Machine learning for the diagnosis of early stage diabetes using temporal glucose profiles

Machine learning shows remarkable success for recognizing patterns in data. Here we apply the machine learning (ML) for the diagnosis of early stage diabetes, which is known as a challenging task in medicine. Blood glucose levels are tightly regulated by two counter-regulatory hormones, insulin and glucagon, and the failure of the glucose homeostasis leads to the common metabolic disease, diabetes mellitus. It is a chronic disease that has a long latent period the complicates detection of the disease at an early stage. The vast majority of diabetics result from that diminished effectiveness of insulin action. The insulin resistance must modify the temporal profile of blood glucose. Thus we propose to use ML to detect the subtle change in the temporal pattern of glucose concentration. Time series data of blood glucose with sufficient resolution is currently unavailable, so we confirm the proposal using synthetic data of glucose profiles produced by a biophysical model that considers the glucose regulation and hormone action. Multi-layered perceptrons, convolutional neural networks, and recurrent neural networks all identified the degree of insulin resistance with high accuracy above $85\%$.Comment: 4 pages, 2 figur

Superelasticity of ThCr2Si2-structured intermetallic compounds at the micrometer scale

The work represents a report of the discovery of superelasticity in ThCr2Si2-structured novel intermetallic compound (CaFe2As2) and its hybrid structure (CaKFe4As4) under “uni-axial” compression at the micrometer scale and discusses the strong possibility of deformation-induced superconductivity switching. They exhibit unprecedentedly large elastic limit (10~17%), ultrahigh strength (3~5 GPa), and repeatable cyclic loading response through the reversible lattice collapse caused by making and breaking atomic bonds.1-4 This unique superelasticity mechanism produces a modulus of resilience orders of magnitude higher than that of most engineering materials and enables strain engineering, which refers to the modification of material properties through elastic strain. Our experimental and computational results strongly suggest that superconductivity in a high temperature superconductor, CaKFe4As4, could be turned on/off reliably through this superelasticity process, before fracture occurs, even under “uniaxial” compression. Please note that it is extremely rare to see deformation-induced superconductivity switching under uni-axial deformation, which is the preferred loading mode in engineering applications. Note that our result is only one manifestation of a wider class of such transitions found in over 2500 different ThCr2Si2-structured intermetallic compounds. If we consider their hybrid structure, there could be a much larger number of similar intermetallic compounds. Therefore, our observation can be extended to search for a large group of superelastic and strain-engineerable functional materials, and, more broadly, will lead to various research opportunities in materials science, solid-state physics, superconducting device engineering, and machine-learning-based materials research. Please click Additional Files below to see the full abstract

Micromechanical characterization of single-crystalline niobium at low temperature

Due to the significant interests in application of micro-electro-mechanical system to devices used in harsh environments, small-scale mechanical behaviors in different thermal environments have been extensively studied recently. Particularly, the study on mechanical behavior of body-centered-cubic (bcc) metals has received a strong attention because of the significant temperature dependence of screw dislocation mobility and its cross-slip probability. So far, most studies have been done in either room temperature or elevated temperature, but a low temperature study is relatively scarce. In this work, we present our recent development of in-situ cryogenic micromechanical testing system and the results of cryogenic micro-tensile tests on a [0 0 1] bcc niobium single crystal. The dog-bone shaped tensile samples were fabricated via focused-ion beam milling and were tested at room temperature, 100K and 56K. The decrease in temperature increased the flow strengths. In addition, stress-strain curves at room temperature were smooth, but those at 56 and 100K showed a few distinctively large strain bursts. Post-mortem scanning electron microscopy revealed that necking region of samples tested at room temperature underwent uniform and homogeneous plastic deformation, but that at low temperature underwent highly localized plastic deformation followed by brittle fracture with limited ductility. Thus, micro-tensile tests of niobium single crystal show the ductile-to-brittle transition. All the results will be discussed in terms of the temperature dependence of cross-slip and intrinsic lattice resistance of screw dislocation. Our results will enable a deeper understanding of the combined effects of sample dimension and temperature on plasticity and fracture processes in bcc metals

GaIA: Graphical Information Gain based Attention Network for Weakly Supervised Point Cloud Semantic Segmentation

While point cloud semantic segmentation is a significant task in 3D scene understanding, this task demands a time-consuming process of fully annotating labels. To address this problem, recent studies adopt a weakly supervised learning approach under the sparse annotation. Different from the existing studies, this study aims to reduce the epistemic uncertainty measured by the entropy for a precise semantic segmentation. We propose the graphical information gain based attention network called GaIA, which alleviates the entropy of each point based on the reliable information. The graphical information gain discriminates the reliable point by employing relative entropy between target point and its neighborhoods. We further introduce anchor-based additive angular margin loss, ArcPoint. The ArcPoint optimizes the unlabeled points containing high entropy towards semantically similar classes of the labeled points on hypersphere space. Experimental results on S3DIS and ScanNet-v2 datasets demonstrate our framework outperforms the existing weakly supervised methods. We have released GaIA at https://github.com/Karel911/GaIA.Comment: WACV 2023 accepted pape