Tootfinder

Temporal Network Analysis of Microservice Architectural Degradation
Alexander Bakhtin
https://arxiv.org/abs/2508.11571 https://arxiv.org/pdf/2508.11571

Temporal Network Analysis of Microservice Architectural Degradation
Microservice architecture can be modeled as a network of microservices making calls to each other, commonly known as the service dependency graph. Network Science can provide methods to study such networks. In particular, temporal network analysis is a branch of Network Science that analyzes networks evolving with time. In microservice systems, temporal networks can arise if we examine the architecture of the system across releases or monitor a deployed system using tracing. In this research …

An Efficient Medical Image Classification Method Based on a Lightweight Improved ConvNeXt-Tiny Architecture
Jingsong Xia, Yue Yin, Xiuhan Li
https://arxiv.org/abs/2508.11532 htt…

An Efficient Medical Image Classification Method Based on a Lightweight Improved ConvNeXt-Tiny Architecture
Intelligent analysis of medical imaging plays a crucial role in assisting clinical diagnosis. However, achieving efficient and high-accuracy image classification in resource-constrained computational environments remains challenging. This study proposes a medical image classification method based on an improved ConvNeXt-Tiny architecture. Through structural optimization and loss function design, the proposed method enhances feature extraction capability and classification performance while redu…

The AI Shadow War: SaaS vs. Edge Computing Architectures
Rhea Pritham Marpu, Kevin J McNamara, Preeti Gupta
https://arxiv.org/abs/2507.11545 https://

The AI Shadow War: SaaS vs. Edge Computing Architectures
The very DNA of AI architecture presents conflicting paths: centralized cloud-based models (Software-as-a-Service) versus decentralized edge AI (local processing on consumer devices). This paper analyzes the competitive battleground across computational capability, energy efficiency, and data privacy. Recent breakthroughs show edge AI challenging cloud systems on performance, leveraging innovations like test-time training and mixture-of-experts architectures. Crucially, edge AI boasts a 10,000x…

Cross-Platform Scaling of Vision-Language-Action Models from Edge to Cloud GPUs
Amir Taherin, Juyi Lin, Arash Akbari, Arman Akbari, Pu Zhao, Weiwei Chen, David Kaeli, Yanzhi Wang
https://arxiv.org/abs/2509.11480

Cross-Platform Scaling of Vision-Language-Action Models from Edge to Cloud GPUs
Vision-Language-Action (VLA) models have emerged as powerful generalist policies for robotic control, yet their performance scaling across model architectures and hardware platforms, as well as their associated power budgets, remain poorly understood. This work presents an evaluation of five representative VLA models -- spanning state-of-the-art baselines and two newly proposed architectures -- targeting edge and datacenter GPU platforms. Using the LIBERO benchmark, we measure accuracy alongsid…

BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search
Azhar Ikhtiarudin, Aditi Das, Param Thakkar, Akash Kundu
https://arxiv.org/abs/2507.12189

BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search
We introduce BenchRL-QAS, a unified benchmarking framework for systematically evaluating reinforcement learning (RL) algorithms in quantum architecture search (QAS) across diverse variational quantum algorithm tasks and system sizes ranging from 2- to 8-qubit. Our study benchmarks nine RL agents including both value-based and policy-gradient methods on representative quantum problems such as variational quantum eigensolver, variational quantum state diagonalization, quantum classification, and …

Stealing Accuracy: Predicting Day-ahead Electricity Prices with Temporal Hierarchy Forecasting (THieF)
Arkadiusz Lipiecki, Kaja Bilinska, Nicolaos Kourentzes, Rafal Weron
https://arxiv.org/abs/2508.11372

Stealing Accuracy: Predicting Day-ahead Electricity Prices with Temporal Hierarchy Forecasting (THieF)
We introduce the concept of temporal hierarchy forecasting (THieF) in predicting day-ahead electricity prices and show that reconciling forecasts for hourly products, 2- to 12-hour blocks, and baseload contracts significantly (up to 13%) improves accuracy at all levels. These results remain consistent throughout a challenging 4-year test period (2021-2024) in the German power market and across model architectures, including linear regression, a shallow neural network, gradient boosting, and a s…

Mixture of Raytraced Experts
Andrea Perin, Giacomo Lagomarsini, Claudio Gallicchio, Giuseppe Nuti
https://arxiv.org/abs/2507.12419 https://

Mixture of Raytraced Experts
We introduce a Mixture of Raytraced Experts, a stacked Mixture of Experts (MoE) architecture which can dynamically select sequences of experts, producing computational graphs of variable width and depth. Existing MoE architectures generally require a fixed amount of computation for a given sample. Our approach, in contrast, yields predictions with increasing accuracy as the computation cycles through the experts' sequence. We train our model by iteratively sampling from a set of candidate exper…

A Modular PyTheus Quantum Network Interpreter: Automated Analysis and Visualization of Optimized Quantum Architectures
S. K. Rithvik
https://arxiv.org/abs/2507.12997

A Modular PyTheus Quantum Network Interpreter: Automated Analysis and Visualization of Optimized Quantum Architectures
We present a modular interpreter for PyTheus-optimized quantum networks that automatically analyzes and visualizes complex quantum architectures discovered through automated optimization. The interpreter addresses the critical challenge of understanding machine-designed quantum networks by providing robust algorithms for functional role identification, graph-theoretical analysis, and physically meaningful visualization across the major classes of PyTheus-generated networks. Our interpreter acce…

Curriculum Multi-Task Self-Supervision Improves Lightweight Architectures for Onboard Satellite Hyperspectral Image Segmentation
Hugo Carlesso, Josiane Mothe, Radu Tudor Ionescu
https://arxiv.org/abs/2509.13229

Curriculum Multi-Task Self-Supervision Improves Lightweight Architectures for Onboard Satellite Hyperspectral Image Segmentation
Hyperspectral imaging (HSI) captures detailed spectral signatures across hundreds of contiguous bands per pixel, being indispensable for remote sensing applications such as land-cover classification, change detection, and environmental monitoring. Due to the high dimensionality of HSI data and the slow rate of data transfer in satellite-based systems, compact and efficient models are required to support onboard processing and minimize the transmission of redundant or low-value data, e.g. cloud-…

A Proposal for High-Level Architectural Model Capable of Expressing Various Data Collaboration Platform and Data Space Concepts
Masaru Dobashi (NTT DATA Corporation), Kohei Toshimitsu (NTT DATA Corporation), Hirotsugu Seike (Koshizuka Laboratory), Miki Kanno (NTT DATA Corporation), Genki Horie (Koshizuka Laboratory), Noboru Koshizuka (Koshizuka Laboratory)
http…

A Proposal for High-Level Architectural Model Capable of Expressing Various Data Collaboration Platform and Data Space Concepts
This paper proposes "Data Space High-Level Architecture Model" (DS-HLAM) for expressing diverse data collaboration platforms across regional implementations. The framework introduces mathematically rigorous definitions with success conditions formalized through finite state automata theory, enabling interoperability while preserving digital sovereignty requirements.