Tootfinder

Calibrating gravitational-wave search algorithms with conformal prediction
Gregory Ashton, Nicolo Colombo, Ian Harry, Surabhi Sachdev
https://arxiv.org/abs/2402.19313

Calibrating gravitational-wave search algorithms with conformal prediction
In astronomy, we frequently face the decision problem: does this data contain a signal? Typically, a statistical approach is used, which requires a threshold. The choice of threshold presents a common challenge in settings where signals and noise must be delineated, but their distributions overlap. Gravitational-wave astronomy, which has gone from the first discovery to catalogues of hundreds of events in less than a decade, presents a fascinating case study. For signals from colliding compact …

A simple model of global cascades on random hypergraphs
Lei Chen, Yanpeng Zhu, Zhongyuan Ruan, Run-Ran Liu, Fanyuan Meng
https://arxiv.org/abs/2402.18850 h…

A simple model of global cascades on random hypergraphs
This study investigates the dynamics of social contagion within hypergraphs, reflecting the modern intricacies of group influence on individual decision-making as magnified by social media platforms. We introduce a novel social contagion model that captures the nuanced interplay between individual caution in decision-making (\(ϕ_k\)) and the efficiency of information transmission within group interactions (\(ϕ_m\)). By integrating dual-threshold mechanisms, our model delineates the conditions…

Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction
Drew T. Nguyen, Reese Pathak, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan
https://arxiv.org/abs/2403.19605

Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction
Decision-making pipelines are generally characterized by tradeoffs among various risk functions. It is often desirable to manage such tradeoffs in a data-adaptive manner. As we demonstrate, if this is done naively, state-of-the art uncertainty quantification methods can lead to significant violations of putative risk guarantees. To address this issue, we develop methods that permit valid control of risk when threshold and tradeoff parameters are chosen adaptively. Our methodology supports mon…

Efficient and Timely Memory Access
Vishakha Ramani, Ivan Seskar, Roy D. Yates
https://arxiv.org/abs/2404.14596 https://arxiv.org/pdf/…

Efficient and Timely Memory Access
This paper investigates the optimization of memory sampling in status updating systems, where source updates are published in shared memory, and reader process samples the memory for source updates by paying a sampling cost. We formulate a discrete-time decision problem to find a sampling policy that minimizes average cost comprising age at the client and the cost incurred due to sampling. We establish that an optimal policy is a stationary and deterministic threshold-type policy, and subsequen…

Approximate Circular Pattern Matching under Edit Distance
Panagiotis Charalampopoulos, Solon P. Pissis, Jakub Radoszewski, Wojciech Rytter, Tomasz Wale\'n, Wiktor Zuba
https://arxiv.org/abs/2402.14550

Approximate Circular Pattern Matching under Edit Distance
In the $k$-Edit Circular Pattern Matching ($k$-Edit CPM) problem, we are given a length-$n$ text $T$, a length-$m$ pattern $P$, and a positive integer threshold $k$, and we are to report all starting positions of the substrings of $T$ that are at edit distance at most $k$ from some cyclic rotation of $P$. In the decision version of the problem, we are to check if any such substring exists. Very recently, Charalampopoulos et al. [ESA 2022] presented $O(nk^2)$-time and $O(nk \log^3 k)$-time solut…

Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction
Drew T. Nguyen, Reese Pathak, Anastasios N. Angelopoulos, Stephen Bates, Michael I. Jordan
https://arxiv.org/abs/2403.19605

Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction
Decision-making pipelines are generally characterized by tradeoffs among various risk functions. It is often desirable to manage such tradeoffs in a data-adaptive manner. As we demonstrate, if this is done naively, state-of-the art uncertainty quantification methods can lead to significant violations of putative risk guarantees. To address this issue, we develop methods that permit valid control of risk when threshold and tradeoff parameters are chosen adaptively. Our methodology supports mon…

This https://arxiv.org/abs/2306.06743 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepe…

Trees versus Neural Networks for enhancing tau lepton real-time selection in proton-proton collisions
This paper introduces supervised learning techniques for real-time selection (triggering) of hadronically decaying tau leptons in proton-proton colliders. By implementing classic machine learning decision trees and advanced deep learning models, such as Multi-Layer Perceptron or residual neural networks, visible improvements in performance compared to standard threshold tau triggers are observed. We show how such an implementation may lower selection energy thresholds, thus contributing to incr…

Average-case deterministic query complexity of boolean functions with fixed weight
Yuan Li, Haowei Wu, Yi Yang
https://arxiv.org/abs/2403.03530 https://

Average-case deterministic query complexity of boolean functions with fixed weight
We explore the $\textit{average-case deterministic query complexity}$ of boolean functions under the $\textit{uniform distribution}$, denoted by $\mathrm{D}_\mathrm{ave}(f)$, the minimum average depth of zero-error decision tree computing a boolean function $f$. This measure found several applications across diverse fields. We study $\mathrm{D}_\mathrm{ave}(f)$ of several common functions, including penalty shoot-out functions, symmetric functions, linear threshold functions and tribes function…