October 2023 arXiv papers — page 76

Shujie Bai, Yueqiang Song, Dušan D. Repovš

We study the following critical Choquard equation on the Heisenberg group: \begin{equation*} \begin{cases} \displaystyle {-\Delta_H u }={\mu} |u|^{q-2}u+\int_{\Omega} \frac{|u(\eta)|^{Q_{\lambda}^{\ast}}} {|\eta^{-1}\xi|^{\lambda}} d\eta|u|^{Q_{\lambda}^{\ast}-2}u &\mbox{in }\ \Omega, u=0 &\mbox{on }\ \partial\Omega, \end{cases} \end{equation*} where $\Omega

Marius Kuhn, Evelyn Heylen, Willem Leterme

The power system of the future will be governed by complex interactions and non-linear phenomena at small time-scales, that should be studied more and more through computationally expensive software simulations. To solve the abovementioned problems, power system engineers face problems with following characteristics: (i) a computationally expensive simulator

Yangyang Luo, Shiyu Tian, Caixia Yuan, Xiaojie Wang

Conversational Machine Reading (CMR) requires answering a user's initial question through multi-turn dialogue interactions based on a given document. Although there exist many effective methods, they largely neglected the alignment between the document and the user-provided information, which significantly affects the intermediate decision-making and subsequ

Yohsuke Matsuzawa, Yuta Suzuki

We prove an asymptotic formula for the average number of rational points on Fano hypersurfaces that are contained in a small ball centered at a given adelic point. We also prove an asymptotic formula for the number of hypersurfaces admitting adelic points that are contained in a small ball.

Committee on Radio Astronomy Frequencies, Benjamin Winkel, Simon Garrington, Francesco Colomer

The Committee on Radio Astronomy Frequencies (CRAF) is an Expert Committee of the European Science Foundation. It aims to provide a cost-effective single voice on frequency protection issues for European radio astronomy observatories and research institutes, achieving a significantly greater impact than that achievable by individual national institutions. By

J. R. Ockendon, H. Ockendon, R. H. Tew, D. P. Hewett

We present an asymptotic and numerical study of the evolution of an incoming wavefield which has a caustic close to a curve with an inflection point. Our results reveal the emergence of a wavefield which resembles that of a shadow boundary but has a maximum amplitude along the tangent at the inflection point.

Federico Stachurski, Christopher Messenger, Martin Hendry

We present a machine learning approach using normalising flows for inferring cosmological parameters from gravitational wave events. Our methodology is general to any type of compact binary coalescence event and cosmological model and relies on the generation of training data representing distributions of gravitational wave event parameters. These parameters

Mikel D. Jedrusiak, Thomas Harweg, Timo Haselhoff, Bryce T. Lawrence

Soundscapes have been studied by researchers from various disciplines, each with different perspectives, goals, approaches, and terminologies. Accordingly, depending on the field, the concept of a soundscape's components changes, consequently changing the basic definition. This results in complicating interdisciplinary communication and comparison of results

Yang Li, Chunhe Xia, Chang Li, Tianbo Wang

With the increasing importance of machine learning, the privacy and security of training data have become critical. Federated learning, which stores data in distributed nodes and shares only model parameters, has gained significant attention for addressing this concern. However, a challenge arises in federated learning due to the Byzantine Attack Problem, wh

Maciej Falkiewicz, Naoya Takeishi, Imahn Shekhzadeh, Antoine Wehenkel

Bayesian inference allows expressing the uncertainty of posterior belief under a probabilistic model given prior information and the likelihood of the evidence. Predominantly, the likelihood function is only implicitly established by a simulator posing the need for simulation-based inference (SBI). However, the existing algorithms can yield overconfident pos

Anshuman Acharya, Enrico Garaldi, Benedetta Ciardi, Qing-bo Ma

The 21-cm line emitted by neutral hydrogen is the most promising probe of the Epoch of Reionization (EoR). Multiple radio interferometric instruments are on the cusp of detecting its power spectrum. It is therefore essential to deliver robust theoretical predictions, enabling sound inference of the coeval Universe properties. The nature of this signal tradit

Goncalo dos Reis, Zac Wilde

In this short note, we establish Malliavin differentiability of McKean-Vlasov Stochastic Differential Equations (MV-SDEs) with drifts satisfying both a locally Lipschitz and a one-sided Lipschitz assumption, and where the diffusion coefficient is assumed to be uniformly Lipschitz in its variables. As a secondary contribution, we investigate how Malliavin dif

Saminathan Ramakrishnan, Zihao Chen, Yair Augusto Gutierrez Fosado, Luca Tubiana

The kinetoplast DNA (kDNA) is the archetype of a two-dimensional Olympic network, composed of thousands of DNA minicircles and found in the mitochondrion of certain parasites. The evolution, replication and self-assembly of this structure are fascinating open questions in biology that can also inform us how to realise synthetic Olympic networks in vitro. To

Yijie Zhou, Likun Cai, Xianhui Cheng, Zhongxue Gan

In the era of big data and large models, automatic annotating functions for multi-modal data are of great significance for real-world AI-driven applications, such as autonomous driving and embodied AI. Unlike traditional closed-set annotation, open-vocabulary annotation is essential to achieve human-level cognition capability. However, there are few open-voc

Aviv Navon, Aviv Shamsian, Ethan Fetaya, Gal Chechik

Permutation symmetries of deep networks make basic operations like model merging and similarity estimation challenging. In many cases, aligning the weights of the networks, i.e., finding optimal permutations between their weights, is necessary. Unfortunately, weight alignment is an NP-hard problem. Prior research has mainly focused on solving relaxed version

Nico Bohlinger, Klaus Dorer

This paper presents the new Deep Reinforcement Learning (DRL) library RL-X and its application to the RoboCup Soccer Simulation 3D League and classic DRL benchmarks. RL-X provides a flexible and easy-to-extend codebase with self-contained single directory algorithms. Through the fast JAX-based implementations, RL-X can reach up to 4.5x speedups compared to w

Ilias Stogiannidis, Stavros Vassos, Prodromos Malakasiotis, Ion Androutsopoulos

Prompting Large Language Models (LLMs) performs impressively in zero- and few-shot settings. Hence, small and medium-sized enterprises (SMEs) that cannot afford the cost of creating large task-specific training datasets, but also the cost of pretraining their own LLMs, are increasingly turning to third-party services that allow them to prompt LLMs. However,

Chang Shu, Jiuzhou Han, Fangyu Liu, Ehsan Shareghi

Embodied language comprehension emphasizes that language understanding is not solely a matter of mental processing in the brain but also involves interactions with the physical and social environment. With the explosive growth of Large Language Models (LLMs) and their already ubiquitous presence in our daily lives, it is becoming increasingly necessary to ve

P. N. Karthik, Vincent Y. F. Tan, Arpan Mukherjee, Ali Tajer

We study best arm identification in a restless multi-armed bandit setting with finitely many arms. The discrete-time data generated by each arm forms a homogeneous Markov chain taking values in a common, finite state space. The state transitions in each arm are captured by an ergodic transition probability matrix (TPM) that is a member of a single-parameter

Luis Fernando dos Prazeres, Thiago R. de Oliveira

It is usually expected and observed that non-integrable isolated quantum systems thermalize. However, for some non-integrable spin chain models, in a numerical study, initial states with oscillations that persisted for some time were found and the phenomenon was named weak thermalization. Later, it was argued that such oscillations will eventually decay sugg

Evgenii Dzhivelikian, Petr Kuderov, Aleksandr I. Panov

This paper presents a novel approach to address the challenge of online sequence learning for decision making under uncertainty in non-stationary, partially observable environments. The proposed algorithm, Distributed Hebbian Temporal Memory (DHTM), is based on the factor graph formalism and a multi-component neuron model. DHTM aims to capture sequential dat

Barbara Opozda

In the paper a Riemannian structure on the tangent bundle is defined by using a statistical structure $(g,\nabla)$ on the base manifold. Expressions for various curvatures of the structure are derived. Some rigidity results of the structure are proved. The main goal of the paper is to initiate the study of sphere bundles over statistical manifolds. Basic for

Stefanos Koffas, Praveen Kumar Vadnala

We investigate the influence of clock frequency on the success rate of a fault injection attack. In particular, we examine the success rate of voltage and electromagnetic fault attacks for varying clock frequencies. Using three different tests that cover different components of a System-on-Chip, we perform fault injection while its CPU operates at different

Kamil Akesbi, Dorian Desblancs, Benjamin Martin

Audio fingerprinting is a well-established solution for song identification from short recording excerpts. Popular methods rely on the extraction of sparse representations, generally spectral peaks, and have proven to be accurate, fast, and scalable to large collections. However, real-world applications of audio identification often happen in noisy environme

Francesco Montagna, Atalanti A. Mastakouri, Elias Eulig, Nicoletta Noceti

When domain knowledge is limited and experimentation is restricted by ethical, financial, or time constraints, practitioners turn to observational causal discovery methods to recover the causal structure, exploiting the statistical properties of their data. Because causal discovery without further assumptions is an ill-posed problem, each algorithm comes wit

Alessandro Coppo, Alessandro Cuccoli, Paola Verrucchi

We present an implementation of a recently proposed procedure for defining time, based on the description of the evolving system and its clock as non-interacting, entangled systems, according to the Page and Wootters approach. We study how the quantum dynamics transforms into a classical-like behaviour when conditions related with macroscopicity are met by t

Haoran Li, Yiran Liu, Xingxing Zhang, Wei Lu

Instruction tuning of open-source large language models (LLMs) like LLaMA, using direct outputs from more powerful LLMs such as Instruct-GPT and GPT-4, has proven to be a cost-effective way to align model behaviors with human preferences. However, the instruction-tuned model has only seen one response per instruction, lacking the knowledge of potentially bet

Mohammad Malekzadeh, Fahim Kawsar

In split inference, a deep neural network (DNN) is partitioned to run the early part of the DNN at the edge and the later part of the DNN in the cloud. This meets two key requirements for on-device machine learning: input privacy and computation efficiency. Still, an open question in split inference is output privacy, given that the outputs of the DNN are ob

Zikang Dong, Weijia Wang, Hao Zhang

In this article, we investigate the behaviour of values of zeta sums $\sum_{n\le x}n^{it}$ when $t$ is large. We show some asymptotic behaviour and Omega results of zeta sums, which are analogous to previous results of large character sums $\sum_{n\le x}\chi(n)$.

A. A. Zhukov, W. V. Pogosov

We address a learning-based quantum error mitigation method, which utilizes deep neural network applied at the postprocessing stage, and study its performance in presence of different types of quantum noises. We concentrate on the simulation of Trotterized dynamics of 2D spin lattice in the regime of high noise, when expectation values of bounded traceless o

Mihaly Novak, Rocco Langone, Carlos Alzate, Johan Suykens

An improved version of the sparse multiway kernel spectral clustering (KSC) is presented in this brief. The original algorithm is derived from weighted kernel principal component (KPCA) analysis formulated within the primal-dual least-squares support vector machine (LS-SVM) framework. Sparsity is achieved then by the combination of the incomplete Cholesky de

Pei Wang, Keqing He, Yutao Mou, Xiaoshuai Song

Detecting out-of-domain (OOD) intents from user queries is essential for a task-oriented dialogue system. Previous OOD detection studies generally work on the assumption that plenty of labeled IND intents exist. In this paper, we focus on a more practical few-shot OOD setting where there are only a few labeled IND data and massive unlabeled mixed data that m

Rene Hiemstra, Thi-Hoa Nguyen, Sascha Eisentrager, Wolfgang Dornisch

This paper introduces a mathematical framework for explicit structural dynamics, employing approximate dual functionals and rowsum mass lumping. We demonstrate that the approach may be interpreted as a Petrov-Galerkin method that utilizes rowsum mass lumping or as a Galerkin method with a customized higher-order accurate mass matrix. Unlike prior work, our m

Jingyi Yu, Zizhao Zhang, Shengfu Xia, Jizhang Sang

We propose a novel end-to-end pipeline for online long-range vectorized high-definition (HD) map construction using on-board camera sensors. The vectorized representation of HD maps, employing polylines and polygons to represent map elements, is widely used by downstream tasks. However, previous schemes designed with reference to dynamic object detection ove

Marien Chenaud, José Alves, Frédéric Magoulès

Since the seminal work of [9] and their Physics-Informed neural networks (PINNs), many efforts have been conducted towards solving partial differential equations (PDEs) with Deep Learning models. However, some challenges remain, for instance the extension of such models to complex three-dimensional geometries, and a study on how such approaches could be comb

Alva Markelius, Sofia Sjöberg, Zakaria Lemhauori, Laura Cohen

This paper presents a novel human-robot interaction setup for robot and human learning of symbolic language for identifying robot homeostatic needs. The robot and human learn to use and respond to the same language symbols that convey homeostatic needs and the stimuli that satisfy the homeostatic needs, respectively. We adopted a differential outcomes traini

Ukyo Suzuki, Yoriyuki Yamagata

This paper explores proof-theoretic semantics, a formal approach to inferential semantics. It derives sentence meaning from formalized proofs, building upon Gentzen and Prawitz's work. The study addresses challenges in understanding how proofs contribute to sentence meaning. In this setting, classical logic poses "Dummett's challenge" due to its mismatch wit

Qian Huang, Weiwen Qian, Chang Li, Xuan Ding

Informationization is a prevailing trend in today's world. The increasing demand for information in decision-making processes poses significant challenges for investigation activities, particularly in terms of effectively allocating limited resources to plan investigation programs. This paper addresses the investigation path planning problem by formulating i

Mervin Lim Pac Chong, Kareem J. Garriga Francis, Yiwen E, Xi-Cheng Zhang

We present a single-shot detection method of terahertz-correlated second harmonic generation in plasma-based sources by directly mixing an optical probe into femtosecond laser-induced plasma filaments in air. The single-shot second harmonic trace is obtained by measuring second harmonic generation on a conventional CCD with a spatio-temporally distorted prob

Albert Garifullin, Nikolay Maiorov, Vladimir Frolov

We propose an approach to 3D reconstruction via inverse procedural modeling and investigate two variants of this approach. The first option consists in the fitting set of input parameters using a genetic algorithm. We demonstrate the results of our work on tree models, complex objects, with the reconstruction of which most existing methods cannot handle. The

Adeel A. Khan

We construct a cdh-local motivic homotopy category SH_cdh(S) over an arbitrary base scheme S, and show that there is a canonical equivalence between SH_cdh(S) and SH(S). We learned this result from D.-C. Cisinski.

Georg Hartl, Conrad Gstöttner, Bernd Kolar, Markus Schöberl

We study the exact linearization of configuration flat Lagrangian control systems with p degrees of freedom and p-1 inputs by quasi-static feedback of classical states. First, we present a detailed analysis of the structure of the parameterization of the system variables by the flat output. Based on that, we systematically construct a linearizing quasi-stati

Hee-Jin Kim, Hyun-Chul Kim

We investigate the dynamical generation of the $D_{s0}^*(2317)$ and $B_{s0}^*$ mesons using a meson-exchange model with a coupled-channelformalism. Our primary focus is on the $D_s^+\pi^0$ channel below the $DK$ threshold. First, we construct the invariant kernel amplitudes, incorporating effective Lagrangians based on heavy-quark symmetry, flavor SU(3) symm

Anh Tong, Thanh Nguyen-Tang, Dongeun Lee, Toan Tran

Deep hedging is a promising direction in quantitative finance, incorporating models and techniques from deep learning research. While giving excellent hedging strategies, models inherently requires careful treatment in designing architectures for neural networks. To mitigate such difficulties, we introduce SigFormer, a novel deep learning model that combines

Yu Kato, Jiquan Xie, Tutomu Murase, Sumiko Miyata

For multi-transmission rate environments, access point (AP) connection methods have been proposed for maximizing system throughput, which is the throughput of an entire system, on the basis of the cooperative behavior of users. These methods derive optimal positions for the cooperative behavior of users, which means that new users move to improve the system

Shuo Wang, Keke Gai, Jing Yu, Liehuang Zhu

Vertical federated learning has garnered significant attention as it allows clients to train machine learning models collaboratively without sharing local data, which protects the client's local private data. However, existing VFL methods face challenges when dealing with heterogeneous local models among participants, which affects optimization convergence a

Felix Liawi, Yun-Da Tsai, Guan-Lun Lu, Shou-De Lin

Scene Text Editing (STE) aims to substitute text in an image with new desired text while preserving the background and styles of the original text. However, present techniques present a notable challenge in the generation of edited text images that exhibit a high degree of clarity and legibility. This challenge primarily stems from the inherent diversity fou

Yu Ji, Qi Shen, Shixuan Zhu, Hang Yu

Conversational recommendation systems (CRS) could acquire dynamic user preferences towards desired items through multi-round interactive dialogue. Previous CRS mainly focuses on the single conversation (subsession) that user quits after a successful recommendation, neglecting the common scenario where user has multiple conversations (multi-subsession) over a

Rūta Binkytė, Sami Zhioua, Yassine Turki

Accurately measuring discrimination in machine learning-based automated decision systems is required to address the vital issue of fairness between subpopulations and/or individuals. Any bias in measuring discrimination can lead to either amplification or underestimation of the true value of discrimination. This paper focuses on a class of bias originating i

Yong-Jie Xie, Ang Qian, Bin He, Yu-Biao Wu

Magnetic skyrmions, the topological states possessing chiral magnetic structure with nontrivial topology, have been widely investigated as a promising candidate for spintronic devices. They can also couple with superconducting vortices to form skyrmion-vortex pairs, hosting Majorana zero mode, which is a potential candidate for topological quantum computing.

Junjie Wu, Lemao Liu, Dit-Yan Yeung

Behavioral testing offers a crucial means of diagnosing linguistic errors and assessing capabilities of NLP models. However, applying behavioral testing to machine translation (MT) systems is challenging as it generally requires human efforts to craft references for evaluating the translation quality of such systems on newly generated test cases. Existing wo

Wenyu Guo, Qingkai Fang, Dong Yu, Yang Feng

Multimodal machine translation (MMT) simultaneously takes the source sentence and a relevant image as input for translation. Since there is no paired image available for the input sentence in most cases, recent studies suggest utilizing powerful text-to-image generation models to provide image inputs. Nevertheless, synthetic images generated by these models

Serge Kernbach

Formation of hydronium and carbonate ions from CO2 in the aqueous phase is a reversible process and can produce and consume ions. These equilibrium reactions represent molecular electrochemical oscillators with chaotic dynamics. As demonstrated in previous works, para- and ortho- isomers of water have different reactivity; weak variations of magnetic fields

Daniel Selvaratnam, Amritam Das, Henrik Sandberg

Motivated by the need to localise faults along electrical power lines, this paper adopts a frequency-domain approach to parameter estimation for an infinite-dimensional linear dynamical system with one spatial variable. Since the time of the fault is unknown, and voltages and currents are measured at only one end of the line, distance information must be ext

Matteo Di Carlo, Maxwell T. Hansen, Nils Hermansson-Truedsson, Antonin Portelli

We consider electromagnetic finite-volume effects through order $1/L^3$ in different formulations of QED, where $L$ is the periodicity of the spatial volume. An inherent problem at this order is the appearance of structure-dependent quantities related to form factors and the analytical structure of the correlation functions. The non-local constraint of the w

Spyros Makridakis, Evangelos Spiliotis, Ross Hollyman, Fotios Petropoulos

The M6 forecasting competition, the sixth in the Makridakis' competition sequence, is focused on financial forecasting. A key objective of the M6 competition was to contribute to the debate surrounding the Efficient Market Hypothesis (EMH) by examining how and why market participants make investment decisions. To address these objectives, the M6 competition

Seoha Kim, Jeongmin Bae, Youngsik Yun, Hahyun Lee

Recent advancements in 4D scene reconstruction using neural radiance fields (NeRF) have demonstrated the ability to represent dynamic scenes from multi-view videos. However, they fail to reconstruct the dynamic scenes and struggle to fit even the training views in unsynchronized settings. It happens because they employ a single latent embedding for a frame w

Muhammad Ferjad Naeem, Yongqin Xian, Xiaohua Zhai, Lukas Hoyer

Image-Text pretraining on web-scale image caption datasets has become the default recipe for open vocabulary classification and retrieval models thanks to the success of CLIP and its variants. Several works have also used CLIP features for dense prediction tasks and have shown the emergence of open-set abilities. However, the contrastive objective used by th

Lu Meng, Yan-Ke Chen, Yao Ma, Shi-Lin Zhu

We investigate the tetraquark bound states that are manifestly exotic using three distinct few-body methods: Gaussian Expansion Method (GEM), Resonating Group Method (RGM), and Diffusion Monte Carlo (DMC). We refer to manifestly exotic states that do not involve a mixture with the conventional mesons through the creation and annihilation of $n\bar{n}$, where

Debashree Priyadarsini Das, Sasmita Mishra

We study a model where the Standard Model is augmented with three sterile neutrinos. By adopting a particular parameterization of a $(6\times6)$ unitary matrix - in this context, light neutrino masses being generated via a type-I seesaw mechanism - we analytically derive the masses of the sterile states using an exact seesaw relation. The masses of the steri

Daan Beelen

In this study, a new theory for the spontaneous formation of sand dunes and related bedforms is proposed. The theory is based on the concept that smaller accumulations of sediment outpace larger ones due to differences in surface-to-volume ratio. From this geometric principle, it follows algebraically that for any nonzero bedload transport, the bed (sediment

Weijie Liu, Xiaoxi Zhang, Jingpu Duan, Carlee Joe-Wong

Federated Learning (FL) is a distributed learning paradigm that can coordinate heterogeneous edge devices to perform model training without sharing private data. While prior works have focused on analyzing FL convergence with respect to hyperparameters like batch size and aggregation frequency, the joint effects of adjusting these parameters on model perform

Yangyiwei Yang, Somnath Bharech, Nick Finger, Xiandong Zhou

Residual stress and plastic strain in additive manufactured materials can exhibit significant microscopic variation at the powder scale, profoundly influencing the overall properties of printed components. This variation depends on processing parameters and stems from multiple factors, including differences in powder bed morphology, non-uniform thermo-struct

Torben Teepe, Philipp Wolters, Johannes Gilg, Fabian Herzog

Multi-view aggregation promises to overcome the occlusion and missed detection challenge in multi-object detection and tracking. Recent approaches in multi-view detection and 3D object detection made a huge performance leap by projecting all views to the ground plane and performing the detection in the Bird's Eye View (BEV). In this paper, we investigate if

Taehyo Kim, Hai Shu, Qiran Jia, Mony J. de Leon

Voxel-based multiple testing is widely used in neuroimaging data analysis. Traditional false discovery rate (FDR) control methods often ignore the spatial dependence among the voxel-based tests and thus suffer from substantial loss of testing power. While recent spatial FDR control methods have emerged, their validity and optimality remain questionable when

Lisa Beinborn, Yuval Pinter

Subword tokenization has become the de-facto standard for tokenization, although comparative evaluations of subword vocabulary quality across languages are scarce. Existing evaluation studies focus on the effect of a tokenization algorithm on the performance in downstream tasks, or on engineering criteria such as the compression rate. We present a new evalua

Ming Hu, Lin Wang, Siyuan Yan, Don Ma

The application of deep learning to nursing procedure activity understanding has the potential to greatly enhance the quality and safety of nurse-patient interactions. By utilizing the technique, we can facilitate training and education, improve quality control, and enable operational compliance monitoring. However, the development of automatic recognition s

Luca Gherardini, Giacomo Cabri, Manuela Montangero

The coordination of autonomous vehicles is an open field that is addressed by different researches comprising many different techniques. In this paper we focus on decentralized approaches able to provide adaptability to different infrastructural and traffic conditions. We formalize an Emergent Behavior Approach that, as per our knowledge, has never been perf

Xilie Xu, Keyi Kong, Ning Liu, Lizhen Cui

The wide-ranging applications of large language models (LLMs), especially in safety-critical domains, necessitate the proper evaluation of the LLM's adversarial robustness. This paper proposes an efficient tool to audit the LLM's adversarial robustness via a prompt-based adversarial attack (PromptAttack). PromptAttack converts adversarial textual attacks int

Yuhang Huang, Takashi Kanai

In the field of brittle fracture animation, generating realistic destruction animations using physics-based simulation methods is computationally expensive. While techniques based on Voronoi diagrams or pre-fractured patterns are effective for real-time applications, they fail to incorporate collision conditions when determining fractured shapes during runti

Xiaoliang Chen, Liangbin Li, Le Chang, Yunhe Huang

With the development of large language models (LLMs) like the GPT series, their widespread use across various application scenarios presents a myriad of challenges. This review initially explores the issue of domain specificity, where LLMs may struggle to provide precise answers to specialized questions within niche fields. The problem of knowledge forgettin

Kwai-Kong Ng, Min-Fong Yang

In this paper, a modified method of anomaly detection using convolutional autoencoders is employed to predict phase transitions in several statistical mechanical models on a square lattice. We show that, when the autoencoder is trained with input data of various phases, the mean-square-error loss function can serve as a measure of disorder, and its standard

Pierre Hoppenot, Mathis Martin, Zoltán Szigeti

The seminal papers of Edmonds \cite{Egy}, Nash-Williams \cite{NW} and Tutte \cite{Tu} have laid the foundations of the theories of packing arborescences and packing trees. The directed version has been extensively investigated, resulting in a great number of generalizations. In contrast, the undirected version has been marginally considered. The aim of this

Han Jiang, Rui Wang, Zhihua Wei, Yu Li

Opinion summarization is expected to digest larger review sets and provide summaries from different perspectives. However, most existing solutions are deficient in epitomizing extensive reviews and offering opinion summaries from various angles due to the lack of designs for information selection. To this end, we propose SUBSUMM, a supervised summarization f

Tommaso Lando, Sirio Legramanti

Given a pair of non-negative random variables $X$ and $Y$, we introduce a class of nonparametric tests for the null hypothesis that $X$ dominates $Y$ in the total time on test order. Critical values are determined using bootstrap-based inference, and the tests are shown to be consistent. The same approach is used to construct tests for the excess wealth orde

I. M. Suslov

One-dimensional disordered systems with a random potential of a small amplitude and short-range correlations are considered near the initial band edge. The evolution equation is obtained for the mutual ditribution P(\rho,\psi) of the Landauer resistance \rho and the phase variable \psi=\theta-\varphi (\theta and \varphi are phases entering the transfer matri

R. Jafari, A. Langari, S. Eggert, Henrik Johannesson

We study how time-dependent energy fluctuations impact the dynamical quantum phase transitions (DQPTs) following a noisy ramped quench of the transverse magnetic field in a quantum Ising chain. By numerically solving the stochastic Schr\"odinger equation of the mode-decoupled fermionic Hamiltonian of the problem, we identify two generic scenarios: Depending

Anatol Garioud, Nicolas Gonthier, Loic Landrieu, Apolline De Wit

We introduce the French Land cover from Aerospace ImageRy (FLAIR), an extensive dataset from the French National Institute of Geographical and Forest Information (IGN) that provides a unique and rich resource for large-scale geospatial analysis. FLAIR contains high-resolution aerial imagery with a ground sample distance of 20 cm and over 20 billion individua

Cheng Luo, Jie Hu, Luping Xiang, Kun Yang

This paper presents a reconfigurable intelligent sensing surface (RISS) that combines passive and active elements to achieve simultaneous reflection and direction of arrival (DOA) estimation tasks. By utilizing DOA information from the RISS instead of conventional channel estimation, the pilot overhead is reduced and the RISS becomes independent of the hybri

Zisheng Liu, Ting Zhang

From a dual perspective of the sparse representation model, Nam et al. proposed the cosparse analysis model. In this paper, we aim to investigate the convergence of the alternating direction method of multipliers (ADMM) for the cosparse optimization problem. First, we examine the variational inequality representation of the cosparse optimization problem by i

Kentaro Kasai, Masahiro Kawasaki, Naoya Kitajima, Kai Murai

We study a modification of the primordial black hole (PBH) formation model from axion bubbles. We assume that the Peccei-Quinn scalar rolls down in the radial direction from a large field value to the potential minimum during inflation, which suppresses the axion fluctuations and weakens the clustering of PBHs on large scales. We find that the modified model

Zhaoyang Wang, Shaohan Huang, Yuxuan Liu, Jiahai Wang

Large language models (LLMs) exhibit impressive emergent abilities in natural language processing, but their democratization is hindered due to huge computation requirements and closed-source nature. Recent research on advancing open-source smaller LMs by distilling knowledge from black-box LLMs has obtained promising results in the instruction-following abi

Tadashi Udagawa

We construct harmonic maps into SU(1,1)/U(1) starting from Smyth potentials \xi, by the DPW method, In this method, harmonic maps are obtained from the Iwasawa factorization of a solution L of L^{-1} dL = \xi. However, the Iwasawa factorization in the case of a noncompact group is not always global. We show that L can be expressed in terms of Bessel function

Yuya Saito, Shinnosuke Matsuo, Seiichi Uchida, Daiki Suehiro

This paper tackles the problem of the worst-class error rate, instead of the standard error rate averaged over all classes. For example, a three-class classification task with class-wise error rates of 10%, 10%, and 40% has a worst-class error rate of 40%, whereas the average is 20% under the class-balanced condition. The worst-class error is important in ma

Ryo Terasawa, Ryuichi Takahashi, Takahiro Nishimichi, Masahiro Takada

The growth of large-scale structure, together with the geometrical information of cosmic expansion history and cosmological distances, can be used to obtain constraints on the spatial curvature of the universe that probes the early universe physics, whereas modeling the nonlinear growth in a nonflat universe is still challenging due to computational expense

Xiaoxiang Chai, Juncheol Pyo, Xueyuan Wan

A theorem of Llarull says that if a smooth metric $g$ on the $n$-sphere $\mathbb{S}^n$ is bounded below by the standard round metric and the scalar curvature $R_g$ of $g$ is bounded below by $n (n - 1)$, then the metric $g$ must be the standard round metric. We prove a spectral Llarull theorem by replacing the bound $R_g \geq n (n - 1)$ by a lower bound on t

Boqian Ma, Vir Nath Pathak, Lanping Liu, Sushmita Ruj

A sparse Merkle tree is a Merkle tree with fixed height and indexed leaves given by a map from indices to leaf values. It allows for both efficient membership and non-membership proofs. It has been widely used as an authenticated data structure in various applications, such as layer-2 rollups for blockchains. zkSync Lite, a popular Ethereum layer-2 rollup so

Marcel Zöllner, Hendrik Mannel, Fabio Rimek, Britta Maib

In quantum dots (QDs) the Auger recombination is a non-radiative scattering process in which the optical transition energy of a charged exciton (trion) is transferred to an additional electron leaving the dot. Electron tunneling from a reservoir is the competing process that replenishes the QD with an electron again. Here, we study the dependence of the tunn

Sylwester J. Rzoska, Aleksandra Drozd-Rzoska, Weronika Bulejak, Joanna Los

The model discussion focused on links between the unique properties of relaxor ceramics and the basics of critical phenomena physics and glass transition physics. It indicates the significance of uniaxiality for appearing mean-field features near paraelectric_ferroelectric transition. Pretransitional fluctuations, increasing up to grain size and leading to i

Xin Zhang, Andrew Curtis

Bayesian inference has become an important tool to solve inverse problems and to quantify uncertainties in their solutions. Variational inference is a method that provides probabilistic, Bayesian solutions efficiently by using optimization. In this study we present a Python Variational Inversion Package (VIP), to solve inverse problems using variational infe

Zhehan Li, Rui Mao, Nanhe Chen, Chao Xu

Perception is necessary for autonomous navigation in an unknown area crowded with obstacles. It's challenging for a robot to navigate safely without any sensors that can sense the environment, resulting in a $\textit{blind}$ robot, and becomes more difficult when comes to a group of robots. However, it could be costly to equip all robots with expensive perce

Benjamin Damilano, Rémi Aristégui, Henryk Teisseyre, Stéphane Vézian

GaN/AlxGa1-xN quantum wells were grown by molecular beam epitaxy on high quality bulk (0001) GaN substrates. The quantum well thickness was set in the 6-8 nm range to favor the photoluminescence emission of indirect excitons. Indeed, such excitons are known to be spatially indirect, due to the presence of the internal electric field which spatially separates

Yuxuan Sheng, Menghao Wu

Ferroelectric crystals must adopt one of the 10 polar point groups according to the Neumann's principle. In this paper we propose that this conclusion is based on perfect bulk crystals without taking the boundaries into account, and we show first-principles evidence that ferroelectric polarizations may also be formed in some non-polar point groups as the edg

Zecheng Tang, Kaifeng Qi, Juntao Li, Min Zhang

Recent studies have revealed that grammatical error correction methods in the sequence-to-sequence paradigm are vulnerable to adversarial attack, and simply utilizing adversarial examples in the pre-training or post-training process can significantly enhance the robustness of GEC models to certain types of attack without suffering too much performance loss o

Shaoan Wang, Mingzhu Zhu, Yaoqing Hu, Dongyue Li

High-precision pose estimation based on visual markers has been a thriving research topic in the field of computer vision. However, the suitability of traditional flat markers on curved objects is limited due to the diverse shapes of curved surfaces, which hinders the development of high-precision pose estimation for curved objects. Therefore, this paper pro

Ilya Karzhemanov, Ludmil Katzarkov

We provide an explicit description of exceptional collection of maximal length in the derived category $D^b(Y)$ for a particular class of elliptic surfaces $Y$. The existence of non\,-\,trivial semiorthogonal complement (a "\,phantom\,") of this collection is also established.

Alessandro Grecucci, Parisa Ahmadi Ghomroudi, Bianca Monachesi, Irene Messina

Acceptance is an adaptive emotion regulation strategy characterized by an open and non-judgmental attitude toward mental and sensory experiences. While a few studies have investigated the neural correlates of acceptance in task-based fMRI studies, a gap remains in the scientific literature in dispositional use of acceptance, and how this is sedimented at a s

Ajai Choudhry, Bibekananda Maji

This paper is concerned with finite sequences of integers that may be written as sums of squares of two nonzero integers. We first find infinitely many integers $n$ such that $n, n+h$ and $n+k$ are all sums of two squares where $h$ and $k$ are two arbitrary integers, and as an immediate corollary obtain, in parametric terms, three consecutive integers that a

Kaikai An, Ce Zheng, Bofei Gao, Haozhe Zhao

Frame identification aims to find semantic frames associated with target words in a sentence. Recent researches measure the similarity or matching score between targets and candidate frames by modeling frame definitions. However, they either lack sufficient representation learning of the definitions or face challenges in efficiently selecting the most suitab

Miaoxi Zhu, Qihuang Zhong, Li Shen, Liang Ding

Quantization is a promising approach for reducing memory overhead and accelerating inference, especially in large pre-trained language model (PLM) scenarios. While having no access to original training data due to security and privacy concerns has emerged the demand for zero-shot quantization. Most of the cutting-edge zero-shot quantization methods primarily