Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph

Knowledge graphs (KGs) are commonly used as side information to enhance collaborative signals and improve recommendation quality. In the context of knowledge-aware recommendation (KGR), graph neural networks (GNNs) have emerged as promising solutions for modeling factual and semantic information in KGs. However, the long-tail distribution of entities leads to sparsity in supervision signals, which weakens the quality of item representation when utilizing KG enhancement. Additionally, the binary relation representation of KGs simplifies hyper-relational facts, making it challenging to model complex real-world information. Furthermore, the over-smoothing phenomenon results in indistinguishable representations and information loss. To address these challenges, we propose the SDK (Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph) framework. This framework establishes a cross-view hypergraph self-supervised learning mechanism for KG enhancement. Specifically, we model hyper-relational facts in KGs to capture interdependencies between entities under complete semantic conditions. With the refined representation, a hypergraph is dynamically constructed to preserve features in the deep vector space, thereby alleviating the over-smoothing problem. Furthermore, we mine external supervision signals from both the global perspective of the hypergraph and the local perspective of collaborative filtering (CF) to guide the model prediction process. Extensive experiments conducted on different datasets demonstrate the superiority of the SDK framework over state-of-the-art models. The results showcase its ability to alleviate the effects of over-smoothing and supervision signal sparsity

VideoReTalking: Audio-based Lip Synchronization for Talking Head Video Editing In the Wild

We present VideoReTalking, a new system to edit the faces of a real-world talking head video according to input audio, producing a high-quality and lip-syncing output video even with a different emotion. Our system disentangles this objective into three sequential tasks: (1) face video generation with a canonical expression; (2) audio-driven lip-sync; and (3) face enhancement for improving photo-realism. Given a talking-head video, we first modify the expression of each frame according to the same expression template using the expression editing network, resulting in a video with the canonical expression. This video, together with the given audio, is then fed into the lip-sync network to generate a lip-syncing video. Finally, we improve the photo-realism of the synthesized faces through an identity-aware face enhancement network and post-processing. We use learning-based approaches for all three steps and all our modules can be tackled in a sequential pipeline without any user intervention. Furthermore, our system is a generic approach that does not need to be retrained to a specific person. Evaluations on two widely-used datasets and in-the-wild examples demonstrate the superiority of our framework over other state-of-the-art methods in terms of lip-sync accuracy and visual quality.Comment: Accepted by SIGGRAPH Asia 2022 Conference Proceedings. Project page: https://vinthony.github.io/video-retalking

Identification and Functional Analysis of ThADH1 and ThADH4 Genes Involved in Tolerance to Waterlogging Stress in Taxodium hybrid ‘Zhongshanshan 406’

The Taxodium hybrid ‘Zhongshanshan 406’ (T. hybrid ‘Zhongshanshan 406’) [Taxodium mucronatum Tenore × Taxodium distichum (L.). Rich] has an outstanding advantage in flooding tolerance and thus has been widely used in wetland afforestation in China. Alcohol dehydrogenase genes (ADHs) played key roles in ethanol metabolism to maintain energy supply for plants in low-oxygen conditions. Two ADH genes were isolated and characterized—ThADH1 and ThADH4 (GenBank ID: AWL83216 and AWL83217—basing on the transcriptome data of T. hybrid ‘Zhongshanshan 406’ grown under waterlogging stress. Then the functions of these two genes were investigated through transient expression and overexpression. The results showed that the ThADH1 and ThADH4 proteins both fall under ADH III subfamily. ThADH1 was localized in the cytoplasm and nucleus, whereas ThADH4 was only localized in the cytoplasm. The expression of the two genes was stimulated by waterlogging and the expression level in roots was significantly higher than those in stems and leaves. The respective overexpression of ThADH1 and ThADH4 in Populus caused the opposite phenotype, while waterlogging tolerance of the two transgenic Populus significantly improved. Collectively, these results indicated that genes ThADH1 and ThADH4 were involved in the tolerance and adaptation to anaerobic conditions in T. hybrid ‘Zhongshanshan 406’

ToonTalker: Cross-Domain Face Reenactment

We target cross-domain face reenactment in this paper, i.e., driving a cartoon image with the video of a real person and vice versa. Recently, many works have focused on one-shot talking face generation to drive a portrait with a real video, i.e., within-domain reenactment. Straightforwardly applying those methods to cross-domain animation will cause inaccurate expression transfer, blur effects, and even apparent artifacts due to the domain shift between cartoon and real faces. Only a few works attempt to settle cross-domain face reenactment. The most related work AnimeCeleb requires constructing a dataset with pose vector and cartoon image pairs by animating 3D characters, which makes it inapplicable anymore if no paired data is available. In this paper, we propose a novel method for cross-domain reenactment without paired data. Specifically, we propose a transformer-based framework to align the motions from different domains into a common latent space where motion transfer is conducted via latent code addition. Two domain-specific motion encoders and two learnable motion base memories are used to capture domain properties. A source query transformer and a driving one are exploited to project domain-specific motion to the canonical space. The edited motion is projected back to the domain of the source with a transformer. Moreover, since no paired data is provided, we propose a novel cross-domain training scheme using data from two domains with the designed analogy constraint. Besides, we contribute a cartoon dataset in Disney style. Extensive evaluations demonstrate the superiority of our method over competing methods

Mild traumatic brain injury is associated with effect of inflammation on structural changes of default mode network in those developing chronic pain

BACKGROUND: Mild traumatic brain injury (mTBI) has a higher prevalence (more than 50%) of developing chronic posttraumatic headache (CPTH) compared with moderate or severe TBI. However, the underlying neural mechanism for CPTH remains unclear. This study aimed to investigate the inflammation level and cortical volume changes in patients with acute PTH (APTH) and further examine their potential in identifying patients who finally developed CPTH at follow-up. METHODS: Seventy-seven mTBI patients initially underwent neuropsychological measurements, 9-plex panel of serum cytokines and MRI scans within 7 days post-injury (T-1) and 54 (70.1%) of patients completed the same protocol at a 3-month follow-up (T-2). Forty-two matched healthy controls completed the same protocol at T-1 once. RESULTS: At baseline, mTBI patients with APTH presented significantly increased GM volume mainly in the right dorsal anterior cingulate cortex (dACC) and dorsal posterior cingulate cortex (dPCC), of which the dPCC volume can predict much worse impact of headache on patients\u27 lives by HIT-6 (β = 0.389, P = 0.007) in acute stage. Serum levels of C-C motif chemokine ligand 2 (CCL2) were also elevated in these patients, and its effect on the impact of headache on quality of life was partially mediated by the dPCC volume (mean [SE] indirect effect, 0.088 [0.0462], 95% CI, 0.01-0.164). Longitudinal analysis showed that the dACC and dPCC volumes as well as CCL2 levels had persistently increased in patients developing CPTH 3 months postinjury. CONCLUSION: The findings suggested that structural remodelling of DMN brain regions were involved in the progression from acute to chronic PTH following mTBI, which also mediated the effect of inflammation processes on pain modulation. TRIAL REGISTRATION: ClinicalTrial.gov ID: NCT02868684 ; registered 16 August 2016

Pulse Width Modulation Electro-Acupuncture on Cardiovascular Remodeling and Plasma Nitric Oxide in Spontaneously Hypertensive Rats

This study was designed to investigate the effect of pulse width modulation electro-acupuncture (PWM-EA) on cardiovascular remodeling and nitric oxide (NO) in spontaneously hypertensive rats (SHR). Thirty-four male SHR were randomly divided into control, captopril, and two PWM-EA groups, which were treated with 350 Hz (SHR-350 Hz) and whole audio bandwith electro-acupuncture (SHR-WAB group) respectively, on the ST 36 point located on the outside of the hind leg. Systolic blood pressure (BP), plasma and myocardial NO were measured. Histological studies were also performed on the aortic wall and the left ventricle. The BP in the SHR-350 Hz, SHR-WAB and the captopril groups was lower than in the control group following the treatment (P < .05). The average aortic media wall thickness in the two electro-acupuncture groups was less than in the control group (P < .05). The left ventricle/heart weight ratio in the captopril and SHR-350 Hz groups was less than in the control group (P < .01), but was similar between the SHR-WAB and the control group (P > .05). The plasma and myocardium NO levels were elevated in the captopril and the SHR-350 Hz group (P < .05 and .01, resp.). The plasma level of NO in the SHR-WAB group was also higher than in the control group (P < .05). We concluded that pulse width modulation electro-acupuncture on the ST 36 point prevents the progression of hypertension and diminishes the cardiovascular remodeling in SHR. It also elevates plasma and cardiac NO in this animal model