Re-parameterizing Your Optimizers rather than Architectures

The well-designed structures in neural networks reflect the prior knowledge incorporated into the models. However, though different models have various priors, we are used to training them with model-agnostic optimizers such as SGD. In this paper, we propose to incorporate model-specific prior knowledge into optimizers by modifying the gradients according to a set of model-specific hyper-parameters. Such a methodology is referred to as Gradient Re-parameterization, and the optimizers are named RepOptimizers. For the extreme simplicity of model structure, we focus on a VGG-style plain model and showcase that such a simple model trained with a RepOptimizer, which is referred to as RepOpt-VGG, performs on par with or better than the recent well-designed models. From a practical perspective, RepOpt-VGG is a favorable base model because of its simple structure, high inference speed and training efficiency. Compared to Structural Re-parameterization, which adds priors into models via constructing extra training-time structures, RepOptimizers require no extra forward/backward computations and solve the problem of quantization. We hope to spark further research beyond the realms of model structure design. The code and models are publicly available at https://github.com/DingXiaoH/RepOptimizers.Comment: Under revie

Overcoming Cabbage Crossing Incompatibility by the Development and Application of Self-Compatibility-QTL- Specific Markers and Genome-Wide Background Analysis

Cabbage hybrids, which clearly present heterosis vigor, are widely used in agricultural production. We compared two S5 haplotype (Class II) cabbage inbred-lines 87–534 and 94–182: the former is highly SC while the latter is highly SI; sequence analysis of SI-related genes including SCR, SRK, ARC1, THL1, and MLPK indicates the some SNPs in ARC1 and SRK of 87–534; semi-quantitative analysis indicated that the SI-related genes were transcribed normally from DNA to mRNA. To unravel the genetic basis of SC, we performed whole-genome mapping of the quantitative trait loci (QTLs) governing self-compatibility using an F2 population derived from 87–534 × 96–100. Eight QTLs were detected, and high contribution rates (CRs) were observed for three QTLs: qSC7.2 (54.8%), qSC9.1 (14.1%) and qSC5.1 (11.2%). 06–88 (CB201 × 96–100) yielded an excellent hybrid. However, F1 seeds cannot be produced at the anthesis stage because the parents share the same S-haplotype (S57, class I). To overcome crossing incompatibility, we performed rapid introgression of the self-compatibility trait from 87–534 to 96–100 using two self-compatibility-QTL-specific markers, BoID0709 and BoID0992, as well as 36 genome-wide markers that were evenly distributed along nine chromosomes for background analysis in recurrent back-crossing (BC). The transfer process showed that the proportion of recurrent parent genome (PRPG) in BC4F1 was greater than 94%, and the ratio of individual SC plants in BC4F1 reached 100%. The newly created line, which was designated SC96–100 and exhibited both agronomic traits that were similar to those of 96–100 and a compatibility index (CI) greater than 5.0, was successfully used in the production of the commercial hybrid 06–88. The study herein provides new insight into the genetic basis of self-compatibility in cabbage and facilitates cabbage breeding using SC lines in the male-sterile (MS) system

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

RepMLPNet:Hierarchical Vision MLP with Re-parameterized Locality

Compared to convolutional layers, fully-connected (FC) layers are better at modeling the long-range dependencies but worse at capturing the local patterns, hence usually less favored for image recognition. In this paper, we propose a methodology, Locality Injection, to incorporate local priors into an FC layer via merging the trained parameters of a parallel conv kernel into the FC kernel. Locality Injection can be viewed as a novel Structural Re-parameterization method since it equivalently converts the structures via transforming the parameters. Based on that, we propose a multi-layer-perceptron (MLP) block named RepMLP Block, which uses three FC layers to extract features, and a novel architecture named RepMLPNet. The hierarchical design distinguishes RepMLPNet from the other concurrently proposed vision MLPs. As it produces feature maps of different levels, it qualifies as a backbone model for downstream tasks like semantic segmentation. Our results reveal that 1) Locality Injection is a general methodology for MLP models; 2) RepMLPNet has favorable accuracy-efficiency trade-off compared to the other MLPs; 3) RepMLPNet is the first MLP that seamlessly transfer to Cityscapes semantic segmentation. The code and models are available at https://github.com/DingXiaoH/RepMLP.Comment: Accepted by CVPR-2022. This is the latest versio

Study of NO2 sensors based on La0.8Sr0.2Co0.2Fe0.8O3-δ/Ag biphasic composite sensing electrodes

In order to enhance the sensing performance of the NO2 sensor, La0.8Sr0.2Co0.2Fe0.8O3-δ (LSCF) and LSCF+0.050Ag, LSCF+0.075Ag, LSCF+0.100Ag biphasic sensing electrodes were prepared by the self-demixing method, and the impedance spectrum type NO2 sensor was successfully prepared with YSZ as the solid electrolyte. The effect of Ag doping on the performance of the sensor was studied. The results show that compared with Z′, Z″ and |Z|, when θ is used as the response signal, the response recovery time of the sensor is shorter and the response signal is more stable. The resutts show that using θ as the response signal, the sensitivities of the sensors based on LSCF, LSCF+0.050Ag, LSCF+0.075Ag and LSCF+0.100Ag sensitive electrodes are 11.12°, 11.28°, 13.62° and 9.56° at the optimal operating temperature of 450 ℃, and the sensitivity of LSCF+0.075Ag is higher. The sensor also exhibits excellent reproducibility and long-term stability. In addition, the sensor shows excellent immunity to CH4, CO2, H2, CO and NH3. Therefore, the preparation of biphasic sensing electrode materials with a homogeneous distribution of the second phase and excellent catalytic activity by the self-demixing method effectively improves the sensitivity and selectivity of the sensor, which provides a new idea for the preparation of high-performance NO2 sensors

Digitized Construction of Iontronic Pressure Sensor with Self-Defined Configuration and Widely Regulated Performance

Flexible pressure sensors are essential components for wearable smart devices and intelligent systems. Significant progress has been made in this area, reporting on excellent sensor performance and fascinating sensor functionalities. Nevertheless, geometrical and morphological engineering of pressure sensors is usually neglected, which, however, is significant for practical application. Here, we present a digitized manufacturing methodology to construct a new class of iontronic pressure sensors with optionally defined configurations and widely modulated performance. These pressure sensors are composed of self-defined electrode patterns prepared by a screen printing method and highly tunable pressure-sensitive microstructures fabricated using 3D printed templates. Importantly, the iontronic pressure sensors employ an iontronic capacitive sensing mechanism based on mechanically regulating the electrical double layer at the electrolyte/electrode interfaces. The resultant pressure sensors exhibit high sensitivity (58 kPa−1), fast response/recovery time (45 ms/75 ms), low detectability (6.64 Pa), and good repeatability (2000 cycles). Moreover, our pressure sensors show remarkable tunability and adaptability in device configuration and performance, which is challenging to achieve via conventional manufacturing processes. The promising applications of these iontronic pressure sensors in monitoring various human physiological activities, fabricating flexible electronic skin, and resolving the force variation during manipulation of an object with a robotic hand are successfully demonstrated

Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images

Abstract Background Quantizing the Breast Imaging Reporting and Data System (BI-RADS) criteria into different categories with the single ultrasound modality has always been a challenge. To achieve this, we proposed a two-stage grading system to automatically evaluate breast tumors from ultrasound images into five categories based on convolutional neural networks (CNNs). Methods This new developed automatic grading system was consisted of two stages, including the tumor identification and the tumor grading. The constructed network for tumor identification, denoted as ROI-CNN, can identify the region contained the tumor from the original breast ultrasound images. The following tumor categorization network, denoted as G-CNN, can generate effective features for differentiating the identified regions of interest (ROIs) into five categories: Category “3”, Category “4A”, Category “4B”, Category “4C”, and Category “5”. Particularly, to promote the predictions identified by the ROI-CNN better tailor to the tumor, refinement procedure based on Level-set was leveraged as a joint between the stage and grading stage. Results We tested the proposed two-stage grading system against 2238 cases with breast tumors in ultrasound images. With the accuracy as an indicator, our automatic computerized evaluation for grading breast tumors exhibited a performance comparable to that of subjective categories determined by physicians. Experimental results show that our two-stage framework can achieve the accuracy of 0.998 on Category “3”, 0.940 on Category “4A”, 0.734 on Category “4B”, 0.922 on Category “4C”, and 0.876 on Category “5”. Conclusion The proposed scheme can extract effective features from the breast ultrasound images for the final classification of breast tumors by decoupling the identification features and classification features with different CNNs. Besides, the proposed scheme can extend the diagnosing of breast tumors in ultrasound images to five sub-categories according to BI-RADS rather than merely distinguishing the breast tumor malignant from benign