March 2024 arXiv papers — page 89

Soumitra Nandi, Shantanu Sahoo, Ria Sain

We study the four-fold angular distribution of the semileptonic $\Lambda_b \to \Lambda_c^{+}(\to \Lambda \pi^{+})\ell^- \bar{\nu}_{\ell}$ decay and find out analytical expressions for various asymmetric and angular observables in the standard model (SM) and the relevant new physics (NP) scenarios. Using the available inputs on the form factors from the latti

Mariam Hassan, Florent Forest, Olga Fink, Malcolm Mielle

Thermal scene reconstruction holds great potential for various applications, such as analyzing building energy consumption and performing non-destructive infrastructure testing. However, existing methods typically require dense scene measurements and often rely on RGB images for 3D geometry reconstruction, projecting thermal information post-reconstruction.

Roland Kaminski, Torsten Schaub, Tran Cao Son, Jiří Švancara

We present alternative approaches to routing and scheduling in Answer Set Programming (ASP), and explore them in the context of Multi-agent Path Finding. The idea is to capture the flow of time in terms of partial orders rather than time steps attached to actions and fluents. This also abolishes the need for fixed upper bounds on the length of plans. The tra

Yuting Zhang, Boyang Liu, Karina V. Bunting, David Brind

The echocardiographic measurement of left ventricular ejection fraction (LVEF) is fundamental to the diagnosis and classification of patients with heart failure (HF). In order to quantify LVEF automatically and accurately, this paper proposes a new pipeline method based on deep neural networks and ensemble learning. Within the pipeline, an Atrous Convolution

Filippos Gouidis, Katerina Papantoniou, Konstantinos Papoutsakis, Theodore Patkos

Domain-specific knowledge can significantly contribute to addressing a wide variety of vision tasks. However, the generation of such knowledge entails considerable human labor and time costs. This study investigates the potential of Large Language Models (LLMs) in generating and providing domain-specific information through semantic embeddings. To achieve th

Callum W. Duncan

Adiabatic protocols are often employed in state preparation schemes but require the system to be driven by a slowly varying Hamiltonian so that transitions between instantaneous eigenstates are exponentially suppressed. Counterdiabatic driving is a technique to speed up adiabatic protocols by including additional terms calculated from the instantaneous eigen

Mani Amani, Reza Akhavian

Robots can serve as safety catalysts on construction job sites by taking over hazardous and repetitive tasks while alleviating the risks associated with existing manual workflows. Research on the safety of physical human-robot interaction (pHRI) is traditionally focused on addressing the risks associated with potential collisions. However, it is equally impo

Nicolas Crampe, Luc Frappat, Julien Gaboriaud, Eric Ragoucy

Bivariate Griffiths polynomials of Racah type are constructed from univariate Racah polynomials. The bispectral properties of the former are deduced from simple properties of the latter. A duality relation and the orthogonality of these polynomials are provided. The domain of validity for the indices and variables of these polynomials is also determined. Par

Benjamin Hinrichs, Oliver Matte

We prove Feynman-Kac formulas for the semigroups generated by selfadjoint operators in a class containing Fr\"ohlich Hamiltonians known from solid state physics. The latter model multi-polarons, i.e., a fixed number of quantum mechanical electrons moving in a polarizable crystal and interacting with the quantized phonon field generated by the crystal's vibra

Kareem El-Badry

Stellar multiplicity is among the oldest and richest problems in astrophysics. Binary stars are a cornerstone of stellar mass and radius measurements that underpin modern stellar evolutionary models. Binaries are the progenitors of many of the most interesting and exotic astrophysical phenomena, ranging from type Ia supernovae to gamma ray bursts, hyperveloc

Ashwin Daswani, Rohan Sawant, Najoung Kim

Sensitivity to false assumptions (or false premises) in information-seeking questions is critical for robust question-answering (QA) systems. Recent work has shown that false assumptions in naturally occurring questions pose challenges to current models, with low performance on both generative QA and simple detection tasks (Kim et al. 2023). However, the foc

Stefano de Nicola, Jens Thomas, Roberto P. Saglia, Matthias Kluge

We report the discovery of a $(1.0 \pm 0.28) \times 10^{10}$ M$_\odot$ Supermassive Black Hole (BH) at the centre of NGC 708, the Brightest Cluster Galaxy of Abell 262. Such high BH masses are very rare and allow to investigate BH - host galaxy scaling relations at the high mass end, which in turn provide hints about the (co)evolution of such systems. NGC~70

Miltiadis Kofinas, Boris Knyazev, Yan Zhang, Yunlu Chen

Neural networks that process the parameters of other neural networks find applications in domains as diverse as classifying implicit neural representations, generating neural network weights, and predicting generalization errors. However, existing approaches either overlook the inherent permutation symmetry in the neural network or rely on intricate weight-s

Maria Derda, Andreas Helset, Julio Parra-Martinez

We derive a soft theorem for a massless scalar in an effective field theory with generic field content using the geometry of field space. This result extends the geometric soft theorem for scalar effective field theories by allowing the massless scalar to couple to other scalars, fermions, and gauge bosons. The soft theorem keeps its geometric form, but wher

Kang Wang, Shi Feng, Penghao Zhu, Runze Chi

We investigate the signatures of fractionalization in quantum spin liquids by studying different phases of the Kitaev honeycomb model in the presence of an out-of-plane magnetic field through which the model becomes non-integrable. Using the infinite projected entangled pair states (iPEPS) ansatz, along with analytical calculations and exact diagonalization,

Elia Pizzati, Joseph F. Hennawi, Joop Schaye, Matthieu Schaller

Recent observations from the EIGER JWST program have measured for the first time the quasar-galaxy cross-correlation function at $z\approx6$. The auto-correlation function of faint $z\approx6$ quasars was also recently estimated. These measurements provide key insights into the properties of quasars and galaxies at high redshift and their relation with the h

Steven A. Kivelson, Chaitanya Murthy

Existing quantum Hall interferometers measure twice the braiding phase, $e^{i2\theta}$, of Abelian anyons, i.e. the phase accrued when one quasi-particle encircles another clockwise. We propose a modified Fabry-P\'{e}rot or Mach-Zehnder interferometer that can measure $e^{i\theta}$.

Martin D. Weinberg

We investigate the coupling between the temporal variation from galaxy-formation feedback and the bar instability. We show that fluctuations from mass outflow on star-formation time scales affect the radial motion of disk orbits. The resulting incoherence in orbital phase leads to the disruption of the bar-forming dynamics. Bar formation is suppressed in sta

L. Vleeschower, A. Corongiu, B. W. Stappers, P. C. C. Freire

Using MeerKAT, we have discovered three new millisecond pulsars (MSPs) in the bulge globular cluster M62: M62H, M62I, and M62J. All three are in binary systems, which means all ten known pulsars in the cluster are in binaries. M62H has a planetary-mass companion with a median mass $M_{\rm c,med} \sim 3$ M$_{\rm J}$ and a mean density of $\rho \sim 11$ g cm$^

Harkirat Singh Sahota, Kinjalk Lochan

One of the foremost concerns in the analysis of quantum gravity is whether the locations of classical horizons are stable under a full quantum analysis. In principle, any classical description, when interpolated to the microscopic level, can become prone to fluctuations. The curious question in that case is if there indeed are such fluctuations at the Planck

Chengzhe Li, Hui Li, Wei Cui, Federico Marinacci

Stellar feedback plays a crucial role in regulating baryon cycles of a galactic ecosystem, and may manifest itself in the formation of superbubbles in the interstellar medium. In this work, we used a set of high-resolution simulations to systematically study the properties and evolution of superbubbles in galactic environments. The simulations were based on

Pau Amaro Seoane, Yiren Lin, Kostas Tzanavaris

The gravitational capture of a stellar-mass object by a supermassive black hole represents a unique probe of warped spacetime. The small object, typically a stellar-mass black hole, describes a very large number of cycles before crossing the event horizon. Because of the mass difference, we call these captures extreme-mass ratio inspirals (EMRIs). Their merg

Victor Enguita, Belen Gavela, Benjamin Grinstein, Pablo Quilez

We compute the one-loop contribution to the $\bar{\theta}$-parameter of an axion-like particle (ALP) with CP-odd derivative couplings. Its contribution to the neutron electric dipole moment is shown to be orders of magnitude larger than that stemming from the one-loop ALP contributions to the up- and down-quark electric and chromoelectric dipole moments. Thi

Nachiket Joshi, Mahavir Sharma

We study the reionization of the Universe due to haloes that host galaxies undergoing bursts of star formation. By comparing the recent results from the James Webb Space Telescope (JWST) with the cosmological hydrodynamical simulation EAGLE at $z\ge 6$, we find that bursty galaxies have specific star formation rate, sSFR $>10^{-2}$ Myr$^{-1}$, and magnitude,

Liang Ma, Peng-Ju Hu, Yi Pang, Hong Lu

Computing leading higher curvature contributions to thermodynamic quantities of AdS black hole is drastically simplified once the higher curvature terms are expressed in terms of powers of Weyl tensor by applying proper field redefinitions, avoiding the usual complications caused by higher derivative Gibbons-Hawking-York (GHY) term or surface counterterms. W

Lin Du, Wei Du, Cheng Cheng, Ming Zhu

We report the discovery of eight optical counterparts of ALFALFA extragalactic objects from DECaLS, five of which are discovered for the first time. These objects were flagged as HI emission sources with no optical counterparts in SDSS before. Multi-band data reveal their unusual physical properties. They are faint and blue ($g-r=-0.35\sim0.55$), with quite

Federica Chiti, Jennifer L. van Saders, Tyler M. Heintz, J. J. Hermes

Gyrochronology, a valuable tool for determining ages of low-mass stars where other techniques fail, relies on accurate calibration. We present a sample of 185 wide ($>$$100$ au) white dwarf + main sequence (WD + MS) binaries. Total ages of WDs are computed using all-sky survey photometry, Gaia parallaxes, and WD atmosphere models. Using a magnetic braking la

Sergei Gukov, Pavel Putrov

We consider a finite-dimensional oscillatory integral which provides a "finite-dimensional model" for analytically continued $SU(2)$ Chern-Simons theory on closed 3-manifolds that are described by plumbing trees. This model allows an efficient description of Stokes phenomenon for perturbative expansions in Chern-Simons theory around classical solutions - $SL

Yiwen Pan, Peihe Yang

In this paper we analytically explore the modularity of the flavored Schur index of 4d $\mathcal{N} = 2$ SCFTs. We focus on the $A_1$ theories of class-$\mathcal{S}$ and $\mathcal{N} = 4$ theories with $SU(N)$ gauge group. We work out the modular orbit of the flavored index and defect index, compute the dimension of the space spanned by the orbit, and provid

Ya-Hui Zhang

The recent experimental observation of a potential fractional quantum spin Hall (FQSH) state in the twisted MoTe$_2$ system has sparked theoretical explorations at total filling $\nu_T=1$ of a pair of $C=\pm 1$ Chern bands from the two spins (locked to valley). One intriguing candidate is a vortex spin liquid (VSL), which can be viewed as an exciton version

Luisa Lucie-Smith, Giulia Despali, Volker Springel

We present a machine-learning-based model for the total density profiles of subhaloes with masses $M \gtrsim 7\times 10^8\,h^{-1}{\rm M}_\odot$ in the IllustrisTNG100 simulation. The model is based on an interpretable variational encoder (IVE) which returns the independent factors of variation in the density profiles within a low-dimensional representation,

A. Zurlo, P. Weber, S. Pérez, L. Cieza

Eruptive stars are a class of young stellar objects that show an abrupt increase in their luminosity. These burst-like episodes are thought to dominate the stellar accretion process during the class 0/class I stage. We present an overview of a survey of seven episodically accreting protostars aimed at studying their potentially complex circumstellar surround

Csaba Csáki, Ameen Ismail

We present a holographic calculation of energy correlators in a simple model of confinement based on a warped extra dimension with an IR brane. For small distances we reproduce the constant correlators of a strongly-coupled conformal field theory, while for large distances the effects of confinement dominate and the correlators decay exponentially. We find e

Nathan A. Kaib, Alex Parsells, Simon Grimm, Billy Quarles

We perform simulations here that include the gravitational effects of the primordial planetesimal belt consisting of ~10^5 massive bodies. In our simulations, Neptune unlocks from resonance with the other giant planets and begins to migrate outward due to interactions with planetesimals before a planetary orbital instability is triggered, and afterward, resi

Nico Kirchner, Wonjune Choi, Frank Pollmann

Entanglement of mixed quantum states can be quantified using the partial transpose and its corresponding entanglement measure, the logarithmic negativity. Recently, the notion of partial transpose has been extended to systems of anyons, which are exotic quasiparticles whose exchange statistics go beyond the bosonic and fermionic case. Studying the fundamenta

Siddharth Chaini, Ashish Mahabal, Ajit Kembhavi, Federica B. Bianco

The rise of synoptic sky surveys has ushered in an era of big data in time-domain astronomy, making data science and machine learning essential tools for studying celestial objects. While tree-based models (e.g. Random Forests) and deep learning models dominate the field, we explore the use of different distance metrics to aid in the classification of astrop

Andreas Bauer

We propose a family of explicit geometrically local circuits on a 2-dimensional planar grid of qudits, realizing any abelian non-chiral topological phase as an actively error-corrected fault-tolerant memory. These circuits are constructed from measuring 1-form symmetries in discrete fixed-point path integrals, which we express through cellular cohomology and

Sean K. Terry, Jean-Philippe Beaulieu, David P. Bennett, Euan Hamdorf

We present an analysis of high angular resolution images of the microlensing target MOA-2007-BLG-192 using Keck adaptive optics and the Hubble Space Telescope. The planetary host star is robustly detected as it separates from the background source star in nearly all of the Keck and Hubble data. The amplitude and direction of the lens-source separation allows

Charles J. Aouad, Paolo A. Mazzali, Chris Ashall, Masaomi Tanaka

Observations of Type Ia supernovae (SNe\,Ia) reveal diversity, even within assumed subcategories. Here, the composition of the peculiar iPTF16abc (SN\,2016bln) is derived by modeling a time series of optical spectra. iPTF16abc's early spectra combine traits of SNe 1999aa and 1991T known for weak \SiII\ $\lambda$ 6355 and prominent \FeIII\ features. However,

Zixin Zhu, Xuelu Feng, Dongdong Chen, Junsong Yuan

In this paper, we explore the visual representations produced from a pre-trained text-to-video (T2V) diffusion model for video understanding tasks. We hypothesize that the latent representation learned from a pretrained generative T2V model encapsulates rich semantics and coherent temporal correspondences, thereby naturally facilitating video understanding.

S. A. Rappaport, T. Borkovits, T. Mitnyan, R. Gagliano

We have identified nearly a hundred close triply eclipsing hierarchical triple star systems from data taken with the space telescope TESS. These systems are noteworthy in that we can potentially determine their dynamical and astrophysical parameters with a high precision. In the present paper, we report the comprehensive study of seven new compact triply ecl

Asaf Shul, Eliahu Horwitz, Yedid Hoshen

Dataset distillation aims to compress a dataset into a much smaller one so that a model trained on the distilled dataset achieves high accuracy. Current methods frame this as maximizing the distilled classification accuracy for a budget of K distilled images-per-class, where K is a positive integer. In this paper, we push the boundaries of dataset distillati

Hongyu Li, Taskin Padir, Huaizu Jiang

Visual navigation has received significant attention recently. Most of the prior works focus on predicting navigation actions based on semantic features extracted from visual encoders. However, these approaches often rely on large datasets and exhibit limited generalizability. In contrast, our approach draws inspiration from traditional navigation planners t

Xinle Cheng, Congyue Deng, Adam Harley, Yixin Zhu

Correspondences emerge from large-scale vision models trained for generative and discriminative tasks. This has been revealed and benchmarked by computing correspondence maps between pairs of images, using nearest neighbors on the feature grids. Existing work has attempted to improve the quality of these correspondence maps by carefully mixing features from

Enshen Zhou, Yiran Qin, Zhenfei Yin, Yuzhou Huang

It is a long-lasting goal to design a generalist-embodied agent that can follow diverse instructions in human-like ways. However, existing approaches often fail to steadily follow instructions due to difficulties in understanding abstract and sequential natural language instructions. To this end, we introduce MineDreamer, an open-ended embodied agent built u

Gaurav Parmar, Taesung Park, Srinivasa Narasimhan, Jun-Yan Zhu

In this work, we address two limitations of existing conditional diffusion models: their slow inference speed due to the iterative denoising process and their reliance on paired data for model fine-tuning. To tackle these issues, we introduce a general method for adapting a single-step diffusion model to new tasks and domains through adversarial learning obj

Bojia Zi, Shihao Zhao, Xianbiao Qi, Jianan Wang

Recent advancements in video generation have been remarkable, yet many existing methods struggle with issues of consistency and poor text-video alignment. Moreover, the field lacks effective techniques for text-guided video inpainting, a stark contrast to the well-explored domain of text-guided image inpainting. To this end, this paper proposes a novel text-

Junlin Han, Filippos Kokkinos, Philip Torr

This paper presents a novel method for building scalable 3D generative models utilizing pre-trained video diffusion models. The primary obstacle in developing foundation 3D generative models is the limited availability of 3D data. Unlike images, texts, or videos, 3D data are not readily accessible and are difficult to acquire. This results in a significant d

Ce Zhang, Simon Stepputtis, Joseph Campbell, Katia Sycara

Being able to understand visual scenes is a precursor for many downstream tasks, including autonomous driving, robotics, and other vision-based approaches. A common approach enabling the ability to reason over visual data is Scene Graph Generation (SGG); however, many existing approaches assume undisturbed vision, i.e., the absence of real-world corruptions

Hansheng Chen, Ruoxi Shi, Yulin Liu, Bokui Shen

Open-domain 3D object synthesis has been lagging behind image synthesis due to limited data and higher computational complexity. To bridge this gap, recent works have investigated multi-view diffusion but often fall short in either 3D consistency, visual quality, or efficiency. This paper proposes MVEdit, which functions as a 3D counterpart of SDEdit, employ

Qitian Jason Hu, Jacob Bieker, Xiuyu Li, Nan Jiang

As the range of applications for Large Language Models (LLMs) continues to grow, the demand for effective serving solutions becomes increasingly critical. Despite the versatility of LLMs, no single model can optimally address all tasks and applications, particularly when balancing performance with cost. This limitation has led to the development of LLM routi

Da-Wei Zhou, Hai-Long Sun, Han-Jia Ye, De-Chuan Zhan

Class-Incremental Learning (CIL) requires a learning system to continually learn new classes without forgetting. Despite the strong performance of Pre-Trained Models (PTMs) in CIL, a critical issue persists: learning new classes often results in the overwriting of old ones. Excessive modification of the network causes forgetting, while minimal adjustments le

Justin Kay, Timm Haucke, Suzanne Stathatos, Siqi Deng

Object detectors often perform poorly on data that differs from their training set. Domain adaptive object detection (DAOD) methods have recently demonstrated strong results on addressing this challenge. Unfortunately, we identify systemic benchmarking pitfalls that call past results into question and hamper further progress: (a) Overestimation of performanc

Mingjin Chen, Junhao Chen, Xiaojun Ye, Huan-ang Gao

3D human body reconstruction has been a challenge in the field of computer vision. Previous methods are often time-consuming and difficult to capture the detailed appearance of the human body. In this paper, we propose a new method called \emph{Ultraman} for fast reconstruction of textured 3D human models from a single image. Compared to existing techniques,

Kung-Hsiang Huang, Hou Pong Chan, Yi R. Fung, Haoyi Qiu

Data visualization in the form of charts plays a pivotal role in data analysis, offering critical insights and aiding in informed decision-making. Automatic chart understanding has witnessed significant advancements with the rise of large foundation models in recent years. Foundation models, such as large language models, have revolutionized various natural

Debidatta Dwibedi, Vidhi Jain, Jonathan Tompson, Andrew Zisserman

We introduce FlexCap, a vision-language model that generates region-specific descriptions of varying lengths. FlexCap is trained to produce length-conditioned captions for input boxes, enabling control over information density, with descriptions ranging from concise object labels to detailed captions. To achieve this, we create large-scale training datasets

Stephen R. Pfohl, Heather Cole-Lewis, Rory Sayres, Darlene Neal

Large language models (LLMs) hold promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities. Reliably evaluating equity-related model failures is a critical step toward developing systems that promote health equity. We present resources and methodologies for surfacing biases with potenti

Bo-Han Lu, Yi-Hsuan Lin, En-Shiun Annie Lee, Richard Tzong-Han Tsai

Machine translation focuses mainly on high-resource languages (HRLs), while low-resource languages (LRLs) like Taiwanese Hokkien are relatively under-explored. The study aims to address this gap by developing a dual translation model between Taiwanese Hokkien and both Traditional Mandarin Chinese and English. We employ a pre-trained LLaMA 2-7B model speciali

Joshua Hoegerman, Shahabedin Sagheb, Benjamin A. Christie, Dylan P. Losey

Assistive robot arms can help humans by partially automating their desired tasks. Consider an adult with motor impairments controlling an assistive robot arm to eat dinner. The robot can reduce the number of human inputs -- and how precise those inputs need to be -- by recognizing what the human wants (e.g., a fork) and assisting for that task (e.g., moving

Sergei Igonin

Matrix differential-difference Lax pairs play an essential role in the theory of integrable nonlinear differential-difference equations. We present sufficient conditions which allow one to simplify such a Lax pair by matrix gauge transformations. Furthermore, we describe a procedure for such a simplification and present applications of it to constructing new

Hannah J. Manetsch, Gyohei Nomura, Elie Bataille, Kon H. Leung

Optical tweezer arrays have transformed atomic and molecular physics, now forming the backbone for a range of leading experiments in quantum computing, simulation, and metrology. Typical experiments trap tens to hundreds of atomic qubits, and recently systems with around one thousand atoms were realized without defining qubits or demonstrating coherent contr

Alexander Mramor

In this article we partially classify the space of eternal mean convex flows in $\mathbb{R}^3$ of finite total curvature type, a condition implied by finite total curvature. In particular we show that topologically nonplanar ones must flow out of a catenoid in a natural sense.

Yushi Lan, Fangzhou Hong, Shangchen Zhou, Shuai Yang

The field of neural rendering has witnessed significant progress with advancements in generative models and differentiable rendering techniques. Though 2D diffusion has achieved success, a unified 3D diffusion pipeline remains unsettled. This paper introduces a novel framework called LN3Diff++ to address this gap and enable fast, high-quality, and generic co

Julius T. Gohsrich, Jacob Fauman, Flore K. Kunst

Exceptional points (EPs) are truly non-Hermitian (NH) degeneracies where matrices become defective. The order of such an EP is given by the number of coalescing eigenvectors. On the one hand, most work focuses on studying $N$th-order EPs in $(N\leq4)$-dimensional NH Bloch Hamiltonians. On the other hand, some works have remarked on the existence of EPs of or

Hao Sun

The prevailing approach to aligning Large Language Models (LLMs) typically relies on human or AI feedback and assumes access to specific types of preference datasets. In our work, we question the efficacy of such datasets and explore various scenarios where alignment with expert demonstrations proves more realistic. We build a sequential decision-making fram

Emily Cairncross, Dhruv Mubayi

We consider three extremal problems about the number of copies of a fixed graph in another larger graph. First, we correct an error in a result of Reiher and Wagner and prove that the number of $k$-edge stars in a graph with density $x \in [0, 1]$ is asymptotically maximized by a clique and isolated vertices or its complement. Next, among ordered $n$-vertex

Axel Sauer, Frederic Boesel, Tim Dockhorn, Andreas Blattmann

Diffusion models are the main driver of progress in image and video synthesis, but suffer from slow inference speed. Distillation methods, like the recently introduced adversarial diffusion distillation (ADD) aim to shift the model from many-shot to single-step inference, albeit at the cost of expensive and difficult optimization due to its reliance on a fix

Abhay Zala, Jaemin Cho, Han Lin, Jaehong Yoon

Recent SOTA approaches for embodied learning via interaction directly employ large language models (LLMs) as agents to determine the next steps in an environment. Due to their world knowledge and reasoning capabilities, LLM agents achieve stronger performance than previous smaller agents based on reinforcement learning (RL); however, frequently calling LLMs

Xiao Fu, Wei Yin, Mu Hu, Kaixuan Wang

We introduce GeoWizard, a new generative foundation model designed for estimating geometric attributes, e.g., depth and normals, from single images. While significant research has already been conducted in this area, the progress has been substantially limited by the low diversity and poor quality of publicly available datasets. As a result, the prior works

Lingkai Kong, Molei Tao

Explicit, momentum-based dynamics for optimizing functions defined on Lie groups was recently constructed, based on techniques such as variational optimization and left trivialization. We appropriately add tractable noise to the optimization dynamics to turn it into a sampling dynamics, leveraging the advantageous feature that the trivialized momentum variab

Mengqi Zhang, Yang Fu, Zheng Ding, Sifei Liu

3D hand-object interaction data is scarce due to the hardware constraints in scaling up the data collection process. In this paper, we propose HOIDiffusion for generating realistic and diverse 3D hand-object interaction data. Our model is a conditional diffusion model that takes both the 3D hand-object geometric structure and text description as inputs for i

Qi Zuo, Xiaodong Gu, Lingteng Qiu, Yuan Dong

Generating multi-view images based on text or single-image prompts is a critical capability for the creation of 3D content. Two fundamental questions on this topic are what data we use for training and how to ensure multi-view consistency. This paper introduces a novel framework that makes fundamental contributions to both questions. Unlike leveraging images

K. P. Santoso, R. V. H. Ginardi, R. A. Sastrowardoyo, F. A. Madany

In the realm of skin lesion image classification, the intricate spatial and semantic features pose significant challenges for conventional Convolutional Neural Network (CNN)-based methodologies. These challenges are compounded by the imbalanced nature of skin lesion datasets, which hampers the ability of models to learn minority class features effectively. D

Luhuan Wu, Sinead Williamson

In this paper, we approach the problem of uncertainty quantification in deep learning through a predictive framework, which captures uncertainty in model parameters by specifying our assumptions about the predictive distribution of unseen future data. Under this view, we show that deep ensembling (Lakshminarayanan et al., 2017) is a fundamentally mis-specifi

Vikram Voleti, Chun-Han Yao, Mark Boss, Adam Letts

We present Stable Video 3D (SV3D) -- a latent video diffusion model for high-resolution, image-to-multi-view generation of orbital videos around a 3D object. Recent work on 3D generation propose techniques to adapt 2D generative models for novel view synthesis (NVS) and 3D optimization. However, these methods have several disadvantages due to either limited

Aneta Lisowska, Szymon Wilk, Laura Locati, Mimma Rizzo

Digital Behavior Change Interventions (DBCIs) are supporting development of new health behaviors. Evaluating their effectiveness is crucial for their improvement and understanding of success factors. However, comprehensive guidance for developers, particularly in small-scale studies with ethical constraints, is limited. Building on the CAPABLE project, this

Ananta Kant Rai, Vaibhav Katewa

The robustness of the stability properties of dynamical systems in the presence of unknown/adversarial perturbations to system parameters is a desirable property. In this paper, we present methods to efficiently compute and improve the approximate stability radius of linear time-invariant systems. We propose two methods to derive closed-form expressions of a

Angelos Chatzimparmpas, Kostiantyn Kucher, Andreas Kerren

Visualization for explainable and trustworthy machine learning remains one of the most important and heavily researched fields within information visualization and visual analytics with various application domains, such as medicine, finance, and bioinformatics. After our 2020 state-of-the-art report comprising 200 techniques, we have persistently collected p

Zhuoyan Li, Chen Liang, Jing Peng, Ming Yin

Recent advances in generative AI technologies like large language models raise both excitement and concerns about the future of human-AI co-creation in writing. To unpack people's attitude towards and experience with generative AI-powered writing assistants, in this paper, we conduct an experiment to understand whether and how much value people attach to AI

Xiaojie Li, Yibo Yang, Xiangtai Li, Jianlong Wu

Self-supervised learning has achieved remarkable success in acquiring high-quality representations from unlabeled data. The widely adopted contrastive learning framework aims to learn invariant representations by minimizing the distance between positive views originating from the same image. However, existing techniques to construct positive views highly rel

Hyeonho Jeong, Jinho Chang, Geon Yeong Park, Jong Chul Ye

Text-driven diffusion-based video editing presents a unique challenge not encountered in image editing literature: establishing real-world motion. Unlike existing video editing approaches, here we focus on score distillation sampling to circumvent the standard reverse diffusion process and initiate optimization from videos that already exhibit natural motion

Chenyang Ma, Kai Lu, Ta-Ying Cheng, Niki Trigoni

Current state-of-the-art spatial reasoning-enhanced VLMs are trained to excel at spatial visual question answering (VQA). However, we believe that higher-level 3D-aware tasks, such as articulating dynamic scene changes and motion planning, require a fundamental and explicit 3D understanding beyond current spatial VQA datasets. In this work, we present Spatia

Gerd Wachsmuth, Daniel Walter

Over the last years, minimization problems over spaces of measures have received increased interest due to their relevance in the context of inverse problems, optimal control and machine learning. A fundamental role in their numerical analysis is played by the assumption that the optimal dual state admits finitely many global extrema and satisfies a second-o

Victor Shepardson, Jack Armitage, Thor Magnusson

Deep learning-based probabilistic models of musical data are producing increasingly realistic results and promise to enter creative workflows of many kinds. Yet they have been little-studied in a performance setting, where the results of user actions typically ought to feel instantaneous. To enable such study, we designed Notochord, a deep probabilistic mode

Ting Yao, Yehao Li, Yingwei Pan, Tao Mei

The hybrid deep models of Vision Transformer (ViT) and Convolution Neural Network (CNN) have emerged as a powerful class of backbones for vision tasks. Scaling up the input resolution of such hybrid backbones naturally strengthes model capacity, but inevitably suffers from heavy computational cost that scales quadratically. Instead, we present a new hybrid b

Cunxiang Wang, Ruoxi Ning, Boqi Pan, Tonghui Wu

Recent advancements in Large Language Models (LLMs) have pushed the boundaries of natural language processing, especially in long-context understanding. However, the evaluation of these models' long-context abilities remains a challenge due to the limitations of current benchmarks. To address this gap, we introduce NovelQA, a benchmark tailored for evaluatin

Vincent Herrmann, Francesco Faccio, Jürgen Schmidhuber

Recurrent Neural Networks (RNNs) are general-purpose parallel-sequential computers. The program of an RNN is its weight matrix. How to learn useful representations of RNN weights that facilitate RNN analysis as well as downstream tasks? While the mechanistic approach directly looks at some RNN's weights to predict its behavior, the functionalist approach ana

Josua Stadelmaier, Brandon Malone, Ralf Eggeling

We study the prediction of T-cell response for specific given peptides, which could, among other applications, be a crucial step towards the development of personalized cancer vaccines. It is a challenging task due to limited, heterogeneous training data featuring a multi-domain structure; such data entail the danger of shortcut learning, where models learn

Christopher Harvey-Hawes, David L. Wiltshire

Strong gravitational lens system catalogues are typically used to constrain a combination of cosmological and empirical power-law lens mass model parameters, often introducing additional empirical parameters and constraints from high resolution imagery. We investigate these lens models using Bayesian methods through a novel alternative that treats spatial cu

Markus J. Buehler

Leveraging generative Artificial Intelligence (AI), we have transformed a dataset comprising 1,000 scientific papers into an ontological knowledge graph. Through an in-depth structural analysis, we have calculated node degrees, identified communities and connectivities, and evaluated clustering coefficients and betweenness centrality of pivotal nodes, uncove

Leo Joon Il Moon, Mandar M. Sohoni, Michael A. Shimizu, Praveen Viswanathan

The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm for quantum simulation that can be run on near-term quantum hardware. A challenge in VQE -- as well as any other heuristic algorithm for finding ground states of Hamiltonians -- is to know how close the algorithm's output solution is to the true ground state, when the true grou

Serhii Myroshnychenko, Colin Tang, Kateryna Tatarko, Tomasz Tkocz

We establish dimension-free stability of Webb's sharp simplex slicing (1996). Incidentally, we investigate Lipschitzness of volume of hyperplane central sections of arbitrary (not necessarily symmetric) convex bodies.

Alix Leroy, Benedict Leimkuhler, Jonas Latz, Desmond J. Higham

We discuss the design of an invariant measure-preserving transformed dynamics for the numerical treatment of Langevin dynamics based on rescaling of time, with the goal of sampling from an invariant measure. Given an appropriate monitor function which characterizes the numerical difficulty of the problem as a function of the state of the system, this method

Takuto Ishikawa, Yoko Takeo, Kai Sakurai, Kyota Yoshinaga

Single-shot imaging with femtosecond X-ray lasers is a powerful measurement technique that can achieve both high spatial and temporal resolution. However, its accuracy has been severely limited by the difficulty of applying conventional noise-reduction processing. This study uses deep learning to validate noise reduction techniques, with autoencoders serving

D. A. Baiko

Crystallization of dense matter in neutron star crusts and white dwarf cores may be similar to epitaxial crystal growth in terrestrial laboratories. However in stellar crystals, the spacing between horizontal planes has to gradually increase with the outward movement of the crystallization front, tracing decrease of the electron density. This process produce

Zhaoyang Lyu, Ben Fei, Jinyi Wang, Xudong Xu

Mesh is a fundamental representation of 3D assets in various industrial applications, and is widely supported by professional softwares. However, due to its irregular structure, mesh creation and manipulation is often time-consuming and labor-intensive. In this paper, we propose a highly controllable generative model, GetMesh, for mesh generation and manipul

Ivan Novikau, Ilya Y. Dodin, Edward A. Startsev

We propose an algorithm for encoding of linear kinetic plasma problems in quantum circuits. The focus is made on modeling electrostatic linear waves in one-dimensional Maxwellian electron plasma. The waves are described by the linearized Vlasov-Amp\`ere system with a spatially localized external current that drives plasma oscillations. This system is formula

Abdesslam Arhrib, Rachid Benbrik, Mohammed Boukidi, Stefano Moretti

Expanding upon our ongoing investigation of Vector-Like Quark (VLQ) phenomenology within a 2-Higgs Doublet Model (2HDM) framework, in this paper, we complement a previous one dedicated to Vector-Like Top-quarks (VLTs) by studying Vector-Like Bottom-quarks (VLBs), specifically focusing on their behavior in the alignment limit of a Type-II Yukawa structure. We

Liam A. Pocher, Temitayo N. Adeyeye, Sidra Gibeault, Philippe Talatchian

Superparamagnetic tunnel junctions are important devices for a range of emerging technologies, but most existing compact models capture only their mean switching rates. Capturing qualitatively accurate analog dynamics of these devices will be important as the technology scales up. Here we present results using a one-dimensional overdamped Langevin equation t

Matteo Caldara, Andrea Richaud, Massimo Capone, Pietro Massignan

We characterize the dynamical instability responsible for the breakdown of regular rows and necklaces of quantized vortices that appear at the interface between two superfluids in relative motion. Making use of a generalized point-vortex model, we identify several mechanisms leading to the suppression of this instability. They include a non-zero mass of the