HDTR-Net: A Real-Time High-Definition Teeth Restoration Network for Arbitrary Talking Face Generation Methods

Talking Face Generation (TFG) aims to reconstruct facial movements to achieve high natural lip movements from audio and facial features that are under potential connections. Existing TFG methods have made significant advancements to produce natural and realistic images. However, most work rarely takes visual quality into consideration. It is challenging to ensure lip synchronization while avoiding visual quality degradation in cross-modal generation methods. To address this issue, we propose a universal High-Definition Teeth Restoration Network, dubbed HDTR-Net, for arbitrary TFG methods. HDTR-Net can enhance teeth regions at an extremely fast speed while maintaining synchronization, and temporal consistency. In particular, we propose a Fine-Grained Feature Fusion (FGFF) module to effectively capture fine texture feature information around teeth and surrounding regions, and use these features to fine-grain the feature map to enhance the clarity of teeth. Extensive experiments show that our method can be adapted to arbitrary TFG methods without suffering from lip synchronization and frame coherence. Another advantage of HDTR-Net is its real-time generation ability. Also under the condition of high-definition restoration of talking face video synthesis, its inference speed is $300\%$ faster than the current state-of-the-art face restoration based on super-resolution.Comment: 15pages, 6 figures, PRCV202

Performance and Emission Optimisation of an Ammonia/Hydrogen Fuelled Linear Joule Engine Generator

This paper presents a Linear Joule Engine Generator (LJEG) powered by ammonia and hydrogen co-combustion to tackle decarbonisation in the electrification of transport propulsion systems. A dynamic model of the LJEG, which integrates mechanics, thermodynamics, and electromagnetics sub-models, as well as detailed combustion chemistry analysis for emissions, is presented. The dynamic model is integrated and validated, and the LJEG performance is optimised for improved performance and reduced emissions. At optimal conditions, the engine could generate 1.96 kWe at a thermal efficiency of 34.3% and an electrical efficiency of 91%. It is found that the electromagnetic force of the linear alternator and heat addition from the external combustor and engine valve timing have the most significant influences on performance, whereas the piston stroke has a lesser impact. The impacts of hydrogen ratio, oxygen concentration, inlet pressure, and equivalence ratio of ammonia-air on nitric oxide (NO) formation and reduction are revealed using a detailed chemical kinetic analysis. Results indicated that rich combustion and elevated pressure are beneficial for NO reduction. The rate of production analysis indicates that the equivalence ratio significantly changes the relative contribution among the critical NO formation and reduction reaction pathways

Efficient Core-selecting Incentive Mechanism for Data Sharing in Federated Learning

Federated learning is a distributed machine learning system that uses participants' data to train an improved global model. In federated learning, participants cooperatively train a global model, and they will receive the global model and payments. Rational participants try to maximize their individual utility, and they will not input their high-quality data truthfully unless they are provided with satisfactory payments based on their data quality. Furthermore, federated learning benefits from the cooperative contributions of participants. Accordingly, how to establish an incentive mechanism that both incentivizes inputting data truthfully and promotes stable cooperation has become an important issue to consider. In this paper, we introduce a data sharing game model for federated learning and employ game-theoretic approaches to design a core-selecting incentive mechanism by utilizing a popular concept in cooperative games, the core. In federated learning, the core can be empty, resulting in the core-selecting mechanism becoming infeasible. To address this, our core-selecting mechanism employs a relaxation method and simultaneously minimizes the benefits of inputting false data for all participants. However, this mechanism is computationally expensive because it requires aggregating exponential models for all possible coalitions, which is infeasible in federated learning. To address this, we propose an efficient core-selecting mechanism based on sampling approximation that only aggregates models on sampled coalitions to approximate the exact result. Extensive experiments verify that the efficient core-selecting mechanism can incentivize inputting high-quality data and stable cooperation, while it reduces computational overhead compared to the core-selecting mechanism

Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring

We present an effective and efficient method that explores the properties of Transformers in the frequency domain for high-quality image deblurring. Our method is motivated by the convolution theorem that the correlation or convolution of two signals in the spatial domain is equivalent to an element-wise product of them in the frequency domain. This inspires us to develop an efficient frequency domain-based self-attention solver (FSAS) to estimate the scaled dot-product attention by an element-wise product operation instead of the matrix multiplication in the spatial domain. In addition, we note that simply using the naive feed-forward network (FFN) in Transformers does not generate good deblurred results. To overcome this problem, we propose a simple yet effective discriminative frequency domain-based FFN (DFFN), where we introduce a gated mechanism in the FFN based on the Joint Photographic Experts Group (JPEG) compression algorithm to discriminatively determine which low- and high-frequency information of the features should be preserved for latent clear image restoration. We formulate the proposed FSAS and DFFN into an asymmetrical network based on an encoder and decoder architecture, where the FSAS is only used in the decoder module for better image deblurring. Experimental results show that the proposed method performs favorably against the state-of-the-art approaches. Code will be available at \url{https://github.com/kkkls/FFTformer}.Comment: Code will be available at \url{https://github.com/kkkls/FFTformer

Chiral charge density wave and backscattering-immune orbital texture in monolayer 1T-TiTe2

Non-trivial electronic states are attracting intense attention in low-dimensional physics. Though chirality has been identified in charge states with a scalar order parameter, its intertwining with charge density waves (CDW), film thickness and the impact on the electronic behaviors remain less well understood. Here, using scanning tunneling microscopy, we report a 2 x 2 chiral CDW as well as a strong suppression of the Te-5p hole-band backscattering in monolayer 1T-TiTe2. These exotic characters vanish in bilayer TiTe2 with a non-CDW state. Theoretical calculations approve that chirality comes from a helical stacking of the triple-q CDW components and therefore can persist at the two-dimensional limit. Furthermore, the chirality renders the Te-5p bands an unconventional orbital texture that prohibits electron backscattering. Our study establishes TiTe2 as a promising playground for manipulating the chiral ground states at the monolayer limit and provides a novel path to engineer electronic properties from an orbital degree.Comment: 21 pages, 5 figure

Don't worry about mistakes! Glass Segmentation Network via Mistake Correction

Recall one time when we were in an unfamiliar mall. We might mistakenly think that there exists or does not exist a piece of glass in front of us. Such mistakes will remind us to walk more safely and freely at the same or a similar place next time. To absorb the human mistake correction wisdom, we propose a novel glass segmentation network to detect transparent glass, dubbed GlassSegNet. Motivated by this human behavior, GlassSegNet utilizes two key stages: the identification stage (IS) and the correction stage (CS). The IS is designed to simulate the detection procedure of human recognition for identifying transparent glass by global context and edge information. The CS then progressively refines the coarse prediction by correcting mistake regions based on gained experience. Extensive experiments show clear improvements of our GlassSegNet over thirty-four state-of-the-art methods on three benchmark datasets

Downregulation of TSPAN13 by miR-369-3p inhibits cell proliferation in papillary thyroid cancer (PTC)

Previous studies demonstrated dysregulation of different microRNAs in thyroid cancer. Tetraspanins (TSPANs) are cell surface proteins with critical roles in many cellular processes, and implications in tumor development. Here we investigated the role of miR-369-3p in papillary thyroid cancer (PTC) and its association with TSPAN13. miR-369-3p and the TSPAN13 gene expression profiles of 513 thyroid cancer and 59 normal thyroid tissues were downloaded from the Cancer Genome Atlas database. Thyroid cancer tissues were classified according to the histological type, grouped based on low and high median miR-369-3p and TSPAN13 expression, and analyzed in relation to overall survival (OS) of patients. Human PTC cell lines (TPC-1 and GLAG-66) and human embryonic kidney 293T (HEK293T) cells were used for in vitro analysis. Transfection experiments were performed with synthetic miRNA mimics for miR-369-3p and small interfering RNAs for TSPAN13. Relative expression of miR-369-3p and TSPAN13 mRNA was determined by RT-qPCR. Protein levels of TSPAN13 were determined by western blotting. Cell proliferation (CCK-8 assay), colony formation, and apoptosis (flow cytometry) were analyzed in transfected cells. Binding sites of miR-369-3p in TSPAN13 mRNA were determined by bioinformatics analysis and dual luciferase reporter assay. miR-369-3p was downregulated and TSPAN13 upregulated in PTC, follicular thyroid cancer, and tall cell variant tissues. Both low expression of miR-369-3p and high expression of TSPAN13 were associated with shorter OS in thyroid cancer patients. Overexpression of miR-369-3p significantly suppressed proliferation and promoted apoptosis in PTC cells. TSPAN13 was a direct target of miR-369-3p, and silencing of TSPAN13 phenocopied the effect of miR-369-3p mimics in PTC cells. Overall, the downregulation of miR-369-3p and consequent upregulation of its target TSPAN13 appear to be involved in pathophysiology of PTC