Tootfinder

EnergyKit: iPhone-Apps sollen lernen, grünen Strom zu verwenden
Apple hat ein Framework vorgestellt, das aktuelle Stromnetzdaten an Apps liefert. Es nutzt ein Feature aus Apple Home, läuft allerdings zunächst nur in den USA.

A Comparative Analysis of Statistical and Machine Learning Models for Outlier Detection in Bitcoin Limit Order Books
Ivan Letteri
https://arxiv.org/abs/2507.14960

A Comparative Analysis of Statistical and Machine Learning Models for Outlier Detection in Bitcoin Limit Order Books
The detection of outliers within cryptocurrency limit order books (LOBs) is of paramount importance for comprehending market dynamics, particularly in highly volatile and nascent regulatory environments. This study conducts a comprehensive comparative analysis of robust statistical methods and advanced machine learning techniques for real-time anomaly identification in cryptocurrency LOBs. Within a unified testing environment, named AITA Order Book Signal (AITA-OBS), we evaluate the efficacy of…

Learning From the Past with Cascading Eligibility Traces
Tokiniaina Raharison Ralambomihanta, Ivan Anokhin, Roman Pogodin, Samira Ebrahimi Kahou, Jonathan Cornford, Blake Aaron Richards
https://arxiv.org/abs/2506.14598

Learning From the Past with Cascading Eligibility Traces
Animals often receive information about errors and rewards after a significant delay. For example, there is typically a delay of tens to hundreds of milliseconds between motor actions and visual feedback. The standard approach to handling delays in models of synaptic plasticity is to use eligibility traces. However, standard eligibility traces that decay exponentially mix together any events that happen during the delay, presenting a problem for any credit assignment signal that occurs with a s…

Detecting and Mitigating Reward Hacking in Reinforcement Learning Systems: A Comprehensive Empirical Study
Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma
https://arxiv.org/abs/2507.05619

Detecting and Mitigating Reward Hacking in Reinforcement Learning Systems: A Comprehensive Empirical Study
Reward hacking in Reinforcement Learning (RL) systems poses a critical threat to the deployment of autonomous agents, where agents exploit flaws in reward functions to achieve high scores without fulfilling intended objectives. Despite growing awareness of this problem, systematic detection and mitigation approaches remain limited. This paper presents a large-scale empirical study of reward hacking across diverse RL environments and algorithms. We analyze 15,247 training episodes across 15 RL e…

Machine Learning-Driven Compensation for Non-Ideal Channels in AWG-Based FBG Interrogator
Ivan A. Kazakov, Iana V. Kulichenko, Egor E. Kovalev, Angelina A. Treskova, Daria D. Barma, Kirill M. Malakhov, Arkady V. Shipulin
https://arxiv.org/abs/2506.13575

Machine Learning-Driven Compensation for Non-Ideal Channels in AWG-Based FBG Interrogator
We present an experimental study of a fiber Bragg grating (FBG) interrogator based on a silicon oxynitride (SiON) photonic integrated arrayed waveguide grating (AWG). While AWG-based interrogators are compact and scalable, their practical performance is limited by non-ideal spectral responses. To address this, two calibration strategies within a 2.4 nm spectral region were compared: (1) a segmented analytical model based on a sigmoid fitting function, and (2) a machine learning (ML)-based regre…

Constructing targeted minimum loss/maximum likelihood estimators: a simple illustration to build intuition
Rachael K. Ross, Lina M. Montoya, Dana E. Goin, Ivan Diaz, Audrey Renson
https://arxiv.org/abs/2507.11680

Constructing targeted minimum loss/maximum likelihood estimators: a simple illustration to build intuition
Use of machine learning to estimate nuisance functions (e.g. outcomes models, propensity score models) in estimators used in causal inference is increasingly common, as it can mitigate bias due to model misspecification. However, it can be challenging to achieve valid inference (e.g., estimate valid confidence intervals). The efficient influence function (EIF) provides a recipe to go from a statistical estimand relevant to our causal question, to an estimator that can validly incorporate machin…

SemanticST: Spatially Informed Semantic Graph Learning for1 Clustering, Integration, and Scalable Analysis of Spatial2 Transcriptomics
Roxana Zahedi, Ahmadreza Argha, Nona Farbehi, Ivan Bakhshayeshi, Youqiong Ye, Nigel H. Lovell, Hamid Alinejad-Rokny
https://arxiv.org/abs/2506.11491

SemanticST: Spatially Informed Semantic Graph Learning for1 Clustering, Integration, and Scalable Analysis of Spatial2 Transcriptomics
Spatial transcriptomics (ST) technologies enable gene expression profiling with spatial resolution, offering unprecedented insights into tissue organization and disease heterogeneity. However, current analysis methods often struggle with noisy data, limited scalability, and inadequate modelling of complex cellular relationships. We present SemanticST, a biologically informed, graph-based deep learning framework that models diverse cellular contexts through multi-semantic graph construction. Sem…

Leveraging machine learning features for linear optical interferometer control
Sergei S. Kuzmin, Ivan V. Dyakonov, Stanislav S. Straupe
https://arxiv.org/abs/2505.24032

Leveraging machine learning features for linear optical interferometer control
We have developed an algorithm that constructs a model of a reconfigurable optical interferometer, independent of specific architectural constraints. The programming of unitary transformations on the interferometer's optical modes relies on either an analytical method for deriving the unitary matrix from a set of phase shifts or an optimization routine when such decomposition is not available. Our algorithm employs a supervised learning approach, aligning the interferometer model with a trainin…

Self-learning signal classifier for decameter coherent scatter radars
Oleg Berngardt, Ivan Lavygin
https://arxiv.org/abs/2506.10305 https://

Self-learning signal classifier for decameter coherent scatter radars
The paper presents a method for automatic constructing a classifier for processed data obtained by decameter coherent scatter radars. Method is based only on the radar data obtained, the results of automatic modeling of radio wave propagation in the ionosphere, and mathematical criteria for estimating the quality of the models. The final classifier is the model trained at data obtained by 12 radars of the SuperDARN and SECIRA networks over two years for each radar. The number of the model coeff…

This https://arxiv.org/abs/2505.18565 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Learning Fluid-Structure Interaction Dynamics with Physics-Informed Neural Networks and Immersed Boundary Methods
We introduce neural network architectures that combine physics-informed neural networks (PINNs) with the immersed boundary method (IBM) to solve fluid-structure interaction (FSI) problems. Our approach features two distinct architectures: a Single-FSI network with a unified parameter space, and an innovative Eulerian-Lagrangian network that maintains separate parameter spaces for fluid and structure domains. We study each architecture using standard Tanh and adaptive B-spline activation functio…