Tootfinder

Regression Discontinuity Designs for Functional Data and Random Objects in Geodesic Spaces
Daisuke Kurisu, Yidong Zhou, Taisuke Otsu, Hans-Georg M\"uller
https://arxiv.org/abs/2506.18136

Regression Discontinuity Designs for Functional Data and Random Objects in Geodesic Spaces
Regression discontinuity designs have been widely used in observational studies to estimate causal effects of an intervention or treatment at a cutoff point. We propose a generalization of regression discontinuity designs to handle complex non-Euclidean outcomes, such as networks, compositional data, functional data, and other random objects residing in geodesic metric spaces. A key challenge in this setting is the absence of algebraic operations, which makes it difficult to define treatment ef…

On the Linear Speedup of the Push-Pull Method for Decentralized Optimization over Digraphs
Liyuan Liang, Gan Luo, Kun Yuan
https://arxiv.org/abs/2506.18075

On the Linear Speedup of the Push-Pull Method for Decentralized Optimization over Digraphs
The linear speedup property is essential for demonstrating the advantage of distributed algorithms over their single-node counterparts. In this paper, we study the stochastic Push-Pull method, a widely adopted decentralized optimization algorithm over directed graphs (digraphs). Unlike methods that rely solely on row-stochastic or column-stochastic mixing matrices, Push-Pull avoids nonlinear correction and has shown superior empirical performance across a variety of settings. However, its theor…

Rethinking LLM Training through Information Geometry and Quantum Metrics
Riccardo Di Sipio
https://arxiv.org/abs/2506.15830 https://a…

Rethinking LLM Training through Information Geometry and Quantum Metrics
Optimization in large language models (LLMs) unfolds over high-dimensional parameter spaces with non-Euclidean structure. Information geometry frames this landscape using the Fisher information metric, enabling more principled learning via natural gradient descent. Though often impractical, this geometric lens clarifies phenomena such as sharp minima, generalization, and observed scaling laws. We argue that curvature-aware approaches deepen our understanding of LLM training. Finally, we specula…

A Study of Hybrid and Evolutionary Metaheuristics for Single Hidden Layer Feedforward Neural Network Architecture
Gautam Siddharth Kashyap, Md Tabrez Nafis, Samar Wazir
https://arxiv.org/abs/2506.15737

A Study of Hybrid and Evolutionary Metaheuristics for Single Hidden Layer Feedforward Neural Network Architecture
Training Artificial Neural Networks (ANNs) with Stochastic Gradient Descent (SGD) frequently encounters difficulties, including substantial computing expense and the risk of converging to local optima, attributable to its dependence on partial weight gradients. Therefore, this work investigates Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) - two population-based Metaheuristic Optimizers (MHOs) - as alternatives to SGD to mitigate these constraints. A hybrid PSO-SGD strategy is …

Higher-Order Transverse Discontinuity Mapping in Filippov Systems: Analysis and Experimental Validation using an Electronic Circuit
Rohit Chawla, Soumyajit Seth, Aasifa Rounak, Vikram Pakrashi
https://arxiv.org/abs/2506.18379

Higher-Order Transverse Discontinuity Mapping in Filippov Systems: Analysis and Experimental Validation using an Electronic Circuit
This paper shows that linearizing the transverse discontinuity mapping (TDM) in Filippov systems can produce inaccurate predictions of the dynamics in impact oscillators operating near a pre-stressed soft barrier. This discrepancy arises from the limitations of the linearized saltation matrix, which inaccurately predicts impacts in the local neighborhood of the discontinuity boundary. To address this issue, a higher-order approximation of the TDM is derived, which accurately captures the onset …

Learning to Coordinate Under Threshold Rewards: A Cooperative Multi-Agent Bandit Framework
Michael Ledford, William Regli
https://arxiv.org/abs/2506.15856 …

Learning to Coordinate Under Threshold Rewards: A Cooperative Multi-Agent Bandit Framework
Cooperative multi-agent systems often face tasks that require coordinated actions under uncertainty. While multi-armed bandit (MAB) problems provide a powerful framework for decentralized learning, most prior work assumes individually attainable rewards. We address the challenging setting where rewards are threshold-activated: an arm yields a payoff only when a minimum number of agents pull it simultaneously, with this threshold unknown in advance. Complicating matters further, some arms are de…

Was speaking to some journalists recently who didn't realise how long a history the use of radar has in measuring ice thickness over glaciers.
The technique was discovered by pilots, who must have had the frankly terrifying experience of landing on the ice sheet with their radars telling them they still had a few thousand metres of descent to reach the surface...
It's not mentioned in this obituary but I wonder if this guy would have seen it? Perhaps he didn't even have radar to assist in landing?
https://flipboard.com/@newyorktimes/obituaries-bq1u22g8z/-/a-gd4LiLY4RbOMXavSDWOBQQ:a:3195393-/0
newyorktimes@flipboard.com - Conrad Shinn, First Pilot to Land at the South Pole, Dies at 102
https://www.nytimes.com/2025/06/08/obituaries/conrad-shinn-dead.html?utm_source=flipboard&utm_medium=activitypub
Posted into Obituaries @obituaries-newyorktimes

Decentralized Uplink Adaptive Compression for Cell-Free MIMO with Limited Fronthaul
Zehua Li, Jingjie Wei, Raviraj Adve
https://arxiv.org/abs/2506.11284 ht…

Decentralized Uplink Adaptive Compression for Cell-Free MIMO with Limited Fronthaul
We study the problem of uplink compression for cell-free multi-input multi-output networks with limited fronthaul capacity. In compress-forward mode, remote radio heads (RRHs) compress the received signal and forward it to a central unit for joint processing. While previous work has focused on a transform-based approach, which optimizes the transform matrix that reduces signals of high dimension to a static pre-determined lower dimension, we propose a rate-based approach that simultaneously fin…

Alternating steepest descent methods for tensor completion with applications to spectromicroscopy
Oliver Townsend, Sergey Dolgov, Silvia Gazzola, Misha Kilmer
https://arxiv.org/abs/2506.10661

Alternating steepest descent methods for tensor completion with applications to spectromicroscopy
In this paper we develop two new Tensor Alternating Steepest Descent algorithms for tensor completion in the low-rank $\star_{M}$-product format, whereby we aim to reconstruct an entire low-rank tensor from a small number of measurements thereof. Both algorithms are rooted in the Alternating Steepest Descent (ASD) method for matrix completion, first proposed in [J. Tanner and K. Wei, Appl. Comput. Harmon. Anal., 40 (2016), pp. 417-429]. In deriving the new methods we target the X-ray spectromic…

The Integral Decimation Method for Quantum Dynamics and Statistical Mechanics
Ryan T. Grimm, Alexander J. Staat, Joel D. Eaves
https://arxiv.org/abs/2506.11341

The Integral Decimation Method for Quantum Dynamics and Statistical Mechanics
We present a method to numerically evaluate functional integrals called integral decimation (ID). It constructs a separable decomposition of the integrand as a spectral tensor train (STT), a continuous generalization of the matrix product state. ID builds the STT by mapping the integrand to an auxiliary many-body wavefunction that evolves in time from an initially unentangled state. Each body-ordered term of the action corresponds to a quantum gate, applied to the state during its evolution. Th…