Biomimetic piezoelectric mobile acoustic sensor is fabricated for exceptional accuracy of AI-based biometric authentication.

Summary Inferences about unobserved random variables, such as future observations, effects and latent are of interest. In this paper, to make probability statements variables without assuming priors on fixed parameters, we propose the use confidence distribution for parameters. We focus their interval estimators related statements. random‐effect models, intervals can be formed either (yet‐to‐be‐realised) or realised values effects. The consistency these two cases requires different...

Abstract We study a Bayesian analysis of the proportional hazards model with time‐varying coefficients. consider two priors for coefficients – one based on B‐spline basis functions and other Gamma processes we use beta process prior baseline hazard functions. show that provide optimal posterior convergence rates (up to term) Bayes factor is consistent testing assumption when are used an alternative hypothesis. In addition, adaptive considered theoretical investigation, in which smoothness...

This paper defines fair principal component analysis (PCA) as minimizing the maximum mean discrepancy (MMD) between dimensionality-reduced conditional distributions of different protected classes. The incorporation MMD naturally leads to an exact and tractable mathematical formulation fairness with good statistical properties. We formulate problem PCA subject constraints a non-convex optimization over Stiefel manifold solve it using Riemannian Exact Penalty Method Smoothing (REPMS)....

Summary We study properties of the maximum h‐likelihood estimators for random effects in clustered data. To define optimality predictions, several foundational concepts statistics such as likelihood, unbiasedness, consistency, confidence distribution and Cramer–Rao lower bound are extended. Exact probability statements about interval can be made asymptotically without a prior assumption. Using binary‐matched pair example, we illustrated that use recover information, leading to boon on...

Gene–environment interaction (GxE) is emphasized as one potential source of missing genetic variation on disease traits, and the ultimate goal GxE research prediction individual risk prevention complex diseases. However, there are various challenges in statistical analysis GxE. In this paper, we focus three methodological challenges: (i) high dimensions genes; (ii) hierarchical structure between effects their corresponding main effects; (iii) correlation among subjects from family-based...

In this study, a survival analysis of the time to death caused by coronavirus disease 2019 is presented. The dataset from East Asian region with focus on data Philippines revealed that hazard was associated symptoms and background variables patients. Machine learning algorithms, i.e., dimensionality reduction boosting, were used along conventional Cox regression. algorithms solved diverging problem observed when using traditional regression improved performance maximizing concordance index...

Active learning is a machine paradigm that aims to improve the performance of model by strategically selecting and querying unlabeled data. One effective selection strategy base it on model's predictive uncertainty, which can be interpreted as measure how informative sample is. The sample's distance decision boundary natural but often intractable compute, especially for complex boundaries formed in multiclass classification tasks. To address this issue, paper proposes {\it least disagree...

ABSTRACT An accelerated failure time (AFT) model assumes a log‐linear relationship between times and set of covariates. In contrast to other popular survival models that work on hazard functions, the effects covariates are directly times, interpretation which is intuitive. The semiparametric AFT does not specify error distribution sufficiently flexible robust depart from distributional assumption. Owing its desirable features, this class has been considered promising alternative Cox in...

We suggested a lightweight AUTOSAR software platform consisting of an OS and development tool. This is new model the automotive sector. tool composed editing, project management, System Generator by extending OSEK OIL 2.5 syntax structure. extended OSEK/VDX adding system protection functions, real-time scheduling timer processing functions additional object classes attributes. well suited for developing time-critical application can be utilized controlling parts modules.

Over the decades, testing for equivalence of hazard functions has received a wide attention in survival analysis. In this paper, we proposed Bayesian test to address problem, Most all, is methodologically flexible so that procedure determining weights not required when proportional assumption violated. comparison with popularly exploited methods, shown be more powerful and robust differences functions, spite presence crossing functions. Extensive applications simulation real data were...

In this paper, we study Bayesian asymptotic properties of the proportional hazards model where link function is modeled by generalized additive model. As standard is, a useful tool in finding nonlinearity covariate effects to survival times. We develop data-dependent sieve prior for and use bootstrap baseline hazard function. prove that posterior contraction rate minimax optimal up logn term when carefully selected. By analyzing simulated as well real data, verify our theoretical results...

Recent studies have raised concerns regarding racial and gender disparity in facial attribute classification performance. As these attributes are directly indirectly correlated with the sensitive a complex manner, simple disparate treatment is ineffective reducing performance disparity. This paper focuses on achieving counterfactual fairness for classification. Each labeled input image used to generate two synthetic replicas: one under factual assumptions about counterfactual. The proposed...

When the data is imbalanced, often observed in real-world, important minor class instances that are conducive to accurately predicting decision boundary less likely be queried active learning for classification task. Therefore, mitigating effect of imbalance necessary achieving better generalization. For alleviation this problem, paper considers an algorithm referred as blending minority preferential (BMP). The BMP systematically adapts blend conventional query strategies with proposed...

Recently, HTTP Adaptive Streaming is a de facto video delivery mechanism which adapts quality to user-specific circumstance for better QoE. In this paper, we apply statistical method bandwidth estimation procedures stabilize adaptation in highly dynamic networks, e.g., WiFi. Simulation results reveal that our proposal can achieve smaller variation of and less switch events by smoothing estimated while keeping high utilization wireless capacity, i.e, the same average bit rate as other schemes.

빅 데이터의 출현은 여러가지 과학적 난제에 대답 할 수 있는 기회를 제공하지만 흥미로운 도전을 또한 제공한다. 이러한 빅데이터의 주요 특징으로 "고차원"과 "대용량"을 들 수가 있다. 본 논문은 두 가지 특징에 동반되는 다음과 같은 도전문제에 대한 개요를 제시한다 : (1) 고차원 자료에서의 소음 축적과 위 상관 관계; (ii) 대용량 자료분석을 위한 계산 확장성. 논문에서는 재난예측, 디지털 인문학과 세이버메트릭스 등 다양한 분야에서 응용사례를 The advent of big data brings the opportunity to answer many open scientic questions but also presents some interesting challenges. Main features contemporary datasets are high dimensionality and massive sample size. In this paper, we give an...