Tootfinder

5 #Grafana in #Docker examples to get started with metrics, logs, and traces
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Sony unveils the Fair Human-Centric Image Benchmark dataset to test the fairness of computer vision models, saying it was compiled in a fair and ethical manner (Thomas Claburn/The Register)
https://www.theregister.com/2025/11/05/sony_ai_vision_model_benchmark/…

Sony rolls out a standard way to measure bias in how AI describes what it 'sees'
: Images in the test dataset were all sourced with consent

From Noisy Traces to Stable Gradients: Bias-Variance Optimized Preference Optimization for Aligning Large Reasoning Models
Mingkang Zhu, Xi Chen, Bei Yu, Hengshuang Zhao, Jiaya Jia
https://arxiv.org/abs/2510.05095

From Noisy Traces to Stable Gradients: Bias-Variance Optimized Preference Optimization for Aligning Large Reasoning Models
Large reasoning models (LRMs) generate intermediate reasoning traces before producing final answers, yielding strong gains on multi-step and mathematical tasks. Yet aligning LRMs with human preferences, a crucial prerequisite for model deployment, remains underexplored. The statistically correct objective for preference alignment requires marginalizing over reasoning traces, but this computation is intractable in practice. A common workaround optimizes a single sampled trajectory, which introdu…

PAD-TRO: Projection-Augmented Diffusion for Direct Trajectory Optimization
Jushan Chen, Santiago Paternain
https://arxiv.org/abs/2510.04436 https://arxiv.o…

PAD-TRO: Projection-Augmented Diffusion for Direct Trajectory Optimization
Recently, diffusion models have gained popularity and attention in trajectory optimization due to their capability of modeling multi-modal probability distributions. However, addressing nonlinear equality constraints, i.e, dynamic feasi- bility, remains a great challenge in diffusion-based trajectory optimization. Recent diffusion-based trajectory optimization frameworks rely on a single-shooting style approach where the denoised control sequence is applied to forward propagate the dynamical sy…

ReeMark: Reeb Graphs for Simulating Patterns of Life in Spatiotemporal Trajectories
Anantajit Subrahmanya, Chandrakanth Gudavalli, Connor Levenson, Umang Garg, B. S. Manjunath
https://arxiv.org/abs/2510.03152

ReeMark: Reeb Graphs for Simulating Patterns of Life in Spatiotemporal Trajectories
Accurately modeling human mobility is critical for urban planning, epidemiology, and traffic management. In this work, we introduce Markovian Reeb Graphs, a novel framework for simulating spatiotemporal trajectories that preserve Patterns of Life (PoLs) learned from baseline data. By combining individual- and population-level mobility structures within a probabilistic topological model, our approach generates realistic future trajectories that capture both consistency and variability in daily l…

EFSA: Kaksi virikettä ei riitä turkistarhojen ketuille #eläinoikeudet

EFSA: Kaksi virikettä ei riitä turkistarhojen ketuille  | Animalia-media
EFSA:n viesti oli selkeä: nykyisiä tarhoja ei kyetä muuttamaan niin, että turkiseläinten hyvinvointi saataisiin edes kohtuulliselle tasolle.

The first actual physical part for my #Tricycle project has arrived today. This is an axle — actually a Cannondale Lefty axle — and it's nominally the front axle, although it doesn't really matter which axle it ends up being because they will all be identical.
#BikeTooter

Modeling Student Learning with 3.8 Million Program Traces
Alexis Ross, Megha Srivastava, Jeremiah Blanchard, Jacob Andreas
https://arxiv.org/abs/2510.05056 https://

Modeling Student Learning with 3.8 Million Program Traces
As programmers write code, they often edit and retry multiple times, creating rich "interaction traces" that reveal how they approach coding tasks and provide clues about their level of skill development. For novice programmers in particular, these traces reflect the diverse reasoning processes they employ to code, such as exploratory behavior to understand how a programming concept works, re-strategizing in response to bugs, and personalizing stylistic choices. In this work, we explore what ca…

Optimal Smooth Coverage Trajectory Planning for Quadrotors in Cluttered Environment
Duanjiao Li, Yun Chen, Ying Zhang, Junwen Yao, Dongyue Huang, Jianguo Zhang, Ning Ding
https://arxiv.org/abs/2510.03169

Optimal Smooth Coverage Trajectory Planning for Quadrotors in Cluttered Environment
For typical applications of UAVs in power grid scenarios, we construct the problem as planning UAV trajectories for coverage in cluttered environments. In this paper, we propose an optimal smooth coverage trajectory planning algorithm. The algorithm consists of two stages. In the front-end, a Genetic Algorithm (GA) is employed to solve the Traveling Salesman Problem (TSP) for Points of Interest (POIs), generating an initial sequence of optimized visiting points. In the back-end, the sequence is…

Reliable and Real-Time Highway Trajectory Planning via Hybrid Learning-Optimization Frameworks
Yujia Lu, Chong Wei, Lu Ma
https://arxiv.org/abs/2508.04436 https://

Reliable and Real-Time Highway Trajectory Planning via Hybrid Learning-Optimization Frameworks
Autonomous highway driving presents a high collision risk due to fast-changing environments and limited reaction time, necessitating reliable and efficient trajectory planning. This paper proposes a hybrid trajectory planning framework that integrates the adaptability of learning-based methods with the formal safety guarantees of optimization-based approaches. The framework features a two-layer architecture: an upper layer employing a graph neural network (GNN) trained on real-world highway dat…