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The core challenge of seismic data interpolation is how to capture latent spatial-temporal relationships between unknown and known traces in 3-D space. The prevailing tensor-based interpolation schemes seek a globally low-rank approximation to mine the high-dimensional relationships hidden in 3-D seismic data. However, when the low-rank assumption is violated for data involving complex geological structures, the existing interpolation schemes fail to precisely capture the trace relationships, which may influence the interpolation results. As an alternative, this article presents a basic deep tensor autoencoder (DTAE) and two variants to implicitly learn a data-driven, nonlinear, and high-dimensional mapping to explore the complicated relationship among traces without the need for any underlying assumption. Then, tensor backpropagation (TBP), which can be essentially viewed as a tensor version of traditional backpropagation (BP), is introduced to solve for the new model parameters. For ease of implementation, a mathematical relationship between tensor and matrix autoencoders is constructed by taking advantage of the properties of a tensor-tensor product. Based on the derived relationship, the DTAE weight parameters are inferred by applying a matrix autoencoder to each frontal slice in the discrete cosine transform (DCT) domain, and this process is further summarized into a general theoretical and practical framework. Finally, the performance benefits of the proposed DTAE-based method are demonstrated in experiments with both synthetic and real field seismic data.

Efficient urban traffic monitoring is a key enabler for intelligent planning and management of modern cities. Network tomography can monitor the urban traffic with a comparably small number of traffic detectors like cameras, and has become an appealing technique for urban traffic management. However, previous work on network tomography based traffic monitoring focuses primarily on developing estimators using the given end-to-end travel time measurements, while the design of data collection for efficiently distributed collecting and processing the raw monitoring videos to such measurements is often neglected. We fill this gap by exploring the vision of edge intelligence for optimal urban traffic monitoring, and tackle the following two problems in regard of limited telecommunications resources: 1) when the total number of monitoring videos that are successfully processed into the end-to-end travel time measurements is pre-bounded, we employ a Fisher Information Matrix (FIM) to help determine the best quota scheme for the monitoring videos that each traffic detector need to generate and 2) when the centralised processing of monitoring videos alone is insufficient, we make use of the computation capabilities from these edge devices, i.e., traffic detectors, and employ a multi-agent reinforcement learning approach to help them conduct intelligent computation offloading individually. Extensive simulations demonstrate that our proposed scheme effectively reduces the estimation error of network tomography compared to common approaches with either uniform or random strategy.

Network congestion will result in significant performance degradation or even failures of many bandwidth-hungry Internet of Things (IoT) applications. Accurate and efficient congested link identification has become a foundational issue to IoT applications like self-driving cars, digital health, smart city, and so on. However, directly monitoring the massive number of interior links often introduces high operation cost or even is infeasible in practice, giving rise to indirect monitoring techniques like network Boolean tomography. Nevertheless, in many networks, the number of their interior links is larger than their end-to-end paths, making it very challenging for network Boolean tomography to find a determined solution. To resolve this issue, most of current methods try to utilize some prerequisites, such as the link congestion probabilities. While these probabilities might be hard or even unable to be obtained accurately in dynamical networks, limiting the practical deployment. In this paper, we are motivated to design a framework of congested link identification without any prerequisite or assumption. We first novelly model the congested link identification procedures as a Markov decision processes (MDPs), and then employ a reinforcement learning technology, i.e., ${Q}$ -learning, to solve this MDP. The simulation results show that our proposed scheme can autonomously and efficiently explore the unknown network environment, and is able to achieve better adaptivity and correctness, without any prior knowledge comparing to existing methods.

Edge computing has been regarded as an ideal platform to provision computation and storage resources at the network edge thanks to its vast distribution and user proximity. Owing to such features, it is also promising to provision energy resources at the network edge via wireless energy transferring. With the consideration of the mobility of wireless devices and the diversity of charging attenuation in the edge computing environment, the device shall make the right charging decision at runtime during the movement to maximize the long-term average utility. In this article, we discover that this problem can be described as a Markov Decision Process (MDP) and accordingly design an MDP based wireless energy harvesting strategy. Based on extensive simulation-based experimental results, we observe that the proposed strategy outperforms several existing schemes and significantly improves the system utility.

Resistance to 5-Fluorouracil (5-FU) is a frequent occurrence in patients with colorectal cancer (CRC). MicroRNAs (miRNAs) from cancer-associated fibroblasts (CAFs)-secreted exosomes have been associated with 5-FU sensitivity. The potential molecular mechanism of CAFs-exosomal miRNAs in CRC remains unclear. The aim of the present study was to elucidate the role of exosomal miRNAs in 5-FU sensitivity in CRC. Exosomes derived from CAFs were extracted. Exosomal miR-181d-5p was identified as a miRNA associated with 5-FU sensitivity. The putative function of exosomal miR-181d-5p was evaluated by ethynyl-2-deoxyuridine staining, flow cytometry, RNA immunoprecipitation, luciferase reporter assay, tumor xenograft formation, reverse transcription-quantitative PCR and western blot analysis. Modification of miR-181d-5p by the RNA N6-methyladenosine (m6A) methyltransferase like (METTL)3 was examined by m6A methylation analysis. The results indicated that m6A modification and METTL3 expression were upregulated in CRC patients. METTL3-dependent m6A methylation promoted the miR-181b-5p process by DiGeorge Syndrome Critical Region 8 (DGCR8) in CAFs. CAFs-derived exosomes inhibited 5-FU sensitivity in CRC cells through the METTL3/miR-181d-5p axis. A mechanistic study revealed that miR-181d-5p directly targeted neurocalcin δ (NCALD) to inhibit the 5-FU sensitivity of CRC cells. Patients with higher NCALD levels exhibited a higher survival rate. Taken together, METTL3-dependent m6A methylation was upregulated in CRC to promote the processing of miR-181d-5p by DGCR8. This led to increased miR-181d-5p expression, which inhibited the 5-FU sensitivity of CRC cells by targeting NCALD. The results of the present study provided novel insight into exosomal microRNAs in 5-FU sensitivity in CRC cells. Furthermore, exosomal miR-181d-5p may represent a potential prognostic marker for CRC.

Iron-based soft magnetic composites insulated with parylene C film were prepared via chemical vapor deposition polymerization, and their morphologies, magnetic properties, and corrosive resistance were investigated. Scanning electron microscopy and Fourier transform infrared spectrum confirmed that the surface of the iron particle was completely uniform insulated with parylene C, resulting in reducing the core loss of the synthetic compacts. The results show that compressed pressure has an important influence on the magnetic properties of the soft magnetic composites, enhance the magnetic induction value from 0.64 to 1.01 T and is obtained with increased the compressed pressure from 300 to 650 MPa. The loss separation results show that the hysteresis loss coefficient of 4 wt% parylene C insulated samples decreased from 1.743 to 1.657 and eddy current loss coefficient decreased from 0.019 to 0.012 as compared with uncoated compacts under the same compress pressure. The acid and salt corrosion resistance of the iron particles is apparently enhanced when the chemically stable parylene C layer was exited in the surface.

Multipath routing, which provides multiple paths for ubiquitous communications, has been considered as promising routing mechanism to optimize network performance for Internet. However, it will incur adverse effects on the existing and emerging network measurement schemes, for example incomplete and inaccurate measurement results, to understand network characteristics, since many of them commonly do the work under single-path routing rather than multipath-routing. In order to eliminate this emerging issue on single-path-based network measurement in Internet, it requires to identify whether there is multipath routing between two reachable hosts in the network. Notice that no out-of-order delivery among a strip of packets along multiple paths seldom occurs, in this paper, an efficient multipath routing identification approach has been proposed to achieve this goal, by introducing a composite probe built on out-of-order delivery. We have elaborated our theoretical observation on the current probe composed of a strip of packets, and then presented our composite probe design in detail. Our proposed approach not only can efficiently identify the existing multipath routing, but also accurately recognize its type, referring to flow-based or packet-based routing. Corroborated by experiments and simulations, conducted on Planetlab and NS2, respectively, our approach outperforms other schemes in terms of effectiveness and accuracy.