work proposes a novel AL architecture to support and reduce human annotations of both labels and explanations in low-resource scenarios. Our AL architecture incorporates an explanation-generation model that can explicitly generate natural language explanations for the prediction model and for assisting humans’ decision-making in real-world. For our AL framework, we design a data diversity-based AL data selection strategy that leverages the explanation annotations. The automated AL simulation evaluations demonstrate that our data selection strategy consistently outperforms traditional data diversity-based strategy; furthermore, human evaluation demonstrates that humans prefer our generated explanations to the SOTA explanation-generation system.
Mitigating Data Imbalance and Representation Degeneration in Multilingual Machine Translation

Despite advances in multilingual neural machine translation (MNMT), we argue that there are still two major challenges in this area: data imbalance and representation degeneration. The data imbalance problem refers to the imbalance in the amount of parallel corpora for all language pairs, especially for long-tail languages (i.e., very low-resource languages). The representation degeneration problem refers to the problem of encoded tokens tending to appear only in a small subspace of the full space available to the MNMT model. To solve these two issues, we propose Bi-ACL, a framework that uses only target-side monolingual data and a bilingual dictionary to improve the performance of the MNMT model. We define two modules, named bidirectional autoencoder and bidirectional contrastive learning, which we combine with an online constrained beam search and a curriculum learning sampling strategy. Extensive experiments show that our proposed method is more effective both in long-tail languages and in high-resource languages. We also demonstrate that our approach is capable of transferring knowledge between domains and languages in zero-shot scenarios.
On the Study of the Klein-Gordon Equation in the Dunkl Setting

In Dunkl theory on $\mathbb{R}^{n}$ which generalizes classical Fourier analysis, we study the solution of the Klein-Gordon-equation defined by: \begin{eqnarray} \nonumber \partial_{t}^{2}u-\Delta_{k}u=-m^{2}u \ , \ \ \ u (x,0)=g(x) \ , \ \ \ \partial_{t}u(x,0)=f(x) \end{eqnarray} with \ $m>0$ \ and \ $\partial_{t}^{2}u$ \ is the second derivative of the solution $u$ with respect to $t$ and $\Delta_{k}u$ is the Dunkl Laplacian with respect to $x$ where $f$ and $g$ the two functions in $\mathcal{S}(\mathbb{R}^{n})$ which surround the initial conditions. We obtain an integral representation for its solution which we gives some properties. As a specific result, we studied the associated energies to the Dunkl-Klein-Gordon equation.
Estimating Ejecta Masses of Stripped-envelope Supernovae Using Late-time Light Curves

Stripped-envelope supernovae (SESNe) are a subclass of core-collapse supernovae that are deficient in hydrogen (SN IIb, SN Ib) and possibly helium (SN Ic) in their spectra. Their progenitors are likely stripped of this material through a combination of stellar winds and interactions with a close binary companion, but the exact ejecta mass range covered by each subtype and how it relates to the zero-age main-sequence progenitor mass is still unclear. Using a combination of semianalytic modeling and numerical simulations, we discuss how the properties of SESN progenitors can be constrained through different phases of the bolometric light curve. We find that the light-curve rise time is strongly impacted by the strength of radioactive nickel mixing and treatment of helium recombination. These can vary between events and are often not accounted for in simpler modeling approaches, leading to large uncertainties in ejecta masses inferred from the rise. Motivated by this, we focus on the late-time slope, which is determined by gamma-ray leakage. We calibrate the relationship between ejecta mass, explosion energy, and gamma-ray escape time T 0 using a suite of numerical models. Application of the fitting function we provide to bolometric light curves of SESNe should result in ejecta masses with approximately 20% uncertainty. With large samples of SESNe coming from current and upcoming surveys, our methods can be utilized to better understand the diversity and origin of the progenitor stars.
Lookdown construction for a Moran seed-bank model

We present a lookdown construction for a Moran seed-bank model with variable active and inactive population sizes and we show that the empirical measure of our model coincides with that of the Seed-Bank-Moran Model with latency of Greven, den Hollander and Oomen, 2022. Furthermore, we prove that the time to the most recent common ancestor, starting from $N$ individuals with stationary distribution over its state (active or inactive), has the same asymptotic order as the largest inactivity period. We then obtain an asymptotic distribution of the TMRCA, and use this result to find the first order of the asymptotic distribution of the fixation time of a single beneficial mutant conditioned to invade the whole population, which surprisingly is of order $\ln(N)$.
Regularized Integrals on Configuration Spaces of Riemann Surfaces and Cohomological Pairings

We extend the notion of regularized integrals introduced by Li-Zhou that aims to assign finite values to divergent integrals on configuration spaces of Riemann surfaces. We then give cohomological formulations for the extended notion using the tools of current cohomology and mixed Hodge structures. We also provide practical ways of constructing representatives of the corresponding cohomology classes in terms of smooth differential forms.
JNV Corpus: A Corpus of Japanese Nonverbal Vocalizations with Diverse Phrases and Emotions

We present JNV (Japanese Nonverbal Vocalizations) corpus, a corpus of Japanese nonverbal vocalizations (NVs) with diverse phrases and emotions. Existing Japanese NV corpora lack phrase or emotion diversity, which makes it difficult to analyze NVs and support downstream tasks like emotion recognition. We first propose a corpus-design method that contains two phases: (1) collecting NVs phrases based on crowd-sourcing; (2) recording NVs by stimulating speakers with emotional scenarios. We then collect $420$ audio clips from $4$ speakers that cover $6$ emotions based on the proposed method. Results of comprehensive objective and subjective experiments demonstrate that the collected NVs have high emotion recognizability and authenticity that are comparable to previous corpora of English NVs. Additionally, we analyze the distributions of vowel types in Japanese NVs. To our best knowledge, JNV is currently the largest Japanese NVs corpus in terms of phrase and emotion diversities.