Intersection Of Sets Research Articles

Privacy Protection Based on Special Identifiers of Intersection Base Computing Technology

Private Set Intersection Cardinality (PSI-CA) and Private Set Union Cardinality (PSU-CA) are two cryptographic primitives whereby two or more parties are able to obtain the cardinalities of the intersection and the union of their respective private sets, and the privacy of their sets is preserved. In this paper, we propose a new privacy protection intersection cardinality protocol, which can quickly deal with set inequality and asymmetry problems and can obtain 100% correct results, and, in terms of efficiency, we are much faster than using the polynomial method. Our protocol adopts the Paillier addition homomorphic encryption scheme and applies the identifier guidance technology, using identifier determination, to the semi-homomorphic encryption ciphertext environment, excluding a large number of different options and quickly finding the base of the intersection of two sides.

Numerical Modeling of Cutting Characteristics during Short Hole Drilling: Part 2—Modeling of Thermal Characteristics

The modeling of machining process characteristics and, in particular, of various cutting processes occupies a significant part of modern research. Determining the thermal characteristics in short hole drilling processes by numerical simulation is the object of the present study. For different contact conditions of the workpiece with the drill cutting inserts, the thermal properties of the machined material were determined. The above-mentioned properties and parameters of the model components were established using a three-dimensional finite element model of orthogonal cutting. Determination of the generalized values of the machined material thermal properties was performed by finding the set intersection of individual properties values using a previously developed software algorithm. A comparison of experimental and simulated values of cutting temperature in the workpiece points located at different distances from the drilled hole surface and on the lateral clearance face of the drill outer cutting insert shows the validity of the developed numerical model for drilling short holes. The difference between simulated and measured temperature values did not exceed 22.4% in the whole range of the studied cutting modes.

Identification of important genes related to HVSMC proliferation and migration in graft restenosis based on WGCNA

The great saphenous vein is the most commonly used vessel for coronary artery bypass grafting (CABG), but its use has been associated with a high restenosis rate at 10-year follow-up. This study sought to determine the key genes associated with vein graft restenosis that could serve as novel therapeutic targets. A total of 3075 upregulated and 1404 downregulated genes were identified after transcriptome sequencing of three pairs of restenosed vein grafts and intraoperative spare great saphenous veins. Weighted gene co-expression network analysis showed that the floralwhite module had the highest correlation with vein graft restenosis. The intersection of the floralwhite module gene set and the upregulated gene set contained 615 upregulated genes strongly correlated with vein graft restenosis. Protein–protein interaction network analysis identified six hub genes (ITGAM, PTPRC, TLR4, TYROBP, ITGB2 and CD4), which were obtained using the STRING database and CytoHubba. Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses showed that the common hub genes were mainly involved in the composition of the cell membrane; in biological processes such as neutrophil degranulation, receptor binding and intercellular adhesion, innate immune deficiency; and other signaling pathways. Finally, ITGB2 was selected as the target gene, and its expression was verified in tissues. The results showed that ITGB2 was significantly overexpressed in occluded vein grafts. To study the function of ITGB2 in HVSMCs, primary HVSMCs were cultured and successfully identified. EdU incorporation, wound healing and transwell assays showed that ITGB2 silencing significantly inhibited the proliferation and migration of HVSMCs stimulated by PDGF-BB. Overall, our study provides a basis for future studies on preventing restenosis following CABG.

Reduction of ETV1 is Identified as a Prominent Feature of Age-Related Cataract

Purpose To identify the inactive genes in cataract lenses and explore their function in lens epithelial cells (LECs). Methods Lens epithelium samples obtained from both age-related cataract (ARC) patients and normal donors were subjected to two forms of histone H3 immunoprecipitation: H3K9ac and H3K27me3 chromatin immunoprecipitation (ChIP), followed by ChIP-seq. The intersection set of “active genes in normal controls” and “repressed genes in cataract lenses” was identified. To validate the role of a specific gene, ETV1, within this set, quantitative polymerase chain reaction (qPCR), western blot, and immunofluorescence were performed using clinical lens epithelium samples. Small interference RNA (siRNA) was utilized to reduce the mRNA level of ETV1 in cultured LECs. Following this, transwell assay and western blot was performed to examine the migration ability of the cells. Furthermore, RNA-seq analysis was conducted on both cell samples with ETV1 knockdown and control cells. Additionally, the expression level of ETV1 in LECs was examined using qPCR under H2O2 treatment. Results Six genes were identified in the intersection set of “active genes in normal controls” and “repressed genes in ARC lenses”. Among these genes, ETV1 showed the most significant fold-change decrease in the cataract samples compared to the control samples. After ETV1 knockdown by siRNA in cultured LECs, reduced cell migration was observed, along with a decrease in the expression of β-Catenin and Vimentin, two specific genes associated with cell migration. In addition, under the oxidative stress induced by H2O2 treatment, the expression level of ETV1 in LECs significantly decreased. Conclusions Based on the findings of this study, it can be concluded that ETV1 is significantly reduced in human ARC lenses. The repression of ETV1 in ARC lenses appears to contribute to the disrupted differentiation of lens epithelium, which is likely caused by the inhibition of both cell differentiation and migration processes.

A Totally Relaxed Self-Adaptive Subgradient Extragradient Scheme for Equilibrium and Fixed Point Problems in a Banach Space

The goal of this paper is to introduce a Totally Relaxed Self adaptive Subgradient Extragradient Method (TRSSEM) together with an Halpern iterative method for approximating a common solution of Fixed Point Problem (FPP) and Equilibrium Problem (EP) in 2-uniformly convex and uniformly smooth Banach space. We prove the strong convergence of the sequence generated by our proposed method. The proposed method does not require the computation of a projection onto a feasible set, it instead requires a projection onto a finite intersection of sub-level sets of convex functions. Our result generalizes, unifies and extends some related results in the literature.

Bioinformatics gene analysis for potential biomarkers and therapeutic targets of Parkinson's disease based on neutrophil extracellular traps.

Neutrophil extracellular traps (NETs) provide key innate immune mechanisms, and studies have shown innate immunity and adaptive immunity are directly linked to Parkinson's disease (PD) pathology. However, limited research has been conducted on NETs in the context of PD. A differential analysis was implemented to acquire differentially expressed genes (DEGs) between PD and control as well as between high- and low-score groups determined by a gene set variation analysis (GSVA). Then, the genes within the critical module, obtained through a weighted gene co-expression network analysis (WGCNA), were intersected with the DEGs to identify the overlapping genes. Then, five kinds of algorithms in the protein-protein interaction (PPI) were performed to identify potential biomarkers. Subsequently, a nomogram for forecasting PD probability was created. An enrichment analysis and an immune infiltration analysis were performed on the identified biomarkers. qRT-PCR was performed to validate the expression trends of three biomarkers. We revealed 798 DEGs between PD and control groups as well as 168 DEGs between high- and low-score groups obtained by differential analyses. The pink module containing 926 genes was identified as the critical module. According to the intersection of these gene sets, a total of 43 overlapping genes were screened out. Furthermore, GPR78, CADM3, and CACNA1E were confirmed as biomarkers. Moreover, we found that biomarkers mainly participated in pathways, such as the 'hydrogen peroxide catabolic process', and 'cell cycle'; five kinds of differential immune cells between PD and control groups were identified. Finally, the qRT-PCR analysis demonstrated the up-regulation of GPR78, CADM3, and CACNA1E in the PD group. Our study authenticated GPR78, CADM3, and CACNA1E as the biomarkers associated with PD. These findings provide an original reference for the diagnosis and treatment of PD.

A robust optimization approach to a repair shop network planning

Robust optimization model is a paradigm for decision-making under uncertainty, where parameters are given in the form of uncertainty sets. In this paper, we develop a robust optimization model for a repair shop network planning problem. The linear optimization model involves product of uncertain parameters in the constraints. We formulate its robust counterpart with the help of min-max regret and Lagrangian dual approach, considering the partial information of uncertain parameters is given in the form of ellipsoidal and polyhedral uncertainty sets. We also consider ellipsoidal+polyhedral uncertainty set, which is the intersection of ellipsoidal and polyhedral uncertainty sets. We apply the robust optimization model to a bi-objective multi-plant repair shop network planning problem where multiple pieces of equipment are repaired and overhauled using several resources over a multi-period planning horizon. We consider uncertainty in resource and demand parameters. Numerical examples are presented for illustrating the theoretical results.

Douglas–Rachford algorithm for control-constrained minimum-energy control problems

Splitting and projection-type algorithms have been applied to many optimization problems due to their simplicity and efficiency, but the application of these algorithms to optimal control is less common. In this paper we utilize the Douglas–Rachford (DR) algorithm to solve control-constrained minimum-energy optimal control problems. Instead of the traditional approach where one discretizes the problem and solves it using large-scale finite-dimensional numerical optimization techniques we split the problem in two subproblems and use the DR algorithm to find an optimal point in the intersection of the solution sets of these two subproblems hence giving a solution to the original problem. We derive general expressions for the projections and propose a numerical approach. We obtain analytic closed-form expressions for the projectors of pure, under-, critically- and over-damped harmonic oscillators. We illustrate the working of our approach to solving not only these example problems but also a challenging machine tool manipulator problem. Through numerical case studies, we explore and propose desirable ranges of values of an algorithmic parameter which yield smaller number of iterations.

An algorithm to optimize explainability using feature ensembles

Feature Ensembles are a robust and effective method for finding the feature set that yields the best predictive accuracy for learning agents. However, current feature ensemble algorithms do not consider explainability as a key factor in their construction. To address this limitation, we present an algorithm that optimizes for the explainability and performance of a model – the Optimizing Feature Ensembles for Explainability (OFEE) algorithm. OFEE uses intersections of feature sets to produce a feature ensemble that optimally balances explainability and performance. Furthermore, OFEE is parameter-free and as such optimizes itself to a given dataset and explainability requirements. To evaluated OFEE, we considered two explainability measures, one based on ensemble size and the other based on ensemble stability. We found that OFEE was overall extremely effective within the nine canonical datasets we considered. It outperformed other feature selection algorithms by an average of over 8% and 7% respectively when considering the size and stability explainability measures.

Enabling Threshold Functionality for Private Set Intersection Protocols in Cloud Computing

Enabling Threshold Functionality for Private Set Intersection Protocols in Cloud Computing

Updatable Private Set Intersection With Forward Privacy

Updatable Private Set Intersection With Forward Privacy

Overview of Privacy Set Intersection Protocol Based on Heterogeneous Network and Social Network User Alignment

Overview of Privacy Set Intersection Protocol Based on Heterogeneous Network and Social Network User Alignment

Private set intersection with constant-size encryptions using RSA subgroups

Private set intersection with constant-size encryptions using RSA subgroups

Comparing Deep Learning Performance for Chronic Lymphocytic Leukaemia Cell Segmentation in Brightfield Microscopy Images.

This article focuses on the detection of cells in low-contrast brightfield microscopy images; in our case, it is chronic lymphocytic leukaemia cells. The automatic detection of cells from brightfield time-lapse microscopic images brings new opportunities in cell morphology and migration studies; to achieve the desired results, it is advisable to use state-of-the-art image segmentation methods that not only detect the cell but also detect its boundaries with the highest possible accuracy, thus defining its shape and dimensions. We compared eight state-of-the-art neural network architectures with different backbone encoders for image data segmentation, namely U-net, U-net++, the Pyramid Attention Network, the Multi-Attention Network, LinkNet, the Feature Pyramid Network, DeepLabV3, and DeepLabV3+. The training process involved training each of these networks for 1000 epochs using the PyTorch and PyTorch Lightning libraries. For instance segmentation, the watershed algorithm and three-class image semantic segmentation were used. We also used StarDist, a deep learning-based tool for object detection with star-convex shapes. The optimal combination for semantic segmentation was the U-net++ architecture with a ResNeSt-269 background with a data set intersection over a union score of 0.8902. For the cell characteristics examined (area, circularity, solidity, perimeter, radius, and shape index), the difference in mean value using different chronic lymphocytic leukaemia cell segmentation approaches appeared to be statistically significant (Mann-Whitney U test, P < .0001). We found that overall, the algorithms demonstrate equal agreement with ground truth, but with the comparison, it can be seen that the different approaches prefer different morphological features of the cells. Consequently, choosing the most suitable method for instance-based cell segmentation depends on the particular application, namely, the specific cellular traits being investigated.

Modelos de redes neurais artificiais para apoiar a gestão de resíduos urbanos: um recurso tecnológico que impulsiona a concretização dos Objetivos de Desenvolvimento Sustentável

ABSTRACT Waste management is crucial to achieving the Sustainable Development Goals (SDGs) established by the United Nations. However, traditional on-site waste characterization techniques require specialized professionals, who are exposed to biological, chemical, and physical risks. In this sense, the use of artificial neural networks (ANN) in models for characterizing municipal solid waste has been discussed, especially after the advent of the COVID-19 pandemic. Predictions made by ANN can be carried out with little or no handling of waste, making the process faster, cleaner, and safer. However, ANN models rely on datasets often provided by third parties, so they require diligent monitoring to ensure that an updated dataset is available at the appropriate regularity. This study presented two standard ANN models that were not available due to a lack of up-to-date datasets and demonstrated that dataset interchangeability may be critical for the long-term use of ANN developed to achieve SDG. Furthermore, interchangeability led to the formulation of a hypothesis about the relevance of the variable associated with basic sanitation in the greater assertiveness of one of the models during the pandemic period, resulting in the identification of abnormal patterns relating to the disposal of textiles and sanitary papers in the years 2020 and 2021. Additionally, this study can be the starting point for the development of more sophisticated interchangeable models developed with alternative datasets, meticulously chosen to reduce the effective error of the desired predictions by reducing the amplitude of the intersection set formed by different models.

A Fuzzy Logic Approach for Best Crop Selection

The agricultural sector faces challenges in selecting suitable crop due to complexity and uncertainty of environmental factors. By using fuzzy technique, we enhance the accuracy in crop selection compare to traditional statistical method. Statistical approaches often rely on precise numerical threshold while, fuzzy logic excels in capturing the vagueness associated with linguistic terms by standard intersection. Considering all parameters like temperature, soil compatibility, water required, production cost and profit enabling farmers to make context aware approach for crop selection. First calculating the gradation of variables and taking minimum intersection of all fuzzy sets from all parameters and then preferring the crop with maximum index. Finite accuracy got by fuzzy logic can be trusted more than statistical method.

FunPredCATH: An ensemble method for predicting protein function using CATH

MotivationThe growth of unannotated proteins in UniProt increases at a very high rate every year due to more efficient sequencing methods. However, the experimental annotation of proteins is a lengthy and expensive process. Using computational techniques to narrow the search can speed up the process by providing highly specific Gene Ontology (GO) terms. MethodologyWe propose an ensemble approach that combines three generic base predictors that predict Gene Ontology (BP, CC and MF) terms from sequences across different species. We train our models on UniProtGOA annotation data and use the CATH domain resources to identify the protein families. We then calculate a score based on the prevalence of individual GO terms in the functional families that is then used as an indicator of confidence when assigning the GO term to an uncharacterised protein. MethodsIn the ensemble, we use a statistics-based method that scores the occurrence of GO terms in a CATH FunFam against a background set of proteins annotated by the same GO term. We also developed a set-based method that uses Set Intersection and Set Union to score the occurrence of GO terms within the same CATH FunFam. Finally, we also use FunFams-Plus, a predictor method developed by the Orengo Group at UCL to predict GO terms for uncharacterised proteins in the CAFA3 challenge. EvaluationWe evaluated the methods against the CAFA3 benchmark and DomFun. We used the Precision, Recall and Fmax metrics and the benchmark datasets that are used in CAFA3 to evaluate our models and compare them to the CAFA3 results. Our results show that FunPredCATH compares well with top CAFA methods in the different ontologies and benchmarks. ContributionsFunPredCATH compares well with other prediction methods on CAFA3, and the ensemble approach outperforms the base methods. We show that non-IEA models obtain higher Fmax scores than the IEA counterparts, while the models including IEA annotations have higher coverage at the expense of a lower Fmax score.

A Practical Multiparty Private Set Intersection Protocol Based on Bloom Filters for Unbalanced Scenarios

Multiparty Private Set Intersection (MPSI) is dedicated to finding the intersection of datasets of multiple participants without disclosing any other information. Although many MPSI protocols have been presented, there are still some important practical scenarios that require in-depth consideration such as an unbalanced scenario, where the server’s dataset is much larger than the clients’ datasets, and in cases where the number of participants is large. This paper proposes a practical MPSI protocol for unbalanced scenarios. The protocol uses the Bloom filter, an efficient data structure, and the ElGamal encryption algorithm to reduce the computation of clients and the server; adopts randomization technology to solve the encryption problem of the 0s in the Bloom filter; and introduces the idea of the Shamir threshold secret-sharing scheme to adapt to multiple environments. A formal security proof and three detailed experiments are given. The results of the experiments showed that the new protocol is very suitable for unbalanced scenarios with a large number of participants, and it has a significant improvement in efficiency compared with the typical related protocol (TIFS 2022).

A novel quantum private set intersection scheme with a semi-honest third party

A novel quantum private set intersection scheme with a semi-honest third party

An in-reachability based classification of invariant synchrony patterns in weighted coupled cell networks

This paper presents an in-reachability based classification of invariant synchrony patterns in coupled cell networks (CCNs). These patterns are encoded through partitions on the set of cells, whose subsets of synchronised cells are called colours. We study the influence of the structure of the network in the qualitative behaviour of invariant synchrony sets, in particular, with respect to the different types of (cumulative) in-neighbourhoods and the in-reachability sets. This motivates the proposed approach to classify the partitions into the categories of strong, rooted and weak, according to how their colours are related with respect to the connectivity structure of the network. Furthermore, we show how this classification system acts under the partition join () operation, which gives us the synchrony pattern that corresponds to the intersection of synchrony sets.