This paper aims at a comprehensive understanding of the novel elastic property of double-stranded DNA (dsDNA) discovered very recently through single-molecule manipulation techniques. A general elastic model for doubl...
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This paper aims at a comprehensive understanding of the novel elastic property of double-stranded DNA (dsDNA) discovered very recently through single-molecule manipulation techniques. A general elastic model for double-stranded biopolymers is proposed, and a structural parameter called the folding angle φ is introduced to characterize their deformations. The mechanical property of long dsDNA molecules is then studied based on this model, where the base-stacking interactions between DNA adjacent nucleotide base pairs, the steric effects of base pairs, and the electrostatic interactions along DNA backbones are taken into account. Quantitative results are obtained by using a path integral method, and excellent agreement between theory and the observations reported by five major experimental groups are attained. The strong intensity of the base stacking interactions ensures the structural stability of DNA, while the short-ranged nature of such interactions makes externally stimulated large structural fluctuations possible. The entropic elasticity, highly extensibility, and supercoiling property of DNA are all closely related to this account. The present work also suggests the possibility that negative torque can induce structural transitions in highly extended DNA from the right-handed B form to left-handed configurations similar to the Z-form configuration. Some formulas concerned with the application of path integral methods to polymeric systems are listed in the Appendixes.
An elastic model for double-stranded polymers is constructed to study the recently observed DNA entropic elasticity, cooperative extensibility, and supercoiling property. With the introduction of a new structural para...
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An elastic model for double-stranded polymers is constructed to study the recently observed DNA entropic elasticity, cooperative extensibility, and supercoiling property. With the introduction of a new structural parameter (the folding angle ϕ), bending deformations of sugar-phosphate backbones, steric effects of nucleotide base pairs, and base-stacking interactions are considered. The comprehensive agreements between theory and experiments both on torsionally relaxed DNA and on negatively supercoiled DNA strongly indicate that base-stacking interactions, although short-ranged in nature, dominate the elasticity of DNA and, hence, are of vital biological significance.
Recently, the paradigm of pre-training and fine-tuning has achieved impressive performance owing to their ability to transfer general knowledge from pre-trained domain to target domain. Meanwhile, Graph Neural Network...
Recently, the paradigm of pre-training and fine-tuning has achieved impressive performance owing to their ability to transfer general knowledge from pre-trained domain to target domain. Meanwhile, Graph Neural Networks (GNNs) have gained prominence in recommender systems. However, there is a lack of unified pre-training and fine-tuning paradigms in graph-based recommendation systems. Applying pre-training and fine-tuning in graph-based recommendation is challenging due to the unique characteristics of recommendation data, including the non-uniform representation, negative transfer effects, and skewed data distributions. To overcome these challenges, we introduce ProRec (Pre-training and prompting Recommendation), a novel model that synergizes uniform graph pre-training with prompt-tuning for recommendation systems. Specifically, to address the challenge of inconsistent features across different recommendation datasets, ProRec constructs unified input features at the subgraph level and uses a Graph Auto-Encoder for pre-training, laying the foundation for uniform knowledge transfer from the pre-trained domain to the downstream domain. Additionally, ProRec employs prompt-tuning during the fine-tuning phase, which, in a parameter-efficient manner, enhancing the generalization of pre-trained knowledge to downstream tasks and thereby reducing negative transfer effects. Furthermore, a cross-layer contrastive learning strategy is adopted to eliminate uneven data distribution, promoting more evenly distributed and informative representations. Finally, extensive benchmark comparisons have demonstrated that ProRec outperforms the latest state-of-the-art methods. The source code necessary for replication is available at https://***/Code2Q/ProRec.
Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data manag...
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Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in IoV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention Networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. It employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in IoV.
Since the mid 1990s, data hiding has been proposed as an enabling technology for securing multimedia communication and is now used in various applications including broadcast monitoring, movie fingerprinting, steganog...
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ISBN:
(数字)9783642550461
ISBN:
(纸本)9783642550454
Since the mid 1990s, data hiding has been proposed as an enabling technology for securing multimedia communication and is now used in various applications including broadcast monitoring, movie fingerprinting, steganography, video indexing and retrieval and image authentication. Data hiding and cryptographic techniques are often combined to complement each other, thus triggering the development of a new research field of multimedia security. Besides, two related disciplines, steganalysis and data forensics, are increasingly attracting researchers and becoming another new research field of multimedia security. This journal, LNCS Transactions on Data Hiding and Multimedia Security, aims to be a forum for all researchers in these emerging fields, publishing both original and archival research results. The seven papers included in this special issue were carefully reviewed and selected from 21 submissions. They address the challenges faced by the emerging area of visual cryptography and provide the readers with an overview of the state of the art in this field of research.
The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT service...
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The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT services, Mobile Edge computing (MEC) has emerged as an indispensable technology in smart health. Benefiting from the cost-effectiveness of deployment, unmanned aerial vehicles (UAVs) equipped with MEC servers in Non-Orthogonal Multiple Access (NOMA) have emerged as a promising solution for providing smart health services in proximity to medical devices (MDs). However, the escalating number of MDs and the limited availability of communication resources of UAVs give rise to a significant increase in transmission latency. Moreover, due to the limited communication range of UAVs, the geographically-distributed MDs lead to workload imbalance of UAVs, which deteriorates the service response delay. To this end, this paper proposes a UAV-enabled Distributed computation Offloading and Power control method with Multi-Agent, named DOPMA, for NOMA-based IoMT environment. Specifically, this paper introduces computation and transmission queue models to analyze the dynamic characteristics of task execution latency and energy consumption. Moreover, a credit assignment scheme-based reward function is designed considering both system-level rewards and rewards tailored to each MD, and an improved multi-agent deep deterministic policy gradient algorithm is developed to derive offloading and power control decisions independently. Extensive simulations demonstrate that the proposed method outperforms existing schemes, achieving \(7.1\%\) reduction in energy consumption and \(16\%\) decrease in average delay.
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