Catching Image Retrieval Generalization

The concepts of overfitting and generalization are vital for evaluating machine learning models. In this work, we show that the popular Recall@K metric depends on the number of classes in the dataset, which limits its ability to estimate generalization. To fix this issue, we propose a new metric, which measures retrieval performance, and, unlike Recall@K, estimates generalization. We apply the proposed metric to popular image retrieval methods and provide new insights about deep metric learning generalization.Comment: 4 pages, 3 figures, 2 table

Diffusive transport of adsorbed n-alkanes along e-beam irradiated plane surfaces and nanopillars

Diffusion of adsorbed n-alkanes was studied by means of electron beam induced deposition (EBID) technique. Carbon ring-like and pillar-like deposits were produced on bulk and thin substrates in a scanning electron microscope (SEM) operated in a “spot” mode. Residual nalkanes used as a precursor gas were delivered to the beam interaction region (BIR) via surface diffusion. The model of adsorbate diffusion along a heterogeneous surface with different diffusion coefficients D1 and D2 outside and inside the BIR, respectively, was proposed to explain the measured deposition rates. The estimates for diffusion coefficients ranging from ~1x10-10 to ~1x10-7 cm2s-1 at room temperature on surfaces with different roughness were obtained. These estimates most likely should be attributed to n-decane molecules expected to play the key role in the deposition process. Clusters of polymerized molecules produced by irradiation were assumed to act as effective traps hampering surface diffusion. For high D1/D2 ratios the deposition rates were found to be practically independent of the substrate material and initial roughness

Implicit Neural Convolutional Kernels for Steerable CNNs

Steerable convolutional neural networks (CNNs) provide a general framework for building neural networks equivariant to translations and other transformations belonging to an origin-preserving group $G$, such as reflections and rotations. They rely on standard convolutions with $G$-steerable kernels obtained by analytically solving the group-specific equivariance constraint imposed onto the kernel space. As the solution is tailored to a particular group $G$, the implementation of a kernel basis does not generalize to other symmetry transformations, which complicates the development of group equivariant models. We propose using implicit neural representation via multi-layer perceptrons (MLPs) to parameterize $G$-steerable kernels. The resulting framework offers a simple and flexible way to implement Steerable CNNs and generalizes to any group $G$ for which a $G$-equivariant MLP can be built. We apply our method to point cloud (ModelNet-40) and molecular data (QM9) and demonstrate a significant improvement in performance compared to standard Steerable CNNs

Amortized Bayesian Inference of GISAXS Data with Normalizing Flows

Grazing-Incidence Small-Angle X-ray Scattering (GISAXS) is a modern imaging technique used in material research to study nanoscale materials. Reconstruction of the parameters of an imaged object imposes an ill-posed inverse problem that is further complicated when only an in-plane GISAXS signal is available. Traditionally used inference algorithms such as Approximate Bayesian Computation (ABC) rely on computationally expensive scattering simulation software, rendering analysis highly time-consuming. We propose a simulation-based framework that combines variational auto-encoders and normalizing flows to estimate the posterior distribution of object parameters given its GISAXS data. We apply the inference pipeline to experimental data and demonstrate that our method reduces the inference cost by orders of magnitude while producing consistent results with ABC

Enteral insufficiency in different phases of acute pancreatitis

Aim – to study the integral scale for assessing enteral insufficiency in patients with acute pancreatitis during the phases of enzyme toxemia and purulent-destructive complications; to develop a strategy for choosing a method for enteral insufficiency correction. Material and methods. We have analyzed the treatment outcomes in 232 patients with acute pancreatitis admitted to the surgical department of the Samara Regional Clinical Hospital n. a. V.D. Seredavin for the period from 2013 to 2019. Two study groups were formed: retrospective and prospective. The retrospective (control) group included 175 patients whose treatment outcomes were assessed retrospectively for the parameters of the enteral insufficiency syndrome and measures were suggested for its differentiated correction. The prospective (intervention) group included 57 patients who were treated using the developed principles of enteral insufficiency correction. Results. The occurrence of I and II degrees of enteral insufficiency differed significantly depending on the phase of acute pancreatitis. When using complex correction of enteral insufficiency according to the proposed scheme, in patients in the phase of enzyme toxemia the I degree enteral insufficiency was prevalent, the patients in the purulent-destructive phase were more likely to have II degree enteral insufficiency. Conclusion. The timely inclusion of enteral insufficiency correction into the complex of therapeutic measures for pancreatitis may improve the treatment outcome in patients with moderate and severe phases of acute pancreatitis

Diffusive transport of adsorbed n-alkanes along e-beam irradiated plane surfaces and nanopillars

Diffusion of adsorbed n-alkanes was studied by means of electron beam induced deposition (EBID) technique. Carbon ring-like and pillar-like deposits were produced on bulk and thin substrates in a scanning electron microscope (SEM) operated in a “spot” mode. Residual nalkanes used as a precursor gas were delivered to the beam interaction region (BIR) via surface diffusion. The model of adsorbate diffusion along a heterogeneous surface with different diffusion coefficients D1 and D2 outside and inside the BIR, respectively, was proposed to explain the measured deposition rates. The estimates for diffusion coefficients ranging from ~1x10-10 to ~1x10-7 cm2s-1 at room temperature on surfaces with different roughness were obtained. These estimates most likely should be attributed to n-decane molecules expected to play the key role in the deposition process. Clusters of polymerized molecules produced by irradiation were assumed to act as effective traps hampering surface diffusion. For high D1/D2 ratios the deposition rates were found to be practically independent of the substrate material and initial roughness

Diffusive transport of adsorbed n-alkanes along e-beam irradiated plane surfaces and nanopillars

Diffusion of adsorbed n-alkanes was studied by means of electron beam induced deposition (EBID) technique. Carbon ring-like and pillar-like deposits were produced on bulk and thin substrates in a scanning electron microscope (SEM) operated in a “spot” mode. Residual nalkanes used as a precursor gas were delivered to the beam interaction region (BIR) via surface diffusion. The model of adsorbate diffusion along a heterogeneous surface with different diffusion coefficients D1 and D2 outside and inside the BIR, respectively, was proposed to explain the measured deposition rates. The estimates for diffusion coefficients ranging from ~1x10-10 to ~1x10-7 cm2s-1 at room temperature on surfaces with different roughness were obtained. These estimates most likely should be attributed to n-decane molecules expected to play the key role in the deposition process. Clusters of polymerized molecules produced by irradiation were assumed to act as effective traps hampering surface diffusion. For high D1/D2 ratios the deposition rates were found to be practically independent of the substrate material and initial roughness

Modeling of Surfactant Adsorption in the Carbonate Reservoir

The problem of surfactant absorption in carbonate reservoir during surfactant injection as enhanced oil recovery method becomes more on the front burner. Moreover, since reservoir simulation progress improves invariably it forces to apply special programs to reach enlargement of the amount of recoverable oil. Therefore, in this article surfactant adsorption in carbonate reservoir was simulated. As a result, it has been got that recovery was improved with surfactant application.1-

Dataset for Inversion of GISAXS data (3 layers)

The dataset consists of 50000 X-ray diffraction patterns simulated by BornAgain [1] software. For each simulation, a multilayer sample model of the following structure was used: air, tantalum oxide, tantalum, copper nitride, silicon dioxide, and substrate. Parameters of air, silicon dioxide, and substrate were kept fixed. Hence, each diffraction pattern is set to depend on the parameters of tantalum oxide, tantalum, and copper nitride layers. For each layer, those are real and complex parts of refractive index, thickness, roughness, Hurst parameter, and correlation length. Each simulation output is stored in an h5py file consisting of 1) diffraction image as a NumPy array of shape [1200, 120]; 2) parameters of a sample as a NumPy array with 18 elements. For further details regarding simulation see https://github.com/maxxxzdn/gisaxs-reconstruction/simulation/simulation. [1] Pospelov, G., Van Herck, W., Burle, J., Carmona Loaiza, J.M., Durniak, C., Fisher, J., Ganeva, M., Yurov, D., & Wuttke, J. (2020). BornAgain: software for simulating and fitting grazing-incidence small-angle scattering. Journal of Applied Crystallography, 53, 262 - 276

Neural Solvers

Neural Solvers are neural network-based solvers for partial differential equations and inverse problems. The framework implements scalable physics-informed neural networks Physics-informed neural networks allow strong scaling by design. Therefore, we have developed a framework that uses data parallelism to accelerate the training of physics-informed neural networks significantly. To implement data parallelism, we use the Horovod framework, which provides near-ideal speedup on multi-GPU regimes