November 2024 arXiv papers — page 113

Feng Gu, Wichayaporn Wongkamjan, Jonathan K. Kummerfeld, Denis Peskoff

AIs can beat humans in game environments; however, how helpful those agents are to human remains understudied. We augment CICERO, a natural language agent that demonstrates superhuman performance in Diplomacy, to generate both move and message advice based on player intentions. A dozen Diplomacy games with novice and experienced players, with varying advice

Selestine Melchane, Youssef Elmir, Farid Kacimi, Larbi Boubchir

Artificial Intelligence (AI) and infectious diseases prediction have recently experienced a common development and advancement. Machine learning (ML) apparition, along with deep learning (DL) emergence, extended many approaches against diseases apparition and their spread. And despite their outstanding results in predicting infectious diseases, conflicts app

Christiane Rousseau

The paper studies the generic complex 1-dimensional polynomial vector fields of the form $iP(z)\frac{\partial}{\partial z}$, where $P$ is a polynomial with real coefficients, under topological orbital equivalence preserving the separatrices of the pole at infinity. The number of generic strata is determined, and a complete parametrization of the strata is gi

Anne Larsen, Anda Tenie

Using Langer's construction of Bridgeland stability conditions on normal surfaces, we prove Reider-type theorems generalizing the work done by Arcara-Bertram in the smooth case. Our results still hold in positive characteristic or when $\omega_X \otimes L$ is not necessarily a line bundle. They also hold when the dualizing sheaf is replaced by a variant aris

Xu Wang, Mi-Xiang Lan, Qing-Wen Tang, Xue-Feng Wu

The multi-window observations, including the light curve and the evolutions of the spectral peak energy ($E_p$), the polarization degree (PD) and the polarization angle (PA), are used to infer the model parameters to predict the time-integrated PD in gamma-ray burst (GRB) prompt phase. We select 23 GRBs co-detected by Fermi/GBM and polarization detectors (i.

Chenglin Li, Qianglong Chen, Zhi Li, Feng Tao

Recent advancements in Large Video-Language Models (LVLMs) have led to promising results in multimodal video understanding. However, it remains unclear whether these models possess the cognitive capabilities required for high-level tasks, particularly those involving symbolic and abstract perception. Existing benchmarks typically rely on real-world, annotate

Jia Guo, Yuanwei Liu, Hyundong Shin, Arumugam Nallanathan

A DeepCAPA (Deep Learning for Continuous Aperture Array (CAPA)) framework is proposed to learn beamforming in CAPA systems. The beamforming optimization problem is firstly formulated, and it is mathematically proved that the optimal beamforming lies in the subspace spanned by users' conjugate channel responses. Two challenges are encountered when directly ap

Qiang Sun, Eitan Dvorquez, Felipe Pinto, Mohan C. Mathpal

Topological insulators (TIs) are materials with unique surface conductive properties that distinguish them from normal insulators and have attracted significant interest due to their potential applications in electronics and spintronics. However, their weak magnetic field response in traditional setups has limited their practical applications. Here, we show

Samantha Dalal, Siobhan Mackenzie Hall, Nari Johnson

The demands for accurate and representative generative AI systems means there is an increased demand on participatory evaluation structures. While these participatory structures are paramount to to ensure non-dominant values, knowledge and material culture are also reflected in AI models and the media they generate, we argue that dominant structures of commu

Ashim Dahal, Saydul Akbar Murad, Nick Rahimi

Vision Transformers (ViT) have recently brought a new wave of research in the field of computer vision. These models have performed particularly well in image classification and segmentation. Research on semantic and instance segmentation has accelerated with the introduction of the new architecture, with over 80% of the top 20 benchmarks for the iSAID datas

Alexander Kagan, Elizaveta Levina, Ji Zhu

Modeling information spread through a network is one of the key problems of network analysis, with applications in a wide array of areas such as marketing and public health. Most approaches assume that the spread is governed by some probabilistic diffusion model, often parameterized by the strength of connections between network members (edge weights), highl

D. Fernández Gil, J. A. Hodgson, B. L'Huillier, J. Asorey

The relationship between galaxies and their supermassive black holes (SMBHs) is an area of active research. One way to investigate this is to compare parsec-scale jets formed by SMBHs with the projected shape of their kiloparsec-scale host galaxies. We analyse Very Long Baseline Interferometry (VLBI) images of Active Galactic Nuclei (AGN) and optical images

Daniel M. Castro, Ernesto P. Raposo, Mauro Copelli, Fernando A. N. Santos

We apply the phenomenological renormalization group to resting-state fMRI time series of brain activity in a large population. By recursively coarse-graining the data, we compute scaling exponents for the series variance, log probability of silence, and largest covariance eigenvalue. The exponents clearly exhibit linear interdependencies, which we derive ana

Mahdi Nouraie, Samuel Muller

Stability selection is a widely adopted resampling-based framework for high-dimensional variable selection. This paper seeks to broaden the use of an established stability estimator to evaluate the overall stability of the stability selection results, moving beyond single-variable analysis. We suggest that the stability estimator offers two advantages: it ca

Cheongho Han, Andrzej Udalski, Ian A. Bond, Chung-Uk Lee

We carried out a project involving the systematic analysis of microlensing data from the Korea Microlensing Telescope Network survey. The aim of this project is to identify lensing events with complex anomaly features that are difficult to explain using standard binary-lens or binary-source models. Our investigation reveals that the light curves of microlens

Radu Slobodeanu, Martin Speight

A Skyrme type energy functional for maps $φ$ from an oriented Riemannian 3-manifold $M$ to a contact 3-manifold $N$ is defined, generalizing the BPS Skyrme energy of Ferreira and Zakrzewski. This energy has a topological lower bound, attained by solutions of a first order self-duality equation which we call (strong) Beltrami maps. In the case where $N$ is th

Taketo Handa, Chun-Ying Huang, Yiliu Li, Nicholas Olsen

Exfoliation and stacking of two-dimensional (2D) van der Waals (vdW) crystals have created unprecedented opportunities in the discovery of quantum phases. A major obstacle to the advancement of this field is the limited spectroscopic access due to a mismatch in sample sizes (1 - 10 micrometer) and wavelengths (0.1 - 1 millimeter) of electromagnetic radiation

Gyanendra Chaubey, Aiman Farooq, Azad Singh, Deepak Mishra

The recovery of damaged or resected ribcage structures requires precise, custom-designed implants to restore the integrity and functionality of the thoracic cavity. Traditional implant design methods rely mainly on manual processes, making them time-consuming and susceptible to variability. In this work, we explore the feasibility of automated ribcage implan

Samrith Ram

Bender, Coley, Robbins and Rumsey posed the problem of counting the number of subspaces which have a given profile with respect to a linear endomorphism defined on a finite vector space. Several special cases of this problem have been solved in the literature. We settle this problem in full generality by giving an explicit counting formula in terms of symmet

Andrzej Czygrinow, Xiaofan Yuan

Let $G = (V, E)$ be a graph on $n$ vertices, and let $c: E \to P$, where $P$ is a set of colors. Let $\delta^c(G) = \min_{v \in V} \{ d^{c}(v) \}$ where $d^c(v)$ is the number of colors on edges incident to a vertex $v$ of $G$. In 2011, Fujita and Magnant showed that if $G$ is a graph on $n$ vertices that satisfies $\delta^c(G)\geq n/2$, then for every two v

Moon-Jin Kang, Bongsuk Kwon, Wanyong Shim

We study the stability of shock profiles in one spatial dimension for the isothermal Navier-Stokes-Poisson (NSP) system, which describes the dynamics of ions in a collision-dominated plasma. The NSP system admits a one-parameter family of smooth traveling waves, called shock profiles, for a given far-field condition satisfying the Lax entropy condition. In t

Ishita Agarwal, Taylor L. Patti, Rodrigo Araiza Bravo, Susanne F. Yelin

Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML). At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation. Canonically, a QP features $N$ input qu

Harshit Kothari, James R. Luedtke

Sample average approximation (SAA) is a technique for obtaining approximate solutions to stochastic programs that uses the average from a random sample to approximate the expected value that is being optimized. Since the outcome from solving an SAA is random, statistical estimates on the optimal value of the true problem can be obtained by solving multiple S

Stefan Henneking, Jacob Grosek, Leszek Demkowicz

This article presents an ultraweak discontinuous Petrov-Galerkin (DPG) formulation of the time-harmonic Maxwell equations for the vectorial envelope of the electromagnetic field in a weakly-guiding multi-mode fiber waveguide. This formulation is derived using an envelope ansatz for the vector-valued electric and magnetic field components, factoring out an os

J. S. Muthmaina

This study addresses the persistent challenges of Workplace Gender Equality (WGE) in Indonesia, examining regional disparities in gender empowerment and inequality through the Gender Empowerment Index (IDG) and Gender Inequality Index (IKG). Despite Indonesia's economic growth and incremental progress in gender equality, as indicated by improvements in the I

Isaac Stone, Nathan R. Sturtevant, Jonathan Schaeffer

Retrograde analysis is used in game-playing programs to solve states at the end of a game, working backwards toward the start of the game. The algorithm iterates through and computes the perfect-play value for as many states as resources allow. We introduce setrograde analysis which achieves the same results by operating on sets of states that have the same

Saulo V. Moreira, Marco Radaelli, Alessandro Candeloro, Felix C. Binder

Thermodynamic (TUR) and kinetic (KUR) uncertainty relations are fundamental bounds constraining the fluctuations of current observables in classical, non-equilibrium systems. Several works have verified, however, violations of these classical bounds in open quantum systems, motivating the derivation of new quantum TURs and KURs that account for the role of q

Maria Esteban

In this paper we address the reverse isoperimetric inequality for convex bodies with uniform curvature constraints in the hyperbolic plane $\mathbb{H}^2$. We prove that the\textit{ thick $\lambda$-sausage} body, that is, the convex domain bounded by two equal circular arcs of curvature $\lambda$ and two equal arcs of hypercircle of curvature $1 / \lambda$, i

Josh Brown, Yutong Bu, Zachary Cheesman, Benjamin Orman

In this research, we examine the capabilities of different mathematical models to accurately predict various levels of the English football pyramid. Existing work has largely focused on top-level play in European leagues; however, our work analyzes teams throughout the entire English Football League system. We modeled team performance using weighted Colley a

Tobias Hartung, Stephan Schuster, Joachim von Zanthier, Karl Jansen

We propose a quantum error mitigation scheme for single-qubit measurement errors, particularly suited for one-way quantum computation. Contrary to well established error mitigation methods for circuit-based quantum computation that require the circuits to be run several times, our method is capable of mitigating measurement errors in real-time, during the pr

S. Matsukiyo, K. Oshida, S. Isayama, R. Yamazaki

Mechanism of generating collisionless shock in magnetized gas plasma driven by laser-ablated target plasma is investigated by using one-dimensional full particle-in-cell simulation. The effect of finite injection time of target plasma, mimicking finite width of laser pulse, is taken into account. It was found that the formation of a seed-shock requires a rec

Abigail Devereaux, Roger Koppl

Economists model knowledge use and acquisition as a cause-and-effect calculus associating observations made by a decision-maker about their world with possible underlying causes. Knowledge models are well-established for static contexts, but not for contexts of innovative and unbounded change. We develop a representation of knowledge use and acquisition in o

Davi Costa, Clay Córdova, Michele Del Zotto, Dan Freed

Global Categorical Symmetries are a powerful new tool for analyzing quantum field theories. This volume compiles lecture notes from the 2022 and 2023 summer schools on Global Categorical Symmetries, held at the Perimeter Institute for Theoretical Physics and at the Swiss Map Research Station in Les Diableret. Specifically, this volume collects the lectures:

Jonathan Regan, Marios Kalomenopoulos, Kelly Kosmo O'Neil

The formation of supermassive black holes (SMBHs) in the early Universe is a subject of significant debate. In this study, we examine whether non-evaporating primordial black holes (PBHs) can offer a solution. We establish initial constraints on the range of PBH masses that correspond to Hawking radiation (HR) effective temperatures in the range needed to su

Emmanouil Nikolakakis, Joann Ching, Emmanouil Karystinaios, Gabrielle Sipin

Music therapy has been shown in recent years to provide multiple health benefits related to emotional wellness. In turn, maintaining a healthy emotional state has proven to be effective for patients undergoing treatment, such as Parkinson's patients or patients suffering from stress and anxiety. We propose fine-tuning MusicGen, a music-generating transformer

Said Boulite, Abdellatif Elgrou, Lahcen Maniar

We prove the null controllability of a cascade system of \(n\) coupled backward stochastic parabolic equations involving both reaction and convection terms, as well as general second-order parabolic operators, with \(n \geq 2\). To achieve this, we apply a single distributed control to the first equation, while the other equations are controlled through the

S. Matsukiyo, Y. Matsumoto

Injection process of pickup ion acceleration at a heliospheric termination shock is investigated. Using two-dimensional fully kinetic particle-in-cell simulation, accelerated pickup ions are self-consistently reproduced by tracking long time evolution of shock with unprecedentedly large system size in the shock normal direction. Reflected pickup ions drive u

Mariusz Wisniewski, Zeeshan A. Rana, Ivan Petrunin, Alan Holt

Drone detection has benefited from improvements in deep neural networks, but like many other applications, suffers from the availability of accurate data for training. Synthetic data provides a potential for low-cost data generation and has been shown to improve data availability and quality. However, models trained on synthetic datasets need to prove their

Jiseong Kim

In this paper, we study the sum of the divisor function over sets with digit restrictions.

Brice Huang, Sidhanth Mohanty, Amit Rajaraman, David X. Wu

There has been a recent surge of powerful tools to show rapid mixing of Markov chains, via functional inequalities such as Poincar\'e inequalities. In many situations, Markov chains fail to mix rapidly from a worst-case initialization, yet are expected to approximately sample from a random initialization. For example, this occurs if the target distribution h

Kenny Peng

We consider equilibrium one-on-one conversations between neighbors on a circular table, with the goal of assessing the likelihood of a (perhaps) familiar situation: sitting at a table where both of your neighbors are talking to someone else. When $n$ people in a circle randomly prefer their left or right neighbor, we show that the probability a given person

Chris Jennings-Shaffer, David H Rich, Matthew Macaulay, Michael D Karcher

Bayesian phylogenetics typically estimates a posterior distribution, or aspects thereof, using Markov chain Monte Carlo methods. These methods integrate over tree space by applying local rearrangements to move a tree through its space as a random walk. Previous work explored the possibility of replacing this random walk with a systematic search, but was quic

Wenbo Zhang, Aditya Majumdar, Amulya Yadav

Large Language Models (LLMs) have demonstrated remarkable capabilities across various NLP tasks but struggle with code-mixed (or code-switched) language understanding. For example, prior work benchmarking the performance of multilingual LLMs on code-mixed translation tasks has demonstrated that current state-of-the-art multilingual LLMs are ineffective in de

Sanggeon Yun, Ryozo Masukawa, William Youngwoo Chung, Minhyoung Na

The increasing demand for robust security solutions across various industries has made Video Anomaly Detection (VAD) a critical task in applications such as intelligent surveillance, evidence investigation, and violence detection. Traditional approaches to VAD often rely on finetuning large pre-trained models, which can be computationally expensive and impra

L. Alborghetti, B. Bertoni, L. Vicarelli, S. Zanotto

A common approach to detecting weak signals or minute quantities involves leveraging the localized spectral features of resonant modes, whose sharper lines (i.e. high Q-factors) enhance transduction sensitivity. However, maximizing the Q-factor often introduces technical challenges in fabrication and design. In this work, we propose an alternative strategy t

Xu Wang, Mi-Xiang Lan, Qing-Wen Tang, Xue-Feng Wu

The observed twice abrupt ${90}^{\circ}$ rotations of the polarization angle (PA) in the prompt phase of gamma-ray bursts (GRBs) are difficult to be understandable within the current one-emitting-shell models. Here, we apply a model with multiple emitting shells to solve this new challenging problem. Two configurations of large-scale ordered magnetic fields

Eduard Schesler, Rachel Skipper, Xiaolei Wu

We provide a family of generating sets $S_{\alpha}$ of the Higman--Thompson groups $V_n$ that are parametrized by certain sequences $\alpha$ of elements in $V_n$. These generating sets consist of $3$ involutions $\sigma$, $\tau$, and $s_{\alpha}$, where the latter involution is inspired by the class of spinal elements in the theory of branch groups. In parti

Utsav Dutta, Yifan Lin, Zhaoyang Larry Jin

This paper proposes a novel reinforcement learning framework to address the Liner Shipping Network Design Problem (LSNDP), a challenging combinatorial optimization problem focused on designing cost-efficient maritime shipping routes. Traditional methods for solving the LSNDP typically involve decomposing the problem into sub-problems, such as network design

Tong Liu

In Anderson localization, eigenstates of disordered quantum systems are broadly classified as extended, localized, or critical. Although critical states exhibit multifractal character, a precise and operational criterion for their identification remains an open challenge, as Lyapunov exponents in real space cannot uniquely distinguish them from extended stat

Ross Cutler, Babak Naderi, Vishak Gopal, Dharmendar Palle

Photorealistic avatars are human avatars that look, move, and talk like real people. The performance of photorealistic avatars has significantly improved recently based on objective metrics such as PSNR, SSIM, LPIPS, FID, and FVD. However, recent photorealistic avatar publications do not provide subjective tests of the avatars to measure human usability fact

Shiyu Wang, Hao Ding, Yupeng Gu, Sergul Aydore

The growth of recommender systems (RecSys) is driven by digitization and the need for personalized content in areas such as e-commerce and video streaming. The content in these systems often changes rapidly and therefore they constantly face the ongoing cold-start problem, where new items lack interaction data and are hard to value. Existing solutions for th

Jongmin Mun, Seungwoo Kwak, Ilmun Kim

We explore the trade-off between privacy and statistical utility in private two-sample testing under local differential privacy (LDP) for both multinomial and continuous data. We begin by addressing the multinomial case, where we introduce private permutation tests using practical privacy mechanisms such as Laplace, discrete Laplace, and Google's RAPPOR. We

Steve Fan, Paul Pollack

For an atomic domain $D$, the $elasticity$ $\rho(D)$ of $D$ is defined as $\sup\{r/s: \pi_1\cdots \pi_r = \rho_1 \cdots \rho_s,~ \text{where each $\pi_i, \rho_j$ is irreducible}\}$; the elasticity provides a concrete measure of the failure of unique factorization in $D$. Fix a quadratic number field $K$ with discriminant $\Delta_K$, and for each positive int

Nazanin Mahjourian, Vinh Nguyen

Manufacturing requires reliable object detection methods for precise picking and handling of diverse types of manufacturing parts and components. Traditional object detection methods utilize either only 2D images from cameras or 3D data from lidars or similar 3D sensors. However, each of these sensors have weaknesses and limitations. Cameras do not have dept

Christian Rosendal, Jenna Zomback

We define a notion of asymptotically spherical topological groups, which says that spheres of large radius with respect to any maximal length function are still spherical with respect to any other maximal length function. This is a strengthening of a related condition introduced by Sebastian Hurtado, which we call bounded eccentricity. Our main result is a p

Michael Filaseta, Florian Luca

We show that if $n\ge n_0$, $b\ge 2$ are integers, $p\ge 7$ is prime and $n!-(b^p-1)/(b-1)\ge 0$, then $n!-(b^p-1)/(b-1) \ge 0.5\log\log n/\log\log\log n$. Further results are obtained, in particular for the case $n!-(b^p-1)/(b-1) < 0$.

Sepideh Mahabadi, Mohammad Roghani, Jakub Tarnawski, Ali Vakilian

We study the metric Steiner tree problem in the sublinear query model. In this problem, for a set of $n$ points $V$ in a metric space given to us by means of query access to an $n\times n$ matrix $w$, and a set of terminals $T\subseteq V$, the goal is to find the minimum-weight subset of the edges that connects all the terminal vertices. Recently, Chen, Khan

Alex Iosevich, Azita Mayeli, Emmett Wyman

The purpose of this paper is to develop a Fourier uncertainty principle on compact Riemannian manifolds and contrast the underlying ideas with those arising in the setting of locally compact abelian groups. The key obstacle is the growth of eigenfunctions, and connections to Bourgain's celebrated $\Lambda_q$ theorem are discussed in this context.

Quan Zhou

Ensuring fairness has emerged as one of the primary concerns in AI and its related algorithms. Over time, the field of machine learning fairness has evolved to address these issues. This paper provides an extensive overview of this field and introduces two formal frameworks to tackle open questions in machine learning fairness. In one framework, operator-val

Akhil Singampalli, Danish Gufran, Sudeep Pasricha

Machine learning (ML) based indoor localization solutions are critical for many emerging applications, yet their efficacy is often compromised by hardware/software variations across mobile devices (i.e., device heterogeneity) and the threat of ML data poisoning attacks. Conventional methods aimed at countering these challenges show limited resilience to the

Pranav Kantroo, Günter P. Wagner, Benjamin B. Machta

The structure and function of a protein are determined by its amino acid sequence. While random mutations change a protein's sequence, evolutionary forces shape its structural fold and biological activity. Studies have shown that neutral networks can connect a local region of sequence space by single residue mutations that preserve viability. However, the la

Patrick Steinert, Jan Mischkies, Stefan Wagenpfeil, Ingo Frommholz

Metaverse Recordings (MVRs) represent an emerging and underexplored media type within the field of Multimedia Information Retrieval (MMIR). This paper presents findings from a field study aimed at understanding the users information needs and search behaviors specific to MVR retrieval. By conducting and analyzing expert interviews, the study identifies appli

Sanjay Haresh, Daniel Dijkman, Apratim Bhattacharyya, Roland Memisevic

Robotics tasks are highly compositional by nature. For example, to perform a high-level task like cleaning the table a robot must employ low-level capabilities of moving the effectors to the objects on the table, pick them up and then move them off the table one-by-one, while re-evaluating the consequently dynamic scenario in the process. Given that large vi

Lanyin Luo, Xueting Tang, Junhee Park, Chih-Wei Wang

Cooperative emission of photons from an ensemble of quantum dots (QDs) as superradiance can arise from the electronically coupled QDs with a coherent emitting excited state. This contrasts with superfluorescence (Dicke superradiance), where the cooperative photon emission occurs via a spontaneous buildup of coherence in an ensemble of incoherently excited QD

Ralph C. Bohlin, Susana Deustua, Gautham Narayan, Abhijit Saha

Fainter standard stars are essential for the calibration of larger telescopes. This work adds to the CALSPEC (calibration spectra) database 19 faint white dwarfs (WDs) with all-sky coverage and V magnitudes between 16.5 and 18.7. Included for these stars is new UV (ultraviolet) HST (Hubble Space Telescope) STIS (Space Telescope Imaging Spectrometer) spectrop

Tom Benhamou, Dima Sinapova

Combining stationary reflection (a compactness property) with the failure of SCH (an instance of non-compactness) has been a long-standing theme. We obtain this at $\aleph_{\omega_1}$, answering a question of Ben-Neria, Hayut, and Unger: We prove from the existence of uncountably many supercompact cardinals the consistency of $\aleph_{\omega_1}$ is strong li

Mohammad Saiful Islam, Mohamed Sami Rakha, William Pourmajidi, Janakan Sivaloganathan

As Large-Scale Cloud Systems (LCS) become increasingly complex, effective anomaly detection is critical for ensuring system reliability and performance. However, there is a shortage of large-scale, real-world datasets available for benchmarking anomaly detection methods. To address this gap, we introduce a new high-dimensional dataset from IBM Cloud, collect

Alberto Coccarelli, Ioannis Polydoros, Alex Drysdale, Osama F. Harraz

Cerebral autoregulation plays a key physiological role by limiting blood flow changes in the face of pressure fluctuations. Although the involved cellular processes are mechanically driven, the quantification of haemodynamic forces in in-vivo settings remains extremely difficult and uncertain. In this work, we propose a novel computational framework for eval

Hanieh Moradi, Yas Vaseghi, Arash Abbasi Larki, Akram Shojaei

The objective assessment of gait kinematics is crucial in evaluating human movement, informing clinical decisions, and advancing rehabilitation and assistive technologies. Assessing gait symmetry, in particular, holds significant importance in clinical rehabilitation, as it reflects the intricate coordination between nerves and muscles during human walking.

C. Ng, A. Pandhi, R. Mckinven, A. P. Curtin

As part of the Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) project, we report 41 new Rotation Measures (RMs) from 20 repeating Fast Radio Bursts (FRBs) obtained between 2019 and 2023 for which no previous RM was determined. We also report 22 additional RM measurements for eight further repeating FRBs. We observe temporal RM va

Klaus Ziegler

In this paper we study localized states in a monitored evolution on a finite graph and how they are distinguished from the delocalized states in terms of the transition probabilities and the mean transition times. Monitoring is performed by repeated projective measurements with respect to a single quantum state. Our constructive approach is based on a mappin

Wolfgang Bietenholz

On August 4 this year, Tsung-Dao Lee, a renowned theoretical physicist of Chinese origin, passed away at the age of 97. His most famous discovery dates back to 1956, when -- together with Chen-Ning Yang -- he postulated that parity symmetry might be broken by the weak interaction. They suggested experimental tests of this revolutionary idea, which were condu

Andrea Addazi, Salvatore Capozziello, Qingyu Gan, Gaetano Lambiase

Two notable anomalies in radio observations -- the excess radiation in the Rayleigh-Jeans tail of the cosmic microwave background, revealed by ARCADE2, and the twice-deeper absorption trough of the global 21cm line, identified by EDGES -- remain unresolved. These phenomena may have a shared origin, as the enhancement of the 21cm absorption trough could arise

Xin Jin

We compute the rational cohomology of the universal centralizer $J_G$ (also known as the Toda system or BFM space) for a complex (connected) semisimple group $G$ of adjoint form. While $J_G$ exhibits interesting and increasingly complex topology as the rank of $G$ rises, its rational cohomology is surprisingly simple--it coincides with that of a point. In a

Howard S. Cohl, Roberto S. Costas-Santos, Xiang-Sheng Wang

In this survey we summarize the current state of known orthogonality relations for the $q$ and $q^{-1}$-symmetric and dual subfamilies of the Askey--Wilson polynomials in the $q$-Askey scheme. These polynomials are the continuous dual $q$ and $q^{-1}$-Hahn polynomials, the $q$ and $q^{-1}$-Al-Salam--Chihara polynomials, the continuous big $q$ and $q^{-1}$-He

Arghadip Koner, Joel Yuen-Zhou

Linear spectra of molecular polaritons formed by $N$ molecules coupled to a microcavity photon mode are usually well described by classical linear optics, raising the question of where the expected nonlinear effects in these strongly coupled systems are. In this work, we derive a general expression for the polaritonic linear spectra that reveal previously ov

Muyang Ye, Tianrui Xia, Tianxin Zu, Qian Wang

The Max-Flow/Min-Cut problem is a fundamental tool in graph theory, with applications in many domains, including data mining, image segmentation, transportation planning, and many types of assignment problems, in addition to being an essential building block for many other algorithms. The Ford-Fulkerson Algorithm for Max-Flow/Min-Cut and its variants are the

Abhishek Arora, Benjamin M. Ward, Caglar Oskay

This manuscript presents the Quantum Finite Element Method (Q-FEM) developed for use in noisy intermediate-scale quantum (NISQ) computers and employs the variational quantum linear solver (VQLS) algorithm. The proposed method leverages the classical FEM procedure to perform the unitary decomposition of the stiffness matrix and employs generator functions to

Uros Zivanovic, Ivan Pilkov, Carlos Eduardo Cancino-Chacón

Visual piano transcription (VPT) is the task of obtaining a symbolic representation of a piano performance from visual information alone (e.g., from a top-down video of the piano keyboard). In this work we propose a VPT system based on the vision transformer (ViT), which surpasses previous methods based on convolutional neural networks (CNNs). Our system is

José Nogueira, Carlos Vieira, Marcelo Terra Cunha

This work explores the implications of the exclusivity principle (EP) in the context of quantum and postquantum correlations. We first establish a key technical result demonstrating that given the set of correlations for a complementary experiment, the EP restricts the maximum set of correlations for the original experiment to the antiblocking set. Based on

Jonah R. Adelman, Hugo Laurell, Lauren B. Drescher, Han K. D. Le

The narrow bandgap semiconductor elemental tellurium (Te) has a unique electronic structure due to strong spin-orbit splitting and a lack of inversion symmetry of it's helical lattice. Using broadband extreme ultraviolet core-level transient absorption, we measure simultaneously the coherently coupled photo-induced carrier and lattice dynamics at the Te N$_{

Beniamin Goldys, Agus L. Soenjaya, Thanh Tran

We study the long-time behaviour of solutions to some classes of fourth-order nonlinear PDEs with non-monotone nonlinearities, which include the Landau--Lifshitz--Baryakhtar (LLBar) equation (with all relevant fields and spin torques) and the convective Cahn--Hilliard/Allen--Cahn (CH-AC) equation with a proliferation term, in dimensions $d=1,2,3$. Firstly, w

K. Telikova, J. González-López, M. Aravena, A. Posses

The history of gas assembly in early galaxies is reflected in their complex kinematics. While a considerable fraction of galaxies at z~5 are consistent with rotating disks, current studies indicate that the dominant galaxy assembly mechanism corresponds to mergers. Despite the important progress, the dynamical classification of galaxies at these epochs is st

Carlos Joaquin, Miguel Alcubierre

In this paper we consider families of solutions for excited states of Proca stars in spherical symmetry. We focus on the first two excited configurations and perform a series of fully non-linear dynamical simulations in order to study their properties and stability. Our analysis reveals that excited Proca stars are always unstable against even very small per

I. M. Cota-Martínez, R. López Antón, A. M. Garay-Tapia, J. A. Matutes-Aquino

Hexagonal antiferromagnetic D019-Mn3X (X = Sn, Ge, Ga) compounds, with a non-collinear Kagome spin structure, are Weyl semimetals exhibiting novel topological transport properties. The longitudinal magnetoresistance of c-oriented epitaxial Ru/Mn3Ga thin films exhibits a positive quadratic dependence on magnetic field over a wide range of temperatures. Here w

Takeshi Miki, Chun-Wei Chang, Po-Ju Ke, Arndt Telschow

Quantifying interaction strengths between state variables in dynamical systems is essential for understanding ecological networks. Within the empirical dynamic modeling approach, multivariate S-map infers the interaction Jacobian from time series data without assuming specific dynamical models. This approach enables the non-parametric statistical inference o

Rusdrael Antony de Araújo Freire, Francisco Márcio Barboza, Márcio Matheus de Lima Barboza

This work investigates the application of the Newton's method for the numerical solution of a nonlinear boundary value problem formulated through an ordinary differential equation (ODE). Nonlinear ODEs arise in various mathematical modeling contexts, where an exact solution is often unfeasible due to the intrinsic complexity of these equations. Thus, a numer

Andrei O. Barvinsky, Alexander V. Kurov, Sergey M. Sibiryakov

We report a comprehensive numerical study of the renormalization group flow of marginal couplings in $(3+1)$-dimensional projectable Ho\v{r}ava gravity. First, we classify all fixed points of the flow and analyze their stability matrices. We find that some of the stability matrices possess complex eigenvalues and discuss why this does not contradict unitarit

Jon Harrison, Clare Pruss

A discrete analog of quantum unique ergodicity was proved for Cayley graphs of quasirandom groups by Magee, Thomas and Zhao. They show that for large graphs there exist real orthonormal basis of eigenfunctions of the adjacency matrix such that quantum probability measures of the eigenfunctions put approximately the correct proportion of their mass on subsets

Soham Gadgil, Joshua Galanter, Mohammadreza Negahdar

Chronic Obstructive Pulmonary Disorder (COPD) is an irreversible and progressive disease which is highly heritable. Clinically, COPD is defined using the summary measures derived from a spirometry test but these are not always adequate. Here we show that using the high-dimensional raw spirogram can provide a richer signal compared to just using the summary m

Leah London Arazi, Amir Shpilka

This paper studies the hazard-free formula complexity of Boolean functions. Our first result shows that unate functions are the only Boolean functions for which the monotone formula complexity of the hazard-derivative equals the hazard-free formula complexity of the function itself. Consequently, they are the only functions for which the hazard-derivative ap

Jacob Englert, Stefanie Ebelt, Howard Chang

Studying the association between mixtures of environmental exposures and health outcomes can be challenging due to issues such as correlation among the exposures and non-linearities or interactions in the exposure-response function. For this reason, one common strategy is to fit flexible nonparametric models to capture the true exposure-response surface. How

Dong-Hoon Kim

We consider a situation in which light emitted from the neighborhood of a binary interacts with gravitational waves from the binary (e.g., a supermassive black hole binary in a quasar, a binary pulsar, etc.). The effect is cumulative over the long path lengths of light propagation and might be appreciable if the interaction initially takes place close to the

Xuming Zhang, Xingfa Gu, Qingjiu Tian, Lorenzo Bruzzone

Improving hyperspectral image (HSI) semantic segmentation by exploiting complementary information from a supplementary data type (referred to X-modality) is promising but challenging due to differences in imaging sensors, image content, and resolution. Current techniques struggle to enhance modality-specific and modality-shared information, as well as to cap

Yongdong Wang, Runze Xiao, Jun Younes Louhi Kasahara, Ryosuke Yajima

Large Language Models (LLMs) have demonstrated promising reasoning capabilities in robotics; however, their application in multi-robot systems remains limited, particularly in handling task dependencies. This paper introduces DART-LLM, a novel framework that employs Directed Acyclic Graphs (DAGs) to model task dependencies, enabling the decomposition of natu

Pierre Coulombel, Fabian Denner

We propose a low-dimensional modeling approach to simulate the dynamics, acoustic emissions and interactions of cavitation bubbles, based on a quasi-acoustic assumption. This quasi-acoustic assumption accounts for the compressibility of the medium surrounding the bubble and its finite speed of sound, whereby the potential of the acoustic wave emitted by the

Anirvan Dutta, Etienne Burdet, Mohsen Kaboli

Interactive exploration of the unknown physical properties of objects such as stiffness, mass, center of mass, friction coefficient, and shape is crucial for autonomous robotic systems operating continuously in unstructured environments. Precise identification of these properties is essential to manipulate objects in a stable and controlled way, and is also

Xihang Shi, Wen Wei Lee, Aviv Karnieli, Leon Merten Lohse

Rapid progress in precision nanofabrication and atomic design over the past 50 years has ushered in a succession of transformative eras for molding the generation and flow of light. The use of nanoscale and atomic features to design light sources and optical elements-encapsulated by the term nanophotonics-has led to new fundamental science and innovative tec

Moran Yanuka, Assaf Ben Kish, Yonatan Bitton, Idan Szpektor

Recent research increasingly focuses on training vision-language models (VLMs) with long, detailed image captions. However, small-scale VLMs often struggle to balance the richness of these captions with the risk of hallucinating content during fine-tuning. In this paper, we explore how well VLMs adapt to such captions. To quantify caption quality, we propose

Eric R. Morenz, Charles J. Wolock, Marco Carone

Learning causal effects of a binary exposure on time-to-event endpoints can be challenging because survival times may be partially observed due to censoring and systematically biased due to truncation. In this work, we present debiased machine learning-based nonparametric estimators of the joint distribution of a counterfactual survival time and baseline cov