Fine-Grained Unsupervised Temporal Action Segmentation and Distributed Representation for Skeleton-Based Human Motion Analysis.

Understanding the fine-grained temporal structure of human actions and its semantic interpretation is beneficial to many real-world tasks, such as sports movements, rehabilitation exercises, and daily-life activities analysis. Current action segmentation methods mainly rely on deep neural networks to derive feature embedding of actions from motion data, while works on analyzing human actions in fine-granularity are still lacking due to the lack of clear and generic definitions of subactions and related datasets. On the other hand, the motion representations obtained in current action segmentation methods lack semantic or mathematical interpretability that can be used to evaluate action/subaction similarity in quantitative motion analysis.

Training Data Selection and Optimal Sensor Placement for Deep-Learning-Based Sparse Inertial Sensor Human Posture Reconstruction.

Although commercial motion-capture systems have been widely used in various applications, the complex setup limits their application scenarios for ordinary consumers. To overcome the drawbacks of wearability, human posture reconstruction based on a few wearable sensors have been actively studied in recent years. In this paper, we propose a deep-learning-based sparse inertial sensor human posture reconstruction method.

Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning.

This paper focuses on communication-efficient federated learning problem, and develops a novel distributed quantized gradient approach, which is characterized by adaptive communications of the quantized gradients. Specifically, the federated learning builds upon the server-worker infrastructure, where the workers calculate local gradients and upload them to the server; then the server obtain the global gradient by aggregating all the local gradients and utilizes it to update the model parameter. The key idea to save communications from the worker to the server is to quantize gradients as well as skip less informative quantized gradient communications by reusing previous gradients.

A K-Medoids based Point-Process Modeling on Neural Spike Transformation using Binless Kernel.

A neural prosthesis is designed to compensate for cognitive functional losses by modeling the information transmission among cortical areas. Existing methods generally build a generalized linear model to approximate the nonlinear transformation among two areas, and use the temporal information of the neural spike with low efficiency. It is essential to efficiently model the nonlinearity embedded in spike generation and transmission for the real-time.

Tracking the Time Varying Neural Tuning via Adam on Point Process Observations.

Brain machine interfaces(BMIs) translate the neural activity into the control of movement by understanding how the neural activity responds to the movement intension. However, the neural tuning property, where the modulation depth and preferred direction describe how a neuron responses to stimuli, is time varying gradually and abruptly during the interaction with environment. There has been some research to address such an issue considering either one of the cases, but never address them in a general framework.

Detecting abrupt change in neuronal tuning via adaptive point process estimation.

Neuronal tuning property such as preferred direction and modulation depth could change gradually or abruptly in brain machine interface (BMI). The decoding performance will decay in static algorithms where dynamic neuronal tuning property is regarded as stationary. Many adaptive algorithms have been proposed to update the time-varying decoding parameter with main consideration on the decoding performance, but seldom focus on exploring how individual neuronal tuning property changes physiologically.

Selective capture and rapid identification of Panax notoginseng metabolites in rat faeces by the integration of magnetic molecularly imprinted polymers and high-performance liquid chromatography coupled with orbitrap mass spectrometry.

In the present work, an advanced pretreatment method magnetic molecular imprinted polymers-dispersive solid phase extraction (MMIPs-DSPE) combined with the high sensitivity LTQ-Orbitrap mass spectrometry was applied to the complicated metabolites analysis of Traditional Chinese Medicines (TCMs) in complex matrices. The ginsenoside Rb1 magnetic molecular imprinted polymers (Rb1-MMIPs) were successfully synthesized for specific recognition and selective enrichment of Panax notoginseng saponin metabolites in rat faeces. The polymers were prepared by using Fe3O4@SiO2 as the supporting material, APTES as the functional monomer and TEOS as the cross-linker.

Selective separation and determination of the synthetic colorants in beverages by magnetic solid-phase dispersion extraction based on a Fe3 O4 /reduced graphene oxide nanocomposite followed by high-performance liquid chromatography with diode array detection.

A facile adsorbent, a nanocomposite of Fe3 O4 and reduced graphene oxide, was fabricated for the selective separation and enrichment of synthetic aromatic azo colorants by magnetic solid-phase dispersion extraction. The nanocomposite was synthesized in a one-step reduction reaction and characterized by atomic force microscopy, scanning electron microscopy, Fourier transform infrared spectroscopy, Raman spectroscopy, X-ray diffraction and Brunauer-Emmett-Teller analysis. The colorants in beverages were quickly adsorbed onto the surface of the nanocomposite with strong π-π interactions between colorants and reduced graphene oxide, and separated with the assistance of an external magnetic field.

Vanillin-molecularly targeted extraction of stir bar based on magnetic field induced self-assembly of multifunctional Fe3O4@Polyaniline nanoparticles for detection of vanilla-flavor enhancers in infant milk powders.

A molecularly imprinted stir bar was constructed based on Fe3O4@Polyaniline nanoparticles with magnetic field-induced self-assembly process. The monomer, methacrylic acid, was pre-assembled into the pre-polymers with vanillin as template by the formation of hydrogen bonds. After that, the magnetic complexes were generated by the hydrogen bonding, the hydrophobic and π-π interaction between the pre-polymers and Fe3O4@Polyaniline.

Adaptive Neural Control of Nonaffine Systems With Unknown Control Coefficient and Nonsmooth Actuator Nonlinearities.

This brief considers the asymptotic tracking problem for a class of high-order nonaffine nonlinear dynamical systems with nonsmooth actuator nonlinearities. A novel transformation approach is proposed, which is able to systematically transfer the original nonaffine nonlinear system into an equivalent affine one. Then, to deal with the unknown dynamics and unknown control coefficient contained in the affine system, online approximator and Nussbaum gain techniques are utilized in the controller design.

Fe₃O₄@rGO doped molecularly imprinted polymer membrane based on magnetic field directed self-assembly for the determination of amaranth.

Based on magnetic field directed self-assembly (MDSA) of Fe3O4@rGO composites, a novel magnetic molecularly imprinted electrochemical sensor (MIES) was fabricated and developed for the determination of the azo dye amaranth. Fe3O4@rGO composites were obtained by a one-step approach involving the initial intercalating of iron ions between the graphene oxide layers via the electrostatic interaction, followed by the reduction with hydrazine hydrate to deposit Fe3O4 nanoparticles onto the reduced oxide graphene nanosheets. In molecular imprinting, the complex including the function monomer of aniline, the template of amaranth and Fe3O4@rGO was pre-assembled through π-π stacking and hydrogen bonding interactions, and then was self-assembled on the surface of magnetic glassy carbon electrode (MGCE) with the help of magnetic field induction before electropolymerization.

Universal neural network control of MIMO uncertain nonlinear systems.

In this brief, a continuous tracking control law is proposed for a class of high-order multi-input-multi-output uncertain nonlinear dynamic systems with external disturbance and unknown varying control direction matrix. The proposed controller consists of high-gain feedback, Nussbaum gain matrix selector, online approximator (OLA) model and a robust term. The OLA model is represented by a two-layer neural network.