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Teppei Suzuki

Publications and source records attributed to Teppei Suzuki.

25 records · Page 2Linked to original sources
arXiv2018

Drive Video Analysis for the Detection of Traffic Near-Miss Incidents

Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, the dangerous traffic (including near-miss incident) scenarios that provide normal drivers with strong motivation to drive safely. Accordingly, as a means of providing learning depth, this paper presents a novel traffic database that contains information on a large number of traffic near-miss incidents that were obtained by mounting driving recorders in more than 100 taxis over the course of a decade. The study makes the following two main contributions: (i) In order to assist automated systems in detecting near-miss incidents based on database instances, we created a large-scale traffic near-miss incident database (NIDB) that consists of video clip of dangerous events captured by monocular driving recorders. (ii) To illustrate the applicability of NIDB traffic near-miss incidents, we provide two primary database-related improvements: parameter fine-tuning using various near-miss scenes from NIDB, and foreground/background separation into motion representation. Then, using our new database in conjunction with a monocular driving recorder, we developed a near-miss recognition method that provides automated systems with a performance level that is comparable to a human-level understanding of near-miss incidents (64.5% vs. 68.4% at near-miss recognition, 61.3% vs. 78.7% at near-miss detection).

cs.CV↗
arXiv2017

Cysteine and cystine adsorption on FeS$_{2}$(100)

Iron pyrite (FeS$_{2}$) is the most abundant metal sulfide on Earth. Owing to its reactivity and catalytic activity, pyrite has been studied in various research fields such as surface science, geochemistry, and prebiotic chemistry. Importantly, native iron-sulfur clusters are typically coordinated by cysteinyl ligands of iron-sulfur proteins. In the present paper, we study the adsorption of L-cysteine and its oxidized dimer, L-cystine, on the FeS$_{2}$ surface, using electronic structure calculations based density functional theory and Raman spectroscopy measurements. Our calculations suggest that sulfur-deficient surfaces play an important role in the adsorption of cysteine and cystine. In the thiol headgroup adsorption on the sulfur-vacancy site, dissociative adsorption is found to be energetically favorable compared with molecular adsorption. In addition, the calculations indicate that, in the cystine adsorption on the defective surface under vacuum conditions, the formation of the S-Fe bond is energetically favorable compared with molecular adsorption. Raman spectroscopic measurements suggest the formation of cystine molecules through the S-S bond on the pyrite surface in aqueous solution. Our results might have implications for chemical evolution at mineral surfaces on the early Earth and the origin of iron-sulfur proteins, which are believed to be one of the most ancient families of proteins.

physics.chem-ph↗
arXiv2017

Canonical-ensemble extended Lagrangian Born-Oppenheimer molecular dynamics for the linear scaling density functional theory

We discuss the development and implementation of a constant temperature (NVT) molecular dynamics scheme that combines the Nosé-Hoover chain thermostat with the extended Lagrangian Born-Oppenheimer molecular dynamics (BOMD) scheme, using a linear scaling density functional theory (DFT) approach. An integration scheme for this canonical-ensemble extended Lagrangian BOMD is developed and discussed in the context of the Liouville operator formulation. Linear scaling DFT canonical-ensemble extended Lagrangian BOMD simulations are tested on bulk silicon and silicon carbide systems to evaluate our integration scheme. The results show that the conserved quantity remains stable with no systematic drift even in the presence of the thermostat.

cond-mat.mtrl-sci↗
arXiv2017

Changing Fashion Cultures

The paper presents a novel concept that analyzes and visualizes worldwide fashion trends. Our goal is to reveal cutting-edge fashion trends without displaying an ordinary fashion style. To achieve the fashion-based analysis, we created a new fashion culture database (FCDB), which consists of 76 million geo-tagged images in 16 cosmopolitan cities. By grasping a fashion trend of mixed fashion styles,the paper also proposes an unsupervised fashion trend descriptor (FTD) using a fashion descriptor, a codeword vetor, and temporal analysis. To unveil fashion trends in the FCDB, the temporal analysis in FTD effectively emphasizes consecutive features between two different times. In experiments, we clearly show the analysis of fashion trends and fashion-based city similarity. As the result of large-scale data collection and an unsupervised analyzer, the proposed approach achieves world-level fashion visualization in a time series. The code, model, and FCDB will be publicly available after the construction of the project page.

cs.CV↗
arXiv2017

Semantic Change Detection with Hypermaps

Change detection is the study of detecting changes between two different images of a scene taken at different times. By the detected change areas, however, a human cannot understand how different the two images. Therefore, a semantic understanding is required in the change detection research such as disaster investigation. The paper proposes the concept of semantic change detection, which involves intuitively inserting semantic meaning into detected change areas. We mainly focus on the novel semantic segmentation in addition to a conventional change detection approach. In order to solve this problem and obtain a high-level of performance, we propose an improvement to the hypercolumns representation, hereafter known as hypermaps, which effectively uses convolutional maps obtained from convolutional neural networks (CNNs). We also employ multi-scale feature representation captured by different image patches. We applied our method to the TSUNAMI Panoramic Change Detection dataset, and re-annotated the changed areas of the dataset via semantic classes. The results show that our multi-scale hypermaps provided outstanding performance on the re-annotated TSUNAMI dataset.

cs.CV↗
arXiv2016

Machine Learning for Atomic Forces in a Crystalline Solid: Transferability to Various Temperatures

Recently, machine learning has emerged as an alternative, powerful approach for predicting quantum-mechanical properties of molecules and solids. Here, using kernel ridge regression and atomic fingerprints representing local environments of atoms, we trained a machine-learning model on a crystalline silicon system in order to directly predict the atomic forces at a wide range of temperatures. Our idea is to construct a machine-learning model using a quantum-mechanical data set taken from canonical-ensemble simulations at a higher temperature, or an upper bound of the temperature range. With our model, the force prediction errors were about 2% or smaller with respect to the corresponding force ranges, in the temperature region between 300 and 1650 K. We also verified the applicability to a larger system, ensuring the transferability with respect to system size.

cond-mat.mtrl-sci↗