Experimental study of laminated glass window responses under impulsive and blast loading

Laminated glass panes are widely adopted as blast-resistant glass windows to mitigate the hazard from ejecting fractured glass fragments. The response of laminated glass windows under blast loads is often predicted by equivalent static analysis or simplified equivalent single degree of freedom (SDOF) analysis. The equivalent SDOF and equivalent static analyses are also respectively adopted in UFC and ASTM design guide for glass window designs. Owing to the inherent problems, the SDOF analysis can only predict the global responses of glass windows and the predictions are not necessarily always satisfactory. Therefore the accuracy and applicability of the SDOF analysis is sometimes questioned. Often numerical simulations and/or experimental tests have to be carried out for reliable predictions of laminated glass window responses to blast loads. In this study, experimental tests on laminated glass windows subjected to impact and blast loads were carried out to evaluate the accuracy of available analyses and design methods. Pendulum impact tests were conducted first on laminated panes of various thicknesses. Full-scale field blast tests were performed on laminated glass windows of dimension 1.5 m × 1.2 m. Glass pane deflections were monitored by mechanical linear voltage displacement transducer (LVDT) and high-speed cameras. The responses of the tested windows are compared with the estimations of SDOF models and design standards in this paper. Available blast testing data by other researchers are also included together with the current testing data to evaluate the accuracy of the SDOF and equivalent static analyses defined in the design guides. The adequacy of these simplified approaches in predicting laminated glass window responses to blast loads is discussed

Efficient Semiparametric Marginal Estimation for Longitudinal/Clustered Data

We consider marginal generalized semiparametric partially linear models for clustered data. Lin and Carroll (2001a) derived the semiparametric efficinet score funtion for this problem in the mulitvariate Gaussian case, but they were unable to contruct a semiparametric efficient estimator that actually achieved the semiparametric information bound. We propose such an estimator here and generalize the work to marginal generalized partially liner models. Asymptotic relative efficincies of the estimation or throughout are investigated. The finite sample performance of these estimators is evaluated through simulations and illustrated using a longtiudinal CD4 count data set. Both theoretical and numerical results indicate that properly taking into account the within-subject correlation among the responses can substantially improve efficiency

Asymmetric double-winged multi-view clustering network for exploring Diverse and Consistent Information

In unsupervised scenarios, deep contrastive multi-view clustering (DCMVC) is becoming a hot research spot, which aims to mine the potential relationships between different views. Most existing DCMVC algorithms focus on exploring the consistency information for the deep semantic features, while ignoring the diverse information on shallow features. To fill this gap, we propose a novel multi-view clustering network termed CodingNet to explore the diverse and consistent information simultaneously in this paper. Specifically, instead of utilizing the conventional auto-encoder, we design an asymmetric structure network to extract shallow and deep features separately. Then, by aligning the similarity matrix on the shallow feature to the zero matrix, we ensure the diversity for the shallow features, thus offering a better description of multi-view data. Moreover, we propose a dual contrastive mechanism that maintains consistency for deep features at both view-feature and pseudo-label levels. Our framework's efficacy is validated through extensive experiments on six widely used benchmark datasets, outperforming most state-of-the-art multi-view clustering algorithms

Equivalent Kernels of Smoothing Splines in Nonparametric Regression for Clustered/Longitudinal Data

We compare spline and kernel methods for clustered/longitudinal data. For independent data, it is well known that kernel methods and spline methods are essentially asymptotically equivalent (Silverman, 1984). However, the recent work of Welsh, et al. (2002) shows that the same is not true for clustered/longitudinal data. First, conventional kernel methods fail to account for the within- cluster correlation, while spline methods are able to account for this correlation. Second, kernel methods and spline methods were found to have different local behavior, with conventional kernels being local and splines being non-local. To resolve these differences, we show that a smoothing spline estimator is asymptotically equivalent to a recently proposed seemingly unrelated kernel estimator of Wang (2003) for any working covariance matrix. To gain insight into this asymptotic equivalence, we show that both the seemingly unrelated kernel estimator and the smoothing spline estimator using any working covariance matrix can be obtained iteratively by applying conventional kernel or spline smoothing to pseudo-observations. This result allows us to study the asymptotic properties of the smoothing spline estimator by deriving its asymptotic bias and variance. We show that smoothing splines are asymptotically consistent for an arbitrary working covariance and have the smallest variance when assuming the true covariance. We further show that both the seemingly unrelated kernel estimator and the smoothing spline estimator are nonlocal (unless working independence is assumed) but have asymptotically negligible bias. Their finite sample performance is compared through simulations. Our results justify the use of efficient, non-local estimators such as smoothing splines for clustered/longitudinal data

A DenseNet-based method for decoding auditory spatial attention with EEG

Auditory spatial attention detection (ASAD) aims to decode the attended spatial location with EEG in a multiple-speaker setting. ASAD methods are inspired by the brain lateralization of cortical neural responses during the processing of auditory spatial attention, and show promising performance for the task of auditory attention decoding (AAD) with neural recordings. In the previous ASAD methods, the spatial distribution of EEG electrodes is not fully exploited, which may limit the performance of these methods. In the present work, by transforming the original EEG channels into a two-dimensional (2D) spatial topological map, the EEG data is transformed into a three-dimensional (3D) arrangement containing spatial-temporal information. And then a 3D deep convolutional neural network (DenseNet-3D) is used to extract temporal and spatial features of the neural representation for the attended locations. The results show that the proposed method achieves higher decoding accuracy than the state-of-the-art (SOTA) method (94.4% compared to XANet's 90.6%) with 1-second decision window for the widely used KULeuven (KUL) dataset, and the code to implement our work is available on Github: https://github.com/xuxiran/ASAD_DenseNe

The ethnic difference of the cultural background of education for national minorities in China

The complexity of the cultural background of education for national minorities is an important formative factor of the complexity of education for national minorities. Only disclose the complexity of the cultural background of education for national minorities could fundamentally know and understand the essence and characteristic of education for national minorities. The study of a cultural background of education for national minorities is an important subject of research of education for national minorities. This paper compare between the cultural background of education for national minorities and the cultural background of education for national majorities. Put forward the ethnic difference\u27s concept of the cultural background of education for national minorities. Describe the ethnic difference\u27s contents and forms in distribution and types and grade and multi-media of education for national minorities. Explain objective basis of complexity of the cultural background of education for national minorities. Make complete theory of the cultural background of education for national minorities in china

Evaluation of viable dynamic treatment regimes in a sequentially randomized trial of advanced prostate cancer

We present new statistical analyses of data arising from a clinical trial designed to compare two-stage dynamic treatment regimes (DTRs) for advanced prostate cancer. The trial protocol mandated that patients be initially randomized among four chemotherapies, and that those who responded poorly be re-randomized to one of the remaining candidate therapies. The primary aim was to compare the DTRs' overall success rates, with success defined by the occurrence of successful responses in each of two consecutive courses of the patient's therapy. Of the 150 study participants, 47 did not complete their therapy as per the algorithm. However, 35 of them did so for reasons that precluded further chemotherapy, that is, toxicity and/or progressive disease. Consequently, rather than comparing the overall success rates of the DTRs in the unrealistic event that these patients had remained on their assigned chemotherapies, we conducted an analysis that compared viable switch rules defined by the per-protocol rules but with the additional provision that patients who developed toxicity or progressive disease switch to a non-prespecified therapeutic or palliative strategy. This modification involved consideration of bivariate per-course outcomes encoding both efficacy and toxicity.We used numerical scores elicited from the trial's principal investigator to quantify the clinical desirability of each bivariate per-course outcome, and defined one endpoint as their average over all courses of treatment. Two other simpler sets of scores as well as log survival time were also used as endpoints. Estimation of each DTR-specific mean score was conducted using inverse probability weighted methods that assumed that missingness in the 12 remaining dropouts was informative but explainable in that it only depended on past recorded data.We conducted additional worst-and best-case analyses to evaluate sensitivity of our findings to extreme departures from the explainable dropout assumption.Fil: Wang, Lu. University of Michigan; Estados UnidosFil: Rotnitzky, Andrea Gloria. Universidad Torcuato Di Tella. Departamento de Economía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lin, Xihong. Harvard University; Estados UnidosFil: Millikan, Randall. University of Texas; Estados UnidosFil: Thall, Peter. University of Texas; Estados Unido

Semantic reconstruction of continuous language from MEG signals

Decoding language from neural signals holds considerable theoretical and practical importance. Previous research has indicated the feasibility of decoding text or speech from invasive neural signals. However, when using non-invasive neural signals, significant challenges are encountered due to their low quality. In this study, we proposed a data-driven approach for decoding semantic of language from Magnetoencephalography (MEG) signals recorded while subjects were listening to continuous speech. First, a multi-subject decoding model was trained using contrastive learning to reconstruct continuous word embeddings from MEG data. Subsequently, a beam search algorithm was adopted to generate text sequences based on the reconstructed word embeddings. Given a candidate sentence in the beam, a language model was used to predict the subsequent words. The word embeddings of the subsequent words were correlated with the reconstructed word embedding. These correlations were then used as a measure of the probability for the next word. The results showed that the proposed continuous word embedding model can effectively leverage both subject-specific and subject-shared information. Additionally, the decoded text exhibited significant similarity to the target text, with an average BERTScore of 0.816, a score comparable to that in the previous fMRI study