Timed Fault Tree Models of the China Yongwen Railway Accident

Safety is an essential requirement for railway transportation. There are many methods that have been developed to predict, prevent and mitigate accidents in this context. All of these methods have their own purpose and limitations. This paper presents a new useful analysis technique: timed fault tree analysis. This method extends traditional fault tree analysis with temporal events and fault characteristics. Timed Fault Trees (TFTs) can determine which faults need to be eliminated urgently, and it can also provide a safe time window to repair them. They can also be used to determine the time taken for railway maintenance requirements, and thereby improve maintenance efficiency, and reduce risks. In this paper, we present the features and functionality of a railway transportation system based on timed fault tree models. We demonstrate the applicability of our framework via a case study of the China Yongwen line railway accident

One-to-many face recognition with bilinear CNNs

The recent explosive growth in convolutional neural network (CNN) research has produced a variety of new architectures for deep learning. One intriguing new architecture is the bilinear CNN (B-CNN), which has shown dramatic performance gains on certain fine-grained recognition problems [15]. We apply this new CNN to the challenging new face recognition benchmark, the IARPA Janus Benchmark A (IJB-A) [12]. It features faces from a large number of identities in challenging real-world conditions. Because the face images were not identified automatically using a computerized face detection system, it does not have the bias inherent in such a database. We demonstrate the performance of the B-CNN model beginning from an AlexNet-style network pre-trained on ImageNet. We then show results for fine-tuning using a moderate-sized and public external database, FaceScrub [17]. We also present results with additional fine-tuning on the limited training data provided by the protocol. In each case, the fine-tuned bilinear model shows substantial improvements over the standard CNN. Finally, we demonstrate how a standard CNN pre-trained on a large face database, the recently released VGG-Face model [20], can be converted into a B-CNN without any additional feature training. This B-CNN improves upon the CNN performance on the IJB-A benchmark, achieving 89.5% rank-1 recall.Comment: Published version at WACV 201

Nitrogen Abundances and the Distance Moduli of the Pleiades and Hyades

Recent reanalyses of HIPPARCOS parallax data confirm a previously noted discrepancy with the Pleiades distance modulus estimated from main-sequence fitting in the color-magnitude diagram. One proposed explanation of this distance modulus discrepancy is a Pleiades He abundance that is significantly larger than the Hyades value. We suggest that, based on our theoretical and observational understanding of Galactic chemical evolution, nitrogen abundances may serve as a proxy for helium abundances of disk stars. Utilizing high-resolution near-UV Keck/HIRES spectroscopy, we determine N abundances in the Pleiades and Hyades dwarfs from NH features in the 3330 Ang region. While our Hyades N abundances show a modest 0.2 dex trend over a 800 K Teff range, we find the Pleiades N abundance (by number) is 0.13+/-0.05 dex lower than in the Hyades for stars in a smaller overlapping Teff range around 6000 K; possible systematic errors in the lower Pleiades N abundance result are estimated to be at the <0.10 dex level. Our results indicate [N/Fe]=0 for both the Pleiades and Hyades, consistent with the ratios exhibited by local Galactic disk field stars in other studies. If N production is a reliable tracer of He production in the disk, then our results suggest the Pleiades He abundance is no larger than that in the Hyades. This finding is supported by the relative Pleiades-Hyades C, O, and Fe abundances interpreted in the current context of Galactic chemical evolution, and is resistant to the effects on our derived N abundances of a He abundance difference like that needed to explain the Pleiades distance modulus discrepancy. A physical explanation of the Pleiades distance modulus discrepancy does not appear to be related to He abundance.Comment: Accepted for publication in the Publications of the Astronomical Society of the Pacifi

Physical drivers of the summer 2019 North Pacific marine heatwave.

Summer 2019 observations show a rapid resurgence of the Blob-like warm sea surface temperature (SST) anomalies that produced devastating marine impacts in the Northeast Pacific during winter 2013/2014. Unlike the original Blob, Blob 2.0 peaked in the summer, a season when little is known about the physical drivers of such events. We show that Blob 2.0 primarily results from a prolonged weakening of the North Pacific High-Pressure System. This reduces surface winds and decreases evaporative cooling and wind-driven upper ocean mixing. Warmer ocean conditions then reduce low-cloud fraction, reinforcing the marine heatwave through a positive low-cloud feedback. Using an atmospheric model forced with observed SSTs, we also find that remote SST forcing from the central equatorial and, surprisingly, the subtropical North Pacific Ocean contribute to the weakened North Pacific High. Our multi-faceted analysis sheds light on the physical drivers governing the intensity and longevity of summertime North Pacific marine heatwaves