Tootfinder

Root Cause Analysis of Outliers in Unknown Cyclic Graphs
Daniela Schkoda, Dominik Janzing
https://arxiv.org/abs/2510.06995 https://arxiv.org/pdf/2510.06995…

Root Cause Analysis of Outliers in Unknown Cyclic Graphs
We study the propagation of outliers in cyclic causal graphs with linear structural equations, tracing them back to one or several "root cause" nodes. We show that it is possible to identify a short list of potential root causes provided that the perturbation is sufficiently strong and propagates according to the same structural equations as in the normal mode. This shortlist consists of the true root causes together with those of its parents lying on a cycle with the root cause. Notably, our m…

ErrorPrism: Reconstructing Error Propagation Paths in Cloud Service Systems
Junsong Pu, Yichen Li, Zhuangbin Chen, Jinyang Liu, Zhihan Jiang, Jianjun Chen, Rui Shi, Zibin Zheng, Tieying Zhang
https://arxiv.org/abs/2509.26463

ErrorPrism: Reconstructing Error Propagation Paths in Cloud Service Systems
Reliability management in cloud service systems is challenging due to the cascading effect of failures. Error wrapping, a practice prevalent in modern microservice development, enriches errors with context at each layer of the function call stack, constructing an error chain that describes a failure from its technical origin to its business impact. However, this also presents a significant traceability problem when recovering the complete error propagation path from the final log message back t…

Integrated broadband optical isolator via dynamic rotating destructive interference
Kyunghun Han, Yiliang Bao, Junyeob Song, David Long, Sean Bresler, Daron Westly, Jason Gorman, Thomas LeBrun, Kartik Srinivasan, Vladimir Aksyuk
https://arxiv.org/abs/2509.02866

Integrated broadband optical isolator via dynamic rotating destructive interference
Photonic integrated circuits route and shape light on a chip, but back-reflections feed back into coherent on-chip lasers, destabilizing operation and corrupting signals. Robust operation requires an integrated optical isolator that strongly suppresses backward propagation while maintaining low-loss, broadband forward transmission. However, prior on-chip isolators rely on magneto-optic materials or resonance-based filters, which respectively demand non-standard processes or inherently constrain…

Statistical Multiport-Network Modeling and Efficient Discrete Optimization of RIS
Cheima Hammami, Luc Le Magoarou, Philipp del Hougne
https://arxiv.org/abs/2508.01776 https://…

Statistical Multiport-Network Modeling and Efficient Discrete Optimization of RIS
This Letter fills the research gap on physics-consistent optimization for reconfigurable intelligent surfaces (RISs) with mutual coupling (MC) and 1-bit-tunable elements, a common hardware constraint in existing RIS prototypes. We compare a model-based method (temperature-annealed back-propagation) and model-agnostic methods (coordinate descent, genetic algorithm), and evaluate potential benefits of intelligently initializing these methods. To facilitate our evaluation, we introduce a technique…

Designing Coherent Optical Environment for Dynamic Optical Manipulation with a Simple Control Beam
Xiaoshu Zhao, Xu Yuan, Hongxia Zheng, Huajin Chen, Zhifang Lin
https://arxiv.org/abs/2510.04228

Designing Coherent Optical Environment for Dynamic Optical Manipulation with a Simple Control Beam
We propose a framework for designing coherent optical environments that enable versatile and dynamic optical manipulation. In contrast to conventional material-based near-field platforms, our approach employs a structured coherent light field -- optimized via a back-propagation-based inverse design algorithm -- as the manipulation environment. This light-based platform allows a simple control beam, such as a single plane wave or a low-numerical-aperture Gaussian beam, to steer micro-objects eff…

Neural Architecture Search with Mixed Bio-inspired Learning Rules
Imane Hamzaoui, Riyadh Baghdadi
https://arxiv.org/abs/2507.13485 https://

Neural Architecture Search with Mixed Bio-inspired Learning Rules
Bio-inspired neural networks are attractive for their adversarial robustness, energy frugality, and closer alignment with cortical physiology, yet they often lag behind back-propagation (BP) based models in accuracy and ability to scale. We show that allowing the use of different bio-inspired learning rules in different layers, discovered automatically by a tailored neural-architecture-search (NAS) procedure, bridges this gap. Starting from standard NAS baselines, we enlarge the search space to…

Replaced article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[2/2]:
- NoProp: Training Neural Networks without Full Back-propagation or Full Forward-propagation
Qinyu Li, Yee Whye Teh, Razvan Pascanu