Computer Vision Self-supervised Learning Methods on Time Series

Self-supervised learning (SSL) has had great success in both computer vision and natural language processing. These approaches often rely on cleverly crafted loss functions and training setups to avoid feature collapse. In this study, the effectiveness of mainstream SSL frameworks from computer vision and some SSL frameworks for time series are evaluated on the UCR, UEA and PTB-XL datasets, and we show that computer vision SSL frameworks can be effective for time series. In addition, we propose a new method that improves on the recently proposed VICReg method. Our method improves on a \textit{covariance} term proposed in VICReg, and in addition we augment the head of the architecture by an IterNorm layer that accelerates the convergence of the model

Application of Recent Developments in Deep Learning to ANN-based Automatic Berthing Systems

Previous studies on Artificial Neural Network (ANN)-based automatic berthing showed considerable increases in performance by training ANNs with a set of berthing datasets. However, the berthing performance deteriorated when an extrapolated initial position was given. To overcome the extrapolation problem and improve the training performance, recent developments in Deep Learning (DL) are adopted in this paper. Recent activation functions, weight initialization methods, input data-scaling methods, a higher number of hidden layers, and Batch Normalization (BN) are considered, and their effectiveness has been analyzed based on loss functions, berthing performance histories, and berthing trajectories. Finally, it is shown that the use of recent activation and weight initialization method results in faster training convergence and a higher number of hidden layers. This leads to a better berthing performance over the training dataset. It is found that application of the BN can overcome the extrapolated initial position problem

VNIbCReg: VICReg with Neighboring-Invariance and better-Covariance Evaluated on Non-stationary Seismic Signal Time Series

One of the latest self-supervised learning (SSL) methods, VICReg, showed a great performance both in the linear evaluation and the fine-tuning evaluation. However, VICReg is proposed in computer vision and it learns by pulling representations of random crops of an image while maintaining the representation space by the variance and covariance loss. However, VICReg would be ineffective on non-stationary time series where different parts/crops of input should be differently encoded to consider the non-stationarity. Another recent SSL proposal, Temporal Neighborhood Coding (TNC) is effective for encoding non-stationary time series. This study shows that a combination of a VICReg-style method and TNC is very effective for SSL on non-stationary time series, where a non-stationary seismic signal time series is used as an evaluation dataset

The amount of astrocytic GABA positively correlates with the degree of tonic inhibition in hippocampal CA1 and cerebellum

A tonic form of synaptic inhibition occurs in discrete regions of the central nervous system and has an important role in controlling neuronal excitability. Recently, we reported that GABA present in astrocyte is the major source of tonic inhibition in cerebellum and that GABA is released through Bestrophin-1 channel by direct permeation. In this study, we screened for the presence of astrocytic GABA in various brain regions such as hippocampus, thalamus, hypothalamus and cerebellum using immunohistochemistry. We found that astrocytic GABA was present in the regions that were reported to show tonic inhibition. Because the existence of tonic inhibition in hippocampal CA1 is somewhat controversial, we compared the amount of astrocytic GABA and tonic inhibition between the hippocampal CA1 pyramidal cell layer and the cerebellar granule cell layer. Unlike cerebellar glial cells, hippocampal astrocytes did not contain GABA. The tonic inhibition was also much lower in the pyramidal neurons of hippocampal CA1 compared to the granule cells of cerebellum. Nevertheless, most of the hippocampal astrocytes expressed Bestrophin-1 channel. These data indicate that the absence of astrocytic GABA results in a low level of tonic inhibition in hippocampal CA1 region

Pretrained FCN on the UCR Archive

The pretrained FCN models on the UCR archive datasets. </p

Grid-Based Set Point Generation Strategy for Position Control of Dynamic Positioning Assisted Mooring System

Unlike typical a dynamic positioning (DP) system, a DP-assisted mooring system must determine a set point (SP) that can ensure a mooring tension safety range to prevent an excessive increase in mooring tension. In this paper, a new algorithm for determining the SP is suggested in order to reduce the tension on all the mooring lines. To determine the SP, a working area around the vessel is represented by a rectangular grid. Thus, the size of the grid area is limited considering the offset of a vessel with a mooring system. At each grid’s nodes, the resultant tension from all the mooring lines is estimated using the time history of the tension and vessel’s position. The results of static analyses for each grid position are used to estimate the global tension. Consequently, the SP is automatically selected as a position satisfying criterion for minimizing the total tension. In order to validate the suggested algorithm, a motion simulation with the control system in the time domain and a discussion of the results are presented

Exosome engineering for efficient intracellular delivery of soluble proteins using optically reversible protein-protein interaction module

Nanoparticle-mediated delivery of functional macromolecules is a promising method for treating a variety of human diseases. Among nanoparticles, cell-derived exosomes have recently been highlighted as a new therapeutic strategy for the in vivo delivery of nucleotides and chemical drugs. Here we describe a new tool for intracellular delivery of target proteins, named &apos;exosomes for protein loading via optically reversible protein-protein interactions&apos; (EXPLORs). By integrating a reversible protein-protein interaction module controlled by blue light with the endogenous process of exosome biogenesis, we are able to successfully load cargo proteins into newly generated exosomes. Treatment with protein-loaded EXPLORs is shown to significantly increase intracellular levels of cargo proteins and their function in recipient cells in vitro and in vivo. These results clearly indicate the potential of EXPLORs as a mechanism for the efficient intracellular transfer of protein-based therapeutics into recipient cells and tissues. © The Author(s) 2016146481sciescopu

Study of spin-ordering and spin-reorientation transitions in hexagonal manganites through Raman spectroscopy

Spin-wave (magnon) scattering, when clearly observed by Raman spectroscopy, can be simple and powerful for studying magnetic phase transitions. In this paper, we present how to observe magnon scattering clearly by Raman spectroscopy, then apply the Raman method to study spin-ordering and spin-reorientation transitions of hexagonal manganite single crystal and thin films and compare directly with the results of magnetization measurements. Our results show that by choosing strong resonance condition and appropriate polarization configuration, magnon scattering can be clearly observed, and the temperature dependence of magnon scattering can be simple and powerful quantity for investigating spin-ordering as well as spin-reorientation transitions. Especially, the Raman method would be very helpful for investigating the weak spin-reorientation transitions by selectively probing the magnons in the Mn 3+ sublattices, while leaving out the strong effects of paramagnetic moments of the rare earth ions11081sciescopu