On-Demand Resource Management for 6G Wireless Networks Using Knowledge-Assisted Dynamic Neural Networks

On-demand service provisioning is a critical yet challenging issue in 6G wireless communication networks, since emerging services have significantly diverse requirements and the network resources become increasingly heterogeneous and dynamic. In this paper, we study the on-demand wireless resource orchestration problem with the focus on the computing delay in orchestration decision-making process. Specifically, we take the decision-making delay into the optimization problem. Then, a dynamic neural network (DyNN)-based method is proposed, where the model complexity can be adjusted according to the service requirements. We further build a knowledge base representing the relationship among the service requirements, available computing resources, and the resource allocation performance. By exploiting the knowledge, the width of DyNN can be selected in a timely manner, further improving the performance of orchestration. Simulation results show that the proposed scheme significantly outperforms the traditional static neural network, and also shows sufficient flexibility in on-demand service provisioning

Imperfect Digital Twin Assisted Low Cost Reinforcement Training for Multi-UAV Networks

Deep Reinforcement Learning (DRL) is widely used to optimize the performance of multi-UAV networks. However, the training of DRL relies on the frequent interactions between the UAVs and the environment, which consumes lots of energy due to the flying and communication of UAVs in practical experiments. Inspired by the growing digital twin (DT) technology, which can simulate the performance of algorithms in the digital space constructed by coping features of the physical space, the DT is introduced to reduce the costs of practical training, e.g., energy and hardware purchases. Different from previous DT-assisted works with an assumption of perfect reflecting real physics by virtual digital, we consider an imperfect DT model with deviations for assisting the training of multi-UAV networks. Remarkably, to trade off the training cost, DT construction cost, and the impact of deviations of DT on training, the natural and virtually generated UAV mixing deployment method is proposed. Two cascade neural networks (NN) are used to optimize the joint number of virtually generated UAVs, the DT construction cost, and the performance of multi-UAV networks. These two NNs are trained by unsupervised and reinforcement learning, both low-cost label-free training methods. Simulation results show the training cost can significantly decrease while guaranteeing the training performance. This implies that an efficient decision can be made with imperfect DTs in multi-UAV networks

Distilling Knowledge from Resource Management Algorithms to Neural Networks: A Unified Training Assistance Approach

As a fundamental problem, numerous methods are dedicated to the optimization of signal-to-interference-plus-noise ratio (SINR), in a multi-user setting. Although traditional model-based optimization methods achieve strong performance, the high complexity raises the research of neural network (NN) based approaches to trade-off the performance and complexity. To fully leverage the high performance of traditional model-based methods and the low complexity of the NN-based method, a knowledge distillation (KD) based algorithm distillation (AD) method is proposed in this paper to improve the performance and convergence speed of the NN-based method, where traditional SINR optimization methods are employed as ``teachers" to assist the training of NNs, which are ``students", thus enhancing the performance of unsupervised and reinforcement learning techniques. This approach aims to alleviate common issues encountered in each of these training paradigms, including the infeasibility of obtaining optimal solutions as labels and overfitting in supervised learning, ensuring higher convergence performance in unsupervised learning, and improving training efficiency in reinforcement learning. Simulation results demonstrate the enhanced performance of the proposed AD-based methods compared to traditional learning methods. Remarkably, this research paves the way for the integration of traditional optimization insights and emerging NN techniques in wireless communication system optimization

Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling

Task scheduling is a critical problem when one user offloads multiple different tasks to the edge server. When a user has multiple tasks to offload and only one task can be transmitted to server at a time, while server processes tasks according to the transmission order, the problem is NP-hard. However, it is difficult for traditional optimization methods to quickly obtain the optimal solution, while approaches based on reinforcement learning face with the challenge of excessively large action space and slow convergence. In this paper, we propose a Digital Twin (DT)-assisted RL-based task scheduling method in order to improve the performance and convergence of the RL. We use DT to simulate the results of different decisions made by the agent, so that one agent can try multiple actions at a time, or, similarly, multiple agents can interact with environment in parallel in DT. In this way, the exploration efficiency of RL can be significantly improved via DT, and thus RL can converges faster and local optimality is less likely to happen. Particularly, two algorithms are designed to made task scheduling decisions, i.e., DT-assisted asynchronous Q-learning (DTAQL) and DT-assisted exploring Q-learning (DTEQL). Simulation results show that both algorithms significantly improve the convergence speed of Q-learning by increasing the exploration efficiency

Pore and fracture scale characterization of oil shale at different microwave temperatures

The spatial complexity of oil shale systems is manifested by microstructure, pore space randomness and extensive heterogeneity. A microwave pyrolysis device developed for this study was used to pyrolyze oil shale, and the microstructure before and after pyrolysis was visually examined and quantified. The internal structure of the rock and the extent of pore and fracture expansion are more accurately determined in this way. The microstructure of oil shale at different temperatures before and after microwave pyrolysis is identified by X-ray microcomputed tomography (μCT) with automatic ultra-high-resolution scanning electron microscopy (SEM), to observe the heterogeneous state of oil shale on 2D and 3D scales and define the distribution of internal pores and fractures by post-processing μCT visualization. The study found that fractures sized from microns to millimeters along with pore fractures were observed at increasing microwave temperatures. The fractures gradually expanded with increasing temperature in the direction of horizontal or vertical laminae and generated a more connected pore network. The kerogen gradually decreased with a rise in temperature. The porosity increased from 0.26% to 13.69% at the initial temperature. This research is essential for the qualitative as well as quantitative analysis of the internal structure of oil shales under microwave radiation

Direct conversion of astrocytes into neuronal cells by drug cocktail

Direct conversion of astrocytes into neuronal cells by drug cocktail Cell Research advance online publication 2 October 2015; doi:10.1038/cr.2015.120 Dear Editor, Neurological disorder is one of the greatest threats to public health according to the World Health Organization. Because neurons have little or no regenerative capacity, conventional therapies for neurological disorders yielded poor outcomes. While the introduction of exogenous neural stem cells or neurons holds promise, many challenges still need to be tackled, including cell resource, delivery strategy, cell integration and cell maturation. Reprogramming of fibroblasts into induced pluripotent stem cells or directly into desirable neuronal cells by transcription factors (TFs) or small molecules can solve some problems, but other issues remain to be addressed, including safety, conversion efficiency and epigenetic memory [1, 2]. Astrocytes are considered to be the ideal starting candidate cell type for generating new neurons, due to their proximity in lineage distance to neurons and ability to proliferate after brain damage. Many studies have already revealed that astrocytes of the central nervous system can be reprogrammed into induced neuronal cells by virus-mediated overexpression of specific TFs in vitro and in vivo [3-6]. However, application of this virus-mediated direct conversion is still limited due to concerns on clinical safety. We have previously reported direct conversion of somatic cells into neural progenitor cells (NPCs) in vitro by cocktail of small molecules under hypoxia [7]. Here we set out to explore whether astrocytes can be induced into neuronal cells by the chemical cocktail in vitro

Mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) in Chinese patients with congenital bilateral absence of vas deferens

AbstractBackgroundGenetic testing of the cystic fibrosis transmembrane conductance (CFTR) gene is currently performed in patients with congenital bilateral absence of vas deferens (CBAVD). This study was conducted to investigate the role of mutations in the CFTR gene in CBAVD-dependent male infertility.Methods73 Chinese patients diagnosed with CBAVD were studied. The entire coding regions and splice sites of 27 exons of the CFTR gene were sequenced in 146 chromosomes from the 73 CBAVD patients. Screening was carried out using PCR, gel electrophoresis and DNA sequencing to identify novel variants of the entire coding regions and boundaries of the 27 exons.ResultsFive novel nonsynonymous mutations, three novel splice site mutations and one deletion were identified by sequencing. Apart from the novel variants, we also found 19 previously reported mutations and polymorphism sites. Thirty-four patients (46.57%) had the 5T variant (6 homozygous and 28 heterozygous) and in two of them it was not associated with any detectable mutation of the CFTR gene. All potential pathogenic mutations are not contained in the 1000 Genome Project database. In total, the present study identified 30 potential pathogenic variations in the CFTR gene, 9 of which had not previously been described.ConclusionsMost patients with CBAVD have mutations in the CFTR gene. A mild genotype with one or two mild or variable mutations was observed in all the patients. These findings improve our understanding of the distribution of CFTR alleles in CBAVD patients and will facilitate the development of more sensitive CFTR mutation screening

Is Fermi 1544-0649 a misaligned blazar? discovering the jet structure with VLBI

Fermi J1544-0649 is a transient GeV source first detected during its GeV flares in 2017. Multi-wavelength observations during the flaring time demonstrate variability and spectral energy distribution(SED) that are typical of a blazar. Other than the flare time, Fermi J1544-0649 is quiet in the GeV band and looks rather like a quiet galaxy (2MASX J15441967-0649156) for a decade. Together with the broad absorption lines feature we further explore the "misaligned blazar scenario". We analyzed the Very Long Baseline Array (VLBA) and East Asian VLBI Network (EAVN) data from 2018 to 2020 and discovered the four jet components from Fermi J1544-0649. We found a viewing angle around 3.7{\deg} to 7.4{\deg}. The lower limit of the viewing angle indicates a blazar with an extremely low duty cycle of the gamma-ray emission, the upper limit of it supports the "misaligned blazar scenario". Follow-up multi-wavelength observations after 2018 show Fermi J1544-0649 remains quiet in GeV, X-ray, and optical bands. Multi-messenger search of neutrinos is also performed, and an excess of 3.1 {\sigma} significance is found for this source.Comment: Accepted for publication in ApJ. 13 pages, 7 figure

Recurrent renal secondary hyperparathyroidism caused by supernumerary mediastinal parathyroid gland and parathyromatosis: A case report

BackgroundSurgical parathyroidectomy (PTX) is necessary for patients with severe and progressive secondary hyperparathyroidism (SHPT) refractory to medical treatment. Recurrence of SHPT after PTX is a serious clinical problem. Both supernumerary mediastinal parathyroid gland and parathyromatosis are the rare causes of recurrent renal SHPT. We report a rare case of recurrent renal SHPT due to supernumerary mediastinal parathyroid gland and parathyromatosis.Case presentationA 53-year-old man underwent total parathyroidectomy with autotransplantation due to the drug-refractory SHPT 17 years ago. In the last 11 months, the patient experienced symptoms including bone pain and skin itch, and the serum intact parathyroid hormone (iPTH) level elevated to 1,587 pg/ml. Ultrasound detected two hypoechoic lesions located at the dorsal area of right lobe of the thyroid gland, and both lesions presented as characteristics of hyperparathyroidism in contrast-enhanced ultrasound. 99mTc-MIBI/SPECT detected a nodule in the mediastinum. A reoperation involved a cervicotomy for excising parathyromatosis lesions and the surrounding tissue and a thoracoscopic surgery for resecting a mediastinal parathyroid gland. According to a histological examination, two lesions behind the right thyroid lobe and one lesion in the central region had been defined as parathyromatosis. A nodule in the mediastinum was consistent with hyperplastic parathyroid. The patient remained well for 10 months with alleviated symptoms and stabilized iPTH levels in the range of 123–201 pg/ml.ConclusionAlthough rare, recurrent SHPT may be caused by a coexistence of both supernumerary parathyroid glands and parathyromatosis, which should receive more attention. The combination of imaging modalities is important for reoperative locations of parathyroid lesions. To successfully treat parathyromatosis, all the lesions and the surrounding tissue must be excised. Thoracoscopic surgery is a reliable and safe approach for the resection of ectopic mediastinal parathyroid glands