Paraoxon Attenuates Vascular Smooth Muscle Contraction through Inhibiting Ca2+ Influx in the Rabbit Thoracic Aorta

We investigated the effect of paraoxon on vascular contractility using organ baths in thoracic aortic rings of rabbits and examined the effect of paraoxon on calcium homeostasis using a whole-cell patch-clamp technique in isolated aortic smooth muscle cells of rabbits. The findings show that administration of paraoxon (30 μM) attenuated thoracic aorta contraction induced by phenylephrine (1 μM) and/or a high K+ environment (80 mM) in both the presence and absence of thoracic aortic endothelium. This inhibitory effect of paraoxon on vasoconstrictor-induced contraction was abolished in the absence of extracellular Ca2+, or in the presence of the Ca2+ channel inhibitor, verapamil. But atropine had little effect on the inhibitory effect of paraoxon on phenylephrine-induced contraction. Paraoxon also attenuated vascular smooth muscle contraction induced by the cumulative addition of CaCl2 and attenuated an increase of intracellular Ca2+ concentration induced by K+ in vascular smooth muscle cells. Moreover, paraoxon (30 μM) inhibited significantly L-type calcium current in isolated aortic smooth muscle cells of rabbits. In conclusion, our results demonstrate that paraoxon attenuates vasoconstrictor-induced contraction through inhibiting Ca2+ influx in the rabbits thoracic aorta

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

Instance-Aware Domain Generalization for Face Anti-Spoofing

Face anti-spoofing (FAS) based on domain generalization (DG) has been recently studied to improve the generalization on unseen scenarios. Previous methods typically rely on domain labels to align the distribution of each domain for learning domain-invariant representations. However, artificial domain labels are coarse-grained and subjective, which cannot reflect real domain distributions accurately. Besides, such domain-aware methods focus on domain-level alignment, which is not fine-grained enough to ensure that learned representations are insensitive to domain styles. To address these issues, we propose a novel perspective for DG FAS that aligns features on the instance level without the need for domain labels. Specifically, Instance-Aware Domain Generalization framework is proposed to learn the generalizable feature by weakening the features' sensitivity to instance-specific styles. Concretely, we propose Asymmetric Instance Adaptive Whitening to adaptively eliminate the style-sensitive feature correlation, boosting the generalization. Moreover, Dynamic Kernel Generator and Categorical Style Assembly are proposed to first extract the instance-specific features and then generate the style-diversified features with large style shifts, respectively, further facilitating the learning of style-insensitive features. Extensive experiments and analysis demonstrate the superiority of our method over state-of-the-art competitors. Code will be publicly available at https://github.com/qianyuzqy/IADG.Comment: Accepted to IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 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

Whole-genome sequencing of <em>Oryza brachyantha</em> reveals mechanisms underlying <em>Oryza</em> genome evolution

The wild species of the genus Oryza contain a largely untapped reservoir of agronomically important genes for rice improvement. Here we report the 261-Mb de novo assembled genome sequence of Oryza brachyantha. Low activity of long-terminal repeat retrotransposons and massive internal deletions of ancient long-terminal repeat elements lead to the compact genome of Oryza brachyantha. We model 32,038 protein-coding genes in the Oryza brachyantha genome, of which only 70% are located in collinear positions in comparison with the rice genome. Analysing breakpoints of non-collinear genes suggests that double-strand break repair through non-homologous end joining has an important role in gene movement and erosion of collinearity in the Oryza genomes. Transition of euchromatin to heterochromatin in the rice genome is accompanied by segmental and tandem duplications, further expanded by transposable element insertions. The high-quality reference genome sequence of Oryza brachyantha provides an important resource for functional and evolutionary studies in the genus Oryza

SoccerNet 2023 Challenges Results

peer reviewedThe SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on https://www.soccer-net.org. Baselines and development kits can be found on https://github.com/SoccerNet

MICROWAVE ABSORPTION PROPERTIES OF β-SiC–C COMPOSITES WITH SOLID PHASE SINTERING AT X BAND

In this paper, by using β-SiC powder as a matrix and mixing different content of C, a series of SiC–C composites with solid phase sintering under different temperature were prepared. The relative density, electrical properties and microwave absorption properties at X band were measured systemically. The microwave absorption mechanisms of the composites were studied comprehensively by the test results, together with the microstructure and composition analysis. The main results show that the composites are good microwave absorption ceramics at X band because of the good interface's match of wave impedance by the control of properties and process, C content and sintering process influence effectively all test properties. For the SiC–3wt%C composites (which is the best microwave absorption one) under 2200° sintering, the biggest microwave attenuation is -40.5 dB and almost all attenuations are above -30 dB in the whole X band.Solid phase sintering, microwave absorption properties, X band, interface's match