The Compact Support Neural Network

Neural networks are popular and useful in many fields, but they have the problem of giving high confidence responses for examples that are away from the training data. This makes the neural networks very confident in their prediction while making gross mistakes, thus limiting their reliability for safety-critical applications such as autonomous driving, space exploration, etc. This paper introduces a novel neuron generalization that has the standard dot-product-based neuron and the {\color{black} radial basis function (RBF)} neuron as two extreme cases of a shape parameter. Using a rectified linear unit (ReLU) as the activation function results in a novel neuron that has compact support, which means its output is zero outside a bounded domain. To address the difficulties in training the proposed neural network, it introduces a novel training method that takes a pretrained standard neural network that is fine-tuned while gradually increasing the shape parameter to the desired value. The theoretical findings of the paper are a bound on the gradient of the proposed neuron and a proof that a neural network with such neurons has the universal approximation property. This means that the network can approximate any continuous and integrable function with an arbitrary degree of accuracy. The experimental findings on standard benchmark datasets show that the proposed approach has smaller test errors than state-of-the-art competing methods and outperforms the competing methods in detecting out-of-distribution samples on two out of three datasets.Comment: 13 pages, 6 figure

Panel Data Visualization in R (panelView) and Stata (panelview)

We develop an R package panelView and a Stata package panelview for panel data visualization. They are designed to assist causal analysis with panel data and have three main functionalities: (1) They plot the treatment status and missing values in a panel dataset; (2) they visualize the temporal dynamics of the main variables of interest; and (3) they depict the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate. These tools can help researchers better understand their panel datasets before conducting statistical analysis

All-fiber passively mode-locked Tm-doped NOLM-based oscillator operating at 2-μm in both soliton and noisy-pulse regimes

A self-starting all-fiber passively mode-locked Tm-doped fiber laser based on nonlinear loop mirror (NOLM) is demonstrated. Stable soliton pulses centered at 2017.33 nm with 1.56 nm FWHM were produced at a repetition rate of 1.514 MHz with pulse duration of 2.8 ps and pulse energy of 83.8 pJ. As increased pump power, the oscillator can also operate at noise-like (NL) regime. Stable NL pulses with coherence spike width of 341 fs and pulse energy of up to 249.32 nJ was achieved at a center wavelength of 2017.24 nm with 21.33 nm FWHM. To the best of our knowledge, this is the first 2 μm region NOLM-based mode-locked fiber laser operating at two regimes with the highest single pulse energy for NL pulses

COVID−19 hospitalization increases the risk of developing glioblastoma: a bidirectional Mendelian-randomization study

BackgroundAs a result of the COVID-19 pandemic, patients with glioblastoma (GBM) are considered a highly vulnerable population. Despite this, the extent of the causative relationship between GBM and COVID-19 infection is uncertain.MethodsGenetic instruments for SARS-CoV-2 infection (38,984 cases and 1,644,784 control individuals), COVID-19 hospitalization (8,316 cases and 1,549,095 control individuals), and COVID-19 severity (4,792 cases and 1,054,664 control individuals) were obtained from a genome-wide association study (GWAS) from European populations. A total of 6,183 GBM cases and 18,169 controls from GWAS were enrolled in our study. Their associations were evaluated by applying Mendelian randomization (MR) including IVW meta-analysis, MR-Egger regression, and weighted-median analysis. To make the conclusions more robust and reliable, sensitivity analyses were performed.ResultsOur results showed that genetically predicted COVID−19 hospitalization increases the risk of GBM (OR = 1.202, 95% CI = 1.035–1.395, p = 0.016). In addition, no increased risk of SARS-CoV-2 infection, COVID-19 hospitalization and severity were observed in patients with any type of genetically predicted GBM.ConclusionOur MR study indicated for the first time that genetically predicted COVID−19 hospitalization was demonstrated as a risk factor for the development of GBM

Associations of Amylin with Inflammatory Markers and Metabolic Syndrome in Apparently Healthy Chinese

BACKGROUND: Cellular and animal studies implicate multiple roles of amylin in regulating insulin action, glucose and lipid metabolisms. However, the role of amylin in obesity related metabolic disorders has not been thoroughly investigated in humans. Therefore, we aimed to evaluate the distribution of circulating amylin and its association with metabolic syndrome (MetS) and explore if this association is influenced by obesity, inflammatory markers or insulin resistance in apparently healthy Chinese. METHODS: A population-based sample of 1,011 Chinese men and women aged 35-54 years was employed to measure plasma amylin, inflammatory markers (C-reactive protein [CRP] and interleukin-6 [IL-6]), insulin, glucose and lipid profiles. MetS was defined according to the updated National Cholesterol Education Program Adult Treatment Panel III criteria for Asian-Americans. RESULTS: Plasma amylin concentrations were higher in overweight/obese participants than normal-weight counterparts (P<0.001) without sex difference. Circulating amylin was positively associated with CRP, IL-6, BMI, waist circumference, blood pressure, fasting glucose, insulin, amylin/insulin ratio, HOMA-IR, LDL cholesterol and triglycerides, while negatively associated with HDL cholesterol (all P<0.001). After multiple adjustments, the risk of MetS was significantly higher (odds ratio 3.71; 95% confidence interval: 2.53 to 5.46) comparing the highest with the lowest amylin quartile. The association remained significant even further controlling for BMI, inflammatory markers, insulin or HOMA-IR. CONCLUSIONS: Our study suggests that amylin is strongly associated with inflammatory markers and MetS. The amylin-MetS association is independent of established risk factors of MetS, including obesity, inflammatory markers and insulin resistance. The causal role of hyperamylinemia in the development of MetS needs to be confirmed prospectively

Design of OpenFOAM solver client based on service architecture

Aiming at the problem that the OpenFOAM solving software lacks GUI function, the user interaction experience is poor, this paper studies and designs an OpenFOAM solving client software. The paper designs a service-based "cloud + terminal" computing architecture, designs and develops an OpenFOAM solver service, which can be remotely called and accessed by the GUI client, and integrates the visualization service to realize the data visualization analysis function and realize the cloud collaborative solution calculation function. This paper researches and designs a dynamic GUI interface generation method based on interface template, and realizes the user-defined configuration function of the client software interface. The software supports service-based function extension integration,and supports user interface customization and solver user setting interface customization functions. The user interface is friendly and the expansibility is strong. Through the actual calculation example, the verification shows that the client software can realize the complete solution calculation process by calling the cloud solution service and visualization service remotely

CFD data visualization analysis service for cloud computing environment

Aiming at the problems that the data visualization analysis of Computational Fluid Dynamics (CFD) software requires a large amount of calculation and the operation is inflexible, this paper implements a CFD data visualization analysis service prototype software for the needs of CFD data visualization analysis. In this prototype software, the adaptive Quality of Service (QoS) optimization technology of the adaptive client is studied to reduce the impact of different client computing environments on the quality of service. The service integration interface based on message middleware is studied and designed to improve the flexibility of service usage. The CFD data visualization analysis service oriented to the cloud computing environment takes advantage of the performance advantages of the cloud server, applies the new development model of "cloud + terminal", and has flexibility and scalability

Metagenomic insights into direct interspecies electron transfer and quorum sensing in blackwater anaerobic digestion reactors supplemented with granular activated carbon

The addition of granular activated carbon (GAC) enhanced the performance of up-flow anaerobic sludge blanket (UASB) reactor treating blackwater at 35 °C. DNA were extracted from the sludge and biofilms attached to GAC and submitted for shotgun sequencing. In addition, the acyl-homoserine lactones (AHLs) were quantified. Diverse partners for direct interspecies electron transfer (DIET) were enriched in the sludge or biofilm (GAC-biofilm) of GAC amended UASB. Pedosphaera parvula, Syntrophus aciditrophicus and Syntrophorhabus aromaticivorans were dominant syntrophs. The analysis for type IV pilus assembly genes further suggested DIET may be functioned through GAC for GAC-biofilm, while through conductive pili for sludge aggregates. AHLs quantification and the analysis for quorum sensing (QS) related genes indicated higher QS activity at the population level was induced by GAC. Overall, the work illustrated the different DIET patterns, and suggested that QS played an important role in controlling the performance in GAC amended USAB

Metagenomic insights into direct interspecies electron transfer and quorum sensing in blackwater anaerobic digestion reactors supplemented with granular activated carbon

The addition of granular activated carbon (GAC) enhanced the performance of up-flow anaerobic sludge blanket (UASB) reactor treating blackwater at 35 °C. DNA were extracted from the sludge and biofilms attached to GAC and submitted for shotgun sequencing. In addition, the acyl-homoserine lactones (AHLs) were quantified. Diverse partners for direct interspecies electron transfer (DIET) were enriched in the sludge or biofilm (GAC-biofilm) of GAC amended UASB. Pedosphaera parvula, Syntrophus aciditrophicus and Syntrophorhabus aromaticivorans were dominant syntrophs. The analysis for type IV pilus assembly genes further suggested DIET may be functioned through GAC for GAC-biofilm, while through conductive pili for sludge aggregates. AHLs quantification and the analysis for quorum sensing (QS) related genes indicated higher QS activity at the population level was induced by GAC. Overall, the work illustrated the different DIET patterns, and suggested that QS played an important role in controlling the performance in GAC amended USAB.</p

Prediction of Pedestrian Crossing Behavior Based on Surveillance Video

Prediction of pedestrian crossing behavior is an important issue faced by the realization of autonomous driving. The current research on pedestrian crossing behavior prediction is mainly based on vehicle camera. However, the sight line of vehicle camera may be blocked by other vehicles or the road environment, making it difficult to obtain key information in the scene. Pedestrian crossing behavior prediction based on surveillance video can be used in key road sections or accident-prone areas to provide supplementary information for vehicle decision-making, thereby reducing the risk of accidents. To this end, we propose a pedestrian crossing behavior prediction network for surveillance video. The network integrates pedestrian posture, local context and global context features through a new cross-stacked gated recurrence unit (GRU) structure to achieve accurate prediction of pedestrian crossing behavior. Applied onto the surveillance video dataset from the University of California, Berkeley to predict the pedestrian crossing behavior, our model achieves the best results regarding accuracy, F1 parameter, etc. In addition, we conducted experiments to study the effects of time to prediction and pedestrian speed on the prediction accuracy. This paper proves the feasibility of pedestrian crossing behavior prediction based on surveillance video. It provides a reference for the application of edge computing in the safety guarantee of automatic driving