Tootfinder

Question!
For a long time YouTube is a pain in the ass! If you see one video about a pimple on your ass, your feed is only about that topic and it makes me sick! I follow a few content creators on peertube, But i would like to have some more accounts to look into, where do i find them?
This can go from a Tech talk, a bike restore, a podcast, ghost stories, you name it.
For now it stick with podcasts, but i would like to federate my video watching also a bit!
:pinguno…

A Striking First Impression: CGI Commissioning Observations of the AB Aurigae Protoplanetary System
Thayne Currie, Kellen Lawson, Erica Dykes, Mona El Morsy
https://arxiv.org/abs/2509.02681

A Striking First Impression: CGI Commissioning Observations of the AB Aurigae Protoplanetary System
For one of the first set of Roman Coronagraph project images, we propose to target AB Aurigae. AB Aurigae is a complex and visually stunning system, surrounded by a gas rich protoplanetary disk showing numerous spiral arms, an enigmatic embedded protoplanet (AB Aurigae b) at 0\farcs{}6 separation, and hints of potential additional sites of planet formation. Even a marginally-successful dark hole generation (e.g. 10$^{-5}$--10$^{-6}$ contrast) with CGI would yield a vastly improved view of AB Au…

Mean-Variance Stackelberg Games with Asymmetric Information
Yu-Jui Huang, Shihao Zhu
https://arxiv.org/abs/2509.03669 https://arxiv.org/pdf/2509.03669

Mean-Variance Stackelberg Games with Asymmetric Information
This paper considers two investors who perform mean-variance portfolio selection with asymmetric information: one knows the true stock dynamics, while the other has to infer the true dynamics from observed stock evolution. Their portfolio selection is interconnected through relative performance concerns, i.e., each investor is concerned about not only her terminal wealth, but how it compares to the average terminal wealth of both investors. We model this as Stackelberg competition: the partiall…

A Martingale approach to continuous Portfolio Optimization under CVaR like constraints
J\'er\^ome Lelong (LJK), V\'eronique Maume-Deschamps, William Thevenot
https://arxiv.org/abs/2509.26009

A Martingale approach to continuous Portfolio Optimization under CVaR like constraints
We study a continuous-time portfolio optimization problem under an explicit constraint on the Deviation Conditional Value-at-Risk (DCVaR), defined as the difference between the CVaR and the expected terminal wealth. While the mean-CVaR framework has been widely explored, its time-inconsistency complicates the use of dynamic programming. We follow the martingale approach in a complete market setting, as in Gao et al. [4], and extend it by retaining an explicit DCVaR constraint in the problem for…

Simultaneous Multi-objective Alignment Across Verifiable and Non-verifiable Rewards
Yiran Shen, Yu Xia, Jonathan Chang, Prithviraj Ammanabrolu
https://arxiv.org/abs/2510.01167 h…

Simultaneous Multi-objective Alignment Across Verifiable and Non-verifiable Rewards
Aligning large language models to human preferences is inherently multidimensional, yet most pipelines collapse heterogeneous signals into a single optimizeable objective. We seek to answer what it would take to simultaneously align a model across various domains spanning those with: verifiable rewards (mathematical accuracy), non-verifiable subjective preferences (human values), and complex interactive scenarios (multi-turn AI tutoring dialogues). Such multi-objective reinforcement learning se…

ActiveUMI: Robotic Manipulation with Active Perception from Robot-Free Human Demonstrations
Qiyuan Zeng, Chengmeng Li, Jude St. John, Zhongyi Zhou, Junjie Wen, Guorui Feng, Yichen Zhu, Yi Xu
https://arxiv.org/abs/2510.01607

ActiveUMI: Robotic Manipulation with Active Perception from Robot-Free Human Demonstrations
We present ActiveUMI, a framework for a data collection system that transfers in-the-wild human demonstrations to robots capable of complex bimanual manipulation. ActiveUMI couples a portable VR teleoperation kit with sensorized controllers that mirror the robot's end-effectors, bridging human-robot kinematics via precise pose alignment. To ensure mobility and data quality, we introduce several key techniques, including immersive 3D model rendering, a self-contained wearable computer, and effic…

Exploratory Semantic Reliability Analysis of Wind Turbine Maintenance Logs using Large Language Models
Max Malyi, Jonathan Shek, Andre Biscaya
https://arxiv.org/abs/2509.22366 h…

Exploratory Semantic Reliability Analysis of Wind Turbine Maintenance Logs using Large Language Models
A wealth of operational intelligence is locked within the unstructured free-text of wind turbine maintenance logs, a resource largely inaccessible to traditional quantitative reliability analysis. While machine learning has been applied to this data, existing approaches typically stop at classification, categorising text into predefined labels. This paper addresses the gap in leveraging modern large language models (LLMs) for more complex reasoning tasks. We introduce an exploratory framework t…

Optimal Portfolio Construction -- A Reinforcement Learning Embedded Bayesian Hierarchical Risk Parity (RL-BHRP) Approach
Shaofeng Kang, Zeying Tian
https://arxiv.org/abs/2508.11856

Optimal Portfolio Construction -- A Reinforcement Learning Embedded Bayesian Hierarchical Risk Parity (RL-BHRP) Approach
We propose a two-level, learning-based portfolio method (RL-BHRP) that spreads risk across sectors and stocks, and adjusts exposures as market conditions change. Using U.S. Equities from 2012 to mid-2025, we design the model using 2012 to 2019 data, and evaluate it out-of-sample from 2020 to 2025 against a sector index built from exchange-traded funds and a static risk-balanced portfolio. Over the test window, the adaptive portfolio compounds wealth by approximately 120 percent, compared with 1…

Multiple Giant Impacts and Chemical Equilibria: An Integrated Approach to Rocky Planet Formation
Haruya Maeda, Takanori Sasaki
https://arxiv.org/abs/2509.12713 https://

Multiple Giant Impacts and Chemical Equilibria: An Integrated Approach to Rocky Planet Formation
During the formation of rocky planets, the surface environments of growing protoplanets were dramatically different from those of present-day planets. The release of gravitational energy during accretion would have maintained a molten surface layer, forming a magma ocean. Simultaneously, sufficiently massive protoplanets could acquire hydrogen-rich proto-atmospheres by capturing gas from the protoplanetary disk. Chemical equilibration among the atmosphere, magma ocean, and iron core plays a key…

Tackling estimation risk in Kelly investing using options
Fabrizio Lillo, Piero Mazzarisi, Ioanna-Yvonni Tsaknaki
https://arxiv.org/abs/2508.18868 https://…

Tackling estimation risk in Kelly investing using options
The Kelly criterion provides a general framework for optimizing the growth rate of an investment portfolio over time by maximizing the expected logarithmic utility of wealth. However, the optimality condition of the Kelly criterion is highly sensitive to accurate estimates of the probabilities and investment payoffs. Estimation risk can lead to greatly suboptimal portfolios. In a simple binomial model, we show that the introduction of a European option in the Kelly framework can be used to cons…