RDMNet: Reliable Dense Matching Based Point Cloud Registration for Autonomous Driving

Point cloud registration is an important task in robotics and autonomous driving to estimate the ego-motion of the vehicle. Recent advances following the coarse-to-fine manner show promising potential in point cloud registration. However, existing methods rely on good superpoint correspondences, which are hard to be obtained reliably and efficiently, thus resulting in less robust and accurate point cloud registration. In this paper, we propose a novel network, named RDMNet, to find dense point correspondences coarse-to-fine and improve final pose estimation based on such reliable correspondences. Our RDMNet uses a devised 3D-RoFormer mechanism to first extract distinctive superpoints and generates reliable superpoints matches between two point clouds. The proposed 3D-RoFormer fuses 3D position information into the transformer network, efficiently exploiting point clouds' contextual and geometric information to generate robust superpoint correspondences. RDMNet then propagates the sparse superpoints matches to dense point matches using the neighborhood information for accurate point cloud registration. We extensively evaluate our method on multiple datasets from different environments. The experimental results demonstrate that our method outperforms existing state-of-the-art approaches in all tested datasets with a strong generalization ability.Comment: 11 pages, 9 figure

Single-site catalyst promoters accelerate metal- catalyzed nitroarene hydrogenation

Atomically dispersed supported metal catalysts are drawing wide attention because of the opportunities they offer for new catalytic properties combined with efficient use of the metals. We extend this class of materials to catalysts that incorporate atomically dispersed metal atoms as promoters. The catalysts are used for the challenging nitroarene hydro- genation and found to have both high activity and selectivity. The promoters are single-site Sn on TiO2 supports that incorporate metal nanoparticle catalysts. Represented as M/Sn- TiO2 (M = Au, Ru, Pt, Ni), these catalysts decidedly outperform the unpromoted supported metals, even for hydrogenation of nitroarenes substituted with various reducible groups. The high activity and selectivity of these catalysts result from the creation of oxygen vacancies on the TiO2 surface by single-site Sn, which leads to efficient, selective activation of the nitro group coupled with a reaction involving hydrogen atoms activated on metal nanoparticles

Calibrationless Reconstruction of Uniformly-Undersampled Multi-Channel MR Data with Deep Learning Estimated ESPIRiT Maps

Purpose: To develop a truly calibrationless reconstruction method that derives ESPIRiT maps from uniformly-undersampled multi-channel MR data by deep learning. Methods: ESPIRiT, one commonly used parallel imaging reconstruction technique, forms the images from undersampled MR k-space data using ESPIRiT maps that effectively represents coil sensitivity information. Accurate ESPIRiT map estimation requires quality coil sensitivity calibration or autocalibration data. We present a U-Net based deep learning model to estimate the multi-channel ESPIRiT maps directly from uniformly-undersampled multi-channel multi-slice MR data. The model is trained using fully-sampled multi-slice axial brain datasets from the same MR receiving coil system. To utilize subject-coil geometric parameters available for each dataset, the training imposes a hybrid loss on ESPIRiT maps at the original locations as well as their corresponding locations within the standard reference multi-slice axial stack. The performance of the approach was evaluated using publicly available T1-weighed brain and cardiac data. Results: The proposed model robustly predicted multi-channel ESPIRiT maps from uniformly-undersampled k-space data. They were highly comparable to the reference ESPIRiT maps directly computed from 24 consecutive central k-space lines. Further, they led to excellent ESPIRiT reconstruction performance even at high acceleration, exhibiting a similar level of errors and artifacts to that by using reference ESPIRiT maps. Conclusion: A new deep learning approach is developed to estimate ESPIRiT maps directly from uniformly-undersampled MR data. It presents a general strategy for calibrationless parallel imaging reconstruction through learning from coil and protocol specific data

On the topological surface states of the intrinsic magnetic topological insulator Mn-Bi-Te family

We review recent progress in the electronic structure study of intrinsic magnetic topological insulators (MnBi$_2$Te$_4$)(Bi$_2$Te$_3$)$_n$ ($n=0,1,2,3$) family. Specifically, we focus on the ubiquitously (nearly) gapless behavior of the topological surface state Dirac cone observed by photoemission spectroscopy, even though a large Dirac gap is expected because of surface ferromagnetic order. The dichotomy between experiment and theory concerning this gap behavior is perhaps the most critical and puzzling question in this frontier. We discuss various proposals accounting for the lack of magnetic effect on the topological surface state Dirac cone, which are mainly categorized into two pictures, magnetic reconfiguration, and topological surface state redistribution. Band engineering towards opening a magnetic gap of topological surface states provides great opportunities to realize quantized topological transport and axion electrodynamics at higher temperatures

Comparison of pneumonitis risk between immunotherapy alone and in combination with chemotherapy: an observational, retrospective pharmacovigilance study

Importance: Checkpoint inhibitor pneumonitis (CIP) is a rare but serious adverse event that may impact treatment decisions. However, there is limited information comparing CIP risks between immune checkpoint inhibitor (ICI) monotherapy and combination with chemotherapy due to a lack of direct cross-comparison in clinical trials.Objective: To determine whether ICI combination with chemotherapy is superior to ICI in other drug regimens (including monotherapy) in terms of CIP risk.Study Design and Methods: This observational, cross-sectional and worldwide pharmacovigilance cohort study included patients who developed CIP from the World Health Organization database (WHO) VigiBase and the US Food and Drug Administration Adverse Event Reporting System (FAERS) database. Individual case safety reports (ICSR) were extracted from 2015 to 2020 in FAERS and from 1967 to 2020 in VigiBase. Timing and reporting odds ratio (ROR) of CIP in different treatment strategies were used to detect time-to-onset and the risk of pneumonitis after different immunotherapy regimens.Results: A total of 93,623 and 114,704 ICI-associated ICSRs were included in this study from VigiBase and FAERS databases respectively. 3450 (3.69%) and 3278 (2.86%) CIPs occurred after therapy initiation with a median of 62 days (VigiBase) and 40 days (FAERS). Among all the CIPs, 274 (7.9%) and 537 (16.4%) CIPs were associated with combination therapies. ICIs plus chemotherapy combination was associated with pneumonitis in both VigiBase [ROR 1.35, 95% CI 1.18-1.52] and FAERS [ROR 1.39, 95% CI 1.27–1.53]. The combination of anti-PD-1 antibodies and anti-CTLA-4 antibodies with chemotherapy demonstrated an association with pneumonitis in both VigiBase [PD-1+chemotherapy: 1.76, 95% CI 1.52-2.05; CTLA-4+chemotherapy: 2.36, 95% CI 1.67-3.35] and FAERS [PD-1+chemotherapy: 1.70, 95% CI 1.52-1.91; CTLA-4+chemotherapy: 1.70, 95% CI 1.31-2.20]. Anti-PD-L1 antibodies plus chemotherapy combinations did not show the association.Conclusion: Compared to ICI in other drug regimens (including monotherapy), the combination of ICI plus chemotherapy is significantly associated with higher pneumonitis toxicity. Anti-PD-1/CTLA4 medications in combination with chemotherapy should be obviated in patients with potential risk factors for CIP.Trial Registration: clinicaltrials.gov, ChiCTR220005906