October 2024 arXiv papers — page 51

Soojin Woo, Seong-Woo Kim

In vision-based robot localization and SLAM, Visual Place Recognition (VPR) is essential. This paper addresses the problem of VPR, which involves accurately recognizing the location corresponding to a given query image. A popular approach to vision-based place recognition relies on low-level visual features. Despite significant progress in recent years, plac

Lingkun Wen, Hong Guo, Wenlin Ma, Lizhi Xie

We investigate the distribution and evolution of HI gas in different theoretical models, including hydro-dynamical simulations (Illustris-TNG and SIMBA), semi-analytic models (GAEA), and the empirical models ( NeutralUniverseMachine; NUM). By comparing model predictions for the HI mass function (HIMF), HI-halo and HI-stellar mass relations, conditional HI ma

Matej Belšak, Matjaž Omladič, Martin Vuk, Aljaž Zalar

The recent survey published in Fuzzy Sets and Systems nicknamed ``Hitchhiker's Guide'' has raised the rating of quasi-copula problems in the dependence modeling community in spite of the lack of statistical interpretation of quasi-copulas. Some of the open problems listed there were solved, and some conjectured one way or the other. This paper concentrates o

Peeter Laud, Alisa Pankova, Jelizaveta Vakarjuk

In this paper, we consider encryption systems with two-out-of-two threshold decryption, where one of the parties (the client) initiates the decryption and the other one (the server) assists. Existing threshold decryption schemes disclose to the server the ciphertext that is being decrypted. We give a construction, where the identity of the ciphertext is not

Daehyeon An, Se Kwon Kim

The Nernst effect of the magnon orbital moment is theoretically investigated in a honeycomb ferromagnet, whose Hamiltonian contains the Heisenberg exchange, the Dzyaloshinskii-Moriya, the Kitaev, and the Zeeman interactions. More specifically, we obtain the magnon band structure, the Berry curvature, the magnon orbital moment Berry curvature, and the magnon

Muhammad Zaeem Shahzad, Muhammad Abdullah Hanif, Muhammad Shafique

The proliferation of smartphones and other mobile devices provides a unique opportunity to make Advanced Driver Assistance Systems (ADAS) accessible to everyone in the form of an application empowered by low-cost Machine/Deep Learning (ML/DL) models to enhance road safety. For the critical feature of Collision Avoidance in Mobile ADAS, lightweight Deep Neura

Jameel-Un Nabi, Mahmut Boyukata, Asim Ullah, Muhammad Riaz

Nuclear structure properties and weak interaction rates of neutron rich even even iron (Fe) isotopes (A = 50 70) are investigated using the Interacting Boson Model1 (IBM1) and the proton neutron Quasiparticle Random Phase Approximation (pnQRPA) model. The IBM1 is used for the calculation of energy levels and the B(E2) values of neutron rich Fe isotopes. Late

Shin Sun, Kenneth Goodenough, Daniel Bhatti, David Elkouss

Realizing secure communication between distant parties is one of quantum technology's main goals. Although quantum key distribution promises information-theoretic security for sharing a secret key, the key rate heavily depends on the level of noise in the quantum channel. To overcome the noise, both quantum and classical techniques exist, i.e., entanglement

Jianning Chi, Zelan Li, Huixuan Wu, Wenjun Zhang

Weakly-supervised methods typically guided the pixel-wise training by comparing the predictions to single-level labels containing diverse segmentation-related information at once, but struggled to represent delicate feature differences between nodule and background regions and confused incorrect information, resulting in underfitting or overfitting in the se

Timothée Audinet, Umberto Morellini, Antoine Levitt, Julien Toulouse

With the aim of progressing toward a practical implementation of an effective quantum-electrodynamics (QED) theory of atoms and molecules, which includes the effects of vacuum polarization through the creation of virtual electron-positron pairs but without the explicit photon degrees of freedom, we study a one-dimensional effective QED model of the hydrogen-

Yimeng Tang, Aaron J. Romanowsky, Pieter G. van Dokkum, T. H. Jarrett

NGC 1052-DF2 and -DF4 are two ultra-diffuse galaxies that have been reported as deficient in dark matter and associated with the same galaxy group. Recent findings suggest that DF2 and DF4 are part of a large linear substructure of dwarf galaxies that could have been formed from a high-velocity head-on encounter of two gas-rich galaxies, known as a bullet dw

Min Wang

We study a class of positive random variables having moments of Gamma type, whose density can be expressed by the three-parametric Mittag-Leffler functions. We give some necessary conditions and some sufficient conditions for their existence. As a corollary, we give some conditions for non-negativity of the three-parametric Mittag-Leffler functions. As an ap

E. Alici

In this study, we investigate anomalous flavour-changing neutral current (FCNC) interactions related to the top quark, particularly the $t\rightarrow q\gamma$ transition, within the Standard Model Effective Field Theory (SMEFT) framework. These rare processes are largely suppressed in the Standard Model (SM) and are strong indicators for new physics scenario

Taki Eddine Djidjekh, Lamoussa Sanogo, Gaël Loubet, Alassane Sidibe

This article addresses an innovative concept to enhance the security for IoT applications in the case of Simultaneous Wireless Information and Power Transfer. This is achieved by integrating a complementary security and identification mechanism through Wireless Power Transfer link within the network of autonomous wireless nodes. This mechanism is implemented

Michel Mollard

The Fibonacci-run graphs $\mathcal{R}_n$ are a family of an induced subgraph of hypercubes introduced by E\u{g}ecio\u{g}lu and Ir\v{s}i\v{c} in 2021. A cyclic version of $\mathcal{R}_n$, the Lucas-run graph $\mathcal{R}_n^l$, was also recently proposed (Jianxin Wei, 2024). We prove that the generating function previously given for the polynomial $D_{\mathcal

Julien Bichon, Agustín García

We describe Hopf-Galois objects over bicrossed product Hopf algebras. More precisely, we show that any right Hopf-Galois object over a bicrossed product of Hopf algebras is obtained from Hopf-Galois objects over the two factors and a certain twisting map, while the unique Hopf algebra making it into a bi-Galois object is again a bicrossed product.

Emiel Hoogeboom, Thomas Mensink, Jonathan Heek, Kay Lamerigts

Latent diffusion models have become the popular choice for scaling up diffusion models for high resolution image synthesis. Compared to pixel-space models that are trained end-to-end, latent models are perceived to be more efficient and to produce higher image quality at high resolution. Here we challenge these notions, and show that pixel-space models can b

Ian W. McBrearty, Gregory C. Beroza

Double difference earthquake relocation is an essential component of many earthquake catalog development workflows. This technique produces high-resolution relative relocations between events by minimizing differential measurements of the arrival times of waves from nearby sources, which highlights the resolution of faults and improves interpretation of seis

Artur Bille, Victor Buchstaber, Evgeny Spodarev

Fullerenes are hollow carbon molecules where each atom is connected to exactly three other atoms, arranged in pentagonal and hexagonal rings. Mathematically, they can be combinatorially modeled as planar, 3-regular graphs with facets composed only of pentagons and hexagons. In this work, we outline a few of the many open questions about fullerenes, beginning

Mengmeng Chen, Xiaohu Wu, Xiaoli Tang, Tiantian He

Federated learning (FL) is a machine learning paradigm that allows multiple FL participants (FL-PTs) to collaborate on training models without sharing private data. Due to data heterogeneity, negative transfer may occur in the FL training process. This necessitates FL-PT selection based on their data complementarity. In cross-silo FL, organizations that enga

Prabhanjan Ananth, John Bostanci, Aditya Gulati, Yao-Ting Lin

We study the (in)feasibility of quantum pseudorandom notions in a quantum analog of the random oracle model, where all the parties, including the adversary, have oracle access to the same Haar random unitary. In this model, we show the following: - (Unbounded-query secure) pseudorandom unitaries (PRU) exist. Moreover, the PRU construction makes two calls to

Xiaoyu Wang, Xuxing Chen, Shiqian Ma, Tong Zhang

This paper focuses on decentralized stochastic bilevel optimization (DSBO) where agents only communicate with their neighbors. We propose Decentralized Stochastic Gradient Descent and Ascent with Gradient Tracking (DSGDA-GT), a novel algorithm that only requires first-order oracles that are much cheaper than second-order oracles widely adopted in existing wo

Wei Han, Pan Zhou, Soujanya Poria, Shuicheng Yan

The limited context window of contemporary large language models (LLMs) remains a huge barrier to their broader application across various domains. While continual pre-training on long-context data is a straightforward and effective solution, it incurs substantial costs in terms of data acquisition and computational resources. To alleviate this issue, we pro

Zhiting Fan, Ruizhe Chen, Tianxiang Hu, Zuozhu Liu

The growing use of large language model (LLM)-based chatbots has raised concerns about fairness. Fairness issues in LLMs can lead to severe consequences, such as bias amplification, discrimination, and harm to marginalized communities. While existing fairness benchmarks mainly focus on single-turn dialogues, multi-turn scenarios, which in fact better reflect

Weiqi Liu, Xi Ai, Zhijian Zhou, Chao Qu

Artificial intelligence is revolutionizing computational chemistry, bringing unprecedented innovation and efficiency to the field. To further advance research and expedite progress, we introduce the Quantum Open Organic Molecular (QO2Mol) database -- a large-scale quantum chemistry dataset designed for professional and transformative research in organic mole

Hadi Vafaii, Dekel Galor, Jacob L. Yates

Inference in both brains and machines can be formalized by optimizing a shared objective: maximizing the evidence lower bound (ELBO) in machine learning, or minimizing variational free energy (F) in neuroscience (ELBO = -F). While this equivalence suggests a unifying framework, it leaves open how inference is implemented in neural systems. Here, we introduce

Leander Girrbach, Stephan Alaniz, Yiran Huang, Trevor Darrell

Pre-trained large language models (LLMs) have been reliably integrated with visual input for multimodal tasks. The widespread adoption of instruction-tuned image-to-text vision-language assistants (VLAs) like LLaVA and InternVL necessitates evaluating gender biases. We study gender bias in 22 popular open-source VLAs with respect to personality traits, skill

Haocheng Xi, Han Cai, Ligeng Zhu, Yao Lu

FP8 training has emerged as a promising method for improving training efficiency. Existing frameworks accelerate training by applying FP8 computation to linear layers while leaving optimizer states and activations in higher precision, which fails to fully optimize memory usage. This paper introduces COAT (Compressing Optimizer States and Activations for FP8

Naveen Gupta, Sivananthan Sampath

The functional linear regression model has been widely studied and utilized for dealing with functional predictors. In this paper, we study the Nystr\"om subsampling method, a strategy used to tackle the computational complexities inherent in big data analytics, especially within the domain of functional linear regression model in the framework of reproducin

Matt Victor Dalziel, Krystal Schaffer, Neil Martin

This review began with the modest goal of drafting a brief commentary on how the social work profession engages with and is impacted by artificial intelligence (AI). However, it quickly became apparent that a deeper exploration was required to adequately capture the profound influence of AI, one of the most transformative and debated innovations in modern hi

Gerrit Behner, Abdur Rehman Jalil, Alina Rupp, Hans Lüth

The combination of an ordinary s-type superconductor with three-dimensional topological insulators creates a promising platform for fault-tolerant topological quantum computing circuits based on Majorana braiding. The backbone of the braiding mechanism are three-terminal Josephson junctions. It is crucial to understand the transport in these devices for furt

Zemin Huang, Zhengyang Geng, Weijian Luo, Guo-jun Qi

In the realm of Artificial Intelligence Generated Content (AIGC), flow-matching models have emerged as a powerhouse, achieving success due to their robust theoretical underpinnings and solid ability for large-scale generative modeling. These models have demonstrated state-of-the-art performance, but their brilliance comes at a cost. The process of sampling f

R. M. Goldblatt, N. Dontschuk, D. J. McCloskey, A. M. Martin

The microscopic electric environment surrounding a spin defect in a wide-bandgap semiconductor plays a determining role in the spin coherence and charge stability of a given qubit and has an equally important role in defining the electrical properties of the host material. Here, we use electrometry of quantum defects embedded within a diamond to observe stab

Jamie Milton Freestone

The word semantics, in robotics and AI, has no canonical definition. It usually serves to denote additional data provided to autonomous agents to aid HRI. Most researchers seem, implicitly, to understand that such data cannot simply be extracted from environmental data. I try to make explicit why this is so and argue that so-called semantics are best underst

Robert L. Bray

This tutorial addresses the challenge of incorporating large language models (LLMs), such as ChatGPT, in a data analytics class. It details several new in-class and out-of-class teaching techniques enabled by AI. For example, instructors can parallelize instruction by having students interact with different custom-made GPTs to learn different parts of an ana

Wanyu Zhang, Jiaqi Zhang, Dongdong Ge, Yu Lin

This paper addresses the problem of vision-based pedestrian localization, which estimates a pedestrian's location using images and camera parameters. In practice, however, calibrated camera parameters often deviate from the ground truth, leading to inaccuracies in localization. To address this issue, we propose an anchor-based method that leverages fixed-pos

Kira Sam

Bacteraemia, a bloodstream infection with high morbidity and mortality rates, poses significant diagnostic challenges. Accurate diagnosis through blood cultures is resource-intensive. Developing a machine learning model to predict blood culture outcomes in emergency departments offers potential for improved diagnosis, reduced healthcare costs, and mitigated

Zhengyang Lu, Tianhao Guo, Feng Wang

Classical Chinese poetry and painting represent the epitome of artistic expression, but the abstract and symbolic nature of their relationship poses a significant challenge for computational translation. Most existing methods rely on large-scale paired datasets, which are scarce in this domain. In this work, we propose a semi-supervised approach using cycle-

Luis A. Cedeño-Pérez, Hernando Quevedo

We construct a new kind of measures, called projection families, which generalize the classical notion of vector and operator-valued measures. The maximal class of reasonable functions admits an integral with respect to a projection family, where the integral is defined as an element of the second dual instead of the original space. We show that projection f

Elon Lindenstrauss, Gregory Margulis, Amir Mohammadi, Nimish Shah

We prove an effective closing lemma for unipotent flows on quotients of perfect real groups. This is largely motivated by recent developments in effective unipotent dynamics.

Hambur Wang

This study investigates the impact of industrial agglomeration on land use intensification in the Yangtze River Delta (YRD) urban agglomeration. Utilizing spatial econometric models, we conduct an empirical analysis of the clustering phenomena in manufacturing and producer services. By employing the Location Quotient (LQ) and the Relative Diversification Ind

Seyeong Park, Jaewook Lee, Seongmin Heo

For the efficient and safe use of lithium-ion batteries, diagnosing their current state and predicting future states are crucial. Although there exist many models for the prediction of battery cycle life, they typically have very complex input structures, making it very difficult and expensive to develop such models. As an alternative, in this work, a model

Josephine Dias, Hui Wang, Kae Nemoto, Franco Nori

We explore a protocol that efficiently charges multiple open quantum batteries in parallel using a single charger. This protocol shows super-extensive charging through collective coupling of the charger and the battery to the same thermal reservoir. When applied to multiple quantum batteries, each coupled to different thermal reservoirs, the energy cannot be

Emiliano Penaloza, Olivier Gouvert, Haolun Wu, Laurent Charlin

Traditional recommender systems rely on high-dimensional (latent) embeddings for modeling user-item interactions, often resulting in opaque representations that lack interpretability. Moreover, these systems offer limited control to users over their recommendations. Inspired by recent work, we introduce TExtuAl Representations for Scrutable recommendations (

Abhijnan Nath, Videep Venkatesha, Mariah Bradford, Avyakta Chelle

Question-asking in collaborative dialogue has long been established as key to knowledge construction, both in internal and collaborative problem solving. In this work, we examine probing questions in collaborative dialogues: questions that explicitly elicit responses from the speaker's interlocutors. Specifically, we focus on modeling the causal relations th

Wenjing Yang, Yuhong Yang

Many machine learning applications deal with high dimensional data. To make computations feasible and learning more efficient, it is often desirable to reduce the dimensionality of the input variables by finding linear combinations of the predictors that can retain as much original information as possible in the relationship between the response and the orig

Vickie Chen, Brandon Wang, Joseph D. Peterson

Entangled polymers are an important class of materials for their toughness, processability, and functionalizability. However, physically detailed modeling of highly entangled polymers can prove challenging, especially as one considers additional layers of physical or chemical complexity. To address these challenges, we present a series of generalizations for

Xinqiao Lin, Ousi Pan, Zhimin Yang, Yanchao Zhang

The bidirectional space charge effects in photon-enhanced thermionic emission (PETE) devices are investigated systematically. First, we precisely determine the carrier concentrations and cathode temperatures by taking into account the electron recycling effect, energy balance constraints, and space charge effects arising from the concurrent discharge of the

Xiaoman Liu

Forecasting CPU performance, which involves estimating performance scores based on hardware characteristics during operation, is crucial for computational system design and resource management. This research field currently faces two primary challenges. First, the diversity of CPU products and the specialized nature of hardware characteristics make real-worl

David P. Nicholls, Jon Wilkening, Xinyu Zhao

Dirichlet-Neumann Operators (DNOs) are important to the formulation, analysis, and simulation of many crucial models found in engineering and the sciences. For instance, these operators permit moving-boundary problems, such as the classical water wave problem (free-surface ideal fluid flow under the influence of gravity and capillarity), to be restated in te

Rutger Campbell, James Davies, Marc Distel, Bryce Frederickson

Treewidth and Hadwiger number are two of the most important parameters in structural graph theory. This paper studies graph classes in which large treewidth implies the existence of a large complete graph minor. To formalise this, we say that a graph class $\mathcal{G}$ is (tw,had)-bounded if there is a function $f$ (called the (tw,had)-bounding function) su

Xingyu Zhu, Beier Zhu, Yi Tan, Shuo Wang

Vision-language models, such as CLIP, have shown impressive generalization capacities when using appropriate text descriptions. While optimizing prompts on downstream labeled data has proven effective in improving performance, these methods entail labor costs for annotations and are limited by their quality. Additionally, since CLIP is pre-trained on highly

Jin-Sheng Wu, Roberto Abril Valenzuela, Mark J. Bowick, Ivan I. Smalyukh

We study topologically stable defect structures in systems where the defect line classification in three dimensions and associated algebra of interactions (the fundamental group) are governed by the non-Abelian 8-element group, the quaternions Q_8. The non-Abelian character of the defect algebra leads to a topological rigidity of bound defect pairs, and triv

G. Martin, F. R. Pearce, N. A. Hatch, A. Contreras-Santos

The stellar stripping of satellites in cluster haloes is understood to play an important role in the production of intracluster light. Increasingly, cosmological simulations have been utilised to investigate its origin and assembly. However, such simulations typically model individual galaxies at relatively coarse resolutions, raising concerns about their ac

Zhiyuan Pei, Jianqi Yan, Jin Yan, Bailing Yang

Recently, deep learning in stock prediction has become an important branch. Image-based methods show potential by capturing complex visual patterns and spatial correlations, offering advantages in interpretability over time series models. However, image-based approaches are more prone to overfitting, hindering robust predictive performance. To improve accura

Yujian Liu, Shiyu Chang, Tommi Jaakkola, Yang Zhang

Recent studies have identified one aggravating factor of LLM hallucinations as the knowledge inconsistency between pre-training and fine-tuning, where unfamiliar fine-tuning data mislead the LLM to fabricate plausible but wrong outputs. In this paper, we propose a novel fine-tuning strategy called Prereq-Tune to address this knowledge inconsistency and reduc

Mark van Hoeij, Wei-Lun Tsai, Dongxi Ye

In this work, we establish modular parameterizations for two general formulas for $\frac{1}{\pi}$ that subsume conjectural Ramanujan type formulas due to Z.-W. Sun, which have remained open since 2011. As an application of this, in a conceptual way we interpret how Sun's conjectural formulas arise and can be verified, as well as recover other cases that were

Siyuan Dong, Zhuotong Cai, Gilbert Hangel, Wolfgang Bogner

Magnetic Resonance Spectroscopic Imaging (MRSI) is a non-invasive imaging technique for studying metabolism and has become a crucial tool for understanding neurological diseases, cancers and diabetes. High spatial resolution MRSI is needed to characterize lesions, but in practice MRSI is acquired at low resolution due to time and sensitivity restrictions cau

Xiaoan Lin

Quantum computing is an advanced area of computing that leverages the principles of quantum mechanics. Quantum computing holds the potential to revolutionize various fields by handling problems that are currently intractable for classical computers. This research focuses on Variational Quantum Eigensolvers (VQEs) in the Noisy Intermediate Scale Quantum (NISQ

Tatsuya Yoshida, Shungo Koyama, Yuki Nakamura, Naoki Terada

Earth is expected to have acquired a reduced proto-atmosphere enriched in H2 and CH4 through the accretion of building blocks that contain metallic Fe and/or the gravitational trapping of surrounding nebula gas. Such an early, wet, reduced atmosphere that covers a proto-ocean would then ultimately evolve toward oxidized chemical compositions through photoche

Yuma Nakamura, Naoya Ozawa, Toshi Kusano, Rei Yokoyama

We present the development of a high-power ultraviolet laser system operating at a wavelength of 325 nm for Rydberg excitation from the ${}^3\mathrm{P}_2$ state of ytterbium. Utilizing a two-stage frequency doubling scheme, we achieved an output power exceeding 800 mW. The system effectively suppresses frequency noise in the MHz range, which is critical for

Liang Qiu, Liyue Shen, Lianli Liu, Junyan Liu

During and after a course of therapy, imaging is routinely used to monitor the disease progression and assess the treatment responses. Despite of its significance, reliably capturing and predicting the spatial-temporal anatomic changes from a sequence of patient-specific image series presents a considerable challenge. Thus, the development of a computational

Ziyuan Zhu, Paul H. Siegel

This paper investigates properties of concatenated polar codes and their potential applications. We start with reviewing previous work on stopping set analysis for conventional polar codes, which we extend in this paper to concatenated architectures. Specifically, we present a stopping set analysis for the factor graph of concatenated polar codes, deriving a

Hisashi Kasuya

We show that the moduli space of simple flat bundles over a compact Sasakian manifold is a finite disjoint union of moduli spaces of simple flat bundles with fixed basic structures. This gives a detailed description of the non-abelian Hodge correspondence on a compact Sasakian manifold at the level of moduli spaces. As an application, we give an analogue of

Haoyu Bian, Bin Guo, Sicong Liu, Yasan Ding

Ubiquitous on-device heart rate sensing is vital for high-stress individuals and chronic patients. Non-contact sensing, compared to contact-based tools, allows for natural user monitoring, potentially enabling more accurate and holistic data collection. However, in open and uncontrolled mobile environments, user movement and lighting introduce. Existing meth

Eoin Farrell, Yeu-Tong Lau, Arthur Conmy

We investigate whether sparse autoencoders (SAEs) can be used to remove knowledge from language models. We use the biology subset of the Weapons of Mass Destruction Proxy dataset and test on the gemma-2b-it and gemma-2-2b-it language models. We demonstrate that individual interpretable biology-related SAE features can be used to unlearn a subset of WMDP-Bio

Dmytro Humeniuk, Houssem Ben Braiek, Thomas Reid, Foutse Khomh

Testing autonomous robotic manipulators is challenging due to the complex software interactions between vision and control components. A crucial element of modern robotic manipulators is the deep learning based object detection model. The creation and assessment of this model requires real world data, which can be hard to label and collect, especially when t

Liang Chen, Yu-Jing Wang, Sheng-Yan Li

We derive analytic expressions for the Helmholtz free energy, Casimir force, and Casimir entropy for both one-dimensional and three-dimensional scalar fields with Dirichlet boundary conditions at finite temperature. We investigate the negative Casimir entropy problem in these systems, as well as for a scalar field in the bulk of a three-dimensional sphere, a

Tuowei Wang, Ruwen Fan, Minxing Huang, Zixu Hao

Large Language Models (LLMs) have achieved remarkable success across various domains, yet deploying them on mobile devices remains an arduous challenge due to their extensive computational and memory demands. While lightweight LLMs have been developed to fit mobile environments, they suffer from degraded model accuracy. In contrast, sparsity-based techniques

Qianyun Miao, Changhui Tan, Liutang Xue, Zhilong Xue

In this paper, we investigate a class of inviscid generalized surface quasi-geostrophic (SQG) equations on the half-plane with a rigid boundary. Compared to the Biot-Savart law in the vorticity form of the 2D Euler equation, the velocity formula here includes an additional Fourier multiplier operator $m(\Lambda)$. When $m(\Lambda) = \Lambda^\alpha$, where $\

Manita Pote, Tuğrulcan Elmas, Alessandro Flammini, Filippo Menczer

Coordinated reply attacks are a tactic observed in online influence operations and other coordinated campaigns to support or harass targeted individuals, or influence them or their followers. Despite its potential to influence the public, past studies have yet to analyze or provide a methodology to detect this tactic. In this study, we characterize coordinat

Borna Bateni, Peyman Bateni, Bishwadeep Bhattacharyya, Devin Reeh

In recurrent survival analysis where the event of interest can occur multiple times for each subject, frailty models play a crucial role by capturing unobserved heterogeneity at the subject level within a population. Frailty models traditionally face challenges due to the lack of a closed-form solution for the maximum likelihood estimation that is unconditio

Bui Ngoc Muoi, Nung-Sing Sze

Entanglement breaking (EB) channels, as completely positive and trace-preserving linear operators, sever the entanglement between the input system and other systems. In the realm of infinite-dimensional systems, a related concept known as strongly entanglement breaking (SEB) channels emerges. This paper delves into characterizations of SEB channels, delineat

Salvatore Simone Perrotta, Cole Davis Pruitt, Oliver C. Gorton, Jutta E. Escher

Optical-model potentials (OMPs) are critical ingredients for basic and applied nuclear physics. Present-day computational capabilities allow us to generate data-driven nucleon-nucleus OMPs that are non-local and exactly dispersive (as theoretically required to be), include statistically-sound uncertainty quantification, and are trained on both scattering and

Yuxun Sun

We prove a version of Quillen's theorems for a map of semi-Segal spaces. We construct a bi-semi-simplicial resolution similar to the one associated to a functor of non-unital topological categories. As a consequence we can represent the homotopy fiber of a map of semi-Segal spaces as the geometric realization of a certain semi-simplicial space.

Makoto Miyoshi, Yoshiaki Kato, Junichiro Makino

We propose that the ring structure found by the Event Horizon Telescope Collaboration (EHTC) as the black hole shadow of Sgr A*is an artifact by the bumpy PSF (Point Spread Function) of the EHT2017. The imaging using sparse u-v data requires detailed scrutiny of the PSF. The estimated shadow diameter (48.7 +- 7 muas) is equal to the spacing between the main

Ting-Ting Duan, Pei-Pin Yang, Peng-Cheng Zhang, Hai-Ling Lao

This study investigates the transverse momentum ($p_T$) spectra of identified light charged hadrons produced in gold--gold (Au+Au) collisions across various centrality classes at center-of-mass energies per nucleon pair, $\sqrt{s_{NN}}$, ranging from 7.7 to 200 GeV, as measured by the STAR Collaboration at the Relativistic Heavy Ion Collider (RHIC). The anal

Kexin Zhang, Shuhan Liu, Song Wang, Weili Shi

Distribution shifts on graphs -- the discrepancies in data distribution between training and employing a graph machine learning model -- are ubiquitous and often unavoidable in real-world scenarios. These shifts may severely deteriorate model performance, posing significant challenges for reliable graph machine learning. Consequently, there has been a surge

Meixia Lin, Ziyang Zeng, Yangjing Zhang

Matrix regression plays an important role in modern data analysis due to its ability to handle complex relationships involving both matrix and vector variables. We propose a class of regularized regression models capable of predicting both matrix and vector variables, accommodating various regularization techniques tailored to the inherent structures of the

Davide Soranzio, Matteo Savoini, Fabian Graf, Rafael T. Winkler

$FeCr_2S_4$ is a ferrimagnet with two oppositely ordered spin sublattices (Fe and Cr), connected via superexchange interaction, giving a non-zero net magnetic moment. We show, using time-resolved measurements of the magneto-optic Kerr effect, how the magnetic dynamics of the sublattices can be selectively manipulated by resonantly perturbing the Fe sublattic

Reachsak Ly, Alireza Shojaei

Current autonomous building research primarily focuses on energy efficiency and automation. While traditional artificial intelligence has advanced autonomous building research, it often relies on predefined rules and struggles to adapt to complex, evolving building operations. Moreover, the centralized organizational structures of facilities management hinde

Yizhi Zhan, Hengyu Xu, Shao-Jun Zhang

We examine the stability of a spherically symmetric regular black hole when subjected to perturbations from a charged scalar field. This particular black hole is constructed by deforming the Minkowski spacetime. It has been observed that the charged superradiant instability arises only within a specific range of the deformation parameter, potentially resulti

Liang-Jun He, Juan Sanchez-Baena, Fabian Maucher, Yong-Chang Zhang

It has been shown that dipolar Bose-Einstein condensates that are tightly trapped along the polarization direction can feature a rich phase diagram. In this paper we show that finite temperature can assist in accessing parts of the phase diagram that otherwise appear hard to realise due to excessively large densities and number of atoms being required. These

Jian-Dong Zhang, Mei-Ming Zhang, Chuang Li, Shuai Wang

Detecting the presence of multiple incoherent sources is a fundamental and challenging task for quantum imaging, especially within sub-Rayleigh region. In this paper, the discrimination of one-versus-two point-like incoherent sources in symmetric and asymmetric scenarios is studied. We calculate the quantum lower bounds on error probabilities of making a dec

Yu Fu, Zefan Cai, Abedelkadir Asi, Wayne Xiong

Key-Value (KV) caching is a common technique to enhance the computational efficiency of Large Language Models (LLMs), but its memory overhead grows rapidly with input length. Prior work has shown that not all tokens are equally important for text generation, proposing layer-level KV cache compression to selectively retain key information. Recognizing the dis

Brayden Leicester, Kenji Bekki, Takuji Tsujimoto

The super star cluster NGC1569-B has recently been observed to have an extremely high [Ba/Fe]. We consider that the observed high [Ba/Fe] ($\sim$ 1.3) is due to the chemical enrichment of giant molecular clouds by either collapsars, neutron star mergers, or magneto-rotational supernovae, and thereby investigate which of the three polluters can best reproduce

Yiqing Guo, Karel Mokany, Shaun R. Levick, Jinyan Yang

Earth observation data have shown promise in predicting species richness of vascular plants ($\alpha$-diversity), but extending this approach to large spatial scales is challenging because geographically distant regions may exhibit different compositions of plant species ($\beta$-diversity), resulting in a location-dependent relationship between richness and

ZhuangEn Fu, Piumi I. Samarawickrama, John Ackerman, Yanglin Zhu

Effective control of magnetic phases in two-dimensional magnets would constitute crucial progress in spintronics, holding great potential for future computing technologies. Here, we report a new approach of leveraging tunneling current as a tool for controlling spin states in CrI3. We reveal that a tunneling current can deterministically switch between spin-

Nan Liu, Maria Lugaro, Jan Leitner, Bradley S. Meyer

We provide an overview of the isotopic signatures of presolar supernova grains, specifically focusing on 44Ti-containing grains with robustly inferred supernova origins and their implications for nucleosynthesis and mixing mechanisms in supernovae. Recent technique advancements have enabled the differentiation between radiogenic (from 44Ti decay) and nonradi

Elijah Mullens, Nikole K. Lewis, Ryan J. MacDonald

Aerosols are a ubiquitous feature of planetary atmospheres and leave clear spectral imprints in exoplanet spectra. Pre-JWST, exoplanet retrieval frameworks mostly adopted simple parametric approximations. With JWST, we now have access to mid-infrared wavelengths where aerosols have detectable composition-specific resonance features. Here, we implement new fe

Sadhana Matth, S. Pandey, Himanshu Pandey

In the ongoing search for innovative thermoelectric (TE) materials with superior TE performance globally, we aim to investigate the possible use of half-Heusler alloy CoHfSi in TE applications. We analyzed the structure stability, thermodynamic inertia and electrical and thermal transport properties using density functional formalism and semi-classical Boltz

Jacob Bedrossian, Patrick Flynn, Sam Punshon-Smith

We consider the negative regularity mixing properties of random volume preserving diffeomorphisms on a compact manifold without boundary. We give general criteria so that the associated random transfer operator mixes $H^{-\delta}$ observables exponentially fast in $H^{-\delta}$ (with a deterministic rate), a property that is false in the deterministic settin

Xinyu Wang, Wenbo Zhang, Sai Koneru, Hangzhi Guo

With the rise of AI-generated content spewed at scale from large language models (LLMs), genuine concerns about the spread of fake news have intensified. The perceived ability of LLMs to produce convincing fake news at scale poses new challenges for both human and automated fake news detection systems. To address this gap, this paper presents the findings fr

Hu-Wei Jia, Ning-Hua Tong

In this paper, we study the thermodynamical properties of the classical one-dimensional Klein-Gordan lattice model ($n \ge 2$) by using the cluster variation method with linear response theory. The results of this method are exact in the thermodynamical limit. We present the single site reduced density matrix $\rho^{(1)}(z)$, averages such as $\langle z^2 \r

Eric Cai, Octavian Donca, Ben Eisner, David Held

The task of "relative placement" is to predict the placement of one object in relation to another, e.g. placing a mug onto a mug rack. Through explicit object-centric geometric reasoning, recent methods for relative placement have made tremendous progress towards data-efficient learning for robot manipulation while generalizing to unseen task variations. How

Yu Chen, Zihan Tan

We study the following distance realization problem. Given a quasi-metric $D$ on a set $T$ of terminals, does there exist a directed Okamura-Seymour graph that realizes $D$ as the (directed) shortest-path distance metric on $T$? We show that, if we are further given the circular ordering of terminals lying on the boundary, then Monge property is a sufficient

Zixiao Zhao, Jing Sun, Zhe Hou, Zhiyuan Wei

With the rapid advancement of Large Language Models (LLMs), LLM-based approaches have demonstrated strong problem-solving capabilities across various domains. However, in automatic programming, a single LLM is typically limited to function-level code generation, while multi-agent systems composed of multiple LLMs often suffer from inefficient task planning.

Toshiki Tsuda, Masaaki Imaizumi

We study the universality property of estimators for high-dimensional linear models, which implies that the distribution of estimators is independent of whether the covariates follow a Gaussian distribution. Recent developments in high-dimensional statistics typically require covariates to strictly follow a Gaussian distribution to precisely characterize the

Sun-Sig Byun, Kyeong Bae Kim, Deepak Kumar

We establish several fine boundary regularity results of weak solutions to non-homogeneous $s$-fractional Laplacian type equations. In particular, we prove sharp Calder\'on-Zygmund type estimates of $u/d^s$ depending on the regularity assumptions on the associated kernel coefficient including VMO, Dini continuity or the H\"older continuity, where $u$ is a we

Zicheng Ye, Yuan Li, Zhichao Liu, Huazi Zhang

The weight spectrum plays a crucial role in the performance of error-correcting codes. Despite substantial theoretical exploration of polar codes with mother code length, a framework for the weight spectrum of rate-compatible polar codes remains elusive. In this paper, we address this gap by presenting the theoretical results for enumerating the number of mi