Pattern classification approaches for breast cancer identification via MRI: state‐of‐the‐art and vision for the future

Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI) of breast tissue are discussed. The algorithms are based on recent advances in multidimensional signal processing and aim to advance current state‐of‐the‐art computer‐aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi‐parametric computer‐aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi‐supervised deep learning and self‐supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high‐dimensional medical imaging analysis platform that is based on multi‐task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE‐MRI. Since some of the approaches discussed are also based on time‐lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis

Partition-based differentially private synthetic data generation

Private synthetic data sharing is preferred as it keeps the distribution and nuances of original data compared to summary statistics. The state-of-the-art methods adopt a select-measure-generate paradigm, but measuring large domain marginals still results in much error and allocating privacy budget iteratively is still difficult. To address these issues, our method employs a partition-based approach that effectively reduces errors and improves the quality of synthetic data, even with a limited privacy budget. Results from our experiments demonstrate the superiority of our method over existing approaches. The synthetic data produced using our approach exhibits improved quality and utility, making it a preferable choice for private synthetic data sharing

Sensitivity estimation for differentially private query processing

Differential privacy has become a popular privacy-preserving method in data analysis, query processing, and machine learning, which adds noise to the query result to avoid leaking privacy. Sensitivity, or the maximum impact of deleting or inserting a tuple on query results, determines the amount of noise added. Computing the sensitivity of some simple queries such as counting query is easy, however, computing the sensitivity of complex queries containing join operations is challenging. Global sensitivity of such a query is unboundedly large, which corrupts the accuracy of the query answer. Elastic sensitivity and residual sensitivity offer upper bounds of local sensitivity to reduce the noise, but they suffer from either low accuracy or high computational overhead. We propose two fast query sensitivity estimation methods based on sampling and sketch respectively, offering competitive accuracy and higher efficiency compared to the state-of-the-art methods

Sketches-based join size estimation under local differential privacy

Join size estimation on sensitive data poses a risk of privacy leakage. Local differential privacy (LDP) is a solution to preserve privacy while collecting sensitive data, but it introduces significant noise when dealing with sensitive join attributes that have large domains. Employing probabilistic structures such as sketches is a way to handle large domains, but it leads to hash-collision errors. To achieve accurate estimations, it is necessary to reduce both the noise error and hash-collision error. To tackle the noise error caused by protecting sensitive join values with large domains, we introduce a novel algorithm called LDPJoinSketch for sketch-based join size estimation under LDP. Additionally, to address the inherent hash-collision errors in sketches under LDP, we propose an enhanced method called LDPJoinSketch+. It utilizes a frequency-aware perturbation mechanism that effectively separates high-frequency and low-frequency items without compromising privacy. The proposed methods satisfy LDP, and the estimation error is bounded. Experimental results show that our method outperforms existing methods, effectively enhancing the accuracy of join size estimation under LDP

LIO-GVM: an Accurate, Tightly-Coupled Lidar-Inertial Odometry with Gaussian Voxel Map

This letter presents an accurate and robust Lidar Inertial Odometry framework. We fuse LiDAR scans with IMU data using a tightly-coupled iterative error state Kalman filter for robust and fast localization. To achieve robust correspondence matching, we represent the points as a set of Gaussian distributions and evaluate the divergence in variance for outlier rejection. Based on the fitted distributions, a new residual metric is proposed for the filter-based Lidar inertial odometry, which demonstrates an improvement from merely quantifying distance to incorporating variance disparity, further enriching the comprehensiveness and accuracy of the residual metric. Due to the strategic design of the residual metric, we propose a simple yet effective voxel-solely mapping scheme, which only necessities the maintenance of one centroid and one covariance matrix for each voxel. Experiments on different datasets demonstrate the robustness and accuracy of our framework for various data inputs and environments. To the benefit of the robotics society, we open source the code at https://github.com/Ji1Xingyu/lio_gvm

Physiological and visible injury responses in different growth stages of winter wheat to ozone stress and the protection of spermidine

AbstractThe open top chamber (OTC) method was used in a farmland to study the influence of different levels of O3 concentrations (40 ppb, 80 ppb and 120 ppb) on the enzymatic activity and metabolite contents of the antioxidation system of the winter wheat leaves during the jointing, heading and milk stage. The protective effect of exogenous spermidine (Spd) against the antioxidation of winter wheat under the O3 stress was investigated. With the increasing O3 concentrations and fumigation time, the injuries of the winter wheat leaves were observed to be more serious. For instance, when the O3 concentration reached 120 ppb, the activities of superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX) and nitrate reductase (NR) in the jointing stage decreased by 50.3%, 64.9%, 75.5% and 92.9%, respectively; peroxidase (POD) and glutathione reductase (GR) increased by 45.1% and 80.5%, respectively; the contents of malondialdehyde (MDA), ascorbic acid (AsA) and reduced glutathione (GSH) increased by 314.3%, 8.4% and 31.7%, respectively; and the soluble protein (SP) content decreased by 47.5%. The O3 stress also had significant impact on the contents of proline (Pro), NO3––N and NH4+–N of the winter wheat leaves. During the heading stage, when the O3 concentration was 40 ppb and 80 ppb, the content of Pro was 163.9% and 173.2% higher than that in the control group, respectively. But under 120 ppb, it was decreased by 42.4%. Exogenous application of Spd increased the activities of SOD, POD, CAT, APX and GR, as well as the contents of GSH and SP, but decreased the contents of MDA and AsA. This indicates that Spd is an effective antioxidant to relieve the O3 stress on winter wheat leaves, thereby might be applicable to protect winter wheat from the harm of O3

Robust model of fresh jujube soluble solids content with near-infrared (NIR) spectroscopy

A robust partial least square (PLS) calibration model with high accuracy and stability was established for the measurement of soluble solids content (SSC) of fresh jujube using near-infrared (NIR) spectroscopytechnique. Fresh jujube samples were collected in different areas of Taigu and Taiyuan cities, central China in 2008 and 2009. A partial least squares (PLS) calibration model was established based on the NIR spectra of 70 fresh jujube samples collected in 2008. A good calibration result was obtained with correlation coefficient (Rc) of 0.9530 and the root mean square error of calibration (RMSEC) of 0.3951 °Brix. Another PLS calibration model was established based on the NIR spectral of 180 samples collected in 2009; it resulted in the Rc of 0.8536 and the RMSEC of 1.1410 °Brix. It could be seen that the accuracy of established PLS models were different when samples harvested in different years were used for the model calibration. In order to improve the accuracy and robustness of model, different numbers (5, 10, 15, 20, 30 and 40) of samples harvested in 2008 were added to the calibration sample set of the model with samples harvested in 2009, respectively. The established PLS models obtained Rc with the range of 0.8846 to 0.8893 and RMSEC with the range of 1.0248 to 0.9645 °Brix. The obtained results werebetter than the result of the model which was established only with samples harvested in 2009. Moreover, the models established using different numbers of added samples had similar results. Therefore, it was concluded that adding samples from another harvest year could improve the accuracy and robustness of the model for SSC prediction of fresh jujube. The overall results proved that the consideration of samples from different harvest places and years would be useful for establishing an accuracy and robustness spectral model.Keywords: Near-infrared (NIR) spectroscopy, Huping jujube, soluble solids content (SSC), partial least squares (PLS), accuracy, stabilit

Outram: One-shot Global Localization via Triangulated Scene Graph and Global Outlier Pruning

One-shot LiDAR localization refers to the ability to estimate the robot pose from one single point cloud, which yields significant advantages in initialization and relocalization processes. In the point cloud domain, the topic has been extensively studied as a global descriptor retrieval (i.e., loop closure detection) and pose refinement (i.e., point cloud registration) problem both in isolation or combined. However, few have explicitly considered the relationship between candidate retrieval and correspondence generation in pose estimation, leaving them brittle to substructure ambiguities. To this end, we propose a hierarchical one-shot localization algorithm called Outram that leverages substructures of 3D scene graphs for locally consistent correspondence searching and global substructure-wise outlier pruning. Such a hierarchical process couples the feature retrieval and the correspondence extraction to resolve the substructure ambiguities by conducting a local-to-global consistency refinement. We demonstrate the capability of Outram in a variety of scenarios in multiple large-scale outdoor datasets. Our implementation is open-sourced: https://github.com/Pamphlett/Outram.Comment: 8 pages, 5 figure

Experimental and Numerical Investigation on the Irregularity of Carbonation Depth of Concrete Under Supercritical Condition

The heterogeneity of a cement-based material results in a random spatial distribution of carbonation depth, which may significantly affect the mechanical properties and durability of the material. Currently, there is a lack of both experimental and numerical investigations aiming at a statistical understanding of this important phenomenon. This paper presents both experimental and numerical supercritical carbonation test results of concrete blocks. The random fields of porosity and two-dimension random aggregate model of concrete were proposed for the simulation. The carbonation depths are measured and distributed along the carbonation boundary by the proposed rapid image processing technique, which are then statistically studied. The study has shown that considering the random distribution of coarse aggregates and using a random field of porosity with due consideration of spatial correlation and variance, the irregularity of carbonation depth can be realistically captured by the numerical model. Overall the methodology adopted in the paper can provide a foundation for future investigations on probability analysis of carbonation depth and other similar work based on multi-scale and –physics modelling

Modeling the Health Impact and Cost-Effectiveness of a Combined Schoolgirl HPV Vaccination and Cervical Cancer Screening Program in Guangdong Province, China

Low human papillomavirus (HPV) vaccine uptake is a key barrier to cervical cancer elimination. We aimed to evaluate the health impact and cost-effectiveness of introducing different HPV vaccines into immunization programs and scaling up the screening program in Guangdong. We used a dynamic compartmental model to estimate the impact of intervention strategies during 2023-2100. We implemented the incremental cost-effectiveness ratio (ICER) in costs per averted disability-adjusted life year (DALY) as an indicator to assess the effectiveness of the intervention. We used an age-standardized incidence of 4 cases per 100,000 women as the threshold for the elimination of cervical cancer. Compared with the status quo, scaling up cervical cancer screening coverage alone would prevent 215,000 (95% CI: 205,000 to 227,000) cervical cancer cases and 49,000 (95% CI: 48,000 to 52,000) deaths during 2023-2100. If the coverage of vaccination reached 90%, domestic two-dose 2vHPV vaccination would be more cost-effective than single-dose and two-dose 9vHPV vaccination. If Guangdong introduced domestic two-dose 2vHPV vaccination at 90% coverage for schoolgirls from 2023 and increased the screening coverage, cervical cancer would be eliminated by 2049 (95% CI 2047 to 2051). Introducing two doses of domestic 2vHPV vaccination for schoolgirls and expanding cervical cancer screening is estimated to be highly cost-effective to accelerate the elimination of cervical cancer in Guangdong