Next Decade of Telecommunications Artificial Intelligence

It has been an exciting journey since the mobile communications and artificial intelligence (AI) were conceived in 1983 and 1956. While both fields evolved independently and profoundly changed communications and computing industries, the rapid convergence of 5th generation mobile communication technology (5G) and AI is beginning to significantly transform the core communication infrastructure, network management, and vertical applications. The paper first outlined the individual roadmaps of mobile communications and AI in the early stage, with a concentration to review the era from 3rd generation mobile communication technology (3G) to 5G when AI and mobile communications started to converge. With regard to telecommunications AI, the progress of AI in the ecosystem of mobile communications was further introduced in detail, including network infrastructure, network operation and management, business operation and management, intelligent applications towards business supporting system (BSS) & operation supporting system (OSS) convergence, verticals and private networks, etc. Then the classifications of AI in telecommunication ecosystems were summarized along with its evolution paths specified by various international telecommunications standardization organizations. Towards the next decade, the prospective roadmap of telecommunications AI was forecasted. In line with 3rd generation partnership project (3GPP) and International Telecommunication Union Radiocommunication Sector (ITU-R) timeline of 5G & 6th generation mobile communication technology (6G), the network intelligence following 3GPP and open radio access network (O-RAN) routes, experience and intent-based network management and operation, network AI signaling system, intelligent middle-office based BSS, intelligent customer experience management and policy control driven by BSS & OSS convergence, evolution from service level agreement (SLA) to experience level agreement (ELA), and intelligent private network for verticals were further explored. The paper is concluded with the vision that AI will reshape the future beyond 5G (B5G)/6G landscape, and we need pivot our research and development (R&D), standardizations, and ecosystem to fully take the unprecedented opportunities

Unsupervised Domain Adaptation for Forest Fire Recognition Using Transferable Knowledge from Public Datasets

Deep neural networks (DNNs) have driven the recent advances in fire detection. However, existing methods require large-scale labeled samples to train data-hungry networks, which are difficult to collect and even more laborious to label. This paper applies unsupervised domain adaptation (UDA) to transfer knowledge from a labeled public fire dataset to another unlabeled one in practical application scenarios for the first time. Then, a transfer learning benchmark dataset called Fire-DA is built from public datasets for fire recognition. Next, the Deep Subdomain Adaptation Network (DSAN) and the Dynamic Adversarial Adaptation Network (DAAN) are experimented on Fire-DA to provide a benchmark result for future transfer learning research in fire recognition. Finally, two transfer tasks are built from Fire-DA to two public forest fire datasets, the aerial forest fire dataset FLAME and the large-scale fire dataset FD-dataset containing forest fire scenarios. Compared with traditional handcrafted feature-based methods and supervised CNNs, DSAN reaches 82.5% performance of the optimal supervised CNN on the testing set of FLAME. In addition, DSAN achieves 95.8% and 83.5% recognition accuracy on the testing set and challenging testing set of FD-dataset, which outperform the optimal supervised CNN by 0.5% and 2.6%, respectively. The experimental results demonstrate that DSAN achieves an impressive performance on FLAME and a new state of the art on FD-dataset without accessing their labels during training, a fundamental step toward unsupervised forest fire recognition for industrial applications

The Next Decade of Telecommunications Artificial Intelligence

It has been an exciting journey since the mobile communications and artificial intelligence were conceived 37 years and 64 years ago. While both fields evolved independently and profoundly changed communications and computing industries, the rapid convergence of 5G and deep learning is beginning to significantly transform the core communication infrastructure, network management and vertical applications. The paper first outlines the individual roadmaps of mobile communications and artificial intelligence in the early stage, with a concentration to review the era from 3G to 5G when AI and mobile communications started to converge. With regard to telecommunications artificial intelligence, the paper further introduces in detail the progress of artificial intelligence in the ecosystem of mobile communications. The paper then summarizes the classifications of AI in telecom ecosystems along with its evolution paths specified by various international telecommunications standardization bodies. Towards the next decade, the paper forecasts the prospective roadmap of telecommunications artificial intelligence. In line with 3GPP and ITU-R timeline of 5G & 6G, the paper further explores the network intelligence following 3GPP and ORAN routes respectively, experience and intention driven network management and operation, network AI signalling system, intelligent middle-office based BSS, intelligent customer experience management and policy control driven by BSS and OSS convergence, evolution from SLA to ELA, and intelligent private network for verticals. The paper is concluded with the vision that AI will reshape the future B5G or 6G landscape and we need pivot our R&D, standardizations, and ecosystem to fully take the unprecedented opportunities.Comment: 50 pages in English 24 figures. (Note version 5 is 19 pages, in Chinese, with 24 figures

Circular RNA MAPK4 (circ-MAPK4) inhibits cell apoptosis via MAPK signaling pathway by sponging miR-125a-3p in gliomas

BackgroundRecent evidences have shown that circular RNAs (circRNAs) are frequently dysregulated and play paramount roles in various cancers. circRNAs are abundant in central nervous system (CNS); however, few studies describe the clinical significance and role of circRNAs in gliomas, which is the most common and aggressive primary malignant tumor in the CNS.MethodsA bioinformatics analysis was performed to profile and screen the dyregulated circRNAs during early neural development. Quantitative real-time PCR was used to detect the expression of circ-MAPK4 and target miRNAs. Glioma cells were transfected with circ-MAPK4 siRNAs, then cell proliferation, apoptosis, transwell assays, as well as tumorigenesis and TUNEL assays, were performed to examine effect of circ-MAPK4 in vitro and vivo. Biotinylated-circ-MAPK4 probe based pull-down assay was conducted to confirm the relationship between circ-MAPK4 and miR-125-3p.ResultsIn this study, we identified a circRNA, circ-MAPK4 (has_circ_0047688), which was downregulated during early neural differentiation. In gliomas, circ-MAPK4 acted as an oncogene, was inversely upregulated and linked to clinical pathological stage of gliomas (P < 0.05). Next, we verified that circ-MAPK4 promoted the survival and inhibited the apoptosis of glioma cells in vitro and in vivo. Furthermore, we proved that circ-MAPK4 was involved in regulating p38/MAPK pathway, which affected glioma proliferation and apoptosis. Finally, miR-125a-3p, a miRNA exhibited tumor-suppressive function through impairing p38/MAPK pathway, which was increased by inhibiting circ-MAPK4 and could be pulled down by circ-MAPK4. Inhibition of miR-125a-3p could partly rescue the increased phosphorylation levels of p38/MAPK and the elevated amount of apoptosis inducing by knockdown of circ-MAPK4.ConclusionsOur findings suggest that circ-MAPK4 is a critical player in glioma cell survival and apoptosis via p38/MAPK signaling pathway through modulation of miR-125a-3p, which can serve as a new therapeutic target for treatment of gliomas

N-Acetyltransferase 10 represses Uqcr11 and Uqcrb independently of ac4C modification to promote heart regeneration

Abstract Translational control is crucial for protein production in various biological contexts. Here, we use Ribo-seq and RNA-seq to show that genes related to oxidative phosphorylation are translationally downregulated during heart regeneration. We find that Nat10 regulates the expression of Uqcr11 and Uqcrb mRNAs in mouse and human cardiomyocytes. In mice, overexpression of Nat10 in cardiomyocytes promotes cardiac regeneration and improves cardiac function after injury. Conversely, treating neonatal mice with Remodelin—a Nat10 pharmacological inhibitor—or genetically removing Nat10 from their cardiomyocytes both inhibit heart regeneration. Mechanistically, Nat10 suppresses the expression of Uqcr11 and Uqcrb independently of its ac4C enzyme activity. This suppression weakens mitochondrial respiration and enhances the glycolytic capacity of the cardiomyocytes, leading to metabolic reprogramming. We also observe that the expression of Nat10 is downregulated in the cardiomyocytes of P7 male pig hearts compared to P1 controls. The levels of Nat10 are also lower in female human failing hearts than non-failing hearts. We further identify the specific binding regions of Nat10, and validate the pro-proliferative effects of Nat10 in cardiomyocytes derived from human embryonic stem cells. Our findings indicate that Nat10 is an epigenetic regulator during heart regeneration and could potentially become a clinical target