Diversified Adversarial Attacks based on Conjugate Gradient Method

Deep learning models are vulnerable to adversarial examples, and adversarial attacks used to generate such examples have attracted considerable research interest. Although existing methods based on the steepest descent have achieved high attack success rates, ill-conditioned problems occasionally reduce their performance. To address this limitation, we utilize the conjugate gradient (CG) method, which is effective for this type of problem, and propose a novel attack algorithm inspired by the CG method, named the Auto Conjugate Gradient (ACG) attack. The results of large-scale evaluation experiments conducted on the latest robust models show that, for most models, ACG was able to find more adversarial examples with fewer iterations than the existing SOTA algorithm Auto-PGD (APGD). We investigated the difference in search performance between ACG and APGD in terms of diversification and intensification, and define a measure called Diversity Index (DI) to quantify the degree of diversity. From the analysis of the diversity using this index, we show that the more diverse search of the proposed method remarkably improves its attack success rate.Comment: Proceedings of the 39th International Conference on Machine Learning (ICML 2022

Chronic neutrophilic leukaemia and plasma cell-related neutrophilic leukaemoid reactions

Many cases reported as ‘chronic neutrophilic leukaemia’ have had an associated plasma cell neoplasm. Recent evidence suggests that the great majority of such cases represent a neutrophilic leukaemoid reaction to the underlying multiple myeloma or monoclonal gammopathy of undetermined significance. We have analysed all accessible reported cases to clarify the likely diagnosis and to ascertain whether toxic granulation, Döhle bodies and an increased neutrophil alkaline phosphatase score were useful in making a distinction between chronic neutrophilic leukaemia and a neutrophilic leukaemoid reaction. We established that all these changes occur in both conditions. Toxic granulation and Döhle bodies are more consistently present in leukaemoid reactions but also occur quite frequently in chronic neutrophilic leukaemia. The neutrophil alkaline phosphatase score is increased in both conditions and is of no value in making a distinction

The Implementation of the Primal-Dual Interior-Point Method for the Semidefinite Programs and its Engineering Applications

this paper is to explain the implementation of SDPA (SemiDefinite Programming Algorithm) [4] for semidefinite programs and report some numerical experiments of SDPA. Besides SDPA, there are some computer programs SDPpack [2], SDPSOL [19], CSDP [3], SDPHA [15] and SDPT3 [17] for semidefinite programs which are available through the Internet. The SDPA is a C++ implementation of a Mehrotra-type [10] primal-dual predictor-corrector interior-point method. Although three types of search directions, the HRVW/KSH/M direction [9], the NT direction [13] and the AHO direction [1] are available in SDPA, we employed the HRVW/KSH/M direction in our numerical experiments because its computation is the cheapest among the three directions (particularly, for sparse data matrices) when we employ the method proposed by Fujisawa et al. [5]. Monteiro et al. [12] recently showed that in theory, the NT direction requires less computation for dense matrices. However, their method needs large amount of computational memory and does not efficiently exploit the sparse data structures. Actually, according to their numerical results, the computation of the HRVW/KSH/M direction is favorable compared to the the computation of the NT and AHO directions. Let

Experimental Analyses of the Life Span Method for the Quadratic Assignment Problem

In this paper, we report an application of the life span method (LSM), a variant of tabu search introduced by the authors, to the quadratic assignment problem which has applications on facility location and backboard wiring, etc. We discuss how to adapt the LSM to the quadratic assignment problem and compare the performance with previous heuristics. The main purpose of this paper is to perform experimental analyses composed of optimizing the various parameters and to estimate the performance not only in the best case but the average behavior. Key words: life span method, tabu search, combinatorial optimization, approximate algorithms, experimental analysis, quadratic assignment problem. 1 Introduction The Quadratic Assignment Problem (QAP) is a combinatorial optimization problem having many applications including facility location, ordering of data on a disk, backboard wiring, machine scheduling, analyzing chemical, the location of departments (or offices), etc. [9]. In the context o..

Experimental Analyses of the Life Span Method for the Maximum Stable Set Problem

An efficient algorithm for the approximate solution of the maximum cardinality stable set problem is presented. The algorithm is based on a variant of tabu search which we call the life span method. Numerical experiments on random and benchmark instances show that our algorithm dominates all the algorithms given in the literature both in accuracy of solutions and in speed. We also investigate how to tune up our implementation and to optimize the parameters via extensive numerical experiments