Towards machine learning enabled future-generation wireless network optimization

We anticipate that there will be an enormous amount of wireless devices connected to the Internet through the future-generation wireless networks. Those wireless devices vary from self-driving vehicles to smart wearable devices and intelligent house- hold electrical appliances. Under such circumstances, the network resource optimization faces the challenge of the requirement of both flexibility and performance. Current wireless communication still relies on one-size-fits-all optimization algorithms, which require meticulous design and elaborate maintenance, thus not flexible and cannot meet the growing requirements well. The future-generation wireless networks should be “smarter”, which means that the artificial intelligence-driven software-level design will play a more significant role in network optimization. In this thesis, we present three different ways of leveraging artificial intelligence (AI) and machine learning (ML) to design network optimization algorithms for three wireless Internet of things network optimization problems. Our ML-based approaches cover the use of multi-layer feed-forward artificial neural network and the graph convolutional network as the core of our AI decision-makers. The learning methods are supervised learning (for static decision-making) and reinforcement learning (for dynamic decision-making). We demonstrate the viability of applying ML in future- generation wireless network optimizations through extensive simulations. We summarize our discovery on the advantage of using ML in wireless network optimizations as the following three aspects: 1. Enabling the distributed decision-making to achieve the performance that near a centralized solution, without the requirement of multi-hop information; 2. Tackling with dynamic optimization through distributed self-learning decision- making agents, instead of designing a sophisticated optimization algorithm; 3. Reducing the time used in optimizing the solution of a combinatorial optimization problem. We envision that in the foreseeable future, AI and ML could help network service designers and operators to improve the network quality of experience swiftly and less expensively

An Efficient Refocusing Scheme for Camera-Array Captured Light Field Video for Improved Visual Immersiveness

Light field video technology attempts to acquire human-like visual data, offering unprecedented immersiveness and a viable path for producing high-quality VR content. Refocusing that is one of the key properties of light field and a must for mixed reality applications has shown to work well for microlens based cameras, but as light field videos acquired by camera arrays have a low angular resolution, the refocused quality suffers. In this paper, we present an approach to improve the visual quality of refocused content captured by a camera array-based setup. Increasing the angular resolution using existing deep learning-based view synthesis method and refocusing the video using shift and sum refocusing algorithm produces over blurring of the in-focus region. Our enhancement method targets these blurry pixels and improves their quality by similarity detection and blending. Experimental results show that the proposed approach achieves better refocusing quality compared to traditional methods

Impact of citalopram combined with mindfulness-based stress reduction on symptoms, cognitive functions and self-confidence in patients with depression

Purpose: To investigate the impact of the combination of citalopram and mindfulness-based stress reduction (MBSR) on the symptoms, cognitive functions and self-confidence of patients with depression.Methods: A total of 98 patients with depression were selected as study subjects and divided into combination therapy group (CT, n = 51) and conventional group (C, n = 47. The conventional group was treated with citalopram, while the combined group was treated with a combination of citalopram and MBSR. Depressive symptoms and self-confidence were evaluated using the 17-item Hamilton Depression Rating Scale (HAMD-17) and General Self-efficacy Scale (GSES). Cognitive functions were assessed by Wisconsin Card Sorting Test (WCST) and Trail Making Test (TMT). Changes in depressive symptoms, cognitive functions, self-confidence and clinical efficacies between the two groups were compared.Results: At weeks 1, 4 and 8 after treatment, CT group had lower HAMD-17 scores but higher GSES scores when compared with the conventional group (p < 0.05). In addition, CT group was superior to the conventional group in efficacy and overall response rate (100.00 vs. 85.11 %, p < 0.05). Also, CT group showed a shorter time of perseverative and non-perseverative errors on WCST and a shorter time for TMT-A and TMT-B, compared with the conventional group (p < 0.05).Conclusion: The combination therapy of citalopram and MBSR is effective in ameliorating depressive symptoms, and enhancing cognitive functions and self-confidence in patients with depression. These findings will increase the understanding of this combination therapy, and provide a clinical reference for the treatment of depression

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Identification of complex and cryptic chromosomal rearrangements by optical genome mapping

Abstract Background Optical genome mapping (OGM) has developed into a highly promising method for detecting structural variants (SVs) in human genomes. Complex chromosomal rearrangements (CCRs) and cryptic translocations are rare events that are considered difficult to detect by routine cytogenetic methods. In this study, OGM was applied to delineate the precise chromosomal rearrangements in three cases with uncertain or unconfirmed CCRs detected by conventional karyotyping and one case with a cryptic translocation suggested by fetal chromosomal microarray analysis (CMA). Results In the three cases with CCRs, OGM not only confirmed or revised the original karyotyping results but also refined the precise chromosomal structures. In the case with a suspected translocation not detected by karyotyping, OGM efficiently identified the cryptic translocation and defined the genomic breakpoints with relatively high accuracy. Conclusions Our study confirmed OGM as a robust alternative approach to karyotyping for the detection of chromosomal structural rearrangements, including CCRs and cryptic translocations

Are there too many screw holes in plates for fracture fixation?

Abstract Background Implant breakage after the fixation of traumatic fractures is rare; however, when it occurs, it is debilitating for the patients and a challenge for surgeons. The purpose of this study was to analyze and identify the independent risk factors for implant breakage of traumatic fractures treated with plate osteosynthesis. Methods We reviewed the medical records of patients with a fracture to any part of their four extremities, clavicle, hand or foot, who underwent surgical plate osteosynthesis from January 2005 to January 2015, and who sustained a subsequent implant breakage. Kaplan–Meier univariate and multivariate Cox regressions were performed to identify independent associations of potential risk factors for implant breakage in this cohort. Results We identified 168 patients who underwent plate osteosynthesis surgery and had subsequent internal fixator breakage. The mean patient age was 40.63 ± 16.71 years (range, 3 to 78 years), with 72.0% (121) males and 28.0% (47) females. The average time between surgery and implant breakage was 12.85 ± 12.42 months (range, 1 to 60 months). In the final regression model, we show that inserting screws close to the fracture line is an independent predictive risk factor for implant breakage (HR, 2.165, 95%CI, 1.227 to 3.822; P = 0.008). Conclusions We found that inserting screws close to the fracture line is related to an increased risk of internal fixator breakage in patients treated with plate osteosynthesis after fracture. Plates with additional holes likely lead to an increased risk of implant breakage, presumably because surgeons cannot resist inserting extra screws into the holes adjacent to the fracture line, which reduces the stiffness of the plate. We have addressed this problem by designing a plate without holes adjacent to the fracture line

Average Daily Gain in Lambs Weaned at 60 Days of Age Is Correlated with Rumen and Rectum Microbiota

Colonization of gastrointestinal microbiota in mammals during early life is vital to host health. The objective of this study was to investigate whether lambs with high and low ADG have a different rumen and rectum microbial community. Thus, we investigated potential relationships between rumen and rectum microbiota and average daily gain (ADG) in weaned lambs. Sixteen lambs with similar body weights (7.63 ± 1.18 kg) were selected at 30 days of age. At 60 days of age, lambs were weaned, and ADG was calculated from 60 to 90 days. Then, two groups were generated: higher ADG (HG, 134.17 ± 13.48 g/day) and lower ADG (LG, 47.50 ± 19.51 g/day). Microbiota was evaluated at 30, 60, and 90 days of age. The final live weight and ADG at 90 days of age was higher (p p < 0.05) in the HG group for the 30 days vs. 90 days comparison and 60 days vs. 90 days comparison. Linear discriminant analysis effect size (LEfSe) analysis revealed a total of 18 bacterial biomarkers that are ADG-specific in the rumen and 35 bacterial biomarkers in the rectum. Meanwhile, 15 fungal biomarkers were found in the rumen and 8 biomarkers were found in the rectum. Our findings indicated that ADG is related to the rumen and rectum microbiota in lambs

Cyperus (<i>Cyperus esculentus</i> L.): A Review of Its Compositions, Medical Efficacy, Antibacterial Activity and Allelopathic Potentials

Cyperus (Cyperus esculentus L.) is an edible perennial grass-like plant, which propagates exclusively with underground tubers. Its tubers are rich in starch (20–30%), fat (25–35%), sugar (10–20%), protein (10–15%) and dietary fiber (8–9%). In addition, the tubers also contain alkaloids, organic acids, vitamins (C and E), steroids, terpenoids and other active components. The contents of oleic acid and linoleic acid in Cyperus oil are very high, which have important medicinal value and health-promoting properties. Most of the extracts from the tubers, stems and leaves of Cyperus have allelopathic potential and antibacterial, antioxidant and insecticidal activities. In recent years, the planting area of Cyperus has increased significantly all over the world, especially in China and some other countries. This paper presents the current status of Cyperus and the recent trend in research in this area. Published reports on its nutritional contents, active ingredients, medicinal efficacy, antibacterial activity and allelopathic potential were also reviewed

Electrospun polycrystalline LixFe0.2Mn0.8PO4/carbon composite fibers for lithium-ion battery

LiFeMnPO/C (LiFMP/C, x = 1.2, 1.1, 1.05) composite fibers were successfully prepared by stabilization and calcination of the electrospun fibers from the precursor solution. The structural and morphological characterizations revealed that the LiFeMnPO with high purity was evenly coated with an amorphous carbon layer. Experimental data testified the well-crystalline structure of composite fibers based on the results of the X-ray diffraction (XRD) and selected area electron diffractions (SAED). The galvanostatic charge-discharge measurements indicated that LiFeMnPO displayed the highest capacity of 174 mA h g at 0.05 C and the best cycling stability. The charge-transfer impedance of LiFeMnPO/C was decreased negatively with the content of lithium. It was found that the molecular structure of carbon sources and calcination procedure played key factor in determining the electrochemical properties of the composite fibers. These results suggested that electrospinning should be a promising method for fabricating crystalline LFMP/C composite fibers as electrode materials for lithium-ion battery