Tootfinder

Listening to CBC Radio Barrie this morning and they were interviewing the manager responsible for housing starts for the Town of Innisfil. The discussion was about missed targets for housing starts and what can be done to get back on track. You could tell that he was frustrated, but the reason for less homes being built was almost solely put on the downturn in the housing market. No one is buying, so private industry isn't building.
All levels of government are throwing money at the problem, with most of it incentivizing for-profit companies to put shovels in the ground. At some point, the Federal, Provincial, and Municipal governments have to realize that public housing, government leasehold development, co-operative housing, or subsidized family apartments must be considered to solve the housing shortage. Private industry will never have the same objectives of getting families into homes - without a calculation of significant profits first.
#CanPoli #AffordableHousing

Placeit! A Framework for Learning Robot Object Placement Skills
Amina Ferrad, Johann Huber, Fran\c{c}ois H\'el\'enon, Julien Gleyze, Mahdi Khoramshahi, St\'ephane Doncieux
https://arxiv.org/abs/2510.09267

Placeit! A Framework for Learning Robot Object Placement Skills
Robotics research has made significant strides in learning, yet mastering basic skills like object placement remains a fundamental challenge. A key bottleneck is the acquisition of large-scale, high-quality data, which is often a manual and laborious process. Inspired by Graspit!, a foundational work that used simulation to automatically generate dexterous grasp poses, we introduce Placeit!, an evolutionary-computation framework for generating valid placement positions for rigid objects. Placei…

Constraining the Cosmological Evolution of Post-Newtonian Parameters with Gravitational Wave Signals from Compact Binary Inspirals
Oliver Pitt, Timothy Clifton
https://arxiv.org/abs/2510.08756

Constraining the Cosmological Evolution of Post-Newtonian Parameters with Gravitational Wave Signals from Compact Binary Inspirals
Gravitational waves from compact binary inspirals offer a new opportunity to constrain the cosmological time dependence of gravitational coupling parameters, due to the high precision of the observations themselves as well as the significant cosmological redshifts at which such systems exist. We calculate theory-independent equations of motion for compact objects in a binary system, implementing a new approach to sensitivities, and subsequently determine the gravitational wave signal that one s…

Evaluating Earth-Observing Satellite Sampling Effectiveness Using Kullback-Leibler Divergence
Negin Esmaeili, Paul T. Grogan
https://arxiv.org/abs/2510.10859 https://

Evaluating Earth-Observing Satellite Sampling Effectiveness Using Kullback-Leibler Divergence
This work presents an objective, repeatable, automatic, and fast methodology for assessing the representativeness of geophysical variables sampled by Earth-observing satellites. The primary goal is to identify and mitigate potential sampling biases attributed to orbit selection during pre-Phase A mission studies. This methodology supports current incubation activities for a future Planetary Boundary Layer observing system by incorporating a sampling effectiveness measure into a broader architec…

Enhancing Speech Emotion Recognition via Fine-Tuning Pre-Trained Models and Hyper-Parameter Optimisation
Aryan Golbaghi, Shuo Zhou
https://arxiv.org/abs/2510.07052 https://

Enhancing Speech Emotion Recognition via Fine-Tuning Pre-Trained Models and Hyper-Parameter Optimisation
We propose a workflow for speech emotion recognition (SER) that combines pre-trained representations with automated hyperparameter optimisation (HPO). Using SpeechBrain wav2vec2-base model fine-tuned on IEMOCAP as the encoder, we compare two HPO strategies, Gaussian Process Bayesian Optimisation (GP-BO) and Tree-structured Parzen Estimators (TPE), under an identical four-dimensional search space and 15-trial budget, with balanced class accuracy (BCA) on the German EmoDB corpus as the objective.…

Automated Defect Detection for Mass-Produced Electronic Components Based on YOLO Object Detection Models
Wei-Lung Mao, Chun-Chi Wang, Po-Heng Chou, Yen-Ting Liu
https://arxiv.org/abs/2510.01914

Automated Defect Detection for Mass-Produced Electronic Components Based on YOLO Object Detection Models
Since the defect detection of conventional industry components is time-consuming and labor-intensive, it leads to a significant burden on quality inspection personnel and makes it difficult to manage product quality. In this paper, we propose an automated defect detection system for the dual in-line package (DIP) that is widely used in industry, using digital camera optics and a deep learning (DL)-based model. The two most common defect categories of DIP are examined: (1) surface defects, and (…

From #RobertReich @rbreich@instagram.com and @imcivicaction@instagram.com @…
#Trump wants to invoke the

DeLTa: Demonstration and Language-Guided Novel Transparent Object Manipulation
Taeyeop Lee, Gyuree Kang, Bowen Wen, Youngho Kim, Seunghyeok Back, In So Kweon, David Hyunchul Shim, Kuk-Jin Yoon
https://arxiv.org/abs/2510.05662

DeLTa: Demonstration and Language-Guided Novel Transparent Object Manipulation
Despite the prevalence of transparent object interactions in human everyday life, transparent robotic manipulation research remains limited to short-horizon tasks and basic grasping capabilities.Although some methods have partially addressed these issues, most of them have limitations in generalizability to novel objects and are insufficient for precise long-horizon robot manipulation. To address this limitation, we propose DeLTa (Demonstration and Language-Guided Novel Transparent Object Manip…

PEPS: Quantum-Inspired Reinforcement Learning for Coherent Reasoning Traces in LLMs
Venkat Margapuri, Garik Kazanjian, Naren Kosaraju
https://arxiv.org/abs/2509.20105 https://…

PEPS: Quantum-Inspired Reinforcement Learning for Coherent Reasoning Traces in LLMs
Large Language Models (LLMs) often struggle with maintaining coherent multi-step reasoning traces, particularly in tasks that require a structured logical flow. This work introduces a quantum-inspired approach to address the challenge by incorporating a fidelity-based reward derived from Projected Entangled Pair States (PEPS) into Proximal Policy Optimization. Unlike prior approaches that use direct supervision or contrastive objectives, the proposed method guides learning through structural co…

Sight Over Site: Perception-Aware Reinforcement Learning for Efficient Robotic Inspection
Richard Kuhlmann, Jakob Wolfram, Boyang Sun, Jiaxu Xing, Davide Scaramuzza, Marc Pollefeys, Cesar Cadena
https://arxiv.org/abs/2509.17877

Sight Over Site: Perception-Aware Reinforcement Learning for Efficient Robotic Inspection
Autonomous inspection is a central problem in robotics, with applications ranging from industrial monitoring to search-and-rescue. Traditionally, inspection has often been reduced to navigation tasks, where the objective is to reach a predefined location while avoiding obstacles. However, this formulation captures only part of the real inspection problem. In real-world environments, the inspection targets may become visible well before their exact coordinates are reached, making further movemen…