November 2024 arXiv papers — page 157

Jonathan Ben-Naim, Victor David, Anthony Hunter

An argument can be seen as a pair consisting of a set of premises and a claim supported by them. Arguments used by humans are often enthymemes, i.e., some premises are implicit. To better understand, evaluate, and compare enthymemes, it is essential to decode them, i.e., to find the missing premisses. Many enthymeme decodings are possible. We need to disting

Qiang Wu, Gechang Yao, Zhixi Feng, Shuyuan Yang

Time series analysis finds wide applications in fields such as weather forecasting, anomaly detection, and behavior recognition. Previous methods attempted to model temporal variations directly using 1D time series. However, this has been quite challenging due to the discrete nature of data points in time series and the complexity of periodic variation. In t

Daheng Min

We first determine the asymptotic cone of the steady gradient K\"ahler-Ricci soliton of the Taub-NUT type constructed by Apostolov and Cifarell. Then we study a special case and prove that it is an ALF Calabi-Yau metric in a certain sense. Finally we construct new ALF Calabi-Yau metrics on crepant resolution of its quotients modeled on it using the method of

Pablo Pedregal

We investigate functionals defined on manifolds through parameterizations. If they are to be meaningful, from a geometrical viewpoint, they ought to be invariant under reparameterizations. Standard, local, integral functionals with this invariance property are well-known. We would like to focus though on the passage from a given arbitrary functional to its i

Lukas Ebner, Berndt Müller, Andreas Schäfer, Leonhard Schmotzer

We review recent and present new results on thermalization of nonabelian gauge theory obtained by exact numerical simulation of the real-time dynamics of $(2+1)$-dimensional SU(2) lattice gauge theory. We discuss: (1) tests confirming the Eigenstate Thermalization Hypothesis; (2) the entanglement entropy of sublattices, including the Page curve, the transiti

Yecheng Jason Ma, Joey Hejna, Ayzaan Wahid, Chuyuan Fu

Predicting temporal progress from visual trajectories is important for intelligent robots that can learn, adapt, and improve. However, learning such progress estimator, or temporal value function, across different tasks and domains requires both a large amount of diverse data and methods which can scale and generalize. To address these challenges, we present

Chen Zhige, Qin Chengxuan

The individual variabilities of electroencephalogram signals pose great challenges to cross-subject motor imagery (MI) classification, especially for the data-scarce single-source to single-target (STS) scenario. The multi-scale spatial data distribution differences can not be fully eliminated in MI experiments for the topological structure and connection ar

Bowen Song, Chenxuan Wu, Andrea Iannelli

This paper revisits and extends the convergence and robustness properties of value and policy iteration algorithms for discrete-time linear quadratic regulator problems. In the model-based case, we extend current results concerning the region of exponential convergence of both algorithms. In the case where there is uncertainty on the value of the system matr

Seyed Mahdi Shavarani, Mahmoud Golabi, Richard Allmendinger, Lhassane Idoumghar

Evolutionary Multi-Objective Optimization Algorithms (EMOAs) are widely employed to tackle problems with multiple conflicting objectives. Recent research indicates that not all objectives are equally important to the decision-maker (DM). In the context of interactive EMOAs, preference information elicited from the DM during the optimization process can be le

Anup Teejo Mathew, Frederic Boyer, Vincent Lebastard, Federico Renda

Algorithms that use derivatives of governing equations have accelerated rigid robot simulations and improved their accuracy, enabling the modeling of complex, real-world capabilities. However, extending these methods to soft and hybrid soft-rigid robots is significantly more challenging due to the complexities in modeling continuous deformations inherent in

Yongqi Jiang, Yansong Gao, Chunyi Zhou, Hongsheng Hu

With the growing applications of Deep Learning (DL), especially recent spectacular achievements of Large Language Models (LLMs) such as ChatGPT and LLaMA, the commercial significance of these remarkable models has soared. However, acquiring well-trained models is costly and resource-intensive. It requires a considerable high-quality dataset, substantial inve

J. Furtado, Alan C. Santos

The phenomenon where a quantum system can be exponentially accelerated to its stationary state has been referred to as the Quantum Mpemba Effect (QMpE). Due to its analogy with the classical Mpemba effect, hot water freezes faster than cold water, this phenomenon has garnered significant attention. Although QMpE has been characterized and experimentally veri

L. Herrera, A. Di Prisco, J. Ospino

Exact solutions are presented which describe, either the evolution of fluid distributions corresponding to a ghost star (vanishing total mass), or describing the evolution of fluid distributions which attain the ghost star status at some point of their lives. The first two solutions correspond to the former case, they admit a conformal Killing vector (CKV) a

Hao Liu, Jia-Rui Li, Yi-Fu Cai

It is well known that multiple Galactic thermal dust emission components may exist along the line of sight, but a single-component approximation is still widely used, since a full multi-component estimation requires a large number of frequency bands that are only available with future experiments. In light of this, we present a reliable, quantitative, and se

Sultan Ahmed, Salman Rakin, Khadija Urmi, Chandan Kumar Nag

The increasing growth of social media provides us with an instant opportunity to be informed of the opinions of a large number of politically active individuals in real-time. We can get an overall idea of the ideologies of these individuals on governmental issues by analyzing the social media texts. Nowadays, different kinds of news websites and popular soci

Guozhi Xu, Zekun Niu, Lyu Li, Weisheng Hu

We propose and experimentally validate a joint estimation method for chromatic dispersion and time-frequency offset based on the fractional Fourier transform, which reduces computational complexity by more than 50% while keeping estimation accuracy.

Wei-Ting Wang, Yen-Jui Chang, Ching Ray Chang

We propose a Continuous-Time Quantum Walks (CTQW) model for one-dimensional Dirac dynamics simulation with higher-order approximation. Our model bridges CTQW with a discrete-time model called Dirac Cellular Automata (DCA) via Quantum Fourier Transformation (QFT). From our continuous-time model, we demonstrate how varying time intervals and position space siz

Dezhi Ye, Junwei Hu, Jiabin Fan, Bowen Tian

Recent studies have highlighted the significant potential of Large Language Models (LLMs) as zero-shot relevance rankers. These methods predominantly utilize prompt learning to assess the relevance between queries and documents by generating a ranked list of potential documents. Despite their promise, the substantial costs associated with LLMs pose a signifi

Abdulrezzak Zekiye, Ozan Sina Bankaoglu, Ouns Bouachir, Oznur Ozkasap

With escalating energy demands, innovative solutions have emerged to supply energy affordably and sustainably. Energy sharing has also been proposed as a solution, addressing affordability issues while reducing consumers' greed. In this paper, we analyse the feasibility of two energy sharing algorithms, centralized and peer-to-peer, within two scenarios, bet

Feng Gao, Jianmin Shen, Shanshan Wang, Wei Li

In recent years, neural networks have increasingly been employed to identify critical points of phase transitions. For the tricritical directed percolation model, its steady-state configurations encompass both first-order and second-order phase transitions. Due to the presence of crossover effects, identifying the critical points of phase transitions becomes

Pablo Pedregal

We propose a framework to define solutions of ODE systems under a novel condition that goes well beyond the usual continuity condition required in the classical theory of ODEs (Peano's or Picard's theorems). We illustrate our results with some simple but enlightening examples, including some facts about Sobolev fields, and mention some relevant questions to

Lisa Alazraki, Marek Rei

External tools help large language models succeed at tasks where they would otherwise typically fail. In existing frameworks, choosing tools at test time relies on naive greedy decoding, regardless of whether the model has been fine-tuned on tool-annotated data or prompted with in-context examples. In contrast, we find that gathering and choosing among a sui

Yi Shen, Hanyan Huang

Offline reinforcement learning has received extensive attention from scholars because it avoids the interaction between the agent and the environment by learning a policy through a static dataset. However, general reinforcement learning methods cannot get satisfactory results in offline reinforcement learning due to the out-of-distribution state actions that

Haim Fisher, Moni Shahar, Yehezkel S. Resheff

Deep learning models for image classification have become standard tools in recent years. A well known vulnerability of these models is their susceptibility to adversarial examples. These are generated by slightly altering an image of a certain class in a way that is imperceptible to humans but causes the model to classify it wrongly as another class. Many a

Hai-Yen Phan Nguyen, Phi-Lan Ly, Duc-Manh Le, Trong-Hop Do

In the context of modern life, particularly in Industry 4.0 within the online space, emotions and moods are frequently conveyed through social media posts. The trend of sharing stories, thoughts, and feelings on these platforms generates a vast and promising data source for Big Data. This creates both a challenge and an opportunity for research in applying t

Jérémy Saucourt, Benjamin Gobé, David Helbert, Agnès Desfarges-Berthelemot

We investigate a method to retrieve full-complex models (Transmission Matrix and Neural Network) of a highly multimode fiber (140 LP modes/polarization) using a straightforward machine learning approach, without the need of a reference beam. The models are first validated by the high fidelity between the predicted and the experimental images in the near fiel

Crystina Zhang, Jing Lu, Vinh Q. Tran, Tal Schuster

Human understanding of text depends on general semantic concepts of words rather than their superficial forms. To what extent does our human intuition transfer to language models? In this work, we study the degree to which current multilingual language models (mLMs) understand based on subword-level semantic concepts. To this end, we form "semantic tokens" b

Siying Hu, Zhicong Lu

Video-sharing platforms (VSPs) have been increasingly embracing social features such as likes, comments, and Danmaku to boost user engagement. However, viewers may post inappropriate content through video commentary to gain attention or express themselves anonymously and even toxically. For example, on VSPs that support Danmaku, users may even intentionally

Tuomas Orponen, Kevin Ren

We show that the "sharp Kaufman projection theorem" from 2023 is sharp in the class of Ahlfors $(1,\delta^{-\epsilon})$-regular sets. This is in contrast with a recent result of the first author, which improves the projection theorem in the class of Ahlfors $(1,C)$-regular sets.

Björn J. Wurst, Dante M. Kennes, Jonas B. Profe

Finding reliable approximations to the quantum many-body problem is one of the central challenges of modern physics. Elemental to this endeavor is the development of advanced numerical techniques pushing the limits of what is tractable. One such recently proposed numerical technique are neural quantum states. This new type of wavefunction based Ans\"atze uti

Kei Itoh

Developing strong AI could provide a powerful tool for addressing social and scientific challenges. Neural networks (NNs), inspired by biological systems, have the potential to achieve this. However, weight optimization techniques using error backpropagation are not observed in biological systems, raising doubts about current NNs approaches. In this context,

Alan S. Cornell, Benjamin Fuks, Mark D. Goodsell, Anele M. Ncube

We demonstrate that neural networks can be used to improve search strategies, over existing strategies, in LHC searches for light electroweak-charged scalars that decay to a muon and a heavy invisible fermion. We propose a new search involving a neural network discriminator as a final cut and show that different signal regions can be defined using networks t

Pavel Sulimov, Claude Lehmann, Kurt Stockinger

Query optimization has become a research area where classical algorithms are being challenged by machine learning algorithms. At the same time, recent trends in learned query optimizers have shown that it is prudent to take advantage of decades of database research and augment classical query optimizers by shrinking the plan search space through different ty

Sultan Ahmed, Salman Rakin, Khadija Urmi, Chandan Kumar Nag

The Gender Identification (GI) problem is concerned with determining the gender of the author from a given text. It has numerous applications in different fields like forensics, literature, security, marketing, trade, etc. Due to its importance, researchers have put extensive efforts into identifying gender from the text for different languages. Unfortunatel

István Juhász, Jan van Mill

Answering a question raised by V. V. Tkachuk, we present several examples of $\sigma$-compact spaces, some only consistent and some in ZFC, that are not countably tight but in which the closure of any discrete subset is countably tight. In fact, in some of our examples the closures of all discrete subsets are even first countable.

Natalie Neumeyer, Leonie Selk

We consider linear models with scalar responses and covariates from a separable Hilbert space. The aim is to detect change points in the error distribution, based on sequential residual empirical distribution functions. Expansions for those estimated functions are more challenging in models with infinite-dimensional covariates than in regression models with

Tanmoy Dutta, Mayukh Pahari, Anish Sarkar, Sudip Bhattacharyya

`Z' type neutron star low-mass X-ray binaries typically show a `Z'-like three-branched track in their hardness intensity diagram. However, a few such `Z' sources show an additional branch known as the extended flaring branch (EFB). EFB has been poorly studied, and its origin is not known. It is thought to be an extension of the flaring branch (FB) or associa

Martin Metodiev, Marie Perrot-Dockès, Sarah Ouadah, Bailey K. Fosdick

We consider the problem of estimating a high-dimensional covariance matrix from a small number of observations when covariates on pairs of variables are available and the variables can have spatial structure. This is motivated by the problem arising in demography of estimating the covariance matrix of the total fertility rate (TFR) of 195 different countries

Gargi Panda, Soumitra Kundu, Saumik Bhattacharya, Aurobinda Routray

Multi-modal image fusion (MMIF) enhances the information content of the fused image by combining the unique as well as common features obtained from different modality sensor images, improving visualization, object detection, and many more tasks. In this work, we introduce an interpretable network for the MMIF task, named FNet, based on an $\ell_0$-regulariz

Edgar Assing

In this note we prove a joint equidistribution result for discrete low lying horocycles. This generalizes previous work of Blomer and Michel, where it was crucially assumed that the number of discrete points is prime.

Sharvani Srivastava, Sudhakar Singh, Pooja, Shiv Prakash

Sign languages are the language of hearing-impaired people who use visuals like the hand, facial, and body movements for communication. There are different signs and gestures representing alphabets, words, and phrases. Nowadays approximately 300 sign languages are being practiced worldwide such as American Sign Language (ASL), Chinese Sign Language (CSL), In

Chunyu Guo, Lucheng Sun, Shilong Li, Zelong Yuan

Solving partial differential equations (PDEs) is essential in scientific forecasting and fluid dynamics. Traditional approaches often incur expensive computational costs and trade-offs in efficiency and accuracy. Recent deep neural networks have improved the accuracy but require high-quality training data. Physics-informed neural networks (PINNs) effectively

Mikel Armentia, Mikel Abasolo, Ibai Coria, Nicolas Saintier

Statement of problem. Dental implant geometry affects the mechanical performance and fatigue behavior of butt-joint implant-supported restorations. However, failure of the implant component has been generally studied by ignoring the prosthetic screw, which is frequently the critical restoration component Purpose. Evaluate the effect of 3 main implant geometr

Dolors Herbera, Michal Hrbek, Giovanna Le Gros

For a commutative noetherian ring $R$, we classify all the hereditary cotorsion pairs cogenerated by pure-injective modules of finite injective dimension. The classification is done in terms of integer-valued functions on the spectrum of the ring. Each such function gives rise to a system of local depth conditions which describes the left-hand class in the c

Julia Stadler, Fabian Schmidt, Martin Reinecke, Matteo Esposito

Forward modeling the galaxy density within the Effective Field Theory of Large Scale Structure (EFT of LSS) enables field-level analyses that are robust to theoretical uncertainties. At the same time, they can maximize the constraining power from galaxy clustering on the scales amenable to perturbation theory. In order to apply the method to galaxy surveys,

Danish Ebadulla, Aditya Gulati, Ambuj Singh

We introduce a manifold analysis technique for neural network representations. Normalized Space Alignment (NSA) compares pairwise distances between two point clouds derived from the same source and having the same size, while potentially possessing differing dimensionalities. NSA can act as both an analytical tool and a differentiable loss function, providin

Yunfan Zhang, Zekun Niu, Minghui Shi, Weisheng Hu

We utilize the Feature Decoupling Distributed (FDD) method to enhance the capability of deep learning to fit the Nonlinear Schrodinger Equation (NLSE), significantly reducing the NLSE loss compared to non decoupling model.

Mruganka Kashyap

Vehicle roll control has been a well studied problem. One of the ubiquitous methods to mitigate vehicle rollover in the automobile industry is via a mechanical anti-roll bar. However with the advent of electric vehicles, rollover mitigation can be pursued using electric actuation. In this work, we study a roll control algorithm using sliding mode control for

Liangrui Pan, Mao Huang, Lian Wang, Pinle Qin

Hematoxylin and Eosin (H&E) staining of whole slide images (WSIs) is considered the gold standard for pathologists and medical practitioners for tumor diagnosis, surgical planning, and post-operative assessment. With the rapid advancement of deep learning technologies, the development of numerous models based on convolutional neural networks and transformer-

Panagiotis Kourtesis

This scoping review examines the broad applications, risks, and ethical challenges associated with Extended Reality (XR) technologies, including Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR), within the context of Metaverse. XR is revolutionizing fields such as immersive learning in education, medical and professional training, neurops

Yang-Yang Xu, Jun-Hao Cheng, You-Tian Zou, Qiong Xiao

In the present work, we investigate the excitation rates and population dynamics of $^{229}$Th nuclei induced by inelastic electron scattering, focusing on how electron energy, flux, and ionic charge state influence the excitation process of the nuclei. Using the Dirac Hartree-Fock-Slater method, we calculate cross sections for both the isomeric state (8.36

Quoc-Tung Le, Léon Zheng, Elisa Riccietti, Rémi Gribonval

In this paper, we investigate the butterfly factorization problem, i.e., the problem of approximating a matrix by a product of sparse and structured factors. We propose a new formal mathematical description of such factors, that encompasses many different variations of butterfly factorization with different choices of the prescribed sparsity patterns. Among

F. A. Ivanyuk, N. Carjan

We have calculated the fission fragments' mass distributions for several isotopes of heavy and super-heavy nuclei from uranium to flerovium within an improved scission point model. For all considered nuclei, in addition to the standard mass-asymmetric fission mode we have found the mass super-asymmetric mode with the mass of heavy fragments equal 190. For th

David Perrella

As a generalisation of the periodic orbit structure often seen in reflection or mirror symmetric MHD equilibria, we consider equilibria with other orientation-reversing symmetries. An example of such a symmetry, which is a not a reflection, is the parity transformation $(x,y,z) \mapsto (-x,-y,-z)$ in $\mathbb{R}^3$. It is shown under any orientation-reversin

François Dubois

In this communication, we propose a tentative to set the fundamental problem of measuring process done by a large structure on a microsopic one. We consider the example of voting when an entire society tries to measure globally opinions of all social actors in order to elect a delegate. We present a quantum model to interpret an operational voting system and

Sunan Zhao, Zhijie Li, Boyu Fan, Yunpeng Wang

Acquisition of large datasets for three-dimensional (3D) partial differential equations (PDE) is usually very expensive. Physics-informed neural operator (PINO) eliminates the high costs associated with generation of training datasets, and shows great potential in a variety of partial differential equations. In this work, we employ physics-informed neural op

Ali K. AlShami, Terrance Boult, Jugal Kalita

Tracking the trajectory of tennis players can help camera operators in production. Predicting future movement enables cameras to automatically track and predict a player's future trajectory without human intervention. Predicting future human movement in the context of complex physical tasks is also intellectually satisfying. Swift advancements in sports anal

Lin Wang, Zhengyan Wu

We explore probabilistic approaches to the deterministic energy equality for the forced Surface Quasi-Geostrophic (SQG) equation on a torus. First, we prove the zero-noise dynamical large deviations for a corresponding stochastic SQG equation, where the lower bound matches the upper bound on a certain closure of the weak-strong uniqueness class for the deter

Lei Han, Yu Zhou, Qiongyan Chen, David Yip

We present our approach to using AI-generated content (AIGC) and multiple media to develop an immersive, game-based, interactive story experience. The narrative of the story, "Memory Remedy", unfolds through flashbacks, allowing the audience to gradually uncover the story and the complex relationship between the robot protagonist and the older adults. This e

M. Andrés-Carcasona, O. J. Piccinni, M. Martínez, Ll. M. Mir

The search for gravitational waves generated by the inspiral phase of binaries of light compact objects holds significant promise in testing the existence of primordial black holes and/or other exotic objects. In this paper, we present a new method to detect such signals exploiting some techniques typically applied in searches for continuous quasi-monochroma

Megala Anandan, Benjamin Boutin, Nicolas Crouseilles

This work deals with the numerical approximation of plasmas which are confined by the effect of a fast oscillating magnetic field (see \cite{Bostan2012}) in the Vlasov model. The presence of this magnetic field induces oscillations (in time) to the solution of the characteristic equations. Due to its multiscale character, a standard time discretization would

Young-Jun Lee, Dokyong Lee, Junyoung Youn, Kyeongjin Oh

To increase social bonding with interlocutors, humans naturally acquire the ability to respond appropriately in a given situation by considering which conversational skill is most suitable for the response - a process we call skill-of-mind. For large language model (LLM)-based conversational agents, planning appropriate conversational skills, as humans do, i

Siddharth Malviy, Vipul Kakkar

The commuting graph ${\Gamma(G)}$ of a group $G$ is the simple undirected graph with group elements as a vertex set and two elements $x$ and $y$ are adjacent if and only if $xy=yx$ in $G$. By eliminating the identity element of $G$ and all the dominant vertices of $\Gamma(G)$, the resulting subgraphs of $\Gamma(G)$ are $\Gamma^*(G)$ and $\Gamma^{**}(G)$, res

Linzhu Yue, Zhitao Song, Jinhu Dong, Zhongyu Li

Natural terrain complexity often necessitates agile movements like jumping in animals to improve traversal efficiency. To enable similar capabilities in quadruped robots, complex real-time jumping maneuvers are required. Current research does not adequately address the problem of online omnidirectional jumping and neglects the robot's kinodynamic constraints

Tao Wang, Xinlin Zhang, Yuanbin Chen, Yuanbo Zhou

Semi-supervised learning has received considerable attention for its potential to leverage abundant unlabeled data to enhance model robustness. Pseudo labeling is a widely used strategy in semi supervised learning. However, existing methods often suffer from noise contamination, which can undermine model performance. To tackle this challenge, we introduce a

Zhong-Ying Fan

In this work, we relate the growth rate of Krylov complexity in the boundary to the radial momentum of an infalling particle in AdS geometry. We show that in general AdS black hole background, our proposal captures the universal behaviors of Krylov complexity at both initial and late times. Hence it can be generally considered as an approximate dual of the K

Hao Yang, Zhanbo Feng, Feng Zhou, Robert C Qiu

Diffusion models have risen to prominence in time series forecasting, showcasing their robust capability to model complex data distributions. However, their effectiveness in deterministic predictions is often constrained by instability arising from their inherent stochasticity. In this paper, we revisit time series diffusion models and present a comprehensiv

Joni-Roy Piispanen, Tinja Myllyviita, Ville Vakkuri, Rebekah Rousi

Currently artificial intelligence (AI)-enabled chatbots are capturing the hearts and imaginations of the public at large. Chatbots that users can build and personalize, as well as pre-designed avatars ready for users' selection, all of these are on offer in applications to provide social companionship, friends and even love. These systems, however, have demo

Kathrin Gerling, Anna-Lena Meiners, Louisa Schumm, Jan Rixen

Creating accessible Virtual Reality (VR) is an ongoing concern in the Human-Computer Interaction (HCI) research community. However, there is little reflection on how accessibility should be conceptualized in the context of an experiential technology. We address this gap in our work: We first explore how accessibility is currently defined, highlighting a grow

Harald Schmid

This paper deals with the question of how to calculate the volume of a body in the three-dimensional Euclidean space when it is cut into slices perpendicular to a given curve. The answer is provided by a formula that can be considered as a generalized version of the second Pappus-Guldin theorem. It turns out that the computation becomes very simple if the cu

Yu Wang, Wen Qu, Xin Ye

Political scientists often grapple with data scarcity in text classification. Recently, fine-tuned BERT models and their variants have gained traction as effective solutions to address this issue. In this study, we investigate the potential of GPT-based models combined with prompt engineering as a viable alternative. We conduct a series of experiments across

Mihir R. Athavale, Ruqaiya Al-Abri, Stephen Church, Wei Wen Wong

On-chip coherent laser sources are crucial for the future of photonic integrated circuits, yet progress has been hindered by the complex interplay between material quality, device geometry, and performance metrics. We combine high-throughput characterization, statistical analysis, experimental design, and multi-objective Bayesian optimization to accelerate t

Shuang Pan, Shaoteng Wu, Huixue Ren, Jiarong Zhao

Thermal emission is a ubiquitous electromagnetic wave with an extreme broad spectrum in nature, and controlling thermal emission can be used to develop low-cost and convenient infrared light sources with wavelength tunable in a wide range that is currently difficult to other sources. Conventional metasurfaces are commonly used to control light but lack the f

Ran Lu

Interpolatory filters are of great interest in subdivision schemes and wavelet analysis. Due to the high-order linear-phase moment property, interpolatory refinement filters are often used to construct wavelets and framelets with high-order vanishing moments. In this paper, given a general dilation matrix $\mathsf{M}$, we propose a method that allows us to c

Marc Barthelemy

Mobility is a complex phenomenon encompassing diverse transportation modes, infrastructure elements, and human behaviors. Tackling the persistent challenges of congestion, pollution, and accessibility requires a range of modeling approaches to optimize these systems. While AI offers transformative potential, it should not be the sole solution. Parsimonious m

A. Bondar, V. Parkhomchuk, A. Petrozhitsky, T. Shakirova

The Accelerator Mass Spectrometry technique makes it possible to measure rare long-lived isotopes such as $^{10}$Be, $^{14}$C, $^{26}$Al, $^{36}$Cl, $^{41}$Ca and $^{129}$I. The content of these isotopes can be at the level of 10$^{-15}$ of the total element content. The Accelerator Mass Spectrometer developed by Budker Institute of Nuclear Physics (BINP AMS

Alper Cakan, Vipul Goyal, Takashi Yamakawa

Quantum information allows us to build quantum money schemes, where a bank can issue banknotes in the form of authenticatable quantum states that cannot be cloned or counterfeited. Similar to paper banknotes, in existing quantum money schemes, a banknote consists of an unclonable quantum state and a classical serial number, signed by bank. Thus, they lack on

Haidi Wang, Qingqing Feng, Shuo Li, Wei Lin

Ferrimagnetic semiconductors, integrated with net magnetization, antiferromagnetic coupling and semi-conductivity, have constructed an ideal platform for spintronics. For practical applications, achieving high N$\acute{e}$el temperatures ($T_{\mathrm{N}}$) is very desirable, but remains a significant challenge. Here, via high-throughput density-functional-th

Laiyan Ding, Hualie Jiang, Rui Xu, Rui Huang

Depth completion using lightweight time-of-flight (ToF) depth sensors is attractive due to their low cost. However, lightweight ToF sensors usually have a limited field of view (FOV) compared with cameras. Thus, only pixels in the zone area of the image can be associated with depth signals. Previous methods fail to propagate depth features from the zone area

Yuriy Tarannikov

We prove that the number of partitions of the hypercube ${\bf Z}_q^n$ into $q^m$ subcubes of dimension $n-m$ each for fixed $q$, $m$ and growing $n$ is asymptotically equal to $n^{(q^m-1)/(q-1)}$. For the proof, we introduce the operation of the bang of a star matrix and demonstrate that any star matrix, except for a fractal, is expandable under some bang, w

Guohua Qian, Yu Zeng

For an irreducible complex character $\chi$ of a finite group $G$, the codegree of $\chi$ is defined by $|G:\ker(\chi)|/\chi(1)$, where $\ker(\chi)$ is the kernel of $\chi$. Given a prime $p$, we provide a classification of finite groups in which every irreducible complex character has either $p'$-degree or $p'$-codegree.

Hongxu Chen

I investigated the capability of medial quandle, quandle whose operation satisfying that $(a_1*b_1)*(a_2*b_2)=(a_1*a_2)*(b_1*b_2)$, to detect causality in (2+1)-dimensional globally hyperbolic spacetime by determining if they can distinguished the connected sum of two Hopf links from an infinite series of relevant three-component links constructed by Allen a

Laifa Tao, Qixuan Huang, Xianjun Wu, Weiwei Zhang

The increasing use of smart devices has emphasized the critical role of maintenance in production activities. Interactive Electronic Technical Manuals (IETMs) are vital tools that support the maintenance of smart equipment. However, traditional IETMs face challenges such as transitioning from Graphical User Interfaces (GUIs) to natural Language User Interfac

Trong-Nhan Phan, Hoang-Hai Nguyen, Thi-Thu-Hien Ha, Huy-Tan Thai

Visual inspections of bridges are critical to ensure their safety and identify potential failures early. This inspection process can be rapidly and accurately automated by using unmanned aerial vehicles (UAVs) integrated with deep learning models. However, choosing an appropriate model that is lightweight enough to integrate into the UAV and fulfills the str

Eduard Sopin, Vyacheslav Begishev, Vladislav Prosvirov, Konstantin Samouylov

The statistical characteristics of the propagation environment and traffic arrival process are known to affect the user performance in 5G/6G millimeter wave (mmWave) and subterahertz (sub-THz) systems. While the former topic has received considerable attention recently, little is known about the impact of traffic statistics. In this study, we characterize th

Yohei Seki, Hakusen Shu, Anaïs Lhuissier, Hanwool Lee

Promises made by politicians, corporate leaders, and public figures have a significant impact on public perception, trust, and institutional reputation. However, the complexity and volume of such commitments, coupled with difficulties in verifying their fulfillment, necessitate innovative methods for assessing their credibility. This paper introduces the con

Sheng Lei

Accurate calculation of switching events is important for electromagnetic transient simulation to obtain reliable results. The common presumption of continuous differential state variables could prevent the accurate calculation, thus leading to unreliable results. This paper explores accurately calculating switching events without presuming continuous differ

Duy-Phuong Dao, Hyung-Jeong Yang, Jahae Kim

Alzheimers disease progresses slowly and involves complex interaction between various biological factors. Longitudinal medical imaging data can capture this progression over time. However, longitudinal data frequently encounter issues such as missing data due to patient dropouts, irregular follow-up intervals, and varying lengths of observation periods. To a

Tuan Hai Vu, Vu Trung Duong Le, Hoai Luan Pham, Quoc Chuong Nguyen

Quantum computers are promising powerful computers for solving complex problems, but access to real quantum hardware remains limited due to high costs. Although the software simulators on CPUs/GPUs such as Qiskit, ProjectQ, and Qsun offer flexibility and support for many qubits, they struggle with high power consumption and limited processing speed, especial

M. Abbasi, S. Mahdavifar, M. Motamedifar

We study the spin-1/2 XX chain with a modulated Gamma interaction (GI), which results from the superposition of uniform and staggered Gamma terms. We diagonalize the Hamiltonian of the model exactly using the Fermionization technique. We then probe the energy gap and identify the gapped and gapless regions. We also examine the staggered chiral, staggered nem

Aoru Xue, Yiming Ren, Zining Song, Mao Ye

We propose a novel hybrid calibration-free method FreeCap to accurately capture global multi-person motions in open environments. Our system combines a single LiDAR with expandable moving cameras, allowing for flexible and precise motion estimation in a unified world coordinate. In particular, We introduce a local-to-global pose-aware cross-sensor human-matc

Adam Fourney, Gagan Bansal, Hussein Mozannar, Cheng Tan

Modern AI agents, driven by advances in large foundation models, promise to enhance our productivity and transform our lives by augmenting our knowledge and capabilities. To achieve this vision, AI agents must effectively plan, perform multi-step reasoning and actions, respond to novel observations, and recover from errors, to successfully complete complex t

Israel Abebe Azime, Atnafu Lambebo Tonja, Tadesse Destaw Belay, Yonas Chanie

With the rapid development of evaluation datasets to assess LLMs understanding across a wide range of subjects and domains, identifying a suitable language understanding benchmark has become increasingly challenging. In this work, we explore LLM evaluation challenges for low-resource language understanding and introduce \proverbeval, LLM evaluation benchmark

Qianni Cao, Chen Shen

As the proportion of renewable energy and power electronics in the power system increases, modeling frequency dynamics under power deficits becomes more challenging. Although data-driven methods help mitigate these challenges, they are exposed to data noise and training errors, leading to uncertain prediction errors. To address uncertain and limited statisti

Robby Costales, Stefanos Nikolaidis

The wider application of end-to-end learning methods to embodied decision-making domains remains bottlenecked by their reliance on a superabundance of training data representative of the target domain. Meta-reinforcement learning (meta-RL) approaches abandon the aim of zero-shot generalization--the goal of standard reinforcement learning (RL)--in favor of fe

Santak Panda, Kartikeya Rai, Amitabha Tripathi

For a set $A$ of positive integers with $\gcd(A)=1$, let $\langle A \rangle$ denote the set of all finite linear combinations of elements of $A$ over the non-negative integers. The it is well known that only finitely many positive integers do not belong to $\langle A \rangle$. The Frobenius number and the genus associated with the set $A$ is the largest numb

Louis Golowich, Venkatesan Guruswami

Quantum low-density parity-check (qLDPC) codes are an important component in the quest for quantum fault tolerance. Dramatic recent progress on qLDPC codes has led to constructions which are asymptotically good, and which admit linear-time decoders to correct errors affecting a constant fraction of codeword qubits. These constructions, while theoretically ex

Tsuyoshi Kato, Daisuke Kishimoto, Mitsunobu Tsutaya

We establish Morse inequalities for a noncompact manifold with a cocompact and properly discontinuous action of a discrete group, where Morse functions are not necessarily invariant under the group action. The inequalities are given in terms of the $L^2$-Betti numbers and functions on the acting group which describe rough configurations of critical points of

Emery Cooper, Caspar Oesterheld, Vincent Conitzer

In Newcomb's problem, causal decision theory (CDT) recommends two-boxing and thus comes apart from evidential decision theory (EDT) and ex ante policy optimisation (which prescribe one-boxing). However, in Newcomb's problem, you should perhaps believe that with some probability you are in a simulation run by the predictor to determine whether to put a millio

Xianhe Zhang, Yu Xiao, Xiaodong Xu, Biao Luo

Traditional approaches to stabilizing hyperbolic PDEs, such as PDE backstepping, often encounter challenges when dealing with high-dimensional or complex nonlinear problems. Their solutions require high computational and analytical costs. Recently, neural operators (NOs) for the backstepping design of first-order hyperbolic partial differential equations (PD