April 2024 arXiv papers — page 51

M. Emin Özdemir

This short study consists of two parts, firstly we obtain some inequalities on Caputo Fractional derivatives using the elementary inequalities. Secondly we establish several new inequalities including Caputo fractional derivatives for m-convex functions. In general, in this work we obtain upper bounds for the left sides of Lemma 1[10] and lemma 2[20].

Christian de Ronde, Raimundo Fernandez Moujan, Cesar Massri

In orthodox Standard Quantum Mechanics (SQM) bases and factorizations are considered to define quantum states and entanglement in relativistic terms. While the choice of a basis (interpreted as a measurement context) defines a state incompatible to that same state in a different basis, the choice of a factorization (interpreted as the separability of systems

Haozhe Cheng, Cheng Ju, Haicheng Wang, Jinxiang Liu

As one of the fundamental video tasks in computer vision, Open-Vocabulary Action Recognition (OVAR) recently gains increasing attention, with the development of vision-language pre-trainings. To enable generalization of arbitrary classes, existing methods treat class labels as text descriptions, then formulate OVAR as evaluating embedding similarity between

Karel Dekimpe, Lore De Weerdt

In [8,9], the authors developed a nice formula to compute the Nielsen number of a self-map on an infra-nilmanifold. For the case of nilmanifolds this formula was extended to $n$-valued maps in [4]. In this paper, we extend these results further and establish the averaging formula to compute the Nielsen number of any $n$-valued affine map on an infra-nilmanif

Jinpeng Liu, Yuanyuan Liu, Lin Zhou

Hoeffding's inequality is a fundamental tool widely applied in probability theory, statistics, and machine learning. In this paper, we establish Hoeffding's inequalities specifically tailored for an irreducible and positive recurrent continuous-time Markov chain (CTMC) on a countable state space with the invariant probability distribution ${\pi}$ and an $\ma

Jonas Landgraf, Vittorio Peano, Florian Marquardt

In optics and photonics, a small number of building blocks, like resonators, waveguides, arbitrary couplings, and parametric interactions, allow the design of a broad variety of devices and functionalities, distinguished by their scattering properties. These include transducers, amplifiers, and nonreciprocal devices, like isolators or circulators. Usually, t

Weixing Wang, Haojin Yang, Christoph Meinel, Hasan Yagiz Özkan

In recent years, there has been a growing interest in using Machine Learning (ML), especially Deep Learning (DL) to solve Network Intrusion Detection (NID) problems. However, the feature distribution shift problem remains a difficulty, because the change in features' distributions over time negatively impacts the model's performance. As one promising solutio

Qincheng Lu, Sitao Luan, Xiao-Wen Chang

Massive MIMO (multiple-input multiple-output) detection is an important topic in wireless communication and various machine learning based methods have been developed recently for this task. Expectation Propagation (EP) and its variants are widely used for MIMO detection and have achieved the best performance. However, EP-based solvers fail to capture the co

Yugan Chen, Lin Zhao, Yalong Xu, Honglei Zu

Domain adaptive pose estimation aims to enable deep models trained on source domain (synthesized) datasets produce similar results on the target domain (real-world) datasets. The existing methods have made significant progress by conducting image-level or feature-level alignment. However, only aligning at a single level is not sufficient to fully bridge the

Henrik Ekström

We study the Coulomb chain where particles are restricted to one dimension and experience three-dimensional Coulomb interactions with their nearest and next-to-nearest neighbours. The distances between consecutive particles are treated as random variables. It is shown that the correlation between clusters of consecutive variables decay exponentially with the

Vittoria Dentella, Fritz Guenther, Evelina Leivada

Understanding the limits of language is a prerequisite for Large Language Models (LLMs) to act as theories of natural language. LLM performance in some language tasks presents both quantitative and qualitative differences from that of humans, however it remains to be determined whether such differences are amenable to model size. This work investigates the c

Brice Ozenne, Martin Norgaard, Cyril Pernet, Melanie Ganz

Even though novel imaging techniques have been successful in studying brain structure and function, the measured biological signals are often contaminated by multiple sources of noise, arising due to e.g. head movements of the individual being scanned, limited spatial/temporal resolution, or other issues specific to each imaging technology. Data preprocessin

Roberto V. Maluf, Gerardo Mora-Pérez, Gonzalo J. Olmo, Diego Rubiera-Garcia

We study the space-time geometry generated by coupling a free scalar field with a non-canonical kinetic term to General Relativity in $(2+1)$ dimensions. After identifying a family of scalar Lagrangians that yield exact analytical solutions in static and circularly symmetric scenarios, we classify the various types of solutions and focus on a branch that yie

Koyena Bose, Ajit C. Balram

Fractional quantum Hall (FQH) fluids host quasiparticle excitations that carry a fraction of the electronic charge. Moreover, in contrast to bosons and fermions that carry exchange statistics of $0$ and $\pi$ respectively, these quasiparticles of FQH fluids, when braided around one another, can accumulate a Berry phase, which is a fractional multiple of $\pi

Jiguang He, Charles Vanwynsberghe, Hui Chen, Chongwen Huang

In this paper, we investigate the potential of reconfigurable intelligent surfaces (RISs) in facilitating passive/device-free three-dimensional (3D) drone localization within existing cellular infrastructure operating at millimeter-wave (mmWave) frequencies and employing multiple antennas at the transceivers. The developed localization system operates in the

Karim A. Adiprasito, Louis Funar

We prove the arborescence of any locally finite complex that is $CAT(0)$ with a polyhedral metric for which all vertex stars are convex. In particular locally finite $CAT(0)$ cube complexes or equilateral simplicial complexes are arborescent. Moreover, a triangulated manifold admits a $CAT(0)$ polyhedral metric if and only if it admits arborescent triangulat

Qianru Meng, Xiao Zhang, Guus Ramackers, Visser Joost

In the realm of Duplicate Bug Report Detection (DBRD), conventional methods primarily focus on statically analyzing bug databases, often disregarding the running time of the model. In this context, complex models, despite their high accuracy potential, can be time-consuming, while more efficient models may compromise on accuracy. To address this issue, we pr

Jiahua Duan, Aitana Tarazaga Martin-Luengo, Christian Lanza, Stefan Partel

Canalization is an optical phenomenon that enables unidirectional propagation of light in a natural way, i.e., without the need for predefined waveguiding designs. Predicted years ago, it was recently demonstrated using highly confined phonon polaritons (PhPs) in twisted layers of the van der Waals (vdW) crystal alpha-MoO3, offering unprecedented possibiliti

Adeyemi D. Adeoye, Philipp Christian Petersen, Alberto Bemporad

The generalized Gauss-Newton (GGN) optimization method incorporates curvature estimates into its solution steps, and provides a good approximation to the Newton method for large-scale optimization problems. GGN has been found particularly interesting for practical training of deep neural networks, not only for its impressive convergence speed, but also for i

Stefano Buzzi, Carmen D'Andrea, Sergi Liesegang

This paper addresses the problem of scalability for a cell-free massive MIMO (CF-mMIMO) system that performs integrated sensing and communications (ISAC). Specifically, the case where a large number of access points (APs) are deployed to perform simultaneous communication with mobile users and monitoring of the surrounding environment in the same time-freque

Hyeontae Jo, Sung Woong Cho, Hyung Ju Hwang

Differential equations are pivotal in modeling and understanding the dynamics of various systems, offering insights into their future states through parameter estimation fitted to time series data. In fields such as economy, politics, and biology, the observation data points in the time series are often independently obtained (i.e., Repeated Cross-Sectional

Jan E. Grabowski, Lauren Hindmarch

We show that under mild assumptions the Segre product of two graded cluster algebras has a natural cluster algebra structure.

Beatriz Cabrero-Daniel, Tomas Herda, Victoria Pichler, Martin Eder

This action research study focuses on the integration of "AI assistants" in two Agile software development meetings: the Daily Scrum and a feature refinement, a planning meeting that is part of an in-house Scaled Agile framework. We discuss the critical drivers of success, and establish a link between the use of AI and team collaboration dynamics. We conclud

Llewellyn Forward, Io Limmer, Joseph Hallett, Dan Page

In a case study spanning four classic Super Mario games and the analysis of 237 known glitches within them, we classify a variety of weaknesses that are exploited by speedrunners to enable them to beat games quickly and in surprising ways. Using the Seven Pernicious Kingdoms software defect taxonomy and the Common Weakness Enumeration, we categorize the glit

Wangdan Liao, Weidong Wang

Brain-computer interfaces (BCIs) harness electroencephalographic signals for direct neural control of devices, offering a significant benefit for individuals with motor impairments. Traditional machine learning methods for EEG-based motor imagery (MI) classification encounter challenges such as manual feature extraction and susceptibility to noise.This paper

Chao Ren, Han Yu, Hongyi Peng, Xiaoli Tang

The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI). This integration offers enhanced capabilities, while addressing concerns of privacy, data decentralization and computational efficiency. This paper provides a comprehensive survey of the emerging field of Federated Found

Roberto Pittau

Reference [1] introduces a method for computing numerically four-dimensional multi-loop integrals without performing an explicit analytic contour deformation around threshold singularities. In this paper, we extend such a technique to massless scalar one-loop integrals regularized in the framework of dimensional regularization. A two-loop example is also dis

Zhong Wang

This article reviews the psychological and neuroscience achievements in concept learning since 2010 from the perspectives of individual learning and social learning, and discusses several issues related to concept learning, including the assistance of machine learning about concept learning. 1 In terms of individual learning, current evidences shown that the

Yuanshao Zhu, James Jianqiao Yu, Xiangyu Zhao, Qidong Liu

Generating trajectory data is among promising solutions to addressing privacy concerns, collection costs, and proprietary restrictions usually associated with human mobility analyses. However, existing trajectory generation methods are still in their infancy due to the inherent diversity and unpredictability of human activities, grappling with issues such as

Eiji Nakano, Takahiko Miyakawa, Hiroyuki Yabu

The polarized dipolar Fermi gas shows exotic properties at low temperatures, characterized by an axially-deformed Fermi surface and anisotropic single-particle energy, due to the long-range and anisotropic nature of dipole-dipole interaction. In cold-atom experiments such a system has been realized, e.g., in degenerate gas of Er and Dy atoms. In the case tha

Sebastian Rietsch, Abhishek Y. Dubey, Christian Ufrecht, Maniraman Periyasamy

This paper presents a deep reinforcement learning approach for synthesizing unitaries into quantum circuits. Unitary synthesis aims to identify a quantum circuit that represents a given unitary while minimizing circuit depth, total gate count, a specific gate count, or a combination of these factors. While past research has focused predominantly on continuou

Liwei Tan, Minsheng Huang, Wenjun Ying

This paper introduces a second-order method for solving general elliptic partial differential equations (PDEs) on irregular domains using GPU acceleration, based on Ying's kernel-free boundary integral (KFBI) method. The method addresses limitations imposed by CFL conditions in explicit schemes and accuracy issues in fully implicit schemes for the Laplacian

Federico Ghimenti, Ludovic Berthier, Grzegorz Szamel, Frédéric van Wijland

Exerting a nonequilibrium drive on an otherwise equilibrium Langevin process brings the dynamics out of equilibrium but can also speedup the approach to the Boltzmann steady-state. Transverse forces are a minimal framework to achieve dynamical acceleration of the Boltzmann sampling. We consider a simple liquid in three space dimensions subjected to additiona

Bohao Lu, Zhiqing Wei, Huici Wu, Xinrui Zeng

Utilizing widely distributed communication nodes to achieve environmental reconstruction is one of the significant scenarios for Integrated Sensing and Communication (ISAC) and a crucial technology for 6G. To achieve this crucial functionality, we propose a deep learning based multi-node ISAC 4D environment reconstruction method with Uplink-Downlink (UL-DL)

Mamta Yadav, Aman Singh Katariya, Animesh Sharma, Amita Das

This paper investigates the dynamics of crystalline clusters observed in Molecular Dynamics (MD) studies conducted earlier [Yadav, M., et al. Physical Review E, 107(5), 055214(2023)] for ultra-cold neutral plasmas. An external oscillatory forcing is applied for this purpose and the evolution is tracked with the help of MD simulations using the open source LA

Tsubasa Ochiai, Kazuma Iwamoto, Marc Delcroix, Rintaro Ikeshita

It is challenging to improve automatic speech recognition (ASR) performance in noisy conditions with a single-channel speech enhancement (SE) front-end. This is generally attributed to the processing distortions caused by the nonlinear processing of single-channel SE front-ends. However, the causes of such degraded ASR performance have not been fully investi

Ju Kang, Yiyuan Niu, Xin Wang

Explaining biodiversity is a central focus in theoretical ecology. A significant obstacle arises from the Competitive Exclusion Principle (CEP), which states that two species competing for the same type of resources cannot coexist at constant population densities, or more generally, the number of consumer species cannot exceed that of resource species at ste

Hongfeng Zhang, Aritra Sarkar, Koen Bertels

Optimizing the mRNA codon has an essential impact on gene expression for a specific target protein. It is an NP-hard problem; thus, exact solutions to such optimization problems become computationally intractable for realistic problem sizes on both classical and quantum computers. However, approximate solutions via heuristics can substantially impact the app

Thomas A. Archbold, Ieva Kazlauskaite, Fehmi Cirak

Surrogate models provide a quick-to-evaluate approximation to complex computational models and are essential for multi-query problems like design optimisation. The inputs of current deterministic computational models are usually high-dimensional and uncertain. We consider Bayesian inference for constructing statistical surrogates with input uncertainties and

Zhuhang Li, Ning Yang

Recommender systems use users' historical interactions to learn their preferences and deliver personalized recommendations from a vast array of candidate items. Current recommender systems primarily rely on the assumption that the training and testing datasets have identical distributions, which may not hold true in reality. In fact, the distribution shift b

Jonathan Richard Shewchuk, Sagnik Bhattacharya

We characterize the geometry and topology of the set of all weight vectors for which a linear neural network computes the same linear transformation $W$. This set of weight vectors is called the fiber of $W$ (under the matrix multiplication map), and it is embedded in the Euclidean weight space of all possible weight vectors. The fiber is an algebraic variet

Christopher D. Marcotte

We develop a linear theory for the prediction of excitation wave quenching -- the construction of minimal perturbations which return stable excitations to quiescence -- for localized pulse solutions in models of excitable media. The theory accounts for an additional equivariance compared to the homogeneous ignition problem, and thus requires a reconsideratio

Ting-Ting Zhu, Rong Hu, Ya-Ping Fang

A Tikhonov regularized inertial primal\mbox{-}dual dynamical system with time scaling and vanishing damping is proposed for solving a linearly constrained convex optimization problem in Hilbert spaces. The system under consideration consists of two coupled second order differential equations and its convergence properties depend upon the decaying speed of th

Xingyue Zhao, Zhongyu Li, Xiangde Luo, Peiqi Li

Recent advances in deep learning have greatly facilitated the automated segmentation of ultrasound images, which is essential for nodule morphological analysis. Nevertheless, most existing methods depend on extensive and precise annotations by domain experts, which are labor-intensive and time-consuming. In this study, we suggest using simple aspect ratio an

Xiaoxi Li, Jiajie Jin, Yujia Zhou, Yuyao Zhang

Information Retrieval (IR) systems are crucial tools for users to access information, which have long been dominated by traditional methods relying on similarity matching. With the advancement of pre-trained language models, generative information retrieval (GenIR) emerges as a novel paradigm, attracting increasing attention. Based on the form of information

Yang Tan, Mingchen Li, Bingxin Zhou, Bozitao Zhong

Fine-tuning Pre-trained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised learning approaches. As a widely applied powerful technique in natural language processing, employing Parameter-Efficient Fine-Tuning techniques could potentially enhance the performa

Michael Lampis, Manolis Vasilakis

We revisit the Maximum Node-Disjoint Paths problem, the natural optimization version of Node-Disjoint Paths, where we are given a graph $G$, $k$ pairs of vertices $(s_i, t_i)$ and an integer $\ell$, and are asked whether there exist at least $\ell$ vertex-disjoint paths in $G$ whose endpoints are given pairs. We present several results, with an emphasis towa

Nitesh Narayana GS, Abhijit Das

The rapid advancements in memory systems, CPU technology, and emerging technologies herald a transformative potential in computing, promising to revolutionize memory hierarchies. Innovations in DDR memory are delivering unprecedented bandwidth, while advancements in on-chip wireless technology are reducing size and increasing speed. The introduction of godsp

Moji Shi, Gang Chen, Álvaro Serra Gómez, Siyuan Wu

Dynamic obstacle avoidance is a popular research topic for autonomous systems, such as micro aerial vehicles and service robots. Accurately evaluating the performance of dynamic obstacle avoidance methods necessitates the establishment of a metric to quantify the environment's difficulty, a crucial aspect that remains unexplored. In this paper, we propose fo

Srinivasa Rao Konda, Puspendu Barik, Subshash Singh, Venkatesh Mottamchetty

Research of the nonlinear optical characteristics of transition metal dichalcogenides in the presence of photoactive particles, plasmonic nanocavities, waveguides, and metamaterials is still in its early stages. This investigation delves into the high-order harmonic generation (HHG) from laser induced plasma of MoS2 nanosheets in the presence of semiconducto

Benedetta Tessa, Lorenzo Cima, Amaury Trujillo, Marco Avvenuti

Current content moderation follows a reactive, trial-and-error approach, where interventions are applied and their effects are only measured post-hoc. In contrast, we introduce a proactive, predictive approach that enables moderators to anticipate the impact of their actions before implementation. We propose and tackle the new task of predicting user abandon

Tobias Fischer, Dimitrios S. Karachalios, Ievgen Zhavzharov, Hossam S. Abbas

Identifying and controlling an unstable, underactuated robot to enable reference tracking is a challenging control problem. In this paper, a ballbot (robot balancing on a ball) is used as an experimental setup to demonstrate and test proposed strategies to tackle this control problem. A double-loop control system, including a state-feedback gain in the outer

Amandeep Kaur Kalsi, Teruki Kamon, Seulgi Kim, Jason S. H. Lee

A minimal non-thermal dark matter model that can explain both the existence of dark matter and the baryon asymmetry in the universe is studied. It requires two color-triplet, iso-singlet scalars with $\mathcal{O}$(TeV) masses and a singlet Majorana fermion with a mass of $\mathcal{O}$(GeV). The fermion becomes stable and can play the role of the dark matter

Noam Soker

I demonstrate the usage of planetary nebulae (PNe) to infer that a pair of jets shaped the ejecta of the core-collapse supernova (CCSN) SN 1987A. The main structure of the SN 1987A inner ejecta, the keyhole, comprised two low-intensity zones. The northern one has a bright rim on its front, while the southern one has an elongated nozzle. Earlier comparison of

Chuchu Chen, Xinyu Chen, Tonghe Dang, Jialin Hong

The superiority of stochastic symplectic methods over non-symplectic counterparts has been verified by plenty of numerical experiments, especially in capturing the asymptotic behaviour of the underlying solution process. How can one theoretically explain this superiority? This paper gives an answer to this problem from the perspective of the law of iterated

Yingying Han, Shuanghao Zhang, Meijuan Zhang, Q. Guan

Monochromatically driven two-level systems (i.e., Rabi models) are ubiquitous in various fields of physics. Though they have been exactly solved, the physical pictures in these exact solutions are not clear. Recently, approximate analytical solutions with neat physics have been obtained by using the counterrotating hybridized rotating wave (CHRW) method, whi

Alessandro Gasparini, Michael J. Crowther, Emiel O. Hoogendijk, Fan Li

Stepped wedge cluster-randomized trial (CRTs) designs randomize clusters of individuals to intervention sequences, ensuring that every cluster eventually transitions from a control period to receive the intervention under study by the end of the study period. The analysis of stepped wedge CRTs is usually more complex than parallel-arm CRTs due to more comple

Aida Abiad, Alessandro Neri, Luuk Reijnders

We derive eigenvalue bounds for the $t$-distance chromatic number of a graph, which is a generalization of the classical chromatic number. We apply such bounds to hypercube graphs, providing alternative spectral proofs for results by Ngo, Du and Graham [Inf. Process. Lett., 2002], and improving their bound for several instances. We also apply the eigenvalue

Alexander Stahl, Michael Kewming, John Goold, Janine Hilder

Within the framework of microscopic thermodynamics, correlations can play a crucial role for energy extraction. Our work sheds light on this connection by demonstrating that entanglement governs the amount of extractable energy in a controllable setting. We experimentally investigate a fundamental link between information, encoded in tunable non-classical co

Zhengzheng Tu, Le Gu, Xixi Wang, Bo Jiang

Segment Anything Model (SAM) has recently achieved amazing results in the field of natural image segmentation. However, it is not effective for medical image segmentation, owing to the large domain gap between natural and medical images. In this paper, we mainly focus on ultrasound image segmentation. As we know that it is very difficult to train a foundatio

Jonas Van Gompel, Bert Claessens, Chris Develder

Keeping the balance between electricity generation and consumption is becoming increasingly challenging and costly, mainly due to the rising share of renewables, electric vehicles and heat pumps and electrification of industrial processes. Accurate imbalance forecasts, along with reliable uncertainty estimations, enable transmission system operators (TSOs) t

Kexin Meng, Ruirui Li, Daguang Jiang

Human pose estimation is a fundamental and challenging task in computer vision. Larger-scale and more accurate keypoint annotations, while helpful for improving the accuracy of supervised pose estimation, are often expensive and difficult to obtain. Semi-supervised pose estimation tries to leverage a large amount of unlabeled data to improve model performanc

Matias Lasen, Dario Di Maio, Damaso De Bono, Michelle Peluzzo

The increasing use of polymer composites in industry asks for the creation of better, faster and cost-effective methods to detect the damage state of such materials. This work presents the investigation of the phase decay , $\Delta{\Phi}$, as a new parameter to characterise crack growth in composites materials utilising an experimental framework of High Freq

Kingman Cheung, Chih-Ting Lu, C. J. Ouseph, Priyanka Sarmah

We observe that most studies of axion-like particle (ALP) production channels at the Large Hadron Collider (LHC) focus on a single type of ALP operator for each process in the effective field theory framework. In this work, we propose an alternative approach that considers two or more types of relevant ALP effective operators together in some specific ALP pr

Binghui Shi, Yongpeng Wu, Yin Xu, Xiqi Gao

In this work, we propose the integration of GLDPC codes with short polar-like component codes, termed GLDPC codes with polar component codes (GLDPC-PC). This approach leverages the good distance properties of polar-like codes and mitigates their high decoding latency in long block lengths. A recently proposed soft-input soft-output decoder for polar-like cod

Tianshu Wang, Hongyu Lin, Xianpei Han, Xiaoyang Chen

Blocking is a critical step in entity resolution, and the emergence of neural network-based representation models has led to the development of dense blocking as a promising approach for exploring deep semantics in blocking. However, previous advanced self-supervised dense blocking approaches require domain-specific training on the target domain, which limit

Teodor Chiaburu, Frank Haußer, Felix Bießmann

Mounting evidence in explainability for artificial intelligence (XAI) research suggests that good explanations should be tailored to individual tasks and should relate to concepts relevant to the task. However, building task specific explanations is time consuming and requires domain expertise which can be difficult to integrate into generic XAI methods. A p

Aojun Lu, Tao Feng, Hangjie Yuan, Xiaotian Song

Efforts to overcome catastrophic forgetting have primarily centered around developing more effective Continual Learning (CL) methods. In contrast, less attention was devoted to analyzing the role of network architecture design (e.g., network depth, width, and components) in contributing to CL. This paper seeks to bridge this gap between network architecture

Li Shen, Yongpeng Wu, Yin Xu, Xiaohu You

The sixth generation (6G) wireless communication system will improve the key technical indicators by one to two orders of magnitude, and come with some new features. As a crucial technique to enhance the reliability and efficiency of data transmission, the next-generation channel coding is thus confronted with new challenges in terms of complexity, latency,

Jingxuan Wei, Linzhuang Sun, Yichong Leng, Xu Tan

Knowledge distillation, transferring knowledge from a teacher model to a student model, has emerged as a powerful technique in neural machine translation for compressing models or simplifying training targets. Knowledge distillation encompasses two primary methods: sentence-level distillation and token-level distillation. In sentence-level distillation, the

Hang Yu, Lei Zhang, Yiwei Li, Kwan-Wu Chin

Many Internet of Things (IoT) networks with Radio Frequency (RF) powered devices operate over a shared medium. They thus require a channel access protocol. Unlike conventional networks where devices have unlimited energy, in an RF-powered IoT network, devices must first harvest RF energy in order to transmit or/and receive data. To this end, this survey pres

Qionglei Chen, Yao Nie, Weikui Ye

In this paper, we prove a sharp ill-posedness result for the incompressible non-resistive MHD equations. In any dimension $d\ge 2$, we show the ill-posedness of the non-resistive MHD equations in $H^{\frac{d}{2}-1}(\mathbb{R}^d)\times H^{\frac{d}{2}}(\mathbb{R}^d)$, which is sharp in view of the results of the local well-posedness in $H^{s-1}(\mathbb{R}^d)\t

Pengyu Xue, Linhao Wu, Zhongxing Yu, Zhi Jin

Commit Message Generation (CMG) approaches aim to automatically generate commit messages based on given code diffs, which facilitate collaboration among developers and play a critical role in Open-Source Software (OSS). Very recently, Large Language Models (LLMs) have demonstrated extensive applicability in diverse code-related task. But few studies systemat

Han Fu, Sigrid Eldh, Kristian Wiklund, Andreas Ermedahl

Industrial embedded systems often require specialized hardware. However, software engineers have access to such domain-specific hardware only at the continuous integration (CI) stage and have to use simulated hardware otherwise. This results in a higher proportion of compilation errors at the CI stage than in other types of systems, warranting a deeper study

Ziheng Jiao, Hongyuan Zhang, Xuelong Li

Although the convolutional neural network (CNN) has achieved excellent performance in vision tasks by extracting the intra-sample representation, it will take a higher training expense because of stacking numerous convolutional layers. Recently, as the bilinear models, graph neural networks (GNN) have succeeded in exploring the underlying topological relatio

Valerio Piomponi, Miroslav Krepl, Jiri Sponer, Giovanni Bussi

N6-methyladenosine (m6A) is a prevalent RNA post-transcriptional modification that plays crucial roles in RNA stability, structural dynamics, and interactions with proteins. The YT521-B (YTH) family of proteins, which are notable m6A readers, function through their highly conserved YTH domain. Recent structural investigations and molecular dynamics (MD) simu

Atsushi Hori, Kazuyuki Sekitani

Incorporating an assurance region (AR) into the slacks-based measure (SBM) improves practicality; however, its efficiency measure may not have desirable properties, such as monotonicity. We incorporate a closer target setting approach into the SBM with AR and a variant of the SBM with AR. We demonstrate that the efficiency measure with the hybrid approach ha

Yiping Xie, Giancarlo Troni, Nils Bore, John Folkesson

This research addresses the challenge of estimating bathymetry from imaging sonars where the state-of-the-art works have primarily relied on either supervised learning with ground-truth labels or surface rendering based on the Lambertian assumption. In this letter, we propose a novel, self-supervised framework based on volume rendering for reconstructing bat

Thorben Kastenholz

In this note we investigate the simplicial volume of fiber bundles with connected structure group. We are able to show that if the structure group is either compact or a Lie group, or if the fiber is aspherical that the simplicial volume of the total space agrees with the simplicial volume of the trivial bundle.

Önder Gürcan

There is a need for a simulation framework, which is develop as a software using modern engineering approaches (e.g., modularity --i.e., model reuse--, testing, continuous development and continuous integration, automated management of builds, dependencies and documentation) and agile principles, (1) to make rapid prototyping of industrial cases and (2) to c

Yunxiang Jiang, Qing Xu, Kai Zhen, Yu Chen

In driving tasks, the driver's situation awareness of the surrounding scenario is crucial for safety driving. However, current methods of measuring situation awareness mostly rely on subjective questionnaires, which interrupt tasks and lack non-intrusive quantification. To address this issue, our study utilizes objective gaze motion data to provide an interf

Kirill A. Bronnikov

The so-called black bounce mechanism of singularity suppression, proposed by Simpson and Visser, consists in replacing the spherical radius $r$ in the metric tensor with $\sqrt{r^2 + a^2}$, $a = \rm const >0$. This removes a singularity at $r=0$ and its neighborhood from space-time, and there emerges a regular minimum of the spherical radius that can be a wo

Shibo Li, Hengliang Cheng, Weihua Li

The widespread application of Electronic Health Records (EHR) data in the medical field has led to early successes in disease risk prediction using deep learning methods. These methods typically require extensive data for training due to their large parameter sets. However, existing works do not exploit the full potential of EHR data. A significant challenge

Alexander Gall, Anja Heim, Eduard Gröller, Christoph Heinzl

Rich material data is complex, large and heterogeneous, integrating primary and secondary non-destructive testing data for spatial, spatio-temporal, as well as high-dimensional data analyses. Currently, materials experts mainly rely on conventional desktop-based systems using 2D visualisation techniques, which render respective analyses a time-consuming and

Andrea Erroi, Francesco Carulli, Francesca Cova, Isabel Frank

Lead halide perovskite nanocrystals (LHP-NCs) embedded in polymer matrices are gaining traction for next-generation radiation detectors. While progress has been made on green-emitting CsPbBr3 NCs, scant attention has been given to the scintillation properties of CsPbCl3 NCs, which emit size-tunable UV-blue light matching the peak efficiency of ultrafast phot

Yufeng Zhang, Xuepeng Wang, Lingxiang Wu, Jinqiao Wang

Chain of Thought (CoT) prompting can encourage language models to engage in multi-step logical reasoning. The quality of the provided demonstrations significantly influences the success of downstream inference tasks. Current unsupervised CoT methods primarily select examples based on the semantics of the questions, which can introduce noise and lack interpre

Bingnan Xiao, Jingjing Zhang, Wei Ni, Xin Wang

Wireless federated learning (WFL) suffers from heterogeneity prevailing in the data distributions, computing powers, and channel conditions of participating devices. This paper presents a new Federated Learning with Adjusted leaRning ratE (FLARE) framework to mitigate the impact of the heterogeneity. The key idea is to allow the participating devices to adju

Vikash Kumar, Srimanta Pakhira

An electrocatalyst is needed to efficiently lower the reaction barriers to produce hydrogen through the H2 evolution reaction (HER). Recently, two-dimensional transition metal dichalcogenides (2D TMDs), such as the pure 2D monolayer MoTe2 TMD, have become attractive materials for HER. Using the first principle-based hybrid DFT-D method, we have computational

Wenbo Shang, Xin Huang

A graph is a fundamental data model to represent various entities and their complex relationships in society and nature, such as social networks, transportation networks, and financial networks. Recently, large language models (LLMs) have showcased a strong generalization ability to handle various natural language processing tasks to answer users' arbitrary

Wenjin Hou, Shiming Chen, Shuhuang Chen, Ziming Hong

Generative Zero-shot learning (ZSL) learns a generator to synthesize visual samples for unseen classes, which is an effective way to advance ZSL. However, existing generative methods rely on the conditions of Gaussian noise and the predefined semantic prototype, which limit the generator only optimized on specific seen classes rather than characterizing each

Siyuan Mei, Fuxin Fan, Mareike Thies, Mingxuan Gu

Recently, X-ray microscopy (XRM) and light-sheet fluorescence microscopy (LSFM) have emerged as pivotal tools in preclinical research, particularly for studying bone remodeling diseases such as osteoporosis. These modalities offer micrometer-level resolution, and their integration allows for a complementary examination of bone microstructures which is essent

Marc Lambert, Francis Bach, Silvère Bonnabel

We consider the problem of stochastic optimal control, where the state-feedback control policies take the form of a probability distribution and where a penalty on the entropy is added. By viewing the cost function as a Kullback- Leibler (KL) divergence between two joint distributions, we bring the tools from variational inference to bear on our optimal cont

Mohammed Tobrej, Binay Rai, Manoj Ghising, Bikash Chandra Paul

We present the broadband X-ray study of the BeXRB pulsar IGR J06074+2205 using NuSTAR observations. The temporal and spectral characteristics of the source are investigated. We detect coherent X-ray pulsations of the source and determine the spin period evolution. Using the current spin period data of the source, we show that the source is spinning down at 0

Ben Wooding, Viacheslav Horbanov, Abolfazl Lavaei

We develop an open-source software tool, called PRoTECT, for the parallelized construction of safety barrier certificates (BCs) for nonlinear polynomial systems. This tool employs sum-of-squares (SOS) optimization programs to systematically search for polynomial-type BCs, while aiming to verify safety properties over four classes of dynamical systems: (i) di

Michaël Fanuel, Rémi Bardenet

We consider a probability measure on cycle-rooted spanning forests (CRSFs) introduced by Kenyon. CRSFs are spanning subgraphs, each connected component of which has a unique cycle; they generalize spanning trees. A generalization of Wilson's celebrated CyclePopping algorithm for uniform spanning trees has been proposed for CRSFs, and several concise proofs h

Oliver Maximilian Zobel, Francesco Trainotti, Daniel J. Rixen

Dynamic substructuring, especially the frequency-based variant (FBS) using frequency response functions (FRF), is gaining in popularity and importance, with countless successful applications, both numerically and experimentally. One drawback, however, is found when the responses to shocks are determined. Numerically, this might be especially expensive when a

Jieru Lin, Danqing Huang, Tiejun Zhao, Dechen Zhan

A well-executed graphic design typically achieves harmony in two levels, from the fine-grained design elements (color, font and layout) to the overall design. This complexity makes the comprehension of graphic design challenging, for it needs the capability to both recognize the design elements and understand the design. With the rapid development of Multimo

Yonghong Yao, Lateef O. Jolaoso, Yekini Shehu, Jen-Chih Yao

In this work, we propose some new Douglas-Rashford splitting algorithms for solving a class of generalized DC (difference of convex functions) in real Hilbert spaces. The proposed methods leverage the proximal properties of the nonsmooth component and a fasten control parameter which improves the convergence rate of the algorithms. We prove the convergence o

Cristian Axenie

The stability--robustness--resilience--adaptiveness continuum in neuronal processing follows a hierarchical structure that explains interactions and information processing among the different time scales. Interestingly, using "canonical" neuronal computational circuits, such as Homeostatic Activity Regulation, Winner-Take-All, and Hebbian Temporal Correlatio

Jintao Wang, Dongdong Zhu, Chunqiu Li

We consider a stochastic lattice Cahn-Hilliard equation with nonautonomous nonlinear noise. First, we prove the existence of pullback random attractors in $\ell^2$ for the generated nonautonomous random dynamical system. Then, we construct the time-dependent invariant sample Borel probability measures based on the pullback random attractor. Moreover, we deve