December 2024 arXiv papers — page 32

Jianbo Zhang, Chunyi Li, Jie Hao, Jun Jia

Image Quality Assessment (IQA) of User-Generated Content (UGC) is a critical technique for human Quality of Experience (QoE). However, does the the image quality of Robot-Generated Content (RGC) demonstrate traits consistent with the Moravec paradox, potentially conflicting with human perceptual norms? Human subjective scoring is more based on the attractive

Seth Nabat, Aishik Ghosh, Edmund Witkowski, Gregor Kasieczka

Recognizing symmetries in data allows for significant boosts in neural network training, which is especially important where training data are limited. In many cases, however, the exact underlying symmetry is present only in an idealized dataset, and is broken in actual data, due to asymmetries in the detector, or varying response resolution as a function of

Mohammad Hassan Naddaf

This paper presents a preliminary investigation into the influence of radial behavior of disk outflow on the structure and dynamics of the broad line region (BLR) in active galactic nuclei (AGNs), with the emphasis on how the mass ejection rate contribute in shaping the broad emission line profiles. Specifically, we analyze how varying the radial efficiency

Sushil Kumar, Soumya P. Dash, Debasish Ghose, George C. Alexandropoulos

In this paper, a multiple-input multiple-output (MIMO) wireless system incorporating a reconfigurable intelligent surface (RIS) to efficiently operate at terahertz (THz) frequencies is considered. The transmitter, Alice, employs continuous-variable quantum key distribution (CV-QKD) to communicate secret keys to the receiver, Bob, which utilizes either homody

Yu-An Liu, Ruqing Zhang, Jiafeng Guo, Maarten de Rijke

Neural ranking models (NRMs) have been shown to be highly effective in terms of retrieval performance. Unfortunately, they have also displayed a higher degree of sensitivity to attacks than previous generation models. To help expose and address this lack of robustness, we introduce a novel ranking attack framework named Attack-in-the-Chain, which tracks inte

A. Flores, C. Stegner, S. S. Chabysheva, J. R. Hiller

We solve the Schr\"odinger-Newton problem of Newtonian gravity coupled to a nonrelativistic scalar particle for solutions with axial symmetry. The gravitational potential is driven by a mass density assumed to be proportional to the probability density of the scalar. Unlike related calculations for condensates of ultralight dark matter or boson stars, no ass

Yu-An Liu, Ruqing Zhang, Jiafeng Guo, Changjiang Zhou

Generative information retrieval methods retrieve documents by directly generating their identifiers. Much effort has been devoted to developing effective generative IR models. Less attention has been paid to the robustness of these models. It is critical to assess the out-of-distribution (OOD) generalization of generative IR models, i.e., how would such mod

Y. C. Xiang, P. Feng, X. F. Lan

We employ an efficient method for identifying gamma-ray sources across the entire sky, leveraging advanced algorithms from Fermi p y, and cleverly utilizing the Galactic diffuse background emission model to partition the entire sky into 72 regions,thereby greatly enhancing the efficiency of discovering new sources throughout the sky through multi-threaded pa

Hadi Vafaii

This Dissertation is comprised of two main projects, addressing questions in neuroscience through applications of generative modeling. Project #1 (Chapter 4) explores how neurons encode features of the external world. I combine Helmholtz's "Perception as Unconscious Inference" -- paralleled by modern generative models like variational autoencoders (VAE) -- w

Ruiqi Liu, Xingyu Liu, Xiaohao Xu, Yixuan Zhang

Group Re-identification (G-ReID) faces greater complexity than individual Re-identification (ReID) due to challenges like mutual occlusion, dynamic member interactions, and evolving group structures. Prior graph-based approaches have aimed to capture these dynamics by modeling the group as a single topological structure. However, these methods struggle to ge

Min Zhang, Cui-Wen Zhang, De-Sheng Zhang, Hai-Bo Sang

By using two-dimensional particle-in-cell simulations, attosecond electron bunches with high density, high energy and small divergence angle can be obtained by p-polarized laser irradiation in conical channel with curved wall. We find that some electrons in the wall are pulled into the channel by the transverse electric field and are directly accelerated. Me

Akihiro Ishibashi, Kengo Maeda, Takashi Okamura

In the framework of AdS/CFT duality, we consider the semiclassical problem in general quadratic theory of gravity. We construct asymptotically global AdS and hyperbolic~(topological) AdS black hole solutions with non-trivial quantum hair in $4$ and $5$-dimensions by perturbing the maximally symmetric AdS solutions to the holographic semiclassical equations.

Yuhao Li, Yuanhao Wei, Ruiping Guo, Yifei Wang

Topological polar textures in ferroelectrics have attracted significant interest for their potential for energy-efficient and high-density data storage and processing. Among these, polar merons and antimerons are predicted in strained and twisted bilayers of inversion symmetry broken systems. However, experimental observation of these polar textures within t

Yufei Zhao, Yong Liang Guan, Dong Chen, Afkar Mohamed Ismail

This research proposes a novel approach utilizing Orbital Angular Momentum (OAM) beams to enhance Radar Cross Section (RCS) diversity for target detection in future transportation systems. Unlike conventional OAM beams with hollow-shaped divergence patterns, the new proposed OAM beams provide uniform illumination across the target without a central energy vo

Haijun Li

Archimedean copulas generated by Laplace transforms have been extensively studied in the literature, with much of the focus on tail dependence limited only to cases where the Laplace transforms exhibit regular variation with positive tail indices. In this paper, we extend the investigation to include Archimedean copulas associated with both slowly varying an

Tai Dinh, Wong Hauchi, Daniil Lisik, Michal Koren

This paper explores the critical role of data clustering in data science, emphasizing its methodologies, tools, and diverse applications. Traditional techniques, such as partitional and hierarchical clustering, are analyzed alongside advanced approaches such as data stream, density-based, graph-based, and model-based clustering for handling complex structure

Haiying Shan, Xiaoqi Liu

Let $\mathcal{G}^M$ denote the set of connected graphs with distinct $M$-eigenvalues. This paper explores the $M$-spectrum and eigenvectors of a new product $G\circ_C H$ of graphs $G$ and $H$. We present the necessary and sufficient condition for $G\circ_C H$ to have distinct $M$-eigenvalues. Specifically, for the rooted product $G\circ H$, we present a more

Cai Heng Li, Luyi Liu

This is one of a series of papers which aim towards a classification of edge-transitive maps of which the Euler characteristic and the edge number are coprime. This one establishes a framework and carries out the classification work for arc-transitive maps with solvable automorphism groups, which illustrates how the edge number impacts on the Euler character

Huaxia Zhou, Mengyi Sun

Authorship disambiguation is crucial for advancing studies in science of science. However, assessing the quality of authorship disambiguation in large-scale databases remains challenging since it is difficult to manually curate a gold-standard dataset that contains disambiguated authors. Through estimating the timing of when 5.8 million biomedical researcher

Lei Wang, Juan Kang, Ni Liu, Chaohua Wu

The interplay among interaction, non-Hermiticity, and disorder opens a new avenue for engineering novel phase transitions. We here study the spectral and localization features of two interacting bosons in one-dimensional nonreciprocal quasicrystals. Specifically, by considering a quasiperiodic Hubbard lattice with nonreciprocal hoppings, we show that the int

Sakiko Noda, Hiroto Adachi, Masanori Ichioka

Within a framework of two-component Ginzburg-Landau theory as a model of superconducting FeSe, we study the spatial structure of vortex states in the presence of nematic twin boundary in an s + d wave nematic superconductor. The result shows that the orientation of the nematic vortex core is rotated 90 degrees across the twin boundary, and just at the twin b

Norihiro Hanihara

Roots of shifted Serre functors appear naturally in representation theory and algebraic geometry. We give an analogue of Keller's Calabi-Yau completion for roots of shifted inverse dualizing bimodules over dg categories. Given a positive integer $a$, we introduce the notion of the $a$-th root pair on smooth dg categories and define its Calabi-Yau completion.

Bibhushan Shakya

Collisions of vacuum bubbles in the early Universe can act as cosmic-scale high-energy colliders with energy reach close to the Planck scale. Such "cosmic colliders" would represent the most energetic phenomena in our cosmic history, transcending any temperature or energy scale ever reached in our Universe, opening tremendous opportunities for particle physi

Md Nakhla Rafi, Dong Jae Kim, Tse-Hsun Chen, Shaowei Wang

Large Language Models (LLMs) show great promise in software engineering tasks like Fault Localization (FL) and Automatic Program Repair (APR). This study investigates the impact of input order and context size on LLM performance in FL, a crucial step for many downstream software engineering tasks. We test different orders for methods using Kendall Tau distan

Likang Zhang, Qinghe Du, Yijing Ren, Xiao Tang

In this paper, we study reconfigurable intelligent surface (RIS)-assisted simultaneous legitimate monitoring and jamming techniques for industrial environments, so that egitimate monitor (LM) and legitimate jammers (LJs) can sustainably monitor and interfere with suspicious communications with minimum transmission power. Specifically, we propose a Block Coor

Yuan Zhao, Rui Liu, Gaoxiang Cong

Automatic Video Dubbing (AVD) generates speech aligned with lip motion and facial emotion from scripts. Recent research focuses on modeling multimodal context to enhance prosody expressiveness but overlooks two key issues: 1) Multiscale prosody expression attributes in the context influence the current sentence's prosody. 2) Prosody cues in context interact

X. M. Tong, K. Tokesi, D. Kato, T. Okumura

We study the energy structures of muonic Ar atoms and find the muon orbital collapses at a critical angular momentum $l_c$ using density-functional theory (DFT). The $l_c$ may provide an upper limit for the muon-captured states in muon-Ar collisions. We confirm the existence of this upper limit by calculating the state-specified capture probability using the

Hiroki Aoki, Tomoyoshi Ibukiyama, Cris Poor

We prove for general paramodular level that formal series of scalar Jacobi forms with an involution condition necessarily converge and are therefore the Fourier-Jacobi expansions at the standard 1-cusp of paramodular Fricke eigenforms.

Joseph R. Burger

Natural selection has produced an extraordinary diversity of life histories spanning many orders of magnitude in body size, vital rates, and biological times. In general, big and cold organisms grow and reproduce slowly and live long lives; small and warm organisms grow and reproduce quickly and live short lives. The Metabolic Theory of Ecology (MTE) predict

Feihu Liu, Guoce Xin, Chen Zhang

In this paper, we provide an overview of Ehrhart polynomials associated with order polytopes of finite posets, a concept first introduced by Stanley. We focus on their combinatorial interpretations for many sequences listed on the OEIS. We begin by exploring the Ehrhart series of order polytopes resulting from various poset operations, specifically the ordin

Ye Shen

Many theoretical models were come up with to figure out the properties of magnetic reconnection process, among which the Sweet-Parker model is the most famous since it describes the magnetic reconnection in a concise way. However, the low reconnection rate expected by this model is generally not available in most astrophysical systems, which motivates people

Milton L. Montero, Jeffrey S. Bowers, Gaurav Malhotra

In recent years, it has been shown empirically that standard disentangled latent variable models do not support robust compositional learning in the visual domain. Indeed, in spite of being designed with the goal of factorising datasets into their constituent factors of variations, disentangled models show extremely limited compositional generalisation capab

Hanbing Li, Doudou Wang, Quansen Zhang, Qiang Zhang

We propose and design a stoichiometric silicon-nitride membrane resonator featuring highly reflective at multi-wavelengths and high mechanical quality factor. The membrane resonator has a thickness of 100 nm and 2D-photonic and phononic crystal patterns. By designing concentric holes of suitable radius on both sides of the membrane, high reflectivity at mult

Jiahao Zhao, Jiayi Nan, Lai Wei, Yichen Yang

Due to the exceptional performance of Large Language Models (LLMs) in diverse downstream tasks,there has been an exponential growth in edge-device requests to cloud-based models.However, the current authentication mechanism using static Bearer Tokens in request headersfails to provide the flexibility and backend control required for edge-device deployments.T

Cory H. Colbert

The union-closed sets conjecture (sometimes referred to as Frankl's conjecture) states that every finite, nontrivial union-closed family of sets has an element that is in at least half of its members. Although the conjecture is known to be false in the infinite setting, we show that many interesting results can still be recovered by imposing suitable chain c

Xue Yang, Yan-Han Yang, Xin-Zhu Liu, Jun-Li Jiang

Quantum batteries, consisting of quantum cells, are anticipated to surpass their classical counterparts in performance because of the presence of quantum correlations. Recent theoretical study introduces the quantum battery capacity that is defined according to the highest and the lowest energy during the charging and discharging procedures. Here, we present

Xiao Zhang, Shaoxuan Wu, Peilin Zhang, Zhuo Jin

Creating fully annotated labels for medical image segmentation is prohibitively time-intensive and costly, emphasizing the necessity for innovative approaches that minimize reliance on detailed annotations. Scribble annotations offer a more cost-effective alternative, significantly reducing the expenses associated with full annotations. However, scribble ann

Andrea Legramandi, Neil Talwar

In chaotic quantum systems the spectral form factor exhibits a universal linear ramp and plateau structure with superimposed erratic oscillations. The mean signal and the statistics of the noise can be probed by the moments of the spectral form factor, also known as higher-point spectral form factors. We identify saddle points in the SYK model that describe

Ji-Gui Cheng, Le Zou

Numerical simulations suggest that dark matter halos surrounding galaxies host numerous small subhalos, which might be detectable by the Fermi-LAT. In this work, we revisit the search for gamma-ray subhalo candidates using the latest Fermi-LAT 4FGL-DR4 catalog. The search is performed by fitting the spectral data of unassociated point sources in the catalog

Xin He, Wenqi Fan, Mingchen Sun, Ying Wang

In recent years, researchers have leveraged social relations to enhance recommendation performance. However, most existing social recommendation methods require carefully designed auxiliary social tasks tailored to specific scenarios, which depend heavily on domain knowledge and expertise. To address this limitation, we propose Automatic Self-supervised Lear

Yanna Ding, Zijie Huang, Malik Magdon-Ismail, Jianxi Gao

Many real-world complex systems, such as epidemic spreading networks and ecosystems, can be modeled as networked dynamical systems that produce multivariate time series. Learning the intrinsic dynamics from observational data is pivotal for forecasting system behaviors and making informed decisions. However, existing methods for modeling networked time serie

Zhenqi Jia, Rui Liu

Conversational Speech Synthesis (CSS) aims to effectively take the multimodal dialogue history (MDH) to generate speech with appropriate conversational prosody for target utterance. The key challenge of CSS is to model the interaction between the MDH and the target utterance. Note that text and speech modalities in MDH have their own unique influences, and t

Yan Wang, Jin-Lei Wu, Ya-Feng Jiao, Tian-Xiang Lu

Tripartite entanglement as a remarkable resource in quantum information science has been extensively investigated in hybrid quantum systems, whereas it is generally weak and prone to be suppressed by noise, restricting its practical application in quantum technologies. Here, we propose how to enhance the tripartite entanglement among magnons, photons and pho

Jiajia Chen, Jiancan Wu, Jiawei Chen, Chongming Gao

Collaborative recommendation fundamentally involves learning high-quality user and item representations from interaction data. Recently, graph convolution networks (GCNs) have advanced the field by utilizing high-order connectivity patterns in interaction graphs, as evidenced by state-of-the-art methods like PinSage and LightGCN. However, one key limitation

Zhengchao Wan, Qingsong Wang, Gal Mishne, Yusu Wang

Flow matching (FM) models extend ODE sampler based diffusion models into a general framework, significantly reducing sampling steps through learned vector fields. However, the theoretical understanding of FM models, particularly how their sample trajectories interact with underlying data geometry, remains underexplored. A rigorous theoretical analysis of FM

Jiacheng Hu, Xiaoxuan Liao, Jia Gao, Zhen Qi

This study proposes a large language model optimization method based on the improved LoRA fine-tuning algorithm, aiming to improve the accuracy and computational efficiency of the model in natural language processing tasks. We fine-tune the large language model through a low-rank adaptation strategy, which significantly reduces the consumption of computing r

Fabián Belmonte, Giuseppe de Nittis

In this article we compute and analyze the spectrum of operators defined by the metaplectic representation $\mu$ on the unitary group $\mathbb{U}(d)$ or operators defined by the corresponding induced representation $d\mu$ of the Lie algebra $\mathfrak{u}(d)$. It turns out that the point spectrum of both types of operators can be expressed in terms of the eig

Taohong Zhu, Adrians Skapars, Fardeen Mackenzie, Declan Kehoe

Fuzz testing effectively uncovers software vulnerabilities; however, it faces challenges with Autonomous Systems (AS) due to their vast search spaces and complex state spaces, which reflect the unpredictability and complexity of real-world environments. This paper presents a universal framework aimed at improving the efficiency of fuzz testing for AS. At its

Charles Tahan

Instead of writing a review article on the state of the field, I'm going to instead write a retrospective from the year 2040. I'll tell you how this whole "quantum computing thing" turned out.

Robert Morelos-Zaragoza, Jeffrey Ma

In recent years, polar codes have been considered for communication systems that require high re-liability and ultra-low latency, such as sixth generation (6G) wireless communications. This paper presents simulation results showing that short binary extended BCH (eBCH) codes with low-complexity decoding outperform polar codes for lengths 64 and 128. In the s

Andrei Grekov, Nikita Nekrasov

Several generalizations of Vershik-Kerov limit shape problem are motivated by topological string theory and supersymmetric gauge theory instanton count. In this paper specifically we study the circular and linear quiver theories. We also briefly discuss the double-elliptic generalization of the Vershik-Kerov problem, related to six dimensional gauge theory c

Arya Bangun, Zhuo Cao, Alessio Quercia, Hanno Scharr

Magnetic Resonance Imaging (MRI) is a powerful imaging technique widely used for visualizing structures within the human body and in other fields such as plant sciences. However, there is a demand to develop fast 3D-MRI reconstruction algorithms to show the fine structure of objects from under-sampled acquisition data, i.e., k-space data. This emphasizes the

Ho Truong Nam Hai, Thanh Tam Nguyen, Maiko Nishibori, Tatsumi Ishihara

The persistent existence of plastic waste causes serious problems for the environment, directly and indirectly affecting the health of organisms and humans. Photoreforming is a nature-friendly method that only uses solar energy to convert plastic waste into green hydrogen (H2) and valuable organic products. This study shows that a high-entropy oxynitride (HE

Xinyue Yin, Shuai Xu, Sibo Zheng

It is known that single-field freeze-in dark matter barely leaves footprints in dark matter direct detection and collider experiments. This situation can be altered in two-field context. In this work we propose a two-field freeze-in dark matter model through Higgs portal. The observed dark matter relic abundance is obtained by a decay of scalar mediator ther

Gyeongmin Gu, Minseo Jeon, Hyun-Je Song, Jinhong Jung

How can we effectively and efficiently learn node representations in signed bipartite graphs? A signed bipartite graph is a graph consisting of two nodes sets where nodes of different types are positively or negative connected, and it has been extensively used to model various real-world relationships such as e-commerce, etc. To analyze such a graph, previou

Chris Impey, Matthew Wenger, Nikhil Garuda, Shahriar Golchin

Assessing writing in large classes for formal or informal learners presents a significant challenge. Consequently, most large classes, particularly in science, rely on objective assessment tools such as multiple-choice quizzes, which have a single correct answer. The rapid development of AI has introduced the possibility of using large language models (LLMs)

Amirhossein Nazeri, Chunheng Zhao, Pierluigi Pisu

Robust object detection is critical for autonomous driving and mobile robotics, where accurate detection of vehicles, pedestrians, and obstacles is essential for ensuring safety. Despite the advancements in object detection transformers (DETRs), their robustness against adversarial attacks remains underexplored. This paper presents a comprehensive evaluation

Chao Wang, Huiwen Zheng, Raymond Chan, Youwei Wen

Tensor Robust Principal Component Analysis (TRPCA) holds a crucial position in machine learning and computer vision. It aims to recover underlying low-rank structures and to characterize the sparse structures of noise. Current approaches often encounter difficulties in accurately capturing the low-rank properties of tensors and balancing the trade-off betwee

Karen Sowon, Collins W. Munyendo, Lily Klucinec, Eunice Maingi

Mobile Money (MoMo), a technology that allows users to complete financial transactions using a mobile phone without requiring a bank account, is a common method for processing financial transactions in Africa and other developing regions. Users can deposit and withdraw money with the help of human agents. During deposit and withdraw operations, know-your-cus

Haowei Yang, Longfei Yun, Jinghan Cao, Qingyi Lu

With the rapid development of large language models (LLMs) and the growing demand for personalized content, recommendation systems have become critical in enhancing user experience and driving engagement. Collaborative filtering algorithms, being core to many recommendation systems, have garnered significant attention for their efficiency and interpretabilit

Charles F. Manski

The relationship of policy choice by majority voting and by maximization of utilitarian welfare has long been discussed. I consider choice between a status quo and a proposed policy when persons have interpersonally comparable cardinal utilities taking values in a bounded interval, voting is compulsory, and each person votes for a policy that maximizes utili

Xueting Lin, Zhan Cheng, Longfei Yun, Qingyi Lu

With the advent of the information explosion era, the importance of recommendation systems in various applications is increasingly significant. Traditional collaborative filtering algorithms are widely used due to their effectiveness in capturing user behavior patterns, but they encounter limitations when dealing with cold start problems and data sparsity. L

Xiaolei Xu, Jack F. Douglas, Wen-Sheng Xu

We utilize the generalized entropy theory (GET) of glass formation to address one of the most singular and least understood properties of polymer glass-forming liquids in comparison to atomic and small molecule liquids -- the often relatively high fragility of the polymer dynamics on a segmental scale, $m_s$. We first analyze the relation between $m_s$ and t

Chirag Nagpal, Subhashini Venugopalan, Jimmy Tobin, Marilyn Ladewig

We introduce a large language model (LLM) capable of processing speech inputs and show that tuning it further with reinforcement learning on human preference (RLHF) enables it to adapt better to disordered speech than traditional fine-tuning. Our method replaces low-frequency text tokens in an LLM's vocabulary with audio tokens and enables the model to recog

S. Jeon, H. I. Kim, J. H. Choi, H. I. Jang

We report an updated neutrino mixing angle of $\theta_{13}$ obtained from a complete data sample of the RENO experiment. The experiment has measured the amplitude and frequency of reactor anti-electron-neutrinos ($\bar{\nu}_{e}$) oscillations at the Hanbit nuclear power plant, Younggwang, Korea, since August 2011. As of March 2023, the data acquisition was c

Yunyi Liu, Craig Jin

Generating sound effects with controllable variations is a challenging task, traditionally addressed using sophisticated physical models that require in-depth knowledge of signal processing parameters and algorithms. In the era of generative and large language models, text has emerged as a common, human-interpretable interface for controlling sound synthesis

Feng-Feng Song, Hanggai Nuomin, Naoki Kawashima

We investigate the finite-temperature phase diagram of the classical $J_1$-$J_2$ XY model on a square lattice using a tensor network approach designed for frustrated spin systems. This model, characterized by competing nearest-neighbor and next-to-nearest-neighbor interactions, exhibits a complex interplay between $U(1)$ and $Z_2$ symmetries. Our study revea

Aren A. Babikian, Attila Ficsor, Oszkár Semeráth, Gunter Mussbacher

To ensure their safe use, autonomous vehicles (AVs) must meet rigorous certification criteria that involve executing maneuvers safely within (arbitrary) scenarios where other actors perform their intended maneuvers. For that purpose, existing scenario generation approaches optimize search to derive scenarios with high probability of dangerous situations. In

Zefan Du, Wenrui Zhang, Wenqi Wei, Juntao Chen

Despite advancements, current quantum hardware faces significant challenges, including limited qubit counts and high susceptibility to noise, which hinder the execution of large, complex algorithms. To address these limitations, multi-node quantum systems and quantum circuit cutting techniques partition large circuits into smaller subcircuits that can be exe

Vivek Vellaiyappan Surulimuthu, Aditya Karnam Gururaj Rao

We present Chunked Augmented Generation (CAG), an architecture specifically designed to overcome the context window limitations of Google Chrome's built-in Gemini Nano model. While Chrome's integration of Gemini Nano represents a significant advancement in bringing AI capabilities directly to the browser, its restricted context window poses challenges for pr

Si Wu, John Wieting, David A. Smith

Multiple valid translations of a single literary work naturally exist. We investigate strategies for leveraging these multi-reference datasets to improve literary machine translation. We propose a filtering framework based on semantic similarity to identify source texts whose references display meaningful variation while remaining faithful. We find that fine

Mohsen Nayebi Kerdabadi, Arya Hadizadeh Moghaddam, Bin Liu, Mei Liu

Survival analysis (SA) models have been widely studied in mining electronic health records (EHRs), particularly in forecasting the risk of critical conditions for prioritizing high-risk patients. However, their vulnerability to adversarial attacks is much less explored in the literature. Developing black-box perturbation algorithms and evaluating their impac

Shuwen Kan, Yanni Li, Hao Wang, Sara Mouradian

Circuit cutting is a promising technique that leverages both quantum and classical computational resources, enabling the practical execution of large quantum circuits on noisy intermediate-scale quantum (NISQ) hardware. Recent approaches typically focus exclusively on either gate cuts or wire cuts, modeling quantum circuits as graphs. However, identifying op

Wenxiao Cai, Dongting Hu, Ruoyan Yin, Jiankang Deng

Stereo matching plays a crucial role in various applications, where understanding uncertainty can enhance both safety and reliability. Despite this, the estimation and analysis of uncertainty in stereo matching have been largely overlooked. Previous works struggle to separate it into data (aleatoric) and model (epistemic) components and often provide limited

Yanlin Feng, Simone Papicchio, Sajjadur Rahman

Retrieval from graph data is crucial for augmenting large language models (LLM) with both open-domain knowledge and private enterprise data, and it is also a key component in the recent GraphRAG system (edge et al., 2024). Despite decades of research on knowledge graphs and knowledge base question answering, leading LLM frameworks (e.g. Langchain and LlamaIn

Vishwak Srinivasan, Andre Wibisono, Ashia Wilson

In this work, we propose a first-order sampling method called the Metropolis-adjusted Preconditioned Langevin Algorithm for approximate sampling from a target distribution whose support is a proper convex subset of $\mathbb{R}^{d}$. Our proposed method is the result of applying a Metropolis-Hastings filter to the Markov chain formed by a single step of the p

Lara Marie Tomasch, Fabian Spallek, Guido W. Fuchs, Thomas F. Giesen

We consider the discriminatory interaction of a chiral molecule with circularly polarised modes inside a cavity. Starting from a generalised Jaynes--Cummings model that includes both electric and magnetic dipole couplings, we derive the Rabi frequency and associated Casimir--Polder potential for a cavity with a single mode of given handedness. One finds that

Marina Kholod, Nikita Mokrenko

The research identifies association rules that can inform marketing strategies and enhance operational efficiency. A structured methodology is applied to extract and interpret meaningful relationships within transactional data, emphasizing their implications for managerial decision-making. By demonstrating the potential of data mining to transform raw data i

Andreas Debrouwere, Michiel Huttener, Jasson Vindas

We show a strong factorization theorem of Dixmier-Malliavin type for ultradifferentiable vectors associated with compact Lie group representations on sequentially complete locally convex Hausdorff spaces. In particular, this solves a conjecture by Gimperlein et al. [J. Funct. Anal. 262 (2012), 667-681] for analytic vectors in the case of compact Lie groups.

Cong Jiang, Xiaolei Yang

The justice system has increasingly employed AI techniques to enhance efficiency, yet limitations remain in improving the quality of decision-making, particularly regarding transparency and explainability needed to uphold public trust in legal AI. To address these challenges, we propose a large language model based multi-agent framework named AgentsBench, wh

Anushrut Jignasu, Ethan Herron, Zhanhong Jiang, Soumik Sarkar

We present STITCH, a novel approach for neural implicit surface reconstruction of a sparse and irregularly spaced point cloud while enforcing topological constraints (such as having a single connected component). We develop a new differentiable framework based on persistent homology to formulate topological loss terms that enforce the prior of a single 2-man

Neiwen Ling, Guojun Chen, Lin Zhong

Large Language Models (LLMs) such as GPT-4 and Llama3 can already comprehend complex commands and process diverse tasks. This advancement facilitates their application in controlling drones and robots for various tasks. However, existing LLM serving systems typically employ a first-come, first-served (FCFS) batching mechanism, which fails to address the time

Ayçin Iplikçi Arodirik

This paper presents a technique for viewing quasi-coherent sheaves of ideals of a given blowup as regular ideals of a ring. In the paper, we first describe (Zariski) models as integral schemes that are separated and of finite type over an integral domain $D$. We then construct a ring $D^*$ for a given projective model (e.g. blowup of $D$ over a finitely gene

Alex Beutel, Kai Xiao, Johannes Heidecke, Lilian Weng

Automated red teaming can discover rare model failures and generate challenging examples that can be used for training or evaluation. However, a core challenge in automated red teaming is ensuring that the attacks are both diverse and effective. Prior methods typically succeed in optimizing either for diversity or for effectiveness, but rarely both. In this

Gautam Chinta, Kelly Isham, Nathan Kaplan

If $\Lambda \subseteq \mathbb{Z}^n$ is a sublattice of index $m$, then $\mathbb{Z}^n/\Lambda$ is a finite abelian group of order $m$ and rank at most $n$. Several authors have studied statistical properties of these groups as we range over all sublattices of index at most $X$. In this paper we investigate quotients by sublattices that have additional algebra

V. Gorkavenko, I. Hrynchak, O. Khasai, M. Tsarenkova

Extension of the Standard Model with Chern-Simons type interaction contains a new vector massive boson (Chern-Simons boson) that couples to electroweak gauge bosons by the so-called effective Chern-Simons interaction. There is no direct interaction between the Chern-Simons bosons and SM fermions. We consider existing restrictions on the parameters of this SM

Ainesh Chatterjee, Samuel Miller, Nithin Parepally

We introduce AgreeMate, a framework for training Large Language Models (LLMs) to perform strategic price negotiations through natural language. We apply recent advances to a negotiation setting where two agents (i.e. buyer or seller) use natural language to bargain on goods using coarse actions. Specifically, we present the performance of Large Language Mode

Ehsan Faridi, Se Kwon Kim, Giovanni Vignale

We theoretically investigate magnon heat transport in an antiferromagnetic (AFM) insulator containing a domain wall (DW) in the presence of a magnetic field applied along the easy axis. We show that the intrinsic spin of the DW couples to the external magnetic field which modifies the transmission of spin wave (SW) through the DW. Applying the magnetic field

Maria Nareklishvili, Nick Polson, Vadim Sokolov

We organize the generative-modeling literature around three classes of generators, corresponding to three distinct inferential tasks: estimating counterfactual outcome distributions in causal inference, recovering posteriors from simulated parameter--outcome pairs, and forming predictive outcome distributions. The unifying representation relies on the noise

Faraz Waseem, Muhammad Shahzad

An image may convey a thousand words, but a video composed of hundreds or thousands of image frames tells a more intricate story. Despite significant progress in multimodal large language models (MLLMs), generating extended videos remains a formidable challenge. As of this writing, OpenAI's Sora, the current state-of-the-art system, is still limited to produ

Hyunjin Shim

Bacteriophages are promising alternative antimicrobial agents due to their high specificity for host bacteria and minimal immunogenicity in humans. However, their therapeutic application is limited by their nature as biological entities, which can lead to unintended evolutionary consequences such as horizontal gene transfer. In this study, we address these c

D. Pricopi, C. C. Popescu, M. T. Rushton, D. Murphy

Solving the inverse problem in spiral galaxies, that allows the derivation of the spatial distribution of dust, gas and stars, together with their associated physical properties, directly from panchromatic imaging observations, is one of the main goals of this work. To this end we used radiative transfer models to decode the spatial and spectral distribution

Falko Ruppenthal, Denis Ridzal, Dmitri Kuzmin, Pavel Bochev

Optimization-based (OB) alternatives to traditional flux limiters couch preservation of properties such as local bounds and maximum principles into optimization problems, which impose these properties through inequality constraints. In this paper, we propose a new potential-target OB approach that enforces these properties using an optimal control formulatio

Vinicius Gomes de Paula, Wanisson S. Santana, Clebson Cruz, Mario Reis

Modeling quantum thermal machines provides a practical approach to describing the thermodynamic properties of quantum technologies and devices. For this purpose, power-law potentials are often employed as working mediums of quantum thermodynamic cycles to investigate the concepts of heat, work, and efficiency. With this in mind, we present the results for th

G. C. Borba, R. S. N. Moreira, L. S. Cruz, M. Martinelli

In this work, we explore both the internal and external atomic degrees of freedom to observe quantum entanglement between the modes produced by a mirrorless optical parametric oscillator operating below the oscillation threshold in a sample of free-space cold cesium atoms. Using a new heterodyne technique, we recover the covariance matrix that reveals the qu

Hendrik J. Algra, Zichao Li, Matthijs Langelaar, Farbod Alijani

The quality factor ($Q$ factor) of nanomechanical resonators is influenced by geometry and stress, a phenomenon called dissipation dilution. Studies have explored maximizing this effect, leading to softly-clamped resonator designs. This paper proposes a topology optimization methodology to design two-dimensional nanomechanical resonators with high $Q$ factor

Asaf Pe'er

I discuss here the progress made in the last decade on few of the key open problems in GRB physics. These include: (1) the nature of GRB progenitors, and the outliers found to the collapsar/merger scenarios; (2) Jet structures, whose existence became evident following GRB/GW170817; (3) the great progress made in understanding the GRB jet launching mechanisms

Alicja Wierzcholska, Stefan Wagner

BL Lacertae is a unique blazar for which the X-ray band can cover either the synchrotron or the inverse Compton, or both parts of the broadband spectral energy distribution. In the latter case, when the spectral upturn is located in the X-ray range, it allows contemporaneous study of the low- and high-energy ends of the electron distribution function. In thi

Ben Elias, Daniel Juteau, Benjamin Young

There is a q-deformation of the reflection representation of the affine symmetric group, which arises in the quantum geometric Satake equivalence, and in the study of the complex reflection groups $G(m,m,n)$. Demazure operators (often called divided difference operators) act on the polynomial ring of this deformed representation. When $n=3$ we prove an expli

Ben Elias, Daniel Juteau, Benjamin Young

In a previous paper of the first author, the type A affine Cartan matrix was q-deformed to produce a deformation of the reflection representation of the affine Weyl group. This deformation plays a role in the quantum geometric Satake equivalence. In this paper we introduce the study of q-deformed divided difference operators. When q is specialized to a primi