November 2024 arXiv papers — page 34

Zhiming Xu, Suorong Yang, Baile Xu, Furao Shen

Class-incremental learning (CIL) aims to learn new classes while retaining previous knowledge. Although pre-trained model (PTM) based approaches show strong performance, directly fine-tuning PTMs on incremental task streams often causes renewed catastrophic forgetting. This paper proposes a Dual-Prototype Network with Task-wise Adaptation (DPTA) for PTM-base

Hui Guo, Grace Y. Yi, Boyu Wang

While crowdsourcing has emerged as a practical solution for labeling large datasets, it presents a significant challenge in learning accurate models due to noisy labels from annotators with varying levels of expertise. Existing methods typically estimate the true label posterior, conditioned on the instance and noisy annotations, to infer true labels or adju

A. Jain, Pranali Parab, G. Saxena, Mamta Aggarwal

The influence of nuclear deformation on proton-decay half-lives has been systematically studied in microscopic theoretical frameworks for a wide range of nuclei with Z<82. Correlation between 1p-decay half-lives and the deformed nuclear shapes of both the parent and daughter nuclei has been investigated. Since the deformations of proton emitters and their re

Alexander Cholach, Dmitry Yakovin

The fractionation of isotopes of natural Ar near the condensation (Tc) and freezing point has been studied using mass spectrometry (MS), numerical modeling and density functional theory. The heat of formation of 0.30, 0.52 and 0.70 kJ per Ar atom of the clusters Ar2, Ar3 and Ar4, respectively, shows the tendency of Ar to clusterization. At T > Tc apparent se

Arash Dargahi Nobari, Davood Rafiei

The integration of tabular data from diverse sources is often hindered by inconsistencies in formatting and representation, posing significant challenges for data analysts and personal digital assistants. Existing methods for automating tabular data transformations are limited in scope, often focusing on specific types of transformations or lacking interpret

Yinshan Chang, Qinwei Chen

We study the maximal correlation coefficient $R(X,Y)$ between two stochastic processes $X$ and $Y$. In the case when $(X,Y)$ is a random walk, we find $R(X,Y)$ using the Cs\'{a}ki-Fischer identity and the lower semicontinuity of the map $\text{Law}(X,Y) \to R(X,Y)$. When $(X,Y)$ is a two-dimensional L\'{e}vy process, we express $R(X,Y)$ in terms of the L\'{e

Xiang Tang, Xian Shi, Mohamed Ramy El-Maarry

European Space Agency's Rosetta mission is the only space mission that performed long-term monitoring of comet at close distances. Its over two years' rendezvous with comet 67P/Churyumov-Gerasimenko revealed diverse evolutionary processes of the cometary nucleus. One of the most striking events is the migration of a 30-m boulder in the southern hemisphere re

Dangyang He

In our investigation, we focus on the reverse Riesz transform within the framework of manifolds with ends. Such manifolds can be described as the connected sum of finite number of Cartesian products $\mathbb{R}^{n_i} \times \mathcal{M}_i$, where $\mathcal{M}_i$ are compact manifolds. We rigorously establish the boundedness of this transform across all $L^p$

Libo Zhu, Jianze Li, Haotong Qin, Wenbo Li

Diffusion-based image super-resolution (SR) models have shown superior performance at the cost of multiple denoising steps. However, even though the denoising step has been reduced to one, they require high computational costs and storage requirements, making it difficult for deployment on hardware devices. To address these issues, we propose a novel post-tr

Luiz L. Lopes, Adamu Issifu

The existence of the ultracompact object XTE J1814-338, with an inferred mass and radius of $M$ = 1.21 $\pm$ 0.05 $M_\odot$ and R = 7.0 $\pm$ 0.4 km, presents a great challenge for the theory of neutron stars. Within this context, we revisit the theory of dark-matter-admixed neutron stars and infer the physical properties of this compact object, such as the

Xinwei Feng, Lu Wang

We investigate two-barriers-reflected backward stochastic differential equations with data from rank-based stochastic differential equation. More specifically, we focus on the solution of backward stochastic differential equations restricted to two prescribed upper-boundary and lower-boundary processes. We rigorously show that this solution gives a probabili

Renyu Zhao, Yunxin Chen

The task of scholar name disambiguation is crucial in various real-world scenarios, including bibliometric-based candidate evaluation for awards, application material anti-fraud measures, and more. Despite significant advancements, current methods face limitations due to the complexity of heterogeneous data, often necessitating extensive human intervention.

Xiaolei Hu, Dongcheng Li, W. Eric Wong, Ya Zou

Software fault localization remains challenging due to limited feature diversity and low precision in traditional methods. This paper proposes a novel approach that integrates multi-objective optimization with deep learning models to improve both accuracy and efficiency in fault localization (FL). By framing feature selection as a multi-objective optimizatio

Yifan Yang, Jianheng Zhuo, Zengrui Jin, Ziyang Ma

Self-supervised learning (SSL) has achieved great success in speech-related tasks. While Transformer and Conformer architectures have dominated SSL backbones, encoders like Zipformer, which excel in automatic speech recognition (ASR), remain unexplored in SSL. Concurrently, inefficiencies in data processing within existing SSL training frameworks, such as fa

Hanyang Jiang, Yao Xie, Feng Qiu

In recent years, increasingly unpredictable and severe global weather patterns have frequently caused long-lasting power outages. Building resilience, the ability to withstand, adapt to, and recover from major disruptions, has become crucial for the power industry. To enable rapid recovery, accurately predicting future outage numbers is essential. Rather tha

Hai-Peng Li, Wei-Hong Liang, Chu-Wen Xiao, Ju-Jun Xie

We study the $\Lambda_{b}\to\Lambda_{c}\bar{D}^{0}K^{-}$ and $\Lambda_{b}\to \Lambda_{c}D^{-}\bar{K}^{0}$ decays which proceed via a Cabibbo and $N_c$ favored process of external emission, and we determine the $\bar{D}^{0}K^{-}$ and $D^{-}\bar{K}^{0}$ mass distributions close to the $\bar{D} \bar{K}$ threshold. For this, we use the tree level contribution pl

Seong Jang, Geon-Hyoung Park, Sein Park, Hyeon-Woo Jeong

Graphene's exceptional electronic mobility, gate-tunability, and contact transparency with superconducting materials make it ideal for exploring the superconducting proximity effect. However, the work function difference between graphene and superconductors causes unavoidable doping of graphene near contacts, forming a p-n junction in the hole-doped regime a

Wanquan Feng, Tianhao Qi, Jiawei Liu, Mingzhen Sun

Motion controllability is crucial in video synthesis. However, most previous methods are limited to single control types, and combining them often results in logical conflicts. In this paper, we propose a disentangled and unified framework, namely I2VControl, to overcome the logical conflicts. We rethink camera control, object dragging, and motion brush, ref

Tewodros Ayalew, Xiao Zhang, Kevin Yuanbo Wu, Tianchong Jiang

We present PROGRESSOR, a novel framework that learns a task-agnostic reward function from videos, enabling policy training through goal-conditioned reinforcement learning (RL) without manual supervision. Underlying this reward is an estimate of the distribution over task progress as a function of the current, initial, and goal observations that is learned in

A. Sukhanov, C. Contreras-Martinez, C. Grimm, B. Hanna

As part of the PIP-II project at Fermilab, a pre-production cryomodule featuring 325 MHz Single Spoke Resonator type 2 (SSR2) superconducting RF cavities is under construction. These SSR2 cavities are fabricated by industry partners and undergo initial cold testing at our collaborating institution, IJCLab in France, utilizing low-power coupler. Subsequently,

Xiang Li, Zixuan Huang, Anh Thai, James M. Rehg

Symmetry is a ubiquitous and fundamental property in the visual world, serving as a critical cue for perception and structure interpretation. This paper investigates the detection of 3D reflection symmetry from a single RGB image, and reveals its significant benefit on single-image 3D generation. We introduce Reflect3D, a scalable, zero-shot symmetry detecto

Zijie Su, Yunpu Liu, Sheng Pan, Zheng Li

Physics-Informed Neural Networks (PINN) has evolved into a powerful tool for solving partial differential equations, which has been applied to various fields such as energy, environment, en-gineering, etc. When utilizing PINN to solve partial differential equations, it is common to rely on Automatic Differentiation (AD) to compute the residuals of the govern

Yue Shao, Tian-Yang Sun, Meng-Lin Zhao, Xin Zhang

The 21-cm forest, composed of spectral absorption features from high-redshift background radio sources, provides a unique probe for studying small-scale structures during the epoch of reionization. It is particularly sensitive to detecting small-scale structures and early heating processes. Despite the rich information contained in the 21-cm forest signal, t

Yang Luo, Yongjie Wang

In this article, we provide a comprehensive characterization of invariants of classical Lie superalgebras from the super-analog of the Schur-Weyl duality in a unified way. We establish $\mathfrak{g}$-invariants of the tensor algebra $T(\mathfrak{g})$, the supersymmetric algebra $S(\mathfrak{g})$, and the universal enveloping algebra $\mathrm{U}(\mathfrak{g})

Tomonari Mizoguchi, Mina Maruyama, Yasuhiro Hatsugai, Susumu Okada

Theoretical quest of flat-band tight-binding models usually relies on lattice structures on which electrons reside. Typical examples of candidate lattice structures include the Lieb-type lattices and the line graphs. Meanwhile, there can be accidental flat-band systems that belong to neither of such typical classes and deriving flat-band energies and wave fu

Zhiyang Cheng, Zizhen Zhu, Haoran Dang, Hai Wan

In recent years, cyber attacks have become increasingly sophisticated and persistent. Detection and investigation based on the provenance graph can effectively mitigate cyber intrusion. However, in the long time span of defenses, the sheer size of the provenance graph will pose significant challenges to the storage systems. Faced with long-term storage tasks

He-Xia Zhang, Yu-Xin Xiao

The impact of non-extensive statistics on the hard thermal loop (HTL) resummation technique is investigated, in the absence and presence of a magnetic field. By utilizing the non-extensive bare propagators in the real-time formalism of finite temperature field theory, we determine the non-extensive deformations of both HTL gluon self-energies and resummed gl

Chaoyi Jiang, Lei Gao, Hossein Entezari Zarch, Murali Annavaram

Inference for Large Language Models (LLMs) is computationally demanding. To reduce the cost of auto-regressive decoding, Key-Value (KV) cache is used to store intermediate activations, which significantly lowers the computational overhead for token generation. However, the memory required for the KV cache grows rapidly, often exceeding the capacity of GPU me

Max Roberts, Ian Colwell, Clara Chew, Dallas Masters

Muon Space (Muon) is building a constellation of small satellites, many of which will carry global navigation satellite system-reflectometry (GNSS-R) receivers. In preparation for the launch of this constellation, we have developed a generalized deep learning retrieval pipeline, which now produces operational GNSS-R near-surface soil moisture retrievals usin

Chang Li, Yu Wang, Ce Zhang, Yongjun Zhang

Terraced field is a significant engineering practice for soil and water conservation (SWC). Terraced field extraction from remotely sensed imagery is the foundation for monitoring and evaluating SWC. This study is the first to propose a novel dual-modal {\Omega}-like super-resolution Transformer network for intelligent TFVE, offering the following advantages

Arunav Bhowmick, Arun Kumar Kuchibhotla

Many functionals of interest in statistics and machine learning can be written as minimizers of expected loss functions. Such functionals are called $M$-estimands, and can be estimated by $M$-estimators -- minimizers of empirical average losses. Traditionally, statistical inference (e.g., hypothesis tests and confidence sets) for $M$-estimands is obtained by

Jinqi Xiao, Shen Sang, Tiancheng Zhi, Jing Liu

Training large-scale neural networks in vision, and multimodal domains demands substantial memory resources, primarily due to the storage of optimizer states. While LoRA, a popular parameter-efficient method, reduces memory usage, it often suffers from suboptimal performance due to the constraints of low-rank updates. Low-rank gradient projection methods (e.

Suk Kim, Soo-Chang Rey, Youngdae Lee

We present a study on the evolution of transitional dwarf galaxies, specifically dwarf lenticulars (dS0s) and early-type dwarfs with blue cores (ETdG(bc)s), driven by environmental processes in the Virgo cluster utilizing the Extended Virgo Cluster Catalog. We investigated the morphological fraction and stellar mass of transitional dwarf galaxies in relation

Kenichi Umeda, Noriyuki Kodera

Atomic force microscopy (AFM) is a versatile nanoscale imaging technique. Since its spatiotemporal resolution is fundamentally limited by the minimum detectable force (MDF) arising from system noise, a deep understanding of MDF is essential for improving instrumentation. However, the theoretical MDF of amplitude-modulation (AM) AFM has long remained inconsis

Zeqing Zhang, Ruixing Jia, Youcan Yan, Ruihua Han

The proximity perception of objects in granular materials is significant, especially for applications like minesweeping. However, due to particles' opacity and complex properties, existing proximity sensors suffer from high costs from sophisticated hardware and high user-cost from unintuitive results. In this paper, we propose a simple yet effective proximit

Gabriel Dospinescu, Juan Esteban Rodríguez Camargo

We study the locally analytic theory of infinite level local Shimura varieties. As a main result, we prove that in the case of a duality of local Shimura varieties, the locally analytic vectors of different period sheaves at infinite level are independent of the actions of the $p$-adic Lie groups $G$ and $G_b$ of the two towers; this generalizes a result of

Seongphill Moon, Yuxuan Jiang, Jennifer Neu, Theo Siegrist

Theories have revealed the universality of the band tilting effect in topological Weyl semimetals (WSMs) and its implications for the material's physical properties. However, the experimental identification of tilted Weyl bands remains much less explored. Here, by combining magneto-infrared optical studies with a four-band coupled Weyl point model, we report

Saeed Nasehi, Farhana Choudhury, Egemen Tanin, Majid Sarvi

The rapid growth of online retail and e-commerce has made effective and efficient Vehicle Routing Problem (VRP) solutions essential. To meet rising demand, companies are adding more depots, which changes the VRP problem to a complex optimization task of Multi-Depot VRP (MDVRP) where the routing decisions of vehicles from multiple depots are highly interdepen

Xiao Tan, Ersin Das, Aaron D. Ames, Joel W. Burdick

We propose a novel zero-order control barrier function (ZOCBF) for sampled-data systems to ensure system safety. Our formulation generalizes conventional control barrier functions and straightforwardly handles safety constraints with high-relative degrees or those that explicitly depend on both system states and inputs. The proposed ZOCBF condition does not

Rongchang Xie, Chen Du, Ping Song, Chang Liu

We introduce MUSE-VL, a Unified Vision-Language Model through Semantic discrete Encoding for multimodal understanding and generation. Recently, the research community has begun exploring unified models for visual generation and understanding. However, existing vision tokenizers (e.g., VQGAN) only consider low-level information, which makes it difficult to al

Kazumasa Narita

Given a Riemannian submersion $(M,g) \to (B,j)$ each of whose fibers is connected and totally geodesic, we consider a certain 1-parameter family of Riemannian metrics $(g_{t})_{t > 0}$ on $M$, which is called the canonical variation. Let $\lambda_{1}(g_{t})$ be the first positive eigenvalue of the Laplace--Beltrami operator $\Delta^{M}_{g_{t}}$ and $\mbox{Vo

Meng-Hua Chen, Li-Xin Li, En-Wei Liang, Ning Wang

Due to the lack of experimental data on extremely neutron-rich nuclei, theoretical values derived from nuclear physics models are essential for the rapid neutron capture process ($r$-process). Metal-poor stars enriched by the $r$-process offer valuable cases for studying the impact of nuclear physics models on $r$-process nucleosynthesis. This study analyzes

Yuhang Wang, Yuxiang Zhang, Yanxu Zhu, Xinyan Wen

The o1 system card identifies the o1 models as the most robust within OpenAI, with their defining characteristic being the progression from rapid, intuitive thinking to slower, more deliberate reasoning. This observation motivated us to investigate the influence of System-2 thinking patterns on model safety. In our preliminary research, we conducted safety e

Yifan Lu, Zachary S. C. Picker, Stefano Profumo, Alexander Kusenko

Fermi balls are non-topological solitons that can naturally form in an early universe containing a dark sector with heavy fermions and an attractive interaction mediated by a light scalar field. We compute the Fermi ball mass and radius scaling relations when the potential of the scalar field $\varphi$ has a non-negligible quartic coupling $\lambda\varphi^4$

Awais Naeem, Tianhao Li, Huang-Ru Liao, Jiawei Xu

Accurate diagnosis and prognosis assisted by pathology images are essential for cancer treatment selection and planning. Despite the recent trend of adopting deep-learning approaches for analyzing complex pathology images, they fall short as they often overlook the domain-expert understanding of tissue structure and cell composition. In this work, we focus o

Pablo Ahumada

General Equilibrium Theory is the benchmark of economics, especially its results concerning the efficient allocation of resources, known as the First and Second Welfare Theorems. Yet, General Equilibrium Theory is beyond the scope of most economists. This paper is pitched as the first entry point into the theory. General Equilibrium Theory proves that at lea

David Sweet

Thompson sampling (TS) has optimal regret and excellent empirical performance in multi-armed bandit problems. Yet, in Bayesian optimization, TS underperforms popular acquisition functions (e.g., EI, UCB). TS samples arms according to the probability that they are optimal. A recent algorithm, P-Star Sampler (PSS), performs such a sampling via Hit-and-Run. We

Yikun Chen, Hanke Feng, Zhenzheng Wang, Ke Zhang

Optical single-sideband (SSB) modulation features high spectral efficiency, substantial dispersion tolerance, and straightforward detection, making it a versatile technology for applications in optical communications, microwave photonics, optical sensing, satellite communication, etc. However, conventional SSB generators typically require two radio-frequency

Xinyu Yang, Xiaotian Zhu, Caitlin Murray, Chawaphon Paryoonyong

The deterministic generation of robust soliton comb has significant meaning for the optical frequency combs to be widely used in various applications. As a novel form of microcomb, Soliton crystal holds the advantages of easy generation, high conversion efficiency, and excellent thermal robustness. Here, we report the turnkey deterministic generation of "Pal

Hongxu Chen, Renjun Duan, Junhao Zhang

Let rarefied gas be confined in an infinite layer with diffusely reflecting boundaries that are isothermal and non-moving. The initial-boundary value problem on the nonlinear Boltzmann equation governing the rarefied gas flow in such setting is challenging due to unboundedness of both domain and its boundaries as well as the presence of physical boundary con

Kaiwen Jiang, Venkataram Sivaram, Cheng Peng, Ravi Ramamoorthi

Geometric reconstruction of opaque surfaces from images is a longstanding challenge in computer vision, with renewed interest from volumetric view synthesis algorithms using radiance fields. We leverage the geometry field proposed in recent work for stochastic opaque surfaces, which can then be converted to volume densities. We adapt Gaussian kernels or surf

Colin Conwell, Rupert Tawiah-Quashie, Tomer Ullman

Despite remarkable progress in multi-modal AI research, there is a salient domain in which modern AI continues to lag considerably behind even human children: the reliable deployment of logical operators. Here, we examine three forms of logical operators: relations, negations, and discrete numbers. We asked human respondents (N=178 in total) to evaluate imag

Gang Dang, Dianhui Wang

Recurrent stochastic configuration networks (RSCNs) have shown great potential in modelling nonlinear dynamic systems with uncertainties. This paper presents an RSCN with hybrid regularization to enhance both the learning capacity and generalization performance of the network. Given a set of temporal data, the well-known least absolute shrinkage and selectio

Naomi Imasato, Kazuki Miyazawa, Takayuki Nagai, Takato Horii

With the growing popularity of generative AI for images, video, and music, we witnessed models rapidly improve in quality and performance. However, not much attention is paid towards enabling AI's ability to "be creative". In this study, we implemented and simulated the systems model of creativity (proposed by Csikszentmihalyi) using virtual agents utilizing

Nanako Shitara, Andrés Montoya-Castillo

Detecting and characterizing decoherence-inducing noise sources is critical for developing robust quantum technologies and deploying quantum sensors operating at molecular scales. However, current noise spectroscopies rely on severe approximations that sacrifice accuracy and precision. We propose a novel approach to overcome these limitations. It self-consis

Xinyi Gao, Yayong Li, Tong Chen, Guanhua Ye

With the increasing computation of training graph neural networks (GNNs) on large-scale graphs, graph condensation (GC) has emerged as a promising solution to synthesize a compact, substitute graph of the large-scale original graph for efficient GNN training. However, existing GC methods predominantly employ classification as the surrogate task for optimizat

Nan Yin

Currently, most Graph Structure Learning (GSL) methods, as a means of learning graph structure, improve the robustness of GNN merely from a local view by considering the local information related to each edge and indiscriminately applying the mechanism across edges, which may suffer from the local structure heterogeneity of the graph (\ie the uneven distribu

Guoan Xu, Jiaming Chen, Wenfeng Huang, Wenjing Jia

The Vision Transformer (ViT) has achieved notable success in computer vision, with its variants widely validated across various downstream tasks, including semantic segmentation. However, as general-purpose visual encoders, ViT backbones often do not fully address the specific requirements of task decoders, highlighting opportunities for designing decoders o

Mark M. Iskarous, Zan Chaudhry, Fangjie Li, Samuel Bello

Humans have an exquisite sense of touch which robotic and prosthetic systems aim to recreate. We developed algorithms to create neuron-like (neuromorphic) spiking representations of texture that are invariant to the scanning speed and contact force applied in the sensing process. The spiking representations are based on mimicking activity from mechanorecepto

Zachary Cooper-Baldock, Paulo E. Santos, Russell S. A. Brinkworth, Karl Sammut

Computational fluid dynamics (CFD) simulations are crucial in automotive, aerospace, maritime and medical applications, but are limited by the complexity, cost and computational requirements of directly calculating the flow, often taking days of compute time. Machine-learning architectures, such as controlled generative adversarial networks (cGANs) hold sign

Tingmin Wu, Shuiqiao Yang, Shigang Liu, David Nguyen

Threat modeling is a crucial component of cybersecurity, particularly for industries such as banking, where the security of financial data is paramount. Traditional threat modeling approaches require expert intervention and manual effort, often leading to inefficiencies and human error. The advent of Large Language Models (LLMs) offers a promising avenue for

Kateryna Andrych, Devika Kamath, Hans Van Winckel, Jacques Kluska

Exploring the formation and evolution of second-generation circumbinary discs around evolved binary stars, such as post-Asymptotic Giant Branch (post-AGB) and post-Red Giant Branch (post-RGB) binaries, provides valuable insights into the complex binary interaction process that concludes the red-giant phase of evolution in these systems. Additionally, it offe

Zhengqing Qiu, Yijie Mao, Shuai Ma, Bruno Clerckx

This paper addresses the robust beamforming design for rate splitting multiple access (RSMA)-aided visible light communication (VLC) networks with imperfect channel state information at the transmitter (CSIT). In particular, we first derive the theoretical lower bound for the channel capacity of RSMA-aided VLC networks. Then we investigate the beamforming de

Benjamin Li, Luis Modes

We generalize a result of M. Kapranov, O. Schiffmann, and E. Vasserot by showing that, for a number field $K$ with class number one, the spherical Hall algebra of $\overline{\operatorname{Spec}(\mathcal{O}_K)}$, where $\mathcal{O}_K$ is the ring of integers of $K$, is isomorphic to the Paley-Wiener shuffle algebra associated to a Hecke $L$-function correspon

Changhong Linghu, Wentao Mao, Haoyu Jiang, Huajian Gao

Shape memory polymers (SMPs) are emerging as innovative smart adhesive materials with broad application potential. Compared to conventional elastomeric adhesives, SMP adhesives are distinguished by the so-called rubber-to-glass (R2G) adhesion, which involves contact in the rubbery state followed by detachment in the glassy state. This process, through a shap

Zhihang Yin, Fa Wu, Ziqian Wang, Jianmin Yang

Traditional offline redundancy resolution of trajectories for redundant manipulators involves computing inverse kinematic solutions for Cartesian space paths, constraining the manipulator to a fixed path without real-time adjustments. Online redundancy resolution can achieve real-time adjustment of paths, but it cannot consider subsequent path points, leadin

Chuyi Li, Kedi Zheng, Hongye Guo, Chongqing Kang

The batch and online workload of Internet data centers (IDCs) offer temporal and spatial scheduling flexibility. Given that power generation costs vary over time and location, harnessing the flexibility of IDCs' energy consumption through workload regulation can optimize the power flow within the system. This paper focuses on multi-geographically distributed

Adway Patra, Alexander Barg

Starting with an explicit framework for designing logical Clifford circuits for CSS codes, we construct logical gates for Hypergraph Product Codes. We first derive symplectic matrices for CNOT, CZ, Phase, and Hadamard operators, which together generate the Clifford group. This enables us to design explicit transformations that result in targeted logical gate

Sungsoon Lim, Eric W. Peng, Patrick Côté, Laura Ferrarese

We present an analysis of the spatial distribution of globular cluster (GC) systems of 118 nearby early-type galaxies in the Next Generation Virgo Cluster Survey (NGVS) and Mass Assembly of early-Type GaLAxies with their fine Structures (MATLAS) survey programs, which both used MegaCam on the Canada-France-Hawaii Telescope. We describe the procedure used to

Hengjia Li, Haonan Qiu, Shiwei Zhang, Xiang Wang

The current text-to-video (T2V) generation has made significant progress in synthesizing realistic general videos, but it is still under-explored in identity-specific human video generation with customized ID images. The key challenge lies in maintaining high ID fidelity consistently while preserving the original motion dynamic and semantic following after t

Bhuvana G. R., Anuj Nandi

Extragalactic Black Hole X-ray Binaries (BH-XRBs) are the most intriguing X-ray sources as some of them are `home' to the most massive stellar-mass BHs ever found. In this work, we conduct a comprehensive study of three massive, eclipsing extragalactic BH-XRBs i.e., M33X-7, NGC300X-1, and IC10X-1 and using entire X-ray observations available from \textit{XMM

Minh-Tuan Tran, Trung Le, Xuan-May Le, Jianfei Cai

Data-Free Knowledge Distillation (DFKD) is an advanced technique that enables knowledge transfer from a teacher model to a student model without relying on original training data. While DFKD methods have achieved success on smaller datasets like CIFAR10 and CIFAR100, they encounter challenges on larger, high-resolution datasets such as ImageNet. A primary is

Yuchen Ling, Shengcheng Yu, Chunrong Fang, Guobin Pan

Context: Crowdsourced testing has gained popularity in software testing, especially for mobile app testing, due to its ability to bring diversity and tackle fragmentation issues. However, the openness of crowdsourced testing presents challenges, particularly in the manual review of numerous test reports, which is time-consuming and labor-intensive. Objective

Woong Oh Cho, In Cho, Seoha Kim, Jeongmin Bae

Modeling dynamic scenes through 4D Gaussians offers high visual fidelity and fast rendering speeds, but comes with significant storage overhead. Recent approaches mitigate this cost by aggressively reducing the number of Gaussians. However, this inevitably removes Gaussians essential for high-quality rendering, leading to severe degradation in dynamic region

Anders Johansen, Peter Olson, Zachary Sharp

Morbidelli, Kleine & Nimmo (2024) (MKN) recently published a critical analysis on whether the terrestrial planets in the Solar System formed by rapid pebble accretion or by the classical route of multiple giant impacts between planetary embryos after the dissipation of the protoplanetary disc. They arrive at the conclusion that the terrestrial planets did no

Eshant English, Christoph Lippert

Conformal Prediction offers a powerful framework for quantifying uncertainty in machine learning models, enabling the construction of prediction sets with finite-sample validity guarantees. While easily adaptable to non-probabilistic models, applying conformal prediction to probabilistic generative models, such as Normalising Flows is not straightforward. Th

Jaemin Kim, Bryan Sangwoo Kim, Jong Chul Ye

Diffusion models have achieved impressive results in generative tasks for text-to-video (T2V) synthesis. However, achieving accurate text alignment in T2V generation remains challenging due to the complex temporal dependencies across frames. Existing reinforcement learning (RL)-based approaches to enhance text alignment often require differentiable reward fu

Songtao Li, Hao Tang

This survey provides a comprehensive overview of recent advances in multimodal alignment and fusion within the field of machine learning, driven by the increasing availability and diversity of data modalities such as text, images, audio, and video. Unlike previous surveys that often focus on specific modalities or limited fusion strategies, our work presents

Yu Yang, Pingan He, Xiaoling Peng, Qiaolin He

Traditional Monte Carlo integration using uniform random sampling exhibits degraded efficiency in low-regularity or high-dimensional problems. We propose a novel deep learning framework based on deterministic number-theoretic sampling points, which is a robust approach specifically designed to handle partial differential equations with rough solutions or in

Kevin Federico, Stefano Profumo

The final stage of black hole evaporation is a potent probe of physics beyond the Standard Model: Hawking-Bekenstein radiation may be affected by quantum gravity "memory burden effects", or by the presence of "dark", beyond-the-Standard-Model degrees of freedom in ways that are testable with high-energy gamma-ray observations. We argue that information on ei

Daniel Jardón, Iván Sánchez, Manuel Sanchis

Given a uniform space $(X, \mathcal{U})$, we denote by $\mathcal{F}(X)$ to the family of all normal upper semicontinuous fuzzy sets $u \colon X \to [0,1]$ with compact support. In this paper, we study transitivity on some uniformities on $\mathcal{F}(X)$: the level-wise uniformity $\mathcal{U}_{\infty}$, the Skorokhod uniformity $\mathcal{U}_{0}$, and the se

Manuela Girotti, Tamara Grava, Ken D. T-R McLaughlin, Joseph Najnudel

We study a random configuration of $N$ soliton solutions $\psi_N(x,t;\boldsymbol{\lambda})$ of the cubic focusing Nonlinear Schr\"odinger (fNLS) equation in one space dimension. The $N$ soliton solutions are parametrized by $2N$ complex numbers $(\boldsymbol{\lambda}, \boldsymbol{c})$ where $\boldsymbol{\lambda}\in\mathbb{C}_+^N$ are the eigenvalues of the Z

Gaurab Hore, Tucker McElroy, Anindya Roy

Utility-preserving data privatization is of utmost importance for data-producing agencies. The popular noise-addition privacy mechanism distorts autocorrelation patterns in time series data, thereby marring utility; in response, McElroy et al. (2023) introduced all-pass filtering (FLIP) as a utility-preserving time series data privatization method. Adapting

Zhihang Yin, Fa Wu, Ruofan Bian, Ziqian Wang

This paper proposes a redundancy resolution algorithm for a redundant manipulator based on dynamic programming. This algorithm can compute the desired joint angles at each point on a pre-planned discrete path in Cartesian space, while ensuring that the angles, velocities, and accelerations of each joint do not exceed the manipulator's constraints. We obtain

Zain Khan, Daniel Malinsky, Martin Picard, Alan A. Cohen

Graphical structure learning is an effective way to assess and visualize cross-biomarker dependencies in biomedical settings. Standard approaches to estimating graphs rely on conditional independence tests that may not be sensitive to associations that manifest at the tails of joint distributions, i.e., they may miss connections among variables that exhibit

Z. Keszthelyi, K. Kurahara, Y. Iwata, Y. Fujii

Magnetic hot stars can emit both coherent and incoherent non-thermal radio emission. Understanding the nature of these emissions and their connection to stellar rotation and magnetic field characteristics remains incomplete. The RAdio Magnetospheres of B and O stars (RAMBO) project aims to address this gap by systematically detecting and characterizing gyros

Gonzalo Gutierrez, Marco Farinati

In this paper, we generalize the Tits construction for Lie superalgebras such that $\mathfrak{sl}_2$ acts by even derivations and decompose, as $\mathfrak{sl}_2$-module, into a direct sum of copies of the adjoint, the natural and the trivial representations. This construction generalizes the one provided by Elduque et al in \cite{EBCC23}, and it is possible

Zihan Wang, Gim Hee Lee

We introduce Generalizable 3D-Language Feature Fields (g3D-LF), a 3D representation model pre-trained on large-scale 3D-language dataset for embodied tasks. Our g3D-LF processes posed RGB-D images from agents to encode feature fields for: 1) Novel view representation predictions from any position in the 3D scene; 2) Generations of BEV maps centered on the ag

Zhongyu Xia, Jishuo Li, Zhiwei Lin, Xinhao Wang

Open-world perception aims to develop a model adaptable to novel domains and various sensor configurations and can understand uncommon objects and corner cases. However, current research lacks sufficiently comprehensive open-world 3D perception benchmarks and robust generalizable methodologies. This paper introduces OpenAD, the first real open-world autonomo

Zhuowen Zhao, Thomas R. Bieler, Philip Eisenlohr

We introduce a cluster-based technique to automate pixel-wise reconstruction of $\beta$ orientations from parent $\alpha$ orientations over large, indexed regions. This approach provides a valuable tool for analyzing problems that require historical information about current $\alpha$ microstructures, such as investigating variant selection mechanisms during

R. Li, A. Picksley, C. Benedetti, F. Filippi

Modern laser plasma accelerators (LPAs) often require plasma waveguides tens of cm long to propagate a high-intensity drive laser pulse. Tapering the longitudinal gas density profile in 10 cm scale gas jets could allow for single stage laser plasma acceleration well beyond 10 GeV with current petawatt-class laser systems. Via simulation and interferometry me

Haiming Zhang, Xu Yan, Ying Xue, Zixuan Guo

This technical report summarizes the second-place solution for the Predictive World Model Challenge held at the CVPR-2024 Workshop on Foundation Models for Autonomous Systems. We introduce D$^2$-World, a novel World model that effectively forecasts future point clouds through Decoupled Dynamic flow. Specifically, the past semantic occupancies are obtained vi

Cristóbal Rojas, Michael Yampolsky

In 1946, S. Ulam invented Monte Carlo method, which has since become the standard numerical technique for making statistical predictions for long-term behaviour of dynamical systems. We show that this, or in fact any other numerical approach can fail for the simplest non-linear discrete dynamical systems given by the logistic maps $f_{a}(x)=ax(1-x)$ of the u

Jinghan Yang

The classification of road signs by autonomous systems, especially those reliant on visual inputs, is highly susceptible to adversarial attacks. Traditional approaches to mitigating such vulnerabilities have focused on enhancing the robustness of classification models. In contrast, this paper adopts a fundamentally different strategy aimed at increasing robu

Yu-Fu Shen

Bayesian statistics emphasizes the importance of prior distributions, yet finding an appropriate one is practically challenging. When multiple sample results are taken regarding the frequency of the same event, these samples may be influenced by different selection effects. In the absence of suitable prior distributions to correct for these selection effects

Emmanuel Humbert, Kilian Raschel

We consider a collection of independent standard Brownian particles (or random walks), starting from a configuration where at least one particle is positive, and study the first time they all become negative. This is clearly equivalent to studying the first hitting time from the negative orthant or the first exit time from the complement of the negative orth

Sahel Ashhab, Mohammad Ayyash

We analyze the properties and dynamics of generalized squeezed states. We find that, in stark contrast to displacement and two-photon squeezing, higher-order squeezing leads to oscillatory dynamics. The state is squeezed in the initial stages of the dynamics but the squeezing reverses at later stages, and the state reverts almost completely back to the initi

Srijan Bhattacharyya, Thomas Sayer, Andrés Montoya-Castillo

Predicting the exact many-body quantum dynamics of polarons in materials with strong carrier-phonon interactions presents a fundamental challenge, often necessitating one to adopt approximations that sacrifice the ability to predict the transition from nonequilibrium relaxation to thermodynamic equilibrium. Here, we exploit a recent breakthrough that general

Yuxuan Guo, Yuto Ashida

Preparing highly entangled quantum states is a key challenge in quantum metrology and quantum information science. Measurements, especially those of global observables, offer a simple and efficient way to generate entanglement between subsystems when they are measured as a whole. We introduce a log-depth protocol leveraging quantum phase estimation to measur

Ning Wang, Jinming Chen, Yicheng Wang, Hong Yao

A universal Wong formula is proposed with refined model parameters for a systematic description of the capture cross sections for heavy-ion fusion reactions from C+C to Ni+U, in which the barrier parameters and the barrier distribution are determined by the entrance-channel nucleus-nucleus potential based on the Skyrme energy density functional. With introdu