Learning While Scheduling in Multi-Server Systems with Unknown Statistics: MaxWeight with Discounted UCB

Multi-server queueing systems are widely used models for job scheduling in machine learning, wireless networks, crowdsourcing, and healthcare systems. This paper considers a multi-server system with multiple servers and multiple types of jobs, where different job types require different amounts of processing time at different servers. The goal is to schedule jobs on servers without knowing the statistics of the processing times. To fully utilize the processing power of the servers, it is known that one has to at least learn the service rates of different job types on different servers. Prior works on this topic decouple the learning and scheduling phases which leads to either excessive exploration or extremely large job delays. We propose a new algorithm, which combines the MaxWeight scheduling policy with discounted upper confidence bound (UCB), to simultaneously learn the statistics and schedule jobs to servers. We prove that under our algorithm the asymptotic average queue length is bounded by one divided by the traffic slackness, which is order-wise optimal. We also obtain an exponentially decaying probability tail bound for any-time queue length. These results hold for both stationary and nonstationary service rates. Simulations confirm that the delay performance of our algorithm is several orders of magnitude better than previously proposed algorithms

Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image Segmentation

Single-source domain generalization (SDG) in medical image segmentation is a challenging yet essential task as domain shifts are quite common among clinical image datasets. Previous attempts most conduct global-only/random augmentation. Their augmented samples are usually insufficient in diversity and informativeness, thus failing to cover the possible target domain distribution. In this paper, we rethink the data augmentation strategy for SDG in medical image segmentation. Motivated by the class-level representation invariance and style mutability of medical images, we hypothesize that unseen target data can be sampled from a linear combination of $C$ (the class number) random variables, where each variable follows a location-scale distribution at the class level. Accordingly, data augmented can be readily made by sampling the random variables through a general form. On the empirical front, we implement such strategy with constrained B$\acute{\rm e}$zier transformation on both global and local (i.e. class-level) regions, which can largely increase the augmentation diversity. A Saliency-balancing Fusion mechanism is further proposed to enrich the informativeness by engaging the gradient information, guiding augmentation with proper orientation and magnitude. As an important contribution, we prove theoretically that our proposed augmentation can lead to an upper bound of the generalization risk on the unseen target domain, thus confirming our hypothesis. Combining the two strategies, our Saliency-balancing Location-scale Augmentation (SLAug) exceeds the state-of-the-art works by a large margin in two challenging SDG tasks. Code is available at https://github.com/Kaiseem/SLAug

Amplification and adaptation of centromeric repeats in polyploid switchgrass species.

Centromeres in most higher eukaryotes are composed of long arrays of satellite repeats from a single satellite repeat family. Why centromeres are dominated by a single satellite repeat and how the satellite repeats originate and evolve are among the most intriguing and long-standing questions in centromere biology. We identified eight satellite repeats in the centromeres of tetraploid switchgrass (Panicum virgatum). Seven repeats showed characteristics associated with classical centromeric repeats with monomeric lengths ranging from 166 to 187 bp. Interestingly, these repeats share an 80-bp DNA motif. We demonstrate that this 80-bp motif may dictate translational and rotational phasing of the centromeric repeats with the cenH3 nucleosomes. The sequence of the last centromeric repeat, Pv156, is identical to the 5S ribosomal RNA genes. We demonstrate that a 5S ribosomal RNA gene array was recruited to be the functional centromere for one of the switchgrass chromosomes. Our findings reveal that certain types of satellite repeats, which are associated with unique sequence features and are composed of monomers in mono-nucleosomal length, are favorable for centromeres. Centromeric repeats may undergo dynamic amplification and adaptation before the centromeres in the same species become dominated by the best adapted satellite repeat

Study on the Emission Characteristics in Renewable Energy Combustion under Different Working Conditions of Marine Two-Stroke Diesel Engine

In this paper, MAN 6S35ME-B9 two-stroke diesel engine is taken as the research object. By constructing a detailed combustion reaction mechanism including CH4, C4H10O, nitrides and other substances, CHEMKIN-PRO is used to simulate the same fuel mixing ratio and excess air coefficient. Under the condition of 1.5, the temperature, NO mole fraction and NH3 mole fraction in the reactor change and study the factors affecting the pollutant emission of marine diesel engine with the crank angle under different working conditions. The simulation shows that with the decrease of diesel engine speed, the maximum temperature of combustion reaction and the temperature at exhaust opening are obviously reduced. At the same time, mole fraction of NO and NH3 decreases with the decrease of rotational speed, and there is no nitride production in the combustion reaction at 25%

Atomically Well-defined Nitrogen Doping in the Cross-plane Transport through Graphene Heterojunctions

The nitrogen doping of graphene leads to graphene heterojunctions with a tunable bandgap, suitable for electronics, electrochemical, and sensing applications. However, the microscopic nature and charge transport properties of atomic-level nitrogen-doped graphene are still unknown, mainly due to the multiple doping sites with topological diversities. In this work, we fabricated the atomically well-defined N-doped graphene heterojunctions and investigated the cross-plane transport through these heterojunctions to reveal the effects of doping on their electronic properties. We found that different doping number of nitrogen atoms leads to a conductance difference of up to ~288, and the conductance of graphene heterojunctions with nitrogen-doping at different positions in the conjugated framework can also lead to a conductance difference of ~170. Combined ultraviolet photoelectron spectroscopy measurements and theoretical calculations reveal that the insertion of nitrogen atoms into the conjugation framework significantly stabilizes the frontier molecular orbitals, leading to a change in the relative positions of HOMO and LUMO to the Fermi level of the electrodes. Our work provides a unique insight into the role of nitrogen doping on the charge transport through graphene heterojunctions and materials at the single atomic level

A Fisher’s Criterion-Based Linear Discriminant Analysis for Predicting the Critical Values of Coal and Gas Outbursts Using the Initial Gas Flow in a Borehole

The risk of coal and gas outbursts can be predicted using a method that is linear and continuous and based on the initial gas flow in the borehole (IGFB); this method is significantly superior to the traditional point prediction method. Acquiring accurate critical values is the key to ensuring accurate predictions. Based on ideal rock cross-cut coal uncovering model, the IGFB measurement device was developed. The present study measured the data of the initial gas flow over 3 min in a 1 m long borehole with a diameter of 42 mm in the laboratory. A total of 48 sets of data were obtained. These data were fuzzy and chaotic. Fisher’s discrimination method was able to transform these spatial data, which were multidimensional due to the factors influencing the IGFB, into a one-dimensional function and determine its critical value. Then, by processing the data into a normal distribution, the critical values of the outbursts were analyzed using linear discriminant analysis with Fisher’s criterion. The weak and strong outbursts had critical values of 36.63 L and 80.85 L, respectively, and the accuracy of the back-discriminant analysis for the weak and strong outbursts was 94.74% and 92.86%, respectively. Eight outburst tests were simulated in the laboratory, the reverse verification accuracy was 100%, and the accuracy of the critical value was verified

Identification of WRKY gene family members in amaranth based on a transcriptome database and functional analysis of AtrWRKY42-2 in betalain metabolism

IntroductionWRKY TFs (WRKY transcription factors) contribute to the synthesis of secondary metabolites in plants. Betalains are natural pigments that do not coexist with anthocyanins within the same plant. Amaranthus tricolor (‘Suxian No.1’) is an important leaf vegetable rich in betalains. However, the WRKY family members in amaranth and their roles in betalain synthesis and metabolism are still unclear.MethodsTo elucidate the molecular characteristics of the amaranth WRKY gene family and its role in betalain synthesis, WRKY gene family members were screened and identified using amaranth transcriptome data, and their physicochemical properties, conserved domains, phylogenetic relationships, and conserved motifs were analyzed using bioinformatics methods.ResultsIn total, 72 WRKY family members were identified from the amaranth transcriptome. Three WRKY genes involved in betalain synthesis were screened in the phylogenetic analysis of WRKY TFs. RT-qPCR showed that the expression levels of these three genes in red amaranth ‘Suxian No.1’ were higher than those in green amaranth ‘Suxian No.2’ and also showed that the expression level of AtrWRKY42 gene short-spliced transcript AtrWRKY42-2 in Amaranth ‘Suxian No.1’ was higher than that of the complete sequence AtrWRKY42-1, so the short-spliced transcript AtrWRKY42-2 was mainly expressed in ‘Suxian No.2’ amaranth. Moreover, the total expression levels of AtrWRKY42-1 and AtrWRKY42-2 were down-regulated after GA3 treatment, so AtrWRKY42-2 was identified as a candidate gene. Therefore, the short splice variant AtrWRKY42-2 cDNA sequence, gDNA sequence, and promoter sequence of AtrWRKY42 were cloned, and the PRI 101-AN-AtrWRKY42-2-EGFP vector was constructed to evaluate subcellular localization, revealing that AtrWRKY42-2 is located in the nucleus. The overexpression vector pRI 101-AN-AtrWRKY42-2-EGFP and VIGS (virus-induced gene silencing) vector pTRV2-AtrWRKY42-2 were transferred into leaves of ‘Suxian No.1’ by an Agrobacterium-mediated method. The results showed that AtrWRKY42-2 overexpression could promote the expression of AtrCYP76AD1 and increase betalain synthesis. A yeast one-hybrid assay demonstrated that AtrWRKY42-2 could bind to the AtrCYP76AD1 promoter to regulate betalain synthesis.DiscussionThis study lays a foundation for further exploring the function of AtrWRKY42-2 in betalain metabolism

A Review of Fibre Reinforced Polymer (FRP) Reinforced Concrete Composite Column Members Modelling and Analysis Techniques

The use of fibre-reinforced polymer (FRP) to confine concrete columns improves the strength and ductility of the columns by reducing passive lateral confinement pressure. Many numerical and analytical formulations have been proposed in the literature to describe the compressive behaviour of FRP confined concrete under both monotonic and cyclic loads. However, the effect of a stress/strain level in the columns has not been well defined because of the lack of well-defined strategies of modelling and oversimplification of the model. This paper reviews the existing FRP combinations and the available numerical and analytical methods to determine the effectiveness of the adopted method. An effort has been made to examine the usage of FRP materials in column applications in existing building regimes and highlights the possible future scopes to improve the use of FRP confined concrete in civil applications

Apolipoprotein E deficiency potentiates macrophage against Staphylococcus aureus in mice with osteomyelitis via regulating cholesterol metabolism

IntroductionStaphylococcus aureus (S. aureus) osteomyelitis causes a variety of metabolism disorders in microenvironment and cells. Defining the changes in cholesterol metabolism and identifying key factors involved in cholesterol metabolism disorders during S. aureus osteomyelitis is crucial to understanding the mechanisms of S. aureus osteomyelitis and is important in designing host-directed therapeutic strategies.MethodsIn this study, we conducted in vitro and in vivo experiments to define the effects of S. aureus osteomyelitis on cholesterol metabolism, as well as the role of Apolipoprotein E (ApoE) in regulating cholesterol metabolism by macrophages during S. aureus osteomyelitis.ResultsThe data from GSE166522 showed that cholesterol metabolism disorder was induced by S. aureus osteomyelitis. Loss of cholesterol from macrophage obtained from mice with S. aureus osteomyelitis was detected by liquid chromatography-tandem mass spectrometry(LC-MS/MS), which is consistent with Filipin III staining results. Changes in intracellular cholesterol content influenced bactericidal capacity of macrophage. Subsequently, it was proven by gene set enrichment analysis and qPCR, that ApoE played a key role in developing cholesterol metabolism disorder in S. aureus osteomyelitis. ApoE deficiency in macrophages resulted in increased resistance to S. aureus. ApoE-deficient mice manifested abated bone destruction and decreased bacteria load. Moreover, the combination of transcriptional analysis, qPCR, and killing assay showed that ApoE deficiency led to enhanced cholesterol biosynthesis in macrophage, ameliorating anti-infection ability.ConclusionWe identified a previously unrecognized role of ApoE in S. aureus osteomyelitis from the perspective of metabolic reprogramming. Hence, during treating S. aureus osteomyelitis, considering cholesterol metabolism as a potential therapeutic target presents a new research direction

A novel 3D unsupervised domain adaptation framework for cross-modality medical image segmentation

We consider the problem of volumetric (3D) unsupervised domain adaptation (UDA) in cross-modality medical image segmentation, aiming to perform segmentation on the unannotated target domain (e.g. MRI) with the help of labeled source domain (e.g. CT). Previous UDA methods in medical image analysis usually suffer from two challenges: 1) they focus on processing and analyzing data at 2D level only, thus missing semantic information from the depth level; 2) one-to-one mapping is adopted during the style-transfer process, leading to insufficient alignment in the target domain. Different from the existing methods, in our work, we conduct a first of its kind investigation on multi-style image translation for complete image alignment to alleviate the domain shift problem, and also introduce 3D segmentation in domain adaptation tasks to maintain semantic consistency at the depth level. In particular, we develop an unsupervised domain adaptation framework incorporating a novel quartet self-attention module to efficiently enhance relationships between widely separated features in spatial regions on a higher dimension, leading to a substantial improvement in segmentation accuracy in the unlabeled target domain. In two challenging cross-modality tasks, specifically brain structures and multi-organ abdominal segmentation, our model is shown to outperform current state-of-the-art methods by a significant margin, demonstrating its potential as a benchmark resource for the biomedical and health informatics research community