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

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 with Learnable Privacy Budgets in Frequency Domain

Face recognition technology has been used in many fields due to its high recognition accuracy, including the face unlocking of mobile devices, community access control systems, and city surveillance. As the current high accuracy is guaranteed by very deep network structures, facial images often need to be transmitted to third-party servers with high computational power for inference. However, facial images visually reveal the user's identity information. In this process, both untrusted service providers and malicious users can significantly increase the risk of a personal privacy breach. Current privacy-preserving approaches to face recognition are often accompanied by many side effects, such as a significant increase in inference time or a noticeable decrease in recognition accuracy. This paper proposes a privacy-preserving face recognition method using differential privacy in the frequency domain. Due to the utilization of differential privacy, it offers a guarantee of privacy in theory. Meanwhile, the loss of accuracy is very slight. This method first converts the original image to the frequency domain and removes the direct component termed DC. Then a privacy budget allocation method can be learned based on the loss of the back-end face recognition network within the differential privacy framework. Finally, it adds the corresponding noise to the frequency domain features. Our method performs very well with several classical face recognition test sets according to the extensive experiments.Comment: ECCV 2022; Code is available at https://github.com/Tencent/TFace/tree/master/recognition/tasks/dctd

Frailty in hypertensive population and its association with all-cause mortality: data from the National Health and Nutrition Examination Survey

ObjectivesThis study aimed to investigate the relationship between frailty and all-cause mortality in hypertensive population.MethodsWe used data from the National Health and Nutrition Examination Survey (NHANES) 1999–2002 and mortality data from the National Death Index. Frailty was assessed using the revised version of the Fried frailty criteria (weakness, exhaustion, low physical activity, shrinking, and slowness). This study aimed to evaluate the association between frailty and all-cause mortality. Cox proportional hazard models were used to evaluate the association between frailty category and all-cause mortality, adjusted for age, sex, race, education, poverty–income ratio, smoking, alcohol, diabetes, arthritis, congestive heart failure, coronary heart disease, stroke, overweight, cancer or malignancy, chronic obstructive pulmonary disease, chronic kidney disease, and taking medicine for hypertension.ResultsWe gathered data of 2,117 participants with hypertension; 17.81%, 28.77%, and 53.42% were classified as frail, pre-frail, and robust, respectively. We found that frail [hazard ratio (HR) = 2.76, 95% confidence interval (CI) = 2.33–3.27] and pre-frail (HR = 1.38, 95% CI = 1.19–1.59] were significantly associated with all-cause mortality after controlling for variables. We found that frail (HR = 3.02, 95% CI = 2.50–3.65) and pre-frail (HR = 1.35, 95% CI = 1.15–1.58) were associated with all-cause mortality in the age group ≥65 years. For the frailty components, weakness (HR = 1.77, 95% CI = 1.55–2.03), exhaustion (HR = 2.25, 95% CI = 1.92–2.65), low physical activity (HR = 2.25, 95% CI = 1.95–2.61), shrinking (HR = 1.48, 95% CI = 1.13–1.92), and slowness (HR = 1.44, 95% CI = 1.22–1.69) were associated with all-cause mortality.ConclusionThis study demonstrated that frailty and pre-frailty were associated with an increased risk of all-cause mortality in patients with hypertension. More attention should be paid to frailty in hypertensive patients, and interventions to reduce the burden of frailty may improve outcomes in these patients