Decoding flat bands from compact localized states

The flat band system is an ideal quantum platform to investigate the kaleidoscope created by the electron-electron correlation effects. The central ingredient of realizing a flat band is to find its compact localized states. In this work, we develop a systematic way to generate the compact localized states by designing destructive interference pattern from 1-dimensional chains. A variety of 2-dimensional new flat band systems are constructed with this method. Furthermore, we show that the method can be extended to generate the compact localized states in multi-orbital systems by carefully designing the block hopping scheme, as well as in quasicrystal and disorder systems

Event-triggered joint connectivity topology containment control for unmanned surface ship systems under time delay

For the containment control problem of unmanned surface ship systems (USSs) with time delay and limited communication bandwidth, this paper proposes a distributed event-triggered control strategy using a joint connection switching topology. The communication of unmanned surface ship systems inevitably has delay and the topology is time-varying. Firstly, a joint connectivity switching topology model and the state control method of USSs with delay are designed. Secondly, an event-triggered control mechanism is established, and a new trigger condition of USSs communication is designed. In case of time delay, the USS updates its information and sends it to its neighboring USSs under time delay, minimizes communication consumption and saves energy, and rapidly converges to the steady state. Based on the Lyapunov method, the stability of the system is analyzed, and the Zeno behavior when event-triggered is excluded. It is proved that under the designed control strategy, if the communication topology is jointly connected in a certain time, the follower USS can converge to the convex hull formed by multiple leader USS within a certain delay range. Finally, the correctness and validity of the conclusions are verified by simulation

Metabolomics in Childhood Asthma

As the most common chronic disease in children, bronchial asthma is highly underdiagnosed with complex pathogenesis. By qualitative and quantitative analyses of changes in low molecular weight molecules or metabolites in biological samples, metabolomics provides a new method to search biomarkers and pathogenesis. We reviewed the application of metabolomics in childhood asthma, which attempts to find the potential biomarkers and pathogenesis of childhood asthma by analyzing the samples of blood, exhaled breath, feces and urine of asthmatic children and healthy children using targeted or untargeted research approaches, providing help for clinical diagnosis and treatment of childhood asthma. Considerable progress has been made in metabolomics in childhood asthma, but due to factors such as individual differences, sample collection, data analysis, and genomic heterogeneity, metabolomics analysis of childhood asthma is still facing challenges

A user-centred collective system design approach for Smart Product-Service Systems:A case study on fitness product design

Emerging technologies have significantly contributed to the evolution of traditional product-service systems (PSS) into smart PSS. This transformation demands a fresh perspective and a more inventive design approach. In response, this study proposes a new User-Centred Collective System Design (CSD) framework and process for Smart PSS design, aiming to enhance stakeholder engagement during the entire design process, thus promoting highly effective and creative design solutions. A case study, titled ‘Next-G Smart Fitness PSS Design’, was carried out to test and implement this approach, contrasting the results of the CSD method with a designer-centred method. The outcomes showed a marked improvement in product novelty and user desirability of the design outcomes when using the proposed design framework. The proposed CSD framework could offer beneficial insights and user-centric viewpoints for practitioners dealing with complex challenges linked to smart PSS design

DuetFace: Collaborative Privacy-Preserving Face Recognition via Channel Splitting in the Frequency Domain

With the wide application of face recognition systems, there is rising concern that original face images could be exposed to malicious intents and consequently cause personal privacy breaches. This paper presents DuetFace, a novel privacy-preserving face recognition method that employs collaborative inference in the frequency domain. Starting from a counterintuitive discovery that face recognition can achieve surprisingly good performance with only visually indistinguishable high-frequency channels, this method designs a credible split of frequency channels by their cruciality for visualization and operates the server-side model on non-crucial channels. However, the model degrades in its attention to facial features due to the missing visual information. To compensate, the method introduces a plug-in interactive block to allow attention transfer from the client-side by producing a feature mask. The mask is further refined by deriving and overlaying a facial region of interest (ROI). Extensive experiments on multiple datasets validate the effectiveness of the proposed method in protecting face images from undesired visual inspection, reconstruction, and identification while maintaining high task availability and performance. Results show that the proposed method achieves a comparable recognition accuracy and computation cost to the unprotected ArcFace and outperforms the state-of-the-art privacy-preserving methods. The source code is available at https://github.com/Tencent/TFace/tree/master/recognition/tasks/duetface.Comment: Accepted to ACM Multimedia 202

Privacy-Preserving Face Recognition Using Random Frequency Components

The ubiquitous use of face recognition has sparked increasing privacy concerns, as unauthorized access to sensitive face images could compromise the information of individuals. This paper presents an in-depth study of the privacy protection of face images' visual information and against recovery. Drawing on the perceptual disparity between humans and models, we propose to conceal visual information by pruning human-perceivable low-frequency components. For impeding recovery, we first elucidate the seeming paradox between reducing model-exploitable information and retaining high recognition accuracy. Based on recent theoretical insights and our observation on model attention, we propose a solution to the dilemma, by advocating for the training and inference of recognition models on randomly selected frequency components. We distill our findings into a novel privacy-preserving face recognition method, PartialFace. Extensive experiments demonstrate that PartialFace effectively balances privacy protection goals and recognition accuracy. Code is available at: https://github.com/Tencent/TFace.Comment: ICCV 202