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Variance Reduced Policy Gradient Method for Multi-Objective Reinforcement Learning
Davide Guidobene, Lorenzo Benedetti, Diego Arapovic
https://arxiv.org/abs/2508.10608 https://

Variance Reduced Policy Gradient Method for Multi-Objective Reinforcement Learning
Multi-Objective Reinforcement Learning (MORL) is a generalization of traditional Reinforcement Learning (RL) that aims to optimize multiple, often conflicting objectives simultaneously rather than focusing on a single reward. This approach is crucial in complex decision-making scenarios where agents must balance trade-offs between various goals, such as maximizing performance while minimizing costs. We consider the problem of MORL where the objectives are combined using a non-linear scalarizati…

🇺🇦 Auf radioeins läuft...
The Passions:
🎵 I'm In Love With A German Film Star
#NowPlaying #ThePassions
https://theboojays.bandcamp.com/track/im-in-love-with-a-german-film-star-passions
https://open.spotify.com/track/7A3rywP8wA9HbL36NMnD81

🇺🇦 #NowPlaying on BBCRadio3's #EssentialClassics
Arvo Pärt, Philharmonia Orchestra & Neeme Järvi:
🎵 Fratres
#ArvoPärt #PhilharmoniaOrchestra #NeemeJärvi
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Competitive Adsorption of Toluene and Water in MFI-type Zeolites
Gavriel Arbiv, Sambhu Radhakrishnan, Alysson F. Morais, C. Vinod Chandran, Dries Vandenabeele, Dirk Dom, Karel Duerinckx, Christine E. A. Kirschhock, Eric Breynaert
https://arxiv.org/abs/2509.10219

Competitive Adsorption of Toluene and Water in MFI-type Zeolites
Competitive adsorption is a major challenge in understanding catalytic activity, selectivity and reaction mechanisms in confined environments such as zeolites. This study investigated competitive adsorption in MFI-type zeolites (ZSM-5) using solid-state NMR, focusing on the interplay between toluene and water. Quantitative 1H NMR spectroscopy identified three distinct populations of adsorbed toluene evolving with increasing toluene loading. The adsorption behavior was consistent across a series…

Software Defect Prediction using Autoencoder Transformer Model
Seshu Barma, Mohanakrishnan Hariharan, Satish Arvapalli
https://arxiv.org/abs/2510.10840 https://

Software Defect Prediction using Autoencoder Transformer Model
An AI-ML-powered quality engineering approach uses AI-ML to enhance software quality assessments by predicting defects. Existing ML models struggle with noisy data types, imbalances, pattern recognition, feature extraction, and generalization. To address these challenges, we develop a new model, Adaptive Differential Evolution (ADE) based Quantum Variational Autoencoder-Transformer (QVAET) Model (ADE-QVAET). ADE combines with QVAET to obtain high-dimensional latent features and maintain sequent…

Fear of flying therapy is about reframing the flight experience;
if my mind’s eye squints,
I can almost interpret my sweating palms and pounding heartbeat as a kind of buzz.
But this requires repeated, strenuous effort,
and the result is a mental exhaustion that makes the worst-case scenario appealing:
succumbing to my fears and never flying again.
I imagine this as analogous to what many Americans, aerophobic or not, are feeling.
It’s simply easier…

Winging It | Noelle Mateer
For those trying to overcome their fears of flying, it’s been . . . a challenging time to say the least.

Social media is dead – here’s what comes next - New Scientist https://apple.news/Arpwv7NmsT7uY1vStyjNfRg

Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration
Mohanakrishnan Hariharan, Satish Arvapalli, Seshu Barma, Evangeline Sheela
https://arxiv.org/abs/2510.10824

Agentic RAG for Software Testing with Hybrid Vector-Graph and Multi-Agent Orchestration
We present an approach to software testing automation using Agentic Retrieval-Augmented Generation (RAG) systems for Quality Engineering (QE) artifact creation. We combine autonomous AI agents with hybrid vector-graph knowledge systems to automate test plan, case, and QE metric generation. Our approach addresses traditional software testing limitations by leveraging LLMs such as Gemini and Mistral, multi-agent orchestration, and enhanced contextualization. The system achieves remarkable accurac…