Kazuki Yoshizoe

A study on security evaluation methodology for image-based biometrics authentication systems

We propose here a security evaluation methodology of image-based biometrics authentication systems against wolf attacks. A wolf attack is an attempt to impersonate a victim by feeding wolves into the system to be attacked. The wolf is input data that can be falsely accepted as a match with multiple templates. To create a secure system, we must evaluate the possibility of wolf attacks. Existing studies have relied on theoretical analysis of algorithms carried out by human beings, which is only effective if theoretical analysis is possible. Therefore, we propose a more generic approach based on a search to assist the developers of matching algorithms. We searched for wolves by using a recently developed algorithm called Monte-Carlo Tree Search (MCTS). We succeeded in detecting wolves in a matching algorithm, which appears promising considering that this is the first trial for this kind of approach.

ChemTS: an efficient python library for de novo molecular generation

Abstract Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel Python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS. Graphical Abstract

MDTS: automatic complex materials design using Monte Carlo tree search

Abstract Complex materials design is often represented as a black-box combinatorial optimization problem. In this paper, we present a novel python library called MDTS (Materials Design using Tree Search). Our algorithm employs a Monte Carlo tree search approach, which has shown exceptional performance in computer Go game. Unlike evolutionary algorithms that require user intervention to set parameters appropriately, MDTS has no tuning parameters and works autonomously in various problems. In comparison to a Bayesian optimization package, our algorithm showed competitive search efficiency and superior scalability. We succeeded in designing large Silicon-Germanium (Si-Ge) alloy structures that Bayesian optimization could not deal with due to excessive computational cost. MDTS is available at https://github.com/tsudalab/MDTS. Graphical Abstract

Scalable parallel numerical constraint solver using global load balancing

We present a scalable parallel solver for numerical constraint satisfaction problems (NCSPs). Our parallelization scheme consists of homogeneous worker solvers, each of which runs on an available core and communicates with others via the global load balancing (GLB) method. The search tree of the branch and prune algorithm is split and distributed through the two phases of GLB: a random workload stealing phase and a workload distribution and termination phase based on a hyper-cube-shaped graph called lifeline. The parallel solver is simply implemented with X10 that provides an implementation of GLB as a library. In experiments, several NCSPs from the literature were solved and attained up to 516-fold speedup using 600 cores of the TSUBAME2.5 supercomputer. Optimal GLB configurations are analyzed.

MP-LAMP: parallel detection of statistically significant multi-loci markers on cloud platforms

Abstract Summary Exhaustive detection of multi-loci markers from genome-wide association study datasets is a computationally challenging problem. This paper presents a massively parallel algorithm for finding all significant combinations of alleles and introduces a software tool termed MP-LAMP that can be easily deployed in a cloud platform, such as Amazon Web Service, as well as in an in-house computer cluster. Multi-loci marker detection is an unbalanced tree search problem that cannot be parallelized by simple tree-splitting using generic parallel programming frameworks, such as Map-Reduce. We employ work stealing and periodic reduce-broadcast to decrease the running time almost linearly to the number of cores. Availability and implementation MP-LAMP is available at https://github.com/tsudalab/mp-lamp. Supplementary information Supplementary data are available at Bioinformatics online.

RNA inverse folding using Monte Carlo tree search

BackgroundArtificially synthesized RNA molecules provide important ways for creating a variety of novel functional molecules. State-of-the-art RNA inverse folding algorithms can design simple and short RNA sequences of specific GC content, that fold into the target RNA structure. However, their performance is not satisfactory in complicated cases.ResultWe present a new inverse folding algorithm called MCTS-RNA, which uses Monte Carlo tree search (MCTS), a technique that has shown exceptional performance in Computer Go recently, to represent and discover the essential part of the sequence space. To obtain high accuracy, initial sequences generated by MCTS are further improved by a series of local updates. Our algorithm has an ability to control the GC content precisely and can deal with pseudoknot structures. Using common benchmark datasets for evaluation, MCTS-RNA showed a lot of promise as a standard method of RNA inverse folding.ConclusionMCTS-RNA is available at https://github.com/tsudalab/MCTS-RNA.