Tootfinder

Warum Augmented Reality auf der Stelle tritt
Was hält Augmented Reality zurück? Der AR-Pionier Kharis O'Connell hat der Technologie den Rücken gekehrt und spricht offen über seine Desillusion.
https://www…

Designs from magic-augmented Clifford circuits
Yuzhen Zhang, Sagar Vijay, Yingfei Gu, Yimu Bao
https://arxiv.org/abs/2507.02828 https://

Designs from magic-augmented Clifford circuits
We introduce magic-augmented Clifford circuits -- architectures in which Clifford circuits are preceded and/or followed by constant-depth circuits of non-Clifford (``magic") gates -- as a resource-efficient way to realize approximate $k$-designs, with reduced circuit depth and usage of magic. We prove that shallow Clifford circuits, when augmented with constant-depth circuits of magic gates, can generate approximate unitary and state $k$-designs with $ε$ relative error. The total circuit depth…

Tout va bien :
"Le nombre de cas de dépression et de burnout entraînant une incapacité de travail de plus d'un an a augmenté de 44% entre 2018 et 2023, selon les chiffres publiés lundi par l'Institut national d'assurance maladie-invalidité (INAMI)."
#santé

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Empire of the Sun:
🎵 Awakening
#EmpireoftheSun
https://downstance.bandcamp.com/track/awakening-empire-of-the-sun
https://open.spotify.com/track/5Jqw0825zvMVexnevoFoBV

RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelines
Xiwei Xu, Hans Weytjens, Dawen Zhang, Qinghua Lu, Ingo Weber, Liming Zhu
https://arxiv.org/abs/2506.03401

RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelines
Recent studies show that 60% of LLM-based compound systems in enterprise environments leverage some form of retrieval-augmented generation (RAG), which enhances the relevance and accuracy of LLM (or other genAI) outputs by retrieving relevant information from external data sources. LLMOps involves the practices and techniques for managing the lifecycle and operations of LLM compound systems in production environments. It supports enhancing LLM systems through continuous operations and feedback …

A Newton Augmented Lagrangian Method for Symmetric Cone Programming with Complexity Analysis
Rui-Jin Zhang, Ruoyu Diao, Xin-Wei Liu, Yu-Hong Dai
https://arxiv.org/abs/2506.04802

A Newton Augmented Lagrangian Method for Symmetric Cone Programming with Complexity Analysis
Symmetric cone programming incorporates a broad class of convex optimization problems, including linear programming, second-order cone programming, and semidefinite programming. Although the augmented Lagrangian method (ALM) is well-suited for large-scale scenarios, its subproblems are often not second-order continuously differentiable, preventing direct use of classical Newton methods. To address this issue, we observe that barrier functions from interior-point methods (IPMs) naturally serve a…

USAD: An Unsupervised Data Augmentation Spatio-Temporal Attention Diffusion Network
Ying Yu, Hang Xiao, Siyao Li, Jiarui Li, Haotian Tang, Hanyu Liu, Chao Li
https://arxiv.org/abs/2507.02827

USAD: An Unsupervised Data Augmentation Spatio-Temporal Attention Diffusion Network
The primary objective of human activity recognition (HAR) is to infer ongoing human actions from sensor data, a task that finds broad applications in health monitoring, safety protection, and sports analysis. Despite proliferating research, HAR still faces key challenges, including the scarcity of labeled samples for rare activities, insufficient extraction of high-level features, and suboptimal model performance on lightweight devices. To address these issues, this paper proposes a comprehensi…

A Reference Architecture for Gamified Cultural Heritage Applications Leveraging Generative AI and Augmented Reality
Federico Martusciello, Henry Muccini, Antonio Bucchiarone
https://arxiv.org/abs/2506.04090

A Reference Architecture for Gamified Cultural Heritage Applications Leveraging Generative AI and Augmented Reality
The rapid advancement of Information and Communication Technologies is transforming Cultural Heritage access, experience, and preservation. However, many digital heritage applications lack interactivity, personalization, and adaptability, limiting user engagement and educational impact. This short paper presents a reference architecture for gamified cultural heritage applications leveraging generative AI and augmented reality. Gamification enhances motivation, artificial intelligence enables ad…

This https://arxiv.org/abs/2505.19746 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Improving Heart Rejection Detection in XPCI Images Using Synthetic Data Augmentation
Accurate identification of acute cellular rejection (ACR) in endomyocardial biopsies is essential for effective management of heart transplant patients. However, the rarity of high-grade rejection cases (3R) presents a significant challenge for training robust deep learning models. This work addresses the class imbalance problem by leveraging synthetic data generation using StyleGAN to augment the limited number of real 3R images. Prior to GAN training, histogram equalization was applied to sta…

Across Programming Language Silos: A Study on Cross-Lingual Retrieval-augmented Code Generation
Qiming Zhu, Jialun Cao, Xuanang Chen, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun, Shing-Chi Cheung
https://arxiv.org/abs/2506.03535

Across Programming Language Silos: A Study on Cross-Lingual Retrieval-augmented Code Generation
Current research on large language models (LLMs) with retrieval-augmented code generation (RACG) mainly focuses on single-language settings, leaving cross-lingual effectiveness and security unexplored. Multi-lingual RACG systems are valuable for migrating code-bases across programming languages (PLs), yet face risks from error (e.g. adversarial data corruption) propagation in cross-lingual transfer. We construct a dataset spanning 13 PLs with nearly 14k instances to explore utility and robustne…