The interference of two-dimensional superconducting induced current in vector potential A field

If a two-dimensional superconducting mental surface is passed through by two infinite straight magnetic flux which is shielded by superconductivity, and it was supposed that the change rate of flux was not equal to zero at the beginning, which would induce two opposite and equal currents on the two-dimensional superconducting mental surface. In this situation, when the change rate of flux changed to zero, and both magnetic fluxes remain constant, new physical interference effect would appear. In this paper, the interference streamline distribution on two dimensional superconducting mental surface are calculated and simulated. We named the new interference phenomenon L-J effect, it is considered as a two-dimensional A-B effect

Can Quantum Mechanics and Relativity be Considered Per se Complete? - A discussion on Quantum Mechanics and Relativity in Full Space-Time Domain

This paper points out the incompleteness of the traditional quantum mechanics and relativity, which is embodied in space-time domains of definition, not in physical quantities for description. The real time and space are not continuous. The phenomena called “ghost-like long-range action†by Einstein in fact occur in the time discontinuity points, that is, Time Quantum Worm Holes put forward by Hawking. This paper also gives an essential difference between the macroscopic random motion and the microscopic random motion, which is critical for understanding wave-particle duality

Trees and water: smallholder agroforestry on irrigated lands in Northern India

Trees / Populus deltoids / Agroforestry / Afforestation / Reforestation / Models / Water use / Water balance / Evapotranspiration / Precipitation / Remote sensing / Irrigation requirements / India

Interpreting AI for Networking: Where We Are and Where We Are Going

In recent years, artificial intelligence (AI) techniques have been increasingly adopted to tackle networking problems. Although AI algorithms can deliver high-quality solutions, most of them are inherently intricate and erratic for human cognition. This lack of interpretability tremendously hinders the commercial success of AI-based solutions in practice. To cope with this challenge, networking researchers are starting to explore explainable AI (XAI) techniques to make AI models interpretable, manageable, and trustworthy. In this article, we overview the application of AI in networking and discuss the necessity for interpretability. Next, we review the current research on interpreting AI-based networking solutions and systems. At last, we envision future challenges and directions. The ultimate goal of this article is to present a general guideline for AI and networking practitioners and motivate the continuous advancement of AI-based solutions in modern communication networks

Multi-organ Segmentation via Co-training Weight-averaged Models from Few-organ Datasets

Multi-organ segmentation has extensive applications in many clinical applications. To segment multiple organs of interest, it is generally quite difficult to collect full annotations of all the organs on the same images, as some medical centers might only annotate a portion of the organs due to their own clinical practice. In most scenarios, one might obtain annotations of a single or a few organs from one training set, and obtain annotations of the the other organs from another set of training images. Existing approaches mostly train and deploy a single model for each subset of organs, which are memory intensive and also time inefficient. In this paper, we propose to co-train weight-averaged models for learning a unified multi-organ segmentation network from few-organ datasets. We collaboratively train two networks and let the coupled networks teach each other on un-annotated organs. To alleviate the noisy teaching supervisions between the networks, the weighted-averaged models are adopted to produce more reliable soft labels. In addition, a novel region mask is utilized to selectively apply the consistent constraint on the un-annotated organ regions that require collaborative teaching, which further boosts the performance. Extensive experiments on three public available single-organ datasets LiTS, KiTS, Pancreas and manually-constructed single-organ datasets from MOBA show that our method can better utilize the few-organ datasets and achieves superior performance with less inference computational cost.Comment: Accepted by MICCAI 202