December 2024 arXiv papers — page 186

M. Singh, R. Yokoyama, R. Grzywacz, A. Keeler

A segmented-scintillator-based implantation detector was developed to study the energy distribution of beta-delayed neutrons emitted from exotic isotopes. The detector comprises a 34 $\times$ 34 YSO scintillator coupled to an 8 $\times$ 8 Position-Sensitive Photo-Multiplier Tube (PSPMT) via a tapered light guide. The detector was used at RIBF, RIKEN, for tim

Shouwei Ruan, Hanqing Liu, Yao Huang, Xiaoqi Wang

Vision Language Models (VLMs) have exhibited remarkable generalization capabilities, yet their robustness in dynamic real-world scenarios remains largely unexplored. To systematically evaluate VLMs' robustness to real-world 3D variations, we propose AdvDreamer, the first framework capable of generating physically reproducible Adversarial 3D Transformation (A

Tae Ung Gang, Yong Hyun Shin

We study an optimal control problem encompassing investment, consumption, and retirement decisions under exponential (CARA-type) utility. The financial market comprises a bond with constant drift and a stock following geometric Brownian motion. The agent receives continuous income, consumes over time, and has the option to retire irreversibly, gaining increa

Kenneth W. Johnson, Donald St. P. Richards

For a given positive integer $m$, the concept of hyperdeterminantal total positivity is defined for a kernel $K\colon {\mathbb R}^{2m} \to {\mathbb R}$, thereby generalizing the classical concept of total positivity. Extending the fundamental example, $K(x,y) = \exp(xy)$, $x, y \in \mathbb{R}$, of a classical totally positive kernel, the hyperdeterminantal t

D. Fernando, R. O'Shaughnessy, D. Williams

Different waveform models can yield notably different conclusions about the properties of individual gravitational wave events. For instance, previous analyses using the SEOBNRv4PHM, IMRPhenomXPHM models, and NRSur7dq4 have led to varying results regarding event properties. This variability complicates the interpretation of the data and understanding of the

Ji Wu, Huai Yu, Shu Han, Xi-Meng Cai

In the realm of large-scale point cloud registration, designing a compact symbolic representation is crucial for efficiently processing vast amounts of data, ensuring registration robustness against significant viewpoint variations and occlusions. This paper introduces a novel point cloud registration method, i.e., QuadricsReg, which leverages concise quadri

Zi-Yan Yang, Qian Wang, Wei Chen

The observation of $X_0(2900)$ in $B^+\rightarrow D^+D^-K^+$ decay process indicates the existence of open flavor tetraquark states. We study the production and decay of $X_0(2900)$ state with final state interaction mechanism, where we calculate the strong vertices such as $g_{\bar{D}KX_0}$, $g_{\bar{D}^\ast K^\ast X_0}$, $g_{D_s^{\ast}\bar{D}K}$ and $g_{D_

XiuYu Zhang, Xiaolei Ye, Jui-Che Chang, Yue Fang

Three-dimensional (3D) objects have wide applications. Despite the growing interest in 3D modeling in academia and industries, designing and/or creating 3D objects from scratch remains time-consuming and challenging. With the development of generative artificial intelligence (AI), designers discover a new way to create images for ideation. However, generativ

Xiao Luo, Yoshinobu Kuramashi

We study (1+1)-dimensional O(3) nonlinear sigma model using the tensor renormalization group method with the infinite limit of the bond dimension $D_{\rm cut}\rightarrow \infty$. At the vanishing chemical potential $\mu=0$, we investigate the von Neumann and R\'enyi types of entanglement entropies. The central charge is determined to be $c=1.97(9)$ by using

Nicole F. Bell, Matthew J. Dolan, Avirup Ghosh, Michael Virgato

Large-scale neutrino detectors currently under construction will have the unique ability to probe the annihilation of low-mass thermal-relic dark matter to neutrinos. This represents an essential test of the thermal freezeout paradigm. This raises the question: what viable UV-complete models are there in which dark matter annihilates dominantly to neutrinos?

Jiongtong Hu, Wei Zhuo, Jun Cheng, Yingying Liu

In clinical practice of echocardiography examinations, multiple planes containing the heart structures of different view are usually required in screening, diagnosis and treatment of cardiac disease. AI models for echocardiography have to be tailored for each specific plane due to the dramatic structure differences, thus resulting in repetition development a

Xiao-Ji Weng, Yi Zhu, Ying Xu, Jie Bai

Metalloborophene, characterized by the presence of metal-centered boron wheels denoted as M\c{opyright}Bn, has garnered considerable attention in recent years due to its versatile properties and potential applications in fields such as electronics, spintronics, and catalysis. However, the experimental verification of metalloborophene has been challenging, ma

Junyi Shan

Optical nonlinearity, especially the second harmonic generation (SHG), is generally weak in materials but has the potential to be applied in high-speed optical computers and energy-efficient artificial intelligence systems. In order to program such photonic circuits, electrical and all-optical modulation mechanisms of optical nonlinearity have been proposed.

Naoki Onishi

We investigate the global structures of neutron stars within the framework of general relativity, treating the entire star as a quantum-degenerate system. Rather than relying on the Tolman-Oppenheimer-Volkoff (TOV) equation, we solve the Einstein-Cartan (EC) field equations self-consistently, incorporating the energy-momentum tensor contributions from neutro

Michael H. Freedman

In an earlier note [arXiv:2301.00295] it was shown that there is an upper bound to the number of disjoint Hopf links (and certain related links) that can be embedded in the unit cube where there is a fixed separation required between the components within each copy of the Hopf link. The arguments relied on multi-linear properties of linking number and certai

Apurv Shukla, Debabrota Basu

We study the preference-based pure exploration problem for bandits with vector-valued rewards. The rewards are ordered using a (given) preference cone $\mathcal{C}$ and our goal is to identify the set of Pareto optimal arms. First, to quantify the impact of preferences, we derive a novel lower bound on sample complexity for identifying the most preferred pol

XiuYu Zhang, Zening Luo

Mental health has increasingly become a global issue that reveals the limitations of traditional conversational psychotherapy, constrained by location, time, expense, and privacy concerns. In response to these challenges, we introduce SoulSpeak, a Large Language Model (LLM)-enabled chatbot designed to democratize access to psychotherapy. SoulSpeak improves u

Shibo Han, Yuhao Zhang, Xiaotong Shi, Xingwei Zhao

This paper proposes a novel robust model predictive control (RMPC) method for the stabilization of constrained systems subject to additive disturbance (AD) and multiplicative disturbance (MD). Concentric containers are introduced to facilitate the characterization of MD, and varying tubes are constructed to bound reachable states. By restricting states and t

Daisuke Uchida, Karthik Duraisamy

This work presents a data-driven Koopman operator-based modeling method using a model averaging technique. While the Koopman operator has been used for data-driven modeling and control of nonlinear dynamics, it is challenging to accurately reconstruct unknown dynamics from data and perform different decision-making tasks, mainly due to its infinite dimension

Anton M. Graf, Saul Atwood, Mingxuan Xiao, Roland Ketzmerick

A hallmark of classical ergodicity is the complete loss of memory of the initial conditions due to eventual uniform covering of {\it a priori} available phase space. In quantum counterparts of such systems, however, this classical ergodic ideal is fundamentally limited: Here, we introduce the concept of a \emph{quantum birthmark}, a permanent signature left

Snezana Stanimirovic

Atomic hydrogen (HI) is a vital player in the star-formation process in galaxies. It is the raw fuel for making molecules, an important shielding agent against interstellar radiation, and a buffer that soaks up the energy and momentum of stellar feedback. While for many years detailed studies of the HI thermal structure have been possible only in the Milky W

Alex Havrilla, Andrew Dai, Laura O'Mahony, Koen Oostermeijer

Synthetic data generation with Large Language Models is a promising paradigm for augmenting natural data over a nearly infinite range of tasks. Given this variety, direct comparisons among synthetic data generation algorithms are scarce, making it difficult to understand where improvement comes from and what bottlenecks exist. We propose to evaluate algorith

Pablo G. Camara

We present a diffeomorphism-invariant formulation of differential entropy for Riemannian spaces, providing a fine-grained, coordinate-independent notion of quantum information for continuous variables in physical space. To this end, we consider the generalization of the Wigner quasiprobability density function to arbitrary Riemannian manifolds and analytical

Qing Zhang, Hang Guo, Siyuan Yang, Qingli Li

Pathological cell semantic segmentation is a fundamental technology in computational pathology, essential for applications like cancer diagnosis and effective treatment. Given that multiple cell types exist across various organs, with subtle differences in cell size and shape, multi-organ, multi-class cell segmentation is particularly challenging. Most exist

Kazutaka Yamaoka, Toshihiro Kawaguchi, Michael L. McCollough, Ruben Farinelli

We compiled the X-ray and soft gamma-ray observations of the Galactic black hole binary XTE J1859+226 in the 1999--2000 outburst from RXTE, ASCA, BeppoSAX and CGRO. Throughout systematic spectral analysis using a two-component model consisting of a multi-temperature accretion disk plus a fraction of its flux convolved with an empirical Comptonized powerlaw c

Yongcheng Li, Lingcong Cai, Ying Lu, Xianghua Fu

Blood cell identification is critical for hematological analysis as it aids physicians in diagnosing various blood-related diseases. In real-world scenarios, blood cell image datasets often present the issues of domain shift and data imbalance, posing challenges for accurate blood cell identification. To address these issues, we propose a novel blood cell cl

Lijie Chen, Binghui Peng, Hongxun Wu

Transformers, especially the decoder-only variants, are the backbone of most modern large language models; yet we do not have much understanding of their expressive power except for the simple $1$-layer case. Due to the difficulty of analyzing multi-layer models, all previous work relies on unproven complexity conjectures to show limitations for multi-layer

Jane Hsieh, Angie Zhang, Mialy Rasetarinera, Erik Chou

The proliferating adoption of platform-based gig work increasingly raises concerns for worker conditions. Past studies documented how platforms leveraged design to exploit labor, withheld information to generate power asymmetries, and left workers alone to manage logistical overheads as well as social isolation. However, researchers also called attention to

Daisuke Uchida, Karthik Duraisamy

This paper develops a methodology for adaptive data-driven Model Predictive Control (MPC) using Koopman operators. While MPC is ubiquitous in various fields of engineering, the controller performance can deteriorate if the modeling error between the control model and the true dynamics persists, which may often be the case with complex nonlinear dynamics. Ada

Arnold Caleb Asiimwe, Dídac Surís, Pranav Rajpurkar, Carl Vondrick

In medical reporting, the accuracy of radiological reports, whether generated by humans or machine learning algorithms, is critical. We tackle a new task in this paper: image-conditioned autocorrection of inaccuracies within these reports. Using the MIMIC-CXR dataset, we first intentionally introduce a diverse range of errors into reports. Subsequently, we p

Thomas Y. Sun, Julia C. Schedler, Daniel R. Kowal, Rebecca Schneider

Monitoring wastewater concentrations of SARS-CoV-2 yields a low-cost, noninvasive method for tracking disease prevalence and provides early warning signs of upcoming outbreaks in the serviced communities. There is tremendous clinical and public health interest in understanding the exact dynamics between wastewater viral loads and infection rates in the popul

Hanti Lin

While the traditional conception of inductive logic is Carnapian, I develop a Peircean alternative and use it to unify formal learning theory, statistics, and a significant part of machine learning: supervised learning. Some crucial standards for evaluating non-deductive inferences have been assumed separately in those areas, but can actually be justified by

Christopher Homan, Flip Korn, Deepak Pandita, Chris Welty

A cornerstone of machine learning evaluation is the (often hidden) assumption that model and human responses are reliable enough to evaluate models against unitary, authoritative, ``gold standard'' data, via simple metrics such as accuracy, precision, and recall. The generative AI revolution would seem to explode this assumption, given the critical role stoc

Aref Hashemi, Aliakbar Izadkhah

We present a Graph Neural Network (GNN) that accurately simulates a multidisperse suspension of interacting spherical particles. Our machine learning framework is built upon the recent work of Sanchez-Gonzalez et al. ICML, PMLR, 119, 8459-8468 (2020) on graph network simulators, and efficiently learns the intricate dynamics of the interacting particles. Node

H. L. Ning, M. Q. Dong, X. Zhang, J. S. Huang

Recent experiments have demonstrated efficient spin transfer across layers in the van der Waals heterostructure composed of WTe2 and Fe3GeTe2, signaling a potential breakthrough in developing all-van der Waals spin-orbit torque devices. However, the reasons behind the unusually high interlayer spin transparency observed, despite the weak van der Waals intera

Kostya Druzhkov, Alexei Cheviakov

For a system of partial differential equations that has an extended Kovalevskaya form, a reduction procedure is presented that allows one to use a local (point, contact, or higher) symmetry of a system and a symmetry-invariant conservation law to algorithmically calculate constants of motion holding for symmetry-invariant solutions. Several examples includin

Fubao Yang, Yuhong Zhou, Peng Jin, Liujun Xu

Intelligent metamaterials have attracted widespread research interest due to their self-adaptive capabilities and controllability. They hold great potential for advancing fluid control by providing responsive and flexible solutions. However, current designs of passive hydrodynamic metamaterials are limited by their fixed shapes and specific environments, lac

Junichi Baba, Takuji Tsujimoto, Takayuki R. Saitoh

Astrophysical evidence suggests that the Sun was born near 5 kpc from the Galactic center, within the corotation radius of the Galactic bar, around 6-7 kpc. This presents challenges for outward migration due to the Jacobi energy constraint, preventing stars from easily overcoming the corotation barrier. In this study, we use test particle simulations to expl

XiuYu Zhang, Zening Luo, Michelle E. Lu

Diffusion models have exhibited exciting capabilities in generating images and are also very promising for video creation. However, the inference speed of diffusion models is limited by the slow sampling process, restricting its use cases. The sequential denoising steps required for generating a single sample could take tens or hundreds of iterations and thu

Martin Lotz, Abhiram Natarajan, Nicolai Vorobjov

We generalize the seminal polynomial partitioning theorems of Guth and Katz to a set of semi-Pfaffian sets. Specifically, given a set $\Gamma \subseteq \mathbb{R}^n$ of $k$-dimensional semi-Pfaffian sets, where each $\gamma \in \Gamma$ is defined by a fixed number of Pfaffian functions, and each Pfaffian function is in turn defined with respect to a Pfaffian

Jiahua Xiao, Jiawei Zhang, Dongqing Zou, Xiaodan Zhang

Real-world image super-resolution (Real-ISR) has achieved a remarkable leap by leveraging large-scale text-to-image models, enabling realistic image restoration from given recognition textual prompts. However, these methods sometimes fail to recognize some salient objects, resulting in inaccurate semantic restoration in these regions. Additionally, the same

Yongshuo Chen, Cheng Ma, Boning Cui, Tian Cui

Nonlocal kinetic energy density functionals (KEDFs) with density-dependent kernels are currently the most accurate functionals available for orbital-free density functional theory (OF-DFT) calculations. However, despite advances in numerical techniques and using only (semi)local density-dependent kernels, nonlocal KEDFs still present substantial computationa

Hashim A. Yamani, Zouhaïr Mouayn

While dealing with the J-Matrix method for the harmonic oscillator to write down its tridiagonal matrix representation in an orthonormal basis of L2(R); we rederive a set of generalized coherent states (GCS) of Perelomov type labeled by points z of the complex plane C and depending on a positive integer number m The number states expansion of these GCS gives

Namkyeong Lee, Yunhak Oh, Heewoong Noh, Gyoung S. Na

Molecular Relational Learning (MRL) is a rapidly growing field that focuses on understanding the interaction dynamics between molecules, which is crucial for applications ranging from catalyst engineering to drug discovery. Despite recent progress, earlier MRL approaches are limited to using only the 2D topological structure of molecules, as obtaining the 3D

Kaidi Jia, Yanxia Wu, Ming Liu, Rongsheng Li

Recently, utilizing large language models (LLMs) for metaphor detection has achieved promising results. However, these methods heavily rely on the capabilities of closed-source LLMs, which come with relatively high inference costs and latency. To address this, we propose a method for metaphor detection by fine-tuning open-source LLMs, effectively reducing in

Hexiang Lin, Huihui Zhu, Zan Tang, Wei Luo

Quantum computing holds promise across various fields, particularly with the advent of Noisy Intermediate-Scale Quantum (NISQ) devices, which can outperform classical supercomputers in specific tasks. However, challenges such as noise and limited qubit capabilities hinder its practical applications. Variational Quantum Algorithms (VQAs) offer a viable strate

Zhiyuan Geng

For the two dimensional Allen-Cahn system with a triple-well potential, previous results established the existence of a minimizing solution $u:\mathbb{R}^2\rightarrow\mathbb{R}^2$ with a triple junction structure at infinity. We show that along each of three sharp interfaces, $u$ is asymptotically invariant in the direction of the interface and can be well-a

Sungjin Lim, Illés Vörös, Yongseob Lim, Gábor Orosz

In this study, we analyze the stability of a path-tracking controller designed for a four-wheel steering vehicle, incorporating the effects of the reference path curvature. By employing a simplified kinematic model of the vehicle with steerable front and rear wheels, we derive analytical expressions for the stability regions and optimal control gains specifi

Pablo Amster, Gonzalo Robledo, Daniel Sepúlveda

We study an almost periodic version of a metapopulation model developed by Tilman \textit{et.al} and Nee \textit{et.al} in the nineties, which generalizes the classical Levins approach by considering several species in competition affected by habitat destruction. The novelty is to assume that the colonization and extinction rates are positive almost periodic

Weisi Fan, Jesse Lane, Qisai Liu, Soumik Sarkar

Perception components in autonomous systems are often developed and optimized independently of downstream decision-making and control components, relying on established performance metrics like accuracy, precision, and recall. Traditional loss functions, such as cross-entropy loss and negative log-likelihood, focus on reducing misclassification errors but fa

Abdelhak Bougouffa, Emmanuel Seignez, Samir Bouaziz, Florian Gardes

Autonomous Mobile Robots operating in indoor industrial environments require a localization system that is reliable and robust. While Visual Odometry (VO) can offer a reasonable estimation of the robot's state, traditional VO methods encounter challenges when confronted with dynamic objects in the scene. Alternatively, an upward-facing camera can be utilized

Yair Carmon, Arun Jambulapati, Liam O'Carroll, Aaron Sidford

We provide a general method to convert a "primal" black-box algorithm for solving regularized convex-concave minimax optimization problems into an algorithm for solving the associated dual maximin optimization problem. Our method adds recursive regularization over a logarithmic number of rounds where each round consists of an approximate regularized primal o

Rama Cont, Fang Rui Lim

We study properties of causal couplings for probability measures on the space of continuous functions. We first provide a characterization of bicausal couplings between weak solutions of stochastic differential equations. We then provide a complete description of all such bicausal Monge couplings. In particular, we show that bicausal Monge couplings of $d$-d

Huabin Ge, Bobo Hua, Longsong Jia, Puchun Zhou

In this article, we prove the decay estimate for the discrete Schr\"odinger equation (DS) on the hexagonal triangulation. The $l^1\rightarrow l^\infty$ dispersive decay rate is $\left\langle t\right\rangle^{-\frac{3}{4}}$, which is faster than the decay rate of DS on the 2-dimensional lattice $\mathbb{Z}^2$, which is $\left\langle t\right\rangle^{-\frac{2}{3

Po-Hsuan Huang, Jeng-Lin Li, Chin-Po Chen, Ming-Ching Chang

Recent advancements in large vision-language models (LVLM) have significantly enhanced their ability to comprehend visual inputs alongside natural language. However, a major challenge in their real-world application is hallucination, where LVLMs generate non-existent visual elements, eroding user trust. The underlying mechanism driving this multimodal halluc

Dongliang Zhang, Masoud Asgharian, Martin A. Lindquist

Outlying observations are frequently encountered across a wide spectrum of scientific domains, posing notable challenges to the generalizability of statistical models and the reproducibility of downstream analysis. They are identified through influential diagnostics, which aim to capture observations that unduly bias model estimation. To date, methods for id

Anju Rani, Vardaan Mongia, Parvatesh Parvatikar, Rutuj Gharate

The BB84 quantum key distribution protocol set the foundation for achieving secure quantum communication. Since its inception, significant advancements have aimed to overcome experimental challenges and enhance security. In this paper, we report the implementation of a passive polarization-encoded BB84 protocol using a heralded single-photon source. By passi

Zixin Jiang, Bing Dong

Model predictive control can achieve significant energy savings, offer grid flexibility, and mitigate carbon emissions. However, the challenge of identifying individual control-oriented building dynamic models limits large-scale real-world applications. To address this issue, this study proposed a Modularized Neural Network Incorporating Physical Priors (Mod

Silu He, Peng Shen, Pingzhen Xu, Qinyao Luo

Existing works typically treat spatial-temporal prediction as the task of learning a function $F$ to transform historical observations to future observations. We further decompose this cross-time transformation into three processes: (1) Encoding ($E$): learning the intrinsic representation of observations, (2) Cross-Time Mapping ($M$): transforming past repr

Xianzhe Dai, Yukai Sun, Changliang Wang

We prove a Geroch type result for isolated conical singularity. Namely, we show that there is no Riemannian metric $g$ on $ X \# T^n $ with an isolated conical singularity which has nonnegative scalar curvature on the regular part, and is positive at some point. In particular, this implies that there is no metric on tori with an isolated conical singularity

Vignesh Sivaramakrishnan, Krishna C. Kalagarla, Rosalyn Devonport, Joshua Pilipovsky

We present a neural network verification toolbox to 1) assess the probability of satisfaction of a constraint, and 2) synthesize a set expansion factor to achieve the probability of satisfaction. Specifically, the tool box establishes with a user-specified level of confidence whether the output of the neural network for a given input distribution is likely t

Xin Liao, Xing-Yu Liu, An-Qi Wang, Qing Yin

Nonlinear transport plays a vital role in probing the quantum geometry of Bloch electrons, valley chirality, and carrier scattering mechanisms. The nonlinear Hall effect, characterized by a nonlinear scaling of Hall voltage with longitudinal current, has been explored to reveal the Berry curvature and quantum metric related physics. In this work, we extend t

Xing-Guo Ye, Peng-Fei Zhu, Wen-Zheng Xu, Tong-Yang Zhao

In materials with spin-momentum locked spin textures, such as Rashba states and topological surface states, the current-induced shift of the Fermi contour in the k space leads to spin polarization, known as the Edelstein effect, which depends linearly on the applied current. However, its nonlinear counterpart has not yet been discovered. Here, we report the

An-Qi Wang, Dong Li, Tong-Yang Zhao, Xing-Yu Liu

We report on the observation of the linear anomalous Hall effect (AHE) in the nonmagnetic Weyl semimetal TaIrTe4. This is achieved by applying a direct current Idc and an alternating current Iac (Iac<<Idc) in TaIrTe4, where the former induces time-reversal symmetry breaking and the latter probes the triggered AHE. The anomalous Hall resistance VacH/Iac shows

Jhordan Silveira de Borba, Sebastian Gonçalves

The ecosystem considered in this study is the outcome of a lengthy sequence of historical and ecological events. Patagonia's indigenous fauna comprises survivors of five significant extinction events, with the notable presence of the puma and the guanaco, two of the largest native mammals. In addition to these, European immigrants introduced sheep into the e

Yuntao Shou, Tao Meng, Wei Ai, Keqin Li

Multimodal emotion recognition in conversation (MERC) refers to identifying and classifying human emotional states by combining data from multiple different modalities (e.g., audio, images, text, video, etc.). Most existing multimodal emotion recognition methods use GCN to improve performance, but existing GCN methods are prone to overfitting and cannot capt

Xianzhi Zhang, Yipeng Zhou, Miao Hu, Di Wu

To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without exposing their raw user-item rating data. The differentially private federated recommender (DPFR) further enhances FR by injecting differentially private (DP) noises into clients. Yet,

Linqiang Guo, Wei Liu, Yi Wen Heng, Tse-Hsun

Graphical User Interfaces (GUIs) are the primary means by which users interact with mobile applications, making them crucial to both app functionality and user experience. However, a major challenge in automated testing is the frequent appearance of app-blocking pop-ups, such as ads or system alerts, which obscure critical UI elements and disrupt test execut

Peter L. Guo, Zhuowei Lin

This paper investigates the number of supports of the Schubert polynomial $\mathfrak{S}_w(x)$ indexed by a permutation $w$. This number also equals the number of lattice points in the Newton polytope of $\mathfrak{S}_w(x)$. We establish a lower bound for this number in terms of the occurrences of patterns in $w$. The analysis is carried out in the general fr

Simon Sinong Zhan, Qingyuan Wu, Zhian Ruan, Frank Yang

Inverse Reinforcement Learning (IRL) has demonstrated effectiveness in a variety of imitation tasks. In this paper, we introduce an IRL framework designed to extract rewarding features from expert trajectories affected by delayed disturbances. Instead of relying on direct observations, our approach employs an efficient off-policy adversarial training framewo

Fawad Javed Fateh, Umer Ahmed, Hamza Khan, M. Zeeshan Zia

We introduce TemporalVLM, a video large language model (video LLM) for temporal reasoning and fine-grained understanding in long videos. Our approach includes a visual encoder for mapping a long-term video into features which are time-aware and contain both local and global cues. It first divides an input video into short-term clips, which are jointly encode

Yinghan Long, Kaushik Roy

Recently, diffusion models have demonstrated impressive capabilities in text-guided and image-conditioned image generation. However, existing diffusion models cannot simultaneously generate an image and a panoptic segmentation of objects and stuff from the prompt. Incorporating an inherent understanding of shapes and scene layouts can improve the creativity

Michael Fuchs, Mike Steel, Qiang Zhang

Phylogenetic networks provide a more general description of evolutionary relationships than rooted phylogenetic trees. One way to produce a phylogenetic network is to randomly place $k$ arcs between the edges of a rooted binary phylogenetic tree with $n$ leaves. The resulting directed graph may fail to be a phylogenetic network, and even when it is (and ther

Guang-Wei Mi, Xiaofen Huang, Shao-Ming Fei, Tinggui Zhang

Wu et al. [J. High Energ. Phys. 2023, 232 (2023)] first found that the fidelity of quantum teleportation with a bipartite entangled resource state, completely determined by the fully entangled fraction (FEF) characterized by the maximal fidelity between the given quantum state and the set of maximally entangled states, can monotonically increase in Schwarzsc

Mia Kumamoto, Catherine Welch

Some neutron stars known as magnetars possess very strong magnetic fields, with surface fields as large as $10^{15}\,\rm G$ and internal fields that are possibly stronger. Recent observations of the radio pulsar GLEAM-X J1627 suggest it may have a surface field as strong as $10^{16} \,\rm G$. In the presence of a strong magnetic field, the energy levels of e

Indu Kant Deo, Rajeev Jaiman

Accurate prediction over long time horizons is crucial for modeling complex physical processes such as wave propagation. Although deep neural networks show promise for real-time forecasting, they often struggle with accumulating phase and amplitude errors as predictions extend over a long period. To address this issue, we propose a novel loss decomposition s

Rahul N. Chacko

Dynamical heterogeneity is a signature phenomenon of deeply supercooled liquids and glasses. Here, we demonstrate that the spatiotemporal correlations between local relaxation events that underpin it are the result of local relaxation events raising the likelihood that other relaxation events subsequently occur nearby, confirming a widely held, but until now

Fanze Kong, Yonghui Tong, Xiaoyu Zeng, Huan-song Zhou

In this paper, we investigate the concentration behaviors of ground states to stationary Mean-field Games systems (MFGs) with the nonlocal coupling in $\mathbb R^n$, $n\geq 2.$ With the mass critical exponent imposed on Riesz potentials, we first discuss the existence of ground states to potential-free MFGs, which corresponds to the establishment of Gagliard

Lyryl H. C. Vaecairn, Jarrod T. Reilly, John Drew Wilson, Simon B. Jaeger

We propose schemes to design and control a time-dependent decoherence-free subspace (DFS) in a dissipative atom-cavity system. These schemes use atoms with three internal energy levels, which allows for the DFS to be multi-dimensional--a condition important for quantum sensing, simulation, and computation. We consider the use of tunable external driving lase

Prabhat Kc, Rongping Zeng

The remarkable success of deep learning methods in solving computer vision problems, such as image classification, object detection, scene understanding, image segmentation, etc., has paved the way for their application in biomedical imaging. One such application is in the field of CT image denoising, whereby deep learning methods are proposed to recover den

Soroush Omranpour, Guillaume Rabusseau, Reihaneh Rabbany

Modern datasets are increasingly high-dimensional and multiway, often represented as tensor-valued data with multi-indexed variables. While Transformers excel in sequence modeling and high-dimensional tasks, their direct application to multiway data is computationally prohibitive due to the quadratic cost of dot-product attention and the need to flatten inpu

Yibo Liu, Liwei Duan, Qing-Hu Chen

The $\mathcal{PT}$ symmetric semiclassical Rabi model explores the fundamental interaction between a two-level atom and a classical field, revealing novel phenomena in open systems through the inclusion of non-Hermitian terms. We propose a single similarity transformation that yields an effective Hamiltonian in rotating-wave approximation, enabling an analyt

Christopher J. Fewster, L. H. Ford

The probability distribution for vacuum fluctuations of the energy flux in two dimensions will be constructed, along with the joint distribution of energy flux and energy density. Our approach will be based on previous work on probability distributions for the energy density in two dimensional conformal field theory. In both cases, the relevant stress tensor

Nicola Pranzini, Lorenzo Maccone

While playing an important role in the foundations of quantum theory, Quantum Speed Limits (QSL) have no role in discussions about the search for quantum gravity. We fill this gap by analysing what QSL arises when superposing spherically symmetric masses in canonical quantum gravity. By this procedure, we find that the quantum mechanical Mandelstam-Tamm and

Chao Duan, Mu Wang, Ahmad Ghobadi, David M. Eike

Quantifying the critical micelle concentration (CMC) and understanding its relationship with both the intrinsic molecular structures and environmental conditions are crucial for the rational design of surfactants. Here, we develop a self-consistent field theory which unifies the study of CMC, micellar structure and kinetic pathway of micellization in one fra

Ivan Duchemin, Xavier Blase

We introduce an alternative route to quasiparticle self-consistent $GW$ calculations ($\mathrm{qs}GW$) on the basis of a Joint Approximate Diagonalization of the one-body $GW$ Green&#39;s functions $G(\varepsilon_n^{QP})$ taken at the input quasiparticle energies. Such an approach allows working with the full dynamical self-energy, without approximating the

Peyman Malekzadeh, Emil Zeuthen, Eric Langman, Albert Schliesser

We experimentally demonstrate a source of squeezed light featuring simultaneous ponderomotive squeezing from two mechanical modes of an optomechanical system. We use ultra-coherent vibrational modes ($Q$ factors on the order of $10^{8}$) of a soft-clamped membrane placed in a Fabry-Pérot optical cavity at cryogenic conditions ($T=11\,\mathrm{K}$) and driven

Zhiyu Jiang, Masatoshi Sato, Hideaki Obuse

Quantum spin Hall insulators, which possess a non-trivial $\mathbb{Z}_2$ topological phase, have attracted great attention for two decades. It is generally believed that when an even number of layers of the quantum spin Hall insulators are stacked, the $\mathbb{Z}_2$ topological phase becomes unstable due to $\mathbb{Z}_2$ nature. While the counterexamples o

Kieran G. O&#39;Grady

We partially extend to hyperkähler fourfolds of Kummer type the results that we have proved regarding stable rigid vector bundles on hyperkähler (HK) varieties of type $K3^{[n]}$. Let $(M,h)$ be a general polarized HK fourfold of Kummer type such that $q_M(h)\equiv -6\pmod{16}$ and the divisibility of $h$ is $2$, or $q_M(h)\equiv -6\pmod{144}$ and the divisi

Samuel P. Gleason, Matthew R. Carbone, Deyu Lu, Jim Ciston

In this work, we have developed CuXASNet, a dense neural network that predicts simulated Cu L-edge X-ray absorption spectra (XAS) from atomic structures. Featurization of the Cu local environment is performed using a component of M3GNet, a graph neural network developed for predicting the potential energy surface. CuXASNet is trained on simulated spectra fro

Belal Abdelhadi, David Rubin

The Roman Space Telescope, equipped with a 2.4 meter primary mirror and optical--NIR wide-field camera, promises to revolutionize our understanding of dark energy, exoplanets, and infrared astrophysics. One of the Roman Core Community Surveys is the High Latitude Time Domain Survey (HLTDS), which will measure more than 10,000 SN Ia light curves but obtain a

Junhao Liu, Siwei Xu, Lei Zhang, Jing Zhang

Over the past decade, the revolution in single-cell sequencing has enabled the simultaneous molecular profiling of various modalities across thousands of individual cells, allowing scientists to investigate the diverse functions of complex tissues and uncover underlying disease mechanisms. Among all the analytical steps, assigning individual cells to specifi

Weijie Kong, Qi Tian, Zijian Zhang, Rox Min

Recent advancements in video generation have significantly impacted daily life for both individuals and industries. However, the leading video generation models remain closed-source, resulting in a notable performance gap between industry capabilities and those available to the public. In this report, we introduce HunyuanVideo, an innovative open-source vide

Alexander Novikov

We introduce a class of equivariant vector bundles on isotropic symplectic Grassmannians $\mathrm{IGr}(k,2n)$ defined as appropriate truncations of staircase complexes and show that these bundles can be assembled into a number of complexes quasi-isomorphic to the symplectic wedge powers of the symplectic bundle on $\mathrm{IGr}(k,2n)$. We are planning to use

Maher Boudabra, Dhaker Kroumi, Boubaker Smii

In this work, we investigate the problem of the boundedness of the Gross' solutions of the planar Skorokhod embedding problem, where we show that the solution is bounded under some mild conditions on the underlying probability distribution.

Dmitry Petrov, Pradyumn Goyal, Divyansh Shivashok, Yuanming Tao

We introduce ShapeWords, an approach for synthesizing images based on 3D shape guidance and text prompts. ShapeWords incorporates target 3D shape information within specialized tokens embedded together with the input text, effectively blending 3D shape awareness with textual context to guide the image synthesis process. Unlike conventional shape guidance met

Xinchen Yu, Hayden Arnold, Benjamin Su, Eduardo Blanco

This paper focuses on the task of measuring and forecasting incivility in conversations following replies to hate speech. Identifying replies that steer conversations away from hatred and elicit civil follow-up conversations sheds light into effective strategies to engage with hate speech and proactively avoid further escalation. We propose new metrics that

A. Barros, I. Evangelista, E. Viana

The aim of this article is to investigate the presence of a conformal vector $\xi$ with conformal factor $\rho$ on a compact Riemannian manifold $M$ with or without boundary $\partial M$. We firstly prove that a compact Riemannian manifold $(M^n, g)\,,n \geq 3,$ with constant scalar curvature, with boundary $\partial M$ totally geodesic, in such way that the

Shiveswarran Ratneswaran, Uthayasanker Thayasivam, Sivakumar Thillaiambalam

The gps2gtfs package addresses a critical need for converting raw Global Positioning System (GPS) trajectory data from public transit vehicles into the widely used GTFS (General Transit Feed Specification) format. This transformation enables various software applications to efficiently utilize real-time transit data for purposes such as tracking, scheduling,

Ankit Tiwari, Daniel Burgarth, Linran Fan, Saikat Guha

We develop squeezing protocols to enhance cross-Kerr interactions. We show that through alternating between squeezing along different quadratures of a single photonic mode, the cross-Kerr interaction strength can be generically amplified. As an application of the squeezing protocols we discuss speeding up the deterministic implementation of controlled phase