Tootfinder

Pattern, which helps brands optimize their sales on online marketplaces, closes up 11.6% at $15.63 per share in its Nasdaq debut, after raising $300M in its IPO (Annie Palmer/CNBC)
https://www.cnbc.com/2025/09/19/amazon-reseller-patt…

Top Amazon reseller Pattern opens at $13.50 in Nasdaq debut after IPO raised $300 million
The Utah-based company ranks No. 2 among the top U.S. sellers on Amazon's third-party marketplace.

Been working on n-dimensional sampling/filtering functions for https://thi.ng/tensors and created an interactive Desmos graph to illustrate 1D Lanczos filtering/interpolation:
http…

1D/2D/3D/4D tensors with extensible polymorphic operations and customizable storage

⚛️ 3D printing reshapes construction for nuclear energy
#energy

3D printing reshapes construction for nuclear energy
In a bold step toward transforming how nuclear infrastructure is built, the Department of Energy's Manufacturing Demonstration Facility, or MDF, at Oak Ridge National Laboratory, in partnership with Kairos Power and Barnard Construction, has successfully developed and validated large-scale, 3D-printed polymer composite forms for casting complex, high-precision concrete structures that would be technically challenging and costly to produce using conventional methods. The concrete forms are being…

LOOP: A Plug-and-Play Neuro-Symbolic Framework for Enhancing Planning in Autonomous Systems
Ronit Virwani, Ruchika Suryawanshi
https://arxiv.org/abs/2508.13371 https://

LOOP: A Plug-and-Play Neuro-Symbolic Framework for Enhancing Planning in Autonomous Systems
Planning is one of the most critical tasks in autonomous systems, where even a small error can lead to major failures or million-dollar losses. Current state-of-the-art neural planning approaches struggle with complex domains, producing plans with missing preconditions, inconsistent goals, and hallucinations. While classical planners provide logical guarantees, they lack the flexibility and natural language understanding capabilities needed for modern autonomous systems. Existing neuro-symbolic…

Geometry Matters in Planar Storyplans
Alexander Dobler, Maximilian Holzm\"uller, Martin N\"ollenburg
https://arxiv.org/abs/2508.12747 https://arx…

Geometry Matters in Planar Storyplans
A storyplan visualizes a graph $G=(V,E)$ as a sequence of $\ell$ frames $Γ_1, \dots, Γ_\ell$, each of which is a drawing of the induced subgraph $G[V_i]$ of a vertex subset $V_i \subseteq V$. Moreover, each vertex $v \in V$ is contained in a single consecutive sequence of frames $Γ_i, \dots, Γ_j$, all vertices and edges contained in consecutive frames are drawn identically, and the union of all frames is a drawing of $G$. In GD 2022, the concept of planar storyplans was introduced, in which…

On 3-Connected Cubic Planar Graphs and their Strong Embeddings on Orientable Surfaces
Meike Wei{\ss}, Reymond Akpanya, Alice C. Niemeyer
https://arxiv.org/abs/2509.14964 https:/…

On 3-Connected Cubic Planar Graphs and their Strong Embeddings on Orientable Surfaces
Although the strong embedding of a 3-connected planar graph $G$ on the sphere is unique, $G$ can have different inequivalent strong embeddings on a surface of positive genus. If $G$ is cubic, then the strong embeddings of $G$ on the projective plane, the torus and the Klein bottle each are in one-to-one correspondence with certain subgraphs of the dual graph $G^\ast$. Here, we exploit this characterisation and show that two strong embeddings of $G$ on the projective plane, the torus or the Klei…

BEV-ODOM2: Enhanced BEV-based Monocular Visual Odometry with PV-BEV Fusion and Dense Flow Supervision for Ground Robots
Yufei Wei, Wangtao Lu, Sha Lu, Chenxiao Hu, Fuzhang Han, Rong Xiong, Yue Wang
https://arxiv.org/abs/2509.14636

BEV-ODOM2: Enhanced BEV-based Monocular Visual Odometry with PV-BEV Fusion and Dense Flow Supervision for Ground Robots
Bird's-Eye-View (BEV) representation offers a metric-scaled planar workspace, facilitating the simplification of 6-DoF ego-motion to a more robust 3-DoF model for monocular visual odometry (MVO) in intelligent transportation systems. However, existing BEV methods suffer from sparse supervision signals and information loss during perspective-to-BEV projection. We present BEV-ODOM2, an enhanced framework addressing both limitations without additional annotations. Our approach introduces: (1) dens…

Lipshitz--Sarkar stable homotopy type for certain planar trivalent graphs with perfect matchings
Nilangshu Bhattacharyya
https://arxiv.org/abs/2508.12272 https://

Lipshitz--Sarkar stable homotopy type for certain planar trivalent graphs with perfect matchings
We develop a space-level refinement of the $2$-factor homology by constructing a stable homotopy type associated to a certain family $\mathscr{G}$ of planar trivalent graphs equipped with perfect matchings. Specifically, we define a cover functor from the $2$-factor flow category $\mathscr{C}(Γ_{M})$ to the cube flow category $\mathscr{C}_{C}(n)$, where the perfect matching graph $Γ_{M}$ represents a planar trivalent graph $G$ together with a perfect matching $M$, such that $(G,M) \in \mathsc…

COMPASS: Confined-space Manipulation Planning with Active Sensing Strategy
Qixuan Li, Chen Le, Dongyue Huang, Jincheng Yu, Xinlei Chen
https://arxiv.org/abs/2509.14787 https://

COMPASS: Confined-space Manipulation Planning with Active Sensing Strategy
Manipulation in confined and cluttered environments remains a significant challenge due to partial observability and complex configuration spaces. Effective manipulation in such environments requires an intelligent exploration strategy to safely understand the scene and search the target. In this paper, we propose COMPASS, a multi-stage exploration and manipulation framework featuring a manipulation-aware sampling-based planner. First, we reduce collision risks with a near-field awareness scan …

BOW: Bayesian Optimization over Windows for Motion Planning in Complex Environments
Sourav Raxit, Abdullah Al Redwan Newaz, Paulo Padrao, Jose Fuentes, Leonardo Bobadilla
https://arxiv.org/abs/2508.13052

BOW: Bayesian Optimization over Windows for Motion Planning in Complex Environments
This paper introduces the BOW Planner, a scalable motion planning algorithm designed to navigate robots through complex environments using constrained Bayesian optimization (CBO). Unlike traditional methods, which often struggle with kinodynamic constraints such as velocity and acceleration limits, the BOW Planner excels by concentrating on a planning window of reachable velocities and employing CBO to sample control inputs efficiently. This approach enables the planner to manage high-dimension…