DA-RAW: Domain Adaptive Object Detection for Real-World Adverse Weather Conditions

Despite the success of deep learning-based object detection methods in recent years, it is still challenging to make the object detector reliable in adverse weather conditions such as rain and snow. For the robust performance of object detectors, unsupervised domain adaptation has been utilized to adapt the detection network trained on clear weather images to adverse weather images. While previous methods do not explicitly address weather corruption during adaptation, the domain gap between clear and adverse weather can be decomposed into two factors with distinct characteristics: a style gap and a weather gap. In this paper, we present an unsupervised domain adaptation framework for object detection that can more effectively adapt to real-world environments with adverse weather conditions by addressing these two gaps separately. Our method resolves the style gap by concentrating on style-related information of high-level features using an attention module. Using self-supervised contrastive learning, our framework then reduces the weather gap and acquires instance features that are robust to weather corruption. Extensive experiments demonstrate that our method outperforms other methods for object detection in adverse weather conditions.Comment: Accepted to ICRA 2024. Our project website can be found at https://bit.ly/3yccTR

Weyl Fermion with various chiralities in a f-electron ferromagnetic system: PrB4

Rare-earth tetraborides ($R$B$_{4}$) have attracted a lot of recent attention due to their intriguing electronic, magnetic, and topological properties. We have theoretically investigated topological properties of PrB$_{4}$, which is unique among $R$B$_{4}$ family due to its ferromagnetic ground state. We have discovered that PrB$_{4}$ is an intrinsic magnetic Weyl system possessing multiple topological band crossings with various chiral charges. Density-functional-theory band calculations combined with tight-binding band analysis reveal large Fermi-arc surface states, which are characteristic fingerprints of Weyl fermions. Anomalous Hall conductivity is estimated to be very large, ranging from 500 to 1000 ($Ω\cdot$cm)$^{-1}$ near the Fermi level, which also demonstrates the topological Weyl character of ferromagnetic PrB$_{4}$. These findings suggest that PrB$_{4}$, being a potential candidate of magnetic Weyl system, would be a promising rare-earth topological system for applications to next-generation spintronic and photonic devices.7 pages, 6 figure

Angle-resolved and resonant photoemission spectroscopy study of the Fermi surface reconstruction in the charge density wave systems CeTe2 and PrTe2

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Diagnostic accuracy of a three-protein signature in women with suspicious breast lesions: a multicenter prospective trial

Background Mammography screening has been proven to detect breast cancer at an early stage and reduce mortality; however, it has low accuracy in young women or women with dense breasts. Blood-based diagnostic tools may overcome the limitations of mammography. This study assessed the diagnostic performance of a three-protein signature in patients with suspicious breast lesions. Findings This trial (MAST; KCT0004847) was a prospective multicenter observational trial. Three-protein signature values were obtained using serum and plasma from women with suspicious lesions for breast malignancy before tumor biopsy. Additionally, blood samples from women who underwent clear or benign mammography were collected for the assays. Among 642 participants, the sensitivity, specificity, and overall accuracy values of the three-protein signature were 74.4%, 66.9%, and 70.6%, respectively, and the concordance index was 0.698 (95% CI 0.656, 0.739). The diagnostic performance was not affected by the demographic features, clinicopathologic characteristics, and co-morbidities of the participants. Conclusions The present trial showed an accuracy of 70.6% for the three-protein signature. Considering the value of blood-based biomarkers for the early detection of breast malignancies, further evaluation of this proteomic assay is warranted in larger, population-level trials. This Multi-protein Assessment using Serum to deTermine breast lesion malignancy (MAST) was registered at the Clinical Research Information Service of Korea with the identification number of KCT0004847 (https://cris.nih.go.kr).This study was supported by the Bertis Inc. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication

Evidential Semantic Mapping in Off-road Environments with Uncertainty-aware Bayesian Kernel Inference

Robotic mapping with Bayesian Kernel Inference (BKI) has shown promise in creating semantic maps by effectively leveraging local spatial information. However, existing semantic mapping methods face challenges in constructing reliable maps in unstructured outdoor scenarios due to unreliable semantic predictions. To address this issue, we propose an evidential semantic mapping, which can enhance reliability in perceptually challenging off-road environments. We integrate Evidential Deep Learning into the semantic segmentation network to obtain the uncertainty estimate of semantic prediction. Subsequently, this semantic uncertainty is incorporated into an uncertainty-aware BKI, tailored to prioritize more confident semantic predictions when accumulating semantic information. By adaptively handling semantic uncertainties, the proposed framework constructs robust representations of the surroundings even in previously unseen environments. Comprehensive experiments across various off-road datasets demonstrate that our framework enhances accuracy and robustness, consistently outperforming existing methods in scenes with high perceptual uncertainties.Comment: Our project website can be found at https://kjyoung.github.io/Homepage/#/Projects/Evidential-Semantic-Mappin