Tootfinder

Lumos: Performance Characterization of WebAssembly as a Serverless Runtime in the Edge-Cloud Continuum
Cynthia Marcelino, Noah Krennmair, Thomas Pusztai, Stefan Nastic
https://arxiv.org/abs/2510.05118 …

Lumos: Performance Characterization of WebAssembly as a Serverless Runtime in the Edge-Cloud Continuum
WebAssembly has emerged as a lightweight and portable runtime to execute serverless functions, particularly in heterogeneous and resource-constrained environments such as the Edge Cloud Continuum. However, the performance benefits versus trade-offs remain insufficiently understood. This paper presents Lumos, a performance model and benchmarking tool for characterizing serverless runtimes. Lumos identifies workload, system, and environment-level performance drivers in the Edge-Cloud Continuum. W…

Inverse Portfolio Optimization with Synthetic Investor Data: Recovering Risk Preferences under Uncertainty
Jinho Cha, Long Pham, Thi Le Hoa Vo, Jaeyoung Cho, Jaejin Lee
https://arxiv.org/abs/2510.06986

Inverse Portfolio Optimization with Synthetic Investor Data: Recovering Risk Preferences under Uncertainty
This study develops an inverse portfolio optimization framework for recovering latent investor preferences including risk aversion, transaction cost sensitivity, and ESG orientation from observed portfolio allocations. Using controlled synthetic data, we assess the estimator's statistical properties such as consistency, coverage, and dynamic regret. The model integrates robust optimization and regret-based inference to quantify welfare losses under preference misspecification and market shocks.…

Beating the Best Constant Rebalancing Portfolio in Long-Term Investment: A Generalization of the Kelly Criterion and Universal Learning Algorithm for Markets with Serial Dependence
Duy Khanh Lam
https://arxiv.org/abs/2507.05994

Beating the Best Constant Rebalancing Portfolio in Long-Term Investment: A Generalization of the Kelly Criterion and Universal Learning Algorithm for Markets with Serial Dependence
In the online portfolio optimization framework, existing learning algorithms generate strategies that yield significantly poorer cumulative wealth compared to the best constant rebalancing portfolio in hindsight, despite being consistent in asymptotic growth rate. While this unappealing performance can be improved by incorporating more side information, it raises difficulties in feature selection and high-dimensional settings. Instead, the inherent serial dependence of assets' returns, such as …

Universal Embeddings of Tabular Data
Astrid Franz, Frederik Hoppe, Marianne Michaelis, Udo G\"obel
https://arxiv.org/abs/2507.05904 https://

Universal Embeddings of Tabular Data
Tabular data in relational databases represents a significant portion of industrial data. Hence, analyzing and interpreting tabular data is of utmost importance. Application tasks on tabular data are manifold and are often not specified when setting up an industrial database. To address this, we present a novel framework for generating universal, i.e., task-independent embeddings of tabular data for performing downstream tasks without predefined targets. Our method transforms tabular data into …

Leading & Trailing Spiral Arms in a Nearly Broken Protoplanetary Disc
Sahl Rowther, Rebecca Nealon, Richard Alexander, Farzana Meru
https://arxiv.org/abs/2508.04914 https://…

Leading & Trailing Spiral Arms in a Nearly Broken Protoplanetary Disc
We perform three-dimensional smoothed particle hydrodynamics simulations to investigate the formation of spiral arms in misaligned circumbinary discs. In a nearly broken disc the misaligned inner and outer discs interact at two nodes, launching leading spiral arms that do not rotate with the disc. These spirals vanish when the disc is fully broken or aligned. Our results show that the formation of leading spirals is driven by the relative misalignment of the inner and outer disc, and does not d…

Performance Analysis for Multi-User Holographic MIMO Downlink with Matched Filter Precoding
Gayathri Shekar, Saman Atapattu, Prathapasinghe Dharmawansa, Kandeepan Sithamparanathan
https://arxiv.org/abs/2510.04530

Performance Analysis for Multi-User Holographic MIMO Downlink with Matched Filter Precoding
Holographic MIMO (HMIMO) has emerged as a promising solution for future wireless systems by enabling ultra-dense, spatially continuous antenna deployments. While prior studies have primarily focused on electromagnetic (EM) modeling or simulation-based performance analysis, a rigorous communication-theoretic framework remains largely unexplored. This paper presents the first analytical performance study of a multi-user HMIMO downlink system with matched filter (MF) precoding - a low-complexity b…

M3Retrieve: Benchmarking Multimodal Retrieval for Medicine
Arkadeep Acharya, Akash Ghosh, Pradeepika Verma, Kitsuchart Pasupa, Sriparna Saha, Priti Singh
https://arxiv.org/abs/2510.06888

M3Retrieve: Benchmarking Multimodal Retrieval for Medicine
With the increasing use of RetrievalAugmented Generation (RAG), strong retrieval models have become more important than ever. In healthcare, multimodal retrieval models that combine information from both text and images offer major advantages for many downstream tasks such as question answering, cross-modal retrieval, and multimodal summarization, since medical data often includes both formats. However, there is currently no standard benchmark to evaluate how well these models perform in medica…

from my link log —
VBARE / versioned BARE: A simple alternative to Protobuf & Cap'n Proto for schema evolution.
https://www.rivet.dev/blog/2025-09-24-vbare-simple-schema-evolution-with-maximum-performance
saved 2025-…

VBARE: A simple alternative to Protobuf & Cap'n Proto for schema evolution
At Rivet, we're building an open-source alternative to Cloudflare Durable Objects — a tool for running stateful compute workloads. VBARE is a small but crucial component in meeting the demanding performance requirements of Rivet Actors.

Replaced article(s) found for cs.PF. https://arxiv.org/list/cs.PF/new
[1/1]:
- Taking GPU Programming Models to Task for Performance Portability
Davis, Sivaraman, Kitson, Parasyris, Menon, Minn, Georgakoudis, Bhatele

A Flexible Programmable Pipeline Parallelism Framework for Efficient DNN Training
Lijuan Jiang, Xingjian Qian, Zhenxiang Ma, Zan Zong, Hengjie Li, Chao Yang, Jidong Zhai
https://arxiv.org/abs/2510.05112

A Flexible Programmable Pipeline Parallelism Framework for Efficient DNN Training
Pipeline parallelism is an essential distributed parallelism method. Increasingly complex and diverse DNN models necessitate meticulously customized pipeline schedules for performance. However, existing practices typically rely on predefined schedules, each with strengths, but fail to adapt automatically to the emerging model architectures. Exploring novel high-efficiency schedules is daunting due to the enormous and varying schedule space. Besides, manually implementing schedules can be challe…