DTBS: Dual-Teacher Bi-directional Self-training for Domain Adaptation in Nighttime Semantic Segmentation

Due to the poor illumination and the difficulty in annotating, nighttime conditions pose a significant challenge for autonomous vehicle perception systems. Unsupervised domain adaptation (UDA) has been widely applied to semantic segmentation on such images to adapt models from normal conditions to target nighttime-condition domains. Self-training (ST) is a paradigm in UDA, where a momentum teacher is utilized for pseudo-label prediction, but a confirmation bias issue exists. Because the one-directional knowledge transfer from a single teacher is insufficient to adapt to a large domain shift. To mitigate this issue, we propose to alleviate domain gap by incrementally considering style influence and illumination change. Therefore, we introduce a one-stage Dual-Teacher Bi-directional Self-training (DTBS) framework for smooth knowledge transfer and feedback. Based on two teacher models, we present a novel pipeline to respectively decouple style and illumination shift. In addition, we propose a new Re-weight exponential moving average (EMA) to merge the knowledge of style and illumination factors, and provide feedback to the student model. In this way, our method can be embedded in other UDA methods to enhance their performance. For example, the Cityscapes to ACDC night task yielded 53.8 mIoU (\%), which corresponds to an improvement of +5\% over the previous state-of-the-art. The code is available at \url{https://github.com/hf618/DTBS}

Understanding gas transport mechanisms in shale gas reservoir: Pore network modelling approach

This report summarizes the recent findings on gas transport mechanisms in shale gas reservoir by pore network modelling. Multi-scale pore network model was developed to accurately characterize the shale pore structure. The pore network single component gas transport model was established considering the gas slippage and real gas property. The gas transport mechanisms in shale pore systems were elaborated on this basis. A multicomponent hydrocarbon pore network transport model was further proposed considering the influences of capillary pressure and fluid occurrence on fugacity balance. The hydrocarbon composition and pore structure influences on condensate gas transport were analyzed. These results provide valuable insights on gas transport mechanisms in shale gas reservoir.Cited as: Song, W., Yao, J., Zhang, K., Yang, Y., Sun, H. Understanding gas transport mechanisms in shale gas reservoir: Pore network modelling approach. Advances in Geo-Energy Research, 2022, 6(4): 359-360. https://doi.org/10.46690/ager.2022.04.1

Evidence for quasi-one-dimensional charge density wave in CuTe by angle-resolved photoemission spectroscopy

We report the electronic structure of CuTe with a high charge density wave (CDW) transition temperature Tc = 335 K by angle-resolved photoemission spectroscopy (ARPES). An anisotropic charge density wave gap with a maximum value of 190 meV is observed in the quasi-one-dimensional band formed by Te px orbitals. The CDW gap can be filled by increasing temperature or electron doping through in situ potassium deposition. Combining the experimental results with calculated electron scattering susceptibility and phonon dispersion, we suggest that both Fermi surface nesting and electron-phonon coupling play important roles in the emergence of the CDW

Energy Optimization for WSN in Ubiquitous Power Internet of Things

This paper attempts to solve the problems of uneven energy consumption and premature death of nodes in the traditional routing algorithm of rechargeable wireless sensor network in the ubiquitous power Internet of things. Under the application environment of the UPIoT, a multipath routing algorithm and an opportunistic routing algorithm were put forward to optimize the network energy and ensure the success of information transmission. Inspired by the electromagnetic propagation theory, the author constructed a charging model for a single node in the wireless sensor network (WSN). On this basis, the network energy optimization problem was transformed into the network lifecycle problem, considering the energy consumption of wireless sensor nodes. Meanwhile, the traffic of each link was computed through linear programming to guide the distribution of data traffic in the network. Finally, an energy optimization algorithm was proposed based on opportunistic routing, in a more realistic low power mode. The experimental results show that the two proposed algorithms achieved better energy efficiency, network lifecycle and network reliability than the shortest path routing (SPR) and the expected duty-cycled wakeups minimal routing (EDC). The research findings provide a reference for the data transmission of UPIoT nodes

Specific detection and deletion of the sigma-1 receptor widely expressed in neurons and glial cells in vivo

The chaperon protein sigma-1 receptor (S1R) has been discovered over 40 years ago. Recent pharmacological studies using S1R exogenous ligands demonstrated a promising therapeutical potential of targeting the S1R in several neurological disorders. Although intensive in vitro studies have revealed S1Rs are mainly residing at the membrane of the endoplasmic reticulum (ER), the cell-specific in vivo expression pattern of S1Rs is still unclear, mainly because of the lack of a reliable detection method which also prevented a comprehensive functional analysis. Here, first, we identified a highly specific antibody using S1R knockout (KO) mice and established an immunohistochemical protocol involving a 1% sodium dodecyl sulphate (SDS) antigen retrieval step. Second, we characterized the S1R expression in the mouse brain and can demonstrate that the S1R is widely expressed: in principal neurons, interneurons and all glial cell types. In addition, unlike reported in previous studies, we showed that the S1R expression in astrocytes is not colocalized with the astrocytic cytoskeleton protein GFAP. Thus, our results raise concerns over previously reported S1R properties. Finally, we generated a Credependent S1R conditional KO mouse (S1R flox) to study cell-type-specific functions of the S1R. As a proof of concept, we successfully ablated S1R expressions in neurons or microglia employing neuronal and microglial Cre-expressing mice, respectively. In summary, we provide powerful tools to cell-specifically detect, delete and functionally characterize S1R in vivo