March 2025 arXiv papers — page 2

Huanshu Zhang, Lei Kang, Sawyer D. Campbell, Kaishun Zhang

The traditional design approaches for high-degree-of-freedom metamaterials have been computationally intensive and, in many cases, even intractable due to the vast design space. In this work, we introduce a novel fixed-attention mechanism into a deep learning framework to address the computational challenges of metamaterial design. We consider a 3D plasmonic

Dionysios Kokkinos, Taxiarchis Papakostas

This work follows earlier investigations in which the existence of canonical Killing tensor forms and the application of general null tetrad transformations led to a variety of solutions, Petrov types D, III, N, in vacuum with a cosmological constant. Among those, a distinct Petrov type D family was extracted and characterized by a topological product of two

Yukiyoshi Nakkajima

For a family of log points with constant log structure and for a proper SNCL scheme with an SNCD over the family, we construct a fundamental l-adic bifiltered complex as a geometric application of the theory of the derived category of (bi)filtered complexes in our papers. By using this bifiltered complex, we give the formulation of the log l-adic relative mo

Savinay Nagendra, Kashif Rashid

The deployment of a continuous methane monitoring system requires determining the optimal number and placement of fixed sensors. However, planning is labor-intensive, requiring extensive site setup and iteration to meet client restrictions. This challenge is amplified when evaluating multiple sites, limiting scalability. To address this, we introduce SmartSc

Prateek Agrawal, Gaurang Ramakant Kane, Vazha Loladze, Mario Reig

We study general properties of confinement phase transitions in the early universe. An observable gravitational wave signal from such transitions requires significant supercooling. However, in almost all understood examples of confining gauge theories the degree of supercooling is too small to give interesting gravitational wave signals. We review and highli

Osvaldo M. Moreschi

The detections\citep{KAGRA:2023pio,LIGOScientific:2019lzm} and analysis of gravitational waves(GWs) have introduced us in a new era of our understanding of the cosmos, providing new insights into astrophysical systems involving massive objects as black holes and neutron stars. Normally the precise sky localization of a GW source needs data from three or more

Baptiste Gros, Jorge L. Ramirez Alfonsin

In this paper, we introduce the notion of strong geometry, a structure composed by both the chirotope of a set of points X in the d-dimensional space and the wedge chirotope which is the specific adjoint chirotope induced by the hyperplanes spanned by X. We present various properties relating these two chirotopes, for instance, by introducing the witness chi

Lukas Schwenkel, Johannes Köhler, Matthias A. Müller, Frank Allgöwer

In this work, we propose an output-feedback tube-based model predictive control (MPC) scheme for linear systems under dynamic uncertainties that are described via integral quadratic constraints (IQC). By leveraging IQCs, a large class of nonlinear and dynamic uncertainties can be addressed. We leverage recent IQC synthesis tools to design a dynamic controlle

John J. Felice, Ronak Desai, Nathaniel Tamminga, Joseph R. Smith

Advances in ultra-intense laser technology have increased repetition rates and average power for chirped-pulse laser systems, which offers a promising solution for many applications including energetic proton sources. An important challenge is the need to optimize and control the proton source by varying some of the many degrees of freedom inherent to the la

Brianna Chrisman, Lucius Bushnaq, Lee Sharkey

Much of mechanistic interpretability has focused on understanding the activation spaces of large neural networks. However, activation space-based approaches reveal little about the underlying circuitry used to compute features. To better understand the circuits employed by models, we introduce a new decomposition method called Local Loss Landscape Decomposit

J. A. A. S. Reis, L. Lisboa-Santos, Fabiano M. Andrade, Frankbelson dos S. Azevedo

Fermions in two-dimensional space, commonly called $(1+2)$-dimensional fermions, exhibit intriguing and distinctive characteristics that distinguish them from their higher-dimensional counterparts. This paper offers a comprehensive theoretical examination of planar fermionic systems, presenting novel findings by incorporating nonminimal coupling. Our analysi

Vincenzo Galluzzi, Meriem Behiri, Marika Giulietti, Andrea Lapi

We present an updated repository of sub-mJy extragalactic radio source counts between $150$ MHz and $10$ GHz, incorporating recent advances in radio surveys and observational techniques. By compiling and refining previous datasets, we provide a comprehensive catalog that enhances the understanding of faint radio-source populations, including Dusty Star-Formi

Lin Zhao, Xin Yu, Yikang Liu, Xiao Chen

Accurate correspondence matching in coronary angiography images is crucial for reconstructing 3D coronary artery structures, which is essential for precise diagnosis and treatment planning of coronary artery disease (CAD). Traditional matching methods for natural images often fail to generalize to X-ray images due to inherent differences such as lack of text

Hongjie Dong, Dong-ha Kim, Seick Kim

This paper investigates the Dirichlet problem for a non-divergence form elliptic operator $L$ in a bounded domain of $\mathbb{R}^2$. Assuming that the principal coefficients satisfy the Dini mean oscillation condition, we establish the equivalence between regular points for $L$ and those for the Laplace operator. This result closes a gap left in the authors'

Ahmed M. Taha, Salah A. Aly, Mohamed F. Darwish

Accurate and quick diagnosis of normal brain tissue Glioma, Meningioma, and Pituitary Tumors is crucial for optimal treatment planning and improved medical results. Magnetic Resonance Imaging (MRI) is widely used as a non-invasive diagnostic tool for detecting brain abnormalities, including tumors. However, manual interpretation of MRI scans is often time-co

Pouya Pezeshkpour, Estevam Hruschka

Retrieval Augmented Generation (RAG) frameworks have shown significant promise in leveraging external knowledge to enhance the performance of large language models (LLMs). However, conventional RAG methods often retrieve documents based solely on surface-level relevance, leading to many issues: they may overlook deeply buried information within individual do

Olawale Salaudeen, Nicole Chiou, Shiny Weng, Sanmi Koyejo

Spurious correlations, unstable statistical shortcuts a model can exploit, are expected to degrade performance out-of-distribution (OOD). However, across many popular OOD generalization benchmarks, vanilla empirical risk minimization (ERM) often achieves the highest OOD accuracy. Moreover, gains in in-distribution accuracy generally improve OOD accuracy, a p

Atharva Sehgal, Patrick Yuan, Ziniu Hu, Yisong Yue

We study the problem of building a visual concept library for visual recognition. Building effective visual concept libraries is challenging, as manual definition is labor-intensive, while relying solely on LLMs for concept generation can result in concepts that lack discriminative power or fail to account for the complex interactions between them. Our appro

Natalie Priebe Frank, May Mei, Kitty Yang

Infinite sequences are of tremendous theoretical and practical importance, and in the Information Age sequences of 0s and 1s are of particular interest. Over the past century, the field of symbolic dynamics has developed to study sequences with entries from a finite set. In this paper we will show you an interesting method for constructing sequences, and the

Peter Jenniskens, Gerardo J. Soto, Gabriel Goncalves Silva, Oscar Lücke

The Aguas Zarcas (Costa Rica) CM2 carbonaceous chondrite fell during night time in April 2019. Security and dashboard camera video of the meteor were analyzed to provide a trajectory, lightcurve, and orbit of the meteoroid. The trajectory was near vertical, 81{\deg} steep, arriving from an ~109{\deg} (WNW) direction with apparent entry speed of 14.6 +/- 0.6

Jakob Reiffenstein, Harald Woracek

In this survey paper we review classical results and recent progress about a certain topic in the spectral theory of two-dimensional canonical systems. Namely, we consider the questions whether the spectrum $\sigma$ is discrete, and if it is, what is its density. Here we measure density by the growth of the counting function of $\sigma$ in the sense of integ

Zhaolin Wang, Chongjun Ouyang, Yuanwei Liu

An efficient beamforming design is proposed for continuous aperture array (CAPA)-based point-to-point multiple-input multiple-output (MIMO) systems. In contrast to conventional spatially discrete array (SPDA)-MIMO systems, whose optimal beamforming can be obtained using singular-value decomposition, CAPA-MIMO systems require solving the eigendecomposition of

Vignesh Gokul, Srikanth Tenneti, Alwarappan Nakkiran

Retrieval Augmented Generation (RAG) systems have emerged as a powerful method for enhancing large language models (LLMs) with up-to-date information. However, the retrieval step in RAG can sometimes surface documents containing contradictory information, particularly in rapidly evolving domains such as news. These contradictions can significantly impact the

Craig W. Schmidt, Varshini Reddy, Chris Tanner, Yuval Pinter

Pre-tokenization, the initial step in many modern tokenization pipelines, segments text into smaller units called pretokens, typically splitting on whitespace and punctuation. While this process encourages having full, individual words as tokens, it introduces a fundamental limitation in most tokenization algorithms such as Byte Pair Encoding (BPE). Specific

Satyanath Howladar

We show that Baumslag-Solitar groups are virtually 2-avoidable, that is, they admit finite index subgroup whose first homology is devoid of $\mathbb{Z}_2$ summand. We also prove virtual 2-avoidability for some other classes of one-relator groups, which generalizes non-orientable surface groups. This along with result from a previous paper confirms Gromov's C

Marco Canducci, Lida Abdi, Alessandro Prete, Roland J. Veen

In a given classification task, the accuracy of the learner is often hampered by finiteness of the training set, high-dimensionality of the feature space and severe overlap between classes. In the context of interpretable learners, with (piecewise) linear separation boundaries, these issues can be mitigated by careful construction of optimization procedures

Cristobal Arrieta, Carlos A. Sing Long

Chemical Shift Imaging (CSI) or Chemical Shift Encoded Magnetic Resonance Imaging (CSE-MRI) enables the quantification of different chemical species in the human body, and it is one of the most widely used imaging modalities used to quantify fat in the human body. Although there have been substantial improvements in the design of signal acquisition protocols

Sijia Li, Young D. Kwon, Lik-Hang Lee, Pan Hui

Meta-Continual Learning (Meta-CL) enables models to learn new classes from limited labelled samples, making it promising for IoT applications where manual labelling is costly. However, existing studies focus on accuracy while ignoring deployment viability on resource-constrained hardware. Thus, we present MetaCLBench, a benchmark framework that evaluates Met

Anna Schwarz, Daniel Kempf, Jens Keim, Patrick Kopper

High-order methods are well-suited for the numerical simulation of complex compressible turbulent flows, but require additional stabilization techniques to capture instabilities arising from the underlying non-linear hyperbolic equations. This paper provides a detailed comparison of the effectiveness of entropy stable discontinuous Galerkin methods for the s

Jonathan H. Cohn, Emmanuel Durodola, Quinn O. Casey, Erini Lambrides

Recent observations have identified an abundance of high-redshift active galactic nuclei (AGN) with supermassive black holes (BHs) that are over-massive compared to the local BH mass$-$total stellar mass ($M_{\mathrm{BH}}-M_\star$) relation. $M_{\mathrm{BH}}$ measurements at high-$z$ are critical for probing the growth histories of BHs and their host galaxie

Hengrui Jia, Sierra Wyllie, Akram Bin Sediq, Ahmed Ibrahim

It is common practice to outsource the training of machine learning models to cloud providers. Clients who do so gain from the cloud's economies of scale, but implicitly assume trust: the server should not deviate from the client's training procedure. A malicious server may, for instance, seek to insert backdoors in the model. Detecting a backdoored model wi

Antoine Dailly, Tuomo Lehtilä

We generalize the problem of reconstructing strings from their substring compositions first introduced by Acharya et al. in 2015 motivated by polymer-based advanced data storage systems utilizing mass spectrometry. Namely, we see strings as labeled path graphs, and as such try to reconstruct labeled graphs. For a given integer t, the subgraph compositions co

Alfonso Artigue

We show that for a compact surface without boundary $M$ the set of cw-expansive homeomorphisms is dense in the set of all the homeomorphisms of $M$ with respect to the $C^0$ topology. After this we show that for a generic homeomorphism $f$ of $M$ it holds that: for all $\epsilon>0$ there is a cw-expansive homeomorphism $g$ of $M$ which is $\epsilon$-close to

Alfonso Artigue

In this article we study the dynamics of one-dimensional relativistic billiards containing particles with positive and negative energy. We study configurations with two identical positive masses and symmetric positions with two massless particles between them of negative energy and symmetric positions. We show that such systems have finitely many collisions

Qian Feng, Wei Xing Zheng, Xiaoyu Wang, Feng Xiao

This paper introduces an effective framework for designing memoryless dissipative full-state feedback for general linear delay systems via the Krasovski\u{i} functional (KF) approach, where an arbitrary finite number of pointwise and general distributed delays (DDs) exists in the state, input and output. To handle the infinite dimensionality of DDs, we emplo

Igor V. Nikolaev

It is proved that the Minkowski question-mark function comes from the K-theory of Cuntz-Pimsner algebras. We apply this result to calculate the action of Frobenius endomorphism at the infinite prime. Such a problem was raised by Serre and Deninger in the theory of local factors of zeta functions of projective varieties.

Yilin Qi, Dong Won Lee, Cynthia Breazeal, Hae Won Park

Cognitive Reframing, a core element of Cognitive Behavioral Therapy (CBT), helps individuals reinterpret negative experiences by finding positive meaning. Recent advances in Large Language Models (LLMs) have demonstrated improved performance through reasoning-based strategies. This inspires a promising direction of leveraging the reasoning capabilities of LL

Elna Svegborn, Jef Pauwels, Armin Tavakoli

We investigate prepare-and-measure scenarios in which a sender and a receiver use entanglement to send quantum information over a channel with limited capacity. We formalise this framework, identify its basic properties and provide numerical tools for optimising quantum protocols for generic communication tasks. The seminal protocol for sending quantum infor

Suzanne Stathatos, Michael Hobley, Pietro Perona, Markus Marks

Low signal-to-noise ratio videos -- such as those from underwater sonar, ultrasound, and microscopy -- pose significant challenges for computer vision models, particularly when paired clean imagery is unavailable. We present Spatiotemporal Augmentations and denoising in Video for Downstream Tasks (SAVeD), a novel self-supervised method that denoises low-SNR

Derrick M. Wang, Sebastian Cmentowski, Reza Hadi Mogavi, Kaushall Senthil Nathan

Digital games offer rich social experiences and promote valuable skills, but they fall short in addressing physical inactivity. Exergames, which combine exercise with gameplay, have the potential to tackle this issue. However, current exergames are primarily single-player or competitive. To explore the social benefits of cooperative exergaming, we designed a

Advaith V. Sethuraman, Max Rucker, Onur Bagoren, Pou-Chun Kung

In this paper, we present SonarSplat, a novel Gaussian splatting framework for imaging sonar that demonstrates realistic novel view synthesis and models acoustic streaking phenomena. Our method represents the scene as a set of 3D Gaussians with acoustic reflectance and saturation properties. We develop a novel method to efficiently rasterize Gaussians to pro

Alexandre Boistard, Laurence Carassus, Safae Issaoui

Drawing on set theory, this paper contributes to a deeper understanding of the structural condition of mathematical finance under Knightian uncertainty. We adopt a projective framework in which all components of the model -- prices, priors and trading strategies -- are treated uniformly in terms of measurability. This contrasts with the quasi-sure setting of

Ulrich Nierste

I present a consistent way to include $\eta$-$\eta^\prime$ mixing in global analyses of two-body decays of heavy hadrons employing the approximate flavour-SU(3) symmetry of QCD. The framework is applied to $D\to P \eta^\prime$ decays, where $P$ denotes a pseudoscalar meson. The result shows that flavour-SU(3) symmetry holds in the decay rates of these modes

Leo Tunkle, Kamal Abdulraheem, Linyu Lin, Majdi I. Radaideh

The economic feasibility of nuclear microreactors will depend on minimizing operating costs through advancements in autonomous control, especially when these microreactors are operating alongside other types of energy systems (e.g., renewable energy). This study explores the application of deep reinforcement learning (RL) for real-time drum control in micror

Karol Kolodziej

Multidimensional phase space integrals must be calculated in order to obtain predictions for total or differential cross sections, or to simulate unweighted events of multiparticle reactions. The corresponding matrix elements, already in the leading order, receive contributions typically from dozens of thousands of the Feynman diagrams, many of which often i

Nasrin Estaji, Ismaeil Abdolhosseini Sarsari, Gergő Thiering, Adam Gali

Two-dimensional (2D) materials hosting color centers and spin defects are emerging as key platforms for quantum technologies. However, the impact of reduced dimensionality on the spin-lattice relaxation time ($T_1$) of embedded defect spins -- critical for quantum applications -- remains largely unexplored. In this study, we present a systematic first-princi

Jaime Cesar dos Santos Filho

Building upon specific compatibility conditions, we establish fundamental structural results concerning ordering relations for triangular fuzzy numbers. We demonstrate that orders satisfying compatibility with arithmetic operations, MIN-MAX operators, and the Weak Law of Trichotomy (WLT) are completely determined on the fibers of the natural projection to re

Aristotelis Chaniotis, Hidde Koerts, Sophie Spirkl

Given $k$ graphs $G_{1}, \ldots, G_{k}$, their intersection is the graph $(\cap_{i\in [k]}V(G_{i}), \cap_{i\in [k]}E(G_{i}))$. Given $k$ graph classes $\mathcal{G}_{1}, \ldots , \mathcal{G}_{k}$, we call the class $\{G: \forall i \in[k], \exists G_{i} \in \mathcal{G}_{i} \text{ such that } G=G_{1}\cap \ldots \cap G_{k}\}$ the graph-intersection of $\mathcal{

Jan-Christoph Schlage-Puchta

Let $\mathcal{L}$ be the closure of the set of all real numbers $\alpha$, such that there exist infinitely many integers $n$, such that $\alpha=\log\frac{d(n+1)}{d(n)}$, where $d$ is the number of divisors of $n$. We give improved lower bounds for the density of $\mathcal{L}$.

Marc S. Klinger, Robert G. Leigh

In this essay, we argue that the problem of time should not be regarded as an issue to be resolved within the prevailing framework for studying quantum gravity, but rather as an indication that there is an issue within the framework itself. We suggest a possible resolution inspired by the observation that the quantization of gravity on null hypersurfaces lea

Caleb Helbling, Graham Leach-Krouse, Sam Lasser, Greg Sullivan

This paper introduces cozy, a tool for analyzing and visualizing differences between two versions of a software binary. The primary use case for cozy is validating "micropatches": small binary or assembly-level patches inserted into existing compiled binaries. To perform this task, cozy leverages the Python-based angr symbolic execution framework. Our tool a

Yongyi Shi, Ge Wang

Leveraging multi-center data for medical analytics presents challenges due to privacy concerns and data heterogeneity. While distributed approaches such as federated learning has gained traction, they remain vulnerable to privacy breaches, particularly in sensitive domains like medical imaging. Generative models, such as diffusion models, enhance privacy by

Masato Tamura

Precise action spotting has attracted considerable attention due to its promising applications. While existing methods achieve substantial performance by employing well-designed model architecture, they overlook a significant challenge: the temporal misalignment inherent in ground-truth labels. This misalignment arises when frames labeled as containing event

M. Kuźniak, S. Choudhary, S. Pawłowski, A. F. V. Cortez

Polymeric wavelength shifters are of particular interest for large liquid argon detectors. Inspired by the success of polyethylene naphthalate (PEN), other new polymers exhibiting a similar type of excimer fluorescence were investigated. We report on the preliminary results of the first cryogenic wavelength shifting test of a solution-cast film of PVN, poly(

Collin Zhang, John X. Morris, Vitaly Shmatikov

Embedding inversion, i.e., reconstructing text given its embedding and black-box access to the embedding encoder, is a fundamental problem in both NLP and security. From the NLP perspective, it helps determine how much semantic information about the input is retained in the embedding. From the security perspective, it measures how much information is leaked

Tudor-Stefan Cotet, Igor Krawczuk

Bayesian optimization (BO) has recently become more prevalent in protein engineering applications and hence has become a fruitful target of benchmarks. However, current BO comparisons often overlook real-world considerations like risk and cost constraints. In this work, we compare 72 model combinations of encodings, surrogate models, and acquisition function

Hao Cheng, Katrin Löer, Max A. N. Hendriks, Yuguang Yang

The concept of acoustoelasticity pertains to changes in elastic wave velocity within a medium when subjected to initial stresses. However, existing acoustoelastic expressions are predominantly developed for waves propagating parallel or perpendicular to the principal stress directions, where no shear stresses are involved. In our previous publication, we dem

Giulio Iannelli, Pablo Villegas, Tommaso Gili, Andrea Gabrielli

A dynamic concordia discors, a finely tuned equilibrium between opposing forces, is hypothesized to drive historical transformations. Similarly, a precise interplay of excitation and inhibition, often approximated by an 80:20 ratio, underlies the normal functionality of neural systems. In artificial neural networks, reinforcement learning enables the fine-tu

Hendrik Wulfmeier, Uliana Yakhnevych, Cornelius Boekhoff, Allan Diima

Conductive ferroelectric domain walls (DWs) represent a promising topical system for the development of nanoelectronic components and device sensors to be operational at elevated temperatures. DWs show very different properties as compared to their hosting bulk crystal, in particular with respect to the high local electrical conductivity. The objective of th

Carlos J. Costa, Joao Tiago Aparicio

This paper explores artificial intelligence's potential societal and economic impacts (AI) through generating scenarios that assess how AI may influence various sectors. We categorize and analyze key factors affecting AI's integration and adoption by applying an Impact-Uncertainty Matrix. A proposed methodology involves querying academic databases, identifyi

Srinitish Srinivasan, Omkumar CU

While hyperbolic GNNs show promise for hierarchical data, they often have limited discriminative power compared to Euclidean counterparts or the WL test, due to non-injective aggregation. To address this expressivity gap, we propose the Lorentzian Graph Isomorphic Network (LGIN), a novel HGNN designed for enhanced discrimination within the Lorentzian model.

Victor Gustafsson, Marcus Brüggen, Cyril Tasse, Torsten Enßlin

Modern radio interferometers enable high-resolution polarization imaging, offering insights into cosmic magnetism through Rotation Measure (RM) synthesis. Traditional 2+1D RM synthesis treats the 2D spatial and 1D spectral transforms separately. A fully 3D approach transforms data directly from visibility-frequency space to sky-Faraday depth space using a 3D

Ariana Muñoz, Gustavo Rubio, Sebastián Salgado

In this paper we consider the construction of a free differential algebra as an extension of the extended Bargmann algebra in arbitrary dimensions. This is achieved by introducing a new Maurer-Cartan equation for a three-form gauge multiplet in the adjoint representation of the extended Bargmann algebra. The new Maurer-Cartan equation is provided of non-triv

Yannick Burkhardt, Simon Schaefer, Stefan Leutenegger

Event-based keypoint detection and matching holds significant potential, enabling the integration of event sensors into highly optimized Visual SLAM systems developed for frame cameras over decades of research. Unfortunately, existing approaches struggle with the motion-dependent appearance of keypoints and the complex noise prevalent in event streams, resul

Maria Concepción Ausín, Maria Kalli

We introduce a novel bivariate copula model able to capture both the central and tail dependence of the joint probability distribution. Model that can capture the dependence structure within the joint tail have important implications in many application areas where the focus is risk management (e.g. macroeconomics and finance). We use a Bayesian nonparametri

Ju Yong Lee, Seung-Won Keum, Sang Min Oh, Dang-Oh Kim

In 6th generation wireless communication technology, it is important to utilize space resources efficiently. Recently, holographic multiple-input multiple-output (HMIMO) and meta-surface technology have attracted attention as technologies that maximize space utilization for 6G mobile communications. However, studies on HMIMO communications are still in an in

Ling Chen, Susu Zhang, Jingchen Liu

Testing fairness is a major concern in psychometric and educational research. A typical approach for ensuring testing fairness is through differential item functioning (DIF) analysis. DIF arises when a test item functions differently across subgroups that are typically defined by the respondents' demographic characteristics. Most of the existing research has

Lais Borbolato, Silvia Rossi, Hélio D. Perottoni, Guilherme Limberg

The Milky Way serves as a template for understanding the formation and evolution of late-type massive disk galaxies since we can obtain detailed chemical and kinematic information for large samples of individual stars. However, the early formation of the disk and the dichotomy between the chemical thick and thin disks remain under intense debate. Some mechan

Dominik Beck

In this paper, we show a physics-free derivation of a Landau-Zener type integral introduced by Kholodenko and Silagadze.

Aleksandra Bakalova, Yana Veitsman, Xinting Huang, Michael Hahn

In-Context Learning (ICL) is an intriguing ability of large language models (LLMs). Despite a substantial amount of work on its behavioral aspects and how it emerges in miniature setups, it remains unclear which mechanism assembles task information from the individual examples in a fewshot prompt. We use causal interventions to identify information flow in G

Quim Llorens

We generalize and extend results on the localization of gravity on Karch-Randall-Sundrum brane-worlds with positive, negative, or zero cosmological constant on the brane. We do so both from the study of bulk metric perturbations, and from their reinterpretation through brane-world holography: an induced higher-derivative theory of gravity coupled to a cut-of

Brenner S. Rego, Guilherme V. Raffo, Marco H. Terra, Joseph K. Scott

This paper presents a new algorithm for set-based state estimation of nonlinear discrete-time systems with bounded uncertainties. The novel method builds upon essential properties and computational advantages of constrained zonotopes (CZs) and polyhedral relaxations of factorable representations of nonlinear functions to propagate CZs through nonlinear funct

Annemarie Geertsema, Chris Godsil, Krystal Guo

We look at the question of which distance-regular graphs are core-complete, meaning they are isomorphic to their own core or have a complete core. We build on Roberson's homomorphism matrix approach by which method he proved the Cameron-Kazanidis conjecture that strongly regular graphs are core-complete. We develop the theory of the homomorphism matrix for d

Antoine Leclerc, Erwan Koch, Monika Feldmann, Daniele Nerini

Issuing timely severe weather warnings helps mitigate potentially disastrous consequences. Recent advancements in Neural Weather Models (NWMs) offer a computationally inexpensive and fast approach for forecasting atmospheric environments on a 0.25{\deg} global grid. For thunderstorms, these environments can be empirically post-processed to predict wind gust

Boyu Li

We introduce the notion of the weak Brehmer's condition and prove that the Cauchy transform for a representation of a right-angled Artin monoid is bounded under such conditions. As a result, we obtain the Poisson transform and $*$-regular dilation for a family of operators that satisfies the weak Brehmer's condition and the property (P). This generalizes Pop

Reghukrishnan Gangadharan, Sukanya Mitra, Victor Roy

We investigate the impact of momentum-dependent relaxation time approximation in the Boltzmann equation within the Bjorken flow framework by analyzing the moments of the single-particle distribution function. The moment equations, which form an infinite hierarchy, provide important insights about the system dynamics and the approach towards equilibrium for s

Ahsan Bilal, David Ebert, Beiyu Lin

Large Language Models (LLMs) offer a promising approach to enhancing Explainable AI (XAI) by transforming complex machine learning outputs into easy-to-understand narratives, making model predictions more accessible to users, and helping bridge the gap between sophisticated model behavior and human interpretability. AI models, such as state-of-the-art neural

Ergon Cugler de Moraes Silva, Arthur Ataide Ferreira Garcia, Guilherme de Almeida, Julie Ricard

How do conspiracy theory communities in Latin America and the Caribbean structure, articulate, and sustain the dissemination of disinformation about autism? To answer this question, this research investigates the structuring, articulation, and promotion of autism-related disinformation in conspiracy theory communities in Latin America and the Caribbean. By a

Gustavo Arciniega, Luisa G. Jaime, Susana J. Landau, Matías Leizerovich

We present a modification to General Relativity by making a redefinition of the coupling constant in front of the Ricci curvature scalar along with the Generalized Quasi-topological Gravity theories added to the action, that we named Geometric Cosmology. We give four different exponential convergent models for this class of theories belonging to three differ

Vivek Pandey, Sudhir K. Pandey

The anomalous Hall conductivity (AHC) in materials has long been a topic of debate. Studies reveal that AHC originates from the Berry curvature ($\boldsymbol\Omega$) of Bloch states. Accurate computation of AHC is crucial for predicting material properties and guiding experimental studies in topological and spintronic applications. Traditional approaches oft

Asaju Babajide, Almustapha Wakili, Michaela Barnett, Lucas Potter

The rapid development of Internet of Things (IoT) technology has significantly impacted various market sectors. According to Li et al. (2024), an estimated 75 billion devices will be on the market in 2025. The healthcare industry is a target to improve patient care and ease healthcare provider burdens. Chronic respiratory disease is likely to benefit from th

Sayed Ali Akbar Ghorashi, Qiang Li

Altermagnets have been identified as the third category of magnetic materials, exhibiting momentum-dependent spin splitting characterized by even powers of momentum. In this study, we show that when subjected to elliptically polarized light, these materials serve as an exemplary framework for the dynamic generation of topological bands featuring higher-order

Xavier Mootoo, Hina Tabassum, Luca Chiaraviglio

With the recent advancements in wireless technologies, forecasting electromagnetic field (EMF) exposure has become critical to enable proactive network spectrum and power allocation, as well as network deployment planning. In this paper, we develop a deep learning (DL) time series forecasting framework referred to as \textit{EMForecaster}. The proposed DL ar

Ritvik Nair, Timothy Merino, Julian Togelius

In this paper, we present God's Innovation Project (GIP), a god game where players collect words to dynamically terraform the landscape using generative AI. A god game is a genre where players take on the role of a deity, indirectly influencing Non-Player Characters (NPCs) to perform various tasks. These games typically grant players supernatural abilities,

Tom Benhamou, Victoria Gitman

We investigate forms of filter extension properties in the two-cardinal setting involving filters on $P_\kappa(\lambda)$. We generalize the filter games introduced by Holy and Schlicht in \cite{HolySchlicht:HierarchyRamseyLikeCardinals} to filters on $P_\kappa(\lambda)$ and show that the existence of a winning strategy for Player II in a game of a certain le

Reza Nematirad, Anil Pahwa, Balasubramaniam Natarajan

Time series forecasting is an important application in various domains such as energy management, traffic planning, financial markets, meteorology, and medicine. However, real-time series data often present intricate temporal variability and sharp fluctuations, which pose significant challenges for time series forecasting. Previous models that rely on 1D tim

Sergei Dyda, Randall C. Dannen, Timothy R. Kallman, Shane W. Davis

We use a combination of radiation hydrodynamics (rad-HD) and photoionization modeling to study line-driven disc winds for a range of black hole masses. We refined previous models by incorporating heating, cooling, and radiation forces from spectral lines calculated using a photoionization code, assuming that composite AGN spectra irradiate the gas. For black

Seiichi Azuma

The OEIS sequence A051221 consists of nonnegative integers of the form 10^x - y^2. The known values are those less than or equal to 2000 with x <= 7, and it is conjectured that no new values in this range appear for x >= 8. In this paper, we give an elementary proof that this is indeed the case by using Pell-type equations.

Shiyue Zhao, Junzhi Zhang, Neda Masoud, Heye Huang

Reliable collision avoidance under extreme situations remains a critical challenge for autonomous vehicles. While large language models (LLMs) offer promising reasoning capabilities, their application in safety-critical evasive maneuvers is limited by latency and robustness issues. Even so, LLMs stand out for their ability to weigh emotional, legal, and ethi

Shiang-Yu Huang, Shreya Kumar, Jeldrik Huster, Yannick Augenstein

Photonic quantum technologies enter a new phase when realized in photonic integrated circuits, leading to a great advance in practical applications. In the pursuit of high integration density and low circuit complexity, ultracompact devices delivered by topology optimization offer a promising solution to miniaturize these photonic systems even further. Howev

Arnab Sarkar, Michael McDonald, Lindsey Bleem, Mark Bautz

We present joint South Pole Telescope (SPT) and XMM-Newton observations of 8 massive galaxy clusters (0.8--1.7$\times$10$^{15}$ M$_{\odot}$) spanning a redshift range of 0.16 to 0.35. Employing a novel SZ+X-ray fitting technique, we effectively constrain the thermodynamic properties of these clusters out to the virial radius. The resulting best-fit electron

S. Di Noi, R. Gröber, P. Olgoso

We explore the relation between two distinct prescriptions for $\gamma_5$ in dimensional regularization -- the Breitenlohner Maison t'Hooft Veltman (BMHV) scheme and Naive Dimensional Regularisation (NDR). The BMHV scheme is the only algebraically consistent scheme, but necessitates chiral symmetry restoring counterterms and it is computationally more expens

Gennaro Zanfardino, Stefano Paesani, Luca Leuzzi, Raffaele Santagati

In the present work, we introduce, develop, and investigate a connection between multiphoton quantum interference, a core element of emerging photonic quantum technologies, and Hopfieldlike Hamiltonians of classical neural networks, the paradigmatic models for associative memory and machine learning in systems of artificial intelligence. Specifically, we sho

Stephen E. Gant, Antonios M. Alvertis, Christopher J. N. Coveney, Jonah B. Haber

Monoclinic bismuth vanadate (m-BiVO$_4$) is a promising indirect band gap semiconductor for photoelectrochemical water splitting, yet the characteristics of its low-lying photoexcitations, or excitons, remain poorly understood. Here, we use an ab initio Bethe-Salpeter equation approach that incorporates phonon screening to compute the nature and lifetimes of

Satyabrata Mahapatra, Partha Kumar Paul, Narendra Sahu, Prashant Shukla

We propose a new extension of the Standard Model that incorporates a gauged \( U(1)_{\rm B-L} \) symmetry and the type-III seesaw mechanism to explain neutrino mass generation and provide a viable dark matter (DM) candidate. Unlike the type-I seesaw, the type-III seesaw extension under \( U(1)_{\rm B-L} \) is not automatically anomaly-free. We show that thes

Hong-Yi Wang

Quantum measurement is a fundamental yet experimentally challenging ingredient of quantum information processing. Many recent studies on quantum dynamics focus on expectation values of nonlinear observables; however, their experimental measurement is hindered by the post-selection problem -- namely, the substantial overhead caused by uncontrollable measureme

Bang Liu, Xinfeng Li, Jiayi Zhang, Jinlin Wang

The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence, paving the way for advanced intelligent agents capable of sophisticated reasoning, robust perception, and versatile action across diverse domains. As these agents increasingly drive AI research and practical applications, their design, evaluation, and

Mateus Malato Corrêa, Caio F. B. Macedo, Rodrigo Panosso Macedo, Leandro A. Oliveira

Small deviations in the spacetime around black holes can lead to instabilities in the underlying quasinormal mode spectrum, potentially altering the hierarchy of its overtones. A practical way to induce such spectral instability is by introducing small modifications to the effective potential governing the dynamics of fluctuations in the black hole spacetime

Lucas Tobias de Paula, Pedro Henrique Croti Siqueira, Rodrigo Panosso Macedo, Maurício Richartz

Analyzing the stability of quasinormal modes (QNM) is essential for understanding black hole dynamics, particularly in the context of gravitational wave emissions and black hole spectroscopy. In this study, we employ the hyperboloidal approach to reformulate the quasinormal mode problem of a rotating analog black hole, effectively transforming it into an eig

Dario Fiore Mosca, Leonid V. Pourovskii

"Hidden"-order phases with high-rank multipolar order parameters have been recently detected in several cubic double perovskites of 5$d$ transition metals. Here, by constructing and solving an ab initio low-energy Hamiltonian, we show that an antiferroic order of magnetic octupoles also forms in the tetragonal 5d$^1$ double perovskite Sr$_2$MgReO$_6$. The lo