DA-STC: Domain Adaptive Video Semantic Segmentation via Spatio-Temporal Consistency

Video semantic segmentation is a pivotal aspect of video representation learning. However, significant domain shifts present a challenge in effectively learning invariant spatio-temporal features across the labeled source domain and unlabeled target domain for video semantic segmentation. To solve the challenge, we propose a novel DA-STC method for domain adaptive video semantic segmentation, which incorporates a bidirectional multi-level spatio-temporal fusion module and a category-aware spatio-temporal feature alignment module to facilitate consistent learning for domain-invariant features. Firstly, we perform bidirectional spatio-temporal fusion at the image sequence level and shallow feature level, leading to the construction of two fused intermediate video domains. This prompts the video semantic segmentation model to consistently learn spatio-temporal features of shared patch sequences which are influenced by domain-specific contexts, thereby mitigating the feature gap between the source and target domain. Secondly, we propose a category-aware feature alignment module to promote the consistency of spatio-temporal features, facilitating adaptation to the target domain. Specifically, we adaptively aggregate the domain-specific deep features of each category along spatio-temporal dimensions, which are further constrained to achieve cross-domain intra-class feature alignment and inter-class feature separation. Extensive experiments demonstrate the effectiveness of our method, which achieves state-of-the-art mIOUs on multiple challenging benchmarks. Furthermore, we extend the proposed DA-STC to the image domain, where it also exhibits superior performance for domain adaptive semantic segmentation. The source code and models will be made available at \url{https://github.com/ZHE-SAPI/DA-STC}.Comment: 18 pages,9 figure

Differential Proteome Analysis of Bone Marrow Mesenchymal Stem Cells from Adolescent Idiopathic Scoliosis Patients

Adolescent idiopathic scoliosis (AIS) is a complex three-dimensional deformity of the spine. The cause and pathogenesis of scoliosis and the accompanying generalized osteopenia remain unclear despite decades of extensive research. In this study, we utilized two-dimensional fluorescence difference gel electrophoresis (2D-DIGE) coupled with mass spectrometry (MS) to analyze the differential proteome of bone marrow mesenchymal stem cells (BM-MSCs) from AIS patients. In total, 41 significantly altered protein spots were detected, of which 34 spots were identified by MALDI-TOF/TOF analysis and found to represent 25 distinct gene products. Among these proteins, five related to bone growth and development, including pyruvate kinase M2, annexin A2, heat shock 27 kDa protein, γ-actin, and β-actin, were found to be dysregulated and therefore selected for further validation by Western blot analysis. At the protein level, our results supported the previous hypothesis that decreased osteogenic differentiation ability of MSCs is one of the mechanisms leading to osteopenia in AIS. In summary, we analyzed the differential BM-MSCs proteome of AIS patients for the first time, which may help to elucidate the underlying molecular mechanisms of bone loss in AIS and also increase understanding of the etiology and pathogenesis of AIS

Predictive Current Control of Boost Three-Level and T-Type Inverters Cascaded in Wind Power Generation Systems

A topology structure based on boost three-level converters (BTL converters) and T-type three-level inverters for a direct-drive wind turbine in a wind power generation system is proposed. In this structure, the generator-side control can be realized by the boost-TL converter. Compared with the conventional boost converter, the boost-TL converter has a low inductor current ripple, which reduces the torque ripple of the generator, increases the converter’s capacity, and minimizes switching losses. The boost-TL converter can boost the DC output from the rectifier at low speeds. The principles of the boost-TL converter and the T-type three-level inverter are separately introduced. Based on the cascaded structure of the proposed BTL converter and three-level inverter, a model predictive current control (MPCC) method is adopted, and the optimization of the MPCC is presented. The prediction model is derived, and the simulation and experimental research are carried out. The results show that the algorithm based on the proposed cascaded structure is feasible and superior

Steganalysis based on multiple features formed by statistical moments of wavelet characteristic functions

Abstract. In this paper 1, a steganalysis scheme based on multiple features formed by statistical moments of wavelet characteristic functions is proposed. Our theoretical analysis has pointed out that the defined n-th statistical moment of a wavelet characteristic function is related to the n-th derivative of the corresponding wavelet histogram, and hence is sensitive to data embedding. The selection of the first three moments of the characteristic functions of wavelet subbands of the three-level Haar wavelet decomposition as well as the test image has resulted in total 39 features for steganalysis. The effectiveness of the proposed system has been demonstrated by extensive experimental investigation. The detection rate for Cox et al.’s non-blind spread spectrum (SS) data hiding method, Piva et al.’s blind SS method, Huang and Shi’s 8 × 8 block SS method, a generic LSB method (as embedding capacity being 0.3 bpp), and a generic QIM method (as embedding capacity being 0.1 bpp) are all above 90 % over all of the 1096 images in the CorelDraw image database using the Bayes classifier. Furthermore, when these five typical data hiding methods are jointly considered for steganalysis, i.e., when the proposed steganalysis scheme is first trained sequentially for each of these five methods, and is then tested blindly for stegoimages generated by all of these methods, the success classification rate is 86%, thus pointing out a new promising approach to general blind steganalysis. The detection results of steganalysis on Jsteg, Outguess and F5 have further demonstrated the effectiveness of the proposed steganalysis scheme.

Theoretical proposal of a low-loss wide-bandwidth silicon photonic crystal fiber for supporting 30 orbital angular momentum modes - Fig 3

<p>(a) n_eff of vector modes guided in PCF2# as a function of wavelength. (b) Δn_eff of vector modes guided in PCF2# as a function of wavelength.</p