108 research outputs found
Decentralized Risk-Aware Tracking of Multiple Targets
We consider the setting where a team of robots is tasked with tracking
multiple targets with the following property: approaching the targets enables
more accurate target position estimation, but also increases the risk of sensor
failures. Therefore, it is essential to address the trade-off between tracking
quality maximization and risk minimization. In our previous work, a centralized
controller is developed to plan motions for all the robots -- however, this is
not a scalable approach. Here, we present a decentralized and risk-aware
multi-target tracking framework, in which each robot plans its motion trading
off tracking accuracy maximization and aversion to risk, while only relying on
its own information and information exchanged with its neighbors. We use the
control barrier function to guarantee network connectivity throughout the
tracking process. Extensive numerical experiments demonstrate that our system
can achieve similar tracking accuracy and risk-awareness to its centralized
counterpart.Comment: DARS2022 submission preprin
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
Mechanism of inflammasomes in cancer and targeted therapies
Inflammasomes, composed of the nucleotide-binding oligomerization domain(NOD)-like receptors (NLRs), are immune-functional protein multimers that are closely linked to the host defense mechanism. When NLRs sense pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs), they assemble into inflammasomes. Inflammasomes can activate various inflammatory signaling pathways, including nuclear factor kappa B (NF-κB) and mitogen-activated protein kinase (MAPK) signaling pathways, and produce a large number of proinflammatory cytokines, which are closely associated with multiple cancers. They can also accelerate the occurrence and development of cancer by providing suitable tumor microenvironments, promoting tumor cell proliferation, and inhibiting tumor cell apoptosis. Therefore, the exploitation of novel targeted drugs against various inflammasomes and proinflammatory cytokines is a new idea for the treatment of cancer. In recent years, more than 50 natural extracts and synthetic small molecule targeted drugs have been reported to be in the research stage or have been applied to the clinic. Herein, we will overview the mechanisms of inflammasomes in common cancers and discuss the therapeutic prospects of natural extracts and synthetic targeted agents
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
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