Online VNF Scaling in Datacenters

Network Function Virtualization (NFV) is a promising technology that promises to significantly reduce the operational costs of network services by deploying virtualized network functions (VNFs) to commodity servers in place of dedicated hardware middleboxes. The VNFs are typically running on virtual machine instances in a cloud infrastructure, where the virtualization technology enables dynamic provisioning of VNF instances, to process the fluctuating traffic that needs to go through the network functions in a network service. In this paper, we target dynamic provisioning of enterprise network services - expressed as one or multiple service chains - in cloud datacenters, and design efficient online algorithms without requiring any information on future traffic rates. The key is to decide the number of instances of each VNF type to provision at each time, taking into consideration the server resource capacities and traffic rates between adjacent VNFs in a service chain. In the case of a single service chain, we discover an elegant structure of the problem and design an efficient randomized algorithm achieving a e/(e-1) competitive ratio. For multiple concurrent service chains, an online heuristic algorithm is proposed, which is O(1)-competitive. We demonstrate the effectiveness of our algorithms using solid theoretical analysis and trace-driven simulations.Comment: 9 pages, 4 figure

Hydroclimatic variability in loess delta D-wax records from the central Chinese Loess Plateau over the past 250 ka

This study reports hydrogen isotopic records from the central Chinese Loess Plateau (CLP) over the past 250 ka. After eliminating the influence of ice and local temperatures, the delta D-wax records extracted from two loess sites at Xifeng and Luochuan can be taken to represent arid/humid alternations in the hydrological environment in this marginal Asian Summer Monsoon (ASM) region; they also contain integrated information on summer precipitation patterns and the corresponding responses to these changes by predominant vegetation cover types. These arid/humid alternations show 100 ka, 40 ka and 20 ka cycles. An increase in precipitation in association with an enhanced summer monsoon has historically been taken to be the major factor driving a humid environment in the central CLP. However, hydroclimatic changes in delta D-wax records differ for the central CLP, central China and southern China. Over a 20 ka cycle, the influence of solar insolation on hydroclimatic changes can be shown to be consistent throughout the central CLP. However, changes in the relative location of the land and sea may have caused different hydroclimatic responses between southern China and the central CLP on a glacial-interglacial scale. The hydroclimatic variability in the central CLP would suggest that an enhanced summer monsoon due to climatic warming is the key to understanding decreased drought degree in this marginal monsoonal region

Holistic Dynamic Frequency Transformer for Image Fusion and Exposure Correction

The correction of exposure-related issues is a pivotal component in enhancing the quality of images, offering substantial implications for various computer vision tasks. Historically, most methodologies have predominantly utilized spatial domain recovery, offering limited consideration to the potentialities of the frequency domain. Additionally, there has been a lack of a unified perspective towards low-light enhancement, exposure correction, and multi-exposure fusion, complicating and impeding the optimization of image processing. In response to these challenges, this paper proposes a novel methodology that leverages the frequency domain to improve and unify the handling of exposure correction tasks. Our method introduces Holistic Frequency Attention and Dynamic Frequency Feed-Forward Network, which replace conventional correlation computation in the spatial-domain. They form a foundational building block that facilitates a U-shaped Holistic Dynamic Frequency Transformer as a filter to extract global information and dynamically select important frequency bands for image restoration. Complementing this, we employ a Laplacian pyramid to decompose images into distinct frequency bands, followed by multiple restorers, each tuned to recover specific frequency-band information. The pyramid fusion allows a more detailed and nuanced image restoration process. Ultimately, our structure unifies the three tasks of low-light enhancement, exposure correction, and multi-exposure fusion, enabling comprehensive treatment of all classical exposure errors. Benchmarking on mainstream datasets for these tasks, our proposed method achieves state-of-the-art results, paving the way for more sophisticated and unified solutions in exposure correction

Genome-resolved metagenomics provides insights into the microbial-mediated sulfur and nitrogen cycling in temperate seagrass meadows

The presence of seagrasses facilitates numerous microbial-mediated biogeochemical cycles, with sulfur- and nitrogen-cycling microorganisms playing crucial roles as regulators. Despite efforts to comprehend the diversity of microbes in seagrass ecosystems, the metabolic functions of these benthic microorganisms in seagrass sediments remain largely unknown. Using metagenomics, we provide insights into the sulfur- and nitrogen-cycling pathways and key metabolic capacities of microorganisms in both Z. japonica-colonized and unvegetated sediments over a seasonal period. Taxonomic analysis of N and S cycling genes revealed that δ- and γ- proteobacteria dominated the benthic sulfate-reducing bacteria, while α- and γ-proteobacteria played a significant role in the sulfur-oxidation processes. The proteobacterial lineages were also major contributors to the benthic nitrogen cycling. However, at a finer taxonomic resolution, microbial participants in different processes were observed to be highly diverse and mainly driven by environmental factors such as temperature and salinity. The gene pools of sulfur and nitrogen cycles in the seagrass sediments were dominated by genes involved in sulfide oxidation (fccA) and hydroxylamine oxidation (hao), respectively. Seagrass colonization elevated the relative abundance of genes responsible for sulfite production (phsC), hydroxylamine oxidation (hao), and nitrogen fixation (nifK), but suppressed sulfur oxidation (soxXYZ) and denitrification (nosZ and nirS). The prevalence of proteobacterial lineages functioned with versatile capabilities in both sulfur and nitrogen cycles in seagrass ecosystems, highlighting tight couplings between these processes, which was further supported by the recovery of 83 metagenome-assembled genomes (MAGs). These findings broaden our understanding of the biogeochemical processes that are mediated by microorganisms in seagrass ecosystems

Greenhouse gas emissions from croplands of China

China possesses cropland of 1.33 million km 2. Cultivation of the cropland not only altered the biogeochemical cycles of carbon (C) and nitrogen (N) in the agroecosystems but also affected global climate. The impacts of agroecosystems on global climate attribute to emissions of three greenhouse gases, namely carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O)

Chinese herbal medicine Guizhi Fuling Formula for treatment of uterine fibroids : a systematic review of randomized clinical trials

Background: Guizhi Fuling Formula is widely applied for uterine fibroids in China. Many clinical trials are reported. This study assessed the efficacy and safety of Guizhi Fuling Formula for the treatment of uterine fibroids. Methods: PubMed, Cochrane CENTRAL, EMBASE, and four Chinese databases were searched through May 2013. We included randomised controlled trials (RCTs) that tested Guizhi Fuling Formula for uterine fibroids, compared with no intervention, placebo, pharmaceutical medication, or other Chinese patent medicines approved by the State Food and Drug Administration of China. Authors extracted data and assessed the quality independently. We applied RevMan 5.2.0 software to analyse data of included randomised trials. Results: A total of 38 RCTs involving 3816 participants were identified. The methodological quality of the included trials was generally poor. Meta-analyses demonstrated that Guizhi Fuling Formula plus mifepristone were more effective than mifepristone alone in reducing the volume of fibroids (in total volume of multiple fibroids, MD −19.41 cm3, 95% CI −28.68 to −10.14; in average volume of multiple fibroids, MD −1.00 cm3, 95% CI −1.23 to −0.76; in average volume of maximum fibroids, MD −3.35 cm3, 95% CI −4.84 to −1.87, I2 = 93%, random effects model). Guizhi Fuling Formula significantly improved symptoms of dysmenorrhea either when it was used alone (RR 2.27, 95% CI 1.04 to 4.97) or in combination with mifepristone (RR 2.35, 95% CI 1.15 to 4.82). No serious adverse events were reported. Conclusions: Guizhi Fuling Formula appears to have additional benefit based on mifepristone treatment in reducing volume of fibroids. However, due to high risk of bias of the trials, we could not draw confirmative conclusions on its benefit. Future clinical trials should be well-designed and avoid the issues that are identified in this study

COVID-19 Multi-Targeted Drug Repurposing Using Few-Shot Learning

The life-threatening disease COVID-19 has inspired significant efforts to discover novel therapeutic agents through repurposing of existing drugs. Although multi-targeted (polypharmacological) therapies are recognized as the most efficient approach to system diseases such as COVID-19, computational multi-targeted compound screening has been limited by the scarcity of high-quality experimental data and difficulties in extracting information from molecules. This study introduces MolGNN , a new deep learning model for molecular property prediction. MolGNN applies a graph neural network to computational learning of chemical molecule embedding. Comparing to state-of-the-art approaches heavily relying on labeled experimental data, our method achieves equivalent or superior prediction performance without manual labels in the pretraining stage, and excellent performance on data with only a few labels. Our results indicate that MolGNN is robust to scarce training data, and hence a powerful few-shot learning tool. MolGNN predicted several multi-targeted molecules against both human Janus kinases and the SARS-CoV-2 main protease, which are preferential targets for drugs aiming, respectively, at alleviating cytokine storm COVID-19 symptoms and suppressing viral replication. We also predicted molecules potentially inhibiting cell death induced by SARS-CoV-2. Several of MolGNN top predictions are supported by existing experimental and clinical evidence, demonstrating the potential value of our metho

Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training

Modern supervised learning neural network models require a large amount of manually labeled data, which makes the construction of domain-specific knowledge graphs time-consuming and labor-intensive. In parallel, although there has been much research on named entity recognition and relation extraction based on distantly supervised learning, constructing a domain-specific knowledge graph from large collections of textual data without manual annotations is still an urgent problem to be solved. In response, we propose an integrated framework for adapting and re-learning knowledge graphs from one coarse domain (biomedical) to a finer-define domain (oncology). In this framework, we apply distant-supervision on cross-domain knowledge graph adaptation. Consequently, no manual data annotation is required to train the model. We introduce a novel iterative training strategy to facilitate the discovery of domain-specific named entities and triples. Experimental results indicate that the proposed framework can perform domain adaptation and construction of knowledge graph efficiently