December 2023 arXiv papers — page 40

Vivek M. Vyas

An operator generalisation of the notion of geometric phase has been recently proposed purely based on physical grounds. Here we provide a mathematical foundation for its existence, while uncovering new geometrical structures in quantum systems. While probing the average of any observable it is found that a quantum system exhibits different ray spaces and as

Hui Zhong, Keyi Ju, Manojna Sistla, Xinyue Zhang

Quantum computing is a promising paradigm for efficiently solving large and high-complexity problems. To protect quantum computing privacy, pioneering research efforts proposed to redefine differential privacy (DP) in quantum computing, i.e., quantum differential privacy (QDP), and harvest inherent noises generated by quantum computing to implement QDP. Howe

Bruno Bertrand, Michal Cuadrat-Grzybowski, Pascale Defraigne, Michel Van Camp

In this proceedings, we study the possible gravitational impact of primordial black holes (PBHs) or dark matter (DM) clumps on GNSS satellite orbits and gravimeter measurements. It provides a preliminary step to the future exhaustive statistical analysis over 28 years of gravimeter and GNSS data to get constraints over the density of asteroid-mass PBH and DM

Christian Henriksen, Carsten Lunde Petersen, Eva Uhre

Suppose $C \subset \mathbb{C}$ is compact. Let $q_k$ be a sequence of polynomials of degree $n_k \to \infty$, such that the locus of roots of all the polynomials is bounded, and the number of roots of $q_k$ in any closed set $L$ not meeting $C$ is uniformly bounded. Supposing that $(q_k)_k$ has an asymptotic root distribution $\mu$ we provide conditions on $

Minghui Liao, Guojia Wan, Bo Du

Determining the types of neurons within a nervous system plays a significant role in the analysis of brain connectomics and the investigation of neurological diseases. However, the efficiency of utilizing anatomical, physiological, or molecular characteristics of neurons is relatively low and costly. With the advancements in electron microscopy imaging and a

François Bernard

This paper is devoted to the study of the relative Lipschitz saturation of complex algebraic varieties. More precisely, we investigate the concept of Lipschitz saturation of a variety in another, and we focus on the case where the dominant morphism between the two varieties is not necessarily finite. In particular, we answer, in the case of algebraic varieti

Anom Trenggana, Freddy P. Zen

In this research, we generalize the transformation of the vacuum state that generated gravitational waves in the early universe which is usually transformed using a two-mode into a three-mode Bogoliubov transformation. Based on the calculation of quantum discord this transformation allows the universe to be classical when the squeezed parameter is large if o

Mathieu Gomes, Sylvain Marsat, Jonathan Peillex, Guillaume Pijourlet

We analyze the influence of religious social norms on corporate greenwashing behavior. Specifically, we focus on a specific form of greenwashing: selective disclosure. Using a large sample of US firms between 2005 and 2019, we show that firms located in counties where religious adherence is high are less likely to engage in greenwashing. We also find that a

Francis Wlazinski

Words whose three successive factors of the same length are all different i.e. 3-anti-power words are a natural extension of square-free words (two successive factors of the same length are different). We give a way to verify whether a uniform morphism preserves 3-anti-power words (the image of a 3-anti-power word is a 3-anti-power word). A consequence of th

Bum-Hoon Lee, Hocheol Lee, Wonwoo Lee, Nils A. Nilsson

We present a non-abelian cousin of the model presented in \cite{Lee:2022rtz} which induces cosmological anisotropies on top of standard FLRW geometry. This is in some sense doing a cosmological mean field approximation, where the mean field cosmological model under consideration would be the standard FLRW, and the induced anisotropies are small perturbative

Magalie Bénéfice

In this article we continue the study of couplings of subelliptic Brownian motions on the subRiemannian manifolds SU (2) and SL(2, R). Similar to the case of the Heisenberg group, this subelliptic Brownian motion can be considered as a Brownian motion on the sphere (resp. the hyperbolic plane) together with its swept area modulo 4$\pi$. Using this structure,

Kewei Song, Chunfeng Xiong, Ze Zhang, Kunlin Wu

Shape memory structures are playing an important role in many cutting-edge intelligent fields. However, the existing technologies can only realize 4D printing of a single polymer or metal, which limits practical applications. Here, we report a construction strategy for TSMP/M heterointerface, which uses Pd2+-containing shape memory polymer (AP-SMR) to induce

Fei Wu, Thomas Thiery, Stefanos Leonardos, Carmine Ventre

The Ethereum block-building process has changed significantly since the emergence of Proposer-Builder Separation. Validators access blocks through a marketplace, where block builders bid for the right to construct the block and earn MEV (Maximal Extractable Value) rewards in an on-chain competition, known as the MEV-boost auction. While more than 90% of bloc

Pablo Alvarez, Stéphane Cotin

Numerous regularization methods for deformable image registration aim at enforcing smooth transformations, but are difficult to tune-in a priori and lack a clear physical basis. Physically inspired strategies have emerged, offering a sound theoretical basis, but still necessitating complex discretization and resolution schemes. This study introduces a regula

Ümit Ertem

Modifications of Dirac operators in supergravity flux backgrounds are considered. Modified spin curvature operators and squares of modified Dirac operators corresponding to Schr\"odinger-Lichnerowicz-like formulas are obtained for different types of flux modifications. Symmetry operators of modified massless and massive Dirac equations are found in terms of

Tatsuki Yamaguchi

Inspired by Schoutens' results, we introduce a variant of sharp $F$-purity and sharp $F$-injectivity in equal characteristic zero via ultraproducts. As an application, we show that if $R\to S$ is pure and $S$ is of dense $F$-pure type, then $R$ is of dense $F$-pure type.

Julien Ferry, Ulrich Aïvodji, Sébastien Gambs, Marie-José Huguet

Machine learning techniques are increasingly used for high-stakes decision-making, such as college admissions, loan attribution or recidivism prediction. Thus, it is crucial to ensure that the models learnt can be audited or understood by human users, do not create or reproduce discrimination or bias, and do not leak sensitive information regarding their tra

Bernardo Torres, Geoffroy Peeters, Gaël Richard

In neural audio signal processing, pitch conditioning has been used to enhance the performance of synthesizers. However, jointly training pitch estimators and synthesizers is a challenge when using standard audio-to-audio reconstruction loss, leading to reliance on external pitch trackers. To address this issue, we propose using a spectral loss function insp

Ran Cohen, Pouyan Forghani, Juan Garay, Rutvik Patel

It is well known that without randomization, Byzantine agreement (BA) requires a linear number of rounds in the synchronous setting, while it is flat out impossible in the asynchronous setting. The primitive which allows to bypass the above limitation is known as oblivious common coin (OCC). It allows parties to agree with constant probability on a random co

Ruobing Bai, Jia Shen

In this work, we prove the existence of wave operator for the following generalized derivative nonlinear Schr\"odinger equation \begin{align*} i\partial_t u+\partial_x^2 u +i |u|^{2\sigma}\partial_x u=0, \end{align*} with $(t,x)\in\mathbb{R}\times\mathbb{R}$, $\sigma\in \mathbb{N}$, and $\sigma\geq 3$. The study of wave operators is an important part of the

Hisaichi Shibata

This study aims to acquire knowledge for creating very large language models that are immune to hallucinations. Hallucinations in contemporary large language models are often attributed to a misunderstanding of real-world social relationships. Therefore, I hypothesize that very large language models capable of thoroughly grasping all these relationships will

Thomas Bellotti

Numerical analysis for linear constant-coefficients Finite Difference schemes was developed approximately fifty years ago. It relies on the assumption of scheme stability and in particular -- for the $L^2$ setting -- on the absence of multiple roots of the amplification polynomial on the unit circle. This allows to decouple, while discussing the convergence

Fu-Jen Tsai, Yan-Tsung Peng, Chen-Yu Chang, Chan-Yu Li

Video restoration is a low-level vision task that seeks to restore clean, sharp videos from quality-degraded frames. One would use the temporal information from adjacent frames to make video restoration successful. Recently, the success of the Transformer has raised awareness in the computer-vision community. However, its self-attention mechanism requires mu

Krystian Gajdzica, Piotr Miska, Maciej Ulas

In recent literature concerning integer partitions one can find many results related to both the Bessenrodt-Ono type inequalities and log-concavity property. In this note we offer some general approach to this type of problems. More precisely, we prove that under some mild conditions on an increasing function $F$ of at most exponential growth satisfying the

Basile Dubois-Bonnaire, Sylvain Meignen, Kévin Polisano

In this paper, we develop a general method to estimate the instantaneous frequencies of the modes making up a multicomponent signal when the former exhibit interference in the time-frequency plane. In particular, studying the representation given by the spectrogram, we show that it is possible to characterize the interference between the modes using the Pron

Zheyuan Hu, Zekun Shi, George Em Karniadakis, Kenji Kawaguchi

Physics-Informed Neural Networks (PINNs) have proven effective in solving partial differential equations (PDEs), especially when some data are available by seamlessly blending data and physics. However, extending PINNs to high-dimensional and even high-order PDEs encounters significant challenges due to the computational cost associated with automatic differ

Yi-Yu Lin, Jun Zhang

In the framework of the holographic principle, focusing on a central concept, conditional mutual information, we construct a class of coarse-grained states, which are intuitively connected to a family of thread configurations. These coarse-grained states characterize the entanglement structure of holographic systems at a coarse-grained level. Importantly, th

Emily Roff, Masahiko Yoshinaga

The magnitude of a metric space is a real-valued function whose parameter controls the scale of the metric. A metric space is said to have the one-point property if its magnitude converges to 1 as the space is scaled down to a point. Not every finite metric space has the one-point property: to date, exactly one example has been found of a finite space for wh

Shengnan Wang, Yi Li, Zhou Chen, Yunjie Yang

Three-dimensional electrical capacitance tomography (3D-ECT) has shown promise for visualizing industrial multiphase flows. However, existing 3D-ECT approaches suffer from limited imaging resolution and lack assessment metrics, hampering their effectiveness in quantitative multiphase flow imaging. This paper presents a digital twin (DT)-assisted 3D-ECT, aimi

Wan-Ting Shih, Chao-Kai Wen, Shang-Ho Tsai, Shi Jin

Millimeter-wave (mmWave) networks offer the potential for high-speed data transfer and precise localization, leveraging large antenna arrays and extensive bandwidths. However, these networks are challenged by significant path loss and susceptibility to blockages. In this study, we delve into the use of situational awareness for beam prediction within the 5G

Pierre Lafourcade

We study the problem of optimal games for the solo and coop modes of the board game Room 25 (season 1). We show that the game cannot be won in a single turn for any starting configuration, but that it can be done in two for some configurations. We introduce an opening that wins in two turns with enough luck, while having a low probability of losing immediate

Anish Madan, Neehar Peri, Shu Kong, Deva Ramanan

The era of vision-language models (VLMs) trained on web-scale datasets challenges conventional formulations of "open-world" perception. In this work, we revisit the task of few-shot object detection (FSOD) in the context of recent foundational VLMs. First, we point out that zero-shot predictions from VLMs such as GroundingDINO significantly outperform state-

Elodie Germani, Elisa Fromont, Pierre Maurel, Camille Maumet

Results of functional Magnetic Resonance Imaging (fMRI) studies can be impacted by many sources of variability including differences due to: the sampling of the participants, differences in acquisition protocols and material but also due to different analytical choices in the processing of the fMRI data. While variability across participants or across acquis

Seungjun An, Seonghoon Park, Gyeongnyeon Kim, Jeongyeol Baek

With the increasing importance of video data in real-world applications, there is a rising need for efficient object detection methods that utilize temporal information. While existing video object detection (VOD) techniques employ various strategies to address this challenge, they typically depend on locally adjacent frames or randomly sampled images within

Hannah Och, Shabhrish Reddy Uddehal, Tilo Strutz, André Kaup

Soft context formation is a lossless image coding method for screen content. It encodes images pixel by pixel via arithmetic coding by collecting statistics for probability distribution estimation. Its main pipeline includes three stages, namely a context model based stage, a color palette stage and a residual coding stage. Each subsequent stage is only empl

Danté M. Hewitt, Shivani Bhandari, Benito Marcote, Jason W. T. Hessels

We present very-long-baseline interferometry (VLBI) observations of the hyperactive repeating FRB 20220912A using the European VLBI Network (EVN) with an EVN-Lite setup. We detected 150 bursts from FRB 20220912A over two observing epochs in October 2022. Combining the data of these bursts allows us to localise FRB 20220912A to a precision of a few milliarcse

Yann Sakref, Olivier Rivoire

Selection among autocatalytic species fundamentally depends on their growth law: exponential species, whose number of copies grows exponentially, are mutually exclusive, while sub-exponential ones, whose number of copies grows polynomially, can coexist. Here we consider competitions between autocatalytic species with different growth laws and make the simple

Aoxiong Yin, Tianyun Zhong, Haoyuan Li, Siliang Tang

The primary objective of simultaneous machine translation (SiMT) is to minimize latency while preserving the quality of the final translation. Drawing inspiration from CPU branch prediction techniques, we propose incorporating branch prediction techniques in SiMT tasks to reduce translation latency. Specifically, we utilize a language model as a branch predi

Federico Rollo, Gennaro Raiola, Nikolaos Tsagarakis, Marco Roveri

Robots with a high level of autonomy are increasingly requested by smart industries. A way to reduce the workers' stress and effort is to optimize the working environment by taking advantage of autonomous collaborative robots. A typical task for Human-Robot Collaboration (HRC) which improves the working setup in an industrial environment is the \textit{"brin

Yi-Yu Lin, Jun Zhang, Jie-Chen Jin

In this paper, we make use of holographic Boundary Conformal Field Theory (BCFT) to simulate the black hole information problem in the semi-classical picture. We investigate the correlation between a portion of Hawking radiation and entanglement islands by the area of an entanglement wedge cross-section. Building on the understanding of the relationship betw

Fangze Liu, Zhantao Chen, Tianyi Liu, Ruyi Song

Drawing inspiration from the achievements of natural language processing, we adopt self-supervised learning and utilize an equivariant graph neural network to develop a unified platform designed for training generative models capable of generating crystal structures, as well as efficiently adapting to downstream tasks in material property prediction. To miti

Tanja Kirchner, Wael Elkamhawy, Hans-Werner Hammer

We investigate the spin entanglement in few-nucleon scattering processes involving nucleons and deuterons. For this purpose, we consider the entanglement power introduced by Beane et al. We analyze different entanglement entropies as a basis to define the entanglement power of the strong interaction and calculate the corresponding entanglement powers for pro

Yasmina Khiar, Esmeralda Mainar, Eduardo Royo-Amondarain, Beatriz Rubio

In recent years many efforts have been devoted to finding bidiagonal factorizations of nonsingular totally positive matrices, since their accurate computation allows to numerically solve several important algebraic problems with great precision, even for large ill-conditioned matrices. In this framework, the present work provides the factorization of the col

Yizhen Jia, Hui Chen, Wen-Qin Wang, Jie Cheng

The smart morphing wing aircraft (SMWA) is a highly adaptable platform that can be widely used for intelligent warfare due to its real-time variable structure. The flexible conformal array (FCA) is a vital detection component of SMWA, when the deformation parameters of FCA are mismatched or array elements are mutually coupled, detection performance will be d

Wenxi Yue, Jing Zhang, Kun Hu, Qiuxia Wu

The Segment Anything Model (SAM) exhibits promise in generic object segmentation and offers potential for various applications. Existing methods have applied SAM to surgical instrument segmentation (SIS) by tuning SAM-based frameworks with surgical data. However, they fall short in two crucial aspects: (1) Straightforward model tuning with instrument masks t

Hongyin Zhu

Large language models (LLMs) are increasingly being used in Metaverse environments to generate dynamic and realistic content and to control the behavior of non-player characters (NPCs). However, the cybersecurity concerns associated with LLMs have become increasingly prominent. Previous research has primarily focused on patching system vulnerabilities to enh

Stefan Catheline

This paper aims at studying the spin once again. The departure point is thus the Stern and Gerlach experimental results that can be described in a coherent way in the frame of quantum mechanics only. Instead, the relativistic mechanics point of view is proposed here following the work presented in a previous article about rigid body rotation. Indeed, a caref

Aishwarya Upadhyay, Vijay Laxmi, Smita Naval

As technology continues to advance and we usher in the era of Industry 5.0, there has been a profound paradigm shift in operating systems, file systems, web, and network applications. The conventional utilization of multiprocessing and multicore systems has made concurrent programming increasingly pervasive. However, this transformation has brought about a n

Xuan Gong, Shanglin Li, Yuxiang Bao, Barry Yao

Federated learning (FL) is a machine learning paradigm in which distributed local nodes collaboratively train a central model without sharing individually held private data. Existing FL methods either iteratively share local model parameters or deploy co-distillation. However, the former is highly susceptible to private data leakage, and the latter design re

Maria Joiţa

In this paper, we show that the local boundary representations of a local operator system in a Frechet locally C*-algebra on quantized Frechet domains introduced by Arunkumar [Local boundary representations of locally C*-algebras, J. Math. Anal. Appl. 515(2022),2,Paper No. 126416, 14pp.] are in fact local boundary representations on Hilbert spaces. Thus, the

Halimah Harfah, Yusuf Wicaksono, Gagus Ketut Sunnardianto, Muhammad Aziz Majidi

We presents a new strategy to create a van der Waals-based magnetic tunnel junction (MTJ) that consists of a three-atom layer thickness of graphene (Gr) sandwiched with hexagonal boron nitride (hBN) by introducing a monoatomic Boron vacancy in both hBN layers. The magnetic properties and electronic structure of the system were investigated using density func

Toshiki Matsusaka, Hideki Murahara, Tomokazu Onozuka

Due to their singularities, multiple zeta functions behave sensitively at non-positive integer points. In this article, we focus on the asymptotic behavior at the origin $(0,\dots, 0)$ and unveil the generating series of the asymptotic coefficients as a generalization of the classical Gregory coefficients. This enables us to reveal the underlying symmetry of

Zhenjia Li, Jinrang Jia, Yifeng Shi

In the field of autonomous driving, monocular 3D detection is a critical task which estimates 3D properties (depth, dimension, and orientation) of objects in a single RGB image. Previous works have used features in a heuristic way to learn 3D properties, without considering that inappropriate features could have adverse effects. In this paper, sample selecti

Yangjun Zeng, Buxiang Zhou, Jie Zhu, Jiarong Li

Utility-scale renewable power-to-hydrogen (ReP2H) production typically uses thyristor rectifiers (TRs) to supply power to multiple electrolyzers (ELZs). They exhibit a nonlinear and non-decouplable relation between active and reactive power. The on-off scheduling and load allocation of multiple ELZs simultaneously impact energy conversion efficiency and AC-s

Jinmin He, Kai Li, Yifan Zang, Haobo Fu

Multi-task reinforcement learning endeavors to accomplish a set of different tasks with a single policy. To enhance data efficiency by sharing parameters across multiple tasks, a common practice segments the network into distinct modules and trains a routing network to recombine these modules into task-specific policies. However, existing routing approaches

Lei Liu, Chenglong Li, Futian Wang, Longfeng Shen

Cross-modal object tracking is an important research topic in the field of information fusion, and it aims to address imaging limitations in challenging scenarios by integrating switchable visible and near-infrared modalities. However, existing tracking methods face some difficulties in adapting to significant target appearance variations in the presence of

Honghao Wei, Xin Liu, Lei Ying

This paper studies safe Reinforcement Learning (safe RL) with linear function approximation and under hard instantaneous constraints where unsafe actions must be avoided at each step. Existing studies have considered safe RL with hard instantaneous constraints, but their approaches rely on several key assumptions: $(i)$ the RL agent knows a safe action set f

Kento Yasuda

Active microscopic objects, e.g., an enzyme molecule, are modeled by the Langevin system with the odd elasticity, in which energy injection from the substrate to the enzyme is described by the antisymmetric part of the elastic matrix. By applying the Onsager--Machlup integral and large deviation theory to the Langevin system with odd elasticity, we can calcu

Chao Cheng

We consider an incidence problem in $\mathbb{R}^4$ which asks, for a set of $L$ lines and a set of $S$ planes in general position, what the maximum number of line-plane incidences is. A line-plane incidence is defined as a point where a line and a plane intersect. We prove that, when the lines and planes are in a truly 4-dimensional configuration such that n

Jiangwei Jian, Qimao Huang, Bang Huang, Wen-Qin Wang

Considering that frequency diverse array multiple-input multiple-output (FDA-MIMO) possesses extra range information to enhance sensing performance, this paper explores the FDA-MIMO-based integrated sensing and communication (ISAC) system. To reinforce the system communication capability, we propose the frequency offset permutation index modulation (FOPIM) s

Shahriyar Jafarzade

In this thesis, I present the results for light mesons with $J=2,3$ within an effective hadronic model, the so-called extended Linear Sigma Model (eLSM). This model is based on the approximate chiral symmetry of QCD. Qualitative agreement between model results and PDG as well as LQCD data is obtained. Various predictions for the radiative decays can be teste

Maceon Knopke, Liguo Zhu, Peter Corke, Fangyi Zhang

Grasping compliant objects is difficult for robots - applying too little force may cause the grasp to fail, while too much force may lead to object damage. A robot needs to apply the right amount of force to quickly and confidently grasp the objects so that it can perform the required task. Although some methods have been proposed to tackle this issue, perfo

Dongmei Zhang, Chang Li, Ray Zhang, Shenghao Xie

The superior performances of pre-trained foundation models in various visual tasks underscore their potential to enhance the 2D models' open-vocabulary ability. Existing methods explore analogous applications in the 3D space. However, most of them only center around knowledge extraction from singular foundation models, which limits the open-vocabulary abilit

Sarit Maitra

The existing variants of the Differential Evolution (DE) algorithm come with certain limitations, such as poor local search and susceptibility to premature convergence. This study introduces Adaptive Differential Evolution with Diversification (ADED), a method that dynamically modifies the neighborhood structure by evaluating the trial solutions' fitness. De

Prakash Mallick, Zhiyong Chen

This article proposes an improved trajectory optimization approach for stochastic optimal control of dynamical systems affected by measurement noise by combining optimal control with maximum likelihood techniques to improve the reduction of the cumulative cost-to-go. A modified optimization objective function that incorporates dynamic programming-based contr

Yu-Hao Huang, Jui-Hsu Han, Wei-Bang Liao, Chen-Yu Hu

Recent theoretical and experimental studies of the interlayer Dzyaloshinskii-Moriya interaction (DMI) has sparked great interest in its implementation into practical magnetic random-access memory (MRAM) devices, due to its capability to mediate long-range chiral spin textures. So far, experimental reports focused on the observation of interlayer DMI, leaving

Sarthak Choudhary, Aashish Kolluri, Prateek Saxena

Training modern neural networks or models typically requires averaging over a sample of high-dimensional vectors. Poisoning attacks can skew or bias the average vectors used to train the model, forcing the model to learn specific patterns or avoid learning anything useful. Byzantine robust aggregation is a principled algorithmic defense against such biasing.

Zengtao Kuang, Yongchun Xu, Qun Huang, Jie Yang

As a crossover frontier of physics and mechanics, quantum computing is showing its great potential in computational mechanics. However, quantum hardware noise remains a critical barrier to achieving accurate simulation results due to the limitation of the current hardware. In this paper, we integrate error-mitigated quantum computing in data-driven computati

Aishvarya Kumar, Ram Prakash Bharti

The present study assesses RANS-based turbulence models to simulate isothermal flow in a combustor representing a constituent can combustor of can-annular configuration used in jet engines. Two-equation models (standard $k-\epsilon$, realizable $k-\epsilon$, standard $k-\omega$, SST $k-\omega$), and Linear Pressure Strain - Reynolds Stress Model (LPS-RSM), a

Chun-Ren Phang, Akimasa Hirata

Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment. In this study, we integrated deep RL and BCI to improve beneficial human interventions in autonomous systems

Pengxiang Ding, Han Zhao, Wenjie Zhang, Wenxuan Song

The important manifestation of robot intelligence is the ability to naturally interact and autonomously make decisions. Traditional approaches to robot control often compartmentalize perception, planning, and decision-making, simplifying system design but limiting the synergy between different information streams. This compartmentalization poses challenges i

Peng Zhang, Yuyang Dong, Dayu Yan, Bei Jiang

The opening of an energy gap in the electronic structure generally indicates the presence of interactions. In materials with low carrier density and short screening length, long-range Coulomb interaction favors the spontaneous formation of electron-hole pairs, so-called excitons, opening an excitonic gap at the Fermi level. Excitonic materials host unique ph

Jierui Huang, Bei Jiang, Jingyu Yao, Dayu Yan

The excitonic insulator (EI) is an exotic ground state of narrow-gap semiconductors and semimetals arising from spontaneous condensation of electron-hole pairs bound by attractive Coulomb interaction. Despite research on EIs dating back to half a century ago, their existence in real materials remains a subject of ongoing debate. In this study, through system

Mukul Dwivedi, Tanmay Sarkar

In this paper, we study the stability and convergence of a fully discrete finite difference scheme for the initial value problem associated with the Korteweg-De Vries (KdV) equation. We employ the Crank-Nicolson method for temporal discretization and establish that the scheme is $L^2$-conservative. The convergence analysis reveals that utilizing inherent Kat

Bailun Jiang, Boyang Li, Ching-Wei Chang, Chih-Yung Wen

It is challenging to model and control a tail-sitter unmanned aerial vehicle (UAV) because its blended wing body generates complicated nonlinear aerodynamic effects, such as wing lift, fuselage drag, and propeller-wing interactions. We therefore devised a hybrid aerodynamic modeling method and model predictive control (MPC) design for a quadrotor tail-sitter

Soumya Suvra Ghosal, Yiyou Sun, Yixuan Li

Machine learning models deployed in the wild can be challenged by out-of-distribution (OOD) data from unknown classes. Recent advances in OOD detection rely on distance measures to distinguish samples that are relatively far away from the in-distribution (ID) data. Despite the promise, distance-based methods can suffer from the curse-of-dimensionality proble

Jing Guo, Nan Li, Jianchuan Qi, Hang Yang

Large language models (LLMs) have achieved impressive linguistic capabilities. However, a key limitation persists in their lack of human-like memory faculties. LLMs exhibit constrained memory retention across sequential interactions, hindering complex reasoning. This paper explores the potential of applying cognitive psychology's working memory frameworks, t

G. G. L. Nashed

Due to the absence of spherically symmetric black hole solutions in $f(\mathbb{Q})$ because of the constraint derived from its field equations, which yields either $\mathbb{Q}=0 $ or $f_{\mathbb{Q} \mathbb{Q}}=0 $ \cite{Heisenberg:2023lru,Maurya:2023muz}. We are going to introduce a tours solutions for charged anti-de-Sitter black holes in $N$-dimensions wit

Chi Hoi Yip

Recently, Hajdu and S\'{a}rk\"{o}zy studied the multiplicative decompositions of polynomial sequences. In particular, they showed that when $k \geq 3$, each infinite subset of $\{x^k+1: x \in \mathbb{N}\}$ is multiplicatively irreducible. In this paper, we attempt to make their result effective by building a connection between this problem and the bipartite

Gergő Nemes

We consider a class of $n^{\text{th}}$-order linear ordinary differential equations with a large parameter $u$. Analytic solutions of these equations can be described by (divergent) formal series in descending powers of $u$. We demonstrate that, given mild conditions on the potential functions of the equation, the formal solutions are Borel summable with res

Yu Zhang, Yanmin Gong, Lei Fan, Yu Wang

Low earth orbit (LEO) satellite network can complement terrestrial networks for achieving global wireless coverage and improving delay-sensitive Internet services. This paper proposes an integrated satellite-terrestrial network (ISTN) architecture to provide ground users with seamless and reliable content delivery services. For optimal service provisioning i

L. Van Warren

This paper describes a machine learning method to automate reading of cockpit gauges, using a CNN to invert affine transformations and deduce aircraft states from instrument images. Validated with synthetic images of a turn-and-bank indicator, this research introduces methods such as generating datasets from a single image, the 'Clean Training Principle' for

Xin Xin, Liu Yang, Ziqi Zhao, Pengjie Ren

Session-based recommendation predicts users' future interests from previous interactions in a session. Despite the memorizing of historical samples, the request of unlearning, i.e., to remove the effect of certain training samples, also occurs for reasons such as user privacy or model fidelity. However, existing studies on unlearning are not tailored for the

Lei Liu, Mengya Zhang, Cheng Li, Chenglong Li

Visual tracking often faces challenges such as invalid targets and decreased performance in low-light conditions when relying solely on RGB image sequences. While incorporating additional modalities like depth and infrared data has proven effective, existing multi-modal imaging platforms are complex and lack real-world applicability. In contrast, near-infrar

Mark A. Atkins

Since the key to artificial general intelligence (AGI) is commonly believed to be commonsense reasoning (CSR) or, roughly equivalently, discovery of a knowledge representation method (KRM) that is particularly suitable for CSR, the author developed a custom KRM for CSR. This novel KRM called Tumbug was designed to be pictorial in nature because there exists

C. Bender, S. E. Ferrando, K. Gajewski, A. L. Gonzalez

Supermartingales are here defined on a non-probabilistic setting and can be interpreted solely in terms of superhedging operations. The classical expectation operator is replaced by a pair of subadditive operators one of them providing a class of null sets and the other one acting as an outer integral. These operators are motivated by a financial theory of n

Ritong Li, Dongkui Ma, Rui Kuang, Xiaojiang Ye

The shadowable points of dynamical systems has been well-studied by Morales \cite{MR3535492}. This paper aims to generalize the main results obtained by Morales to free semigroup actions. To this end, we introduce the notion of shadowable points of a free semigroup action. Let $G$ be a free semigroup generated by finite continuous self-maps acting on compact

Da-Wei Luo, Ting Yu

We propose a protocol to overcome the shot noise limit and reach the Heisenberg scaling limit for parameter estimation by using quantum optimal control and a time-reversal strategy. Exemplified through the phase estimation, which can play an important role in quantum navigation and measurement, we show that the uncertainty arising from a photon number measur

Kiichi Tashiro

We show that a mean curvature flow obtained as the limit of the Allen--Cahn equation is not only a Brakke flow but also a generalized BV flow proposed by Stuvard and Tonegawa.

Chen Ding, Christopher Kanan, Dylan McKellips, Toranosuke Ozawa

The greatest demand for today's computing is machine learning. This paper analyzes three machine learning algorithms: transformers, spatial convolution, and FFT. The analysis is novel in three aspects. First, it measures the cost of memory access on an abstract memory hierarchy, instead of traditional time or space complexity. Second, the analysis is asympto

Haz Sameen Shahgir, Xianghao Kong, Greg Ver Steeg, Yue Dong

The widespread use of Text-to-Image (T2I) models in content generation requires careful examination of their safety, including their robustness to adversarial attacks. Despite extensive research on adversarial attacks, the reasons for their effectiveness remain underexplored. This paper presents an empirical study on adversarial attacks against T2I models, f

Yilun Liu, Ruihong Qiu, Yanran Tang, Hongzhi Yin

When handling streaming graphs, existing graph representation learning models encounter a catastrophic forgetting problem, where previously learned knowledge of these models is easily overwritten when learning with newly incoming graphs. In response, Continual Graph Learning (CGL) emerges as a novel paradigm enabling graph representation learning from stream

Bingheng Li, Erlin Pan, Zhao Kang

Recently, many carefully crafted graph representation learning methods have achieved impressive performance on either strong heterophilic or homophilic graphs, but not both. Therefore, they are incapable of generalizing well across real-world graphs with different levels of homophily. This is attributed to their neglect of homophily in heterophilic graphs, a

Zongxia Liang, Keyu Zhang

In this paper we study a time-inconsistent portfolio optimization problem for competitive agents with CARA utilities and non-exponential discounting. The utility of each agent depends on her own wealth and consumption as well as the relative wealth and consumption to her competitors. Due to the presence of a non-exponential discount factor, each agent's opti

Nannan Li, Qing Liu, Krishna Kumar Singh, Yilin Wang

Human image editing includes tasks like changing a person's pose, their clothing, or editing the image according to a text prompt. However, prior work often tackles these tasks separately, overlooking the benefit of mutual reinforcement from learning them jointly. In this paper, we propose UniHuman, a unified model that addresses multiple facets of human ima

Souradip Chakraborty, Anukriti Singh, Amisha Bhaskar, Pratap Tokekar

The effectiveness of reinforcement learning (RL) agents in continuous control robotics tasks is mainly dependent on the design of the underlying reward function, which is highly prone to reward hacking. A misalignment between the reward function and underlying human preferences (values, social norms) can lead to catastrophic outcomes in the real world especi

Leonard Mushunje

Revenues obtained from the corporate tax heads play significant roles in any economy as they can be prioritized for producing public goods and employment creations, among others. As such, corporate tax revenue should be paid enough attention. This study, therefore, explores the tax-revenue harvesting system of an economy where we focused on the corporate tax

Atsushi Kamimura, Yuki Sughiyama, Tetsuya J. Kobayashi

We delve into growing open chemical reaction systems (CRSs) characterized by autocatalytic reactions within a variable volume, which changes in response to these reactions. Understanding the thermodynamics of such systems is crucial for comprehending biological cells and constructing protocells, as it sheds light on the physical conditions necessary for thei

Brian Kishiyama, Izzat Alsmadi

Cloud Service Providers, such as Google Cloud Platform, Microsoft Azure, or Amazon Web Services, offer continuously evolving cloud services. It is a growing industry. Businesses, such as Netflix and PayPal, rely on the Cloud for data storage, computing power, and other services. For businesses, the cloud reduces costs, provides flexibility, and allows for gr

Zhenyang Li, Fan Liu, Yinwei Wei, Zhiyong Cheng

Recommendation algorithms forecast user preferences by correlating user and item representations derived from historical interaction patterns. In pursuit of enhanced performance, many methods focus on learning robust and independent representations by disentangling the intricate factors within interaction data across various modalities in an unsupervised man