Progressive Learning with Cross-Window Consistency for Semi-Supervised Semantic Segmentation

Semi-supervised semantic segmentation focuses on the exploration of a small amount of labeled data and a large amount of unlabeled data, which is more in line with the demands of real-world image understanding applications. However, it is still hindered by the inability to fully and effectively leverage unlabeled images. In this paper, we reveal that cross-window consistency (CWC) is helpful in comprehensively extracting auxiliary supervision from unlabeled data. Additionally, we propose a novel CWC-driven progressive learning framework to optimize the deep network by mining weak-to-strong constraints from massive unlabeled data. More specifically, this paper presents a biased cross-window consistency (BCC) loss with an importance factor, which helps the deep network explicitly constrain confidence maps from overlapping regions in different windows to maintain semantic consistency with larger contexts. In addition, we propose a dynamic pseudo-label memory bank (DPM) to provide high-consistency and high-reliability pseudo-labels to further optimize the network. Extensive experiments on three representative datasets of urban views, medical scenarios, and satellite scenes demonstrate our framework consistently outperforms the state-of-the-art methods with a large margin. Code will be available publicly

A Comparison of Chitosan Adhesion to KOH and H2O2 Pre-Treated Electrospun Poly(3-Hydroxybutyrate) Nanofibers

Chitosan coatings could effectively increase the biostability and biocompatibility of biomaterials while maintaining their structural integrity. In this study, electrospun fibrous polyhydroxybutyrate (PHB) membranes were pre-treated with potassium hydroxide (KOH) or hydrogen peroxide (H2O2) and then modified with dopamine (DA) and glutaraldehyde (GA) to improve their adhesion with chitosan (CS). Scanning electron microscopy (SEM), water contact angles (WCA), and Fourier transform infrared spectroscopy (FTIR) were used to demonstrate the successful generation of DA and GA-modified PHB fibers. KOH pre-treated PHB membranes exhibited superior binding efficiency with CS at low concentrations compared to their H2O2 pre-treated counterparts. The thermal analysis demonstrated a considerable decrease in the degradation temperature and crystallinity of KOH pre-treated membranes, with temperatures dropping from 309 °C to 265.5 °C and crystallinity reducing from 100% to 25.59% as CS concentration increased from 0 to 2 w/v%. In comparison, H2O2 pre-treated membranes experienced a mild reduction in degradation temperature, from 309 °C to 284.4 °C, and a large decrease in crystallinity from 100% to 43%. UV-vis analysis using Cibacron Brilliant Red 3B-A dye (CBR) indicated similar binding efficiencies at low CS concentrations for both pre-treatments, but decreased stability at higher concentrations for KOH pre-treated membranes. Mechanical testing revealed a considerable increase in Young’s modulus (2 to 14%), toughness (31 to 60%), and ultimate tensile stress (UTS) (14 to 63%) for KOH-treated membranes compared with H2O2 pre-treated membranes as CS concentration increased from 0 to 2 w/v%

A Comparison of Chitosan Adhesion to KOH and H_{2}O_{2} Pre-Treated Electrospun Poly(3-Hydroxybutyrate) Nanofibers

Chitosan coatings could effectively increase the biostability and biocompatibility of biomaterials while maintaining their structural integrity. In this study, electrospun fibrous polyhydroxybutyrate (PHB) membranes were pre-treated with potassium hydroxide (KOH) or hydrogen peroxide (H2O2) and then modified with dopamine (DA) and glutaraldehyde (GA) to improve their adhesion with chitosan (CS). Scanning electron microscopy (SEM), water contact angles (WCA), and Fourier transform infrared spectroscopy (FTIR) were used to demonstrate the successful generation of DA and GA-modified PHB fibers. KOH pre-treated PHB membranes exhibited superior binding efficiency with CS at low concentrations compared to their H2O2 pre-treated counterparts. The thermal analysis demonstrated a considerable decrease in the degradation temperature and crystallinity of KOH pre-treated membranes, with temperatures dropping from 309 °C to 265.5 °C and crystallinity reducing from 100% to 25.59% as CS concentration increased from 0 to 2 w/v%. In comparison, H2O2 pre-treated membranes experienced a mild reduction in degradation temperature, from 309 °C to 284.4 °C, and a large decrease in crystallinity from 100% to 43%. UV-vis analysis using Cibacron Brilliant Red 3B-A dye (CBR) indicated similar binding efficiencies at low CS concentrations for both pre-treatments, but decreased stability at higher concentrations for KOH pre-treated membranes. Mechanical testing revealed a considerable increase in Young’s modulus (2 to 14%), toughness (31 to 60%), and ultimate tensile stress (UTS) (14 to 63%) for KOH-treated membranes compared with H2O2 pre-treated membranes as CS concentration increased from 0 to 2 w/v%

Global solutions to the Nernst-Planck-Euler system on bounded domain

We show that the Nernst-Planck-Euler system, which models ionic electrodiffusion in fluids, has global strong solutions for arbitrarily large data in the two dimensional bounded domains. The assumption on species is either there are two species or the diffusivities and the absolute values of ionic valences are the same if the species are arbitrarily many. In particular, the boundary conditions for the ions are allowed to be inhomogeneous. The proof is based on the energy estimates, integration along the characteristic line and the regularity theory of elliptic and parabolic equations

ElegantSeg: End-to-End Holistic Learning for Extra-Large Image Semantic Segmentation

This paper presents a new paradigm for Extra-large image semantic Segmentation, called ElegantSeg, that capably processes holistic extra-large image semantic segmentation (ELISS). The extremely large sizes of extra-large images (ELIs) tend to cause GPU memory exhaustion. To tackle this issue, prevailing works either follow the global-local fusion pipeline or conduct the multi-stage refinement. These methods can only process limited information at one time, and they are not able to thoroughly exploit the abundant information in ELIs. Unlike previous methods, ElegantSeg can elegantly process holistic ELISS by extending the tensor storage from GPU memory to host memory. To the best of our knowledge, it is the first time that ELISS can be performed holistically. Besides, ElegantSeg is specifically designed with three modules to utilize the characteristics of ELIs, including the multiple large kernel module for developing long-range dependency, the efficient class relation module for building holistic contextual relationships, and the boundary-aware enhancement module for obtaining complete object boundaries. ElegantSeg outperforms previous state-of-the-art on two typical ELISS datasets. We hope that ElegantSeg can open a new perspective for ELISS. The code and models will be made publicly available

The Expanded Electrodeionization Method for Sewage Reclamation

The aim of the chapter is to introduce a new technology using the flow-divided electrodes for sewage reclamation based on redox and concentrating ions. This creative system can be used directly for sewage treatment without the need for costly ion-exchange membranes and other chemical reagents. Under experimental conditions, the removal percentage of TDS (total dissolved salts) is 83 %and the removal percentage of COD (chemical oxygen demand) is 92 %

Fluctuation characteristics and rolling control for an underactuated spherical underwater exploration robot

Compared with other underwater exploration robots, Spherical underwater robot has an outstanding advantage for the underwater exploration, whose spherical shell has the excellent resiliency to protect the internal electronic components. In addition, this steering resistance is very small to move flexibly. In this paper, a type of spherical underwater robot with the pendulums and a propeller was studied on moving at the water bottom in a rolling manner. The structure and force were analyzed to understand that the hydrodynamic force’s affection on the robot’s rolling at the water bottom. A mathematical model was established with the mass parameters and speeding parameters. The virtual simulation environment was established in Adams software. Furthermore, the coupling fluctuation characteristics of the speed, swing angle and the torque were studied by the simulation and the experiment in a pool. The study proved that this robot not only can use the propeller to move in water, but also can roll at the water bottom by driving the spherical shell. Especially, the result also can be obtained that the robot can roll at water bottom stably by increasing the pendulum mass and lowering the motor speed