Probability Of Discrimination Research Articles

Investigating the effectiveness of forensic genetics and population genetic diversity using a multi-InDel system in Chinese Hezhou and Southern Shaanxi Han populations.

Multiple insertion-deletion (multi-InDel) has greater potential in forensic genetics than InDel, and its efficacy in kinship testing, individual identification, DNA mixture detection and ancestry inference remains to be explored. Consequently, we designed an efficient and robust system consisting of 41 multi-InDels to evaluate its efficacy in forensic applications in Chinese Hezhou Han (HZH) and Southern Shaanxi Han (SNH) populations and explore the genetic relationships between the SNH, HZH, and 26 reference populations. The obtained results showed that 38 out of the 41 multi-InDels had fairly high genetic variations. The the cumulative probability of discrimination and exclusion values of the multi-InDels (except MI38) in HZH and SNH populations both exceeded 1-e-25 and 1-e-6, correspondingly. The genetic compositions of HZH and SNH individuals were similar to that of East Asians and the Naive Bayes model could well distinguish East Asians, Africans and Americans. These results indicated that the multi-InDel systerm can serve as an effective tool to provide important evidence for the development of multi-InDels in forensic practice and better analyse the genetic background of the Han Chinese populations.

Wasserstein Generative Adversarial Networks Based Differential Privacy Metaverse Data Sharing.

Although differential privacy metaverse data sharing can avoid privacy leakage of sensitive data, randomly perturbing local metaverse data will lead to an imbalance between utility and privacy. Therefore, this work proposed models and algorithms of differential privacy metaverse data sharing using Wasserstein generative adversarial networks (WGAN). Firstly, this study constructed the mathematical model of differential privacy metaverse data sharing by introducing appropriate regularization term related to generated data's discriminant probability into WGAN. Secondly, we established basic model and algorithm for differential privacy metaverse data sharing using WGAN based on the constructed mathematical model, and theoretically analyzed basic algorithm. Thirdly, we established federated model and algorithm for differential privacy metaverse data sharing using WGAN by serialized training based on basic model, and theoretically analyzed federated algorithm. Finally, based on utility and privacy metrics, we conducted a comparative analysis for the basic algorithm of differential privacy metaverse data sharing using WGAN, and experimental results validate theoretical results, which show that algorithms of differential privacy metaverse data sharing using WGAN maintaining equilibrium between privacy and utility.

Closing the Gap Between Theory and Practice During Alternating Optimization for GANs.

Synthesizing high-quality and diverse samples is the main goal of generative models. Despite recent great progress in generative adversarial networks (GANs), mode collapse is still an open problem, and mitigating it will benefit the generator to better capture the target data distribution. This article rethinks alternating optimization in GANs, which is a classic approach to training GANs in practice. We find that the theory presented in the original GANs does not accommodate this practical solution. Under the alternating optimization manner, the vanilla loss function provides an inappropriate objective for the generator. This objective forces the generator to produce the output with the highest discriminative probability of the discriminator, which leads to mode collapse in GANs. To address this problem, we introduce a novel loss function for the generator to adapt to the alternating optimization nature. When updating the generator by the proposed loss function, the reverse Kullback-Leibler divergence between the model distribution and the target distribution is theoretically optimized, which encourages the model to learn the target distribution. The results of extensive experiments demonstrate that our approach can consistently boost model performance on various datasets and network structures.

Reconfigurable Intelligent Surface-Assisted Radar Deception Electronic Counter-Countermeasures

A reconfigurable intelligent surface (RIS) is a promising technology for wireless communication and radar detection, owing to its superior ability to realize smart radio environments. Inspired by previous studies on RISs, this study deals with the use of RISs for radar electronic counter-countermeasures (ECCMs) in deception jamming scenarios. At first, a RIS was applied to a monostatic radar, constructing a virtual multi-radar system combined with multi-beam receiving technology. Then, a data-fusion-based deception ECCM method for the proposed virtual multi-radar system was studied to discriminate the active false targets generated by deception jamming. A theoretical analysis of the target discrimination probability was derived. Because the location of RISs is the key to determining the target discrimination ability, the location optimization of the RIS was considered based on the theoretical analysis. Simulation results corroborate the deception ECCM ability of the proposed RIS-assisted virtual multi-radar system, enhancing the survivability of a radar system in a complex electromagnetic environment.

PROBABILITY OF TARGET DISCRIMINATION BY THE CONTRAST-LIMITED THERMAL VISION SYSTEM OF A UAV

Equipping modern unmanned aerial vehicles (UAVs) with thermal imaging cameras expands their potential utilization in various environmental conditions, enabling efficient aerial reconnaissance and execution of combat-related tasks. The primary objec- tives for target discrimination encompass detection, recognition, and identification. However, existing methods and algorithms for determining the probability of distinguishing targets do not offer an efficient and swift means of calculating these probabili- ties based on the target’s distance. This article aims to develop a novel method for calculating the probability of detecting, recognizing, and identifying an object (target) using a thermal imaging surveillance system. The proposed approach involves an improved algorithm that utilizes the Johnson criterion, as per the NATO standard 4347, the Schultz approximation of the threshold contrast for the operator’s perception of the image on the display screen, and incorporates the objective function of probability transfer along with probability transfer functions based on the target’s distance. An example illustrating the calculation of the target discrimination probability is included to provide clarity. With the suggested algorithm, the probability of detecting, recognizing, and identifying the target through the contrast-limited thermal imaging system of the drone can be rapidly calculated.

A Periodic Multiple Phases Modulation Active Deception Jamming for Multistatic Radar System

Aiming at the problem that the amplitudes of false targets (FTs) are correlated among receivers in multistatic radar system (MSRS), a multi-jammer cooperative deception jamming method based on periodic multiple phases modulation (PMPM) is proposed. A FT string with random fluctuations in amplitude can be generated after the signal undergoes PMPM. In the distributed jammer system, each jammer adopts PMPM jamming method with different parameters, and then jointly transmit beamforming to forward jamming signals. Finally, the amplitude of the FTs between receivers is jointly affected by the azimuth angle and the phase modulation coefficient, which destroys the amplitude "coherence" of the FTs between receivers, and eliminates the amplitude symmetry and high-order attenuation characteristics of the FT strings generated by traditional jamming method. We study the amplitude compensation and amplitude correlation performance of the proposed jamming method, derive the rank of the ratio matrix of amplitude correlation factors, and prove that FTs have dispersiveness in the amplitude ratio space. In order to obtain the optimal and stable jamming effect, a genetic algorithm (GA)-based jamming modulation parameter optimization method is designed, which effectively drops the discrimination probability of physical targets (PTs) to 14% in the benchmark scenario. Simulation experiments demonstrate the feasibility and effectiveness of the jamming method.

Effect of Racial Discrimination Activated at Teacher–Student Interaction Context on Academic Self-Efficacy: A Latent Transition Analysis

This study uses latent transitional analysis to examine the longitudinal association between racial discrimination and academic self-efficacy in teacher–student interactions. Two levels of teacher–student interactions are examined: low-risk, in which students perceive no probability of racial discrimination, and high-risk, in which students perceive probability of racial discrimination. Participants were drawn from the Maryland Adolescent Development in Context Study ( N = 574: 202 White and 372 Black, mean age = 13.64 [ SD = .42]). Findings revealed that students perceiving no racial discrimination, regardless of sociodemographic factors, showed consistently strong positive academic self-efficacy as they transitioned from lower to higher grade levels compared with those perceiving racial discrimination. Accounting for racial discrimination, there were no differences in academic self-efficacy beliefs between Black and White students. Students’ perceived racial discrimination in teacher–student interactions impacted negatively on academic self-efficacy.

Hydraulic conductivity estimation and lithological classification of an esker aquifer system using surface electrical resistivity surveys and a neural network

Empirical and theoretical relationships between hydraulic conductivity, lithology, and electrical resistivity provide a basis for the use of electrical resistivity for aquifer characterization in unconsolidated sediments. This study demonstrates a meaningful field-scale correlation between vertically distributed hydraulic conductivity obtained from packer-based borehole hydraulic tests, and electrical resistivity obtained from surface-based geophysical surveys over the Vars-Winchester esker aquifer system, Ontario, Canada. Electrical resistivity alone has order-of-magnitude predictive capacity for hydraulic conductivity, but is insufficient to reliably discriminate between aquifer and aquitard lithology. An alternative methodology is developed that takes advantage of the observed correlation between hydraulic conductivity and elevation, and the separability of lithology in terms of elevation. Electrical resistivity and elevation are combined as predictor variables for hydraulic conductivity using both multiple linear regression and nonlinear neural network regression, and for neural network classification of lithology. Neural network regression results in prediction accuracy for log-transformed hydraulic conductivity of 0.38–0.52 with clear definition of vertical and lateral heterogeneity. Classification accuracy for lithology is 83–84% with high probability of discrimination between the unconsolidated aquifer and aquitard sediments, and lower probability identification of the bedrock surface due to fewer samples at depth and limited penetration depth of the resistivity survey.

Privacy-utility equilibrium data generation based on Wasserstein generative adversarial networks

To solve the contradictory problem of local data sharing and privacy protection, this paper presented the models and algorithms for privacy-utility equilibrium data generation based on Wasserstein generative adversarial networks. First, we introduce the expected estimation error between the discriminant probability of real data and generated data into Wasserstein generative adversarial networks, and we formally construct a basic mathematical model of privacy-utility equilibrium data generation based on computationally indistinguishable. Second, we construct the basic model and the basic algorithm of privacy-utility equilibrium data generation based on Wasserstein generative adversarial networks according to the constructed basic mathematical model, and our theoretical analysis results show that the basic algorithm can achieve the equilibrium between local data sharing and privacy protection. Third, according to the constructed basic model, we construct the federated model and the federated algorithm of privacy-utility equilibrium data generation based on Wasserstein generative adversarial networks using the serialized training method of federated learning, and our theoretical analysis results also show that the federated algorithm can achieve the equilibrium between local data sharing and privacy protection in a distributed environment. Finally, our experimental results show that the proposed algorithms can achieve the equilibrium between local data sharing and privacy protection in this paper. Therefore, the constructed basic mathematical model provides a theoretical basis of achieving the equilibrium between local data sharing and privacy protection. At the same time, the proposed basic model and the federated model of privacy-utility equilibrium data generation provide a concrete method of achieving the equilibrium between local data sharing and privacy protection in centralized and distributed environment respectively.

A Novel Topology Optimization Protocol Based on an Improved Crow Search Algorithm for the Perception Layer of the Internet of Things

In wireless sensor networks, each sensor node has a finite amount of energy to expend. The clustering method is an efficient way to deal with the imbalance in node energy consumption. A topology optimization technique for wireless sensor networks based on the Cauchy variation optimization crow search algorithm (CM-CSA) is suggested to address the issues of rapid energy consumption, short life cycles, and unstable topology in wireless sensor networks. At the same time, a clustering approach for wireless sensor networks based on the enhanced Cauchy mutation crow search algorithm is developed to address the issue of the crow algorithm's sluggish convergence speed and ease of falling into the local optimum. It utilizes the Cauchy mutation to improve the population's variety and prevent settling for the local optimum, as well as to broaden the range of variation and the capacity to carry out global searches. When the leader realizes he is being followed, the discriminative probability is introduced to improve the current person's location update approach. According to the simulation findings, the suggested CM-CSA algorithm decreases the network's average energy consumption by 66.7%, 50%, and 33.3% and enhances its connectivity performance by 52.9%, 37.6%, and 23.5% when compared to the PSO algorithm, AFSA method, and basic CSA algorithm.

Local approximation for perfect discrimination of quantum states

Quantum state discrimination involves identifying a given state out of a set of possible states. When the states are mutually orthogonal, perfect state discrimination is alway possible using a global measurement. In the case of multipartite systems when the parties are constrained to use multiple rounds of local operations and classical communications (LOCC), perfect state discrimination is often impossible even with the use of \emph{asymptotic LOCC}, wherein an error is allowed but must vanish in the limit of an infinite number of rounds. Utilizing our recent results on asymptotic LOCC, we derive a lower bound on the error probability for LOCC discrimination of any given set of mutually orthogonal pure states. We use this lower bound to prove necessary conditions for perfect state discrimination by asymptotic LOCC. We then illustrate by example the power of these necessary conditions in significantly simplifying the determination of whether perfect discrimination of a given set of states can be accomplished arbitrarily closely using LOCC. The latter examples include a proof that perfect discrimination by asymptotic LOCC is impossible for any \emph{minimal} unextendible product basis (UPB), where minimal means that for the given multipartite system, no UPB with a smaller number of states can exist. We also give a simple proof that what has been called \emph{strong nonlocality without entanglement} is considerably stronger than had previously been demonstrated.

Subset Selection Strategies Based on Target Positioning Characteristics for Anti-Jamming Technology

For the discrimination of false targets, the discrimination probability can be improved by increasing the number of radar stations. However, that may result in a serious waste of equipment resources when too many radars are involved. An asymptotic subset selection strategy based on target positioning characteristics is proposed to address the above issues. Several effective strategies are considered to select some transmitters and receivers to form a radar subset, such as the rapid shrinkage method, global shrinkage method, and predetermined size method, which can guarantee the preset discrimination performance of limited equipment resources and reduce the waste of resources. All of the selected stations have good spatial distribution or strong discrimination capacity in multistatic radar system. Compared with the exhaustive search, the proposed subset selection strategy affords a significant reduction in terms of time complexity. The simulation results show that the radar subset can maintain approximate discrimination performance with the original multistatic radar systems. At the same time, the proposed method optimizes the number of radar stations and reduces data processing time and required communication links, thus effectively saving operating costs.

Parallel Combined Spread Spectrum Error Analysis And Succesive Error Correction Method

Traditional spread spectrum techniques provide a strong anti-interference capability but a relatively low transmission rate. This is addressed by the parallel combination spread spectrum technique which provides higher transmission efficiency, security, and spread gain. Nevertheless, the receiver of a parallel combined spread spectrum system requires to distinguish multiple spread spectrum sequences. Therefore, any reception errors are propagated in the receiver hence leading to a larger number of errors. Moreover, spread spectrum sequences with different mode values experience different probability of errors at the receiver. The existing analysis of the error location in the parallel combined spread spectrum is only an estimate after countless experiments. In this paper, we theoretically analyze the probability of correct discrimination of parallel combination spread spectrum with different modulus values. Furthermore, according to the probability analysis of correct discrimination therefore the parallel combination spread spectrum is improved. This significantly reduces the bit error rate of the system and improves the reliability only with a slight degradation of transmission efficiency.

Quantum Communication with Polarization-Encoded Qubits under Majorization Monotone Dynamics

Quantum communication can be realized by transmitting photons that carry quantum information. Due to decoherence, the information encoded in the quantum state of a single photon can be distorted, which leads to communication errors. In particular, we consider the impact of majorization monotone dynamical maps on the efficiency of quantum communication. The mathematical formalism of majorization is revised with its implications for quantum systems. The discrimination probability for two arbitrary orthogonal states is used as a figure of merit to track the quality of quantum communication in the time domain.

Multimodal MRI-Based Radiomic Nomogram for the Early Differentiation of Recurrence and Pseudoprogression of High-Grade Glioma.

Objective To evaluate the diagnostic value of multimodal MRI radiomics based on T2-weighted fluid attenuated inversion recovery imaging (T2WI-FLAIR) combined with T1-weighted contrast enhanced imaging (T1WI-CE) in the early differentiation of high-grade glioma recurrence from pseudoprogression. Methods A total of one hundred eighteen patients with brain gliomas who were diagnosed from March 2014 to April 2020 were retrospectively analyzed. According to the clinical characteristics, the patients were randomly split into a training group (n = 83) and a test group (n = 35) at a 7 : 3 ratio. The region of interest (ROI) was delineated, and 2632 radiomic features were extracted. We used multiple logistic regression to establish a classification model, including the T1 model, T2 model, and T1 + T2 model, to differentiate recurrence from pseudoprogression. The diagnostic efficiency of the model was evaluated by calculating the area under the receiver operating characteristic curve (AUC) and accuracy (ACC) and by analyzing the calibration curve of the nomogram and decision curve. Results There were 75 cases of recurrence and 43 cases of pseudoprogression. The diagnostic efficacies of the multimodal MRI-based radiomic model were relatively high. The AUC values and ACC of the training group were 0.831 and 77.11%, respectively, and the AUC values and ACC of the test group were 0.829 and 88.57%, respectively. The calibration curve of the nomogram showed that the discrimination probability was consistent with the actual occurrence in the training group, and the discrimination probability was roughly the same as the actual occurrence in the test group. In the decision curve analysis, the T1 + T2 model showed greater overall net efficiency. Conclusion The multimodal MRI radiomic model has relatively high efficiency in the early differentiation of recurrence from pseudoprogression, and it could be helpful for clinicians in devising correct treatment plans so that patients can be treated in a timely and accurate manner.

An upper bound on the success probability of minimum-error discrimination

Minimum-error discrimination of the ensemble of pure states {|ϕj〉,wj} with given probabilities {wj} is considered by introducing an ancillary degree of freedom, and therefore relating the success probability ps of minimum-error discrimination to the l1-norm coherence in the ancillary degree of freedom. A new upper bound of the success probability ps is obtained this way.

Prognostic risk score development to predict birth asphyxia using maternal and fetal characteristics in South Gondar zone hospitals, north West Ethiopia

BackgroundBirth asphyxia leads to profound systemic and neurological sequela to decrease blood flow or oxygen to the fetus followed by lethal progressive or irreversible life-long pathologies. In low resource setting countries, birth asphyxia remains a critical condition. This study aimed to develop and validate prognostic risk scores to forecast birth asphyxia using maternal and neonatal characteristics in south Gondar zone hospitals.MethodsProspective cohorts of 404 pregnant women were included in the model in south Gondar Zone Hospitals, Northwest Ethiopia. To recognize potential prognostic determinants for birth asphyxia, multivariable logistic regression was applied. The model discrimination probability was checked using the receiver operating characteristic curve (AUROC) and the model calibration plot was assessed using the ‘givitiR’ R-package. To check the clinical importance of the model, a cost-benefit analysis was done through a decision curve and the model was internally validated using bootstrapping. Lastly, a risk score prediction measurement was established for simple application.ResultsOf 404, 108 (26.73%) (95% CI: 22.6–31.3) newborns were exposed to birth asphyxia during the follow-up time. Premature rupture of membrane, meconium aspiration syndrome, malpresentation, prolonged labor, Preterm, and tight nuchal was the significant prognostic predictors of birth asphyxia. The AUROC curve for birth asphyxia was 88.6% (95% CI: 84.6-92.2%), which indicated that the tool identified the newborns at risk for birth asphyxia very well. The AUROC of the simplified risk score algorithm, was 87.9 (95% CI, 84.0– 91.7%) and the risk score value of 2 was selected as the optimal cut-off value, with a sensitivity of 78.87%, a specificity of 83.26%, a positive predictive value of 63.23%, and a negative predictive value of 91.52%.ConclusionsWe established birth asphyxia prediction tools by applying non-sophisticated maternal and neonatal characteristics for resource scares countries. The driven score has very good discriminative ability and prediction performance. This risk score tool would allow reducing neonatal morbidity and mortality related to birth asphyxia. Consequently, it will improve the overall neonatal health / under-five child health in low-income countries.

Development and validation of a risk prediction model for lost to follow-up among adults on active antiretroviral therapy in Ethiopia: a retrospective follow-up study

BackgroundOver 420,000 people have initiated life-saving antiretroviral therapy (ART) in Ethiopia; however, lost-to-follow-up (LTFU) rates continues to be high. A clinical decision tool is needed to identify patients at higher risk for LTFU to provide individualized risk prediction to intervention. Therefore, this study aimed to develop and validate a statistical risk prediction tool that predicts the probability of LTFU among adult clients on ART.MethodsA retrospective follow-up study was conducted among 432 clients on ART in Gondar Town, northwest, Ethiopia. Prognostic determinates included in the analysis were determined by multivariable logistic regression. The area under the receiver operating characteristic (AUROC) and calibration plot were used to assess the model discriminative ability and predictive accuracy, respectively. Individual risk prediction for LTFU was determined using both regression formula and score chart rule. Youden index value was used to determine the cut-point for risk classification. The clinical utility of the model was evaluated using decision curve analysis (DCA).ResultsThe incidence of LTFU was 11.19 (95% CI 8.95–13.99) per 100-persons years of observation. Potential prognostic determinants for LTFU were rural residence, not using prophylaxis (either cotrimoxazole or Isoniazid or both), patient on appointment spacing model (ASM), poor drug adherence level, normal Body mass index (BMI), and high viral load (viral copies > 1000 copies/ml). The AUROC was 85.9% (95% CI 82.0–89.6) for the prediction model and the risk score was 81.0% (95% CI 76.7–85.3) which was a good discrimination probability. The maximum sensitivity and specificity of the probability of LTFU using the prediction model were 72.07% and 83.49%, respectively. The calibration plot of the model was good (p-value = 0.350). The DCA indicated that the model provides a higher net benefit following patients based on the risk prediction tool.ConclusionThe incidence of LTFU among clients on ART in Gondar town was high (> 3%). The risk prediction model presents an accurate and easily applicable prognostic prediction tool for clients on ART. A prospective follow-up study and external validation of the model is warranted before using the model.

Bound on optimal local discrimination of multipartite quantum states

We consider the unambiguous discrimination of multipartite quantum states and provide an upper bound for the maximum success probability of optimal local discrimination. We also provide a necessary and sufficient condition to realize the upper bound. We further establish a necessary and sufficient condition for this upper bound to be saturated. Finally, we illustrate our results using examples in multidimensional multipartite quantum systems.

A Ship Discrimination Method Based on High-Frequency Electromagnetic Theory

Ship target detection using radar has important applications in the military and civilian fields. As a decoy, the corner reflector (CR) can successfully deceive a radar by its strong radar cross-section (RCS) to protect a ship. In order to discriminate between a CR and ship, this paper proposes a discrimination method based on three-dimensional characteristics. First, we deduce the basic scattering of CR by the high-frequency electromagnetic theory, and propose a CR decomposition which can solve the problem that the Krogager decomposition has terrible errors in clutter. Then, we introduce the definition of the main scattering polarization and give the multi-dimensional characteristic of CR. Subsequently, we analyze the spatial-time characteristic of radar based on the three-dimensional proportional guidance. With the CR mean square error (MSE), a CR discrimination method is proposed based on the time-spatial-polarization (TSP) joint domains. Finally, the proposed method is analyzed and compared using the fully polarimetric data of Feko software, which can achieve 95% discrimination probability and 4.1% false alarm probability.