Smart and Functional Polymers

This book is based on the Special Issue of the journal Molecules on “Smart and Functional Polymers”. The collected research and review articles focus on the synthesis and characterization of advanced functional polymers, polymers with specific structures and performances, current improvements in advanced polymer-based materials for various applications, and the opportunities and challenges in the future. The topics cover the emerging synthesis and characterization technology of smart polymers, core?shell structure polymers, stimuli-responsive polymers, anhydrous electrorheological materials fabricated from conducting polymers, reversible polymerization systems, and biomedical polymers for drug delivery and disease theranostics. In summary, this book provides a comprehensive overview of the latest synthesis approaches, representative structures and performances, and various applications of smart and functional polymers. It will serve as a useful reference for all researchers and readers interested in polymer sciences and technologies

Analysis of Alphalactalbumin and Betalactoglobulin from the Rehydration of Bovine Colostrum Powder Using Cloud Point Extraction and Mass Spectrometry

Alphalactalbumin (α-La) and betalactoglobulin (β-Lg) in the rehydration of bovine colostrum powder were successfully separated by cloud point extraction using a nonionic surfactant Triton X-114. The effects of different factors, including the surfactant concentration, sample volume, electrolyte, and pH were discussed. The optimized conditions for cloud point extraction of alphalactalbumin (α-La) and betalactoglobulin (β-Lg) can be concluded that the best surfactant is 1% (w/v) Triton X-114, 200 μL of sample volume, 150 mmol/L NaCl, and 6% (w/v) sucrose. After cloud point extraction, the capillary electrophoresis is used to check the efficiency of the extraction procedure. The results had been effectively confirmed by the characterization with matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS)

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

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