March 2024 arXiv papers — page 135

Marek Elias, Haim Kaplan, Yishay Mansour, Shay Moran

Recent advances in algorithmic design show how to utilize predictions obtained by machine learning models from past and present data. These approaches have demonstrated an enhancement in performance when the predictions are accurate, while also ensuring robustness by providing worst-case guarantees when predictions fail. In this paper we focus on online prob

Qilong Pan, Sameh Abdulah, Marc G. Genton, David E. Keyes

Gaussian processes (GPs) are commonly used for geospatial analysis, but they suffer from high computational complexity when dealing with massive data. For instance, the log-likelihood function required in estimating the statistical model parameters for geospatial data is a computationally intensive procedure that involves computing the inverse of a covarianc

Dae Gwan Lee, Goetz E. Pfander, David Walnut

We consider tilings $(\mathcal{Q},\Phi)$ of $\mathbb{R}^d$ where $\mathcal{Q}$ is the $d$-dimensional unit cube and the set of translations $\Phi$ is constrained to lie in a pre-determined lattice $A \mathbb{Z}^d$ in $\mathbb{R}^d$. We provide a full characterization of matrices $A$ for which such cube tilings exist when $\Phi$ is a sublattice of $A\mathbb{Z

Bernhard Haeupler, Shyamal Patel, Antti Roeyskoe, Cliff Stein

This paper addresses point-to-point packet routing in undirected networks, which is the most important communication primitive in most networks. The main result proves the existence of routing tables that guarantee a polylog-competitive completion-time $\textbf{deterministically}$: in any undirected network, it is possible to give each node simple stateless

Neri Merhav

We establish a coding theorem and a matching converse theorem for separate encodings and joint decoding of individual sequences using finite-state machines. The achievable rate region is characterized in terms of the Lempel-Ziv (LZ) complexities, the conditional LZ complexities and the joint LZ complexity of the two source sequences. An important feature tha

Beibei Lin, Yeying Jin, Wending Yan, Wei Ye

Masked autoencoder (MAE) shows that severe augmentation during training produces robust representations for high-level tasks. This paper brings the MAE-like framework to nighttime image enhancement, demonstrating that severe augmentation during training produces strong network priors that are resilient to real-world night haze degradations. We propose a nove

Dyke Ferber, Georg Wölflein, Isabella C. Wiest, Marta Ligero

Medical image classification requires labeled, task-specific datasets which are used to train deep learning networks de novo, or to fine-tune foundation models. However, this process is computationally and technically demanding. In language processing, in-context learning provides an alternative, where models learn from within prompts, bypassing the need for

Eduard Hogea, Adrian Popescu, Darian Onchis, Grégoire Petit

Exemplar-free class-incremental learning (EFCIL) poses significant challenges, primarily due to catastrophic forgetting, necessitating a delicate balance between stability and plasticity to accurately recognize both new and previous classes. Traditional EFCIL approaches typically skew towards either model plasticity through successive fine-tuning or stabilit

Kyungmin Seo, Hyunsoo Kim, Yeongduk Kim, Hyeyoung Lee

The radiation emitted from radon is a critical background in rare event search experiments conducted at the Yemi Underground Laboratory (Yemilab) in Jeongseon, Korea. A Radon Reduction System(RRS) has been developed and installed in Yemilab to reduce radon concentration in the air. The RRS primarily provides a purified air of 50 m3/h to the cleanroom used to

Filip Szatkowski, Yaoyue Zheng, Fei Yang, Bartłomiej Twardowski

Continual learning is crucial for applying machine learning in challenging, dynamic, and often resource-constrained environments. However, catastrophic forgetting - overwriting previously learned knowledge when new information is acquired - remains a major challenge. In this work, we examine the intermediate representations in neural network layers during co

Guoshan Liu, Yang Jiao, Jingjing Chen, Bin Zhu

The precise recognition of food categories plays a pivotal role for intelligent health management, attracting significant research attention in recent years. Prominent benchmarks, such as Food-101 and VIREO Food-172, provide abundant food image resources that catalyze the prosperity of research in this field. Nevertheless, these datasets are well-curated fro

Sungwon Jung, Sang Hoon Lee, Jaeyoon Cho

While renormalization groups are fundamental in physics, renormalization of complex networks remains vague in its conceptual definition and methodology. Here, we propose a novel strategy to renormalize complex networks. Rather than resorting to handling the bare structure of a network, we overlay it with a readily renormalizable physical model, which reflect

Nedjoua Zine, Benaoumeur Bayour, Delfim F. M. Torres

A susceptible, asymptomatic, infectious, quarantined, and hospitalized (SAIQH) compartmental model on time scales is introduced and a suitable Lyapunov function is defined. Main results include: the proof that the system is permanent; proof of existence of solution; and sufficient conditions implying the dynamic system to have a unique almost periodic soluti

Max Sponner, Lorenzo Servadei, Bernd Waschneck, Robert Wille

Deep Learning is becoming increasingly relevant in Embedded and Internet-of-things applications. However, deploying models on embedded devices poses a challenge due to their resource limitations. This can impact the model's inference accuracy and latency. One potential solution are Early Exit Neural Networks, which adjust model depth dynamically through addi

Max Sponner, Lorenzo Servadei, Bernd Waschneck, Robert Wille

Early Exit Neural Networks (EENNs) present a solution to enhance the efficiency of neural network deployments. However, creating EENNs is challenging and requires specialized domain knowledge, due to the large amount of additional design choices. To address this issue, we propose an automated augmentation flow that focuses on converting an existing model int

A. Lapi, G. Gandolfi, L. Boco, F. Gabrielli

[abridged] We aim to constrain the stellar initial mass function (IMF) during the epoch of reionization. To this purpose, we build up a semi-empirical model for the reionization history of the Universe, based on various ingredients: the latest determination of the UV galaxy luminosity function from JWST out to redshift $z\lesssim 12$; data-inferred and simul

Baptiste Lefort, Eric Benhamou, Jean-Jacques Ohana, David Saltiel

This paper introduces a new risk-on risk-off strategy for the stock market, which combines a financial stress indicator with a sentiment analysis done by ChatGPT reading and interpreting Bloomberg daily market summaries. Forecasts of market stress derived from volatility and credit spreads are enhanced when combined with the financial news sentiment derived

Kenji Yajima

We prove that Sobolev norms of solutions to time dependent Schr\"odinger equations for $d$-dimensional $N$-partcles interacting via time dependent two body potentials are bounded in time if certain Lebesgue norms of the potentials are small uniformly in time. The proof uses the scattering theory in the extended phase space which proves that all particles sca

Kwangwoo Lee

We prove that the automorphism group of Hilbert square of a Cayley's K3 surface of Picard number 2 is the free product of three cyclic groups of order two. The generators are three Beauville involutions.

Tianqing Fang, Zeming Chen, Yangqiu Song, Antoine Bosselut

Event commonsense reasoning requires the ability to reason about the relationship between events, as well as infer implicit context underlying that relationship. However, data scarcity makes it challenging for language models to learn to generate commonsense inferences for contexts and questions involving interactions between complex events. To address this

Klaus Raab, Maurice Schmitt, Maarten A. Brems, Jan Rothörl

Magnetic skyrmions, topologically stabilized chiral magnetic textures with particle-like properties have so far primarily been studied statically. Here, we experimentally investigate the dynamics of skyrmion ensembles in metallic thin film conduits where they behave as quasi-particle fluids. By exploiting our access to the full trajectories of all fluid part

Zhiyuan Zhou, Xingkai Cheng, Mengli Hu, Junwei Liu

Symmetry plays a fundamental role in condensed matter. The unique entanglement between magnetic sublattices and alternating crystal environment in altermagnets provides a unique opportunity for designing magnetic space symmetry. There have been extensive experimental efforts concentrated on tuning the Neel vector to reconstruct altermagnetic symmetry. Howeve

B. Tripathi, A. J. Barker, A. E. Fraser, P. W. Terry

Differentially rotating stars and planets transport angular momentum internally due to turbulence at rates that have long been a challenge to predict reliably. We develop a self-consistent saturation theory, using a statistical closure approximation, for hydrodynamic turbulence driven by the axisymmetric Goldreich--Schubert--Fricke (GSF) instability at the s

She Yang

Let $K$ be an algebraically closed field of arbitrary characteristic and let $X$ be an irreducible projective variety over $K$. Let $G\subseteq\text{Bir}(X)$ be a bounded-degree subgroup. We prove that there exists an irreducible projective variety $Y$ birational to $X$, such that every element of $G$ becomes an automorphism of $Y$ after the birational trans

Zongxin Liu, Pengfei Yang, Lijun Zhang, Xiaowei Huang

Neural networks in safety-critical applications face increasing safety and security concerns due to their susceptibility to little disturbance. In this paper, we propose DeepCDCL, a novel neural network verification framework based on the Conflict-Driven Clause Learning (CDCL) algorithm. We introduce an asynchronous clause learning and management structure,

Yihang Chen, Wenbin Li

We consider end-to-end learning approaches for inverse problems of gravimetry. Due to ill-posedness of the inverse gravimetry, the reliability of learning approaches is questionable. To deal with this problem, we propose the strategy of learning on the correct class. The well-posedness theorems are employed when designing the neural-network architecture and

Chunlong Xia, Xinliang Wang, Feng Lv, Xin Hao

Although Vision Transformer (ViT) has achieved significant success in computer vision, it does not perform well in dense prediction tasks due to the lack of inner-patch information interaction and the limited diversity of feature scale. Most existing studies are devoted to designing vision-specific transformers to solve the above problems, which introduce ad

Lucian A. Constantin, Szymon Śmiga, Fabio Della Sala

The Adiabatic Connection Integrand Interpolation (ACII) method represents a general path for calculating correlation energies in electronic systems within the Den sity Functional Theory. ACII functionals include both exact-exchange and the second-order correlation energy, as well as an interpolating function toward the strictly-correlated electron (SCE) regi

Haochen Sun, Yan Yuan, Lijuan Su, Haotian Shao

Previous approaches for blind image super-resolution (SR) have relied on degradation estimation to restore high-resolution (HR) images from their low-resolution (LR) counterparts. However, accurate degradation estimation poses significant challenges. The SR model's incompatibility with degradation estimation methods, particularly the Correction Filter, may s

Nicolas Brieu, Nicolas Triltsch, Philipp Wortmann, Dominik Winter

Generative models enable the translation from a source image domain where readily trained models are available to a target domain unseen during training. While Cycle Generative Adversarial Networks (GANs) are well established, the associated cycle consistency constrain relies on that an invertible mapping exists between the two domains. This is, however, not

Maxime Bergman, Yixuan Xu, José Muñéton Díaz, Zhang Chi

Polymer microgels exhibit intriguing macroscopic flow properties arising from their unique microscopic structure. Microgel colloids usually comprise a crosslinked polymer network with a radially decaying density profile, resulting in a dense core surrounded by a fuzzy corona. Notably, microgels synthesized from poly(N-isopropyl acrylamide) (PNIPAM) are therm

Lunyuan Chen, Jie Gong

In recent years, semantic communication is progressively emerging as an effective means of facilitating intelligent and context-aware communication. However, current researches seldom simultaneously consider the reliability and timeliness of semantic communication, where scheduling and resource allocation (SRA) plays a crucial role. In contrast, conventional

So Matsuura, Kazutoshi Ohta

We examine the fundamental Kazakov-Migdal (FKM) model on a generic graph, whose partition function is represented by the Ihara zeta function weighted by unitary matrices. The FKM model becomes unstable in the critical strip of the Ihara zeta function. We discover a duality between small and large couplings, associated with the functional equation of the Ihar

Yu Yang, Siddhartha Mishra, Jeffrey N Chiang, Baharan Mirzasoleiman

Despite the effectiveness of data selection for large language models (LLMs) during pretraining and instruction fine-tuning phases, improving data efficiency in supervised fine-tuning (SFT) for specialized domains poses significant challenges due to the complexity of fine-tuning data. To bridge this gap, we introduce an effective and scalable data selection

Takuya Saito, Sakumi Sugawara

In this paper, we give a characterization of homogeneous quandles with abelian inner automorphism groups. In particular, we show that such a quandle is expressed as an abelian extension of a trivial quandle. Our construction is a generalization of the recent work by Furuki and Tamaru, which gives a construction of disconnected flat quandles.

W. Al Saidi, R. Sbiaa, Y. Dusch, N. Tiercelin

In the present research, we demonstrate the usage of plasmonic effects in thin film structures to control magnetic topological textures, specifically skyrmions and skyrmioniums. We investigate numerically the generation and alteration of these topological structures caused by hemisphere gold nanoparticle placed over a magnetic layer coated with a dielectric

Boujemaa Agrebaoui, Walid Mhiri

Let $A_n=\mathbb{C}[t_i^{\pm1},~1\leq i\leq n]$ and $\mathbf{W}(n)_\mu=A_nd_\mu$ the solenoidal Lie algebra introduced by Y.Billig and V.Futorny in \cite{BiFu2}, where $\mu=(\mu_1,\ldots,\mu_n)\in\mathbb{C}^n$ is a generic vector and $$d_\mu=\sum_{i=1}^n\mu_it_i\frac{\partial}{\partial t_i}.$$ We consider the semi-direct product Lie algebra $\mathbf{WA}(n)_\

Sijin He, Guangfeng Lin

Image deraining have have gained a great deal of attention in order to address the challenges posed by the effects of harsh weather conditions on visual tasks. While convolutional neural networks (CNNs) are popular, their limitations in capturing global information may result in ineffective rain removal. Transformer-based methods with self-attention mechanis

Sidak Pal Singh, Bobby He, Thomas Hofmann, Bernhard Schölkopf

We propose a fresh take on understanding the mechanisms of neural networks by analyzing the rich directional structure of optimization trajectories, represented by their pointwise parameters. Towards this end, we introduce some natural notions of the complexity of optimization trajectories, both qualitative and quantitative, which hallmark the directional na

Xin Wang, Yu Zheng, Zhongwei Wan, Mi Zhang

The advancements in Large Language Models (LLMs) have been hindered by their substantial sizes, which necessitates LLM compression methods for practical deployment. Singular Value Decomposition (SVD) offers a promising solution for LLM compression. However, state-of-the-art SVD-based LLM compression methods have two key limitations: truncating smaller singul

Luc Hillairet, Chris M. Judge

We prove that the Laplace spectrum of the generic ellipse is simple, both with Neumann and Dirichlet boundary condition. We rely on the known multiplicities in the spectrum of the disk (Bourget's hypothesis) and on a refined version of our method of asymptotic separation of variables. In v1, the statement of prop. 2.1 is correct but the proof is not.

Bingqian Lin, Yunshuang Nie, Ziming Wei, Jiaqi Chen

Vision-and-Language Navigation (VLN), as a crucial research problem of Embodied AI, requires an embodied agent to navigate through complex 3D environments following natural language instructions. Recent research has highlighted the promising capacity of large language models (LLMs) in VLN by improving navigational reasoning accuracy and interpretability. How

Xiaonan Xu, Zheng Xu, Zhipeng Ling, Zhengyu Jin

Natural Language Processing (NLP) is an important branch of artificial intelligence that studies how to enable computers to understand, process, and generate human language. Text classification is a fundamental task in NLP, which aims to classify text into different predefined categories. Text classification is the most basic and classic task in natural lang

Takato Tokuno, Akihiko Fukui, Takeru K. Suzuki

We propose a novel method to constrain the tidal quality factor, $Q'$, from an observed non-synchronized star-planet system consisting of a slowly rotating low-mass star and a close-in Jovian planet, taking into account the co-evolution of stellar spin and planetary orbit due to the tidal interaction and the magnetic braking. On the basis of dynamical system

Linan Yue, Qi Liu, Yichao Du, Li Wang

The remarkable success in neural networks provokes the selective rationalization. It explains the prediction results by identifying a small subset of the inputs sufficient to support them. Since existing methods still suffer from adopting the shortcuts in data to compose rationales and limited large-scale annotated rationales by human, in this paper, we prop

Matthias Hamann

In this paper, we prove that infinite cancellative finitely generated hyperbolic monoids never contain $\mathbb N\times\mathbb N$ as a submonoid but that they contain an element of infinite order and, if they are elementary, then they also contain a free monoid of rank at least 2. As a corollary we obtain that the latter have exponential growth. We prove the

Shalabh Mishra, K. Sasikumar Raja, Sanal Krishnan V U, Venkata Suresh Narra

Aditya-L1 is the first Indian space mission to explore the Sun and solar atmosphere with seven multi-wavelength payloads, with Visible Emission Line Coronagraph (VELC) being the prime payload. It is an internally occulted coronagraph with four channels to image the Sun at 5000 \AA~ in the field of view 1.05 - 3 \rsun, and to pursue spectroscopy at 5303 \AA,

Yiyang Gu, Yougen Zhou, Qin Chen, Ningning Zhou

Depression-diagnosis-oriented chat aims to guide patients in self-expression to collect key symptoms for depression detection. Recent work focuses on combining task-oriented dialogue and chitchat to simulate the interview-based depression diagnosis. Whereas, these methods can not well capture the changing information, feelings, or symptoms of the patient dur

Jiahui Fu, Chen Gao, Zitian Wang, Lirong Yang

Recent 3D object detectors typically utilize multi-sensor data and unify multi-modal features in the shared bird's-eye view (BEV) representation space. However, our empirical findings indicate that previous methods have limitations in generating fusion BEV features free from cross-modal conflicts. These conflicts encompass extrinsic conflicts caused by BEV f

Phuong Dam, Jihoon Jeong, Anh Tran, Daeyoung Kim

This study discusses the critical issues of Virtual Try-On in contemporary e-commerce and the prospective metaverse, emphasizing the challenges of preserving intricate texture details and distinctive features of the target person and the clothes in various scenarios, such as clothing texture and identity characteristics like tattoos or accessories. In additi

Xingjun Wu, Haitian Su, Chuanchang Zeng, Ji-Yin Wang

The absence of odd-order Shapiro steps is a predicted signature of topological superconductors. Experimentally, the missing first-order Shapiro step has been reported in both putative topological superconducting systems and topologically trivial superconductor-semiconductor Josephson junctions. Here, we revisit this phenomenon in topologically trivial Al-InS

Haiyang Zheng, Nan Pu, Wenjing Li, Nicu Sebe

In this paper, we study the problem of Generalized Category Discovery (GCD), which aims to cluster unlabeled data from both known and unknown categories using the knowledge of labeled data from known categories. Current GCD methods rely on only visual cues, which however neglect the multi-modality perceptive nature of human cognitive processes in discovering

Vincent Bagayoko

We introduce a formalism of infinite, linearly ordered products in general groups. Using this, we define infinite compositions in certain groups of formal power series such as transseries. We show that such groups can sometimes be represented as infinite, linearly ordered, semidirect products of ordered Abelian groups.

B. Riaz, D. Stamatellos, M. Machida

Spirals and streamers are the hallmarks of mass accretion during the early stages of star formation. We present the first observations of a large-scale spiral and a streamer towards a very young brown dwarf candidate in its early formation stages. These observations show, for the first time, the influence of external environment that results in asymmetric ma

Jonghyun Lee, Dahuin Jung, Saehyung Lee, Junsung Park

Test-time adaptation (TTA) fine-tunes pre-trained deep neural networks for unseen test data. The primary challenge of TTA is limited access to the entire test dataset during online updates, causing error accumulation. To mitigate it, TTA methods have utilized the model output's entropy as a confidence metric that aims to determine which samples have a lower

Khalil Idiab, Stijn J. van Tongeren

We study Yang-Baxter deformations of the flat space string that result in exactly solvable models, finding the Nappi-Witten model and its higher dimensional generalizations. We then consider the spectra of these models obtained by canonical quantization in light-cone gauge, and match them with an integrability-based Bethe ansatz approach. By considering a ge

Yubo Ye, Huafeng Liu, Linwei Wang

In dynamic positron emission tomography (PET) reconstruction, the importance of leveraging the temporal dependence of the data has been well appreciated. Current deep-learning solutions can be categorized in two groups in the way the temporal dynamics is modeled: data-driven approaches use spatiotemporal neural networks to learn the temporal dynamics of trac

Yingtao Ren, Xiaomin Zhu, Kaiyuan Bai, Runtong Zhang

Classification is essential to the applications in the field of data mining, artificial intelligence, and fault detection. There exists a strong need in developing accurate, suitable, and efficient classification methods and algorithms with broad applicability. Random forest is a general algorithm that is often used for classification under complex condition

Chongyu Fan, Jiancheng Liu, Alfred Hero, Sijia Liu

The trustworthy machine learning (ML) community is increasingly recognizing the crucial need for models capable of selectively 'unlearning' data points after training. This leads to the problem of machine unlearning (MU), aiming to eliminate the influence of chosen data points on model performance, while still maintaining the model's utility post-unlearning.

Sayak Ray, Sudipta Ray, Suman Chakraborty

A tiny drop of one liquid, suspended within another, may be set into motion aligned with an imposed thermal gradient, as influenced by thermocapillary action stemming from the gradients in interfacial tension due to the local variations in temperature. In real-world situations, however, such drops do not remain in isolation, as they interact with their neigh

Jungang Chen, Eduardo Gildin, Georgy Kompantsev

Injecting greenhouse gas into deep underground reservoirs for permanent storage can inadvertently lead to fault reactivation, caprock fracturing and greenhouse gas leakage when the injection-induced stress exceeds the critical threshold. Extraction of pre-existing fluids at various stages of injection process, referred as pressure management, can mitigate as

Xianzu Wu, Xianfeng Wu, Tianyu Luan, Yajing Bai

While previous studies have demonstrated successful 3D object shape completion with a sufficient number of points, they often fail in scenarios when a few points, e.g. tens of points, are observed. Surprisingly, via entropy analysis, we find that even a few points, e.g. 64 points, could retain substantial information to help recover the 3D shape of the objec

Guanghan Li, Chunwu Wang, Zhicheng Hu

We develop a novel fast iterative moment method for the steady-state simulation of near-continuum flows, which are modeled by the high-order moment system derived from the Boltzmann-BGK equation. The fast convergence of the present method is mainly achieved by alternately solving the moment system and the hydrodynamic equations with consistent constitutive r

Akito Kawasaki, Hector Brunel, Ryuhoh Ide, Takumi Suzuki

Entanglement is a fundamental resource of various optical quantum-information-processing (QIP) applications. Towards high-speed QIP system, entanglement should be encoded in short wavepackets. We report real-time observation of ultrafast optical Einstein-Podolsky-Rosen (EPR) correlation at a picosecond timescale in a continuous-wave (CW) system. Optical phas

Mark D. McDonnell, Dong Gong, Ehsan Abbasnejad, Anton van den Hengel

Continual learning requires a model to adapt to ongoing changes in the data distribution, and often to the set of tasks to be performed. It is rare, however, that the data and task changes are completely unpredictable. Given a description of an overarching goal or data theme, which we call a realm, humans can often guess what concepts are associated with it.

Junyong Shin, Yujin Kang, Yo-Seb Jeon

This paper presents a finite-rate deep-learning (DL)-based channel state information (CSI) feedback method for massive multiple-input multiple-output (MIMO) systems. The presented method provides a finite-bit representation of the latent vector based on a vector-quantized variational autoencoder (VQ-VAE) framework while reducing its computational complexity

Keke Huang, Wencai Cao, Hoang Ta, Xiaokui Xiao

Graph Neural Networks (GNNs), known as spectral graph filters, find a wide range of applications in web networks. To bypass eigendecomposition, polynomial graph filters are proposed to approximate graph filters by leveraging various polynomial bases for filter training. However, no existing studies have explored the diverse polynomial graph filters from a un

Geonhwa Jeong, Po-An Tsai, Abhimanyu R. Bambhaniya, Stephen W. Keckler

Exploiting sparsity in deep neural networks (DNNs) has been a promising area for meeting the growing computation requirements. To minimize the overhead of sparse acceleration, hardware designers have proposed structured sparsity support, but it provides limited flexibility and requires extra model fine-tuning. Moreover, any sparse model fine-tuned for certai

Qihang Fang, Chengcheng Tang, Shugao Ma, Yanchao Yang

Skeleton-based motion representations are robust for action localization and understanding for their invariance to perspective, lighting, and occlusion, compared with images. Yet, they are often ambiguous and incomplete when taken out of context, even for human annotators. As infants discern gestures before associating them with words, actions can be concept

Jiahao Zhang, Lin Wang, Shijie Wang, Wenqi Fan

Graph Neural Networks (GNNs) have achieved remarkable success in various real-world applications. However, GNNs may be trained on undesirable graph data, which can degrade their performance and reliability. To enable trained GNNs to efficiently unlearn unwanted data, a desirable solution is retraining-based graph unlearning, which partitions the training gra

Andrew Sullivan, Chris Power, Connor Bottrell

We investigate the relationship between a dark matter halo's mass profile and measures of the velocity dispersion of kinematic tracers within its gravitational potential. By predicting the scaling relation of the halo mass with the aperture velocity dispersion, $M_\mathrm{vir} - \sigma_\mathrm{ap}$, we present the expected form and dependence of this halo ma

Ma-Cheng Yang, Cong-Feng Qiao

We propose in this work a practical approach to address the longstanding and challenging problem of quantum separability, leveraging the correlation matrices of generic observables. General separability conditions are obtained by dint of constructing the measurement-induced Bloch space, which in essence come from the intrinsic constraints in the space of qua

Han Huang, Haitian Zhong, Tao Yu, Qiang Liu

Recently, knowledge editing on large language models (LLMs) has received considerable attention. Compared to this, editing Large Vision-Language Models (LVLMs) faces extra challenges from diverse data modalities and complicated model components, and data for LVLMs editing are limited. The existing LVLM editing benchmark, which comprises three metrics (Reliab

Andreas Malmendier, Michael T. Schultz

We propose a mechanism for computing the genus zero invariants of local Calabi-Yau fourfolds arising as the total space of the canonical bundle of a rank-1 Fano threefold. Our method relies heavily on modular parameterizations of the associated Landau-Ginzburg model, as well as extension regulator classes and higher normal functions studied by Doran and Kerr

Luis Eduardo García-Hernández, Ben Williams

Let $n\le 5$ be an integer, and let $\Gamma$ be a finite group. We prove that if $\rho , \rho': \Gamma \to O(n)$ are two representations that are conjugate by an orientation-preserving diffeomorphism, then they are conjugate by an element of $SO(n)$. In the process, we prove that if $G \subset O(4)$ is a finite group, then exactly one of the following is tru

Fei Wang, Dan Guo, Kun Li, Zhun Zhong

Video Motion Magnification (VMM) aims to reveal subtle and imperceptible motion information of objects in the macroscopic world. Prior methods directly model the motion field from the Eulerian perspective by Representation Learning that separates shape and texture or Multi-domain Learning from phase fluctuations. Inspired by the frequency spectrum, we observ

Jianping Jiang, Xinyu Zhou, Bingxuan Wang, Xiaoming Deng

Reliable hand mesh reconstruction (HMR) from commonly-used color and depth sensors is challenging especially under scenarios with varied illuminations and fast motions. Event camera is a highly promising alternative for its high dynamic range and dense temporal resolution properties, but it lacks key texture appearance for hand mesh reconstruction. In this p

Takuya Mine, Nobuo Yoshida

We consider an operator $P_V=(1+V)P$ on $\ell^2(Z^d)$, where $P$ is the transition operator of a symmetric irreducible random walk, and $V$ is a ``sparse'' potential. We first characterize the essential spectra of this operator. Secondly, we prove that all the eigenfunctions which correspond to discrete spectra decay exponentially fast. Thirdly, we give a su

Wenjie Sun, Zhicheng Jiang, Chengliang Xia, Bo Hao

Revealing the momentum-resolved electronic structure of infinite-layer nickelates is essential for understanding this new class of unconventional superconductors, but has been hindered by the formidable challenges in improving the sample quality. In this work, we report for the first time the angle-resolved photoemission spectroscopy of superconducting La$_{

Sayantan Choudhury

Renormalization of quantum loop effects generated from large fluctuations is a hugely debatable topic of research these days which rules out the Primordial Black Hole (PBH) formation within the framework of single-field inflation. In this article, we briefly discuss that the correct implementation of regularization, renormalization, and resummation technique

Qiao Sun, Liujia Yang, Minghao Ma, Nanyang Ye

Aspect Sentiment Triplet Extraction (ASTE) is a burgeoning subtask of fine-grained sentiment analysis, aiming to extract structured sentiment triplets from unstructured textual data. Existing approaches to ASTE often complicate the task with additional structures or external data. In this research, we propose a novel tagging scheme and employ a contrastive l

Osamu Hatori, Shiho Oi

We study maps between positive definite or positive semidefinite cones of unital $C^*$-algebras. We describe surjective maps that preserve (1) the norm of the quotient or multiplication of elements; (2) the spectrum of the quotient or multiplication of elements; (3) the spectral seminorm of the quotient or multiplication of elements. These maps relate to the

Guanghui Hu, Andreas Kirsch

This is a continuation of the authors' previous work (A. Kirsch, Math. Meth. Appl. Sci., 45 (2022): 5737-5773.) on well-posedness of time-harmonic scattering by locally perturbed periodic curves of Dirichlet kind. The scattering interface is supposed to be given by a non-self-intersecting Lipschitz curve. We study properties of the Green's function and prove

Zhanpeng Zeng, Karthikeyan Sankaralingam, Vikas Singh

GEneral Matrix Multiply (GEMM) is a central operation in deep learning and corresponds to the largest chunk of the compute footprint. Therefore, improving its efficiency is an active topic of ongoing research. A popular strategy is the use of low bit-width integers to approximate the original entries in a matrix. This allows efficiency gains, but often requi

Jianhao Huang, Kai Yuan, Chuan Huang, Kaibin Huang

Semantic communications (SemCom) have emerged as a new paradigm for supporting sixth-generation applications, where semantic features of data are transmitted using artificial intelligence algorithms to attain high communication efficiencies. Most existing SemCom techniques utilize deep neural networks (DNNs) to implement analog source-channel mappings, which

Haoyan Wei, Hongtao Zhang

Rapid signal fluctuations due to blockage effects cause excessive handovers (HOs) and degrade mobility performance. By reconfiguring line-of-sight (LoS) Links through passive reflections, intelligent reflective surface (IRS) has the potential to address this issue. Due to the lack of introducing blocking effects, existing HO analyses cannot capture excessive

Shuto Kawai, Shun Sato, Takayasu Matsuo

Furihata and Matsuo proposed in 2010 an energy-conserving scheme for the Zakharov equations, as an application of the discrete variational derivative method (DVDM). This scheme is distinguished from conventional methods (in particular the one devised by Glassey in 1992) in that the invariants are consistent with respect to time, but it has not been sufficien

Si-wen Li, Yi-peng Zhang, Hao-qian Li

We study the out-of-time-order correlators (OTOC) of Skyrmion as baryon in the D0-D4/D8 model which is expected to be holographically dual to QCD with instantons as D0-branes or with a non-zero theta angle. Baryon states are identified to the excitations of the Skyrmion which are described by a holographic quantum mechanical system in this model. By employin

Shin-itiro Goto

The Fokker-Planck equation is one of the fundamental equations in nonequilibrium statistical mechanics, and this equation is known to be derived from the Wasserstein gradient flow equation with a free energy. This gradient flow equation describes relaxation processes and is formulated on a Riemannian manifold. Meanwhile contact Hamiltonian systems are also k

Lina Wu, Jin-Ke Shen, Tianjun Li, Junle Pei

We develop generalized no-scale supergravity models of inflation, and then study the corresponding cosmological predictions as well as the formation of primordial black holes (PBHs) and scalar-induced gravitational waves (SIGWs). With a new parameter $0<a\leq 1$, the generalized no-scale supergravity provides the continuous connections among the generic no-s

Jinhong Wang, Jintai Chen, Danny Chen, Jian Wu

In clinical practice, medical image segmentation provides useful information on the contours and dimensions of target organs or tissues, facilitating improved diagnosis, analysis, and treatment. In the past few years, convolutional neural networks (CNNs) and Transformers have dominated this area, but they still suffer from either limited receptive fields or

Ziqi Yin, Shanshan Feng, Shang Liu, Gao Cong

With the proliferation of spatio-textual data, Top-k KNN spatial keyword queries (TkQs), which return a list of objects based on a ranking function that considers both spatial and textual relevance, have found many real-life applications. To efficiently handle TkQs, many indexes have been developed, but the effectiveness of TkQ is limited. To improve effecti

Jing Zhou, Kazem Bitaghsir Fadafan, Xun Chen

In this work, we use the AdS/CFT correspondence to study the behavior of a triply heavy baryon within anisotropic backgrounds. Beginning with the total action of the three quarks, we derive the balance equation for the three-quark system and compute the separation distance and potential energy. Our results reveal a consistent decrease in both the separation

Seungjae Shin, HeeSun Bae, Byeonghu Na, Yoon-Yeong Kim

The objective of domain generalization (DG) is to enhance the transferability of the model learned from a source domain to unobserved domains. To prevent overfitting to a specific domain, Sharpness-Aware Minimization (SAM) reduces source domain's loss sharpness. Although SAM variants have delivered significant improvements in DG, we highlight that there's st

Tanmay Inamdar, Pallavi Jain, Daniel Lokshtanov, Abhishek Sahu

In MaxSAT with Cardinality Constraint problem (CC-MaxSAT), we are given a CNF-formula $\Phi$, and $k \ge 0$, and the goal is to find an assignment $\beta$ with at most $k$ variables set to true (also called a weight $k$-assignment) such that the number of clauses satisfied by $\beta$ is maximized. MaxCov can be seen as a special case of CC-MaxSAT, where the

X. G. Wang, A. W. Thomas

Precise measurements of electron and positron scattering, including parity violation, offer great promise in the search for physics beyond the Standard Model. In this context it is crucial to understand the corrections which might arise from charge symmetry violation, as well as the less well known strange and charm quark distributions. Our analysis, using s

Xingyu Lu, Lei Sun, Diyang Gu, Kaiwei Wang

Fast and accurate depth sensing has long been a significant research challenge. Event camera, as a device that quickly responds to intensity changes, provides a new solution for structured light (SL) systems. In this paper, we introduce Gray code into event-based SL systems for the first time. Our setup includes an event camera and a Digital Light Processing

Kenta Suzuki

In this paper, we study massless/massive vector and $p$-form field perturbations in AdS spacetime with an end-of-the-world brane. By imposing $U(1)$ preserving Neumann boundary condition on the end-of-the-world brane, we study their spectrum and discuss their implications for dual BCFT operators. When the perturbation is massless, the dual BCFT operator is a

Biswajit Khan, Abir Mukherjee, Yordan M. Georgiev, J. P. Colinge

In the pursuit of room temperature quantum hardware, our study introduces a gate voltage tunable quantum wire within a tri-gated n-type junctionless MOSFET. The application of gate voltage alters the parabolic potential well of the tri-gated junctionless MOSFET, enabling modification of the nanowire's potential well profile. In the presence of light, photoge

Haoyan Wei, Hongtao Zhang

Owning to the reflection gain and double path loss featured by intelligent reflecting surface (IRS) channels, handover (HO) locations become irregular and the signal strength fluctuates sharply with variations in IRS connections during HO, the risk of HO failures (HOFs) is exacerbated and thus HO parameters require reconfiguration. However, existing HO model