On the performance of a mixed RF/MIMO FSO variable gain dual-hop transmission system

In this work, we propose a mixed radio frequency (RF) and multiple-input-multiple-output (MIMO) free-space optical (FSO) system based on a variable-gain dual-hop relay transmission scheme. The RF channel is modeled by Rayleigh distribution and Gamma–Gamma turbulence distribution is adopted for the MIMO FSO link, which accounts for the equal gain combining diversity technique. Moreover, new closed-form mathematical formulas are obtained including the cumulative distribution function, probability density function, moment generating function, and moments of equivalent signal-to-noise ratio of the dual-hop relay system based on Meijer’s G function. As such, we derive the novel analytical expressions of the outage probability, the higher-order fading, and the average bit error rate for a range of modulations in terms of Meijer’s G function. Furthermore, the exact closed-form formula of the ergodic capacity is derived based on the bivariate Meijer’s G function. The evaluation and simulation are provided for system performance, and the effect of spatial diversity technique is discussed as well

Towards Best Practice of Interpreting Deep Learning Models for EEG-based Brain Computer Interfaces

As deep learning has achieved state-of-the-art performance for many tasks of EEG-based BCI, many efforts have been made in recent years trying to understand what have been learned by the models. This is commonly done by generating a heatmap indicating to which extent each pixel of the input contributes to the final classification for a trained model. Despite the wide use, it is not yet understood to which extent the obtained interpretation results can be trusted and how accurate they can reflect the model decisions. In order to fill this research gap, we conduct a study to evaluate different deep interpretation techniques quantitatively on EEG datasets. The results reveal the importance of selecting a proper interpretation technique as the initial step. In addition, we also find that the quality of the interpretation results is inconsistent for individual samples despite when a method with an overall good performance is used. Many factors, including model structure and dataset types, could potentially affect the quality of the interpretation results. Based on the observations, we propose a set of procedures that allow the interpretation results to be presented in an understandable and trusted way. We illustrate the usefulness of our method for EEG-based BCI with instances selected from different scenarios

Accelerating the exploration of high-entropy alloys: Synergistic effects of integrating computational simulation and experiments

High-entropy alloys (HEAs) are novel materials composed of multiple elements with nearly equal concentrations and they exhibit exceptional properties such as high strength, ductility, thermal stability, and corrosion resistance. However, the intricate and diverse structures of HEAs pose significant challenges to understanding and predicting their behavior at different length scales. This review summarizes recent advances in computational simulations and experiments of structure-property relationships in HEAs at the nano/micro scales. Various methods such as first-principles calculations, molecular dynamics simulations, phase diagram calculations, and finite element simulations are discussed for revealing atomic/chemical and crystal structures, defect formation and migration, diffusion and phase transition, phase formation and stability, stress-strain distribution, deformation behavior, and thermodynamic properties of HEAs. Emphasis is placed on the synergistic effects of computational simulations and experiments in terms of validation and complementarity to provide insights into the underlying mechanisms and evolutionary rules of HEAs. Additionally, current challenges and future directions for computational and experimental studies of HEAs are identified, including accuracy, efficiency, and scalability of methods, integration of multiscale and multiphysics models, and exploration of practical applications of HEAs

Impact of Wall Configurations on Seismic Fragility of Steel-Sheathed Cold-Formed Steel-Framed Buildings

Seismic fragility of steel-sheathed cold-formed steel-framed (CFSF) structures is scarcely investigated; thus, the information for estimation of seismic losses of the steel-sheathed CFSF buildings is insufficient. This study aims to investigate the seismic fragility of steel-sheathed CFSF buildings with different wall configurations. Analytic models for four 2-story steel-sheathed CFSF buildings are established based on shaking table tests on steel-sheathed CFS walls. Then, a group of fragility curves for these buildings are generated. The results show that the thickness of steel sheathing and the fastener spacing of the wall have significant impact on seismic fragility of steel-sheathed CFSF buildings. The seismic fragility of the CFSF building can be reduced by increasing the thickness of steel sheathing or decreasing the fastener spacing. By increasing the thickness of steel sheathing, the reduction on probability is more obvious for the CP limit. It is also found that the exceeding probability is approximately linear with fastener spacing, with a slope in the range from 0.25%/mm to 0.50%/mm

Unsupervised Temporal Action Localization via Self-paced Incremental Learning

Recently, temporal action localization (TAL) has garnered significant interest in information retrieval community. However, existing supervised/weakly supervised methods are heavily dependent on extensive labeled temporal boundaries and action categories, which is labor-intensive and time-consuming. Although some unsupervised methods have utilized the ``iteratively clustering and localization'' paradigm for TAL, they still suffer from two pivotal impediments: 1) unsatisfactory video clustering confidence, and 2) unreliable video pseudolabels for model training. To address these limitations, we present a novel self-paced incremental learning model to enhance clustering and localization training simultaneously, thereby facilitating more effective unsupervised TAL. Concretely, we improve the clustering confidence through exploring the contextual feature-robust visual information. Thereafter, we design two (constant- and variable- speed) incremental instance learning strategies for easy-to-hard model training, thus ensuring the reliability of these video pseudolabels and further improving overall localization performance. Extensive experiments on two public datasets have substantiated the superiority of our model over several state-of-the-art competitors

Free-standing kinked nanowire transistor probes for targeted intracellular recording in three dimensions

Recording intracellular bioelectrical signals is central to understanding the fundamental behaviour of cells and cell-networks in, for example, neural and cardiac systems1–4. The standard tool for intracellular recording, the patch-clamp micropipette5 is widely applied, yet remains limited in terms of reducing the tip size, the ability to reuse the pipette5, and ion exchange with the cytoplasm6. Recent efforts have been directed towards developing new chip-based tools1–4,7–13, including micro-to-nanoscale metal pillars7–9, transistor-based kinked nanowire10,11 and nanotube devices12,13. These nanoscale tools are interesting with respect to chip-based multiplexing, but, to date, preclude targeted recording from specific cell regions and/or subcellular structures. Here we overcome this limitation in a general manner by fabricating free-standing probes where a kinked silicon nanowire with encoded field-effect transistor detector serves as the tip end. These probes can be manipulated in three dimensions (3D) within a standard microscope to target specific cells/cell regions, and record stable full-amplitude intracellular action potentials from different targeted cells without the need to clean or change the tip. Simultaneous measurements from the same cell made with free-standing nanowire and patch-clamp probes show that the same action potential amplitude and temporal properties are recorded without corrections to the raw nanowire signal. In addition, we demonstrate real-time monitoring of changes in the action potential as different ion-channel blockers are applied to cells, and multiplexed recording from cells by independent manipulation of two free-standing nanowire probes

Design and Synthesis of Diverse Functional Kinked Nanowire Structures for Nanoelectronic Bioprobes

Functional kinked nanowires (KNWs) represent a new class of nanowire building blocks, in which functional devices, for example, nanoscale field-effect transistors (nanoFETs), are encoded in geometrically controlled nanowire superstructures during synthesis. The bottom-up control of both structure and function of KNWs enables construction of spatially isolated point-like nanoelectronic probes that are especially useful for monitoring biological systems where finely tuned feature size and structure are highly desired. Here we present three new types of functional KNWs including (1) the zero-degree KNW structures with two parallel heavily doped arms of U-shaped structures with a nanoFET at the tip of the “U”, (2) series multiplexed functional KNW integrating multi-nanoFETs along the arm and at the tips of V-shaped structures, and (3) parallel multiplexed KNWs integrating nanoFETs at the two tips of W-shaped structures. First, U-shaped KNWs were synthesized with separations as small as 650 nm between the parallel arms and used to fabricate three-dimensional nanoFET probes at least 3 times smaller than previous V-shaped designs. In addition, multiple nanoFETs were encoded during synthesis in one of the arms/tip of V-shaped and distinct arms/tips of W-shaped KNWs. These new multiplexed KNW structures were structurally verified by optical and electron microscopy of dopant-selective etched samples and electrically characterized using scanning gate microscopy and transport measurements. The facile design and bottom-up synthesis of these diverse functional KNWs provides a growing toolbox of building blocks for fabricating highly compact and multiplexed three-dimensional nanoprobes for applications in life sciences, including intracellular and deep tissue/cell recordings.Chemistry and Chemical Biolog