Miracle-dz/UniFAR-FTUs huggingface dataset:
HEETR: Pretraining for Robotic Manipulation on Heteromodal Data

: A good representation is a key to unlock efficient learning for real-world robot manipulation. However, common manipulation-relevant datasets do not always have all the modalities (e.g., videos, actions, proprioceptive states) presented in robotic manipulation. As a result, existing approaches to representation learning, which assume full data modalities, cannot be easily scaled to consume all the data; instead, they can only be applied to a subset of modality sufficient data, which limits the effectiveness of representation learning. In this work, we present an end-to-end transformer-based pretraining method called HEETR ( H eteromodal E nd-to-E nd T ransformer for R obotic manipulation) that can learn a representation for efficient adaptation using all data regardless of their available modalities. We demonstrate the merits of this design and establish new state-of-the-art performance on Robosuite/Robomimic and Meta-World benchmarks.
FoMC69 Gene in Fusarium oxysporum f. sp. radicis-lycopersici Is Essential for Pathogenicity by Involving Normal Function of Chlamydospores

Fusarium oxysporum f. sp. radicis-lycopersici (Forl) causes crown and root rot disease in tomato, effecting severe economic losses. However, research on the pathogenicity genes and infection strategy of Forl is limited compared to that on F. oxysporum f. sp. lycopersici (Fol). In this study, we characterized FoMC69 gene in Forl as a homolog of MC69 required for pathogenicity in rice blast pathogen—Magnaporthe oryzae. Gene expression analysis revealed that FoMC69 expressionin Forl is higher than that in Folin planta. FoMC69-knockout mutant of Forl had significantly reduced root rot symptoms compared to the wild-type strain, and full pathogenicity was restored by complementation. By contrast, ΔFoMC69 mutant of Fol presented the same symptoms as the wild type, suggesting that FoMC69 of Forl, but not of Fol, was essential for full virulence in tomato plants. Morphological differences between the Forl and ΔFoMC69 in the roots were observed by fluorescent labeling using WGA-FITC. Chlamydospores of the ΔFoMC69 mutant of Forlcontinuously increased during infection and were three times higher than that of the wild type at 21 days post-inoculation. These observations suggest that FoMC69 of Forl is required for virulence to tomato plants by involving the normal development and germination of chlamydospores.
A Review of the Optimization and Control Techniques in the Presence of Uncertainties for the Energy Management of Microgrids

This paper reviews the current techniques used in energy management systems to optimize energy schedules into microgrids, accounting for uncertainties for various time frames (day-ahead and real-time operations). The current uncertainties affecting applications, including residential, commercial, virtual power plants, electric mobility, and multi-carrier microgrids, are the main subjects of this article. We outline the most recent modeling approaches to describe the uncertainties associated with various microgrid applications, such as prediction errors, load consumption, degradation, and state of health. The modeling approaches discussed in this article are probabilistic, possibilistic, information gap theory, and deterministic. Then, the paper presents and compares the current optimization techniques, considering the uncertainties in their problem formulations, such as stochastic, robust, fuzzy optimization, information gap theory, model predictive control, multiparametric programming, and machine learning techniques. The optimization techniques depend on the model used, the data available, the specific application, the real-time platform, and the optimization time. We hope to guide researchers to identify the best optimization technique for energy scheduling, considering the specific uncertainty and application. Finally, the most challenging issues to enhance microgrid operations, despite uncertainties by considering new trends, are discussed.
Assessing Potential Spontaneous Combustion of Coal Gangue Dumps after Reclamation by Simulating Alfalfa Heat Stress Based on the Spectral Features of Chlorophyll Fluorescence Parameters

The spontaneous combustion of coal gangue dumps after reclamation causes severe harm to the ecological environment surrounding mining areas. Using remote sensing technology to determine vegetation heat stress levels is an important way to evaluate the probability of a spontaneous combustion disaster. The canopy spectra and chlorophyll fluorescence (ChlF) parameters of alfalfa were collected through pot experiments to simulate different heat stress levels. Time series analyses of three ChlF (chlorophyll fluorescence) parameters showed that the regularity of the quantum efficiency of photosystem II (PSII) in light-adapted conditions (Fv′/Fm′) was stronger during the monitoring period. The correlation coefficients between the three ChlF parameters and the canopy raw spectrum, first derivative spectrum, and vegetation indices were calculated, and the spectral features were found to be more correlated. Lasso regression was used to further screen spectral features, and the optimal spectral features were the raw spectral value at 741 nm (abbreviated as RS (741)) and NDVI (652, 671). To discriminate among heat stress levels accurately and automatically, we built a time convolution neural network. The classification results showed that when the sequence length is 3, the heat stress is divided into three categories, and the model obtains the highest accuracy. In combination with relevant research conclusions on the temperature distribution law of spontaneous combustion in coal gangue dumps, three heat stress levels can be used to assess the potential of spontaneous combustion in coal gangue dumps after reclamation. The research results provide an important theoretical basis and technical support for early warnings regarding spontaneous combustion disasters in reclaimed coal gangue dumps.
A Reference Current Source with Cascaded Nagata Current Mirrors Insensitive to Supply Voltage and Temperature

This paper investigates a supply voltage and temperature insensitive CMOS current reference with cascaded Nagata current mirrors. The Nagata current mirror has a peak with respect to its input current, and the current change rate is small in the vicinity of the peak. By the cascade structure design, the output current can be insensitive to a wide range of supply voltage. Further, we show here that its temperature insensitivity is realized by biasing the gate-source voltage at the zero temperature coefficient condition for the drain current. The investigated circuit is designed using TSMC 180nm CMOS process with 3.3 V supply voltage. Our LTspice simulation shows that it achieves a 4μA current with temperature variation of less than 3% or 180ppm/°C over the range of -45°C to 95°C with simple configuration.
TEDOP: A Tiny Event-Driven Neural Network Hardware Core Enabling On-Chip Spike-Driven Synaptic Plasticity

For edge intelligent applications, this work proposes a tiny neuromorphic hardware core embedding high-speed on-chip synaptic plasticity, by adopting the proposed Temporal-Integrate neuron model and a simplified supervised spike-driven synaptic plasticity rule for on-chip learning. The proposed hardware core was prototyped on a very-low-cost Zybo Zynq-7010 FPGA device, and attained comparably high classification accuracies on many datasets (e.g. 90.4% on MNIST), with a learning and inference speed as high as 11,268 and 11,749 f $r$ ame/s, respectively, while dissipating only 39 mW power under a 250 MHz clock frequency.
Secure Analytics for Embedded Systems through Machine Learning Applications using TrustZones

With the advent of edge computing, there has been a substantial growth of connected devices. Several of these devices, including internet-of-things (IoT), smartphones, embedded systems, and even drones and satellites, often rely on smaller computing components. Most often, these devices fall under the set of reduced instruction set computers (RISC), and several contain ARM (Advanced RISC Machine) processors. Because of the growth of connected devices, there has been an increased need for cybersecurity. TrustZones, a hardware technology extension that acts to partition applications and memory in a secure manner, serves as a method to guarantee the security of applications on ARM processors. We briefly introduce the TrustZone architecture and apply TrustZones on the ARM processor through construction of secure machine learning applications.
Simulation on the Influence of Square Wave Electric Field on Space Charge Behavior in Polyimide

The polyimide in the inverter-fed motors works under square wave electric field with the periodic variation of voltage polarity and steep rise time. Thus, it is important to investigate the charge behavior under square wave electric field. With the limit of present space charge detection technology under the square wave, this paper focuses on numerical simulations of charge behavior. The multi-mobility mode was introduced into the bipolar carrier model and the simulation of charge transport in polyimide under square wave electric field with different strength and rise time was carried out. It shows that the increase of strength of electric field significantly enhances the accumulation of space charge. When the electric field strength is low, the charge responds rapidly to the change of electric field polarity. However, with the increase of the electric field strength, the heterocharge accumulation becomes obvious, resulting in the distortion of local electric field. The rise time has little effect on the charge distribution. Further, the effect of recombination was used to study the relationship between rise time and charge behavior. When the voltage polarity is reversed, there is a surge in the recombination rate, which is convincing evidence of the coexistence of positive and negative charges. As the rise time increases, the maximum total recombination rate presents a significant downward trend and the mean recombination rate fluctuates accordingly. Spatially, the recombination rate near the boundary is much higher than that inside the specimen. The recombination rate inside the sample slowly raises, while the recombination rate near the boundary has an opposite change with the increase of rise time.
Mixed Imaging Sequences for Improved Spatiotemporal Resolution in Cardiac Imaging

Ultrasound is a crucial tool for cardiac imaging. Even though modern imaging sequences efficiently manage the trade-off between spatial and temporal resolution, motion in the field-of-view readily degrades image quality. In this work, we seek to design an ultrasound imaging sequence that resolves cardiac motion with fewer total transmissions than conventional sequences, thereby increasing temporal resolution and maintaining spatial resolution. Specifically, we used REFoCUS to combine focused and plane wave transmissions in a “mixed sequence. ” First, the motion sensitivity of REFoCUS was evaluated in simulation for a variety of transmit beam geometries. Focused transmissions resolve motion much more effectively than broad transmissions. Second, a mixed sequence was designed that insonifies key targets with focused transmissions and the full field-of-view with plane wave transmissions. This mixed sequence (n=70) was used to image cardiac motion in-vivo and was compared to a focused sequence (n=102) and a plane sequence (n=60). Overall, mixed sequences have strong potential to both resolve cardiac motion and improve imaging frame rate.
An Improved Bidirectional Variable Structure LLC Resonant Converter

With the development of electric vehicles, LLC converter for bidirectional energy transmission has received more and more attention. In order to achieve bidirectional boost operation, the L-LLC converter is applied. However, the forward operation efficiency of L-LLC converter is reduced compared to the traditional LLC converter due to the addition of an extra inductor. This paper proposes an improved bidirectional variable structure LLC (VS-LLC) resonant converter to improve forward operation efficiency. Compared with L-LLC converter, the VS-LLC converter improves the efficiency in forward mode by changing the resonant network. The working principle of the proposed converter is introduced, and the parameters of a VS-LLC converter are optimized. Bidirectional boost operation and soft switching capabilities of VS-LLC converter are validated by the simulation results. The comparison results show that VS-LLC converter has higher efficiency than L-LLC converter in forward mode, and the backward operation efficiency of two converters is approximate.
Strain Engineering on Lithium Niobate Crystal Based SAW Resonators Through Ion Implantation

In this work, a novel quasi-heterostructure consisting of a shallow surface layer of strained LiNbO3 and a virgin LiNbO3 substrate is demonstrated. The quasi-heterostructure is constructed by implanting helium ions into the surface layer of a single-crystalline X-cut LiNbO3 wafer, where the material properties of the surface layer will be modulated by the implanted ions. The X-ray diffraction (XRD) rocking curves were measured to characterize the crystalline quality and strain distribution of the ion-implanted layer. The Rayleigh-SAW resonators on the quasi-heterostructure and virgin LiNbO3 were fabricated. The resonators on the quasi-heterostructure exhibit higher resonance frequencies (1.15-1.17 times), larger electromechanical coupling factors (1.1-1.4 times), higher quality factors (1.1 times), and larger static capacitances (1.33 times), indicating that the elastic constants, piezoelectric constants, and dielectric constants of the strained LiNbO3 layers have been effectively modulated. Although the correlations between the improved performances and the modulated material constants are still under study, the improvements reveal the feasibility of performance modulation of SAW resonators by strain engineering.
Acoustic Hologram Lens Made of Nanoparticle-Epoxy Composite Molding for Directing Predefined Therapeutic Ultrasound Beams

We present an acoustic hologram lens fabrication method for prototyping nondeformed hologram lenses with a tailored acoustic impedance. A pixelized hologram pattern is typically manufactured by photo-curing 3D printing methods, such as stereolithography (SLA) printing. However, SLA printing has major limitations for lens fabrication: vulnerability to deformation during photo-curing of a thin-plate shape lens structure and limited controllability of acoustic impedance. To overcome these limitations, we adopted a synthesized epoxy composite molding technique in this work. The used alumina nanoparticle (300 nm)-epoxy composite contains 22.5% alumina particles in volume. The characterized acoustic impedance of the composite was 4.68 MRayl whereas the conventional photopolymer exhibited 3.13 MRayl. We used these acoustic properties in lens modeling and acoustic hologram simulations. In simulations, the composite lens generated 145% pressure amplitude of the photopolymer lens due to improved acoustic impedance matching between a piezoelectric ceramic and water medium. We prototyped a composite lens through 1) 3D printing a lens cavity, 2) silicone rubber molding, and 3) epoxy composite lens molding. We observed no deformation of the prototyped composite lens whereas the photopolymer lens showed deformed edges. The beam mapping result using the composite lens showed 17% improved structural similarity with the designed pressure pattern compared to the photopolymer result. Due to the air bubbles trapped in a composite lens, the expected improvement of pressure amplitude over a photopolymer lens was not experimentally demonstrated. The additional degassing procedure will be included for future prototypes and pressure transmission will be evaluated.
An interchangeable data structure used in automated PCB layout design and optimisation for power electronics applications

An exploration of interchangeable data structure used in automated printed circuit boards (PCB) design for power electronics applications is presented in this paper. The key features include generation, modification and assessment needed by the automatic design and optimisation programmes are identified and discussed and the essential capabilities are defined. Existing PCB file formats from commercial software have been reviewed and the insufficiency is observed. A prototype data structure, named copper polygon areas (CPA), is illustrated and typical use case examples are shown to justify the simplicity, compatibility and effectiveness of the new design. This study also lays the foundation stone and sets the baseline benchmark for future automated trace shape optimisation algorithms in versatile power electronics applications.
Generalized binomial distributions

The definition, properties, and calculational advantages of the binomial transform representation of a class of finitely enumerated discrete probability distributions that generalize the standard binomial distribution are investigated. Discrete distributions of this type appear in various models of hadron multiparticle production among other physical applications. The compact representations of the generating functions for factorial moments of these discrete distributions obtained using the binomial transform are used to relate the behavior of the complex zeros of the generating functions to different models for the distribution of charged and neutral particles in multihadron production.
CLA-U-Net: Convolutional Long-short-term-memory Attention-gated U-Net for Automatic Segmentation of the Left Ventricle in 2-D Echocardiograms

Left ventricular ejection fraction is one of the important indices to evaluate cardiac function. Manual segmentation of the left ventricle (LV) in 2-D echocardiograms is tedious and time-consuming. We proposed a deep learning method called convolutional long-short-term-memory attention-gated U-Net (CLA-U-Net) for automatic segmentation of the LV in 2-D echocardiograms. The CLA-U-Net model was trained and tested using the EchoNet-Dynamic dataset. The dataset contained 9984 annotated echocardiogram videos (training set: 7456; validation set: 1296; test set 1232). The model was also tested on a private clinical dataset of 20 echocardiogram videos. U-Net was used as the basic encoder and decoder structure, and some very useful structures were designed. In the encoding part, we incorporated a convolutional long-short-term-memory (C-LSTM) block to guide the network to capture the temporal information between frames in the videos. In addition, we replaced the skip-connection structure of the original U-Net with a channel attention mechanism, which can amplify the desired feature signals and suppress the noise. With the proposed CLA-U-Net, the LV was segmented automatically on the EchoNet-Dynamic test set, and a Dice similarity coefficient (DSC) of 0.9311 was obtained. The DSC obtained by the DeepLabV3 network was 0.9236. The hyperparameters of CLA-U-Net were only 19.9 MB, reduced by ~91.6% as compared with DeepLabV3 network. For the private clinical dataset, a DSC of 0.9192 was obtained. Our CLA-U-Net achieved a desirable LV segmentation accuracy, with a lower amount of hyperparameters. The CLA-U-Net may be used as a new lightweight deep learning method for automatic LV segmentation in 2-D echocardiograms.
Deep Unfolding RPCA for High Resolution Flow Estimation

Numerous techniques have been proposed to produce high precision blood flow or contrast-enhanced ultrasound estimates from fast ultrasound sequences. Among them, robust principal component analysis (RPCA)-based methods are known as superior to most state-of-the-art techniques. In particular, these techniques may include a deconvolution step which allows for further improvement of the resolution of the estimated blood flow images. However, they rely on many hyperparameters that have to be manually adjusted to obtain the optimal solution. To overcome this limitation, we propose a new deep unfolding neural network based on the DRPCA iterative algorithm, which enables the reconstruction of high-resolution and high-sensitivity blood flow components. Compared to other state-of-the-art methods, the proposed algorithm showed interesting performances, in terms of PSNR and SSIM on simulation data.
Research on Underwater Small Target Detection Algorithm Based on Improved YOLOv3

Underwater target detection has important research significance and value in the fields of fish exploration technology, fishery resources research, fishery prediction, aquaculture and so on. In this paper, an improved YOLOv3-ST model is proposed to improve the detection accuracy of underwater small targets. In this model, a linear scaling K-means clustering algorithm is designed to adapt to multi-scale feature map detection. At the same time, the 8-fold down-sampling scale feature map of the Darknet-53 network is removed, and the splice-conv module is used for up-sampling and feature fusion, finally the 4-fold down-sampling scale feature map is output as the third detection layer. On this basis, in order to further accurately detect targets with inconspicuous features such as waterweeds, two kinds of attention mechanisms are embedded in the YOLOv3-ST model. The experimental results show that the YOLOv3-ST model with the attentional mechanism of SEnet can effectively improve the underwater small target detection accuracy. The detection accuracy of echinus is 91.80%, and the detection accuracy of waterweeds is 22.33% higher than that of the original yolov3 model, while the average detection accuracy of all categories is increased by 12.13%.
Utilizing Deep Learning Techniques to Detect Zero Day Exploits in Network Traffic Flows

In recent times, the cybersecurity of naval systems has become a major concern; in particular, there is an increased need for network traffic analysis and detecting the presence of threat actors. In industry, commercial intrusion detection and prevention systems have been extremely useful of protecting systems on the general internet; for submarine networks, a more targeted approach is clearly needed. To accommodate large amounts of traffic, flow analysis using such technologies as NetFlow and sFlow have shown clear advantage for real-time concerns. However, for submarine networks, the question of which parameter settings are most ideal and how to use the flow data most effectively still do not have clear answers. In this technical memo, we present a deep learning approach to analyzing flow traffic. Whereas deep neural networks would take considerable time to process raw network PCAP files, training a deep neural network on flow data would show significant time improvement in recognizing modern cyberattacks. Our results aim to show which flow parameter and neural network hyperparameter settings are optimal for recognizing and reacting to various cyberattacks in minimal time.
Mechanical Fault Diagnosis of GIS Disconnector Based on Synchrosqueezing Wavelet Transform and Stacked Autoencoder

As one of the essential equipment of Gas Insulated Switchgear (GIS), the abnormal mechanical condition of the disconnector will directly lead to the interruption of its opening and closing operation and the delay of the power outage and transmission. To effectively and efficiently recognize the mechanical condition of GIS disconnector, this paper presents a mechanical fault diagnosis method consisting of synchrosqueezing wavelet transform (SWT) and stacked autoencoder (SAE) based on the vibration signal originating from the opening and closing operation of GIS disconnector. The time-frequency representation (TFR) of vibration signal is calculated and the deep-learning framework based on the SAE is built. The analysis results of a three-phased GIS disconnector under the normal and typical mechanical fault conditions show that the proposed fault diagnosis method can identify the different mechanical conditions with high accuracy.