Curriculumformer: Taming Curriculum Pre-Training for Enhanced 3-D Point Cloud Understanding.

Learning universal representations of 3-D point clouds is essential for reducing the need for manual annotation of large-scale and irregular point cloud datasets. The current modus operandi for representative learning is self-supervised learning, which has shown great potential for improving point cloud understanding. Nevertheless, it remains an open problem how to employ auto-encoding for learning universal 3-D representations of irregularly structured point clouds, as previous methods focus on either global shapes or local geometries.

3D Gaussian Splatting as New Era: A Survey.

3D Gaussian Splatting (3D-GS) has emerged as a significant advancement in the field of Computer Graphics, offering explicit scene representation and novel view synthesis without the reliance on neural networks, such as Neural Radiance Fields (NeRF). This technique has found diverse applications in areas such as robotics, urban mapping, autonomous navigation, and virtual reality/augmented reality, just name a few. Given the growing popularity and expanding research in 3D Gaussian Splatting, this paper presents a comprehensive survey of relevant papers from the past year.

Porous Carbon Architecture Assembled by Cross-Linked Carbon Leaves with Implanted Atomic Cobalt for High-Performance Li-S Batteries.

The practical application of lithium-sulfur batteries is severely hampered by the poor conductivity, polysulfide shuttle effect and sluggish reaction kinetics of sulfur cathodes. Herein, a hierarchically porous three-dimension (3D) carbon architecture assembled by cross-linked carbon leaves with implanted atomic Co-N has been delicately developed as an advanced sulfur host through a SiO-mediated zeolitic imidazolate framework-L (ZIF-L) strategy. The unique 3D architectures not only provide a highly conductive network for fast electron transfer and buffer the volume change upon lithiation-delithiation process but also endow rich interface with full exposure of Co-N active sites to boost the lithium polysulfides adsorption and conversion.

A flexible electromagnetic wave-electricity harvester.

Developing an ultimate electromagnetic (EM)-absorbing material that can not only dissipate EM energy but also convert the generated heat into electricity is highly desired but remains a significant challenge. Here, we report a hybrid Sn@C composite with a biological cell-like splitting ability to address this challenge. The composite consisting of Sn nanoparticles embedded within porous carbon would split under a cycled annealing treatment, leading to more dispersed nanoparticles with an ultrasmall size.

Vacancy Occupation-Driven Polymorphic Transformation in Cobalt Ditelluride for Boosted Oxygen Evolution Reaction.

Transition-metal dichalcogenides (TMDs) hold great potential as an advanced electrocatalyst for oxygen evolution reaction (OER), but to date the activity of transition metal telluride catalysts are demonstrated to be poor for this reaction. In this study, we report the activation of CoTe for OER by doping secondary anions into Te vacancies to trigger a structural transition from the hexagonal to the orthorhombic phase. The achieved orthorhombic CoTe with partial vacancies occupied by P-doping exhibits an exceptional OER catalytic activity with an overpotential of only 241 mV at 10 mA cm and a robust stability more than 24 h.

Binary tree of SVM: a new fast multiclass training and classification algorithm.

We present a new architecture named Binary Tree of support vector machine (SVM), or BTS, in order to achieve high classification efficiency for multiclass problems. BTS and its enhanced version, c-BTS, decrease the number of binary classifiers to the greatest extent without increasing the complexity of the original problem. In the training phase, BTS has N - 1 binary classifiers in the best situation (N is the number of classes), while it has log4/3 ((N + 3)/4) binary tests on average when making a decision.