Tootfinder

This https://arxiv.org/abs/2403.17124 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Grounding Language Plans in Demonstrations Through Counterfactual Perturbations
Grounding the common-sense reasoning of Large Language Models (LLMs) in physical domains remains a pivotal yet unsolved problem for embodied AI. Whereas prior works have focused on leveraging LLMs directly for planning in symbolic spaces, this work uses LLMs to guide the search of task structures and constraints implicit in multi-step demonstrations. Specifically, we borrow from manipulation planning literature the concept of mode families, which group robot configurations by specific motion co…

#Meta #AI: Introducing #SceneScript, a novel approach for 3D scene reconstruction

Introducing SceneScript, a novel approach for 3D scene reconstruction
Today, we’re introducing SceneScript, a novel method for reconstructing environments and representing the layout of physical spaces.

This https://arxiv.org/abs/2312.09270 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Towards establishing a connection between two-level quantum systems and physical spaces
This work seeks to make explicit the operational connection between the preparation of two-level quantum systems with their corresponding description (as states) in a Hilbert space. This may sound outdated, but we show there is more to this connection than common sense may lead us to believe. To bridge these two separated realms -- the actual laboratory and the space of states -- we rely on a paradigmatic mathematical object: the Hopf fibration. We illustrate how this connection works in practi…

This https://arxiv.org/abs/2402.11016 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Holographic phenomenology via overlapping degrees of freedom
The holographic principle suggests that regions of space contain fewer physical degrees of freedom than would be implied by conventional quantum field theory. Meanwhile, in Hilbert spaces of large dimension $2^n$, it is possible to define $N \gg n$ Pauli algebras that are nearly anti-commuting (but not quite) and which can be thought of as "overlapping degrees of freedom". We propose to model the phenomenology of holographic theories by allowing field-theory modes to be overlapping, and derive …

💥 Understanding Physical Breakdowns in Virtual Reality
#vr #technology

Understanding Physical Breakdowns in Virtual Reality
Virtual Reality (VR) moves away from well-controlled laboratory environments into public and personal spaces. As users are visually disconnected from the physical environment, interacting in an uncontrolled space frequently leads to collisions and raises safety concerns. In my thesis, I investigate this phenomenon which I define as the physical breakdown in VR. The goal is to understand the reasons for physical breakdowns, provide solutions, and explore future mechanisms that could perpetuate s…

Meta releases OpenEQA, a benchmark to measure an AI agent's understanding of physical spaces by probing it with questions about the environment (Michael Nuñez/VentureBeat)
https://venturebeat.com/ai/meta-ai-releases-opene…

Due to the overwhelming success of the 2023 Repair Cafe Series, SPEC and the City of #Vancouver have scheduled more Cafes throughout 2024.
These interactive community spaces encourage individuals to bring one broken small appliance, electronic product, bicycle or item of clothing to be repaired for FREE by volunteer fixers. Participants may be asked to engage with the physical repair of their it…

Synergistic integration of physical embedding and machine learning enabling precise and reliable force field
Lifeng Xu, Jian Jiang
https://arxiv.org/abs/2404.13368

Synergistic integration of physical embedding and machine learning enabling precise and reliable force field
The machine learning force field has achieved significant strides in accurately reproducing the potential energy surface with quantum chemical accuracy. However, it still faces significant challenges, e.g., extrapolating to uncharted chemical spaces, interpreting long-range electrostatics, and mapping complex macroscopic properties. To address these issues, we advocate for a synergistic integration of physical principles and machine learning techniques within the framework of a physically infor…

This https://arxiv.org/abs/2403.13295 has been replaced.
initial toot: https://mastoxiv.page/@…

Interaction-Induced Dimensional Crossover through Full 3D to 1D
The exploration of dimensional crossover carries profound fundamental significance, serving as a crucial bridge in comprehending the remarkable disparities observed in transitional phenomena across the two distinct dimensions of a physical system. The prevalent strategy for manipulating the dimensionality involves meticulously controlling the external trapping geometry, thereby restricting the degrees of freedom of the kinetic energy from three-dimensional (3D) to lower-dimensional spaces, whil…

Stokes phenomenon of Kloosterman and Airy connections
Andreas Hohl, Konstantin Jakob
https://arxiv.org/abs/2404.09582 https://arxiv.o…

Stokes phenomenon of Kloosterman and Airy connections
We define categories of Stokes filtered and Stokes graded $G$-local systems for reductive groups $G$ and use the formalism of Tannakian categories to show that they are equivalent to the category of $G$-connections. We then use the interpretation of moduli spaces of Stokes filtered $G$-local systems as braid varieties to prove physical rigidity of two well-known families of cohomologically rigid connections, the Kloosterman and Airy connections. In the Kloosterman case, our proof relies on Stei…

This https://arxiv.org/abs/2310.12544 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Neural Likelihood Approximation for Integer Valued Time Series Data
Stochastic processes defined on integer valued state spaces are popular within the physical and biological sciences. These models are necessary for capturing the dynamics of small systems where the individual nature of the populations cannot be ignored and stochastic effects are important. The inference of the parameters of such models, from time series data, is challenging due to intractability of the likelihood. To work at all, current simulation based inference methods require the generation…

Towards a representer theorem for identification of passive systems
Brayan M. Shali, Henk J. van Waarde
https://arxiv.org/abs/2404.08297 https://

Towards a representer theorem for identification of passive systems
A major problem in system identification is the incorporation of prior knowledge about the physical properties of the given system, such as stability, positivity and passivity. In this paper, we present first steps towards tackling this problem for passive systems. In particular, using ideas from the theory of reproducing kernel Hilbert spaces, we solve the problem of identifying a nonnegative input-output operator from data consisting of input-output trajectories of the system. We prove a repr…

IKSPARK: An Inverse Kinematics Solver using Semidefinite Relaxation and Rank Minimization
Liangting Wu, Roberto Tron
https://arxiv.org/abs/2403.12235 https…

IKSPARK: An Inverse Kinematics Solver using Semidefinite Relaxation and Rank Minimization
Inverse kinematics (IK) is a fundamental problem frequently occurred in robot control and motion planning. However, the problem is nonconvex because the kinematic map between the configuration and task spaces is generally nonlinear, which makes it challenging for fast and accurate solutions. The problem can be more complicated with the existence of different physical constraints imposed by the robot structure. In this paper, we develop an inverse kinematics solver named IKSPARK (Inverse Kinemat…

This https://arxiv.org/abs/2302.11508 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

nSimplex Zen: A Novel Dimensionality Reduction for Euclidean and Hilbert Spaces
Dimensionality reduction techniques map values from a high dimensional space to one with a lower dimension. The result is a space which requires less physical memory and has a faster distance calculation. These techniques are widely used where required properties of the reduced-dimension space give an acceptable accuracy with respect to the original space. Many such transforms have been described. They have been classified in two main groups: linear and topological. Linear methods such as Princ…

Dynamic Gaussian Graph Operator: Learning parametric partial differential equations in arbitrary discrete mechanics problems
Chu Wang, Jinhong Wu, Yanzhi Wang, Zhijian Zha, Qi Zhou
https://arxiv.org/abs/2403.02810

Dynamic Gaussian Graph Operator: Learning parametric partial differential equations in arbitrary discrete mechanics problems
Deep learning methods have access to be employed for solving physical systems governed by parametric partial differential equations (PDEs) due to massive scientific data. It has been refined to operator learning that focuses on learning non-linear mapping between infinite-dimensional function spaces, offering interface from observations to solutions. However, state-of-the-art neural operators are limited to constant and uniform discretization, thereby leading to deficiency in generalization on …

VR-GS: A Physical Dynamics-Aware Interactive Gaussian Splatting System in Virtual Reality
Ying Jiang, Chang Yu, Tianyi Xie, Xuan Li, Yutao Feng, Huamin Wang, Minchen Li, Henry Lau, Feng Gao, Yin Yang, Chenfanfu Jiang
https://arXiv.org/abs/2401.16663

VR-GS: A Physical Dynamics-Aware Interactive Gaussian Splatting System in Virtual Reality
As consumer Virtual Reality (VR) and Mixed Reality (MR) technologies gain momentum, there's a growing focus on the development of engagements with 3D virtual content. Unfortunately, traditional techniques for content creation, editing, and interaction within these virtual spaces are fraught with difficulties. They tend to be not only engineering-intensive but also require extensive expertise, which adds to the frustration and inefficiency in virtual object manipulation. Our proposed VR-GS syste…

This https://arxiv.org/abs/2309.02953 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Blended learning: A data-literate science teacher is a better teacher
The COVID-19 pandemic has underscored the importance of blended learning in contemporary physics and, more generally, STEM education. In this contribution, we summarize current pedagogical models of blended learning, such as rotational and flexible non-rotational models, and customizable configurations of physical and virtual learning spaces. With the inevitable integration of digital technology as one of the pillars of blended learning, teachers find themselves in an unprecedented position to …

This https://arxiv.org/abs/2305.09722 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Effective Field Theories as Lagrange Spaces
We present a formulation of scalar effective field theories in terms of the geometry of Lagrange spaces. The horizontal geometry of the Lagrange space generalizes the Riemannian geometry on the scalar field manifold, inducing a broad class of affine connections that can be used to covariantly express and simplify tree-level scattering amplitudes. Meanwhile, the vertical geometry of the Lagrange space characterizes the physical validity of the effective field theory, as a torsion component compr…

On steady solutions of the Hall-MHD system in Besov spaces
Jin Tan, Hiroyuki Tsurumi, Xin Zhang
https://arxiv.org/abs/2404.03402 https://

On steady solutions of the Hall-MHD system in Besov spaces
In this paper, we investigate the well-posedness and ill-posedness issues for the incompressible stationary Hall-magnetohydrodynamic (Hall-MHD) system in $\mathbb{R}^3.$ We first show the existence and uniqueness of solutions provided with the forces in $\dot B^{3/p-3}_{p,r}(\mathbb{R}^3)$ for $1\leq p <3$ and $r=1$. Moreover, this result can be extended to any $1\leq r\leq \infty$ whenever $p=2,$ without any additional assumption on the physical parameters. On the other hand, we establish some…

This https://arxiv.org/abs/2310.12544 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Neural Likelihood Approximation for Integer Valued Time Series Data
Stochastic processes defined on integer valued state spaces are popular within the physical and biological sciences. These models are necessary for capturing the dynamics of small systems where the individual nature of the populations cannot be ignored and stochastic effects are important. The inference of the parameters of such models, from time series data, is challenging due to intractability of the likelihood. To work at all, current simulation based inference methods require the generation…

This https://arxiv.org/abs/2401.14574 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Quantization of Kähler Manifolds via Brane Quantization
In their physical proposal for quantization [20], Gukov-Witten suggested that, given a symplectic manifold $M$ with a complexification $X$, the A-model morphism spaces $\operatorname{Hom}(\mathcal{B}_{\operatorname{cc}}, \mathcal{B}_{\operatorname{cc}})$ and $\operatorname{Hom}(\mathcal{B}, \mathcal{B}_{\operatorname{cc}})$ should recover holomorphic deformation quantization of $X$ and geometric quantization of $M$ respectively, where $\mathcal{B}_{\operatorname{cc}}$ is a canonical coisotropic…

This https://arxiv.org/abs/2403.09583 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

ExploRLLM: Guiding Exploration in Reinforcement Learning with Large Language Models
In image-based robot manipulation tasks with large observation and action spaces, reinforcement learning struggles with low sample efficiency, slow training speed, and uncertain convergence. As an alternative, large pre-trained foundation models have shown promise in robotic manipulation, particularly in zero-shot and few-shot applications. However, using these models directly is unreliable due to limited reasoning capabilities and challenges in understanding physical and spatial contexts. This…

GeoDirDock: Guiding Docking Along Geodesic Paths
Ra\'ul Mi\~n\'an, Javier Gallardo, \'Alvaro Ciudad, Alexis Molina
https://arxiv.org/abs/2404.06481

GeoDirDock: Guiding Docking Along Geodesic Paths
This work introduces GeoDirDock (GDD), a novel approach to molecular docking that enhances the accuracy and physical plausibility of ligand docking predictions. GDD guides the denoising process of a diffusion model along geodesic paths within multiple spaces representing translational, rotational, and torsional degrees of freedom. Our method leverages expert knowledge to direct the generative modeling process, specifically targeting desired protein-ligand interaction regions. We demonstrate tha…

Towards a representer theorem for identification of passive systems
Brayan M. Shali, Henk J. van Waarde
https://arxiv.org/abs/2404.08297 https://

Towards a representer theorem for identification of passive systems
A major problem in system identification is the incorporation of prior knowledge about the physical properties of the given system, such as stability, positivity and passivity. In this paper, we present first steps towards tackling this problem for passive systems. In particular, using ideas from the theory of reproducing kernel Hilbert spaces, we solve the problem of identifying a nonnegative input-output operator from data consisting of input-output trajectories of the system. We prove a repr…

Hypothesis Spaces for Deep Learning
Rui Wang, Yuesheng Xu, Mingsong Yan
https://arxiv.org/abs/2403.03353 https://arxiv.org/pdf/2403.0…

Hypothesis Spaces for Deep Learning
This paper introduces a hypothesis space for deep learning that employs deep neural networks (DNNs). By treating a DNN as a function of two variables, the physical variable and parameter variable, we consider the primitive set of the DNNs for the parameter variable located in a set of the weight matrices and biases determined by a prescribed depth and widths of the DNNs. We then complete the linear span of the primitive DNN set in a weak* topology to construct a Banach space of functions of the…

This https://arxiv.org/abs/2306.10771 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Oxidation, Reduction and Semi-Classical Limit for Quantum Matrix Geometries
Matrix configurations define noncommutative spaces endowed with extra structure including a generalized Laplace operator, and hence a metric structure. Made dynamical via matrix models, they describe rich physical systems including noncommutative gauge theory and emergent gravity. Refining the construction in [1], we construct a semi-classical limit through an immersed submanifold of complex projective space based on quasi-coherent states. We observe the phenomenon of oxidation, where the resul…

Interaction-Induced Dimensional Crossover through Full 3D to 1D
Tao Yu, Xiaoran Ye, Zhaoxin Liang
https://arxiv.org/abs/2403.13295 https://

Interaction-Induced Dimensional Crossover through Full 3D to 1D
The exploration of dimensional crossover carries profound fundamental significance, serving as a crucial bridge in comprehending the remarkable disparities observed in transitional phenomena across the two distinct dimensions of a physical system. The prevalent strategy for manipulating the dimensionality involves meticulously controlling the external trapping geometry, thereby restricting the degrees of freedom of the kinetic energy from three-dimensional (3D) to lower-dimensional spaces, whil…

Hypothesis Spaces for Deep Learning
Rui Wang, Yuesheng Xu, Mingsong Yan
https://arxiv.org/abs/2403.03353 https://arxiv.org/pdf/2403.0…

Hypothesis Spaces for Deep Learning
This paper introduces a hypothesis space for deep learning that employs deep neural networks (DNNs). By treating a DNN as a function of two variables, the physical variable and parameter variable, we consider the primitive set of the DNNs for the parameter variable located in a set of the weight matrices and biases determined by a prescribed depth and widths of the DNNs. We then complete the linear span of the primitive DNN set in a weak* topology to construct a Banach space of functions of the…

This https://arxiv.org/abs/2204.13813 has been replaced.
link: https://scholar.google.com/scholar?q=a

On the fractional heat semigroup and product estimates in Besov spaces and applications in theoretical analysis of the fractional Keller-Segel system
This paper is concerned with the fractional Keller-Segel system in the temporal and spatial variables. We consider fractional dissipation for the physical variables including a fractional dissipation mechanism for the chemotactic diffusion, as well as a time fractional variation assumed in the Caputo sense. We analyze the fractional heat semigroup obtaining time decay and integral estimates of the Mittag-Leffler operators in critical Besov spaces, and prove a bilinear estimate derived from the …

This https://arxiv.org/abs/2402.11016 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hept…

Holographic phenomenology via overlapping degrees of freedom
The holographic principle suggests that regions of space contain fewer physical degrees of freedom than would be implied by conventional quantum field theory. Meanwhile, in Hilbert spaces of large dimension $2^n$, it is possible to define $N \gg n$ Pauli algebras that are nearly anti-commuting (but not quite) and which can be thought of as "overlapping degrees of freedom". We propose to model the phenomenology of holographic theories by allowing field-theory modes to be overlapping, and derive …