March 2024 arXiv papers — page 2

Tao Chen, Chenxi Huang, Bryce Gadway, Jacob P. Covey

Coherent dynamics of interacting quantum particles plays a central role in the study of strongly correlated quantum matter and the pursuit of quantum information processors. Here, we present the state-space of interacting Rydberg atoms as a synthetic landscape on which to control and observe coherent and correlated dynamics. With full control of the coupling

Giuseppe G. A. Celano

In this article, the beta version 0.1.0 of Opera Graeca Adnotata (OGA), the largest open-access multilayer corpus for Ancient Greek (AG) is presented. OGA consists of 1,687 literary works and 34M+ tokens coming from the PerseusDL and OpenGreekAndLatin GitHub repositories, which host AG texts ranging from about 800 BCE to about 250 CE. The texts have been enr

Sheng-Yang Kevin Ho

Let $\mathfrak{n} = \mathfrak{p}^r$ be a prime power ideal of $\mathbb{F}_q[T]$ with $r \geq 2$. We study the rational torsion subgroup $\mathcal{T}(\mathfrak{p}^r)$ of the Drinfeld modular Jacobian $J_0(\mathfrak{p}^r)$. We prove that the prime-to-$q(q-1)$ part of $\mathcal{T}(\mathfrak{p}^r)$ is equal to that of the rational cuspidal divisor class group $\

Tao Chen, Chenxi Huang, Ivan Velkovsky, Tomoki Ozawa

Flat bands play a central role in hosting emergent states of matter in many condensed matter systems, from the nascent insulating states of twisted bilayer graphene to the fractionalized excitations found in frustrated magnets and quantum Hall materials. Here, we report on the experimental realization of highly tunable flat-band models populated by strongly

Bartosz Malman, Daniel Seco

We study conditions for containment of a given space $X$ of analytic functions on the unit disk $\mathbb{D}$ in the de Branges-Rovnyak space $\mathcal{H}(b)$. We deal with the non-extreme case in which $b$ admits a Pythagorean mate $a$, and derive a multiplier boundedness criterion on the function $\phi = b/a$ which implies the containment $X \subset \mathca

Chang Liu, AmirEmad Ghassami

Influence function (IF)-based estimators are widely used in mediation analysis due to their modeling flexibility, but standard implementations require direct estimation of the distribution functions of the mediator and treatment variables. Since these functions appear in the denominator of IF-based estimators, they can induce significant instability, particu

Thejus Mary S., Avijit Hazra, Neelesh Sharma, Rohit Dhir

We present a comprehensive analysis of weak transition form factors, semileptonic decays, and nonleptonic decays of $B_c$ meson involving pseudoscalar ($P$) and vector ($V$) meson for bottom-conserving and bottom-changing decay modes. We employ self-consistent covariant light-front quark model (CLFQM), termed as Type-II correspondence, to calculate the $B_c$

Fernando Palafox, Jesse Milzman, Dong Ho Lee, Ryan Park

We present a mathematical framework for modeling two-player noncooperative games in which one player is uncertain of the other player's costs but can preemptively allocate information-gathering resources to reduce this uncertainty. We refer to the players as the uncertain player (UP) and the certain player (CP), respectively. We obtain UP's decisions by solv

Katy Blumer, Kate Donahue, Katie Fritz, Kate Ivanovich

In this paper, we study the highly competitive arena of baby naming. Through making several Extremely Reasonable Assumptions (namely, that parents are myopic, perfectly knowledgeable agents who pick a name based solely on its uniqueness), we create a model which is not only tractable and clean, but also perfectly captures the real world. We then extend our i

Tyler Dunaisky, David Krumm

Motivated by a uniform boundedness conjecture of Morton and Silverman, we study the graphs of pre-periodic points for maps in three families of dynamical systems, namely the collections of rational functions of degree two having a periodic critical point of period $n$, where $n\in\{2,3,4\}$. In particular, we provide a conjecturally complete list of possible

Xuan-Qian Bao, Xue-Dong Tian, Dong-Xiao Li, Yi-Mou Liu

We propose a theoretical scheme for dipole exchange-induced grating (DEIG) based on a hybrid system consisting of ultra-cold Rubidium ($^{87}$Rb) atomic ensemble and movable Rydberg spin atoms. The optical response of the grating appears as a superposition of three- and four-level configurations, similar to the cooperative optical nonlinear effect caused by

Minghui Chen, Zichao Meng, Yanping Liu, Longbo Luo

In this paper, we introduce a nonparametric end-to-end method for probabilistic forecasting of distributed renewable generation outputs while including missing data imputation. Firstly, we employ a nonparametric probabilistic forecast model utilizing the long short-term memory (LSTM) network to model the probability distributions of distributed renewable gen

Luis Morales-Navarro, Yasmin B. Kafai

Despite recent calls for including artificial intelligence (AI) literacy in K-12 education, not enough attention has been paid to studying youths' everyday knowledge about machine learning (ML). Most research has examined how youths attribute intelligence to AI/ML systems. Other studies have centered on youths' theories and hypotheses about ML highlighting t

Ian Porada, Xiyuan Zou, Jackie Chi Kit Cheung

All state-of-the-art coreference resolution (CR) models involve finetuning a pretrained language model. Whether the superior performance of one CR model over another is due to the choice of language model or other factors, such as the task-specific architecture, is difficult or impossible to determine due to lack of a standardized experimental setup. To reso

Chen Peng, Zhiqin Qian, Kunyu Wang, Qi Luo

Biomedical image segmentation is a very important part in disease diagnosis. The term "colonic polyps" refers to polypoid lesions that occur on the surface of the colonic mucosa within the intestinal lumen. In clinical practice, early detection of polyps is conducted through colonoscopy examinations and biomedical image processing. Therefore, the accurate po

Michael Hassid, Tal Remez, Jonas Gehring, Roy Schwartz

It is a common belief that large language models (LLMs) are better than smaller-sized ones. However, larger models also require significantly more time and compute during inference. This begs the question: what happens when both models operate under the same budget? (e.g., compute, run-time). To address this question, we analyze code generation LLMs of vario

Jia Guo, Haonan Han, Shuai Lu, Weihang Zhang

Conventional unsupervised anomaly detection (UAD) methods build separate models for each object category. Recent studies have proposed to train a unified model for multiple classes, namely model-unified UAD. However, such methods still implement the unified model separately on each class during inference with respective anomaly decision thresholds, which hin

Qian Zhang, Jie Wang, Tian-Xiang Lu, Franco Nori

Cavity magnomechanics, exhibiting remarkable experimental tunability, rich magnonic nonlinearities, and compatibility with various quantum systems, has witnessed considerable advances in recent years. However, the potential benefits of using cavity magnomechanical (CMM) systems in further improving the performance of quantum-enhanced sensing for weak forces

Chih-Chung Hsu, Chia-Ming Lee, Yi-Shiuan Chou

In recent years, Vision Transformer-based approaches for low-level vision tasks have achieved widespread success. Unlike CNN-based models, Transformers are more adept at capturing long-range dependencies, enabling the reconstruction of images utilizing non-local information. In the domain of super-resolution, Swin-transformer-based models have become mainstr

Naoki Hiraiwa, Mai Bando, Isaia Nisoli, Yuzuru Sato

Modern space missions with uncrewed spacecraft require robust trajectory design to connect multiple chaotic orbits by small controls. To address this issue, we propose a control scheme to design robust trajectories by leveraging a geometrical structure in chaotic zones, known as a {\it lobe}. Our scheme shows that appropriately selected lobes reveal possible

Carlos Araújo Álvarez, Henry W. Y. Wong, Anna Liu, Juan Calderón Bustillo

Pair-instability supernova (PISN) prevents black-hole formation from stellar collapse within the approximate mass range $M\in [65,130]M_\odot$. However, such black holes may form hierarchically through merging ancestral black holes, whose properties determine those of the ``child'' one: mass, spin, and recoil velocity. Crucially, the child will leave its hos

Zi-Kui Liu, Shun-Li Shang

A coherent theory for the superconductivity of both conventional and unconventional superconductors is currently lacking. Here we show that superconductivity arises from the formation of a symmetry-broken superconducting configuration (SCC) due to atomic perturbation of the normal conducting configuration (NCC). This electron-phonon interaction creates strai

Mohd Ali, Vardarajan Suneeta

In this paper, we show a local generalized second law (the generalized entropy is nondecreasing) in crossed product constructions for maximally extended static and Kerr black holes using modular theory. The new ingredient is the use of results from a recent paper discussing the entropy of the algebra of operators in subregions of arbitrary spacetimes. These

Haibao Yu, Wenxian Yang, Jiaru Zhong, Zhenwei Yang

Cooperatively utilizing both ego-vehicle and infrastructure sensor data via V2X communication has emerged as a promising approach for advanced autonomous driving. However, current research mainly focuses on improving individual modules, rather than taking end-to-end learning to optimize final planning performance, resulting in underutilized data potential. I

L. Kneale

Since the Super-Kamiokande (SK) neutrino experiment in Japan added gadolinium sulphate octahydrate (Gd) to the pure water in its detector, it has entered a new era in supernova neutrino detection. The addition of Gd makes it possible to tag neutrons from inverse beta decay (IBD) interactions of electron antineutrinos with free protons in the water. This has

Alessio Belfiglio, Orlando Luongo, Stefano Mancini, Sebastiano Tomasi

We discuss the entanglement entropy for a massive Klein-Gordon field in two Schwarzschild-like quantum black hole spacetimes, also including a nonminimal coupling term with the background scalar curvature. To compute the entanglement entropy, we start from the standard spherical shell discretisation procedure, tracing over the degrees of freedom residing ins

Mingyuan Yao, Yukang Huo, Yang Ran, Qingbin Tian

In recent years, Neural Radiance Fields (NeRF) has made remarkable progress in the field of computer vision and graphics, providing strong technical support for solving key tasks including 3D scene understanding, new perspective synthesis, human body reconstruction, robotics, and so on, the attention of academics to this research result is growing. As a revo

Jianqing Jia, Ashley Prater-Bennette, Lixin Shen

This paper investigates the computation of proximity operators for scale and signed permutation invariant functions. A scale-invariant function remains unchanged under uniform scaling, while a signed permutation invariant function retains its structure despite permutations and sign changes applied to its input variables. Noteworthy examples include the $\ell

Jesse Atuhurra, Hidetaka Kamigaito

Natural language processing (NLP) has grown significantly since the advent of the Transformer architecture. Transformers have given birth to pre-trained large language models (PLMs). There has been tremendous improvement in the performance of NLP systems across several tasks. NLP systems are on par or, in some cases, better than humans at accomplishing speci

Yan Zhuang, Qi Liu, Haoyang Bi, Zhenya Huang

Computerized Adaptive Testing (CAT) offers an efficient and personalized method for assessing examinee proficiency by dynamically adjusting test questions based on individual performance. Compared to traditional, non-personalized testing methods, CAT requires fewer questions and provides more accurate assessments. As a result, CAT has been widely adopted acr

Michael Allen, Brian Grove, Ling Long, Fang-Ting Tu

The theories of hypergeometric functions and modular forms are highly intertwined. For example, particular values of truncated hypergeometric functions and hypergeometric character sums are often congruent or equal to Fourier coefficients of modular forms. In this series of papers, we develop and explore an explicit "Hypergeometric-Modularity" method for ass

Mainak Singha, Ankit Jha, Shirsha Bose, Ashwin Nair

We delve into Open Domain Generalization (ODG), marked by domain and category shifts between training's labeled source and testing's unlabeled target domains. Existing solutions to ODG face limitations due to constrained generalizations of traditional CNN backbones and errors in detecting target open samples in the absence of prior knowledge. Addressing thes

Peter Morfe, Felix Otto, Christian Wagner

This note is about a drift-diffusion process $X$ with a time-independent, divergence-free drift $b$, where $b$ is a smooth Gaussian field that decorrelates over large scales. In two space dimensions, this just fails to fall into the standard theory of stochastic homogenization, and leads to a borderline super-diffusive behavior. In a previous paper by Chatzi

M. I. Ayzatsky

In this work we present the results of a study of the possibility of using a homogeneous basis and a new generalization of coupled modes theory to describe non-periodic structured waveguides. It was shown that for the studied inhomogeneities the single wave approximation gives good results. Within the framework of single wave approximation, the fields are re

Anna Musilová, Aleš Nekvinda, Dalimil Peša, Hana Turčinová

This paper explores some important aspects of the theory of rearrangement-invariant quasi-Banach function spaces. We focus on two main topics. Firstly, we prove an analogue of the Luxemburg representation theorem for rearrangement-invariant quasi-Banach function spaces over resonant measure spaces. Secondly, we develop the theory of fundamental functions and

Shuai Yang, Zhiyu Dong, Yan Chen

The behavior of strongly interacting electrons in bands with Berry curvature is a problem of wide interest. In this paper, we study this problem by numerically studying a fluxed Hubbard-type model on square lattice. Using this model, we demonstrate a metallic ferromagnet in electron bands equipped with Berry curvature can develop non-coplanar spin order in w

Dusan Sarenac, Melissa E. Henderson, Huseyin Ekinci, Charles W. Clark

Access to the neutron orbital degree of freedom has been enabled by the recent actualization of methods to prepare and characterize neutron helical waves carrying orbital angular momentum (OAM) at small-angle neutron scattering (SANS) facilities. This provides new avenues of exploration in fundamental science experiments as well as in material characterizati

Kamran Razavi, Saeid Ghafouri, Max Mühlhäuser, Pooyan Jamshidi

Mobile and IoT applications increasingly adopt deep learning inference to provide intelligence. Inference requests are typically sent to a cloud infrastructure over a wireless network that is highly variable, leading to the challenge of dynamic Service Level Objectives (SLOs) at the request level. This paper presents Sponge, a novel deep learning inference s

Boris Botvinnik, Jonathan Rosenberg

Let $(M,L)$ be a (compact) non-spin spin$^c$ manifold. Fix a Riemannian metric $g$ on $M$ and a connection $A$ on $L$, and let $D_L$ be the associated spin$^c$ Dirac operator. Let $R^{tw}_{(g,A)}:=R_g + 2ic(\Omega)$ be the twisted scalar curvature (which takes values in the endomorphisms of the spinor bundle), where $R_g$ is the scalar curvature of $g$ and $

Wenlin Zhang, Chuhan Wu, Xiangyang Li, Yuhao Wang

The lack of training data gives rise to the system cold-start problem in recommendation systems, making them struggle to provide effective recommendations. To address this problem, Large Language Models (LLMs) can model recommendation tasks as language analysis tasks and provide zero-shot results based on their vast open-world knowledge. However, the large s

Wenfang Sun, Yingjun Du, Gaowen Liu, Ramana Kompella

Recent advancements in open vocabulary models, like CLIP, have notably advanced zero-shot classification and segmentation by utilizing natural language for class-specific embeddings. However, most research has focused on improving model accuracy through prompt engineering, prompt learning, or fine-tuning with limited labeled data, thereby overlooking the imp

Omri Nisan Solan

We show that for every nonarithmetic lattice $\Gamma<{\rm SL}_2(\mathbb{C})$ there is a gap $\varepsilon_\Gamma>0$ such that for every $g\in {\rm SL}_2(\mathbb{C})$ the intersection ${\rm SL}_2(\mathbb{R})\cap g\Gamma g^{-1}$ is either a lattice in ${\rm SL}_2(\mathbb{R})$ or has critical exponent $\delta({\rm SL}_2(\mathbb{R})\cap g\Gamma g^{-1}) \leq 1 - \

Mathieu Ravaut, Bosheng Ding, Fangkai Jiao, Hailin Chen

With the rise of Large Language Models (LLMs) in recent years, abundant new opportunities are emerging, but also new challenges, among which contamination is quickly becoming critical. Business applications and fundraising in Artificial Intelligence (AI) have reached a scale at which a few percentage points gained on popular question-answering benchmarks cou

Hajime Otsuka, Ryo Yokokura

We study primordial electromagnetic fields in effective actions of string theory. In contrast to a conventional scenario of producing primordial electromagnetic fields induced by the axion inflation, we deal with the Dirac-Born-Infeld action as a non-linear generation of Maxwell theory. It turns out that the intensity of generated electromagnetic fields is b

Haoran Li, Zhenzhou Yuan, Rui Yue, Guangchuan Yang

The strategy of permitting general traffic to use the bus lane for improved utilization while ensuring bus priority has gained increasingly attention, particularly with the support of vehicle-to-everything technology. In this study, we propose a novel lane usage strategy called Dynamic Spatial-Temporal Priority (DSTP) to ensure bus priority and optimize bus

Abdallah Alshantti, Adil Rasheed, Frank Westad

Generative models are subject to overfitting and thus may potentially leak sensitive information from the training data. In this work. we investigate the privacy risks that can potentially arise from the use of generative adversarial networks (GANs) for creating tabular synthetic datasets. For the purpose, we analyse the effects of re-identification attacks

Jingyue Wang, Junwei Huang, Daniel Kaplan, Xuehan Zhou

In the presence of high magnetic field, quantum Hall systems usually host both even- and odd-integer quantized states because of lifted band degeneracies. Selective control of these quantized states is challenging but essential to understand the exotic ground states and manipulate the spin textures. Here, we study the quantum Hall effect in Bi2O2Se thin film

Haolin Qin, Tingfa Xu, Peifu Liu, Jingxuan Xu

Hyperspectral salient object detection (HSOD) has exhibited remarkable promise across various applications, particularly in intricate scenarios where conventional RGB-based approaches fall short. Despite the considerable progress in HSOD method advancements, two critical challenges require immediate attention. Firstly, existing hyperspectral data dimension r

Zhuoyi Pang, Fei Yao, Jian-Hui Zhang

We propose two approaches for extracting the total gluon helicity contribution to proton spin from lattice QCD, one from local operator matrix elements in a fixed gauge accessible on lattice with feasible renormalization, and the other from gauge-invariant nonlocal gluon correlators. Neither of these approaches requires a matching procedure when converted to

Xiujie Deng, Alexander Wu Chao, Wenhui Huang, Zizheng Li

In this paper, three specific scenarios of a novel accelerator light source mechanism called steady-state micro-bunching (SSMB) have been studied, i.e., longitudinal weak focusing, longitudinal strong focusing and generalized longitudinal strong focusing (GLSF). At present, GLSF is the most promising among them in realizing high-power short-wavelength cohere

Xiu-Cai Jiang, Ze-Yi Song, Ze Ruan, Yu-Zhong Zhang

We propose that a weakly spontaneous charge-ordered insulating state probably exists in Bernal-stacked bilayer graphene which can account for experimentally observed non-monotonic behavior of resistance as a function of the gated field, namely, the gap closes and reopens at a critical gated field. The underlying physics is demonstrated by a simple model on a

Meisam Kabiri, Claudio Cimarelli, Hriday Bavle, Jose Luis Sanchez-Lopez

5G New Radio Time of Arrival (ToA) data has the potential to revolutionize indoor localization for micro aerial vehicles (MAVs). However, its performance under varying network setups, especially when combined with IMU data for real-time localization, has not been fully explored so far. In this study, we develop an error state Kalman filter (ESKF) and a pose

Gerald X. Gilbert-Thorple, Jôsé J. Jesus

An outstanding mystery of dark matter physics is the lack of direct detection signals to date. We suggest that dark matter is scatophobic: due to a repulsive long-range interaction, it is repelled by objects with a large net scat charge, such as the Earth, and is therefore not able to reach direct detection experiments. This represents the first step in a br

Antoine Lemenant, Mohammad Reza Pakzad

We establish a $\Gamma$-convergence result for $h\to 0$ of a thin nonlinearly elastic 3D-plate of thickness $h>0$ which is assumed to be glued to a support region in the 2D-plane $x_3=0$ over the $h$-2D-neighborhood of a given closed set $K$. In the regime of very small vertical forces we identify the $\Gamma$-limit as being the bi-harmonic energy, with Diri

Steven Bilaj, Sofien Dhouib, Setareh Maghsudi

We study the problem of meta-learning several contextual stochastic bandits tasks by leveraging their concentration around a low-dimensional affine subspace, which we learn via online principal component analysis to reduce the expected regret over the encountered bandits. We propose and theoretically analyze two strategies that solve the problem: One based o

Weimin Zhang

Here we consider the following fractional Hamiltonian system \begin{equation*} \begin{cases} \begin{aligned} (-\Delta)^{s} u&=H_v(u,v) \;\;&&\text{in}~\Omega,\\ (-\Delta)^{s} v&=H_u(u,v) &&\text{in}~\Omega,\\ u &= v = 0 &&\text{in} ~ \mathbb{R}^N\setminus\Omega, \end{aligned} \end{cases} \end{equation*} where $s\in (0,1)$, $N>2s$, $H \in C^1(\mathbb{R}^2, \m

Srinjoy Roy, Swagatam Das

Accounting for the uncertainty of value functions boosts exploration in Reinforcement Learning (RL). Our work introduces Maximum Mean Discrepancy Q-Learning (MMD-QL) to improve Wasserstein Q-Learning (WQL) for uncertainty propagation during Temporal Difference (TD) updates. MMD-QL uses the MMD barycenter for this purpose, as MMD provides a tighter estimate o

Michael Timothy Bennett

Simplicity is held by many to be the key to general intelligence. Simpler models tend to "generalise", identifying the cause or generator of data with greater sample efficiency. The implications of the correlation between simplicity and generalisation extend far beyond computer science, addressing questions of physics and even biology. Yet simplicity is a pr

Santiago Cuervo, Ricard Marxer

Speech Language Models (SLMs) aim to learn language from raw audio, without textual resources. Despite significant advances, our current models exhibit weak syntax and semantic abilities. However, if the scaling properties of neural language models hold for the speech modality, these abilities will improve as the amount of compute used for training increases

Shiguang Wu, Wenda Wei, Mengqi Zhang, Zhumin Chen

Generative retrieval generates identifiers of relevant documents in an end-to-end manner using a sequence-to-sequence architecture for a given query. The relation between generative retrieval and other retrieval methods, especially those based on matching within dense retrieval models, is not yet fully comprehended. Prior work has demonstrated that generativ

Zeev Nutov

A classic result of Williamson, Goemans, Mihail, and Vazirani [STOC 1993: 708-717] states that the problem of covering an uncrossable set family by a min-cost edge set admits approximation ratio $2$, by a primal-dual algorithm with a reverse delete phase. Recently, Bansal, Cheriyan, Grout, and Ibrahimpur [ICALP 2023: 15:1-15:19] showed that this algorithm ac

J. -C. Zhang, B. -H. Sun, I. Tanihata, R. Kanungo

We report the charge-changing cross sections ($\sigma_{\text{cc}}$) of 24 $p$-shell nuclides on both hydrogen and carbon at about 900$A$ MeV, of which $^{8,9}$Li, $^{10\textendash12}$Be, $^{10,14,15}$B, $^{14,15,17\textendash22}$N and $^{16}$O on hydrogen and $^{8,9}$Li on carbon are for the first time. Benefiting from the data set,we found a new and robust

Dawei Zhu, Wenhao Wu, Yifan Song, Fangwei Zhu

Coherence evaluation aims to assess the organization and structure of a discourse, which remains challenging even in the era of large language models. Due to the scarcity of annotated data, data augmentation is commonly used for training coherence evaluation models. However, previous augmentations for this task primarily rely on heuristic rules, lacking desi

Yang Luo, Zhineng Chen, Peng Zhou, Zuxuan Wu

Images suffer from heavy spatial redundancy because pixels in neighboring regions are spatially correlated. Existing approaches strive to overcome this limitation by reducing less meaningful image regions. However, current leading methods rely on supervisory signals. They may compel models to preserve content that aligns with labeled categories and discard c

Alexander Gambashidze, Aleksandr Dadukin, Maksim Golyadkin, Maria Razzhivina

This paper addresses the critical challenges of sparsity and occlusion in LiDAR-based 3D object detection. Current methods often rely on supplementary modules or specific architectural designs, potentially limiting their applicability to new and evolving architectures. To our knowledge, we are the first to propose a versatile technique that seamlessly integr

Hakyeong Kim, Andreas Meuleman, Hyeonjoong Jang, James Tompkin

We present a method to reconstruct indoor and outdoor static scene geometry and appearance from an omnidirectional video moving in a small circular sweep. This setting is challenging because of the small baseline and large depth ranges, making it difficult to find ray crossings. To better constrain the optimization, we estimate geometry as a signed distance

Wei Wang, Zhifei Zhang

We study a class of Landau-de Gennes energy functionals with a sextic bulk energy density in a three-dimensional domain. We examine the asymptotic behavior of uniformly bounded minimizers in two distinct scenarios: one where their energy remains uniformly bounded, and another where it logarithmically diverges as a function of the elastic constant. In the fir

Dongyoung Choi, Hyeonjoong Jang, Min H. Kim

Omnidirectional cameras are extensively used in various applications to provide a wide field of vision. However, they face a challenge in synthesizing novel views due to the inevitable presence of dynamic objects, including the photographer, in their wide field of view. In this paper, we introduce a new approach called Omnidirectional Local Radiance Fields (

Yassir Bendou, Giulia Lioi, Bastien Pasdeloup, Lukas Mauch

We consider the problem of zero-shot one-class visual classification, extending traditional one-class classification to scenarios where only the label of the target class is available. This method aims to discriminate between positive and negative query samples without requiring examples from the target class. We propose a two-step solution that first querie

Wonseok Lee, Yeonghun Kang, Taeun Bae, Jihan Kim

This research was focused on the efficient collection of experimental Metal-Organic Framework (MOF) data from scientific literature to address the challenges of accessing hard-to-find data and improving the quality of information available for machine learning studies in materials science. Utilizing a chain of advanced Large Language Models (LLMs), we develo

Wenxiao Cai, Xinyue Lei, Xinyu He, Junming Leo Chen

We present Knowledge NeRF to synthesize novel views for dynamic scenes. Reconstructing dynamic 3D scenes from few sparse views and rendering them from arbitrary perspectives is a challenging problem with applications in various domains. Previous dynamic NeRF methods learn the deformation of articulated objects from monocular videos. However, qualities of the

Thanh Tam Nguyen, Thanh Trung Huynh, Zhao Ren, Thanh Toan Nguyen

As the adoption of explainable AI (XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and the

Wenxuan Huang, Yunhang Shen, Jiao Xie, Baochang Zhang

The remarkable performance of Vision Transformers (ViTs) typically requires an extremely large training cost. Existing methods have attempted to accelerate the training of ViTs, yet typically disregard method universality with accuracy dropping. Meanwhile, they break the training consistency of the original transformers, including the consistency of hyper-pa

Metin Gürses, Aslı Pekcan

To obtain new integrable nonlinear differential equations there are some well-known methods such as Lax equations with different Lax representations. There are also some other methods which are based on integrable scalar nonlinear partial differential equations. We show that some systems of integrable equations published recently are the ${\cal M}_{2}$-exten

Youngseo Cho, In Hee Kwak, Dohyeon Kim, Jinhee Na

Bradykinesia, characterized by involuntary slowing or decrement of movement, is a fundamental symptom of Parkinson's Disease (PD) and is vital for its clinical diagnosis. Despite various methodologies explored to quantify bradykinesia, computer vision-based approaches have shown promising results. However, these methods often fall short in adequately address

Vadim V. Bobylev, Anisa T. Bajkova

The trajectories of the clusters $\eta$ Cha, $\epsilon$ Cha and Cha I, Cha II, constructed backward in time, have been studied. We concluded that the hypothesis about the joint formation of all four of these clusters from one molecular cloud cannot be completely excluded. However, 10-15 Myr ago, all these four clusters were located at approximately the same

Yong Lin, Shi Wan

By the connection graph we mean an underlying weighted graph with a connection which associates edge set with an orthogonal group. This paper centers its investigation on the connection heat kernels on connection lattices and connection discrete torus. For one dimensional connection lattice, we derive the connection heat kernel expression by doing the Taylor

Dafei Qiu, Shan Xiong, Jiajin Yi, Jialin Peng

Accurate segmentation of organelle instances from electron microscopy (EM) images plays an essential role in many neuroscience researches. However, practical scenarios usually suffer from high annotation costs, label scarcity, and large domain diversity. While unsupervised domain adaptation (UDA) that assumes no annotation effort on the target data is promis

Itai Kreisler, Maor Ivgi, Oliver Hinder, Yair Carmon

We propose a method that achieves near-optimal rates for smooth stochastic convex optimization and requires essentially no prior knowledge of problem parameters. This improves on prior work which requires knowing at least the initial distance to optimality d0. Our method, U-DoG, combines UniXGrad (Kavis et al., 2019) and DoG (Ivgi et al., 2023) with novel it

Santosh Kumar Chaudhary, Nitin Gupta, Achintya Roy

In this paper, we introduce the cumulative past information generating function (CPIG) and relative cumulative past information generating function (RCPIG). We study its properties. We establish its relation with generalized cumulative past entropy (GCPE). We defined CPIG stochastic order and its relation with dispersive order. We provide the results for the

Vladimir Kozlov

We consider solitary water waves on a rotational, unidirectional flow in a two-dimensional channel of finite depth. Ovsyannikov has conjectured in 1983 that the solitary wave is uniquely determined by the Bernoulli constant, mass flux and by the flow force. This conjecture was disproved by Plotnikov in 1992 for the ir-rotational flow. In this paper we show t

Yingjin Bi

In this paper, we give a geometrization of semicanonical bases of quantum groups via Grothendieck groups of the derived categories of Lusztig's nilpotent varieties. Meanwhile, we describe the dual semicanonical bases in terms of Serre polynomials of Grassmannians of modules over preprojective algebras.

René Rühr

We associate to each unit volume lattice of $\R^d$ the Ising model with bond variables equal to the inverse successive minima of that lattice. This induces the notion of a critical temperature for a random lattice for which integrability exponents are proven.

Chunyang Bi, Xin Luo, Sheng Shen, Mengxi Zhang

Diffusion models, known for their powerful generative capabilities, play a crucial role in addressing real-world super-resolution challenges. However, these models often focus on improving local textures while neglecting the impacts of global degradation, which can significantly reduce semantic fidelity and lead to inaccurate reconstructions and suboptimal s

Syl Shaw, Rudolf A. Römer

The real-space renormalisation group method can be applied to the Chalker-Coddington model of the quantum Hall transition to provide a convenient numerical estimation of the localisation critical exponent, $\nu$. Previous such studies found $\nu\sim 2.39$ which falls considerably short of the current best estimates by transfer matrix ($\nu\approx 2.593$) and

Bowen Chen, Xiaokai Wu, Zhiyu Liao, Zhendong Fu

The search for exotic spin configurations and related novel transport properties continues to be fueled by the promise of new electronic states and outstanding candidate components for spintronic applications. In layered Cr$_{7-\delta}$Te$_8$, the applied field drives a before unreported magnetic transition revealed by the alternating current magnetic suscep

Jihua Peng, Yanghong Zhou, P. Y. Mok

This paper presents a novel Kinematics and Trajectory Prior Knowledge-Enhanced Transformer (KTPFormer), which overcomes the weakness in existing transformer-based methods for 3D human pose estimation that the derivation of Q, K, V vectors in their self-attention mechanisms are all based on simple linear mapping. We propose two prior attention modules, namely

Sumit Soman, Sujoy Roychowdhury

Retrieval augmented generation (RAG) for technical documents creates challenges as embeddings do not often capture domain information. We review prior art for important factors affecting RAG and perform experiments to highlight best practices and potential challenges to build RAG systems for technical documents.

Shujie Hu, Long Zhou, Shujie Liu, Sanyuan Chen

The recent advancements in large language models (LLMs) have revolutionized the field of natural language processing, progressively broadening their scope to multimodal perception and generation. However, effectively integrating listening capabilities into LLMs poses significant challenges, particularly with respect to generalizing across varied contexts and

Haibo Li

The generalized singular value decomposition (GSVD) of a matrix pair $\{A, L\}$ with $A\in\mathbb{R}^{m\times n}$ and $L\in\mathbb{R}^{p\times n}$ generalizes the singular value decomposition (SVD) of a single matrix. In this paper, we provide a new understanding of GSVD from the viewpoint of SVD, based on which we propose a new iterative method for computin

Osamu Seto, Takashi Shimomura, Yoshiki Uchida

We consider the gauged $U(1)_{B-L}$ model and examine the situation where the sterile neutrino is a dark matter candidate produced by the freeze-in mechanism. In our model, the dark matter $N$ is mainly produced by the decay of a $U(1)_{B-L}$ breaking scalar boson $\phi$. We point out that the on-shell production of $\phi$ through annihilation of the $U(1)_{

Yuhan Zhu, Guozhen Zhang, Jing Tan, Gangshan Wu

Temporal Action Detection (TAD) aims to identify the action boundaries and the corresponding category within untrimmed videos. Inspired by the success of DETR in object detection, several methods have adapted the query-based framework to the TAD task. However, these approaches primarily followed DETR to predict actions at the instance level (i.e., identify e

Sergey Rybakov

We refine and generalize the results of K. E. Lauter and E. W. Howe on principal polarizations on products of abelian varieties over finite fields. Firstly, we study the reasons for the absence of an irreducible principal polarization in the isogeny class of the product of an ordinary and a supersingular abelian variety. Secondly, we provide a necessary cond

Yibo Wang, Jiang Zhao

Recent advancements in deep reinforcement learning (RL) have demonstrated notable progress in sample efficiency, spanning both model-based and model-free paradigms. Despite the identification and mitigation of specific bottlenecks in prior works, the agent's exploration ability remains under-emphasized in the realm of sample-efficient RL. This paper investig

Xiaorui Huang, Gen Luo, Chaoyang Zhu, Bo Tong

Recently, Segment Anything Model (SAM) has become a research hotspot in the fields of multimedia and computer vision, which exhibits powerful yet versatile capabilities on various (un) conditional image segmentation tasks. Although SAM can support different types of segmentation prompts, we note that, compared to point- and box-guided segmentations, it perfo

Sergey Volkov

The total 5-loop quantum electrodynamics universal contribution to the anomalous magnetic moments of the leptons was calculated by the author. The obtained value $A_1^{(10)}=5.891(61)$ provides the first complete verification of the previously known value obtained by T. Aoyama, M. Hayakawa, T. Kinoshita, M. Nio (AHKN). The discrepancy is $5 \sigma$. The comp

Haojie Mu, Burhan Ul Tayyab, Nicholas Chua

We present SpiralMLP, a novel architecture that introduces a Spiral FC layer as a replacement for the conventional Token Mixing approach. Differing from several existing MLP-based models that primarily emphasize axes, our Spiral FC layer is designed as a deformable convolution layer with spiral-like offsets. We further adapt Spiral FC into two variants: Self

Tomonari Michiyama, Yoshiyuki Inoue, Akihiro Doi, Tomoya Yamada

GRS 1734-292 is a radio-quiet galaxy, exhibiting neither intense starburst nor jet activities. However, Fermi-LAT detected this object in the GeV band. The origin of non-thermal activity in this Seyfert galaxy is an intriguing question. We report Atacama Large Millimeter/submillimeter Array (ALMA) observations of GRS 1734-292 at frequencies of 97.5, 145, and

Shinya Kanemura, Masanori Tanaka, Ke-Pan Xie

We investigate the formation of primordial black holes (PBHs) through delayed vacuum decay during slow cosmic first-order phase transitions. Two specific models, the polynomial potential and the real singlet extension of the Standard Model, are used as illustrative examples. Our findings reveal that models with zero-temperature scalar potential barriers are

Abdul Aziz A. B, Aindri Bajpai

This research introduces an innovative security enhancement approach, employing advanced image analysis and soft computing. The focus is on an intelligent surveillance system that detects unauthorized individuals in restricted areas by analyzing attire. Traditional security measures face challenges in monitoring unauthorized access. Leveraging YOLOv8, an adv