Measurement Errors and their Propagation in the Registration of Remote Sensing Images (?)

Reference control points (RCPs) used in establishing the regression model in the registration or geometric correction of remote sensing images are generally assumed to be ?perfect?. That is, the RCPs, as explanatory variables in the regression equation, are accurate and the coordinates of their locations have no errors. Thus ordinary least squares (OLS) estimator has been applied extensively to the registration or geometric correction of remotely sensed data. However, this assumption is often invalid in practice because RCPs always contain errors. Moreover, the errors are actually one of the main sources which lower the accuracy of geometric correction of an uncorrected image. Under this situation, the OLS estimator is biased. It cannot handle explanatory variables with errors and cannot propagate appropriately errors from the RCPs to the corrected image. Therefore, it is essential to develop new feasible methods to overcome such a problem. In this paper, we introduce the consistent adjusted least squares (CALS) estimator and propose a relaxed consistent adjusted least squares (RCALS) method, with the latter being more general and flexible, for geometric correction or registration. These estimators have good capability in correcting errors contained in the RCPs, and in propagating appropriately errors of the RCPs to the corrected image with and without prior information. The objective of the CALS and our proposed RCALS estimators is to improve the accuracy of measurement value by weakening the measurement errors. The validity of the CALS and RCALS estimators are first demonstrated by applying them to perform geometric corrections of controlled simulated images. The conceptual arguments are further substantiated by a real-life example. Compared to the OLS estimator, the CALS and RCALS estimators give a superior overall performances in estimating the regression coefficients and variance of measurement errors. Keywords: error propagation, geometric correction, ordinary least squares, registration, relaxed consistent adjusted least squares, remote sensing images.

Asymmetric feature interaction for interpreting model predictions

In natural language processing (NLP), deep neural networks (DNNs) could model complex interactions between context and have achieved impressive results on a range of NLP tasks. Prior works on feature interaction attribution mainly focus on studying symmetric interaction that only explains the additional influence of a set of words in combination, which fails to capture asymmetric influence that contributes to model prediction. In this work, we propose an asymmetric feature interaction attribution explanation model that aims to explore asymmetric higher-order feature interactions in the inference of deep neural NLP models. By representing our explanation with an directed interaction graph, we experimentally demonstrate interpretability of the graph to discover asymmetric feature interactions. Experimental results on two sentiment classification datasets show the superiority of our model against the state-of-the-art feature interaction attribution methods in identifying influential features for model predictions. Our code is available at https://github.com/StillLu/ASIV.Comment: Accepted by Findings of the Association for Computational Linguistics: ACL 2023 (long paper

Measurement Errors and their Propagation in the Registration of Remote Sensing Images

Reference control points (RCPs) used in establishing the regression model in the registration or geometric correction of remote sensing images are generally assumed to be ?perfect?. That is, the RCPs, as explanatory variables in the regression equation, are accurate and the coordinates of their locations have no errors. Thus ordinary least squares (OLS) estimator has been applied extensively to the registration or geometric correction of remotely sensed data. However, this assumption is often invalid in practice because RCPs always contain errors. Moreover, the errors are actually one of the main sources which lower the accuracy of geometric correction of an uncorrected image. Under this situation, the OLS estimator is biased. It cannot handle explanatory variables with errors and cannot propagate appropriately errors from the RCPs to the corrected image. Therefore, it is essential to develop new feasible methods to overcome such a problem. In this paper, we introduce the consistent adjusted least squares (CALS) estimator and propose a relaxed consistent adjusted least squares (RCALS) method, with the latter being more general and flexible, for geometric correction or registration. These estimators have good capability in correcting errors contained in the RCPs, and in propagating appropriately errors of the RCPs to the corrected image with and without prior information. The objective of the CALS and our proposed RCALS estimators is to improve the accuracy of measurement value by weakening the measurement errors. The validity of the CALS and RCALS estimators are first demonstrated by applying them to perform geometric corrections of controlled simulated images. The conceptual arguments are further substantiated by a real-life example. Compared to the OLS estimator, the CALS and RCALS estimators give a superior overall performances in estimating the regression coefficients and variance of measurement errors. Keywords: error propagation, geometric correction, ordinary least squares, registration, relaxed consistent adjusted least squares, remote sensing images

Study of the adsorption of Co(II) on the chitosan-hydroxyapatite

The adsorption of cobalt ions (Co2+) from aqueous solution onto chitosan-hydroxyapatite composite is investigated in this study. The effects of adsorption time, initial concentration, temperature, and pH are studied in details. Kinetics and thermodynamics of the adsorption of Co2+ onto the chitosan-hydroxyapatite are also investigated and the adsorption kinetics is found to follow the pseudo-second-order model with an activation energy (Ea) of 10.73 kJ/mol. Thermodynamic studies indicates that the adsorption follows the Langmuir adsorption equation. The value of entropy change (∆Sө) and enthalpy change (∆Hө) are found to be 83.50 and 18.09 kJ/mol, respectively. The Gibbs free energy change (∆Gө) is found to be negative at all fives temperatures, demonstrating that the adsorption process is spontaneous and endothermic.

Meta-analysis of interleukin 6, 8, and 10 between off-pump and on-pump coronary artery bypass groups

This study aimed to evaluate the role of off-pump coronary artery bypass (CAB) surgery on the decrease of postoperative inflammatory responses in patients. We systematically searched databases of PubMed and Embase to select the related studies. Interleukin (IL) 6, 8, and 10 were used as outcomes and pooled analysis was performed using R 3.12 software. Standardized mean differences (SMDs) and their 95% confidence intervals (95% CIs) were considered as effect estimates. A total of 27 studies, including 1340 participants, were recruited in this meta-analysis. The pooled analyses showed that postoperative concentration of IL-10 at 12 hours was significantly lower in off-pump CAB group compared to on-pump CAB group (SMD = −1.3640, 95% CI = −2.0086-−0.7193). However, no significant differences were found in pre and postoperative concentrations of IL-6 and 8 between off-pump and on-pump CAB groups. These results suggest that there is no advantage of off-pump CAB surgery in the reduction of inflammation compared to on-pump CAB surgery

The New Method and Application of Friction Torque for Extended Reach Well

The extended reach well has the characteristics of long horizontal displacement, big hold angle and long open hole. The forecast and control of friction torque is one of the key factors of successful drilling of extended reach well. Based on the characteristics of extended reach well, considering the effect of drill string stiffness and drill string buckling on the friction & torque of the tube, a modified 3D soft-string calculation model for friction & torque of the extended reach well is proposed. The corresponding software has been programmed, and the model is applied in well A. The calculation result shows that the new method’s calculation result is consistent with the experimental result, and the torque and hook load errors are within 10%. The method could satisfy the engineering requirement, which provide a good guidance for the friction & torque analysis in the process of profile optimal design and drilling operation for the extended reach well. Key words: Extended reach well; Friction＆torque; Drill string stiffness; Drill string buckling; Drilling operatio

Droplets microfluidics platform—A tool for single cell research

Cells are the most basic structural and functional units of living organisms. Studies of cell growth, differentiation, apoptosis, and cell-cell interactions can help scientists understand the mysteries of living systems. However, there is considerable heterogeneity among cells. Great differences between individuals can be found even within the same cell cluster. Cell heterogeneity can only be clearly expressed and distinguished at the level of single cells. The development of droplet microfluidics technology opens up a new chapter for single-cell analysis. Microfluidic chips can produce many nanoscale monodisperse droplets, which can be used as small isolated micro-laboratories for various high-throughput, precise single-cell analyses. Moreover, gel droplets with good biocompatibility can be used in single-cell cultures and coupled with biomolecules for various downstream analyses of cellular metabolites. The droplets are also maneuverable; through physical and chemical forces, droplets can be divided, fused, and sorted to realize single-cell screening and other related studies. This review describes the channel design, droplet generation, and control technology of droplet microfluidics and gives a detailed overview of the application of droplet microfluidics in single-cell culture, single-cell screening, single-cell detection, and other aspects. Moreover, we provide a recent review of the application of droplet microfluidics in tumor single-cell immunoassays, describe in detail the advantages of microfluidics in tumor research, and predict the development of droplet microfluidics at the single-cell level

Correlation functions quantify super-resolution images and estimate apparent clustering due to over-counting

We present an analytical method to quantify clustering in super-resolution localization images of static surfaces in two dimensions. The method also describes how over-counting of labeled molecules contributes to apparent self-clustering and how the effective lateral resolution of an image can be determined. This treatment applies to clustering of proteins and lipids in membranes, where there is significant interest in using super-resolution localization techniques to probe membrane heterogeneity. When images are quantified using pair correlation functions, the magnitude of apparent clustering due to over-counting will vary inversely with the surface density of labeled molecules and does not depend on the number of times an average molecule is counted. Over-counting does not yield apparent co-clustering in double label experiments when pair cross-correlation functions are measured. We apply our analytical method to quantify the distribution of the IgE receptor (Fc{\epsilon}RI) on the plasma membranes of chemically fixed RBL-2H3 mast cells from images acquired using stochastic optical reconstruction microscopy (STORM) and scanning electron microscopy (SEM). We find that apparent clustering of labeled IgE bound to Fc{\epsilon}RI detected with both methods arises from over-counting of individual complexes. Thus our results indicate that these receptors are randomly distributed within the resolution and sensitivity limits of these experiments.Comment: 22 pages, 5 figure

Graphene/silicon heterojunction for reconfigurable phase-relevant activation function in coherent optical neural networks

Optical neural networks (ONNs) herald a new era in information and communication technologies and have implemented various intelligent applications. In an ONN, the activation function (AF) is a crucial component determining the network performances and on-chip AF devices are still in development. Here, we first demonstrate on-chip reconfigurable AF devices with phase activation fulfilled by dual-functional graphene/silicon (Gra/Si) heterojunctions. With optical modulation and detection in one device, time delays are shorter, energy consumption is lower, reconfigurability is higher and the device footprint is smaller than other on-chip AF strategies. The experimental modulation voltage (power) of our Gra/Si heterojunction achieves as low as 1 V (0.5 mW), superior to many pure silicon counterparts. In the photodetection aspect, a high responsivity of over 200 mA/W is realized. Special nonlinear functions generated are fed into a complex-valued ONN to challenge handwritten letters and image recognition tasks, showing improved accuracy and potential of high-efficient, all-component-integration on-chip ONN. Our results offer new insights for on-chip ONN devices and pave the way to high-performance integrated optoelectronic computing circuits

Nicotine aggravates vascular adiponectin resistance via ubiquitin-mediated adiponectin receptor degradation in diabetic Apolipoprotein E knockout mouse

There is limited and discordant evidence on the role of nicotine in diabetic vascular disease. Exacerbated endothelial cell dysregulation in smokers with diabetes is associated with the disrupted adipose function. Adipokines possess vascular protective, anti-inflammatory, and anti-diabetic properties. However, whether and how nicotine primes and aggravates diabetic vascular disorders remain uncertain. In this study, we evaluated the alteration of adiponectin (APN) level in high-fat diet (HFD) mice with nicotine (NIC) administration. The vascular pathophysiological response was evaluated with vascular ring assay. Confocal and co-immunoprecipitation analysis were applied to identify the signal interaction and transduction. These results indicated that the circulating APN level in nicotine-administrated diabetic Apolipoprotein E-deficient (ApoE−/−) mice was elevated in advance of 2 weeks of diabetic ApoE−/− mice. NIC and NIC addition in HFD groups (NIC + HFD) reduced the vascular relaxation and signaling response to APN at 6 weeks. Mechanistically, APN receptor 1 (AdipoR1) level was decreased in NIC and further significantly reduced in NIC + HFD group at 6 weeks, while elevated suppressor of cytokine signaling 3 (SOCS3) expression was induced by NIC and further augmented in NIC + HFD group. Additionally, nicotine provoked SOCS3, degraded AdipoR1, and attenuated APN-activated ERK1/2 in the presence of high glucose and high lipid (HG/HL) in human umbilical vein endothelial cells (HUVECs). MG132 (proteasome inhibitor) administration manifested that AdipoR1 was ubiquitinated, while inhibited SOCS3 rescued the reduced AdipoR1. In summary, this study demonstrated for the first time that nicotine primed vascular APN resistance via SOCS3-mediated degradation of ubiquitinated AdipoR1, accelerating diabetic endothelial dysfunction. This discovery provides a potential therapeutic target for preventing nicotine-accelerated diabetic vascular dysfunction