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arXiv 2023年

作者： Naor, Moni Nissim, Kobbi Stemmer, Uri Yan, Chao Department of Computer Science and Applied Math Weizmann Institute of Science Blavatnik School of Computer Science Tel Aviv University Google Research Department of Computer Science Georgetown University United States

A private learner is trained on a sample of labeled points and generates a hypothesis that can be used for predicting the labels of newly sampled points while protecting the privacy of the training set [Kasiviswannathan et al., FOCS 2008]. Research uncovered that private learners may need to exhibit significantly higher sample complexity than non-private learners as is the case with, e.g., learning of one-dimensional threshold functions [Bun et al., FOCS 2015, Alon et al., STOC 2019]. We explore prediction as an alternative to learning. Instead of putting forward a hypothesis, a predictor answers a stream of classification queries. Earlier work has considered a private prediction model with just a single classification query [Dwork and Feldman, COLT 2018]. We observe that when answering a stream of queries, a predictor must modify the hypothesis it uses over time, and, furthermore, that it must use the queries for this modification, hence introducing potential privacy risks with respect to the queries themselves. We introduce private everlasting prediction taking into account the privacy of both the training set and the (adaptively chosen) queries made to the predictor. We then present a generic construction of private everlasting predictors in the PAC model. The sample complexity of the initial training sample in our construction is quadratic (up to polylog factors) in the VC dimension of the concept class. Our construction allows prediction for all concept classes with finite VC dimension, and in particular threshold functions with constant size initial training sample, even when considered over infinite domains, whereas it is known that the sample complexity of privately learning threshold functions must grow as a function of the domain size and hence is impossible for infinite domains. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Adaptive particle swarm optimization model for resource leveling

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Evolving Systems 2023年第4期14卷 593-604页

作者： Lin, Jerry Chun-Wei Lv, Qing Yu, Dehu Srivastava, Gautam Chen, Chun-Hao School of Information and Control Engineering Qingdao University of Technology Qingdao China Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway School of Civil Engineering Qingdao University of Technology Qingdao China School of Civil Engineering Shandong Jianzhu University Jinan China Department of Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Department of Information and Finance Management National Taipei University of Technology Taipei Taiwan

In the process of engineering project construction, the balanced allocation of resources has an important impact on the purchase of actual materials, the progress of the site construction and the arrangement of temporary facilities. Although there have been many studies on the use of evolutionary computation (EC) to solve the fixed duration of the resource-leveling problem, the optimization effect of standard EC to solve the resource-balancing problem in large-scale network design planning is reduced due to the phenomenon of premature convergence of PSO and the complexity of large-scale network planning. Therefore, in this paper, an adaptive dissipative particle swarm optimization (ADPSO) algorithm is used to solve the resource balancing optimization problem for different network-plans scales, and the feasibility of the developed model is verified using four different scaled network plans (databases). At the same time, a solution to the dynamic resource-leveling problem with optimization for a fixed duration is proposed. The results show that the proposed models are suitable for solving the problem of resource-leveling with a fixed duration of optimization of different scale network plans, and their accuracy and stability are significantly improved compared to the state-of-the-art approaches. Moreover, the proposed models are accurate and suitable for combining the calculation method of network planning time parameters with the ADPSO algorithm to realize dynamic resource balancing optimization. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Particle swarm optimization (PSO)

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Towards Early Diagnosis and Intervention: An Ensemble Voting Model for Precise Vital Sign Prediction in Respiratory Disease

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IEEE Journal of Biomedical and Health Informatics 2023年 PP卷 1-13页

作者： Ahmed, Usman Lin, Jerry Chun-Wei Srivastava, Gautam Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway Dept. of Math and Computer Science Brandon University Canada

Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naïve Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is utilized to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends. IEEE

关键词： Decision trees

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Low Acceptance Agreement Tests via Bounded-Degree Symplectic HDXs

Low Acceptance Agreement Tests via Bounded-Degree Symplectic...

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Annual IEEE Symposium on Foundations of computer science

作者： Yotam Dikstein Irit Dinur Alexander Lubotzky Computer Science and Discrete Mathematics Institute for Advanced Study Princeton New Jersey USA Dept. of Applied Math and Computer Science The Weizmann Institute of Science Rehovot Israel Department of Mathematics The Weizmann Institute of Science Rehovot Israel

ISBN: (数字)9798331516741

ISBN: (纸本)9798331516758

We solve the derandomized direct product testing question in the low acceptance regime, by constructing new high dimensional expanders that have no small connected covers. We show that our complexes have swap cocycle expansion, which allows us to deduce the agreement theorem by relying on previous work. Derandomized direct product testing, also known as agreement testing, is the following problem. Let $X$ be a family of k-element subsets of $[N]$ and let $\{f_{s}:s\rightarrow\Sigma\vert s\in X\}$ be an ensemble of local functions, each defined over a subset $s\subset\lceil N$ . Suppose that we run the following so-called agreement test: choose a random pair of sets $s_{1}, s_{2}\in X$ that intersect on $\sqrt{k}$ elements, and accept if $f_{s_{1}}, f_{s_{2}}$ agree on the elements in $s_{1}\cap s_{2}$ . We denote the success probability of this test by Agree $\{f_{s}\})$ Given that Agree $(\{f_{s}\})=\varepsilon > 0$ is there a global function $G:[N]\rightarrow\Sigma$ such that $f_{s}=G\vert _{s}$ for a non-negligible fraction of $s\in X\ ?$ We construct a family $X$ of k-subsets of $[N]$ such that $\vert X\vert =O(N)$ , and such that it satisfies the low acceptance agreement theorem. Namely, $\text{Agree}\left(\left\{f_s\right\}\right)>\varepsilon \Longrightarrow \exists G:[N] \rightarrow \Sigma, \quad \underset{s}{\mathbb{P}}\left[\left.f_s \stackrel{0.99}{\approx} G\right\vert_s\right] \geqslant \text{poly}(\varepsilon)$ . A key idea is to replace the well-studied LSV complexes by symplectic high dimensional expanders (HDXs). The family $X$ is just the k-faces of the new symplectic HDXs. The latter serve our needs better since their fundamental group satisfies the congruence subgroup property, which implies that they lack small covers. We also give a polynomial-time algorithm to construct this family of sym-plectic HDXs.

关键词： computer science Testing

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Expansion of high-dimensional cubical complexes with application to quantum locally testable codes

arXiv

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arXiv 2024年

作者： Dinur, Irit Lin, Ting-Chun Vidick, Thomas Department of Applied Math and Computer Science The Weizmann Institute of Science Department of Physics University of California San Diego CA United States Hon Hai Research Institute Taipei Taiwan

We introduce a high-dimensional cubical complex, for any dimension t ∈ N, and apply it to the design of quantum locally testable codes. Our complex is a natural generalization of the constructions by Panteleev and Kalachev and by Dinur et. al of a square complex (case t = 2), which have been applied to the design of classical locally testable codes (LTC) and quantum low-density parity check codes (qLDPC) respectively. We turn the geometric (cubical) complex into a chain complex by relying on constant-sized local codes h1, . . . , ht as gadgets. A recent result of Panteleev and Kalachev on existence of tuples of codes that are product expanding enables us to prove lower bounds on the cycle and co-cycle expansion of our chain complex. For t = 4 our construction gives a new family of "almost-good" quantum LTCs — with constant relative rate, inverse-polylogarithmic relative distance and soundness, and constant-size parity checks. Both the distance of the quantum code and its local testability are proven directly from the cycle and co-cycle expansion of our chain complex. Copyright © 2024, The Authors. All rights reserved.

关键词： Expansion

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Noisy Circumnutations Facilitate Self-Organized Shade Avoidance in Sunflowers

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Physical Review X 2024年第3期14卷 031027-031027页

作者： Chantal Nguyen Imri Dromi Ahron Kempinski Gabriella E. C. Gall Orit Peleg Yasmine Meroz BioFrontiers Institute School of Plant Science and Food Security The Center for Physics and Chemistry of Living Systems Zukunftskolleg Department of Biology Centre for the Advanced Study of Collective Behaviour Konstanz Germany Department of Computer Science Department of Physics Department of Applied Math Department of Ecology and Evolutionary Biology Santa Fe New Mexico USA

Circumnutations are widespread in plants and typically associated with exploratory movements; however, a quantitative understanding of their role remains elusive. In this study we report, for the first time, the role of noisy circumnutations in facilitating an optimal growth pattern within a crowded group of mutually shading plants. We revisit the problem of self-organization observed for sunflowers, mediated by shade response interactions. Our analysis reveals that circumnutation movements conform to a bounded random walk characterized by a remarkably broad distribution of velocities, covering 3 orders of magnitude. In motile animal systems such wide distributions of movement velocities are frequently identified with enhancement of behavioral processes, suggesting that circumnutations may serve as a source of functional noise. To test our hypothesis, we developed a Langevin-type parsimonious model of interacting growing disks, informed by experiments, successfully capturing the characteristic dynamics of individual and multiple interacting plants. Employing our simulation framework we examine the role of circumnutations in the system, and find that the observed breadth of the velocity distribution represents a sharp transition in the force-noise ratio, conferring advantageous effects by facilitating exploration of potential configurations, leading to an optimized arrangement with minimal shading. These findings represent the first report of functional noise in plant movements and establish a theoretical foundation for investigating how plants navigate their environment by employing computational processes such as task-oriented processes, optimization, and active sensing. Since plants move by growing, space and time are coupled, and dynamics of self-organization lead to emergent 3D patterns. As such, this system provides conceptual insight for other interacting growth-driven systems such as fungal hyphae, neurons and self-growing robots, as well as active matter sys

关键词： Animal behavior, social interactions & emergent phenomena Biological self-organization Emergence of patterns Emergent biological functions from complex interactions Foraging Patterns in complex systems Plant development Scaling laws of complex systems

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Having Hope in Hops: New Spanners, Preservers and Lower Bounds for Hopsets

Having Hope in Hops: New Spanners, Preservers and Lower Boun...

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Annual IEEE Symposium on Foundations of computer science

作者： Shimon Kogan Merav Parter Department of Computer Science and Applied Math Weizmann Institute Rehovot Israel

ISBN: (数字)9781665455190

ISBN: (纸本)9781665455206

Hopsets and spanners are fundamental graph structures, playing a key role in shortest path computation, distributed communication, and more. A (near-exact) hopset for a given graph G is a (small) subset of weighted edges H that when added to the graph G reduces the number of hops (edges) of near-exact shortest paths. Spanners and distance preservers, on the other hand, ask for removing many edges from the graph while approximately preserving shortest path *** provide a general reduction scheme from graph hopsets to the known metric compression schemes of spanners, emulators and distance preservers. Consequently, we get new and improved upper bound constructions for the latter, as well as, new lower bound results for hopsets. Our main results include:•For n-vertex directed weighted graphs, one can provide $(1+\epsilon)$-approximate distance preservers 1 for p pairs in $V\times V$ with $O_{\epsilon}(n\cdot p^{2/5}+(np)^{2/3})$ edges. For $p\geq n^{5/4}$, this matches the state-of-the art bounds for reachability preservers by [Abboud and Bodwin, SODA 2018] and the lower bound for exact-distance preservers by [Bodwin, SODA 2016].•For n-vertex undirected weighted graphs, one can provide $(1+\epsilon)$ distance preserves with $\overline{O}_{\epsilon}(n^{1+o(1)}+p\cdot n^{o(1)})$ edges. So far, such bounds could be obtained only for unweighted graphs. Consequently, we also get improved sourcewise spanners [Roditty, Thorup and Zwick, ICALP 2005] and spanners with slack [Chan, Dinitz and Gupta, ESA 2006].•Exact hopsets of linear size admit a worst-case hopbound of $\beta=\Omega(n^{1/3})$. This holds even for undirected weighted graphs, improving upon the $\Omega(n^{1/6})$ lower bound by [Huang and Pettie, SIAM J. Discret. math 2021]. Interestingly this matches the recent diameter bound achieved for linear directed shortcuts. 1 I.e., subgraphs that preserve the pairwise distances up to a multiplicative stretch of (1+$\epsilon$).More conceptually, our work makes a si

关键词： Measurement computer science Upper bound Art

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BUZZ: Beehive-structured Sparse KV Cache with Segmented Heavy Hitters for Efficient LLM Inference

arXiv

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arXiv 2024年

作者： Zhao, Junqi Fang, Zhijin Li, Shu Yang, Shaohui He, Shichao Department of Computer Science New York University New York United States Department of Statistics and Data Science University of California Los Angeles United States Department of Mathematical Science Beihang University Beijing China School of Computer Science University of Leed Leeds United Kingdom Department of Computer Science and Applied Math Brandeis University Boston United States

Large language models (LLMs) are essential in natural language processing but often struggle with inference speed and computational efficiency, limiting real-time deployment. The key-value (KV) cache mechanism reduces computational overhead in transformer models, but challenges in maintaining contextual understanding remain. In this paper, we propose BUZZ, a novel KV caching algorithm that leverages structured contextual information to minimize cache memory usage while enhancing inference speed. BUZZ employs a beehive-structured sparse cache, incorporating a sliding window to capture recent information and dynamically segmenting historical tokens into chunks to prioritize important tokens in local neighborhoods. We evaluate BUZZ on four real-world datasets: CNN/Daily Mail, XSUM, Wikitext, and 10-QA. Our results demonstrate that BUZZ (1) reduces cache memory usage by 2.5× in LLM inference while maintaining over 99% accuracy in long-text summarization, and (2) surpasses state-of-the-art performance in multi-document question answering by 7.69% under the same memory limit, where full cache methods encounter out-of-memory issues. Additionally, BUZZ achieves significant inference speedup with a log n time complexity. The code is available at: https://***/JunqiZhao888/buzz-llm. Copyright © 2024, The Authors. All rights reserved.

关键词： Cache memory

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Expansion of High-Dimensional Cubical Complexes: with Application to Quantum Locally Testable Codes

Expansion of High-Dimensional Cubical Complexes: with Applic...

引用

Annual IEEE Symposium on Foundations of computer science

作者： Irit Dinur Ting-Chun Lin Thomas Vidick Department of Applied Math and Computer Science The Weizmann Institute of Science Rehovot Israel Department of Physics University of California at San Diego San Diego USA Hon Hai Research Institute Taipei Taiwan

ISBN: (数字)9798331516741

ISBN: (纸本)9798331516758

We introduce a high-dimensional cubical complex, for any dimension $t \in \mathbb{N}$ , and apply it to the design of quantum locally testable codes. Our complex is a natural generalization of the constructions by Panteleev and Kalachev and by Dinur et. al of a square complex (case $t=2$ ), which have been applied to the design of classical locally testable codes (LTC) and quantum low-density parity check codes (qLDPC) respectively. We turn the geometric (cubical) complex into a chain complex by relying on constant-sized local codes $h_{1}, \ldots,h_{t}$ as gadgets. A recent result of Panteleev and Kalachev on existence of tuples of codes that are product expanding enables us to prove lower bounds on the cycle and co-cycle expansion of our chain complex. For $t=4$ our construction gives a new family of “almost-good” quantum LTCs - with constant relative rate, inverse-polylogarithmic relative distance and soundness, and constant-size parity checks. Both the distance of the quantum code and its local testability are proven directly from the cycle and co-cycle expansion of our chain complex.

关键词： computer science Parity check codes

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RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate Models

arXiv

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arXiv 2024年

作者： Nath, Pritthijit Moss, Henry Shuckburgh, Emily Webb, Mark Department of Applied Math and Theoretical Physics University of Cambridge United Kingdom Department of Computer Science and Technology University of Cambridge United Kingdom Met Office Hadley Centre United Kingdom

This study explores integrating reinforcement learning (RL) with idealised climate models to address key parameterisation challenges in climate science. Current climate models rely on complex mathematical parameterisations to represent sub-grid scale processes, which can introduce substantial uncertainties. RL offers capabilities to enhance these parameterisation schemes, including direct interaction, handling sparse or delayed feedback, continuous online learning, and long-term optimisation. We evaluate the performance of eight RL algorithms on two idealised environments: one for temperature bias correction, another for radiative-convective equilibrium (RCE) imitating real-world computational constraints. Results show different RL approaches excel in different climate scenarios with exploration algorithms performing better in bias correction, while exploitation algorithms proving more effective for RCE. These findings support the potential of RL-based parameterisation schemes to be integrated into global climate models, improving accuracy and efficiency in capturing complex climate dynamics. Overall, this work represents an important first step towards leveraging RL to enhance climate model accuracy, critical for improving climate understanding and predictions. Code accessible at https://***/p3jitnath/climate-rl. © 2024, CC BY-NC-SA.

关键词： Digital elevation model

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