Tootfinder

This https://arxiv.org/abs/2505.00546 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Directly Forecasting Belief for Reinforcement Learning with Delays
Reinforcement learning (RL) with delays is challenging as sensory perceptions lag behind the actual events: the RL agent needs to estimate the real state of its environment based on past observations. State-of-the-art (SOTA) methods typically employ recursive, step-by-step forecasting of states. This can cause the accumulation of compounding errors. To tackle this problem, our novel belief estimation method, named Directly Forecasting Belief Transformer (DFBT), directly forecasts states from ob…

A New Lifetime Distribution: Exponentiated Exponential-Pareto-HalfNormal Mixture Model for Biomedical Applications
Oriyomi Ahmad Hassan, Aisha Tunrayo Maradesa, Abdulazeez Toyosi Alabi, Oyejide Surajudeen Salam, Ajani Busari, Akinwale Victor Famotire, Habeeb Abiodun Afolabi, Solomon Adeleke, Abayomi Ayodele Akomolafe
https://arx…

A New Lifetime Distribution: Exponentiated Exponential-Pareto-HalfNormal Mixture Model for Biomedical Applications
This study introduces the Exponentiated-Exponential-Pareto-Half Normal Mixture Distribution (EEPHND), a novel hybrid model developed to overcome the limitations of classical distributions in modeling complex real-world data. By compounding the Exponentiated-Exponential-Pareto (EEP) and Half-Normal distributions through a mixture mechanism, EEPHND effectively captures both early-time symmetry and long-tail behavior, features which are commonly observed in survival and reliability data. The model…

Pseudo-Simulation for Autonomous Driving
Wei Cao, Marcel Hallgarten, Tianyu Li, Daniel Dauner, Xunjiang Gu, Caojun Wang, Yakov Miron, Marco Aiello, Hongyang Li, Igor Gilitschenski, Boris Ivanovic, Marco Pavone, Andreas Geiger, Kashyap Chitta
https://arxiv.org/abs/2506.04218

Pseudo-Simulation for Autonomous Driving
Existing evaluation paradigms for Autonomous Vehicles (AVs) face critical limitations. Real-world evaluation is often challenging due to safety concerns and a lack of reproducibility, whereas closed-loop simulation can face insufficient realism or high computational costs. Open-loop evaluation, while being efficient and data-driven, relies on metrics that generally overlook compounding errors. In this paper, we propose pseudo-simulation, a novel paradigm that addresses these limitations. Pseudo…

Lightweight Physics-Informed Zero-Shot Ultrasound Plane Wave Denoising
Hojat Asgariandehkordi, Mostafa Sharifzadeh, Hassan Rivaz
https://arxiv.org/abs/2506.21499

Lightweight Physics-Informed Zero-Shot Ultrasound Plane Wave Denoising
Ultrasound Coherent Plane Wave Compounding (CPWC) enhances image contrast by combining echoes from multiple steered transmissions. While increasing the number of angles generally improves image quality, it drastically reduces the frame rate and can introduce blurring artifacts in fast-moving targets. Moreover, compounded images remain susceptible to noise, particularly when acquired with a limited number of transmissions. We propose a zero-shot denoising framework tailored for low-angle CPWC ac…

My favorite historical mystery is “what really happened in the 6th century???”
We have few clues. We know there were at least 2 big eruptions in the 530s, most likely in Iceland, Kamchatka, or Alaska: nowhere with a surviving writing culture. Then a wave of Plague. Then probably other shit no one bothered writing down. It shattered the recording of history for many decades. We will never have a comprehensive history of the “Dark Ages” because no one wrote the pieces.

Making Leveraged Exchange-Traded Funds Work for your Portfolio
Peter Forsyth, Pieter van Staden, Yuying Li
https://arxiv.org/abs/2506.19200 https://…

Making Leveraged Exchange-Traded Funds Work for your Portfolio
We examine strategically incorporating broad stock market leveraged exchange-traded funds (LETFs) into investment portfolios. We demonstrate that easily understandable and implementable strategies can enhance the risk-return profile of a portfolio containing LETFs. Our analysis shows that seemingly reasonable investment strategies may result in undesirable Omega ratios, with these effects compounding across rebalancing periods. By contrast, relatively simple dynamic strategies that systematical…

scSiameseClu: A Siamese Clustering Framework for Interpreting single-cell RNA Sequencing Data
Ping Xu, Zhiyuan Ning, Pengjiang Li, Wenhao Liu, Pengyang Wang, Jiaxu Cui, Yuanchun Zhou, Pengfei Wang
https://arxiv.org/abs/2505.12626

scSiameseClu: A Siamese Clustering Framework for Interpreting single-cell RNA Sequencing Data
Single-cell RNA sequencing (scRNA-seq) reveals cell heterogeneity, with cell clustering playing a key role in identifying cell types and marker genes. Recent advances, especially graph neural networks (GNNs)-based methods, have significantly improved clustering performance. However, the analysis of scRNA-seq data remains challenging due to noise, sparsity, and high dimensionality. Compounding these challenges, GNNs often suffer from over-smoothing, limiting their ability to capture complex biol…