December 2024 arXiv papers — page 96

Henning Bahl, Nina Elmer, Luigi Favaro, Manuel Haußmann

Neural networks for LHC physics have to be accurate, reliable, and controlled. Using neural surrogates for the prediction of loop amplitudes as a use case, we first show how activation functions are systematically tested with Kolmogorov-Arnold Networks. Then, we train neural surrogates to simultaneously predict the target amplitude and an uncertainty for the

Fangzhou Lin, Shang Gao, Yichuan Tang, Xihan Ma

Spectroscopic photoacoustic (sPA) imaging uses multiple wavelengths to differentiate chromophores based on their unique optical absorption spectra. This technique has been widely applied in areas such as vascular mapping, tumor detection, and therapeutic monitoring. However, sPA imaging is highly susceptible to noise, leading to poor signal-to-noise ratio (S

Hidetaka Manabe, Yuichi Sano

The quantum state preparation of probability distributions is an important subroutine for many quantum algorithms. When embedding $D$-dimensional multivariate probability distributions by discretizing each dimension into $2^n$ points, we need a state preparation circuit comprising a total of $nD$ qubits, which is often difficult to compile. In this study, we

Hao He, Xingwei Gao, Alexander Cerjan, Chia Wei Hsu

One of the key features of lasers operating near exceptional points (EPs) is that the gain medium can support an oscillating population inversion above a pump threshold, leading to self-modulated laser dynamics. This unusual behavior opens up new possibilities for frequency comb generation and temporal modulation. However, the dynamic population inversion co

H. Jerome Keisler

We prove analogues of the Craig interpolation theorem for the continuous model theory of metric structures.

Haiduo Wang, Ruizhe Tang, Xilin Liu

This report details the design and development of a compact high-voltage functional electrical stimulation (FES) device. Unlike conventional FES systems, the proposed design prioritizes user comfort by leveraging rapid switching times to effectively activate muscles while minimizing stimulation of pain receptors. The device is equipped with a high compliance

Marius Belly, Nathanaël Fijalkow, Hugo Gimbert, Florian Horn

Partially observable Markov decision processes (POMDPs) form a prominent model for uncertainty in sequential decision making. We are interested in constructing algorithms with theoretical guarantees to determine whether the agent has a strategy ensuring a given specification with probability 1. This well-studied problem is known to be undecidable already for

Samuel Falcon, Carmen Alvarez-Alvarez, Jaime Leon

Engaging messages delivered by teachers are a key aspect of the classroom discourse that influences student outcomes. However, improving this communication is challenging due to difficulties in obtaining observations. This study presents a methodology for efficiently extracting actual observations of engaging messages from audio-recorded lessons. We collecte

Farnaz Nouraei, Keith Rebello, Mina Fallah, Prasanth Murali

Many laypeople are motivated to improve the health behavior of their family or friends but do not know where to start, especially if the health behavior is potentially stigmatizing or controversial. We present an approach that uses virtual agents to coach community-based volunteers in health counseling techniques, such as motivational interviewing, and allow

Henrique Miranda, Ciro Pappalardo, José Afonso, Polychronis Papaderos

The neglect of modelling both stellar and nebular emission significantly affects the derived physical properties of galaxies, particularly those with high star formation rates. While this issue has been studied, it has not been established a clear threshold for a significant impact on the estimated physical properties of galaxies due to accounting for both s

Jidong Wang

We introduce a notion of Lorentzian proper position in close analogy to proper position of stable polynomials. Using this notion, we give a new characterization of elementary quotients of M-convex function that parallels the Lorentzian characterization of M-convex functions. We thereby use Lorentzian proper position to study the incidence geometry of tropica

Peyman Rostami, Alexander Erb, Reza Azizmalayeri, Johanna Steinmann

This study investigated the coalescence of polymer solution drops on the solid substrates. When two drops meet at their contact line on a substrate, the liquid bridge connecting the two drops increases in size with time. The height and radius of the liquid bridge have a power law dependence on time. In the early stage of drop coalescence, the exponents $\alp

Hjalte Frellesvig, Roger Morales, Sebastian Pögel, Stefan Weinzierl

We study a recently identified four-loop Feynman integral that contains a three-dimensional Calabi-Yau geometry and contributes to the scattering of black holes in classical gravity at fifth post-Minkowskian and second self-force order (5PM 2SF) in the conservative sector. In contrast to previously studied Calabi-Yau Feynman integrals, the higher-order diffe

Juliette Becker, Andrew Vanderburg, Joseph Livesey

Several groups have recently suggested that small planets orbiting very closely around white dwarf stars could be promising locations for life to arise, even after stellar death. There are still many uncertainties, however, regarding the existence and habitability of these worlds. Here, we consider the retention of water during post-main-sequence evolution o

Bao-Yuan Zhao, Kai Zhang, Dai Zhou, Shiliang Hu

This study investigates the effects of Thom disks on alleviating ground effects by wall-mounted rotating cylinders, also known as Flettner rotors, which utilize wind energy for ship propulsion. Through three-dimensional direct numerical simulations, our findings reveal that introducing a secondary Thom disk near the ground significantly reduces the three-dim

Xin Tong

Ever since the introduction of motivic homotopy theory, as a well-proposed approximation of Grothendieck's dream, algebraic geometers then have the chance to study schemes via a homotopy theory. However topologists also found that lifting the usual homotopy theory over a sphere spectrum to the motivic homotopy category over a motivic bigraded sphere spectrum

Jannis Bolik, Thomas Hofmann

We discuss optimal prediction for families of probability distributions with a locally compact topological group structure. Right-invariant priors were previously shown to yield a posterior predictive distribution minimizing the worst-case Kullback-Leibler risk among all predictive procedures. However, the assumptions for the proof are so strong that they ra

Albert Le Floch, Guy Ropars

While the existence of an asymmetry between the two Maxwell centroids at the centre of the two foveas recorded using a foveascope, leads to the ocular dominance in good readers, the lack of asymmetry in most of the observers with dyslexia leads to their non-dominance and their difficulties in reading and writing. Indeed, the lack of asymmetry between the two

Simon J. Perkins, Jonathan S. Kenyon, Lexy A. L. Andati, Hertzog L. Bester

New radio interferometers such as MeerKAT, SKA, ngVLA, and DSA-2000 drive advancements in software for two key reasons. First, handling the vast data from these instruments requires subdivision and multi-node processing. Second, their improved sensitivity, achieved through better engineering and larger data volumes, demands new techniques to fully exploit it

Patricia Alonso Ruiz, Valentia Fragkiadaki

This paper revisits classical fractional Sobolev embedding theorems and the algebra property of the fractional Sobolev space $H^s(\mathbb{R})$ by means of Haar functions and dyadic decompositions. The aim is to provide an alternative, hands-on approach without Fourier transform that may be transferred to settings where the latter is not available. Explicit c

Hongwei Niu, Linhuang Xie, Jianghang Lin, Shengchuan Zhang

Domain Generalized Semantic Segmentation (DGSS) seeks to utilize source domain data exclusively to enhance the generalization of semantic segmentation across unknown target domains. Prevailing studies predominantly concentrate on feature normalization and domain randomization, these approaches exhibit significant limitations. Feature normalization-based meth

Mohammad Sadegh Salehi, Subhadip Mukherjee, Lindon Roberts, Matthias J. Ehrhardt

Bilevel learning has gained prominence in machine learning, inverse problems, and imaging applications, including hyperparameter optimization, learning data-adaptive regularizers, and optimizing forward operators. The large-scale nature of these problems has led to the development of inexact and computationally efficient methods. Existing adaptive methods pr

Chenxi Liu, Towaki Takikawa, Alec Jacobson

Recent advances in diffusion models and parameter-efficient fine-tuning (PEFT) have made text-to-image generation and customization widely accessible, with Low Rank Adaptation (LoRA) able to replicate an artist's style or subject using minimal data and computation. In this paper, we examine the relationship between LoRA weights and artistic styles, demonstra

Eduardo Flández, Alejandro Zamorano, Víctor Muñoz

In this paper, solar cycles 21 to 24 were compared using complex network analysis. A network was constructed for these four solar cycles to facilitate the comparison. In these networks, the nodes represent the active regions of the Sun that emit flares, and the connections correspond to the sequence of solar flares over time. This resulted in a directed netw

Erica Caden, Stephen Sekula, Stanley Yen

Neutrinos carry most of the energy released by a core-collapse supernova. SNOLAB has two neutrino-capable detectors, SNO+ and HALO, that have complementary neutrino flavour sensitivities. SNOLAB is also host to existing facilities, or plans to host future projects, that can enhance sensitivity to these neutrinos. These detectors, together with others worldwi

Ritwik Raj Saxena

Existing research on AI-based traffic management systems, utilizing techniques such as fuzzy logic, reinforcement learning, deep neural networks, and evolutionary algorithms, demonstrates the potential of AI to transform the traffic landscape. This article endeavors to review the topics where AI and traffic management intersect. It comprises areas like AI-po

Shyamal Biswas

We have re-examined the integral form of Gauss' law for arbitrarily moving charges inside and outside an arbitrarily expanding (or contracting) and deforming Gaussian surface. We have explicitly calculated the time-dependent Gauss' flux integral for such a generic non-static case with the Maxwell equations under consideration. We have obtained an evolution e

D. Bazeia, I. Bezerra, R. Menezes

This work deals with two real scalar fields in two-dimensional spacetime, with the fields coupled to allow the study of localized configurations. We consider models constructed to engender geometric constrictions, and use them to investigate solutions of the lump type, which attain no topological properties. We show how to modify the internal structure of th

Grigory Belousov

We consider a real del Pezzo surface without points. We prove that the same surface over complex numbers field $\mathbb{C}$ has Picard number is at least two.

Julián N. Acosta, Siddhant Dogra, Subathra Adithan, Kay Wu

Radiologists face increasing workload pressures amid growing imaging volumes, creating risks of burnout and delayed reporting times. While artificial intelligence (AI) based automated radiology report generation shows promise for reporting workflow optimization, evidence of its real-world impact on clinical accuracy and efficiency remains limited. This study

Arnaud Mayeux

Prime numbers are fascinating by the way they appear in the set of natural numbers. Despite several results enlighting us about their repartition, the set of prime numbers is often informally qualified as misterious. In the present paper, we introduce a formalism allowing to state a formal conjecture: the set of prime numbers is supernatural. Our conjecture

Anthony Hughes, Ning Ma, Nikolaos Aletras

In sensitive domains such as medical and legal, protecting sensitive information is critical, with protective laws strictly prohibiting the disclosure of personal data. This poses challenges for sharing valuable data such as medical reports and legal cases summaries. While language models (LMs) have shown strong performance in text summarization, it is still

Ira Ceka, Feitong Qiao, Anik Dey, Aastha Valecha

Despite their remarkable success, large language models (LLMs) have shown limited ability on safety-critical code tasks such as vulnerability detection. Typically, static analysis (SA) tools, like CodeQL, CodeGuru Security, etc., are used for vulnerability detection. SA relies on predefined, manually-crafted rules for flagging various vulnerabilities. Thus,

Ki-Hwan Oh, Leonardo Borgioli, Alberto Mangano, Valentina Valle

In recent years, the application of machine learning to minimally invasive surgery (MIS) has attracted considerable interest. Datasets are critical to the use of such techniques. This paper presents a unique dataset recorded during ex vivo pseudo-cholecystectomy procedures on pig livers using the da Vinci Research Kit (dVRK). Unlike existing datasets, it add

Marco Budinich

We refine the formulation of the Boolean satisfiability problem with $n$ Boolean variables in Clifford algebra ${\cal C}\ell(\mathbb{R}^{n,n})$ [3] and exploit this continuous setting to outline a new unsatisfiability test. This algorithm is not combinatorial and can prove unsatisfiability in polynomial time.

Connor Lawless, Yingxi Li, Anders Wikum, Madeleine Udell

Mixed integer linear programming (MILP) solvers expose hundreds of parameters that have an outsized impact on performance but are difficult to configure for all but expert users. Existing machine learning (ML) approaches require training on thousands of related instances, generalize poorly and can be difficult to integrate into existing solver workflows. We

Xinze Lyu, Sundar Aditya, Bruno Clerckx

A canonical use case of Integrated Sensing and Communications (ISAC) in multiple-input multiple-output (MIMO) systems involves a multi-antenna transmitter communicating with $K$ users and sensing targets in its vicinity. For this setup, precoder and multiple access designs are of utmost importance, as the limited transmit power budget must be efficiently dir

Rana Danesh, Farrokh Janabi-Sharifi

This paper presents a study on the backstepping control of tendon-driven continuum robots for large deflections using the Cosserat rod model. Continuum robots are known for their flexibility and adaptability, making them suitable for various applications. However, modeling and controlling them pose challenges due to their nonlinear dynamics. To model their d

Lucas Tesán, David González, Pedro Martins, Elías Cueto

The growing importance of real-time simulation in the medical field has exposed the limitations and bottlenecks inherent in the digital representation of complex biological systems. This paper presents a novel methodology aimed at advancing current lines of research in soft tissue simulation. The proposed approach introduces a hybrid model that integrates th

Jan M. Pawlowski, Jonas Wessely

We study the causal structure of the quark propagator with the spectral DSE. The spectral gap equation is solved with the input of the spectral representation of the gluon and a causal STI-construction for the quark-gluon vertex. The latter includes a potential infrared enhancement of the vertex strength of the classical tensor structure that accommodates fo

Gaojian Wang, Feng Lin, Tong Wu, Zhenguang Liu

This work asks: with abundant, unlabeled real faces, how to learn a robust and transferable facial representation that boosts various face security tasks with respect to generalization performance? We make the first attempt and propose a self-supervised pretraining framework to learn fundamental representations of real face images, FSFM, that leverages the s

Jie Zhang, Xun Gong, Zhonglin Sun

Face recognition has made remarkable strides, driven by the expanding scale of datasets, advancements in various backbone and discriminative losses. However, face recognition performance is heavily affected by the label noise, especially closed-set noise. While numerous studies have focused on handling label noise, addressing closed-set noise still poses cha

Honglin Yang, Ji Ma, Xiao Yu

The optimization-based meta-learning approach is gaining increased traction because of its unique ability to quickly adapt to a new task using only small amounts of data. However, existing optimization-based meta-learning approaches, such as MAML, ANIL and their variants, generally employ backpropagation for upper-level gradient estimation, which requires us

Rafaela M. Brinn, Peter Meisenheimer, Medha Dandu, Elyse Barré

Er3+ color centers are promising candidates for quantum science and technology due to their long electron and nuclear spin coherence times, as well as their desirable emission wavelength. By selecting host materials with suitable, controllable properties, we introduce new parameters that can be used to tailor the Er3+ emission spectrum. PbTiO3 is a well-stud

Zhaoyi Li, Sawyer D. Campbell, Joon-Suh Park, Ronald P. Jenkins

Metasurfaces, with their ability to control electromagnetic waves, hold immense potential in optical device design, especially for applications requiring precise control over dispersion. This work introduces an approach to dispersion engineering using heterogeneous freeform metasurfaces, which overcomes the limitations of conventional metasurfaces that often

Tomojit Chowdhury, Aurélie Champagne, Patrick Knüppel, Zehra Naqvi

Bilayer crystals, formed by stacking monolayers of two-dimensional (2D) crystals, create interlayer potentials that govern excitonic phenomena but are constrained by their fixed covalent lattices. Replacing one layer with an atomically thin molecular crystal overcomes this limitation, as precise control of functional groups enables tunable 2D molecular latti

Milind Hegde, Zongrui Yang

We consider the stationary measure of the asymmetric simple exclusion process (ASEP) on a finite interval in $\mathbb{Z}$ with open boundaries. Fixing all the jump rates and letting the system size approach infinity, the height profile of such a sequence of stationary measures satisfies a large deviation principle (LDP), whose rate function was predicted in

Shalu Yadav, Ankit Singh, Praveen Kumar Srivastava, Abhay Kumar Choubey

This study is a continuation of previous work, which highlights the nutrient enhancement by using rice straw (RS) and pressmud (PM) on vermicomposting. Herein, we demonstrate the significant impact of Moringa oleifera derived Cu/Ni/Co oxide nanoparticles (TmONs) in conjunction with these vermicompost on the growth performance of Abelmoschus esculentus. Vermi

Linfeng Zhao, Owen Howell, Xupeng Zhu, Jung Yeon Park

Reinforcement learning (RL) algorithms for continuous control tasks require accurate sampling-based action selection. Many tasks, such as robotic manipulation, contain inherent problem symmetries. However, correctly incorporating symmetry into sampling-based approaches remains a challenge. This work addresses the challenge of preserving symmetry in sampling-

Linfeng Zhao, Lawson L. S. Wong

Learning navigation capabilities in different environments has long been one of the major challenges in decision-making. In this work, we focus on zero-shot navigation ability using given abstract $2$-D top-down maps. Like human navigation by reading a paper map, the agent reads the map as an image when navigating in a novel layout, after learning to navigat

Antoine Aurillard

We extend the classical coding of measured $\mathbb R$-trees by continuous excursion-type functions to c\`adl\`ag excursion-type functions through the notion of parametric representations. The main feature of this extension is its continuity properties with respect to the Gromov-Hausdorff-Prokhorov topology for $\mathbb R$-trees and Skorokhod's $M_1$ topolog

Antoine Pinardin, Arman Sarikyan, Egor Yasinsky

We give a complete solution of the linearization problem in the plane Cremona group over an algebraically closed field of characteristic zero.

Pierre-Antoine Bernard, Riccarda Bonsignori, Viktor Eisler, Gilles Parez

We study the entanglement Hamiltonian for free-fermion chains with a particular form of inhomogeneity. The hopping amplitudes and chemical potentials are chosen such that the single-particle eigenstates are related to discrete orthogonal polynomials of the Askey scheme. Due to the bispectral properties of these functions, one can construct an operator which

Tomás Ortín

Supergravity has played a very important role in many advances and developments in black hole physics. Here I will review the history of some of them.

Shekhar Madhav Khairnar, Huu Phong Nguyen, Alexis Desir, Carla Holcomb

Automated assessment of surgical skills using artificial intelligence (AI) provides trainees with instantaneous feedback. After bimanual tool motions are captured, derived kinematic metrics are reliable predictors of performance in laparoscopic tasks. Implementing automated tool tracking requires time-intensive human annotation. We developed AI-based tool tr

J. Bayron Orjuela-Quintana, Jose Beltrán Jiménez

We study a class of homogeneous but anisotropic cosmologies within the family of shift-symmetric Horndeski theories, where the scalar field features an inhomogeneous profile but it preserves a translational symmetry that is realised as a combination of spatial translations and internal shifts. The spatial gradient of the scalar field introduces a preferred d

Anton Biryukov, Gregory Beskin

It is thought that isolated neutron stars receive a natal kick velocity at birth nearly aligned with their spin axis. Direct observational confirmation of this alignment is currently limited to a single source in a supernova remnant (PSR J0538+2817), for which the three-dimensional velocity has been well constrained. Meanwhile, pulsar polarisation statistics

Tim Sziburis, Susanne Blex, Tobias Glasmachers, Ioannis Iossifidis

The identification of individual movement characteristics sets the foundation for the assessment of personal rehabilitation progress and can provide diagnostic information on levels and stages of movement disorders. This work presents a preliminary study for differentiating individual motion patterns using a dataset of 3D upper-limb transport trajectories me

Christophe Grojean, Patrick Janot

The paper entitled "Sustainability Strategy for the Cool Copper Collider" by M. Breidenbach et al. defines a metric to weigh the electricity consumption and the carbon footprint of future Higgs factory concepts. We show that this metric is flawed in many respects and gives an incorrect representation of reality. We also demonstrates that, irrespective of the

NA62 Collaboration

A measurement of the $K^{+}\rightarrow\pi^{+}\nu\bar{\nu}$ decay by the NA62 experiment at the CERN SPS is presented, using data collected in 2021 and 2022. This dataset was recorded, after modifications to the beamline and detectors, at a higher instantaneous beam intensity with respect to the 2016--2018 data taking. Combining NA62 data collected in 2016--2

Nong Minh Hieu, Antoine Ledent, Yunwen Lei, Cheng Yeaw Ku

In this paper, we present generalization bounds for the unsupervised risk in the Deep Contrastive Representation Learning framework, which employs deep neural networks as representation functions. We approach this problem from two angles. On the one hand, we derive a parameter-counting bound that scales with the overall size of the neural networks. On the ot

Ole Sönnerborn

In holonomic quantum computation, quantum logic gates are realized by cyclic parallel transport of the computational space. The resulting quantum gate corresponds to the holonomy associated with the closed path traced by the computational space. The isoholonomic inequality for gates establishes a fundamental lower bound on the path length of such cyclic tran

Elijah Sheridan, Federico Carta, Naomi Gendler, Mudit Jain

We study fuzzy axion dark matter in type IIB string theory, for axions descending from the Ramond-Ramond four-form in compactifications on orientifolds of Calabi-Yau hypersurfaces. Such models can be tested by cosmological measurements if a significant relic abundance of fuzzy dark matter arises, which we argue is most common in models with small numbers of

Sylwia Kondej, Kacper Ślipko

In this paper we consider two dimensional quantum system with an infinite waveguide of the width $d$ and a transversally invariant profile. Furthermore, we assume that at a distant $\rho$ there is a perturbation defined by the Kato measure. We show that, under certain conditions, the resolvent of the Hamiltonian has the second sheet pole which reproduces the

Mohammad Mortezaei Nobahari, Mahmood Rezaei Roknabadi

1T$^{\prime}$ phase of the monolayer transition metal dichalcogenides has recently attracted attention for its potential in nanoelectronic applications. We theoretically prove the topological behavior and phase transition of 1T$^{\prime}$-MoS$_2$ using $k.p$ Hamiltonian and linear response theory. The spin texture in momentum space reveals a strong spin-mome

Yueqian Lin, Yuzhe Fu, Jingyang Zhang, Yudong Liu

We introduce Speech Information Retrieval (SIR), a new long-context task for Speech Large Language Models (Speech LLMs), and present SPIRAL, a 1,012-sample benchmark testing models' ability to extract critical details from approximately 90-second spoken inputs. While current Speech LLMs excel at short-form tasks, they struggle with the computational and repr

Melih İs, İsmet Karaca

This work aims to define the concept of manifold, which has a very important place in the topology, on digital images. So, a general perspective is provided for two and three-dimensional imaging studies on digital curves and digital surfaces. Throughout the study, the features present in topological manifolds but that are not satisfied in the discrete versio

MohammadReza Askari, Navid Parsa

Abstract: The rising global temperatures caused by climate change significantly impact energy consumption and electricity generation. Fluctuating temperatures and frequent extreme weather events disrupt energy production and consumption patterns. Addressing these challenges has become a priority, prompting governments, industries, and societies to pursue sus

Gilles Mordant

We study the statistical properties of the entropic optimal (self) transport problem for smooth probability measures. We provide an accurate description of the limit distribution for entropic (self-)potentials and plans as the regularization parameter shrinks with the sample size; this regime is largely unexplored in the prior statistical literature, where $

Krzysztof Sienicki

Algorithmic idealism represents a transformative approach to understanding reality, emphasizing the informational structure of self-states and their algorithmic transitions over traditional notions of an external, objective universe. Rooted in algorithmic information theory, it redefines reality as a sequence of self-state transitions governed by principles

Rajat Khanda

Technical troubleshooting in enterprise environments often involves navigating diverse, heterogeneous data sources to resolve complex issues effectively. This paper presents a novel agentic AI solution built on a Weighted Retrieval-Augmented Generation (RAG) Framework tailored for enterprise technical troubleshooting. By dynamically weighting retrieval sourc

Giacomo Micheli, Vincenzo Pallozzi Lavorante, Phillip Waitkevich

Let $q$ be a prime power. This paper provides a new class of linear codes that arises from the action of the alternating group on $\mathbb F_q[x_1,\dots,x_m]$ combined with the ideas in (M. Datta and T. Johnsen, 2022). Compared with Generalized Reed-Muller codes with similar parameters, our codes have the same asymptotic relative distance but a better rate.

Jiya Manchanda, Laura Boettcher, Matheus Westphalen, Jasser Jasser

Large language models (LLMs) have rapidly advanced natural language processing, driving significant breakthroughs in tasks such as text generation, machine translation, and domain-specific reasoning. The field now faces a critical dilemma in its approach: closed-source models like GPT-4 deliver state-of-the-art performance but restrict reproducibility, acces

Gayana Jayasinghe, Hadrian Quan, Xinran Yu

In this paper we construct Witten instanton complexes on stratified pseudomanifolds with wedge metrics, for all choices of mezzo-perversities which classify the self-adjoint extensions of the Hodge Dirac operator. In this singular setting we introduce a generalization of the Morse-Bott condition and in so doing can consider a class of functions with certain

Ishfaq Ahmad Rather, Kau D. Marquez, Prashant Thakur, Odilon Lourenço

We study the effects of hyperons, delta baryons, and quark matter phase transitions on $f$-mode oscillations in neutron stars. Using the density-dependent relativistic mean-field model (DDME2) for the hadronic phase and the density-dependent quark mass (DDQM) model for the quark phase, we construct hadronic and hybrid equations of state (EoSs) consistent wit

Zhuhao Wang, Yihua Sun, Zihan Li, Xuan Yang

Drafting radiology reports is a complex task requiring flexibility, where radiologists tail content to available information and particular clinical demands. However, most current radiology report generation (RRG) models are constrained to a fixed task paradigm, such as predicting the full ``finding'' section from a single image, inherently involving a misma

Senkang Hu, Yihang Tao, Guowen Xu, Yiqin Deng

Collaborative Perception (CP) has shown a promising technique for autonomous driving, where multiple connected and autonomous vehicles (CAVs) share their perception information to enhance the overall perception performance and expand the perception range. However, in CP, ego CAV needs to receive messages from its collaborators, which makes it easy to be atta

Kassie Archer, Aaron Geary, Robert P. Laudone

Shallow permutations were defined in 1977 to be those that satisfy the lower bound of the Diaconis-Graham inequality. Recently, there has been renewed interest in these permutations. In particular, Berman and Tenner showed they satisfy certain pattern avoidance conditions in their cycle form and Woo showed they are exactly those whose cycle diagrams are unli

Savinay Nagendra, Kashif Rashid, Chaopeng Shen, Daniel Kifer

Few-shot segmentation is the problem of learning to identify specific types of objects (e.g., airplanes) in images from a small set of labeled reference images. The current state of the art is driven by resource-intensive construction of models for every new domain-specific application. Such models must be trained on enormous labeled datasets of unrelated ob

Jiaxuan Cai, Xinmiao Zhang

The medium-density parity-check (MDPC) code-based Bit Flipping Key Encapsulation (BIKE) mechanism remains a candidate of post-quantum cryptography standardization. The latest version utilizes a new bit-flipping (BF) decoding algorithm, which decides the BF threshold by an affine function with high-precision coefficients. Previous BF decoder implementations c

Nguyen Dong Yen, Duong Thi Viet An, Vu Thi Huong, Nguyen Ngoc Luan

The recent results of An, Luan, and Yen [Differential stability in convex optimization via generalized polyhedrality. Vietnam J. Math. https://-doi.org/10.1007/s10013-024-00721-y] on differential stability of parametric optimization problems described by proper generalized polyhedral convex functions and generalized polyhedral convex set-valued maps are anal

Devika Venugopalan, Ziwen Yan, Conrad Borchers, Jionghao Lin

Caregivers (i.e., parents and members of a child's caring community) are underappreciated stakeholders in learning analytics. Although caregiver involvement can enhance student academic outcomes, many obstacles hinder involvement, most notably knowledge gaps with respect to modern school curricula. An emerging topic of interest in learning analytics is hybri

Filip Cano, Thomas A. Henzinger, Bettina Könighofer, Konstantin Kueffner

As AI-based decision-makers increasingly influence human lives, it is a growing concern that their decisions are often unfair or biased with respect to people's sensitive attributes, such as gender and race. Most existing bias prevention measures provide probabilistic fairness guarantees in the long run, and it is possible that the decisions are biased on sp

Patrick J. Park, Alexander Glaser

Our work explores North Korea's 100 MW-th Experimental Light Water Reactor (ELWR) and its potential contributions to the country's nuclear weapons program. Built at the Yongbyon Nuclear Research Center, the ELWR began operations in October 2023 and represents North Korea's first attempts at a light-water reactor using domestically-enriched, ceramic fuel. Our

Francesca Pacifico, Paolo Pergola, Charlotte Sleight

Holographic correlators on the celestial sphere of Minkowski space were recently defined in arXiv:2301.01810 as the extrapolation of bulk time-ordered correlation functions to the celestial sphere. In this work we explore the Mellin representation of such celestial correlators, which is based on the Mellin representation of conformal correlators introduced b

Paul Manns

Trust-region algorithms can be applied to very abstract optimization problems because they do not require a specific direction of descent or gradient. This has lead to recent interest in them, in particular in the area of integer optimal control problems, where the infinite-dimensional problem formulations do not assume vector space structure. We analyze a t

Jian Yang, Jiajun Zhang, Jiaxi Yang, Ke Jin

Code completion has become an essential tool for daily software development. Existing evaluation benchmarks often employ static methods that do not fully capture the dynamic nature of real-world coding environments and face significant challenges, including limited context length, reliance on superficial evaluation metrics, and potential overfitting to train

ATLAS Collaboration

A measurement of the ratio of the branching fractions, $R_{\tau/e} = B(W \to \tau \nu)/ B(W \to e \nu)$, is performed using a sample of $W$ bosons originating from top-quark decays to final states containing $\tau$-leptons or electrons. This measurement uses $pp$ collisions at $\sqrt{s}=13$ TeV, collected by the ATLAS experiment at the Large Hadron Collider

Karthik Sivakoti

While Natural Language Inference (NLI) models have achieved high performances on benchmark datasets, there are still concerns whether they truly capture the intended task, or largely exploit dataset artifacts. Through detailed analysis of the Stanford Natural Language Inference (SNLI) dataset, we have uncovered complex patterns of various types of artifacts

Debarshi Kundu

Consider the problem: ``If one man and one woman can produce one child in one year, how many children will be produced by one woman and three men in 0.5 years?" Current large language models (LLMs) such as GPT-4o, GPT-o1-preview, and Gemini Flash frequently answer "0.5," which does not make sense. While these models sometimes acknowledge the unrealistic natu

Harrison Nicholls, Raymond T. Pierrehumbert, Tim Lichtenberg, Laurent Soucasse

Atmospheric energy transport is central to the cooling of primordial magma oceans. Theoretical studies of atmospheres on lava planets have assumed that convection is the only process involved in setting the atmospheric temperature structure. This significantly influences the ability for a magma ocean to cool. It has been suggested that convective stability i

Kate Knill, Diane Nicholls, Mark J. F. Gales, Mengjie Qian

We introduce the Speak & Improve Corpus 2025, a dataset of L2 learner English data with holistic scores and language error annotation, collected from open (spontaneous) speaking tests on the Speak & Improve learning platform. The aim of the corpus release is to address a major challenge to developing L2 spoken language processing systems, the lack of publicl

Mengjie Qian, Kate Knill, Stefano Banno, Siyuan Tang

This paper presents the "Speak & Improve Challenge 2025: Spoken Language Assessment and Feedback" -- a challenge associated with the ISCA SLaTE 2025 Workshop. The goal of the challenge is to advance research on spoken language assessment and feedback, with tasks associated with both the underlying technology and language learning feedback. Linked with the ch

Yannai A. Gonczarowski, Ella Segev

We axiomatically define a cardinal social inefficiency function, which, given alternatives and individuals' vNM preferences, assigns a number -- the social inefficiency -- to each alternative. These numbers induce not only an ordinal comparison but also a cardinal comparison between alternatives that is uniquely defined by our axioms despite no exogenous

Taiyan Zhang, Renchi Yang, Yurui Lai, Mingyu Yan

Graph neural networks (GNNs) have become the preferred models for node classification in graph data due to their robust capabilities in integrating graph structures and attributes. However, these models heavily depend on a substantial amount of high-quality labeled data for training, which is often costly to obtain. With the rise of large language models (LL

Clement Dinh, Yunhao Fan, Gia-Wei Chern

An echo state network (ESN) is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer. Compared with other recurrent neural networks, one great advantage of ESN is the simplicity of its training process. Yet, despite the seemingly restricted learnable parameters, ESN has been shown to successfully capture the

Sheikh Junaid Fayaz, Nestor Montiel-Bohorquez, Shashank Bishnoi, Matteo Romano

Cement production, exceeding 4.1 billion tonnes and contributing 2.4 tonnes of CO2 annually, faces critical challenges in quality control and process optimization. While traditional process models for cement manufacturing are confined to steady-state conditions with limited predictive capability for mineralogical phases, modern plants operate under dynamic c

Luis A. Medina-Dozal, Alejandro R. Urzúa, José Récamier-Angelini

In this work, we make use of Lie algebraic methods to obtain the time evolution operator for an optomechanical system with linear and quadratic couplings between the field and the mechanical oscillator. Firstly, we consider the case of a non-driven system and find its exact time evolution operator, secondly we consider the case of a forced system whose time

Oren Neumann, Claudius Gros

Neural scaling laws are observed in a range of domains, to date with no universal understanding of why they occur. Recent theories suggest that loss power laws arise from Zipf's law, a power law observed in domains like natural language. One theory suggests that language scaling laws emerge when Zipf-distributed task quanta are learned in descending order of

Beomseok Lee, Marco Gaido, Ioan Calapodescu, Laurent Besacier

While crowdsourcing is an established solution for facilitating and scaling the collection of speech data, the involvement of non-experts necessitates protocols to ensure final data quality. To reduce the costs of these essential controls, this paper investigates the use of Speech Foundation Models (SFMs) to automate the validation process, examining for the