Ground state degeneracy of the Ising cage-net model

The Ising cage-net model, first proposed in Phys. Rev. X 9, 021010 (2019), is a representative type I fracton model with nontrivial non-abelian features. In this paper, we calculate the ground state degeneracy of this model and find that, even though it follows a similar coupled layer structure as the X-cube model, the Ising cage-net model cannot be "foliated" in the same sense as X-cube as defined in Phys. Rev. X 8, 031051 (2018). A more generalized notion of "foliation'' is hence needed to understand the renormalization group transformation of the Ising cage-net model. The calculation is done using an operator algebra approach that we develop in this paper, and we demonstrate its validity through a series of examples

The Optimization of Jaw Crusher with Complex Motion Aimed at Reducing Stroke Feature Value of Its Outlet

Volume 8 Issue 1 (January 201

Flow Patterns and Reaction Rate Estimation of RedOx Electrolyte in the Presence of Natural Convection

Transport processes in an upright, concentric, annular, electrochemical reactor filled with RedOx electrolyte solution are studied experimentally and theoretically. The electrodes form the two vertical surfaces of the reactor. The theoretical calculations consist of the solution of the Navier-Stokes and the Nernst-Planck equations accounting for species\u27 diffusion, migration, convection, and electrochemical reactions on the electrodes\u27 surfaces as a function of the difference in the electrodes\u27 potentials and the average concentration of the electrolyte. Since the convection is driven by density gradients, the momentum and mass transport equations are strongly coupled. In spite of the small dimensions (mm-scale) of the reactor, the current transmitted through the electrolyte is significantly enhanced by natural convection. The current is measured as a function of the difference in the electrodes\u27 potentials. To obtain the reaction rate constants, an inverse problem is solved and the reaction rate constants are determined by minimizing the discrepancy between theoretical predictions and experimental observations. As an example, we study the reversible electrochemical reaction Fe++++e- = Fe++ on platinum electrodes. The paper demonstrates that natural convection plays a significant role even when the reactor’s dimensions are on the millimeter scale and that it is possible to predict reaction rate constants while accounting for significant mass transfer effects

Thermally-actuated, phase change flow control for microfluidic systems

An easy to implement, thermally-actuated, noninvasive method for flow control in microfluidic devices is described. This technique takes advantage of the phase change of the working liquid itself—the freezing and melting of a portion of a liquid slug—to noninvasively close and open flow passages (referred to as a phase change valve). The valve was designed for use in a miniature diagnostic system for detecting pathogens in oral fluids at the point of care. The paper describes the modeling, construction, and characteristics of the valve. The experimental results favorably agree with theoretical predictions. In addition, the paper demonstrates the use of the phase change valves for flow control, sample metering and distribution into multiple analysis paths, sealing of a polymerase chain reaction (PCR) chamber, and sample introduction into and withdrawal from a closed loop. The phase change valve is electronically addressable, does not require any moving parts, introduces only minimal dead volume, is leakage and contamination free, and is biocompatible

Towards Novel Class Discovery: A Study in Novel Skin Lesions Clustering

Existing deep learning models have achieved promising performance in recognizing skin diseases from dermoscopic images. However, these models can only recognize samples from predefined categories, when they are deployed in the clinic, data from new unknown categories are constantly emerging. Therefore, it is crucial to automatically discover and identify new semantic categories from new data. In this paper, we propose a new novel class discovery framework for automatically discovering new semantic classes from dermoscopy image datasets based on the knowledge of known classes. Specifically, we first use contrastive learning to learn a robust and unbiased feature representation based on all data from known and unknown categories. We then propose an uncertainty-aware multi-view cross pseudo-supervision strategy, which is trained jointly on all categories of data using pseudo labels generated by a self-labeling strategy. Finally, we further refine the pseudo label by aggregating neighborhood information through local sample similarity to improve the clustering performance of the model for unknown categories. We conducted extensive experiments on the dermatology dataset ISIC 2019, and the experimental results show that our approach can effectively leverage knowledge from known categories to discover new semantic categories. We also further validated the effectiveness of the different modules through extensive ablation experiments. Our code will be released soon.Comment: 10 pages, 1 figure,Accepted by miccai 202

Towards Open-Scenario Semi-supervised Medical Image Classification

Semi-supervised learning (SSL) has attracted much attention since it reduces the expensive costs of collecting adequate well-labeled training data, especially for deep learning methods. However, traditional SSL is built upon an assumption that labeled and unlabeled data should be from the same distribution e.g., classes and domains. However, in practical scenarios, unlabeled data would be from unseen classes or unseen domains, and it is still challenging to exploit them by existing SSL methods. Therefore, in this paper, we proposed a unified framework to leverage these unseen unlabeled data for open-scenario semi-supervised medical image classification. We first design a novel scoring mechanism, called dual-path outliers estimation, to identify samples from unseen classes. Meanwhile, to extract unseen-domain samples, we then apply an effective variational autoencoder (VAE) pre-training. After that, we conduct domain adaptation to fully exploit the value of the detected unseen-domain samples to boost semi-supervised training. We evaluated our proposed framework on dermatology and ophthalmology tasks. Extensive experiments demonstrate our model can achieve superior classification performance in various medical SSL scenarios