Inversion of the wavelet transform using Riemannian sums

AbstractWe study the approximation of the inverse wavelet transform using Riemannian sums. For a large class of wavelet functions, we show that the Riemannian sums converge to the original function as the sampling density tends to infinity. When the analysis and synthesis wavelets are the same, we also give some necessary conditions for the Riemannian sums to be convergent

Discovering Dynamic Causal Space for DAG Structure Learning

Discovering causal structure from purely observational data (i.e., causal discovery), aiming to identify causal relationships among variables, is a fundamental task in machine learning. The recent invention of differentiable score-based DAG learners is a crucial enabler, which reframes the combinatorial optimization problem into a differentiable optimization with a DAG constraint over directed graph space. Despite their great success, these cutting-edge DAG learners incorporate DAG-ness independent score functions to evaluate the directed graph candidates, lacking in considering graph structure. As a result, measuring the data fitness alone regardless of DAG-ness inevitably leads to discovering suboptimal DAGs and model vulnerabilities. Towards this end, we propose a dynamic causal space for DAG structure learning, coined CASPER, that integrates the graph structure into the score function as a new measure in the causal space to faithfully reflect the causal distance between estimated and ground truth DAG. CASPER revises the learning process as well as enhances the DAG structure learning via adaptive attention to DAG-ness. Grounded by empirical visualization, CASPER, as a space, satisfies a series of desired properties, such as structure awareness and noise robustness. Extensive experiments on both synthetic and real-world datasets clearly validate the superiority of our CASPER over the state-of-the-art causal discovery methods in terms of accuracy and robustness.Comment: Accepted by KDD 2023. Our codes are available at https://github.com/liuff19/CASPE

Status and Prospects of the PandaX-III Experiment

The PandaX-III experiment searches the neutrinoless double beta decay of $^{136}$Xe with a high-pressure xenon gaseous time projection chamber~(TPC). Thermal-bonding Micromegas modules are used for charge collection. Benefitting from the excellent energy resolution and imaging capability, the background rate can be significantly suppressed through the topological information of events. The technology is successfully demonstrated by a prototype detector. The final detector has been constructed. In this paper, we will report the status of the PandaX-III experiment, including the construction and commissioning of the final detector, and the Micromegas-based TPC performance test in the prototype detector

Implementing Infopipes: The SIP/XIP Experiment

We describe an implementation of the Infopipe abstraction for information flow applications. We have implemented software tools that translate the SIP/XIP variant of Infopipe specification into executable code. These tools are evaluated through the rewriting of two realistic applications using Infopipes: a multimedia streaming program and a web source combination application. Measurements show that Infopipe-generated code has the same execution overhead as the manually written original version. Source code of Infopipe version is reduced by 36% to 85% compared to the original

Low-Rank Linear Dynamical Systems for Motor Imagery EEG

The common spatial pattern (CSP) and other spatiospectral feature extraction methods have become the most effective and successful approaches to solve the problem of motor imagery electroencephalography (MI-EEG) pattern recognition from multichannel neural activity in recent years. However, these methods need a lot of preprocessing and postprocessing such as filtering, demean, and spatiospectral feature fusion, which influence the classification accuracy easily. In this paper, we utilize linear dynamical systems (LDSs) for EEG signals feature extraction and classification. LDSs model has lots of advantages such as simultaneous spatial and temporal feature matrix generation, free of preprocessing or postprocessing, and low cost. Furthermore, a low-rank matrix decomposition approach is introduced to get rid of noise and resting state component in order to improve the robustness of the system. Then, we propose a low-rank LDSs algorithm to decompose feature subspace of LDSs on finite Grassmannian and obtain a better performance. Extensive experiments are carried out on public dataset from “BCI Competition III Dataset IVa” and “BCI Competition IV Database 2a.” The results show that our proposed three methods yield higher accuracies compared with prevailing approaches such as CSP and CSSP

Low-Rank Linear Dynamical Systems for Motor Imagery EEG

The common spatial pattern (CSP) and other spatiospectral feature extraction methods have become the most effective and successful approaches to solve the problem of motor imagery electroencephalography (MI-EEG) pattern recognition from multichannel neural activity in recent years. However, these methods need a lot of preprocessing and postprocessing such as filtering, demean, and spatiospectral feature fusion, which influence the classification accuracy easily. In this paper, we utilize linear dynamical systems (LDSs) for EEG signals feature extraction and classification. LDSs model has lots of advantages such as simultaneous spatial and temporal feature matrix generation, free of preprocessing or postprocessing, and low cost. Furthermore, a low-rank matrix decomposition approach is introduced to get rid of noise and resting state component in order to improve the robustness of the system. Then, we propose a low-rank LDSs algorithm to decompose feature subspace of LDSs on finite Grassmannian and obtain a better performance. Extensive experiments are carried out on public dataset from "BCI Competition III Dataset IVa" and "BCI Competition IV Database 2a." The results show that our proposed three methods yield higher accuracies compared with prevailing approaches such as CSP and CSSP

A Novel Apoptosis Correlated Molecule: Expression and Characterization of Protein Latcripin-1 from Lentinula edodes C91–3

An apoptosis correlated molecule—protein Latcripin-1 of Lentinula edodes C91–3—was expressed and characterized in Pichia pastoris GS115. The total RNA was obtained from Lentinula edodes C91–3. According to the transcriptome, the full-length gene of Latcripin-1 was isolated with 3′-Full Rapid Amplification of cDNA Ends (RACE) and 5′-Full RACE methods. The full-length gene was inserted into the secretory expression vector pPIC9K. The protein Latcripin-1 was expressed in Pichia pastoris GS115 and analyzed by Sodium Dodecylsulfonate Polyacrylate Gel Electrophoresis (SDS-PAGE) and Western blot. The Western blot showed that the protein was expressed successfully. The biological function of protein Latcripin-1 on A549 cells was studied with flow cytometry and the 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyl-tetrazolium Bromide (MTT) method. The toxic effect of protein Latcripin-1 was detected with the MTT method by co-culturing the characterized protein with chick embryo fibroblasts. The MTT assay results showed that there was a great difference between protein Latcripin-1 groups and the control group (p < 0.05). There was no toxic effect of the characterized protein on chick embryo fibroblasts. The flow cytometry showed that there was a significant difference between the protein groups of interest and the control group according to apoptosis function (p < 0.05). At the same time, cell ultrastructure observed by transmission electron microscopy supported the results of flow cytometry. The work demonstrates that protein Latcripin-1 can induce apoptosis of human lung cancer cells A549 and brings new insights into and advantages to finding anti-tumor proteins