161 research outputs found
Online Adaptation for Implicit Object Tracking and Shape Reconstruction in the Wild
Tracking and reconstructing 3D objects from cluttered scenes are the key
components for computer vision, robotics and autonomous driving systems. While
recent progress in implicit function has shown encouraging results on
high-quality 3D shape reconstruction, it is still very challenging to
generalize to cluttered and partially observable LiDAR data. In this paper, we
propose to leverage the continuity in video data. We introduce a novel and
unified framework which utilizes a neural implicit function to simultaneously
track and reconstruct 3D objects in the wild. Our approach adapts the DeepSDF
model (i.e., an instantiation of the implicit function) in the video online,
iteratively improving the shape reconstruction while in return improving the
tracking, and vice versa. We experiment with both Waymo and KITTI datasets and
show significant improvements over state-of-the-art methods for both tracking
and shape reconstruction tasks. Our project page is at
https://jianglongye.com/implicit-tracking .Comment: Accepted to RA-L 2022 & IROS 2022. Project page:
https://jianglongye.com/implicit-trackin
Learning Continuous Grasping Function with a Dexterous Hand from Human Demonstrations
We propose to learn to generate grasping motion for manipulation with a
dexterous hand using implicit functions. With continuous time inputs, the model
can generate a continuous and smooth grasping plan. We name the proposed model
Continuous Grasping Function (CGF). CGF is learned via generative modeling with
a Conditional Variational Autoencoder using 3D human demonstrations. We will
first convert the large-scale human-object interaction trajectories to robot
demonstrations via motion retargeting, and then use these demonstrations to
train CGF. During inference, we perform sampling with CGF to generate different
grasping plans in the simulator and select the successful ones to transfer to
the real robot. By training on diverse human data, our CGF allows
generalization to manipulate multiple objects. Compared to previous planning
algorithms, CGF is more efficient and achieves significant improvement on
success rate when transferred to grasping with the real Allegro Hand. Our
project page is at https://jianglongye.com/cgf .Comment: Project page: https://jianglongye.com/cg
Causal relationships between susceptibility and severity of COVID-19 and neuromyelitis optica spectrum disorder (NMOSD) in European population: a bidirectional Mendelian randomized study
BackgroundNeurological disorders can be caused by viral infections. The association between viral infections and neuromyelitis optica spectrum disorder (NMOSD) has been well-documented for a long time, and this connection has recently come to attention with the occurrence of SARS-CoV-2 infection. However, the precise nature of the causal connection between NMOSD and COVID-19 infection remains uncertain.MethodsTo investigate the causal relationship between COVID-19 and NMOSD, we utilized a two-sample Mendelian randomization (MR) approach. This analysis was based on the most extensive and recent genome-wide association study (GWAS) that included SARS-CoV-2 infection data (122616 cases and 2475240 controls), hospitalized COVID-19 data (32519 cases and 2062805 controls), and data on severe respiratory confirmed COVID-19 cases (13769 cases and 1072442 controls). Additionally, we incorporated a GWAS meta-analysis comprising 132 cases of AQP4-IgG-seropositive NMOSD (NMO-IgG+), 83 cases of AQP4-IgG-seronegative NMOSD (NMO-IgG−), and 1244 controls.ResultsThe findings of our study indicate that the risk of developing NMO-IgG+ is elevated when there is a genetic predisposition to SARS-CoV-2 infection (OR = 5.512, 95% CI = 1.403-21.657, P = 0.014). Furthermore, patients with genetically predicted NMOSD did not exhibit any heightened susceptibility to SARS-CoV2 infection, COVID-19 hospitalization, or severity.Conclusionour study using Mendelian randomization (MR) revealed, for the first time, that the presence of genetically predicted SARS-CoV2 infection was identified as a contributing factor for NMO-IgG+ relapses
MVDream: Multi-view Diffusion for 3D Generation
We propose MVDream, a multi-view diffusion model that is able to generate
geometrically consistent multi-view images from a given text prompt. By
leveraging image diffusion models pre-trained on large-scale web datasets and a
multi-view dataset rendered from 3D assets, the resulting multi-view diffusion
model can achieve both the generalizability of 2D diffusion and the consistency
of 3D data. Such a model can thus be applied as a multi-view prior for 3D
generation via Score Distillation Sampling, where it greatly improves the
stability of existing 2D-lifting methods by solving the 3D consistency problem.
Finally, we show that the multi-view diffusion model can also be fine-tuned
under a few shot setting for personalized 3D generation, i.e. DreamBooth3D
application, where the consistency can be maintained after learning the subject
identity.Comment: Our project page is https://MV-Dream.github.i
GIS-based landslide susceptibility modeling using data mining techniques
Introduction: Landslide is one of the most widespread geohazards around the world. Therefore, it is necessary and meaningful to map regional landslide susceptibility for landslide mitigation. In this research, landslide susceptibility maps were produced by four models, namely, certainty factors (CF), naive Bayes (NB), J48 decision tree (J48), and multilayer perceptron (MLP) models.Methods: In the first step, 328 landslides were identified via historical data, interpretation of remote sensing images, and field investigation, and they were divided into two subsets that were assigned different uses: 70% subset for training and 30% subset for validating. Then, twelve conditioning factors were employed, namely, altitude, slope angle, slope aspect, plan curvature, profile curvature, TWI, NDVI, distance to rivers, distance to roads, land use, soil, and lithology. Later, the importance of each conditioning factor was analyzed by average merit (AM) values, and the relationship between landslide occurrence and various factors was evaluated using the certainty factor (CF) approach. In the next step, the landslide susceptibility maps were produced based on four models, and the effect of the four models were quantitatively compared by receiver operating characteristic (ROC) curves, area under curve (AUC) values, and non-parametric tests.Results: The results demonstrated that all the four models can reasonably assess landslide susceptibility. Of these four models, the CF model has the best predictive performance for the training (AUC=0.901) and validating data (AUC=0.892).Discussion: The proposed approach is an innovative method that may also help other scientists to develop landslide susceptibility maps in other areas and that could be used for geo-environmental problems besides natural hazard assessments
Copper-based charge transfer multiferroics with a configuration
Multiferroics are materials with a coexistence of magnetic and ferroelectric
order allowing the manipulation of magnetism by applications of an electric
field through magnetoelectric coupling effects. Here we propose an idea to
design a class of multiferroics with a configuration using the magnetic
order in copper-oxygen layers appearing in copper oxide high-temperature
superconductors by inducing ferroelectricity. Copper-based charge transfer
multiferroics SnCuO2 and PbCuO2 having the inversion symmetry breaking
polar space group are predicted to be such materials. The active inner s
electrons in Sn and Pb hybridize with O states leading the buckling in
copper-oxygen layers and thus induces ferroelectricity, which is known as the
lone pair mechanism. As a result of the configuration, SnCuO2 and PbCuO2
are charge transfer insulators with the antiferromagnetic ground state of the
moment on Cu retaining some strongly correlated physical properties of parent
compounds of copper oxide high-temperature superconductors. Our work reveals
the possibility of designing multiferroics based on copper oxide
high-temperature superconductors.Comment: 18 pages, 5 figures, 1 tabl
Elemental topological ferroelectrics and polar metals of few-layer materials
Ferroelectricity can exist in elemental phases as a result of charge
transfers between atoms occupying inequivalent Wyckoff positions. We
investigate the emergence of ferroelectricity in two-dimensional elemental
materials with buckled honeycomb lattices. Various multi-bilayer structures
hosting ferroelectricity are designed by stacking-engineering. Ferroelectric
materials candidates formed by group IV and V elements are predicted
theoretically. Ultrathin Bi films show layer-stacking-dependent physical
properties of ferroelectricity, topology, and metallicity. The two-bilayer Bi
film with a polar stacking sequence is found to be an elemental topological
ferroelectric material. Three and four bilayers Bi films with polar structures
are ferroelectric-like elemental polar metals with topological nontrivial edge
states. For Ge and Sn, trivial elemental polar metals are predicted. Our work
reveals the possibility of design two-dimensional elemental topological
ferroelectrics and polar metals by stacking-engineering.Comment: 18 pages, 6 figure
GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields
It is a long-standing problem in robotics to develop agents capable of
executing diverse manipulation tasks from visual observations in unstructured
real-world environments. To achieve this goal, the robot needs to have a
comprehensive understanding of the 3D structure and semantics of the scene. In
this work, we present , a visual behavior cloning agent for
multi-task robotic manipulation with eneralizable eural
feature ields. GNFactor jointly optimizes a generalizable neural
field (GNF) as a reconstruction module and a Perceiver Transformer as a
decision-making module, leveraging a shared deep 3D voxel representation. To
incorporate semantics in 3D, the reconstruction module utilizes a
vision-language foundation model (, Stable Diffusion) to distill
rich semantic information into the deep 3D voxel. We evaluate GNFactor on 3
real robot tasks and perform detailed ablations on 10 RLBench tasks with a
limited number of demonstrations. We observe a substantial improvement of
GNFactor over current state-of-the-art methods in seen and unseen tasks,
demonstrating the strong generalization ability of GNFactor. Our project
website is https://yanjieze.com/GNFactor/ .Comment: CoRL 2023 Oral. Website: https://yanjieze.com/GNFactor
Effects of Welding Heat Input on Microstructure and Electrochemical Behavior of Flux-Cored Arc-Welded Q690 HSLA Steel
Anisotropic, Intermediate Coupling Superconductivity in Cu0.03TaS2
The anisotropic superconducting state properties in Cu0.03TaS2 have been
investigated by magnetization, magnetoresistance, and specific heat
measurements. It clearly shows that Cu0.03TaS2 undergoes a superconducting
transition at TC = 4.03 K. The obtained superconducting parameters demonstrate
that Cu0.03TaS2 is an anisotropic type-II superconductor. Combining specific
heat jump = 1.6(4), gap ratio 2/kBTC = 4.0(9) and the estimated electron-phonon
coupling constant ~ 0.68, the superconductivity in Cu0.03TaS2 is explained
within the intermediate coupling BCS scenario. First-principles electronic
structure calculations suggest that copper intercalation of 2H-TaS2 causes a
considerable increase of the Fermi surface volume and the carrier density,
which suppresses the CDW fluctuation and favors the raise of TC.Comment: 16pages, 5figure
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