Automated Driving Systems (ADS) open up a new domain for the automotive industry and offer new possibilities for future transportation with higher efficiency and comfortable experiences. However, ...perception and sensing for autonomous driving under adverse weather conditions have been the problem that keeps autonomous vehicles (AVs) from going to higher autonomy for a long time. This paper assesses the influences and challenges that weather brings to ADS sensors in a systematic way, and surveys the solutions against inclement weather conditions. State-of-the-art algorithms and deep learning methods on perception enhancement with regard to each kind of weather, weather status classification, and remote sensing are thoroughly reported. Sensor fusion solutions, weather conditions coverage in currently available datasets, simulators, and experimental facilities are categorized. Additionally, potential ADS sensor candidates and developing research directions such as V2X (Vehicle to Everything) technologies are discussed. By looking into all kinds of major weather problems, and reviewing both sensor and computer science solutions in recent years, this survey points out the main moving trends of adverse weather problems in perception and sensing, i.e., advanced sensor fusion and more sophisticated machine learning techniques; and also the limitations brought by emerging 1550 nm LiDARs. In general, this work contributes a holistic overview of the obstacles and directions of perception and sensing research development in terms of adverse weather conditions.
A soil bacterium, designated XQ2
T
, was isolated from Lang Mountain in Hunan province, P. R. China. The strain is Gram stain negative, facultative anaerobic, and the cells are motile and rod-shaped. ...The 16S rRNA gene sequence of strain XQ2
T
shared the highest similarities with
Hyphomicrobium sulfonivorans
S1
T
(97.1%),
Pedomicrobium manganicum
ACM 3038
T
(95.9%) and
Hyphomicrobium aestuarii
DSM 1564
T
(95.4%) and grouped with
H. sulfonivorans
S1
T
. The average nucleotide identity (ANI) values and the DNA–DNA hybridization (dDDH) values between strain XQ2
T
and
H. sulfonivorans
S1
T
were 86.6% and 55.4% respectively. Strain XQ2
T
had a genome size of 3.91 Mb and the average G+C content was 65.1%. The major fatty acids (> 5%) were C
18:1
ω
6
c
, C
18:1
ω
7
c
, C
19:0
cyclo
ω
8
c
, C
16:0
and C
18:0
. The major respiratory quinone was Q-9 (82.8%) and the minor one was Q-8 (17.2%). The polar lipids were diphosphatidylglycerol, phosphatidylglycerol, phosphatidylethanolamine, phosphatidylcholine, unidentified phospholipid and two unidentified lipids. On the basis of phenotypic, chemotaxonomic and phylogenetic characteristics, strain XQ2
T
represents a novel species of the genus
Hyphomicrobium,
for which the name
Hyphomicrobium album
sp. nov. is proposed. The type strain is XQ2
T
(= KCTC 82378
T
= CCTCC AB 2020178
T
). The genus description is also emended.
Most of the approaches used for Landslide inventory mapping (LIM) rely on traditional feature extraction and unsupervised classification algorithms. However, it is difficult to use these approaches ...to detect landslide areas because of the complexity and spatial uncertainty of landslides. In this letter, we propose a novel approach based on a fully convolutional network within pyramid pooling (FCN-PP) for LIM. The proposed approach has three advantages. First, this approach is automatic and insensitive to noise because multivariate morphological reconstruction is used for image preprocessing. Second, it is able to take into account features from multiple convolutional layers and explore efficiently the context of images, which leads to a good tradeoff between wider receptive field and the use of context. Finally, the selected PP module addresses the drawback of global pooling employed by convolutional neural network, FCN, and U-Net, and, thus, provides better feature maps for landslide areas. Experimental results show that the proposed FCN-PP is effective for LIM, and it outperforms the state-of-the-art approaches in terms of five metrics, <inline-formula> <tex-math notation="LaTeX">Precision </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">Recall </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">Overall~Error </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">F </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">score </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">Accuracy </tex-math></inline-formula>.
This study investigated the sensitivity and specificity of immunohistochemical (IHC) analysis using an anti-BRAF antibody to detect the presence of the BRAF V600E mutation in patients with metastatic ...melanoma. A total of 100 patients with American Joint Committee on Cancer stage IIIC unresectable or stage IV melanoma and who underwent tumor DNA BRAF mutation testing were selected. Paraffin-embedded, formalin-fixed melanoma biopsies were analyzed for the BRAF mutation status by independent, blinded observers using both conventional DNA molecular techniques and IHC with the novel BRAF V600E mutant-specific antibody, VE1. The antibody had a sensitivity of 97% (37/38) and a specificity of 98% (58/59) for detecting the presence of a BRAF V600E mutation. Of the BRAF-mutated cases, none of the non-V600E cases (including V600K) stained positive with the antibody (0/11). There were 5 cases with discordant BRAF mutation results. Additional molecular analysis confirmed the immunohistochemically obtained BRAF result in 3 cases, suggesting that the initial molecular testing results were incorrect. Two of these patients would not have received a BRAF inhibitor on the basis of the initial false-negative mutation testing result. Two cases remained discordant. The reported IHC method is an accurate, rapid, and cost-effective method for detecting V600E BRAF mutations in melanoma patients. Clinical use of the V600E BRAF antibody should be a valuable supplement to conventional mutation testing and allow V600E mutant metastatic melanoma patients to be triaged rapidly into appropriate treatment pathways.
In the field of intelligent vehicle technology, there is a high dependence on images captured under challenging conditions to develop robust perception algorithms. However, acquiring these images can ...be both time-consuming and dangerous. To address this issue, unpaired image-to-image translation models offer a solution by synthesizing samples of the desired domain, thus eliminating the reliance on ground truth supervision. However, the current methods predominantly focus on single projections rather than multiple solutions, not to mention controlling the direction of generation, which creates a scope for enhancement. In this study, we propose a generative adversarial network (GAN)–based model, which incorporates both a style encoder and a content encoder, specifically designed to extract relevant information from an image. Further, we employ a decoder to reconstruct an image using these encoded features, while ensuring that the generated output remains within a permissible range by applying a self-regression module to constrain the style latent space. By modifying the hyperparameters, we can generate controllable outputs with specific style codes. We evaluate the performance of our model by generating snow scenes on the Cityscapes and the EuroCity Persons datasets. The results reveal the effectiveness of our proposed methodology, thereby reinforcing the benefits of our approach in the ongoing evolution of intelligent vehicle technology.
Weather variation in the distribution of image data can cause a decline in the performance of existing visual algorithms during evaluation. Adding additional samples of target domain to training data ...or using pre-trained image restoration methods such as de-hazing, de-raining, and de-snowing, to improve the quality of input images are two promising solutions. In this work, we propose Multiple Weather Translation GAN (MWTG), a CycleGAN-based, dual-purpose framework that simultaneously learns weather generation and its removal from image data. MWTG consists of four GANs constrained using cycle consistency that carry out domain translation tasks between hazy, rainy, snowy, and clear weather, using an asymmetric approach. To increase network capacity, we employ a spatial feature transform (SFT) layer to fuse the features extracted from the weather layer, which contains high-level domain information from the previous generators. Further, we collect an unpaired, real-world driving dataset recorded under various weather conditions called Realistic Driving Scenes under Bad Weather (RDSBW). We qualitatively and quantitatively evaluate MWTG using the RDSBW and the variation of Cityscapes that synthesize weather effects, eg., FoggyCityscape. Our experimental results suggest that MWTG can generate realistic weather in clear images and also accurately remove noise from weather images. Furthermore, the SOTA pedestrian detector ASCP is shown to achieve an impressive gain in detection precision after image restoration using the proposed MWTG method.
Abstract
The topological materials have attracted much attention for their unique electronic structure and peculiar physical properties. ZrTe
5
has host a long-standing puzzle on its anomalous ...transport properties manifested by its unusual resistivity peak and the reversal of the charge carrier type. It is also predicted that single-layer ZrTe
5
is a two-dimensional topological insulator and there is possibly a topological phase transition in bulk ZrTe
5
. Here we report high-resolution laser-based angle-resolved photoemission measurements on the electronic structure and its detailed temperature evolution of ZrTe
5
. Our results provide direct electronic evidence on the temperature-induced Lifshitz transition, which gives a natural understanding on underlying origin of the resistivity anomaly in ZrTe
5
. In addition, we observe one-dimensional-like electronic features from the edges of the cracked ZrTe
5
samples. Our observations indicate that ZrTe
5
is a weak topological insulator and it exhibits a tendency to become a strong topological insulator when the layer distance is reduced.
PDF
