Data-driven Test Research Articles

Prospect of Precision Cosmology and Testing General Relativity using Binary Black Holes- Galaxies Cross-correlation

Abstract Modified theories of gravity predict deviations from General Relativity (GR) in the propagation of gravitational waves (GW) across cosmological distances. A key prediction is that the GW luminosity distance will vary with redshift, differing from the electromagnetic (EM) luminosity distance due to varying effective Planck mass. We introduce a model-independent, data-driven approach to explore these deviations using multi-messenger observations of dark standard sirens (Binary Black Holes, BBH). By combining GW luminosity distance measurements from dark sirens with Baryon Acoustic Oscillation (BAO) measurements, BBH redshifts inferred from cross-correlation with spectroscopic or photometric galaxy surveys, and sound horizon measurements from the Cosmic Microwave Background (CMB), we can make a data-driven test of GR (jointly with the Hubble constant) as a function of redshift. Using the multi-messenger technique with the spectroscopic DESI galaxy survey, we achieve precise measurements of deviations in the effective Planck mass variation with redshift. For the Cosmic Explorer and Einstein Telescope (CEET), the best precision is approximately 3.6%, and for LIGO-Virgo-KAGRA (LVK), it is 7.4% at a redshift of $\rm {z = 0.425}$. Additionally, we can measure the Hubble constant with a precision of about 1.1% from CEET and 7% from LVK over five years of observation with a 75% duty cycle. We also explore the potential of cross-correlation with photometric galaxy surveys from the Rubin Observatory, extending measurements up to a redshift of $\rm {z \sim 2.5}$. This approach can reveal potential deviations from models affecting GW propagation using numerous dark standard sirens in synergy with DESI and the Rubin Observatory.

The role of social-evaluative threat for cortisol profiles in response to psychosocial stress: A person-centered approach.

Heterogeneity in individuals' physiological stress responses is central to theories linking stress with vulnerability to disease. Although multiple cortisol profiles have been reported in response to acute psychological stress, most prior work focuses on a single, average pattern and relative deviations from it, such as greater or lesser response peaks or reactivity. The present aims were to identify cortisol stress response trajectory classes using a data-driven approach and test whether social-evaluative threat (SET), a reliable elicitor of cortisol, predicted a greater likelihood of membership in the more reactive profiles. Data were pooled from 13 acute laboratory stressor studies from two geographically distinct U.S. university communities. Participants included 1,258 adults ranging from 18 to 52 years (Mage = 20.5; 62% women; 38% men) with diverse racial/ethnic identities and socioeconomic statuses. Studies included a version of the Trier Social Stress Test and at least three salivary cortisol assessments. SET was tested in three ways: study conditions with evaluators present, perceptions of evaluation, and ratings of shame-related emotions. Latent group-based trajectory modeling was applied to identify cortisol response patterns that best fit the data. Results revealed five unique cortisol response profiles. Consistent with hypotheses, SET conditions, greater perceived evaluation, and greater shame-related emotions predicted membership in the most reactive response trajectories. The findings highlight the high degree of heterogeneity that characterizes cortisol stress response profiles, which has important implications for theories of stress and health and methodological approaches in future research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A data-driven test approach to identify COVID-19 surge phases: an alert-warning tool

The effective monitoring of the pandemic emergency and, specifically, the early detection of surge phases are crucial to define proper health policies. We propose a statistical testing approach to identify the acceleration in contagion growth that potentially marks the start of new waves, based on the study of the reproduction rate dynamics. The proposed method can be considered as a supplementary early warning system that can assist policymakers in the attempt to anticipate and tailor countermeasures. It can also be used as an ex-post tool to date-stamp surge phases and evaluate the impact of the implemented strategies on their timing. The effectiveness of our approach is exemplified on ten countries' contagion data, reaching robust and insightful results in assessing the timing and severity of COVID-19 surge phases.

A data-driven UUV decision-making test strategy design method

Based on the analysis of UUV control architecture and workflow, a data-driven test strategy design method was proposed to realize comprehensive online inspection of UUV, aiming at the requirements of full-process and full-function commissioning, system-level fault testing, single-step action of the actuator, software boundary testing, emergency control and fault-tolerant function verification of a certain UUV. At the same time, three types of typical faults of software, sensors and actuators are identified, and pseudo-random fault codes are injected into UUV control flow or information flow to realize the test verification of action continuity, function matching and control effectiveness under UUV operating conditions. The engineering application proves that the strategy has clear logic and comprehensive functional coverage, laying the foundation for improving UUV reliability and sailing safety.

A data-driven persistence test for robust (probabilistic) quality control of measured environmental time series: constant value episodes

Abstract. Robust quality control is a prerequisite and an essential component in any data application. That is especially important for time series of environmental observations such as air quality due to their dynamic and irreversible nature. One of the common issues in these data is constant value episodes (CVEs), where a set of consecutive data values remains constant over a given period. Although CVEs are often considered to be an indicator of sensor failure or other measurement errors and are removed during quality control procedures, there are situations when CVEs reflect natural environmental phenomena, and they should not be removed from the data or analysis. Assessing whether the CVEs are erroneous data or valid observations is a challenge. As there are no formal procedures established for this, their classification is based on subjective judgment and is therefore uncertain and irreproducible. This paper presents a novel test procedure, i.e., constant value test, to estimate the probability of CVEs being valid data. The theoretical foundation of this test is based on statistical characteristics and probability theory and takes into account the numerical precision of the data values. The test is a data-driven (parametric) approach, which makes it usable for time series analysis in different environmental research domains, as long as serial dependency is given and the data distribution is not too different from Gaussian. The robustness of the test was demonstrated with sensitivity studies using synthetic data with different distributions. Example applications to measured air temperature and ozone mixing ratio data confirm the versatility of the test.

Short: A data-driven respirator fit test model via human speech signal

Short: A data-driven respirator fit test model via human speech signal

Modeling and operation optimization of hydrogen-based integrated energy system with refined power-to-gas and carbon-capture-storage technologies under carbon trading

As a promising solution to realize the energetic complementarity and energy cascade utilization, integrated energy system with a large-scale integration of renewable energy garners considerable attention recently. However, the renewable energy curtailment and uncertainty become two prominent challenges. To the end, we establish a hydrogen-based integrated energy system (HIES) operation framework comprising a refined power to gas (P2G) and carbon capture system (CCS) under carbon trading. Subsequently, a Wasserstein metric based distributional robust optimization method (WDRO) is developed to address wind power uncertainty. Finally, numerical analysis is performed. Simulations results show that: (1) the proposed approach can realize a low-carbon electric energy supply and consumption mode and satisfies the hydrogen demand through internal equipment of the system; (2) compared with the benchmark, the proposed model reveals outstanding economic, environmental and energy performance by obtaining 31.81% decline in operation cost, a 20.19% mitigation in CO2 emission, 7.89% reduction of wind power curtailment and a higher energy efficiency; (3) the validation of WDRO model is demonstrated by the data-driven and robustness test. Moreover, the model exhibits advantages due to its trade-off between conservativeness and computational complexity. The optimization results provide a good reference for the HIES investment strategy and government carbon pricing decision.

MultiMiAT: an optimal microbiome-based association test for multicategory phenotypes.

Microbes can affect the metabolism and immunity of human body incessantly, and the dysbiosis of human microbiome drives not only the occurrence but also the progression of disease (i.e. multiple statuses of disease). Recently, microbiome-based association tests have been widely developed to detect the association between the microbiome and host phenotype. However, the existing methods have not achieved satisfactory performance in testing the association between the microbiome and ordinal/nominal multicategory phenotypes (e.g. disease severity and tumor subtype). In this paper, we propose an optimal microbiome-based association test for multicategory phenotypes, namely, multiMiAT. Specifically, under the multinomial logit model framework, we first introduce a microbiome regression-based kernel association test for multicategory phenotypes (multiMiRKAT). As a data-driven optimal test, multiMiAT then integrates multiMiRKAT, score test and MiRKAT-MC to maintain excellent performance in diverse association patterns. Massive simulation experiments prove the success of our method. Furthermore, multiMiAT is also applied to real microbiome data experiments to detect the association between the gut microbiome and clinical statuses of colorectal cancer as well as for diverse statuses of Clostridium difficile infections.

Inferring a Cognitive Architecture from Multitask Neuroimaging Data: A Data-Driven Test of the Common Model of Cognition Using Granger Causality.

Cognitive architectures (i.e., theorized blueprints on the structure of the mind) can be used to make predictions about the effect of multiregion brain activity on the systems level. Recent work has connected one high-level cognitive architecture, known as the "Common Model of Cognition," to task-based functional MRI data with great success. That approach, however, was limited in that it was intrinsically top-down, and could thus only be compared with alternate architectures that the experimenter could contrive. In this paper, we propose a bottom-up method to infer a cognitive architecture directly from brain imaging data itself, overcoming this limitation. Specifically, Granger causality modeling was applied to the same task-based fMRI data to infer a network of causal connections between brain regions based on their functional connectivity. The resulting network shares many connections with those proposed by the Common Model of Cognition but also suggests important additions likely related to the role of episodic memory. This combined top-down and bottom-up modeling approach can be used to help formalize the computational instantiation of cognitive architectures and further refine a comprehensive theory of cognition.

Cheating on Testing: Cramming on Test Preparation

Professor Glenn Fulcher’s research has influenced language testing and assessment in two ways: in proposing a new understanding of data-driven speaking test design, and in drawing attention to the core identity of language testers. The former has had a powerful impact on practical speaking testing and assessment. The latter has asked a very essential question of what language testers do, exploring philosophical, historical, and social ideas from a pragmatic view of language testing and assessment. In particular, Fulcher (2015)’s chapters on meritocracy and values in language tests explain why we have been using tests throughout history to discern vital roles of language tests and yet why people attempt to cheat on them, in relation to ‘three Ms’ ideas including meritocracy, material success, and motivation. Within this view, this paper aims to review TOEFL cramming practices on test preparation, using Fulcher (2015)’s notion of cheating. Korean test takers study TOEFL cramming skills at test preparation companies to acquire admission to schools, called Hagwons in Korean. However, there is little research regarding cramming practices in terms of cheating. Collected data from my doctoral study (Kim, 2016) were reviewed. It was found that cramming skills as a form of cheating were explicitly taught in class, including use of actual test questions, memorisation of sample answers, and the use of a testing environment. The study revealed reasons to lead the cramming skills: (1) practical benefits of test scores, (2) speaking and writing sections and (3) investments of test preparation. The implications of the study are to raise awareness of the importance of links between cheating and cramming in the field of language testing and teaching.

Data-Driven Optimal Test Selection Design for Fault Detection and Isolation Based on CCVKL Method and PSO

Accurate fault detection and isolation (FDI) relies on the information collection. This can be done by the optimal test selection which can also reduce the life cost of engineering systems. In recent years, some researchers have made lots of achievement on solving the test selection design (TSD) problem. However, few of them concerned a method to deal with the ambiguity problem caused by the multiple fault modes. In this article, a data-driven-based method for test selection is proposed to build an accurate TSD model. Then, we propose a copula function on cross validation-based Kullback–Leibler divergence (CCVKL) method to construct an accurate constraint model. An improved discrete binary particle swarm optimization (IBPSO) algorithm is used to obtain the optimal test design solution. The proposed method also in comparison to three other existing methods are performed in an electrical circuit.

Data-driven test strategy for COVID-19 using machine learning: A study in Lahore, Pakistan

AimsWe aimed at giving a preliminary analysis of the weakness of a current test strategy, and proposing a data-driven strategy that was self-adaptive to the dynamic change of pandemic. The effect of driven-data selection over time and space was also within the deep concern. MethodsA mathematical definition of the test strategy were given. With the real COVID-19 test data from March to July collected in Lahore, a significance analysis of the possible features was conducted. A machine learning method based on logistic regression and priority ranking were proposed for the data-driven test strategy. With performance assessed by the area under the receiver operating characteristic curve (AUC), time series analysis and spatial cross-test were conducted. ResultsThe transition of risk factors accounted for the failure of the current test strategy. The proposed data-driven strategy could enhance the positive detection rate from 2.54% to 28.18%, and the recall rate from 8.05% to 89.35% under strictly limited test capacity. Much more optimal utilization of test resources could be realized where 89.35% of total positive cases could be detected with merely 48.17% of the original test amount. The strategy showed self-adaptability with the development of pandemic, while the strategy driven by local data was proved to be optimal. ConclusionsWe recommended a generalization of such a data-driven test strategy for a better response to the global developing pandemic. Besides, the construction of the COVID-19 data system should be more refined on space for local applications.

Implementing cognitive technologies in an assembly line based on two case studies

Due to mass customization, the product variances have considerably increased. Hence, achieving high quality, high variety, and small batch size production can be expensive. A thorough literature review and research findings indicate the new technologies that can be considered for mitigating these disparities. This paper then presents a case study for achieving high quality reliable e-axle bearings through a developed data-driven Bearing Test Rig (BTR). The second case study indicates the feasibility of high variety assembly of products in an e-axle through a cobot that is utilized for bearing assembly, sealing application, and bolting operations. Both depict the human-machine interaction as a core element in future e-axle assembly. The results are evaluated with the help of FMEA analysis, LoA matrix, and a simulation model.

Data-Driven Tests for Controllability

The fundamental lemma due to Willems et al. “A note on persistency of excitation,” Syst. Control Lett., vol. 54, no. 4, pp. 325-329, 2005 plays an important role in system identification and data-driven control. One of the assumptions for the fundamental lemma is that the underlying linear time-invariant system is controllable. In this letter, the fundamental lemma is extended to address system identification for uncontrollable systems. Then, a data-driven algebraic test is derived to check whether the underlying system is controllable or not. An algorithm based on the singular value decomposition of a Hankel matrix constructed from the data is provided to implement the developed test. The algorithm has cubic computational cost. Examples are given to illustrate the theoretical results.

A powerful microbial group association test based on the higher criticism analysis for sparse microbial association signals

BackgroundIn human microbiome studies, it is crucial to evaluate the association between microbial group (e.g., community or clade) composition and a host phenotype of interest. In response, a number of microbial group association tests have been proposed, which account for the unique features of the microbiome data (e.g., high-dimensionality, compositionality, phylogenetic relationship). These tests generally fall in the class of aggregation tests which amplify the overall group association by combining all the underlying microbial association signals, and, therefore, they are powerful when many microbial species are associated with a given host phenotype (i.e., low sparsity). However, in practice, the microbial association signals can be highly sparse, and this is especially the situation where we have a difficulty to discover the microbial group association.MethodsHere, we introduce a powerful microbial group association test for sparse microbial association signals, namely, microbiome higher criticism analysis (MiHC). MiHC is a data-driven omnibus test taken in a search space spanned by tailoring the higher criticism test to incorporate phylogenetic information and/or modulate sparsity levels and including the Simes test for excessively high sparsity levels. Therefore, MiHC robustly adapts to diverse phylogenetic relevance and sparsity levels.ResultsOur simulations show that MiHC maintains a high power at different phylogenetic relevance and sparsity levels with correct type I error controls. We also apply MiHC to four real microbiome datasets to test the association between respiratory tract microbiome and smoking status, the association between the infant’s gut microbiome and delivery mode, the association between the gut microbiome and type 1 diabetes status, and the association between the gut microbiome and human immunodeficiency virus status.ConclusionsIn practice, the true underlying association pattern on the extent of phylogenetic relevance and sparsity is usually unknown. Therefore, MiHC can be a useful analytic tool because of its high adaptivity to diverse phylogenetic relevance and sparsity levels. MiHC can be implemented in the R computing environment using our software package freely available at https://github.com/hk1785/MiHC.

Performance of Two Commercial Dengue NS1 Rapid Tests for the Diagnosis of Adult Patients with Dengue Infection

Objective: to determine the diagnostic performance of two commercially available dengue NS1 antigen rapid detection tests (RDTs); namely the SD BIOLINE Dengue NS1 RDT and the ImmuneMed Dengue NS1 Rapid, for the diagnosis of adult patients with dengue infection. Methods: The study was performed by using archived samples from 237 patients with the laboratory-confirmed dengue infection. Archived, well-characterized samples from an additional 208 febrile individuals from Thailand were used as the control group. Reference testing to confirm the diagnosis in dengue patients included RT-PCR and in-house NS1 ELISA, in-house IgM and IgG capture ELISAs. Results: The sensitivity of the SD BIOLINE Dengue NS1 RDT was 100%, and the specificity was 99% (95%CI 96.6 to 99.7%). False positive was found in 2 samples from patient with scrub typhus. The sensitivity and specificity of the ImmuneMed Dengue NS1 Ag Rapid were 97.4% (95% CI, 95.5 to 99.5%) and 96.6% (95%CI 93.5 to 98.4%) respectively. False positive results were found in 7 patients with murine typhus, scrub typhus, and influenza. Â Conclusion: Both RDTs showed comparable sensitivity and specificity in this study population. Data from this study could be used to facilitate data-driven laboratory test choices for managing patient care during dengue outbreaks.

Deep CM-CNN for Spectrum Sensing in Cognitive Radio

One of the key problems in spectrum sensing is to design the test statistic. Existing methods generally exploit the model-based features as the test statistic, such as energies and eigenvalues. However, these features could not accurately characterize the real environment. Motivated by this, in this paper, we use a deep neural network (DNN) to intelligently explore the data-driven test statistic. Firstly, we introduce a DNN-based detection framework, where a DNN-based likelihood ratio test (DNN-LRT) is derived to guarantee the optimality of the designed test statistic. As a realization of the developed DNN-based framework, we use the sample covariance matrix as the input of a convolutional neural network (CNN), and propose a covariance matrix-aware CNN (CM-CNN)-based spectrum sensing algorithm, which further improves the performance. In addition, we also provide the theoretical analysis of the proposed method. To the best of our knowledge, it’s the first time to analyze the theoretical performance of CNN-based methods. Finally, simulation results demonstrate that the performance of the proposed method is close to that of the optimal detector. Particularly, the proposed method could achieve a detection probability of 96.7% with a false alarm probability of 1.9% at SNR = −18dB, which significantly outperforms the conventional methods.

Comparing the marginal densities of two strictly stationary linear processes

In this paper, we adapt a data-driven smooth test to the comparison of the marginal distributions of two independent, short or long memory, strictly stationary linear sequences. Some illustrations are shown to evaluate the performances of our test.

A Distance-Based Kernel Association Test Based on the Generalized Linear Mixed Model for Correlated Microbiome Studies.

Researchers have increasingly employed family-based or longitudinal study designs to survey the roles of the human microbiota on diverse host traits of interest (e. g., health/disease status, medical intervention, behavioral/environmental factor). Such study designs are useful to properly control for potential confounders or the sensitive changes in microbial composition and host traits. However, downstream data analysis is challenging because the measurements within clusters (e.g., families, subjects including repeated measures) tend to be correlated so that statistical methods based on the independence assumption cannot be used. For the correlated microbiome studies, a distance-based kernel association test based on the linear mixed model, namely, correlated sequence kernel association test (cSKAT), has recently been introduced. cSKAT models the microbial community using an ecological distance (e.g., Jaccard/Bray-Curtis dissimilarity, unique fraction distance), and then tests its association with a host trait. Similar to prior distance-based kernel association tests (e.g., microbiome regression-based kernel association test), the use of ecological distances gives a high power to cSKAT. However, cSKAT is limited to handling Gaussian traits [e.g., body mass index (BMI)] and a single chosen distance measure at a time. The power of cSKAT differs a lot by which distance measure is used. However, choosing an optimal distance measure is challenging because of the unknown nature of the true association. Here, we introduce a distance-based kernel association test based on the generalized linear mixed model (GLMM), namely, GLMM-MiRKAT, to handle diverse types of traits, such as Gaussian (e.g., BMI), Binomial (e.g., disease status, treatment/placebo) or Poisson (e.g., number of tumors/treatments) traits. We further propose a data-driven adaptive test of GLMM-MiRKAT, namely, aGLMM-MiRKAT, so as to avoid the need to choose the optimal distance measure. Our extensive simulations demonstrate that aGLMM-MiRKAT is robustly powerful while correctly controlling type I error rates. We apply aGLMM-MiRKAT to real familial and longitudinal microbiome data, where we discover significant disparity in microbial community composition by BMI status and the frequency of antibiotic use. In summary, aGLMM-MiRKAT is a useful analytical tool with its broad applicability to diverse types of traits, robust power and valid statistical inference.

Primary Sensorimotor Cortex Drives the Common Cortical Network for Gamma Synchronization in Voluntary Hand Movements.

Background: Gamma synchronization (GS) may promote the processing between functionally related cortico-subcortical neural populations. Our aim was to identify the sources of GS and to analyze the direction of information flow in cerebral networks at the beginning of phasic movements, and during medium-strength isometric contraction of the hand.Methods: We measured 64-channel electroencephalography in 11 healthy volunteers (age: 25 ± 8 years; four females); surface electromyography detected the movements of the dominant hand. In Task 1, subjects kept a constant medium-strength contraction of the first dorsal interosseus muscle, and performed a superimposed repetitive voluntary self-paced brisk squeeze of an object. In Task 2, brisk, and in Task 3, constant contractions were performed. Time-frequency analysis of the EEG signal was performed with the multitaper method. GS sources were identified in five frequency bands (30–49, 51–75, 76–99, 101–125, and 126–149 Hz) with beamformer inverse solution dynamic imaging of coherent sources. The direction of information flow was estimated by renormalized partial directed coherence for each frequency band. The data-driven surrogate test, and the time reversal technique were performed to identify significant connections.Results: In all tasks, we depicted the first three common sources for the studied frequency bands that were as follows: contralateral primary sensorimotor cortex (S1M1), dorsolateral prefrontal cortex (dPFC) and supplementary motor cortex (SMA). GS was detected in narrower low- (∼30–60 Hz) and high-frequency bands (>51–60 Hz) in the contralateral thalamus and ipsilateral cerebellum in all three tasks. The contralateral posterior parietal cortex was activated only in Task 1. In every task, S1M1 had efferent information flow to the SMA and the dPFC while dPFC had no detected afferent connections to the network in the gamma range. Cortical-subcortical information flow captured by the GS was dynamically variable in the narrower frequency bands for the studied movements.Conclusion: A distinct cortical network was identified for GS in voluntary hand movement tasks. Our study revealed that S1M1 modulated the activity of interconnected cortical areas through GS, while subcortical structures modulated the motor network dynamically, and specifically for the studied movement program.