Tootfinder

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff
Paul Scherer, Andreas Kirsch, Jake P. Taylor-King
https://arxiv.org/abs/2509.04363

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff
Real-world experimental scenarios are characterized by the presence of heteroskedastic aleatoric uncertainty, and this uncertainty can be correlated in batched settings. The bias--variance tradeoff can be used to write the expected mean squared error between a model distribution and a ground-truth random variable as the sum of an epistemic uncertainty term, the bias squared, and an aleatoric uncertainty term. We leverage this relationship to propose novel active learning strategies that directl…

Bills CB Tre'Davious White 'doubtful' for season opener against Ravens

https://www.cbssports.com/nfl/news/bills-cb-tredavious-white-doubtful-for-seaso…

from my link log —
Build system tradeoffs.
https://jyn.dev/build-system-tradeoffs
saved 2025-11-02 https://dotat.at/:/5ZUHD.html

Tradeoffs on the volume of fault-tolerant circuits
Anirudh Krishna, Gilles Z\'emor
https://arxiv.org/abs/2510.03057 https://arxiv.org/pdf/2510.03057

Tradeoffs on the volume of fault-tolerant circuits
Dating back to the seminal work of von Neumann [von Neumann, Automata Studies, 1956], it is known that error correcting codes can overcome faulty circuit components to enable robust computation. Choosing an appropriate code is non-trivial as it must balance several requirements. Increasing the rate of the code reduces the relative number of redundant bits used in the fault-tolerant circuit, while increasing the distance of the code ensures robustness against faults. If the rate and distance wer…

Hardware-Aware Data and Instruction Mapping for AI Tasks: Balancing Parallelism, I/O and Memory Tradeoffs
Md Rownak Hossain Chowdhury, Mostafizur Rahman
https://arxiv.org/abs/2509.03846

Hardware-Aware Data and Instruction Mapping for AI Tasks: Balancing Parallelism, I/O and Memory Tradeoffs
We introduce a mapping framework for deep learning inference that takes advantage of predictable neural network behavior to plan both computation and communication ahead of time. The framework generates a unified stream of instructions and data, enabling the hardware to execute operations and route information on its own, without frequent involvement from the host and with minimal off-chip memory use. This naturally reduces reliance on I/O, off-chip memory, and host control. By leveraging fine-…

Was mir ja auch nicht so bewusst war: wie sehr ich es mag, dass in egal was für Zügen Ausstiegshöhe und Perronhöhe (in Bussen Trottoirkante) übereinstimmen und die letzte Stufe sich wirklich zu einer vollen Trittfläche ausfährt. Danke #SBB

Ein herbstlicher #Schnappschuss aus dem Volkspark #Rehberge bei strahlendem Sonnenschein. Hier lässt es sich hervorragend #Spazieren,

Algorithmic Tradeoff Exploration for Component Placement and Wire Routing in Nanomodular Electronics
Peidi Song, Alexandros Daglis, Michael Filler, Ahmed Saeed
https://arxiv.org/abs/2510.03126

Algorithmic Tradeoff Exploration for Component Placement and Wire Routing in Nanomodular Electronics
Advances in fabrication technology have enabled modularizing electronic components at the micro- or nano-scale and composing these modules on demand into larger circuits. Micromodular and nanomodular electronics (ME and NE) open a new design space in electronics, promising a degree of flexibility, extensibility, and accessibility far superior to traditional monolithic methods. ME/NE leverage a multi-stage process of initial imprecise component deposition, followed by precise wire printing to co…

Lagrange-Mesh Method in Momentum Space: an Alternative Formulation
Cyrille Chevalier, Joachim Viseur
https://arxiv.org/abs/2510.03015 https://arxiv.org/pdf…

Lagrange-Mesh Method in Momentum Space: an Alternative Formulation
This work presents a new methodology for computing potential matrix elements within the Lagrange-mesh method in momentum space. The proposed approach extends the range of treatable potentials to include previously inaccessible cases, such as Coulomb and linear interactions. The method is validated across a variety of systems. A particular attention is given to the representation of both momentum and position probability densities.

Rethinking Layer-wise Gaussian Noise Injection: Bridging Implicit Objectives and Privacy Budget Allocation
Qifeng Tan (Xi'an Jiaotong University), Shusen Yang (Xi'an Jiaotong University), Xuebin Ren (Xi'an Jiaotong University), Yikai Zhang (Xi'an Jiaotong University)
https://arxiv.org/abs/2509.04232

Rethinking Layer-wise Gaussian Noise Injection: Bridging Implicit Objectives and Privacy Budget Allocation
Layer-wise Gaussian mechanisms (LGM) enhance flexibility in differentially private deep learning by injecting noise into partitioned gradient vectors. However, existing methods often rely on heuristic noise allocation strategies, lacking a rigorous understanding of their theoretical grounding in connecting noise allocation to formal privacy-utility tradeoffs. In this paper, we present a unified analytical framework that systematically connects layer-wise noise injection strategies with their im…