November 2024 arXiv papers — page 144

Carlos Garcia-Azpeitia, Ziad Ghanem, Wieslaw Krawcewicz

In this paper, we use the equivariant degree theory to establish a global bifurcation result for the existence of non-stationary branches of solutions to a nonlinear, two-parameter family of hyperbolic wave equations with local delay and non-trivial damping. As a motivating example, we consider an application of our result to a system of $N$ identical vibrat

Grant Buster, Jordan Cox, Brandon N. Benton, Ryan N. King

As urbanization and climate change progress, urban heat becomes a priority for climate adaptation efforts. High temperatures concentrated in urban heat can drive increased risk of heat-related death and illness as well as increased energy demand for cooling. However, estimating the effects of urban heat is an ongoing field of research typically burdened by a

Marcin Wątorek, Marcin Królczyk, Jarosław Kwapień, Tomasz Stanisz

Multifractality is a concept that helps compactly grasping the most essential features of the financial dynamics. In its fully developed form, this concept applies to essentially all mature financial markets and even to more liquid cryptocurrencies traded on the centralized exchanges. A new element that adds complexity to cryptocurrency markets is the possib

Youssef Aiache, Abderrahim El Allati, Khadija El Anouz

Quantum probes, such as single- and two-qubit probes, can accurately measure the temperature of a bosonic bath. The current investigation assesses the precision of temperature estimate using quantum Fisher information and the accompanying quantum signal-to-noise ratio. Employing an ancilla as a mediator between the probe and the bath improves thermometric se

Adnan Machado, Pierre E. Sullivan

This paper synthesizes prior studies to develop a comprehensive three-dimensional description of reattached flow over an airfoil controlled by finite-span SJA arrays. When applied to a longer-span wing, the flow field exhibits three distinct regions: a directly controlled region, an unaffected region, and a transitional zone between them. Through the integra

Dheeraj Pasham, Eric Coughlin, Chris Nixon, Michal Zajacek

SwJ023017.0+283603 (SwJ0230) exhibited soft X-ray (0.3-1.0 keV) eruptions recurring roughly every 22 days. We present results from an extended monitoring campaign of SwJ0230 using Swift, NICER, and deep XMM-Newton observations. Our main findings are: 1) SwJ0230 did not display any eruptions during two 80-day periods (June-September 2023 and July-September 20

Omar Alrabiah, Prabhanjan Ananth, Miranda Christ, Yevgeniy Dodis

Pseudorandom codes are error-correcting codes with the property that no efficient adversary can distinguish encodings from uniformly random strings. They were recently introduced by Christ and Gunn [CRYPTO 2024] for the purpose of watermarking the outputs of randomized algorithms, such as generative AI models. Several constructions of pseudorandom codes have

Jacob Anderson, Georgios Fainekos, Bardh Hoxha, Hideki Okamoto

Perception in fields like robotics, manufacturing, and data analysis generates large volumes of temporal and spatial data to effectively capture their environments. However, sorting through this data for specific scenarios is a meticulous and error-prone process, often dependent on the application, and lacks generality and reproducibility. In this work, we i

Yen-Ting Lin, Zhehuai Chen, Piotr Zelasko, Zhen Wan

Construction of a general-purpose post-recognition error corrector poses a crucial question: how can we most effectively train a model on a large mixture of domain datasets? The answer would lie in learning dataset-specific features and digesting their knowledge in a single model. Previous methods achieve this by having separate correction language models, r

Alexei Kuzmin

This chapter introduces the use of X-ray absorption spectroscopy (XAS) in studying the local electronic and atomic structure of high-entropy materials. The element selectivity of XAS makes it particularly suitable to address the challenges posed by the study of multicomponent compounds. By analysing different parts of the X-ray absorption spectra for each el

Takuya Ito, Murray Campbell, Lior Horesh, Tim Klinger

The rapid development of artificial intelligence (AI) systems has created an urgent need for their scientific quantification. While their fluency across a variety of domains is impressive, AI systems fall short on tests requiring algorithmic reasoning -- a glaring limitation given the necessity for interpretable and reliable technology. Despite a surge of re

Simon van Bellen, Lucía Céspedes

Scholarly publishing involves multiple stakeholders having various types of interest. In Canada, the implication of universities, the presence of societies and the availability of governmental support for periodicals seem to have contributed to a rather diverse ecosystem of journals. This study presents in detail the current state of these journals, in addit

Kathrin Bringmann, Guoniu Han, Bernhard Heim, Ben Kane

In this paper, we study vanishing of Fourier coefficients of holomorphic $\eta$-quotients. We investigate examples of two different types: the first one involves integral weight CM newforms, while the second one involves half-integral weight $\eta$-quotients associated with sums of squares and Hurwitz class numbers.

Muhammad Rizwan Akram, Abbas Semnani

This paper presents a novel metamaterial topology incorporating gas discharge tubes for high-power microwave protection. The design features two split ring resonators positioned side by side with their splits oriented orthogonally. When exposed to low-power microwaves, each split ring resonator induces a resonance that interacts to create a passband within a

Moseli Mots'oehli, kyungim Baek

Active learning aims to train accurate classifiers while minimizing labeling costs by strategically selecting informative samples for annotation. This study focuses on image classification tasks, comparing AL methods on CIFAR10, CIFAR100, Food101, and the Chest X-ray datasets under varying label noise rates. We investigate the impact of model architecture by

Eric Vansteenberghe

Measures of inflation uncertainty and directional risk derived from higher moments of forecast distributions are contaminated by the first moment, but in distinct ways. Using individual density forecasts from the ECB Survey of Professional Forecasters, this paper shows that 42% of the variation in raw forecast variance reflects the distance of expected infla

Jorge Neyra, Vishal B. Siramshetty, Huthaifa I. Ashqar

This study examines the effect that different feature selection methods have on models created with XGBoost, a popular machine learning algorithm with superb regularization methods. It shows that three different ways for reducing the dimensionality of features produces no statistically significant change in the prediction accuracy of the model. This suggests

Anantha Sharma, Sheeba Elizabeth John, Fatemeh Rezapoor Nikroo, Krupali Bhatt

The growth of digital documents presents significant challenges in efficient management and knowledge extraction. Traditional methods often struggle with complex documents, leading to issues such as hallucinations and high latency in responses from Large Language Models (LLMs). ZeroG, an innovative approach, significantly mitigates these challenges by levera

Leonardo Ripoli, Richard G. Everitt

Monte Carlo methods, such as Markov chain Monte Carlo (MCMC), remain the most regularly-used approach for implementing Bayesian inference. However, the computational cost of these approaches usually scales worse than linearly with the dimension of the parameter space, limiting their use for models with a large number of parameters. However, it is not uncommo

Saad Tahmid, Sourav Sarker

We present an innovative approach for solving mathematical problems in Bengali, developed for the DL Sprint 3.0 BUET CSE Fest 2024 Competition. Our method uses advanced deep learning models, notably the Qwen 2.5 series, with improvements made through prompt engineering, model quantization, and Tool Integrated Reasoning (TIR) to handle complex calculations. I

Feng-Yu Yue, Daniel Zelazo

This work deals with the output consensus problem for multiagent systems over balanced digraphs by passivity analysis. As the standard diffusive coupling structure only models the undirected interconnection, we propose a general approach capable of processing directed coupling and performing passivity analysis. To mitigate the complexity arising from the non

Eugenio P. Balanzario

In this note we consider the distribution of values of weighted sums of the von Mangoldt arithmetical function. By using a formula for the distribution of values of trigonometric polynomials, we are able to present evidence supporting the claim that these weighted sums follow a distribution with a normal-like behavior.

Sean Fiscus, Eric Myzelev, Hongyi Zhang

There is a famous problem in geometric graph theory to find the chromatic number of the unit distance graph on Euclidean space; it remains unsolved. A theorem of Erdos and De-Bruijn simplifies this problem to finding the maximum chromatic number of a finite unit distance graph. Via a construction built on sequential finite graphs obtained from a generalizati

Allaa Boutaleb, Jerome Picault, Guillaume Grosjean

Detecting and tracking emerging trends and weak signals in large, evolving text corpora is vital for applications such as monitoring scientific literature, managing brand reputation, surveilling critical infrastructure and more generally to any kind of text-based event detection. Existing solutions often fail to capture the nuanced context or dynamically tra

Xiaojia Wang, Silas Alben

Oscillations of a heated solid surface in an oncoming fluid flow can increase heat transfer from the solid to the fluid. Previous studies have investigated the resulting heat transfer enhancement for the case of a circular cylinder undergoing translational or rotational motions. Another common geometry, the flat plate, has not been studied as thoroughly. The

Zijun Wu, Bingyuan Liu, Ran Yan, Lei Chen

Recent advancements in Large Language Models (LLMs) have significantly enhanced their capacity to process long contexts. However, effectively utilizing this long context remains a challenge due to the issue of distraction, where irrelevant information dominates lengthy contexts, causing LLMs to lose focus on the most relevant segments. To address this, we pr

Sjoerd van Steenkiste, Daniel Zoran, Yi Yang, Yulia Rubanova

Current vision models typically maintain a fixed correspondence between their representation structure and image space. Each layer comprises a set of tokens arranged "on-the-grid," which biases patches or tokens to encode information at a specific spatio(-temporal) location. In this work we present Moving Off-the-Grid (MooG), a self-supervised video represen

Charles Klewicki, Bjoern F. Klose, Gustaaf B. Jacobs, Geoffrey R. Spedding

When a chordwise Reynolds number (Re) falls below about $10^5$ the performance of wings and aerodynamic sections become sensitive to viscous phenomena, including boundary layer separation and possible reattachment. Here, detailed measurements of the flow inside the boundary layer on the suction surface are shown for an aspect ratio 3 wing with wall boundarie

Mira Todorova, Stefan Wippermann, Jörg Neugebauer

Ab initio techniques have revolutionised the way in which theory can help practitioners to explore critical mechanisms that govern reactions or properties, and to develop new strategies for materials discovery and design. Yet, their application to electrochemical systems is still limited, due to the challenges electronic structure calculations face in achiev

Ben Hambly, Dörte Kreher, Konstantins Starovoitovs

We construct non-negative martingale solutions to the stochastic porous medium equation in one dimension with homogeneous Dirichlet boundary conditions which exhibit a type of sticky behavior at zero. The construction uses the stochastic Faedo--Galerkin method via spatial semidiscretization, so that the pre-limiting system is given by a finite-dimensional di

Mike Van Ness, Billy Block, Madeleine Udell

Survival analysis is a classic problem in statistics with important applications in healthcare. Most machine learning models for survival analysis are black-box models, limiting their use in healthcare settings where interpretability is paramount. More recently, glass-box machine learning models have been introduced for survival analysis, with both strong pr

Devang Shah, Hriday Ranka, Lynnette Hui Xian NG, Swapneel Mehta

In recent years, mass-broadcast messaging platforms like Telegram have gained prominence for both, serving as a harbor for private communication and enabling large-scale disinformation campaigns. The encrypted and networked nature of these platforms makes it challenging to identify intervention targets since most channels that promote misleading information

Danielius Kramnik, Imbert Wang, Anirudh Ramesh, Josep M. Fargas Cabanillas

Silicon photonics is a leading platform for realizing the vast numbers of physical qubits needed for useful quantum information processing because it leverages mature complementary metal-oxide-semiconductor (CMOS) manufacturing to integrate on-chip thousands of optical devices for generating and manipulating quantum states of light. A challenge to the practi

Mohammad Mehboudi, Fatemeh Rezaeinia, Saleh Rahimi-Keshari

We investigate the measurement incompatibility of continuous-variable systems with infinite-dimensional Hilbert spaces under the influence of pure losses, a fundamental noise source in quantum optics, and a significant challenge for long-distance quantum communication. We show that loss channels with transmissivities less than $1/n$ make any set of $n$ measu

Ruairidh Sutcliffe, Kathleen Hart, Gil Refael, Arun Paramekanti

The dynamics of magnetic moments coupled to phonons is of great interest for understanding spin transport in solids as well as for our ability to control magnetism via tailored phonon modes. For spin $S > 1/2$, spin-orbit coupling permits an unusual linear coupling of phonons to quadrupolar moments, so that phonons act as a dynamical transverse field for the

Ismaël Ahlouche Lahlali, Josh A. O'Connor

In these lectures we review two approaches to constructing particle actions from coset spaces of symmetry groups: non-linear realisations and coadjoint orbits. At the level of particle actions, we observe that they coincide. We also provide an introduction to symplectic geometry and we sketch the theory of coadjoint orbits for the Poincar\'e group.

William O. Balmer, Kyle Franson, Antoine Chomez, Laurent Pueyo

Direct imaging observations are biased towards wide-separation, massive companions that have degenerate formation histories. Although the majority of exoplanets are expected to form via core accretion, most directly imaged exoplanets have not been convincingly demonstrated to follow this formation pathway. We obtained new interferometric observations of the

Ryan O'Donnell, Noah G. Singer

Recent major results in property testing~\cite{BLM24,DDL24} and PCPs~\cite{BMV24} were unlocked by moving to high-dimensional expanders (HDXs) constructed from $\widetilde{C}_d$-type buildings, rather than the long-known $\widetilde{A}_d$-type ones. At the same time, these building quotient HDXs are not as easy to understand as the more elementary (and more

Anthony N. Ciavarella

Quantum simulations of non-Abelian gauge theories require efficient mappings onto quantum computers and practical state preparation and measurement procedures. A truncation of the Hilbert space of non-Abelian lattice gauge theories with matter in the heavy quark limit is developed. This truncation is applied to $SU(2)$ lattice gauge theory in $1+1D$ to map t

Giorgio Arcadi, David Cabo-Almeida, Sven Fabian, Florian Goertz

In this work we illustrate a general framework to describe the LHC phenomenology of extended scalar (and fermion) sectors, with focus on dark matter (DM) physics, based on an effective field theory (EFT) with non-linearly realized electroweak symmetry. Generalizing Higgs EFT (HEFT), the setup allows to include a generic set of new scalar resonances, without

K. Tanidis, J. Asorey, C. S. Saraf, C. L. Hale

We measured the harmonic-space power spectrum of galaxy clustering auto-correlation from the Evolutionary Map of the Universe Pilot Survey 1 data (EMU PS1) and its cross-correlation with the lensing convergence map of cosmic microwave background (CMB) from Planck Public Release 4 at the linear scale range from $\ell=2$ to 500. We applied two flux density cut

Benjamin Muntz, Antonio Padilla, Paul M. Saffin

We propose a scenario of a de Sitter universe living on an End-of-the-World brane. Motivated by the Swampland programme and in particular the Cobordism Conjecture, we consider a compact region of AdS$_5$ nucleating from nothing, with a dS$_4$ living on its boundary. We show that it can equivalently be interpreted as an up-tunnelling from AdS$_5$ with cosmolo

Alexis Ortega, Tatsuya Daniel, Savvas M. Koushiappas

We investigate the effects of Chern-Simons-Gauss-Bonnet gravity on fundamental metrics. This theory involves perturbative corrections to general relativity, as well as two scalar fields, the axion and the dilaton, that arise from Chern-Simons and Gauss-Bonnet gravity modifications respectively. The combined Chern-Simons-Gauss-Bonnet gravity is motivated by a

Seongbong Han, J. K. Jang, Emanuele Contini, Yohan Dubois

The formation pathways of lenticular galaxies (S0s) in field environments remain a matter of debate. We utilize the cosmological hydrodynamic simulation, NewHorizon, to investigate the issue. We select two massive star-formation quenched S0s as our main sample. By closely tracing their physical and morphological evolution, we identify two primary formation c

Lucca Radicce Justino, Clarissa Siqueira, Aion Viana

We investigate constraints on the Inert Doublet Model (IDM) that features a scalar dark matter candidate, using data from recent and future gamma-ray observatories. The relevance of the model for indirect searches of dark matter stems from two key features: first, in the high mass regime, IDM can achieve the correct dark matter relic abundance for masses bet

Daniele Guerci, J. H. Pixley, Andrew J. Millis

We study the formation of ferromagnetic and magnetic polaron states in weakly doped heterobilayer transition metal dichalcogenides in the ``heavy fermion'' limit in which one layer hosts a dense set of local moments and the other hosts a low density of itinerant holes. We show that interactions among the carriers in the itinerant layer induces a ferromagneti

Thibault D. Décoppet, Peter Huston, Theo Johnson-Freyd, Dmitri Nikshych

We classify (multi)fusion 2-categories in terms of braided fusion categories and group cohomological data. This classification is homotopy coherent -- we provide an equivalence between the 3-groupoid of (multi)fusion 2-categories up to monoidal equivalences and a certain 3-groupoid of commuting squares of $\mathrm{B}\mathbb{Z}/2$-equivariant spaces. Rank fin

Yifan Chen, Matthias Daniel, Daniel J. D'Orazio, Xuanye Fan

The detection of a stochastic gravitational wave background by pulsar-timing arrays indicates the presence of a population of supermassive black hole binaries. Although the observed spectrum generally matches predictions for orbital evolution driven by gravitational-wave emission in circular orbits, there is a preference for a spectral turnover at the lowest

Andreas Filipp, Yashar Hezaveh, Laurence Perreault-Levasseur

We investigate the robustness of Neural Ratio Estimators (NREs) and Neural Posterior Estimators (NPEs) to distributional shifts in the context of measuring the abundance of dark matter subhalos using strong gravitational lensing data. While these data-driven inference frameworks can be accurate on test data from the same distribution as the training sets, in

Fangyuan Xu, Tanya Goyal, Eunsol Choi

Generating long sequences of tokens given a long-context input is a very compute-intensive inference scenario for large language models (LLMs). One prominent inference speed-up approach is to construct a smaller key-value (KV) cache, relieving LLMs from computing attention over a long sequence of tokens. While such methods work well to generate short sequenc

Meng Cheng, Juven Wang, Xinping Yang

We investigate (3+1)d topological orders in fermionic systems with an anomalous $\mathbb{Z}_{2N}^{\mathrm{F}}$ symmetry, where its $\mathbb{Z}_2^{\mathrm{F}}$ subgroup is the fermion parity. Such an anomalous symmetry arises as the discrete subgroup of the chiral U(1) symmetry of $\nu$ copies of Weyl fermions of the same chirality. Inspired by the crystallin

Jonas Kiemel, Ludovic Righetti, Torsten Kröger, Tamim Asfour

In this paper, we present an approach for learning collision-free robot trajectories in the presence of moving obstacles. As a first step, we train a backup policy to generate evasive movements from arbitrary initial robot states using model-free reinforcement learning. When learning policies for other tasks, the backup policy can be used to estimate the pot

Kayo Yin, Chinmay Singh, Fyodor O. Minakov, Vanessa Milan

Deaf and hard-of-hearing (DHH) students face significant barriers in accessing science, technology, engineering, and mathematics (STEM) education, notably due to the scarcity of STEM resources in signed languages. To help address this, we introduce ASL STEM Wiki: a parallel corpus of 254 Wikipedia articles on STEM topics in English, interpreted into over 300

Mingyue Zha, Ho-Chun Herbert Chang

Content-creator collaborations are a widespread strategy for enhancing digital viewership and revenue. While existing research has explored the efficacy of collaborations, few have looked at inequities in collaborations, particularly from the perspective of the supply and demand of attention. Leveraging 42,376 videos and 6,117,441 comments from YouTube (acro

Josh Barua, Sanjay Subramanian, Kayo Yin, Alane Suhr

In translation, a concept represented by a single word in a source language can have multiple variations in a target language. The task of lexical selection requires using context to identify which variation is most appropriate for a source text. We work with native speakers of nine languages to create DTAiLS, a dataset of 1,377 sentence pairs that exhibit c

Trong Thang Pham, Tien-Phat Nguyen, Yuki Ikebe, Akash Awasthi

Medical eye-tracking data is an important information source for understanding how radiologists visually interpret medical images. This information not only improves the accuracy of deep learning models for X-ray analysis but also their interpretability, enhancing transparency in decision-making. However, the current eye-tracking data is dispersed, unprocess

Maxime Jacovella, Ali Keshavarzi, Elsa Angelini

Despite advances with deep learning (DL), automated airway segmentation from chest CT scans continues to face challenges in segmentation quality and generalization across cohorts. To address these, we propose integrating Curriculum Learning (CL) into airway segmentation networks, distributing the training set into batches according to ad-hoc complexity score

Colin Doyle

We introduce "Method Actors" as a mental model for guiding LLM prompt engineering and prompt architecture. Under this mental model, LLMs should be thought of as actors; prompts as scripts and cues; and LLM responses as performances. We apply this mental model to the task of improving LLM performance at playing Connections, a New York Times word puzzle game t

Vojtech Formanek, Ondrej Sotolar

A growing amount of literature critiques the current operationalizations of empathy based on loose definitions of the construct. Such definitions negatively affect dataset quality, model robustness, and evaluation reliability. We propose an empathy evaluation framework that operationalizes empathy close to its psychological origins. The framework measures th

G. G. Kenning, M. Brandt, R. Brake, M. Hepler

Time-dependent Thermoremanent Magnetization (TRM) studies have been instrumental in probing energy dynamics within the spin glass phase. In this paper, we will review the evolution of the TRM experiment over the last half century and discuss some aspects related to how it has been employed in the understanding of spin glasses. We will also report on recent e

Giorgio Palma, Andrea Bardazzi, Alessia Lucarelli, Chiara Pilloton

This article presents the data-driven equation-free modeling of the dynamics of a hexafloat floating offshore wind turbine based on the application of dynamic mode decomposition (DMD). All the analyses are performed on experimental data collected from an operating prototype. The DMD has here used i) to extract knowledge from the dynamic system through its mo

Veronica Chatrath, Marcelo Lotif, Shaina Raza

Political misinformation poses significant challenges to democratic processes, shaping public opinion and trust in media. Manual fact-checking methods face issues of scalability and annotator bias, while machine learning models require large, costly labelled datasets. This study investigates the use of state-of-the-art large language models (LLMs) as reliabl

Antonio J. Muñoz-Montoro, Pablo Revuelta-Sanz, Damian Martínez-Muñoz, Juan Torre-Cruz

In this paper, a parallel computing method is proposed to perform the background denoising and wheezing detection from a multi-channel recording captured during the auscultation process. The proposed system is based on a non-negative matrix factorization (NMF) approach and a detection strategy. Moreover, the initialization of the proposed model is based on s

Margarita Arias, Abdelkarim Hajjaj, Amine Sbai

In this paper we study the local boundary controllability for a non linear system of two degenerate parabolic equations with a control acting on only one equation. We analyze boundary null controllability properties for the linear system via the moment method by Fattorini and Russell, together with some results on biorthogonal families. Moreover, we provide

Frederick Skiff, Gregory G. Howes

Weakly collisional plasmas contain a wealth of information about the dynamics of the plasma in the particle velocity distribution functions, yet our ability to exploit fully that information remains relatively primitive. Here we aim to present the fundamentals of a new technique denoted Plasma Seismology that aims to invert the information from measurements

Guixian Xu, Jinglai Li, Junqi Tang

Equivariant Imaging (EI) regularization has become the de-facto technique for unsupervised training of deep imaging networks, without any need of ground-truth data. Observing that the EI-based unsupervised training paradigm currently has significant computational redundancy leading to inefficiency in high-dimensional applications, we propose a sketched EI re

Kaisa Matomäki, Maksym Radziwiłł, Xuancheng Shao, Terence Tao

We study higher uniformity properties of the von Mangoldt function $\Lambda$, the M\"obius function $\mu$, and the divisor functions $d_k$ on short intervals $(x,x+H]$ for almost all $x \in [X, 2X]$. Let $\Lambda^\sharp$ and $d_k^\sharp$ be suitable approximants of $\Lambda$ and $d_k$, $G/\Gamma$ a filtered nilmanifold, and $F\colon G/\Gamma \to \mathbb{C}$

MacKenzie Harnett, Paras Kumar, Rebecca F. Friesen

Friction modulation allows for a range of different sensations and textures to be simulated on flat touchscreens, yet is largely unable to render fundamental tactile interactions such as path following or shape discrimination due to lack of spatial force distribution across the fingerpad. In order to expand the range of sensations rendered via friction modul

Noor A. Rashed, Yossra H. Ali Tarik A. Rashid, Seyedali Mirjalili

This paper presents the Multi-Objective Ant Nesting Algorithm (MOANA), a novel extension of the Ant Nesting Algorithm (ANA), specifically designed to address multi-objective optimization problems (MOPs). MOANA incorporates adaptive mechanisms, such as deposition weight parameters, to balance exploration and exploitation, while a polynomial mutation strategy

Douglas A. Barlow, Kylene Monaghan

We show, in this report, how a population balance model differential equation describing batch crystal growth from solution can be solved in closed form for the case of diffusion limited growth with and without modeling the effects of growth rate dispersion. By letting the growth rate diffusivity be directly proportional to the supersaturation, a closed form

G. Lusztig

Let G be a connected reductive group over the complex numbers with a fixed pinning. We define and study the totally positive part of the set of maximal tori of G.

Poetri Sonya Tarabunga, Martina Frau, Tobias Haug, Emanuele Tirrito

We introduce a nonstabilizerness monotone which we name basis-minimised stabilizerness asymmetry (BMSA). It is based on the notion of $G$-asymmetry, a measure of how much a certain state deviates from being symmetric with respect to a symmetry group $G$. For pure states, we show that the BMSA is a strong monotone for magic-state resource theory, while it can

Heli Elorreaga, Juan Peña, Gonzalo Robledo

The property of exponential dichotomy can be seen as a generalization of the hyperbolicity condition for non autonomous linear finite dimensional systems of ordinary differential equations. In 1978 W.A. Coppel proved that the exponential dichotomy on the half line is equivalent to the property of noncritical uniformity provided that a condition of bounded gr

Yilun Zhao, Yitao Long, Yuru Jiang, Chengye Wang

We introduce FinDVer, a comprehensive benchmark specifically designed to evaluate the explainable claim verification capabilities of LLMs in the context of understanding and analyzing long, hybrid-content financial documents. FinDVer contains 2,400 expert-annotated examples, divided into three subsets: information extraction, numerical reasoning, and knowled

Amirhossein Iraniparast, Dominic Groß

In this paper, we study a constrained network flow problem and associated networked dynamics that resemble but are distinct from the well-known primal-dual dynamics of the constrained flow problem. Crucially, under a change of coordinates, the networked dynamics coincide with primal-dual dynamics associated with the constrained flow problem in edge coordinat

Christopher Malon

Separating disinformation from fact on the web has long challenged both the search and the reasoning powers of humans. We show that the reasoning power of large language models (LLMs) and the retrieval power of modern search engines can be combined to automate this process and explainably verify claims. We integrate LLMs and search under a multi-hop evidence

Johan Helsing, Shidong Jiang

A numerical scheme is presented for solving the Helmholtz equation with Dirichlet or Neumann boundary conditions on piecewise smooth open curves, where the curves may have corners and multiple junctions. Existing integral equation methods for smooth open curves rely on analyzing the exact singularities of the density at endpoints for associated integral oper

Abhinav Deshpande, Marcel Hinsche, Khadijeh Najafi, Kunal Sharma

Classical optimization of parameterized quantum circuits is a widely studied methodology for the preparation of complex quantum states, as well as the solution of machine learning and optimization problems. However, it is well known that many proposed parameterized quantum circuit architectures suffer from drawbacks which limit their utility, such as their c

Javal Vyas, Mehmet Mercangöz

As chemical plants evolve towards full autonomy, the need for effective fault handling and control in dynamic, unpredictable environments becomes increasingly critical. This paper proposes an innovative approach to industrial automation, introducing validation and reprompting architectures utilizing large language model (LLM)-based autonomous control agents.

Maryam Reza, Farbod Faraji, Aaron Knoll

Across many plasma applications, the underlying phenomena and interactions among the involved processes are known to exhibit three-dimensional characteristics. Furthermore, the global properties and evolution of plasma systems are often determined by a process called inverse energy cascade, where kinetic plasma processes at the microscopic scale interact and

Ankita Joshi, Ashutosh Sharma, Anoushkrit Goel, Ranjeet Ranjan Jha

Fiber tractography is a cornerstone of neuroimaging, enabling the detailed mapping of the brain's white matter pathways through diffusion MRI. This is crucial for understanding brain connectivity and function, making it a valuable tool in neurological applications. Despite its importance, tractography faces challenges due to its complexity and susceptibility

Hernan Olguin, Pascale Domingo, Luc Vervisch, Christian Hasse

In combustion theory, flames are usually described in terms of the dynamics of iso-surfaces of a specific scalar. The flame displacement speed is then introduced as a local variable quantifying the progression of these iso-surfaces relative to the flow field. While formally defined as a scalar, the physical meaning of this quantity allows relating it with a

Dylan Goetting, Himanshu Gaurav Singh, Antonio Loquercio

We present VLMnav, an embodied framework to transform a Vision-Language Model (VLM) into an end-to-end navigation policy. In contrast to prior work, we do not rely on a separation between perception, planning, and control; instead, we use a VLM to directly select actions in one step. Surprisingly, we find that a VLM can be used as an end-to-end policy zero-s

Michele Maggiore, Francesco Iacovelli, Enis Belgacem, Michele Mancarella

We study the performances of a world-wide network made by a European third-generation gravitational-wave (GW) detector, together with a 40km Cosmic Explorer detector in the US, considering three scenarios for the European detector: (1) Einstein Telescope (ET) in its 10km triangle configuration; (2) ET in its configuration featuring two 15km L-shaped detector

Arttu Rajantie

I give a theoretical overview of magnetic monopoles, focusing on the physical perspective of monopoles as hypothetical particles rather than as mathematical objects. I argue that monopoles are exceptionally interesting hypothetical particles and discuss the prospects of addressing the question of their existence, and possibly producing them, in particle phys

Shreen Gul, Mohamed Elmahallawy, Sanjay Madria, Ardhendu Tripathy

Deep learning (DL) models are popular across various domains due to their remarkable performance and efficiency. However, their effectiveness relies heavily on large amounts of labeled data, which are often time-consuming and labor-intensive to generate manually. To overcome this challenge, it is essential to develop strategies that reduce reliance on extens

Maryam Reza, Farbod Faraji, Aaron Knoll

The particle-in-cell (PIC) method is a well-established and widely used kinetic plasma modelling approach that provides a hybrid Lagrangian-Eulerian approach to solve the plasma kinetic equation. Despite its power in capturing details of the underlying physics of plasmas, conventional PIC implementations are associated with a significant computational cost,

Jerry Yao-Chieh Hu, Erzhi Liu, Han Liu, Zhao Song

Given a database of bit strings $A_1,\ldots,A_m\in \{0,1\}^n$, a fundamental data structure task is to estimate the distances between a given query $B\in \{0,1\}^n$ with all the strings in the database. In addition, one might further want to ensure the integrity of the database by releasing these distance statistics in a secure manner. In this work, we propo

Arijit Manna, Sabyasachi Pal

The study of complex organic molecules containing thiol ($-$SH) groups is essential in interstellar media because $-$SH plays an important role in the polymerization of amino acids (R-CH(NH$_{2}$)-COOH). Some quantum chemical studies have shown that there is a high chance of detecting the emission lines of dithioformic acid (HC(S)SH) in the highly dense and

Ziang Yin, Yu Yao, Jeff Zhang, Jiaqi Gu

Diffractive optical neural networks (DONNs), leveraging free-space light wave propagation for ultra-parallel, high-efficiency computing, have emerged as promising artificial intelligence (AI) accelerators. However, their inherent lack of reconfigurability due to fixed optical structures post-fabrication hinders practical deployment in the face of dynamic AI

Shreyans Jain, Viraj Vekaria, Karan Gandhi, Aadya Arora

Shadow removal and segmentation remain challenging tasks in computer vision, particularly in complex real world scenarios. This study presents a novel approach that enhances the ShadowFormer model by incorporating Masked Autoencoder (MAE) priors and Fast Fourier Convolution (FFC) blocks, leading to significantly faster convergence and improved performance. W

Rhys Gould, Hidenori Tanaka

Adaptive optimization algorithms, particularly Adam and its variant AdamW, are fundamental components of modern deep learning. However, their training dynamics lack comprehensive theoretical understanding, with limited insight into why common practices -- such as specific hyperparameter choices and normalization layers -- contribute to successful generalizat

Patrycja Tulewicz, Karol Bartkiewicz, Adam Miranowicz, Franco Nori

Measuring complex properties in quantum systems, such as measures of quantum entanglement and Bell nonlocality, is inherently challenging. Traditional methods, like quantum state tomography (QST), necessitate a full reconstruction of the density matrix for a given system and demand resources that scale exponentially with system size. We propose an alternativ

B. De Simone, M. H. P. M. van Putten, M. G. Dainotti, G. Lambiase

$\Lambda$CDM provides a leading framework in the interpretation of modern cosmology. Nevertheless, the scientific community still struggles with many open problems in cosmology. Among the most noticeable ones, the tension in the Hubble constant $H_0$ is particularly intriguing, prompting a wide range of possible solutions. In the present work, the flat scale

Joseph Pollock, Igor Shilov, Euodia Dodd, Yves-Alexandre de Montjoye

Membership inference attacks (MIAs) are widely used to empirically assess privacy risks in machine learning models, both providing model-level vulnerability metrics and identifying the most vulnerable training samples. State-of-the-art methods, however, require training hundreds of shadow models with the same architecture as the target model. This makes the

Wangyang Ying, Haoyue Bai, Kunpeng Liu, Yanjie Fu

Feature space is an environment where data points are vectorized to represent the original dataset. Reconstructing a good feature space is essential to augment the AI power of data, improve model generalization, and increase the availability of downstream ML models. Existing literature, such as feature transformation and feature selection, is labor-intensive

Alex J. Vernon, Sebastian Golat, Francisco J. Rodríguez-Fortuño

Singular optics aims to understand and manipulate light's topological defects, pioneered by the discovery that phase vortex lines, strands of destructive interference, naturally occur in scalar wave fields. Monochromatic electromagnetic fields, however, are described by complex three-dimensional vectors that make individual scalar phase vortices in their vec

Amy R. Smith, Muhammad Zulqurnain, Angus G. M. Mathieson, Marek Szablewski

Thin films and van der Waals heterostructures derived from two-dimensional solids offer enormous potential for a broad range of novel, energy efficient devices, however, their use is currently hampered by slow, labor-intensive fabrication methods often employing hazardous chemicals. We demonstrate a novel technique for rapid, low-cost and environmentally-fri

Yuze He, Yanning Zhou, Wang Zhao, Zhongkai Wu

We present StdGEN, an innovative pipeline for generating semantically decomposed high-quality 3D characters from single images, enabling broad applications in virtual reality, gaming, and filmmaking, etc. Unlike previous methods which struggle with limited decomposability, unsatisfactory quality, and long optimization times, StdGEN features decomposability,

Nicolas Dalbec-Constant, Guillaume Thekkadath, Duncan England, Benjamin Sussman

We compare methods for signal classification applied to voltage traces from transition-edge sensors (TES) which are photon-number resolving detectors fundamental for accessing quantum advantages in information processing, communication and metrology. We quantify the impact of numerical analysis on the distinction of such signals. Furthermore, we explore dime