Deep Learning Based Vehicle Make-Model Classification

This paper studies the problems of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is proposed to combine an SSD (Single Shot Multibox Detector) model with a CNN (Convolutional Neural Network) model to train on the database. In the pipeline, we first detect the vehicles by following an algorithm which reduces the time for annotation. Then, we feed them into the CNN model. It is reached approximately 4% better classification accuracy result than using a conventional CNN model. Next, we propose to use the detected vehicles as ground truth bounding box (GTBB) of the images and feed them into an SSD model in another pipeline. At this stage, it is reached reasonable classification accuracy result without using perfectly shaped GTBB. Lastly, an application is implemented in a use case by using our proposed pipelines. It detects the unauthorized vehicles by comparing their license plate numbers and make & models. It is assumed that license plates are readable.Comment: 10 pages, ICANN 2018: Artificial Neural Networks and Machine Learnin

Coexistence of localized and itinerant electrons in BaFe2X3 (X = S and Se) revealed by photoemission spectroscopy

We report a photoemission study at room temperature on BaFe2X3 (X = S and Se) and CsFe2Se3 in which two-leg ladders are formed by the Fe sites. The Fe 2p core-level peaks of BaFe2X3 are broad and exhibit two components, indicating that itinerant and localized Fe 3d sites coexist similar to KxFe2-ySe2. The Fe 2p core-level peak of CsFe2Se3 is rather sharp and is accompanied by a charge-transfer satellite. The insulating ground state of CsFe2Se3 can be viewed as a Fe2+ Mott insulator in spite of the formal valence of +2.5. The itinerant versus localized behaviors can be associated with the stability of chalcogen p holes in the two-leg ladder structure.Comment: 5 pages, 5 figures, Accepted in publication for Physical Review

Hybrid Superpixel Segmentation

Poster presentation: paper no. 27postprin

Charmless two-body B decays: A global analysis with QCD factorization

In this paper, we perform a global analysis of $B \to PP$ and $PV$ decays with the QCD factorization approach. It is encouraging to observe that the predictions of QCD factorization are in good agreement with experiment. The best fit $\gamma$ is around $79^\circ$. The penguin-to-tree ratio $|P_{\pi \pi}/T_{\pi \pi}|$ of $\pi^+ \pi^-$ decays is preferred to be larger than 0.3. We also show the confidence levels for some interesting channels: $B^0 \to \pi^0 \pi^0$, $K^+ K^-$ and $B^+ \to \omega \pi^+$, $\omega K^+$. For $B \to \pi K^\ast$ decays, they are expected to have smaller branching ratios with more precise measurements.Comment: 20 pages, 4 figures, version to appear in Phys. Rev.

Quantum divide-and-conquer anchoring for separable non-negative matrix factorization

© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. It is NP-complete to find non-negative factors W and H with fixed rank r from a non-negative matrix X by minimizing ||X − WHτ||2F. Although the separability assumption (all data points are in the conical hull of the extreme rows) enables polynomial-time algorithms, the computational cost is not affordable for big data. This paper investigates how the power of quantum computation can be capitalized to solve the non-negative matrix factorization with the separability assumption (SNMF) by devising a quantum algorithm based on the divide-and-conquer anchoring (DCA) scheme [Zhou et al., 2013]. The design of quantum DCA (QDCA) is challenging. In the divide step, the random projections in DCA is completed by a quantum algorithm for linear operations, which achieves the exponential speedup. We then devise a heuristic post-selection procedure which extracts the information of anchors stored in the quantum states efficiently. Under a plausible assumption, QDCA performs efficiently, achieves the quantum speedup, and is beneficial for high dimensional problems

Investigation of the fire hazard of underground space fire scenarios in urban metro tunnels under natural ventilation: Analysis of the impact of tunnel slope on smoke back-layering length

The smoke back-layering length is a crucial parameter for evacuating people in both road and subway tunnel fires. This study investigates the fire hazard induced by carriage fire in inclined metro tunnels under natural ventilation. The parameter ‘transition slope’ is defined to measure the smoke flow from the carriage head in the upstream direction to the tunnel or not due to the stack effect of the tunnel slope. The aim of this paper is to analyse the effects of changes in cross-section, downstream length, tunnel slope, and carriage side-door coupling on smoke behaviour characteristics by experiment and simulation methods. A piecewise function expression between dimensionless smoke back-layering length, downstream length, and tunnel slope for carriage fires in an inclined tunnel under natural ventilation is proposed by theoretical analysis. At the same time, a 1:15 scale model experiment was conducted to initially analyse the characteristics of smoke movement. Following this, full-scale numerical simulations were employed to complement the model experiment and quantify the principles governing smoke movement. The experimental results show that the tunnel slope has a significant effect on the smoke back-layering length. In contrast, the influence of the heat release rate was found to be relatively minor. In addition, simulation results show that the tunnel slope has no significant effect on the smoke back-layering length when the fire location is approximately 20 m from the train head, and the tunnel slope is in the range of 2.29° ∼ 3.43° (4% ∼ 6%). For small tunnel slopes, smoke spreads in the tunnel, and the smoke back-layering length produced by the virtual fire source shows a different law from the previous study model. Finally, the correlation coefficient of the piecewise function in theoretical analysis is fitted by combining the experimental and numerical simulation results. Practical application This study provides valuable insights into the practical implications of controlling and mitigating the impact of fires in inclined metro tunnels. By understanding the critical role of tunnel slope and providing a quantitative tool for smoke spread law assessment, this study contributes to the enhancement of safety measures and the protection of lives in tunnel environments during fire incidents