Zobrazeno 1 - 10
of 7 636
pro vyhledávání: '"Ghaffari P"'
Autor:
Song, Jingwei, Ghaffari, Maani
This paper addresses a special Perspective-n-Point (PnP) problem: estimating the optimal pose to align 3D and 2D shapes in real-time without correspondences, termed as correspondence-free PnP. While several studies have focused on 3D and 2D shape reg
Externí odkaz:
http://arxiv.org/abs/2409.18457
Tabular reinforcement learning methods cannot operate directly on continuous state spaces. One solution for this problem is to partition the state space. A good partitioning enables generalization during learning and more efficient exploitation of pr
Externí odkaz:
http://arxiv.org/abs/2409.16791
Autor:
Ghaffari, Mohsen, Trygub, Anton
We present the first (randomized) parallel dynamic algorithm for maximal matching, which can process an arbitrary number of updates simultaneously. Given a batch of edge deletion or insertion updates to the graph, our parallel algorithm adjusts the m
Externí odkaz:
http://arxiv.org/abs/2409.15476
Autor:
Ghaffari, Mohsen, Trygub, Anton
We present a low-energy deterministic distributed algorithm that computes exact Single-Source Shortest Paths (SSSP) in near-optimal time: it runs in $\tilde{O}(n)$ rounds and each node is awake during only $poly(\log n)$ rounds. When a node is not aw
Externí odkaz:
http://arxiv.org/abs/2409.15470
A major limitation of minimally invasive surgery is the difficulty in accurately locating the internal anatomical structures of the target organ due to the lack of tactile feedback and transparency. Augmented reality (AR) offers a promising solution
Externí odkaz:
http://arxiv.org/abs/2409.11688
Two-plasmon decay instability emerges as the parametric decay of laser beams into two plasma waves which is expected for hohlraum in inertial confinement fusion. The behavior of this instability in magnetized plasma is investigated in the present stu
Externí odkaz:
http://arxiv.org/abs/2409.07982
Autor:
Zhang, Ray, Zhou, Zheming, Sun, Min, Ghasemalizadeh, Omid, Kuo, Cheng-Hao, Eustice, Ryan, Ghaffari, Maani, Sen, Arnie
This paper introduces a robust unsupervised SE(3) point cloud registration method that operates without requiring point correspondences. The method frames point clouds as functions in a reproducing kernel Hilbert space (RKHS), leveraging SE(3)-equiva
Externí odkaz:
http://arxiv.org/abs/2407.20223
Partial point cloud registration is a challenging problem in robotics, especially when the robot undergoes a large transformation, causing a significant initial pose error and a low overlap between measurements. This work proposes exploiting equivari
Externí odkaz:
http://arxiv.org/abs/2407.16823
We present Simplified Text-Attributed Graph Embeddings (STAGE), a straightforward yet effective method for enhancing node features in Graph Neural Network (GNN) models that encode Text-Attributed Graphs (TAGs). Our approach leverages Large-Language M
Externí odkaz:
http://arxiv.org/abs/2407.12860
Autor:
Balliu, Alkida, Ghaffari, Mohsen, Kuhn, Fabian, Modanese, Augusto, Olivetti, Dennis, Rabie, Mikaël, Suomela, Jukka, Uitto, Jara
By prior work, we have many results related to distributed graph algorithms for problems that can be defined with local constraints; the formal framework used in prior work is locally checkable labeling problems (LCLs), introduced by Naor and Stockme
Externí odkaz:
http://arxiv.org/abs/2407.05445