arXiv subjects

Hiroki Ishikura

Publications and source records attributed to Hiroki Ishikura.

8 recordsLinked to original sources

arXiv2026

Tracks on planar complexes and soficity

We show that every probability-measure-preserving equivalence relation generated by a locally-finite Borel graph with planar connected components is sofic in the sense of Elek--Lippner. In particular, every unimodular random planar graph is sofic. This removes the additional assumptions in the works of Angel--Hutchcroft--Nachmias--Ray and Timár on the soficity of unimodular random planar maps and graphs. To prove this, we investigate Borel simplicial complexes with planar components and approximate them by treeable covering spaces. To construct these coverings, we use a canonical family of tracks on planar simplicial complexes introduced by Dunwoody.

math.CO↗

arXiv2026

Decomposition of Borel graphs and cohomology

We give a cohomological criterion for certain decomposition of Borel graphs, which is an analog of Dunwoody's work on accessibility of groups. As an application, we prove that a Borel graph $(X,G)$ with uniformly bounded degrees of cohomological dimension one is Lipschitz equivalent to a Borel acyclic graph on $X$. This gives a new proof of a result of Chen-Poulin-Tao-Tserunyan on Borel graphs with components quasi-isometric to trees.

math.GR↗

arXiv2025

Superpixel Attack: Enhancing Black-box Adversarial Attack with Image-driven Division Areas

Deep learning models are used in safety-critical tasks such as automated driving and face recognition. However, small perturbations in the model input can significantly change the predictions. Adversarial attacks are used to identify small perturbations that can lead to misclassifications. More powerful black-box adversarial attacks are required to develop more effective defenses. A promising approach to black-box adversarial attacks is to repeat the process of extracting a specific image area and changing the perturbations added to it. Existing attacks adopt simple rectangles as the areas where perturbations are changed in a single iteration. We propose applying superpixels instead, which achieve a good balance between color variance and compactness. We also propose a new search method, versatile search, and a novel attack method, Superpixel Attack, which applies superpixels and performs versatile search. Superpixel Attack improves attack success rates by an average of 2.10% compared with existing attacks. Most models used in this study are robust against adversarial attacks, and this improvement is significant for black-box adversarial attacks. The code is avilable at https://github.com/oe1307/SuperpixelAttack.git.

cs.CR↗

arXiv2025

Enhancing Quantum-ready QUBO-based Suppression for Object Detection with Appearance and Confidence Features

Quadratic Unconstrained Binary Optimization (QUBO)-based suppression in object detection is known to have superiority to conventional Non-Maximum Suppression (NMS), especially for crowded scenes where NMS possibly suppresses the (partially-) occluded true positives with low confidence scores. Whereas existing QUBO formulations are less likely to miss occluded objects than NMS, there is room for improvement because existing QUBO formulations naively consider confidence scores and pairwise scores based on spatial overlap between predictions. This study proposes new QUBO formulations that aim to distinguish whether the overlap between predictions is due to the occlusion of objects or due to redundancy in prediction, i.e., multiple predictions for a single object. The proposed QUBO formulation integrates two features into the pairwise score of the existing QUBO formulation: i) the appearance feature calculated by the image similarity metric and ii) the product of confidence scores. These features are derived from the hypothesis that redundant predictions share a similar appearance feature and (partially-) occluded objects have low confidence scores, respectively. The proposed methods demonstrate significant advancement over state-of-the-art QUBO-based suppression without a notable increase in runtime, achieving up to 4.54 points improvement in mAP and 9.89 points gain in mAR.

cs.CV↗

arXiv2024

Enhancing Output Diversity Improves Conjugate Gradient-based Adversarial Attacks

Deep neural networks are vulnerable to adversarial examples, and adversarial attacks that generate adversarial examples have been studied in this context. Existing studies imply that increasing the diversity of model outputs contributes to improving the attack performance. This study focuses on the Auto Conjugate Gradient (ACG) attack, which is inspired by the conjugate gradient method and has a high diversification performance. We hypothesized that increasing the distance between two consecutive search points would enhance the output diversity. To test our hypothesis, we propose Rescaling-ACG (ReACG), which automatically modifies the two components that significantly affect the distance between two consecutive search points, including the search direction and step size. ReACG showed higher attack performance than that of ACG, and is particularly effective for ImageNet models with several classification classes. Experimental results show that the distance between two consecutive search points enhances the output diversity and may help develop new potent attacks. The code is available at \url{https://github.com/yamamura-k/ReACG}

cs.LG↗

arXiv2023

Free metabelian groups are permutation stable

We prove that all finitely generated free metabelian groups are permutation stable. This partially answers to the question asked by Levit and Lubotzky whether all finitely generated metabelian groups are permutation stable. Our proof extends the range of application of Levit-Lubotzky's method, which is used to show permutation stability of permutational wreath products of finitely generated abelian groups, to non-split and non-permutational metabelian groups.

math.GR↗

arXiv2022

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.

cs.LG↗

arXiv2022

Long-Term Optimal Delivery Planning for Replacing the Liquefied Petroleum Gas Cylinder

In the daily operation of liquefied petroleum gas service, gas providers visit customers and replace cylinders if the gas is about to run out. For a long time, frequent visits to customers were required because they could not determine the amount of remaining gas without a staff visit and observation. To solve this problem, smart meters are started to be employed to acquire gas consumption more frequently without visiting customers. In this study, we construct a system to optimize plans for cylinder replacement, and evaluate it with a large-scale field test. We propose an algorithm to create a replacement plan with three steps: estimating the replacement date, acquiring the customer list for replacement, and determining the delivery route. A more accurate estimation of the replacement date can be acquired with a smart meter, which is used for making a customer list for replacement. The formulation for making a customer list enables the gas provider to replace cylinders some days before the date when the gas would run out. It can suppress the concentration of replacements on certain days. Large-scale verification experiments were performed with more than 1,000 customers in Chiba prefecture in Japan. In the field test, the gas provider incorporated the system into its replacement operations. Moreover, the replacement plans developed by the proposed system were compared with that by the gas provider. Our system reduced the number of gas cylinders with gas shortage, the number of visits without replacement due to plenty of gas remaining, and the working duration per customer, which shows that our system benefits both gas providers and customers.

eess.SY↗