Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning

Deep neural networks require a large amount of labeled training data during supervised learning. However, collecting and labeling so much data might be infeasible in many cases. In this paper, we introduce a source-target selective joint fine-tuning scheme for improving the performance of deep learning tasks with insufficient training data. In this scheme, a target learning task with insufficient training data is carried out simultaneously with another source learning task with abundant training data. However, the source learning task does not use all existing training data. Our core idea is to identify and use a subset of training images from the original source learning task whose low-level characteristics are similar to those from the target learning task, and jointly fine-tune shared convolutional layers for both tasks. Specifically, we compute descriptors from linear or nonlinear filter bank responses on training images from both tasks, and use such descriptors to search for a desired subset of training samples for the source learning task. Experiments demonstrate that our selective joint fine-tuning scheme achieves state-of-the-art performance on multiple visual classification tasks with insufficient training data for deep learning. Such tasks include Caltech 256, MIT Indoor 67, Oxford Flowers 102 and Stanford Dogs 120. In comparison to fine-tuning without a source domain, the proposed method can improve the classification accuracy by 2% - 10% using a single model.Comment: To appear in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017

RankDNN: Learning to Rank for Few-shot Learning

This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classification, ranking relation classification is sample efficient and domain agnostic. Besides, it provides a new perspective on few-shot learning and is complementary to state-of-the-art methods. The core component of our deep neural network is a simple MLP, which takes as input an image triplet encoded as the difference between two vector-Kronecker products, and outputs a binary relevance ranking order. The proposed RankMLP can be built on top of any state-of-the-art feature extractors, and our entire deep neural network is called the ranking deep neural network, or RankDNN. Meanwhile, RankDNN can be flexibly fused with other post-processing methods. During the meta test, RankDNN ranks support images according to their similarity with the query samples, and each query sample is assigned the class label of its nearest neighbor. Experiments demonstrate that RankDNN can effectively improve the performance of its baselines based on a variety of backbones and it outperforms previous state-of-the-art algorithms on multiple few-shot learning benchmarks, including miniImageNet, tieredImageNet, Caltech-UCSD Birds, and CIFAR-FS. Furthermore, experiments on the cross-domain challenge demonstrate the superior transferability of RankDNN.The code is available at: https://github.com/guoqianyu-alberta/RankDNN.Comment: 12 pages, 4 figures. Accepted to AAAI2023. The code is available at: https://github.com/guoqianyu-alberta/RankDN

Numerical study on complex conductivity characteristics of hydrate-bearing porous media

The complex conductivity method is frequently used in hydro-/petro-/environmental geophysics, and considered to be a promising tool for characterizing and quantifying the properties of subsurface rocks, sediments and soils. We report a study on the complex conductivity characteristics of porous media containing gas hydrates through numerical modelling. The effects of the hydrate saturation, pore-water salinity and micro-distribution mode were studied, and hydrate-saturation evaluation correlations based on complex conductivity parameters were developed. A pore-scale numerical approach for developing the finite-element based models for hydrate-bearing porous media is proposed and a two-dimensional (2D) model is built to compute the complex conductivity responses of porous media under various conditions. We demonstrate that the simple 2D model can capture the dominant characteristics of the complex conductivity of hydrate-bearing porous media within the frequency range related to the induced polarization. The in-phase conductivity, quadrature conductivity and effective dielectric constant can be correlated with the saturation based on a power law in the log-log space, by which the hydrate-saturation evaluation models can be derived. A constant saturation exponent of the power-law correlation between the hydrate saturation and quadrature conductivity can be obtained when the pore-water conductivity exceeds 1.0 S/m. This is highly desirable in the hydrate-saturation models due to the variations of the pore-water conductivity in the processes of hydrate formation and dissociation. Within the framework of the complex conductivity analysis, the micro-distribution modes of hydrates in porous media can be categorized into two types. These are the fluid-suspending mode and grain-attaching mode. The in-phase conductivity exhibits significant variations under the same saturation and salinity but different micro-distribution modes, which can be attributed to the change in the tortuosity of the electrical conduction paths in the void space of porous media

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

History of kidney stones and risk of chronic kidney disease: a meta-analysis

Background Although the relationship between a history of kidney stones and chronic kidney disease (CKD) has been explored in many studies, it is still far from being well understood. Thus, we conducted a meta-analysis of studies comparing rates of CKD in patients with a history of kidney stones. Methods PubMed, EMBASE, and the reference lists of relevant articles were searched to identify observational studies related to the topic. A random-effects model was used to combine the study-specific risk estimates. We explored the potential heterogeneity by subgroup analyses and meta-regression analyses. Results Seven studies were included in this meta-analysis. Pooled results suggested that a history of kidney stones was associated with an increased adjusted risk estimate for CKD [risk ratio (RR), 1.47 95% confidence interval (CI) [1.23–1.76])], with significant heterogeneity among these studies (I2 = 93.6%, P < 0.001). The observed positive association was observed in most of the subgroup analyses, whereas the association was not significant among studies from Asian countries, the mean age ≥50 years and male patients. Conclusion A history of kidney stones is associated with increased risk of CKD. Future investigations are encouraged to reveal the underlying mechanisms in the connection between kidney stones and CKD, which may point the way to more effective preventive and therapeutic measures