Unsupervised Visible-Infrared Person ReID by Collaborative Learning with Neighbor-Guided Label Refinement

Unsupervised learning visible-infrared person re-identification (USL-VI-ReID) aims at learning modality-invariant features from unlabeled cross-modality dataset, which is crucial for practical applications in video surveillance systems. The key to essentially address the USL-VI-ReID task is to solve the cross-modality data association problem for further heterogeneous joint learning. To address this issue, we propose a Dual Optimal Transport Label Assignment (DOTLA) framework to simultaneously assign the generated labels from one modality to its counterpart modality. The proposed DOTLA mechanism formulates a mutual reinforcement and efficient solution to cross-modality data association, which could effectively reduce the side-effects of some insufficient and noisy label associations. Besides, we further propose a cross-modality neighbor consistency guided label refinement and regularization module, to eliminate the negative effects brought by the inaccurate supervised signals, under the assumption that the prediction or label distribution of each example should be similar to its nearest neighbors. Extensive experimental results on the public SYSU-MM01 and RegDB datasets demonstrate the effectiveness of the proposed method, surpassing existing state-of-the-art approach by a large margin of 7.76% mAP on average, which even surpasses some supervised VI-ReID methods

Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID

Unsupervised visible-infrared person re-identification (USL-VI-ReID) aims to match pedestrian images of the same identity from different modalities without annotations. Existing works mainly focus on alleviating the modality gap by aligning instance-level features of the unlabeled samples. However, the relationships between cross-modality clusters are not well explored. To this end, we propose a novel bilateral cluster matching-based learning framework to reduce the modality gap by matching cross-modality clusters. Specifically, we design a Many-to-many Bilateral Cross-Modality Cluster Matching (MBCCM) algorithm through optimizing the maximum matching problem in a bipartite graph. Then, the matched pairwise clusters utilize shared visible and infrared pseudo-labels during the model training. Under such a supervisory signal, a Modality-Specific and Modality-Agnostic (MSMA) contrastive learning framework is proposed to align features jointly at a cluster-level. Meanwhile, the cross-modality Consistency Constraint (CC) is proposed to explicitly reduce the large modality discrepancy. Extensive experiments on the public SYSU-MM01 and RegDB datasets demonstrate the effectiveness of the proposed method, surpassing state-of-the-art approaches by a large margin of 8.76% mAP on average

Ultrafast Spin-To-Charge Conversion at the Surface of Topological Insulator Thin Films

Strong spin-orbit coupling, resulting in the formation of spin-momentum-locked surface states, endows topological insulators with superior spin-to-charge conversion characteristics, though the dynamics that govern it have remained elusive. Here, we present an all-optical method that enables unprecedented tracking of the ultrafast dynamics of spin-to-charge conversion in a prototypical topological insulator Bi$_2$Se$_3$/ferromagnetic Co heterostructure, down to the sub-picosecond timescale. Compared to pure Bi$_2$Se$_3$ or Co, we observe a giant terahertz emission in the heterostructure than originates from spin-to-charge conversion, in which the topological surface states play a crucial role. We identify a 0.12-picosecond timescale that sets a technological speed limit of spin-to-charge conversion processes in topological insulators. In addition, we show that the spin-to-charge conversion efficiency is temperature independent in Bi$_2$Se$_3$ as expected from the nature of the surface states, paving the way for designing next-generation high-speed opto-spintronic devices based on topological insulators at room temperature.Comment: 19 pages, 4 figure

Governance mechanisms for chronic disease diagnosis and treatment systems in the post-pandemic era

“Re-visits and drug renewal” is difficult for chronic disease patients during COVID-19 and will continue in the post-pandemic era. To overcome this dilemma, the scenario of chronic disease diagnosis and treatment systems was set, and an evolutionary game model participated by four stakeholder groups including physical medical institutions, medical service platforms, intelligent medical device providers, and chronic disease patients, was established. Ten possible evolutionary stabilization strategies (ESSs) with their mandatory conditions were found based on Lyapunov's first method. Taking cardiovascular and cerebrovascular diseases, the top 1 prevalent chronic disease, as a specific case context, and resorting to the MATLAB simulation, it is confirmed that several dual ESSs and four unique ESS circumstances exist, respectively, and the evolution direction is determined by initial conditions, while the evolution speed is determined by the values of the conditions based on the quantitative relations of benefits, costs, etc. Accordingly, four governance mechanisms were proposed. By their adjustment, the conditions along with their values can be interfered, and then the chronic disease diagnosis and treatment systems can be guided toward the desired direction, that is, toward the direction of countermeasure against the pandemic, government guidance, global trends of medical industry development, social welfare, and lifestyle innovation. The dilemma of “Re-visits and drug renewal” actually reflects the uneven distribution problem of qualified medical resources and the poor impact resistance capability of social medical service systems under mass public emergency. Human lifestyle even the way of working all over the world will get a spiral upgrade after experiencing COVID-19, such as consumption, and meeting, while medical habits react not so rapidly, especially for mid or aged chronic disease patients. We believe that telemedicine empowered by intelligent medical devices can benefit them and will be a global trend, governments and the four key stakeholders should act according to the governance mechanisms suggested here simultaneously toward novel social medical ecosystems for the post-pandemic era

A Novel E-DVA Module Synthesis Featuring of Synergy between Driving and Vibration Attenuation

To attenuate the negative effects brought by heavy unsprung mass of the decentralized driving electric vehicle, a novel e-DVA module featuring of synergy between driving and vibration attenuation is proposed in this paper. It presents the advantages of compact structure and low cost. Structure design proves the feasibility of the e-DVA module. Kinematic analysis of the slider-crank mechanism is carried out to conclude the transmission ratio ripple under road excitation. After parameter matching and optimization of the e-DVA module based on the H2/H∞ norm criterions, vertical dynamics analyses in both frequency and time domains are conducted theoretically to prove the performance improvements on the ride comfort and handling stability under the constraint of DVA deflection bound