May 2025 arXiv papers — page 8

Charig Yang, Samiul Alam, Shakhrul Iman Siam, Michael J. Proulx

To enable egocentric contextual AI in always-on smart glasses, it is crucial to be able to keep a record of the user's interactions with the world, including during reading. In this paper, we introduce a new task of reading recognition to determine when the user is reading. We first introduce the first-of-its-kind large-scale multimodal Reading in the Wild d

Yiming Wang, Changle Liu, Shan Wu, Jianda Wu

The phase diagram of iron-based superconductors contains a host of electronic orders, which are intimately connected with their superconductivity. Here we analyze the fluctuations of one type of nematic order in another. Our analysis leads to an emergent U(1) symmetry at a first-order transition between a nematic phase and a $C_4$-symmetric charge-ordered ph

Raj Patel, Himanshu Tripathi, Jasper Stone, Noorbakhsh Amiri Golilarz

The rapid adoption of machine learning (ML) technologies has driven organizations across diverse sectors to seek efficient and reliable methods to accelerate model development-to-deployment. Machine Learning Operations (MLOps) has emerged as an integrative approach addressing these requirements by unifying relevant roles and streamlining ML workflows. As the

Jingyan Shen, Jiarui Yao, Rui Yang, Yifan Sun

Reward modeling is a key step in building safe foundation models when applying reinforcement learning from human feedback (RLHF) to align Large Language Models (LLMs). However, reward modeling based on the Bradley-Terry (BT) model assumes a global reward function, failing to capture the inherently diverse and heterogeneous human preferences. Hence, such over

Muhammad Rizwan Akram, Abbas Semnani

The natural source of a magnetic dipole in antennas is typically an electrically small loop, which can be utilized in conjunction with an electric dipole to realize an electrically small Huygens' antenna. However, these antennas suffer from low radiation efficiency and their theoretical directivity limit is 4.8 dBi. Magnetic dipoles with an electrical size l

Wanyun Xie, Francesco Tonin, Volkan Cevher

Training data mixtures greatly impact the generalization performance of large language models. Existing domain reweighting methods often rely on costly weight computations and require retraining when new data is introduced. To this end, we introduce a flexible and efficient data mixing framework, Chameleon, that employs leverage scores to quantify domain imp

Ruqi Bai, Yao Ji, Zeyu Zhou, David I. Inouye

Models that learn spurious correlations from training data often fail when deployed in new environments. While many methods aim to learn invariant representations to address this, they often underperform standard empirical risk minimization (ERM). We propose a data-centric alternative that shifts the focus from learning invariant representations to leveragin

Harsh Chaudhari, Jamie Hayes, Matthew Jagielski, Ilia Shumailov

Model distillation has become essential for creating smaller, deployable language models that retain larger system capabilities. However, widespread deployment raises concerns about resilience to adversarial manipulation. This paper investigates vulnerability of distilled models to adversarial injection of biased content during training. We demonstrate that

Calum Hawcroft, Claus Leitherer, Oskar Arangure, John Chisholm

STARBURST99 is a population synthesis code tailored to predict the integrated properties or observational characteristics of star-forming galaxies. Here we present an update to STARBURST99 where we port the code to python, include new evolutionary tracks both rotating and non-rotating at a range of low metallicity environments. We complement these tracks wit

Yuwen Tan, Yuan Qing, Boqing Gong

This paper reveals that many open-source large language models (LLMs) lack hierarchical knowledge about our visual world, unaware of even well-established biology taxonomies. This shortcoming makes LLMs a bottleneck for vision LLMs' hierarchical visual recognition (e.g., recognizing Anemone Fish but not Vertebrate). We arrive at these findings using about on

Shilpa Sarkar

A novel methodology to obtain global transonic solutions around compact objects is reported here. A unified methodology to obtain accretion as well as wind solutions around these objects has been presented. Flows around compact objects are dissipative, and the conservation equations are therefore stiff. In such conditions, obtaining of sonic point(s) and hen

Ashik E Rasul, Hyung-Jin Yoon

Accurate state estimation is critical for optimal policy design in dynamic systems. However, obtaining true system states is often impractical or infeasible, complicating the policy learning process. This paper introduces a novel neural architecture that integrates spatial feature extraction using convolutional neural networks (CNNs) and temporal modeling th

Brandon Man, Ghadi Nehme, Md Ferdous Alam, Faez Ahmed

Computer-Aided Design (CAD) is a time-consuming and complex process, requiring precise, long-horizon user interactions with intricate 3D interfaces. While recent advances in AI-driven user interface (UI) agents show promise, most existing datasets and methods focus on short, low-complexity tasks in mobile or web applications, failing to capture the demands o

Yinglian Zhu, Haiyang Yu, Qizao Wang, Wei Lu

Chinese Character Recognition (CCR) is a fundamental technology for intelligent document processing. Unlike Latin characters, Chinese characters exhibit unique spatial structures and compositional rules, allowing for the use of fine-grained semantic information in representation. However, existing approaches are usually based on auto-regressive as well as ed

A. Jamraoui, Y. Selmani, A. Jabar, L. Bahmad

In this work we reported the structural and electronic properties of the Heusler compound Co2FeGe using the AKAI-KKR code under the GGA approximation. We established that this material presents not only magnetic character but also has a metallic behavior. Our calculations have been conducted using the DFT method in the framework of the AKAI-KKR code. This st

Fuyuan Lyu, Linfeng Du, Yunpeng Weng, Qiufang Ying

Fund allocation has been an increasingly important problem in the financial domain. In reality, we aim to allocate the funds to buy certain assets within a certain future period. Naive solutions such as prediction-only or Predict-then-Optimize approaches suffer from goal mismatch. Additionally, the introduction of the SOTA time series forecasting model inevi

Roksana Goworek, Haim Dubossarsky

Cross-lingual transfer is central to modern NLP, enabling models to perform tasks in languages different from those they were trained on. A common assumption is that training on more languages improves zero-shot transfer. We test this on sense-aware tasks-polysemy and lexical semantic change-and find that multilinguality is not necessary for effective transf

Duxing Hao, Chun-I Lu, Ziqi Sun, Yu-Chen Chang

Circular dichroism spectroscopy is known to provide important insights into the interplay of different degrees of freedom in quantum materials, and yet spectroscopic study of the optoelectronic responses of quantum materials to structured optical fields, such as light with finite spin and orbital angular momentum, has not yet been widely explored, particular

John X. Morris, Chawin Sitawarin, Chuan Guo, Narine Kokhlikyan

We propose a new method for estimating how much a model knows about a datapoint and use it to measure the capacity of modern language models. Prior studies of language model memorization have struggled to disentangle memorization from generalization. We formally separate memorization into two components: unintended memorization, the information a model conta

Ruixue Jing, Ryota Kobayashi, Luis Enrique Correa Rocha

The rapidly evolving cryptocurrency market presents unique challenges for investment due to its inherent volatility and evolving regulatory environment. Collective price movements can be exploited to construct diversified portfolios with improved risk-return profiles. This paper introduces an integrated framework that combines network analysis, price forecas

Juraj Vladika, Annika Domres, Mai Nguyen, Rebecca Moser

Large language models (LLMs) exhibit extensive medical knowledge but are prone to hallucinations and inaccurate citations, which pose a challenge to their clinical adoption and regulatory compliance. Current methods, such as Retrieval Augmented Generation, partially address these issues by grounding answers in source documents, but hallucinations and low fac

Giacomo Galloni, Paolo Campeti, Luca Pagano, Martina Gerbino

Accurate parameter estimation from cosmic microwave background data requires reliable likelihood modeling, particularly at large angular scales where angular power spectrum estimators exhibit non-Gaussian statistics. We present a novel approach, based on the Hamimeche-Lewis formalism, that marginalizes over auto-spectra, thus reducing residual biases from no

J. Douglas Wright, Udoh Akpan

We consider long range variants of Fermi-Pasta-Ulam-Tsingou lattice and in particular allow for particles to interact over arbitrarily long distances. We develop sufficient conditions which allow for the construction of solitary wave solutions.

Li yunhan, Wu gengshen

As large language models (LLMs) are increasingly used in legal applications, current evaluation benchmarks tend to focus mainly on factual accuracy while largely neglecting important linguistic quality aspects such as clarity, coherence, and terminology. To address this gap, we propose three steps: First, we develop a regression model to evaluate the quality

Hung Le, Shay Solomon, Cuong Than, Csaba D. Tóth

In their seminal paper, Alth\"{o}fer et al. (DCG 1993) introduced the {\em greedy spanner} and showed that, for any weighted planar graph $G$, the weight of the greedy $(1+\epsilon)$-spanner is at most $(1+\frac{2}{\epsilon}) \cdot w(MST(G))$, where $w(MST(G))$ is the weight of a minimum spanning tree $MST(G)$ of $G$. This bound is optimal in an {\em existen

Marta López-Rauhut, Hongyu Zhou, Mathieu Aubry, Loic Landrieu

Historical maps offer an invaluable perspective into territory evolution across past centuries--long before satellite or remote sensing technologies existed. Deep learning methods have shown promising results in segmenting historical maps, but publicly available datasets typically focus on a single map type or period, require extensive and costly annotations

Yinggan Xu, Yue Liu, Zhiqiang Gao, Changnan Peng

Large language models (LLMs) have rapidly advanced and are increasingly capable of tackling complex scientific problems, including those in physics. Despite this progress, current LLMs often fail to emulate the concise, principle-based reasoning characteristic of human experts, instead generating lengthy and opaque solutions. This discrepancy highlights a cr

Yang Bai, Ting-Kuo Chen, Jia Liu, Xiaolin Ma

We present a complete Lagrangian describing axion interactions with pseudoscalar and (axial-)vector mesons within the three light-flavor quark framework. This formulation incorporates both the standard chiral Lagrangian and the full Wess-Zumino-Witten (WZW) term. By including instanton effects associated with the anomalous $U(1)_A$ symmetry, we demonstrate t

Anna Brandenberger, Byron Chin, Elchanan Mossel

Motivated by the connection to a probabilistic model of phylogenetic trees introduced by Aldous, we study the recursive sequence governed by the rule $x_n = \sum_{i=1}^{n-1} \frac{1}{h_{n-1}(n-i)} x_i$ where $h_{n-1} = \sum_{j=1}^{n-1} 1/j$, known as the harmonic descent chain. While it is known that this sequence converges to an explicit limit $x$, not much

Matthew Rayman

Effective versions of strong measure zero sets are developed for various levels of complexity and computability. It is shown that the sets can be equivalently defined using a generalization of supermartingales called odds supermartingales, success rates on supermartingales, predictors, and coverings. We show Borel's conjecture of a set having strong measure

Yu Xi, Xiaoyu Gu, Haoyu Li, Jun Song

RNN-T-based keyword spotting (KWS) with autoregressive decoding~(AR) has gained attention due to its streaming architecture and superior performance. However, the simplicity of the prediction network in RNN-T poses an overfitting issue, especially under challenging scenarios, resulting in degraded performance. In this paper, we propose a masked self-distilla

Aarathi Parameswaran, Andrea Benigni, Dirk Witthaut, Iva Bačić

Both natural and engineered supply networks exhibit universal structural patterns, such as the formation of loops, yet the principles governing optimal structures remain unclear. These patterns can be interpreted as solutions of optimization models, assuming that biological networks evolve toward optimal states and engineered systems are designed accordingly

Eric C. Nelson, Kyle J. Charbonnet, Haytham H. Effarah, Trevor Reutershan

A characterization of the focused space-time structures of radially chirped beams is provided, detailing different tunable properties such as: variable on-axis centroid velocity, symmetric pulse front tilt, transverse intensity modulations, and polarization states. While the practical generation of ideal radially chirped beams and polarizations can be proble

Jiangpeng He, Zhihao Duan, Fengqing Zhu

Class-Incremental Learning (CIL) aims to learn new classes sequentially while retaining the knowledge of previously learned classes. Recently, pre-trained models (PTMs) combined with parameter-efficient fine-tuning (PEFT) have shown remarkable performance in rehearsal-free CIL without requiring exemplars from previous tasks. However, existing adapter-based m

V Varagapriya, Vikas Vikram Singh, Abdel Lisser

Constrained Markov decision processes (CMDPs) are used as a decision-making framework to study the long-run performance of a stochastic system. It is well-known that a stationary optimal policy of a CMDP problem under discounted cost criterion can be obtained by solving a linear programming problem when running costs and transition probabilities are exactly

Debendro Mookerjee, Sarah Kostinski

We present a closed-form expression for the survival probability of a biased random walker to first reach a target site on a 1D lattice. The expression holds for any step number $N$ and is computationally faster than non-closed-form results in the literature. Because our result is exact even in the intermediate step number range, it serves as a tool to study

Yannick Feld, Marc Barthelemy

Supply networks are essential for modern production, yet their critical properties remain understudied. We present a stochastic model with random production capacities to analyze material flow to a root node, focusing on topology and buffer stocks. The critical demand, where unsatisfied demand diverges, is examined mostly through numerical simulations. Witho

Marcelo Fiore, Sanjiv Ranchod

We develop a unified categorical theory of substructural abstract syntax with variable binding and single-variable (capture-avoiding) substitution. This is done for the gamut of context structural rules given by exchange (linear theory) with weakening (affine theory) or with contraction (relevant theory) and with both (cartesian theory). Specifically, in all

Alexander Kent, Thomas B. Berrett, Yi Yu

We consider the problem of two-sample testing under a local differential privacy constraint where a permutation procedure is used to calibrate the tests. We develop testing procedures which are optimal up to logarithmic factors, for general discrete distributions and continuous distributions subject to a smoothness constraint. Both non-interactive and intera

Xiaocong Ai, Stefan Antusch, Peter Athron, Yunxiang Bai

The Circular Electron-Positron Collider (CEPC), a proposed next-generation Higgs factory, provides new opportunities to explore physics beyond the Standard Model (SM). With its clean electron-positron collision environment and the ability to collect large samples of Higgs, W, and Z bosons, the CEPC enables precision measurements and searches for new physics.

Friederike Elisa Wührl, Antonia Rieche, Anne Oelschläger, Kathrin Dörr

Strong electron correlations in perovskite oxides give rise to rich and often unexpected electronic phenomena. In this study, we present a comprehensive surface-science investigation of epitaxial thin films of the charge-transfer insulator LaFeO$_3$(001). The characterization includes low-energy electron diffraction (LEED), high-resolution electron energy lo

Wenhao Ding, Sushant Veer, Yuxiao Chen, Yulong Cao

Learning-based planners generate natural human-like driving behaviors by learning to reason about nuanced interactions from data, overcoming the rigid behaviors that arise from rule-based planners. Nonetheless, data-driven approaches often struggle with rare, safety-critical scenarios and offer limited controllability over the generated trajectories. To addr

Vishikh Athavale, Nikita Fedik, William Colglazier, Anders M. N. Niklasson

We report the implementation of electronic excited states for semi-empirical quantum chemical methods at the configuration interaction singles (CIS) and time-dependent Hartree-Fock (TDHF) level of theory in the PySEQM software. Built on PyTorch, this implementation leverages GPU acceleration to significantly speed up molecular property calculations. Benchmar

Somaye Imanpour, Ahmadreza Montazerolghaem, Saeed Afshari

The Internet of Multimedia Things (IoMT) represents a significant advancement in the evolution of IoT technologies, focusing on the transmission and management of multimedia streams. As the volume of data continues to surge and the number of connected devices grows exponentially, internet traffic has reached unprecedented levels, resulting in challenges such

Hernan Haimovich, Shenyu Liu, Antonio Russo, Jose L. Mancilla-Aguilar

When the state of a system may remain bounded even if both the input amplitude and energy are unbounded, then the state bounds given by the standard input-to-state stability (ISS) and integral-ISS (iISS) properties may provide no useful information. This paper considers an ISS-related concept suitable in such a case: input-power-to-state stability (IPSS). Ne

Chunjie Wang, Xuhui Zhang, Wenchao Liu, Jinke Ren

Emerging as a cornerstone for next-generation wireless networks, integrated sensing and communication (ISAC) systems demand innovative solutions to balance spectral efficiency and sensing accuracy. In this paper, we propose a coordinated beamforming framework for a reconfigurable intelligent surface (RIS)-empowered ISAC system, where the active precoding at

Zhijun Pan, Antonios Andronis, Eva Hayek, Oscar AP Wilkinson

Large language models (LLMs) have shown great potential in story generation, but challenges remain in maintaining long-form coherence and effective, user-friendly control. Retrieval-augmented generation (RAG) has proven effective in reducing hallucinations in text generation; while knowledge-graph (KG)-driven storytelling has been explored in prior work, thi

Marc González, Rachid Guerraoui, Rafael Pinot, Geovani Rizk

We present ByzFL, an open-source Python library for developing and benchmarking robust federated learning (FL) algorithms. ByzFL provides a unified and extensible framework that includes implementations of state-of-the-art robust aggregators, a suite of configurable attacks, and tools for simulating a variety of FL scenarios, including heterogeneous data dis

Dorian Quelle, Frederic Denker, Prashant Garg, Alexandre Bovet

Social media platforms mediate professional communication, political expression, and community formation, making the rare instances when users collectively abandon an incumbent platform particularly consequential. Strong network effects raise switching costs and strengthen incumbents' positions, making coordinated exit difficult. Here we link 276,431 scholar

Sergey A. Dyakov, Ilia A. Smagin, Natalia S. Salakhova, Oleg Blokhin

Maximizing the interaction between chiral light and chiral matter is pivotal for the advancement of technologies enabling optical detection that distinguishes between different handedness in chiral organic molecules. One strategy involves developing a resonator that sustains photonic modes with non-zero electromagnetic handedness, which interact differently

Aditya Retnanto, Son Le, Sebastian Mueller, Armin Leitner

Super-resolution aims to increase the resolution of satellite images by reconstructing high-frequency details, which go beyond na\"ive upsampling. This has particular relevance for Earth observation missions like Sentinel-2, which offer frequent, regular coverage at no cost; but at coarse resolution. Its pixel footprint is too large to capture small features

Martin Folwarczny, Ao Li, Rushvi Shah, Aaron Chote

We present a method for obtaining qualitatively accurate grain boundary plane distributions (GBPD) for textured microstructures using a stereological calculation applied to two-dimensional electron backscatter diffraction (EBSD) orientation maps. Stereology, applied to 2D EBSD orientation maps, is currently the fastest method of obtaining GBPDs. Existing ste

Zilang Chen

Accurate and reproducible wine-quality assessment is critical for production control yet remains dominated by subjective, labour-intensive tasting panels. We present the first unified benchmark of five ensemble learners (Random Forest, Gradient Boosting, XGBoost, LightGBM, CatBoost) on the canonical Vinho Verde red- and white-wine datasets (1,599 and 4,898 i

F. Bossi, R. De Sangro, C. Di Giulio, E. Di Meco

The PADME Experiment at the Frascati DA$\Phi$NE LINAC has searched for a hypothetical particle with mass around 17 MeV, commonly referred to as the X17, using a positron beam incident on a fixed target. The beam energy was varied between 262 and 296 MeV, corresponding to center-of-mass energies $\sqrt{s}$ between 16.4 and 17.4 MeV. The X17 should be produced

Zimu Liao, Jifeng Ding, Siwei Cui, Ruixuan Gong

3D Gaussian Splatting (3DGS) renders pixels by rasterizing Gaussian primitives, where conditional alpha-blending dominates the computational cost in the rendering pipeline. This paper proposes TC-GS, an algorithm-independent universal module that expands the applicability of Tensor Core (TCU) for 3DGS, leading to substantial speedups and seamless integration

Sibei Liu, Yuanzhe Zhang, Xiang Li, Yunbo Liu

Multimodal recommendation has emerged as a promising solution to alleviate the cold-start and sparsity problems in collaborative filtering by incorporating rich content information, such as product images and textual descriptions. However, effectively integrating heterogeneous modalities into a unified recommendation framework remains a challenge. Existing a

Eva Rifà, Julian Vicens, Emanuele Cozzo

We study the dynamics and intervention strategies of a rumor using the modified Maki-Thompson model. A key challenge in social networks is distinguishing between natural increases in transmissibility and artificial injections of rumor spreaders, such as through broadcast events or astroturfing. Using stochastic simulations, we compare two scenarios: one with

Benedek Kovács, Zoltán Lóránt Nagy

We study the set of numbers the total number of independent sets can admit in $n$-vertex graphs. In this paper, we prove that the cardinality $\mathcal{N}i(n)$ of this set is very close to $2^n$ in the following sense: $\mathcal{N}i(n)/2^n = O(n^{-1/5})$ while for infinitely many $n$, we have $\log_2(\mathcal{N}i(n)/2^n)\ge -2^{(1+o(1)\sqrt{\log_2 n}}$. This

Yu Gao, Chong Chen

For nonlinear multispectral computed tomography (CT), accurate and fast image reconstruction is challenging when the scanning geometries under different X-ray energy spectra are inconsistent or mismatched. Motivated by this, we propose an Accurate and Fast Image REconstruction (AFIRE) algorithm to address such problems in the case of mildly full scan. From t

Xinliu Zhong, Leo Hwa Liang, Angela S. Koh, Yeo Si Yong

Traditional diagnostic methods like colonoscopy are invasive yet critical tools necessary for accurately diagnosing colorectal cancer (CRC). Detection of CRC at early stages is crucial for increasing patient survival rates. However, colonoscopy is dependent on obtaining adequate and high-quality endoscopic images. Prolonged invasive procedures are inherently

Jiaru Zhang, Juanwu Lu, Xiaoyu Wu, Ziran Wang

Discrete normalizing flows are promising generative models with advantages such as analytical log-likelihood computation and end-to-end training. However, the architectural constraints to ensure invertibility and tractable Jacobian computation limit their expressive power and practical usability. Recent advancements utilize autoregressive modeling, significa

Anandita De, Roozbeh Kiani, Luca Mazzucato

Recent advancements in neurotechnology enable precise spatiotemporal patterns of microstimulations with single-cell resolution. The choice of perturbation sites must satisfy two key criteria: efficacy in evoking significant responses and selectivity for the desired target effects. This choice is currently based on laborious trial-and-error procedures, unfeas

K. Abd El Dayem, F. H. Vincent, G. Heissel, T. Paumard

Measuring the astrometric and spectroscopic data of stars orbiting the central black hole in our galaxy (Sgr A*) offers a promising way to measure relativistic effects. In principle, the "no-hair" theorem can be tested at the Galactic Center by monitoring the orbital precession of S-stars due to the angular momentum (spin) and quadrupole moment of Sgr A*. Cl

Houjun Liu, John Bauer, Christopher D. Manning

Originally, dropout was seen as a breakthrough regularization technique that reduced overfitting and improved performance in almost all applications of deep learning by reducing overfitting. Yet, single-epoch pretraining tasks common to modern LLMs yield minimal overfitting, leading to dropout not being used for large LLMs. Nevertheless, no thorough empirica

Yucheng Zhou, Jiahao Yuan, Qianning Wang

Recent advancements in text-to-image (T2I) generation have enabled models to produce high-quality images from textual descriptions. However, these models often struggle with complex instructions involving multiple objects, attributes, and spatial relationships. Existing benchmarks for evaluating T2I models primarily focus on general text-image alignment and

Eran Bamani Beeri, Eden Nissinman, Avishai Sintov

Dynamic hand gestures play a pivotal role in assistive human-robot interaction (HRI), facilitating intuitive, non-verbal communication, particularly for individuals with mobility constraints or those operating robots remotely. Current gesture recognition methods are mostly limited to short-range interactions, reducing their utility in scenarios demanding rob

Patrick Tser Jern Kon, Jiachen Liu, Xinyi Zhu, Qiuyi Ding

Automating AI research holds immense potential for accelerating scientific progress, yet current AI agents struggle with the complexities of rigorous, end-to-end experimentation. We introduce EXP-Bench, a novel benchmark designed to systematically evaluate AI agents on complete research experiments sourced from influential AI publications. Given a research q

Conor Heins, Toon Van de Maele, Alexander Tschantz, Hampus Linander

Current deep reinforcement learning (DRL) approaches achieve state-of-the-art performance in various domains, but struggle with data efficiency compared to human learning, which leverages core priors about objects and their interactions. Active inference offers a principled framework for integrating sensory information with prior knowledge to learn a world m

Mark E. Glickman

Paired comparison models, such as the Bradley-Terry (1952) model and its variants, are commonly used to measure competitor strength in games and sports. Extensions have been proposed to account for order effects (e.g., home-field advantage) as well as the possibility of a tie as a separate outcome, but such models are rarely adopted in practice due to poor f

Max Conti, Manuel Faysse, Gautier Viaud, Antoine Bosselut

A limitation of modern document retrieval embedding methods is that they typically encode passages (chunks) from the same documents independently, often overlooking crucial contextual information from the rest of the document that could greatly improve individual chunk representations. In this work, we introduce ConTEB (Context-aware Text Embedding Benchmark

Karim Abou-Moustafa

We consider the problem of estimating a regularization parameter, or a shrinkage coefficient $\alpha \in (0,1)$ for Regularized Tyler's M-estimator (RTME). In particular, we propose to estimate an optimal shrinkage coefficient by setting $\alpha$ as the solution to a suitably chosen objective function; namely the leave-one-out cross-validated (LOOCV) log-lik

Run-Ze He, Jun-Jian Su, Su-Juan Qin, Zheng-Ping Jin

Quantum neural networks converge faster and achieve higher accuracy than classical models. However, data augmentation in quantum machine learning remains underexplored. To tackle data scarcity, we integrate quantum generative adversarial networks (QGANs) with hybrid quantum-classical neural networks (HQCNNs) to develop an augmentation framework. We propose t

Yidong Luo, Chenguang Wang, Dong Li, Tianshu Yu

The proliferation of machine learning-based methods for Mixed-Integer Linear Programming (MILP) instance generation has surged, driven by the need for diverse training datasets. However, a critical question remains: Are these generated instances truly useful and realistic? Current evaluation protocols often rely on superficial structural metrics or simple so

Jiayu Liu, Qing Zong, Weiqi Wang, Yangqiu Song

As large language models (LLMs) are increasingly used in high-stakes domains, accurately assessing their confidence is crucial. Humans typically express confidence through epistemic markers (e.g., "fairly confident") instead of numerical values. However, it remains unclear whether LLMs consistently use these markers to reflect their intrinsic confidence due

Parameshwar R. Pasnoori, Patrick Azaria, Colin Rylands, Natan Andrei

The interplay between bulk properties and boundary conditions in one-dimensional quantum systems, gives rise to many intriguing phenomena. These include the emergence of zero energy modes which are of significant interest to a variety of fields. In this work we investigate the presence of such zero modes in cases where the boundary conditions are dynamical a

Zachary Bastiani, Robert M. Kirby, Jacob Hochhalter, Shandian Zhe

Diffusion has emerged as a powerful framework for generative modeling, achieving remarkable success in applications such as image and audio synthesis. Enlightened by this progress, we propose a novel diffusion-based approach for symbolic regression. We construct a random mask-based diffusion and denoising process to generate diverse and high-quality equation

Madhura Limaye, Yezhuo Li, Qiong Zhang, Gang Li

The present study aimed to solve the cure optimization problem of laminated composites through a statistical approach. The approach consisted of using constrained Bayesian Optimization (cBO) along with a Gaussian process model as a surrogate to rapidly solve the cure optimization problem. The approach was implemented to two case studies including the cure of

Phuc Thien Tran, Long-Hao Xu, Christian Röver, Tim Friede

Meta-analysis is a well-established tool used to combine data from several independent studies, each of which usually compares the effect of an experimental treatment with a control group. While meta-analyses are often performed using aggregated study summaries, they may also be conducted using individual participant data (IPD). Classical meta-analysis model

Yajie Zhou, Xiaoyi Pang, Zhibo Wang

Federated fine-tuning has emerged as a promising approach to adapt foundation models to downstream tasks using decentralized data. However, real-world deployment remains challenging due to the high computational and communication demands of fine-tuning Large Language Models (LLMs) on clients with data and system resources that are heterogeneous and constrain

S. H. Curnoe, D. Gajera, C. Wei

We present a method to quantify entanglement in mixed states of highly symmetric systems. Symmetry constrains interactions between parts and predicts the degeneracies of the states. While symmetry alone produces entangled eigenstates, the thermal mixed state (density) which contains all of the eigenstate densities weighted by their Boltzmann factors is not n

Pablo G. Arce, Sonali Das, David Ríos Insua

In a world of utility-driven marketing, each company acts as an adversary to other contenders, with all having competing interests. A major challenge for companies launching a new product is that, despite testing, flaws in their product can remain, potentially risking a loss in market share. However, delayed launch decisions can lead to losing first-mover ad

Jun Tang, Dong-Qing Wang, Wei Zhong, Lan Zhou

We investigate phase estimation in a lossy interferometer using entangled coherent states, with particular focus on a scenario where no reference beam is employed. By calculating the quantum Fisher information, we reveal two key results: (1) the metrological equivalence between scenarios with and without a reference beam, established under ideal lossless con

Claudia Merger, Sebastian Goldt

Diffusion models are powerful generative models that produce high-quality samples from complex data. While their infinite-data behavior is well understood, their generalization with finite data remains less clear. Classical learning theory predicts that generalization occurs at a sample complexity that is exponential in the dimension, far exceeding practical

Haoyu Li, Xuhong Li, Yiming Dong, Kun Liu

Dataset diversity plays a pivotal role for the successful training of many machine learning models, particularly in the supervised fine-tuning (SFT) stage of large language model (LLM) development. Despite increasing recognition of its importance, systematic analyses of dataset diversity still remain underexplored. To address this gap, this work presents a s

Nabasmita Talukdar, Xiaodan Zhang, Shreya Paithankar, Hui Wang

Electronic Health Records (EHRs) have been increasingly used as real-world evidence (RWE) to support the discovery and validation of new drug indications. This paper surveys current approaches to EHR-based drug repurposing, covering data sources, processing methodologies, and representation techniques. It discusses study designs and statistical frameworks fo

Thomas M. Gaudin, Jamie A. Kennea, Malcolm J. Coe, Phil A. Evans

It has long been known that a large population of Be/X-ray Binaries (BeXRBs) exists in the Milky Way's neighboring dwarf galaxy, the Small Magellanic Cloud (SMC), due to a recent period of intense star formation. Since 2016, efforts have been made to monitor this population and identify new BeXRBs through the Swift SMC Survey (S-CUBED). S-CUBED's weekly obse

Srikanth Thudumu, Jason Fisher, Hung Du

Supervised Quantum Machine Learning (QML) represents an intersection of quantum computing and classical machine learning, aiming to use quantum resources to support model training and inference. This paper reviews recent developments in supervised QML, focusing on methods such as variational quantum circuits, quantum neural networks, and quantum kernel metho

Rui Zhang, Zhenhuan Liu, Chendi Yang, Yue-Yang Fei

Entanglement detection is a fundamental task in quantum information science, serving as a cornerstone for quantum benchmarking and foundational studies. With an increasing qubit number that can be effectively controlled, there is a pressing need for a scalable and robust detection protocol which requires minimal resources while maintaining high detection cap

Steve Blandino, Nada Golmie, Anirudha Sahoo, Thao Nguyen

The integration of sensing capabilities into 5G New Radio (5G NR) networks offers an opportunity to enable the detection of airborne objects without the need for dedicated radars. This paper investigates the feasibility of using standardized Positioning Reference Signals (PRS) to detect UAVs in Urban Micro (UMi) and Urban Macro (UMa) propagation environments

Wenjun Li, Rongyuan Liu, Guohao Chen, Aijin Lin

In this paper we introduce the branched $\alpha$-flows on closed surfaces with Euler characteristic \(\chi \leq 0\). Based on the strict convexity of the branched $\alpha$-potentials, we establish the long time existence and convergence of the solutions to the branched $\alpha$-flows, which generalizes Ge and Xu's main results \cite{2015,2015A} on the $\alph

Elias Zikkos

Let $\Lambda=\{\lambda_n\}_{n=1}^{\infty}$ be a strictly increasing sequence of positive real numbers such that $\sum_{n=1}^{\infty}\frac{1}{\lambda_n}<\infty$ and $\inf(\lambda_{n+1}-\lambda_n)>0$. We investigate properties of the closed span of the system $\{t^{\lambda_n}\}_{n=1}^{\infty}$ in $L^2 (0,1)$, denoted by $\overline{M_{\Lambda}}$, and of the uni

Zafir Stojanovski, Oliver Stanley, Joe Sharratt, Richard Jones

We introduce Reasoning Gym (RG), a library of reasoning environments for reinforcement learning with verifiable rewards. It provides over 100 data generators and verifiers spanning multiple domains including algebra, arithmetic, computation, cognition, geometry, graph theory, logic, and various common games. Its key innovation is the ability to generate virt

Mu Qiao

Identifying evolutionary correspondences between cell types across species is a fundamental challenge in comparative genomics and evolutionary biology. Existing approaches often rely on either reference-based matching, which imposes asymmetry by designating one species as the reference, or projection-based matching, which may increase computational complexit

Christian Jaumann, Andreas Wiedholz, Annemarie Friedrich

The scientific literature is growing rapidly, making it hard to keep track of the state-of-the-art. Systematic literature reviews (SLRs) aim to identify and evaluate all relevant papers on a topic. After retrieving a set of candidate papers, the abstract screening phase determines initial relevance. To date, abstract screening methods using large language mo

Dario Olianas, Diego Clerissi, Maurizio Leotta, Filippo Ricca

Web applications play a crucial role in our daily lives, making it essential to employ testing methods that ensure their quality. Typically, Web testing automation frameworks rely on locators to interact with the graphical user interface, acting as connection points to the elements on a Web page. Nevertheless, locators are widely recognized as a major vulner

Dennis Zaritsky, Richard Donnerstein, Donghyeon J. Khim

We re-examine the 7,070 candidate ultra-diffuse galaxies (UDGs) in the SMUDGes survey and provide classifications based on their visual morphology. Among the more interesting cases, we identify objects along a low surface brightness galaxy merger sequence (ongoing mergers (8) and post-mergers (7)) and a distinct set of dwarf ring galaxies (29). The ring gala

Leo Egghe, Ronald Rousseau

We make precise what is meant by stating that modified fractional counting (MFC) lies between full counting and complete-normalized fractional counting by proving that for individuals, the MFC-values are weighted geometric averages of these two extremes. There are two essentially different ways to consider the production of institutes in multi-institutional

Yingchaojie Feng, Yiqun Sun, Yandong Sun, Minfeng Zhu

In this work, we investigate an important task named instruction-following text embedding, which generates dynamic text embeddings that adapt to user instructions, highlighting specific attributes of text. Despite recent advancements, existing approaches suffer from significant computational overhead, as they require re-encoding the entire corpus for each ne

Jessica I. Bellone, Maria Colonna, Danilo Gambacurta, Horst Lenske

Collisional heavy ion double charge exchange (DCE) reactions, induced by second order nucleon-nucleon interactions, are shown to provide access to the two-body transition densities of the complementary DCE transitions in the interacting nuclei. Corresponding two-body operators are introduced, treating the second order distorted wave reaction amplitude in the

Gregor Kemper, Christian Liedtke, Christiane Ott

This paper establishes Noether's classical degree bound $\beta(G) \le |G|$ for finite and linearly reductive group schemes. On the other hand, we provide examples of infinitesimal group schemes where $\beta(G)$ is unbounded. We also generalize Molien's formula to finite and linearly reductive group schemes.