March 2025 arXiv papers — page 26

Xiaofei Zhou, Yi Zhang, Yufei Jiang, Yunfan Gong

Understanding how AI recommendations work can help the younger generation become more informed and critical consumers of the vast amount of information they encounter daily. However, young learners with limited math and computing knowledge often find AI concepts too abstract. To address this, we developed Briteller, a light-based recommendation system that m

Wei Liu, Zixiang Chen

We study the potential collider discovery for the right-handed neutrinos (RHNs) in the minimal left-right symmetric model (LRSM). In this model, the RHNs can be either produced from the right-handed gauge boson $W_R^\pm$, or the scalar triplet $\Delta$ decays. The RHNs can subsequently decay via the heavy $W_R^\pm$, making them potentially long-lived. In loo

Yechul Shin, Ji-Hoon Oh, Sebastian Bathiany, Maya Ben-Yami

Paleoclimate proxy records and models suggest that the Atlantic Meridional Overturning Circulation (AMOC) can transition abruptly between a strong and a weak state. Empirical warning signals in observational fingerprints indeed suggest a stability decline and raise concerns that the system may be approaching a tipping point. However, state-of-the-art Earth S

Stephen Lacina, Grace Stadnyk

We present two perhaps surprisingly small posets, one graded and one non-graded, that are CC-shellable in the sense of Kozlov and TCL-shellable in the sense of Hersh, but not CL-shellable in the sense of Bj\"orner and Wachs. In the spirit of Bj\"orner and Wachs' recursive atom orderings (RAO) and Hersh and Stadnyk's generalized recursive atom orderings (GRAO

Brian R. La Cour, Noah A. Davis

Recent astonishing experiments with quantum computers have demonstrated unambiguously the existence of a quantum multiverse, where calculations of mind-boggling complexity are effortlessly computed in just a few minutes. Here, we investigate whether a similar computation on a digital computer can demonstrate the existence of a classical multiverse. To this e

Seungjae Lee, Hyung Mok Lee, Ji-hoon Kim, Rainer Spurzem

We investigate the evolution of star clusters containing intermediate-mass black hole (IMBH) of $300$ to $5000\ \mathrm{M}_\odot$, focusing on the formation and evolution of IMBH-stellar mass black holes (SBHs; $M_{\rm BH} \lesssim 10^2\ \mathrm{M}_\odot$) binaries. Dense stellar systems like globular clusters (GCs) or nuclear star clusters offer unique labo

Long D. H. My, Akshaya Jayashankar, Prabha Mandayam, Hui Khoon Ng

Designing efficient fault tolerance schemes is crucial for building useful quantum computers. Most standard schemes assume no knowledge of the underlying device noise and rely on general-purpose quantum error-correcting (QEC) codes capable of handling arbitrary errors. Biased-noise alternatives focus on only correcting a subset of some generic error basis (e

Shival Dasu, Ben Criger, Cameron Foltz, Justin A. Gerber

Constructing an efficient and robust quantum memory is central to the challenge of engineering feasible quantum computer architectures. Quantum error correction codes can solve this problem in theory, but without careful design it can introduce daunting requirements that call for machines many orders of magnitude larger than what is available today. Bringing

C. Granados, Bikash K. Das, M. Ciappina, W . Gao

We explore the impact of spatiotemporal couplings (STCs) on high-order harmonic generation (HHG) driven by spatiotemporal vortex beams. Our investigation demonstrates how STCs shape key properties of the generated harmonic beams, including their intensity distribution and different chirps. By analyzing these chirps, we establish a clear connection between ST

Jin-Wei Wang

Blazar-Boosted Dark Matter (BBDM) is a novel mechanism whereby dark matter (DM) particles are accelerated to ultrarelativistic energies through interactions with blazar jets. Focusing on a vector portal DM model, we systematically investigate both elastic and inelastic scattering processes between DM and protons. By analyzing multi-messenger data from ground

Daisuke Niizumi, Daiki Takeuchi, Masahiro Yasuda, Binh Thien Nguyen

Contrastive language-audio pre-training (CLAP), which learns audio-language representations by aligning audio and text in a common feature space, has become popular for solving audio tasks. However, CLAP's audio features lack generalizability, whereas self-supervised learning (SSL) models offer general-purpose features that perform well across diverse audio

Grayson W. White, Andrew O. Finley, Josh K. Yamamoto, Jennifer L. Green

National Forest Inventory (NFI) data are typically limited to sparse networks of sample locations due to cost constraints. While design-based estimators provide reliable forest parameter estimates for large areas, there is increasing interest in model-based small area estimation (SAE) methods to improve precision for smaller spatial, temporal, or biophysical

K. Mizuyama, T. Dieu Thuy

This study investigates resonance states in $^{16}$O, specifically the $E$1 giant resonance and isoscalar/isovector quadrupole resonances, using the Jost-RPA method. By applying the Mittag-Leffler theorem, we decompose the RPA response function to analyze the individual contributions from poles corresponding to collective excitation modes. This decomposition

Liwei Wang

Magnetic soft robots embedded with hard magnetic particles enable untethered actuation via external magnetic fields, offering remote, rapid, and precise control, which is highly promising for biomedical applications. However, designing such systems is challenging due to the complex interplay of magneto-elastic dynamics, large deformation, solid contacts, tim

T. Nandi, Yash Kumar, Adya P. Mishra, Nishchal R. Dwivedi

Nuclear orbiting resonances have been revealed at the sub-barrier energies as an atomic phenomenon by means of x-ray spectroscopy experiments. This interpretation is supported by several phenomenological models and theoretical estimates of the nuclear orbiting timescale and cross-section, inelastic scattering cross section including both nuclear and Coulomb

Changjian Fu, Zhanhong Liang

We introduce a pseudo $\mathbb{N}$-grading on the cluster auotmorphism group $\operatorname{Aut}(\mathcal{A})$ with respect to an initial seed of $\mathcal{A}$, which consists of a family of subsets $\{G_i\}_{i\in \mathbb{N}}$ of $\operatorname{Aut}(\mathcal{A})$ such that $\operatorname{Aut}(\mathcal{A})=\bigcup_{i\in \mathbb{N}}G_i$ and $G_k\cdot G_l\subse

Tao Wang, Yu-xin Cheng, Dong-kang Li, Xiao-shen Kang

Recently, a new mechanism for explaining the B(E2) anomaly was given by F. Pan \emph{et al.} (PRC, 110, 054324, 2024), which is realized in the parameter region from the SU(3) symmetry limit to the O(6) symmetry limit, and seems to be not related to the SU(3) symmetry. However, through SU(3) analysis, a new technique proposed recently, we found that it is no

Jia-Cheng Huang, Hao-En Li, Yi-Cheng Wang, Guang-Ze Zhang

In this paper, we introduce a novel and general framework for the variational quantum simulation of Lindblad equations. Building on the close relationship between the unraveled Lindblad dynamics, stochastic Magnus integrators, and variational quantum simulation, we propose a high-order scheme for solving the quantum state diffusion equation using exponential

Lintong Wang, Sherry H. F. Yan

A partially ordered pattern (abbreviated POP) is a partially ordered set (poset) that generalizes the notion of a pattern when we are not concerned with the relative order of some of its letters. The notion of partially ordered patterns provides a convenient language to deal with large sets of permutation patterns. In analogy to the shape-Wilf-equivalence fo

Haoyan Xu, Zhengtao Yao, Yushun Dong, Ziyi Wang

Existing methods for graph out-of-distribution (OOD) detection typically depend on training graph neural network (GNN) classifiers using a substantial amount of labeled in-distribution (ID) data. However, acquiring high-quality labeled nodes in text-attributed graphs (TAGs) is challenging and costly due to their complex textual and structural characteristics

Zi-Jian Li, Yi-Ting Tu, Sankar Das Sarma

We study many-body localization (MBL) in a nearest-neighbor hopping 1D lattice with a slowly varying (SV) on-site potential $U_j = \lambda\cos(\pi\alpha j^s)$ with $0<s<1$. The corresponding non-interacting 1D lattice model is known to have single-particle localization with mobility edges. Using exact diagonalization, we find that the MBL of this model has s

Zahra Sharifi Soltani, Arash Rezaee, Orlando Arias, Vinod M Vokkarane

Only the chairs can edit The rapid growth of high-bandwidth applications in fifth-generation (5G) networks and beyond has driven a substantial increase in traffic within transport optical networks. While network slicing effectively addresses diverse quality of service (QoS) requirements-including bit rate, latency, and reliability-it also amplifies vulnerabi

Jacques Verstraete

The classical Ramsey numbers $r(s,t)$ denote the minimum $n$ such that every red-blue coloring of the edges of the complete graph $K_n$ contains either a red clique of order $s$ or a blue clique of order $t$. These quantities are the centerpiece of graph Ramsey Theory, and have been studied for almost a century. The Erd\H{o}s-Szekeres Theorem (1935) shows th

Ximing Wen, Mallika Mainali, Anik Sen

Vision Language Models (VLMs) have demonstrated strong reasoning capabilities in Visual Question Answering (VQA) tasks; however, their ability to perform Theory of Mind (ToM) tasks, such as inferring human intentions, beliefs, and mental states, remains underexplored. We propose an open-ended question framework to evaluate VLMs' performance across diverse ca

Ziang Yan, Xingyu Zhao, Hanqing Ma, Wei Chen

With the rise of social media and Location-Based Social Networks (LBSN), check-in data across platforms has become crucial for User Identity Linkage (UIL). These data not only reveal users' spatio-temporal information but also provide insights into their behavior patterns and interests. However, cross-platform identity linkage faces challenges like poor data

Dina Albassam, Adam Cross, Chengxiang Zhai

Electronic Health Records (EHRs) often lack explicit links between medications and diagnoses, making clinical decision-making and research more difficult. Even when links exist, diagnosis lists may be incomplete, especially during early patient visits. Discharge summaries tend to provide more complete information, which can help infer accurate diagnoses, esp

Bingqing Lyu, Xiaoli Zhou, Longbin Lai, Yufan Yang

This technical report extends the SIGMOD 2025 paper "A Modular Graph-Native Query Optimization Framework" by providing a comprehensive exposition of GOpt's advanced technical mechanisms, implementation strategies, and extended evaluations. While the original paper introduced GOpt's unified intermediate representation (GIR) and demonstrated its performance be

Georgios Mavrogiannis

We study the Klein--Gordon equation $\Box\psi-\mu^2_{\textit{KG}}\psi=0$ on subextremal Kerr de Sitter black hole backgrounds with parameters $(a,M,l)$, where $l^2=\frac{3}{\Lambda}$. We prove a "relatively non degenerate integrated" decay estimate assuming an appropriate mode stability statement for real frequency solutions of Carter's radial ode. Our resul

Kevin Saric, Gowri Sankar Ramachandran, Raja Jurdak, Surya Nepal

As conventional storage density reaches its physical limits, the cost of a gigabyte of storage is no longer plummeting, but rather has remained mostly flat for the past decade. Meanwhile, file sizes continue to grow, leading to ever fuller drives. When a user's storage is full, they must disrupt their workflow to laboriously find large files that are good ca

Binh Thien Nguyen, Masahiro Yasuda, Daiki Takeuchi, Daisuke Niizumi

Immersive communication has made significant advancements, especially with the release of the codec for Immersive Voice and Audio Services. Aiming at its further realization, the DCASE 2025 Challenge has recently introduced a task for spatial semantic segmentation of sound scenes (S5), which focuses on detecting and separating sound events in spatial sound s

Xuechen Zhang, Xingyong Zhang

We investigate the multiplicity and uniqueness of positive solutions for the superlinear singular $(p,q)$-Laplacian equation \begin{eqnarray*} \begin{cases} -\Delta_p u-\Delta_q u+a(x)u^{p-1}+b(x)u^{q-1}=f(x)u^{-\gamma}+\lambda g(x)u^{\alpha}, \;\;\;\;\hfill \mbox{in}\;\; V,\\ u>0,\;\;u\in W_a^{1,p}(V) \cap W_b^{1,q}(V), \end{cases} \end{eqnarray*} on a weig

Huan Zhao, Yiming Liu, Jina Yao, Ling Xiong

Recent breakthroughs in single-cell technology have ushered in unparalleled opportunities to decode the molecular intricacy of intricate biological systems, especially those linked to diseases unique to humans. However, these progressions have also ushered in novel obstacles-specifically, the efficient annotation of extensive, long-tailed single-cell data pe

Theo F. Motta

For decades now, low-energy models of QCD have shown indications that a crystalline quark phase could be stable at high chemical potentials. Beyond models, however, there are numerous difficulties in investigating such a hypothesis in full QCD, such as the sign problem. Functional methods do not suffer from the sign problem, and thus, can access the high--$\

Yann Bugeaud

Let $q_1, \ldots , q_t$ be distinct prime numbers. Let $a_1, \ldots , a_t$ be nonnegative integers and $x$ a positive integer. We establish an effective lower bound for the greatest prime divisor of $|x^2 - q_1^{a_1} \ldots q_t^{a_t}|$, which tends to infinity with the maximum of $x$, $a_1, \ldots , a_t$.

G. S. Da Costa, T. Nordlander

Spectra have been obtained with multi-fibre instrument 2dF on the Anglo-Australian Telescope of 89 candidate main sequence stars in the globular cluster M55 (NGC 6809). Radial velocities and Gaia proper motions confirm 72 candidates as cluster members. Among these stars one stands out as having a substantially stronger G-band (CH) than the rest of the member

Shreyas Chaudhari, José M. F. Moura

This paper presents a novel framework for understanding trained ReLU networks as random, affine functions, where the randomness is induced by the distribution over the inputs. By characterizing the probability distribution of the network's activation patterns, we derive the discrete probability distribution over the affine functions realizable by the network

Ziyue Huang, Hongxi Yan, Qiqi Zhan, Shuai Yang

The rapid advancement of remote sensing foundation models, particularly vision and multimodal models, has significantly enhanced the capabilities of intelligent geospatial data interpretation. These models combine various data modalities, such as optical, radar, and LiDAR imagery, with textual and geographic information, enabling more comprehensive analysis

Matthias von Davier

This article presents an improved approximation for the effective degrees of freedom in the Satterthwaite (1941, 1946) method which estimates the distribution of a weighted combination of variance components The standard Satterthwaite approximation assumes a scaled chisquare distribution for the composite variance estimator but is known to be biased downward

Kunshan Yang, Wenwei Luo, Yuguo Hu, Jiafu Yan

Flexible objects recognition remains a significant challenge due to its inherently diverse shapes and sizes, translucent attributes, and subtle inter-class differences. Graph-based models, such as graph convolution networks and graph vision models, are promising in flexible objects recognition due to their ability of capturing variable relations within the f

Santiago Palumbo, Pablo S. Cornaglia, Jorge I. Facio

The trigonal Weyl semimetal PtBi$_2$ presents an intriguing superconducting phase, previously reported to be confined to its topological Fermi arcs within a certain temperature range. This observation highlights the importance of a thorough understanding of its normal phase, particularly the roles that spin-orbit coupling (SOC) and inversion-symmetry breakin

Georgios Mavrogiannis

We study the Klein--Gordon equation $\Box_{g_{a,M,l}}\psi-\mu^2_{\textit{KG}}\psi=0$ on subextremal Kerr--de Sitter black hole backgrounds with parameters $(a,M,l)$, where $l^2=\frac{3}{\Lambda}$. We prove boundedness and Morawetz estimates assuming an appropriate mode stability statement for real frequency solutions of Carter's radial ode. Our results in pa

Lena Strobl, Dana Angluin, Robert Frank

While transformers have proven enormously successful in a range of tasks, their fundamental properties as models of computation are not well understood. This paper contributes to the study of the expressive capacity of transformers, focusing on their ability to perform the fundamental computational task of evaluating an arbitrary function from $[n]$ to $[n]$

Wenwen Wang, Zhimin Zhou

We present a comprehensive study of bar structures in the local Universe using data from the DESI Legacy Imaging Surveys. Through isophotal analysis of 232,142 galaxies, we identify bars and classify them into strong and weak categories based on normalized bar length, using a threshold of 0.4. We find a total bar fraction of 42.9%, rising to 62.0% in disk ga

Chuan-Wei Kuo, Siyu Chen, Chenqi Yan, Yu Yang Fredrik Liu

Large language models (LLMs) hold great promise for specialized scientific domains such as materials science, yet adapting them efficiently and accurately to domain-specific knowledge remains challenging due to limited data and high knowledge density. We propose a two-stage framework that combines structured model compression with a scientific fine-tuning re

Igor Minevich, Patrick Morton

If $ABC$ is a given triangle in the plane, $P$ is any point not on the extended sides of $ABC$ or its anticomplementary triangle, $Q$ is the complement of the isotomic conjugate of $P$ with respect to $ABC$, $DEF$ is the cevian triangle of $P$, and $D_0$ and $A_0$ are the midpoints of segments $BC$ and $EF$, respectively, a synthetic proof is given for the f

Yan-Cheng Guo and, Tian-Sheuan Chang, Chih-Sheng Lin, Bo-Cheng Chiou

Computing-in-memory (CIM) is renowned in deep learning due to its high energy efficiency resulting from highly parallel computing with minimal data movement. However, current SRAM-based CIM designs suffer from long latency for loading weight or feature maps from DRAM for large AI models. Moreover, previous SRAM-based CIM architectures lack end-to-end model i

Min Ye, Nicolas Delfosse

We propose a model for quantum computing with long chains of trapped ions and we design quantum error correction schemes for this model. The main components of a quantum error correction scheme are the quantum code and a quantum circuit called the syndrome extraction circuit, which is executed to perform error correction with this code. In this work, we desi

Immanuel Ben Porat, Gui-Qiang G. Chen, Difan Yuan

We provide a rigorous justification of the semiclassical quasi-neutral and the quantum many-body limits to the isothermal Euler equations. We consider the nonlinear Schr\"{o}dinger-Poisson-Boltzmann system under a quasi-neutral scaling and establish the convergence of its solutions to the isothermal Euler equations. Different from the previous results that d

Ekansh Chauhan, Anila Sharma, Amit Sharma, Vikas Nishadham

Breast cancer, the most common malignancy among women, requires precise detection and classification for effective treatment. Immunohistochemistry (IHC) biomarkers like HER2, ER, and PR are critical for identifying breast cancer subtypes. However, traditional IHC classification relies on pathologists' expertise, making it labor-intensive and subject to signi

Zeki Doruk Erden, Boi Faltings

We analyze the ability of computational units to retain past responses after parameter updates, a key property for system-wide continual learning. Neural networks trained with gradient descent lack this capability, prompting us to propose Modelleyen, an alternative approach with inherent response preservation. We demonstrate through experiments on modeling t

Sergey Kitaev, Philip B. Zhang

In this paper, we find distribution of descents over $(n-3)$- and $(n-4)$-stack-sortable permutations in terms of Eulerian polynomials. Our results generalize the enumeration results by Claesson, Dukes, and Steingr\'{\i}msson on $(n-3)$- and $(n-4)$-stack-sortable permutations. Moreover, we find distribution of descents on $(n-2)$-, $(n-3)$- and $(n-4)$-stac

Fubao Zhu, Yang Zhang, Gengmin Liang, Jiaofen Nan

Early and accurate diagnosis of pulmonary hypertension (PH) is essential for optimal patient management. Differentiating between pre-capillary and post-capillary PH is critical for guiding treatment decisions. This study develops and validates a deep learning-based diagnostic model for PH, designed to classify patients as non-PH, pre-capillary PH, or post-ca

Rohit Dandamudi, Ifeoma Adaji, Gema Rodríguez-Pérez

Open-source software communities thrive on global collaboration and contributions from diverse participants. This study explores the Rust programming language ecosystem to understand its contributors' demographic composition and interaction patterns. Our objective is to investigate the phenomenon of participation inequality in key Rust projects and the prese

Maxime Gourceyraud, Rim Ben Salem, Christopher Neal, Frédéric Cuppens

Recent Intrusion Detection System (IDS) research has increasingly moved towards the adoption of machine learning methods. However, most of these systems rely on supervised learning approaches, necessitating a fully labeled training set. In the realm of network intrusion detection, the requirement for extensive labeling can become impractically burdensome. Mo

Wanli Ni, Zhijin Qin, Haofeng Sun, Xiaoming Tao

Artificial intelligence (AI) promises to revolutionize the design, optimization and management of next-generation communication systems. In this article, we explore the integration of large AI models (LAMs) into semantic communications (SemCom) by leveraging their multi-modal data processing and generation capabilities. Although LAMs bring unprecedented abil

Deshani Geethika Poddenige, Sachith Seneviratne, Asela Hevapathige, Damith Senanayake

Existing neural architecture representation learning methods focus on continuous representation learning, typically using Variational Autoencoders (VAEs) to map discrete architectures onto a continuous Gaussian distribution. However, sampling from these spaces often leads to a high percentage of invalid or duplicate neural architectures, likely due to the un

Yihong Du, Xiangdong Fang, Wenjie Ni

For fixed $c\in\mathbb R$, $l>0$ and a general non-symmetric kernel function $J(x)$ satisfying a standard assumption, we consider the nonlocal diffusion operator \begin{align*} \bf{L}^{J, c}_{(-l,l)}[\phi](x):=\int_{-l}^lJ(x-y)\phi(y)\,dy+c\phi'(x), \end{align*} and prove that its principal eigenvalue $\lambda_p(\bf{L}^{J, c}_{(-l,l)})$ has the following asy

D. Martínez-Tibaduiza, Vladimir Vargas-Calderón, J. G. Dueñas, J. Flórez-Jiménez

Many significant quantum physical systems are characterized by Hamiltonians expressible as a linear combination of time-independent generators of a closed Lie algebra, $\hat{H}(t)=\sum_{l=1}^{L}\eta_{l}(t)\hat{g}_{l}$. The Wei-Norman method provides a framework for determining the coefficients of the corresponding time evolution operator in its factorized re

Ukcheol Shin, Jinsun Park

Achieving robust and accurate spatial perception under adverse weather and lighting conditions is crucial for the high-level autonomy of self-driving vehicles and robots. However, existing perception algorithms relying on the visible spectrum are highly affected by weather and lighting conditions. A long-wave infrared camera (i.e., thermal imaging camera) ca

Tohid Kargar Tasooji, Sakineh Khodadadi

This paper addresses the problem of event-based distributed Linear Quadratic Gaussian (LQG) control for multirobot coordination under localization uncertainty. An event-triggered LQG rendezvous control strategy is proposed to ensure coordinated motion while reducing communication overhead. The design framework decouples the LQG controller from the event-trig

Akshay Rangamani

Modular addition tasks serve as a useful test bed for observing empirical phenomena in deep learning, including the phenomenon of \emph{grokking}. Prior work has shown that one-layer transformer architectures learn Fourier Multiplication circuits to solve modular addition tasks. In this paper, we show that Recurrent Neural Networks (RNNs) trained on modular

Lukas Scarfe, Yingwen Zhang, Ebrahim Karimi

Here, we present a proof-of-principle high-dimensional quantum key distribution (QKD) protocol utilizing the position and momentum entanglement of photon pairs. The protocol exploits the fact that position and momentum form mutually unbiased bases, linked via a Fourier transform. One photon of the entangled pair is measured by the sender in a randomly chosen

Wenli Du, Chuan Wang, Chen Fan, Zhi Li

To achieve digital intelligence transformation and carbon neutrality, effective production planning is crucial for integrated refinery-petrochemical complexes. Modern refinery planning relies on advanced optimization techniques, whose development requires reproducible benchmark problems. However, existing benchmarks lack practical context or impose oversimpl

Amarendra Sharma

The U.S. dollar's status as the global reserve currency faces growing challenges from a 36 trillion dollar national debt, geopolitical shifts, and the emergence of digital currencies. This paper introduces the Quantum Reserve Token (QRT), a decentralized digital currency backed by quantum computational capacity - a scarce, productive resource projected to ad

Mehrshad Gholami Anjiraki, Mustafa Meriç Aksen, Jonathan Craig, Hossein Seyedzadeh

We present a coupled large-eddy simulation (LES) and bed morphodynamics study to investigate the impact of sediment dynamics on the wake flow, wake recovery and power production of a utility-scale marine hydrokinetic vertical-axis turbine (VAT). A geometry-resolving immersed boundary method is employed to capture the turbine components, the waterway, and the

Jaime Vera-Jaramillo

tempdisagg is a modern, extensible, and production-ready Python framework for temporal disaggregation of time series data. It transforms low-frequency aggregates into consistent, high-frequency estimates using a wide array of econometric techniques-including Chow-Lin, Denton, Litterman, Fernandez, and uniform interpolation-as well as enhanced variants with a

Oswaldo Otero, Jesús E. López, P. Tsygankov, Carlos J. Páez-González

This study investigates the autoresonant acceleration of electrons using the GYRAC mechanism in a cylindrical cavity excited in the TE$_{011}$ microwave mode, under a slowly increasing external magnetic field. The acceleration process is driven by the interaction between electrons and the right-hand circularly polarized (RHP) component of the electric field,

Jan Hurtado, Joao P. Maia, Cesar A. Sierra-Franco, Alberto Raposo

Mammography stands as the main screening method for detecting breast cancer early, enhancing treatment success rates. The segmentation of landmark structures in mammography images can aid the medical assessment in the evaluation of cancer risk and the image acquisition adequacy. We introduce a series of data-centric strategies aimed at enriching the training

Zeeshan Ahmed, Frank Seide, Zhe Liu, Rastislav Rabatin

Simultaneous or streaming machine translation generates translation while reading the input stream. These systems face a quality/latency trade-off, aiming to achieve high translation quality similar to non-streaming models with minimal latency. We propose an approach that efficiently manages this trade-off. By enhancing a pretrained non-streaming model, whic

Tai An, Weiqiang Huang, Da Xu, Qingyuan He

As a fundamental task in computer vision, semantic segmentation is widely applied in fields such as autonomous driving, remote sensing image analysis, and medical image processing. In recent years, Transformer-based segmentation methods have demonstrated strong performance in global feature modeling. However, they still struggle with blurred target boundarie

Jose Manuel Gómez-Guzmán, Karina Bernert, Anton Devishvili, Christine Klauser

Neutron supermirrors (SMs) are a crucial part of many scattering and particle physics experiments. So far, Ni(Mo)/Ti SMs have been used in experiments that require to transport a polarized neutron beam due to their lower saturation magnetization compared to Ni/Ti SMs. However, next generation $β$ decay experiments require SMs that depolarize below $10^{-4}$

Takaharu Yoshida, Yuta Shingu, Chihaya Shimada, Tetsuro Nikuni

Quantum annealing (QA) is an efficient method for finding the ground-state energy of the problem Hamiltonian. However, in practical implementation, the system suffers from decoherence. On the other hand, recently, ``Localized virtual purification&#34; (LVP) was proposed to suppress decoherence in the context of noisy intermediate-scale quantum (NISQ) devices

C. L. Chang, Y. -Y. Chang, L. Chaplinsky, C. W. Fink

We present results of a search for spin-independent dark matter-nucleon interactions in a 1 cm$^2$ by 1 mm thick (0.233 gram) high-resolution silicon athermal phonon detector operated above ground. For interactions in the substrate, this detector achieves a r.m.s. baseline energy resolution of 361.5 $\pm$ 0.4 MeV/$c^2$, the best for any athermal phonon detec

Amy Rafferty, Rishi Ramaesh, Ajitha Rajan

Deep learning models for medical image classification tasks are becoming widely implemented in AI-assisted diagnostic tools, aiming to enhance diagnostic accuracy, reduce clinician workloads, and improve patient outcomes. However, their vulnerability to adversarial attacks poses significant risks to patient safety. Current attack methodologies use general te

Amy Rafferty, Rishi Ramaesh, Ajitha Rajan

Deep learning models show significant potential for advancing AI-assisted medical diagnostics, particularly in detecting lung cancer through medical image modalities such as chest X-rays. However, the black-box nature of these models poses challenges to their interpretability and trustworthiness, limiting their adoption in clinical practice. This study exami

Jinze Wang, Tiehua Zhang, Lu Zhang, Yang Bai

Next Point-of-Interest (POI) recommendation aims to predict users' next locations by leveraging historical check-in sequences. Although existing methods have shown promising results, they often struggle to capture complex high-order relationships and effectively adapt to diverse user behaviors, particularly when addressing the cold-start issue. To address th

Chung-En Sun, Ge Yan, Tsui-Wei Weng

Recent studies have shown that Large Language Models (LLMs) augmented with chain-of-thought (CoT) reasoning demonstrate impressive problem-solving abilities. However, in this work, we identify a recurring issue where these models occasionally generate overly short reasoning, leading to degraded performance on even simple mathematical problems. Specifically,

Caio B. Naves, Jonas Larson

We introduce Liouville Fock state lattices (LFSLs) as a framework for visualizing open quantum systems through matrix representations of the Lindblad master equation (LME). By vectorizing the LME, the state evolves in a doubled Hilbert space, naturally forming a synthetic lattice. Unlike the unitary evolution of pure states, LFSL states exhibit nontrivial dy

Wim Cosyn, Frank Vera

This paper discusses a framework to parametrize and decompose operator matrix elements for particles with higher spin $(j > 1/2)$ using chiral representations of the Lorentz group, i.e. the $(j,0)$ and $(0,j)$ representations and their parity-invariant direct sum. Unlike traditional approaches that require imposing constraints to eliminate spurious degrees o

Juan Sosa, Carolina Luque, Juan Valero

This study presents a Bayesian spatial voting analysis of the Colombian Senate during the 2006-2010 legislative period, leveraging a newly constructed roll-call dataset comprising 147 senators and 136 plenary votes. We estimate legislators' ideal points under two alternative geometric frameworks: A traditional Euclidean model and a circular model that embeds

Shurui Li, Puneet Gupta

Compute-in-memory (CIM) based neural network accelerators offer a promising solution to the Von Neumann bottleneck by computing directly within memory arrays. However, SRAM CIM faces limitations in executing larger models due to its cell size and on-chip memory constraints. This work proposes CIMPool, a CIM-aware compression and acceleration framework that c

Jarosław Grytczuk, Bartłomiej Pawlik, Andrzej Ruciński

A shuffle square is a word consisting of two shuffled copies of the same word. For instance, the Turkish word $\mathtt{\color{red}{ik}\color{blue}{i}\color{red}{li}\color{blue}{kli}}$ (binary in English) is a shuffle square, as it can be split into two copies of the word $\mathtt{ikli}$. We explore a representation of shuffle squares in terms of \emph{ordere

Stanislav Semenov

Classical set theory constructs the continuum via the power set P(N), thereby postulating an uncountable totality. However, constructive and computability-based approaches reveal that no formal system with countable syntax can generate all subsets of N, nor can it capture the real line in full. In this paper, we propose fractal countability as a constructive

Tongjiang Wang, C. Nick Arge, Shaela I. Jones

Tomography is a powerful technique for recovering the three-dimensional (3D) density structure of the global solar corona. In this work, we present an improved tomography method by introducing radial weighting in the regularization term. Radial weighting provides balanced smoothing of density values across different heights, helping to recover finer structur

Hao Lin, Yongjun Zhang

Large language models (LLMs) have the potential to revolutionize computational social science, particularly in automated textual analysis. In this paper, we conduct a systematic evaluation of the promises and risks associated with using LLMs for text classification tasks, using social movement studies as an example. We propose a framework for social scientis

Sunny Rhoades, Tucker Jones, Keerthi Vasan G. C., Yuguang Chen

The kinematics of star-forming galaxy populations at high redshifts are integral to our understanding of disk properties, merger rates, and other defining characteristics. Nebular gas emission is a common tracer of galaxies' gravitational potentials and angular momenta, but is sensitive to non-gravitational forces as well as galactic outflows, and thus might

Lei Chong

In recent years, non-control-data attacks have be come a research hotspot in the field of network security, driven by the increasing number of defense methods against control-flow hijacking attacks. These attacks exploit memory vulnerabilities to modify non-control data within a program, thereby altering its behavior without compromising control-flow integri

Ngoc Tuong Vy Nguyen, Felix D Childress, Yunting Yin

Phishing attacks remain a critical cybersecurity threat. Attackers constantly refine their methods, making phishing emails harder to detect. Traditional detection methods, including rule-based systems and supervised machine learning models, either rely on predefined patterns like blacklists, which can be bypassed with slight modifications, or require large d

Scott E. Perkins, Peter McGill, William A. Dawson, Ming-Feng Ho

The dark and dynamic parts of the Galaxy, including the bulk shape and movement of the Galactic Bulge and characteristics of dark compact object populations, such as a hypothetical population of primordial black holes (PBHs), are difficult to study directly by their very nature, but are critical to our understanding of the universe. Fortunately, all of these

Indraneil Paul, Haoyi Yang, Goran Glavaš, Kristian Kersting

Language models (LMs) have become a staple of the code-writing toolbox. Their pre-training recipe has, however, remained stagnant over recent years, barring the occasional changes in data sourcing and filtering strategies. In particular, research exploring modifications to Code-LMs' pre-training objectives, geared towards improving data efficiency and better

Oliver Kramer

Large language models (LLMs) demonstrate strong language generation capabilities but often struggle with structured reasoning, leading to inconsistent or suboptimal problem-solving. To mitigate this limitation, Guilford's Structure of Intellect (SOI) model - a foundational framework from intelligence theory - is leveraged as the basis for cognitive prompt en

Isabella Loaiza, Roberto Rigobon

AI is transforming industries, raising concerns about job displacement and decision making reliability. AI, as a universal approximation function, excels in data driven tasks but struggles with small datasets, subjective probabilities, and contexts requiring human judgment, relationships, and ethics.The EPOCH framework highlights five irreplaceable human cap

Mario Di Mauro, Walter Cerroni, Fabio Postiglione, Massimo Tornatore

The rise of Network Function Virtualization (NFV) has transformed network infrastructures by replacing fixed hardware with software-based Virtualized Network Functions (VNFs), enabling greater agility, scalability, and cost efficiency. Virtualization increases the distribution of system components and introduces stronger interdependencies. As a result, failu

James N. Bull, Arun Subramani, Chang Liu, Samuel J. P. Marlton

Several small polycyclic aromatic hydrocarbons (PAHs) with closed-shell electronic structure have been identified in the cold, dark environment Taurus Molecular Cloud-1. We measure efficient radiative cooling through the combination of recurrent fluorescence (RF) and IR emission in the closed-shell indenyl cation (C$_{9}$H$_{7}^{+}$), finding good agreement

Rafal Babilas, Jakub Bicz, Adrian Radon, Mariola Kadziolka-Gawel

In this work, the four high entropy CoCrFeNiX alloys (X=Mo,Nb,B,Si) were prepared by induction melting to comparatively analyse their structure, nanomechanical properties, and corrosion resistance in the chloride ion environment. The CoCrFeNiNb and CoCrFeNiMo alloys are composed of FCC solid solution and intermetallic phases (TM)2Nb and Cr-Mo-TM. In the case

Haowei Yang, Lei Fu, Qingyi Lu, Yue Fan

With the rapid development of short video platforms, recommendation systems have become key technologies for improving user experience and enhancing platform engagement. However, while short video recommendation systems leverage multimodal information (such as images, text, and audio) to improve recommendation effectiveness, they also face the severe challen

Ko Hosokawa, Takayuki Kotani, Hajime Kawahara, Yui Kawashima

Recent high-dispersion spectroscopy from ground-based telescopes and high-precision spectroscopy from space observatories have enabled atmospheric observations of substellar objects, such as brown dwarfs and hot gaseous exoplanets, with sufficient precision to make ambient gas differences in molecular line broadening a significant factor. In this paper, we e

Ali Vaziri, Iman Askari, Huazhen Fang

Robots rely on motion planning to navigate safely and efficiently while performing various tasks. In this paper, we investigate motion planning through Bayesian inference, where motion plans are inferred based on planning objectives and constraints. However, existing Bayesian motion planning methods often struggle to explore low-probability regions of the pl

T. Medjnoun, M. Nillson-Takeuchi, B. Ganapathisubramani

This study explores the effect of friction Reynolds number ($Re_\tau \approx 3{,}000$--$13{,}000$) on secondary flows in three-dimensional turbulent boundary layers induced by spanwise surface heterogeneity. Using a combination of floating-element drag balance and high-resolution hot-wire anemometry, we examine how varying spanwise spacing ($S/\delta$) influ

Xinxin Hu, Shivam Dangwal, Xucheng Wang, Fan Zhang

Zinc-ion batteries are promising alternatives to lithium-ion batteries, offering advantages in safety, cost, and environmental impact. However, their performance is often limited by the functioning of the zinc anode. This study employs severe plastic deformation via the high-pressure torsion (HPT) method to enhance the electrochemical performance of zinc ano