Miracle-dz/UniFAR-FTUs huggingface dataset:
Earthworms and plants can decrease soil greenhouse gas emissions by modulating soil moisture fluctuations and soil macroporosity in a mesocosm experiment

Earthworms can stimulate microbial activity and hence, greenhouse gas (GHG) emissions from soils. However, the extent of this effect in the presence of plants and soil moisture fluctuations, which are influenced by earthworm burrowing activity, remains uncertain. Here we report the effect of earthworms (without, anecic, endogeic, both) and plants (with, without) on GHG (CO2, N2O) emissions in a 3 month-greenhouse mesocosm experiment simulating a simplified agricultural context. The mesocosms allowed for water drainage at the bottom to account for the earthworm engineering effect on water flow during two drying-wetting cycles. N2O cumulative emissions were 34.6 and 44.8% lower when both earthworm species and only endogeic species were present, respectively, and 19.8% lower in presence of plants. The presence of the endogeic species alone or in combination with the anecic species slightly reduced CO2 emissions by 5.9% and 11.4% respectively, and plants presence increased emissions by 6%. Earthworms, plants and soil water content interactively affected weekly N2O emissions, an effect controlled by increased soil dryness due to drainage via earthworm burrows and mesocosm evapotranspiration. Soil macroporosity (measured by X-ray tomography) was affected by earthworm species-specific burrowing activity. Both GHG emissions decreased with top soil macropore volume, presumably due to reduced moisture and microbial activity. N2O emissions decreased with macropore volume in the deepest layer, likely due to fewer anaerobic microsites. Our results indicate that, under experimental conditions allowing for plant and earthworm engineering effects on soil moisture, earthworms do not increase GHG emissions and that endogeic earthworms may even reduce N2O emissions.
Monitoring the regional Ocean Heat Content change over the Atlantic Ocean with the space geodetic approach

The estimation of the regional Ocean Heat Content (OHC) is essential for climate analysis and future climate predictions. In this study, we propose a method to estimate and propagate uncertainties in regional OHC changes. The OHC is estimated with space geodetic steric data corrected from salinity variations estimated with in situ measurements. A variance-covariance matrix method is used to propagate uncertainties from space geodetic data to the OHC change. The integrated OHC change over the Atlantic basin is 0.17 W m − 2 which represents 21% of the global OHC trend, with significant trends observed 5 in 52% of the Atlantic basin. Uncertainties in OHC trends are mainly attributed to manometric sea level change uncertainties. We validate our space geodetic OHC estimates at two test sites, representing the subtropical and subpolar regions of the North Atlantic, highlighting their importance in understanding climate dynamics. Our results show good agreement between space geodetic estimates and in situ measurements in the North Atlantic region. The space geodetic OHC trends reveal a warming pattern in the southern and western parts of the North Atlantic, particularly in the Gulf Stream region, while the northeastern 10 part exhibits cooling trends. Overall, our study provides valuable insights and a new framework to estimate regional OHC change and its uncertainties, contributing to a better understanding of the Earth’s climate system and its future projections. The
A modified gradient‐based algorithm for solving extended Sylvester‐conjugate matrix equations

In this paper, we present a modified gradient‐based algorithm for solving extended Sylvester‐conjugate matrix equations. The idea is from the gradient‐based method introduced in [14] and the relaxed gradient‐based algorithm proposed in [16]. The convergence analysis of the algorithm is investigated. We show that the iterative solution converges to the exact solution for any initial value based on some appropriate assumptions. A numerical example is given to illustrate the effectiveness of the proposed method and to test its efficiency and accuracy compared with those presented in [14] and [16].
Driven Handheld Forceps with Force Display Operated by

—Development of intuitive user interface of a dexterous forceps with wrist joint for minimally-invasive surgery (MIS) is important for reducing the surgeon’s confusion and fatigue. In this paper, a wearable robotic forceps manipulator is proposed. By measuring the wrist attitude of the surgeon by a motion sensor and driving the forceps tip by actuators, intuitive operation is realized, compared with fully mechanical forceps. A compact and lightweight system is achieved since we adopt pneumatic actuators to drive the manipulator. In order to help the force sensing of the surgeon, the external force is measured from the air pressure and displayed in a visual way. Experimental comparison with a conventional handheld forceps and a teleoperation system is conducted and its results show the effectiveness of the proposed handheld forceps.
Gradient bounds and Liouville theorems for quasilinear elliptic equations

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Prediction of the Impact of Covid-19 Vaccine on Public Health Using Twitter

Social media is a source of big data. Media like Twitter and Facebook has been used for collecting and analyzing user data for different purposes. The data can be used to analyze people opinions towards certain topics and incidents by applying sentiment analysis and then certain useful insights can be drawn from the analyzed data. During the current time of Covid-19, people have been sharing information regarding Covid-19 statistics, vaccines, and discussing the effects of the vaccine concerning public health. The purpose of this study is to analyze tweet data regarding the Covid-19 vaccine by applying sentiment analysis and predicting the impact of the vaccine on public health. Also, the tweets are analyzed for hidden topics by applying Topic Modelling using Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). The source of data for this study is Twitter API. The coding and data analysis is done using Python programming language in the Spyder (Scientific Python Development Environment) that is an integrated development environment for scientific programming, testing, and data analysis. The results of the study indicate a greater positive sentiment reflecting a healthy public discussion about the Covid-19 vaccine, information, awareness, and public acceptance. With these results, a positive impact of the Covid-19 vaccine on public health is predicted. The results of topic modeling discovered 10 hidden topics from the tweet dataset. © 2022. All Rights Reserved.
Weak Solutions in the Sense of Schwartz to Dynkin’s Characteristic Operator Equation

We show that if the transition operator P t of a continuous time-homogeneous (strong) Markov process X in IR n maps inﬁnitely diﬀerentiable functions with compact support to twice continuously diﬀerentiable functions for t> 0 , then the action of the inﬁnitesimal operator A of X on its domain coincides with the action of the diﬀerential operator D of X understood in the sense of Schwartz distributions. Applying this fact to the process X stopped at the ﬁrst exit time from a given open subset C of IR n , we derive that the solution to Dynkin’s characteristic operator equation of X arising from the Dirichlet problem on C can be viewed as a weak solution to the diﬀerential operator equation of X in the sense of Schwartz distributions. A useful consequence of this identiﬁcation is that the solution is inﬁnitely diﬀerentiable on C whenever the drift and diﬀusion coeﬃcients are inﬁnitely diﬀer-entiable and the diﬀerential operator D of X is hypoelliptic (e.g. when D satisﬁes the H¨ormander condition). In particular, this is satisﬁed when X is a degenerate diﬀusion process in IR n (in the sense that D is a degenerate parabolic operator) so that the analytic existence results for parabolic PDEs are generally not available. The arguments and results extend to cover more general boundary value problems of this kind including the initial value problems as well.
A New Multiway MFDM Based Technique For EEG Source Localisation And Interpretation

The clinical applications of EEG Source Localization are seen in the analysis of epilepsy, schizophrenia, Parkinson, stress, and tumor. This paper proposes a novel brain source localization method using a multiway Multivariate Fourier Decomposition Method (MFDM) based technique. Firstly, a multiway array (tensor) is formed using MFDM, a novel way of forming a tensor from EEG signals. Then, this tensor is decomposed into various independent components using Canonical Polyadic Decomposition (CPD). Each independent component is used to reconstruct separate EEG signals corresponding to every independent source. Lastly, localization techniques like SPICE and LIKES are used to obtain the source locations. The results are compared with traditional methods like MNE, sLORETA, LASSO, and Space Time Frequency (STF) based tensor decomposition method. Further, real EEG signals during an arithmetic task are used to support and verify the proposed method in real application.
BFT Consensus Algorithms

With the advent of Bitcoin, the blockchain as the underlying technology of Bitcoin has entered people’s field of vision. In recent years, the blockchain and its related technologies have developed rapidly. The consensus algorithm is the key to the realization of blockchain technology. There will be Byzantine nodes in the process of reaching consensus among the nodes in the blockchain. Byzantine nodes are tolerated in the blockchain to achieve consensus. Consensus is an essential performance of consensus algorithms. This paper first introduces blockchain technology and the application of consensus algorithms in it; secondly, introduces the classic PBFT algorithm; then, introduces some existing consensus algorithms; finally summarizes the research status and challenges of consensus algorithms, and analyzes its Prospects.
Occupancy Forecasting for Dynamic Building Controls

Across the globe, buildings account for a substantial portion of energy consumption and CO2 emissions. In this paper, we use time series-based machine learning models to predict building occupancy and control building heating, ventilation, and air conditioning (HVAC) for energy savings. We achieve a 7.4% reduction in daily HVAC energy usage in our target building. Our approach leverages existing wireless access point (WAP) infrastructure to gather ground-truth occupancy data in a privacy-conscious way. Our method can be used across multiple buildings and can be expanded to different use cases.
Modeling Solar Cell Performance at High Temperatures: A Validation Study of Theoretical Equations and Computational Simulations

As the demand for solar renewable energies grows globally, researchers' goal has always been to develop low-cost, high-efficiency cells, knowing that higher panel temperatures lead to poor conversion performance and decreased long-term reliability, posing a well-known challenge in the field of photovoltaics. This study employed both theoretical and computational methodologies to investigate the relationship between temperature and efficiency. To model the performance of the solar cell under varying temperatures, theoretical equations relating the temperature of the cell to the cell's efficiency were developed, and MATLAB SIMULINK was used to develop a computational model showing the output power of a solar cell under varying temperatures. Our findings reveal that the efficiency of an LR5-72HPH 545 Watts solar cell decreases from 21.3% at 25°C to 17.41% at 70°C. The results of the theoretical and computational studies were compared and found to have a small error of 1.05%, proving that computational modeling can be relied on to accurately predict solar cell performance, where this model is valuable since it allows a to analyze the effect of temperature on solar cells in a way that is simpler and faster to achieve than theoretical methods. However, by employing suitable cooling systems, it is possible to limit the impacts of temperature, which is critical for the development of more efficient and dependable solar cells, particularly in high-temperature conditions.
Information Flow Graph for Distributed Caching without Newcomers over a Broadcast Medium

The trade-offs between storage and repair traffic for replacing failed storage nodes with new nodes (newcomers) in data centers with an omniscient controller are well understood. However, in edge storage settings, newcomers are not readily available, necessitating resilient data storage (caching) without newcomers. Edge storage nodes can often communicate via a broadcast wireless medium, which can be exploited to reduce the transmitted repair traffic via network coding. Repairs for resilient distributed caching without newcomers over a broadcast medium with Random Linear Network Coding (RLNC), which does not require an omniscient controller, have not been previously studied. We develop an information-theoretic model to characterize the theoretically achievable trade-offs between stored data and transmitted repair data in the RLNC broadcast setting without newcomers. Specifically, we formulate an Information Flow Graph (FG) model and identify all cuts in the resulting FG. We validate the theoretical FG model with simulations that demonstrate that the practically achievable trade-offs are close to the theoretical trade-offs.
Uniformity Adjustment of Delay-Based Physical Unclonable Function: Modeling and Analysis

We discuss a theoretical model to evaluate the source of entropy from a mathematical point of view targeting optimized intrinsic security mechanisms implemented by Physical Unclonable Functions (PUFs). The results develop a profitable design tool that can address the most crucial model parameters suitable to adjust the system uniformity without imposing additional electronic gates. The study pursues the theoretical approach by a low-complexity CMOS circuit solution implementing the mathematical models aiming at proposing methodical approaches for implementing security mechanisms embedded in hardware.
The Bayes Factor for Case-Control Studies with Misclassified Data

The question of how to test if collected data for a case-control study are misclassified was investigated. A mixed approach was employed to calculate the Bayes factor to assess the validity of the null hypothesis of no-misclassification. A real-world data set on the association between lung cancer and smoking status was used as an example to illustrate the proposed method.
A Low Power and Low Latency FPGA-Based Spiking Neural Network Accelerator

Spiking Neural Networks (SNNs), known as the third generation of the neural network, are famous for their biological plausibility and brain-like characteristics. Recent efforts further demonstrate the potential of SNNs in high-speed inference by designing accelerators with the parallelism of temporal or spatial dimensions. However, with the limitation of hardware resources, the accelerator designs must utilize off-chip memory to store many intermediate data, which leads to both high power consumption and long latency. In this paper, we focus on the data flow between layers to improve arithmetic efficiency. Based on the spike discrete property, we design a convolution-pooling(CONVP) unit that fuses the processing of the convolutional layer and pooling layer to reduce latency and resource utilization. Furthermore, for the fully-connected layer, we apply intra-output parallelism and inter-output parallelism to accelerate network inference. We demonstrate the effectiveness of our proposed hardware architecture by implementing different SNN models with the different datasets on a Zynq XA7Z020 FPGA. The experiments show that our accelerator can achieve about x28 inference speed up with a competitive power compared with FPGA implementation on MNIST dataset and a x15 inference speed up with low power compared with ASIC design on DVSGesture dataset.
Cross-Domain Transformation for Outlier Detection on Tabular Datasets

The overwhelming success of Deep Learning approaches in recent years is often driven by the availability of large public datasets. However, in some domains like finance, creating and sharing realistic datasets is hindered by secrecy or privacy concerns. This can lead to a mismatch, where approaches that have proven to work well on public, research-oriented datasets end up underperforming when applied to real-world (private) datasets. In this work, we focus on the task of Outlier Detection (OD) and bridge the above gap by building an autoencoder based Deep Learning approach that can transform samples between two tabular datasets (e.g., a private and public one). The goal of our approach is that transformed samples become similar to the target dataset, while inliers remain inliers and outliers remain outliers. Among others, after successful transformation, this allows applying of proven methods on public datasets to internal datasets, even if they are of different dimensionality (rows and columns). To evaluate our approach, we introduce metrics to measure dataset similarity and the quality of transformed samples. Our experimental results show that combining public datasets with transformed samples of other datasets leads to higher dataset similarity while sustaining performance w.r. t. common OD algorithms.
Latency analysis of JP and Root DNS servers from packet capture data

IP anycast is widely used for root and TLD DNS servers to reduce latency. DNS operators need to perform measurements to understand and improve their service quality. Although it is vital to measure the effect of IP anycast, the measurement requires enough worldwide measurement points to send queries. In this paper, we try a more manageable approach, i.e., analyzing the existing capture data to reveal the response delays between resolvers and authoritative DNS servers. There are two packet capture datasets, the DITL and JP datasets. The DITL dataset is collected by Root server operators and maintained by DNS-OARC. The JP dataset is collected for JP TLD operations. Specifically, we extracted communication delay information from TCP session data in these datasets.Our analysis that uses multiple datasets, i.e., DITL and JP datasets, reveals: 1) Approximately 30% of IPv4 addresses seen at the M-Root & JP DNS server have RTT information and come from over 200 countries. 2) JP DNS servers offer RTT of less than 20ms for 80% of queries from Japan, and RTT of less than 100ms for 80% of queries from outside of Japan. 3) The newly installed M-root Brisbane node offers shorter RTTs in Australia.
Make BERT-based Chinese Spelling Check Model Enhanced by Layerwise Attention and Gaussian Mixture Model

BERT-based models have shown a remarkable ability in the Chinese Spelling Check (CSC) task recently. However, traditional BERT-based methods still suffer from two limitations. First, although previous works have identified that explicit prior knowledge like Part-Of-Speech (POS) tagging can benefit in the CSC task, they neglected the fact that spelling errors inherent in CSC data can lead to incorrect tags and therefore mislead models. Additionally, they ignored the correlation between the implicit hierarchical information encoded by BERT's intermediate layers and different linguistic phenomena. This results in sub-optimal accuracy. To alleviate the above two issues, we design a heterogeneous knowledge-infused framework to strengthen BERT-based CSC models. To incorporate explicit POS knowledge, we utilize an auxiliary task strategy driven by Gaussian mixture model. Meanwhile, to incorporate implicit hierarchical linguistic knowledge within the encoder, we propose a novel form of n-gram-based layerwise self-attention to generate a multilayer representation. Experimental results show that our proposed framework yields a stable performance boost over four strong baseline models and outperforms the previous state-of-the-art methods on two datasets.
Deep Learning Based COVID-19 Detection from Chest X-ray Images

The Coronavirus Disease 2019 (COVID-19) pandemic has dramatically affected the healthcare systems and economy over the world. To help reduce the negative effects of such a contagious malady, the detection time of COVID-19 becomes crucial. While one of the most promising methods to detect whether a patient is infected with this virus is to track his or her lung tissue with chest x-ray (CXR) images, it is considerably time-consuming if all the images are examined by professional radiologists. Besides, the accuracy of detection is determined mainly by the status and stress of the specific radiologist. To tackle these issues, we present a convolutional neural network that automatically detects infections from CXR images. Due to the limited amount of training data, data augmentation methods are used to reduce overfitting. Besides, training improvements such as warmup, cosine scheduler, and label smoothing are also applied during training. The proposed method is validated on the COVID-QU-Ex dataset and has an accuracy of 95.18% and an F1 score of 95.11 % on the validation set, showing its powerful potential in real-world applications for medical diagnostic systems.
A Novel Hybrid Model Based on Deep Learning and Autoregressive for Air Quality Prediction

With the rapid development of urbanization and industrialization, air pollution problems are gradually aggravated. Efficient and accurate air quality prediction can provide a reference for air pollution control and air quality improvement. Predicting air quality by traditional methods is difficult as air quality is influenced by many complex factors, i.e., temporal and multivariate correlations. Meanwhile, under the constraints of data scale, neural network methods have difficulty capturing the long-term associations of input features. To address these challenges, we propose a novel hybrid model, namely Deep Learning and Autoregressive Network (DLARN). Specifically, our model consists of two parts: a deep learning part for analyzing the nonlinear features of air quality series, and an autoregressive module for capturing the linear trend of the series. The former combines CNN, BiLSTM and the temporal attention mechanism to learn the temporal and multivariate dependence patterns in air quality series, and the latter can address the problem of neural network methods that the output scale is insensitive to the input scale. Experiments on two real-world datasets show that our model outperforms other baselines and improves performance by 7.04% - 10.81% compared to the state-of-the-art baseline.