February 2024 arXiv papers — page 40

Sheng Wang, Boyang Xue, Jiacheng Ye, Jiyue Jiang

With the rapid scaling of large language models (LLMs), serving numerous low-rank adaptations (LoRAs) concurrently has become increasingly impractical, leading to unaffordable costs and necessitating more parameter-efficient finetuning methods. In this work, we introduce Partially Rotation-enhanced Low-Rank Adaptation (PRoLoRA), an intra-layer sharing mechan

Tian Xia, Zhiwei He, Tong Ren, Yibo Miao

Bargaining is an important and unique part of negotiation between humans. As LLM-driven agents learn to negotiate and act like real humans, how to evaluate agents' bargaining abilities remains an open problem. For the first time, we formally described the Bargaining task as an asymmetric incomplete information game, defining the gains of the Buyer and Seller

Carlos Octavio A. Ribeiro Neto, Bertúlio de Lima Bernardo

Landauer's principle sets a fundamental limit on the heat dissipated when one classical bit of information is erased from a memory, thereby establishing a direct link between information theory and thermodynamics. With the advent of quantum technologies, a natural question arises: how does Landauer's principle extend to the quantum regime? In this work, we s

Yifan Fei, Xucong Du, Xiao-Long Chen, Yunbo Zhang

The collective excitation modes in quantum droplets trapped in a two-dimensional harmonic potential in the context of symmetric weakly interacting binary bosonic mixtures are studied. By utilizing the linearization technique, the time-dependent extended Gross-Pitaevskii equation, and a sum-rule approach with a variational approximation, the ground state prop

Fanjin Zhang, Shijie Shi, Yifan Zhu, Bo Chen

With the rapid proliferation of scientific literature, versatile academic knowledge services increasingly rely on comprehensive academic graph mining. Despite the availability of public academic graphs, benchmarks, and datasets, these resources often fall short in multi-aspect and fine-grained annotations, are constrained to specific task types and domains,

ChenRui Duan, Zelin Zang, Yongjie Xu, Hang He

Metagenomic data, comprising mixed multi-species genomes, are prevalent in diverse environments like oceans and soils, significantly impacting human health and ecological functions. However, current research relies on K-mer, which limits the capture of structurally and functionally relevant gene contexts. Moreover, these approaches struggle with encoding bio

Haiteng Zhao, Chang Ma, Guoyin Wang, Jing Su

Large Language Model (LLM) Agents have recently garnered increasing interest yet they are limited in their ability to learn from trial and error, a key element of intelligent behavior. In this work, we argue that the capacity to learn new actions from experience is fundamental to the advancement of learning in LLM agents. While humans naturally expand their

Federica Granese, Marco Romanelli, Pablo Piantanida

This paper explores a scenario in which a malicious actor employs a multi-armed attack strategy to manipulate data samples, offering them various avenues to introduce noise into the dataset. Our central objective is to protect the data by detecting any alterations to the input. We approach this defensive strategy with utmost caution, operating in an environm

Edison Alberto Fernández-Culma

We solve an open problem concerning the well-known $(\alpha,\beta,\gamma)$-derivations, proving that the spaces of $(\alpha,1,0)$-derivations of any Lie algebra are isomorphic ($\alpha\neq 0,1$). Also, we prove sharp bounds for the invariants functions defined by such spaces.

Mingkun Yang, Biao Yang, Minghui Liao, Yingying Zhu

Scene text recognition (STR) is a challenging task that requires large-scale annotated data for training. However, collecting and labeling real text images is expensive and time-consuming, which limits the availability of real data. Therefore, most existing STR methods resort to synthetic data, which may introduce domain discrepancy and degrade the performan

Leo S. I. Lam, Gautham Gopinath, Zichen Zhao, Shuling Wang

The nature of glassy dynamics and the glass transition are long-standing problems under active debate. In the presence of a structural disorder widely believed to be an essential characteristic of structural glass, identifying and understanding key dynamical behaviors are very challenging. In this work, we demonstrate that an energetic disorder, which usuall

CMS Collaboration

A search for the production of long-lived particles in proton-proton collisions at a center-of-mass energy of 13 TeV at the CERN LHC is presented. The search is based on data collected by the CMS experiment in 2016-2018, corresponding to a total integrated luminosity of 137 fb$^{-1}$. This search is designed to be sensitive to long-lived particles with mean

Lorenzo V. Mugnai, Mark R. Swain, Raissa Estrela, Gael M. Roudier

The challenge of inconsistent results from different data pipelines, even when starting from identical data, is a recognized concern in exoplanetary science. As we transition into the James Webb Space Telescope (JWST) era and prepare for the ARIEL space mission, addressing this issue becomes paramount because of its implications on our understanding of exopl

Koji Inui, Yukitoshi Motome

Solving inverse problems to identify Hamiltonians with desired properties holds promise for the discovery of fundamental principles. In quantum systems, quantum entanglement plays a pivotal role in not only characterizing the quantum nature but also developing quantum technology like quantum computing. Nonetheless, the design principles of the quantum entang

Chunhe Ni, Jiang Wu, Hongbo Wang, Wenran Lu

Large Language Models (LLMs) are a class of generative AI models built using the Transformer network, capable of leveraging vast datasets to identify, summarize, translate, predict, and generate language. LLMs promise to revolutionize society, yet training these foundational models poses immense challenges. Semantic vector search within large language models

Guang F. Yang, Hong X. Song, Dan Wang, Hao Wang

First-principles calculations have been used to investigate the electronic and topological properties of the two-dimensional C6-2x(BN)x biphenylene network, a graphene-like structure composed of not only hexagonal ring but also octagonal and square rings. Nontrivial topological properties have been found in two of them, with a stoichiometry of C4BN and C2(BN

Chenwei Zhang, Wenran Lu, Chunhe Ni, Hongbo Wang

With the large language model showing human-like logical reasoning and understanding ability, whether agents based on the large language model can simulate the interaction behavior of real users, so as to build a reliable virtual recommendation A/B test scene to help the application of recommendation research is an urgent, important and economic value proble

Vincent Bagayoko

Log-atomic numbers are surreal numbers whose iterated logarithms are monomials, and consequently have a trivial expansion as transseries. Presenting surreal numbers as sign sequences, we give the sign sequence formula for log-atomic numbers. To that efect, we relate log-atomic numbers to fixed-points of certain surreal functions.

Elena Medvedeva, Raphael Assier, Anastasia Kisil

The diffraction of a time-harmonic plane wave on collinear finite defects in a square lattice is studied. This problem is reduced to a matrix Wiener-Hopf equation. This work adapts the recently developed iterative Wiener-Hopf method to this situation. The method was motivated by wave scattering in continuous media but it is shown here that it can also be emp

Dan Wang, Hongxing Song, Leilei Zhang, Hao Wang

Robust metallic surface states (MSS) of topological insulator (TI) against imperfections and perturbations are important in broad applications such as chemical catalysis and quantum computing. Unfortunately, they are suffered from the narrow band gap that can be accessed. Searching for MSS with large bulk band gap beyond conventional TIs becomes a quest. In

Yu Su, Yongjiao Li, Zhu Cao

Smart wearable devices (SWDs) collect and store sensitive daily information of many people. Its primary method of identification is still the password unlocking method. However, several studies have shown serious security flaws in that method, which makes the privacy and security concerns of SWDs particularly urgent. Gait identification is well suited for SW

Yong Wang, Yanlin Zhou, Huan Ji, Zheng He

In recent years, the rapid development of high-precision map technology combined with artificial intelligence has ushered in a new development opportunity in the field of intelligent vehicles. High-precision map technology is an important guarantee for intelligent vehicles to achieve autonomous driving. However, due to the lack of research on high-precision

Waseem Raza, Fahd Ahmed Khan, Muhammad Umar Bin Farooq, Sabit Ekin

The performance of user-centric ultra-dense networks (UCUDNs) hinges on the Service zone (Szone) radius, which is an elastic parameter that balances the area spectral efficiency (ASE) and energy efficiency (EE) of the network. Accurately determining the Szone radius requires the precise location of the user equipment (UE) and data base stations (DBSs). Even

Changrim Ahn, Tommaso Franzini, Francesco Ravanini

Generalizing the quantum sine-Gordon and sausage models, we construct exact S-matrices for higher spin representations with quantum U_q(su(2)) symmetry, which satisfy unitarity, crossing-symmetry, and the Yang-Baxter equations with minimality assumption, i.e. without any unnecessary CDD factor. The deformation parameter q is related to a coupling constant. B

Xiao L. Pan, Hong X. Song, H. Wang, F. C. Wu

U-based binary alloys have been widely adopted in fast nuclear reactors, but their stability under extreme conditions of high-pressure is almost unknown, mounting up to latent risk in applications. Here, possible ordered phases in U-Zr system up to 200 GPa are comprehensively investigated by unbiased first-principles structure prediction. Stable U2Zr, metast

Qingxin Gu, Chunlei Qu, Yongping Zhang

We study the impact of saturable nonlinearity on the presence and location of exceptional points in a non-Hermitian dimer system. The inclusion of the saturable nonlinearity leads to the emergence of multiple eigenvalues, exceeding the typical two found in the linear counterpart. To identify the exceptional points, we calculate the nonlinear eigenvalues both

Igor I. Smolyaninov, Quirino Balzano, Vera N. Smolyaninova, Daryna Soloviova

A theory of surface electromagnetic waves in gradient media exhibiting arbitrary surface gradients of dielectric permittivity and magnetic permeability has been developed. Novel low-loss propagating surface wave solutions have been found in the gradient media in which both dielectric permittivity and magnetic permeability are dominated by their imaginary par

Fude Li, Hao Wang, Jinlong Li, Hua Y. Geng

Hydrogen and lithium, along with their compounds, are crucial materials for nuclear fusion research. High-pressure studies have revealed intricate structural transitions in all these materials. However, research on lithium hydrides beyond LiH has mostly focused on the low-temperature regime. Here, we use density functional theory and ab initio molecular dyna

Yuan Lin, Xiao Liu, Zishun Zheng

This study focuses on a crucial task in the field of autonomous driving, autonomous lane change. Autonomous lane change plays a pivotal role in improving traffic flow, alleviating driver burden, and reducing the risk of traffic accidents. However, due to the complexity and uncertainty of lane-change scenarios, the functionality of autonomous lane change stil

Charles Parker, Endre Süli

On the reference tetrahedron $K$, we construct, for each $k \in \mathbb{N}_0$, a right inverse for the trace operator $u \mapsto (u, \partial_{n} u, \ldots, \partial_{n}^k u)|_{\partial K}$. The operator is stable as a mapping from the trace space of $W^{s, p}(K)$ to $W^{s, p}(K)$ for all $p \in (1, \infty)$ and $s \in (k+1/p, \infty)$. Moreover, if the data

Peter Holy, Philipp Lücke, Sandra Müller

We introduce and study a new type of compactness principle for strong logics that, roughly speaking, infers the consistency of a theory from the consistency of its small fragments in certain outer models of the set-theoretic universe. We refer to this type of compactness property as outward compactness, and we show that instances of this type of principle fo

A. Hossam, A. Ramadan, M. Magdy, R. Abdelwahab

In response to the significant challenges facing the retail sector, including inefficient queue management, poor demand forecasting, and ineffective marketing, this paper introduces an innovative approach utilizing cutting-edge machine learning technologies. We aim to create an advanced smart retail analytics system (SRAS), leveraging these technologies to e

Günter Rote, Moritz Rüber, Morteza Saghafian

Grid peeling is the process of repeatedly removing the convex hull vertices of the grid-points that lie inside a given convex curve. It has been conjectured that, for a more and more refined grid, grid peeling converges to a continuous process, the affine curve-shortening flow, which deforms the curve based on the curvature. We prove this conjecture for one

Ismail Barakat, Mahmoud Kalash, Dennis Scharwald, Polina Sharapova

Multimode squeezed light is an increasingly popular tool in photonic quantum technologies, including sensing, imaging, and computation. Meanwhile, the existing methods of its characterization are technically complicated, and in the best case, deal with a single mode at a time. Here, we demonstrate experimentally how the squeezing can be measured in multiple

Georgios Dosidis, Bae Jun Park, Lenka Slavikova

We establish boundedness results for bilinear singular integral operators with rough homogeneous kernels whose restriction to the unit sphere belongs to the Orlicz space $L(\log L)^\alpha$. This improves the previously best known condition for boundedness of such bilinear operators obtained in the paper of the first and third authors, and provides estimates

Dongqi Fan, Xin Zhao, Liang Chang

Recently, the contrastive learning paradigm has achieved remarkable success in high-level tasks such as classification, detection, and segmentation. However, contrastive learning applied in low-level tasks, like image restoration, is limited, and its effectiveness is uncertain. This raises a question: Why does the contrastive learning paradigm not yield sati

Alix Malfondet, Alexandre Parriaux, Katarzyna Krupa, Guy Millot

Most of the saturable absorbers commonly used to perform mode locking in laser cavities affect the trigger conditions of laser oscillation, which requires manually forcing the laser start-up by various means such as polarization controllers. We present a procedure for designing a laser cavity driven by a nonlinear optical loop mirror, which allows the laser

Xiaoyue Zhou, Xiaodian Chen, Licai Deng, Shu Wang

Classical Cepheids (CCs) are excellent tracers for understanding the structure of the Milky Way disk. The latest Gaia Data Release 3 provides a large number of line-of-sight velocity information for Galactic CCs, offering an opportunity for studying the kinematics of the Milky Way. We determine the three-dimensional velocities of 2057 CCs relative to the Gal

Donghwan Lee

This paper analyzes multi-step TD-learning algorithms within the `deadly triad' scenario, characterized by linear function approximation, off-policy learning, and bootstrapping. In particular, we prove that n-step TD-learning algorithms converge to a solution as the sampling horizon n increases sufficiently. The paper is divided into two parts. In the first

Zakaria Tolba

In this work, we proposed a new dynamic distributed planning approach that is able to take into account the changes that the agent introduces on his set of actions to be planned in order to take into account the changes that occur in his environment. Our approach fits into the context of distributed planning for distributed plans where each agent can produce

Hidde Lycklama, Alexander Viand, Nicolas Küchler, Christian Knabenhans

Recent advancements in privacy-preserving machine learning are paving the way to extend the benefits of ML to highly sensitive data that, until now, have been hard to utilize due to privacy concerns and regulatory constraints. Simultaneously, there is a growing emphasis on enhancing the transparency and accountability of machine learning, including the abili

Zakaria Tolba

With the rising popularity of the internet and the widespread use of networks and information systems via the cloud and data centers, the privacy and security of individuals and organizations have become extremely crucial. In this perspective, encryption consolidates effective technologies that can effectively fulfill these requirements by protecting public

Junfeng An, Yingzhi Tian

The Wiener index $W(G)$ of a graph $G$ is one of the most well-known topological indices, which is defined as the sum of distances between all pairs of vertices of $G$. The diameter $D(G)$ of $G$ is the maximum distance between all pairs of vertices of $G$; the conditional diameter $D(G;s)$ is the maximum distance between all pairs of vertex subsets with car

Adrian Müller, Pragnya Alatur, Volkan Cevher, Giorgia Ramponi

Constrained Markov decision processes (CMDPs) are a common way to model safety constraints in reinforcement learning. State-of-the-art methods for efficiently solving CMDPs are based on primal-dual algorithms. For these algorithms, all currently known regret bounds allow for error cancellations -- one can compensate for a constraint violation in one round wi

Liam Heidt, Tim Colonius

We demonstrate that accurate computation of the spectral proper orthogonal decomposition (SPOD) critically depends on the choice of frequency resolution. Using both artificially generated data and large-eddy simulation data of a turbulent subsonic jet, we show that the optimal choice depends on how rapidly the SPOD modes change in space at adjacent frequenci

Oleksiy Dovgoshey, Valentino Vito

We will say that an infinite tree $T$ is almost a ray if $T$ is the union of a ray and a finite tree. Let $l$ be a non-degenerate labeling of the vertex set $V$ of almost a ray $T$ and let $d_l$ be the corresponding ultrametric on $V$. It is shown that the ultrametric space $(V, d_l)$ is totally bounded iff this space contains an infinite totally bounded sub

Hugo Pompougnac, Alban Dutilleul, Christophe Guillon, Nicolas Derumigny

Modern Out-of-Order (OoO) CPUs are complex systems with many components interleaved in non-trivial ways. Pinpointing performance bottlenecks and understanding the underlying causes of program performance issues are critical tasks to make the most of hardware resources. We provide an in-depth overview of performance bottlenecks in recent OoO microarchitecture

Christian H. Weiß, Fukang Zhu

In the past four decades, research on count time series has made significant progress, but research on $\mathbb{Z}$-valued time series is relatively rare. Existing $\mathbb{Z}$-valued models are mainly of autoregressive structure, where the use of the rounding operator is very natural. Because of the discontinuity of the rounding operator, the formulation of

Zehui Liu, Qingsong Wang, Chunfeng Cui, Yong Xia

The majority of classic tensor CP decomposition models are designed for squared loss, employing Euclidean distance as a local proximal term. However, the Euclidean distance is unsuitable for the generalized loss function applicable to various types of real-world data, such as integer and binary data. Consequently, algorithms developed under the squared loss

Timothy R. McIntosh, Teo Susnjak, Tong Liu, Paul Watters

This study investigated the integration readiness of four predominant cybersecurity Governance, Risk and Compliance (GRC) frameworks - NIST CSF 2.0, COBIT 2019, ISO 27001:2022, and the latest ISO 42001:2023 - for the opportunities, risks, and regulatory compliance when adopting Large Language Models (LLMs), using qualitative content analysis and expert valid

Zeming Dong, Qiang Hu, Xiaofei Xie, Maxime Cordy

Pre-trained code models lead the era of code intelligence, with multiple models designed with impressive performance. However, one important problem, data augmentation for code data that automatically helps developers prepare training data lacks study in this field. In this paper, we introduce a generic data augmentation framework, GenCode, to enhance the tr

Mitchell Creelman, Kiran Jain, Niles Oien, Thomas M. Wentzel

One of the products of the National Solar Observatory's Integrated Synoptic Program (NISP) is the farside seismic map which shows the magnetic activity on the unobserved side of the Sun. The production of these rudimentary maps began in 2006, and they have since proven to be a valuable tool in tracking solar activity which cannot be directly observed from th

Harshil Vagadia, Mudit Chopra, Abhinav Barnawal, Tamajit Banerjee

Given the task of positioning a ball-like object to a goal region beyond direct reach, humans can often throw, slide, or rebound objects against the wall to attain the goal. However, enabling robots to reason similarly is non-trivial. Existing methods for physical reasoning are data-hungry and struggle with complexity and uncertainty inherent in the real wor

Chunliang Li, Shuhui Yang, Yan Lin

Via the new weight $A_{\vec p}^{\infty}(\varphi)$ and the new $BMO$ function, the authors introduce a new class of multilinear square operators $T$ with generalized kernels. The boundedness of multilinear commutators and multilinear iterative commutators generated by $T$ and the new $BMO$ function on weighted Lebesgue spaces and weighted Morrey spaces is obt

Magali Jay

This work continues the study of tiling billiards, a class of dynamical system introduced by Davis et al. in 2018. We develop the study of generalized tiling billiards in a cyclic polygon. This work shows that the behavior of generalized tiling billiards in cyclic N-gons with N > 4 is considerably different from that of triangular and quadrilateral tiling bi

Haoran Liao, Jidong Tian, Shaohua Hu, Hao He

Large language models (LLMs) still grapple with complex tasks like mathematical reasoning. Despite significant efforts invested in improving prefix prompts or reasoning process, the crucial role of problem context might have been neglected. Accurate recognition of inputs is fundamental for solving mathematical tasks, as ill-formed problems could potentially

Ricardo Correa da Silva, Luca Giorgetti, Gandalf Lechner

We introduce and study the crossing map, a closed linear map acting on operators on the tensor square of a given Hilbert space that is inspired by the crossing property of quantum field theory. This map turns out to be closely connected to Tomita--Takesaki modular theory. In particular, crossing symmetric operators, namely those operators that are mapped to

Serena Dipierro, Enrico Valdinoci, Glen Wheeler, Valentina-Mira Wheeler

In a recent paper, we have introduced a new model to describe front propagation in bushfires. This model describes temperature diffusion in view of an ignition process induced by an interaction kernel, the effect of the environmental wind and that of the fire wind. This has led to the introduction of a new partial differential equation of evolutionary type,

Zilong Zhao, Yao Rong, Dongyang Guo, Emek Gözlüklü

Using Large Language Models for complex mathematical reasoning is difficult, primarily due to the complexity of multi-step reasoning. The main challenges of this process include (1) selecting critical intermediate results to advance the procedure, and (2) limited exploration of potential solutions. To address these issues, we introduce a novel algorithm, nam

Chi-Sheng Chen, Guan-Ying Chen, Dong Zhou, Di Jiang

Food classification is the foundation for developing food vision tasks and plays a key role in the burgeoning field of computational nutrition. Due to the complexity of food requiring fine-grained classification, recent academic research mainly modifies Convolutional Neural Networks (CNNs) and/or Vision Transformers (ViTs) to perform food category classifica

Daesung Park, Changwon Park, Kunihiro Yananose, Eunjung Ko

Imposing incommensurable periodicity on the periodic atomic lattice can lead to complex structural phases consisting of locally periodic structure bounded by topological defects. Twisted trilayer graphene (TTG) is an ideal material platform to study the interplay between different atomic periodicities, which can be tuned by twist angles between the layers, l

Zekun Jiang, Dongjie Cheng, Ziyuan Qin, Jun Gao

This study presents a novel multimodal medical image zero-shot segmentation algorithm named the text-visual-prompt segment anything model (TV-SAM) without any manual annotations. The TV-SAM incorporates and integrates the large language model GPT-4, the vision language model GLIP, and the SAM to autonomously generate descriptive text prompts and visual bound

Ziqian Zeng, Jiahong Yu, Qianshi Pang, Zihao Wang

Large language models (LLMs) have demonstrated remarkable capabilities across various tasks. However, their widespread application is hindered by the resource-intensive decoding process. To address this challenge, current approaches have incorporated additional decoding heads to enable parallel prediction of multiple subsequent tokens, thereby achieving infe

Erdem Bıyık, Nima Anari, Dorsa Sadigh

Data generation and labeling are often expensive in robot learning. Preference-based learning is a concept that enables reliable labeling by querying users with preference questions. Active querying methods are commonly employed in preference-based learning to generate more informative data at the expense of parallelization and computation time. In this pape

Lingji Chen

Conventional tracking paradigm takes in instantaneous measurements such as range and bearing, and produces object tracks across time. In applications such as autonomous driving, lidar measurements in the form of point clouds are usually passed through a "virtual sensor" realized by a deep learning model, to produce "measurements" such as bounding boxes, whic

Mohammad Dehghani

Dental diseases have a significant impact on a considerable portion of the population, leading to various health issues that can detrimentally affect individuals' overall well-being. The integration of automated systems in oral healthcare has become increasingly crucial. Machine learning approaches offer a viable solution to address challenges such as diagno

Yuxuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang

Large language models (LLMs) have emerged as a promising alternative to expensive human evaluations. However, the alignment and coverage of LLM-based evaluations are often limited by the scope and potential bias of the evaluation prompts and criteria. To address this challenge, we propose HD-Eval, a novel framework that iteratively aligns LLM-based evaluator

Serena Dipierro, Enrico Valdinoci, Glen Wheeler, Valentina-Mira Wheeler

We introduce a simple mathematical model for bushfires accounting for temperature diffusion in the presence of a combustion term which is activated above a given ignition state. The model also takes into consideration the effect of the environmental wind and of the pyrogenic flow. The simplicity of the model is highlighted from the fact that it is described

Baihui Chen, Chengjie Zhan, Zhenhua Chai, Baochang Shi

In this work, we develop a phase-field-based lattice Boltzmann (LB) method for a two-scalar model of the two-phase flows with interfacial mass/heat transfer. Through the Chapman-Enskog analysis, we show that the present LB method can correctly recover the governing equations for phase field, flow field and concentration/temperature field. In particular, to d

Rahma Yasmina Moulay Hachemi, Bernt Øksendal

We give an introduction to the time-fractional stochastic heat equation driven by 1+d-parameter fractional time-space white noise, in the following two cases: (i) With additive noise (ii) With multiplicative noise. The fractional time derivative is interpreted as the Caputo derivative of order $\alpha \in (0,2)$ and we assume that the Hurst coefficient $H=(H

Yong Liu, Zirui Zhu, Chaoyu Gong, Minhao Cheng

While fine-tuning large language models (LLMs) for specific tasks often yields impressive results, it comes at the cost of memory inefficiency due to back-propagation in gradient-based training. Memory-efficient Zeroth-order (MeZO) optimizers, recently proposed to address this issue, only require forward passes during training, making them more memory-friend

Markus Haltmeier, Matthias Ye, Karoline Felbermayer, Florian Hinterleitner

Significance: Compressed sensing (CS) uses special measurement designs combined with powerful mathematical algorithms to reduce the amount of data to be collected while maintaining image quality. This is relevant to almost any imaging modality, and in this paper we focus on CS in photoacoustic projection imaging (PAPI) with integrating line detectors (ILDs).

Xunchi Ma

This essay provides a comprehensive analysis of the optimization and performance evaluation of various routing algorithms within the context of computer networks. Routing algorithms are critical for determining the most efficient path for data transmission between nodes in a network. The efficiency, reliability, and scalability of a network heavily rely on t

Himanshu Kumar, Dasika Shishir, Maheshwar Mangat, Siddharth Tallur

Nitrogen vacancy (NV) centers in diamonds have been explored for realizing a wide range of sensing applications in the last decade due to their unique quantum properties. Here we realize a compact and portable magnetometer with an ensemble of NV centers which we call the Quantum MagPI (Quantum Magnetometer with Proportional Integral control). Including the s

Tomohiro Yamada

We give explicit upper bounds for coefficients of polynomials appearing in Gauss-Kra\"{i}tchik formula for cyclotomic polynomials. We use a certain relation between elementary symmetric polynomials and power sums polynomials.

Sixiao Zheng, Jingyang Huo, Yu Wang, Yanwei Fu

With the rise of short video platforms represented by TikTok, the trend of users expressing their creativity through photos and videos has increased dramatically. However, ordinary users lack the professional skills to produce high-quality videos using professional creation software. To meet the demand for intelligent and user-friendly video creation tools,

Yi Zong, Xipeng Qiu

The Large Vision-Language Models (LVLMs) have demonstrated great abilities in image perception and language understanding. However, existing multimodal benchmarks focus on primary perception abilities and commonsense knowledge which are insufficient to reflect the comprehensive capabilities of LVLMs. We propose GAOKAO-MM, a multimodal benchmark based on the

Zhiyuan Li, Chenyang Ge, Shun Li

Recently, many deep image compression methods have been proposed and achieved remarkable performance. However, these methods are dedicated to optimizing the compression performance and speed at medium and high bitrates, while research on ultra low bitrates is limited. In this work, we propose a ultra low bitrates enhanced invertible encoding network guided b

Ting Qing, Fan Wang, Qiuyue Li, Gaogao Dong

The persistence of the global climate system is critical for assuring the sustainability of the natural ecosystem and the further development of the prosperity of socio-economics. In this paper, we develop a framework and analyze the time persistence of the yearly networks of climate and carbon flux, based on cross-correlations between sites, using daily dat

Marcoen J. T. F. Cabbolet

The PBR theorem is widely seen as one of the most important no-go theorems in the foundations of quantum mechanics. Recently, in Found. Phys. 53(3): 64 (2023), it has been argued that there is no reality to the PBR theorem using a pair of bolts as a counterexample. In this addendum we expand on the argument: we disprove the PBR theorem by a generic counterex

Sharang Thimmaiah, Raisa DSouza

We study rings $R$ for which whenever non-zero polynomials $f(x)$ and $g(x)$ satisfy $f(x)g(x)f(x)=0$, it implies that there is a non-zero element $r\in R$ such that $f(x)rf(x)=0$. We call such rings inner McCoy rings. We explore some examples of rings that are inner McCoy, determine relationships between the class of inner McCoy rings and some known classes

Yassir Jedra, William Réveillard, Stefan Stojanovic, Alexandre Proutiere

We study contextual bandits with low-rank structure where, in each round, if the (context, arm) pair $(i,j)\in [m]\times [n]$ is selected, the learner observes a noisy sample of the $(i,j)$-th entry of an unknown low-rank reward matrix. Successive contexts are generated randomly in an i.i.d. manner and are revealed to the learner. For such bandits, we presen

Xinhao Yan, Guanzhong Zhou, Daniel E. Quevedo, Carlos Murguia

Networked systems are increasingly the target of cyberattacks that exploit vulnerabilities within digital communications, embedded hardware, and software. Arguably, the simplest class of attacks -- and often the first type before launching destructive integrity attacks -- are eavesdropping attacks, which aim to infer information by collecting system data and

Gadadhar Misra, E. K. Narayanan, Cherian Varughese

In this semi-expository article, we investigate the relationship between the imprimitivity introduced by Mackey several decades ago and commuting $d$- tuples of homogeneous normal operators. The Hahn-Hellinger theorem gives a canonical decomposition of a $*$- algebra representation $\rho$ of $C_0(\mathbb{S})$ (where $\mathbb S$ is a locally compact Hausdorff

Shuyu Yin, Qixuan Zhou, Fei Wen, Tao Luo

Deep reinforcement learning excels in numerous large-scale practical applications. However, existing performance analyses ignores the unique characteristics of continuous-time control problems, is unable to directly estimate the generalization error of the Bellman optimal loss and require a boundedness assumption. Our work focuses on continuous-time control

Junyuan Shi, Linhe Zhu

Reaction-diffusion processes on networked systems have received mounting attention in the past two decades, and the corresponding theory of network dynamics has been continuously enriched with the advancement of network science. Recently, time-varying features and many-body interactions have been discovered on various and numerous real-world networks, such a

Sipei Zhao, Fei Ma

Acoustic beamforming aims to focus acoustic signals to a specific direction and suppress undesirable interferences from other directions. Despite its flexibility and steerability, beamforming with circular microphone arrays suffers from significant performance degradation at frequencies corresponding to zeros of the Bessel functions. To conquer this constrai

Wuyang Chen, Jialin Song, Pu Ren, Shashank Subramanian

Recent years have witnessed the promise of coupling machine learning methods and physical domain-specific insights for solving scientific problems based on partial differential equations (PDEs). However, being data-intensive, these methods still require a large amount of PDE data. This reintroduces the need for expensive numerical PDE solutions, partially un

Han Wang, Roy Ka-Wei Lee

Online memes have emerged as powerful digital cultural artifacts in the age of social media, offering not only humor but also platforms for political discourse, social critique, and information dissemination. Their extensive reach and influence in shaping online communities' sentiments make them invaluable tools for campaigning and promoting ideologies. Desp

Hazem Darwish, Abdalrahman Al Malah, Khloud Al Jallad, Nada Ghneim

Brain-computer interfaces is an important and hot research topic that revolutionize how people interact with the world, especially for individuals with neurological disorders. While extensive research has been done in EEG signals of English letters and words, a major limitation remains: the lack of publicly available EEG datasets for many non-English languag

Hiroyuki Minamoto

The aim of this short note is to prove the formula of the Hilbert series of the preprojective algebras in arbitrary characteristic by making effective use of the formulas of the Hilbert series of differential graded (dg) algebras with Adams grading. We also compute the Hilbert series of the quiver Heisenberg algebras, a special class of central extensions of

Danial Yazdani, Juergen Branke, Mohammad Sadegh Khorshidi, Mohammad Nabi Omidvar

Clustering in dynamic environments is of increasing importance, with broad applications ranging from real-time data analysis and online unsupervised learning to dynamic facility location problems. While meta-heuristics have shown promising effectiveness in static clustering tasks, their application for tracking optimal clustering solutions or robust clusteri

Ghadeer O. Ghosheh, Jin Li, Tingting Zhu

With the widespread of machine learning models for healthcare applications, there is increased interest in building applications for personalized medicine. Despite the plethora of proposed research for personalized medicine, very few focus on representing missingness and learning from the missingness patterns in time-series Electronic Health Records (EHR) da

Jiaxin Deng, Junbiao Pang, Baochang Zhang, Tian Wang

Sharpness-aware Minimization (SAM) has been proposed recently to improve model generalization ability. However, SAM calculates the gradient twice in each optimization step, thereby doubling the computation costs compared to stochastic gradient descent (SGD). In this paper, we propose a simple yet efficient sampling method to significantly accelerate SAM. Con

Long Li, Xuzheng He, Haozhe Wang, Linlin Wang

Program-of-Thought (PoT) replaces natural language-based Chain-of-Thought (CoT) as the most popular method in Large Language Models (LLMs) mathematical reasoning tasks by utilizing external tool calls to circumvent computational errors. However, our evaluation of the GPT-4 and Llama series reveals that using PoT introduces more reasoning errors, such as inco

Md Khaledur Rahman, Faysal Amin Tanvir, Md Saiful Islam, Md Shameem Ahsan

This article presents a probabilistic modeling method utilizing smart meter data and an innovative agent-based simulator for electric vehicles (EVs). The aim is to assess the effects of different cost-driven EV charging strategies on the power distribution network (PDN). We investigate the effects of a 40% EV adoption on three parts of Frederiksberg's low vo

Daoyuan Wu, Shuai Wang, Yang Liu, Ning Liu

Jailbreaking is an emerging adversarial attack that bypasses the safety alignment deployed in off-the-shelf large language models (LLMs). A considerable amount of research exists proposing more effective jailbreak attacks, including the recent Greedy Coordinate Gradient (GCG) attack, jailbreak template-based attacks such as using "Do-Anything-Now" (DAN), and

Xiao Lin, Minghao Zhu, Ronghao Dang, Guangliang Zhou

Most of existing category-level object pose estimation methods devote to learning the object category information from point cloud modality. However, the scale of 3D datasets is limited due to the high cost of 3D data collection and annotation. Consequently, the category features extracted from these limited point cloud samples may not be comprehensive. This

Duo Ma, Xianghu Yue, Junyi Ao, Xiaoxue Gao

Human language can be expressed in either written or spoken form, i.e. text or speech. Humans can acquire knowledge from text to improve speaking and listening. However, the quest for speech pre-trained models to leverage unpaired text has just started. In this paper, we investigate a new way to pre-train such a joint speech-text model to learn enhanced spee

Yuanhanqing Huang, Jianghai Hu

We explore a class of stochastic multiplayer games where each player in the game aims to optimize its objective under uncertainty and adheres to some expectation constraints. The study employs an offline learning paradigm, leveraging a pre-existing dataset containing auxiliary features. While prior research in deterministic and stochastic multiplayer games p