December 2024 arXiv papers — page 17

Andreas Oliveira, Jian Zheng, Mario Sznaier

The growing complexity of dynamical systems and advances in data collection necessitates robust data-driven control strategies without explicit system identification and robust synthesis. Data-driven stability has been explored in linear and nonlinear systems, often by turning the problem into a linear or positive semidefinite program. This paper focuses on

Leonard Gross

A Schr\"odinger operator that is bounded below and has a unique positive ground state can be transformed into a Dirichlet form operator by the ground state transformation. If the resulting Dirichlet form operator is hypercontractive, Davies and Simon call the Schr\"odinger operator ``intrinsically hypercontractive". I will show that if one adds a suitable po

Luca Benatti, Ariadna León Quirós, Francesca Oronzio, Alessandra Pluda

In this paper, we focus on Hamilton's pinching conjecture formulated in Hamilton's paper "Three-manifolds with positive Ricci curvature". Let $(M, g)$ be a complete, connected, noncompact Riemannian $3$-manifold satisfying the Ricci-pinching condition. Then, it is flat. Here, we give an alternative proof, based on nonlinear potential theory, under the extra

Matija Matijević, Livio Žužić, Jacopo Forneris, Zdravko Siketić

In this work, development of the new Laser and Ion Beam Induced Luminescence (LIBIL) experimental end-station has been presented. To systematically test the capabilities and limitations of the newly developed setup, ionoluminescence (IL) and iono-photoluminescence (IPL) measurements were performed on a type IIa optical grade and a type Ib nitrogen rich diamo

Guan-Horng Liu, Tianrong Chen, Evangelos A. Theodorou

Generalized Schr\"odinger Bridges (GSBs) are a fundamental mathematical framework used to analyze the most likely particle evolution based on the principle of least action including kinetic and potential energy. In parallel to their well-established presence in the theoretical realms of quantum mechanics and optimal transport, this paper focuses on an algori

Zhirayr Avetisyan, Khachatur Khachatryan, Michael Ruzhansky

In this paper we prove the existence and uniqueness of positive mild solutions for the semilinear parabolic equations of the form $u_t+\mathcal{L}u=f+h\cdot G(u)$, where $h$ is a positive function and $G$ a positive concave function (for example, $G(u)=u^\alpha$ for $0<\alpha<1$). In contrast with the case of convex $G$, where the Fujita exponent appears, an

Dhanya G. Nair, Neil M. Nagar, Venkatessh Ramakrishnan, Maciek Wielgus

Using the Event Horizon Telescope (EHT), the gravitationally lensed rings around the supermassive black holes (SMBHs) in Messier 87 (M87) and Sagittarius A* (Sgr A*) have now been successfully imaged at a resolution under 10 gravitational radii (R$_{\rm g}$ $ = \rm{GM/c^2}$). To expand studies beyond M87 and Sgr A*, we have constructed the Event Horizon and

William Yue

The widespread success of artificial intelligence in fields like natural language processing and computer vision has not yet fully transferred to robotics, where progress is hindered by the lack of large-scale training data and the complexity of real-world tasks. To address this, many robot learning researchers are pushing to get robots deployed at scale in

Dang Nguyen, Sunil Gupta

In image classification tasks, deep learning models are vulnerable to image distortion. For successful deployment, it is important to identify distortion levels under which the model is usable i.e. its accuracy stays above a stipulated threshold. We refer to this problem as Model Assurance under Image Distortion, and formulate it as a classification task. Gi

Jeffrey Shi, Benjamin H. November, Stephen Carr, Harris Pirie

Resonators with a high quality factor (Q) are crucial components in a wide range of advanced technologies, including energy harvesting, chemical and biological sensing, and second-harmonic generation. Many applications also require resonance across a broad frequency range. However, single-cavity resonators face a fundamental trade-off between bandwidth and q

Laslo Hunhold

Although not primarily designed for this purpose, floating-point numbers are often used to represent integral values, with some applications explicitly relying on this capability. However, the integral representation properties of IEEE 754 floating-point numbers have not yet been formally investigated. Recently, the bfloat16, posit and takum machine number f

Thiago L. M. Guedes, Guillermo A. Mena Marugán, Markus Müller, Francesca Vidotto

The Hamiltonian constraint remains an elusive object in loop quantum gravity because its action on spinnetworks leads to changes in their corresponding graphs. As a result, calculations in loop quantum gravity are often considered unpractical, and neither the eigenstates of the Hamiltonian constraint, which form the physical space of states, nor the concrete

Ismael T. Freire, Paul Verschure

Social learning, a cornerstone of cultural evolution, enables individuals to acquire knowledge by observing and imitating others. At the heart of its efficacy lies episodic memory, which encodes specific behavioral sequences to facilitate learning and decision-making. This study explores the interrelation between episodic memory and social learning in collec

Anastassia Vybornova, Ane Rahbek Vierø, Kirsten Krogh Hansen, Michael Szell

A bicycle node network is a wayfinding system targeted at recreational cyclists, consisting of numbered signposts placed alongside already existing infrastructure. Bicycle node networks are becoming increasingly popular as they encourage sustainable tourism and rural cycling, while also being flexible and cost-effective to implement. However, the lack of a f

Alfredo Fernandez, Ankur Mali

We propose the Hyperbolic Tangent Exponential Linear Unit (TeLU), a neural network hidden activation function defined as TeLU(x)=xtanh(exp(x)). TeLU's design is grounded in the core principles of key activation functions, achieving strong convergence by closely approximating the identity function in its active region while effectively mitigating the vanishin

Laslo Hunhold, James Quinlan

Solving sparse linear systems lies at the core of numerous computational applications. Consequently, understanding the performance of recently proposed alternatives to the established IEEE 754 floating-point numbers, such as bfloat16 and the tapered-precision posit and takum machine number formats, is of significant interest. This paper examines these format

Umut Kose

Plastic scintillator detectors with three-dimensional granularity and sub-nanosecond time resolution offer simultaneous particle tracking, identification, and calorimetry. However, scaling to larger volumes and finer segmentation poses significant challenges in manufacturing and assembly due to high costs, extensive time, and precision requirements. To addre

Niloofar Sayadi, Sadie Co, Diego Gomez-Zara

As more people meet, interact, and socialize online, Social Virtual Reality (VR) emerges as a technology that bridges the gap between traditional face-to-face and online communication. Unlike traditional screen-based applications, Social VR provides immersive, spatial, and three-dimensional social interactions, making it a potential tool for enhancing remote

Ibrahim Almosallam

Quantum Key Distribution (QKD) enables the sharing of cryptographic keys secured by quantum mechanics. The BB84 protocol assumed single-photon sources, but practical systems rely on weak coherent pulses vulnerable to photon-number-splitting (PNS) attacks. The Gottesman-Lo-L\"utkenhaus-Preskill (GLLP) framework addressed these imperfections, deriving secure k

Henry J. Xie, Jinghan Zhang, Xinhao Zhang, Kunpeng Liu

In recent years, Large Language Models (LLMs) have become increasingly more powerful in their ability to complete complex tasks. One such task in which LLMs are often employed is scoring, i.e., assigning a numerical value from a certain scale to a subject. In this paper, we strive to understand how LLMs score, specifically in the context of empathy scoring.

Ziye Chen, Hao Qi

Mathematical problem-solving is a key field in artificial intelligence (AI) and a critical benchmark for evaluating the capabilities of large language models (LLMs). While extensive research has focused on mathematical problem-solving, most existing work and datasets concentrate on computational tasks, leaving gaps in areas like mathematical analysis, which

Jiaoyang Huang, Theo McKenzie, Horng-Tzer Yau

We consider the normalized adjacency matrix of a random $d$-regular graph on $N$ vertices with any fixed degree $d\geq 3$ and denote its eigenvalues as $\lambda_1=d/\sqrt{d-1}\geq \lambda_2\geq\lambda_3\cdots\geq \lambda_N$. We establish the following two results as $N\rightarrow \infty$. (i) With high probability, all eigenvalues are optimally rigid, up to

Sophie Raynor

Circuit algebras are a symmetric version of Jones's planar algebras. They originated in quantum topology as a framework for encoding virtual crossings. This paper extends existing results for modular operads to construct a graphical calculus and monad for general circuit algebras and prove an abstract nerve theorem. The proof relies on a subtle interplay bet

Chengyu Fang, Jared Miles, Jonathan Goldwin, Martin Lichtman

We demonstrate trapping of individual rubidium (Rb) and cesium (Cs) atoms in an interleaved array of bright tweezers and dark bottle-beam traps, using a microfabricated optical element illuminated by a single laser beam and a 4F system with spatial filtering. Our approach exploits the opposite-sign dynamic polarizabilities of Rb and Cs, ensuring each species

Sophie Raynor

Circuit algebras are a symmetric analogue of Jones's planar algebras introduced to study finite-type invariants of virtual knotted objects. Circuit algebra structures appear, in different forms, across mathematics. This paper provides a dictionary for translating between their diverse incarnations and describing their wider context. A formal definition of a

S. Alekhin, M. V. Garzelli, J. Mazzitelli, S. -O. Moch

We describe our recent NNLO QCD extraction of the top-quark pole mass from fits to experimental data on total inclusive and normalized (multi)-differential cross sections for $t\bar{t} + X$ hadroproduction, using as input various modern PDF + $\alpha_s(M_Z)$ sets. We find top-quark mass values compatible among each other and with the PDG 2024 preferred value

Sara Baradaran, Liyan Huang, Mukund Raghothaman, Weihang Wang

WebAssembly (Wasm) has emerged as a powerful technology for executing high-performance code and reusing legacy code in web browsers. With its increasing adoption, ensuring the reliability of WebAssembly code becomes paramount. In this paper, we investigate how well WebAssembly compilers fulfill code reusability. Specifically, we inquire (1) what challenges a

Thiago L. M. Guedes, Guillermo A. Mena Marugán, Francesca Vidotto, Markus Müller

In loop quantum gravity (LQG), states of the gravitational field are represented by labeled graphs called spin networks. Their dynamics can be described by a Hamiltonian constraint, { which acts on the spin network states modifying both spins and graphs.} Fixed-graph approximations of the dynamics have been extensively studied, but its full graph-changing ac

Yuxin Yang, Hongkuan Zhou, Rajgopal Kannan, Viktor Prasanna

Temporal Graph Neural Networks (TGNNs) have emerged as powerful tools for modeling dynamic interactions across various domains. The design space of TGNNs is notably complex, given the unique challenges in runtime efficiency and scalability raised by the evolving nature of temporal graphs. We contend that many of the existing works on TGNN modeling inadequate

VS Morales-Salgado

This work reflects on mechanics as an epistemological framework on the state of a physical system to regard dynamics as the distribution of mechanical properties over spacetime coordinates. The resulting distribution is taken to be the partition function of the relevant physical quantities over a spacetime parametrized by coordinates. The partition yields a

Chunheng Zhao, Stefano Longari, Michele Carminati, Pierluigi Pisu

As electronic systems become increasingly complex and prevalent in modern vehicles, securing onboard networks is crucial, particularly as many of these systems are safety-critical. Researchers have demonstrated that modern vehicles are susceptible to various types of attacks, enabling attackers to gain control and compromise safety-critical electronic system

Cao Vien Phung, Max Franke, Ehsan Tohidi, June Heinemann

Future smart factories are expected to deploy applications over high-performance indoor wireless channels in the millimeter-wave (mmWave) bands, which on the other hand are susceptible to high path losses and Line-of Sight (LoS) blockages. Low-cost Reconfigurable Intelligent Surfaces (RISs) can provide great opportunities in such scenarios, due to its abilit

Muhammad Irfan Khan, Elina Kontio, Suleiman A. Khan, Mojtaba Jafaritadi

Federated learning (FL) enables collaborative model training across decentralized datasets while preserving data privacy. However, optimally selecting participating collaborators in dynamic FL environments remains challenging. We present RL-HSimAgg, a novel reinforcement learning (RL) and similarity-weighted aggregation (simAgg) algorithm using harmonic mean

S. N. Storchak

Based on a method developed earlier for a finite-dimensional mechanical system, the problem of path integral reduction for scalar electrodynamics is considered. Using the Coulomb gauge, the stochastic differential equations for the reduced dynamics on the orbit space are obtained. It is shown that the geometry of the reduced space is completely determined by

Qing Zong, Zhaowei Wang, Tianshi Zheng, Xiyu Ren

The rapid development of LLMs has sparked extensive research into their factual knowledge. Current works find that LLMs fall short on questions around low-frequency entities. However, such proofs are unreliable since the questions can differ not only in entity frequency but also in difficulty themselves. So we introduce ComparisonQA benchmark, containing 283

Muhammad Irfan Khan, Elina Kontio, Suleiman A. Khan, Mojtaba Jafaritadi

This study presents a robust and efficient client selection protocol designed to optimize the Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2024). In the evolving landscape of FL, the judicious selection of collaborators emerges as a critical determinant for the success and efficiency of collective learning endeavors, p

Chen Chen, Xinkui Zhao, Guanjie Cheng, Yuesheng Xu

Interconnection is crucial for computing systems. However, the current interconnection performance between processors and devices, such as memory devices and accelerators, significantly lags behind their computing performance, severely limiting the overall performance. To address this challenge, Intel proposes Compute Express Link (CXL), an open industry-sta

Ruiwen Shu

Break of radial symmetry for interaction energy minimizers is a phenomenon where a radial interaction potential whose associated energy minimizers are never radially symmetric. Numerically, it has been frequently observed for various types of interaction potentials, however, rigorous justification of this phenomenon was only done in very limited cases. We pr

P. D. Hinds, A. Sharma, M. V. Tretyakov

In this paper, we establish well-posedness of reflected McKean-Vlasov SDEs and their particle approximations in smooth non-convex domains. We prove convergence of the interacting particle system to the corresponding mean-field limit with the optimal rate of convergence. We motivate this study with applications to sampling and optimization in constrained doma

Fernando A. Z. Santamaria, Elizaveta Vishnyakova

We develop the theory of $H$-graded manifolds for any finitely generated abelian group, using tools from representation theory. Furthermore, we introduce and investigate the notion of $H$-graded coverings of supermanifolds in the case where $H$ is a finite abelian group.

Majid Aalizadeh, Morteza Azmoudeh Afshar, Xudong Fan

A novel framework is proposed that combines multi-resonance biosensors with machine learning (ML) to significantly enhance the accuracy of parameter prediction in biosensing. Unlike traditional single-resonance systems, which are limited to one-dimensional datasets, this approach leverages multi-dimensional data generated by a custom-designed nanostructure,

Lucas Hackl, Mario Kieburg, Joel Maldonado

Studying the typical entanglement entropy of a bipartite system when averaging over different ensembles of pure quantum states has been instrumental in different areas of physics, ranging from many-body quantum chaos to black hole evaporation. We extend such analysis to open quantum systems and mixed states, where we compute the typical mutual information in

Bernd Bauerhenne, Lucas Tsunaki, Jan Thieme, Boris Naydenov

We present and characterize advanced attacks on an ensemble-based quantum token protocol that allows for implementing non-clonable quantum coins. Multiple differently initialized tokens of identically prepared qubit ensembles are combined to a quantum coin that can be issued by a bank. A sophisticated attempt to copy tokens can assume that measurements on su

V. M. López-Guerrero, M. A. Arroyo-Ureña, J. L. Díaz-Cruz, O. Félix-Beltrán

We study the production and possible detection of a single Higgs boson in association with a top quark in proton-proton collisions ($pp \to th + X$) at the High-Luminosity Large Hadron Collider. This process absent in the Standard Model is predicted by other models such as the Two-Higgs Doublet Model of type III, which is the theoretical framework adopted in

Denis Nikolaevich Sob'yanin

Analytically solving the magnetostatic Maxwell equations in the bispherical coordinates, we calculate the magnetic field around two uniformly magnetized spheres oriented so that their magnetic moments are parallel to the axis passing through the centers of the spheres. We demonstrate that, contrary to what is often claimed in the literature, the magnetic int

Bolun Zhang, Gan Zheng, Nguyen Van Huynh

This paper investigates the application of quantum machine learning to End-to-End (E2E) communication systems in wireless fading scenarios. We introduce a novel hybrid quantum-classical autoencoder architecture that combines parameterized quantum circuits with classical deep neural networks (DNNs). Specifically, we propose a hybrid quantum-classical autoenco

Alan Hernandez-Flores, Gabriel Montoya-Vega

Polynomial invariants constitute a dynamic and essential area of study in the mathematical theory of knots. From the pioneer Alexander polynomial, the revolutionary Jones polynomial, to the collectively discovered HOMFLYPT polynomial, just to mention a few, these algebraic expressions have been central to the understanding of knots and links. The introductio

Magdalena Czubak, Ian Miller, Svetlana Roudenko

The electromagnetic nonlinear Schr\"odinger (emNLS) equation is a variant of the well-studied nonlinear Schr\"odinger equation. In this article, we consider questions of global existence or blow-up for emNLS in dimensions 3 and higher.

Esther Bou Dagher, Yifu Wang, Boguslaw Zegarlinski

In this work, we study regularity problems of certain Markov generators, which naturally appear in the context of analysis in functional spaces associated to probability measures on nilpotent Lie groups.

Yulin Feng, Hailang Jin, Steven X. Ding, Hao Ye

The robustness of fault detection algorithms against uncertainty is crucial in the real-world industrial environment. Recently, a new probabilistic design scheme called distributionally robust fault detection (DRFD) has emerged and received immense interest. Despite its robustness against unknown distributions in practice, current DRFD focuses on the overall

Rolf Schimmrigk

One of the fundamental open questions in QFT is what kind of functions appear as Feynman integrals. In recent years this question has often been considered in a geometric context by interpreting the polynomials that appear in these integrals as defining algebraic varieties. One focal point of the past decade has in particular been the class of Calabi-Yau var

Md. Zehan Alam, Tonmoy Roy, H. M. Nahid Kawsar, Iffat Rimi

This paper explores and enhances the application of Transfer Learning (TL) for multilabel image classification in medical imaging, focusing on brain tumor class and diabetic retinopathy stage detection. The effectiveness of TL-using pre-trained models on the ImageNet dataset-varies due to domain-specific challenges. We evaluate five pre-trained models-Mobile

Hao Huang, Fei Peng

Seymour's celebrated second neighborhood conjecture, now more than thirty years old, states that in every oriented digraph, there is a vertex $u$ such that the size of its second out-neighborhood $N^{++}(u)$ is at least as large as that of its first out-neighborhood $N^+(u)$. In this paper, we prove the existence of $u$ for which $|N^{++}(u)| \ge 0.715538 |N

Junqing Jia, Xiaoyun Jiang

We establish optimal error bounds on an exponential wave integrator (EWI) for the space fractional nonlinear Schr\"{o}dinger equation (SFNLSE) with low regularity potential and/or nonlinearity. For the semi-discretization in time, under the assumption of $L^\infty$-potential, $C^1$-nonlinearity, and $H^\alpha$-solution with $1<\alpha \leq 2$ being the fracti

Stepan Dergachev, Konstantin Yakovlev

In this work, we study the problem where a group of mobile agents needs to reach a set of goal locations, but it does not matter which agent reaches a specific goal. Unlike most of the existing works on this topic that typically assume the existence of the centralized planner (or controller) and limit the agents' moves to a predefined graph of locations and

Suman Kunwar, Banji Raphael Owabumoye, Abayomi Simeon Alade

With the increasing use of plastic, the challenges associated with managing plastic waste have become more challenging, emphasizing the need of effective solutions for classification and recycling. This study explores the potential of deep learning, focusing on convolutional neural networks (CNNs) and object detection models like YOLO (You Only Look Once), t

Mallory Knodel, Andrés Fábrega, Daniella Ferrari, Jacob Leiken

End-to-end encryption (E2EE) has become the gold standard for securing communications, bringing strong confidentiality and privacy guarantees to billions of users worldwide. However, the current push towards widespread integration of artificial intelligence (AI) models, including in E2EE systems, raises some serious security concerns. This work performs a cr

Athanasios Karagounis

Autonomous vehicles (AVs) rely on sophisticated perception systems to interpret their surroundings, a cornerstone for safe navigation and decision-making. The integration of Large Language Models (LLMs) into AV perception frameworks offers an innovative approach to address challenges in dynamic environments, sensor fusion, and contextual reasoning. This pape

L. -I. Bulyk, D. Wlodarczyk, S. S. Nagorny, V. V. Nahorna

The luminescence and Raman spectra of the Cs2ZrCl6 crystal in a wide range of pressures were studied in this work for the first time. Luminescence measurements were performed up to 10 GPa, while the Raman spectra were measured up to 20 GPa. The luminescence data revealed a linear blue shift of the emission maximum from about 2.5 eV at ambient pressure to 3.1

Alicja Jokiel-Rokita, Sylwester Piątek, Rafał Topolnicki

The classical concept of inequality curves and measures is extended to conditional inequality curves and measures and a curve of conditional inequality measures is introduced. This extension provides a more nuanced analysis of inequality in relation to covariates. In particular, this enables comparison of inequalities between subpopulations, conditioned on c

Shuguang Chen, Guang Lin

Large Language Models (LLMs) have shown remarkable performance in various natural language processing tasks but face challenges in mathematical reasoning, where complex problem-solving requires both linguistic understanding and mathematical reasoning skills. Existing approaches to address this challenge often rely on ensemble methods and suffer from the prob

Nimrod Curtis, Osher Azulay, Avishai Sintov

Learning to navigate in unstructured environments is a challenging task for robots. While reinforcement learning can be effective, it often requires extensive data collection and can pose risk. Learning from expert demonstrations, on the other hand, offers a more efficient approach. However, many existing methods rely on specific robot embodiments, pre-speci

Abdollah Rida

Credit Scoring is one of the problems banks and financial institutions have to solve on a daily basis. If the state-of-the-art research in Machine and Deep Learning for finance has reached interesting results about Credit Scoring models, usage of such models in a heavily regulated context such as the one in banks has never been done so far. Our work is thus

Tansel Dokeroglu, Deniz Canturk, Tayfun Kucukyilmaz

This review examines over 150 new metaheuristics of the last six years (between 2019 and 2024), underscoring their profound influence and performance. Over the past three decades, more than 500 new metaheuristic algorithms have been proposed, with no slowdown in sight. An overwhelming abundance that complicates the process of selecting and assessing the most

d'Artis Kancs

The preparedness and readiness of Europe is currently being challenged not only by Russia, but since recently also by its long-standing allies. In response to the evolving external security environment, the EU's White Paper on European Defence Readiness 2030 outlines the key defence issues in Europe - including critical capability gaps of forces, challenges

Yurii Belov, Alexander Borichev, Alexander Kuznetsov

We establish a relation between the approximation in $L^2[-\pi,\pi]$ by exponentials with the set of frequencies of Beurling--Malliavin density less than $1$ and the meromorphic interpolation at $\mathbb Z$. Furthermore, we show that typical $L^2[-\pi,\pi]$ functions admit such an approximation.

Toyib Ogunremi, Serah Akojenu, Anthony Soronnadi, Olubayo Adekanmbi

This paper introduces AfriHG -- a news headline generation dataset created by combining from XLSum and MasakhaNEWS datasets focusing on 16 languages widely spoken by Africa. We experimented with two seq2eq models (mT5-base and AfriTeVa V2), and Aya-101 LLM. Our results show that Africa-centric seq2seq models such as AfriTeVa V2 outperform the massively multi

Alexey Solyanik

In this note, we use a toy problem of detecting cycles of length two in a tent map to highlight some curious phenomena in the behavior of discrete dynamical systems. This work presents no new results or proofs, only computer experiments and illustrations. Thus, it serves as light reading and does not aim to be a scientific paper but is rather educational in

Jiazhen Shao

The multi-Higgs model (NHDM) is a class of new physics models that go beyond the Standard Model with fewer assumptions, namely by postulating the existence of multiple Higgs doublets, while yielding a rich phenomenology. Among them, the Four-Higgs-Doublet model (4HDM) has attracted increasing attention in recent years, with nearly a hundred papers dedicated

Ziming Mao, Rishabh Iyer, Scott Shenker, Ion Stoica

Caching is widely used in industry to improve application performance by reducing data-access latency and taking the load off the backend infrastructure. TTLs have become the de-facto mechanism used to keep cached data reasonably fresh (i.e., not too out of date with the backend). However, the emergence of real-time applications requires tighter data freshne

Jan-Christoph Kassing, Jürgen Giesl

Dependency pairs are one of the most powerful techniques to analyze termination of term rewrite systems automatically. We adapt dependency pairs to the probabilistic setting and develop an annotated dependency pair framework for automatically proving almost-sure termination of probabilistic term rewrite systems, both for full and innermost rewriting. To eval

Antonino Flachi, Gonçalo M. Quinta

In this letter we propose a new interpretation of the Casimir effect. Concretely, we show that the Casimir energy can be written as the quantum ``Von Neumann'' entropy associated to a 2-qubit, mixed pseudo-density matrix of the relevant quantum fluctuations. The quantum entropy we introduce draws parallels to the concept of quantum inseparability found in qu

Akindele Michael Olawole, Jesujoba O. Alabi, Aderonke Busayo Sakpere, David I. Adelani

In this work, we present Yor\`ub\'a automatic diacritization (YAD) benchmark dataset for evaluating Yor\`ub\'a diacritization systems. In addition, we pre-train text-to-text transformer, T5 model for Yor\`ub\'a and showed that this model outperform several multilingually trained T5 models. Lastly, we showed that more data and larger models are better at diac

Ankur Kumar, Anubhav sinha

This paper presents experimental study of reacting hydrogen jet in crossflow. High speed shadowgraph images are used to capture flame dynamics. Unforced jets with various momentum flux ratios (q) are studied. Proper Orthogonal Decomposition (POD) analysis is used to examine the high-speed instantaneous images and characterize spatio-temporal behavior the rea

Sebastian Siegel, Ming-Jay Yang, John-Paul Strachan

Processing long temporal sequences is a key challenge in deep learning. In recent years, Transformers have become state-of-the-art for this task, but suffer from excessive memory requirements due to the need to explicitly store the sequences. To address this issue, structured state-space sequential (S4) models recently emerged, offering a fixed memory state

Alexey S. Kotykhov, Max Hodapp, Christian Tantardini, Konstantin Kravtsov

We present a protocol for automated fitting of magnetic Moment Tensor Potential explicitly including magnetic moments in its functional form. For the fitting of this potential we use energies, forces, stresses, and magnetic forces (negative derivatives of energies with respect to magnetic moments) of configurations selected with an active learning algorithm.

Guneesh Vats, Priyanka Srivastava, Chiranjeevi Yarra

The current study examines the relationship between self-reported depression and the perception of affective speech within the Indian population. PANAS and PHQ-9 were used to assess current mood and depression, respectively. Participants' emotional reactivity was recorded on a valence and arousal scale against the affective speech audio presented in a sequen

Mohsen Yazdinejad, Marjan Kaedi

Question answering systems provide short, precise, and specific answers to questions. So far, many robust question answering systems have been developed for English, while some languages with fewer resources, like Persian, have few numbers of standard dataset. In this study, a comprehensive open-domain dataset is presented for Persian. This dataset is called

Hongxu Ma, Kai Tian, Tao Zhang, Xuefeng Zhang

Watch time prediction (WTP) has emerged as a pivotal task in short video recommendation systems, designed to quantify user engagement through continuous interaction modeling. Predicting users' watch times on videos often encounters fundamental challenges, including wide value ranges and imbalanced data distributions, which can lead to significant estimation

Bharath Kumar Agnur

This paper presents an advanced mapping system that combines drone imagery with machine learning and computer vision to overcome challenges in speed, accuracy, and adaptability across diverse terrains. By automating processes like feature detection, image matching, and stitching, the system produces seamless, high-resolution maps with minimal latency, offeri

Anirban Chatterjee, Yungui Gong

We employ a linear stability analysis approach to explore the dynamics of matter and curvature-driven dark energy interactions within the framework of two types of viable $f(R)$ gravity models. The interaction is modeled via a source term in the continuity equations, $\mathcal{Q} = \alpha \tilde{\rho}_{\rm m} \Big{(}\frac{3H^3}{\kappa^2 \rho_{\rm curv}} + \f

Nguyen N. Hung, Attila Maróti, Juan Martínez Madrid

Let $G = X \wr H$ be the wreath product of a nontrivial finite group $X$ with $k$ conjugacy classes and a transitive permutation group $H$ of degree $n$ acting on the set of $n$ direct factors of $X^n$. If $H$ is semiprimitive, then $k(G) \leq k^n$ for every sufficiently large $n$ or $k$. This result solves a case of the non-coprime $k(GV)$ problem and provi

Yingze Hou, Hoda Bidkhori, Taposh Banerjee

The problem of quickest detection of a change in the distribution of a sequence of random variables is studied. The objective is to detect the change with the minimum possible delay, subject to constraints on the rate of false alarms and the cost of observations used in the decision-making process. The post-change distribution of the data is known only withi

Abdellatif Mouhssine, Ahmed Ratnani, Hassane Sadok

In the present work, we study how to develop an efficient solver for the fast resolution of large and sparse linear systems that occur while discretizing elliptic partial differential equations using isogeometric analysis. Our new approach combines vector extrapolation methods with geometric multigrid schemes. Using polynomial-type extrapolation methods to s

Jean-Jacques Forneron, Zhongjun Qu

This paper considers filtering, parameter estimation, and testing for potentially dynamically misspecified state-space models. When dynamics are misspecified, filtered values of state variables often do not satisfy model restrictions, making them hard to interpret, and parameter estimates may fail to characterize the dynamics of filtered variables. To addres

Davide Legacci, Panayotis Mertikopoulos, Christos H. Papadimitriou, Georgios Piliouras

The long-run behavior of multi-agent learning - and, in particular, no-regret learning - is relatively well-understood in potential games, where players have aligned interests. By contrast, in harmonic games - the strategic counterpart of potential games, where players have conflicting interests - very little is known outside the narrow subclass of 2-player

Yuta Hozumi, Guo-Wei Wei

Despite decades of effort, understanding the shape of genome space in biology remains a challenge due to the similarity, variability, diversity, and plasticity of evolutionary relationships among species, genes, or other biological entities. We present a k-mer topology method, the first of its kind, to delineate the shape of the genome space. K-mer topology

Wen-Dong Jiang, Chih-Yung Chang, Hsiang-Chuan Chang, Ji-Yuan Chen

Weakly Supervised Monitoring Anomaly Detection (WSMAD) utilizes weak supervision learning to identify anomalies, a critical task for smart city monitoring. However, existing multimodal approaches often fail to meet the real-time and interpretability requirements of edge devices due to their complexity. This paper presents TCVADS (Two-stage Cross-modal Video

Zibin Pan, Zhichao Wang, Chi Li, Kaiyan Zheng

Federated Learning (FL) has received much attention in recent years. However, although clients are not required to share their data in FL, the global model itself can implicitly remember clients' local data. Therefore, it's necessary to effectively remove the target client's data from the FL global model to ease the risk of privacy leakage and implement ``th

Emre Ozan Polat, Zafer Artvin, Yusuf Şaki, Alpan Bek

We demonstrate that the decay rates of a fluorescent molecule can be controlled by electrically shifting a transparency introduced by a Fano resonance. An auxiliary quantum object (QO), located at the hotspot of a plasmonic nanoparticle, suppresses plasmonic excitation at its level spacing {\omega}_QO. As a result, the local density of states (LDOS) associat

Boris Rubin

Many known Radon-type transforms of symmetric (radial or zonal) functions are represented by one-dimensional Riemann-Liouville fractional integrals or their modifications. The present article contains new examples of such transforms in the Euclidean, spherical, and hyperbolic settings, when integration is performed over lower-dimensional geodesic spheres or

Luca Dal Negro, Riccardo Franchi, Marco Ornigotti

We investigate single-photon nonlinear refractive index change and frequency shift of Epsilon-Near-Zero (ENZ) sub-wavelength nanocavities. We apply the rigorous quantum Langevin-noise approach in the framework of Green's tensor quantization method to realistic ENZ materials with causal dispersion and derive closed-form analytical solutions for cavities with

Giuseppe Buttazzo

In this paper we prove the existence of an optimal domain $\Omega_{opt}$ for the shape optimization problem $$\max\Big\{\lambda_q(\Omega)\ :\ \Omega\subset D,\ \lambda_p(\Omega)=1\Big\},$$ where $q<p$ and $D$ is a prescribed bounded subset of ${\bf R}^d$. Here $\lambda_p(\Omega)$ (respectively $\lambda_q(\Omega)$) is the first eigenvalue of the $p$-Laplacian

Alexander Kozachinskiy

In this note, we use the VC dimension technique to prove the first lower bound against one-layer softmax transformers with infinite precision. We do so for two tasks: function composition, considered by Peng, Narayanan, and Papadimitriou, and the SUM$_2$ task, considered by Sanford, Hsu, and Telgarsky.

Krishna Shende, Matreyee Kandpal, Arvind, Kavita Dorai

The finite time operation of a quantum Otto heat engine leads to a trade-off between efficiency and output power, which is due to the deviation of the system from the adiabatic path. This trade-off caveat can be bypassed by using the shortcut-to-adiabaticity protocol. We experimentally implemented a quantum Otto heat engine using spin-1/2 nuclei on a nuclear

Jiangdong Fan, Hongcai He, Paul Weng, Hui Xu

A major bottleneck in imitation learning is the requirement of a large number of expert demonstrations, which can be expensive or inaccessible. Learning from supplementary demonstrations without strict quality requirements has emerged as a powerful paradigm to address this challenge. However, previous methods often fail to fully utilize their potential by di

Daniel A. Serino, Evan Bell, Marc Klasky, Ben S. Southworth

In high energy density physics (HEDP) and inertial confinement fusion (ICF), predictive modeling is complicated by uncertainty in parameters that characterize various aspects of the modeled system, such as those characterizing material properties, equation of state (EOS), opacities, and initial conditions. Typically, however, these parameters are not directl

David Chester, Alessio Marrani, Michael Rios, Klee Irwin

The Grassmann envelope is used to find the $\mathcal{N}=1$ `superquasiconformal' algebra in $D=10+1$. The adjoint representation of this algebra is found to contain $\mathfrak{su}_{2,2}\oplus \mathfrak{u}_{1}\oplus \mathfrak{su}_{5}$ as a submaximal subalgebra, giving a spectrum of conformal gravity with flipped $SU_{5}\times U_{1}$ GUT. Combining Yang-Mills

Daniel Smolyak, Courtney Paulson, Margrét V. Bjarnadóttir

In many healthcare settings, it is both critical to consider fairness when building analytical applications but also uniquely unacceptable to lower model performance for one group to match that of another (e.g. fairness cannot be achieved by lowering the diagnostic ability of a model for one group to match that of another and lose overall diagnostic power).

Sanskar Ranjan, Supratim Shit

Accurate coresets are a weighted subset of the original dataset, ensuring a model trained on the accurate coreset maintains the same level of accuracy as a model trained on the full dataset. Primarily, these coresets have been studied for a limited range of machine learning models. In this paper, we introduce a unified framework for constructing accurate cor