arXiv papers from February 2025

Lucas Seiffert, Felipe Pereira

We consider the theoretical analysis of Multiscale Sampling Methods, which are a new class of gradient-free Markov chain Monte Carlo (MCMC) methods for high dimensional inverse differential equation problems. A detailed presentation of those methods is given, including a review of each MCMC technique that they employ. Then, we propose a two-part framework to

Yanan Niu, Roy Sarkis, Demetri Psaltis, Mario Paolone

Accurate intraday solar irradiance forecasting is crucial for optimizing dispatch planning and electricity trading. For this purpose, we introduce a novel and effective approach that includes three distinguishing components from the literature: 1) the uncommon use of single-frame public camera imagery; 2) solar irradiance time series scaled with a proposed n

Abhiroop Ajith, Gokul Narayanan, Jonathan Zornow, Carlos Calle

Sewing garments using robots has consistently posed a research challenge due to the inherent complexities in fabric manipulation. In this paper, we introduce an intelligent robotic automation system designed to address this issue. By employing a patented technique that temporarily stiffens garments, we eliminate the traditional necessity for fabric modeling.

Lukas William Mayer, Sheer Karny, Jackie Ayoub, Miao Song

Despite the growing interest in collaborative AI, designing systems that seamlessly integrate human input remains a major challenge. In this study, we developed a task to systematically examine human preferences for collaborative agents. We created and evaluated five collaborative AI agents with strategies that differ in the manner and degree they adapt to h

Dac-Nhan-Tam Nguyen

Let $p$ be an odd prime. Consider normalized newforms $f_1,f_2$ that both satisfy the Heegner hypothesis for an imaginary quadratic field $K$ and suppose that they induce isomorphic residual Galois representations. In the work of Greenberg-Vatsal and Emerton-Pollack-Weston, the authors compare the cyclotomic Iwasawa $\mu$ and $\lambda$-invariants of $f_1$ an

Michał Wichrowski

We present a matrix-free approach for implementing ghost penalty stabilization in Cut Finite Element Methods (CutFEM). While matrix-free methods for CutFEM have been developed, the efficient evaluation of high-order, face-based ghost penalties remains a significant challenge, which this work addresses. By exploiting the tensor-product structure of the ghost

Katherine A. Seaton

This paper describes preliminary investigation of hitomezashi stitching designs created on the isometric grid. An imposed constraint is that only every second line of stitching in each of the possible three directions is present. Each vertex visited by the stitching has degree two. Motifs and wallpaper patterns with three- and six-fold rotational symmetry ar

Patrick Huber, Akshat Shrivastava, Ernie Chang, Chinnadhurai Sankar

Sparse Mixture of Expert (MoE) models are popular foundational architectures at large scale, however, under-explored at smaller sizes. Here, we show how to enable Compact Sparse Mixture of Experts (CoSMoEs) for on-device inference. Specifically, we tackle the three main on-device dimensions: Quality, Memory and Latency. Along the quality axis, we show that i

K. K. Ernazarov, V. D. Ivashchuk

We consider the $4D$ gravitational model with a scalar field $\varphi$, Einstein and Gauss-Bonnet terms. The action of the model contains a potential term $U(\varphi)$, Gauss-Bonnet coupling function $f(\varphi)$ and a parameter $\varepsilon = \pm1 $, where $\varepsilon = 1$ corresponds to ordinary scalar field and $\varepsilon = -1 $ - to phantom one. Inspi

Martino Grasselli, Gilles Pagès

We investigate a class of non-Markovian processes that hold particular relevance in the realm of mathematical finance. This family encompasses path-dependent volatility models, including those pioneered by [Platen and Rendek, 2018] and, more recently, by [Guyon and Lekeufack, 2023]. Our study unfolds in two principal phases. In the first phase, we introduce

Ali Keshavarzi, Elsa Angelini

Automated airway segmentation from lung CT scans is vital for diagnosing and monitoring pulmonary diseases. Despite advancements, challenges like leakage, breakage, and class imbalance persist, particularly in capturing small airways and preserving topology. We propose the Boundary-Emphasized Loss (BEL), which enhances boundary preservation using a boundary-

Sharba Bhattacharjee, Ivar Martin

We study the retrieval accuracy and capacity of modern Hopfield networks of with two-state (Ising) spins interacting via modified Hebbian $n$-spin interactions. In particular, we consider systems where the interactions deviate from the Hebb rule through additive or multiplicative noise or through clipping or deleting interactions. We find that the capacity s

Marius F. R. Juston, Ramavarapu S. Sreenivas, William R. Norris, Dustin Nottage

This paper discusses the weight parametrization of two standard 1-Lipschitz network architectures, the Almost-Orthogonal-Layers (AOL) and the SDP-based Lipschitz Layers (SLL). It examines their impact on initialization for deep 1-Lipschitz feedforward networks, and discusses underlying issues surrounding this initialization. These networks are mainly used in

Yunyi Shen, Tamara Broderick

Bayesian posterior approximation has become more accessible to practitioners than ever, thanks to modern black-box software. While these tools provide highly accurate approximations with minimal user effort, certain posterior geometries remain notoriously difficult for standard methods. As a result, research into alternative approximation techniques continue

Victor De Lima, Grace Hui Yang

This paper presents our approach to the TREC Interactive Knowledge Assistance Track (iKAT), which focuses on improving conversational information-seeking (CIS) systems. While recent advancements in CIS have improved conversational agents' ability to assist users, significant challenges remain in understanding context and retrieving relevant documents across

Erik Miehling, Karthikeyan Natesan Ramamurthy, Kush R. Varshney, Matthew Riemer

The endowment of AI with reasoning capabilities and some degree of agency is widely viewed as a path toward more capable and generalizable systems. Our position is that the current development of agentic AI requires a more holistic, systems-theoretic perspective in order to fully understand their capabilities and mitigate any emergent risks. The primary moti

Michal Spiegel, Michal Štefánik, Marek Kadlčík, Josef Kuchař

Can transformers learn to perform algorithmic tasks reliably across previously unseen input/output domains? While pre-trained language models show solid accuracy on benchmarks incorporating algorithmic reasoning, assessing the reliability of these results necessitates an ability to distinguish genuine algorithmic understanding from memorization. In this pape

Timothée Crin-Barat, Lorenzo Liverani, Ling-Yun Shou, Enrique Zuazua

We study the stability of one-dimensional linear hyperbolic systems with non-symmetric relaxation. Introducing a new frequency-dependent Kalman stability condition, we prove an abstract decay result underpinning a form of inhomogeneous hypocoercivity. In contrast with the homogeneous setting, the decay rates depend on how the Kalman condition is fulfilled an

Xiang-Yu Huang, Simon Birrer, Michele Cappellari, Tommaso Treu

Constraining the mass-sheet degeneracy (MSD) is crucial for improving the precision and accuracy of time-delay cosmography. Joint analyses of lensing and stellar kinematics are widely adopted to break the MSD. A 3D mass and stellar tracer population is required to accurately interpret the kinematics data. Our forward-modeling procedure aims at evaluating the

Lukasz Sztukiewicz, Ignacy Stępka, Michał Wiliński, Jerzy Stefanowski

The widespread adoption of machine learning systems has raised critical concerns about fairness and bias, making mitigating harmful biases essential for AI development. In this paper, we investigate the relationship between debiasing and removing artifacts in neural networks for computer vision tasks. First, we introduce a set of novel XAI-based metrics that

Bernardo Langa, Brooke Henry, Ivan Lainez, Richard Haight

Ruthenium (Ru) is a promising candidate for the next-generation of electronic interconnects due to its low resistivity, small mean free path, and superior electromigration reliability at nanometer scales. Additionally, Ru exhibits superconductivity below 1 K, with resistance to oxidation, low diffusivity, and a small superconducting gap, making it a potentia

Kaleab A. Kinfu, René Vidal

Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have led to significant progress in 2D body pose estimation. However, achieving a good balance between accuracy, efficiency, and robustness remains a challenge. For instance, CNNs are computationally efficient but struggle with long-range dependencies, while ViTs excel in capturing such depe

Samar M. Magdy, Sang Yun Kwon, Fakhraddin Alwajih, Safaa Abdelfadil

Recent advancements in instruction fine-tuning, alignment methods such as reinforcement learning from human feedback (RLHF), and optimization techniques like direct preference optimization (DPO) have significantly enhanced the adaptability of large language models (LLMs) to user preferences. However, despite these innovations, many LLMs continue to exhibit b

Wenqi Huang, Nan Wang, Congyu Liao, Yimeng Lin

Echo Planar Imaging (EPI) is widely used for its rapid acquisition but suffers from severe geometric distortions due to B0 inhomogeneities, particularly along the phase encoding direction. Existing methods follow a two-step process: reconstructing blip-up/down EPI images, then estimating B0, which can introduce error accumulation and reduce correction accura

Sharan Vaswani, Reza Babanezhad

Armijo line-search (Armijo-LS) is a standard method to set the step-size for gradient descent (GD). For smooth functions, Armijo-LS alleviates the need to know the global smoothness constant L and adapts to the ``local'' smoothness, enabling GD to converge faster. Existing theoretical analyses show that GD with Armijo-LS (GD-LS) can result in constant factor

Sheng Long, Angelos Chatzimparmpas, Emma Alexander, Matthew Kay

Judging the similarity of visualizations is crucial to various applications, such as visualization-based search and visualization recommendation systems. Recent studies show deep-feature-based similarity metrics correlate well with perceptual judgments of image similarity and serve as effective loss functions for tasks like image super-resolution and style t

Melih İşeri, Erhan Bayraktar

This work introduces a unified framework for analyzing games in greater depth. In the existing literature, players' strategies are typically assigned scalar values, and equilibrium concepts are used to identify compatible choices. However, this approach neglects the internal structure of players, thereby failing to accurately model observed behaviors. To add

Yufei Guo, Xiaode Liu, Yuanpei Chen, Weihang Peng

Transformers have demonstrated outstanding performance across a wide range of tasks, owing to their self-attention mechanism, but they are highly energy-consuming. Spiking Neural Networks have emerged as a promising energy-efficient alternative to traditional Artificial Neural Networks, leveraging event-driven computation and binary spikes for information tr

Rafael Vazquez

This paper extends backstepping to higher-dimensional PDEs by leveraging domain symmetries and structural properties. We systematically address three increasingly complex scenarios. First, for rectangular domains, we characterize boundary stabilization with finite-dimensional actuation by combining backstepping with Fourier analysis, deriving explicit necess

K. O. T. Erziev

We report a peculiar observation that LLMs can assign hidden meanings to sequences that seem visually incomprehensible to humans: for example, a nonsensical phrase consisting of Byzantine musical symbols is recognized by gpt-4o as "say abracadabra". Moreover, some models can communicate using these sequences. Some of these meanings are hypothesized to partly

Pengcheng Jiang, Jiacheng Lin, Lang Cao, Runchu Tian

Information retrieval systems are crucial for enabling effective access to large document collections. Recent approaches have leveraged Large Language Models (LLMs) to enhance retrieval performance through query augmentation, but often rely on expensive supervised learning or distillation techniques that require significant computational resources and hand-l

James M. Shook

A graph $G$ is said to be equitably $c$-colorable if its vertices can be partitioned into $c$ independent sets that pairwise differ in size by at most one. Chen, Lih, and Wu conjectured that every connected graph $G$ with maximum degree $\Delta(G)\geq 2$ has an equitable coloring with $\Delta(G)$ colors, except when $G$ is complete, an odd cycle, or a balanc

Seongmin Kim, In-Saeng Suh

Optimization problems are critical across various domains, yet existing quantum algorithms, despite their great potential, struggle with scalability and accuracy due to excessive reliance on entanglement. To address these limitations, we propose variational quantum optimization algorithm (VQOA), which leverages many-qubit (MQ) operations in an ansatz solely

John C. Duchi

We revisit the problem of constructing predictive confidence sets for which we wish to obtain some type of conditional validity. We provide new arguments showing how ``split conformal'' methods achieve near desired coverage levels with high probability, a guarantee conditional on the validation data rather than marginal over it. In addition, we directly cons

Christos Lytrosyngounis, Ioannis Lytrosyngounis

The Traveling Salesperson Problem (TSP) is a fundamental NP-hard optimisation challenge with widespread applications in logistics, operations research, and network design. While classical algorithms effectively solve small to medium-sized instances, they struggle with scalability due to exponential complexity. In this work, we present a hybrid quantum-classi

Le Minh Anh Nguyen, Brant Bowers, Sara Mouradian

To increase the power of a trapped ion quantum information processor, the qubit number, gate speed, and gate fidelity must all increase. All three of these parameters are influenced by the trapping field which in turn depends on the electrode geometry. Here we consider how the electrode geometry affects the radial trapping parameters: trap height, harmonicit

Adway Gupta, Arunima Singh

Hydrogen gas is a promising alternative to fossil fuels due to its high energy output and environmentally safe byproducts. Various morphologies of photocatalytic materials have been explored for high-efficiency H$_2$ production, for instance, quasi-1D nanoscroll structures that provide larger surface-to-volume ratio. Recently, we predicted layer-by-layer for

Reza Saadati, Fayçal Hammad

We recently studied spin precession in various stationary and axisymmetric spacetimes and applied it to the case of neutrinos propagating in those spacetimes. In this paper, the study of spin precession is extended to the case of a spinning particle propagating within the spacetime of a time-dependent gravitational plane wave. First, the angular velocity of

Abhishek Setty, Lukas Morand, Poojitha Ramachandra, Claas Bierwisch

This study explores the applicability of a graph-based interaction-aware trajectory prediction model, originally developed for the transportation domain, to forecast particle trajectories in three-dimensional discrete element simulations. The model and our enhancements are validated at two typical particle simulation use cases: (i) particle flow in a represe

Haichuan Li, Ziang Zhao, Ziniu Wu, Parth Potdar

Protecting and restoring forest ecosystems has become an important conservation issue. Although various robots have been used for field data collection to protect forest ecosystems, the complex terrain and dense canopy make the data collection less efficient. To address this challenge, an aerial platform with bio-inspired behaviour facilitated by a bio-inspi

Alex Alberts, Ilias Bilionis

Many inverse problems require reconstructing physical fields from limited and noisy data while incorporating known governing equations. A growing body of work within probabilistic numerics formalizes such tasks via Bayesian inference in function spaces by assigning a physically meaningful prior to the latent field. In this work, we demonstrate that Brownian

Jesús Jerónimo-Castro, Luis Montejano, Efrén Morales-Amaya

We prove several localized characterizations of quadrics and, between them, the localized Blaschke's Theorem and use this result to give some characterizations of the ellipsoid related to the flatness of its grazes.

Jiawei Zhang, Xuan Yang, Taiqi Wang, Yu Yao

Traditional autonomous driving systems often struggle to connect high-level reasoning with low-level control, leading to suboptimal and sometimes unsafe behaviors. Recent advances in multimodal large language models (MLLMs), which process both visual and textual data, offer an opportunity to unify perception and reasoning. However, effectively embedding prec

Wenrui Fan, L. M. Riza Rizky, Jiayang Zhang, Chen Chen

Neuropathic pain, affecting up to 10% of adults, remains difficult to treat due to limited therapeutic efficacy and tolerability. Although resting-state functional MRI (rs-fMRI) is a promising non-invasive measurement of brain biomarkers to predict drug response in therapeutic development, the complexity of fMRI demands machine learning models with substanti

Vu Minh Hoang Dang, Rakesh M. Verma

Linguistic richness is essential for advancing natural language processing (NLP), as dataset characteristics often directly influence model performance. However, traditional metrics such as Type-Token Ratio (TTR), Vocabulary Diversity (VOCD), and Measure of Lexical Text Diversity (MTLD) do not adequately capture contextual relationships, semantic richness, a

Mark R Swain, Kyle A Pearson, Thaddeus D. Komacek, Geoffrey Bryden

We present a catalog of uniformly processed 3.6-$\mu$m and 4.5-$\mu$m band exoplanet thermal phase curves based on Infrared Array Camera observations obtained from the Spitzer Heritage Archive. The catalog includes phase curve measurements for 34 planets, 16 of which contain full orbit coverage and have detectable secondary eclipses in both channels. The dat

S M Taslim Uddin Raju

As the population of older adults increases, so will the need for both human and robot care providers. While traditional practices involve hiring human caregivers to serve meals and attend to basic needs, older adults often require continuous companionship and health monitoring. However, hiring human caregivers for this job costs a lot of money. However, usi

Cristina Gheorghe, Adrian Petruşel

The aim of this paper is to give strict fixed point principles for multivalued operators $T:X\rightarrow P(X)$ satisfying some contraction conditions of \'Ciri\' c and of \'Ciri\' c-Reich-Rus type. We are interested, under which conditions, the multi-valued operator has a unique strict fixed point and, additionally, when the sequence of its multi-valued iter

Naveen Mysore

Reinforcement learning (RL) methods frequently assume that each new observation completely reflects the environment's state, thereby guaranteeing Markovian (one-step) transitions. In practice, partial observability or sensor/actuator noise often invalidates this assumption. This paper proposes a systematic methodology for detecting such violations, combining

Jian Gao, Weidong Cao, Junyi Yang, Xuan Zhang

The massive and large-scale design of foundational semiconductor integrated circuits (ICs) is crucial to sustaining the advancement of many emerging and future technologies, such as generative AI, 5G/6G, and quantum computing. Excitingly, recent studies have shown the great capabilities of foundational models in expediting the design of digital ICs. Yet, app

Mikołaj Rogóż, Zofia Dziekan, Piotr Wasylczyk

Depending on multiple parameters, soft robots can exhibit different modes of locomotion that are difficult to model numerically. As a result, improving their performance is complex, especially in small-scale systems characterized by low Reynolds numbers, when multiple aero- and hydrodynamical processes influence their movement. In this work, we optimize ligh

William Nguyen, An Phan, Konobu Kimura, Hitoshi Maeno

Large Language Models (LLMs) have demonstrated substantial potential in addressing complex reasoning tasks, yet their general-purpose nature often limits their effectiveness in specialized domains such as maritime navigation. To bridge this gap, we introduce Llamarine, the first open-source LLM designed specifically for maritime navigation. Llamarine 1.0 is

Ryosuke Kawamura, Hideaki Hayashi, Noriko Takemura, Hajime Nagahara

Dynamic facial expression recognition (DFER) is an important task in the field of computer vision. To apply automatic DFER in practice, it is necessary to accurately recognize ambiguous facial expressions, which often appear in data in the wild. In this paper, we propose MIDAS, a data augmentation method for DFER, which augments ambiguous facial expression d

Keroshan Pillay

This paper demonstrates that Automated Market Maker (AMM) based markets, such as those using constant product formulas (e.g., Uniswap), are inherently path-dependent. We prove mathematically that the sequence of operations in AMMs determines the final state, challenging the notion that market prices solely reflect information. This property has profound impl

Shuang Li, Yihuai Gao, Dorsa Sadigh, Shuran Song

A unified video and action model holds significant promise for robotics, where videos provide rich scene information for action prediction, and actions provide dynamics information for video prediction. However, effectively combining video generation and action prediction remains challenging, and current video generation-based methods struggle to match the p

Jacob M. Hiesener, C. Alex Kaylor, Joshua J. Wong, Prankush Agarwal

We present a seeded topology optimization methodology for integrated photonic devices fabricated on foundry platforms that yields improved performance compared to traditional topology optimization. We employ blurring filters and a design rule check correction algorithm to more readily meet fabrication constraints, resulting in devices with fewer artifacts an

Martin Hartenberger, Huzeyfe Ayaz, Fatih Ozlugedik, Charly Caredda

In this paper, we propose a methodology for extracting molecular tumor biomarkers from hyperspectral imaging (HSI), an emerging technology for intraoperative tissue assessment. To achieve this, we employ spectral unmixing, allowing to decompose the spectral signals recorded by the HSI camera into their constituent molecular components. Traditional unmixing a

Beatrice Moscoloni, Cameron Beeche, Julio A. Chirinos, Patrick Segers

Sex-based differences in cardiovascular disease are well documented, yet the precise nature and extent of these discrepancies in cardiac anatomy remain incompletely understood. Traditional scaling models often fail to capture the interplay of age, blood pressure, and body size, prompting a more nuanced investigation. Here, we employ statistical shape modelin

Amar Kumar, Anita Kriz, Mohammad Havaei, Tal Arbel

Developing reliable and generalizable deep learning systems for medical imaging faces significant obstacles due to spurious correlations, data imbalances, and limited text annotations in datasets. Addressing these challenges requires architectures that are robust to the unique complexities posed by medical imaging data. Rapid advancements in vision-language

Jiawei Zhang, Shuang Yang, Bo Li

Large Language Model (LLM) agents equipped with external tools have become increasingly powerful for complex tasks such as web shopping, automated email replies, and financial trading. However, these advancements amplify the risks of adversarial attacks, especially when agents can access sensitive external functionalities. Nevertheless, manipulating LLM agen

Paula Ebner, Jessica Szczuka

LLM-based chatbots are now being specifically designed to facilitate social companionship, even romantic relationships, incorporating features that parallel human relationship dynamics. This has led a subset of users to form romantic relationships with chatbots. Understanding which interpersonal characteristics drive individuals to form intense, emotional bo

Maarten Baes, Andrea Gebek, Sabelo Kunene, Lerothodi Leeuw

The Tully-Fisher relation (TFR) is one of the most important and widely used empirical correlations in extragalactic astronomy. Apart from its importance as a secondary distance indicator, the TFR relation serves as a test for galaxy evolution models, because it connects the baryonic and dark matter components of galaxies. We aim at simulating the multi-wave

Federico Pizarro Bejarano, Bryson Jones, Daniel Pastor Moreno, Joseph Bowkett

Diffusion models have revolutionized imitation learning, allowing robots to replicate complex behaviours. However, diffusion often relies on cameras and other exteroceptive sensors to observe the environment and lacks long-term memory. In space, military, and underwater applications, robots must be highly robust to failures in exteroceptive sensors, operatin

Md Nur-A-Adam Dony

Reinforcement learning (RL) has emerged as a potent paradigm for autonomous decision-making in complex environments. However, the integration of event-driven decision processes within RL remains a challenge. This paper presents a novel architecture that combines a Discrete Event Supervisory (DES) model with a standard RL framework to create a hybrid decision

Xinhang Ma, Junlin Wu, Hussein Sibai, Yiannis Kantaros

Ensuring safety in autonomous systems with vision-based control remains a critical challenge due to the high dimensionality of image inputs and the fact that the relationship between true system state and its visual manifestation is unknown. Existing methods for learning-based control in such settings typically lack formal safety guarantees. To address this

Matteo Boselli, Joel Grebel, Ambroise Peugeot, Rémy Dassonneville

Amplifying microwave signals with a noise close to the minimum imposed by quantum mechanics is now routinely performed with superconducting quantum devices. In particular, Josephson-based Traveling Wave Parametric Amplifiers (JTWPA) have shown record bandwidth with added noise close to the quantum limit. In this work, we report the appearance of echo signals

Balázs István Szabó

A directed hypergraph is a hypergraph in which the vertex set of each hyperedge is partitioned into two disjoint parts, a head and a tail. Keszegh and P\'alv\"olgyi posed the following conjecture. Let $H$ be a directed hypergraph such that in every hyperedge the number of head-vertices is less than the number of tail-vertices and assume that for every pair o

Ezad Shojaee, James R. van Meter, Karl Mayer, Scott Glancy

A general one-dimensional quantum optical mode is described by a shape in the time or frequency domain. A fundamental problem is to measure a quadrature operator of such a mode. If the shape is narrow in frequency this can be done by pulsed homodyne detection, in which the mode and a matched local oscillator (LO) interfere on a beamsplitter, whose output por

Hanjiang Hu, Alexander Robey, Changliu Liu

Large language models (LLMs) are shown to be vulnerable to jailbreaking attacks where adversarial prompts are designed to elicit harmful responses. While existing defenses effectively mitigate single-turn attacks by detecting and filtering unsafe inputs, they fail against multi-turn jailbreaks that exploit contextual drift over multiple interactions, gradual

R. C. Spiers, A. Bose, C. A. Frank, B. Lahmann

Imposing a magnetic field on inertial confinement fusion (ICF) implosions magnetizes the electrons in the compressed fuel; this suppresses thermal losses which increases temperature and fusion yield. Indirect-drive experiments at the National Ignition Facility (NIF) with 12 T and 26 T applied magnetic fields demonstrate up to $40\%$ increase in temperature,

Jorge Fariña-Asategui, Santiago Radi

We prove that super strongly fractal groups acting on regular rooted trees have null fixed-point proportion. In particular, we show that the fixed-point proportion of an infinite family of iterated monodromy groups of exceptional complex polynomials have the same property. The proof uses the approach of Rafe Jones in [15] based on martingales and a recent re

Michael Park, Erin Leahey, Russell J. Funk

We respond to Holst et al.'s critique that the decline in scientific disruptiveness documented in Park et al. (Nature, 2023) is an artifact of including works with zero backward citations. Using their advocated dataset, metric, and exclusion criteria, we find declines equivalent to major benchmark transformations in science. Their own regression model--desig

Jeffrey D. Adler, Joshua M. Lansky, Loren Spice

Let $k$ be a field, and suppose that $\Gamma$ is a smooth $k$-group that acts on a connected, reductive $k$-group $\widetilde G$. Let $G$ denote the maximal smooth, connected subgroup of the group of $\Gamma$-fixed points in $\widetilde G$. Under fairly general conditions, we show that $G$ is a reductive $k$-group, and that the image of the functorial embedd

Deepak Sah, Manoranjan P. Singh

We report on the dynamical scaling of momentum spectra for particle-antiparticle pairs at finite times within the framework of scalar Quantum Electrodynamics (QED). The analysis focuses on the momentum spectra in two different choices of adiabatic mode functions, which are related by a Wronskian normalization condition. Oscillations in the momentum spectra a

Garrett Nelson

An $(m, n)$-parking function can be characterized as function $f:[n] \to [m]$ such that the partition obtained by reordering the values of $f$ fits inside a right triangle with legs of length $m$ and $n$. Recent work by McCammond, Thomas, and Williams define an action of words in $[m]^n$ on $\mathbb{R}^n$. They show that rational parking functions are exactl

Osama A. Marzouk

This study investigates the Rankine vapor power thermodynamic cycle using steam/water as the working fluid, which is common in commercial power plants for power generation as the source of the rotary shaft power needed to drive electric generators. The four-process cycle version, which comprises a water pump section, a boiler/superheater section, a steam tur

Lili Lu, Chuan Meng, Federico Ravenda, Mohammad Aliannejadi

Clarification need prediction (CNP) is a key task in conversational search, aiming to predict whether to ask a clarifying question or give an answer to the current user query. However, current research on CNP suffers from the issues of limited CNP training data and low efficiency. In this paper, we propose a zero-shot and efficient CNP framework (Zef-CNP), i

Ryan O'Dowd, Raghu G. Raj, Hrushikesh N. Mhaskar

Linear inverse problems are ubiquitous in various science and engineering disciplines. Of particular importance in the past few decades, is the incorporation of sparsity based priors, in particular $\ell_1$ priors, into linear inverse problems, which led to the flowering of fields of compressive sensing (CS) and sparsity based signal processing. More recentl

Reza Bayat, Ali Rahimi-Kalahroudi, Mohammad Pezeshki, Sarath Chandar

A key challenge in AI alignment is guiding large language models (LLMs) to follow desired behaviors at test time. Activation steering, which modifies internal model activations during inference, offers a potential solution. However, prior work in dense activation spaces struggles with superposition, wherein multiple features become entangled, limiting interp

Xin Chen, Zhibin Ye

Quantum illumination leverages entanglement to surpass classical target detection, even in high-noise environments. Remarkably, its quantum advantage persists despite entanglement degradation caused by environmental decoherence. A central challenge lies in designing optimal receivers to exploit this advantage, with the correlation-to-displacement conversion

Xiang Liu, Zhe Su, Yongyi Shi, Yiying Tong

Recently, topological deep learning (TDL), which integrates algebraic topology with deep neural networks, has achieved tremendous success in processing point-cloud data, emerging as a promising paradigm in data science. However, TDL has not been developed for data on differentiable manifolds, including images, due to the challenges posed by differential topo

Akhil Jalan, Yassir Jedra, Arya Mazumdar, Soumendu Sundar Mukherjee

We study transfer learning for matrix completion in a Missing Not-at-Random (MNAR) setting that is motivated by biological problems. The target matrix $Q$ has entire rows and columns missing, making estimation impossible without side information. To address this, we use a noisy and incomplete source matrix $P$, which relates to $Q$ via a feature shift in lat

Ahmed Saoudi, Imen Kallel

The aim of this paper is to establish and study the linear canonical Dunkl wavelet transform. We begin by introducing the generalized translation operator and generalized convolution product for the linear canonical Dunkl transform and we establish their basic properties. Next, we introduce the new proposed wavelet transform and we investigate its fundamenta

Zhiqiu Xia, Jinxuan Xu, Yuqian Zhang, Hang Liu

Large language models (LLMs) have demonstrated remarkable capabilities across various tasks. However, these models could offer biased, hallucinated, or non-factual responses camouflaged by their fluency and realistic appearance. Uncertainty estimation is the key method to address this challenge. While research efforts in uncertainty estimation are ramping up

Denis Musinguzi, Andrew Katumba, Sudi Murindanyi

Tuberculosis (TB) is a infectious global health challenge. Chest X-rays are a standard method for TB screening, yet many countries face a critical shortage of radiologists capable of interpreting these images. Machine learning offers an alternative, as it can automate tasks such as disease diagnosis, and report generation. However, traditional approaches rel

Roi Bar-Zur, Ittay Eyal

Many blockchain-based decentralized services require their validators (operators) to deposit stake (collateral), which is forfeited (slashed) if they misbehave. Restaking networks let validators secure multiple services by reusing stake. These networks have quickly gained traction, leveraging over \$20 billion in stake. However, restaking introduces a new at

Dip Joti Paul, Tony X. Zhou, Karl K. Berggren

In this work, we present a technique to determine the mid-infrared refractive indices of thin superconducting films using Fourier transform infrared spectroscopy (FTIR). In particular, we performed FTIR transmission and reflection measurements on 10-nm-thick NbN and 15-nm-thick MoSi films in the wavelength range of 2.5 to 25 $\mu$m, corresponding to frequenc

Benedikt Blumenstiel, Nassim Ait Ali Braham, Conrad M Albrecht, Stefano Maurogiovanni

This work presents SSL4EO-S12 v1.1, a multimodal, multitemporal Earth Observation dataset designed for pretraining large-scale foundation models. Building on the success of SSL4EO-S12, this extension updates the previous version to fix geospatial alignment inaccuracies and the inefficent data structure. The dataset allows low-barrier, analysis-ready data loa

Kuangyi Chen, Jun Zhang, Friedrich Fraundorfer

Event cameras are bio-inspired sensors with some notable features, including high dynamic range and low latency, which makes them exceptionally suitable for perception in challenging scenarios such as high-speed motion and extreme lighting conditions. In this paper, we explore their potential for localization within pre-existing LiDAR maps, a critical task f

Bin Zhuang, Noé Lugaz, Nada Al-Haddad, Charles J. Farrugia

Understanding the properties, especially the magnetohydrodynamic (MHD) invariants, of coronal mass ejections (CMEs) measured in-situ is key to bridging the CME properties from the Sun to interplanetary space. In order to investigate CMEs from the in-situ measurements that provide a one-dimensional (1-D) cut of the CME parameters over the spacecraft trajector

Enming Xing, Junjie Zhang, Shen Wang, Xiaolin Cheng

AlphaFold2 (AF2) has transformed protein structure prediction by harnessing co-evolutionary constraints embedded in multiple sequence alignments (MSAs). MSAs not only encode static structural information, but also hold critical details about protein dynamics, which underpin biological functions. However, these subtle co-evolutionary signatures, which dictate

Krti Tallam

In an era marked by unprecedented digital complexity, the cybersecurity landscape is evolving at a breakneck pace, challenging traditional defense paradigms. Advanced Persistent Threats (APTs) reveal inherent vulnerabilities in conventional security measures and underscore the urgent need for continuous, adaptive, and proactive strategies that seamlessly int

Luca Orusa, Vicente Valenzuela-Villaseca

The study of collisionless shocks and their role in cosmic ray acceleration has gained importance through observations and simulations, driving interest in reproducing these conditions in laboratory experiments using high-power lasers. In this work, we examine the role of three-dimensional (3D) effects in ion acceleration in quasi-perpendicular shocks under

Kangda Wei, Zhengyu Zhou, Bingqing Wang, Jun Araki

In recent years, online lecture videos have become an increasingly popular resource for acquiring new knowledge. Systems capable of effectively understanding/indexing lecture videos are thus highly desirable, enabling downstream tasks like question answering to help users efficiently locate specific information within videos. This work proposes PreMind, a no

J. P. Cuenca, T. de Ara, A. Martinez-Garcia, F. Guzman

We present an in-depth study of electronic transport in atomic-sized gold contacts using Break-Junction (BJ) techniques under cryogenic and ambient conditions. Our experimental results, supported by classical molecular dynamics (CMD) simulations and ab initio calculations, provide compelling evidence for the formation of three-atom-thick structures in gold n

Philippe Racette, Frédéric Quesnel, Andrea Lodi, François Soumis

The crew rostering problem (CRP) for pilots is a complex crew scheduling task assigning pairings, or sequences of flights starting and ending at the same airport, to pilots to create a monthly schedule. In this paper, we propose an innovative solution method for the CRP that uses a windowing approach. First, using a combination of machine learning (ML) and c

Shashwat Gupta, Sarthak Gupta, Akshan Agrawal, Mahim Naaz

Crohn's disease and intestinal tuberculosis share many overlapping features such as clinical, radiological, endoscopic, and histological features - particularly granulomas, making it challenging to clinically differentiate them. Our research leverages 3D CTE scans, computer vision, and machine learning to improve this differentiation to avoid harmful treatme

A. Zafrar

In the present article, we introduce and study a model addressing the Stokes problem with non-linear boundary conditions of the Tresca type. We suggest a new procedure for regularizing incompressible fluid, i.e. we assume that the divergence $\nabla \cdot {\bf u}\in [-\epsilon,\,\epsilon]$ which leads to class of constrained elliptic variational inequalities

Pierre Monmarché

Systems of stochastic particles evolving in a multi-well energy landscape and attracted to their barycenter is the prototypical example of mean-field process undergoing phase transitions: at low temperature, the corresponding mean-field deterministic limit has several stationary solutions, and the empirical measure of the particle system is then expected to

Aakash Patel, Tianqing Zhang, Camille Avestruz, Jeffrey Regier

Neural posterior estimation (NPE), a type of amortized variational inference, is a computationally efficient means of constructing probabilistic catalogs of light sources from astronomical images. To date, NPE has not been used to perform inference in models with spatially varying covariates. However, ground-based astronomical images have spatially varying s