Tootfinder

DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation
Young Hun Kim, Seungyeon Kim, Yonghyeon Lee, Frank Chongwoo Park
https://arxiv.org/abs/2507.05627

DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation
Partial-view 3D recognition -- reconstructing 3D geometry and identifying object instances from a few sparse RGB images -- is an exceptionally challenging yet practically essential task, particularly in cluttered, occluded real-world settings where full-view or reliable depth data are often unavailable. Existing methods, whether based on strong symmetry priors or supervised learning on curated datasets, fail to generalize to such scenarios. In this work, we introduce DreamGrasp, a framework tha…

SenseCrypt: Sensitivity-guided Selective Homomorphic Encryption for Joint Federated Learning in Cross-Device Scenarios
Borui Li, Li Yan, Junhao Han, Jianmin Liu, Lei Yu
https://arxiv.org/abs/2508.04100

SenseCrypt: Sensitivity-guided Selective Homomorphic Encryption for Joint Federated Learning in Cross-Device Scenarios
Homomorphic Encryption (HE) prevails in securing Federated Learning (FL), but suffers from high overhead and adaptation cost. Selective HE methods, which partially encrypt model parameters by a global mask, are expected to protect privacy with reduced overhead and easy adaptation. However, in cross-device scenarios with heterogeneous data and system capabilities, traditional Selective HE methods deteriorate client straggling, and suffer from degraded HE overhead reduction performance. According…

Local Well-Posedness for the Bartnik Stationary Extension Problem near Schwarzschild Spheres
Ahmed Ellithy
https://arxiv.org/abs/2509.03478 https://arxiv.o…

Local Well-Posedness for the Bartnik Stationary Extension Problem near Schwarzschild Spheres
We investigate the Bartnik stationary extension conjecture, which arises from the definition of the spacetime Bartnik mass for a compact region in a general initial data set satisfying the dominant energy condition. This conjecture posits the existence and uniqueness (up to isometry) of an asymptotically flat stationary vacuum spacetime containing an initial data set $(M, \mathfrak{g}, Π)$ that realizes prescribed Bartnik boundary data on $\partial M$, consisting of the induced metric, mean cu…

Reduced Efficiency in the Right Fronto-Parietal Attentional Network During Distractor Suppression in Mild Cognitive Impairment
Jatupong Oboun, Piyanon Charoenpoonpanich, Anna Raksapatcharawong, Chaipat Chunharas, Itthi Chatnuntawech, Chainarong Amornbunchornvej, Sirawaj Itthipuripat
https://arxiv.org/abs/2507.01433

Reduced Efficiency in the Right Fronto-Parietal Attentional Network During Distractor Suppression in Mild Cognitive Impairment
Mild Cognitive Impairment (MCI) is a critical transitional stage between normal cognitive aging and dementia, making its early detection essential. This study investigates the neural mechanisms of distractor suppression in MCI patients using EEG and behavioral data during an attention-cueing Eriksen flanker task. A cohort of 56 MCIs and 26 healthy controls (HCs) performed tasks with congruent and incongruent stimuli of varying saliency levels. During these tasks, EEG data were analyzed for alph…

Inverse spectral problems with sparse data and applications to passive imaging on manifolds
Ali Feizmohammadi, Katya Krupchyk
https://arxiv.org/abs/2507.22723 https://

Inverse spectral problems with sparse data and applications to passive imaging on manifolds
Motivated by inverse problems with a single passive measurement, we introduce and analyze a new class of inverse spectral problems on closed Riemannian manifolds. Specifically, we establish two general uniqueness results for the recovery of a potential in the stationary Schrödinger operator from partial spectral data, which consists of a possibly sparse subset of its eigenvalues and the restrictions of the corresponding eigenfunctions to a nonempty open subset of the manifold. Crucially, the e…

ECLEIRS: Exact conservation law embedded identification of reduced states for parameterized partial differential equations from sparse and noisy data
Aviral Prakash, Ben S. Southworth, Marc L. Klasky
https://arxiv.org/abs/2506.18855

ECLEIRS: Exact conservation law embedded identification of reduced states for parameterized partial differential equations from sparse and noisy data
Multi-query applications such as parameter estimation, uncertainty quantification and design optimization for parameterized PDE systems are expensive due to the high computational cost of high-fidelity simulations. Reduced/Latent state dynamics approaches for parameterized PDEs offer a viable method where high-fidelity data and machine learning techniques are used to reduce the system's dimensionality and estimate the dynamics of low-dimensional reduced states. These reduced state dynamics appr…

Prokhorov Metric Convergence of the Partial Sum Process for Reconstructed Functional Data
Tim Kutta, Piotr Kokoszka
https://arxiv.org/abs/2506.21172 https:…

Prokhorov Metric Convergence of the Partial Sum Process for Reconstructed Functional Data
Motivated by applications in functional data analysis, we study the partial sum process of sparsely observed, random functions. A key novelty of our analysis are bounds for the distributional distance between the limit Brownian motion and the entire partial sum process in the function space. To measure the distance between distributions, we employ the Prokhorov and Wasserstein metrics. We show that these bounds have important probabilistic implications, including strong invariance principles an…

Nonparametric learning of stochastic differential equations from sparse and noisy data
Arnab Ganguly, Riten Mitra, Jinpu Zhou
https://arxiv.org/abs/2508.11597 https://

Nonparametric learning of stochastic differential equations from sparse and noisy data
The paper proposes a systematic framework for building data-driven stochastic differential equation (SDE) models from sparse, noisy observations. Unlike traditional parametric approaches, which assume a known functional form for the drift, our goal here is to learn the entire drift function directly from data without strong structural assumptions, making it especially relevant in scientific disciplines where system dynamics are partially understood or highly complex. We cast the estimation prob…

Time Resolution Independent Operator Learning
Diab W. Abueidda, Mbebo Nonna, Panos Pantidis, Mostafa E. Mobasher
https://arxiv.org/abs/2507.02524 https://

Time Resolution Independent Operator Learning
Accurately learning solution operators for time-dependent partial differential equations (PDEs) from sparse and irregular data remains a challenging task. Recurrent DeepONet extensions inherit the discrete-time limitations of sequence-to-sequence (seq2seq) RNN architectures, while neural-ODE surrogates cannot incorporate new inputs after initialization. We introduce NCDE-DeepONet, a continuous-time operator network that embeds a Neural Controlled Differential Equation (NCDE) in the branch and a…

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