TENSILE: A Tensor granularity dynamic GPU memory scheduling method towards multiple dynamic workloads system

Recently, deep learning has been an area of intense research. However, as a kind of computing-intensive task, deep learning highly relies on the scale of GPU memory, which is usually prohibitive and scarce. Although there are some extensive works have been proposed for dynamic GPU memory management, they are hard to be applied to systems with multiple dynamic workloads, such as in-database machine learning systems. In this paper, we demonstrated TENSILE, a method of managing GPU memory in tensor granularity to reduce the GPU memory peak, considering the multiple dynamic workloads. TENSILE tackled the cold-starting and across-iteration scheduling problem existing in previous works. We implement TENSILE on a deep learning framework built by ourselves and evaluated its performance. The experiment results show that TENSILE can save more GPU memory with less extra time overhead than prior works in both single and multiple dynamic workloads scenarios

FairNN- Conjoint Learning of Fair Representations for Fair Decisions

In this paper, we propose FairNN a neural network that performs joint feature representation and classification for fairness-aware learning. Our approach optimizes a multi-objective loss function in which (a) learns a fair representation by suppressing protected attributes (b) maintains the information content by minimizing a reconstruction loss and (c) allows for solving a classification task in a fair manner by minimizing the classification error and respecting the equalized odds-based fairness regularized. Our experiments on a variety of datasets demonstrate that such a joint approach is superior to separate treatment of unfairness in representation learning or supervised learning. Additionally, our regularizers can be adaptively weighted to balance the different components of the loss function, thus allowing for a very general framework for conjoint fair representation learning and decision making.Comment: Code will be availabl

Hole transport assisted by the piezoelectric field in In0.4Ga0.6N/GaN quantum wells under electrical injection

The authors observe the significant penetration of electrically injected holes through InGaN/GaN quantum wells (QWs) with an indium mole fraction of 40%. This effect and its current density dependence were analysed by studies on micro-pixel light-emitting diodes, which allowed current densities to be varied over a wide range up to 5 kA/cm2. The systematic changes in electroluminescence spectra are discussed in the light of the piezoelectric field in the high-indium-content QWs and its screening by the carriers. Simulations were also carried out to clarify the unusual hole transport mechanism and the underlying physics in these high-indium QWs

Hole transport assisted by the piezoelectric field in In0.4Ga0.6N/GaN quantum wells under electrical injection

The authors observe the significant penetration of electrically injected holes through InGaN/GaN quantum wells (QWs) with an indium mole fraction of 40%. This effect and its current density dependence were analysed by studies on micro-pixel light-emitting diodes, which allowed current densities to be varied over a wide range up to 5 kA/cm2. The systematic changes in electroluminescence spectra are discussed in the light of the piezoelectric field in the high-indium-content QWs and its screening by the carriers. Simulations were also carried out to clarify the unusual hole transport mechanism and the underlying physics in these high-indium QWs

The Airlines’ Recent Experience Under the Railway Labor Act

Silky-feather has been selected and fixed in some breeds due to its unique appearance. This phenotype is caused by a single recessive gene (hookless, h). Here we map the silky-feather locus to chromosome 3 by linkage analysis and subsequently fine-map it to an 18.9 kb interval using the identical by descent (IBD) method. Further analysis reveals that a C to G transversion located upstream of the prenyl (decaprenyl) diphosphate synthase, subunit 2 (PDSS2) gene is causing silky-feather. All silky-feather birds are homozygous for the G allele. The silky-feather mutation significantly decreases the expression of PDSS2 during feather development in vivo. Consistent with the regulatory effect, the C to G transversion is shown to remarkably reduce PDSS2 promoter activity in vitro. We report a new example of feather structure variation associated with a spontaneous mutation and provide new insight into the PDSS2 function

CgGCS, Encoding a Glucosylceramide Synthase, Is Required for Growth, Conidiation and Pathogenicity in Colletotrichum gloeosporioides

Fungal glucosylceramide plays important role in cell division, hyphal formation and growth, spore germination and the modulation of virulence and has recently been considered as target for small molecule inhibitors. In this study, we characterized CgGCS, a protein encoding a glucosylceramide synthase (GCS) in Colletotrichum gloeosporioides. Disruption of CgGCS resulted in a severe reduction of mycelial growth and defects in conidiogenesis. Sphingolipid profile analysis revealed large decreases in glucosylceramide production in the mutant strains. Pathogenicity assays indicated that the ability of the ΔCgGCS mutants to invade both tomato and mango hosts was almost lost. In addition, the expression levels of many genes, especially those related to metabolism, were shown to be affected by the mutation of CgGCS via transcriptome analysis. Overall, our results demonstrate that C. gloeosporioides glucosylceramide is an important regulatory factor in fungal growth, conidiation, and pathogenesis in hosts

Fusaric acid instigates the invasion of banana by Fusarium oxysporum f. sp. cubense TR4

CITATION: Liu, S. et al. 2020. Fusaric acid instigates the invasion of banana by Fusarium oxysporum f. sp. cubense TR4. New Phytologist, 225:913–929, doi:10.1111/nph.16193.The original publication is available at https://nph.onlinelibrary.wiley.comFusaric acid (FSA) is a phytotoxin produced by several Fusarium species and has been associated with plant disease development, although its role is still not well understood. Mutation of key genes in the FSA biosynthetic gene (FUB) cluster in Fusarium oxysporum f. sp. cubense tropical race 4 (Foc TR4) reduced the FSA production, and resulted in decreased disease symptoms and reduced fungal biomass in the host banana plants. When pretreated with FSA, both banana leaves and pseudostems exhibited increased sensitivity to Foc TR4 invasion. Banana embryogenic cell suspensions (ECSs) treated with FSA exhibited a lower rate of O2 uptake, loss of mitochondrial membrane potential, increased reactive oxygen species (ROS) accumulation, and greater nuclear condensation and cell death. Consistently, transcriptomic analysis of FSA-treated ECSs showed that FSA may induce plant cell death through regulating the expression of genes involved in mitochondrial functions. The results herein demonstrated that the FSA from Foc TR4 functions as a positive virulence factor and acts at the early stage of the disease development before the appearance of the fungal hyphae in the infected tissues.https://nph.onlinelibrary.wiley.com/doi/full/10.1111/nph.16193Publisher's versio

ForestFireDetector: Expanding Channel Depth for Fine-Grained Feature Learning in Forest Fire Smoke Detection

Wildfire is a pressing global issue that transcends geographic boundaries. Many areas, including China, are trying to cope with the threat of wildfires and manage limited forest resources. Effective forest fire detection is crucial, given its significant implications for ecological balance, social well-being and economic stability. In light of the problems of noise misclassification and manual design of the components in the current forest fire detection model, particularly the limited capability to identify subtle and unnoticeable smoke within intricate forest environments, this paper proposes an improved smoke detection model for forest fires utilizing YOLOv8 as its foundation. We expand the channel depth for fine-grain feature learning and retain more feature information. At the same time, lightweight convolution reduces the parameters of the model. This model enhances detection accuracy for smoke targets of varying scales and surpasses the accuracy of mainstream models. The outcomes of experiments demonstrate that the improved model exhibits superior performance, and the mean average precision is improved by 3.3%. This model significantly enhances the detection ability while also optimizing the neural network to make it more lightweight. These advancements position the model as a promising solution for early-stage forest fire smoke detection

Effects of fire disturbance on soil respiration in the non-growing season in a <i>Larix gmelinii</i> forest in the Daxing'an Mountains, China

<div><p>In boreal forests, fire is an important part of the ecosystem that greatly influences soil respiration, which in turn affects the carbon balance. Wildfire can have a significant effect on soil respiration and it depends on the fire severity and environmental factors (soil temperature and snow water equivalent) after fire disturbance. In this study, we quantified post-fire soil respiration during the non-growing season (from November to April) in a <i>Larix gmelinii</i> forest in Daxing'an Mountains of China. Soil respiration was measured in the snow-covered and snow-free conditions with varying degrees of natural burn severity forests. We found that soil respiration decreases as burn severity increases. The estimated annual C efflux also decreased with increased burn severity. Soil respiration during the non-growing season approximately accounted for 4%–5% of the annual C efflux in all site types. Soil temperature (at 5 cm depth) was the predominant determinant of non-growing season soil respiration change in this area. Soil temperature and snow water equivalent could explain 73%–79% of the soil respiration variability in winter snow-covering period (November to March). Mean spring freeze–thaw cycle (FTC) period (April) soil respiration contributed 63% of the non-growing season C efflux. Our finding is key for understanding and predicting the potential change in the response of boreal forest ecosystems to fire disturbance under future climate change.</p></div