Miracle-dz/UniFAR-FTUs huggingface dataset:
Fundamental Concepts and Approaches

This introductory chapter uses fluid mechanics as an example field problem for reference; the applicability of the concepts discussed is, however, not in any way limited to this area. Fluid mechanics is described by nonlinear equations, which cannot generally be solved analytically, but which have been solved using various approximate methods including expansion and perturbation methods, sundry particle and vortex tracing methods, collocation and integral methods, and finite difference, finite volume, and finite element methods. Generally the finite difference, finite volume, and finite element discretization methods have been the most successful, but to use them it is necessary to discretize the field using a grid (mesh). (The terms grid and mesh are used interchangeably throughout with identical meaning.) The mesh can be structured or unstructured, but it must be generated under some of the various constraints described below, which can often be difficult to satisfy completely. In fact, at present it can take orders of magnitude more person-hours to construct the grid than it does to construct and analyze the physical solution on the grid. This is especially true now that solution codes of wide applicability are becoming available. Computational fluid dynamics (CFD) is a prime example, and grid generation has been cited repeatedly as a major pacing item (cf. Thompson [1996]). The same is true for other areas of computational field simulation. The proceedings of the several international conferences on grid generation (Thompson [1982], Hauser and Taylor [1986], Sengupta, et al. [1988], Arcilla, et al. [1991], Eiseman, et al. [1994], Soni et al. [1996]) as well as those of the NASA conferences (Smith [1980], Smith [1992], Choo [1995]) provide numerous illustrations of application to CFD and some other fields. A recent comprehensive text is Carey [1997]. Joe F. Thompson
Deep single-cell RNA-seq data clustering with graph prototypical contrastive learning

Abstract Motivation Single-cell RNA sequencing enables researchers to study cellular heterogeneity at single-cell level. To this end, identifying cell types of cells with clustering techniques becomes an important task for downstream analysis. However, challenges of scRNA-seq data such as pervasive dropout phenomena hinder obtaining robust clustering outputs. Although existing studies try to alleviate these problems, they fall short of fully leveraging the relationship information and mainly rely on reconstruction-based losses that highly depend on the data quality, which is sometimes noisy. Results This work proposes a graph-based prototypical contrastive learning method, named scGPCL. Specifically, scGPCL encodes the cell representations using Graph Neural Networks on cell–gene graph that captures the relational information inherent in scRNA-seq data and introduces prototypical contrastive learning to learn cell representations by pushing apart semantically dissimilar pairs and pulling together similar ones. Through extensive experiments on both simulated and real scRNA-seq data, we demonstrate the effectiveness and efficiency of scGPCL. Availability and implementation Code is available at https://github.com/Junseok0207/scGPCL.
An Evaluation of Information Flows in Digital Euro Transactions Using Contextual Integrity Theory

Privacy is regarded as a crucial factor in the development of Central Bank Digital Currency (CBDC), particularly for the digital euro in Europe. Currently, research on privacy in CBDC is scarce and focuses largely on its technical implementation or its influence on technology adoption. This work aims to act as a first step towards uncovering privacy norms in digital euro transactions for German citizens. To this end, we investigate privacy parameters and acceptable flows of information for digital euro transactions using an exploratory mixed-method approach based and contextual integrity theory. The privacy parameters, derived through the analysis of 21 qualitative interviews of experts and non-experts, are used to measure acceptability of various information flows in digital euro transactions for 129 respondents in a first quantitative evaluation. The results demonstrate the importance of acceptable and unacceptable recipients of transaction- and identity-related information as well as different transmission principles. The contributions of this work, the creation of a contextual integrity framework and the evaluation of first privacy norms in digital euro transactions, can be used by central banks and policy makers to design and implement CBDC that does not violate individuals’ privacy norms.
CLOUDOSCOPE: Detecting Anti-Forensic Malware using Public Cloud Environments

Many modern malware employs runtime anti-forensic techniques in order to evade detection. Anti-forensic tactics can be categorized as anti-virtualization (anti-VM), anti-debugging, anti-sandbox, and anti forensic-tools. The detection of such malware is challenging since they do not reveal their malicious behavior and are therefore considered benign. We present CLOUDOSCOPE, a novel architecture for detecting anti-forensic malware using the power of public cloud environments. The method we use involves running samples on bare metal machines, then running and monitoring them in multiple forensic environments deployed in the cloud. That includes virtual machines, debugging, sandboxes, and forensic environments. We identify anti-forensic behavior by comparing results in forensic and non-forensic environments. Anti-forensic malware would expose a difference between bare-metal, non-forensic, and virtualized forensic executions. Furthermore, our method enables the identification of the specific anti-forensic technique(s) used by the malware. We provide background on anti-forensic malware, present the architecture, design and implementation of CLOUDOSCOPE, and the evaluation of our system. Public cloud environments can be used to identify and detect stealthy, anti-forensic malware, as shown in our evaluation.
Explaining controversy through community analysis on Twitter

Controversy refers to content attracting different point-of-views, as well as positive and negative feedback on a specific event, gathering users into different communities. Research on controversy led to two main categories of works: controversy detection/quantification and controversy explainability. When the former aims to quantify controversy on a topic, the latter aims to understand why a topic is controversial or not. This paper mainly contributes to the controversy explainability. We analyze topic discussions on Twitter from the community perspective to investigate the power of text in classifying tweets into the right community. We propose a SHAP-based pipeline to quantify impactful text features on predictions of three tweet classifiers. We also rely on the use of different text features namely BERT, TF − IDF, and LIWC. The results we obtain from both SHAP plots and statistical analysis show clearly significant impacts of some text features in classifying tweets.It also highlights the relevance of the study as well as the potential benefits of combining text and user interactions to quantify controversy.
A Deep Ensemble Network for Sentiment Analysis in Bi-Lingual Low-Resource Languages

Sentiment analysis (SA) is the systematic identification, extraction, quantification, and study of affective states and subjective information using natural language processing. It is widely used for analysing users’ feedback, such as reviews or social posts. Recently, SA has been one of the favourite research domains in NLP due to their wide range of applications, including E-commerce, healthcare, hotel business and others. Many machine learning and deep learning-based models exist to predict the sentiment of the user’s post. However, the sentiment analysis in low-resource languages like Kannada, Malayalam, Telugu and Tamils received very fewer attention due to language complexity and the low availability of required resources. This research fills the gap by proposing an ensemble model for predicting the sentiment of code-mixed Kannada and Malayalam languages. The ensemble of transformer based models achieved a promising weighted F1-score of 0.66 for Kannada code-mixed language. In contrast, the ensemble model of the deep learning framework performed best by achieving a weighted F1-score of 0.72 for the Malayalam dataset, outperforming existing research.
Risky business: relating probability of direct contact to risk of chronic wasting disease

Chronic wasting disease (CWD) is a fatal, prion disease of cervids that was first detected in Alberta in 2005. Transmission of CWD by direct contact with infected individuals plays a major role in the early phases of an outbreak. Mule deer (Odocoileus hemionus) comprise 85% of CWD-infected animals in the province, and we investigated the seasonal effects of grouping patterns and landscape heterogeneity on direct, pair-wise contacts (distance of 3 m) within and between sex-specific (same or mixed sex) groups of mule deer in east-central Alberta. We determined seasonal contacts of mule deer based on proximity loggers that alter GPS schedules to record contact locations. We modeled the relative risk of contact between sex-specific dyads both within and between social groups based on landscape characteristics at the location of contact. We then assessed the support for 5 hypotheses that linked locations of seasonal contacts to occurrence of CWD on the landscape. Disease occurrence on the landscape was derived by comparing locations of CWD-infected and CWD-uninfected deer collected as part of the Alberta’s CWD hunter-harvest surveillance program. We found that contacts in winter occurred in areas where deer use was concentrated, whereas in summer, contact locations were less constrained in space where patterns of landscape characteristics at contact locations varied between sex-specific dyads. Contact probabilities of within and between-group male dyads in winter and between-group female dyads in summer were the best predictors of CWD risk in east-central Alberta. Our results relate habitat specific, social behaviors between conspecific mule deer to potential routes of CWD transmission and contribute to CWD research that guides management strategies for an emergent wildlife disease.
Novel systems to study vector-pathogen interactions in malaria

Some parasitic diseases, such as malaria, require two hosts to complete their lifecycle: a human and an insect vector. Although most malaria research has focused on parasite development in the human host, the life cycle within the vector is critical for the propagation of the disease. The mosquito stage of the Plasmodium lifecycle represents a major demographic bottleneck, crucial for transmission blocking strategies. Furthermore, it is in the vector, where sexual recombination occurs generating “de novo” genetic diversity, which can favor the spread of drug resistance and hinder effective vaccine development. However, understanding of vector-parasite interactions is hampered by the lack of experimental systems that mimic the natural environment while allowing to control and standardize the complexity of the interactions. The breakthrough in stem cell technologies has provided new insights into human-pathogen interactions, but these advances have not been translated into insect models. Here, we review in vivo and in vitro systems that have been used so far to study malaria in the mosquito. We also highlight the relevance of single-cell technologies to progress understanding of these interactions with higher resolution and depth. Finally, we emphasize the necessity to develop robust and accessible ex vivo systems (tissues and organs) to enable investigation of the molecular mechanisms of parasite-vector interactions providing new targets for malaria control.
Research and optimization of RFID tag anti-collision algorithm

In order to reduce the total identification time of readers and improve the system throughput in Radio Frequency Identification (RFID) systems, a new algorithm is proposed. Based on the Frame-Slotted ALOHA (FSA) algorithm, Tags are grouped according to the number of time slots during the reader's recognition process. For tags in collision time slots after a frame ends, the Collision Tree (CT) algorithm is used for group recognition, solving the starvation problem of the FSA algorithm and significantly reducing the total identification time of the reader. Simulation experiments have demonstrated that the algorithm proposed by the author significantly reduces the processing time when compared to the CT algorithm. and achieves a throughput rate that is higher than the traditional CT algorithm When the number of tags exceeds 260, reaching a maximum of 52% when the number of tags is around 400.
Numerical Simulations Reproduce Field Observations Showing Transient Weakening During Shear Zone Formation by Diffusional Hydrogen Influx and H2O Inflow

Exposures on Holsnøy island (Bergen Arcs, Norway) indicate fluid infiltration through fractures into a dry, metastable granulite, which triggered a kinetically delayed eclogitization, a transient weakening during fluid‐rock interaction, and formation of shear zones that widened during shearing. It remains unclear whether the effects of grain boundary‐assisted aqueous fluid inflow on the duration of granulite hydration were influenced by a diffusional hydrogen influx accompanying the fluid inflow. To better estimate the fluid infiltration efficiencies and the parameter interdependencies, a 1D numerical model of a viscous shear zone is utilized and validated using measured mineral phase abundance distributions and H2O‐contents in nominally anhydrous minerals of the original granulite assemblage to constrain the hydration by aqueous fluid inflow and diffusional hydrogen influx, respectively. Both hydrations are described with a diffusion equation and affect the effective viscosity. Shear zone kinematics are constrained by the observed shear strain and thickness. The model fits the phase abundance and H2O‐content profiles if the effective hydrogen diffusivity is approximately one order of magnitude higher than the diffusivity for aqueous fluid inflow. The observed shear zone thickness is reproduced if the viscosity ratio between dry granulite and deforming, reequilibrating eclogite is ∼104 and that between dry granulite and hydrated granulite is ∼102. The results suggest shear velocities <10−2 cm/a, hydrogen diffusivities of ∼10−13±1 m2/s, and a shearing duration of <10 years. This study successfully links and validates field data to a shear zone model and highlights the importance of hydrogen diffusion for shear zone widening and eclogitization.
A brief analysis of remote sensing image cloud extraction method based on deep learning

In optical remote sensing images, different degrees of cloud cover will have different degrees of obscuration to ground information. Due to the special spectral and shape characteristics of clouds, cloud extraction makes the detection and extraction of cloud cover in the process of remote sensing data processing is one of the primary problems to be solved for further analysis and utilization of image information, and cloud extraction has gradually become a hot spot and frontier of current research in recent years. In view of this, this paper categorizes and summarizes the existing deep learning theories and methods based on a large amount of relevant literature in recent years, and classifies cloud extraction into three types of methods based on convolutional neural networks, recurrent neural networks, and generative adversarial neural networks by analyzing the network architectures adopted by different methods, briefly introduces the current development trends and representative works of cloud extraction methods based on remote sensing images at home and abroad, and finally gives an overview of the current Finally, we suggest and foresee the current problems and future development trend of cloud extraction.
Research on ink style image generation based on deep learning

Most of the current existing style transfer methods are based on photographs or Western paintings. Due to the inherent differences between Chinese and Western paintings, direct application of existing algorithms cannot generate satisfactory style transfer results for Chinese ink paintings. Accordingly, this paper proposes a new feedforward style transfer method, called inkStyle, which uses a draw Network to transfer global style patterns at low resolution and a higher resolution details networks to modify local style patterns in a pyramidal manner based on the multi-level Laplacian filtering output of the content image. The experimental results show that the method in the paper performs better and produces better visual results.
Prairie mouse hole target detection technology based on deep learning

Rat hole detection is a key work in the prevention of rat damage. The digital rat hole detection method of UAV combined with target detection is studied to replace the manual carpet rat hole detection, so as to improve the detection efficiency. In this paper, low-altitude remote sensing of unmanned aerial vehicle was used to collect rat hole images on the Edolechuan grassland in Hohhot, Inner Mongolia. Combined with deep learning models: Faster-Rcnn, Yolov3 and SSD, rat hole detection was compared and analyzed. Data was cut and labeled through image preprocessing method. Then, by comparing the three groups of target detection models, the results show that the SSD model has the best effect on rat hole detection, the accuracy rate can reach 91.8%, and the reasoning speed can reach 7.9ms.
Application of data fusion algorithm based on gray model combined with Kalman Filter in intelligent ammunition wireless sensor network

The sensor system is crucial to intelligent munitions and must function with incredibly high accuracy and in real time based on the characteristics of the munition. Additionally, it is difficult to meet the requirements of the munition system given the significant temporospatial redundancy of the conventional sensor system. In this paper, a data fusion algorithm based on prediction gray model combined with Kalman Filter (GMKF) is proposed. The algorithm combines the advantages of Kalman Filter in dealing with sensor noise with the advantages of gray model in fast modeling and prediction. The missile model is simulated by Ansys Fluent, and the algorithm performance of the obtained temperature data set is analyzed. The results show that in wireless sensor networks, GMKF algorithm can greatly reduce communication redundancy, increase network lifetime, and has a high prediction success rate.
RGB-D SLAM in dynamic environments with deep learning

Traditional visual Simultaneous Localization and Mapping (SLAM) is mostly based on the assumption of static environment, which is susceptible to receive dynamic targets in dynamic environment, leading to the degradation of localization accuracy. In this paper, we introduce the instance segmentation network SOLOv2, which combined with motion consistency detection can effectively eliminate the dynamic feature points in the environment and improve the visual SLAM accuracy with the depth map hole repair algorithm. Tested on the TUM dataset, the positional estimation accuracy in dynamic environments is significantly improved compared to ORB-SLAM2.
A neural network aided classification method for human eye diseases

Patients with eye diseases may have a variety of pathological features. If a certain pathological feature is ignored during diagnosis, the overall treatment effect may be seriously affected. To solve this problem, a new neural network model is proposed, in which all the possible pathological features in an OCT image are trained together as the target of multi task classification. The model uses an improved residual neural network to extract the features of OCT images, and adds a spatial attention module in the residual module to facilitate the model to capture the location of lesions more accurately. MMoE model is used to classify the extracted features in multiple tasks, making full use of the potential relationship between each task to help classification, so as to improve the stability of training. In addition, the patient's personal information features and image features are fused with attention and embedded in the top of the model to improve the classification accuracy. The model is compared with VGG16, ResNet10 general single task model and other multi task learning models on 2729 human eye OCT images containing four pathological features. The experimental results show that the training time of the model is 1/4 of that of the single task model, and the average classification accuracy of the four tasks is improved by 4%.
Application of data fusion algorithm in detection of pesticide residues in food

In order to avoid the excessive pesticide content in the ecological environment caused by the use of a large number of pesticides, it will cause serious harm to people, livestock, water, atmosphere, soil and other environmental elements. Even if it is a small amount of residues, long-term contact will do harm to human health. In this paper, the application of data fusion algorithm in the detection of pesticide residues in food is proposed. A data fusion method suitable for repeated sampling of single sensor is applied to the processing of pesticide residue detection data. The data are grouped by statistical theory, while the support probability is fuzzy, and the support interval is determined by subsection discussion, which makes the fused data more stable than the previous results obtained by absolute support probability and support interval, and further ensures that the data fusion has better anti-interference.
JSON functional dependency and key

JSON is a data format to transform and exchange of different data in various applications, especially in Web applications. JSON format data contains not only the data itself, but also the semantic information behind the data. Like relational data and XML data, functional dependencies and keys are very important semantic information in JSON format data. This paper studies the semantic constraints such as functional dependencies and keys of JSON format data. The concepts of functional dependency and key are proposed based on JSON tree model for JSON format data. We discussed the implied meaning behind JSON functional dependencies and keys. This research is of great significance for efficient management of JSON data, and has important reference significance for other relevant research, such as JSON semantics.
Improved I-Log-SCMA algorithm for low complexity detection

SCMA (sparse code multiple access) is a kind of wireless Communication technology, which is used to process the uplink portion of wireless communication and ensure a high data transmission rate and massive data connection at the age of information technology. The urgent need for SCMA technology is to address the issue that the user capacity of SCMA technology couldn’t be balanced with the complexity of the detection algorithm. As for solving this, an improved low-complexity detection algorithm I-log-MPA was analyzed, which is based on the modification of the traditional algorithm in multi-user detection. It made up for the solving of the bit error performance of MAX-log-MPA algorithm and the problem of high complexity. The simulation results revealed that the I-log-MPA detection algorithm with lower complexity and lower bit error rate could effectively improve the performance of SCMA system.