November 2024 arXiv papers — page 75

Jan Hladký, Daniel Iľkovič, Jared León, Xichao Shu

In this short note, we introduce cospectral graphons, paralleling the notion of cospectral graphs. As in the graph case, we give three equivalent definitions: by equality of spectra, by equality of cycle densities, and by a unitary transformation. We also give an example of two cospectral graphons that cannot be approximated by two sequences of cospectral gr

Adrian Dumitrescu, Arsenii Sagdeev

We introduce and analyze a natural game formulated as follows. In this one-person game, the player is given a random permutation $A=(a_1,\dots, a_n)$ of a multiset $M$ of $n$ reals that sum up to $0$, where each of the $n!$ permutation sequences is equally likely. The player only knows the value of $n$ beforehand. The elements of the sequence are revealed on

Yi Yan, Zhao-Long Wang

The abelian $(p+1)$-form gauge field is inherently coupled to the $p$-brane worldvolume. After quantization, the corresponding $p$-form gauge transformation is associated with the local phase ambiguity of the $p$-brane wave functional. In essence, the $p$-form gauge symmetry can be realized as a special construction of the generic 0-form gauge symmetry in th

Matteo Polimeno, Changho Kim, François Blanchette

We present a numerical model of fractal-structured aggregates in low-Reynolds-number flows. Assuming that aggregates are made of cubic particles, we first use a boundary integral method to compute the stresses acting on the boundary of the aggregates. From these external stresses, we compute the stresses within the aggregates in order to gain insights on the

Sepehr Moalemi, James Richard Forbes

This paper considers gain-scheduling of QSR-dissipative subsystems using scheduling matrices. The corresponding QSR-dissipative properties of the overall matrix-gain-scheduled system, which depends on the QSR properties of the subsystems scheduled, are explicitly derived. The use of scheduling matrices is a generalization of the scalar scheduling signals use

Amit Singh Ubhi, Clive C. Speake, Emilia Chick, Conner Gettings

The Cavendish torsion balance is the instrument of choice for measuring weak forces, such as gravity. Although torsion balances have extremely high sensitivity for measuring forces over ranges of a few cm and more, their dynamics make it difficult to extend this range to much less than fractions of mm. In particular forces such as the Casimir force are usual

Yu Nakayama

Inspired by the possibility of emergent supersymmetry in critical random systems, we study a field theory model with a quartic potential of one superfield, possessing the Parisi-Sourlas supertranslation symmetry. Within perturbative $\epsilon$ expansion, we find nine non-trivial scale invariant renormalization group fixed points, but only one of them is conf

D. E. Ruiz

The Rayleigh--Taylor instability (RTI) is an ubiquitous phenomenon that occurs in inertial-confinement-fusion (ICF) implosions and is recognized as an important limiting factor of ICF performance. To analytically understand the RTI dynamics and its impact on ICF capsule implosions, we develop a first-principle variational theory that describes an imploding s

Alexander G. Ramm

Sufficient conditions are given for a function $F(p)$ to be the Laplace transform of a function $f(t)$ or a distribution $f$. No assumption on $f$ is given a priori. It is not even assumed that $f=0$ for $t<0$.

Chenxin Huang, Zhiyuan Li, Manuel K. -H. Müller, Zelin Ye

In this paper, we investigate the Picard group of the Baily--Borel compactification of orthogonal Shimura varieties. As a key result, we determine the Picard group of the Baily--Borel compactification of the moduli space of quasi-polarized K3 surfaces, proving that it is isomorphic to $\mathbb{Z}$. Notably, this contrasts with the moduli space of smooth curv

Dimple Vijay Kochar, Hanrui Wang, Anantha Chandrakasan, Xin Zhang

Traditional approaches for designing analog circuits are time-consuming and require significant human expertise. Existing automation efforts using methods like Bayesian Optimization (BO) and Reinforcement Learning (RL) are sub-optimal and costly to generalize across different topologies and technology nodes. In our work, we introduce a novel approach, LEDRO,

Lorenzo Gavassino

Radiation is a universal friction-increasing agent. When two fluid layers are in relative motion, the inevitable exchange of radiation between such layers gives rise to an effective force, which tries to prevent the layers from sliding. This friction is often modeled as a Navier-Stokes shear viscosity. However, non-Newtonian corrections are expected to appea

Anne Kétri P. da Fonseca, Felipe Augusto O. Silveira, Célia M. Kuwana, Diego F. M. Oliveira

We revisit a time-dependent, oval-shaped billiard to investigate a phase transition from bounded to unbounded energy growth. In the static case, the phase space exhibits a mixed structure. The chaotic sea in the static scenario leads to average energy growth for a time-dependent boundary. However, inelastic collisions between the particle and the boundary li

Minki Kim, Georgy E. Manucharyan, Monica M. Wilhelmus

Eddies within the meso/submeso-scale range are prevalent throughout the Arctic Ocean, playing a pivotal role in regulating freshwater budget, heat transfer, and sea ice transport. While observations have suggested a strong connection between the dynamics of sea ice and the underlying turbulent flows, quantifying this relationship remains an ambitious task du

David Brandfonbrener, Nikhil Anand, Nikhil Vyas, Eran Malach

While scaling laws provide a reliable methodology for predicting train loss across compute scales for a single data distribution, less is known about how these predictions should change as we change the distribution. In this paper, we derive a strategy for predicting one loss from another and apply it to predict across different pre-training datasets and fro

Wannita Takerngsaksiri, Jirat Pasuksmit, Patanamon Thongtanunam, Chakkrit Tantithamthavorn

Recently, Large Language Models (LLMs)-based multi-agent paradigms for software engineering are introduced to automatically resolve software development tasks (e.g., from a given issue to source code). However, existing work is evaluated based on historical benchmark datasets, rarely considers human feedback at each stage of the automated software developmen

Mark G. Arnold, Thomas A. Bailey, John R. Cowles

Correctness proofs for floating point programs are difficult to verify. To simplify the task, a similar, but less complex system, known as logarithmic arithmetic can be used. The Boyer-Moore Theorem Prover, NQTHM, mechanically verified the correctness of a simple implementation of logarithmic arithmetic. It also verified some useful theorems about accumulate

S. Agarwal, J. A. Aguilar, N. Alden, S. Ali

The Radio Neutrino Observatory in Greenland (RNO-G) is the first in-ice radio array in the northern hemisphere for the detection of ultra-high energy neutrinos via the coherent radio emission from neutrino-induced particle cascades within the ice. The array is currently in phased construction near Summit Station on the Greenland ice sheet, with 7~stations de

Jakub Stetina, Martin Fajcik, Michal Stefanik, Michal Hradis

This article presents a comprehensive evaluation of 7 off-the-shelf document retrieval models: Splade, Plaid, Plaid-X, SimCSE, Contriever, OpenAI ADA and Gemma2 chosen to determine their performance on the Czech retrieval dataset DaReCzech. The primary objective of our experiments is to estimate the quality of modern retrieval approaches in the Czech languag

Horia Mărgărit, Amanda Bowman, Krishnageetha Karuppasamy, Alberto Maldonado-Romo

In this work, we present a case study in implementing a variational quantum algorithm for solving the Poisson equation, which is a commonly encountered partial differential equation in science and engineering. We highlight the practical challenges encountered in mapping the algorithm to physical hardware, and the software engineering considerations needed to

Asad Aali, Marius Arvinte, Sidharth Kumar, Yamin I. Arefeen

We study the effect of incorporating self-supervised denoising as a pre-processing step for training deep learning (DL) based reconstruction methods on data corrupted by Gaussian noise. K-space data employed for training are typically multi-coil and inherently noisy. Although DL-based reconstruction methods trained on fully sampled data can enable high recon

Bastian B. Brandt, Gergely Endrodi, G. Markó

We extend our measurement of the equation of state of isospin asymmetric QCD to small baryon and strangeness chemical potentials, using the leading order Taylor expansion coefficients computed directly at non-zero isospin chemical potentials. Extrapolating the fully connected contributions to vanishing pion sources is particularly challenging, which we overc

Wayne Barrett, Shaun Fallat, Veronika Furst, Shahla Nasserasr

The parameter $q(G)$ of an $n$-vertex graph $G$ is the minimum number of distinct eigenvalues over the family of symmetric matrices described by $G$. We show that all $G$ with $e(\overline{G}) = |E(\overline{G})| \leq \lfloor n/2 \rfloor -1$ have $q(G)=2$. We conjecture that any $G$ with $e(\overline{G}) \leq n-3$ satisfies $q(G) = 2$. We show that this conj

P. Freeman, S. Bottinelli, R. Plume, E. Caux

Star-forming regions host a large and evolving suite of molecular species. Molecular transition lines, particularly of complex molecules, can reveal the physical and dynamical environment of star formation. We aim to study the large-scale structure and environment of high-mass star formation through single-dish observations of CH$_3$CCH, CH$_3$OH, and H$_2$C

Vishwesh Nath, Wenqi Li, Dong Yang, Andriy Myronenko

Generalist vision language models (VLMs) have made significant strides in computer vision, but they fall short in specialized fields like healthcare, where expert knowledge is essential. In traditional computer vision tasks, creative or approximate answers may be acceptable, but in healthcare, precision is paramount.Current large multimodal models like Gemin

Xihe Gu, Greg Fields, Yaman Jandali, Tara Javidi

Trojan attacks are sophisticated training-time attacks on neural networks that embed backdoor triggers which force the network to produce a specific output on any input which includes the trigger. With the increasing relevance of deep networks which are too large to train with personal resources and which are trained on data too large to thoroughly audit, th

Qin Tian, Chen Zhao, Minglai Shao, Wenjun Wang

Domain generalization on graphs aims to develop models with robust generalization capabilities, ensuring effective performance on the testing set despite disparities between testing and training distributions. However, existing methods often rely on static encoders directly applied to the target domain, constraining its flexible adaptability. In contrast to

Ingo Czerwinski, Alexander Pott

A Sidon set $M$ is a subset of $\mathbb{F}_2^t$ such that the sum of four distinct elements of $M$ is never 0. The goal is to find Sidon sets of large size. In this note we show that the graphs of almost perfect nonlinear (APN) functions with high linearity can be used to construct large Sidon sets. Thanks to recently constructed APN functions $\mathbb{F}_2^

Giulia Mescolini, Jules Pitcho, Massimo Sorella

We study the advection equation along vector fields singular at the initial time. More precisely, we prove that for divergence-free vector fields in $L^1_{loc}((0, T ]; BV (\mathbb{T}^d;\mathbb{R}^d))\cap L^2((0, T ) \times\mathbb{T}^d;\mathbb{R}^d)$, there exists a unique vanishing diffusivity solution. This class includes the vector field constructed by De

Adam Falkowski, Panagiotis Marinellis

We propose an on-shell description of spinning binary systems in gravitational theories where compact objects display scalar hair. The framework involves matter particles of arbitrary spin which, in addition to the minimal coupling to gravitons, are conformally coupled to a massless scalar mediating non-standard interactions. We use the unitary factorization

Tom Reichert, Jörg Aichelin

The mechanism for generating directed and elliptic flow in heavy-ion collisions is investigated and quantified for the SIS18 and SIS100 energy regimes. The observed negative elliptic flow $v_2$, at midrapidity has been explained either via (in-plane) shadowing or via (out-of-plane) squeeze-out. To settle this question, we employ the Ultra-relativistic Quantu

Lion Schulz, Miguel Patrício, Daan Odijk

We propose an information-theoretic framework to measure narratives, providing a formalism to understand pivotal moments, cliffhangers, and plot twists. This approach offers creatives and AI researchers tools to analyse and benchmark human- and AI-created stories. We illustrate our method in TV shows, showing its ability to quantify narrative complexity and

Yu Luo, Lina Pu, Aijun Song

This paper presents an underwater acoustic reconfigurable intelligent surface (UA-RIS) designed for long-range, high-speed, and environmentally friendly communication in oceanic environments. The proposed UA-RIS comprises multiple pairs of acoustic reflectors that utilize a synthetic reflection scheme to flexibly control the amplitude and phase of reflected

Liuxin Gu, You Zhou

Nonlinear optics has long been a cornerstone of modern photonic technology, enabling a wide array of applications, from frequency conversion to the generation of ultrafast light pulses. Recent breakthroughs in two-dimensional (2D) materials have opened a frontier in this field, offering new opportunities for both classical and quantum nonlinear optics. These

Tim Strobel, Michal Vyvlecka, Ilenia Neureuther, Tobias Bauer

The quest for a global quantum internet is based on the realization of a scalable network which requires quantum hardware with exceptional performance. Among them are quantum light sources providing deterministic, high brightness, high-fidelity entangled photons and quantum memories with coherence times in the millisecond range and above. To operate the netw

Priyanka Kumari, Olexandr Kurochkin, Vassili G. Nazarenko, Oleg D. Lavrentovich

Domain structure of a fluid ferroelectric nematic is dramatically different from the domain structure of solid ferroelectrics since it is not restricted by rectilinear crystallographic axes and planar surface facets. We demonstrate that thin films of a ferroelectric nematic seeded by colloidal inclusions produce domain walls in the shape of conics such as a

Razvan Gabriel Iagar, Ariel Sánchez

Some qualitative properties of radially symmetric solutions to the non-homogeneous heat equation with critical density and weighted source $$ |x|^{-2}\partial_tu=\Delta u+|x|^{\sigma}u^p, \quad (x,t)\in\mathbb{R}^N\times(0,T), $$ are obtained, in the range of exponents $p>1$, $\sigma\ge-2$. More precisely, we establish conditions fulfilled by the initial dat

Eta Yang

Sign language translation, especially in gloss-free paradigm, is confronting a dilemma of impracticality and unsustainability due to growing resource-intensive methodologies. Contemporary state-of-the-arts (SOTAs) have significantly hinged on pretrained sophiscated backbones such as Large Language Models (LLMs), embedding sources, or extensive datasets, indu

Razvan Gabriel Iagar, Ariel Sánchez

The following Fisher-KPP type equation $$ u_t=Ku_{xx}-Bu^q+Au^p, \quad (x,t)\in\real\times(0,\infty), $$ with $p>q>0$ and $A$, $B$, $K$ positive coefficients, is considered. For both $p>q>1$ and $p>1$, $q=1$, we construct stationary solutions, establish their behavior as $|x|\to\infty$ and prove that they are separatrices between solutions decreasing to zero

Ricardo Gutierrez, Jesse B. Hoagg

This article presents a closed-form adaptive controlbarrier-function (CBF) approach for satisfying state constraints in systems with parametric uncertainty. This approach uses a sampled-data recursive-least-squares algorithm to estimate the unknown model parameters and construct a nonincreasing upper bound on the norm of the estimation error. Together, this

Thomas Flynn, Patrick Johnstone, Shinjae Yoo

In distributed optimization, the communication of model updates can be a performance bottleneck. Consequently, gradient compression has been proposed as a means of increasing optimization throughput. In general, due to information loss, compression introduces a penalty on the number of iterations needed to reach a solution. In this work, we investigate how t

Colverd Grace, Schade Laura, Takami Jumpei, Bot Karol

Tree species classification plays an important role in nature conservation, forest inventories, forest management, and the protection of endangered species. Over the past four decades, remote sensing technologies have been extensively utilized for tree species classification, with Synthetic Aperture Radar (SAR) emerging as a key technique. In this study, we

Jason Forbus, Darshana Wickramaratne, John L. Lyons, M. E. Zvanut

We present photo-electron paramagnetic resonance (EPR) measurements and first-principles calculations that indicate germanium (Ge) is a DX-center in AlGaN. Our photo-EPR measurements on Ge-doped AlGaN samples show no EPR spectra in the dark, while persistent EPR spectra is observed upon photoexcitation with photon energies greater than ~1.3 eV. Thermally ann

Morgan MacLeod, Antonija Oklopčić, Fabienne Nail, Dion Linssen

Planets lose mass to atmospheric outflows, and this mass loss is thought to be central in shaping the bimodal population of gaseous giant and rocky terrestrial exoplanets in close orbits. We model the escape of planetary atmospheres in three dimensional gas dynamic simulations in order to study their emergent morphology. Planetary outflows show a range of sh

Stanley S. Coelho, Lucas Queiroz, Danilo T. Alves

In quantum mechanics courses, students often solve the Schr\"odinger equation for the harmonic oscillator with time-independent parameters. However, time-dependent quantum harmonic oscillators are relevant in modeling several problems as, for instance, the description of quantum motion of particles in traps, shortcuts to adiabaticity, generation of squeezed

Orestis Lagkas Nikolos, Chloe Alverti, Stratos Psomadakis, Georgios Goumas

Resource elasticity is one of the key defining characteristics of the Function-as-a-Service (FaaS) serverless computing paradigm. While compute resources assigned to VM-sandboxed functions can be seamlessly adjusted on the fly, memory elasticity remains challenging. Hot(un)plugging memory resources suffers from long reclamation latencies and occupies valuabl

Xuechen Zhang, Xiangyu Chang, Mingchen Li, Amit Roy-Chowdhury

The attention mechanism within the transformer architecture enables the model to weigh and combine tokens based on their relevance to the query. While self-attention has enjoyed major success, it notably treats all queries $q$ in the same way by applying the mapping $V^\top\text{softmax}(Kq)$, where $V,K$ are the value and key embeddings respectively. In thi

Kannan M Ridings, Eneasi E L Vaka'uta, Sam M Croot

We explore the melting mechanisms of silver nanowires through molecular dynamics simulations and theoretical modelling, where we observe that two distinct mechanisms or pathways emerge that dictate how the solid-liquid interface melts during the phase transition. For wires longer than a critical length ($L>L_{\textrm{crit}}$), an Arrhenius-type diffusion mod

A. Batsidis, B. Milošević, M. D. Jiménez-Gamero

This paper presents and examines computationally convenient goodness-of-fit tests for the family of generalized Poisson distributions, which encompasses notable distributions such as the Compound Poisson and the Katz distributions. The tests are consistent against fixed alternatives and their null distribution can be consistently approximated by a parametric

Roberto Bomfin, Ahmad Bazzi, Hao Guo, Hyeongtaek Lee

The following paper provides a multi-band channel measurement analysis on the frequency range (FR)3. This study focuses on the FR3 low frequencies 6.5 GHz and 8.75 GHz with a setup tailored to the context of integrated sensing and communication (ISAC), where the data are collected with and without the presence of a target. A method based on multiple signal c

V. K. Bhartiya, T. Kim, J. Li, T. P. Darlington

Understanding the ground state of van der Waals (vdW) magnets is crucial for designing devices leveraging these platforms. Here, we investigate the magnetic excitations and charge order in Fe$_{4.75}$GeTe$_2$, a vdW ferromagnet with $\approx$ 315 K Curie temperature. Using Fe $L_3 - $edge resonant inelastic x-ray scattering, we observe a dual nature of magne

Koh Sakano, Kairi Furui, Masahito Ohue

Natural products are substances produced by organisms in nature and often possess biological activity and structural diversity. Drug development based on natural products has been common for many years. However, the intricate structures of these compounds present challenges in terms of structure determination and synthesis, particularly compared to the effic

Nicolaj K. Jyde, Henrik H. Kristensen, Lorenz Kranabetter, Jeppe K. Christensen

Vibrational wave packets are created in the lowest triplet state \triplet of $\mathrm{K_2}$ and $\mathrm{Rb_2}$ residing on the surface of helium nanodroplets, through non-resonant stimulated impulsive Raman scattering induced by a moderately intense near-infrared laser pulse. A delayed, intense 50-fs laser pulse doubly ionizes the alkali dimers via multipho

Slimane Adjerid, Tao Lin, Haroun Meghaichi

This article presents an error analysis of the recently introduced Frenet immersed finite element (IFE) method. The Frenet IFE space employed in this method is constructed to be locally conforming to the function space of the associated weak form for the interface problem. This article further establishes a critical trace inequality for the Frenet IFE functi

Nandika Ramamurthy, Dr Daniel Lumsden, Dr Rachel Sparks

Hyperkinetic movement disorders (HMDs) in children, including dystonia (abnormal twisting) and chorea (irregular, random movements), pose significant diagnostic challenges due to overlapping clinical features. The prevalence of dystonia ranges from 2 to 50 per million, and chorea from 5 to 10 per 100,000. These conditions are often diagnosed with delays aver

Guilherme T. Irumé, Joel Pavan, Rudi Gaelzer

The usual approach on electrostatic wave decay process for a weak beam-plasma system considers two different wave modes interplaying, the Langmuir and ion-sound mode. In the present paper, a single-mode approach is shown to be feasible for conditions where the respective dispersion relations undergo topological changes. Numerical solutions for the dispersion

Xiangzhe Xu, Zian Su, Jinyao Guo, Kaiyuan Zhang

While recent code-specific large language models (LLMs) have greatly enhanced their code generation capabilities, the safety of these models remains under-explored, posing potential risks as insecure code generated by these models may introduce vulnerabilities into real-world systems. Existing methods collect security-focused datasets from real-world vulnera

Juan Alonso, Juan Manuel Burgos, Miguel Paternain

We show that there is a topology on certain groups of loops in Euclidean space such that these groups are embedded in a Fr\'echet-Lie group which is the structural group of a principal bundle with connection whose holonomy coincides with the Chen signature map. We also give an alternative geometric new proof of the Chen signature theorem and a generalization

Yuanyuan Tian, Wenwen Li, Lei Hu, Xiao Chen

Retrieval and recommendation are two essential tasks in modern search tools. This paper introduces a novel retrieval-reranking framework leveraging Large Language Models (LLMs) to enhance the spatiotemporal and semantic associated mining and recommendation of relevant unusual climate and environmental events described in news articles and web posts. This fra

Stefano Scanzio, Gabriele Formis, Tullio Facchinetti, Giacomo Paolini

One of the main goals of wireless sensor networks is to permit the involved nodes to communicate with low energy budgets, as they are typically battery-powered. When such networks are employed in industrial scenarios, constraints about latency may have a significant role, too. The TSCH mechanism, and more in general TDMA schemes, rely on traffic scheduling,

Seok-Jin Kim, Min-hwan Oh

We study the performance guarantees of exploration-free greedy algorithms for the linear contextual bandit problem. We introduce a novel condition, named the \textit{Local Anti-Concentration} (LAC) condition, which enables a greedy bandit algorithm to achieve provable efficiency. We show that the LAC condition is satisfied by a broad class of distributions,

Sachit Kuhar, Wasi Uddin Ahmad, Zijian Wang, Nihal Jain

Recent advancements in code completion models have primarily focused on local file contexts. However, these studies do not fully capture the complexity of real-world software development, which often requires the use of rapidly-evolving public libraries. To fill the gap, we introduce LibEvolutionEval, a detailed study requiring an understanding of library ev

Tingting Liu, Salvatore Giorgi, Ankit Aich, Allison Lahnala

As AI chatbots increasingly incorporate empathy, understanding user-centered perceptions of chatbot empathy and its impact on conversation quality remains essential yet under-explored. This study examines how chatbot identity and perceived empathy influence users' overall conversation experience. Analyzing 155 conversations from two datasets, we found that w

Yucheng Xing, Xin Wang

Modern convolutional neural networks (CNNs) organize computation as a discrete stack of layers whose parameters are independently stored and learned, with the number of layers fixed as an architectural hyperparameter. In this work, we explore an alternative perspective: can network parameterization itself be modeled as a continuous dynamical system? We intro

Hongyi Guan, Negar Ahani, Carlos J. García-Cervera, Ananya Renuka Balakrishna

The width of the magnetic hysteresis loop is often correlated with the material's magnetocrystalline anisotropy constant $\kappa_1$. Traditionally, a common approach to reduce the hysteresis width has been to develop alloys with $\kappa_1$ as close to zero as possible. However, contrary to this widely accepted view, we present evidence that magnetoelastic in

Meryem Altin Karagoz, O. Ufuk Nalbantoglu, Geoffrey C. Fox

Deep learning has proven very promising for interpreting MRI in brain tumor diagnosis. However, deep learning models suffer from a scarcity of brain MRI datasets for effective training. Self-supervised learning (SSL) models provide data-efficient and remarkable solutions to limited dataset problems. Therefore, this paper introduces a generative SSL model for

Roberto Dias Algarte

This article introduces a novel approach to the mathematical development of Ordinary Least Squares and Neural Network regression models, diverging from traditional methods in current Machine Learning literature. By leveraging Tensor Analysis and fundamental matrix computations, the theoretical foundations of both models are meticulously detailed and extended

Clément Bonnet, Ariel N. Lee, Franck Wertel, Antoine Tamano

In the last two years, text-to-image diffusion models have become extremely popular. As their quality and usage increase, a major concern has been the need for better output control. In addition to prompt engineering, one effective method to improve the controllability of diffusion models has been to condition them on additional modalities such as image styl

Rui Feng, Chenlei Leng

Asymmetric relational data is increasingly prevalent across diverse fields, underscoring the need for directed network models to address the complex challenges posed by their unique structures. Unlike undirected models, directed models can capture reciprocity, the tendency of nodes to form mutual links. In this work, we address a fundamental question: what i

Tharnier O. Puel, Adam T. Turflinger, Sebastian P. Horvath, Jeff D. Thompson

We propose a new method for converting single microwave photons to single optical sideband photons based on spinful impurities in magnetic materials. This hybrid system is advantageous over previous proposals because (i) the implementation allows much higher transduction rates ($10^{3}$ times faster at the same optical pump Rabi frequency) than state-of the

Wei Wang, Zhanghao Yu, Yiwei Zou, Joshua E Woods

Miniature bioelectronic implants promise revolutionary therapies for cardiovascular and neurological disorders. Wireless power transfer (WPT) is a significant method for miniaturization, eliminating the need for bulky batteries in devices. Despite successful demonstrations of millimetric battery free implants in animal models, the robustness and efficiency o

Yucheng Xing, Xiaodong Liu, Xin Wang

Diffusion models typically generate data through a fixed denoising trajectory that is shared across all samples. However, generation targets can differ in complexity, suggesting that a single pre-defined diffusion process may not be optimal for every input. In this work, we investigate input-adaptive generative dynamics for diffusion models, where the genera

Ioakeim Ampatzoglou, Tristan Léger

In this article we identify a sharp ill-posedness/well-posedness threshold for kinetic wave equations (KWE) derived from quasilinear Schr\"{o}dinger models. We show well-posedness using a collisional averaging estimate proved in our earlier work \cite{AmLe}. Ill-posedness manifests as instantaneous loss of smoothness for well-chosen initial data. We also pro

Amit Ophir, Claus Sorensen

We investigate under which circumstances there exists nonzero {\it{projective}} smooth $\field[G]$-modules, where $\field$ is a field of characteristic $p$ and $G$ is a locally pro-$p$ group. We prove the non-existence of (non-trivial) projective objects for so-called {\it{fair}} groups -- a family including $\bf{G}(\frak{F})$ for a connected reductive group

Mateusz Czaja, Mikołaj Misiak, Abdur Rehman

In the determination of the Cabibbo-Kobayashi-Maskawa matrix element $|V_{cb}|$ from inclusive semileptonic $B$-meson decays, moments of the leptonic invariant mass spectrum constitute valuable observables. To evaluate them with sufficient precision, perturbative $\mathcal{O}(\alpha_s^2)$ corrections to the analogous spectrum in the partonic $b\to X_c l\bar{

Nigar Alishzade, Jamaladdin Hasanov

Sign language processing technology development relies on extensive and reliable datasets, instructions, and ethical guidelines. We present a comprehensive Azerbaijani Sign Language Dataset (AzSLD) collected from diverse sign language users and linguistic parameters to facilitate advancements in sign recognition and translation systems and support the local

Shashi B. Mishra

Using first-principles calculations, we systematically investigate the spin contributions to the inverse Faraday effect (IFE) in transition metals. The IFE depends on the d-electron filling and asymmetry between excited electron and hole spin moments. Our results reveal that even elements with smaller electron magnetic moments, like Os, can exhibit higher IF

Vadim E. Levit, Eugen Mandrescu

Let $\alpha(G)$ denote the cardinality of a maximum independent set and $\mu(G)$ be the size of a maximum matching of a graph $G=\left( V,E\right) $. If $\alpha(G)+\mu(G)=\left\vert V\right\vert $, then $G$ is a K\"{o}nig-Egerv\'{a}ry graph, and $G$ is a $1$-K\"{o}nig-Egerv\'{a}ry graph whenever $\alpha(G)+\mu(G)=\left\vert V\right\vert -1$. The corona $H\ci

JP Phillips, Emil Y. Sidky, Fatma Terzioglu, Ingrid S. Reiser

The goal of this work is to study occurrences of non-unique solutions in dual-energy CT (DECT) for objects containing water and a contrast agent. Previous studies of the Jacobian of nonlinear systems identified that a vanishing Jacobian determinant indicates the existence of multiple solutions to the system. Vanishing Jacobian determinants are identified for

Maurício Corrêa, José Seade

In this expository article, we study and discuss invariants of vector fields and holomorphic foliations that intertwine the theories of complex analytic singular varieties and singular holomorphic foliations on complex manifolds: two different settings with many points in common.

Terence Highsmith

We study the problem of an organization that matches agents to objects where agents have preference rankings over objects and the organization uses algorithms to construct a ranking over objects on behalf of each agent. Our new framework carries the interpretation that the organization and its agents may be misaligned in pursuing some underlying matching goa

Yunfei Ge, Quanyan Zhu

This chapter explores the symbiotic relationship between Artificial Intelligence (AI) and trust in networked systems, focusing on how these two elements reinforce each other in strategic cybersecurity contexts. AI's capabilities in data processing, learning, and real-time response offer unprecedented support for managing trust in dynamic, complex networks. H

Jan Dubiński, Antoni Kowalczuk, Franziska Boenisch, Adam Dziedzic

Diffusion Models (DMs) benefit from large and diverse datasets for their training. Since this data is often scraped from the Internet without permission from the data owners, this raises concerns about copyright and intellectual property protections. While (illicit) use of data is easily detected for training samples perfectly re-created by a DM at inference

Dimitrios P. Panagoulias, Elissaios Sarmas, Vangelis Marinakis, Maria Virvou

This paper presents an innovative approach to Extreme Value Analysis (EVA) by introducing the Extreme Value Dynamic Benchmarking Method (EVDBM). EVDBM integrates extreme value theory to detect extreme events and is coupled with the novel Dynamic Identification of Significant Correlation (DISC)-Thresholding algorithm, which enhances the analysis of key variab

Jared Pincus, Eric Koskinen

Commutativity of program code (i.e. the equivalence of two code fragments composed in alternate orders) is of ongoing interest in many settings such as program verification, scalable concurrency, and security analysis. While some have explored static analysis for code commutativity, few have specifically catered to heap-manipulating programs. We introduce an

Seong Jong Yoo, Snehesh Shrestha, Irina Muresanu, Cornelia Fermüller

Musicians delicately control their bodies to generate music. Sometimes, their motions are too subtle to be captured by the human eye. To analyze how they move to produce the music, we need to estimate precise 4D human pose (3D pose over time). However, current state-of-the-art (SoTA) visual pose estimation algorithms struggle to produce accurate monocular 4D

Igors Gorbovickis, Johan Taflin

We establish the independence of multipliers for polynomial endomorphisms of $\mathbb C^n$ and endomorphisms of $\mathbb P^n.$ This allows us to extend results about the bifurcation measure and the critical height obtained in \cite{arXiv:2305.02246} to the case of polynomial endomorphisms of $\mathbb C^n$ for $n\geq 3$. An important step in the proof is the

Megan Mazzatenta, Martin A. Erinin, Baptiste Néel, Luc Deike

Bubbles entrained by breaking waves rise to the ocean surface, where they cluster before bursting and release droplets into the atmosphere. The ejected drops and dry aerosol particles, left behind after the liquid drop evaporates, affect the radiative balance of the atmosphere and can act as cloud condensation nuclei. The remaining uncertainties surrounding

Vincent Lemaire, Gilles Pagès, Christian Yeo

We introduce a new class of neural networks designed to be convex functions of their inputs, leveraging the principle that any convex function can be represented as the supremum of the affine functions it dominates. These neural networks, inherently convex with respect to their inputs, are particularly well-suited for approximating the prices of options with

Poorya Khajouie, Titli Sarkar, Krishna Rauniyar, Li Chen

Protein structures represent the key to deciphering biological functions. The more detailed form of similarity among these proteins is sometimes overlooked by the conventional structural comparison methods. In contrast, further advanced methods, such as Triangular Spatial Relationship (TSR), have been demonstrated to make finer differentiations. Still, the c

Md Niaz Imtiaz, Naimul Khan

Emotion recognition has significant potential in healthcare and affect-sensitive systems such as brain-computer interfaces (BCIs). However, challenges such as the high cost of labeled data and variability in electroencephalogram (EEG) signals across individuals limit the applicability of EEG-based emotion recognition models across domains. These challenges a

Murat Arda Onsu, Poonam Lohan, Burak Kantarci, Emil Janulewicz

Virtualization technology, Network Function Virtualization (NFV), gives flexibility to communication and 5G core network technologies for dynamic and efficient resource allocation while reducing the cost and dependability of the physical infrastructure. In the NFV context, Service Function Chain (SFC) refers to the ordered arrangement of various Virtual Netw

Zhao-Feng Wu, Michail Damoulakis, Paz Beniamini, Dimitrios Giannios

Gamma-ray bursts (GRBs) are among the most energetic events in the universe, driven by relativistic jets launched from black holes (BHs) formed during the collapse of massive stars or after the merger of two neutron stars (NSs). The jet power depends on the BH spin and the magnetic flux accreted onto it. In the standard thin disk model, jet power is limited

David Cruz-Uribe, Michael Penrod

In this paper we prove a reverse H\"{o}lder inequality for the variable exponent Muckenhoupt weights $\mathcal{A}_{p(\cdot)}$, introduced by the first author, Fiorenza, and Neugeabauer. All of our estimates are quantitative, showing the dependence of the exponent function on the $\mathcal{A}_{p(\cdot)}$ characteristic. As an application, we use the reverse H

Steffen Kionke

A famous conjecture of Chowla on the least primes in arithmetic progressions implies that the abscissa of convergence of the Weil representation zeta function for a procyclic group $G$ only depends on the set $S$ of primes dividing the order of $G$ and that it agrees with the abscissa of the Dedekind zeta function of $\mathbb{Z}[p^{-1}\mid p \not\in S]$. Her

Mariette Dupuy, Marie Chavent, Remi Dubois

This paper introduces a methodology based on Denoising AutoEncoder (DAE) for missing data imputation. The proposed methodology, called mDAE hereafter, results from a modification of the loss function and a straightforward procedure for choosing the hyper-parameters. An ablation study shows on several UCI Machine Learning Repository datasets, the benefit of u

Laura N Montoya, Jennafer Shae Roberts, Belen Sanchez Hidalgo

Early and accurate melanoma detection is crucial for improving patient outcomes. Recent advancements in artificial intelligence AI have shown promise in this area, but the technologys effectiveness across diverse skin tones remains a critical challenge. This study conducts a systematic review and preliminary analysis of AI based melanoma detection research p

Gabriel Rodriguez-Rondon

This paper introduces a new approach for estimating core inflation indicators based on common factors across a broad range of price indices. Specifically, by utilizing procedures for detecting multiple regimes in high-dimensional factor models, we propose two types of core inflation indicators: one incorporating multiple structural breaks and another based o

Stephanie M. Lukin, Claire Bonial, Matthew Marge, Taylor Hudson

We introduce the Situated Corpus Of Understanding Transactions (SCOUT), a multi-modal collection of human-robot dialogue in the task domain of collaborative exploration. The corpus was constructed from multiple Wizard-of-Oz experiments where human participants gave verbal instructions to a remotely-located robot to move and gather information about its surro

Shang Liu, Yu Pan, Guanting Chen, Xiaocheng Li

Learning a reward model (RM) from human preferences has been an important component in aligning large language models (LLMs). The canonical setup of learning RMs from pairwise preference data is rooted in the classic Bradley-Terry (BT) model that accepts binary feedback, i.e., the label being either Response 1 is better than Response 2, or the opposite. Such