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Entropic optimal transport offers a computationally tractable approximation to the classical problem. In this note, we study the approximation rate of the entropic optimal transport map (in approaching the Brenier map) when the regularization parameter $\varepsilon$ tends to zero in the semidiscrete setting, where the input measure is absolutely continuous while the output is finitely discrete. Previous work shows that the approximation rate is $O(\sqrt{\varepsilon})$ under the $L^2$-norm with respect to the input measure. In this work, we establish faster, $O(\varepsilon^2)$ rates up to polylogarithmic factors, under the dual Lipschitz norm, which is weaker than the $L^2$-norm. For the said dual norm, the $O(\varepsilon^2)$ rate is sharp. As a corollary, we derive a central limit theorem for the entropic estimator for the Brenier map in the dual Lipschitz space when the regularization parameter tends to zero as the sample size increases.

The Legacy Survey of Space and Time (LSST) at Vera C. Rubin Observatory is planned to begin in the Fall of 2025. The LSST survey cadence has been designed via a community-driven process regulated by the Survey Cadence Optimization Committee (SCOC), which recommended up to 3% of the observing time to carry out Target of Opportunity (ToO) observations. Experts from the scientific community, Rubin Observatory personnel, and members of the SCOC were brought together to deliver a recommendation for the implementation of the ToO program during a workshop held in March 2024. Four main science cases were identified: gravitational wave multi-messenger astronomy, high energy neutrinos, Galactic supernovae, and small potentially hazardous asteroids possible impactors. Additional science cases were identified and briefly addressed in the documents, including lensed or poorly localized gamma-ray bursts and twilight discoveries. Trigger prioritization, automated response, and detailed strategies were discussed for each science case. This document represents the outcome of the Rubin ToO 2024 workshop, with additional contributions from members of the Rubin Science Collaborations. The implementation of the selection criteria and strategies presented in this document has been endorsed in the SCOC Phase 3 Recommendations document (PSTN-056). Although the ToO program is still to be finalized, this document serves as a baseline plan for ToO observations with the Rubin Observatory.

Grid-forming technology has a crucial role in achieving the future all renewable power grid. Among different types of grid-forming controllers, Virtual Oscillator (VO) based Controllers (VOCs) are the most advanced. VOCs outperform the conventional droop-based grid-forming controllers in terms of dynamic performance and synchronization stability by adapting time-domain-based implementation. However, because of the time-domain-based implementation, the same Fault Ride-through (FRT) techniques for droop-based controllers are incompatible with VOCs. Existing literature has successfully incorporated current limiting techniques in VOCs to protect the converters during severe transient conditions. Nevertheless, some very important aspects of FRT requirements are not attended to by the existing literature on VOCs, such as maintaining synchronization with the network during a fault, minimizing power oscillation during a fault, and at the fault clearance. First, this article introduces a unique analytical approach for quantifying the underlying dynamics of VOCs during faults. Next, using the mentioned analysis and in-depth reasoning, the systematic development of a unique FRT control architecture for VOCs is presented. The proposed FRT technique has unified both current and voltage synchronization in the same architecture to work successfully under three-phase and unbalanced faults. The performance of the proposed FRT technique is thoroughly validated using simulation studies.

Gravitational-wave detection pipelines have helped to identify over one hundred compact binary mergers in the data collected by the Advanced LIGO and Advanced Virgo interferometers, whose sensitivity has provided unprecedented access to the workings of the gravitational universe. The detectors are, however, subject to a wide variety of noise transients (or glitches) that can contaminate the data. Although detection pipelines utilize a variety of noise mitigation techniques, glitches can occasionally bypass these checks and produce false positives. One class of mitigation techniques is the signal consistency check, which aims to quantify how similar the observed data is to the expected signal. In this work, we describe a new signal consistency check that utilizes a set of bases that spans the gravitational-wave signal space and convolutional neural networks (CNN) to probabilistically identify glitches. We recast the basis response as a grayscale image, and train a CNN to distinguish between gravitational-waves and glitches with similar morphologies. We find that the CNN accurately classifies $\gtrsim 99\%$ of the responses it is shown. We compare these results to a toy detection pipeline, finding that the two methods produce similar false positive rates, but that the CNN has a significantly higher true positive rate. We modify our toy model detection pipeline and demonstrate that including information from the network increases the toy pipeline's true positive rate by $4-7\%$ while decreasing the false positive rate to a data-limited bound of $\lesssim 0.1\%$.

Long-tailed problems in healthcare emerge from data imbalance due to variability in the prevalence and representation of different medical conditions, warranting the requirement of precise and dependable classification methods. Traditional loss functions such as cross-entropy and binary cross-entropy are often inadequate due to their inability to address the imbalances between the classes with high representation and the classes with low representation found in medical image datasets. We introduce a novel polynomial loss function based on Pade approximation, designed specifically to overcome the challenges associated with long-tailed classification. This approach incorporates asymmetric sampling techniques to better classify under-represented classes. We conducted extensive evaluations on three publicly available medical datasets and a proprietary medical dataset. Our implementation of the proposed loss function is open-sourced in the public repository:https://github.com/ipankhi/ALPA., Comment: 13 pages, 1 figures. Accepted in The 35th British Machine Vision Conference (BMVC24)

Current regulations on powerful AI capabilities are narrowly focused on "foundation" or "frontier" models. However, these terms are vague and inconsistently defined, leading to an unstable foundation for governance efforts. Critically, policy debates often fail to consider the data used with these models, despite the clear link between data and model performance. Even (relatively) "small" models that fall outside the typical definitions of foundation and frontier models can achieve equivalent outcomes when exposed to sufficiently specific datasets. In this work, we illustrate the importance of considering dataset size and content as essential factors in assessing the risks posed by models both today and in the future. More broadly, we emphasize the risk posed by over-regulating reactively and provide a path towards careful, quantitative evaluation of capabilities that can lead to a simplified regulatory environment.

We give a conjectural formula for the characteristic number of rational cuspidal curves in the projective plane by extending the idea of Kontsevich's recursion formula (namely, pulling back the equality of two divisors in the four pointed moduli space). The key geometric input that is needed here is that in the closure of rational cuspidal curves, there are two component rational curves which are tangent to each other at the nodal point. While this fact is geometrically quite believable, we haven't as yet proved it; hence our formula is for the moment conjectural. The answers that we obtain agree with what has been computed earlier Ran, Pandharipande, Zinger and Ernstrom and Kennedy. We extend this technique (modulo another conjecture) to obtain the characteristic number of rational quartics with an E6 singularity., Comment: 19 pages, 8 figures. Comments are welcome

The traditional Landau-Lifshitz-Gilbert (LLG) equation has often delineated the linear and nonlinear magnetization dynamics, even at ultrashort timescales e.g., femtoseconds. In contrast, several other non-relativistic and relativistic spin torques have been reported as an extension of the LLG spin dynamics. Here, we explore the contribution of the relativistic field-derivative torque (FDT) in the nonlinear THz magnetization dynamics response applied to ferrimagnets with high Gilbert damping and exchange magnon frequency. Our findings suggest that the FDT plays a significant role in magnetization dynamics in both linear and nonlinear regimes, bridging the gap between the traditional LLG spin dynamics and experimental observations. We find that the coherent THz magnon excitation amplitude is enhanced with the field-derivative torque. Furthermore, a phase shift in the magnon oscillation is induced by the FDT term. This phase shift is almost 90 for the antiferromagnet, while it is almost zero for the ferrimagnet under our investigation. Analyzing the dual THz excitation and their FDT, we find that the nonlinear signals can not be distinctly observed without the FDT terms. However, the inclusion of the FDT terms produces distinct nonlinear signals which matches extremely well with the previously reported experimental results., Comment: 6 figures

The problem of $\mathcal{L}_2$ stabilization of a state feedback stochastic control loop is investigated under different constraints. The discrete time linear time invariant (LTI) open loop plant is chosen to be unstable. The additive white Gaussian noise is assumed to be stationary. The link between the plant and the controller is assumed to be a finite capacity stationary channel, which puts a constraint on the bit rate of the transmission. Moreover, the state of the plant is observed only intermittently keeping the loop open some of the time. In this manuscript both scalar and vector plants under Bernoulli and Markov intermittence models are investigated. Novel bounds on intermittence parameters are obtained to ensure $\mathcal{L}_2$ stability. Moreover, novel recursive quantization algorithms are developed to implement the stabilization scheme under all the constraints. Suitable illustrative examples are provided to elucidate the main results.

The retrieval augmented generation (RAG) framework addresses an ambiguity in user queries in QA systems by retrieving passages that cover all plausible interpretations and generating comprehensive responses based on the passages. However, our preliminary studies reveal that a single retrieval process often suffers from low quality results, as the retrieved passages frequently fail to capture all plausible interpretations. Although the iterative RAG approach has been proposed to address this problem, it comes at the cost of significantly reduced efficiency. To address these issues, we propose the diversify-verify-adapt (DIVA) framework. DIVA first diversifies the retrieved passages to encompass diverse interpretations. Subsequently, DIVA verifies the quality of the passages and adapts the most suitable approach tailored to their quality. This approach improves the QA systems accuracy and robustness by handling low quality retrieval issue in ambiguous questions, while enhancing efficiency.

Traditional medical basic sciences educational approaches follow a one-size-fits-all model, neglecting the diverse learning styles of individual students. I propose an intelligent AI companion which will fill this gap by providing on-the-fly solutions to students' questions in the context of not only USMLE Step 1 but also other similar examinations in other countries, inter alia, PLAB Part 1 in United Kingdom, and NEET (PG) and FMGE in India. I have harnessed Generative AI for dynamic, accurate, human-like responses and for knowledge retention and application. Users were encouraged to employ prompt engineering, in particular, in-context learning, for response optimization and enhancing the model's precision in understanding the intent of the user through the way the query is framed. The implementation of RAG has enhanced the chatbot's ability to combine pre-existing medical knowledge with generative capabilities for efficient and contextually relevant support. Mistral was employed using Python to perform the needed functions. The digital conversational agent was implemented and achieved a score of 0.5985 on a reference-based metric similar to BLEU and ROUGE scores. My approach addresses a critical gap in traditional medical basic sciences education by introducing an intelligent AI companion which specializes in helping medical aspirants with planning and information retention for USMLE Step 1 and other similar exams. Considering the stress that medical aspirants face in studying for the exam and in obtaining spontaneous answers to medical basic sciences queries, especially whose answers are challenging to obtain by searching online, and obviating a student's need to search bulky medical texts or lengthy indices or appendices, I have been able to create a quality assistant capable of producing ad-libitum responses best suited to the user's needs., Comment: 42 pages, 15 figures, 1 table

Magnetic inertia has emerged as a possible way to manipulate ferromagnetic spins at a higher frequency e.g., THz. Theoretical treatments so far have considered the magnetic inertia as a scalar quantity. Here, we explore the magnetic inertial dynamics with a magnetic inertia tensor as macroscopic derivations predicted it to be a tensor. First, the inertia tensor has been decomposed into three terms: (a) scalar and isotropic inertia, (b) anisotropic and symmetric inertia tensor, (c) chiral and antisymmetric tensor. Further, we employ linear response theory to the inertial Landau-Lifshitz-Gilbert equation with the inertia tensor and calculate the effect of chiral and anisotropic inertia on ferromagnets, antiferromagnets, and ferrimagnets. It is established that the precession and nutation resonance frequencies decrease with scalar magnetic inertia. Our results suggest that the nutation resonance frequencies further reduce due to inertia tensor. However, the effective damping of the nutation resonance increases with the chiral and antisymmetric part of the inertia tensor. We show that the precession resonances remain unaffected, while the nutation resonances are modified with the chiral magnetic inertia., Comment: 13 pages, 8 figures

Most existing Question Answering Datasets (QuADs) primarily focus on factoid-based short-context Question Answering (QA) in high-resource languages. However, the scope of such datasets for low-resource languages remains limited, with only a few works centered on factoid-based QuADs and none on non-factoid QuADs. Therefore, this work presents MuNfQuAD, a multilingual QuAD with non-factoid questions. It utilizes interrogative sub-headings from BBC news articles as questions and the corresponding paragraphs as silver answers. The dataset comprises over 370K QA pairs across 38 languages, encompassing several low-resource languages, and stands as the largest multilingual QA dataset to date. Based on the manual annotations of 790 QA-pairs from MuNfQuAD (golden set), we observe that 98\% of questions can be answered using their corresponding silver answer. Our fine-tuned Answer Paragraph Selection (APS) model outperforms the baselines. The APS model attained an accuracy of 80\% and 72\%, as well as a macro F1 of 72\% and 66\%, on the MuNfQuAD testset and the golden set, respectively. Furthermore, the APS model effectively generalizes certain a language within the golden set, even after being fine-tuned on silver labels.

We have investigated the ground state ($1S$), radially excited states ($2S$) and ($3S$) along with the orbitally excited state ($1P$) for the heavy charmonia ($c \bar c$) and bottomonia ($b \bar b$) mesons in the light-front quark model (LFQM). The light-front wave functions have been successful in explaining various physical properties of meson states in the past, especially for the $1S$ and $2S$ states. However, studies regarding the radially excited state $3S$ and orbitally excited state $1P$ have hardly been pursued before. In this study, we take up these two excited states and investigate the electromagnetic form factors (EMFFs), charge radii, decay constants, parton distribution functions (PDFs) and the distribution amplitudes (DAs) for the quarkonia system. For the sake of completeness, we have also included the study of the ground and the first excited states of quarkonia mesons. We have also illustrated the 3D wave functions for the radially excited states in order to study their nodal structures., Comment: 28 pages and 7 figures

Understanding the complete light-spin interactions in magnetic systems is the key to manipulating the magnetization using optical means at ultrafast timescales. The selective addressing of spins by terahertz (THz) electromagnetic fields via Zeeman torque is, by far, one of the most successful ultrafast means of controlling magnetic excitations. Here we show that this traditional Zeeman torque on the spins is not sufficient, rather an additional relativistic field-derivative torque is essential to realize the observed magnetization dynamics. We accomplish this by exploring the ultrafast nonlinear magnetization dynamics of rare-earth, Bi-doped iron garnet when excited by two co-propagating THz pulses. By non-thermal optical pump-probe technique, we, first, find the collective exchange resonance mode between rare-earth and transition metal sublattices at 0.48 THz. We further explore the magnetization dynamics via a rather direct and efficient THz time-domain spectroscopic means. We find that the observed nonlinear trace of the magnetic response cannot be mapped to the magnetization precession induced by the Zeeman torque, while the Zeeman torque supplemented by an additional field-derivative torque follows the experimental evidences. This breakthrough not only enhances our comprehension of ultra-relativistic effects but also paves the way for the development of novel technologies harnessing light-induced control over magnetic systems., Comment: 5 figures

Matched-filter based gravitational-wave search pipelines identify candidate events within seconds of their arrival on Earth, offering a chance to guide electromagnetic follow-up and observe multi-messenger events. Understanding the detectors' response to an astrophysical transient across the searched signal manifold is paramount to inferring the parameters of the progenitor and deciding which candidates warrant telescope time. We describe a framework that uses artificial neural networks to interpolate gravitational waves and, equivalently, the signal-to noise ratio (SNR) across sufficiently local patches of the signal manifold. Our machine-learning based model generates a single waveform in 6 milliseconds on a CPU and 0.4 milliseconds on a GPU. When using a GPU to generate batches of waveforms simultaneously, we find that we can produce $10^4$ waveforms in $\lesssim 1$ ms. This is achieved while remaining faithful, on average, to 1 part in $10^4$ (1 part in $10^5$) for binary black hole (binary neutron star) waveforms. The model we present is designed to directly utilize intermediate detection pipeline outputs in the hopes of facilitating a better real-time understanding of gravitational-wave candidates.

Accurately determining the effective Hubbard parameter $(U_{eff})$ in Density Functional Theory plus U (DFT+U) remains a significant challenge, often relying on empirical methods or linear response theory, which frequently fail to predict accurate material properties. This study introduces BMach, an advanced Bayesian optimization algorithm that refines $U_{eff}$ by incorporating electronic properties, such as band gaps and eigenvalues, alongside structural properties like lattice parameters. Implemented within the Quantum Espresso platform, BMach demonstrates superior accuracy and reduced computational cost compared to traditional methods. The BMach-optimized $U_{eff}$ values yield electronic properties that align closely with experimental and high-level theoretical results, providing a robust framework for high-throughput materials discovery and detailed electronic property characterization across diverse material systems., Comment: 8 pages, 6 figures

The Caporaso-Harris formula gives a recursive algorithm to enumerate delta nodal degree d curves in P^2. The recursion is obtained in terms of curves of lower degree that are tangent to a given divisor. This paper presents two generalizations of this method. The first result is on enumeration of one cuspidal curves on P^2, and the second result is an extension to the fiber bundle setting. We solve the question of counting the characteristic number planar nodal cubics in P^3 by extending the idea of Caporaso-Harris., Comment: 38 pages, 7 figures. Comments are welcome

Large Multimodal Models (LMMs) have made significant strides in visual question-answering for single images. Recent advancements like long-context LMMs have allowed them to ingest larger, or even multiple, images. However, the ability to process a large number of visual tokens does not guarantee effective retrieval and reasoning for multi-image question answering (MIQA), especially in real-world applications like photo album searches or satellite imagery analysis. In this work, we first assess the limitations of current benchmarks for long-context LMMs. We address these limitations by introducing a new vision-centric, long-context benchmark, "Visual Haystacks (VHs)". We comprehensively evaluate both open-source and proprietary models on VHs, and demonstrate that these models struggle when reasoning across potentially unrelated images, perform poorly on cross-image reasoning, as well as exhibit biases based on the placement of key information within the context window. Towards a solution, we introduce MIRAGE (Multi-Image Retrieval Augmented Generation), an open-source, lightweight visual-RAG framework that processes up to 10k images on a single 40G A100 GPU -- far surpassing the 1k-image limit of contemporary models. MIRAGE demonstrates up to 13% performance improvement over existing open-source LMMs on VHs, sets a new state-of-the-art on the RetVQA multi-image QA benchmark, and achieves competitive performance on single-image QA with state-of-the-art LMMs., Comment: Project page: https://visual-haystacks.github.io

Many social and environmental phenomena are associated with macroscopic changes in the built environment, captured by satellite imagery on a global scale and with daily temporal resolution. While widely used for prediction, these images and especially image sequences remain underutilized for causal inference, especially in the context of randomized controlled trials (RCTs), where causal identification is established by design. In this paper, we develop and compare a set of general tools for analyzing Conditional Average Treatment Effects (CATEs) from temporal satellite data that can be applied to any RCT where geographical identifiers are available. Through a simulation study, we analyze different modeling strategies for estimating CATE in sequences of satellite images. We find that image sequence representation models with more parameters generally yield a greater ability to detect heterogeneity. To explore the role of model and data choice in practice, we apply the approaches to two influential RCTs -- Banerjee et al. (2015), a poverty study in Cusco, Peru, and Bolsen et al. (2014), a water conservation experiment in Georgia, USA. We benchmark our image sequence models against image-only, tabular-only, and combined image-tabular data sources, summarizing practical implications for investigators in a multivariate analysis. Land cover classifications over satellite images facilitate interpretation of what image features drive heterogeneity. We also show robustness to data and model choice of satellite-based generalization of the RCT results to larger geographical areas outside the original. Overall, this paper shows how satellite sequence data can be incorporated into the analysis of RCTs, and provides evidence about the implications of data, model, and evaluation metric choice for causal analysis.

Exploratory data analysis (EDA) is an essential step for analyzing a dataset to derive insights. Several EDA techniques have been explored in the literature. Many of them leverage visualizations through various plots. But it is not easy to interpret them for a non-technical user, and producing appropriate visualizations is also tough when there are a large number of columns. Few other works provide a view of some interesting slices of data but it is still difficult for the user to draw relevant insights from them. Of late, conversational data exploration is gaining a lot of traction among non-technical users. It helps the user to explore the dataset without having deep technical knowledge about the data. Towards this, we propose a system that recommends interesting questions in natural language based on relevant slices of a dataset in a conversational setting. Specifically, given a dataset, we pick a select set of interesting columns and identify interesting slices of such columns and column combinations based on few interestingness measures. We use our own fine-tuned variation of a pre-trained language model(T5) to generate natural language questions in a specific manner. We then slot-fill values in the generated questions and rank them for recommendations. We show the utility of our proposed system in a coversational setting with a collection of real datasets.

Weyls law is a fundamental result governing the asymptotic behaviour of the eigenvalues of teh Laplacian. It states that for a compact d dimensional manifold M (without boundary), the eigenvalue counting function has an asymptotic growth, whose leading term is of the order of d and the error term is no worse than order d-1. A natural question is: when is the error term sharp and when can it be improved? It has been known for a long time that the error term is sharp for the round sphere (since 1968). In contrast, it has only recently been shown (in 2019) by Iosevich and Wyman that for the product of spheres, the error term can be polynomially improved. They conjecture that a polynomial improvement should be true for products in general. In this paper we extend both these results to Compact Rank One Symmetric Spaces (CROSSes). We show that for CROSSes, the error term is sharp. Furthermore, we show that for a product of CROSSes, the error term can be polynomially improved. This gives further evidence to the conjecture made by Iosevich and Wyman., Comment: 16 pages. Comments are welcome

Lossless image compression is required in various applications to reduce storage or transmission costs of images, while requiring the reconstructed images to have zero information loss compared to the original. Existing lossless image compression methods either have simple design but poor compression performance, or complex design, better performance, but with no performance guarantees. In our endeavor to develop a lossless image compression method with low complexity and guaranteed performance, we argue that compressibility of a color image is essentially derived from the patterns in its spatial structure, intensity variations, and color variations. Thus, we divide the overall design of a lossless image compression scheme into three parts that exploit corresponding redundancies. We further argue that the binarized version of an image captures its fundamental spatial structure. In this first part of our work, we propose a scheme for lossless compression of binary images. The proposed scheme first learns dictionaries of $16\times16$, $8\times8$, $4\times4$, and $2\times 2$ square pixel patterns from various datasets of binary images. It then uses these dictionaries to encode binary images. These dictionaries have various interesting properties that are further exploited to construct an efficient and scalable scheme. Our preliminary results show that the proposed scheme consistently outperforms existing conventional and learning based lossless compression approaches, and provides, on average, as much as $1.5\times$ better performance than a common general purpose lossless compression scheme (WebP), more than $3\times$ better performance than a state of the art learning based scheme, and better performance than a specialized scheme for binary image compression (JBIG2)., Comment: Corrected axes labels on Figs 8-11, slightly reorganized content with 13 pages, 11 figures, and 5 tables

We present a fluctuating hydrodynamic description of a non-polar active lattice gas model with excluded volume interactions that exhibits motility-induced phase separation under appropriate conditions. For quasi-one dimension and higher, stability analysis of the noiseless hydrodynamics gives quantitative bounds on the phase boundary of the motility-induced phase separation in terms of spinodal and binodal. Inclusion of the multiplicative noise in the fluctuating hydrodynamics describes the exponentially decaying two-point correlations in the stationary-state homogeneous phase. Our hydrodynamic description and theoretical predictions based on it are in excellent agreement with our Monte-Carlo simulations and pseudo-spectral iteration of the hydrodynamics equations. Our construction of hydrodynamics for this model is not suitable in strictly one-dimension with single-file constraints, and we argue that this breakdown is associated with micro-phase separation., Comment: 6 pages, 6 figures, + 16 supplemental pages

The integration of artificial intelligence (AI) into military capabilities has become a norm for major military power across the globe. Understanding how these AI models operate is essential for maintaining strategic advantages and ensuring security. This paper demonstrates an open-source methodology for analyzing military AI models through a detailed examination of the Zhousidun dataset, a Chinese-originated dataset that exhaustively labels critical components on American and Allied destroyers. By demonstrating the replication of a state-of-the-art computer vision model on this dataset, we illustrate how open-source tools can be leveraged to assess and understand key military AI capabilities. This methodology offers a robust framework for evaluating the performance and potential of AI-enabled military capabilities, thus enhancing the accuracy and reliability of strategic assessments.

In $1946$, Mark Kac proved a Central Limit type theorem for a sequence of random variables that were not independent. The random variables under consideration were obtained from the angle-doubling map. The idea behind Kac's proof was to show that although the random variables under consideration were not independent, they were what he calls \textit{statistically independent} (in modern terminology, this concept is called long range independence). The final conclusion of his paper was that the sample averages of the random variables, suitably normalized converges to the standard normal distribution. We describe a new proof of Mark Kac's result by applying Stein's method and show that the normalized sample averages converge to the standard normal distribution in the Wasserstein metric, which is stronger than the convergence in distribution., Comment: unintended typo "Victor Kac" has been corrected to "Mark Kac"; the article has been revised and shorted following comments from experts

We have investigated the spin-orbital angular momentum correlations for the active quark inside the light and heavy mesons for both the spin-0 and spin-1 cases. These correlations can be derived from the generalised transverse momentum dependent distributions (GTMDs) as well as the generalised parton distributions (GPDs). We employ the overlap representation of light-front wave functions in the light-front quark model (LFQM) to calculate our analytical results. The dependence of spin-orbit correlations (SOCs) on the longitudinal momentum fraction $x$ as well as the transverse momentum dependence $\mathbf{k}_{\perp}$ has been graphically presented. Even though the SOCs have already been studied for the spin-0 pions and kaons in other approaches, no calculations for the other light and heavy spin-0 mesons have been reported in literature. Further, the correlations for any of the light and heavy spin-1 mesons have been studied for the first time in the present work., Comment: 23 pages, 4 figures and 3 tables

A/B tests are often required to be conducted on subjects that might have social connections. For e.g., experiments on social media, or medical and social interventions to control the spread of an epidemic. In such settings, the SUTVA assumption for randomized-controlled trials is violated due to network interference, or spill-over effects, as treatments to group A can potentially also affect the control group B. When the underlying social network is known exactly, prior works have demonstrated how to conduct A/B tests adequately to estimate the global average treatment effect (GATE). However, in practice, it is often impossible to obtain knowledge about the exact underlying network. In this paper, we present UNITE: a novel estimator that relax this assumption and can identify GATE while only relying on knowledge of the superset of neighbors for any subject in the graph. Through theoretical analysis and extensive experiments, we show that the proposed approach performs better in comparison to standard estimators., Comment: AISTATS 2024

This technical report delves into an in-depth exploration of adversarial attacks specifically targeted at Deep Neural Networks (DNNs) utilized for image classification. The study also investigates defense mechanisms aimed at bolstering the robustness of machine learning models. The research focuses on comprehending the ramifications of two prominent attack methodologies: the Fast Gradient Sign Method (FGSM) and the Carlini-Wagner (CW) approach. These attacks are examined concerning three pre-trained image classifiers: Resnext50_32x4d, DenseNet-201, and VGG-19, utilizing the Tiny-ImageNet dataset. Furthermore, the study proposes the robustness of defensive distillation as a defense mechanism to counter FGSM and CW attacks. This defense mechanism is evaluated using the CIFAR-10 dataset, where CNN models, specifically resnet101 and Resnext50_32x4d, serve as the teacher and student models, respectively. The proposed defensive distillation model exhibits effectiveness in thwarting attacks such as FGSM. However, it is noted to remain susceptible to more sophisticated techniques like the CW attack. The document presents a meticulous validation of the proposed scheme. It provides detailed and comprehensive results, elucidating the efficacy and limitations of the defense mechanisms employed. Through rigorous experimentation and analysis, the study offers insights into the dynamics of adversarial attacks on DNNs, as well as the effectiveness of defensive strategies in mitigating their impact., Comment: This report pertains to the Capstone Project done by Group 1 of the Fall batch of 2023 students at Praxis Tech School, Kolkata, India. The reports consists of 35 pages and it includes 15 figures and 10 tables. This is the preprint which will be submitted to to an IEEE international conference for review

Modern computer vision pipelines handle large images in one of two sub-optimal ways: down-sampling or cropping. These two methods incur significant losses in the amount of information and context present in an image. There are many downstream applications in which global context matters as much as high frequency details, such as in real-world satellite imagery; in such cases researchers have to make the uncomfortable choice of which information to discard. We introduce xT, a simple framework for vision transformers which effectively aggregates global context with local details and can model large images end-to-end on contemporary GPUs. We select a set of benchmark datasets across classic vision tasks which accurately reflect a vision model's ability to understand truly large images and incorporate fine details over large scales and assess our method's improvement on them. xT is a streaming, two-stage architecture that adapts existing vision backbones and long sequence language models to effectively model large images without quadratic memory growth. We are able to increase accuracy by up to 8.6% on challenging classification tasks and $F_1$ score by 11.6 on context-dependent segmentation on images as large as 29,000 x 29,000 pixels., Comment: Accepted to the 2024 International Conference on Machine Learning (ICML)

Inertial effects in spin dynamics emerge on picosecond time scales, giving rise to nutational excitations at THz frequencies. Here, we describe a general framework for investigating the precessional and nutational excitations in any type of spin structure within linear spin-wave theory. We consider the particular cases of planar and conical spin spirals in detail. We observe a change in the sign of the curvature of the high-frequency nutational spin-wave band as the spiral period is decreased when passing from the ferromagnetic to the antiferromagnetic limit. We identify conditions for the interaction parameters where the curvature changes sign and asymptotical flat bands are formed., Comment: 10 pages, 4 figures

Coreference resolution involves the task of identifying text spans within a discourse that pertain to the same real-world entity. While this task has been extensively explored in the English language, there has been a notable scarcity of publicly accessible resources and models for coreference resolution in South Asian languages. We introduce a Translated dataset for Multilingual Coreference Resolution (TransMuCoRes) in 31 South Asian languages using off-the-shelf tools for translation and word-alignment. Nearly all of the predicted translations successfully pass a sanity check, and 75% of English references align with their predicted translations. Using multilingual encoders, two off-the-shelf coreference resolution models were trained on a concatenation of TransMuCoRes and a Hindi coreference resolution dataset with manual annotations. The best performing model achieved a score of 64 and 68 for LEA F1 and CoNLL F1, respectively, on our test-split of Hindi golden set. This study is the first to evaluate an end-to-end coreference resolution model on a Hindi golden set. Furthermore, this work underscores the limitations of current coreference evaluation metrics when applied to datasets with split antecedents, advocating for the development of more suitable evaluation metrics., Comment: Accepted at LREC-COLING 2024

Whataboutism, a potent tool for disrupting narratives and sowing distrust, remains under-explored in quantitative NLP research. Moreover, past work has not distinguished its use as a strategy for misinformation and propaganda from its use as a tool for pragmatic and semantic framing. We introduce new datasets from Twitter and YouTube, revealing overlaps as well as distinctions between whataboutism, propaganda, and the tu quoque fallacy. Furthermore, drawing on recent work in linguistic semantics, we differentiate the `what about' lexical construct from whataboutism. Our experiments bring to light unique challenges in its accurate detection, prompting the introduction of a novel method using attention weights for negative sample mining. We report significant improvements of 4% and 10% over previous state-of-the-art methods in our Twitter and YouTube collections, respectively., Comment: 14 pages, 5 figures

We introduce a real-time, multichannel speech enhancement algorithm which maintains the spatial cues of stereo recordings including two speech sources. Recognizing that each source has unique spatial information, our method utilizes a dual-path structure, ensuring the spatial cues remain unaffected during enhancement by applying source-specific common-band gain. This method also seamlessly integrates pretrained monaural speech enhancement, eliminating the need for retraining on stereo inputs. Source separation from stereo mixtures is achieved via spatial beamforming, with the steering vector for each source being adaptively updated using post-enhancement output signal. This ensures accurate tracking of the spatial information. The final stereo output is derived by merging the spatial images of the enhanced sources, with its efficacy not heavily reliant on the separation performance of the beamforming. The algorithm runs in real-time on 10-ms frames with a 40 ms of look-ahead. Evaluations reveal its effectiveness in enhancing speech and preserving spatial cues in both fully and sparsely overlapped mixtures., Comment: Accepted for ICASSP 2024, 5 pages

In many real-world causal inference applications, the primary outcomes (labels) are often partially missing, especially if they are expensive or difficult to collect. If the missingness depends on covariates (i.e., missingness is not completely at random), analyses based on fully observed samples alone may be biased. Incorporating surrogates, which are fully observed post-treatment variables related to the primary outcome, can improve estimation in this case. In this paper, we study the role of surrogates in estimating continuous treatment effects and propose a doubly robust method to efficiently incorporate surrogates in the analysis, which uses both labeled and unlabeled data and does not suffer from the above selection bias problem. Importantly, we establish the asymptotic normality of the proposed estimator and show possible improvements on the variance compared with methods that solely use labeled data. Extensive simulations show our methods enjoy appealing empirical performance., Comment: 30 pages, 7 figures

Table question answering is a popular task that assesses a model's ability to understand and interact with structured data. However, the given table often does not contain sufficient information for answering the question, necessitating the integration of external knowledge. Existing methods either convert both the table and external knowledge into text, which neglects the structured nature of the table; or they embed queries for external sources in the interaction with the table, which complicates the process. In this paper, we propose a simple yet effective method to integrate external information in a given table. Our method first constructs an augmenting table containing the missing information and then generates a SQL query over the two tables to answer the question. Experiments show that our method outperforms strong baselines on three table QA benchmarks. Our code is publicly available at https://github.com/UCSB-NLP-Chang/Augment_tableQA.

The quantum anomalous Hall effect resulting from the in-plane magnetization in the OsCl$_3$ monolayer is shown to exhibit different electronic topological phases determined by the crystal symmetries and magnetism. In this Chern insulator, the Os-atoms form a two dimensional planar honeycomb structure with an easy-plane ferromagnetic configuration and the required non-adiabatic paths to tune the topology of electronic structure exist for specific magnetic orientations based on mirror symmetries of the system. Using density functional theory (DFT) calculations, these tunable phases are identified by changing the orientation of the magnetic moments. We argue that in contrast to the buckled system, here the Cl-ligands bring non-trivial topology into the system by breaking the in-plane mirror symmetry. The interplay between the magnetic anisotropy and electronic band-topology changes the Chern number and hence the topological phases. Our DFT study is corroborated with comprehensive analysis of relevant symmetries as well as a detailed explanation of topological phase transitions using a generic tight binding model., Comment: 9 pages, 6 figures

We show that a pair of newforms $(f,g)$ can be uniquely determined by the product of the central $L$-values of their twists. To achieve our goal, we prove an asymptotic formula for the average of the product of the central values of two twisted $L$-functions- $L(1/2, f \times \chi)L(1/2, g \times \chi \psi)$, where $(f,g)$ is a pair of newforms. The average is taken over the primitive Dirichlet characters $\chi$ and $\psi$ of distinct prime moduli., Comment: Comments are welcome

With growing concerns regarding bias and discrimination in predictive models, the AI community has increasingly focused on assessing AI system trustworthiness. Conventionally, trustworthy AI literature relies on the probabilistic framework and calibration as prerequisites for trustworthiness. In this work, we depart from this viewpoint by proposing a novel trust framework inspired by the philosophy literature on trust. We present a precise mathematical definition of trustworthiness, termed $\mathcal{U}$-trustworthiness, specifically tailored for a subset of tasks aimed at maximizing a utility function. We argue that a model's $\mathcal{U}$-trustworthiness is contingent upon its ability to maximize Bayes utility within this task subset. Our first set of results challenges the probabilistic framework by demonstrating its potential to favor less trustworthy models and introduce the risk of misleading trustworthiness assessments. Within the context of $\mathcal{U}$-trustworthiness, we prove that properly-ranked models are inherently $\mathcal{U}$-trustworthy. Furthermore, we advocate for the adoption of the AUC metric as the preferred measure of trustworthiness. By offering both theoretical guarantees and experimental validation, AUC enables robust evaluation of trustworthiness, thereby enhancing model selection and hyperparameter tuning to yield more trustworthy outcomes.

The growth of low-end hardware has led to a proliferation of machine learning-based services in edge applications. These applications gather contextual information about users and provide some services, such as personalized offers, through a machine learning (ML) model. A growing practice has been to deploy such ML models on the user's device to reduce latency, maintain user privacy, and minimize continuous reliance on a centralized source. However, deploying ML models on the user's edge device can leak proprietary information about the service provider. In this work, we investigate on-device ML models that are used to provide mobile services and demonstrate how simple attacks can leak proprietary information of the service provider. We show that different adversaries can easily exploit such models to maximize their profit and accomplish content theft. Motivated by the need to thwart such attacks, we present an end-to-end framework, SODA, for deploying and serving on edge devices while defending against adversarial usage. Our results demonstrate that SODA can detect adversarial usage with 89% accuracy in less than 50 queries with minimal impact on service performance, latency, and storage.

Interpreting tangency as a limit of two transverse intersections, we obtain a concrete formula to enumerate smooth degree d plane curves tangent to a given line at multiple points with arbitrary order of tangency. One nodal curves with multiple tangencies of any order are enumerated. Also, one cuspidal curves, that are tangent to first order to a given line at multiple points, are enumerated. We also present a new way to enumerate curves with one node; it is interpreted as a degeneration of a curve tangent to a given line. That method is extended to enumerate curves with two nodes, and also curves with one tacnode are enumerated. In the final part of the paper, it is shown how this idea can be applied in the setting of stable maps and perform a concrete computation to enumerate rational curves with first order tangency. A large number of low degree cases have been worked out explicitly., Comment: Changed the title of the paper. 48 pages

Current open-source Large Multimodal Models (LMMs) excel at tasks such as open-vocabulary language grounding and segmentation but can suffer under false premises when queries imply the existence of something that is not actually present in the image. We observe that existing methods that fine-tune an LMM to segment images significantly degrade their ability to reliably determine ("see") if an object is present and to interact naturally with humans ("say"), a form of catastrophic forgetting. In this work, we propose a cascading and joint training approach for LMMs to solve this task, avoiding catastrophic forgetting of previous skills. Our resulting model can "see" by detecting whether objects are present in an image, "say" by telling the user if they are not, proposing alternative queries or correcting semantic errors in the query, and finally "segment" by outputting the mask of the desired objects if they exist. Additionally, we introduce a novel False Premise Correction benchmark dataset, an extension of existing RefCOCO(+/g) referring segmentation datasets (which we call FP-RefCOCO(+/g)). The results show that our method not only detects false premises up to 55% better than existing approaches, but under false premise conditions produces relative cIOU improvements of more than 31% over baselines, and produces natural language feedback judged helpful up to 67% of the time., Comment: Project Page: https://see-say-segment.github.io