October 2024 arXiv papers — page 58

Michela Rigoselli, Caterina Tresoldi, Lorenzo Ducci, Sandro Mereghetti

A0538-66 is a neutron star/Be X-ray binary located in the Large Magellanic Cloud and, since its discovery in the seventies, it showed a peculiar behavior which makes it a unique object in the high-mass X-ray binaries scene: the extremely eccentric orbit (e=0.72), the short spin period of the neutron star (P=69 ms), the episodes of super-Eddington accretion.

Tianze Wang, Maryam Honari-Jahromi, Styliani Katsarou, Olga Mikheeva

This pilot study explores the application of language models (LMs) to model game event sequences, treating them as a customized natural language. We investigate a popular mobile game, transforming raw event data into textual sequences and pretraining a Longformer model on this data. Our approach captures the rich and nuanced interactions within game sessions

Alessandro Contu

Using Hernandez-Leclerc's isomorphism between the derived Hall algebra of a representation-finite quiver $Q$ and the quantum Grothendieck ring of the quantum loop algebra of the Dynkin type of $Q$, we lift the (quantum) cluster algebra structure of the quantum Grothendieck ring to the semi-derived Hall algebra, introduced by Gorsky, of the category of bounde

Chengyou Jia, Minnan Luo, Zhuohang Dang, Qiushi Sun

Digital agents capable of automating complex computer tasks have attracted considerable attention due to their immense potential to enhance human-computer interaction. However, existing agent methods exhibit deficiencies in their generalization and specialization capabilities, especially in handling open-ended computer tasks in real-world environments. Inspi

Zixin Gu, Yaoxin Ge, Yao Zhang, Dengji Zhao

Diffusion auction design is a new trend in mechanism design which extends the original incentive compatibility property to include buyers' private connection report. Reporting connections is equivalent to inviting their neighbors to join the auction in practice. Then, the social welfare is collectively accumulated by all participants: reporting high valuatio

Olivia Mallick

We perform Monte Carlo simulation to study the effects of random disorder on equilibrium phase transition of three-dimensional single-site anisotropic XY ferromagnet. The disorder is incorporated in two ways; having a randomly distributed anisotropy and presence of a quenched random field. The ferro-para transition temperature has been found to increase with

Adamantia Kosma, Philipp Rüßmann, Yuriy Mokrousov, Stefan Blügel

We present an ab-initio computational analysis of the topological Hall effect arising from stable magnetic skyrmions in the Pd/Fe/Ir(111) film using non-collinear spin density functional calculations within the Korringa-Kohn-Rostoker (KKR) Green function method. The semiclassical Boltzmann transport equation is employed for the resistivity and the Hall angle

Christian J. Renggli, Edgar S. Steenstra, Alberto E. Saal

This chapter presents a comprehensive overview of the abundances and distribution of S, and the processes that control the behavior of S on the Earth's Moon and on Mercury. The two planetary bodies share notable similarities, such as lacking substantial atmospheres and featuring surfaces with high numbers of impact craters. Both objects are at variably low o

Pietro Klausner, Gianluca Colò, Xavier Roca-Maza, Enrico Vigezzi

State-of-the-art models based on nuclear Density Functional Theory are successful in the description of nuclei throughout the whole nuclear chart. Among them, some differences arise regarding their accuracy. For a given nuclear model, this depends on the procedure adopted to determine the parameters, and, at the same time, new experimental findings constantl

O. Fedchenko, Y. -J. Song, O. Tkach, Y. Lytvynenko

Hard X-ray angle-resolved photoemission spectroscopy reveals significant alterations in the valence band states of EuPd$_2$Si$_2$ at a temperature $T_V$, where the Eu ions undergo a temperature-induced valence crossover from a magnetic Eu$^{2+}$ state to a low-temperature valence-fluctuating state. The introduction of small amounts of Au on Pd lattice sites

Jiange Li, Yetong Sha, Huan Xiong

Core partitions have attracted much attention since Anderson's work (2002) on the number of $(s,t)$-core partitions for coprime $s,t$. Recently, there has been a growing interest in studying the limiting distributions of the sizes of random simultaneous core partitions. In this paper, we prove the asymptotic normality of certain statistics of uniform random

Jörg Brendle, Michael Hrušák, Francesco Parente

We investigate the combinatorial structure of the set of maximal antichains in a Boolean algebra ordered by almost refinement. We also consider the reaping relation and its associated cardinal invariants, focusing in particular on reduced powers of Boolean algebras. As an application, we obtain that, on the one hand, the ultrafilter number of the Cohen algeb

J. P. Rodriguez

We compute the energy spectrum of a nearest-neighbor electron hopping model for bi-layer graphene at commensurate twist angles. Specifically, we focus on the simplest bi-layer lattices, with moire patterns that have no subcells. The electron hopping hamiltonian is analyzed in momentum space, both by degenerate perturbation theory and by exact numerical calcu

Munetake Otsuka, Kohei Mitsuhashi, Ryutaro Takahashi, Yohei Nishino

The Roberts linkage is recognized for enabling long-period pendulum motion in a compact format. Utilizing this characteristic, we are developing a three-point Roberts linkage for vibration isolation systems, with an eye towards its potential contribution to the development of next-generation interferometric gravitational wave antennas. In this article, we de

Jinrui Zhang

In this article, an encoder was trained to obtain the inner structure of the original data by obtain a differential equations. A decoder was trained to resample the original data domain, to generate new data that obey the differential structure of the original data using the physics-informed neural network.

Liang Xiong, Jianzhou Liu

Multipartite quantum scenarios are a significant and challenging resource in quantum information science. Tensors provide a powerful framework for representing multipartite quantum systems. In this work, we introduce the role of tensor-generated matrices that can broadly be defined as the relationships between an $m$-th order $n$-dimensional tensor and an $n

L. A. Shelukhin, A. V. Kuzikova, A. V. Telegin, V. D. Bessonov

The development of spintronic emitters of broadband THz pulses relies on designing heterostructures where processes of laser-driven spin current generation and subsequent spin-to-charge current conversion are the most efficient. An interface between ferromagnetic and nonmagnetic layers in the emitter is one of the critical elements. Here, we study experiment

Jean-Marc Luck, Anita Mehta

We present a model of speech perception which takes into account effects of correlations between sounds. Words in this model correspond to the attractors of a suitably chosen descent dynamics. The resulting lexicon is rich in short words, and much less so in longer ones, as befits a reasonable word length distribution. We separately examine the decryption of

Hungchong Kim, K. S. Kim

There are three scalar nonets in the Particle Data Group (PDG), one of which includes [$a_0(980), K_0^*(700)$], another includes [$a_0(1450), K_0^*(1430)$], and the third includes [$a_0(1710), K_0^*(1950)$]. Motivated by Ref.[1], we examine an alternative mixing mechanism that could potentially explain the small mass difference between the $a_0 (1450)$ and $

Anup Shirgaonkar, Nikhil Pandey, Nazmiye Ceren Abay, Tolga Aktas

Leading open-source large language models (LLMs) such as Llama-3.1-Instruct-405B are extremely capable at generating text, answering questions, and solving a variety of natural language understanding tasks. However, they incur higher inference cost and latency compared to smaller LLMs. Knowledge distillation provides a way to use outputs from these large, ca

Husanboy Hoshimov, Odil Yunusov, Farruh Atamurotov, Mubasher Jamil

In this work, we have worked out dynamical aspects for the particles moving around the GUP-corrected-Schwarzschild (S-GUP) black hole. We have calculated the innermost stable circular orbit (ISCO) around black hole and explored its implications for different microquasars. Additionally, we have shown that the Kerr black hole mimics S-GUP black hole after some

Junyu Zhang, Yao Zhang, Yaoxin Ge, Dengji Zhao

In cooperative games, we study how values created or costs incurred by a coalition are shared among the members within it, and the players may join the coalition in a online manner such as investors invest a startup. Recently, Ge et al. [10] proposed a new property called incentives for early arrival (I4EA) in such games, which says that the online allocatio

Yibo Miao, Bofei Gao, Shanghaoran Quan, Junyang Lin

The last year has witnessed the rapid progress of large language models (LLMs) across diverse domains. Among them, CodeLLMs have garnered particular attention because they can not only assist in completing various programming tasks but also represent the decision-making and logical reasoning capabilities of LLMs. However, current CodeLLMs mainly focus on pre

Gennaro Corcella

The top-quark mass is a fundamental parameter of the Standard Model, as it plays a crucial role in the electroweak precision tests, stability of the vacuum and inflation. I review the method and the main results contained in a recent ATLAS analysis which measures the top mass by using the invariant mass of the leptons coming from W and B-hadron decays. The e

Zhenqian Shen, Mingyang Zhou, Yongqi Zhang, Quanming Yao

Motivation: Emerging drug-drug interaction (DDI) prediction is crucial for new drugs but is hindered by distribution changes between known and new drugs in real-world scenarios. Current evaluation often neglects these changes, relying on unrealistic i.i.d. split due to the absence of drug approval data. Results: We propose DDI-Ben, a benchmarking framework f

Kangwei Xu, Ruidi Qiu, Zhuorui Zhao, Grace Li Zhang

With the rapidly increasing complexity of modern chips, hardware engineers are required to invest more effort in tasks such as circuit design, verification, and physical implementation. These workflows often involve continuous modifications, which are labor-intensive and prone to errors. Therefore, there is an increasing need for more efficient and cost-effe

Rajko Nenadov

An $n$-vertex graph $G$ is locally dense if every induced subgraph of size larger than $\zeta n$ has density at least $d > 0$, for some parameters $\zeta, d > 0$. We show that the number of induced subgraphs of $G$ with $m$ vertices and maximum degree significantly smaller than $dm$ is roughly $\binom{\zeta n}{m}$, for $m \ll \zeta n$ which is not too small.

Sukanya Randhawa, Eren Aygun, Guntaj Randhawa, Benjamin Herfort

We have released an open dataset with global coverage on road surface characteristics (paved or unpaved) derived utilising 105 million images from the world's largest crowdsourcing-based street view platform, Mapillary, leveraging state-of-the-art geospatial AI methods. We propose a hybrid deep learning approach which combines SWIN-Transformer based road sur

Kaiwei Che, Zhaokun Zhou, Li Yuan, Jianguo Zhang

Spiking Neural Networks (SNNs) are considered as a potential candidate for the next generation of artificial intelligence with appealing characteristics such as sparse computation and inherent temporal dynamics. By adopting architectures of Artificial Neural Networks (ANNs), SNNs achieve competitive performances on benchmark tasks like image classification.

Kingshook Biswas, Arkajit Pal Choudhury

The boundary $\partial X$ of a boundary continuous Gromov hyperbolic space $X$ carries a natural Moebius structure on the boundary. For a proper, geodesically complete, boundary continuous Gromov hyperbolic space $X$, the boundary $\partial X$ equipped with its cross-ratio is a particular kind of quasi-metric space, called a quasi-metric antipodal space. Giv

Bing Li, Lingmin Liao, Baowei Wnag, Sanju Velani

We develop the Mass Transference Principle for rectangles of Wang \& Wu (Math. Ann. 2021) to incorporate the `unbounded' setup; that is, when along some direction the lower order (at infinity) of the side lengths of the rectangles under consideration is infinity. As applications, we obtain the Hausdorff dimension of naturally occurring $\limsup$ sets within

Huangbin Liang, Beatriz Moya, Francisco Chinesta, Eleni Chatzi

Infrastructure systems are critical in modern communities but are highly susceptible to various natural and man-made disasters. Efficient post-disaster recovery requires repair-scheduling approaches under the limitation of capped resources that need to be shared across the system. Existing approaches, including component ranking methods, greedy evolutionary

Romain Pierrat, Julia Rocha, Rémi Carminati

We develop a theoretical model to investigate wave propagation in media with random time-varying properties, where temporal fluctuations lead to complex scattering dynamics. Focusing on the ensemble-averaged field, we derive an exact expression for the average Green's function in the presence of finite temporal disorder, and extend the analysis to the thermo

Shivam Adarsh, Kumar Shridhar, Caglar Gulcehre, Nicholas Monath

Large Language Models (LLMs) can transfer their reasoning skills to smaller models by teaching them to generate the intermediate reasoning process required to solve multistep reasoning tasks. While LLMs can accurately solve reasoning tasks through a variety of strategies, even without fine-tuning, smaller models are not expressive enough to fit the LLMs dist

Tomáš Pivoňka, Libor Přeučil

Re-ranking is the second stage of a visual place recognition task, in which the system chooses the best-matching images from a pre-selected subset of candidates. Model-free approaches compute the image pair similarity based on a spatial comparison of corresponding local visual features, eliminating the need for computationally expensive estimation of a model

Chien Van Nguyen, Huy Huu Nguyen, Thang M. Pham, Ruiyi Zhang

Efficient long-context language modeling remains a significant challenge in Natural Language Processing (NLP). While Transformers dominate language tasks, they struggle with long sequences due to quadratic computational complexity in training and linearly scaling memory costs during inference. Recent State Space Models (SSMs) such as Mamba offer alternatives

Julio González-Díaz, Ángel M. González-Rueda, Irene Llana-García, Jorge Rodríguez-Veiga

We study the problem of stock replenishment and transshipment in the retail industry. We develop a model that can accommodate different policies, including centralized redistribution (replenishment) and decentralized redistribution (lateral transshipments), allowing for direct comparisons between them. We present a numeric analysis in which the benchmark ins

Congcong Wen, Yisiyuan Huang, Hao Huang, Yanjia Huang

Object navigation is crucial for robots, but traditional methods require substantial training data and cannot be generalized to unknown environments. Zero-shot object navigation (ZSON) aims to address this challenge, allowing robots to interact with unknown objects without specific training data. Language-driven zero-shot object navigation (L-ZSON) is an ext

Erxi Xiao, Yu Yang, Yingge Huang, Zhen Zhang

Background: The primary goal of studying isospin dynamics via heavy-ion reactions is to explore the isospin dependence of effective interactions within the nuclear equation of state (EOS). Purpose: This work aims to investigate the effects of nuclear incompressibility ($ K_0 $) on neutron-proton equilibration in projectile-like fragments (PLFs). Method: We s

Romanshu Garg, G. P. Singh, Ashutosh Singh

In the symmetric teleparallel gravity framework, we study the cosmic dynamics of the universe with dark energy equation of state (EoS) parameter having non-linear forms. The non-metricity scalar induced by the dark energy EoS parameter evolves with time and, explains the physically reasonable transiting universe evolution in a consistent way. A comparative s

Ye-eun Kim, Seoung Yun Kim, Hyunjoong Kim

Random forest (RF) stands out as a highly favored machine learning approach for classification problems. The effectiveness of RF hinges on two key factors: the accuracy of individual trees and the diversity among them. In this study, we introduce a novel approach called heterogeneous RF (HRF), designed to enhance tree diversity in a meaningful way. This dive

Adam Nohejl, Akio Hayakawa, Yusuke Ide, Taro Watanabe

The tasks of lexical complexity prediction (LCP) and complex word identification (CWI) commonly presuppose that difficult to understand words are shared by the target population. Meanwhile, personalization methods have also been proposed to adapt models to individual needs. We verify that a recent Japanese LCP dataset is representative of its target populati

Ferdinando Frascà, Andrea Beraudo, Michael Strickland

We solve a Boltzmann equation for massless quark and gluon fluids in a transversally homogeneous, longitudinally boost-invariant expansion. Quarks can be out of chemical equilibrium and the relaxation times of the two species are assumed to be connected by Casimir scaling. We numerically calculate moments of the distribution functions, identifying their earl

Krzysztof Ociepa, Łukasz Flis, Krzysztof Wróbel, Adrian Gwoździej

We introduce Bielik 7B v0.1, a 7-billion-parameter generative text model for Polish language processing. Trained on curated Polish corpora, this model addresses key challenges in language model development through innovative techniques. These include Weighted Instruction Cross-Entropy Loss, which balances the learning of different instruction types, and Adap

Mingjun Han, Yukai Zhang, Taotao Yu, Guodong Du

The accurate prediction of solvation free energy is of significant importance as it governs the behavior of solutes in solution. In this work, we apply a variety of machine learning techniques to predict and analyze the alchemical free energy of small molecules. Our methodology incorporates an ensemble of machine learning models with feature processing using

Theodore Modis

The logistic function is used to forecast energy consumed worldwide. The logistic substitution model is used to describe the energy mix since 1965 presenting a picture significantly different from the one covering the previous 100 years. In the new picture the share of heavy pollutants, i.e. coal plus oil, keeps declining systematically in favor of natural g

Justus Bruckamp, Markus Chimani, Martina Juhnke

Given a graph $G$, we study the $2$-edge-connected subgraph polytope $\mathrm{TECSP}(G)$, which is given by the convex hull of the incidence vectors of all $2$-edge-connected subgraphs of $G$. We describe the lattice points of this polytope by linear inequalities which provides an ILP-algorithm for finding a $2$-edge-connected subgraph of maximum weight. Fur

Wantong Huang, Máté Stark, Paul Greule, Kwan Ho Au-Yeung

The design and control of atomic-scale spin structures constitute major challenges for spin-based quantum technology platforms, including quantum dots, color centers, and molecular spins. Here, we showcase a strategy for designing the quantum properties of molecular spin qubits by combining tip-assisted on-surface assembly with electron spin resonance scanni

Xuebao Li, Xuefeng Li, Yanfang Zheng, Ting Li

The existing flare prediction primarily relies on photospheric magnetic field parameters from the entire active region (AR), such as Space-Weather HMI Activity Region Patches (SHARP) parameters. However, these parameters may not capture the details the AR evolution preceding flares. The magnetic structure within the core area of an AR is essential for predic

Xiuwei Shang, Li Hu, Shaoyin Cheng, Guoqiang Chen

Binary Code Similarity Detection (BCSD) plays a crucial role in numerous fields, including vulnerability detection, malware analysis, and code reuse identification. As IoT devices proliferate and rapidly evolve, their highly heterogeneous hardware architectures and complex compilation settings, coupled with the demand for large-scale function retrieval in pr

Seema Aswani, Sujala D. Shetty

Explainable Artificial Intelligence (XAI) methods in text summarization are essential for understanding the model behavior and fostering trust in model-generated summaries. Despite the effectiveness of XAI methods, recent studies have highlighted a key challenge in this area known as the "disagreement problem". This problem occurs when different XAI methods

Fabio Salvati, Mikhail I. Katsnelson, Andrey A. Bagrov

Eigenstate multifractality, a hallmark of non-interacting disordered metals, which may also be observed in many-body localized states, is characterized by anomalous slow dynamics and appears relevant for many areas of quantum physics, from measurement-driven systems to superconductivity. We propose a novel approach to achieve non-ergodic multifractal states

Shuhao Gu, Jialing Zhang, Siyuan Zhou, Kevin Yu

Recently, Vision-Language Models (VLMs) have achieved remarkable progress in multimodal tasks, and multimodal instruction data serves as the foundation for enhancing VLM capabilities. Despite the availability of several open-source multimodal datasets, limitations in the scale and quality of open-source instruction data hinder the performance of VLMs trained

Mingjin Zhang, Jiahao Wang, Jianming Wang, Qi Wang

Gesture recognition based on surface electromyographic signal (sEMG) is one of the most used methods. The traditional manual feature extraction can only extract some low-level signal features, this causes poor classifier performance and low recognition accuracy when dealing with some complex signals. A recognition method, namely SEDCNN-SVM, is proposed to re

David Khachaturov, Robert Mullins

Quantifying robustness in a single measure for the purposes of model selection, development of adversarial training methods, and anticipating trends has so far been elusive. The simplest metric to consider is the number of trainable parameters in a model but this has previously been shown to be insufficient at explaining robustness properties. A variety of o

Yejing Xie, Richard Zanibbi, Harold Mouchère

In this paper, we present a novel approach to Handwritten Mathematical Expression Recognition (HMER) by leveraging graph-based modeling techniques. We introduce an End-to-end model with an Edge-weighted Graph Attention Mechanism (EGAT), designed to perform simultaneous node and edge classification. This model effectively integrates node and edge features, fa

Daniel Maître, Vishal S. Ngairangbam, Michael Spannowsky

The Matrix-Element Method (MEM) has long been a cornerstone of data analysis in high-energy physics. It leverages theoretical knowledge of parton-level processes and symmetries to evaluate the likelihood of observed events. In parallel, the advent of geometric deep learning has enabled neural network architectures that incorporate known symmetries directly i

Duy Dao Do, Hervé Kerivin, Philippe Lacomme, Bogdan Vulpescu

Track finding can be considered as a complex optimization problem initially introduced in particle physics involving the reconstruction of particle trajectories. A track is typically composed of several consecutive segments (track segments) that resembles a smooth curve without bifurcations. In this paper various modeling approaches are explored in order to

Yejing Huo, Guoheng Huang, Lianglun Cheng, Jianbin He

Accurate prediction of mortality in nasopharyngeal carcinoma (NPC), a complex malignancy particularly challenging in advanced stages, is crucial for optimizing treatment strategies and improving patient outcomes. However, this predictive process is often compromised by the high-dimensional and heterogeneous nature of NPC-related data, coupled with the pervas

D. Julien-Neitzert, E. Leung, N. Islam, S. Khorev

Integrated circuit technology enables the scaling of circuit complexity and functionality while maintaining manufacturability and reliability. Integration is expected to play an important role in quantum information technologies, including in the highly demanding task of producing the classical signals to control and measure quantum circuits at scales needed

Shantanu Jain, Tatiana Podladchikova, Astrid M. Veronig, Galina Chikunova

On May 8, 2024, solar active region 13664 produced an X-class flare, several M-class flares, and multiple Earth-directed Coronal Mass Ejections (CMEs). The initial CME caused coronal dimmings, characterized by localized reductions in extreme-ultraviolet (EUV) emissions, indicating mass loss and expansion during the eruption. After one solar rotation, on June

Benjamin Bakri, Nicolas Crouseilles, Paul-Antoine Hervieux, Xue Hong

We construct a mean-field model that describes the nonlinear dynamics of a spin-polarized electron gas interacting with fixed, positively-charged ions possessing a magnetic moment that evolves in time. The mobile electrons are modeled by a four-component distribution function in the two-dimensional phase space $(x,v)$, obeying a Vlasov-Poisson set of equatio

Simon Vendelbo Bylling Jensen, Nicolas Tancogne-Dejean, Angel Rubio, Lars Bojer Madsen

A beyond electric-dipole light-matter theory is needed to describe emerging X-ray and THz applications for characterization and control of quantum materials but inaccessible as nondipole lattice-aperiodic terms impede on the use of Bloch's theorem. To circumvent this, we derive a formalism that captures dominant nondipole effects in intense electromagnetic f

Kuan-Chu Chen, Clement Godfrin, George Simion, Imri Fattal

The spatial distribution of spurious dots in SiMOS single-electron transistors (SETs), fabricated on an industrial 300 mm process line, has been statistically analyzed. To have a deeper understanding of the origin of these spurious dots, we analyzed SETs with three different oxide thicknesses: 8 nm, 12 nm and 20 nm. By combining spurious dot triangulation cr

Sedef Özcan, Matthias Täufer

We investigate the torsion function or landscape function and its integral, the torsional rigidity, of Laplacians on metric graphs subject to $\delta$-vertex conditions. A variational characterization of torsional rigidity and Hadamard-type formulas are obtained, enabling the derivation of surgical principles. We use these principles to prove upper and lower

Mikel Artola, José A. R. Cembranos, Prado Martín-Moruno

Motivated by different approaches to quantum gravity, one could consider that Lorentz invariance is not an exact symmetry of nature at all energy scales. Following this spirit, modified dispersion relations have been used to encapsulate quantum gravity phenomenology. In the present work, we propose a class of Lorentz invariance violating phenomenological dis

Ben Blain, Giampiero Marchegiani, Luigi Amico, Gianluigi Catelani

In quantum information processing, a tension between two different tasks occurs: while qubits' states can be preserved by isolating them, quantum gates can be realized only through qubit-qubit interactions. In arrays of qubits, weak coupling leads to states being spatially localized and strong coupling to delocalized states. Here, we study the average energy

Sander Borst, Marek Eliáš, Moritz Venzin

We propose a $O(\log k \log n)$-competitive randomized algorithm for online node-weighted Steiner forest. This is essentially optimal and significantly improves over the previous bound of $O(\log^2 k \log n)$ by Hajiaghayi et al. [2017]. In fact, our result extends to the more general prize-collecting setting, improving over previous works by a poly-logarith

Omar Naim, Nicholas Asher

This paper explores the much discussed, possible explanatory link between attention weights (AW) in transformer models and predicted output. Contrary to intuition and early research on attention, more recent prior research has provided formal arguments and empirical evidence that AW are not explanatorily relevant. We show that the formal arguments are incorr

Parosh Aziz Abdulla, Yo-Ga Chen, Yu-Fang Chen, Lukáš Holík

We present a new method for the verification of quantum circuits based on a novel symbolic representation of sets of quantum states using level-synchronized tree automata (LSTAs). LSTAs extend classical tree automata by labeling each transition with a set of choices, which are then used to synchronize subtrees of an accepted tree. Compared to the traditional

Marian Longa, João F. Henriques

Learning interpretable representations of visual data is an important challenge, to make machines' decisions understandable to humans and to improve generalisation outside of the training distribution. To this end, we propose a deep learning framework where one can specify nonlinear priors for videos (e.g. of Newtonian physics) that allow the model to learn

Amirhossein Alimohammadi, Sauradip Nag, Saeid Asgari Taghanaki, Andrea Tagliasacchi

Segmenting an object in a video presents significant challenges. Each pixel must be accurately labelled, and these labels must remain consistent across frames. The difficulty increases when the segmentation is with arbitrary granularity, meaning the number of segments can vary arbitrarily, and masks are defined based on only one or a few sample images. In th

Jinghao Hu, Yuhe Zhang, GuoHua Geng, Liuyuxin Yang

Traditionally, style has been primarily considered in terms of artistic elements such as colors, brushstrokes, and lighting. However, identical semantic subjects, like people, boats, and houses, can vary significantly across different artistic traditions, indicating that style also encompasses the underlying semantics. Therefore, in this study, we propose a

Jordi-Lluís Figueras, Joaquim Puig

We present some computer assisted methods to prove the existence of spectral gaps for the Almost Mathieu operator at critical coupling and give rigorous numerical estimates on their size. As an example we show that the first 8 gaps predicted by the Gap Labelling theorem are open when $\omega=(\sqrt{5}-1)/2$ and 12 of them are open when $\omega=e-2$. A dynami

Benjamin Moseley, Aidin Niaparast, R. Ravi

We study an online generalization of the classic Joint Replenishment Problem (JRP) that models the trade-off between ordering costs, holding costs, and backlog costs in supply chain planning systems. A retailer places orders to a supplier for multiple items over time: each request is for some item that the retailer needs in the future, and has an arrival tim

Vuong Bui

Generalizing some popular sequences like Catalan's number, Schr\"oder's number, etc, we consider the sequence $s_n$ with $s_0=1$ and for $n\ge 1$, \begin{multline*} s_n=\sum_{x_1+\dots+x_{\ell_1}=n-1} \kappa_1 s_{x_1}\dots s_{x_{\ell_1}} + \dots +\sum_{x_1+\dots+x_{\ell_{t'}}=n-1} \kappa_{t'} s_{x_1}\dots s_{x_{\ell_{t'}}}+\\ \max_{x_1+\dots+x_{\ell_{t'+1}}=

Ashutosh Pandey

This paper investigates the fundamental connections between linear super-commuting maps, super-biderivations, and centroids in Hom-Lie superalgebras under certain conditions. Our work generalizes the results of Bresar and Zhao on Lie algebras.

Zecheng Tang, Zechen Sun, Juntao Li, Qiaoming Zhu

Long-context models(LCMs) have shown great potential in processing long input sequences(even more than 100M tokens) conveniently and effectively. With significant progress, recent research has pointed out that LCMs can accurately locate token-level salient information within the context. Yet, the generation performance of these LCMs is far from satisfactory

Duc-Bao Nguyen, Duc-Viet Vu

We generalize previous diameter estimates and local non-vanishing of volumes for Kaehler metrics to the case of big cohomology classes. In our proof, among other things, we will prove a uniform diameter estimate for a family of smooth Kaehler metrics only involving an integrability condition. We also have to use fine stability properties of complex Monge-Amp

Jun-ichiro Yasuda, Michael M. Hull, Naohiro Mae, Kentaro Kojima

Although conceptual assessment tests are commonly administered at the beginning and end of a semester, this pre-post approach has inherent limitations. Specifically, education researchers and instructors have limited ability to observe the progression of student conceptual understanding throughout the course. Furthermore, instructors are limited in the usefu

Stalin Abraham, Ameeya A. Bhagwat

Non-Hermitian matrices $H\in M_2(\mathbb{C})$ satisfying the relation $ H^{\dag}G = GH $, for invertible and singular Hermitian matrices $G$ have been studied. The matrices $H$ corresponding to invertible $G$ are known in the literature as G-pseudo Hermitian matrices. We label the matrices corresponding to the singular $G_s$ as $G_s$-pseudo Hermitian. We hav

Abdulfattah Safa, Tamta Kapanadze, Arda Uzunoğlu, Gözde Gül Şahin

Recent advances in large language models have demonstrated promising capabilities in following simple instructions through instruction tuning. However, real-world tasks often involve complex, multi-step instructions that remain challenging for current NLP systems. Robust understanding of such instructions is essential for deploying LLMs as general-purpose ag

Zhiwei Liu, Weiran Yao, Jianguo Zhang, Rithesh Murthy

We introduce the Principled Reasoning and Acting (PRAct) framework, a novel method for learning and enforcing action principles from trajectory data. Central to our approach is the use of text gradients from a reflection and optimization engine to derive these action principles. To adapt action principles to specific task requirements, we propose a new optim

Tanya Chowdhury, Atharva Nijasure, James Allan

Transformer networks, particularly those achieving performance comparable to GPT models, are well known for their robust feature extraction abilities. However, the nature of these extracted features and their alignment with human-engineered ones remain unexplored. In this work, we investigate the internal mechanisms of state-of-the-art, fine-tuned LLMs for p

Yi Liu, Alessandro Russo

This paper introduces a nonconforming virtual element method for general second-order elliptic problems with variable coefficients on domains with curved boundaries and curved internal interfaces. We prove arbitrary order optimal convergence in the energy and $L^2$ norms, confirmed by numerical experiments on a set of polygonal meshes. The accuracy of the nu

A. Anokhina, E. Lanina, A. Morozov

We generalize the recently discovered planar decomposition (Kauffman bracket) for the HOMFLY polynomials of bipartite knot/link diagrams to (anti)symmetrically colored HOMFLY polynomials. Cabling destroys planarity, but it is restored after projection to (anti)symmetric representations. This allows to go beyond arborescent calculus, which so far produced the

Ruiyang Zhao, Kazuya Koyama, Yuting Wang, Gong-Bo Zhao

The baryon acoustic oscillation (BAO) reconstruction plays a crucial role in cosmological analysis for spectroscopic galaxy surveys because it can make the density field effectively more linear and more Gaussian. The combination of the power spectra before and after the BAO reconstruction helps break degeneracies among parameters, then improve the constraint

Parkhi Bhardwaj, Shubhrangshu Dasgupta

In this work, we propose a theoretical model for generating microwave beams with non-zero orbital angular momentum utilizing the atomic vapor medium combined with coherent control techniques. Our method involves a difference frequency generation process within a centrosymmetric medium subjected to a dc electric field, enabling frequency conversion and parame

Yanzhen Cai, Wei Ren, Xijing Dai, Jing Kang

Recently reported van der Waals layered honeycomb rare-earth chalcohalides REChX (RE = rare earth, Ch = chalcogen, and X = halogen) are considered to be promising Kitaev spin liquid (KSL) candidates. The high-quality single crystals of YbOCl, a representative member of the family with an effective spin of 1/2, are available now. The crystalline electric fiel

Ankit Anand, Saeed Noori Gashti, Mohammad Reza Alipour, Mohammad Ali S. Afshar

In this paper, we conduct a comprehensive investigation into the weak cosmic censorship conjecture (WCCC) for Reissner-Nordstr\"om (R-N) AdS black holes that are influenced by Perfect Fluid Dark Matter (PFDM). Our study is framed within the context of Conformal Field Theory (CFT) thermodynamics. We delve into the principles of energy flux and mass-energy equ

Josh Calcino, Daniel J. Price, Thomas Hilder, Valentin Christiaens

Late-stage infall onto evolved protoplanetary discs is an important source of material and angular momentum replenishment, and disc substructures. In this paper we used 3D smoothed particle hydrodynamics simulations to model streamer-disc interactions for a prograde streamer. The initially parabolic streamer interacts with the disc material to excite disc ec

Oona Oinonen, Lassi Ruoppa, Josef Taher, Matti Lehtomäki

The availability of highly accurate urban airborne laser scanning (ALS) data will increase rapidly in the future, especially as acquisition costs decrease, for example through the use of drones. Current challenges in data processing are related to the limited spectral information and low point density of most ALS datasets. Another challenge will be the growi

Uljad Berdica, Matthew Jackson, Niccolò Enrico Veronese, Jakob Foerster

Soft robotic manipulators offer operational advantage due to their compliant and deformable structures. However, their inherently nonlinear dynamics presents substantial challenges. Traditional analytical methods often depend on simplifying assumptions, while learning-based techniques can be computationally demanding and limit the control policies to existin

Xu Ding, KaiFan Ji, ZhiMing Song, NianPing Liu

TIC 157365951 has been classified as a $\delta$ Scuti type by the International Variable Star Index (VSX). Through the spectra from Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) and its light curve, we further discovered that it is a binary system. This binary system comprises a red dwarf star and a compact star. Through the spectral ene

Yifei Yang, Zouying Cao, Qiguang Chen, Libo Qin

The development of large language models (LLMs) has significantly expanded model sizes, resulting in substantial GPU memory requirements during inference. The key and value storage of the attention map in the KV (key-value) cache accounts for more than 80\% of this memory consumption. Nowadays, most existing KV cache compression methods focus on intra-layer

Xueying Zhang, Bin Zhang, Shihai Wei, Hao Li

Light-matter interface is an important building block for long-distance quantum networks. Towards a scalable quantum network with high-rate quantum information processing, it requires to develop integrated light-matter interfaces with broadband and multiplexing capacities. Here we demonstrate a light-matter interface at telecom band in an integrated system.

Jae Hyung Woo, Hae Seong Lee, Joon-Young Moon, Tae-Wook Ko

We investigate hysteresis in a generalized Kuramoto model with identical oscillators, focusing on coupling strength inhomogeneity, which results in oscillators being coupled to others with varying strength, and a simplified, more realistic coupling function. With the more realistic coupling function and the coupling strength inhomogeneity, each oscillator ac

Shen Nie, Fengqi Zhu, Chao Du, Tianyu Pang

Masked diffusion models (MDMs) have shown promise in language modeling, yet their scalability and effectiveness in core language tasks, such as text generation and language understanding, remain underexplored. This paper establishes the first scaling law for MDMs, demonstrating a scaling rate comparable to autoregressive models (ARMs) and a relatively small

Digvijay Boob, Mohammad Khalafi

This paper proposes a novel first-order algorithm that solves composite nonsmooth and stochastic convex optimization problem with function constraints. Most of the works in the literature provide convergence rate guarantees on the average-iterate solution. There is growing interest in the convergence guarantees of the last iterate solution due to its favorab

J. Kováč, J. Veselý, K. Janková

We study the behaviour of discrete dynamical systems generated by a continuous map $f$ of a compact real interval into itself where at randomly chosen times a function different from $f$ - so called impulse function is applied. We show that both the splittting property and the average contraction property guarantee the stability of the system. We give a numb