Almost free of calibration for SSVEP-based brain-computer interfaces.

Objective. Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) is a promising technology that can achieve high information transfer rate (ITR) with supervised algorithms such as ensemble task-related component analysis (eTRCA) and task-discriminant component analysis (TDCA). However, training individual models requires a tedious and time-consuming calibration process, which hinders the real-life use of SSVEP-BCIs. A recent data augmentation method, called source aliasing matrix estimation (SAME), can generate new EEG samples from a few calibration trials. But SAME does not exploit the information across stimuli as well as only reduces the number of calibration trials per command, so it still has some limitations.Approach. This study proposes an extended version of SAME, called multi-stimulus SAME (msSAME), which exploits the similarity of the aliasing matrix across frequencies to enhance the performance of SSVEP-BCI with insufficient calibration trials. We also propose a semi-supervised approach based on msSAME that can further reduce the number of SSVEP frequencies needed for calibration. We evaluate our method on two public datasets, Benchmark and BETA, and an online experiment.Main results. The results show that msSAME outperforms SAME for both eTRCA and TDCA on the public datasets. Moreover, the semi-supervised msSAME-based method achieves comparable performance to the fully calibrated methods and outperforms the conventional free-calibrated methods. Remarkably, our method only needs 24 s to calibrate 40 targets in the online experiment and achieves an average ITR of 213.8 bits min-1with a peak of 242.6 bits min-1.Significance. This study significantly reduces the calibration effort for individual SSVEP-BCIs, which is beneficial for developing practical plug-and-play SSVEP-BCIs.

A data-driven framework for structure-property correlation in ordered and disordered cellular metamaterials.

Extracting the relation between microstructural features and resulting material properties is essential for advancing our fundamental knowledge on the mechanics of cellular metamaterials and to enable the design of novel material systems. Here, we present a unified framework that not only allows the prediction of macroscopic properties but, more importantly, also reveals their connection to key morphological characteristics, as identified by the integration of machine-learning models and interpretability algorithms. We establish the complex manner in which strut orientation can be critical in determining effective stiffness for certain microstructures and highlight cellular metamaterials with counterintuitive material behavior. We further provide a refined version of Maxwell's criteria regarding the rigidity of frame structures and their connection to cellular metamaterials. By examining the shear moduli of these metamaterials, the mean cell compactness emerges as a key morphological feature. The generality of the proposed framework allows its extension to broader classes of architected materials as well as different properties of interest.

Blended fabric with integrated neural network based on attention mechanism qualitative identification method of near infrared spectroscopy.

Near Infrared spectroscopy (NIRS) qualitative analysis technology has shown excellent development potential in the field of blend fabrics. However, the qualitative detection method based on the convolutional neural network (CNN) is difficult to accurately extract the feature of the spectral data, which will lead to missing detection or false detection; when using deep learning to build a qualitative detection model, due to interference of the external environment and other factors, the spectral data collected may have outliers, this means that the knowledge generalization on anomalous testing data, which may have a different distribution of that of the training set, is not trivial, which will also lead to missing detection or false detection. To solve the above problems, this paper proposes a novel qualitative detection neural network by analyzing the near infrared spectral data of blend fabrics. Firstly, we remove the convolutional layer and pooling layer of the CNN, making full use of the feature to enhance the feature representation ability of the model. Secondly, adding the L1 norm of the feature coefficients as a penalty term to the loss function to force those features with high redundancy to become weaker. Thirdly, in order to improve the recognition accuracy of the anomalous spectral data and minimize the model uncertainty, an ensemble machine learning approach utilizing 5 neural networks in parallel is used. To show the superiority of our proposed method, the existing methods are used as competitive methods to compare with our method. Our homemade dataset contains 3482 samples of blend fabrics with 9 different compositions. The results show that the Micro-F1-score, Micro-Specificity, Weight-F1-score, and Weight-Specificity of this method respectively 99.71%, 99.96%, 99.73%, and 99.99%, the results further confirm the method has higher analysis accuracy and stability. In addition, the method proposed in this paper can greatly improve the recognition accuracy of the anomalous spectral data. It has important practical value in the qualitative detection of blend fabrics.

Stretching DNA origami: effect of nicks and Holliday junctions on the axial stiffness.

The axial stiffness of DNA origami is determined as a function of key nanostructural characteristics. Different constructs of two-helix nanobeams with specified densities of nicks and Holliday junctions are synthesized and stretched by fluid flow. Implementing single particle tracking to extract force-displacement curves enables the measurement of DNA origami stiffness values at the enthalpic elasticity regime, i.e. for forces larger than 15 pN. Comparisons between ligated and nicked helices show that the latter exhibit nearly a two-fold decrease in axial stiffness. Numerical models that treat the DNA helices as elastic rods are used to evaluate the local loss of stiffness at the locations of nicks and Holliday junctions. It is shown that the models reproduce the experimental data accurately, indicating that both of these design characteristics yield a local stiffness two orders of magnitude smaller than the corresponding value of the intact double-helix. This local degradation in turn leads to a macroscopic loss of stiffness that is evaluated numerically for multi-helix DNA bundles.

Transferable Kinetic Monte Carlo Models with Thousands of Reactions Learned from Molecular Dynamics Simulations.

Molecular dynamics (MD) simulation of complex chemistry typically involves thousands of atoms propagating over millions of time steps, generating a wealth of data. Traditionally these data are used to calculate some aggregate properties of the system and then discarded, but we propose that these data can be reused to study related chemical systems. Using approximate chemical kinetic models and methods from statistical learning, we study hydrocarbon chemistries under extreme thermodynamic conditions. We discover that a single MD simulation can contain sufficient information about reactions and rates to predict the dynamics of related yet different chemical systems using kinetic Monte Carlo (KMC) simulation. Our learned KMC models identify thousands of reactions and run 4 orders of magnitude faster than MD. The transferability of these models suggests that we can viably reuse data from existing MD simulations to accelerate future simulation studies and reduce the number of new MD simulations required.

Gold nanorods as plasmonic nanotransducers: distance-dependent refractive index sensitivity.

Owing to the facile tunability of the localized surface plasmon resonance wavelength (LSPR) and large refractive index sensitivity, gold nanorods (AuNR) are of high interest as plasmonic nanotransducers for label-free biological sensing. We investigate the influence of gold nanorod dimensions on distance-dependent LSPR sensitivity and electromagnetic (EM) decay length using electrostatic layer-by-layer (LbL) assembly of polyelectrolytes. The electromagnetic decay length was found to increase linearly with both nanorod length and diameter, although to variable degrees. The rate of EM decay length increase with nanorod diameter is significantly higher compared to that of the length, indicating that diameter is a convenient handle to tune the EM decay length of gold nanorods. The ability to precisely measure the EM decay length of nanostructures enables the rational selection of plasmonic nanotransducer dimensions for the particular biosensing application.