December 2024 arXiv papers — page 128

Rui Pan, Hui Chen, Guanxiong Shen, Hongyang Chen

In order to address the issue of limited data samples for the deployment of pre-trained models in unseen environments, this paper proposes a residual channel-based data augmentation strategy for Radio Frequency Fingerprint Identification (RFFI), coupled with a lightweight SimSiam contrastive learning framework. By applying least square (LS) and minimum mean

Tristán Radić

Partial rigidity is a quantitative notion of recurrence and provides a global obstruction which prevents the system from being strongly mixing. A dynamical system $(X, \mathcal{X}, \mu, T)$ is partially rigid if there is a constant $\delta >0$ and sequence $(n_k)_{k \in \mathbb{N}}$ such that $\displaystyle \liminf_{k \to \infty } \mu(A \cap T^{n_k}A) \geq \

Austen Gabrielpillai, Isak G. B. Wold, Sangeeta Malhotra, James Rhoads

We describe the software package $\texttt{ESpRESSO}$ - [E]xtragalactic [Sp]ectroscopic [R]oman [E]mulator and [S]imulator of [S]ynthetic [O]bjects, created to emulate the slitless spectroscopic observing modes of the Nancy Grace Roman Space Telescope (Roman) Wide Field Instrument (WFI). We combine archival Hubble Space Telescope (HST) imaging data of compara

Yanyan Bu, Zexin Yang

We consider the chiral phase transition relevant for QCD matter at finite temperature but with vanishing baryon density. Presumably, the chiral phase transition is of second order for two-flavor QCD in the chiral limit. Near the transition temperature, we apply the Schwinger-Keldysh formalism and construct a low-energy effective field theory for the system,

Mohamed Sennary, Javier Rivera-Dean, Mohamed ElKabbash, Vladimir Pervak

Advancements in quantum optics and squeezed light generation have transformed various domains of quantum science and technology. However, real-time quantum dynamics remain an underexplored frontier. Here, we extend quantum optics into the ultrafast regime, providing direct experimental evidence that quantum uncertainty is not a static constraint but evolves

Prajwal Koirala, Zhanhong Jiang, Soumik Sarkar, Cody Fleming

Safe offline reinforcement learning aims to learn policies that maximize cumulative rewards while adhering to safety constraints, using only offline data for training. A key challenge is balancing safety and performance, particularly when the policy encounters out-of-distribution (OOD) states and actions, which can lead to safety violations or overly conserv

Yongliang Wu, Wenbo Zhu, Jiawang Cao, Yi Lu

The demand for producing short-form videos for sharing on social media platforms has experienced significant growth in recent times. Despite notable advancements in the fields of video summarization and highlight detection, which can create partially usable short films from raw videos, these approaches are often domain-specific and require an in-depth unders

Omer Erdem, Kevin Daley, Gabrielle Hoelzle, Majdi I. Radaideh

As clean energy demand grows to meet sustainability and net-zero goals, nuclear energy emerges as a reliable option. However, high capital costs remain a challenge for nuclear power plants (NPP), where repurposing coal power plant sites (CPP) with existing infrastructure is one way to reduce these costs. Additionally, Brownfield sites-previously developed or

Jianping Xiong, Zhenwei Li, Jiao Li, Xiaobin Zhang

EL CVn-type systems represent a rare evolutionary stage in binary star evolution, providing ideal laboratories for investigating stable mass transfer processes and the formation of extremely low-mass white dwarfs (ELM WDs). The Transiting Exoplanet Survey Satellite (TESS) has delivered an extensive collection of high-precision time-domain photometric data, w

Bo Yu, Jiangning Wei, Minzhen Hu, Zejie Han

Artificial General Intelligence (AGI), widely regarded as the fundamental goal of artificial intelligence, represents the realization of cognitive capabilities that enable the handling of general tasks with human-like proficiency. Researchers in brain-inspired AI seek inspiration from the operational mechanisms of the human brain, aiming to replicate its fun

David D. Dai

We show that the spin-s square-lattice Heisenberg model has exact many-body scars. These scars are simple valence-bond solids with exactly zero energy, and they exist in even-by-even systems and ladders of width 2. Ladders have additional scars corresponding to injecting one or two magnons on top of a parent valence-bond solid scar. These scars have a remark

Hans Moen, Vishnu Raj, Andrius Vabalas, Markus Perola

Health registers contain rich information about individuals' health histories. Here our interest lies in understanding how individuals' health trajectories evolve in a nationwide longitudinal dataset with coded features, such as clinical codes, procedures, and drug purchases. We introduce a straightforward approach for training a Transformer-based deep learn

Eoin M. Kenny, Julie A. Shah

Regulation is increasingly cited as the most important and pressing concern in machine learning. However, it is currently unknown how to implement this, and perhaps more importantly, how it would effect model performance alongside human collaboration if actually realized. In this paper, we attempt to answer these questions by building a regulatable large-lan

Jin Hong, Siyao Liu, Yong Wang

In this paper, we give a Lichnerowicz type formula for the $J$-twist of the Dirac operator with torsion. And we prove a Kastler-Kalau-Walze type theorem for the $J$-twist of the Dirac operator with torsion on 4-dimensional and 6-dimensional almost product Riemannian spin manifold with boundary.

Benji Peng, Chia Xin Liang, Ziqian Bi, Ming Liu

Deep learning-based image generation has undergone a paradigm shift since 2021, marked by fundamental architectural breakthroughs and computational innovations. Through reviewing architectural innovations and empirical results, this paper analyzes the transition from traditional generative methods to advanced architectures, with focus on compute-efficient di

Geon Yeong Park, Sang Wan Lee, Jong Chul Ye

Diffusion distillation models effectively accelerate reverse sampling by compressing the process into fewer steps. However, these models still exhibit a performance gap compared to their pre-trained diffusion model counterparts, exacerbated by distribution shifts and accumulated errors during multi-step sampling. To address this, we introduce Distillation++,

Owen C. Wetherbee, Saswata Roy, Baptiste Royer, Valla Fatemi

Bosonic encodings of quantum information offer hardware-efficient, noise-biased approaches to quantum error correction relative to qubit register encodings. Implementations have focused in particular on error correction of stored, idle quantum information, whereas quantum algorithms are likely to desire high duty cycles of active control. Error-transparent o

Ying Jin, Naoki Egami, Dominik Rothenhäusler

Many existing approaches to generalizing statistical inference amidst distribution shift operate under the covariate shift assumption, which posits that the conditional distribution of unobserved variables given observable ones is invariant across populations. However, recent empirical investigations have demonstrated that adjusting for shift in observed var

Wyatt Scott, Brett Genz, Sarah Elmasry, Sodiq Adewole

In political discourse and geopolitical analysis, national leaders words hold profound significance, often serving as harbingers of pivotal historical moments. From impassioned rallying cries to calls for caution, presidential speeches preceding major conflicts encapsulate the multifaceted dynamics of decision-making at the apex of governance. This project a

Qun Chen, Hongbing Qiu

By carrying out a point-wise estimate for the second fundamental form, we prove a rigidity theorem of complete noncompact ancient solutions to the mean curvature flow in codimension one. Moreover, we derive an optimal growth condition.

David J. Aldous, Svante Janson

In the critical beta-splitting model of a random $n$-leaf rooted tree, clades are recursively split into sub-clades, and a clade of $m$ leaves is split into sub-clades containing $i$ and $m-i$ leaves with probabilities $\propto 1/(i(m-i))$. Study of structure theory and explicit quantitative aspects of the model is an active research topic. It turns out that

Joseph Pollard, Richard G. Morris

The Peach-Koehler force between disclination lines was originally formulated in the study of crystalline solids, and has since been adopted to provide a notion of interactions between disclination lines in nematic liquid crystals. Here, we argue that the standard formulation of this interaction force seemingly fails for materials where there is a symmetry-br

Christopher A. Broderick, Xie Zhang, Mark E. Turiansky, Chris G. Van de Walle

The emergence of hexagonal Ge (2H-Ge) as a candidate direct-gap group-IV semiconductor for Si photonics mandates rigorous understanding of its optoelectronic properties. Theoretical predictions of a "pseudo-direct" band gap, characterized by weak oscillator strength, contrast with a claimed high radiative recombination coefficient $B$ comparable to conventio

Jiankang Wang, Jianjun Xu, Xiaorui Wang, Yuxin Wang

Large-scale, high-quality data is essential for advancing the reasoning capabilities of large language models (LLMs). As publicly available Internet data becomes increasingly scarce, synthetic data has emerged as a crucial research direction. However, existing data synthesis methods often suffer from limited scalability, insufficient sample diversity, and a

Michelle Kunimoto, Zifan Lin, Sarah Millholland, Alexander Venner

We report the validation of multiple planets transiting the nearby ($d = 12.8$ pc) K5V dwarf HD 101581 (GJ 435, TOI-6276, TIC 397362481). The system consists of at least two Earth-size planets whose orbits are near a mutual 4:3 mean-motion resonance, HD 101581 b ($R_{p} = 0.956_{-0.061}^{+0.063}~R_{\oplus}$, $P = 4.47$ days) and HD 101581 c ($R_{p} = 0.990_{

Vikas Vyas, Zheyuan Xu

With the increasing presence of autonomous SAE level 3 and level 4, which incorporate artificial intelligence software, along with the complex technical challenges they present, it is essential to maintain a high level of functional safety and robust software design. This paper explores the necessary safety architecture and systematic approach for automotive

Paolo Lipari, Silvia Vernetto

The Galactic gamma-ray flux can be described as the sum of two components: the first is due to the emission from an ensemble of discrete sources, and the second is formed by the photons produced by cosmic rays propagating in interstellar space and interacting with gas or radiation fields. The source component is partially resolved as the contributions from i

Yining Pang, Chenghan Li

Seasonal time series exhibit intricate long-term dependencies, posing a significant challenge for accurate future prediction. This paper introduces the Multi-scale Seasonal Decomposition Model (MSSD) for seasonal time-series forecasting. Initially, leveraging the inherent periodicity of seasonal time series, we decompose the univariate time series into three

Yuehui Cui, Jinquan Luo

In this paper, we study the differential properties of $x^d$ over $\mathbb{F}_{p^n}$ with $d=p^{2l}-p^{l}+1$. By studying the differential equation of $x^d$ and the number of rational points on some curves over finite fields, we completely determine differential spectrum of $x^{d}$. Then we investigate the $c$-differential uniformity of $x^{d}$. We also calc

Artemis Panagopoulou, Honglu Zhou, Silvio Savarese, Caiming Xiong

Programming based approaches to reasoning tasks have substantially expanded the types of questions models can answer about visual scenes. Yet on benchmark visual reasoning data, when models answer correctly, they produce incorrect programs 33% of the time. These models are often right for the wrong reasons and risk unexpected failures on new data. Unit tests

Tao Xu, Jianping He

Probabilistic prediction of stochastic dynamical systems (SDSs) aims to accurately predict the conditional probability distributions of future states. However, accurate probabilistic predictions tightly hinge on accurate distributional information from a nominal model, which is hardly available in practice. To address this issue, we propose a novel functiona

Reza Hashemi, Taban Baghfalaki, Viviane Philipps, Helene Jacqmin-Gadda

Dynamic event prediction, using joint modeling of survival time and longitudinal variables, is extremely useful in personalized medicine. However, the estimation of joint models including many longitudinal markers is still a computational challenge because of the high number of random effects and parameters to be estimated. In this paper, we propose a model

Yi Li, Yang Sun, Plamen Angelov

In this paper, we present a novel diffusion model-based monaural speech enhancement method. Our approach incorporates the separate estimation of speech spectra's magnitude and phase in two diffusion networks. Throughout the diffusion process, noise clips from real-world noise interferences are added gradually to the clean speech spectra and a noise-aware rev

Joshua Ludolf, Yesmin Reyna-Hernandez, Matthew Trevino

In the current landscape of big data, the reliability and performance of storage systems are essential to the success of various applications and services. as data volumes continue to grow exponentially, the complexity and scale of the storage infrastructures needed to manage this data also increase. a significant challenge faced by data centers and storage

Dvij Kalaria, Chinmay Maheshwari, Shankar Sastry

Autonomous racing extends beyond the challenge of controlling a racecar at its physical limits. Professional racers employ strategic maneuvers to outwit other competing opponents to secure victory. While modern control algorithms can achieve human-level performance by computing offline racing lines for single-car scenarios, research on real-time algorithms f

Jingzhi, Zhou, Alexander B. Watson

Mechanical relaxation in moir\'e materials is often modeled by a continuum model where linear elasticity is coupled to a stacking penalty known as the Generalized Stacking Fault Energy (GSFE). We review and compute minimizers of a one-dimensional version of this model, and then show how it can be formally derived from a natural atomistic model. Specifically,

Sang J. Park, Rajkumar Modak, Ravi Gautam, Abdulkareem Alasli

The global shift towards a carbon-neutral society has accelerated the demand for green energy, driving research into efficient technologies for harvesting energy from low-grade waste heat. Recently, transverse thermoelectrics based on the anomalous Nernst effect (ANE) has gained attention due to their simple device structure, scalability, and manufacturing-f

Kartik Sharma, Peeyush Kumar, Yunqing Li

This paper presents OG-RAG, an Ontology-Grounded Retrieval Augmented Generation method designed to enhance LLM-generated responses by anchoring retrieval processes in domain-specific ontologies. While LLMs are widely used for tasks like question answering and search, they struggle to adapt to specialized knowledge, such as industrial workflows or knowledge w

Maribel Hernández-Márquez, Celia Escamilla-Rivera

In this work we constrain the value of $\sigma_8$ for the normal and self-accelerating branch of a DGP brane embedded in a five-dimensional Minkowski space-time. For that purpose we first constrain the model parameters $H_0$, $\Omega_{m0}$, $\Omega_{r0}$ and $M$ by means of the Pantheon+ catalog and a mock catalog of gravitational waves. Then, we solve numer

Yu-Chao Hsu, Nan-Yow Chen, Tai-Yu Li, Po-Heng

We present the Quantum Kernel-Based Long short-memory (QK-LSTM) network, which integrates quantum kernel methods into classical LSTM architectures to enhance predictive accuracy and computational efficiency in climate time-series forecasting tasks, such as Air Quality Index (AQI) prediction. By embedding classical inputs into high-dimensional quantum feature

Andrew Holmes, Matt Jensen, Sarah Coffland, Hidemi Mitani Shen

The Global Change Analysis Model (GCAM) simulates complex interactions between the coupled Earth and human systems, providing valuable insights into the co-evolution of land, water, and energy sectors under different future scenarios. Understanding the sensitivities and drivers of this multisectoral system can lead to more robust understanding of the differe

Zhongyang Zhang, Jiacheng Qiu, Shuyang Cui, Yijun Luo

Event cameras provide a compelling alternative to traditional frame-based sensors, capturing dynamic scenes with high temporal resolution and low latency. Moving objects trigger events with precise timestamps along their trajectory, enabling smooth continuous-time estimation. However, few works have attempted to optimize the information loss during event rep

Zhuo Zhang, Yaroslav V. Kartashov, Milivoj R. Belić, Yongdong Li

Edge states emerging at the boundaries of materials with nontrivial topology are attractive for many practical applications due to their remarkable robustness to disorder and local boundary deformations, which cannot result in scattering of the energy of the edge states impinging on such defects into the bulk of material, as long as forbidden topological gap

Zheyuan Zhang, Zehong Wang, Tianyi Ma, Varun Sameer Taneja

The prevalence of unhealthy eating habits has become an increasingly concerning issue in the United States. However, major food recommendation platforms (e.g., Yelp) continue to prioritize users' dietary preferences over the healthiness of their choices. Although efforts have been made to develop health-aware food recommendation systems, the personalization

Minsang Kim, Seungjun Baek

Large language models (LLMs) closely interact with humans, and thus need an intimate understanding of the cultural values of human society. In this paper, we explore how open-source LLMs make judgments on diverse categories of cultural values across countries, and its relation to training methodology such as model sizes, training corpus, alignment, etc. Our

Kuan-Cheng Chen, Samuel Yen-Chi Chen, Chen-Yu Liu, Kin K. Leung

In this paper, we introduce Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning (Dist-QTRL), a novel approach to addressing the scalability challenges of traditional Reinforcement Learning (RL) by integrating quantum computing principles. Quantum-Train Reinforcement Learning (QTRL) leverages parameterized quantum circuits to efficiently genera

Parthapratim Pradhan, John W. Moffat

It has been argued in existing literature that the zero mass limit of Kerr spacetime corresponds to either flat Minkowski spacetime or a wormhole exhibiting a locally flat geometry. In this study, we examine that the zero mass limit of the Kerr-MOG black hole is equivalent to a wormhole. Moreover, we derive the Kerr-Schild form of the Kerr-MOG black hole thr

Yingying Fan, Yuxuan Han, Jinchi Lv, Xiaocong Xu

In this paper, we study the behavior of the Upper Confidence Bound-Variance (UCB-V) algorithm for the Multi-Armed Bandit (MAB) problems, a variant of the canonical Upper Confidence Bound (UCB) algorithm that incorporates variance estimates into its decision-making process. More precisely, we provide an asymptotic characterization of the arm-pulling rates for

Mohammad Jalili Torkamani

Understanding and extracting the grammar of a domain-specific language (DSL) is crucial for various software engineering tasks; however, manually creating these grammars is time-intensive and error-prone. This paper presents Kajal, a novel approach that automatically infers grammar from DSL code snippets by leveraging Large Language Models (LLMs) through pro

Xiang Huang, Hao Peng, Li Sun, Hui Lin

Probabilistic embeddings have several advantages over deterministic embeddings as they map each data point to a distribution, which better describes the uncertainty and complexity of data. Many works focus on adjusting the distribution constraint under the Information Bottleneck (IB) principle to enhance representation learning. However, these proposed regul

Prateek Sasan, Daryl Swartzentruber

This study evaluates the effectiveness of the two-for-one strategy in basketball by applying a causal inference framework to play-by-play data from the 2018-19 and 2021-22 National Basketball Association regular seasons. Incorporating factors such as player lineup, betting odds, and player ratings, we compute the average treatment effect and find that the tw

Hiroki Ikegami, Kimitoshi Kono, Yasumasa Tsutsumi

The B phase of superfluid $^\textrm 3$He ($^\textrm 3$He-B) is topologically nontrivial and the surface Andreev bound states formed on a surface are conceived as Majorana fermions. In a magnetic field, the surface Andreev bound states acquire a Zeeman gap. How the Zeeman gap opens when a magnetic field is applied is intimately related to how the topological

Patricio Gaete, J. A. Helayël-Neto

We examine the effects of electromagnetic field non-linearities in $3$ space-time dimensions. We focus on how these non-linearities influence permittivity and susceptibility. This, in turn, leads to changes in the refractive index through the use of the dispersion relation in the context of massless and massive non-linear electrodynamics. We also verify that

Seok-Jun Chang, Rajeshwari Dutta, Max Gronke, Michele Fumagalli

We investigate the properties of cold gas at $10^4~\rm K$ around star-forming galaxies at $z~\sim~1$ using Mg II spectra through radiative transfer modeling. We utilize a comprehensive dataset of 624 galaxies from the MAGG and MUDF programs. We focus on Mg II emission from galaxies and their outskirts to explore the cold gas within galaxies and the circumgal

Sean N. Pike, Steven E. Boggs, Gabriel Brewster, Sophia E. Haight

We present an investigation into the effects of high-energy proton damage on charge trapping in germanium cross-strip detectors, with the goal of accomplishing three important measurements. First, we calibrated and characterized the spectral resolution of a spare COSI-balloon detector in order to determine the effects of intrinsic trapping, finding that elec

Amirreza Shiralinasab Langari, Leila Yeganeh, Kim Khoa Nguyen

Graph Neural Networks (GNNs) are almost universally built on a single primitive: the neighborhood. Regardless of architectural variations, message passing ultimately aggregates over neighborhoods, which intrinsically limits expressivity and often yields power no stronger than the Weisfeiler-Lehman (WL) test. In this work, we challenge this primitive. We intr

Motohiro Sobajima, Kimitoshi Tsutaya, Yuta Wakasugi

In this paper, blowup phenomenon for the semilinear wave equation with time-dependent speed of propagation and scattering damping is considered under the smallness of initial data. Our result contains small data blowup for sub-Strauss exponent for the simplest semilinear wave equation and also the one for semilinear generalized Tricomi equation. Key ingredie

Seong Chan Park

We present a unified framework that simultaneously addresses the dynamics of early-time cosmic inflation and late-time cosmic acceleration within the context of a single scalar field non-minimally coupled to gravity. By employing an exponential coupling function and a scalar potential with dual asymptotic plateaus, our model naturally transitions from inflat

Krish Agarwal, Rishi Astra, Adnan Hoque, Mudhakar Srivatsa

We present HadaCore, a modified Fast Walsh-Hadamard Transform (FWHT) algorithm optimized for the Tensor Cores present in modern GPU hardware. HadaCore follows the recursive structure of the original FWHT algorithm, achieving the same asymptotic runtime complexity but leveraging a hardware-aware work decomposition that benefits from Tensor Core acceleration.

Zhaofeng Tian, Lichen Xia, Weisong Shi

Autonomous driving lacks strong proof of energy efficiency with the energy-model-agnostic trajectory planning. To achieve an energy consumption model-aware trajectory planning for autonomous driving, this study proposes an online nonlinear programming method that optimizes the polynomial trajectories generated by the Frenet polynomial method while considerin

Elizabeth Mone, Brandon Pries, John Wise, Sandrine Ferrans

Most galaxies, including the Milky Way, host a supermassive black hole (SMBH) at the center. These SMBHs can be observed out to high redshifts (z>=6) if the accretion rate is sufficiently large. However, we do not fully understand the mechanism through which these black holes form at early times. The heavy (or direct collapse) seeding mechanism has emerged a

Alvin Sheng, Brian J. Reich, Ana-Maria Staicu, Santhoshi N. Krishnan

Recent advances in multiplex imaging have enabled researchers to locate different types of cells within a tissue sample. This is especially relevant for tumor immunology, as clinical regimes corresponding to different stages of disease or responses to treatment may manifest as different spatial arrangements of tumor and immune cells. Spatial point pattern mo

Jason Murphy, Jiqiang Zheng

We establish a small-data modified scattering result for the $1d$ cubic dispersion-managed NLS (with time-dependent dispersion map) for initial data in a weighted space.

Dingding Dong, Theo McKenzie

We prove that for each $d\geq 3$ and $k\geq 2$, the set of limit points of the first $k$ eigenvalues of sequences of $d$-regular graphs is \[ \{(μ_1,\dots,μ_k): d=μ_1\geq \dots\geq μ_{k}\geq2\sqrt{d-1}\}. \] The result for $k=2$ was obtained by Alon and Wei, and our result confirms a conjecture of theirs. Our proof uses an infinite random graph sampled from

Xin Wang, Junjun He, Zeyang Liao, M. Suhail Zubairy

We present a scheme for achieving broadband complete reflection by constructing photonic bandgap via collective atom-atom interaction in a one-dimensional (1D) waveguide quantum electrodynamics (QED) system. Moreover, we propose several strategies to further expand the ultrahigh reflection windows, including increasing the number of atoms with separations ne

Ruizhi Dong, Ranquan Cao, Dian Tan, Ruixiang Fei

The generation of time-reversal-odd spin-current in metallic altermagnets has attracted considerable interest in spintronics. However, producing pure spin-current in insulating materials remains both challenging and desirable, as insulating states are frequently found in antiferromagnets. Nonlinear photogalvanic effects offer a promising method for generatin

Yuan Li, Ye Xing, Wuhong Zhang, Lixiang Chen

The orbital angular momentum and radial momentum both describe the transverse momentum of a light field. Efficient discriminating and sorting the two kinds of momentum lies at the heart of further application. Here, we propose a parabola-like lens that can transform the orbital angular momentum and the radial momentum into different positions in the parabola

Mike Perkins, Jasper Roe, Leon Furze

Recent developments in Generative Artificial Intelligence (GenAI) have created significant uncertainty in education, particularly in terms of assessment practices. Against this backdrop, we present an updated version of the AI Assessment Scale (AIAS), a framework with two fundamental purposes: to facilitate open dialogue between educators and students about

Zhixian Zhuang, Xiaodong Lee, Jiuqi Wei, Yufan Fu

Cross-border data transfer is vital for the digital economy by enabling data flow across different countries or regions. However, ensuring compliance with diverse data protection regulations during the transfer introduces significant complexities. Existing solutions either focus on a single legal framework or neglect real-time and concurrent processing deman

Enrico Maria Fenoaltea, Dario Mazzilli, Aurelio Patelli, Angelica Sbardella

The integration of artificial intelligence (AI) into the workplace is advancing rapidly, necessitating robust metrics to evaluate its tangible impact on the labour market. Existing measures of AI occupational exposure largely focus on AI's theoretical potential to substitute or complement human labour on the basis of technical feasibility, providing limi

Muzhou Ma, Steven T. Flammia, John Preskill, Yu Tong

We study the problem of learning a $k$-body Hamiltonian with $M$ unknown Pauli terms that are not necessarily geometrically local. We propose a protocol that learns the Hamiltonian to precision $ε$ with total evolution time ${\mathcal{O}}(M^{1/2+1/p}/ε)$ up to logarithmic factors, where the error is quantified by the $\ell^p$-distance between Pauli coefficie

Hongjie Dong, Dong-ha Kim, Seick Kim

This paper investigates the Dirichlet problem for a non-divergence form elliptic operator $L$ in a bounded domain of $\mathbb{R}^d$. Under certain conditions on the coefficients of $L$, we first establish the existence of a unique Green's function in a ball and derive two-sided pointwise estimates for it. Utilizing these results, we demonstrate the equiv

Zheyan Wan, Juven Wang, Shing-Tung Yau, Yi-Zhuang You

Charge conjugation (C), mirror reflection (R), time reversal (T), and fermion parity $(-1)^{\rm F}$ are basic discrete spacetime and internal symmetries of the Dirac fermions. In this article, we determine the group, called the C-R-T fractionalization, which is a group extension of $\mathbb{Z}_2^{\rm C}\times\mathbb{Z}_2^{\rm R}\times\mathbb{Z}_2^{\rm T}$ by

Shi Bo, AmirEmad Ghassami, Debarghya Mukherjee

Mediation analysis is a crucial tool for uncovering the mechanisms through which a treatment affects an outcome, providing deeper causal insights and guiding effective interventions. Despite substantial advances in mediation analysis with fixed- or low-dimensional mediators and covariates, estimation and inference for mediation functionals remain limited whe

Chiara Damiolini, Jiuzu Hong, Shuo Gao

In this paper we investigate line bundles on $\mathrm{Bun}_{\mathcal{G}}$ the moduli stack of parahoric Bruhat--Tits bundles over a smooth projective curve. Translating this problem into one concerning twisted conformal blocks, we are able to establish criteria that detect when line bundles on an appropriate flag variety descend to $\mathrm{Bun}_{\mathcal{G}

Yihe Zhang, Bryce Turney, Purushottam Sigdel, Xu Yuan

Accurate and timely regional weather prediction is vital for sectors dependent on weather-related decisions. Traditional prediction methods, based on atmospheric equations, often struggle with coarse temporal resolutions and inaccuracies. This paper presents a novel machine learning (ML) model, called MiMa (short for Micro-Macro), that integrates both near-s

Calvin W. Johnson, Bui Minh Loc, Austin Keller, Kenneth M. Nollett

The scattering problem can be implemented in a square-integrable basis via the so-called $J$-matrix method. While methods to compute the phase shift in the $J$-matrix approach are known, we introduce a novel formula in square-integrable bases analogous to existing integral relations or overlap integrals in a (continuous) position basis. We demonstrate the me

Abhinav Agrawal, Justin Domke

Normalizing flow-based variational inference (flow VI) is a promising approximate inference approach, but its performance remains inconsistent across studies. Numerous algorithmic choices influence flow VI's performance. We conduct a step-by-step analysis to disentangle the impact of some of the key factors: capacity, objectives, gradient estimators, number

Bouchra El Alaoui, Abdallah Slaoui, Abderrahim Lakhfif, Rachid Ahl Laamara

The Wigner function and Wigner-Yanase skew information are connected through quantum coherence. States with high skew information often exhibit more pronounced negative regions in their Wigner functions, indicative of quantum interference and non-classical behavior. Thus, the relationship between these two concepts is that states with high quantum coherence

Nicholus R. Clinkinbeard, Justin Sehlin, Meharpal Singh Bhatti, Marilyn McNamarra

Selection of solution concentrations and flow rates for the fabrication of microfibers using a microfluidic device is a largely empirical endeavor of trial-and-error, largely due to the difficulty of modeling such a multiphysics process. Machine learning, including deep neural networks, provides the potential for allowing the determination of flow rates and

LCM team, Loïc Barrault, Paul-Ambroise Duquenne, Maha Elbayad

LLMs have revolutionized the field of artificial intelligence and have emerged as the de-facto tool for many tasks. The current established technology of LLMs is to process input and generate output at the token level. This is in sharp contrast to humans who operate at multiple levels of abstraction, well beyond single words, to analyze information and to ge

Jiaheng Chen, Daniel Sanz-Alonso

This paper studies the estimation of large precision matrices and Cholesky factors obtained by observing a Gaussian process at many locations. Under general assumptions on the precision and the observations, we show that the sample complexity scales poly-logarithmically with the size of the precision matrix and its Cholesky factor. The key challenge in these

Albert S. Yue, Lovish Madaan, Ted Moskovitz, DJ Strouse

Math reasoning is becoming an ever increasing area of focus as we scale large language models. However, even the previously-toughest evals like MATH are now close to saturated by frontier models (90.0% for o1-mini and 86.5% for Gemini 1.5 Pro). We introduce HARP, Human Annotated Reasoning Problems (for Math), consisting of 5,409 problems from the US national

Cody Grogan, Som Dutta, Mauricio Tano, Somayajulu L. N. Dhulipala

With increasing computational demand, Neural-Network (NN) based models are being developed as pre-trained surrogates for different thermohydraulics phenomena. An area where this approach has shown promise is in developing higher-fidelity turbulence closures for computational fluid dynamics (CFD) simulations. The primary bottleneck to the widespread adaptatio

Hanwen Yao, Mert Gökduman, Henry D. Pfister

We introduce a new erasure decoder that applies to arbitrary quantum LDPC codes. Dubbed the cluster decoder, it generalizes the decomposition idea of Vertical-Horizontal (VH) decoding introduced by Connelly et al. in 2022. Like the VH decoder, the idea is to first run the peeling decoder and then post-process the resulting stopping set. The cluster decoder b

Ahmad El Ferdaoussi, Eric Plourde, Jean Rouat

Neuromorphic applications emulate the processing performed by the brain by using spikes as inputs instead of time-varying analog stimuli. Therefore, these time-varying stimuli have to be encoded into spikes, which can induce important information loss. To alleviate this loss, some studies use population coding strategies to encode more information using a po

Alejandro Garnung Menéndez

Partial Differential Equations (PDEs) have long been recognized as powerful tools for image processing and analysis, providing a framework to model and exploit structural and geometric properties inherent in visual data. Over the years, numerous PDE-based models have been developed and refined, inspired by natural analogies between physical phenomena and ima

David Hokken

Let $\mathcal{N} \neq \{0\}$ be a fixed set of integers, closed under multiplication, closed under negation, or containing $\{\pm 1\}$. We prove that any zero of a polynomial in $\mathbf{Z}[X]$ whose coefficients lie in $\mathcal{N}$ can be approximated in $\mathbf{C}$ to arbitrary precision by a zero of a polynomial in $\mathbf{Z}[X]$ with square discrimina

Yucheng Liu, Jiwoon Park, Gordon Slade

We present a new unified theory of critical finite-size scaling for lattice statistical mechanical models with periodic boundary conditions above the upper critical dimension. Our theory is based on recent mathematically rigorous results for linear and branched polymers, multi-component spin systems, and percolation. Both short-range and long-range interacti

Daniel Pellicer, Yesenia Villicaña Molina

We introduce the Hoffman-Singleton manifold based on some specific subgraph of the Hoffman-Singleton graph. This manifold is motivated in a combinatorial fashion, and it is defined rigorously in geometric terms. We also present a few geometric properties of this manifold.

Yoonho Lee, Jonathan Williams, Henrik Marklund, Archit Sharma

Reward models trained on aggregate preferences often fail to capture individual users' values, but existing adaptation methods such as fine-tuning or long-context conditioning are too costly for real-time personalization. We propose Hypothesis Reweighting (HyRe), which enables real-time personalization by reweighting ensemble members using just 1-5 labeled e

Carolyn G. Volpert, Emily M. Barrentine, Alberto D. Bolatto, Ari Brown

As superconducting kinetic inductance detectors (KIDs) continue to grow in popularity for sensitive submillimeter detection and other applications, there is a drive to advance toward lower-loss devices. We present measurements of diagnostic thin-film aluminum coplanar waveguide (CPW) resonators designed to inform ongoing KID development at NASA Goddard Space

Fan Li, Xiaoyang Wang, Dawei Cheng, Cong Chen

Data generation is a fundamental research problem in data management due to its diverse use cases, ranging from testing database engines to data-specific applications. However, real-world entities often involve complex interactions that cannot be effectively modeled by traditional tabular data. Therefore, graph data generation has attracted increasing attent

Benjamin M. Boyd, Gautham Narayan, Kaisey S. Mandel, Matthew Grayling

We use hierarchical Bayesian modelling to calibrate a network of 32 all-sky faint DA white dwarf (DA WD) spectrophotometric standards ($16.5 < V < 19.5$) alongside three CALSPEC standards, from 912 \r{A} to 32 $\mu$m. The framework is the first of its kind to jointly infer photometric zeropoints and WD parameters (surface gravity $\log g$, effective temperat

Hikaru Kawai, Nobuyoshi Ohta

We discuss the effect of wave function renormalization (WFR) in asymptotically safe gravity. We show that there are two WFR-invariant quantities, and the renormalization (RG) equations may be written entirely in terms of these quantities. The same set of RG equations can be obtained whether we fix the vacuum energy or Newton coupling along the RG trajectory.

Tomáš Beránek

Working with function spaces in various branches of mathematical analysis introduces optimality problems, where the question of choosing a function space both accessible and expressive becomes a nontrivial exercise. A good middle ground is provided by Orlicz spaces, parameterized by a single Young function and thus accessible, yet expansive. In this work, we

Xiaohu Lu, Hayder Radha

Object detection using LiDAR point clouds relies on a large amount of human-annotated samples when training the underlying detectors' deep neural networks. However, generating 3D bounding box annotation for a large-scale dataset could be costly and time-consuming. Alternatively, unsupervised domain adaptation (UDA) enables a given object detector to operate

Agnieszka Mensfelt, Kostas Stathis, Vince Trencsenyi

Multi-agent simulations are versatile tools for exploring interactions among natural and artificial agents, but their development typically demands domain expertise and manual effort. This work introduces the Generative Agents for Multi-Agent Autoformalization (GAMA) framework, which automates the formalization of interaction scenarios in simulations using a

Franco Golfieri Madriaga, Ariel Pacetti

In the beautiful article [11] Darmon proposed a program to study integral solutions of the generalized Fermat equation $Ax^p+By^q=Cz^r$. In the aforementioned article, Darmon proved many steps of the program, by exhibiting models of hyperelliptic/superelliptic curves lifting what he called ''Frey representations'', Galois representations over a finite field

Alexander Libanov

Within the framework of general relativity, it can be shown that gravitational waves are radiated with the merger of massive compact objects. Such gravitational wave signals are observed on Earth on various detectors, in particular, on Laser Interferometer Gravitational Wave Observatory (LIGO) and Virgo. During the operation of these detectors, many events h