December 2024 arXiv papers — page 104

Chunmei Wang

This paper presents a simplified weak Galerkin (WG) finite element method for solving biharmonic equations avoiding the use of traditional stabilizers. The proposed WG method supports both convex and non-convex polytopal elements in finite element partitions, utilizing bubble functions as a critical analytical tool. The simplified WG method is symmetric and

Dmitry Ustalov

The rapid advancement of natural language processing (NLP) technologies, such as instruction-tuned large language models (LLMs), urges the development of modern evaluation protocols with human and machine feedback. We introduce Evalica, an open-source toolkit that facilitates the creation of reliable and reproducible model leaderboards. This paper presents i

Tran T. A. Nghia, Huy N. Pham, Nghia V. Vo

Recovering a low-complexity signal from its noisy observations by regularization methods is a cornerstone of inverse problems and compressed sensing. Stable recovery ensures that the original signal can be approximated linearly by optimal solutions of the corresponding Morozov or Tikhonov regularized optimization problems. In this paper, we propose new chara

Bruna Salgado, André Sequeira, Luis Paulo Santos

The unit commitment (UC) problem stands as a critical optimization challenge in the electrical power industry. It is classified as NP-hard, placing it among the most intractable problems to solve. This paper introduces a novel hybrid quantum-classical algorithm designed to efficiently (approximately) solve the UC problem in polynomial time. In this approach,

Johnathan Hoggarth, Kari Dalnoki-Veress

We present a simple and inexpensive method for measuring weak cohesive interactions. This technique is applied to the specific case of oil droplets with a depletion interaction, dispersed in an aqueous solution. The experimental setup involves creating a short string of droplets while immobilizing a single droplet. The droplets are held together via depletio

Abolfazl Younesi, Mohsen Ansari, Alireza Ejlali, Mohammad Amin Fazli

Fog computing brings about a transformative shift in data management, presenting unprecedented opportunities for enhanced performance and reduced latency. However, one of the key aspects of fog computing revolves around ensuring efficient power and reliability management. To address this challenge, we have introduced a novel model that proposes a non-coopera

Daniel Bath, Mircea Mustaţă, Uli Walther

We prove that hypersurfaces defined by irreducible square-free polynomials have rational singularities. As an easy consequence, we deduce that certain (possibly non-square-free) polynomials associated to pairs of square-free polynomials define hypersurfaces with rational singularities. This extends results on certain classes of polynomials associated to matr

Daniel B. de Freitas, Cleo V. da Silva, Mackson M. F. Nepomuceno

We study the first gravitational wave, GW150914, detected by advanced LIGO and constructed from the data of measurement of strain relative deformation of the fabric of spacetime. We show that the time series from the gravitational wave obeys a nonadditive entropy, and its dynamics evolve with the three associated Tsallis indices named q-triplet. This fact st

Boris Ruf, Marcin Detyniecki

We propose a simple way to use large language models (LLMs) in education. Specifically, our method aims to improve individual comprehension by adding a novel feature to online videos. We combine the low threshold for interactivity in digital experiences with the benefits of rephrased and elaborated explanations typical of face-to-face interactions, thereby s

Zeguan Wu, Xiu Yang, Tamás Terlaky

Quantum Interior Point Methods (QIPMs) have been attracting significant interests recently due to their potential of solving optimization problems substantially faster than state-of-the-art conventional algorithms. In general, QIPMs use Quantum Linear System Algorithms (QLSAs) to substitute classical linear system solvers. However, the performance of QLSAs d

Thorben Ortmann, Qi Wang, Larissa Putzar

In this study, we explored the potential of utilizing Facial Expression Activations (FEAs) captured via the Meta Quest Pro Virtual Reality (VR) headset for Facial Expression Recognition (FER) in VR settings. Leveraging the EmojiHeroVR Database (EmoHeVRDB), we compared several unimodal approaches and achieved up to 73.02% accuracy for the static FER task with

Cristóbal Méndez, C. J. Thompson, M. F. Van Duinen, S. J. Sibener

We propose a fully $ \textit{ab initio} $ approach to predicting thermal attenuation in elastic helium atom scattering amplitudes, validated through strong agreement with experiments on Nb(100) and (3$\times$1)-O/Nb(100) surfaces. Our results reveal the relative contributions from bulk, resonant, and surface phonon modes, as well as from different surface mo

Nargiz Humbatova, Jinhan Kim, Gunel Jahangirova, Shin Yoo

With the increased popularity of Deep Neural Networks (DNNs), increases also the need for tools to assist developers in the DNN implementation, testing and debugging process. Several approaches have been proposed that automatically analyse and localise potential faults in DNNs under test. In this work, we evaluate and compare existing state-of-the-art fault

Minhui Jiang, Yuansi Chen

We study the problem of drawing samples from a logconcave distribution truncated on a polytope, motivated by computational challenges in Bayesian statistical models with indicator variables, such as probit regression. Building on interior point methods and the Dikin walk for sampling from uniform distributions, we analyze the mixing time of regularized Dikin

Trishita Tiwari, G. Edward Suh

This work quantifies the risk of training data leakage from LLMs (Large Language Models) using sequence-level probabilities. Computing extraction probabilities for individual sequences provides finer-grained information than has been studied in prior benchmarking work. We re-analyze the effects of decoding schemes, model sizes, prefix lengths, partial sequen

Hong Zhang, Ying Liu, Romit Maulik

Classical neural ODEs trained with explicit methods are intrinsically limited by stability, crippling their efficiency and robustness for stiff learning problems that are common in graph learning and scientific machine learning. We present a semi-implicit neural ODE approach that exploits the partitionable structure of the underlying dynamics. Our technique

Sebastian Kilde-Westberg, Andreas Johansson, Jonas Enger

Generative AI tools, including the popular ChatGPT, have had a significant impact on discourses about future work and educational practices. Previous research in science education has highlighted the potential of generative AI in various education-related areas, including generating valuable discussion material, solving physics problems, and acting as a tuto

András Balogh, Márk Jelasity

Measuring the similarity of the internal representations of deep neural networks is an important and challenging problem. Model stitching has been proposed as a possible approach, where two half-networks are connected by mapping the output of the first half-network to the input of the second one. The representations are considered functionally similar if the

Olya Rezaeian, Onur Asan, Alparslan Emrah Bayrak

Advances in machine learning have created new opportunities to develop artificial intelligence (AI)-based clinical decision support systems using past clinical data and improve diagnosis decisions in life-threatening illnesses such breast cancer. Providing explanations for AI recommendations is a possible way to address trust and usability issues in black-bo

Sreeveni Das, Rhodri Mansell, Lukáš Flajšman, Maria-Andromachi Syskaki

Neuromorphic computing aims to revolutionize large-scale data processing by developing efficient methods and devices inspired by neural networks. Among these, the control of magnetism through ion migration has emerged as a promising approach due to the inherent memory and nonlinearity of ionically conducting and magnetic materials. In this work, we present a

Tsao-Hsien Chen

A class of Weyl group equivariant $\ell$-adic complexes on a torus, called the central complexes, was introduced and studied in our previous work on Braverman-Kazhdan conjecture. In this note we show that the category of central complexes admits functorial monoidal transfers with respect to morphisms between the dual groups. Combining with the work of Bezruk

Francesco Dagnino, Fabio Pasquali

Taking a quotient roughly means changing the notion of equality on a given object, set or type. In a quantitative setting, equality naturally generalises to a distance, measuring how much elements are similar instead of just stating their equivalence. Hence, quotients can be understood quantitatively as a change of distance. In this paper, we show how, combi

Gabriele Fioravanti

In this paper, we investigate the Dirichlet problem on lower dimensional manifolds for a class of weighted elliptic equations with coefficients that are singular on such sets. Specifically, we study the problem \[\begin{cases} -{\rm div}(|y|^a A(x,y) \nabla u) = |y|^a f + {\rm div}(|y|^a F), \\ u = \psi, \quad \text{ on } \Sigma_0, \end{cases} \] where $(x,y

Ashish Parmanand Pandey, Alan John Varghese, Sarang Patil, Mengjia Xu

Dynamic graph embedding has emerged as an important technique for modeling complex time-evolving networks across diverse domains. While transformer-based models have shown promise in capturing long-range dependencies in temporal graph data, they face scalability challenges due to quadratic computational complexity. This study presents a comparative analysis

Amime Mohamed Aboussalah, Yassine Abbahaddou

Generative models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) often fail to capture the full diversity of their training data, leading to mode collapse. While this issue is well-explored in image generation, it remains underinvestigated for time series data. We introduce a new definition of mode collapse specific to time s

Stephen Doty

Using the \texttt{WeylModules} \textsf{GAP} Package, we compute structural information about certain Weyl modules for type $G_2$ in characteristic $2$. This gives counterexamples to two conjectures stated by S.~Donkin in 1990. It also illustrates capabilities of the package, which can in principle be applied to Weyl modules for any simple, simply-connected a

Daniel N. Levitin

In this paper, we generalize the results of ($\textit{Groups, Geom. Dyn.}$, forthcoming) to describe the split left-invariant Riemannian distances on higher-rank Sol-type groups $G=\mathbf{N}\rtimes \mathbb{R}^k$. We show that the rough isometry type of such a distance is determined by a specific restriction of the metric to $\mathbb{R}^k$, and therefore the

Jonibek Mansurov, Akhmed Sakip, Alham Fikri Aji

In this paper, we show that knowledge distillation can be subverted to manipulate language model benchmark scores, revealing a critical vulnerability in current evaluation practices. We introduce "Data Laundering," a process that enables the covert transfer of benchmark-specific knowledge through seemingly legitimate intermediate training steps. Through exte

Paulina Stevia Nouwou Mindom, Leuson Da Silva, Amin Nikanjam, Foutse Khomh

Automatically locating buggy changesets associated with bug reports is crucial in the software development process. Deep Learning (DL)-based techniques show promising results by leveraging structural information from the code and learning links between changesets and bug reports. However, since source code associated with changesets evolves, the performance

Padma Srivari, Ella Paasio, Xinye Li, Sayani Majumdar

Thin film ferroelectric devices with ultralow power operation, non-volatile data retention and fast and reliable switching are attractive for non-volatile memory and as synaptic weight elements. However, low thermal budget ferroelectric oxides suffer from crystalline inhomogeneity and defects that makes their large-scale circuit integration challenging. Here

Akanksha Singh, Shaily, J. K. Singh, Ertan Güdekli

In this paper, we construct an isotropic cosmological model in the $ f(Q, T) $ theory of gravity in the frame of a flat FLRW spacetime being $ Q $ the non-metricity tensor and $ T $ the trace of the energy-momentum tensor. The gravity function is taken to be a quadratic equation, $ f(Q, T)=\zeta Q^2 + \gamma T $, where $ \zeta<0 $ and $ \gamma $ are the arbi

Cristina Draper, Thomas L. Meyer, Juana Sánchez-Ortega

A new combinatorial object, called generalised nice set, is classified up to collineations of the Fano plane. This classification is necessary to find the graded contractions of all the exceptional complex Lie algebras of dimension at least 52, endowed with $\mathbb Z_2^3$-gradings coming from the octonions. Our classification is of purely combinatorial natu

Dafna Schwartz, Lori Quinn, Nora E. Fritz, Lisa M. Muratori

Wearable sensors offer a non-invasive way to collect physical activity (PA) data, with walking as a key component. Existing models often struggle to detect gait bouts in individuals with neurodegenerative diseases (NDDs) involving involuntary movements. We developed J-Net, a deep learning model inspired by U-Net, which uses a pre-trained self-supervised foun

Kees Jan van Garderen, Noud van Giersbergen

Mediation analysis is a form of causal inference that investigates indirect effects and causal mechanisms. Confidence intervals for indirect effects play a central role in conducting inference. The problem is non-standard leading to coverage rates that deviate considerably from their nominal level. The default inference method in the mediation model is the p

Dehao Yuan, Levi Burner, Jiayi Wu, Minghui Liu

Event-based motion field estimation is an important task. However, current optical flow methods face challenges: learning-based approaches, often frame-based and relying on CNNs, lack cross-domain transferability, while model-based methods, though more robust, are less accurate. To address the limitations of optical flow estimation, recent works have focused

Felix Lotter, Rosa Preiß

The volume of a cyclic polytope can be obtained by forming an iterated integral along a suitable piecewise linear path running through its edges. Different choices of such a path are related by the action of a subgroup of the combinatorial automorphisms of the polytope. Motivated by this observation, we look for other linear combinations of iterated integral

Stefano Profumo, Liam Colombo-Murphy, Gabriela Huckabee, Maya Diaz Svensson

The question of what is the total entropy of the universe, how it compares to the maximal entropy of de Sitter space, and how it is distributed across the universe's components, bears considerable importance for a number of reasons. Here, we first update the computation of the entropy associated with various sectors of the observed universe, including in the

Peiyu Zeng, Yijiang Huang, Simon Huber, Stelian Coros

Robotic systems are routinely used in the logistics industry to enhance operational efficiency, but the design of robot workspaces remains a complex and manual task, which limits the system's flexibility to changing demands. This paper aims to automate robot workspace design by proposing a computational framework to generate a budget-minimizing layout by sel

K. E. Honasoge, M. Handschuh, W. K. Yam, S. Gandorfer

Superconducting circuits incorporating Josephson elements represent a promising hardware platform for quantum technologies. Potential applications include scalable quantum computing, microwave quantum networks, and quantum-limited amplifiers. However, progress in Josephson junction-based quantum technologies is facing the ongoing challenge of minimizing loss

Hao Shao, Shulun Wang, Yang Zhou, Guanglu Song

Video face swapping is becoming increasingly popular across various applications, yet existing methods primarily focus on static images and struggle with video face swapping because of temporal consistency and complex scenarios. In this paper, we present the first diffusion-based framework specifically designed for video face swapping. Our approach introduce

Gianluca Cubadda

The main aim of this paper is to review recent advances in the multivariate autoregressive index model [MAI], originally proposed by Reinsel (1983), and their applications to economic and financial time series. MAI has recently gained momentum because it can be seen as a link between two popular but distinct multivariate time series approaches: vector autore

Sooyoung Kim, Joonwoo Kwon, Junbeom Kwon, Jungyoun Janice Min

The human brain is a complex system requiring both macroscopic and microscopic components for comprehensive understanding. However, mapping nonlinear relationships between these scales remains challenging due to technical limitations and the high cost of multimodal Magnetic Resonance Imaging (MRI) acquisition. To address this, we introduce Macro2Micro, a dee

Zartasha Mustansar, Haider Ali, Lee Margetts, Saad Ahmad Khan

This paper presents the principal challenges and opportunities associated with computational biomechanics research. The underlying cognitive control involved in the process of human motion is inherently complex, dynamic, multidimensional, and highly non-linear. The dynamics produced by the internal and external forces and the body's ability to react to them

Salar Abbaspourazad, Anshuman Mishra, Joseph Futoma, Andrew C. Miller

Modern wearable devices can conveniently record various biosignals in the many different environments of daily living, enabling a rich view of individual health. However, not all biosignals are the same: high-fidelity biosignals, such as photoplethysmogram (PPG), contain more physiological information, but require optical sensors with a high power footprint.

Jia Xu, Manish Dixit, Xi Wang

Construction robots operate in unstructured construction sites, where effective visual perception is crucial for ensuring safe and seamless operations. However, construction robots often handle large elements and perform tasks across expansive areas, resulting in occluded views from onboard cameras and necessitating the use of multiple environmental cameras

Hamed Vakili, Edward Schwartz, Alexey A. Kovalev

We predict the existence of anisotropic spin-transfer torque effect in textured altermagnets. To this end, we generalize the Zhang-Li torque to incorporate the symmetry associated with prototypical $d$-wave altermagnets and identify the spin-splitter adiabatic and nonadiabatic torques. Applying our results to domain wall dynamics induced by spin-transfer tor

Luke W. Filor, Kaya Mori, Gabriel Bridges, Charles J. Hailey

We present the first NuSTAR X-ray observation of EF Eri, a well-known polar system. The NuSTAR observation was conducted in conjunction with NICER, shortly after EF Eri entered a high accretion state following an unprecedented period of low activity lasting 26 years since 1997. NuSTAR detected hard X-ray emission up to 50 keV with an X-ray flux of $1.2\times

Emilio Santos

The quantum electromagnetic (EM) field is formulated in the Weyl-Wigner representation (WW), which is equivalent to the standard Hilbert space one (HS). In principle it is possible to interpret within WW all experiments involving the EM field interacting with macroscopic bodies, the latter treated classically. In the WW formalism the essential difference bet

Rongxiang Wang, Zhiming Xu, Felix Xiaozhu Lin

Speech foundation models, such as OpenAI's Whisper, become the state of the art in speech understanding due to their strong accuracy and generalizability. Yet, their applications are mostly limited to processing pre-recorded speech, whereas processing of streaming speech, in particular doing it efficiently, remains rudimentary. Behind this inefficiency are m

Justine Singleton, Megan DeCesar, Shi Dai, Deven Bhakta

Globular clusters host large populations of millisecond pulsars (MSPs) due to their high gravitational encounter rates, producing many binary systems and thus MSPs via the recycling process. Seven pulsars with spin periods ranging from 3 ms to 134 ms have been discovered in Terzan 1, which was targeted for pulsar searches with the Green Bank Telescope after

Benjamin Riviere, John Lathrop, Soon-Jo Chung

The ability of a robot to plan complex behaviors with real-time computation, rather than adhering to predesigned or offline-learned routines, alleviates the need for specialized algorithms or training for each problem instance. Monte Carlo Tree Search is a powerful planning algorithm that strategically explores simulated future possibilities, but it requires

Luigi Accardi, Tarek Hamdi, Yun Gang Lu

This paper continues the program of applying beyond physics the technique of \textbf{probabilistic quantization} and extending to the quantum mechanics associated with the arc--sine distributions our previous results on the semi--circle distribution. We derive analytical expressions for the momentum and kinetic energy operators using the arc--sine weighted H

T. A. Movsessian, J. Bally, T. Yu. Magakian, A. V. Moiseev

We continue to present the results of a Byurakan Narrow Band Imaging Survey (BNBIS). In this work we present the results of the search and further detailed investigation of the objects, found in the course of the BNBIS survey in the southern part of the Mon R2 association. For the search of HH objects the narrow band images, obtained with the 1-m Schmidt tel

Jamshid Namdari, Robert T Krafty, Amita Manatunga

Many studies collect data that can be considered as a realization of a point process. Included are medical imaging data where photon counts are recorded by a gamma camera from patients being injected with a gamma emitting tracer. It is of interest to develop analytic methods that can help with diagnosis as well as in the training of inexpert radiologists. Pa

A. Kontogiannis, P. Nair, M. Loecher, D. B. Ennis

We solve a Bayesian inverse Reynolds-averaged Navier-Stokes (RANS) problem that assimilates mean flow data by jointly reconstructing the mean flow field and learning its unknown RANS parameters. We devise an algorithm that learns the most likely parameters of an algebraic effective viscosity model, and estimates their uncertainties, from mean flow data of a

Francesco Fassò, Nicola Sansonetto

There is an important difference between Hamiltonian-like vector fields in an almost-symplectic manifold $(M,\sigma)$, compared to the standard case of a symplectic manifold: in the almost-symplectic case, a vector field such that the contraction iX{\sigma} is closed need not be a symmetry of $\sigma$. We thus call partially-Hamiltonian those vector fields w

Eduardo Abi Jaber

We introduce a simple, efficient and accurate nonnegative preserving numerical scheme for simulating the square-root process. The novel idea is to simulate the integrated square-root process first instead of the square-root process itself. Numerical experiments on realistic parameter sets, applied for the integrated process and the Heston model, display high

Nicolas Roy, Beicheng Lou, Shanhui Fan, Alexandre Mayer

Twisted bilayer photonic crystals introduce a twist between two stacked photonic crystal slabs, enabling strong modulation of their electromagnetic properties. The change in the twist angle strongly influences the resonant frequencies and available propagating diffraction orders with applications including sensing, lasing, slow light or wavefront engineering

Jiageng Li, Zhen Dong, Chong Wang, Haozhen You

Automated library APIs testing is difficult as it requires exploring a vast space of parameter inputs that may involve objects with complex data types. Existing search based approaches, with limited knowledge of relations between object states and program branches, often suffer from the low efficiency issue, i.e., tending to generate invalid inputs. Symbolic

Olivier Pironneau

This article is an attempt to elucidate the effect of a slowly varying refractive index on the temperature in a stratified atmosphere, with a particular focus on greenhouse gases such as CO2. It validates an iterative method for the vector radiative transfer equations (VVRTE) called Iterations on the Source. As the system proposed by Chandrasekhar and Pomran

Kurando IIDA, Kenjiro MIMURA

This paper introduces the Comprehensive AI-assisted Translation Edit Ratio (CATER), a novel and fully prompt-driven framework for evaluating machine translation (MT) quality. Leveraging large language models (LLMs) via a carefully designed prompt-based protocol, CATER expands beyond traditional reference-bound metrics, offering a multidimensional, reference-

Javier Chagoya, I. Díaz-Saldaña, Mario H. Amante, J. C. López-Domínguez

In this work, we derive a generalized modified Friedmann equation based on an entropy-area relation that incorporates established modifications, such as volumetric, linear, and logarithmic terms, in addition to novel entropic modifications that might yield to relevant cosmological implications at different stages of the evolution of the Universe. Some of the

Jean-Philippe Bouchaud

We argue that establishing the phase diagram of Agent Based Models (ABM) is a crucial first step, together with a qualitative understanding of how collective phenomena come about, before any calibration or more quantitative predictions are attempted. Computer-aided *gedanken* experiments are by themselves of genuine value: if we are not able to make sense of

Xinli Xu, Wenhang Ge, Dicong Qiu, ZhiFei Chen

Estimating physical properties for visual data is a crucial task in computer vision, graphics, and robotics, underpinning applications such as augmented reality, physical simulation, and robotic grasping. However, this area remains under-explored due to the inherent ambiguities in physical property estimation. To address these challenges, we introduce Gaussi

Erfan Entezami, Hui Guan

Recent efforts to enhance immersive and interactive user experiences have driven the development of volumetric video, a form of 3D content that enables 6 DoF. Unlike traditional 2D content, volumetric content can be represented in various ways, such as point clouds, meshes, or neural representations. However, due to its complex structure and large amounts of

Valery Alexeev, Anand Deopurkar, Changho Han

We describe Baily-Borel, toroidal, and geometric -- using the KSBA stable pairs -- compactifications of some moduli spaces of K3 surfaces with a nonsymplectic automorphism of order $3$ and $4$ for which the fixed locus of the automorphism contains a curve of genus $\ge2$. For order $3$, we treat all the maximal-dimensional such families. We show that the tor

Danielle R. Thomas, Conrad Borchers, Sanjit Kakarla, Jionghao Lin

Equity is a core concern of learning analytics. However, applications that teach and assess equity skills, particularly at scale are lacking, often due to barriers in evaluating language. Advances in generative AI via large language models (LLMs) are being used in a wide range of applications, with this present work assessing its use in the equity domain. We

Jing-Hang Fu, Yu-Ji Li, Hui-Min Yang, Yu-Bo Li

We explore toponium, the smallest known quantum bound state of a top quark and its antiparticle, bound by the strong force. With a Bohr radius of $8\times 10^{-18}$~m and a lifetime of $2.5 \times 10^{-25}$~s, toponium uniquely probes microphysics. Unlike all other hadrons, it is governed by ultraviolet freedom. This distinction offers novel insights into qu

Guan Wang, Haoyi Niu, Jianxiong Li, Li Jiang

Among various branches of offline reinforcement learning (RL) methods, goal-conditioned supervised learning (GCSL) has gained increasing popularity as it formulates the offline RL problem as a sequential modeling task, therefore bypassing the notoriously difficult credit assignment challenge of value learning in conventional RL paradigm. Sequential modeling,

Timothy Sauer, George Sugihara

Two numerical methods are proposed for detection of coupling between multiple time series generated by deterministic nonlinear systems. The first detects interdependence or the existence of coupling between time series. The second ascertains directionality of coupling, or alternatively, latent coupling, the case when multiple series are driven by another, un

Xixian Wang, Zhongjian Wang

We present an estimate of the Wasserstein distance between the data distribution and the generation of score-based generative models. The sampling complexity with respect to dimension is $\mathcal{O}(\sqrt{d})$, with a logarithmic constant. In the analysis, we assume a Gaussian-type tail behavior of the data distribution and an $\epsilon$-accurate approximat

Sayantan Pal, Souvik Das, Rohini K. Srihari

Large Language Models (LLMs) have significantly improved personalized conversational capabilities. However, existing datasets like Persona Chat, Synthetic Persona Chat, and Blended Skill Talk rely on static, predefined personas. This approach often results in dialogues that fail to capture human personalities' fluid and evolving nature. To overcome these lim

Andrey V. Soldatov

We studied time-dependent features of high-frequency fluorescent radiation from a two-level quantum system with broken inversion spatial symmetry. The system in question was modelled after a one-electron two-level asymmetric polar semiconductor quantum dot whose electric dipole moment operator has permanent unequal diagonal matrix elements. The dot was perma

Pengcheng Zhao, Jinxing Zhou, Yang Zhao, Dan Guo

The Audio-Visual Video Parsing task aims to recognize and temporally localize all events occurring in either the audio or visual stream, or both. Capturing accurate event semantics for each audio/visual segment is vital. Prior works directly utilize the extracted holistic audio and visual features for intra- and cross-modal temporal interactions. However, ea

Yun Gao, Qijun You, Peixiang Lu, Wei Cao

Reflective ptychography is a promising lensless imaging technique with a wide field of view, offering significant potential for applications in semiconductor manufacturing and detection. However, many semiconductor materials are coated with different layers during processing, which leads to the reflected diffraction light being a coherent superposition of mu

Marzieh Mirzaeibonehkhater, Mohammad Ali Labbaf-Khaniki, Mohammad Manthouri

Bearing fault detection is a critical task in predictive maintenance, where accurate and timely fault identification can prevent costly downtime and equipment damage. Traditional attention mechanisms in Transformer neural networks often struggle to capture the complex temporal patterns in bearing vibration data, leading to suboptimal performance. To address

Suzhen Han, Qing Liu

We prove that stable subgroups of Morse local-to-global groups exhibit a growth gap. That is, the growth rate of an infinite-index stable subgroup is strictly less than the growth rate of the ambient Morse local-to-global group. This generalizes a result of Cordes, Russell, Spriano, and Zalloum in the sense that we removed the additional torsion-free or resi

Nikolai N. Bogolyubov,, Andrey V. Soldatov

Spectral properties of fluorescent radiation from a two-level quantum system with broken inversion spatial symmetry, which can be described by a model of an one-electron two-level atom whose electric dipole moment operator has permanent unequal diagonal matrix elements, were studied. The case of the excitation of this system by a polychromatic laser field, c

Lanxiang Hu, Tajana Rosing, Hao Zhang

Specializing large language models (LLMs) for local deployment in domain-specific use cases is necessary for strong performance while meeting latency and privacy constraints. However, conventional task-specific adaptation approaches do not show simultaneous memory saving and inference speedup at deployment time. Practical compression techniques like quantiza

Khang Nguyen, Tuan Dang, Manfred Huber

Reconstructing three-dimensional (3D) scenes with semantic understanding is vital in many robotic applications. Robots need to identify which objects, along with their positions and shapes, to manipulate them precisely with given tasks. Mobile robots, especially, usually use lightweight networks to segment objects on RGB images and then localize them via dep

David T. Plouff, Laura Scheuer, Shreya Shrestha, Weipeng Wu

Altermagnets are a recently discovered class of magnetic material with great potential for applications in the field of spintronics, owing to their non-relativistic spin-splitting and simultaneous antiferromagnetic order. One of the most studied candidates for altermagnetic materials is rutile structured RuO2. However, it has recently come under significant

Gözde Özcan, Chengzhi Shi, Stratis Ioannidis

Ou et al. (2022) introduce the problem of learning set functions from data generated by a so-called optimal subset oracle. Their approach approximates the underlying utility function with an energy-based model, whose parameters are estimated via mean-field variational inference. Ou et al. (2022) show this reduces to fixed point iterations; however, as the nu

Sharmila Duppala, Nathaniel Grammel, Juan Luque, Calum MacRury

Given an edge-colored graph, the goal of the proportional fair matching problem is to find a maximum weight matching while ensuring proportional representation (with respect to the number of edges) of each color. The colors may correspond to demographic groups or other protected traits where we seek to ensure roughly equal representation from each group. It

Konstantin A. Rybakov

The paper gives a new representation for the fractional Brownian motion that can be applied to simulate this self-similar random process in continuous time. Such a representation is based on the spectral form of mathematical description and the spectral method. The Legendre polynomials are used as the orthonormal basis. The paper contains all the necessary a

Max Riffi-Aslett, Christina Fell

Classifying large images with small or tiny regions of interest (ROI) is challenging due to computational and memory constraints. Weakly supervised memory-efficient patch selectors have achieved results comparable with strongly supervised methods. However, low signal-to-noise ratios and low entropy attention still cause overfitting. We explore these issues u

Vasileios Alevizos, Nikitas Gerolimos, Sabrina Edralin, Clark Xu

One requirement of maintaining digital information is storage. With the latest advances in the digital world, new emerging media types have required even more storage space to be kept than before. In fact, in many cases it is required to have larger amounts of storage to keep up with protocols that support more types of information at the same time. In contr

Vaibhav Pandey, Matteo Varbaro

Let $I$ be the ideal generated by the maximal minors of a matrix of indeterminates over a field and let $J$ denote the generic link, i.e., the most general link, of $I$. The generators of the ideal $J$ are not known. We provide an explicit description of the lead terms of the generators of $J$ using Gr\"obner degeneration. Indeed, we construct a degeneration

George Manoussakis

Building on existing algorithms and results, we offer new insights and algorithms for various problems related to detecting maximal and maximum bicliques. Most of these results focus on graphs with small maximum degree, providing improved complexities when this parameter is constant; a common characteristic in real-world graphs.

Yu Li, Jared Coles, Xin Gui, Hyowon Park

We have conducted a comprehensive investigation into the magnetic properties of the chiral multiferroic material CoTeMoO$_6$. In contrast with the previous claim of canted antiferromagnetic order with ferromagnetic components, our investigation reveals an antiferromagnetic ground state with compensated moments, providing an interesting platform for exploring

Yuang Qi, Kejiang Chen, Na Zhao, Zijin Yang

The rapid development of image generation models has facilitated the widespread dissemination of generated images on social networks, creating favorable conditions for provably secure image steganography. However, existing methods face issues such as low quality of generated images and lack of semantic control in the generation process. To leverage provably

Calysta A. Tesiman, Mark Oxborrow, Max Attwood

Quantum technologies offer ways to solve certain tasks more quickly, efficiently, and with greater precision than their classical counterparts. Yet substantial challenges remain in the construction of sufficiently error-free and scalable quantum platforms needed to unlock any real benefits to society. Acknowledging that this hardware can take vastly differen

Tingfeng Hui, Lulu Zhao, Guanting Dong, Yaqi Zhang

Instruction tuning has been widely used to unleash the complete potential of large language models. Notably, complex and diverse instructions are of significant importance as they can effectively align models with various downstream tasks. However, current approaches to constructing large-scale instructions predominantly favour powerful models such as GPT-4

F. Nazari, M. Radin, M. Moeini Arani

In this study, we focus on the radiative capture process of the deuteron on alpha particle leading to the formation of $^6{\textrm{Li}}$ in the two-body formalism through the cluster effective field theory~(CEFT). It was the primitive nuclear reaction to produce ${^6 \textrm{Li}}$ in a few minutes after the Big Bang. In detail, we outline the calculation of

Maryam Taghizadeh, Mahmood Ahmadi

Vehicular fog computing (VFC) can be considered as an important alternative to address the existing challenges in intelligent transportation systems (ITS). The main purpose of VFC is to perform computational tasks through various vehicles. At present, VFCs include powerful computing resources that bring the computational resources nearer to the requesting de

Pranshu Jahagirdar

AlphaFold, a groundbreaking protein prediction model, has revolutionized protein structure prediction, populating the AlphaFold Protein Database (AFDB) with millions of predicted structures. However, AlphaFold's accuracy in predicting proteins with intricate topologies, such as knots, remains a concern. This study investigates AlphaFold's performance in pred

Yulin Wang, Haoji Zhang, Yang Yue, Shiji Song

This paper presents a comprehensive exploration of the phenomenon of data redundancy in video understanding, with the aim to improve computational efficiency. Our investigation commences with an examination of spatial redundancy, which refers to the observation that the most informative region in each video frame usually corresponds to a small image patch, w

Károly J. Böröczky

The Brascamp-Lieb inequality in harmonic analysis was proved by Brascamp and Lieb in the rank one case in 1976, and by Lieb in 1990. It says that in a certain inequality, the optimal constant can be determined by checking the inequality for centered Gaussian distributions. It was Keith M Ball's pioneering work around 1990 that led to various applications of

Desislava Mihaylova, Xinglong Chen, Daniel Phelan, Stephan Rosenkranz

We have explored the relaxation process of the spin glass phase in Bi$_2$Fe$_3$GaO$_9$, a geometrically frustrated magnet with a unique structure consisting of pentagonal building blocks known as the Cairo lattice. Using dc magnetization measurements on single crystals, we estimate the relaxation time across various temperatures and fields. Our results indic

Ali Hamdi, Hozaifa Kassab, Mohamed Bahaa, Marwa Mohamed

Large language models (LLMs) have significantly advanced natural language processing, excelling in areas like text generation, summarization, and question-answering. Despite their capabilities, these models face challenges when fine-tuned on small, domain-specific datasets, often struggling to generalize and deliver accurate results with unfamiliar inputs. T

Zoltán Lelkes

We consider the connected sum of two three-dimensional lens spaces $L_1\#L_2$, where $L_1$ and $L_2$ are non-diffeomorphic and are of a certain "generic" type. Our main result is the calculation of the cohomology ring $H^\ast(B\text{Diff}(L_1\#L_2);\mathbb{Q})$, where $\text{Diff}(L_1\#L_2)$ is the diffeomorphism group of $M$ equipped with the $C^\infty$-top