Stability and Generalization for Minibatch SGD and Local SGD

The increasing scale of data propels the popularity of leveraging parallelism to speed up the optimization. Minibatch stochastic gradient descent (minibatch SGD) and local SGD are two popular methods for parallel optimization. The existing theoretical studies show a linear speedup of these methods with respect to the number of machines, which, however, is measured by optimization errors. As a comparison, the stability and generalization of these methods are much less studied. In this paper, we study the stability and generalization analysis of minibatch and local SGD to understand their learnability by introducing a novel expectation-variance decomposition. We incorporate training errors into the stability analysis, which shows how small training errors help generalization for overparameterized models. We show both minibatch and local SGD achieve a linear speedup to attain the optimal risk bounds

On Performance Estimation in Automatic Algorithm Configuration

Over the last decade, research on automated parameter tuning, often referred to as automatic algorithm configuration (AAC), has made significant progress. Although the usefulness of such tools has been widely recognized in real world applications, the theoretical foundations of AAC are still very weak. This paper addresses this gap by studying the performance estimation problem in AAC. More specifically, this paper first proves the universal best performance estimator in a practical setting, and then establishes theoretical bounds on the estimation error, i.e., the difference between the training performance and the true performance for a parameter configuration, considering finite and infinite configuration spaces respectively. These findings were verified in extensive experiments conducted on four algorithm configuration scenarios involving different problem domains. Moreover, insights for enhancing existing AAC methods are also identified.Comment: accepted by AAAI 202

An Uncertain QFD Approach for the Strategic Management of Logistics Services

Due to customers’ growing concern about logistics performances related to products, logistics service increasingly contributes to the core competence of an enterprise or product, which calls an appropriate tool to develop effective strategic actions to improve logistics performances and gain customer satisfaction. Therefore, an uncertain quality function deployment (QFD) approach for selecting the most effective strategic actions in terms of efficiency to meet the customer requirements is developed in this paper, which integrates uncertainty theory into the traditional QFD methodology in order to rationally deal with imprecise information inherently involved in the QFD process. The framework and systematic procedures of the approach are presented in the context of logistics services. Specifically, the calculations for the prioritization of strategic actions are discussed in detail, in which uncertain variables are used to capture the linguistic judgements given by customers and experts. Applications of the proposed approach are presented as well for illustration

Rotational Cryptanalysis in the Presence of Constants

Rotational cryptanalysis is a statistical method for attacking ARX constructions. It was previously shown that ARX-C, i.e., ARX with the injection of constants can be used to implement any function. In this paper we investigate how rotational cryptanalysis is affected when constants are injected into the state. We introduce the notion of an RX-difference, generalizing the idea of a rotational difference. We show how RX-differences behave around modular addition, and give a formula to calculate their transition probability. We experimentally verify the formula using Speck32/64, and present a 7-round distinguisher based on RX-differences. We then discuss two types of constants: round constants, and constants which are the result of using a fixed key, and provide recommendations to designers for optimal choice of parameters

Technical Difficulties and Countermeasures of Drilling of Φ118mm Sidetracking Horizontal Well in Changqing Oilfield

The boreholes of the horizontal side-drilled wells with casing windows in Changqing Oilfield are all Φ118mm. Difficulties in the construction of small-hole drilling include high landing risk in the horizontal section, difficulty in slope prediction and trajectory control, and limited extension capacity of small wellbore. By optimizing the trajectory of the borehole, designing the trajectory adjustment section before the horizontal landing well, and introducing the equilibrium trend angle method to predict the slope of the directional tool, we improved the trajectory control ability; by calculating the stability of the drill string and the loss of cyclic pressure and analyzing the displacement extension ability, we optimized drill string selection and formed a precise trajectory control method based on sidetrack horizontal well trajectory optimization. Based on the analysis of displacement and extension ability, a non-standard drill pipe with a large diameter of 88.9mm and a small hydrophthalmia was selected. It is considered that the technical countermeasures and suggestions in this paper are of good reference and guiding significance for the development of sidetracked horizontal wells drilling in Changqing Oilfield

An Easy-to-Use Tool for Rotational-XOR Cryptanalysis of ARX Block Ciphers

An increasing number of lightweight cryptographic primitives have been published recently. Some of these proposals are ARX primitives, which have shown a great performance in software. Rotational-XOR cryptanalysis is a statistical technique to attack ARX primitives. In this paper, a computer tool to speed up and make easier the security evaluation of ARX block ciphers against rotational-XOR cryptanalysis is shown. Our tool takes a Python implementation of an ARX block cipher and automatically finds an optimal rotational-XOR characteristic. Compared to most of the automated tools, which only support a small set of primitives, our tool supports any ARX block cipher and it is executed with a simple shell command

Rotational Cryptanalysis From a Differential-linear Perspective: Practical Distinguishers for Round-reduced FRIET, Xoodoo, and Alzette

The differential-linear attack, combining the power of the two most effective techniques for symmetric-key cryptanalysis, was proposed by Langford and Hellman at CRYPTO 1994. From the exact formula for evaluating the bias of a differential-linear distinguisher (JoC 2017), to the differential-linear connectivity table (DLCT) technique for dealing with the dependencies in the switch between the differential and linear parts (EUROCRYPT 2019), and to the improvements in the context of cryptanalysis of ARX primitives (CRYPTO 2020), we have seen significant development of the differential-linear attack during the last four years. In this work, we further extend this framework by replacing the differential part of the attack by rotational-xor differentials. Along the way, we establish the theoretical link between the rotational-xor differential and linear approximations, revealing that it is nontrivial to directly apply the closed formula for the bias of ordinary differential- linear attack to rotational differential-linear cryptanalysis. We then revisit the rotational cryptanalysis from the perspective of differential- linear cryptanalysis and generalize Morawiecki et al.’s technique for analyzing Keccak, which leads to a practical method for estimating the bias of a (rotational) differential-linear distinguisher in the special case where the output linear mask is a unit vector. Finally, we apply the rotational differential-linear technique to the permutations involved in FRIET, Xoodoo, Alzette, and SipHash. This gives significant improvements over existing cryptanalytic results or offers explanations for previous experimental distinguishers without a theoretical foundation. To confirm the validity of our analysis, all distinguishers with practical complexities are verified experimentally