Tootfinder

De afdeling klimaat van CEO, Corporate Europe Observatory, heeft lezenwaardige stukken. Het eerste stuk gaat over de Europese plannen voor groene waterstof en de lobby van Big Oil om daar met hun vuile handen aan te zitten en zo bakken subsidie binnen te harken.
https://corporateeurope.org/en/climate

A 66-Gb/s/5.5-W RISC-V Many-Core Cluster for 5G Software-Defined Radio Uplinks
Marco Bertuletti, Yichao Zhang, Alessandro Vanelli-Coralli, Luca Benini
https://arxiv.org/abs/2508.06176

A 66-Gb/s/5.5-W RISC-V Many-Core Cluster for 5G+ Software-Defined Radio Uplinks
Following the scale-up of new radio (NR) complexity in 5G and beyond, the physical layer's computing load on base stations is increasing under a strictly constrained latency and power budget; base stations must process > 20-Gb/s uplink wireless data rate on the fly, in < 10 W. At the same time, the programmability and reconfigurability of base station components are the key requirements; it reduces the time and cost of new networks' deployment, it lowers the acceptance threshold for industry pl…

Surface Induced Frustration of Inherent Dipolar Order in Nanoconfined Water
Sayantan Mondal, Saumyak Mukherjee, Biman Bagchi
https://arxiv.org/abs/2506.09809

Surface Induced Frustration of Inherent Dipolar Order in Nanoconfined Water
Surface effects could play a dominant role in modifying the natural liquid order. In some cases, the effects of the surface interactions can propagate inwards, and even can interfere with a similar propagation from opposite surfaces. This can be particularly evident in liquid water under nano-confinement. The large dipolar cross-correlations among distinct molecules that give rise to the unusually large dielectric constant of water (and in turn owe their origin to the extended hydrogen bond (HB…

Relative discrepancy of hypergraphs
Diep Luong-Le, Tuan Tran, Dilong Yang
https://arxiv.org/abs/2506.23264 https://arxiv.org/pdf/2506…

Relative discrepancy of hypergraphs
Given $k$-uniform hypergraphs $G$ and $H$ on $n$ vertices with densities $p$ and $q$, their relative discrepancy is defined as $\hbox{disc}(G,H)=\max\big||E(G')\cap E(H')|-pq\binom{n}{k}\big|$, where the maximum ranges over all pairs $G',H'$ with $G'\cong G$, $H'\cong H$, and $V(G')=V(H')$. Let $\hbox{bs}(k)$ denote the smallest integer $m \ge 2$ such that any collection of $m$ $k$-uniform hypergraphs on $n$ vertices with moderate densities contains a pair $G,H$ for which $\…

A Bayesian approach to modelling spectrometer data chromaticity corrected using beam factors -- II. Model priors and posterior odds
Peter H. Sims, Judd D. Bowman, Steven G. Murray, John P. Barrett, Rigel C. Cappallo, Colin J. Lonsdale, Nivedita Mahesh, Raul A. Monsalve, Alan E. E. Rogers, Titu Samson, Akshatha K. Vydula
https://…

A Bayesian approach to modelling spectrometer data chromaticity corrected using beam factors -- II. Model priors and posterior odds
The reliable detection of the global 21-cm signal, a key tracer of Cosmic Dawn and the Epoch of Reionization, requires meticulous data modelling and robust statistical frameworks for model validation and comparison. In Paper I of this series, we presented the Beam-Factor-based Chromaticity Correction (BFCC) model for spectrometer data processed using BFCC to suppress instrumentally induced spectral structure. We demonstrated that the BFCC model, with complexity calibrated by Bayes factor-based …

HARLF: Hierarchical Reinforcement Learning and Lightweight LLM-Driven Sentiment Integration for Financial Portfolio Optimization
Benjamin Coriat, Eric Benhamou
https://arxiv.org/abs/2507.18560

HARLF: Hierarchical Reinforcement Learning and Lightweight LLM-Driven Sentiment Integration for Financial Portfolio Optimization
This paper presents a novel hierarchical framework for portfolio optimization, integrating lightweight Large Language Models (LLMs) with Deep Reinforcement Learning (DRL) to combine sentiment signals from financial news with traditional market indicators. Our three-tier architecture employs base RL agents to process hybrid data, meta-agents to aggregate their decisions, and a super-agent to merge decisions based on market data and sentiment analysis. Evaluated on data from 2018 to 2024, after t…