Benchmarking Self-Supervised Learning on Diverse Pathology Datasets

Computational pathology can lead to saving human lives, but models are annotation hungry and pathology images are notoriously expensive to annotate. Self-supervised learning has shown to be an effective method for utilizing unlabeled data, and its application to pathology could greatly benefit its downstream tasks. Yet, there are no principled studies that compare SSL methods and discuss how to adapt them for pathology. To address this need, we execute the largest-scale study of SSL pre-training on pathology image data, to date. Our study is conducted using 4 representative SSL methods on diverse downstream tasks. We establish that large-scale domain-aligned pre-training in pathology consistently out-performs ImageNet pre-training in standard SSL settings such as linear and fine-tuning evaluations, as well as in low-label regimes. Moreover, we propose a set of domain-specific techniques that we experimentally show leads to a performance boost. Lastly, for the first time, we apply SSL to the challenging task of nuclei instance segmentation and show large and consistent performance improvements under diverse settings

Hafnium metallocene compounds used as cathode interfacial layers for enhanced electron transfer in organic solar cells

We have used hafnium metallocene compounds as cathode interfacial layers for organic solar cells [OSCs]. A metallocene compound consists of a transition metal and two cyclopentadienyl ligands coordinated in a sandwich structure. For the fabrication of the OSCs, poly[3,4-ethylenedioxythiophene]:poly(styrene sulfonate), poly(3-hexylthiophene-2,5-diyl) + [6,6]-phenyl C61 butyric acid methyl ester, bis-(ethylcyclopentadienyl)hafnium(IV) dichloride, and aluminum were deposited as a hole transport layer, an active layer, a cathode interfacial layer, and a cathode, respectively. The hafnium metallocene compound cathode interfacial layer improved the performance of OSCs compared to that of OSCs without the interfacial layer. The current density-voltage characteristics of OSCs with an interfacial layer thickness of 0.7 nm and of those without an interfacial layer showed power conversion efficiency [PCE] values of 2.96% and 2.34%, respectively, under an illumination condition of 100 mW/cm2 (AM 1.5). It is thought that a cathode interfacial layer of an appropriate thickness enhances the electron transfer between the active layer and the cathode, and thus increases the PCE of the OSCs

Design of Cyclone Separator Critical Diameter Model Based on Machine Learning and CFD

In this paper, the characteristics of the cyclone separator was analyzed from the Lagrangian perspective for designing the important dependent variables. The neural network network model was developed for predicting the separation performance parameter. Further, the predictive performances were compared between the traditional surrogate model and the developed neural network model. In order to design the important parameters of the cyclone separator based on the particle separation theory, the force acting until the particles are separated was calculated using the Lagrangian-based computational fluid dynamics (CFD) methodology. As a result, it was proved that the centrifugal force and drag acting on the critical diameter having a separation efficiency of 50% were similar, and the particle separation phenomenon in the cyclone occurred from the critical diameter, and it was set as an important dependent variable. For developing a critical diameter prediction model based on machine learning and multiple regression methods, unsteady-Reynolds averaged Navier-Stokes analyzes according to shape dimensions were performed. The input design variables for predicting the critical diameter were selected as four geometry parameters that affect the turbulent flow inside the cyclone. As a result of comparing the model prediction performances, the machine learning (ML) model, which takes into account the critical diameter and the nonlinear relationship of cyclone design variables, showed a 32.5% improvement in R-square compared to multi linear regression (MLR). The proposed techniques have proven to be fast and practical tools for cyclone design

Assessment and Calibration of a Low-Cost PM2.5 Sensor Using Machine Learning (HybridLSTM Neural Network): Feasibility Study to Build an Air Quality Monitoring System

Commercially available low-cost air quality sensors have low accuracy. The improved accuracy of low-cost PM2.5 sensors allows the use of low-cost sensor systems to reasonably investigate PM2.5 emissions from industrial activities or to accurately estimate individual exposure to PM2.5. In this work, we developed a new PM2.5 calibration model (HybridLSTM) by combining a deep neural network (DNN) optimized in calibration problems and a long short-term memory (LSTM) neural network optimized in time-dependent characteristics to improve the performance of conventional calibration algorithms of low-cost PM sensors. The PM2.5 concentrations, temperature and humidity by low-cost sensors and gravimetric-based PM2.5 measuring instrument were sampled for a sufficiently long time. The proposed model was compared with benchmarks (multiple linear regression model (MLR), DNN model) and low-cost sensor results. The gravimetric measurements were used as reference data to evaluate sensor accuracy. For root-mean-square error (RMSE) for PM2.5 concentrations, the proposed model reduced 41–60% of error when compared with the raw data of low-cost sensors, reduced 30–51% of error when compared with the MLR model and reduced 8–40% of error when compared with the MLR model. R2 of HybridLSTM, DNN, MLR and raw data were 93, 90, 80 and 59%, respectively. HybridLSTM showed the state-of-the-art calibration performance for a low-cost PM sensor. In other words, the proposed ML model has state-of-the-art calibration performance among the tested calibration algorithms

The Koszul-Tate type resolution for Gerstenhaber-Batalin-Vilkovisky algebras

Tate provided an explicit way to kill a nontrivial homology class of a commutative differential graded algebra over a commutative noetherian ring R in Tate (Ill J Math 1: 14-27, 1957). The goal of this article is to generalize his result to the case of GBV (Gerstenhaber-Batalin-Vilkovisky) algebras and, more generally, the descendant L-infinity-algebras. More precisely, for a given GBV algebra ((A) over tilde = circle plus(m >= 0)A(m), delta, l(2)(delta)), we provide another explicit GBV algebra ((A) over tilde = circle plus(m >= 0) (A) over tilde (m), (delta) over tilde, l(2)((delta) over tilde)) such that its total homology is the same as the degree zero part of the homology H-0(A, delta) of the given GBV algebra (A, delta, l(2)(delta)).11Nsciescopu