AdaTask: A Task-aware Adaptive Learning Rate Approach to Multi-task Learning

Multi-task learning (MTL) models have demonstrated impressive results in computer vision, natural language processing, and recommender systems. Even though many approaches have been proposed, how well these approaches balance different tasks on each parameter still remains unclear. In this paper, we propose to measure the task dominance degree of a parameter by the total updates of each task on this parameter. Specifically, we compute the total updates by the exponentially decaying Average of the squared Updates (AU) on a parameter from the corresponding task.Based on this novel metric, we observe that many parameters in existing MTL methods, especially those in the higher shared layers, are still dominated by one or several tasks. The dominance of AU is mainly due to the dominance of accumulative gradients from one or several tasks. Motivated by this, we propose a Task-wise Adaptive learning rate approach, AdaTask in short, to separate the \emph{accumulative gradients} and hence the learning rate of each task for each parameter in adaptive learning rate approaches (e.g., AdaGrad, RMSProp, and Adam). Comprehensive experiments on computer vision and recommender system MTL datasets demonstrate that AdaTask significantly improves the performance of dominated tasks, resulting SOTA average task-wise performance. Analysis on both synthetic and real-world datasets shows AdaTask balance parameters in every shared layer well.Comment: AAAI 202

Efficient refinements on YOLOv3 for real-time detection and assessment of diabetic foot Wagner grades

Currently, the screening of Wagner grades of diabetic feet (DF) still relies on professional podiatrists. However, in less-developed countries, podiatrists are scarce, which led to the majority of undiagnosed patients. In this study, we proposed the real-time detection and location method for Wagner grades of DF based on refinements on YOLOv3. We collected 2,688 data samples and implemented several methods, such as a visual coherent image mixup, label smoothing, and training scheduler revamping, based on the ablation study. The experimental results suggested that the refinements on YOLOv3 achieved an accuracy of 91.95% and the inference speed of a single picture reaches 31ms with the NVIDIA Tesla V100. To test the performance of the model on a smartphone, we deployed the refinements on YOLOv3 models on an Android 9 system smartphone. This work has the potential to lead to a paradigm shift for clinical treatment of the DF in the future, to provide an effective healthcare solution for DF tissue analysis and healing status.Comment: 11 pages with 11 figure

Genome structure of cotton revealed by a genome-wide SSR genetic map constructed from a BC1 population between gossypium hirsutum and G. barbadense

<p>Abstract</p> <p>Background</p> <p>Cotton, with a large genome, is an important crop throughout the world. A high-density genetic linkage map is the prerequisite for cotton genetics and breeding. A genetic map based on simple polymerase chain reaction markers will be efficient for marker-assisted breeding in cotton, and markers from transcribed sequences have more chance to target genes related to traits. To construct a genome-wide, functional marker-based genetic linkage map in cotton, we isolated and mapped expressed sequence tag-simple sequence repeats (EST-SSRs) from cotton ESTs derived from the A₁, D₅, (AD)₁, and (AD)₂genome.</p> <p>Results</p> <p>A total of 3177 new EST-SSRs developed in our laboratory and other newly released SSRs were used to enrich our interspecific BC₁genetic linkage map. A total of 547 loci and 911 loci were obtained from our EST-SSRs and the newly released SSRs, respectively. The 1458 loci together with our previously published data were used to construct an updated genetic linkage map. The final map included 2316 loci on the 26 cotton chromosomes, 4418.9 cM in total length and 1.91 cM in average distance between adjacent markers. To our knowledge, this map is one of the three most dense linkage maps in cotton. Twenty-one segregation distortion regions (SDRs) were found in this map; three segregation distorted chromosomes, Chr02, Chr16, and Chr18, were identified with 99.9% of distorted markers segregating toward the heterozygous allele. Functional analysis of SSR sequences showed that 1633 loci of this map (70.6%) were transcribed loci and 1332 loci (57.5%) were translated loci.</p> <p>Conclusions</p> <p>This map lays groundwork for further genetic analyses of important quantitative traits, marker-assisted selection, and genome organization architecture in cotton as well as for comparative genomics between cotton and other species. The segregation distorted chromosomes can be a guide to identify segregation distortion loci in cotton. The annotation of SSR sequences identified frequent and rare gene ontology items on each chromosome, which is helpful to discover functions of cotton chromosomes.</p

Genome-wide recombination rate variation in a recombination map of cotton

<div><p>Recombination is crucial for genetic evolution, which not only provides new allele combinations but also influences the biological evolution and efficacy of natural selection. However, recombination variation is not well understood outside of the complex species’ genomes, and it is particularly unclear in <i>Gossypium</i>. Cotton is the most important natural fibre crop and the second largest oil-seed crop. Here, we found that the genetic and physical maps distances did not have a simple linear relationship. Recombination rates were unevenly distributed throughout the cotton genome, which showed marked changes along the chromosome lengths and recombination was completely suppressed in the centromeric regions. Recombination rates significantly varied between A-subgenome (At) (range = 1.60 to 3.26 centimorgan/megabase [cM/Mb]) and D-subgenome (Dt) (range = 2.17 to 4.97 cM/Mb), which explained why the genetic maps of At and Dt are similar but the physical map of Dt is only half that of At. The translocation regions between A02 and A03 and between A04 and A05, and the inversion regions on A10, D10, A07 and D07 indicated relatively high recombination rates in the distal regions of the chromosomes. Recombination rates were positively correlated with the densities of genes, markers and the distance from the centromere, and negatively correlated with transposable elements (TEs). The gene ontology (GO) categories showed that genes in high recombination regions may tend to response to environmental stimuli, and genes in low recombination regions are related to mitosis and meiosis, which suggested that they may provide the primary driving force in adaptive evolution and assure the stability of basic cell cycle in a rapidly changing environment. Global knowledge of recombination rates will facilitate genetics and breeding in cotton.</p></div

Metformin promotes osteogenic differentiation and prevents hyperglycaemia-induced osteoporosis by suppressing PPARγ expression

Metformin can prevent hyperglycaemia-induced osteoporosis and decrease the bone fracture rate, but the mechanism has not been fully elucidated. To reveal the mechanism by which metformin affects hyperglycaemia-induced osteoporosis, we treat a mouse osteoporosis cell model with metformin and find that osteoblast mineralization increases and PPARγ expression decreases. Single-cell mRNA sequencing analysis show that PPARγ is highly expressed in the bone tissue of osteoporosis patients, which highlights the role of PPARγ in osteoporosis. Furthermore, we find that PPARγ is the effector through which metformin prevents osteoporosis. We further examine the mechanism of PPARγ regulation and reveal that metformin regulates PPARγ expression through the AMPK pathway and that PPARγ affects osteoblasts through the endoplasmic reticulum stress (ERS) pathway. Moreover, we verify the association between the effect of metformin on bone metabolism and the expression of PPARγ in high-fat diet-induced diabetic rats. Thus, we identify and functionally validate that metformin prevents hyperglycaemia-induced osteoporosis by regulating the AMPK-PPARγ-ERS axis

The correlation of genetic and physical maps, the estimated local recombination rates and its distribution in chromosomal rearrangement regions.

<p>The red dots represent the genetic and physical positions of markers. The red curves below the scatter plots of SSRs represent the estimated local recombination rates. The value of R² represent the correlation between the genetic and physical maps. Shadow represents the centromere regions, inversion regions and translocation regions.</p

The physical distribution statistics of markers on 26 chromosomes in cotton.

<p>The physical distribution statistics of markers on 26 chromosomes in cotton.</p

The linear relationship between recombination rates and density of <i>NBS-LRR</i> genes.

<p>The linear relationship between recombination rates and density of <i>NBS-LRR</i> genes. Error bars represent ± standard error.</p

The GO terms distribution of the genes in the high and low recombination regions.

<p>The GO terms distribution of the genes in the high recombination regions (A) and the low recombination regions (B).</p

Summary of the integrated chromosome features and the correlations.

<p>Summary of the integrated chromosome features and the correlations.</p