418 research outputs found
Energy efficient hybrid satellite terrestrial 5G networks with software defined features
In order to improve the manageability and adaptability
of future 5G wireless networks, the software orchestration mechanism,
named software defined networking (SDN) with Control
and User plane (C/U-plane) decoupling, has become one of the
most promising key techniques. Based on these features, the hybrid
satellite terrestrial network is expected to support flexible
and customized resource scheduling for both massive machinetype-
communication (MTC) and high-quality multimedia requests
while achieving broader global coverage, larger capacity and lower
power consumption. In this paper, an end-to-end hybrid satellite
terrestrial network is proposed and the performance metrics,
e. g., coverage probability, spectral and energy efficiency (SE and
EE), are analysed in both sparse networks and ultra-dense networks.
The fundamental relationship between SE and EE is investigated,
considering the overhead costs, fronthaul of the gateway
(GW), density of small cells (SCs) and multiple quality-ofservice
(QoS) requirements. Numerical results show that compared
with current LTE networks, the hybrid system with C/U split
can achieve approximately 40% and 80% EE improvement in
sparse and ultra-dense networks respectively, and greatly enhance
the coverage. Various resource management schemes, bandwidth
allocation methods, and on-off approaches are compared, and the
applications of the satellite in future 5G networks with software
defined features are proposed
M3C: A Framework towards Convergent, Flexible, and Unsupervised Learning of Mixture Graph Matching and Clustering
Existing graph matching methods typically assume that there are similar
structures between graphs and they are matchable. However, these assumptions do
not align with real-world applications. This work addresses a more realistic
scenario where graphs exhibit diverse modes, requiring graph grouping before or
along with matching, a task termed mixture graph matching and clustering. We
introduce Minorize-Maximization Matching and Clustering (M3C), a learning-free
algorithm that guarantees theoretical convergence through the
Minorize-Maximization framework and offers enhanced flexibility via relaxed
clustering. Building on M3C, we develop UM3C, an unsupervised model that
incorporates novel edge-wise affinity learning and pseudo label selection.
Extensive experimental results on public benchmarks demonstrate that our method
outperforms state-of-the-art graph matching and mixture graph matching and
clustering approaches in both accuracy and efficiency. Source code will be made
publicly available.Comment: 26 pages, 10 figure
Identifying Crypto Addresses with Gambling Behaviors: A Graph Neural Network Approach
The development of blockchain technology has brought prosperity to the cryptocurrency market and has made the blockchain platform a hotbed of crimes. As one of the most rampant crimes, crypto gambling has more high risk of illegal activities due to the lack of regulation. As a result, identifying crypto addresses with gambling behaviors has emerged as a significant research topic. In this work, we propose a novel detection approach based on Graph Neural Networks named CGDetector, consisting of Graph Construction, Subgraph Extractor, Statistical Feature Extraction, and Gambling Address Classification. Extensive experiments of large-scale and heterogeneous Ethereum transaction data are implemented to demonstrate that our proposed approach outperforms state-of-the-art address classifiers of traditional machine learning methods. This work makes the first attempt to detect suspicious crypto gambling addresses via Graph Neural Networks by all EVM-compatible blockchain systems, providing new insights into the field of cryptocurrency crime detection and blockchain security regulation
Make Pixels Dance: High-Dynamic Video Generation
Creating high-dynamic videos such as motion-rich actions and sophisticated
visual effects poses a significant challenge in the field of artificial
intelligence. Unfortunately, current state-of-the-art video generation methods,
primarily focusing on text-to-video generation, tend to produce video clips
with minimal motions despite maintaining high fidelity. We argue that relying
solely on text instructions is insufficient and suboptimal for video
generation. In this paper, we introduce PixelDance, a novel approach based on
diffusion models that incorporates image instructions for both the first and
last frames in conjunction with text instructions for video generation.
Comprehensive experimental results demonstrate that PixelDance trained with
public data exhibits significantly better proficiency in synthesizing videos
with complex scenes and intricate motions, setting a new standard for video
generation.Comment: 12 page
Computational Prediction of Human Salivary Proteins from Blood Circulation and Application to Diagnostic Biomarker Identification
Proteins can move from blood circulation into salivary glands through active transportation, passive diffusion or ultrafiltration, some of which are then released into saliva and hence can potentially serve as biomarkers for diseases if accurately identified. We present a novel computational method for predicting salivary proteins that come from circulation. The basis for the prediction is a set of physiochemical and sequence features we found to be discerning between human proteins known to be movable from circulation to saliva and proteins deemed to be not in saliva. A classifier was trained based on these features using a support-vector machine to predict protein secretion into saliva. The classifier achieved 88.56% average recall and 90.76% average precision in 10-fold cross-validation on the training data, indicating that the selected features are informative. Considering the possibility that our negative training data may not be highly reliable (i.e., proteins predicted to be not in saliva), we have also trained a ranking method, aiming to rank the known salivary proteins from circulation as the highest among the proteins in the general background, based on the same features. This prediction capability can be used to predict potential biomarker proteins for specific human diseases when coupled with the information of differentially expressed proteins in diseased versus healthy control tissues and a prediction capability for blood-secretory proteins. Using such integrated information, we predicted 31 candidate biomarker proteins in saliva for breast cancer
Evolutionary dynamics of rabies viruses highlights the importance of China rabies transmission in Asia
AbstractRabies in Asia is emerging as a serious public health issue. To explore the possible origin, phylogenetic relationships, and evolutionary dynamics of Asian Rabies viruses (RABV), we examined 200 complete nucleoprotein (N) gene sequences from RABV isolates in the region. Phylogeny supported the classification of Asian RABVs into five distinct clusters in lyssavirus genotype 1. Our geospatial and temporal analyses demonstrated that China appears to be the prime source of Asian RABVs. Understanding of rabies transmission and associated human activities, such as dog translocation, can help rabies control and elimination in Asia through collaborative efforts or programs
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