October 2024 arXiv papers — page 68

Mingyang Zheng, Rebika Makaju, Rasul Gazizulin, Alex Levchenko

One-dimensional Coulomb drag has been an essential tool to probe the physics of interacting Tomonaga-Luttinger liquids. To date, most experimental work has focused on the linear regime while the predictions for Luttinger liquids beyond the linear response theory remain largely untested. In this letter, we report measurements of momentum transfer induced Coul

Michael Pichat, Enola Campoli, William Pogrund, Jourdan Wilson

We propose a neuropsychological approach to the explainability of artificial neural networks, which involves using concepts from human cognitive psychology as relevant heuristic references for developing synthetic explanatory frameworks that align with human modes of thought. The analogical concepts mobilized here, which are intended to create such an episte

Mingyan Simon Lin

We derive the cluster structure on the conjugation quotient Coxeter double Bruhat cells of a simple Lie group from that on the double Bruhat cells of the corresponding adjoint Lie group given by Fock and Goncharov using the notion of amalgamation given by Fock and Goncharov, and Williams, thereby generalizing the construction developed by Gekhtman \emph{et a

Zhiwei Dave Li, Chang Liu, Adrian van Kan, Edgar Knobloch

Multiple stationary, localized structures were recently found for inclined porous medium convection with constant-temperature boundaries. We analyze traveling asymmetric, localized convective structures, consisting of 1 to 5 pulses, in a 2D inclined porous layer with fixed temperature at the bottom and an imperfectly conducting boundary at the top, such that

Ivoline C. Ngong, Joseph P. Near, Niloofar Mireshghallah

Differentially private SGD (DPSGD) enables privacy-preserving training of language models, but often reduces utility, diversity, and linguistic quality. We introduce DPRefine, a three-phase method that initializes a model using data synthesis from a small pre-trained LM with rigorous filtering, applies DP finetuning on private data, and performs self-distill

Yingyu Chen, Ziyuan Yang, Ming Yan, Zhongzhou Zhang

Multi-modality (MM) semi-supervised learning (SSL) based medical image segmentation has recently gained increasing attention for its ability to utilize MM data and reduce reliance on labeled images. However, current methods face several challenges: (1) Complex network designs hinder scalability to scenarios with more than two modalities. (2) Focusing solely

Zihao Li, Dongqi Fu, Hengyu Liu, Jingrui He

Hypergraphs naturally arise when studying group relations and have been widely used in the field of machine learning. To the best of our knowledge, the recently proposed edge-dependent vertex weights (EDVW) modeling is one of the most generalized modeling methods of hypergraphs, i.e., most existing hypergraph conceptual modeling methods can be generalized as

Shangshang Yang, Mingyang Chen, Ziwen Wang, Xiaoshan Yu

Existing graph learning-based cognitive diagnosis (CD) methods have made relatively good results, but their student, exercise, and concept representations are learned and exchanged in an implicit unified graph, which makes the interaction-agnostic exercise and concept representations be learned poorly, failing to provide high robustness against noise in stud

Shardul Lendve, Konstantinos Bletsas, Pedro F. Souto

Parallel real-time embedded applications can be modelled as directed acyclic graphs (DAGs) whose nodes model subtasks and whose edges model precedence constraints among subtasks. Efficiently scheduling such parallel tasks can be challenging in itself, particularly in hard real-time systems where it must be ensured offline that the deadlines of the parallel a

Dongni Chen, Stefano Chesi, Mahn-Soo Choi

We have analyzed the dynamics of entanglement in dissipative fermionic and bosonic Su-Schrieffer-Heeger (SSH) models and found that, when the decoherence channel preserves the chiral symmetry, they exhibit a revival of entanglement in a wide range of parameters. This behavior only emerges in the topological phase, with the visibility of the revival dropping

Farzane Zangene, Matei I. Radulescu

This study investigates the role of two inert mono-atomic diluents, argon and helium, on the detonation structure in order to assess the importance of vibrational non-equilibrium and wall losses. When relaxation effects and wall losses are neglected, the detonation waves in mixtures diluted with either of these gases have the same kinetics, Mach number, and

S. Venkataraman, Manisha V. Kulkarni

Let $K=\mathbb{Q}[\iota]$ and $N=K[\sqrt[4]{\alpha}]$, $\alpha\in\mathbb{Z}[\iota]$, $alpha=fg^2h^3$, $f$, $g$, $h\in \mathbb{Z}[\iota]$ are pairwise coprime and square free. Let $\mathcal{O}_N$ be the ring of integers of $N$. In this article we construct normalised integral basis for $\mathcal{O}_N$ over $\mathbb{Z}[\iota]$, that is an integral basis of the

Yong-Qing Chen, Peng-Cheng Hong, Zhuo Chen, Wei Shan

In this work, a formalism is presented for the helicity amplitude analysis of the decays $\psi(2S) \to \gamma_1 \chi_{cJ},~ \chi_{cJ} \to \gamma_2 V (V=\rho^0,~\phi,~\omega)$~(the subscript 1,2 is used to distinguish the two radiative photons), and the polarization expressions of the P-wave charmonia $\chi_{cJ}$ and the vector mesons $\rho^0, \phi, \omega$ f

Bruno Kahn

We give a sheaf-theoretic version of the universal coefficient theorem.

Junnan Dong, Zijin Hong, Yuanchen Bei, Feiran Huang

Large language models (LLMs) have demonstrated their remarkable performance across various language understanding tasks. While emerging benchmarks have been proposed to evaluate LLMs in various domains such as mathematics and computer science, they merely measure the accuracy in terms of the final prediction on multi-choice questions. However, it remains ins

Michael John Fanous, Christopher Michael Seybold, Hanlong Chen, Nir Pillar

We developed a rapid scanning optical microscope, termed "BlurryScope", that leverages continuous image acquisition and deep learning to provide a cost-effective and compact solution for automated inspection and analysis of tissue sections. This device offers comparable speed to commercial digital pathology scanners, but at a significantly lower price point

Kehan Huang, Min Qiu, Jun Tong, Jinhong Yuan

The orthogonal delay-Doppler division multiplexing (ODDM) modulation is a recently proposed multi-carrier modulation that features a realizable pulse orthogonal with respect to the delay-Doppler (DD) plane's fine resolutions. In this paper, we investigate the performance of ODDM systems with imperfect channel estimation considering three detectors, namely th

Wei Chen, Meng Yuan, Zhao Zhang, Ruobing Xie

As trustworthy AI continues to advance, the fairness issue in recommendations has received increasing attention. A recommender system is considered unfair when it produces unequal outcomes for different user groups based on user-sensitive attributes (e.g., age, gender). Some researchers have proposed data augmentation-based methods aiming at alleviating user

Tianhao Fu, Querobin Mascarenhas, Andrew Forti

With the rapid development of electric vehicles, formula races that face high school and university students have become more popular than ever as the threshold for design and manufacturing has been lowered. In many cases, we see teams inspired by or directly using toolkits and technologies inherited from standardized commercial vehicles. These architectures

Norak Rin, Iman Shames, Ian R. Petersen, Elizabeth L. Ratnam

In this paper, we quantify voltage and current phasor-based measurement requirements for the unique estimation of the electric grid topology and admittance parameters. Our approach is underpinned by the concept of a rigidity matrix that has been extensively studied in graph rigidity theory. Specifically, we show that the rank of the rigidity matrix is the sa

Zongqi Wang, Baoyuan Wu, Jingyuan Deng, Yujiu Yang

Embeddings as a Service (EaaS) is emerging as a crucial role in AI applications. Unfortunately, EaaS is vulnerable to model extraction attacks, highlighting the urgent need for copyright protection. Although some preliminary works propose applying embedding watermarks to protect EaaS, recent research reveals that these watermarks can be easily removed. Hence

Bang You, Huaping Liu

Reinforcement learning has achieved promising results on robotic control tasks but struggles to leverage information effectively from multiple sensory modalities that differ in many characteristics. Recent works construct auxiliary losses based on reconstruction or mutual information to extract joint representations from multiple sensory inputs to improve th

He-Guang Xu, Shujie Cheng

Extended and critical states are two common phenomena in Fibonacci quasicrystals. In this paper, we first reveal the difference between the extended phase and the critical phase in the extended-critical Fibonacci quasicrystal from the perspectives of quantum transport and Wigner distribution. The transport conductance indicates that the extended-critical tra

Marcin Marculewicz, Mouyuan Sun, Zhixiang Zhang, Tuan Yi

The measurement of continuum time lags in lensed quasars can effectively probe the accretion physics of quasars. This is because microlensing observations of lensed quasars can provide constraints on the half-light radii of quasar accretion disks. By combining the microlensing results with time lag measurements, one can, for the first time, estimate the prop

Xingfu Li, Sunny Gupta, Boris I. Yakobson

Two-dimensional (2D) materials have a high F\"oppl-von K\'arm\'an number and can be easily bent, much like a paper, making undulations a novel way to design distinct electronic phases. Through first-principles calculations, we reveal the formation of 1D polarization domains and 1D flat electronic bands by 1D bending modulation to a 2D bilayer semiconductor.

Sharath Matada, Luke Bhan, Yuanyuan Shi, Nikolay Atanasov

In this work, we introduce a planning neural operator (PNO) for predicting the value function of a motion planning problem. We recast value function approximation as learning a single operator from the cost function space to the value function space, which is defined by an Eikonal partial differential equation (PDE). Therefore, our PNO model, despite being t

Bowen Wei, Ziwei Zhu

Deep neural networks have achieved remarkable performance in various text-based tasks but often lack interpretability, making them less suitable for applications where transparency is critical. To address this, we propose ProtoLens, a novel prototype-based model that provides fine-grained, sub-sentence level interpretability for text classification. ProtoLen

Xintao Li, Sibei Liu, Dezhi Yu, Yang Zhang

Readmissions among Medicare beneficiaries are a major problem for the US healthcare system from a perspective of both healthcare operations and patient caregiving outcomes. Our study analyzes Medicare hospital readmissions using LSTM networks with feature engineering to assess feature contributions. We selected variables from admission-level data, inpatient

Ngo Phuc Duc Loc

Primordial black holes (PBHs) are the natural candidate of dark matter (DM) as they only interact gravitationally and can evade any experiments on earth. In the standard semiclassical calculation of Hawking radiation, PBHs with mass below $10^{15}\rm g$ evaporated by now and therefore cannot be DM. However, the recently-discovered quantum memory burden effec

Ran An, Ke Chen, Hongwei Li

Deep-learning methods have shown promising performance for low-dose computed tomography (LDCT) reconstruction. However, supervised methods face the problem of lacking labeled data in clinical scenarios, and the CNN-based unsupervised denoising methods would cause excessive smoothing in the reconstructed image. Recently, the normalizing flows (NFs) based meth

Sajal K. Das, Amit Kumar Dhar, Barun Gorain, Madhuri Mahawar

In this paper, we study collision-free graph exploration in an anonymous pot labeled network. Two identical mobile agents, starting from different nodes in $G$ have to explore the nodes of $G$ in such a way that for every node $v$ in $G$, at least one mobile agent visits $v$ and no two agents are in the same node in any round and stop. The agents know the si

Mohammed Saif, Shahrokh Valaee

Reconfigurable intelligent surface (RIS) is pivotal for beyond 5G networks in regards to the surge demand for reliable communication in unmanned aerial vehicle (UAV) networks. This paper presents an innovative approach to maximize connectivity of UAV networks using RIS deployment and virtual partitioning, wherein an RIS is deployed to assist in the communica

Zhuangfei Wu, Lin Bai, Jinpeng Xu, Lin Zhou

We study the two-user broadcast channel with degraded message sets and derive second-order achievability rate regions. Specifically, the channel noises are not necessarily Gaussian and we use spherical codebooks for both users. The weak user with worse channel quality applies nearest neighbor decoding by treating the signal of the other user as interference.

Dipankar Shakya, Mingjun Ying, Theodore S. Rappaport, Peijie Ma

Global allocations in the upper mid-band spectrum (4--24 GHz) necessitate a comprehensive exploration of the propagation behavior to meet the promise of coverage and capacity. This paper presents an extensive Urban Microcell (UMi) outdoor propagation measurement campaign at 6.75 GHz and 16.95 GHz conducted in Downtown Brooklyn, USA, using a 1 GHz bandwidth s

Yang Hu, Tianyi Chen, Na Li, Kai Wang

Off-policy evaluation (OPE) is one of the most fundamental problems in reinforcement learning (RL) to estimate the expected long-term payoff of a given target policy with only experiences from another behavior policy that is potentially unknown. The distribution correction estimation (DICE) family of estimators have advanced the state of the art in OPE by br

Elly Bayona, Hernando Quevedo, Miguel Alcubierre

The initial state of the spherical gravitational collapse in general relativity has been studied with different methods, especially by using {\it a priori} given equations of state that describe the matter as a perfect fluid. We propose an alternative approach, in which the energy density of the perfect fluid is given as a polynomial function of the radial c

Jiarun Ding, Peiwen Jiang, Chao-Kai Wen, Shi Jin

The rapid development of artificial intelligence has significantly advanced semantic communications, particularly in wireless image transmission. However, most existing approaches struggle to precisely distinguish and prioritize image content, and they do not sufficiently incorporate semantic priorities into system design. In this study, we propose an adapti

R. R. S. Oliveira

In this paper, we determine the relativistic bound-state solutions for the charged (DO) Dirac oscillator in a rotating frame in the Bonnor-Melvin-Lambda spacetime in $(2+1)$-dimensions, where such solutions are given by the two-component normalizable Dirac spinor and by the relativistic energy spectrum. To analytically solve our problem, we consider two appr

Haiji Liang, Ruize Han

Open-vocabulary object perception has become an important topic in artificial intelligence, which aims to identify objects with novel classes that have not been seen during training. Under this setting, open-vocabulary object detection (OVD) in a single image has been studied in many literature. However, open-vocabulary object tracking (OVT) from a video has

Yuxin Yang, Qiang Li, Yuan Hong, Binghui Wang

Federated graph learning (FedGL) is an emerging learning paradigm to collaboratively train graph data from various clients. However, during the development and deployment of FedGL models, they are susceptible to illegal copying and model theft. Backdoor-based watermarking is a well-known method for mitigating these attacks, as it offers ownership verificatio

Junhua Liu, Bin Fu

Multilingual Large Language Models (MLLMs) represent a pivotal advancement in democratizing artificial intelligence across linguistic boundaries. While theoretical foundations are well-established, practical implementation guidelines remain scattered. This work bridges this gap by providing a comprehensive end-to-end framework for developing and deploying ML

Romakanta Bhattarai, Xiao Shen

We propose a strategy to make phosphorus nanotubes from two well-known phosphorus allotropes: violet phosphorus and fibrous red phosphorus. First-principles calculations show that doping with sulfur dissociates the covalent bonds between tubular phosphorus structures that form bilayers in these allotropes, resulting in free-standing 1D nanotubes. Due to the

Rohit Kumar Shukla, Levan Chotorlishvili, Sunil K. Mishra, Fernando Iemini

We study the emergence of prethermal Floquet Time Crystal (pFTC) in disordered chiral multiferroic chains. The model is an extension of the usual periodically driven nearest-neighbor disordered Heisenberg chain, with additional next-nearest-neighbor Heisenberg couplings and DMI interactions due to external magnetic and electric couplings. We derive the phase

Yongqiang Huang, Wentao Ye, Liyao Li, Junbo Zhao

This study investigates the potential of Large Language Models (LLMs) for reconstructing and constructing the physical world solely based on textual knowledge. It explores the impact of model performance on spatial understanding abilities. To enhance the comprehension of geometric and spatial relationships in the complex physical world, the study introduces

Ryoya Igata, Myonsok I, Yuya Seki, Yuta Mizuno

We investigate the performance of penalty-based quantum annealing (PQA) and constrained quantum annealing (CQA) in solving the graph partitioning problem under various noise models, including depolarizing, bit-flip, and phase-flip noise. We found that even in the absence of noise, the relative superiority of PQA or CQA is highly problem-dependent. PQA genera

JiuYi Li, ShanKun Liu, Fei Han, Yong Mei

Peridynamics (PD) is widely used to simulate structural failure. However, PD models are time-consuming. To improve the computational efficiency, we developed an adaptive coupling model between PD and classical continuum mechanics (PD-CCM) based on the Morphing method [1], driven by the broken bond or strength criteria. We derived the dynamic equation of the

Zhixia He, Chen Zhao, Minglai Shao, Yujie Lin

Out-of-distribution (OOD) detection poses a significant challenge for Graph Neural Networks (GNNs), particularly in open-world scenarios with varying distribution shifts. Most existing OOD detection methods on graphs primarily focus on identifying instances in test data domains caused by either semantic shifts (changes in data classes) or covariate shifts (c

Xiaoqian Qi, Haoye Chai, Yong Li

In cellular mobile networks, wireless channel quality (CQ) is a crucial factor in determining communication performance and user's network experience. Accurately predicting CQ based on real environmental characteristics, specific base station configurations and user trajectories can help network operators optimize base station deployment, improving coverage

Yusuke Tanaka, Alvin Zhu, Richard Lin, Ankur Mehta

In limbed robotics, end-effectors must serve dual functions, such as both feet for locomotion and grippers for grasping, which presents design challenges. This paper introduces a multi-modal end-effector capable of transitioning between flat and line foot configurations while providing grasping capabilities. MAGPIE integrates 8-axis force sensing using propo

Daisuke Kishimoto, Takahiro Matsushita

We show a criterion for a skeleton of a manifold triangulation being embeddable into Euclidean space in terms of the complement of a submanifold. As an application, we obtain embeddability of a $(q-1)$-skeleton of a triangulation of an $S^p$-bundle over $S^q$ into $\mathbb{R}^{p+q}$.

Chen-Bo Jia, Jia-Qing Huang

The central Delannoy numbers $D_n=\sum_{k=0}^{n}\binom{n}{k}\binom{n+k}{k}$ and the little Schr\"oder number $s_n=\sum_{k=1}^{n}\frac{1}{n}\binom{n}{k}\binom{n}{k-1}2^{n-k}$ are important quantities. In this paper, we confirm \[\frac{2}{3n(n+1)}\sum_{k=1}^n (-1)^{n-k}k^2D_kD_{k-1}\ \text{and}\ \ \frac 1n\sum_{k=1}^n (-1)^{n-k}(4k^2+2k-1)D_{k-1}s_k\]are posit

Jun Cheng, Shan Tan

Real-world noise removal is crucial in low-level computer vision. Due to the remarkable generation capabilities of diffusion models, recent attention has shifted towards leveraging diffusion priors for image restoration tasks. However, existing diffusion priors-based methods either consider simple noise types or rely on approximate posterior estimation, limi

Juyong Lee, Dongyoon Hahm, June Suk Choi, W. Bradley Knox

Autonomous agents powered by large language models (LLMs) show promising potential in assistive tasks across various domains, including mobile device control. As these agents interact directly with personal information and device settings, ensuring their safe and reliable behavior is crucial to prevent undesirable outcomes. However, no benchmark exists for s

Wonje Jeung, Dongjae Jeon, Ashkan Yousefpour, Jonghyun Choi

Existing fairness benchmarks for large language models (LLMs) primarily focus on simple tasks, such as multiple-choice questions, overlooking biases that may arise in more complex scenarios like long-text generation. To address this gap, we introduce the Long Text Fairness Test (LTF-TEST), a framework that evaluates biases in LLMs through essay-style prompts

Qibang Liu, Pengfei Cai, Diab Abueidda, Sagar Vyas

Under some initial and boundary conditions, the rapid reaction-thermal diffusion process taking place during frontal polymerization (FP) destabilizes the planar mode of front propagation, leading to spatially varying, complex hierarchical patterns in thermoset polymeric materials. Although modern reaction-diffusion models can predict the patterns resulting f

Virginia Feldman, Ariel Bendersky

Quantum process tomography is a useful tool for characterizing quantum processes. This task is essential for the development of different areas, such as quantum information processing. In this work, we present a protocol for selective continuous-variable quantum process tomography. Our proposal allows one to selectively estimate any element of an unknown con

David Kogan, Sagnik Nandy, Jiaoyang Huang

We study the spectral norm of random kernel matrices with polynomial scaling, where the number of samples scales polynomially with the data dimension. In this regime, Lu and Yau (2022) proved that the empirical spectral distribution converges to the additive free convolution of a semicircle law and a Marcenko-Pastur law. We demonstrate that the random kernel

Hamid Manoochehri, Bodong Zhang, Beatrice S. Knudsen, Tolga Tasdizen

Self-supervised learning has become a cornerstone in various areas, particularly histopathological image analysis. Image augmentation plays a crucial role in self-supervised learning, as it generates variations in image samples. However, traditional image augmentation techniques often overlook the unique characteristics of histopathological images. In this p

Kailai Sun, Zherui Shao, Yang Miang Goh, Jing Tian

Workplace safety has received increasing attention as millions of workers worldwide suffer from work-related accidents. Despite poor housekeeping is a significant contributor to construction accidents, there remains a significant lack of technological research focused on improving housekeeping practices in construction sites. Recognizing and locating poor ho

Abdallah El Kass, Kevin A. F. Simoes, Cassandra Chua, David J. Reilly

Kinetic inductance travelling-wave parametric amplifiers (KTWPAs) are emerging as core components in many applications where wideband cryogenic rf amplification at or near the quantum limit of added noise is critical. These thin film superconducting devices are unique in their ability to simultaneously provide large dynamic range and quantum-limited amplific

Muhammad Tanzil Furqon, Mahardhika Pratama, Ary Mazharuddin Shiddiqi, Lin Liu

The issue of source-free time-series domain adaptations still gains scarce research attentions. On the other hand, existing approaches rely solely on time-domain features ignoring frequency components providing complementary information. This paper proposes Time Frequency Domain Adaptation (TFDA), a method to cope with the source-free time-series domain adap

Soto Anno, Kota Tsubouchi, Masamichi Shimosaka

Forecasting rail congestion is crucial for efficient mobility in transport systems. We present rail congestion forecasting using reports from passengers collected through a transit application. Although reports from passengers have received attention from researchers, ensuring a sufficient volume of reports is challenging due to passenger's reluctance. The l

Simon Tartakovsky, Alexandre E. Adler, Jason E. Austermann, Steven J. Benton

We describe the cryogenic system being developed for Taurus: a super-pressure balloon-borne microwave polarimeter scheduled to fly in 2027. The Taurus cryogenic system consists of a 660L liquid helium cryostat which achieves a base temperature of <100mK with the help of a capillary-fed superfluid tank and a closed cycle dilution refrigerator. The main tank i

Jinghan Jia, Jiancheng Liu, Yihua Zhang, Parikshit Ram

The need for effective unlearning mechanisms in large language models (LLMs) is increasingly urgent, driven by the necessity to adhere to data regulations and foster ethical generative AI practices. Despite growing interest of LLM unlearning, much of the existing research has focused on varied unlearning method designs to boost effectiveness and efficiency.

Melissa Fuentealba, Danilo M. Rivera, Roberto E. Navarro

The Hamiltonian Mean-Field (HMF) model is a long-range interaction model that exhibits quasi-stationary states associated with a phase transition. Its quasi-stationary states with a lifetime diverging with the number of particles in the system. These states are characterized by homogeneous or non-homogeneous structures in phase-space. There exists a phase-tr

Yusuke Kobayashi, Takashi Noguchi

A triangle-free (simple) 2-matching is an edge set that has at most $2$ edges incident to each vertex and contains no cycle of length $3$. For the problem of finding a maximum cardinality triangle-free 2-matching in a given graph, a complicated exact algorithm was proposed by Hartvigsen. Recently, a simple PTAS using local search was presented by Bosch-Calvo

Sherry He, Brett Hollenbeck, Gijs Overgoor, Davide Proserpio

Online reviews significantly impact consumers' decision-making process and firms' economic outcomes and are widely seen as crucial to the success of online markets. Firms, therefore, have a strong incentive to manipulate ratings using fake reviews. This presents a problem that academic researchers have tried to solve over two decades and on which platforms e

Bohan Wang, Yurui Chang, Wei Jin, Lu Lin

Distribution shifts between training and testing datasets significantly impair the model performance on graph learning. A commonly-taken causal view in graph invariant learning suggests that stable predictive features of graphs are causally associated with labels, whereas varying environmental features lead to distribution shifts. In particular, covariate sh

Yu Wang, Xiaobao Wei, Ming Lu, Guoliang Kang

Previous methods utilize the Neural Radiance Field (NeRF) for panoptic lifting, while their training and rendering speed are unsatisfactory. In contrast, 3D Gaussian Splatting (3DGS) has emerged as a prominent technique due to its rapid training and rendering speed. However, unlike NeRF, the conventional 3DGS may not satisfy the basic smoothness assumption a

Shruthi Chari

Explainable Artificial Intelligence (AI) focuses on helping humans understand the working of AI systems or their decisions and has been a cornerstone of AI for decades. Recent research in explainability has focused on explaining the workings of AI models or model explainability. There have also been several position statements and review papers detailing the

Emir Kamenica, Xiao Lin

When does Sender, in a Sender-Receiver game, strictly value commitment? In a setting with finitely many actions and states, we establish that, generically, commitment has no value if and only if a partitional experiment is optimal. Moreover, if Sender's preferred cheap-talk equilibrium necessarily involves randomization, then Sender values commitment. Our re

Himashi Peiris, Zhaolin Chen

Accurate hippocampus segmentation in brain MRI is critical for studying cognitive and memory functions and diagnosing neurodevelopmental disorders. While high-field MRIs provide detailed imaging, low-field MRIs are more accessible and cost-effective, which eliminates the need for sedation in children, though they often suffer from lower image quality. In thi

Martin Bojowald, Erick I. Duque, S. Shankaranarayanan

Emergent modified gravity is a post-Einsteinian gravitational theory where spacetime geometry is not fundamental but rather emerges from the gravitational degrees of freedom in a non-trivial way. The specific relationship between geometry and these degrees of freedom is unique for each theory, but it is not predetermined. Instead, it is derived from constrai

Ryota Maruo, Koh Takeuchi, Hisashi Kashima

The fair allocation of indivisible resources is a fundamental problem. Existing research has developed various allocation mechanisms or algorithms to satisfy different fairness notions. For example, round robin (RR) was proposed to meet the fairness criterion known as envy-freeness up to one good (EF1). Expert algorithms without mathematical formulations are

Kyla de Villa, Felipe Gonzalez-Cataldo, Burkhard Militzer

The ice giant planets Uranus and Neptune are assumed to contain large amounts of planetary ices such as water, methane, and ammonia. The properties of mixtures of such ices at the extreme pressures and temperatures of planetary interiors are not yet well understood. Ab initio computer simulations predicted that a number of ices exhibit a hydrogen superionic

Paul Smolensky, Roland Fernandez, Zhenghao Herbert Zhou, Mattia Opper

Large Language Models (LLMs) have demonstrated impressive abilities in symbol processing through in-context learning (ICL). This success flies in the face of decades of critiques asserting that artificial neural networks cannot master abstract symbol manipulation. We seek to understand the mechanisms that can enable robust symbol processing in transformer ne

Yue Xiang, Shenghong Gu, A. Collier Cameron, J. R. Barnes

We present a series of 9 Doppler images of the magnetically active K component of the RS CVn-type binary SZ Psc, based on the high-resolution spectroscopic data collected from 2014 to 2018. We apply least-squares deconvolution to all spectra to extract the average profiles with high signal-to-noise ratios (SNRs) for Doppler imaging. The surface maps of the K

Hung Viet Chu, Steven J. Miller, Joshua M. Siktar

One challenge (or opportunity!) that many instructors face is how varied the backgrounds, abilities, and interests of students are. In order to simultaneously instill confidence in those with weaker preparations and still challenge those able to go faster, an instructor must be prepared to give problems of different difficulty levels. Using Dirichlet's Theor

Kushagra Tiwari, M. Amin Rahimian, Mark S. Roberts, Praveen Kumar

The US opioid overdose epidemic has been a major public health concern in recent decades. There has been increasing recognition that its etiology is rooted in part in the social contexts that mediate substance use and access; however, reliable statistical measures of social influence are lacking in the literature. We use Facebook's social connectedness index

Ying-Jen Yang, Ken A. Dill

The great power of EQuilibrium (EQ) statistical physics comes from its principled foundations: its First Law (conservation), Second Law (variational tendency principle), and its Legendre Transforms from observables $(U, V, N)$ to their driving forces $(T, p, \mu)$. Here, we generalize this structure to Non-EQuilibria (NEQ) in \textit{Caliber Force Theory} (C

Jun-En Ding, Chien-Chin Hsu, Chi-Hsiang Chu, Shuqiang Wang

The classification of medical images is a pivotal aspect of disease diagnosis, often enhanced by deep learning techniques. However, traditional approaches typically focus on unimodal medical image data, neglecting the integration of diverse non-image patient data. This paper proposes a novel Cross-Graph Modal Contrastive Learning (CGMCL) framework for multim

Barry P Lawlor, Vatsa Gandhi, Guruswami Ravichandran

Background: Full-field, quantitative visualization techniques, such as digital image correlation (DIC), have unlocked vast opportunities for experimental mechanics. However, DIC has traditionally been a surface measurement technique, and has not been extended to perform measurements on the interior of specimens for dynamic, full-scale laboratory experiments.

Jiaqi Xue, Qian Lou, Mengxin Zheng

Attacking fairness is crucial because compromised models can introduce biased outcomes, undermining trust and amplifying inequalities in sensitive applications like hiring, healthcare, and law enforcement. This highlights the urgent need to understand how fairness mechanisms can be exploited and to develop defenses that ensure both fairness and robustness. W

Wei Liu, Huihua Zhao, Chenran Li, Joydeep Biswas

General-purpose navigation in challenging environments remains a significant problem in robotics, with current state-of-the-art approaches facing myriad limitations. Classical approaches struggle with cluttered settings and require extensive tuning, while learning-based methods face difficulties generalizing to out-of-distribution environments. This paper in

Kasturi Shah

To improve models of the structure and evolution of stellar and planetary interiors, it is important to quantify transport by strongly stratified turbulence in low Prandtl number fluids. Recent numerical studies have shown evidence for scale-separated dynamics in strongly anisotropic flows. Motivated by these results, in the first part of this report we perf

Indrajeet Ghosh, Garvit Chugh, Abu Zaher Md Faridee, Nirmalya Roy

Recent advancements in deep learning-based wearable human action recognition (wHAR) have improved the capture and classification of complex motions, but adoption remains limited due to the lack of expert annotations and domain discrepancies from user variations. Limited annotations hinder the model's ability to generalize to out-of-distribution samples. Whil

Yixuan Wang, Guang Yin, Binghao Huang, Tarik Kelestemur

Diffusion-based policies have shown remarkable capability in executing complex robotic manipulation tasks but lack explicit characterization of geometry and semantics, which often limits their ability to generalize to unseen objects and layouts. To enhance the generalization capabilities of Diffusion Policy, we introduce a novel framework that incorporates e

Cédric Josz, Lexiao Lai

We consider nonsmooth rank-one symmetric matrix factorization. It has no spurious second-order stationary points.

D. F. S. Costa, J. H. M Pontes, W. P. Ferreira, J. S. de Sousa

Surface roughness plays a crucial role in the accuracy of indentation experiments used to measure the elastic properties of materials. In this study, we present a computational analysis of how surface roughness, represented explicitly by fractal geometry, influences the mechanical properties of soft materials. We model two-dimensional elastic samples with a

Yifan Peng, Krishna C. Puvvada, Zhehuai Chen, Piotr Zelasko

Recent studies have augmented large language models (LLMs) with speech capabilities, leading to the development of speech language models (SpeechLMs). Earlier SpeechLMs focused on single-turn speech-based question answering (QA), where user input comprised a speech context and a text question. More recent studies have extended this to multi-turn conversation

He Zhu, Ren Togo, Takahiro Ogawa, Miki Haseyama

Conventional medical artificial intelligence (AI) models face barriers in clinical application and ethical issues owing to their inability to handle the privacy-sensitive characteristics of medical data. We present a novel personalized federated learning (pFL) method for medical visual question answering (VQA) models, addressing privacy reliability challenge

Riley Thornton

We generalize ultraproducts and local-global limits of graphs to hypergraphs and other structures. We show that the local statistics of an ultraproduct of a sequence of hypergraphs are the ultralimits of the local statistics of the hypergraphs. Using some standard results from model theory, we conclude that the space of (equivalence classes of) pmp hypergrap

Hayley Ross, Kathryn Davidson, Najoung Kim

Inferences from adjective-noun combinations like "Is artificial intelligence still intelligence?" provide a good test bed for LLMs' understanding of meaning and compositional generalization capability, since there are many combinations which are novel to both humans and LLMs but nevertheless elicit convergent human judgments. We study a range of LLMs and fin

Swati Srivastava, Justin Bullock

This essay examines how Artificial Intelligence (AI) systems are becoming more integral to international affairs by affecting how global governors exert power and pursue digital sovereignty. We first introduce a taxonomy of multifaceted AI payoffs for governments and corporations related to instrumental, structural, and discursive power in the domains of vio

Nithin Somasekharan, Shaowu Pan

Representation learning for high-dimensional, complex physical systems aims to identify a low-dimensional intrinsic latent space, which is crucial for reduced-order modeling and modal analysis. To overcome the well-known Kolmogorov barrier, deep autoencoders (AEs) have been introduced in recent years, but they often suffer from poor convergence behavior as t

M. Grant Roberts, Lila Braff, Aarna Garg, Stefano Profumo

Evidence for high-redshift supermassive black holes challenges standard scenarios for how such objects form in the early universe. Here, we entertain the possibility that a fraction of the cosmological dark matter could be ultra-strongly self interacting. This would imply that gravothermal collapse occur at early times in the cores of dark matter halos, foll

Xin Shu, Vibhoothi Vibhoothi, Anil Kokaram

Predicting the computational cost of compressing/transcoding clips in a video corpus is important for resource management of cloud services and VOD (Video On Demand) providers. Currently, customers of cloud video services are unaware of the cost of transcoding their files until the task is completed. Previous work concentrated on predicting perclip compressi

E. A. O&#39;Brien, Sunil Kumar Prajapati, Ayush Udeep

The minimal faithful permutation degree $μ(G)$ of a finite group $G$ is the least integer $n$ such that $G$ is isomorphic to a subgroup of the symmetric group $S_n$. If $G$ has a normal subgroup $N$ such that $μ(G/N) > μ(G)$, then $G$ is exceptional. We prove that the proportion of exceptional groups of order $p^6$ for primes $p \geq 5$ is asymptotically 0.

Erwin Mönch, Mariya D. Moldavskaya, Leonid E. Golub, Vasily V. Bel&#39;kov

Nonlinear electron transport induced by polarized terahertz radiation is studied in two-dimensional tellurene at room temperature. A direct current, quadratic in the radiation&#39;s electric field, is observed. Contributions sensitive to radiation helicity, polarization orientation as well as polarization independent current are found. We show that these con

Giulio Bonelli, Pavlo Gavrylenko, Ideal Majtara, Alessandro Tanzini

We study BPS surface observables of $\mathcal{N}=2$ four dimensional $SU(2)$ gauge theory in gravitational $Ω$-background at perturbative and at Argyres-Douglas superconformal fixed points. This is done by formulating the equivariant gauge theory on the blow-up of $\mathbb{C}^2$ and considering the decoupling Nekrasov-Shatashvili limit. We show that in this