357 research outputs found
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
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
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
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