Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining Global and Local Merges in Logic-based Entity Resolution

In the recently proposed LACE framework for collective entity resolution, logical rules and constraints are used to identify pairs of entity references (e.g. author or paper ids) that denote the same entity. This identification is global: all occurrences of those entity references (possibly across multiple database tuples) are deemed equal and can be merged. By contrast, a local form of merge is often more natural when identifying pairs of data values, e.g. some occurrences of 'J. Smith' may be equated with 'Joe Smith', while others should merge with 'Jane Smith'. This motivates us to extend LACE with local merges of values and explore the computational properties of the resulting formalism.
Don't Trust ChatGPT when your Question is not in English: A Study of Multilingual Abilities and Types of LLMs

Large Language Models (LLMs) have demonstrated exceptional natural language understanding abilities and have excelled in a variety of natural language processing (NLP)tasks in recent years. Despite the fact that most LLMs are trained predominantly in English, multiple studies have demonstrated their comparative performance in many other languages. However, fundamental questions persist regarding how LLMs acquire their multi-lingual abilities and how performance varies across different languages. These inquiries are crucial for the study of LLMs since users and researchers often come from diverse language backgrounds, potentially influencing their utilization and interpretation of LLMs' results. In this work, we propose a systematic way of qualifying the performance disparities of LLMs under multilingual settings. We investigate the phenomenon of across-language generalizations in LLMs, wherein insufficient multi-lingual training data leads to advanced multi-lingual capabilities. To accomplish this, we employ a novel back-translation-based prompting method. The results show that GPT exhibits highly translating-like behaviour in multilingual settings.
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks

For regression tasks, standard Gaussian processes (GPs) provide natural uncertainty quantification, while deep neural networks (DNNs) excel at representation learning. We propose to synergistically combine these two approaches in a hybrid method consisting of an ensemble of GPs built on the output of hidden layers of a DNN. GP scalability is achieved via Vecchia approximations that exploit nearest-neighbor conditional independence. The resulting deep Vecchia ensemble not only imbues the DNN with uncertainty quantification but can also provide more accurate and robust predictions. We demonstrate the utility of our model on several datasets and carry out experiments to understand the inner workings of the proposed method.
GOC-Ledger: State-based Conflict-Free Replicated Ledger from Grow-Only Counters

Conventional blockchains use consensus algorithms that totally order updates across all accounts, which is stronger than necessary to implement a replicated ledger. This makes updates slower and more expensive than necessary. More recent consensus-free replicated ledgers forego consensus algorithms, with significant increase in performance and decrease in infrastructure costs. However, current designs are based around reliable broadcast of update operations to all replicas which require reliable message delivery and reasoning over operation histories to establish convergence and safety. In this paper, we present a replicated ledger as a state-based conflict-free replicated data type (CRDT) based on grow-only counters. This design provides two major benefits: 1) it requires a weaker eventual transitive delivery of the latest state rather than reliable broadcast of all update operations to all replicas; 2) eventual convergence and safety properties can be proven easily without having to reason over operation histories: convergence comes from the composition of grow-only counters, themselves CRDTs, and safety properties can be expressed over the state of counters, locally and globally. In addition, applications that tolerate temporary negative balances require no additional mechanisms and applications that require strictly non-negative balances can be supported by enforcing sequential updates to the same account across replicas. Our design is sufficient when executing on replicas that might crash and recover, as common in deployments in which all replicas are managed by trusted entities. It may also provide a good foundation to explore new mechanisms for tolerating adversarial replicas.
Variation of the fine structure constant in light of recent helium abundance measurement

We point out that the recent result of primordial helium-4 ($^4$He) abundance measurement by EMPRESS, which has reported a smaller $^4$He abundance than other measurements, can be well fitted by assuming a time-variation of the fine structure constant $\alpha$ which is slightly smaller than the present value during big bang nucleosynthesis (BBN). We find that the EMPRESS result in combination with deuterium abundance measurement indicates $-2.6\%<\Delta\alpha/\alpha<-1.4 \%$ (68\% C.L.) where $\Delta \alpha$ is the difference between the values of $\alpha$ at the BBN and present epochs, while $-1.2\%<\Delta\alpha/\alpha<0.4 \%$ (68\% C.L.) is obtained from other previous $^4$He abundance data. We also investigate its effects in the framework where the effective number of neutrino species and the lepton asymmetry, which are other typical interpretations of the EMPRESS result, are allowed to vary. Once a smaller $\alpha$ is adopted, the EMPRESS result can be explained without assuming any non-standard values for the effective number of neutrino species and lepton asymmetry.
Gender Lost In Translation: How Bridging The Gap Between Languages Affects Gender Bias in Zero-Shot Multilingual Translation

Neural machine translation (NMT) models often suffer from gender biases that harm users and society at large. In this work, we explore how bridging the gap between languages for which parallel data is not available affects gender bias in multilingual NMT, specifically for zero-shot directions. We evaluate translation between grammatical gender languages which requires preserving the inherent gender information from the source in the target language. We study the effect of encouraging language-agnostic hidden representations on models’ ability to preserve gender and compare pivot-based and zero-shot translation regarding the influence of the bridge language (participating in all language pairs during training) on gender preservation. We find that language-agnostic representations mitigate zero-shot models’ masculine bias, and with increased levels of gender inflection in the bridge language, pivoting surpasses zero-shot translation regarding fairer gender preservation for speaker-related gender agreement.
ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection

Anomaly detection is crucial to the advanced identification of product defects such as incorrect parts, misaligned components, and damages in industrial manufacturing. Due to the rare observations and unknown types of defects, anomaly detection is considered to be challenging in machine learning. To overcome this difficulty, recent approaches utilize the common visual representations pre-trained from natural image datasets and distill the relevant features. However, existing approaches still have the discrepancy between the pre-trained feature and the target data, or require the input augmentation which should be carefully designed, particularly for the industrial dataset. In this paper, we introduce ReConPatch, which constructs discriminative features for anomaly detection by training a linear modulation of patch features extracted from the pre-trained model. ReConPatch employs contrastive representation learning to collect and distribute features in a way that produces a target-oriented and easily separable representation. To address the absence of labeled pairs for the contrastive learning, we utilize two similarity measures between data representations, pairwise and contextual similarities, as pseudo-labels. Our method achieves the state-of-the-art anomaly detection performance (99.72%) for the widely used and challenging MVTec AD dataset. Additionally, we achieved a state-of-the-art anomaly detection performance (95.8%) for the BTAD dataset.
UMSE: Unified Multi-scenario Summarization Evaluation

Summarization quality evaluation is a non-trivial task in text summarization. Contemporary methods can be mainly categorized into two scenarios: (1) reference-based: evaluating with human-labeled reference summary; (2) reference-free: evaluating the summary consistency of the document. Recent studies mainly focus on one of these scenarios and explore training neural models built on PLMs to align with human criteria. However, the models from different scenarios are optimized individually, which may result in sub-optimal performance since they neglect the shared knowledge across different scenarios. Besides, designing individual models for each scenario caused inconvenience to the user. Inspired by this, we propose Unified Multi-scenario Summarization Evaluation Model (UMSE). More specifically, we propose a perturbed prefix tuning method to share cross-scenario knowledge between scenarios and use a self-supervised training paradigm to optimize the model without extra human labeling. Our UMSE is the first unified summarization evaluation framework engaged with the ability to be used in three evaluation scenarios. Experimental results across three typical scenarios on the benchmark dataset SummEval indicate that our UMSE can achieve comparable performance with several existing strong methods which are specifically designed for each scenario.
Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models

Wasserstein distributionally robust estimators have emerged as powerful models for prediction and decision-making under uncertainty. These estimators provide attractive generalization guarantees: the robust objective obtained from the training distribution is an exact upper bound on the true risk with high probability. However, existing guarantees either suffer from the curse of dimensionality, are restricted to specific settings, or lead to spurious error terms. In this paper, we show that these generalization guarantees actually hold on general classes of models, do not suffer from the curse of dimensionality, and can even cover distribution shifts at testing. We also prove that these results carry over to the newly-introduced regularized versions of Wasserstein distributionally robust problems.
FARA: Future-aware Ranking Algorithm for Fairness Optimization

Ranking systems are the key components of modern Information Retrieval (IR) applications, such as search engines and recommender systems. Besides the ranking relevance to users, the exposure fairness to item providers has also been considered an important factor in ranking optimization. Many fair ranking algorithms have been proposed to jointly optimize both ranking relevance and fairness. However, we find that most existing fair ranking methods adopt greedy algorithms that only optimize rankings for the next immediate session or request. As shown in this paper, such a myopic paradigm could limit the upper bound of ranking optimization and lead to suboptimal performance in the long term. To this end, we propose FARA, a novel Future-Aware Ranking Algorithm for ranking relevance and fairness optimization. Instead of greedily optimizing rankings for the next immediate session, FARA plans ahead by jointly optimizing multiple ranklists together and saving them for future sessions. Specifically, FARA first uses the Taylor expansion to investigate how future ranklists will influence the overall fairness of the system. Then, based on the analysis of the Taylor expansion, FARA adopts a two-phase optimization algorithm where we first solve an optimal future exposure planning problem and then construct the optimal ranklists according to the optimal future exposure planning. Theoretically, we show that FARA is optimal for ranking relevance and fairness joint optimization. Empirically, our extensive experiments on three semi-synthesized datasets show that FARA is efficient, effective, and can deliver significantly better ranking performance compared to state-of-the-art fair ranking methods. We make our implementation public at \hrefhttps://github.com/Taosheng-ty/QP_fairness/ https://github.com/Taosheng-ty/QP\_fairness/.
Accelerated evolution of Burgers' turbulence with coarse projective integration and deep learning

The evolution of a turbulent flow to a statistically steady state can be cast as a multiscale problem involving energy redistribution processes that take place on the long, large eddy turnover timescale and chaotic processes that take place on the much shorter timescale of the turbulence fluctuations. But the absence of a way to perform super-resolution reconstructions of the instantaneous velocity field from its lower-dimensional moments has prevented the use of standard multiscale computational approaches for accelerating turbulence simulations. We introduce an encoder-decoder recurrent neural network model, an architecture typically used in natural language processing, for translating between the instantaneous velocity field and energy spectrum in a one-dimensional turbulent flow. We use the model in a multiscale simulation scheme to evolve the stochastic Burgers' equation and recover the final, statistically stationary turbulent Burgers' velocity field up to 443 times faster in wall-clock time than using direct numerical simulation alone.
Asteroseismic Investigation on KIC 10526294 to Probe Convective Core Overshoot Mixing

In the overshoot mixing model with an exponentially decreasing diffusion coefficient, the initial value of the diffusion coefficient plays a crucial role. According to the turbulent convective mixing model, the characteristic length of convection in the convection zone differs from that in the overshoot region, resulting in a rapid decrease of the diffusion coefficient near the convective boundary. To investigate this quick decrease, we conducted an asteroseismic study on the intermediate-mass slowly pulsating B-type star KIC 10526294. We generated stellar models with varied input parameters, including the overshoot parameters, and compared the resulting stellar oscillation periods with observations. To mitigate the potential issue arising from large steps in the stellar parameters and stellar age, we employed a comprehensive interpolation scheme for the stellar oscillatory frequencies, considering all stellar parameters and stellar age. Our analysis revealed that the quick decreasing of the diffusion coefficient has discernible effects on the stellar oscillations, and a quick decrease with 4 orders of magnitude shows the best oscillatory frequencies compared with the observations. This provides weak evidence in support of the prediction made by the turbulent convective mixing model. Furthermore, we examined the residuals of the oscillation periods and discovered a potential association between abundance anomalies in the buoyancy frequency profile and the oscillation-like patterns observed in the residuals.
Handling Realistic Label Noise in BERT Text Classification

Labels noise refers to errors in training labels caused by cheap data annotation methods, such as web scraping or crowd-sourcing, which can be detrimental to the performance of supervised classifiers. Several methods have been proposed to counteract the effect of random label noise in supervised classification, and some studies have shown that BERT is already robust against high rates of randomly injected label noise. However, real label noise is not random; rather, it is often correlated with input features or other annotator-specific factors. In this paper, we evaluate BERT in the presence of two types of realistic label noise: feature-dependent label noise, and synthetic label noise from annotator disagreements. We show that the presence of these types of noise significantly degrades BERT classification performance. To improve robustness, we evaluate different types of ensembles and noise-cleaning methods and compare their effectiveness against label noise across different datasets.
Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning

Federated learning (FL) is a popular privacy-preserving distributed training scheme, where multiple devices collaborate to train machine learning models by uploading local model updates. To improve communication efficiency, over-the-air computation (AirComp) has been applied to FL, which leverages analog modulation to harness the superposition property of radio waves such that numerous devices can upload their model updates concurrently for aggregation. However, the uplink channel noise incurs considerable model aggregation distortion, which is critically determined by the device scheduling and compromises the learned model performance. In this paper, we propose a probabilistic device scheduling framework for over-the-air FL, named PO-FL, to mitigate the negative impact of channel noise, where each device is scheduled according to a certain probability and its model update is reweighted using this probability in aggregation. We prove the unbiasedness of this aggregation scheme and demonstrate the convergence of PO-FL on both convex and non-convex loss functions. Our convergence bounds unveil that the device scheduling affects the learning performance through the communication distortion and global update variance. Based on the convergence analysis, we further develop a channel and gradient-importance aware algorithm to optimize the device scheduling probabilities in PO-FL. Extensive simulation results show that the proposed PO-FL framework with channel and gradient-importance awareness achieves faster convergence and produces better models than baseline methods.
Origins of the Evil Eye: M64's Stellar Halo Reveals the Recent Accretion of an SMC-mass Satellite

M64, often called the “Evil Eye” galaxy, is unique among local galaxies. Beyond its dramatic, dusty nucleus, it also hosts an outer gas disk that counter-rotates relative to its stars. The mass of this outer disk is comparable to the gas content of the Small Magellanic Cloud (SMC), prompting the idea that it was likely accreted in a recent minor merger. Yet, detailed follow-up studies of M64's outer disk have shown no evidence of such an event, leading to other interpretations, such as a “flyby” interaction with the distant diffuse satellite Coma P. We present Subaru Hyper Suprime-Cam observations of M64's stellar halo, which resolve its stellar populations and reveal a spectacular radial shell feature, oriented ∼30° relative to the major axis and along the rotation axis of the outer gas disk. The shell is ∼45 kpc southeast of M64, while a similar but more diffuse plume to the northwest extends to >100 kpc. We estimate a stellar mass and metallicity for the southern shell of M ⋆ = 1.80 ± 0.54 × 108 M ⊙ and [M/H] = −1.0, respectively, and a similar mass of 1.42 ± 0.71 × 108 M ⊙ for the northern plume. Taking into account the accreted material in M64's inner disk, we estimate a total stellar mass for the progenitor satellite of M ⋆,prog ≃ 5 × 108 M ⊙. These results suggest that M64 is in the final stages of a minor merger with a gas-rich satellite strikingly similar to the SMC, in which M64's accreted counter-rotating gas originated, and which is responsible for the formation of its dusty inner star-forming disk.
D-CALM: A Dynamic Clustering-based Active Learning Approach for Mitigating Bias

Despite recent advancements, NLP models continue to be vulnerable to bias. This bias often originates from the uneven distribution of real-world data and can propagate through the annotation process. Escalated integration of these models in our lives calls for methods to mitigate bias without overbearing annotation costs. While active learning (AL) has shown promise in training models with a small amount of annotated data, AL's reliance on the model's behavior for selective sampling can lead to an accumulation of unwanted bias rather than bias mitigation. However, infusing clustering with AL can overcome the bias issue of both AL and traditional annotation methods while exploiting AL's annotation efficiency. In this paper, we propose a novel adaptive clustering-based active learning algorithm, D-CALM, that dynamically adjusts clustering and annotation efforts in response to an estimated classifier error-rate. Experiments on eight datasets for a diverse set of text classification tasks, including emotion, hatespeech, dialog act, and book type detection, demonstrate that our proposed algorithm significantly outperforms baseline AL approaches with both pretrained transformers and traditional Support Vector Machines. D-CALM showcases robustness against different measures of information gain and, as evident from our analysis of label and error distribution, can significantly reduce unwanted model bias.
Fast FPGA Implementations of Inversions for Special Irreducible Polynomials in Finite Field

Inversions in finite field have been playing a key role in areas of cryptography and engineering. The main algorithms for finite field inversions are based on Fermat's little theorem, extended Euclidean algorithm and other methods. We present techniques to exploit special irreducible polynomials for fast inversions in finite fields (2 ) n GF , where n is a positive integer. We propose fast inversions based on Fermat's theorem for two special irreducible polynomials, i.e. trinomials and All-One-Polynomials (AOPs). Trinomials can be represented by polynomials 1 n m x x   and AOPs can be represented by polynomials 1 . 1 n n x x     , where m is a positive integer and 0<m<n. Our designs are programmed in Very-High-Speed Integrated Circuit Hardware Description Language (VHDL) by using Quartus II and implemented on a lowcost Field-Programmable Gate Array (FPGA). The experimental results show that our designs provide significant reductions in executing time.
Causality between investor sentiment and the shares return on the Moroccan and Tunisian financial markets

This paper aims to test the relationship between investor sentiment and the profitability of stocks listed on two emergent financial markets, the Moroccan and Tunisian ones. Two indirect measures of investor sentiment are used, SENT and ARMS. These sentiment indicators show that there is an important relationship between the stocks returns and investor sentiment. Indeed, the results of modeling investor sentiment by past observations show that sentiment has weak memory; on the other hand, series of changes in sentiment have significant memory. The results of the Granger causality test between stock return and investor sentiment show us that profitability causes investor sentiment and not the other way around for the two financial markets studied.Thanks to four autoregressive relationships estimated between investor sentiment, change in sentiment, stock return and change in stock return, we find firstly that the returns predict the changes in sentiments which confirms with our hypothesis and secondly, the variation in profitability negatively affects investor sentiment.We conclude that whatever sentiment measure is used there is a positive and significant relationship between investor sentiment and profitability, but sentiment cannot be predicted from our various variables.