December 2024 arXiv papers — page 19

Davide Buoso, Francesco Ferraresso

We establish the convergence of the resolvent of the Reissner-Mindlin system in any dimension $N \geq 2$, with any of the physically relevant boundary conditions, to the resolvent of the biharmonic operator with suitably defined boundary conditions in the vanishing thickness limit. Moreover, given a thin domain $\Omega_\delta$ in ${\mathbb R}^N$ with $1 \leq

Eugeny Babichev

We demonstrate that Cherenkov radiation can be interpreted as ghost instability of a certain type. Solutions of modified gravity theories often contain ghost instabilities. One type of such ghost instability is associated with existence of different types of species with causal cones that do not share common time, which leads to vacuum decay via creation of

Xiaoqian Zhang, Maolin Luo, Xiaoqi Zhou

The rapid advancement of quantum information technology has increased the demand for precise testing and calibration of quantum modules, especially in optical quantum circuits where module reliability directly impacts system performance. To address this need, we propose a two-photon quantum module evaluation method based on high-dimensional Hong-Ou-Mandel in

A. Manchanda, A. Kaushal, M. G. Dainotti, A. Deepu

Gamma-Ray Bursts (GRBs), observed at high-z, are probes of the evolution of the Universe and can be used as cosmological tools. Thus, we need correlations with small dispersion among key parameters. To reduce such a dispersion, we mitigate gaps in light curves (LCs), including the plateau region, key to building the two-dimensional Dainotti relation between

Jesus Marco de Lucas

In this brief and speculative commentary, we explore ideas inspired by neural networks in machine learning, proposing that a simple neural XOR motif, involving both excitatory and inhibitory connections, may provide the basis for a relevant mode of plasticity in neural circuits of living organisms, with homeostasis as the sole guiding principle. This XOR mot

Honglin Pang, Yi Chang, Tianjing Duan, Xi Yang

Archaeological catalogs, containing key elements such as artifact images, morphological descriptions, and excavation information, are essential for studying artifact evolution and cultural inheritance. These data are widely scattered across publications, requiring automated collection methods. However, existing Large Vision-Language Models (VLMs) and their d

Atmane Ayoub Mansour Bahar, Ahmad Samer Wazan

This research investigates the effectiveness of established vulnerability metrics, such as the Common Vulnerability Scoring System (CVSS), in evaluating attacks against Large Language Models (LLMs), with a focus on Adversarial Attacks (AAs). The study explores the influence of both general and specific metric factors in determining vulnerability scores, prov

Zhaohui Wang, Min Zhang, Jingran Yang, Bojie Shao

Deep neural networks (DNNs) have shown powerful performance in various applications and are increasingly being used in decision-making systems. However, concerns about fairness in DNNs always persist. Some efficient white-box fairness testing methods about individual fairness have been proposed. Nevertheless, the development of black-box methods has stagnate

Xiaoteng Zhou, Katsunori Mizuno

With the development of coastal construction, a large amount of human-generated waste, particularly plastic debris, is continuously entering the ocean, posing a severe threat to marine ecosystems. The key to effectively addressing plastic pollution lies in the ability to autonomously monitor such debris. Currently, marine debris monitoring primarily relies o

Zhangxun Li, Mengyang Zhao, Xuan Yang, Yang Liu

Video anomaly detection (VAD) has been extensively researched due to its potential for intelligent video systems. However, most existing methods based on CNNs and transformers still suffer from substantial computational burdens and have room for improvement in learning spatial-temporal normality. Recently, Mamba has shown great potential for modeling long-ra

Zhiqiang Xiao, Zhiwen Zhou, Qianglong Dai, Yong Zeng

This letter studies an uplink integrated sensing and communication (ISAC) system using discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM) transmission. We try to answer the following fundamental question: With only a fractional bandwidth allocated to the user with sensing task, can the same delay resolution and unambigu

Shuo Wang, Wanting Li, Yongcai Wang, Zhaoxin Fan

Deep visual odometry has demonstrated great advancements by learning-to-optimize technology. This approach heavily relies on the visual matching across frames. However, ambiguous matching in challenging scenarios leads to significant errors in geometric modeling and bundle adjustment optimization, which undermines the accuracy and robustness of pose estimati

Jumpei Yasuda

The knot group is the fundamental group of a knot or link complement. A necessary and sufficient conditions for a group to be realized as the knot group of some link was provided. This result was shown using the closed braid method. Gonz\'alez-Acu\~na and Kamada independently extended this characterization to the knot groups of orientable surface-links. Kama

Azizul Hoque, Srinivas Kotyada

We construct parameterized families of imaginary (resp. real) quadratic fields whose class groups have $n$-rank at least $2$.

Hayashi Ani, Lily Chung, Erik D. Demaine, Jenny Diomidova

We prove PSPACE-completeness of the well-studied pushing-block puzzle Push-1F, a theoretical abstraction of many video games (introduced in 1999). The proof also extends to Push-$k$ for any $k \ge 2$. We also prove PSPACE-completeness of two versions of the recently studied block-moving puzzle game with gravity, Block Dude - a video game dating back to 1994

Xiaojun Yan, Xiuwu Zhu

Let $E/\mathbb{Q}$ be an elliptic curve and $p > 2$ be a prime of good ordinary reduction for $E$. Assume that the residue representation associated with $(E, p)$ is irreducible. In this paper, we prove more cases on several Iwasawa main conjectures for $E$. As applications, we prove more general cases of $p$-converse theorem and $p$-part BSD formula when th

Feng Luo, Yunpeng Li, Zitong Wang, Yi Ren

The development of the automotive industry and automation has led to a growing demand for time-critical systems to have low latency and jitter for critical traffic. To address this issue, the IEEE 802.1 Time-Sensitive Networking (TSN) task group proposed the Time-Aware Shaper (TAS) to implement Time-Triggered (TT) communication, enabling deterministic transm

H. Yoshioka, Y. Yoshioka, M. Tsujimura

Modeling fish growth is an important research topic in ecological and fishery sciences because body weight statistics directly affect the total biomass of fish in a habitat, which in turn affects their population dynamics. Many models of fish growth assume that the fish population in a habitat is homogenous, meaning that there is no physiological spectrum an

Gabriele Calzolari, Vidya Sumathy, Christoforos Kanellakis, George Nikolakopoulos

This paper introduces a novel enhancement to the Decentralized Multi-Agent Reinforcement Learning (D-MARL) exploration by proposing communication-induced action space to improve the mapping efficiency of unknown environments using homogeneous agents. Efficient exploration of large environments relies heavily on inter-agent communication as real-world scenari

Víctor Blanco, Ricardo Gázquez, Marina Leal

This paper studies a practical regional demand continuous multifacility location problems whose main goal is to locate a given number of services and entry points in each region to distribute certain products to the users at minimum transportation cost. Additionally, a minimum satisfaction level is required for the customers in each region. This satisfaction

M. Koussour, S. Bekov, A. Syzdykova, S. Muminov

We investigate the cosmological implications of a generalized total equation of state (EoS) model by constraining its parameters using observational datasets to effectively characterize the universe's expansion history and its dynamic properties. We introduce three parameters: $\alpha$, $\beta$, and $n$ to capture the EoS behavior across different evolutiona

Chongjian Yue, Xinrun Xu, Xiaojun Ma, Lun Du

Large Language Models (LLMs) demonstrate exceptional performance in textual understanding and tabular reasoning tasks. However, their ability to comprehend and analyze hybrid text, containing textual and tabular data, remains unexplored. The hybrid text often appears in the form of hybrid long documents (HLDs), which far exceed the token limit of LLMs. Conse

Mingyue Yuan, Jieshan Chen, Yongquan Hu, Sidong Feng

In automated UI design generation, a key challenge is the lack of support for iterative processes, as most systems focus solely on end-to-end output. This stems from limited capabilities in interpreting design intent and a lack of transparency for refining intermediate results. To better understand these challenges, we conducted a formative study that identi

Zhenyang Cai, Junying Chen, Rongsheng Wang, Weihong Wang

Medical imaging provides essential visual insights for diagnosis, and multimodal large language models (MLLMs) are increasingly utilized for its analysis due to their strong generalization capabilities; however, the underlying factors driving this generalization remain unclear. Current research suggests that multi-task training outperforms single-task as dif

Miao Yu, Junfeng Fang, Yingjie Zhou, Xing Fan

While safety-aligned large language models (LLMs) are increasingly used as the cornerstone for powerful systems such as multi-agent frameworks to solve complex real-world problems, they still suffer from potential adversarial queries, such as jailbreak attacks, which attempt to induce harmful content. Researching attack methods allows us to better understand

Zhaowen Wang

Injection-locked ring oscillators (ILROs) are extensively employed for multi-phase clock generation in wireline and optical links. However, existing injection-locking theorems primarily rely on linearized phase-domain or nonlinear time-domain models, which fail to account for amplitude-to-phase conversion effects inherent in ILROs. This paper introduces an e

Alessandro De Grandi, Federico Ravenda, Andrea Raballo, Fabio Crestani

The increasing demand for mental health services has highlighted the need for innovative solutions, particularly in the realm of psychological conversational AI, where the availability of sensitive data is scarce. In this work, we explored the development of a system tailored for mental health support with a novel approach to psychological assessment based o

Arif Er, Meng-Chwan Tan

This work is a sequel to [arXiv:2410.18575], and a third and final installment of the program initiated in [arXiv:2311.18302]. We show how, via a 3d gauged Landau-Ginzburg model interpretation of certain topologically-twisted 5d $\mathcal{N} = 2$ and 8d $\mathcal{N} = 1$ gauge theories, one can derive novel Fueter type $A_{\infty}$-2-categories that 2-catego

Boyun Li, Haiyu Zhao, Wenxin Wang, Peng Hu

Recent advancements in Mamba have shown promising results in image restoration. These methods typically flatten 2D images into multiple distinct 1D sequences along rows and columns, process each sequence independently using selective scan operation, and recombine them to form the outputs. However, such a paradigm overlooks two vital aspects: i) the local rel

Lan Chen, Haoxiang Yang, Pengpeng Shao, Haoyu Song

Pattern recognition leveraging both RGB and Event cameras can significantly enhance performance by deploying deep neural networks that utilize a fine-tuning strategy. Inspired by the successful application of large models, the introduction of such large models can also be considered to further enhance the performance of multi-modal tasks. However, fully fine

Ishmael Apachigawo, Dhruvil Solanki, Santanu Maity, Pradeep Shukla

Photonics/light localization techniques are important in understanding the structural changes in biological tissues at the nano- to sub-micron scale. It is now known that structural alteration starts at the nanoscale at the beginning of cancer progression. This study examines the molecular-specific nano-structural alterations of chronic alcoholism and probio

Zechao Zhan, Dehong Gao, Jinxia Zhang, Jiale Huang

Text-guided image editing model has achieved great success in general domain. However, directly applying these models to the fashion domain may encounter two issues: (1) Inaccurate localization of editing region; (2) Weak editing magnitude. To address these issues, the MADiff model is proposed. Specifically, to more accurately identify editing region, the Ma

Yanxin Shen, Lun Wang, Chuanqi Shi, Shaoshuai Du

Large Language Models (LLMs) have demonstrated significant effectiveness across various NLP tasks, including text ranking. This study assesses the performance of large language models (LLMs) in listwise reranking for limited-resource African languages. We compare proprietary models RankGPT3.5, Rank4o-mini, RankGPTo1-mini and RankClaude-sonnet in cross-lingua

Haiming Yao, Wei Luo, Tao Zhou, Ang Gao

Raman scattering is based on molecular vibration spectroscopy and provides a powerful technology for pathogenic bacteria diagnosis using the unique molecular fingerprint information of a substance. The integration of deep learning technology has significantly improved the efficiency and accuracy of intelligent Raman spectroscopy (RS) recognition. However, th

Mirza Samad Ahmed Baig, Syeda Anshrah Gillani, Shahid Munir Shah, Mahmoud Aljawarneh

Visual impairment affects the ability of people to live a life like normal people. Such people face challenges in performing activities of daily living, such as reading, writing, traveling and participating in social gatherings. Many traditional approaches are available to help visually impaired people; however, these are limited in obtaining contextually ri

Qiancheng Xu, Ezel Üsten, Ahmed Alia, Biao He

In high-density crowds, close proximity between pedestrians makes the steady state highly vulnerable to disruption by pushing behaviours, potentially leading to serious accidents. However, the scarcity of experimental data has hindered systematic studies of its mechanisms and accurate modelling. Using behavioural data from bottleneck experiments, we investig

Sharath Naganna, Saprativa Bhattacharjee, Biplab Banerjee, Pushpak Bhattacharyya

Humblebragging is a phenomenon in which individuals present self-promotional statements under the guise of modesty or complaints. For example, a statement like, "Ugh, I can't believe I got promoted to lead the entire team. So stressful!", subtly highlights an achievement while pretending to be complaining. Detecting humblebragging is important for machines t

Atticus J. Zeller, Haijuan Wu

We present GSplatLoc, a camera localization method that leverages the differentiable rendering capabilities of 3D Gaussian splatting for ultra-precise pose estimation. By formulating pose estimation as a gradient-based optimization problem that minimizes discrepancies between rendered depth maps from a pre-existing 3D Gaussian scene and observed depth images

Shihang Zhang, Yu He, Peihao Huang

Donor-based spin qubit offers a promising silicon quantum computing route for building large-scale qubit arrays, attributed to its long coherence time and advancements in nanoscale donor placement. However, the state-of-the-art device designs face scalability challenges, notably in achieving tunable two-qubit coupling and ensuring qubit addressability. Here,

Rajesh O. Sharma, Tanmoy Das

Low-energy lattice models are the cornerstone for studying many-body physics and interactions between the system and measurement fields. A key challenge is identifying appropriate quasiparticle states that canonically transform between momentum and real space while retaining the correlation, entanglement, and geometric properties - generally called the Wanni

Andrzej Indrzejczak, Michał Zawidzki

Non-Classical Logics. Theory and Applications (NCL) is an international conference which aims to present novel results and survey works in widely understood non-classical logics and their applications. This year's edition was also an opportunity to hold a special session devoted to the ERC-funded project "ExtenDD" devoted to complex terms and term-forming op

Joseph Zhang, Ruiming Zhang, Kipngeno Koech, David Hill

Steady-State Visual Evoked Potential (SSVEP) spellers are a promising communication tool for individuals with disabilities. This Brain-Computer Interface utilizes scalp potential data from (electroencephalography) EEG electrodes on a subject's head to decode specific letters or arbitrary targets the subject is looking at on a screen. However, deep neural net

Tamali Mukherjee, Banasree Sadhukhan, V Satya Narayana Murthy

Skyrmions refer to small swirling spin structures that emerge in ferromagnetic materials and show promising features to be used as a `bit' of information in future spintronic devices. Our research explores the possibility of nucleating skyrmions in X-Fe/Ir(111) multilayer nano-structure where, X is one of the 4d transition metals, such as, Pd, Rh, Ru, Mo and

Juntao Bai, Na Wang, Shi Dai, Shuangqiang Wang

We report on radio observations of four magnetars SGR 0501+4516, Swift 1834.9-0846, 1E 1841-045, SGR 1900+14 and a magnetar-like pulsar PSR J1846-0258 with the Five-hundred-meter Aperture Spherical radio Telescope (FAST) at 1250 MHz. Notably, PSR J1846-0258 was observed one month after its 2020 X-ray outburst. The data from these observations were searched f

Yuwei Du, Xinyue Liu, Wenxin Liang, Linlin Zong

Temporal knowledge graph (TKG) reasoning has become a hot topic due to its great value in many practical tasks. The key to TKG reasoning is modeling the structural information and evolutional patterns of the TKGs. While great efforts have been devoted to TKG reasoning, the structural and evolutional characteristics of real-world networks have not been consid

Gabriele Calzolari, Vidya Sumathy, Christoforos Kanellakis, George Nikolakopoulos

Collaborative multi-agent exploration of unknown environments is crucial for search and rescue operations. Effective real-world deployment must address challenges such as limited inter-agent communication and static and dynamic obstacles. This paper introduces a novel decentralized collaborative framework based on Reinforcement Learning to enhance multi-agen

Ji-Hoon Kim, Hong-Sun Yang, Yoon-Cheol Ju, Il-Hwan Kim

The goal of this work is to generate natural speech in multiple languages while maintaining the same speaker identity, a task known as cross-lingual speech synthesis. A key challenge of cross-lingual speech synthesis is the language-speaker entanglement problem, which causes the quality of cross-lingual systems to lag behind that of intra-lingual systems. In

Phi Vu Tran

While modern visual recognition systems have made significant advancements, many continue to struggle with the open problem of learning from few exemplars. This paper focuses on the task of object detection in the setting where object classes follow a natural long-tailed distribution. Existing methods for long-tailed detection resort to external ImageNet lab

Shin-Ming Huang, Dimitrios Giataganas

Quantum states defined over a parameter space form a Grassmann manifold. To capture the geometry of the associated gauge structure, gauge-invariant quantities are essential. We employ the projector of a multilevel system to quantify the quantum distance between states. Using the multidimensional scaling method, we transform the quantum distance into a recons

Shayan Mohajer Hamidi, En-Hui Yang

Inverse problems exist in many disciplines of science and engineering. In computer vision, for example, tasks such as inpainting, deblurring, and super resolution can be effectively modeled as inverse problems. Recently, denoising diffusion probabilistic models (DDPMs) are shown to provide a promising solution to noisy linear inverse problems without the nee

S. Pustelny, P. Włodarczyk

Precise magnetometry is vital in numerous scientific and technological applications. At the forefront of sensitivity, optical atomic magnetometry, particularly techniques utilizing nonlinear magneto-optical rotation (NMOR), enables ultraprecise measurements across a broad field range. Despite their potential, these techniques reportedly lose sensitivity at h

Chencheng Zhu, Kazutaka Shimada, Tomoki Taniguchi, Tomoko Ohkuma

Large language models (LLMs) demonstrate the ability to learn in-context, offering a potential solution for scientific information extraction, which often contends with challenges such as insufficient training data and the high cost of annotation processes. Given that the selection of in-context examples can significantly impact performance, it is crucial to

Xijun Wang, Pedro Sandoval-Segura, Chengyuan Zhang, Junyun Huang

Most existing traffic video datasets including Waymo are structured, focusing predominantly on Western traffic, which hinders global applicability. Specifically, most Asian scenarios are far more complex, involving numerous objects with distinct motions and behaviors. Addressing this gap, we present a new dataset, DAVE, designed for evaluating perception met

Yingcheng Lai, Li Chai, Jinming Xu

The sampling of graph signals has recently drawn much attention due to the wide applications of graph signal processing. While a lot of efficient methods and interesting results have been reported to the sampling of band-limited or smooth graph signals, few research has been devoted to non-smooth graph signals, especially to sparse graph signals, which are a

Qidong Liu, Zhaopeng Qiu, Xiangyu Zhao, Xian Wu

Medication recommendation is one of the most critical health-related applications, which has attracted extensive research interest recently. Most existing works focus on a single hospital with abundant medical data. However, many small hospitals only have a few records, which hinders applying existing medication recommendation works to the real world. Thus,

Tongyuan Bao, Qi Luo, Ailun Yin, Yao Zhang

Silicon carbide (SiC) has attracted significant attention as a promising quantum material due to its ability to host long-lived, optically addressable color centers with solid-state photonic interfaces. The CMOS compatibility of 4H-SiCOI (silicon-carbide-on-insulator) makes it an ideal platform for integrated quantum photonic devices and circuits. While micr

Gaoang Wang, Hang Wu, Yang Liao, Zhen Chen

Biotoxins, mainly produced by venomous animals, plants and microorganisms, exhibit high physiological activity and unique effects such as lowering blood pressure and analgesia. A number of venom-derived drugs are already available on the market, with many more candidates currently undergoing clinical and laboratory studies. However, drug design resources rel

Ahmad Al-Badawi, Sanjar Shaymatov

In this paper, we consider a static spherically symmetric black hole (BH) embedded in a Dehnen-(1,4,0) type dark matter (DM) halo in the presence of a cloud string. We examine and present data on how the core density of the DM halo parameter and the cloud string parameter affect BH attributes such as quasinormal modes (QNMs) and shadow cast. To do this, we f

Ting Bai, Weijie Chen, Cheng Yang, Chuan Shi

Previous debiasing studies utilize unbiased data to make supervision of model training. They suffer from the high trial risks and experimental costs to obtain unbiased data. Recent research attempts to use invariant learning to detach the invariant preference of users for unbiased recommendations in an unsupervised way. However, it faces the drawbacks of low

Mingyue Guo, Zhenhua Shi

We discuss a specific type of pseudospherical surfaces defined by a class of third order differential equations, of the form $u_t - u_{xxt} = \lambda u^2 u_{xxx} + G(u, u_x, u_{xx})$, and poses a question about the dependence of the triples $\{a,b,c\}$ of the second fundamental form in the context of local isometric immersion in $\mathbb{E}^3$. It is demonst

Feiping Nie, Shenfei Pei, Zengwei Zheng, Rong Wang

We propose a Greedy strategy to solve the problem of Graph Cut, called GGC. It starts from the state where each data sample is regarded as a cluster and dynamically merges the two clusters which reduces the value of the global objective function the most until the required number of clusters is obtained, and the monotonicity of the sequence of objective func

Xiaoyi Wang, Chenxi Fu, Caimei Yang, Ziman Zhuang

Recursion is the nature of human natural language. Since Chomsky proposed generative grammar, many scholars have studied recursion either theoretically or empirically. However, by observing children's acquisition of tail recursion sequences, we can verify the nativism of language supported by universal grammar and reveal the cognitive mechanism of human brai

Rui Xie, Yue Chen, Xi Weng

Data centers have become one of the major energy consumers, making their low-carbon operations critical to achieving global carbon neutrality. Although distributed data centers have the potential to reduce costs and emissions through cooperation, they are facing challenges due to uncertainties. This paper proposes an online approach to co-optimize the worklo

Thomas Y. He, H. X. Huang, Y. X. Xie, T. T. Zou

Recently, Chen, He, Hu and Xie considered the parity of the number of non-overlined (resp. overlined) parts of size greater than or equal to the size of the smallest overlined (resp. non-overlined) part in an overpartition. In this article, we investigate the parity of the number of non-overlined (resp. overlined) parts of size less than or equal to the size

Ming-Yue Liu, Yuan Gong, Jiaojiao Chen, Yan-Wei Wang

Microwave-optical entanglement is essential for efficient quantum communication, secure information transfer, and integrating microwave and optical quantum systems to advance hybrid quantum technologies. In this work, we demonstrate how the magnon Kerr effect can be harnessed to generate and control nonreciprocal entanglement in cavity optomagnomechanics (CO

Yassine Ait Mohamed

In this paper, we explore functional identities with central values in gr-prime rings involving pairs of homogeneous derivations. We establish commutativity conditions that extend classical results from prime rings to the graded setting. In particular, we show that under certain conditions on homogeneous derivations, the ring must be commutative. Furthermore

Bo Hou, Zhanpeng Cui

We introduce the notion of an anti-Leibniz bialgebra which is equivalent to a Manin triple of anti-Leibniz algebras, is equivalent to a matched pair of anti-Leibniz algebras. The study of some special anti-Leibniz bialgebras leads to the introduction of the anti-Leibniz Yang-Baxter equation in an anti-Leibniz algebra. A symmetric (or an invariant) solution o

A. Nakamula, K. Obuse, N. Sawado, K. Shimasaki

The Zakharov-Kuznetsov equation, originally a three dimensional mathematical model of plasma with a uniform magnetic field, is a direct extension of the KdV equation into higher dimensions and is a typical quasi-integrable system. Physics-Informed Neural Networks (PINNs) are used to study the collision of soliton solutions in the 2+1 dimensional Zakharov-Kuz

Moon Moon Devi, Dharitree Bezboruah, Abinash Medhi, Arnab Sarker

The limitations of the Standard Model in explaining neutrino masses and neutrino mixing lead to the exploration of frameworks beyond the Standard Model (BSM). The possibility of neutrinos interacting with fermions via a scalar mediator is one of the interesting prospects. The study of neutrino non-standard interactions (NSI) is a well-motivated phenomenologi

Chunheng Zhao, Pierluigi Pisu, Gurcan Comert, Negash Begashaw

Deep learning-based discriminative classifiers, despite their remarkable success, remain vulnerable to adversarial examples that can mislead model predictions. While adversarial training can enhance robustness, it fails to address the intrinsic vulnerability stemming from the opaque nature of these black-box models. We present a deep ensemble model that comb

Ting Bai, Jiazheng Kang, Jiayang Fan

We introduce a comprehensive large-scale role-playing agent corpus, termed BaiJia, that comprises various Chinese historical characters. This corpus is noteworthy for being the pioneering compilation of low-resource data that can be utilized in large language models (LLMs) to engage in AI-driven historical role-playing agents. BaiJia addresses the challenges

Ryne Beeson, Anjian Li, Amlan Sinha

Preliminary spacecraft trajectory optimization is a parameter dependent global search problem that aims to provide a set of solutions that are of high quality and diverse. In the case of numerical solution, it is dependent on the original optimal control problem, the choice of a control transcription, and the behavior of a gradient based numerical solver. In

Tsuyoshi Iizuka, Yuki Hibiya, Satoshi Yoshihara, Takehito Hayakawa

The radioactive decay of short-lived 26Al to 26Mg has been used to estimate the timescales over which 26Al was produced in a nearby star and the protosolar disk evolved. The chronology commonly assumes that 26Al was uniformly distributed in the protosolar disk; however, this assumption is challenged by the discordance between the timescales defined by the Al

Mengnan Zhao, Lihe Zhang, Xingyi Yang, Tianhang Zheng

Security concerns surrounding text-to-image diffusion models have driven researchers to unlearn inappropriate concepts through fine-tuning. Recent fine-tuning methods typically align the prediction distributions of unsafe prompts with those of predefined text anchors. However, these techniques exhibit a considerable performance trade-off between eliminating

Sijia Chen, Ningxin Su, Baochun Li

In the context of personalized federated learning, existing approaches train a global model to extract transferable representations, based on which any client could train personalized models with a limited number of data samples. Self-supervised learning is considered a promising direction as the global model it produces is generic and facilitates personaliz

Guoyu Zhang, Dandan Jiang, Fang Yao

Spectral analysis plays a crucial role in high-dimensional statistics, where determining the asymptotic distribution of various spectral statistics remains a challenging task. Due to the difficulties of deriving the analytic form, recent advances have explored data-driven bootstrap methods for this purpose. However, widely used Gaussian approximation-based b

Qianli Liao, Liu Ziyin, Yulu Gan, Brian Cheung

Over the last four decades, the amazing success of deep learning has been driven by the use of Stochastic Gradient Descent (SGD) as the main optimization technique. The default implementation for the computation of the gradient for SGD is backpropagation, which, with its variations, is used to this day in almost all computer implementations. From the perspec

Xiaochuan Gong, Jie Hao, Mingrui Liu

This paper studies the problem of stochastic bilevel optimization where the upper-level function is nonconvex with potentially unbounded smoothness and the lower-level function is strongly convex. This problem is motivated by meta-learning applied to sequential data, such as text classification using recurrent neural networks, where the smoothness constant o

Wei Wang, Da Zhao

We provide a criterion to distinguish two graphs which are indistinguishable by $2$-dimensional Weisfeiler-Lehman algorithm for almost all graphs. Haemers conjectured that almost all graphs are identified by their spectrum. Our approach suggests that almost all graphs are identified by their generalized block Laplacian spectrum.

Heeyeon Kim, Jaewon Song

We find that multiple vertex algebras can arise from a single 4d $\mathcal{N}=2$ superconformal field theory (SCFT). The connection is given by the BPS monodromy operator $M$, which is a wall-crossing invariant quantity that captures the BPS spectrum on the Coulomb branch. For a class of low-rank Argyres-Douglas theories, we find that the trace of the multip

Hanjing Zhou, Mingze Yin, Wei Wu, Mingyang Li

Multi-modality pre-training paradigm that aligns protein sequences and biological descriptions has learned general protein representations and achieved promising performance in various downstream applications. However, these works were still unable to replicate the extraordinary success of language-supervised visual foundation models due to the ineffective u

Ye Lu

We derive explicit formulas for Kendall's tau and Spearman's rho for two broad classes of asymmetric copulas: normal location-scale mixture copulas and skew-normal scale mixture copulas. These classes encompass widely used specifications, including the normal scale mixture, skew-normal, and various skew-$t$ copulas, as special cases. The derived formulas est

Michael Fuchs, Mike Steel

Motivated by applications in medical bioinformatics, Khayatian et al. (2024) introduced a family of metrics on Cayley trees (the $k$-RF distance, for $k=0, \ldots, n-2$) and explored their distribution on pairs of random Cayley trees via simulations. In this paper, we investigate this distribution mathematically, and derive exact asymptotic descriptions of t

La Ode Aman, Aiyi Asnawi

This study aims to develop a deep learning model for predicting the binding affinity of ligands targeting the Peroxisome Proliferator-Activated Receptor (PPAR) family, using 2D molecular descriptors. A dataset of 3,764 small molecules with known binding affinities, sourced from the ChEMBL database, was preprocessed by eliminating duplicates and incomplete da

Tomoya Kato, Akihiko Miyachi, Naoto Shida, Naohito Tomita

For $s > 0$, $s \neq 1$, bilinear Fourier multipliers of the form $e^{i (|\xi|^s + |\eta|^s+ |\xi + \eta|^s)} \sigma (\xi, \eta)$ are considered, where $\sigma(\xi, \eta)$ belongs to the H\"ormander class $S^{m}_{1, 0}(\mathbb{R}^{2n})$. A criterion for $m$ to ensure the $L^{\infty}\times L^{\infty} \to L^\infty$, $L^{1} \times L^{\infty} \to L^{1}$, and $L^

Wei E. I. Sha, Xiaoyu Wang, Wenchao Chen, Yuhao Fu

SolarDesign (https://solardesign.cn/) is an online photovoltaic device simulation and design platform that provides engineering modeling analysis for crystalline silicon solar cells, as well as emerging high-efficiency solar cells such as organic, perovskite, and tandem cells. The platform offers user-updatable libraries of basic photovoltaic materials and d

Linshuo Jiang, Nachuan Xiao, Xin Liu

In this paper, we consider a class of stochastic optimization problems over the expectation-formulated generalized Stiefel manifold (SOEGS), where the objective function $f$ is continuously differentiable. We propose a novel constraint dissolving penalty function with a customized penalty term (CDFDP), which maintains the same order of differentiability as $

Elhoucine Elfatimi, Pratik Shah

Deep learning models (DLMs) frequently achieve accurate segmentation and classification of tumors from medical images. However, DLMs lacking feedback on their image segmentation mechanisms, such as Dice coefficients and confidence in their performance, face challenges when processing previously unseen images in real-world clinical settings. Uncertainty estim

Ryo Ide, Lei Yang

Rapidly growing wildfires have recently devastated societal assets, exposing a critical need for early warning systems to expedite relief efforts. Smoke detection using camera-based Deep Neural Networks (DNNs) offers a promising solution for wildfire prediction. However, the rarity of smoke across time and space limits training data, raising model overfittin

Yujie Luo, Xiangyuan Ru, Kangwei Liu, Lin Yuan

We introduce OneKE, a dockerized schema-guided knowledge extraction system, which can extract knowledge from the Web and raw PDF Books, and support various domains (science, news, etc.). Specifically, we design OneKE with multiple agents and a configure knowledge base. Different agents perform their respective roles, enabling support for various extraction s

Jun Liu, Yunming Liao, Hongli Xu, Yang Xu

Federated fine-tuning (FedFT) has been proposed to fine-tune the pre-trained language models in a distributed manner. However, there are two critical challenges for efficient FedFT in practical applications, i.e., resource constraints and system heterogeneity. Existing works rely on parameter-efficient fine-tuning methods, e.g., low-rank adaptation (LoRA), b

Yabo Dong, Kun Wang, Hailong Yuan, Jingya Zhu

To address the longstanding tension between the Constrained Minimal Supersymmetric Standard Model (CMSSM) and recent experimental data, we investigate non-universal gaugino masses within an SU(5) Grand Unified Theory (GUT) framework, focusing on the $\tilde{g}$-SUGRA scenario where $\lvert M_{3} \rvert \gg \lvert M_{1} \rvert, \lvert M_{2} \rvert$. This hier

You Wu, Yongxin Li, Mengyuan Liu, Xucheng Wang

Transformer-based models have improved visual tracking, but most still cannot run in real time on resource-limited devices, especially for unmanned aerial vehicle (UAV) tracking. To achieve a better balance between performance and efficiency, we propose AVTrack, an adaptive computation tracking framework that adaptively activates transformer blocks through a

Luis Kuffner, Reza Naserasr, Lujia Wang, Xiaowei Yu

The Kneser signed graph $\KS(n,k)$, $k\leq n$, is the graph whose vertices are signed $k$-subsets of $[n]$ (i.e. $k$-subsets $S$ of $\{ \pm 1, \pm 2, \ldots, \pm n\}$ such that $S\cap (-S)=\emptyset$). Two vertices $A$ and $B$ are adjacent with a positive edge if $A\cap (-B)=\emptyset$ and with a negative edge if $A\cap B=\emptyset$. We prove that the balanc

Marius Landry Foka, Michel Bertrand Ngaha Djiadeu, Thomas Bouetou Bouetou

The prescribed Ricci curvature problem involves finding a Riemannian metric g that satisfies the equation ric(g) = T, where T is a fixed symmetric (0, 2)-tensor field on a differential manifold M. In this paper, we introduce the concept of Schouten-like metrics as particular solutions to the prescribed Ricci curvature problem. We classify these metrics on fi

Yashvir Sabharwal, Balaji Rama

Electroencephalography (EEG) is an invaluable tool in neuroscience, offering insights into brain activity with high temporal resolution. Recent advancements in machine learning and generative modeling have catalyzed the application of EEG in reconstructing perceptual experiences, including images, videos, and audio. This paper systematically reviews EEG-to-o

William J. Keith

We investigate reciprocals of false theta functions, producing results such as congruences, simple asymptotic bounds, and combinatorial identities. Of particular interest is a connection between $1/\Psi(-q^2,q)$ and the truncated pentagonal number theorem of Andrews and Merca. We record a useful dissection identity analogous to the known theta function disse

Ling-Fang Li, Shun-Cai Zhao

Recent evidence suggests that the multi charge-separation pathways can contribute to the photosynthetic performance. In this work, the influence of coupled-dipoles on the photosynthetic performance was investigated in a two-charge separation pathways quantum heat engine (QHE) model. And the population dynamics of the two coupled sites, j-V characteristics an

Jiale Huang, Dehong Gao, Jinxia Zhang, Zechao Zhan

Large-scale Vision-Language Pre-training (VLP) has demonstrated remarkable success in the general domain. However, in the fashion domain, items are distinguished by fine-grained attributes like texture and material, which are crucial for tasks such as retrieval. Existing models often fail to leverage these fine-grained attributes from both text and image mod

Yaoqi Yang, Yong Chen, Jiacheng Wang, Geng Sun

Low altitude economy (LAE) holds immense potential to drive urban development across various sectors. However, LAE also faces challenges in data collection and processing efficiency, flight control precision, and network performance. The challenges could be solved by realizing an integration of sensing, communications, computation, and control (ISC3) for LAE