Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition

Meta-learning methods have been widely used in few-shot named entity recognition (NER), especially prototype-based methods. However, the Other(O) class is difficult to be represented by a prototype vector because there are generally a large number of samples in the class that have miscellaneous semantics. To solve the problem, we propose MeTNet, which generates prototype vectors for entity types only but not O-class. We design an improved triplet network to map samples and prototype vectors into a low-dimensional space that is easier to be classified and propose an adaptive margin for each entity type. The margin plays as a radius and controls a region with adaptive size in the low-dimensional space. Based on the regions, we propose a new inference procedure to predict the label of a query instance. We conduct extensive experiments in both in-domain and cross-domain settings to show the superiority of MeTNet over other state-of-the-art methods. In particular, we release a Chinese few-shot NER dataset FEW-COMM extracted from a well-known e-commerce platform. To the best of our knowledge, this is the first Chinese few-shot NER dataset. All the datasets and codes are provided at https://github.com/hccngu/MeTNet

NLRP3 Inflammasome Promotes the Progression of Acute Myeloid Leukemia via IL-1β Pathway

NLRP3 inflammasome has been reported to be associated with the pathogenesis of multiple solid tumors. However, the role of NLRP3 inflammasome in acute myeloid leukemia (AML) remains unclear. We showed that NLRP3 inflammasome is over-expressed and highly activated in AML bone marrow leukemia cells, which is correlated with poor prognosis. The activation of NLRP3 inflammasome in AML cells promotes leukemia cells proliferation, inhibits apoptosis and increases resistance to chemotherapy, while inactivation of NLRP3 by caspase-1 or NF-κB inhibitor shows leukemia-suppressing effects. Bayesian networks analysis and cell co-culture tests further suggest that NLRP3 inflammasome acts through IL-1β but not IL-18 in AML. Knocking down endogenous IL-1β or anti-IL-1β antibody inhibits leukemia cells whereas IL-1β cytokine enhances leukemia proliferation. In AML murine model, up-regulation of NLRP3 increases the leukemia burden in bone marrow, spleen and liver, and shortens the survival time; furthermore, knocking out NLRP3 inhibits leukemia progression. Collectively, all these evidences demonstrate that NLRP3 inflammasome promotes AML progression in an IL-1β dependent manner, and targeting NLRP3 inflammasome may provide a novel therapeutic option for AML

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

MA_W-Net-Based Dual-Output Method for Microseismic Localization in Strong Noise Environments

With the continuous depletion of conventional oil and gas reservoir resources, the beginning of exploration and development of unconventional oil and gas reservoir resources has led to the rapid development of microseismic monitoring technology. Addressing the challenges of low signal-to-noise ratio and inaccurate localization in microseismic data, we propose a new neural network MA_W-Net based on the U-Net network with the following improvements: (1) The foundational U-Net model was refined by evolving the single-channel decoder into a two-channel decoder, aimed at enhancing microseismic event localization and noise suppression capabilities. (2) The integration of attention mechanisms such as the convolutional block attention module (CBAM), coordinate attention (CA), and squeeze-and-excitation (SE) into the encoder to bolster feature extraction. We use synthetic data for evaluating the proposed method. Comparing with the normal U-net network, our accuracy in seismic recordings with a signal-to-noise ratio of −15 is improved from 78 percent to 93.5 percent, and the average error is improved from 2.60 m to 0.76 m. The results show that our method can accurately localize microseismic events and denoising processes from microseismic records with a low signal-to-noise ratio

Signal Reconstruction of Arbitrarily Lack of Frequency Bands from Seismic Wavefields Based on Deep Learning

Due to the limitations of seismic exploration instruments and the impact of the high frequencies absorption by the earth layers during subsurface propagation of seismic waves, recorded seismic data usually lack high and low frequency information that is needed to accurately image geological structures. Traditional methods face challenges such as limitations of model assumptions and poor adaptability to complex geological conditions. Therefore, this paper proposes a deep learning method that introduces the attention mechanism and Bi-directional gated recurrent unit (BiGRU) into the Transformer neural network. This approach can simultaneously capture both global and local characteristics of time series data, establish mappings between different frequency bands, and achieve information compensation and frequency extension. The results show that the BiGRU-Extended Transformer network is capable of compensating and extending the synthetic seismic data sets with the limited frequency band. It has certain generalization capabilities and stability and can effectively handle various problems in the data reconstruction process, which is better than traditional methods

NIR-assisted orchid virus therapy using urchin bimetallic nanomaterials in phalaenopsis

The authors would like to thank the Council of Agriculture (grant number 101AS-9.1.1-FD-Z1) of Taiwan and National Science Council (contracts numbers NSC 101-2113-M-002-014-MY3 and NSC 101-3113-P-002-021) for financially supporting this research.The use of nanoparticles has drawn special attention, particularly in the treatment of plant diseases. Cymbidium mosaic virus (CymMV) and Odontoglossum ring spot virus (ORSV) are the most prevalent and serious diseases that affect the development of the orchid industry. In this study we treated nanoparticles as a strategy for enhancing the resistance of orchids against CymMV and ORSV. After chitosan-modified gold nanoparticles (Au NPs) were injected into Phalaenopsis leaves, the injected leaves were exposed to 980 nm laser for light–heat conversion. To evaluate virus elimination in the treated Phalaenopsis leaves, the transcripts of coat protein genes and the production of viral proteins were assessed by reverse transcription-Polymerase chain reaction and enzyme-linked immunosorbent assay, respectively. The expression of coat protein genes for both CymMV and ORSV was significantly lower in the chitosan-modified Au NP-treated Phalaenopsis leaves than in the control. Similarly, the amount of coat proteins for both viruses in the Phalaenopsis leaves was lower than that in the control (without nanoparticle injection). We propose that the temperature increase in the chitosan-modified Au NP-treated Phalaenopsis tissues after laser exposure reduces the viral population, consequently conferring resistance against CymMV and ORSV. Our findings suggest that the application of chitosan-modified Au NPs is a promising new strategy for orchid virus therapy.Publisher PDFPeer reviewe

Wavefield Decomposition-Based Direct Envelope Inversion and Structure-Guided Perturbation Decomposition for Salt Building

Due to the large-scale and strong perturbation features of salt bodies, it is very difficult to complete a good salt building with the conventional full waveform inversion (FWI) method without low-frequency data and prior information. The direct envelope inversion (DEI) method is quite effective for salt building when seismic data lack low-frequency information. However, in the current DEI studies, the calculation of the envelope field, which needs a nonlinear envelope operator, does not consider the influences of wavefield overlapping, and the inversion quality of subsalt areas needs further improvements. In this paper, we analyze the effects of wavefield overlapping on envelope field calculation and propose a new envelope field calculation method based on wavefield decomposition. Then, we propose a wavefield decomposition-based direct envelope inversion (WDDEI) method, in which the gradient is calculated using the new envelope field. To improve the inversion quality of subsalt structures, we propose a structure-guided perturbation decomposition method, which can separate the strong scattering salt information from the DEI results with the help of reverse time migration images. Finally, numerical tests are conducted on a modified SEG/EAGE salt model to demonstrate the effectiveness and the antinoise performance of the proposed method

Multisource Seismic Full Waveform Inversion of Metal Ore Bodies

The seismic exploration method could explore deep metal ore bodies (depth > 1000 m). However, it is difficult to describe the geometry of the complex metal ore body accurately. Seismic full waveform inversion is a relatively new method to achieve accurate imaging of subsurface structures, but its success requires better initial models and low-frequency data. The seismic data acquired in the metal mine area is usually difficult to meet the requirements of full waveform inversion. The passive seismic data usually contains good low frequency information. In this paper, we use both passive and active seismic datasets to improve the full waveform inversion results in the metal mining area. The results show that the multisource seismic full waveform inversion could obtain a suitable result for high-resolution seismic imaging of metal ore bodies