October 2024 arXiv papers — page 53

Nanshan Jia, Tingyu Zhu, Haoyu Liu, Zeyu Zheng

We propose a class of structured diffusion models, in which the prior distribution is chosen as a mixture of Gaussians, rather than a standard Gaussian distribution. The specific mixed Gaussian distribution, as prior, can be chosen to incorporate certain structured information of the data. We develop a simple-to-implement training procedure that smoothly acc

Finn Temmen, Evan Berkowitz, Anthony Kennedy, Thomas Luu

Despite its many advantages, the sensible application of the Hybrid Monte Carlo (HMC) method is often hindered by the presence of large - or even infinite - potential barriers. These potential barriers partition the configuration space into distinct sectors, which leads to ergodicity violations and biased measurements of observables. In this work, we address

Ahmed Temtam, Megan A. Witherow, Liangsuo Ma, M. Shibly Sadique

Understanding the neurobiology of opioid use disorder (OUD) using resting-state functional magnetic resonance imaging (rs-fMRI) may help inform treatment strategies to improve patient outcomes. Recent literature suggests time-frequency characteristics of rs-fMRI blood oxygenation level-dependent (BOLD) signals may offer complementary information to tradition

Willow Veytsman, Shuang Zhai, Chen Ding, Adam B. Sefkow

Surveys of computational science show that many scientists use languages like C and C++ in order to write code for scientific computing, especially in scenarios where performance is a key factor. In this paper, we seek to evaluate the use of Rust in such a scenario, through implementations of a physics simulation in both C++ and Rust. We also create a parall

T. A. Callister

Ground-based gravitational-wave detectors like the Advanced LIGO, Advanced Virgo, and KAGRA experiments now regularly witness gravitational waves from compact binary mergers: the relativistic collisions of neutron stars and/or stellar-mass black holes. With hundreds of such events observed to date, gravitational-wave observations are enabling increasingly pr

Hassan Alhussein, Pavel Kolesnikov, Viktor Lopatkin

In this paper, we find the Hochschild cohomology groups of the universal associative conformal envelope $U(3)$ of the Virasoro Lie conformal algebra with respect to associative locality $N=3$ on the generator with coefficients in all finite modules. In order to obtain this result, we construct the Anick resolution via the algebraic discrete Morse theory and

Abhirama Subramanyam Penamakuri, Anand Mishra

We revisit knowledge-aware text-based visual question answering, also known as Text-KVQA, in the light of modern advancements in large multimodal models (LMMs), and make the following contributions: (i) We propose VisTEL - a principled approach to perform visual text entity linking. The proposed VisTEL module harnesses a state-of-the-art visual text recognit

Chen Liu, Jingwei Hu, William T. Taitano, Xiangxiong Zhang

For high-order accurate schemes such as discontinuous Galerkin (DG) methods solving Fokker-Planck equations, it is desired to efficiently enforce positivity without losing conservation and high-order accuracy, especially for implicit time discretizations. We consider an optimization-based positivity-preserving limiter for enforcing positivity of cell average

Alain Connes, Joseph Kouneiher

This article reflects on the life and mathematical contributions of Pierre Cartier, a distinguished figure in 20th- and 21st-century mathematics. As a key member of the Bourbaki collective, Cartier played a pivotal role in the formalization and modernization of mathematics. His work spanned fields such as algebraic geometry, representation theory, mathematic

Emily G. Ward, Alexandru B. Georgescu

We provide a methodology to understand materials with complex bonding patterns, and apply it to the example of heteroanionic and lone pair materials. We build a tight-binding model based on Wannier functions fitted on density functional theory results, followed by enforcing symmetry on the atomic orbital basis set, and finally connecting and disconnecting se

Michael Hagenow, Dimosthenis Kontogiorgos, Yanwei Wang, Julie Shah

Previous methods for Learning from Demonstration leverage several approaches for a human to teach motions to a robot, including teleoperation, kinesthetic teaching, and natural demonstrations. However, little previous work has explored more general interfaces that allow for multiple demonstration types. Given the varied preferences of human demonstrators and

Ufuk Beyaztas, Abhijit Mandal, Han Lin Shang

This paper introduces a robust estimation strategy for the spatial functional linear regression model using dimension reduction methods, specifically functional principal component analysis (FPCA) and functional partial least squares (FPLS). These techniques are designed to address challenges associated with spatially correlated functional data, particularly

Shuning Shang, Xuran Meng, Yuan Cao, Difan Zou

Benign overfitting refers to how over-parameterized neural networks can fit training data perfectly and generalize well to unseen data. While this has been widely investigated theoretically, existing works are limited to two-layer networks with fixed output layers, where only the hidden weights are trained. We extend the analysis to two-layer ReLU convolutio

Gábor Somlai

Greenfeld and Lev conjectured that the Cartesian product of two sets $A$ and $B$ is spectral if and only if $A$ and $B$ are spectral. We construct a counterexample to this fact using the existence of a tile that has no spectra.

N. V. Krylov

We prove weak uniqueness for admissible solutions of It\^o's equations with uniformly nondegenerate $a$ which is almost in VMO and $b$ in a Morrey class of functions with low integrability property. If $b\in L_{d}$ any solution is admissible.

Haoji Hu, Jina Kim, Jinwei Zhou, Sofia Kirsanova

Trajectory anomaly detection is crucial for effective decision-making in urban and human mobility management. Existing methods of trajectory anomaly detection generally focus on training a trajectory generative model and evaluating the likelihood of reconstructing a given trajectory. However, previous work often lacks important contextual information on the

Mandana Vaziri, Louis Mandel, Claudio Spiess, Martin Hirzel

Large language models (LLMs) have taken the world by storm by making many previously difficult uses of AI feasible. LLMs are controlled via highly expressive textual prompts and return textual answers. Unfortunately, this unstructured text as input and output makes LLM-based applications brittle. This motivates the rise of prompting frameworks, which mediate

Ziqi Liang, Haoxiang Shi, Hanhui Chen

Speech Emotion Captioning (SEC) has gradually become an active research task. The emotional content conveyed through human speech are often complex, and classifying them into fixed categories may not be enough to fully capture speech emotions. Describing speech emotions through natural language may be a more effective approach. However, existing SEC methods

Cássia F. Sampaio, Plamen E. Koshlukov

Let $K$ be a field of characteristic zero and let $\mathfrak{sl}_2 (K)$ be the 3-dimensional simple Lie algebra over $K$. In this paper we describe a finite basis for the $\mathbb{Z}_2$-graded identities of the adjoint representation of $\mathfrak{sl}_2 (K)$, or equivalently, the $\mathbb{Z}_2$-graded identities for the pair $(M_3(K), \mathfrak{sl}_2 (K))$.

Lester James V. Miranda, Yizhong Wang, Yanai Elazar, Sachin Kumar

Learning from human feedback has enabled the alignment of language models (LMs) with human preferences. However, collecting human preferences is expensive and time-consuming, with highly variable annotation quality. An appealing alternative is to distill preferences from LMs as a source of synthetic annotations, offering a cost-effective and scalable alterna

Brian Jackson, Lori Fenton, Travis Brown, Asier Munguira

We used attitude data from the Mars Ingenuity helicopter with a simple steady-state model to estimate windspeeds and directions at altitudes of 3 meters up to 24 meters, the first time winds at such altitudes have been probed on Mars. We compared our estimates to concurrent wind data at 1.5 m height from the meteorology package MEDA onboard the Mars 2020 Per

Paul A. Ullrich, Elizabeth A. Barnes, William D. Collins, Katherine Dagon

Machine learning (ML) is a revolutionary technology with demonstrable applications across multiple disciplines. Within the Earth science community, ML has been most visible for weather forecasting, producing forecasts that rival modern physics-based models. Given the importance of deepening our understanding and improving predictions of the Earth system on a

Harsh Vardhan Dubey, Ji Ah Lee, Patrick Flaherty

Many Bayesian statistical inference problems come down to computing a maximum a-posteriori (MAP) assignment of latent variables. Yet, standard methods for estimating the MAP assignment do not have a finite time guarantee that the algorithm has converged to a fixed point. Previous research has found that MAP inference can be represented in dual form as a line

Haowei Yang, Mingxiu Sui, Shaobo Liu, Xinyue Qian

With the rapid development of natural language processing technology, large language models have demonstrated exceptional performance in various application scenarios. However, training these models requires significant computational resources and data processing capabilities. Cross-cloud federated training offers a new approach to addressing the resource bo

Pinrui Shen, Donald Booth, Chang Liu, Scott Beattie

We make absolute frequency measurements of Cs Rydberg transitions, $\vert 6S_{1/2}, F=3 \rangle \rightarrow \vert nS_{1/2}~(n=23\rm{-}90)\rangle$ and $\vert nD_{3/2,5/2}~(n=21\rm{-}90)\rangle$, with an accuracy of less than $ 72\,\rm kHz$. The quantum defect parameters for the measured Rydberg series are the most precise obtained to date. The quantum defect

Guimin Hu, Hasti Seifi

Large language models (LLMs) have garnered significant attention in recent years due to their impressive performance. While considerable research has evaluated these models from various perspectives, the extent to which LLMs can perform implicit and explicit emotion retrieval remains largely unexplored. To address this gap, this study investigates LLMs' emot

Kajal Sharma, Abir Mukherjee, Kritika Bhattacharya, Biswarup Satpati

Here, we report the full-fledged journey towards the material synthesis and characterization of few-layered/thin WSe$_2$ using sputtered W-films on SiO$_2$/Si substrates followed by electrical studies under dark and illumination conditions. Growth temperature 500oC and gas pressure 55 sccm are found to be the optimized parameters for formation of thermodynam

Jing-Ren Zhou, Zheng-Cheng Gu

The interplay between symmetry and topological properties plays a very important role in modern physics. In the past decade, the concept of symmetry-enriched topological (SET) phases was proposed and their classifications have been systematically studied for bosonic systems. Very recently, the concept of SET phases has been generalized into fermionic systems

Renat Sergazinov, Armeen Taeb, Irina Gaynanova

Multi-view data provides complementary information on the same set of observations, with multi-omics and multimodal sensor data being common examples. Analyzing such data typically requires distinguishing between shared (joint) and unique (individual) signal subspaces from noisy, high-dimensional measurements. Despite many proposed methods, the conditions fo

Ruobing Mei, Chao-Xing Liu

In this work, we propose a geometric nonlinear current response induced by magnetic resonance in magnetic Weyl semimetals. This phenomenon is in analog to the quantized circular photogalvanic effect previously proposed for Weyl semimetal phases of chiral crystals. However, the nonlinear current response in our case can occur in magnetic Weyl semimetals where

Ruisi Cai, Yeonju Ro, Geon-Woo Kim, Peihao Wang

The proliferation of large language models (LLMs) has led to the adoption of Mixture-of-Experts (MoE) architectures that dynamically leverage specialized subnetworks for improved efficiency and performance. Despite their benefits, MoE models face significant challenges during inference, including inefficient memory management and suboptimal batching, due to

Qingguo Hong, Jiwei Jia, Young Ju Lee, Ziqian Li

The paper presents a priori error analysis of the shallow neural network approximation to the solution to the indefinite elliptic equation and and cutting-edge implementation of the Orthogonal Greedy Algorithm (OGA) tailored to overcome the challenges of indefinite elliptic problems, which is a domain where conventional approaches often struggle due to nontr

Andrzej Pigulski

Nascent binaries (NBs) are binary systems with very low mass ratios, less than ~0.2, in which the more massive component is an O- or B-type main-sequence star, while the secondary is a star contracting onto the main sequence. NBs are of interest because they can help to understand the formation of small-mass ratio systems and shed light on the origin of low-

Daniel Salwasser, Daniel Seemaier, Lars Gottesbüren, Peter Sanders

We present TeraPart, a memory-efficient multilevel graph partitioning method that is designed to scale to extremely large graphs. In balanced graph partitioning, the goal is to divide the vertices into $k$ blocks with balanced size while cutting as few edges as possible. Due to its NP-hard nature, heuristics are prevalent in this field, with the multilevel f

Thiago T. Tsutsui, Antonio S. M. de Castro, Fabiano M. Andrade

In the Jaynes-Cummings (JC) model, the time dependence in the coupling parameter allows changes in the forms of the Rabi oscillations. In the inverse problem approach (IPA), the time-dependent coupling parameter is obtained from the resulting population inversion. In this work, we employ the IPA to obtain a time-dependent coupling that reproduces the effects

Dylan Wilson

This project aims to investigate a novel sequence generation method inspired by the AlphaGo paradigm, adapting it for use with large language models (LLMs). The proposed approach involves creating search trees of different possible completions and evaluating these completions based on model confidence. By considering various paths in the search tree and scor

Fabian Langkabel, Stefan Knecht, Jakob S. Kottmann

In electronic structure theory, variational methods offer a valuable paradigm for approximating electronic ground states. However, for historical reasons, this principle is mostly restricted to model chemistries in pre-defined fixed basis sets. Especially in quantum computation, these model chemistries are far from an accurate description of the initial elec

Ruicheng Wang, Sicheng Xu, Cassie Dai, Jianfeng Xiang

We present MoGe, a powerful model for recovering 3D geometry from monocular open-domain images. Given a single image, our model directly predicts a 3D point map of the captured scene with an affine-invariant representation, which is agnostic to true global scale and shift. This new representation precludes ambiguous supervision in training and facilitate eff

Huiyu Wu, Diego Klabjan

Federated Learning (FL) is a collaborative, privacy-preserving machine learning framework that enables multiple participants to train a single global model. However, the recent advent of powerful Large Language Models (LLMs) with tens to hundreds of billions of parameters makes the naive application of traditional FL methods to LLMs impractical due to high c

Jared C Bronski, Ver Mikyoung Hur, Sarah E Simpson

We study quasi-periodic eigenvalue problems that arise in the stability analysis of periodic traveling wave solutions to Hamiltonian PDEs. We establish bounds on regions in the complex plane when the eigenvalues may deviate from the imaginary axis, and estimates for the number of such off-axis eigenvalues. These relations hold when the dispersion relation gr

Cem Ates Musluoglu, Alexander Bertrand

With the emergence of wireless sensor networks (WSNs), many traditional signal processing tasks are required to be computed in a distributed fashion, without transmissions of the raw data to a centralized processing unit, due to the limited energy and bandwidth resources available to the sensors. In this paper, we propose a distributed independent component

J. A. Morales, C. J. Horowitz

Neutron stars are not observed to spin faster than about half their breakup rate. This limiting rotational frequency may be related to the strength of their crusts. As a star spins up from accretion, centrifugal forces stress the crust. We perform finite-element simulations of rotating neutron stars and find that the crust fails at rotation rates about half

Andrew Liu, Axel Elaldi, Nathan Russell, Olivia Viessmann

Efficient encoding and representation of large 3D molecular structures with high fidelity is critical for biomolecular design applications. Despite this, many representation learning approaches restrict themselves to modeling smaller systems or use coarse-grained approximations of the systems, for example modeling proteins at the resolution of amino acid res

Yifan Wang, Vera Demberg

Despite significant advancements in natural language generation, controlling language models to produce texts with desired attributes remains a formidable challenge. In this work, we introduce RSA-Control, a training-free controllable text generation framework grounded in pragmatics. RSA-Control directs the generation process by recursively reasoning between

Marrick Braam, Paul I. Palmer, Leen Decin, Nathan J. Mayne

Terrestrial exoplanets around M- and K-type stars are important targets for atmospheric characterisation. Such planets are likely tidally locked with the order of spin-orbit resonances (SORs) depending on eccentricity. We explore the impact of SORs on 3D atmospheric dynamics and chemistry, employing a 3D coupled Climate-Chemistry Model to simulate Proxima Ce

Brian Z. Zhu, Dingyue Liu, Xin Wan, Gordon Liao

We study liquidity on decentralized exchanges (DEXs), identifying factors at the platform, blockchain, token pair, and liquidity pool levels with predictive power for market depth metrics. We introduce the v2 counterfactual spread metric, a novel criterion which assesses the degree of liquidity concentration in pools using the ``concentrated liquidity'' mech

Brian Z. Zhu, Xin Wan, Ciamac C. Moallemi, Dan Robinson

Revert protection is a feature provided by some blockchain platforms that prevents users from incurring fees for failed transactions. We study the economic implications and benefits of revert protection in the context of priority gas auctions and maximal extractable value. We develop a model in which searchers bid for a top-of-block arbitrage opportunity und

Tianyu Chen, Vansh Bansal, James G. Scott

Neural posterior estimation (NPE), a simulation-based computational approach for Bayesian inference, has shown great success in approximating complex posterior distributions. Existing NPE methods typically rely on normalizing flows, which approximate a distribution by composing many simple, invertible transformations. But flow-based models, while state of th

A. M. Mathai, H. J. Haubold

It is shown that a Mittag-Leffler density has interesting properties. The Mittag-Leffler random variable has a structural representation in terms of a positive Levy variable and the power of a gamma variable where these two variables are independently distributed. It is shown that several central limit-type properties hold but the limiting forms are positive

Yuhang Li, Priyadarshini Panda

Large language models (LLMs) have revolutionized natural language processing, albeit at the cost of immense memory and computation requirements. Post-training quantization (PTQ) is becoming the de facto method to reduce the memory footprint and improve the inference throughput of LLMs. In this work, we aim to push the upper limit of LLM PTQ by optimizing the

Vassos Hadzilacos, Myles Thiessen, Sam Toueg

In this paper, we first propose a natural generalization of the well-known compare-and-swap object, one that replaces the equality comparison with an arbitrary comparator. We then present a simple wait-free universal construction using this object and prove its correctness.

M. S. Guimaraes, I. Roditi, S. P. Sorella

A concise and self-contained introduction to the Bell inequality in relativistic Quantum Field Theory is presented. Taking the example of a real scalar massive field, the violation of the Bell inequality in the vacuum state and for causal complementary wedges is illustrated.

Lawrence Jang, Yinheng Li, Dan Zhao, Charles Ding

Videos are often used to learn or extract the necessary information to complete tasks in ways different than what text and static imagery alone can provide. However, many existing agent benchmarks neglect long-context video understanding, instead focusing on text or static image inputs. To bridge this gap, we introduce VideoWebArena (VideoWA), a benchmark fo

Newton Solórzano, Dik Lujerio, Víctor León, Alexis Rodríguez

In this paper, we consider the cylindrically symmetric Finsler metrics and we obtain their Douglas curvature. Furthermore, we obtain the differential equation system of the cylindrically symmetric Finsler metrics with vanishing Douglas curvature. Many examples are included.

Zebin Yang, Agus Sudjianto, Xiaoming Li, Aijun Zhang

Tree ensemble models like random forests and gradient boosting machines are widely used in machine learning due to their excellent predictive performance. However, a high-performance ensemble consisting of a large number of decision trees lacks sufficient transparency and explainability. In this paper, we demonstrate that when shallow decision trees are used

Baptiste Blochet, Nathalie Lebas, Pascal Berto, Dimitrios Papadopoulos

Single-shot hyperspectral wavefront sensing is essential for applications like spatio-spectral coupling metrology in high power laser or fast material dispersion imaging. Under broadband illumination, traditional wavefront sensors assume an achromatic wavefront, which makes them unsuitable. We introduce a hyperspectral wavefront sensing scheme based on the H

Ruisi Zhang, Farinaz Koushanfar

The widely adopted and powerful generative large language models (LLMs) have raised concerns about intellectual property rights violations and the spread of machine-generated misinformation. Watermarking serves as a promising approch to establish ownership, prevent unauthorized use, and trace the origins of LLM-generated content. This paper summarizes and sh

R. Zemouri, A. R. Champagne, S. Maiti

In the era of 2D and quasi-2D quantum materials one needs to model strain at the level of the Hamiltonian as opposed to a semi-classical approach. Corrections to the electronic Hamiltonian due to strain arise from two sources: deformations of the lattice and changes in the hoppings. Here, we provide a general theory that takes into account the symmetry of th

Gerard Ben Arous, Pax Kivimae

This is the first of a series of three papers about the Elastic Manifold model. This classical model proposes a rich picture due to the competition between the inherent disorder and the smoothing effect of elasticity. In this paper, we prove a Parisi formula, i.e. we compute the asymptotic quenched free energy and show it is given by the solution to a certai

Andrzej Pigulski, Piotr A. Kołaczek-Szymański, Marta Święch, Piotr Łojko

Blue large-amplitude pulsators (BLAPs) are a newly discovered group of compact pulsating stars whose origin needs to be explained. Of the existing evolutionary scenarios that could lead to the formation of BLAPs, there are two in which BLAPs are the products of the merger of two stars, either a main sequence star and a helium white dwarf or two low-mass heli

Itamar Harel, William M. Hoza, Gal Vardi, Itay Evron

We study the overfitting behavior of fully connected deep Neural Networks (NNs) with binary weights fitted to perfectly classify a noisy training set. We consider interpolation using both the smallest NN (having the minimal number of weights) and a random interpolating NN. For both learning rules, we prove overfitting is tempered. Our analysis rests on a new

Martín Blufstein, Alexandre Martin, Nicolas Vaskou

We study homomorphisms between XL-type Artin groups and show that, in a suitable sense, a generic Artin group is both hopfian and co-hopfian. For XL-type Artin groups over complete graphs, we describe all possible homomorphisms with sufficiently large image, and prove in particular that such groups are both hopfian and co-hopfian. For Artin groups over gener

Jiaze Yin, Christian Pfluegl, Chu C. Teng, Rylie Bolarinho

Photothermal microscopy is an emerging tool for measuring light-matter interactions with single-molecule sensitivity. It is generally believed that the spectral acquisition speed in photothermal microscopy is limited by the slow thermal diffusion process. Here, we demonstrate mid-infrared energy deposition (MIRED) spectroscopy, which offers both microsecond-

Julien Bouvard, Swarnavo Basu, Charlott Leu, Onurcan Bektas

From the vasculature of animals to the porous media making up batteries, the core task of flow networks is to transport solutes and perfuse all cells or media equally with resources. Yet, living flow networks have a key advantage over porous media: they are adaptive and self-organize their geometry for homogeneous perfusion throughout the network. Here, we s

Thomas Gehrmann, Johannes Henn, Petr Jakubčík, Jungwon Lim

Several recent works have demonstrated the powerful algebraic simplifications that can be achieved for scattering amplitudes through a systematic grading of transcendental quantities. We develop these concepts to construct a minimal basis of functions tailored to a scattering amplitude in a general way. Starting with formal solutions for all master integral

Vittoria Brugaletta, Stefanie Walch, Thorsten Naab, Philipp Girichidis

Low-metallicity environments are subject to inefficient cooling. They also have low dust-to-gas ratios and therefore less efficient photoelectric (PE) heating than in solar-neighbourhood conditions, where PE heating is one of the most important heating processes in the warm neutral interstellar medium (ISM). We perform magneto-hydrodynamic simulations of str

Yago Antolín, Martín Blufstein, Luis Paris

We characterize, in terms of the defining graph, when a twisted right-angled Artin group (a group whose only relations among pairs of generators are either commuting or Klein-bottle type relations) is left-orderable.

Serap A. Savari

Sampling and quantization are standard practices in signal and image processing, but a theoretical understanding of their impact is incomplete. We consider discrete image registration when the underlying function is a one-dimensional spatially-limited piecewise constant function. For ideal noiseless sampling the number of samples from each region of the supp

Qifan Zhang, Xiaobin Hong, Jianheng Tang, Nuo Chen

Large Language Models (LLMs) have demonstrated strong reasoning abilities, making them suitable for complex tasks such as graph computation. Traditional reasoning steps paradigm for graph problems is hindered by unverifiable steps, limited long-term reasoning, and poor generalization to graph variations. To overcome these limitations, we introduce GCoder, a

Kaixuan Ye, Akshay Keloth, Yisbel E. Marin, Matteo Cherchi

Silicon photonics, with its CMOS compatibility and high integration density, has enabled a wide range of novel applications. Harnessing stimulated Brillouin scattering (SBS), an optomechanic interaction between optical and GHz acoustic waves, in silicon-on-insulator (SOI) platforms attracts great interests for its potential in narrow-linewidth lasers and mic

Hans J. Haubold

In a paper [8] the authors classify entropy into three categories, as a thermodynamics quantity, as a measure of information production, and as a means of statistical inference. An entropy measure introduced by Mathai falls into the second and third categories. It is shown that this entropy measure is the same whether the variables involved are real or compl

Jiachang Liu, Rui Zhang, Cynthia Rudin

Survival analysis is an important research topic with applications in healthcare, business, and manufacturing. One essential tool in this area is the Cox proportional hazards (CPH) model, which is widely used for its interpretability, flexibility, and predictive performance. However, for modern data science challenges such as high dimensionality (both $n$ an

Vinod Ramakrishnan, Kathryn H. Matlack

Phononic crystals (PnCs) are periodic engineered media that can customize the spatio-temporal characteristics of mechanical energy propagation. PnCs that additionally leverage precisely embedded defects can achieve robust energy localization with desirable spatio-temporal characteristics, opening avenues for critical engineering applications, e.g., energy ha

Lingxiao Li, Kaixiong Gong, Weihong Li, Xili Dai

This paper introduces Bifr\"ost, a novel 3D-aware framework that is built upon diffusion models to perform instruction-based image composition. Previous methods concentrate on image compositing at the 2D level, which fall short in handling complex spatial relationships ($\textit{e.g.}$, occlusion). Bifr\"ost addresses these issues by training MLLM as a 2.5D

Charles Gong

Let $G$ be a plane graph and let $C$ be a cycle in $G$. For each finite face of $G$, count the number of edges of $C$ the face contains. We call this the Slitherlink signature of $C$. The symmetric difference $A$ of two cycles with the same signature is totally even, meaning every vertex is incident to an even number of edges in $A$ and every face contains a

Alexis Bose, Jonathan Ethier, Paul Guinand

This paper introduces Target Strangeness, a novel difficulty estimator for conformal prediction (CP) that offers an alternative approach for normalizing prediction intervals (PIs). By assessing how atypical a prediction is within the context of its nearest neighbours' target distribution, Target Strangeness can surpass the current state-of-the-art performanc

Charles Gong

Given any $r$-edge coloring of $K_{n,n}$, how large is the maximum (over all $r$ colors) sized monochromatic subgraph guaranteed to be? We give answers to this problem for $r \leq 8$, when $r$ is a perfect square, and when $r$ is one less than a perfect square all up to a constant additive term that depends on $r$. We give a lower bound on this quantity that

Oleksandr Sudakov, Volodymyr Maistrenko

This paper addresses the problem of parallelizing computations to study non-linear dynamics in large networks of non-locally coupled oscillators using heterogeneous computing resources. The proposed approach can be applied to a variety of non-linear dynamics models with runtime specification of parameters and network topologies. Parallelizing the solution of

Nayely Vélez-Cruz, Manfred D. Laubichler

In this work, we introduce a generalized framework for multiscale state-space modeling that incorporates nested nonlinear dynamics, with a specific focus on Bayesian learning under switching regimes. Our framework captures the complex interactions between fast and slow processes within systems, allowing for the analysis of how these dynamics influence each o

Herbert Susmann, Yiting Li, Mara A. McAdams-DeMarco, Iván Díaz

Provider profiling has the goal of identifying healthcare providers with exceptional patient outcomes. When evaluating providers, adjustment is necessary to control for differences in case-mix between different providers. Direct and indirect standardization are two popular risk adjustment methods. In causal terms, direct standardization examines a counterfac

Tyson Jordan, Pranav Pandey, Prashant Doshi, Ramviyas Parasuraman

The use of autonomous systems in medical evacuation (MEDEVAC) scenarios is promising, but existing implementations overlook key insights from human-robot interaction (HRI) research. Studies on human-machine teams demonstrate that human perceptions of a machine teammate are critical in governing the machine's performance. Here, we present a mixed factorial de

Aysun Pınarbaşı, Béla Vizvári

The COVID-19 pandemic showed that stocking and procuring vaccines are different from the inventory holding problems of production. The safety of the supply is more important than cost minimization. The Hungarian inventory model is applied to determine the initial stock if the probability of the non-shortage is given. This initial stock is the function of the

Mustazee Rahman, Balint Virag

A basic question about the directed landscape is how much of it can be reconstructed simply by knowing the shapes of its geodesics. We prove that the directed landscape can be reconstructed from the shapes of its semi-infinite geodesics. In order to show this result, we make use of the Busemann process of the directed landscape together with a novel ``coordi

Bharadwaj Vedula, M. A. Moore, Auditya Sharma

We have studied the diluted Heisenberg spin glass model in a 3-component random field for the commonly-used one-dimensional long-range model where the probability that two spins separated by a distance $r$ interact with one another falls as $1/r^{2 \sigma}$, for two values of $\sigma$, $0.75$ and $0.85$. No de Almeida-Thouless line is expected at these $\sig

Bijit Mukherjee, Jeremy M. Hutson

We investigate the prospect of using ultracold alkali diatomic molecules to implement many-body quantum systems with SU($N$) symmetry. Experimentally accessible molecules offer large $N$ for both bosonic and fermionic systems, with both attractive and repulsive interactions. We carry out coupled-channel scattering calculations on pairs of NaK, NaRb and NaCs

Andrew Pangia, Agus Sudjianto, Aijun Zhang, Taufiquar Khan

Fair lending practices and model interpretability are crucial concerns in the financial industry, especially given the increasing use of complex machine learning models. In response to the Consumer Financial Protection Bureau's (CFPB) requirement to protect consumers against unlawful discrimination, we introduce LDA-XGB1, a novel less discriminatory alternat

Aditya Anand, Euiwoong Lee, Amatya Sharma

Given a fixed arity $k \geq 2$, Min-$k$-CSP on complete instances involves a set of $n$ variables $V$ and one nontrivial constraint for every $k$-subset of variables (so there are $\binom{n}{k}$ constraints). The goal is to find an assignment that minimizes unsatisfied constraints. Unlike Max-$k$-CSP that admits a PTAS on dense or expanding instances, the ap

V. Plastovets, A. Buzdin

We study the microscopic mechanism of nucleation of the helical superconducting state maintained by a spin-splitting field and weak Rashba spin-orbit coupling in a 2D superconductor/ferromagnet system and present an exact solution for the Gor'kov equations, which provides a full description of the thermodynamic properties of the system under consideration. T

Yuhan Liu, Zirui Song, Juntian Zhang, Xiaoqing Zhang

With the growing spread of misinformation online, understanding how true news evolves into fake news has become crucial for early detection and prevention. However, previous research has often assumed fake news inherently exists rather than exploring its gradual formation. To address this gap, we propose FUSE (Fake news evolUtion Simulation framEwork), a nov

Farah Alsafadi, Mahmoud Yaseen, Xu Wu

The confluence of ultrafast computers with large memory, rapid progress in Machine Learning (ML) algorithms, and the availability of large datasets place multiple engineering fields at the threshold of dramatic progress. However, a unique challenge in nuclear engineering is data scarcity because experimentation on nuclear systems is usually more expensive an

Avantika Agarwal, Shalev Ben-David

We study the quantum-classical polynomial hierarchy, QCPH, which is the class of languages solvable by a constant number of alternating classical quantifiers followed by a quantum verifier. Our main result is that QCPH is infinite relative to a random oracle (previously, this was not even known relative to any oracle). We further prove that higher levels of

Jesse Vos, Benoit Cerutti, Monika Moscibrodzka, Kyle Parfrey

We present the first collisionless realization of two-dimensional axisymmetric black hole accretion consistent with a persistent magnetically arrested disk state. The accretion flow, consisting of an ion-electron disk plasma combined with magnetospheric pair creation effects, is simulated using first-principles general-relativistic particle-in-cell methods.

Philip Marx, Elie Tamer, Xun Tang

We study the identification of heterogeneous, intertemporal treatment effects (TE) when potential outcomes depend on past treatments. First, applying a dynamic panel data model to observed outcomes, we show that an instrumental variable (IV) version of the estimand in Arellano and Bond (1991) recovers a non-convex (negatively weighted) aggregate of TE plus n

Mitrajyoti Ghosh, Yuval Grossman, Chinhsan Sieng, Bingrong Yu

The Standard Model predicts a long-range force mediated by a pair of neutrinos, known as ``the neutrino force". It scales as $G_F^2/r^5$, where $G_F$ is the Fermi constant. However, as $r \lesssim \sqrt{G_F}$, the four-Fermi theory breaks down and the neutrino force no longer has the $1/r^5$ scaling. For the first time, we derive a complete expression for th

Tom Vanackere, Artur Hermans, Ian Christen, Christopher Panuski

Advancements in light modulator technology have been driving discoveries and progress across various fields. The problem of large-scale coherent optical control of atomic quantum systems-including cold atoms, ions, and solid-state color centers-presents among the most stringent requirements. This motivates a new generation of high-speed large-scale modulator

Juan M. Tizón, Antoni M. Barredo

High-frequency combustion instabilities can lead to significant fluctuations in chamber pressure, affecting the structural integrity and performance of solid rocket motors. Since these instabilities manifest as acoustic oscillations during combustion, a mathematical model has been developed to calculate the chamber modes, providing an accurate method for pre

Marc Magaña

We prove that the solution map for a family of non-linear transport equations in $\mathbb{R}^n$, with a velocity field given by the convolution of the density with a kernel that is smooth away from the origin and homogeneous of degree $-(n-1)$, is continuous in both the little H\"older class and the little Zygmund class. For particular choices of the kernel,

Xiaodong Yu, Ben Zhou, Hao Cheng, Dan Roth

Existing math datasets evaluate the reasoning abilities of large language models (LLMs) by either using the final answer or the intermediate reasoning steps derived from static examples. However, the former approach fails to surface model's uses of shortcuts and wrong reasoning while the later poses challenges in accommodating alternative solutions. In this

Felix Petersen, Christian Borgelt, Tobias Sutter, Hilde Kuehne

When training neural networks with custom objectives, such as ranking losses and shortest-path losses, a common problem is that they are, per se, non-differentiable. A popular approach is to continuously relax the objectives to provide gradients, enabling learning. However, such differentiable relaxations are often non-convex and can exhibit vanishing and ex

Revanth Gangi Reddy, Sagnik Mukherjee, Jeonghwan Kim, Zhenhailong Wang

Despite seemingly performant web agents on the task-completion benchmarks, most existing methods evaluate the agents based on a presupposition: the web navigation task consists of linear sequence of actions with an end state that marks task completion. In contrast, our work focuses on web navigation for information aggregation, wherein the agent must explore