March 2024 arXiv papers — page 29

Lihui Chai, Hengzhun Chen, Xu Yang

We develop a refined Frozen Gaussian approximation (FGA) for the fractional Schr\"odinger equation in the semi-classical regime, where the solution exhibits rapid oscillations as the scaled Planck constant $\varepsilon$ becomes small. Our approach utilizes an integral representation based on asymptotic analysis, offering a highly efficient computational fram

Xiang Lisa Li, Urvashi Khandelwal, Kelvin Guu

Recent work has uncovered promising ways to extract well-calibrated confidence estimates from language models (LMs), where the model's confidence score reflects how likely it is to be correct. However, while LMs may appear well-calibrated over broad distributions, this often hides significant miscalibration within narrower slices (e.g., systemic over-confide

Herbert Egger, Felix Engertsberger, Klaus Roppert

The magnetostatic field distribution in a nonlinear medium amounts to the unique minimizer of the magnetic coenergy over all fields that can be generated by the same current. This is a nonlinear saddlepoint problem whose numerical solution can in principle be achieved by mixed finite element methods and appropriate nonlinear solvers. The saddlepoint structur

Charles Namchaisiri, Tianxiang Liu, Makoto Yamashita

In this paper, we propose a new efficient method for a sparse Gaussian graphical model with hidden clustering structures by extending a dual spectral projected gradient (DSPG) method proposed by Nakagaki et al.~(2020). We establish the global convergence of the proposed method to an optimal solution, and we show that the projection onto the feasible region c

S. Rakhmanov, C. Trunk, M. Znojil, D. Matrasulov

We consider a quantum particle under the dynamical confinement caused by PT-symmetric box with a moving wall. The latter is described in terms of the time-dependent Schr\"{o}dinger equation obeying the time-dependent PT-symmetric boundary conditions. The class of the functions, describing time-dependence of the wall's position and keeping the system as PT-sy

Wenjie Xing, Zhenchao Cui, Jing Qi

The spatial attention mechanism has been widely used to improve object detection performance. However, its operation is currently limited to static convolutions lacking content-adaptive features. This paper innovatively approaches from the perspective of dynamic convolution. We propose Razor Dynamic Convolution (RDConv) to address thetwo flaws in dynamic wei

Changkun Liu, Jianhao Jiao, Huajian Huang, Zhengyang Ma

State-of-the-art hierarchical localisation pipelines (HLoc) employ image retrieval (IR) to establish 2D-3D correspondences by selecting the top-$k$ most similar images from a reference database. While increasing $k$ improves localisation robustness, it also linearly increases computational cost and runtime, creating a significant bottleneck. This paper inves

William Shiao, Mingxuan Ju, Zhichun Guo, Xin Chen

Recommendation systems (RS) are an increasingly relevant area for both academic and industry researchers, given their widespread impact on the daily online experiences of billions of users. One common issue in real RS is the cold-start problem, where users and items may not contain enough information to produce high-quality recommendations. This work focuses

Mario Eudave-Muñoz, Makoto Ozawa

We give a Kuratowski-type classification of a graph-defined class of minimal piecewise-linear obstructions to embeddability in the 3-sphere. A finite simplicial complex \(X\) is called critical for \(S^3\) if \(|X|\) does not embed in \(S^3\), whereas deleting the open star of any simplex in the second barycentric subdivision of \(X\) yields a polyhedron emb

Michael Livanos, Ian Davidson

Deep learning is extensively used in many areas of data mining as a black-box method with impressive results. However, understanding the core mechanism of how deep learning makes predictions is a relatively understudied problem. Here we explore the notion of identifying a backbone of deep learning for a given group of instances. A group here can be instances

Yejin Yoon, Jungyeon Lee, Kangsan Kim, Chanhee Park

Task-oriented dialogue (TOD) systems are commonly designed with the presumption that each utterance represents a single intent. However, this assumption may not accurately reflect real-world situations, where users frequently express multiple intents within a single utterance. While there is an emerging interest in multi-intent detection (MID), existing in-d

Zhichao Xu

Transformer structure has achieved great success in multiple applied machine learning communities, such as natural language processing (NLP), computer vision (CV) and information retrieval (IR). Transformer architecture's core mechanism\, -- \,attention requires $O(n^2)$ time complexity in training and $O(n)$ time complexity in inference. Many works have bee

Wei Huo, Xiaomeng Chen, Lingying Huang, Karl Henrik Johansson

This paper investigates privacy issues in distributed resource allocation over directed networks, where each agent holds a private cost function and optimizes its decision subject to a global coupling constraint through local interaction with other agents. Conventional methods for resource allocation over directed networks require all agents to transmit thei

Jiuming Liu, Dong Zhuo, Zhiheng Feng, Siting Zhu

Information inside visual and LiDAR data is well complementary derived from the fine-grained texture of images and massive geometric information in point clouds. However, it remains challenging to explore effective visual-LiDAR fusion, mainly due to the intrinsic data structure inconsistency between two modalities: Image pixels are regular and dense, but LiD

Debajyoti Choudhuri, Shengda Zeng

We will study a free boundary value problem driven by a source term which is quite {\it irregular}. In the process, we will establish a monotonicity result, and regularity of the solution.

Qingcang Shui, Shu Zhang, Shuangnan Zhang, Yupeng Chen

We propose a method based on the Hilbert-Huang transform (HHT) to recover the high-energy waveform of low-frequency quasi-periodic oscillations (LFQPOs). Based on the method, we successfully obtain the modulation of the phase-folded light curve above 170 keV using the QPO phase reconstructed at lower energies in MAXI J1535-571 with Insight-HXMT observations.

Zhiheng Cheng, Qingyue Wei, Hongru Zhu, Yan Wang

The Segment Anything Model (SAM) has garnered significant attention for its versatile segmentation abilities and intuitive prompt-based interface. However, its application in medical imaging presents challenges, requiring either substantial training costs and extensive medical datasets for full model fine-tuning or high-quality prompts for optimal performanc

He-Hao Liao, Yan-Tsung Peng, Wen-Tao Chu, Ping-Chun Hsieh

The quality of images captured outdoors is often affected by the weather. One factor that interferes with sight is rain, which can obstruct the view of observers and computer vision applications that rely on those images. The work aims to recover rain images by removing rain streaks via Self-supervised Reinforcement Learning (RL) for image deraining (SRL-Der

Kento Urano, Ryo Yuki, Kenji Yamanishi

This paper proposes an early detection method for cluster structural changes. Cluster structure refers to discrete structural characteristics, such as the number of clusters, when data are represented using finite mixture models, such as Gaussian mixture models. We focused on scenarios in which the cluster structure gradually changed over time. For finite mi

Alex Westström, Wenbu Duan, Jian Li

We study a generalization of chiral symmetry applicable to non-Hermitian systems and its topological consequences on one-dimensional chains. We uncover a rich family of topological phases hosting several chiral flavors characterized not by a single winding number, but a vector of of them. This, in turn, leads to a novel type of bulk-boundary correspondence,

Oriel Perets, Nadav Rappoport

Utility and privacy are two crucial measurements of the quality of synthetic tabular data. While significant advancements have been made in privacy measures, generating synthetic samples with high utility remains challenging. To enhance the utility of synthetic samples, we propose a novel architecture called the DownStream Feedback Generative Adversarial Net

Shun-Cai Zhao, Zheng-Dong Liu, Qi-Xuan Wu

In this paper,three external fields interacting with the four-level Y-type atomic system described by the density-matrix approach is investigated .The results show that the left-handedness with zero absorption are achieved.And the zero absorption property displays the possibility of manipulation with varying the phase and the intensity of the coupling field.

Wenzhuo Liu, Fei Zhu, Cheng-Lin Liu

Self-supervised learning (SSL) has emerged as an effective paradigm for deriving general representations from vast amounts of unlabeled data. However, as real-world applications continually integrate new content, the high computational and resource demands of SSL necessitate continual learning rather than complete retraining. This poses a challenge in striki

Darwin Alexander O. Co, Vince Angelo A. Chavez, Denny Lane B. Sombillo

Enhancements in the invariant mass distribution or scattering cross-section are usually associated with resonances. However, the nature of exotic signals found near hadron-hadron thresholds remain a puzzle today due to the presence of experimental uncertainties. In fact, a purely kinematical triangle diagram is also capable of producing similar structures, b

Kaoru Miyamoto, Atsushi Nakamula

An analytic method to calculate the vortex number on a torus is constructed, focusing on analytic vortex solutions to the Chern-Simons-Higgs theory, whose governing equation is the so-called Jackiw-Pi equation. The equation is one of the integrable vortex equations and is reduced to Liouville's equation. The requirement of continuity of the Higgs field stron

Yue Huang, Hang Liang, Man Jiao, Pei Yu

We report new experimental results on exotic spin-spin-velocity-dependent interactions between electron spins. We designed an elaborate setup that is equipped with two nitrogen-vacancy (NV) ensembles in diamonds. One of the NV ensembles serves as the spin source, while the other functions as the spin sensor. By coherently manipulating the quantum states of t

Naonori Sugiyama

While substantial progress has been made in studying the pre-reconstruction galaxy bispectrum, investigations of the post-reconstruction bispectrum are still in nascent stages. In this paper, we present a bispectrum model that incorporates one-loop corrections in the Standard Perturbation Theory (SPT), while simultaneously addressing infrared (IR) effects in

Yong-Geun Oh

For a given coorientable contact manifold $(M^{2n+1},\xi)$, we consider the group $ \operatorname{Cont}_c^{(r,\delta)}(M,\alpha)$ consisting of $C^{r,\delta}$ contactomorphisms with compact support which is equipped with $C^{r,\delta}$-topology of H\"older regularity $(r,\delta)$ for $r \geq 1$ and $0 <\delta \leq 1$. We prove that for all H\"older class exp

Jungbeom Lee, Sanghyuk Chun, Sangdoo Yun

Recent Vision-Language Pre-training (VLP) models have demonstrated significant advancements. Nevertheless, these models heavily rely on image-text pairs that capture only coarse and global information of an image, leading to a limitation in their regional understanding ability. In this work, we introduce \textbf{RegionVLM}, equipped with explicit regional mo

Yang Tian, Jiyao Zhang, Guowei Huang, Bin Wang

Estimating robot pose and joint angles is significant in advanced robotics, enabling applications like robot collaboration and online hand-eye calibration.However, the introduction of unknown joint angles makes prediction more complex than simple robot pose estimation, due to its higher dimensionality.Previous methods either regress 3D keypoints directly or

Shun-Cai Zhao, Zheng-Dong Liu, Jun Zheng, Gen Li

We suggest a scheme of electromagnetic chirality-induced negative refraction utilizing magneto-electric cross coupling in a four-level atomic system. The negative refraction can be achieved with the two chirality coefficients having the same amplitude but the opposite phase,and without requiring the simultaneous presence of an electric-dipole and a magnetic-

Taro Togo, Ren Togo, Keisuke Maeda, Takahiro Ogawa

This study presents a novel approach to Generative Class Incremental Learning (GCIL) by introducing the forgetting mechanism, aimed at dynamically managing class information for better adaptation to streaming data. GCIL is one of the hot topics in the field of computer vision, and this is considered one of the crucial tasks in society, specifically the conti

Kevin Setterstrom, Jeremy Straub

Autonomous vehicle control is growing in availability for new vehicles and there is a potential need to retrofit older vehicles with this capability. Additionally, automotive cybersecurity has become a significant concern in recent years due to documented attacks on vehicles. As a result, researchers have been exploring reverse engineering techniques to auto

Xilin Jiang, Cong Han, Nima Mesgarani

Transformers have been the most successful architecture for various speech modeling tasks, including speech separation. However, the self-attention mechanism in transformers with quadratic complexity is inefficient in computation and memory. Recent models incorporate new layers and modules along with transformers for better performance but also introduce ext

Zikang Xiong, Suresh Jagannathan

Data-driven neural path planners are attracting increasing interest in the robotics community. However, their neural network components typically come as black boxes, obscuring their underlying decision-making processes. Their black-box nature exposes them to the risk of being compromised via the insertion of hidden malicious behaviors. For example, an attac

Yuecai Han, Dingwen Zhang

In this paper, we investigate the parameter estimation for threshold Ornstein$\mathit{-}$Uhlenbeck processes. Least squares method is used to obtain continuous-type and discrete-type estimators for the drift parameters based on continuous and discrete observations, respectively. The strong consistency and asymptotic normality of the proposed least squares es

Jialong Chen, Jimin Wang, Ji-Feng Zhang

This paper proposes a new distributed nonconvex stochastic optimization algorithm that can achieve privacy protection, communication efficiency and convergence simultaneously. Specifically, each node adds general privacy noises to its local state to avoid information leakage, and then quantizes its noise-perturbed state before transmitting to improve communi

Kaidi Jia, Rongsheng Li

Metaphors play a significant role in our everyday communication, yet detecting them presents a challenge. Traditional methods often struggle with improper application of language rules and a tendency to overlook data sparsity. To address these issues, we integrate knowledge distillation and prompt learning into metaphor detection. Our approach revolves aroun

Yiwu Zhong, Zi-Yuan Hu, Michael R. Lyu, Liwei Wang

Visual representation learning has been a cornerstone in computer vision, involving typical forms such as visual embeddings, structural symbols, and text-based representations. Despite the success of CLIP-type visual embeddings, they often lack access to world knowledge critical for visual reasoning. In this work, we propose Visual Table, a novel form of vis

Abteen Ebrahimi, Kenneth Church

English has long been assumed the $\textit{lingua franca}$ of scientific research, and this notion is reflected in the natural language processing (NLP) research involving scientific document representation. In this position piece, we quantitatively show that the literature is largely multilingual and argue that current models and benchmarks should reflect t

Jiachen Guo, Yuchen Cao, Kenle Chen

This paper presents the first-ever highly linear and load-insensitive three-way load-modulation power amplifier (PA) based on reconfigurable hybrid asymmetrical load modulated balanced amplifier (H-ALMBA). Through proper amplitude and phase controls, the carrier, control amplifier (CA), and two peaking balanced amplifiers (BA1 and BA2) can form a linear high

Yanshen Sun, Jianfeng He, Limeng Cui, Shuo Lei

Recent advancements in Large Language Models (LLMs) have enabled the creation of fake news, particularly in complex fields like healthcare. Studies highlight the gap in the deceptive power of LLM-generated fake news with and without human assistance, yet the potential of prompting techniques has not been fully explored. Thus, this work aims to determine whet

Kai Feng, Han Hong, Denis Nekipelov

In this paper, we develop a functional differentiability approach for solving statistical optimal allocation problems. We derive Hadamard differentiability of the value functions through analyzing the properties of the sorting operator using tools from geometric measure theory. Building on our Hadamard differentiability results, we apply the functional delta

Tapaswini Mohanty, Vikas Srivastava, Sumit Kumar Debnath, Debasish Roy

Digital signatures are one of the simplest cryptographic building blocks that provide appealing security characteristics such as authenticity, unforgeability, and undeniability. In 1984, Shamir developed the first Identity-based signature (IBS) to simplify public key infrastructure and circumvent the need for certificates. It makes the process uncomplicated

Shuhan Ding, Shaozhi Li, Yao Wang

Analog quantum simulation based on ultracold atoms in optical lattices has catalyzed significant breakthroughs in the study of quantum many-body systems. These simulations rely on the statistical sampling of electronic Fock states, which are not easily accessible in classical algorithms. In this work, we modify the determinant quantum Monte Carlo by integrat

Elif F. Acar, Martin Lysy, Alan Kuchinsky

Conditional copulas models allow the dependence structure between multiple response variables to be modelled as a function of covariates. LocalCop (Acar & Lysy, 2024) is an R/C++ package for computationally efficient semiparametric conditional copula modelling using a local likelihood inference framework developed in Acar, Craiu, & Yao (2011), Acar, Craiu, &

S. Sharma, A. D. S. Richards, Sajilesh K. P., A. Kataria

We present comprehensive investigations into the structural, superconducting, and topological properties of Bi$_2$PdPt. Magnetization and heat capacity measurements performed on polycrystalline Bi$_2$PdPt demonstrate a superconducting transition at $\approx$ 0.8 K. Moreover, muon spin relaxation/rotation ($\mu$SR) measurements present evidence for a time rev

Linhao Ye, Zhikai Lei, Jianghao Yin, Qin Chen

Retrieval-Augmented Generation (RAG) aims to generate more reliable and accurate responses, by augmenting large language models (LLMs) with the external vast and dynamic knowledge. Most previous work focuses on using RAG for single-round question answering, while how to adapt RAG to the complex conversational setting wherein the question is interdependent on

Stephen Ebert, Christian Ferko, Cian Luke Martin, Gabriele Tartaglino-Mazzucchelli

We study interacting theories of $N$ left-moving and $\overline{N}$ right-moving Floreanini-Jackiw bosons in two dimensions. A parameterized family of such theories is shown to enjoy (non-manifest) Lorentz invariance if and only if its Lagrangian obeys a flow equation driven by a function of the energy-momentum tensor. We discuss the canonical quantization o

Ruikai Cui, Weizhe Liu, Weixuan Sun, Senbo Wang

3D shape generation aims to produce innovative 3D content adhering to specific conditions and constraints. Existing methods often decompose 3D shapes into a sequence of localized components, treating each element in isolation without considering spatial consistency. As a result, these approaches exhibit limited versatility in 3D data representation and shape

Francisco E Enriquez-Mier-y-Teran, Luping Zhou, Steven R Meikle, Andre Z Kyme

Objective: Conventional event positioning algorithms in light-sharing PET detectors are often limited by edge effects and the impact of inter-crystal scattering (ICS). This study explores the feasibility of deep neural network (DNN) techniques for more precise event positioning in finely segmented and highly multiplexed PET detectors with light-sharing. Appr

Renato Feres, Eric Pasewark, Gregory Yablonsky

We consider reaction-transport processes in open reactors in which systems of first order reactions involving a number of gas species and solid catalysts can occur at localized active regions. Reaction products flow out of the reactor into vacuum conditions and are collected at an exit boundary. The output composition problem (OCP) is to determine the compos

Liangyu Xu, Wanxuan Lu, Hongfeng Yu, Yongqiang Mao

As drone technology advances, using unmanned aerial vehicles for aerial surveys has become the dominant trend in modern low-altitude remote sensing. The surge in aerial video data necessitates accurate prediction for future scenarios and motion states of the interested target, particularly in applications like traffic management and disaster response. Existi

Mingpei Lin, Tong Luo, Hayato Chiba

A unified analytical solution is presented for constructing the phase space near collinear libration points in the Circular Restricted Three-body Problem (CRTBP), encompassing Lissajous orbits and quasihalo orbits, their invariant manifolds, as well as transit and non-transit orbits. Traditional methods could only derive separate analytical solutions for the

Shao Shuo

Thanks to its robust learning and search stabilities,the reinforcement learning (RL) algorithm has garnered increasingly significant attention and been exten-sively applied in Automated Guided Vehicle (AGV) path planning. However, RL-based planning algorithms have been discovered to suffer from the substantial variance of neural networks caused by environmen

Liang Wu, Liwei Zhou, Richard D. Braatz

Handling possible infeasibility and providing an execution time certificate are two pressing requirements of real-time Model Predictive Control (MPC). To meet these two requirements simultaneously, this paper proposes an $\ell_1$-penalty soft-constrained MPC formulation that is globally feasible and solvable with an execution time certificate using our propo

Yangyang Deng, Zhongmu Li

Recent studies indicate that some Galactic open clusters (OCs) exhibit extended main-sequence turnoff (eMSTO) in their colour-magnitude diagrams (CMDs). However, the number of Galactic OCs with eMSTO structures detected so far is limited, and the reasons for their formation are still unclear. This work identifies 26 Galactic OCs with undiscovered eMSTOs and

Mohamed Harmanani, Paul F. R. Wilson, Fahimeh Fooladgar, Amoon Jamzad

PURPOSE: Deep learning methods for classifying prostate cancer (PCa) in ultrasound images typically employ convolutional networks (CNNs) to detect cancer in small regions of interest (ROI) along a needle trace region. However, this approach suffers from weak labelling, since the ground-truth histopathology labels do not describe the properties of individual

I. A. Mazur, M. K. Efimenko, A. I. Mazur, I. J. Shin

The SS-HORSE-NCSM method is generalized to the case of democratic decay into an odd number of fragments. This method is applied to the search for resonances in three-neutron system (trineutron) using ab initio No-Core Shell Model calculations with realistic nucleon-nucleon potentials. The $3/2^-$ and $1/2^-$ strongly overlapping resonances are predicted when

Junmin An, Jon-Lark Kim

Chara et al. introduced conorm codes defined over algebraic geometry codes, but the hulls of conorm codes were not determined yet. In this paper, we study the dimension of the hull of conorm codes using the method introduced by Camps et al. For an algebraic geometry code $\mathcal{C}:=C_\mathscr{L}(D, G)$, we consider the divisor $\gcd(G, H)$, where $H$ is t

Chuwen Wang, Shirong Zeng, Cheng Wang

Large language models (LLMs), in conjunction with various reasoning reinforcement methodologies, have demonstrated remarkable capabilities comparable to humans in fields such as mathematics, law, coding, common sense, and world knowledge. In this paper, we delve into the reasoning abilities of LLMs within complex human systems. We propose a novel reasoning f

Reynald Affeldt, Zachary Stone

Formalization of real analysis offers a chance to rebuild traditional proofs of important theorems as unambiguous theories that can be interactively explored. This paper provides a comprehensive overview of the Lebesgue Differentiation Theorem formalized in the Coq proof assistant, from which the first Fundamental Theorem of Calculus (FTC) for the Lebesgue i

Qingyu Wang, Duzhen Zhang, Tilelin Zhang, Bo Xu

Energy-efficient spikformer has been proposed by integrating the biologically plausible spiking neural network (SNN) and artificial Transformer, whereby the Spiking Self-Attention (SSA) is used to achieve both higher accuracy and lower computational cost. However, it seems that self-attention is not always necessary, especially in sparse spike-form calculati

Erjia Chen, Bang Wang

Recent years have witnessed extensive researches on developing two tower recommendation models for relieving information overload. Four building modules can be identified in such models, namely, user-item encoding, negative sampling, loss computing and back-propagation updating. To the best of our knowledge, existing algorithms have researched only on the fi

Habibur Rahman, Saqib Ameen

In this study, we analyzed the correlation between unit test coverage and the occurrence of Single Statement Bugs (SSBs) in open-source Java projects. We analyzed data from the top 100 Maven-based projects on GitHub, which includes 7824 SSBs. Our preliminary findings suggest a weak to moderate correlation, indicating that increased test coverage is somewhat

Shannon M. Duff, Jason Austermann, James A. Beall, David P. Daniel

The Simons Observatory (SO) is a cosmic microwave background instrumentation suite in the Atacama Desert of Chile. More than 65,000 polarization-sensitive transition-edge sensor (TES) bolometers will be fielded in the frequency range spanning 27 to 280 GHz, with three separate dichroic designs. The mid-frequency 90/150 GHz and ultra-high-frequency 220/280 GH

Yuanyuan Li, Hao Zhang, Jianping Huang, Zhenchun Li

Ground-roll attenuation is a challenging seismic processing task in land seismic survey. The ground-roll coherent noise with low frequency and high amplitude seriously contaminate the valuable reflection events, corrupting the quality of seismic data. The transform-based filtering methods leverage the distinct characteristics of the ground roll and seismic r

Kyle Stein, Arash Mahyari, Guillermo Francia, Eman El-Sheikh

As malicious cyber threats become more sophisticated in breaching computer networks, the need for effective intrusion detection systems (IDSs) becomes crucial. Techniques such as Deep Packet Inspection (DPI) have been introduced to allow IDSs analyze the content of network packets, providing more context for identifying potential threats. IDSs traditionally

Bo Wu, Bruce D. Lee, Kostas Daniilidis, Bernadette Bucher

Large-scale robotic policies trained on data from diverse tasks and robotic platforms hold great promise for enabling general-purpose robots; however, reliable generalization to new environment conditions remains a major challenge. Toward addressing this challenge, we propose a novel approach for uncertainty-aware deployment of pre-trained language-condition

Cunyuan Jiang, Matteo Baggioli

Solid materials that deviate from the harmonic crystal paradigm exhibit characteristic anomalies in the specific heat and vibrational density of states (VDOS) with respect to Debye's theory predictions. The boson peak (BP), a low-frequency excess in the VDOS over Debye law $g(\omega) \propto \omega^2$, is certainly the most famous among them; nevertheless, i

Brendan Shanahan, David Bold, Ben Dudson

Isothermal fluid turbulence simulations have been performed in the edge and scrape-off-layer (SOL) of an analytic stellarator configuration with an island divertor, thereby providing numerical insight into edge turbulence in regions around islands in a stellarator. The steady-state transport follows the 1/R curvature drive toward the outboard side, but large

Ergon Cugler de Moraes Silva

Reinforcement learning (RL) algorithms have become indispensable tools in artificial intelligence, empowering agents to acquire optimal decision-making policies through interactions with their environment and feedback mechanisms. This study explores the performance of RL agents in both two-dimensional (2D) and three-dimensional (3D) environments, aiming to r

Yu Wang

Fuzzy string matching remains a key issue when political scientists combine data from different sources. Existing matching methods invariably rely on string distances, such as Levenshtein distance and cosine similarity. As such, they are inherently incapable of matching strings that refer to the same entity with different names such as ''JP Morgan'' and ''Ch

Jun Hu, Zhen Liu, Rui Ma, Ruishu Wang

This paper proposes a mixed variational formulation for the problem of two coupled plates with a rigid {junction}. The proposed mixed {formulation} introduces {the union of} stresses and moments as {an auxiliary variable}, which {are} commonly of great interest in practical applications. The primary challenge lies in determining a suitable {space involving}

Xianli Zeng, Guang Cheng, Edgar Dobriban

Mitigating the disparate impact of statistical machine learning methods is crucial for ensuring fairness. While extensive research aims to reduce disparity, the effect of using a \emph{finite dataset} -- as opposed to the entire population -- remains unclear. This paper explores the statistical foundations of fair binary classification with two protected gro

Katsuya Iwaya, Hiroyuki Mogi, Shoji Yoshida, Yusuke Arashida

Understanding the nanoscale carrier dynamics induced by light excitation is the key to unlocking futuristic devices and innovative functionalities in advanced materials. Optical pump-probe scanning tunneling microscopy (OPP-STM) has opened a window to these phenomena. However, mastering the combination of ultrafast pulsed lasers with STM requires high expert

Bikai Gao, Toru Kojo, Masayasu Harada

Parity doublet model is an effective chiral model that includes the chiral variant and invariant masses of baryons. The chiral invariant mass has large impacts on the density dependence of models which can be constrained by neutron star observations. In the previous work, models of two-flavors have been considered up to a few times nuclear saturation density

Michelle Blom, Adrian R. Pearce, Pascal Cote

We present a Large Neighbourhood Search based approach for solving complex long-term open-pit mine planning problems. An initial feasible solution, generated by a sliding windows heuristic, is improved through repeated solves of a restricted mixed-integer program. Each iteration leaves only a subset of the variables in the planning model free to take on new

Daniel M. German

This paper summarizes the current copyright related risks that Machine Learning (ML) and Artificial Intelligence (AI) systems (including Large Language Models --LLMs) incur. These risks affect different stakeholders: owners of the copyright of the training data, the users of ML/AI systems, the creators of trained models, and the operators of AI systems. This

Hazhar Rahmani, Abhishek N. Kulkarni, Jie Fu

Human preferences are not always represented via complete linear orders: It is natural to employ partially-ordered preferences for expressing incomparable outcomes. In this work, we consider decision-making and probabilistic planning in stochastic systems modeled as Markov decision processes (MDPs), given a partially ordered preference over a set of temporal

Jingyang Huo, Yikai Wang, Xuelin Qian, Yun Wang

Recent fMRI-to-image approaches mainly focused on associating fMRI signals with specific conditions of pre-trained diffusion models. These approaches, while producing high-quality images, capture only a limited aspect of the complex information in fMRI signals and offer little detailed control over image creation. In contrast, this paper proposes to directly

Kazuhisa Ogawa, Takumi Matsuura, Akihisa Tomita

The dynamics of a quantum system are characterized by three components: quantum state, quantum process, and quantum measurement. The proper measurement of these components is a crucial issue in quantum information processing. Recently, direct measurement methods have been proposed and demonstrated wherein each complex matrix element of these three components

Xuemin Hu, Pan Chen, Yijun Wen, Bo Tang

Reinforcement learning (RL) has been widely used in decision-making and control tasks, but the risk is very high for the agent in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving systems. Safe RL methods are developed to handle this issue by constra

Yizhang Xia, Shihao Song, Zhanglu Hou, Junwen Xu

Hand gesture recognition (HGR) based on multimodal data has attracted considerable attention owing to its great potential in applications. Various manually designed multimodal deep networks have performed well in multimodal HGR (MHGR), but most of existing algorithms require a lot of expert experience and time-consuming manual trials. To address these issues

Chihiro Noguchi, Toshiaki Ohgushi, Masao Yamanaka

The detection of unknown traffic obstacles is vital to ensure safe autonomous driving. The standard object-detection methods cannot identify unknown objects that are not included under predefined categories. This is because object-detection methods are trained to assign a background label to pixels corresponding to the presence of unknown objects. To address

Bolun Dai, Rooholla Khorrambakht, Prashanth Krishnamurthy, Farshad Khorrami

The capability to navigate safely in an unstructured environment is crucial when deploying robotic systems in real-world scenarios. Recently, control barrier function (CBF) based approaches have been highly effective in synthesizing safety-critical controllers. In this work, we propose a novel CBF-based local planner comprised of two components: Vessel and M

Yuqi Yang, Xiaowen Huang, Jitao Sang

Large language models (LLMs), renowned for their impressive capabilities in various tasks, have significantly advanced artificial intelligence. Yet, these advancements have raised growing concerns about privacy and security implications. To address these issues and explain the risks inherent in these models, we have devised a three-tiered progressive framewo

Aditi Verma, Todd Allen

The Technology Readiness Level (TRL) scale was initially developed by NASA in the 1970s and is now widely used in space, nuclear, and other complex technology sectors in the US and beyond. The TRL scale is particularly useful for determining where extrapolation of untested sub-systems or features could produce technical risk, cause expensive redesigns, or ac

Nisha Pillai, Athish Ram Das, Moses Ayoola, Ganga Gireesan

Artificial intelligence (AI) techniques are widely applied in the life sciences. However, applying innovative AI techniques to understand and deconvolute biological complexity is hindered by the learning curve for life science scientists to understand and use computing languages. An open-source, user-friendly interface for AI models, that does not require pr

Yuhang Ye, Yuekun Wang, Yinxing Xue, Yueming Wu

The rapid evolution of programming languages and software systems has necessitated the implementation of multilingual and scalable clone detection tools. However, it is difficult to achieve the above requirements at the same time. Most existing tools only focus on one challenge. In this work, we propose TGMM, a tree and GPU-based tool for multilingual and mu

Shenxing Wei, Xing Wei, Zhiheng Ma, Songlin Dong

Detecting anomaly patterns from images is a crucial artificial intelligence technique in industrial applications. Recent research in this domain has emphasized the necessity of a large volume of training data, overlooking the practical scenario where, post-deployment of the model, unlabeled data containing both normal and abnormal samples can be utilized to

Anton A. Stoorvogel, Ali Saberi, Zhenwei Liu

Originally, protocols were designed for multi-agent systems (MAS) using information about the network which might not be available. Recently, there has been a focus on scale-free synchronization where the protocol is designed without any prior information about the network. As long as the network contains a directed spanning tree, a scale-free protocol guara

Daiki Yamashita, Nan Fang, Shun Fujii, Yuichiro K. Kato

We propose and demonstrate hybrid all-optical switching devices that combine silicon nanocavities and two-dimensional semiconductor material. By exploiting the refractive index modulation caused by photo-induced carriers in the two-dimensional material instead of the silicon substrate, we overcome the switching performance limitation imposed by the substrate

Jiayu Huo, Xi Ouyang, Sébastien Ourselin, Rachel Sparks

Rapid advancements in medical image segmentation performance have been significantly driven by the development of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). These models follow the discriminative pixel-wise classification learning paradigm and often have limited ability to generalize across diverse medical imaging datasets. In this

Changyi Lin, Xingyu Liu, Yuxiang Yang, Yaru Niu

Quadrupedal robots have emerged as versatile agents capable of locomoting and manipulating in complex environments. Traditional designs typically rely on the robot's inherent body parts or incorporate top-mounted arms for manipulation tasks. However, these configurations may limit the robot's operational dexterity, efficiency and adaptability, particularly i

Dana Moukheiber, Saurabh Mahindre, Lama Moukheiber, Mira Moukheiber

There has been significant progress in implementing deep learning models in disease diagnosis using chest X- rays. Despite these advancements, inherent biases in these models can lead to disparities in prediction accuracy across protected groups. In this study, we propose a framework to achieve accurate diagnostic outcomes and ensure fairness across intersec

Yuxuan Wan, Kaichen Zhou, jinhong Chen, Hao Dong

Autonomous assembly in robotics and 3D vision presents significant challenges, particularly in ensuring assembly correctness. Presently, predominant methods such as MEPNet focus on assembling components based on manually provided images. However, these approaches often fall short in achieving satisfactory results for tasks requiring long-term planning. Concu

Hsiu-Chung Yeh, Achim Rosch, Aditi Mitra

Results are presented for the dynamics of edge modes in interacting Floquet Ising chains. It is shown that in addition to the quasi-stable $0$ and $\pi$ edge modes, a third long lived edge mode arising from the operator product of the $0$ and $\pi$ edge modes exists. Depending on the microscopic parameters, this Floquet product mode is shown to have a substa

Qiming Wang, Yongqiang Bai, Hongxing Song

RGB-T tracking, a vital downstream task of object tracking, has made remarkable progress in recent years. Yet, it remains hindered by two major challenges: 1) the trade-off between performance and efficiency; 2) the scarcity of training data. To address the latter challenge, some recent methods employ prompts to fine-tune pre-trained RGB tracking models and

Ao Zhou, Bin Liu, Jin Wang, Grigorios Tsoumakas

Deep neural network models have demonstrated their effectiveness in classifying multi-label data from various domains. Typically, they employ a training mode that combines mini-batches with optimizers, where each sample is randomly selected with equal probability when constructing mini-batches. However, the intrinsic class imbalance in multi-label data may b