February 2024 arXiv papers — page 61

Yue Wang, Chao Zhang, Xiaojun Chen

Recurrent Neural Networks (RNNs) are widely used to model sequential data in a wide range of areas, such as natural language processing, speech recognition, machine translation, and time series analysis. In this paper, we model the training process of RNNs with the ReLU activation function as a constrained optimization problem with a smooth nonconvex objecti

Huaisong Yong, Bilin Zhuang, Sissi de Beer

We study the reentrant condensation of polyelectrolytes in dilute solutions of small multivalent salts, whose phase-transition mechanism remains poorly understood. Motivated by recent full atomic simulation results reported by the Caltech group on phase behaviors of polyelectrolytes in presence of multivalent salts (DOIs: 10.1021/acs.macromol.3c02437 and 10.

Stefan Meinecke, Felix Köster, Dominik Christiansen, Kathy Lüdge

We present a data-driven approach to efficiently approximate nonlinear transient dynamics in solid-state systems. Our proposed machine-learning model combines a dimensionality reduction stage with a nonlinear vector autoregression scheme. We report an outstanding time-series forecasting performance combined with an easy to deploy model and an inexpensive tra

Francesco Vaselli, Filippo Cattafesta, Patrick Asenov, Andrea Rizzi

The simulation of high-energy physics collision events is a key element for data analysis at present and future particle accelerators. The comparison of simulation predictions to data allows looking for rare deviations that can be due to new phenomena not previously observed. We show that novel machine learning algorithms, specifically Normalizing Flows and

Hafiz Usman Naseer, Daulet Izbassarov, Zaheer Ahmed, Metin Muradoglu

Cross-stream migration of a deformable bubble is investigated computationally in a pressure-driven channel flow of a viscoelastic fluid via interface-resolved simulations. The flow equations are solved fully coupled with the Giesekus model equations using the front-tracking method and extensive simulations are performed for a wide range of flow parameters to

Patrick Barth, Eva E. Stüeken, Christiane Helling, Edward W. Schwieterman

Lightning has been suggested to play a role in triggering the occurrence of bio-ready chemical species. Future missions (PLATO, ARIEL, HWO, LIFE) and ground-based ELTs will investigate the atmospheres of potentially habitable exoplanets. We aim to study the effect of lightning on the atmospheric chemistry, how it affects false-positive and false-negative bio

Mirco Ciallella, Mario Ricchiuto, Renato Paciorri, Aldo Bonfiglioli

We propose a novel approach to approximate numerically shock waves. The method combines the unstructured shock-fitting approach developed in the last decade by some of the authors, with ideas coming from embedded boundary techniques. The numerical method obtained allows avoiding the re-meshing phase required by the unstructured fitting method, while guarante

Stefano Almi, Riccardo Durastanti, Francesco Solombrino

We derive a first order optimality condition for a class of agent-based systems, as well as for their mean-field counterpart. A relevant difficulty of our analysis is that the state equation is formulated on possibly infinite-dimensional convex subsets of Banach spaces. This is a typical feature of many problems in multi-population dynamics, where a convex s

Fritz Riehle, Sebastian Ulbricht

We revisit the concept of particles as it is used in special relativity. The presented model treats the energy-momentum relation of relativistic particles as the upper branch of a generalized energy-momentum relation of quasi particles. These particles emerge from a forbidden crossing between the constant energy of a massive particle in rest and the linear e

Sam Power, Daniel Rudolf, Björn Sprungk, Andi Q. Wang

Using the framework of weak Poincar\'e inequalities, we provide a general comparison between Hybrid and Ideal Slice Sampling in terms of their corresponding Dirichlet forms. In particular, under suitable assumptions Hybrid Slice Sampling inherits fast convergence from Ideal Slice Sampling and conversely. We apply our results to analyse the convergence of the

Navid Ashrafi, Vera Schmitt, Robert P. Spang, Sebastian Möller

Preservation of private user data is of paramount importance for high Quality of Experience (QoE) and acceptability, particularly with services treating sensitive data, such as IT-based health services. Whereas anonymization techniques were shown to be prone to data re-identification, synthetic data generation has gradually replaced anonymization since it is

Tian Long, Jieyun Pan, Stéphane Zaleski

We present a novel Front-Tracking method, the Edge-Based Interface Tracking (EBIT) method for multiphase flow simulations. In the EBIT method, the markers are located on the grid edges and the interface can be reconstructed without storing the connectivity of the markers. This feature makes the process of marker addition or removal easier than in the traditi

T. Khouri, H. Olofsson, W. H. T. Vlemmings, T. Schirmer

The mass loss experienced on the asymptotic giant branch (AGB) at the end of the lives of low- and intermediate-mass stars is widely accepted to rely on radiation pressure acting on dust grains formed in the extended AGB atmospheres. The interaction of convection, stellar pulsation, and heating and cooling processes cause the density, velocity and temperatur

Zongrui Yang

Consider the stationary measure of open asymmetric simple exclusion process (ASEP) on the lattice $\{1,\dots,n\}$. Taking $n$ to infinity while fixing the jump rates, this measure converges to a measure on the semi-infinite lattice. In the high and low density phases, we characterize the limiting measure and provide bounds on the convergence rates in total v

Atabek E. Atamuratov, Mahkam M. Khalilloev, Ahmed Yusupov, Jean Chamberlain Chedjou

The influence of the channel shape in a junctionless silicon-on-insulator finned field-effect transistor (JL SOI FinFET) on the amplitude of random telegraph noise (RTN) induced by single interface trapped charge has been simulated for the transistors with rectangular, trapezoidal, and triangular fin cross sections. The simulation of the RTN amplitude distri

Santiago Iglesias Álvarez, Enrique Díez Alonso, María Luisa Sánchez Rodríguez, Javier Rodríguez Rodríguez

The transit method allows the detection and characterization of planetary systems by analyzing stellar light curves. Convolutional neural networks appear to offer a viable solution for automating these analyses. In this research, two 1D convolutional neural network models, which work with simulated light curves in which transit-like signals were injected, ar

Zhichen Lai, Huan Li, Dalin Zhang, Yan Zhao

Cyber-physical system sensors emit multivariate time series (MTS) that monitor physical system processes. Such time series generally capture unknown numbers of states, each with a different duration, that correspond to specific conditions, e.g., "walking" or "running" in human-activity monitoring. Unsupervised identification of such states facilitates storag

Gareth A. Jones, Sezgin Sezer

Extending earlier work of Guralnick and of Cai and Zhang, we classify the almost simple groups which have transitive permutation representations of prime power degree $p^k$, and those which have $p$-complements (stabilisers of order coprime to $p$ in such representations). We deduce that every primitive permutation group of prime power degree has a regular s

Michal Spiegel, Dominik Macko

SemEval-2024 Task 8 is focused on multigenerator, multidomain, and multilingual black-box machine-generated text detection. Such a detection is important for preventing a potential misuse of large language models (LLMs), the newest of which are very capable in generating multilingual human-like texts. We have coped with this task in multiple ways, utilizing

Ronny Bergmann, Roland Herzog, Hajg Jasa

We introduce the convex bundle method to solve convex, non-smooth optimization problems on Riemannian manifolds of bounded sectional curvature. Each step of our method is based on a model that involves the convex hull of previously collected subgradients, parallelly transported into the current serious iterate. This approach generalizes the dual form of clas

Zhaorui Yang, Tianyu Pang, Haozhe Feng, Han Wang

The surge in Large Language Models (LLMs) has revolutionized natural language processing, but fine-tuning them for specific tasks often encounters challenges in balancing performance and preserving general instruction-following abilities. In this paper, we posit that the distribution gap between task datasets and the LLMs serves as the primary underlying cau

David Garoz, C. Bueno, C. Larriba, S. Castro

The emissions of Taylor cones from a wide range of ionic liquids (ILs) have been tested in vacuo in an attempt to identify what physical properties favor the purely ionic regime (PIR). This regime is well known in the case of Taylor cones of liquid metals. For nonmetallic liquids, it has been previously observed in conventional (capillary tube) electrospray

Jianghui Zhou, Ya Gao, Jie Liu, Xuemin Zhao

Large language models(LLM) such as ChatGPT have substantially simplified the generation of marketing copy, yet producing content satisfying domain specific requirements, such as effectively engaging customers, remains a significant challenge. In this work, we introduce the Genetic Copy Optimization Framework (GCOF) designed to enhance both efficiency and eng

Simon Cramer, Tobias Müller, Robert H. Schmitt

In the context of industrially mass-manufactured products, quality management is based on physically inspecting a small sample from a large batch and reasoning about the batch's quality conformance. When complementing physical inspections with predictions from machine learning models, it is crucial that the uncertainty of the prediction is known. Otherwise,

Homa Shababi, Theophanes Grammenos, Nikolaos Dimakis, Andronikos Paliathanasis

We uncover the solution space of a five dimensional geometry which we deem it as the direct counterpart of the Bianchi Type V cosmological model. We kinematically reduce the scale factor matrix and then, with an appropriate scaling and choice of time, we cast the spatial equations into a simple "Kasner" like form; thus revealing linear integrals of motion. T

Jacopo D'Alberto, Lorenzo Cardarelli, Davide Emilio Galli, Gianluca Bertaina

We derive analytically the leading beyond-mean field contributions to the zero-temperature equation of state and to the fermionic quasi-particle residue and effective mass of a dilute Bose-Fermi mixture in two dimensions. In the repulsive case, we perform quantum Monte Carlo simulations for two representative bosonic concentrations and equal masses, extendin

Quentin Chauleur

We study the convergence of solutions of the discrete nonlinear Klein-Gordon equation on an infinite lattice in the continuum limit, using recent tools developed in the context of nonlinear discrete dispersive equations. Our approach relies in particular on the use of bilinear estimates of the Shannon interpolation alongside controls on the growth of discret

Nikola Zlatanov

We present a method for upper and lower bounding the right and the left tail probabilities of continuous random variables (RVs). For the right tail probability of RV $X$ with probability density function $f (x)$, this method requires first setting a continuous, positive, and strictly decreasing function $g (x)$ such that $-f (x)/g' (x)$ is a decreasing and i

E. Carretti, C. Baccigalupi

The polarization of the Cosmic Microwave Background (CMB) radiation carries essential information on early stages of the Universe such as the cosmic inflation, forming cosmological structures through gravitational lensing, and the epoch of re-ionization. The signal requires high sensitivity instruments with a large number of detectors (bolometers) and low le

Etienne Levecque, Jan Butora, Patrick Bas

This article presents a refined notion of incompatible JPEG images for a quality factor of 100. It can be used to detect the presence of steganographic schemes embedding in DCT coefficients. We show that, within the JPEG pipeline, the combination of the DCT transform with the quantization function can map several distinct blocks in the pixel domain to the sa

Da Yu, Peter Kairouz, Sewoong Oh, Zheng Xu

Service providers of large language model (LLM) applications collect user instructions in the wild and use them in further aligning LLMs with users' intentions. These instructions, which potentially contain sensitive information, are annotated by human workers in the process. This poses a new privacy risk not addressed by the typical private optimization. To

Maxime Herbrich, Eythan Cousin, Ivan Puga-Gonzalez, Barbara Tiddi

This study investigates the prevalence and implications of nestedness within primate social networks, examining its relationship with cognitive and structural factors. We analysed data from 51 primate groups across 21 species, employing network analysis to evaluate nestedness and its correlation with modularity, neocortex ratio, and group size. We used Bayes

Bang Liu, Li-Hua Zhang, Qi-Feng Wang, Yu Ma

Higher-order and fractional discrete time crystals (DTCs) are exotic phases of matter where the discrete time translation symmetry is broken into higher-order and non-integer category. Generation of these unique DTCs has been widely studied theoretically in different systems. However, no current experimental methods can probe these higher-order and fractiona

B. Baran, P. Benner, J. Saak, T. Stillfjord

By computing a feedback control via the linear quadratic regulator (LQR) approach and simulating a non-linear non-autonomous closed-loop system using this feedback, we combine two numerically challenging tasks. For the first task, the computation of the feedback control, we use the non-autonomous generalized differential Riccati equation (DRE), whose solutio

Morten Blørstad, Berent Å. S. Lunde, Nello Blaser

Updating machine learning models with new information usually improves their predictive performance, yet, in many applications, it is also desirable to avoid changing the model predictions too much. This property is called stability. In most cases when stability matters, so does explainability. We therefore focus on the stability of an inherently explainable

Paul Daoudi, Bojan Mavkov, Bogdan Robu, Christophe Prieur

This paper presents a learning-based control strategy for non-linear throttle valves with an asymmetric hysteresis, leading to a near-optimal controller without requiring any prior knowledge about the environment. We start with a carefully tuned Proportional Integrator (PI) controller and exploit the recent advances in Reinforcement Learning (RL) with Guides

Eli M Carrami, Sahand Sharifzadeh

Understanding protein structure and function is crucial in biology. However, current computational methods are often task-specific and resource-intensive. To address this, we propose zero-shot Protein Question Answering (PQA), a task designed to answer a wide range of protein-related queries without task-specific training. The success of PQA hinges on high-q

Nathan J. Brooks, Chih-Chen Liu, Chia-Lung Hsieh

Interferometric scattering (iSCAT) microscopy is currently among the most powerful techniques available for achieving high-sensitivity single-particle localization. This capability is realized through homodyne detection, where interference with a reference wave offers the promise of exceptionally precise three-dimensional (3D) localization. However, the prac

Mikolaj Czerkawski, Carmine Clemente, Craig Michie, Christos Tachtatzis

With the great capabilities of deep classifiers for radar data processing come the risks of learning dataset-specific features that do not generalize well. In this work, the robustness of two deep convolutional architectures, trained and tested on the same data, is evaluated. When standard training practice is followed, both classifiers exhibit sensitivity t

Philippe Vaslin, Denis N'Chot, Roland Lenain, Jean-Christophe Fauroux

Unmanned ground vehicle obstacle crossing generally relies on two strategies: (i) applying a wheel torque for climbing and (ii) modifying the vehicle shape by using a wheel-leg or wheel-paddle to lift the wheel on top of the obstacle. However, most of those strategies sacrifice speed in order to have a longer contact duration between the wheels and the obsta

Etienne Ménager, Christian Duriez

The ability of a soft robot to perform specific tasks is determined by its contact configuration, and transitioning between configurations is often necessary to reach a desired position or manipulate an object. Based on this observation, we propose a method for controlling soft robots that involves defining a graph of configuration spaces. Different agents,

Luca Marannino

We generalize the $p$-adic explicit reciprocity laws for balanced diagonal classes by Darmon--Rotger and Bertolini--Seveso--Venerucci to the case of geometric balanced triples $(f,g,h)$ of modular eigenforms where $f$ is a $p$-ordinary newform, while $g$ and $h$ are allowed to be (both) supercuspidal at $p$ or (both) ramified principal series at $p$.

Lei Pan, Yunshi Lan, Yang Li, Weining Qian

Unsupervised Text Style Transfer (UTST) has emerged as a critical task within the domain of Natural Language Processing (NLP), aiming to transfer one stylistic aspect of a sentence into another style without changing its semantics, syntax, or other attributes. This task is especially challenging given the intrinsic lack of parallel text pairings. Among exist

Victor Leger, Romain Couillet

This article conducts a large dimensional study of a simple yet quite versatile classification model, encompassing at once multi-task and semi-supervised learning, and taking into account uncertain labeling. Using tools from random matrix theory, we characterize the asymptotics of some key functionals, which allows us on the one hand to predict the performan

Nikolaos Chalmoukis, Alberto Dayan, Giuseppe Lamberti

We study the Kolmogorov 0-1 law for a random sequence with prescribed radii so that it generates a Carleson measure almost surely, both for the Hardy space on the polydisc and the Hardy space on the unit ball, thus providing improved versions of previous results of the first two authors and of a separate result of Massaneda. In the polydisc, the geometry of

Bang Liu, Li-Hua Zhang, Zong-Kai Liu, Jun Zhang

A time crystal is an exotic phase of matter where time-translational symmetry is broken; this phase differs from the spatial symmetry breaking induced in crystals in space. Lots of experiments report the transition from a thermal equilibrium phase to time crystal phase. However, there is no experimental method to probe the bifurcation effect of distinct time

Mingkun Yang, Biao Yang, Minghui Liao, Yingying Zhu

Scene text recognition is a rapidly developing field that faces numerous challenges due to the complexity and diversity of scene text, including complex backgrounds, diverse fonts, flexible arrangements, and accidental occlusions. In this paper, we propose a novel approach called Class-Aware Mask-guided feature refinement (CAM) to address these challenges. O

Ka Long Keith Ho, Hiroki Masuda

We propose an adaptive ridge (AR) estimation scheme for a heteroscedastic linear regression model with log-linear noise in data. We simultaneously estimate the mean and variance parameters, demonstrating new asymptotic distributional and tightness properties in a sparse setting. We also show that estimates for zero parameters shrink with more iterations unde

Yang Zhang, Haiyang Wu, Yuekui Yang

Given a Hyperparameter Optimization(HPO) problem, how to design an algorithm to find optimal configurations efficiently? Bayesian Optimization(BO) and the multi-fidelity BO methods employ surrogate models to sample configurations based on history evaluations. More recent studies obtain better performance by integrating BO with HyperBand(HB), which accelerate

Negar Alizadeh, Fernando Castor

Deep Learning (DL) frameworks such as PyTorch and TensorFlow include runtime infrastructures responsible for executing trained models on target hardware, managing memory, data transfers, and multi-accelerator execution, if applicable. Additionally, it is a common practice to deploy pre-trained models on environments distinct from their native development set

Zhou Zhou, Jun-Jie Zhang, Gemma F. Turner, Stephen A. Moggach

Interlayer stacking order has recently emerged as a unique degree of freedom to control crystal symmetry and physical properties in two-dimensional van der Waals (vdW) materials and heterostructures. By tuning the layer stacking pattern, symmetry-breaking and electric polarization can be created in otherwise non-polar crystals, whose polarization reversal de

Jianhui Lin, Xiangdong Zhang

In this paper, we utilize the effective corrections of the $\bar{\mu}$-scheme in loop quantum black holes to obtain a 4-dimensional spherically symmetric metric with a cosmological constant. By imposing the areal gauge on the components of Ashtekar variables in the classical theory and applying the holonomy corrections, we derive the equations of motion, whi

Jöran Lindeberg, Eric-Oluf Svee, Martin Henkel

Context and motivation: Requirements engineering of complex IT systems needs to manage the many, and often vague and conflicting, organisational rules that exist in the context of a modern enterprise. At the same time, IT systems affect the organisation, essentially setting new rules on how the organisation should work. Question/problem: Gathering requiremen

Ashutosh Sathe, Prachi Jain, Sunayana Sitaram

Vision-language models (VLMs) have gained widespread adoption in both industry and academia. In this study, we propose a unified framework for systematically evaluating gender, race, and age biases in VLMs with respect to professions. Our evaluation encompasses all supported inference modes of the recent VLMs, including image-to-text, text-to-text, text-to-i

Daniel Schwabe, Katinka Becker, Martin Seyferth, Andreas Klaß

The adoption of machine learning (ML) and, more specifically, deep learning (DL) applications into all major areas of our lives is underway. The development of trustworthy AI is especially important in medicine due to the large implications for patients' lives. While trustworthiness concerns various aspects including ethical, technical and privacy requiremen

Shishun Zhang, Qijin She, Wenhao Li, Chenyang Zhu

This work focuses on the dual-arm object rearrangement problem abstracted from a realistic industrial scenario of Cartesian robots. The goal of this problem is to transfer all the objects from sources to targets with the minimum total completion time. To achieve the goal, the core idea is to develop an effective object-to-arm task assignment strategy for min

Haneul Yoo, Jieun Han, So-Yeon Ahn, Alice Oh

Automated essay scoring (AES) is a useful tool in English as a Foreign Language (EFL) writing education, offering real-time essay scores for students and instructors. However, previous AES models were trained on essays and scores irrelevant to the practical scenarios of EFL writing education and usually provided a single holistic score due to the lack of app

Nathan Blacher

We study noncommutative rings whose proper subrings all satisfy the same chain condition. We show that if every proper subring of a ring $R$ is right Noetherian, then $R$ is either right Noetherian or the trivial extension of $\mathbb{Z}$ by the Pr\"ufer $p$-group for a prime $p$. We also prove that if every proper subring of $R$ is right Artinian, then $R$

Huankang Guan, Ke Xu, Rynson W. H. Lau

Shadow detection is a challenging task as it requires a comprehensive understanding of shadow characteristics and global/local illumination conditions. We observe from our experiment that state-of-the-art deep methods tend to have higher error rates in differentiating shadow pixels from non-shadow pixels in dark regions (ie, regions with low-intensity values

Yufei He, Yuan Sui, Xiaoxin He, Bryan Hooi

Foundation models like ChatGPT and GPT-4 have revolutionized artificial intelligence, exhibiting remarkable abilities to generalize across a wide array of tasks and applications beyond their initial training objectives. However, graph learning has predominantly focused on single-graph models, tailored to specific tasks or datasets, lacking the ability to tra

Zhenbo Song, Zhenyuan Zhang, Kaihao Zhang, Zhaoxin Fan

This study delves into the enhancement of Under-Display Camera (UDC) image restoration models, focusing on their robustness against adversarial attacks. Despite its innovative approach to seamless display integration, UDC technology faces unique image degradation challenges exacerbated by the susceptibility to adversarial perturbations. Our research initiall

Dafang Zhao, Zheng Chen, Zhengmao Li, Xiaolei Yuan

Heat, Ventilation and Air Conditioning (HVAC) systems play a critical role in maintaining a comfortable thermal environment and cost approximately 40% of primary energy usage in the building sector. For smart energy management in buildings, usage patterns and their resulting profiles allow the improvement of control systems with prediction capabilities. Howe

Martin Hoefer, Carmine Ventre, Lisa Wilhelmi

The recent banking crisis has again emphasized the importance of understanding and mitigating systemic risk in financial networks. In this paper, we study a market-driven approach to rescue a bank in distress based on the idea of claims trading, a notion defined in Chapter 11 of the U.S. Bankruptcy Code. We formalize the idea in the context of financial netw

Janina Tikko

The field of optimization has the goal to find an optimal solution to a target function, i.e. to minimize (or maximize) the target function. When trying to find such a global minimum, one often encounters local minima due to unfavorable procedures and starting regions. The swarm-based gradient descent method of Prof. Eitan Tadmor offers an alternative method

Giuseppe Cosma Brusca, Davide Donati, Margherita Solci

Variational models of phase transitions take into account double-well energies singularly perturbed by gradient terms, such as the Cahn-Hilliard free energy. The derivation by $\Gamma$-convergence of a sharp-interface limit for such energy is a classical result by Modica and Mortola. We consider a singular perturbation of a double-well energy by derivatives

Wanqing Cui, Keping Bi, Jiafeng Guo, Xueqi Cheng

Since commonsense information has been recorded significantly less frequently than its existence, language models pre-trained by text generation have difficulty to learn sufficient commonsense knowledge. Several studies have leveraged text retrieval to augment the models' commonsense ability. Unlike text, images capture commonsense information inherently but

Sebastian Angrick, Ben Bals, Tobias Friedrich, Hans Gawendowicz

Many real-world networks, such as transportation or trade networks, are dynamic in the sense that the edge set may change over time, but these changes are known in advance. This behavior is captured by the temporal graphs model, which has recently become a trending topic in theoretical computer science. A core open problem in the field is to prove the existe

Sahil Mishra, Ujjwal Sudev, Tanmoy Chakraborty

Taxonomies represent an arborescence hierarchical structure that establishes relationships among entities to convey knowledge within a specific domain. Each edge in the taxonomy signifies a hypernym-hyponym relationship. Taxonomies find utility in various real-world applications, such as e-commerce search engines and recommendation systems. Consequently, the

Lucas Clarté, Adrien Vandenbroucque, Guillaume Dalle, Bruno Loureiro

We investigate popular resampling methods for estimating the uncertainty of statistical models, such as subsampling, bootstrap and the jackknife, and their performance in high-dimensional supervised regression tasks. We provide a tight asymptotic description of the biases and variances estimated by these methods in the context of generalized linear models, s

Hsian-Yang Chen, Ching Hung Lam

We continue our study of cyclic orbifolds of lattice vertex operator algebras and their full automorphism groups. We consider some special isometry $g\in O(L)$ such that $g^i$ is fixed point free on $L$ for any $1\leq i\leq |g|-1$. We show that when $L_2=\emptyset$ and $g^i$ is fixed point free on $L$ for any $1\leq i\leq |g|-1$, $V_L^{\hat{g}}$ has extra au

Kai Xu, Luman Ju, Engui Fan

In this paper, we develop a Riemann-Hilbert (RH) approach to the Cauchy problem for the two-component modified Camassa-Holm (2-mCH) equation based on its Lax pair. Further via a series of deformations to the RH problem by using the $\bar{\partial}$-generalization of Deift-Zhou steepest descent method, we obtain the long-time asymptotic approximations to the

Karl-Hermann Neeb, Francesco G. Russo

Hilbert--Lie groups are Lie groups whose Lie algebra is a real Hilbert space whose scalar product is invariant under the adjoint action. These infinite-dimensional Lie groups are the closest relatives to compact Lie groups. Here we study unitary representations of these groups from various perspectives. First, we address norm-continuous, also called bounded,

Hagit Attiya, Armando Castañeda, Constantin Enea

A powerful tool for designing complex concurrent programs is through composition with object implementations from lower-level primitives. Strongly-linearizable implementations allow to preserve hyper-properties, e.g., probabilistic guarantees of randomized programs. However, the only known wait-free strongly-linearizable implementations for many objects rely

Kirti Gupta, Subham Sahoo, Bijaya Ketan Panigrahi

Power electronic systems (PES) face significant threats from various data availability and integrity attacks, significantly affecting the performance of communication networks and power system operation. As a result, several attack detection and reconstruction techniques are deployed, which makes it a costly \& complex cybersecurity operational platform with

Chien-Yao Wang, I-Hau Yeh, Hong-Yuan Mark Liao

Today's deep learning methods focus on how to design the most appropriate objective functions so that the prediction results of the model can be closest to the ground truth. Meanwhile, an appropriate architecture that can facilitate acquisition of enough information for prediction has to be designed. Existing methods ignore a fact that when input data underg

Tomas Veloz, Olha Sobetska

Since the seminal paper by Tversky and Kahneman, the conjunction fallacy has been the subject of multiple debates and become a fundamental challenge for cognitive theories in decision-making. In this article, we take a rather uncommon perspective on this phenomenon. Instead of trying to explain the nature or causes of the conjunction fallacy (intensional def

C. Li, B. Wang, Y. Liu, C. Guo

The Jiangmen Underground Neutrino Observatory (JUNO), a 20~kton multi-purpose low background Liquid Scintillator (LS) detector, was proposed primarily to determine the neutrino mass ordering. To suppress the radioactivity from the surrounding rocks and tag cosmic muons, the JUNO central detector is submerged in a Water Cherenkov Detector (WCD). In addition t

David Fernández Llorca, Ronan Hamon, Henrik Junklewitz, Kathrin Grosse

This study explores the complexities of integrating Artificial Intelligence (AI) into Autonomous Vehicles (AVs), examining the challenges introduced by AI components and the impact on testing procedures, focusing on some of the essential requirements for trustworthy AI. Topics addressed include the role of AI at various operational layers of AVs, the implica

Hoang-Quynh Le, Duy-Cat Can, Khanh-Vinh Nguyen, Mai-Vu Tran

This paper presents a comprehensive overview of the Comparative Opinion Mining from Vietnamese Product Reviews shared task (ComOM), held as part of the 10$^{th}$ International Workshop on Vietnamese Language and Speech Processing (VLSP 2023). The primary objective of this shared task is to advance the field of natural language processing by developing techni

Yan Guo, Chandreyee Sengupta, Tom. C. Scott, Patricio Lagos

A less explored aspect of dwarf galaxies is their metallicity evolution. Generally, dwarfs have lower metallicities than Hubble sequence late type galaxies but in reality, dwarfs span a wide range of metallicities with several open questions regarding the formation and evolution of the lowest and the highest metallicity dwarfs. We present a catalogue of 3459

Akane Inda, Rikuto Oiwa, Satoru Hayami, Hiroshi M Yamamoto

Chirality ubiquitously appears in nature, however, its quantification remains obscure owing to the lack of microscopic description at the quantum-mechanical level. We propose a way of evaluating chirality in terms of electric toroidal monopole, a practical entity of time-reversal even pseudoscalar (parity-odd) object reflecting relevant electronic wave funct

Bodhisattwa Prasad Majumder, Harshit Surana, Dhruv Agarwal, Sanchaita Hazra

With the accumulation of data at an unprecedented rate, its potential to fuel scientific discovery is growing exponentially. This position paper urges the Machine Learning (ML) community to exploit the capabilities of large generative models (LGMs) to develop automated systems for end-to-end data-driven discovery -- a paradigm encompassing the search and ver

Yutong Wang, Chaoyang Jiang, Xieyuanli Chen

In recent years, object-oriented simultaneous localization and mapping (SLAM) has attracted increasing attention due to its ability to provide high-level semantic information while maintaining computational efficiency. Some researchers have attempted to enhance localization accuracy by integrating the modeled object residuals into bundle adjustment. However,

Ryo Hagiwara, Satoshi Takabe

This study proposes a trainable sampling-based solver for combinatorial optimization problems (COPs) using a deep-learning technique called deep unfolding. The proposed solver is based on the Ohzeki method that combines Markov-chain Monte-Carlo (MCMC) and gradient descent, and its step sizes are trained by minimizing a loss function. In the training process,

Fuwen Luo, Chi Chen, Zihao Wan, Zhaolu Kang

Multimodal large language models (MLLMs) have demonstrated promising results in a variety of tasks that combine vision and language. As these models become more integral to research and applications, conducting comprehensive evaluations of their capabilities has grown increasingly important. However, most existing benchmarks fail to consider that, in certain

Boyang Xue, Hongru Wang, Rui Wang, Sheng Wang

The tendency of Large Language Models (LLMs) to generate hallucinations raises concerns regarding their reliability. Therefore, confidence estimations indicating the extent of trustworthiness of the generations become essential. However, current LLM confidence estimations in languages other than English remain underexplored. This paper addresses this gap by

Jiyoung Lee, Minwoo Kim, Seungho Kim, Junghwan Kim

For Large Language Models (LLMs) to be effectively deployed in a specific country, they must possess an understanding of the nation's culture and basic knowledge. To this end, we introduce National Alignment, which measures an alignment between an LLM and a targeted country from two aspects: social value alignment and common knowledge alignment. Social v

Christian Møller Dahl, Torben Johansen, Christian Vedel

This paper introduces OccCANINE, an open-source tool that maps occupational descriptions to HISCO codes. Manual coding is slow and error-prone; OccCANINE replaces weeks of work with results in minutes. We fine-tune CANINE on 15.8 million description-code pairs from 29 sources in 13 languages. The model achieves 96 percent accuracy, precision, and recall. We

Yixin Wang, Xiao Ma

This paper is concerned with a class of low density generator matrix codes (LDGM), called repetition and superposition (RaS) codes, which have been proved to be capacity-achieving over binary-input output-symmetric (BIOS) channels in terms of bit-error rate (BER). We prove with a recently proposed framework that the RaS codes are also capacity-achieving over

Mehdi Azarafza, Mojtaba Nayyeri, Charles Steinmetz, Steffen Staab

Large Language Models (LLMs) have garnered significant attention for their ability to understand text and images, generate human-like text, and perform complex reasoning tasks. However, their ability to generalize this advanced reasoning with a combination of natural language text for decision-making in dynamic situations requires further exploration. In thi

Sizhong Zhou, Tao Zhang, Qiuxiang Bian

A graph $G$ contains a strong parity factor $F$ if for every subset $X\subseteq V(G)$ with $|X|$ even, $G$ has a spanning subgraph $F$ satisfying $\delta(F)\geq1$, $d_F(u)\equiv1$ (mod 2) for any $u\in X$, and $d_F(v)\equiv0$ (mod 2) for any $v\in V(G)\setminus X$. In this paper, we give a spectral radius condition to guarantee that a connected graph contain

Alexey Potapov, Cornelia Jäger, Harald Mutschke, Thomas Henning

Existence of strongly bound water molecules on silicate surfaces, above the desorption temperature of water ice, has been first predicted by computational studies and recently demonstrated by laboratory experiments. Such trapped water may be present in various astrophysical environments and there is now evidence for its presence in the diffuse interstellar m

Haruka Irie, Yasutaka Shimizu

The scale function holds significant importance within the fluctuation theory of Levy processes, particularly in addressing exit problems. However, its definition is established through the Laplace transform, thereby lacking explicit representations in general. This paper introduces a novel series representation for this scale function, employing Laguerre po

Lin Ning, Luyang Liu, Jiaxing Wu, Neo Wu

Large language models (LLMs) have achieved remarkable success across various domains, but effectively incorporating complex and potentially noisy user timeline data into LLMs remains a challenge. Current approaches often involve translating user timelines into text descriptions before feeding them to LLMs, which can be inefficient and may not fully capture t

Wang Liu, Cunhua Pan, Hong Ren, Jiangzhou Wang

Extremely large-scale multiple-input multiple-output (XL-MIMO) systems are capable of improving spectral efficiency by employing far more antennas than conventional massive MIMO at the base station (BS). However, beam training in multiuser XL-MIMO systems is challenging. To tackle these issues, we conceive a three-phase graph neural network (GNN)-based beam

Lokesh Kumar Sharma, Suresh Parekh, Saibal Ray, Anil Kumar Yadav

We try to find the possibility of a Bianchi V universe in the modified gravitational field theory of $f(R,T)$. We have considered a Lagrangian model in the connection between the trace of the energy-momentum tensor $T$ and the Ricci scalar $R$. In order to solve the field equations a power law for the scaling factor was also considered. To make a comparison

Martin Ryner, Jan Kronqvist, Johan Karlsson

Clustering is one of the most fundamental tools in data science and machine learning, and k-means clustering is one of the most common such methods. There is a variety of approximate algorithms for the k-means problem, but computing the globally optimal solution is in general NP-hard. In this paper we consider the k-means problem for instances with low dimen

Louis-Charles Ippet-Letembet, Rui-Fu Xu, Robin Jeanty, Zhi Yao

To overcome the physical limitations of electrically small antennas, strain-mediated magnetoelectric antennas have been studied experimentally and theoretically. However, current closed-form analytical models include solely one-dimensional approaches. This paper proposes a two-dimensional closed-form analytical model of a laterally-excited multiferroic anten

Mengqi Zhang, Xiaotian Ye, Qiang Liu, Pengjie Ren

Large language models (LLMs) are pivotal in advancing natural language processing (NLP) tasks, yet their efficacy is hampered by inaccuracies and outdated knowledge. Model editing emerges as a promising solution to address these challenges. However, existing editing methods struggle to track and incorporate changes in knowledge associated with edits, which l

Shuo Wang, Bin Xu

A hyperk\"ahler manifold is defined as a Riemannian manifold endowed with three covariantly constant complex structures that are quaternionically related. A twistor space is characterized as a holomorphic fiber bundle $p: \mathcal{Z} \rightarrow \mathbb{CP}^1$ possesses properties such as a family of holomorphic sections whose normal bundle is $\bigoplus^{2n