February 2024 arXiv papers — page 82

Adi Ditkowski, Anne Le Blanc, Chi-Wang Shu

We propose a block finite difference, error inhibiting scheme that is fourth-order accurate for short to moderate times and has a six-order convergence rate for long times. This scheme outperforms the standard fourth-order Finite Difference scheme. We also demonstrate that the proposed scheme is a particular type of nodal-based Discontinuous Galerkin method

Jiaqi Ni

Littlewood's theorem is one of the pioneering results in random analytic functions over the open unit disk. In this paper, we prove some analogues of this theorem for Hardy spaces in infinitely many variables. Our results not only cover finite-variable setting, but also apply in cases of Dirichlet series.

S. Davood Sadatian, S. Mohamad Reza Hosseini

We investigate wormhole solutions using the modified gravity model $f(Q,T)$ with viscosity and aim to find a solution for the existence of wormholes mathematically without violating the energy conditions. We show that there is no need to define a wormhole from exotic matter and analyze the equations with numerical analysis to establish weak energy conditions

Xiao-Wu Chen

For a regular normal element in an arbitrary ring, we study the category of its module factorizations. The cokernel functor relates module factorizations with Gorenstein projective components to Gorenstein projective modules over the quotient ring. The results are vast extensions of Eisenbud's matrix factorization theorem.

Aleksandra Koroleva, Thoai-Khanh Khuu, César Magén, Hervé Roussel

The rapid development of brain-inspired computing requires new artificial components and architectures for its hardware implementation. In this regard, memristive devices emerged as potential candidates for artificial synapses because of their ability to emulate the plasticity of the biological synapses. In this work, the synaptic behavior of the TiN/La2NiO4

Mikhail Nevskii

The paper contains a survey of the results obtained by the author in recent years. These results concern the application in multivariate polynomial interpolation of some geometric constructions and methods. In particular, we give estimates of the projector's norms through the characteristics of sets associated with homothety. The known exact values and nowad

Kelvin Onggadinata, Pawel Kurzynski, Dagomir Kaszlikowski

We demonstrate how to universally simulate ensemble statistics of projective local measurements on any $n$-qubit state shared among $n$ observers with classical communication and shared randomness. Our technique originates from protocols designed to simulate quantum non-locality [in Horizons of the Mind, Springer, Cham (2014)] and classical simulation of qua

Mårten Schultzberg, Sebastian Ankargren, Mattias Frånberg

In the past decade, AB tests have become the standard method for making product decisions in tech companies. They offer a scientific approach to product development, using statistical hypothesis testing to control the risks of incorrect decisions. Typically, multiple metrics are used in AB tests to serve different purposes, such as establishing evidence of s

Louis Jalouzot, Christophe Pallier, Emmanuel Chemla, Yair Lakretz

Understanding how explicit theoretical features are encoded in opaque neural systems is a central challenge now common to neuroscience and AI. We introduce Metric Learning Encoding Models (MLEMs) to address this challenge most directly as a metric learning problem: we fit the distance in the space of theoretical features to match the distance in neural space

Kelvin Onggadinata, Pawel Kurzynski, Dagomir Kaszlikowski

We demonstrate a basic non-classical effect in a quasi-probabilistic toy model with local Alice and Bob who share classical randomness. Our scenario differs from the orthodox demonstrations of non-classicality such as violations of Bell inequalities where both local observers have a free will and randomly choose their measurement settings. The core of the ar

Azat F. Aminov, Alexey A. Sokolik, Yurii E. Lozovik

Two-component systems consisting of mutually interacting particles can demonstrate both intracomponent transport effects and intercomponent entrainment (or drag) effects. In the presence of superfluidity, the intracomponent transport is characterized by dissipative conductivity and superfluid weight in the framework of two-fluid model, and intercomponent ent

Kim Louisa Auth, Jim Brouzoulis, Magnus Ekh

This study addresses ductile fracture of single grains in metals by modeling of the formation and propagation of transgranular cracks. A proposed model integrates gradient extended hardening, phase-field modeling for fracture, and crystal plasticity. It is presented in a thermodynamical framework in large deformation kinematics and accounts for damage irreve

J. Senthilnath, Bangjian Zhou, Zhen Wei Ng, Deeksha Aggarwal

In the realm of sequential decision-making tasks, the exploration capability of a reinforcement learning (RL) agent is paramount for achieving high rewards through interactions with the environment. To enhance this crucial ability, we propose SAQN, a novel approach wherein a self-evolving autoencoder (SA) is embedded with a Q-Network (QN). In SAQN, the self-

L. Banszerus, W. Marshall, C. W. Andersson, T. Lindemann

We report measurements of the current-phase relation of two voltage-controlled semiconductor-superconductor hybrid Josephson junctions (JJs) in series. The two hybrid junctions behave similar to a single-mode JJ with effective transparency determined by the ratio of Josephson coupling strengths of the two junctions. Gate-voltage control of Josephson coupling

Seshadri Majumder, Rwitika Chatterjee, Kiran M. Jayasury, Santabrata Das

We report the results of first ever spectro-polarimetric analyses of the Galactic ultra-luminous X-ray pulsar Swift J0243.6$+6124$ during the 2023 outburst using quasi-simultaneous {\it IXPE}, {\it NICER} and {\it NuSTAR} observations. A pulsation of period $\sim 9.79$ s is detected in {\it IXPE} and {\it NuSTAR} observations with pulse fractions (PFs) $\sim

Bo Peng, Lingke Zhang, Rong Xiong

When a mobile robot plans its path in an environment with obstacles using Artificial Potential Field (APF) strategy, it may fall into the local minimum point and fail to reach the goal. Also, the derivatives of APF will explode close to obstacles causing poor planning performance. To solve the problems, exponential functions are used to modify potential fiel

Xiang Huang, C. Y. Zhao, Hong Wang, Shenghong Ju

Artificial intelligence (AI) promotes the polymer design paradigm from a traditional trial-and-error approach to a data-driven style. Achieving high thermal conductivity (TC) for intrinsic polymers is urgent because of their importance in the thermal management of many industrial applications such as microelectronic devices and integrated circuits. In this w

Andrei Bud

We prove that the projectivized strata of differentials are not contained in pointed Brill-Noether divisors, with only a few exceptions. For a generic element in a stratum of differentials, we show that many of the associated pointed Brill-Noether loci are of expected dimension. We use our results to study the Auel-Haburcak Conjecture: We obtain new non-cont

E. Harikumar, R. P. Malik

We demonstrate the existence of a single pseudo-scalar (PS) field in the mathematically backed and parity preserving modifications of the standard St${\ddot u}$ckelberg formalism (SSF) in the context of the Lagrangian formulation of the (i) two (1 + 1)-dimensional (2D) massive Abelian 1-form gauge theory, (ii) three (2 + 1)-dimensional (3D) massive Abelian 2

Guijin Son, Sangwon Baek, Sangdae Nam, Ilgyun Jeong

Large language models (LLMs) are typically prompted to follow a single instruction per inference call. In this work, we analyze whether LLMs also hold the capability to handle multiple instructions simultaneously, denoted as Multi-Task Inference. For this purpose, we introduce the MTI Bench(Multi-Task Inference Benchmark), a comprehensive evaluation benchmar

M. Zeeshan Gul, M. Sharif, Adeeba Arooj

The main objective of this paper is to investigate the impact of $f(\mathcal{Q},\mathcal{T})$ gravity on the geometry of anisotropic compact stellar objects, where $\mathcal{Q}$ is non-metricity and $\mathcal{T}$ is the trace of the energy-momentum tensor. In this perspective, we use the physically viable non-singular solutions to examine the configuration o

Tomohiro Koana, Magnus Wahlström

We show new algorithms and constructions over linear delta-matroids. We observe an alternative representation for linear delta-matroids, as a contraction representation over a skew-symmetric matrix. This is equivalent to the more standard "twist representation" up to $O(n^\omega)$-time transformations, but is much more convenient for algorithmic tasks. For i

Pietro Ferrero, Dario Francia, Carlo Heissenberg, Matteo Romoli

In the framework of the convolutional double copy, we investigate the asymptotic symmetries of the gravitational multiplet stemming from the residual symmetries of its single-copy constituents at null infinity. We show that the asymptotic symmetries of Maxwell fields in D=4 imply ``double-copy supertranslations", i.e. BMS supertranslations and two-form asymp

Caroline S. Wagner, Xiaojing Cai, Jeroen Baas

A review of researcher mobility between China and the United States shows overall growth in mobility between the two nations. A review based upon a scholar’s change of address from China to the United States or vice versa reveals mobility patterns. Between 2009 and 2018, an overall upward trend is noted for incoming scholars from China, as well as upward t

Thomas Bartz-Beielstein

Batch Machine Learning (BML) reaches its limits when dealing with very large amounts of streaming data. This is especially true for available memory, handling drift in data streams, and processing new, unknown data. Online Machine Learning (OML) is an alternative to BML that overcomes the limitations of BML. OML is able to process data in a sequential manner

Yuri N. Obukhov, G. E. Volovik

The quintet of Dirac $4\times 4$ matrices suggests that the fundamental dimension of the internal (spin) space is $n=5$, instead of the conventional dimension $n=4$. Then extending the usual $4\times 4$ tetrads (vierbein), gravity is described in terms of the 5-bein (f\"unfbein or five legs). We discuss the properties of the spacetime geometry induced from t

Yihua Zhang, Pingzhi Li, Junyuan Hong, Jiaxiang Li

In the evolving landscape of natural language processing (NLP), fine-tuning pre-trained Large Language Models (LLMs) with first-order (FO) optimizers like SGD and Adam has become standard. Yet, as LLMs grow {in size}, the substantial memory overhead from back-propagation (BP) for FO gradient computation presents a significant challenge. Addressing this issue

Giovanni Fusco, Monica Motta, Richard Vinter

We introduce discontinuous solutions to nonlinear impulsive control systems with state time delays in the dynamics and derive necessary optimality conditions in the form of a Maximum Principle for associated optimal control problems. In the case without delays, if the measure control is scalar valued, the corresponding discontinuous state trajectory, underst

Alicia Ouskine, Adrian D. C. Chan, Fateme Rajabiyazdi

Stroke is one of the leading causes of disability worldwide. The efficacy of recovery is determined by a variety of factors, including patient adherence to rehabilitation programs. One way to increase patient adherence to their rehabilitation program is to show patients their progress that is visualized in a simple and intuitive way. We begin to gather preli

Felix Lambrecht, Andreas Kröpelin, Mario Lüttich, Michael Habeck

Cryo-EM data processing typically focuses on the structure of the main conformational state under investigation and discards images that belong to other states. This approach can reach atomic resolution, but ignores vast amounts of valuable information about the underlying conformational ensemble and its dynamics. CowScape analyzes an entire cryo-EM dataset

Shu Yang, Hanzhi Ma, Chengting Yu, Aili Wang

Spiking neural networks (SNNs) have low power consumption and bio-interpretable characteristics, and are considered to have tremendous potential for energy-efficient computing. However, the exploration of SNNs on image generation tasks remains very limited, and a unified and effective structure for SNN-based generative models has yet to be proposed. In this

Xiaonan Hao, Jiaxi Huang, Ning Jiang, Lifeng Zhao

In this article, we consider the evolutionary model for magnetoelasticity with vanishing viscosity/damping, which is a nonlinear dispersive system. The global regularity and scattering of the evolutionary model for magnetoelasticity under small size of initial data is proved. Our proof relies on the idea of vector-field method due to the quasilinearity and t

Ksenia Arkhipova, Lev Astrakhantsev, Nihat Sadik Deger, Anastasia A. Golubtsova

In this work we focus on the study of RG flows of conformal field theories that are holographically dual to Poincar\'e domain wall solutions in $D=3$, $\mathcal{N}=(2,0)$ gauged supergravity coupled to a sigma model with target space $SU(1, 1)/U(1) = H^2$. This theory is truncated to a subsector where the vector field and phase of the scalar field vanish and

Debayan Bhattacharya, Konrad Reuter, Finn Behrendt, Lennart Maack

Commonly employed in polyp segmentation, single image UNet architectures lack the temporal insight clinicians gain from video data in diagnosing polyps. To mirror clinical practices more faithfully, our proposed solution, PolypNextLSTM, leverages video-based deep learning, harnessing temporal information for superior segmentation performance with the least p

Zs. Sándor, O. M. Guilera, Zs. Regály, W. Lyra

The ring-like structures in protoplanetary discs that are observed in the cold dust emission by ALMA, might be explained by dust aggregates trapped aerodynamically in pressure maxima. The effect of a transient pressure maximum is investigated that develops between two regimes with different turbulent levels. We study how such a pressure maximum collects dust

Alexandros Galanakis, Michael Spieß

Nori's Eisenstein cohomology classes and their integral refinements due to Beilinson, Kings and Levin can be used to obtain simple proofs of the rationality and integrality properties of special values of abelian $L$-functions of totally real fields. Here we introduce an adelic refinement of these constructions. This will be used to establish new divisibilit

Prashant Agrawal, Mahabir Prasad Jhanwar, Subodh Vishnu Sharma, Subhashis Banerjee

While existing literature on electronic voting has extensively addressed verifiability of voting protocols, the vulnerability of electoral rolls in large public elections remains a critical concern. To ensure integrity of electoral rolls, the current practice is to either make electoral rolls public or share them with the political parties. However, this ena

Zahra Yazdanparast

Software malfunction presents a significant hurdle within the computing domain, carrying substantial risks for systems, enterprises, and users universally. To produce software with high reliability and quality, effective debugging is essential. Program debugging is an activity to reduce software maintenance costs. In this study, a failure repair method that

Zongxia Liang, Xiaodong Luo

We study a reinsurance Stackelberg game in which both the insurer and the reinsurer adopt the mean-variance (abbr. MV) criterion in their decision-making and the reinsurance is irreversible. We apply a unified singular control framework where irreversible reinsurance contracts can be signed in both discrete and continuous times. The results theoretically ill

Xiaopeng Si, Zi Tang

Climate change has become an unavoidable problem in achieving sustainable development. As one of the major industries worldwide, tourism can make a significant contribution to mitigating climate change. The main objective of the paper is to assess the development level of low-carbon tourism from multi-aspect, using the Yellow River Basin as an example. First

Shigehiro Yasui, Daiki Suenaga, Kei Suzuki

We consider the quantum chromodynamics (QCD) Kondo effect for a single heavy quark in quark matter composed of light quarks with chiral symmetry breaking. Introducing several spinor structures in QCD Kondo condensates, i.e., particle-projected condensate, antiparticle-projected condensate, and normal condensate without projection, we calculate the attractive

Ninglu Shao, Shitao Xiao, Zheng Liu, Peitian Zhang

Large language models (LLMs) call for extension of context to handle many critical applications. However, the existing approaches are prone to expensive costs and inferior quality of context extension. In this work, we propose Extensible Embedding, which realizes high-quality extension of LLM's context with strong flexibility and cost-effectiveness. Extensib

Tim Gorichanaz

Humanity-centered design is a concept of emerging interest in HCI, one motivated by the limitations of human-centered design. As discussed to date, humanity-centered design is compatible with but goes beyond human-centered design in that it considers entire ecosystems and populations over the long term and centers participatory design. Though the intentions

Dmitrii Kobylianskii, Nathalie Soybelman, Etienne Dreyer, Eilam Gross

Denoising diffusion models have gained prominence in various generative tasks, prompting their exploration for the generation of calorimeter responses. Given the computational challenges posed by detector simulations in high-energy physics experiments, the necessity to explore new machine-learning-based approaches is evident. This study introduces a novel gr

Peng Xu, Wenqi Shao, Mengzhao Chen, Shitao Tang

Large language models (LLMs) have demonstrated outstanding performance in various tasks, such as text summarization, text question-answering, and etc. While their performance is impressive, the computational footprint due to their vast number of parameters can be prohibitive. Existing solutions such as SparseGPT and Wanda attempt to alleviate this issue thro

Yucheng Zhou, Xiang Li, Qianning Wang, Jianbing Shen

In Large Visual Language Models (LVLMs), the efficacy of In-Context Learning (ICL) remains limited by challenges in cross-modal interactions and representation disparities. To overcome these challenges, we introduce a novel Visual In-Context Learning (VICL) method comprising Visual Demonstration Retrieval, Intent-Oriented Image Summarization, and Intent-Orie

Kun Luo, Zheng Liu, Shitao Xiao, Kang Liu

Large language models (LLMs) call for extension of context to handle many critical applications. However, the existing approaches are prone to expensive costs and inferior quality of context extension. In this work, we proposeExtensible Embedding, which realizes high-quality extension of LLM's context with strong flexibility and cost-effectiveness. Extensibl

Zheng Ma, Changxin Wang, Yawen Ouyang, Fei Zhao

Evaluating the compatibility between textual descriptions and corresponding images represents a core endeavor within multi-modal research. In recent years, a proliferation of reference-free methods, leveraging visual-language pre-trained models (VLMs), has emerged. Empirical evidence has substantiated that these innovative approaches exhibit a higher correla

Zining Wang, Paul Reisert, Eric Nichols, Randy Gomez

Social robots aim to establish long-term bonds with humans through engaging conversation. However, traditional conversational approaches, reliant on scripted interactions, often fall short in maintaining engaging conversations. This paper addresses this limitation by integrating large language models (LLMs) into social robots to achieve more dynamic and expr

Jin Zhou, Jiahao Mei, Fangguo Zhao, Jiming Chen

Over the past decade, there has been a remarkable surge in utilizing quadrotors for various purposes due to their simple structure and aggressive maneuverability, such as search and rescue, delivery and autonomous drone racing, etc. One of the key challenges preventing quadrotors from being widely used in these scenarios is online waypoint-constrained time-o

Matouš Jelínek, Eric Nichols, Randy Gomez

This study presents an empirical investigation into the design and impact of autonomous dialogues in human-robot interaction for behavior change coaching. We focus on the use of Haru, a tabletop social robot, and explore the implementation of the Tiny Habits method for fostering positive behavior change. The core of our study lies in developing a fully auton

Ali Kashefi, Tapan Mukerji

Fourier neural operators (FNOs) are invariant with respect to the size of input images, and thus images with any size can be fed into FNO-based frameworks without any modification of network architectures, in contrast to traditional convolutional neural networks (CNNs). Leveraging the advantage of FNOs, we propose a novel deep-learning framework for classify

Hendra I. Nurdin

The quantum Cram\'{e}r-Rao bound (QCRB) as the ultimate lower bound for precision in quantum parameter estimation is only known to be saturable in the multiparameter setting in special cases and under conditions such as full or average commutavity of the symmetric logarithmic derivatives (SLDs) associated with the parameters. Moreover, for general mixed stat

Huayi Zhou, Mukun Luo, Fei Jiang, Yue Ding

The 2D human pose estimation (HPE) is a basic visual problem. However, its supervised learning requires massive keypoint labels, which is labor-intensive to collect. Thus, we aim at boosting a pose estimator by excavating extra unlabeled data with semi-supervised learning (SSL). Most previous SSHPE methods are consistency-based and strive to maintain consist

Xikun Zhang, Dongjin Song, Dacheng Tao

Continual learning on graph data has recently attracted paramount attention for its aim to resolve the catastrophic forgetting problem on existing tasks while adapting the sequentially updated model to newly emerged graph tasks. While there have been efforts to summarize progress on continual learning research over Euclidean data, e.g., images and texts, a s

Muhammad Ridwan, Ahmad Jafar Arifi, Terry Mart

Investigating the properties of excited charmonia is important to clarify its internal structure. In this paper, we present the mass spectra (MS) and decay constants (DC) for charmonia up to 3S states calculated by means of the light-front quark model based on a variational approach. In particular, we consider the QCD-motivated effective Hamiltonian, which i

Robert C. Griffiths, Ross A. Maller, Soudabeh Shemehsavar

A Bayesian nonparametric method of James, Lijoi \& Prunster (2009) used to predict future values of observations from normalized random measures with independent increments is modified to a class of models based on negative binomial processes for which the increments are not independent, but are independent conditional on an underlying gamma variable. Like i

Wei Ding, Rui Zhang, Tianning Chen, Shuai Qu

The wave equation governing the wave propagation in chiral phononic crystals, established through force equilibrium law, conceals the underlying physical information. This has led to a controversy over the bandgap mechanism. In this letter, we theoretically unveil the reason of this controversy, and put forward an alternative approach from wave behavior to f

Dongyan Fu, Yubing Dong, S. Kumano

The definitions of the quark and gluon transversity generalized parton distributions (GPDs) in spin-3/2 particles are obtained in the light-cone gauge. It is found that they contain 16 independent components for each parton. Their even or odd property is found in terms of skewness variable, and the odd transversity GPDs vanish in the forward limit. There are

Maria V. Kulikova, Julia V. Tsyganova, Gennady Yu. Kulikov

One of the modern research lines in econometrics studies focuses on translating a wide variety of structural econometric models into their state-space form, which allows for efficient unknown dynamic system state and parameter estimations by the Kalman filtering scheme. The mentioned trend yields advanced state-space model structures, which demand innovative

Chuang Sun, Zixuan Wang, Kian Shen Kiang, Jun-Yu Ou

Dielectric metalens has emerged as an attractive device for advanced imaging system because of its powerful manipulation ability of light beam, small volume, and light weight. However, the applications of silicon nitride (Si3N4) metalens are limited by the low refraction index of Si3N4, and multi-foci metalens has not been realized based on a Si3N4 metalens.

Yakun Chen, Kaize Shi, Zhangkai Wu, Juan Chen

Spatiotemporal data analysis is pivotal across various domains, such as transportation, meteorology, and healthcare. The data collected in real-world scenarios are often incomplete due to device malfunctions and network errors. Spatiotemporal imputation aims to predict missing values by exploiting the spatial and temporal dependencies in the observed data. T

Kanchana Vaishnavi Gandikota, Paramanand Chandramouli, Hannah Droege, Michael Moeller

Low dose computed tomography (CT) acquisition using reduced radiation or sparse angle measurements is recommended to decrease the harmful effects of X-ray radiation. Recent works successfully apply deep networks to the problem of low dose CT recovery on bench-mark datasets. However, their robustness needs a thorough evaluation before use in clinical settings

Taras Panov, Temurbek Rahmatullaev

We relate polyhedral products of topological spaces to graph products of groups. The loop homology algebras of polyhedral products are identified with the universal enveloping algebras of the Lie algebras associated with central series of graph products. By way of application, we describe the restricted Lie algebra associated with the lower 2-central series

Maria V. Kulikova, Gennady Yu. Kulikov

In this paper, a singular value decomposition (SVD) approach is developed for implementing the cubature Kalman filter. The discussed estimator is one of the most popular and widely used method for solving nonlinear Bayesian filtering problem in practice. To improve its numerical stability (with respect to roundoff errors) and practical reliability of computa

Chuang Sun, Hailong Pi, Kian Shen Kiang, Jize Yan

The spiral phase contrast microscope can clearly distinguish the morphological information of the low contrast objects (i.e., biological samples) because of the isotropic edge-enhancement effect, while the bright field microscope can image the overall morphology of amplitude objects. However, the imaging resolution, magnification, and field of view of conven

Niccolò D'Archivio, Robin Vacus

We address the self-stabilizing bit-dissemination problem, designed to capture the challenges of spreading information and reaching consensus among entities with minimal cognitive and communication capacities. Specifically, a group of $n$ agents is required to adopt the correct opinion, initially held by a single informed individual, choosing from two possib

Cristiano Tamborrino, Antonella Falini, Francesca Mazzia

Density estimation is a fundamental technique employed in various fields to model and to understand the underlying distribution of data. The primary objective of density estimation is to estimate the probability density function of a random variable. This process is particularly valuable when dealing with univariate or multivariate data and is essential for

Maria V. Kulikova, Gennady Yu. Kulikov

In mathematical neuroscience, a special interest is paid to a working memory mechanism in the neural tissue modeled by the Dynamic Neural Field (DNF) in the presence of model uncertainties. The working memory facility implies that the neurons' activity remains self-sustained after the external stimulus removal due to the recurrent interactions in the network

Jun Zhao, Can Zu, Hao Xu, Yi Lu

Large language models (LLMs) have demonstrated impressive performance in understanding language and executing complex reasoning tasks. However, LLMs with long context windows have been notorious for their expensive training costs and high inference latency. Even the most advanced models such as GPT-4 and Claude2 often make mistakes when processing inputs of

Yanran Chen, Wei Zhao, Anne Breitbarth, Manuel Stoeckel

Many studies have shown that human languages tend to optimize for lower complexity and increased communication efficiency. Syntactic dependency distance, which measures the linear distance between dependent words, is often considered a key indicator of language processing difficulty and working memory load. The current paper looks at diachronic trends in syn

Guijin Son, Hanwool Lee, Sungdong Kim, Seungone Kim

We propose KMMLU, a new Korean benchmark with 35,030 expert-level multiple-choice questions across 45 subjects ranging from humanities to STEM. While prior Korean benchmarks are translated from existing English benchmarks, KMMLU is collected from original Korean exams, capturing linguistic and cultural aspects of the Korean language. We test 27 public and pr

Konstantinos Ntougias, Symeon Chatzinotas, Ioannis Krikidis

The emerging reflecting intelligent surface (RIS) technology promises to enhance the capacity of wireless communication systems via passive reflect beamforming. However, the product path loss limits its performance gains. Fully-connected (FC) active RIS, which integrates reflect-type power amplifiers into the RIS elements, has been recently introduced in res

Lijia Han, Yue Qiu, Xiaohong Wang

In this paper, we study the standing wave solutions of Klein--Gordon equation with logarithmic nonlinearity. The existence of the standing wave solution related to the ground state $\phi_0(x)$ is obtained. Further, we prove the instability of solutions around $\phi_0(x)$.

Zihao Tang, Zheqi Lv, Shengyu Zhang, Fei Wu

The rapid advancement of Large Language Models (LLMs) has revolutionized various sectors by automating routine tasks, marking a step toward the realization of Artificial General Intelligence (AGI). However, they still struggle to accommodate the diverse and specific needs of users and simplify the utilization of AI models for the average user. In response, w

J. Dick, T. Le Gia, W. McLean, K. Mustapha

In a recent work (Dick et al, arXiv:2310.06187), we considered a linear stochastic elasticity equation with random Lam\'e parameters which are parameterized by a countably infinite number of terms in separate expansions. We estimated the expected values over the infinite dimensional parametric space of linear functionals ${\mathcal L}$ acting on the continuo

Mohamadou Sall, M. Anwar Hasan

Binary field extensions are fundamental to many applications, such as multivariate public key cryptography, code-based cryptography, and error-correcting codes. Their implementation requires a foundation in number theory and algebraic geometry and necessitates the utilization of efficient bases. The continuous increase in the power of computation, and the de

Dov Shirtz, Inna Koberman, Aviad Elyashar, Rami Puzis

Security by design, Sbd is a concept for developing and maintaining systems that are, to the greatest extent possible, free from security vulnerabilities and impervious to security attacks. In addition to technical aspects, such as how to develop a robust industrial control systems hardware, software, communication product, etc., SbD includes also soft aspec

Shashwat Khandelwal, Ziaul Choudhury, Shashwat Shrivastava, Suresh Purini

Images when processed using various enhancement techniques often lead to edge degradation and other unwanted artifacts such as halos. These artifacts pose a major problem for photographic applications where they can denude the quality of an image. There is a plethora of edge-aware techniques proposed in the field of image processing. However, these require t

Xinbang Dai, Huiying Li, Nan Hu, Yongrui Chen

Spatio-temporal knowledge graphs (STKGs) enhance traditional KGs by integrating temporal and spatial annotations, enabling precise reasoning over questions with spatio-temporal dependencies. Despite their potential, research on spatio-temporal knowledge graph question answering (STKGQA) remains limited. This is primarily due to the lack of datasets that simu

Xinbang Dai, Yuncheng Hua, Tongtong Wu, Yang Sheng

When we integrate factual knowledge from knowledge graphs (KGs) into large language models (LLMs) to enhance their performance, the cost of injection through training increases with the scale of the models. Consequently, there is significant interest in developing prompt strategies that effectively incorporate KG information into LLMs. However, the community

Longhuang Wu, Shangxuan Tian, Youxin Wang, Pengfei Xiong

Existing methods for scene text detection can be divided into two paradigms: segmentation-based and anchor-based. While Segmentation-based methods are well-suited for irregular shapes, they struggle with compact or overlapping layouts. Conversely, anchor-based approaches excel for complex layouts but suffer from irregular shapes. To strengthen their merits a

Bo-Hae Im, Hojin Kim, Khac Nhuan Le, Tuan Ngo Dac

Zagier-Hoffman's conjectures predict the dimension and a basis for the $\mathbb Q$-vector spaces spanned by $N$th cyclotomic multiple zeta values (MZV's) of fixed weight where $N$ is a natural number. For $N=1$ (MZV's case), half of these conjectures have been solved by the work of Terasoma, Deligne-Goncharov and Brown with the help of Zagier's identity. The

Junjian Lu, Siwei Liu, Dmitrii Kobylianski, Etienne Dreyer

In high-energy physics, particles produced in collision events decay in a format of a hierarchical tree structure, where only the final decay products can be observed using detectors. However, the large combinatorial space of possible tree structures makes it challenging to recover the actual decay process given a set of final particles. To better analyse th

Yang Zhao, Li Du, Xiao Ding, Kai Xiong

Through pretraining on a corpus with various sources, Large Language Models (LLMs) have gained impressive performance. However, the impact of each component of the pretraining corpus remains opaque. As a result, the organization of the pretraining corpus is still empirical and may deviate from the optimal. To address this issue, we systematically analyze the

Damião J. Araújo, Rafayel Teymurazyan, José Miguel Urbano

We study minimizers of non-differentiable functionals modeled on the degenerate quenching problem. Our main result establishes the finiteness of the $(n-1)-$dimensional Hausdorff measure of the free boundary. The proof is based on optimal gradient decay estimates obtained from an intrinsic Harnack-type inequality, along with a detailed analysis in a flatness

Hanshuang Tong, Jun Li, Ning Wu, Ming Gong

Recent advancements in large language models (LLMs) have opened new pathways for many domains. However, the full potential of LLMs in financial investments remains largely untapped. There are two main challenges for typical deep learning-based methods for quantitative finance. First, they struggle to fuse textual and numerical information flexibly for stock

A. Chalil, C. Ducoin, O. Stézowski, N. Millard-Pinard

The $p$ process nucleosynthesis is responsible for the synthesis of 35 neutron-deficient nuclei from $^{35}$Se to $^{196}$Hg. An important input that can affect the modeling of this process is the nuclear level density at the relevant excitation energies of the nuclei involved in the reaction network. The OSLO method has been extensively used for the measure

Dayuan Fu, Jianzhao Huang, Siyuan Lu, Guanting Dong

Addressing the disparity between forecasts and actual results can enable individuals to expand their thought processes and stimulate self-reflection, thus promoting accurate planning. In this research, we present **PreAct**, an agent framework that integrates **pre**diction, **rea**soning, and **act**ion. By utilizing the information derived from predictions

Alexander Ek, Philip B. Stark, Peter J. Stuckey, Damjan Vukcevic

Various risk-limiting audit (RLA) methods have been developed for instant-runoff voting (IRV) elections. A recent method, AWAIRE, is the first efficient approach that can take advantage of but does not require cast vote records (CVRs). AWAIRE involves adaptively weighted averages of test statistics, essentially "learning" an effective set of hypotheses to te

Jonathan Mosheiff, Nicolas Resch, Kuo Shang, Chen Yuan

We wish to generate list-decodable codes over small alphabets using as little randomness as possible. Specifically, we hope to generate codes achieving what we term the Elias bound, which means that they are $(\rho,L)$-list-decodable with rate $R \geq 1-h(\rho)-O(1/L)$. A long line of work shows that uniformly random linear codes (RLCs) achieve the Elias bou

Shirley Anugrah Hayati, Taehee Jung, Tristan Bodding-Long, Sudipta Kar

Fine-tuning large language models (LLMs) with a collection of large and diverse instructions has improved the model's generalization to different tasks, even for unseen tasks. However, most existing instruction datasets include only single instructions, and they struggle to follow complex instructions composed of multiple subtasks. In this work, we propose a

Moritz Lichter, Simon Raßmann, Pascal Schweitzer

The Weisfeiler-Leman dimension of a graph $G$ is the least number $k$ such that the $k$-dimensional Weisfeiler-Leman algorithm distinguishes $G$ from every other non-isomorphic graph. The dimension is a standard measure of the descriptive complexity of a graph and recently finds various applications in particular in the context of machine learning. In this p

Muyang He, Yexin Liu, Boya Wu, Jianhao Yuan

Multimodal Large Language Models (MLLMs) have demonstrated notable capabilities in general visual understanding and reasoning tasks. However, their deployment is hindered by substantial computational costs in both training and inference, limiting accessibility to the broader research and user communities. A straightforward solution is to leverage smaller pre

Jeremy Lovejoy, Rishabh Sarma

In the first part of this paper we prove a conjecture of Hikami on the values of the radial limits of a family of $q$-hypergeometric false theta functions. Hikami conjectured that the radial limits are obtained by evaluating a truncated version of the series. He proved a special case of his conjecture by computing the Kashaev invariant of certain torus links

Algo Carè, Erik Weyer, Balázs Cs. Csáji, Marco C. Campi

Sign-Perturbed Sums (SPS) is a system identification method that constructs confidence regions for the unknown system parameters. In this paper, we study SPS for ARX systems, and establish that the confidence regions are guaranteed to include the true model parameter with exact, user-chosen, probability under mild statistical assumptions, a property that hol

Tom Banks, Willy Fischler

Evidence has accumulated that there are supermassive black holes (SMBHs) in the centers of most galaxies, and that these were formed in the very early universe by some as yet unknown process. In particular, there is evidence [15] that at least some galaxies formed as early as $10^8$ to $10^9$ years after the Big Bang host SMBHs. We suggest that the holograph

Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri

Statistics about traffic flow and people's movement gathered from multiple geographical locations in a distributed manner are the driving force powering many applications, such as traffic prediction, demand prediction, and restaurant occupancy reports. However, these statistics are often based on sensitive location data of people, and hence privacy has to be

Nuo Xu, Jun Zhao, Can Zu, Sixian Li

Faithfulness, expressiveness, and elegance is the constant pursuit in machine translation. However, traditional metrics like \textit{BLEU} do not strictly align with human preference of translation quality. In this paper, we explore leveraging reinforcement learning with human feedback (\textit{RLHF}) to improve translation quality. It is non-trivial to coll

Lucia Caramellino, Cristian Mendico

We address the well-posedness of subelliptic Fokker-Planck equations arising from stochastic control problems, as well as the properties of the associated diffusion processes. Here, the main difficulty arises from the possible polynomial growth of the coefficients, which is related to the growth of the family of vector fields generating the first layer of th