5,912 research outputs found
COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation
Annotation of large-scale 3D data is notoriously cumbersome and costly. As an
alternative, weakly-supervised learning alleviates such a need by reducing the
annotation by several order of magnitudes. We propose COARSE3D, a novel
architecture-agnostic contrastive learning strategy for 3D segmentation. Since
contrastive learning requires rich and diverse examples as keys and anchors, we
leverage a prototype memory bank capturing class-wise global dataset
information efficiently into a small number of prototypes acting as keys. An
entropy-driven sampling technique then allows us to select good pixels from
predictions as anchors. Experiments on three projection-based backbones show we
outperform baselines on three challenging real-world outdoor datasets, working
with as low as 0.001% annotations
Skeleton Marching-based Parallel Vascular Geometry Reconstruction Using Implicit Functions
Fast high-precision patient-specific vascular tissue and geometric structure reconstruction is an essential task for vascular tissue engineering and computer-aided minimally invasive vascular disease diagnosis and surgery. In this paper, we present an effective vascular geometry reconstruction technique by representing a highly complicated geometric structure of a vascular system as an implicit function. By implicit geometric modelling, we are able to reduce the complexity and level of difficulty of this geometric reconstruction task and turn it into a parallel process of reconstructing a set of simple short tubular-like vascular sections, thanks to the easy-blending nature of implicit geometries on combining implicitly modelled geometric forms. The basic idea behind our technique is to consider this extremely difficult task as a process of team exploration of an unknown environment like a cave. Based on this idea, we developed a parallel vascular modelling technique, called Skeleton Marching, for fast vascular geometric reconstruction. With the proposed technique, we first extract the vascular skeleton system from a given volumetric medical image. A set of sub-regions of a volumetric image containing a vascular segment is then identified by marching along the extracted skeleton tree. A localised segmentation method is then applied to each of these sub-image blocks to extract a point cloud from the surface of the short simple blood vessel segment contained in the image block. These small point clouds are then fitted with a set of implicit surfaces in a parallel manner. A high-precision geometric vascular tree is then reconstructed by blending together these simple tubular-shaped implicit surfaces using the shape-preserving blending operations. Experimental results show the time required for reconstructing a vascular system can be greatly reduced by the proposed parallel technique
Fairy circles reveal the resilience of self-organized salt marshes
Spatial patterning is a fascinating theme in both theoretical and experimental ecology. It reveals resilience and stability to withstand external disturbances and environmental stresses. However, existing studies mainly focus on well-developed persistent patterns rather than transient patterns in self-organizing ecosystems. Here, combining models and experimental evidence, we show that transient fairy circle patterns in intertidal salt marshes can both infer the underlying ecological mechanisms and provide a measure of resilience. The models based on sulfide accumulation and nutrient depletion mechanisms reproduced the field-observed fairy circles, providing a generalized perspective on the emergence of transient patterns in salt marsh ecosystems. Field experiments showed that nitrogen fertilization mitigates depletion stress and shifts plant growth from negative to positive in the center of patches. Hence, nutrient depletion plays an overriding role, as only this process can explain the concentric rings. Our findings imply that the emergence of transient patterns can identify the ecological processes underlying pattern formation and the factors determining the ecological resilience of salt marsh ecosystems
Octaaquabis(μ2-1H-pyrazole-3,5-dicarboxylato)tricopper(II) tetrahydrate
In the trinucler CuII complex molecule of the title compound, [Cu3(C5HN2O4)2(H2O)8]·4H2O, the central CuII atom is located on an inversion centre and is coordinated in a distorted octahedral geometry. The equatorial sites are occupied by two N and two O atoms from two pyrazole-3,5-dicarboxylate ligands and the axial positions are occupied by two water molecules. The two other symmetry-related CuII atoms are pentacoordinated and assume a square-pyramidal geometry. In the crystal structure, coordinated and uncoordinated water molecules and carboxylate O atoms are linked by O—H⋯O hydrogen bonds
Storage of multiple single-photon pulses emitted from a quantum dot in a solid-state quantum memory
Quantum repeaters are critical components for distributing entanglement over
long distances in presence of unavoidable optical losses during transmission.
Stimulated by Duan-Lukin-Cirac-Zoller protocol, many improved quantum-repeater
protocols based on quantum memories have been proposed, which commonly focus on
the entanglement-distribution rate. Among these protocols, the elimination of
multi-photons (multi-photon-pairs) and the use of multimode quantum memory are
demonstrated to have the ability to greatly improve the
entanglement-distribution rate. Here, we demonstrate the storage of
deterministic single photons emitted from a quantum dot in a
polarization-maintaining solid-state quantum memory; in addition,
multi-temporal-mode memory with , and narrow single-photon pulses
is also demonstrated. Multi-photons are eliminated, and only one photon at most
is contained in each pulse. Moreover, the solid-state properties of both
sub-systems make this configuration more stable and easier to be scalable. Our
work will be helpful in the construction of efficient quantum repeaters based
on all-solid-state devicesComment: Published version, including supplementary materia
COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation
International audienceAnnotation of large-scale 3D data is notoriously cumbersome and costly. As an alternative, weakly-supervised learning alleviates such a need by reducing the annotation by several order of magnitudes. We propose COARSE3D, a novel architecture-agnostic contrastive learning strategy for 3D segmentation. Since contrastive learning requires rich and diverse examples as keys and anchors, we leverage a prototype memory bank capturing class-wise global dataset information efficiently into a small number of prototypes acting as keys. An entropy-driven sampling technique then allows us to select good pixels from predictions as anchors. Experiments on three projection-based backbones show we outperform baselines on three challenging real-world outdoor datasets, working with as low as 0.001% annotations
A Random Forest and Current Fault Texture Feature-Based Method for Current Sensor Fault Diagnosis in Three-Phase PWM VSR
Three-phase PWM voltage-source rectifier (VSR) systems have been widely used
in various energy conversion systems, where current sensors are the key
component for state monitoring and system control. The current sensor faults
may bring hidden danger or damage to the whole system; therefore, this paper
proposed a random forest (RF) and current fault texture feature-based method
for current sensor fault diagnosis in three-phase PWM VSR systems. First, the
three-phase alternating currents (ACs) of the three-phase PWM VSR are collected
to extract the current fault texture features, and no additional hardware
sensors are needed to avoid causing additional unstable factors. Then, the
current fault texture features are adopted to train the random forest current
sensor fault detection and diagnosis (CSFDD) classifier, which is a data-driven
CSFDD classifier. Finally, the effectiveness of the proposed method is verified
by simulation experiments. The result shows that the current sensor faults can
be detected and located successfully and that it can effectively provide fault
locations for maintenance personnel to keep the stable operation of the whole
system.Comment: Frontiers in Energy Researc
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