Development of Computer Vision-Enhanced Smart Golf Ball Retriever

An automatic vehicle system was developed to assist golfers in collecting golf balls from a practice field. Computer vision methodology was utilized to enhance the detection of golf balls in shallow and/or deep grass regions. The free software OpenCV was used in this project because of its powerful features and supported repository. The homemade golf ball picker was built with a smart recognition function for golf balls and can lock onto targets by itself. A set of field tests was completed in which the rate of golf ball recognition was as high as 95%. We report that this homemade smart golf ball picker can reduce the tremendous amount of labor associated with having to gather golf balls scattered throughout a practice field

Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis

3D point cloud analysis has drawn a lot of research attention due to its wide applications. However, collecting massive labelled 3D point cloud data is both time-consuming and labor-intensive. This calls for data-efficient learning methods. In this work we propose PointDisc, a point discriminative learning method to leverage self-supervisions for data-efficient 3D point cloud classification and segmentation. PointDisc imposes a novel point discrimination loss on the middle and global level features produced by the backbone network. This point discrimination loss enforces learned features to be consistent with points belonging to the corresponding local shape region and inconsistent with randomly sampled noisy points. We conduct extensive experiments on 3D object classification, 3D semantic and part segmentation, showing the benefits of PointDisc for data-efficient learning. Detailed analysis demonstrate that PointDisc learns unsupervised features that well capture local and global geometry.Comment: This work is published in 3DV 202

2019 Overview

The CNS Neuroscience & Therapeutics provides a medium for rapid publication of original clinical, experimental, and translational research papers, timely reviews, and reports of novel findings of therapeutic relevance to the central nervous system. Its focus includes clinical pharmacology, drug development, and novel methodologies for drug evaluation in neurological and psychiatric diseases. We are pleased to announce that CNS Neuroscience & Therapeutics has become an Open‐Access Journal as of January 2019. This would allow wider dissemination of scientific knowledge and facilitate collaborative efforts toward advancing novel and solid research on the maintenance of brain homeostasis and repairing the aging and dysfunctional brain

Scattering approach for calculating one-loop effective action and vacuum energy

An approach for calculating one-loop effective actions and vacuum energies in nonrelativistic quantum field theory is suggested. The starting point is to regard one-loop effective actions and vacuum energies in quantum field theory and scattering phase shifts and amplitudes in quantum mechanics as spectral functions of an operator. Different spectral functions of the same operator may belong to different fields of physics. Methods for calculating various spectral functions can be interconverted through transform relations among spectral functions. In principle, all methods for calculating scattering phase shifts and amplitudes can be converted to methods for the calculation of the one-loop effective actions and vacuum energies. As an example, we convert the Born approximation in quantum mechanics to a method for calculating one-loop effective actions and vacuum energies

Delayed response to the photovoltaic performance in a double quantum dot photocell with spatially correlated fluctuation

A viable strategy for enhancing photovoltaic performance in a double quantum dot (DQD) photocell is to comprehend the underlying quantum physical regime of charge transfer. This work explores the photovoltaic performance dependent spatially correlated fluctuation in a DQD photocell. A suggested DQD photocell model was used to examine the effects of spatially correlated variation on charge transfer and output photovoltaic efficiency. The charge transfer process and the process of reaching peak solar efficiency were both significantly delayed as a result of the spatially correlated fluctuation, and the anti-spatial correlation fluctuation also resulted in lower output photovoltaic efficiency. Further results revealed that some structural parameters, such as gap difference and tunneling coefficient within two dots, could suppress the delayed response, and a natural adjustment feature was demonstrated on the delayed response in this DQD photocell model. Subsequent investigation verified that the delayed response was caused by the spatial correlation fluctuation, which slowed the generative process of noise-induced coherence, which had previously been proven to improve quantum photovoltaic performance in quantum photocells. While anti-spatial correlation fluctuation and a hotter thermal ambient environment could diminish the condition for noise-induced coherence, as demonstrated by the reduced photovoltaic capabilities in this suggested DQD photocell model. As a result, we expect that regulated noise-induced coherence, via spatially correlated fluctuation, will have a major impact on photovoltaic qualities in a DQD photocell system. The discovery of its underlying physical regime of quantum fluctuation will broaden and deepen understanding of quantum features of electron transfer, as well as provide some indications concerning quantum techniques for high efficiency DQD solar cells.Comment: 16 pages, 5 figure

On Representation Knowledge Distillation for Graph Neural Networks

Knowledge distillation is a learning paradigm for boosting resource-efficient graph neural networks (GNNs) using more expressive yet cumbersome teacher models. Past work on distillation for GNNs proposed the Local Structure Preserving loss (LSP), which matches local structural relationships defined over edges across the student and teacher's node embeddings. This paper studies whether preserving the global topology of how the teacher embeds graph data can be a more effective distillation objective for GNNs, as real-world graphs often contain latent interactions and noisy edges. We propose Graph Contrastive Representation Distillation (G-CRD), which uses contrastive learning to implicitly preserve global topology by aligning the student node embeddings to those of the teacher in a shared representation space. Additionally, we introduce an expanded set of benchmarks on large-scale real-world datasets where the performance gap between teacher and student GNNs is non-negligible. Experiments across 4 datasets and 14 heterogeneous GNN architectures show that G-CRD consistently boosts the performance and robustness of lightweight GNNs, outperforming LSP (and a global structure preserving variant of LSP) as well as baselines from 2D computer vision. An analysis of the representational similarity among teacher and student embedding spaces reveals that G-CRD balances preserving local and global relationships, while structure preserving approaches are best at preserving one or the other