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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Efficiency in managing peer-review of scientific manuscri...
Olgica Nedic, Ivana Drvenica, Marcel Ausloos, Aleksandar Dekansk · 2019-10-12 · via cs.DS updates on arXiv.org

The purpose of this paper is to introduce a model for measuring the efficiency in managing peer-review of scientific manuscripts by editors. The approach employed is based on the assumption that the editorial aim is to manage publication with high efficiency, employing the least amount of editorial resources. Efficiency is defined in this research as a measure based on 7 variables. An on-line survey was constructed and editors of journals originating from Serbia regularly publishing articles in the field of chemistry were invited to participate. An evaluation of the model is given based on responses from 24 journals and 50 editors. With this investigation we aimed to contribute to our understanding of the peer-review process and, possibly, offer a tool to improve the "efficiency" in journal editing. The proposed protocol may be adapted by other journals in order to assess the managing potential of editors.