GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer

We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods have shown great potential through bypassing the detection of repeatable keypoints which is difficult to do especially in low-overlap scenarios. They seek correspondences over downsampled superpoints, which are then propagated to dense points. Superpoints are matched based on whether their neighboring patches overlap. Such sparse and loose matching requires contextual features capturing the geometric structure of the point clouds. We propose Geometric Transformer, or GeoTransformer for short, to learn geometric feature for robust superpoint matching. It encodes pair-wise distances and triplet-wise angles, making it invariant to rigid transformation and robust in low-overlap cases. The simplistic design attains surprisingly high matching accuracy such that no RANSAC is required in the estimation of alignment transformation, leading to $100$ times acceleration. Extensive experiments on rich benchmarks encompassing indoor, outdoor, synthetic, multiway and non-rigid demonstrate the efficacy of GeoTransformer. Notably, our method improves the inlier ratio by $18{\sim}31$ percentage points and the registration recall by over $7$ points on the challenging 3DLoMatch benchmark. Our code and models are available at \url{https://github.com/qinzheng93/GeoTransformer}.Comment: Accepted by TPAMI. Extended version of our CVPR 2022 paper [arXiv:2202.06688

Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer in which the tumor-vascular involvement greatly affects the resectability and, thus, overall survival of patients. However, current prognostic prediction methods fail to explicitly and accurately investigate relationships between the tumor and nearby important vessels. This paper proposes a novel learnable neural distance that describes the precise relationship between the tumor and vessels in CT images of different patients, adopting it as a major feature for prognosis prediction. Besides, different from existing models that used CNNs or LSTMs to exploit tumor enhancement patterns on dynamic contrast-enhanced CT imaging, we improved the extraction of dynamic tumor-related texture features in multi-phase contrast-enhanced CT by fusing local and global features using CNN and transformer modules, further enhancing the features extracted across multi-phase CT images. We extensively evaluated and compared the proposed method with existing methods in the multi-center (n=4) dataset with 1,070 patients with PDAC, and statistical analysis confirmed its clinical effectiveness in the external test set consisting of three centers. The developed risk marker was the strongest predictor of overall survival among preoperative factors and it has the potential to be combined with established clinical factors to select patients at higher risk who might benefit from neoadjuvant therapy.Comment: MICCAI 202

CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans

Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases. This might severely limit AI's clinical adoption. A certain number of AI models need to be assembled non-trivially to match the diagnostic process of a human reading a CT scan. In this paper, we construct a Unified Tumor Transformer (CancerUniT) model to jointly detect tumor existence & location and diagnose tumor characteristics for eight major cancers in CT scans. CancerUniT is a query-based Mask Transformer model with the output of multi-tumor prediction. We decouple the object queries into organ queries, tumor detection queries and tumor diagnosis queries, and further establish hierarchical relationships among the three groups. This clinically-inspired architecture effectively assists inter- and intra-organ representation learning of tumors and facilitates the resolution of these complex, anatomically related multi-organ cancer image reading tasks. CancerUniT is trained end-to-end using a curated large-scale CT images of 10,042 patients including eight major types of cancers and occurring non-cancer tumors (all are pathology-confirmed with 3D tumor masks annotated by radiologists). On the test set of 631 patients, CancerUniT has demonstrated strong performance under a set of clinically relevant evaluation metrics, substantially outperforming both multi-disease methods and an assembly of eight single-organ expert models in tumor detection, segmentation, and diagnosis. This moves one step closer towards a universal high performance cancer screening tool.Comment: ICCV 2023 Camera Ready Versio

Giant Hall Switching by Surface-State-Mediated Spin-Orbit Torque in a Hard Ferromagnetic Topological Insulator

Topological insulators (TI) can apply highly efficient spin-orbit torque (SOT) and manipulate the magnetization with their unique topological surface states, and their magnetic counterparts, magnetic topological insulators (MTI) offer magnetization without shunting and are one of the highest in SOT efficiency. Here, we demonstrate efficient SOT switching of a hard MTI, V-doped (Bi,Sb)2Te3 (VBST) with a large coercive field that can prevent the influence of an external magnetic field and a small magnetization to minimize stray field. A giant switched anomalous Hall resistance of 9.2 $k\Omega$ is realized, among the largest of all SOT systems. The SOT switching current density can be reduced to $2.8\times10^5 A/cm^2$, and the switching ratio can be enhanced to 60%. Moreover, as the Fermi level is moved away from the Dirac point by both gate and composition tuning, VBST exhibits a transition from edge-state-mediated to surface-state-mediated transport, thus enhancing the SOT effective field to $1.56\pm 0.12 T/ (10^6 A/cm^2)$ and the spin Hall angle to $23.2\pm 1.8$ at 5 K. The findings establish VBST as an extraordinary candidate for energy-efficient magnetic memory devices

Bioinspired Universal Flexible Elastomer-Based Microchannels

Flexible and stretchable microscale fluidic devices have a broad range of potential applications, ranging from electronic wearable devices for convenient digital lifestyle to biomedical devices. However, simple ways to achieve stable flexible and stretchable fluidic microchannels with dynamic liquid transport have been challenging because every application for elastomeric microchannels is restricted by their complex fabrication process and limited material selection. Here, a universal strategy for building microfluidic devices that possess exceptionally stable and stretching properties is shown. The devices exhibit superior mechanical deformability, including high strain (967%) and recovery ability, where applications as both strain sensor and pressure‐flow regulating device are demonstrated. Various microchannels are combined with organic, inorganic, and metallic materials as stable composite microfluidics. Furthermore, with surface chemical modification these stretchable microfluidic devices can also obtain antifouling property to suit for a broad range of industrial and biomedical applications.Chemistry and Chemical Biolog

Dendritic Cell‐Mediated Cross‐Priming by a Bispecific Neutralizing Antibody Boosts Cytotoxic T Cell Responses and Protects Mice against SARS‐CoV‐2

SARS-CoV-2 B.1.351 and B.1.167.2 viruses used in this study were obtained through the European Virus Archive Global (EVA-GLOBAL) project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 653316. SARS-CoV-2 B.1 (MAD6 isolate) was kindly provided by José M. Honrubia and Luis Enjuanes (CNB-CSIC, Madrid, Spain). The authors thank Centro de Investigación en Sanidad Animal (CISA)-Instituto Nacional de Investigaciones Agrarias (INIA-CSIC) (Valdeolmos, Madrid, Spain) for the BSL-3 facilities. Research in LAV laboratory was funded by the BBVA Foundation (Ayudas Fundación BBVA a Equipos de Investigación Científica SARS-CoV-2 y COVID19); the MCIN/AEI/10.13039/501100011033 (PID2020-117323RB-I00 and PDC2021-121711-I00), partially supported by the European Regional Development Fund (ERDF); the Carlos III Health Institute (ISCIII) (DTS20/00089), partially supported by the ERDF, the Spanish Association Against Cancer (AECC 19084); the CRIS Cancer Foundation (FCRISIFI-2018 and FCRIS-2021-0090), the Fundación Caixa-Health Research (HR21-00761 project IL7R_LungCan), and the Comunidad de Madrid (P2022/BMD-7225 NEXT_GEN_CART_MAD-CM). Work in the DS laboratory was funded by the CNIC; the European Union’s Horizon 2020 research and innovation program under grant agreement ERC-2016-Consolidator Grant 725091; MCIN/AEI/10.13039/501100011033 (PID2019-108157RB); Comunidad de Madrid (B2017/BMD-3733 Immunothercan-CM); Atresmedia (Constantes y Vitales prize); Fondo Solidario Juntos (Banco Santander); and “La Caixa” Foundation (LCF/PR/HR20/00075). The CNIC was supported by the ISCIII, the MCIN and the Pro CNIC Foundation and is a Severo Ochoa Center of Excellence (CEX2020- 001041-S funded by MCIN/AEI/10.13039/501100011033). Research in RD laboratory was supported by the ISCIII (PI2100989) and CIBERINFEC; the European Commission Horizon 2020 Framework Programme (grant numbers 731868 project VIRUSCAN FETPROACT-2016, and 101046084 project EPIC-CROWN-2); and the Fundación CaixaHealth Research (grant number HR18-00469 project StopEbola). Research in CNB-CSIC laboratory was funded by Fondo Supera COVID19 (Crue Universidades-Banco Santander) grant, CIBERINFEC, and Spanish Research Council (CSIC) grant 202120E079 (to J.G.-A.), CSIC grant 2020E84 (to M.E.), MCIN/AEI/10.13039/501100011033 (PID2020- 114481RB-I00 to J.G-A. and M.E.), and by the European CommissionNextGenerationEU, through CSIC’s Global Health Platform (PTI Salud Global) to J.G.-A. and M.E. Work in the CIB-CSIC laboratory was supported by MCIN/AEI/10.13039/501100011033 (PID2019-104544GB-I00 and 2023AEP105 to CA, and PID2020-113225GB-I00 to F.J.B.). Cryo-EM data were collected at the Maryland Center for Advanced Molecular Analyses which was supported by MPOWER (The University of Maryland Strategic Partnership). I.H.-M. receives the support of a fellowship from la Caixa Foundation (ID 100010434, fellowship code: LCF/BQ/IN17/11620074) and from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 71367. L.R.-P. was supported by a predoctoral fellowship from the Immunology Chair, Universidad Francisco de Vitoria/Merck.S

Polarizabilities of Adsorbed and Assembled Molecules: Measuring the Conductance through Buried Contacts

We have measured the polarizabilities of four families of molecules adsorbed to Au{111} surfaces, with structures ranging from fully saturated to fully conjugated, including single-molecule switches. Measured polarizabilities increase with increasing length and conjugation in the adsorbed molecules and are consistent with theoretical calculations. For single-molecule switches, the polarizability reflects the difference in substrate-molecule electronic coupling in the ON and OFF conductance states. Calculations suggest that the switch between the two conductance states is correlated with an oxidation state change in a nitro functional group in the switch molecules