Miracle-dz/UniFAR-FTUs huggingface dataset:
A hierarchical knowledge-aware neural network for protein-protein interaction article classification

In this paper, we focus on the Protein-Protein Interaction Article Classification (PPIAC) problem. In order to make better use of domain knowledge, we propose a Hierarchical Knowledge-aware Hybrid Neural Network (HKaHNN) model to classify PPI articles. Inspired by existing work, we introduce two kinds of knowledge embeddings and design a Hierarchical Knowledge-aware Attention (HKaATT) component which implements the interaction between the original token representations and external knowledge from the token-level and sentence-level respectively. In addition, in order to improve the anti-interference ability of the model, we adopt the adversarial training strategy. Our model achieves competitive performance on BioCreative II and BioCreative III corpora, with Fl-scores of 83.67% and 68.86%, respectively.
Dual Graph regularized PCA based on Different Norm Constraints for Bi-clustering Analysis on Single-cell RNA-seq Data

In recent years, single-cell RNA sequencing (scRNA-seq) technology has made significant progress in many fields and become an important means to study cell dynamics. How to effectively mine valuable biological information from these sequencing data is a topic worthy of researching. In this paper, two new methods based on traditional principal component analysis (PCA) are proposed and used to scRNA-seq data. The first method named dual graph regularized PCA (DGPPCA) is based on Frobenius-norm and $\mathrm{L}_{2,\mathrm{p}}$-norm constraints, and the method named the dual graph-regularization PCA (DG2PPCA) is based on the nonconvex proximal $\mathrm{L}_{\mathrm{p}}$-norm $(0\lt p\lt 1)$ and the $\mathrm{L}_{2,\mathrm{p}}$-norm constraints. We apply these two new methods to five scRNA-seq datasets, and perform bi-clustering on genes and samples at the same time. Extensive experiments are conducted to explore the influence of the combination of different norm constraints in the two optimization models based on dual Graph regularized PCA. The visual experimental results confirmed the effectiveness of these two methods.
IC4Windows–Hand Gesture for Controlling MS Windows

IC4Windows (Intelligent Companion for Windows) provides a natural Human computer interaction through hand gesture to avoid direct contact with computer device. The hand gestures trigger a windows command that usually used by a user when they use MS windows system. Some gesture has a different purpose depending on the application being operated by the user. The proposed system using external depth camera to capture the frame as an input. The depth data is used to remove unnecessary inputs such as background, face, etc., so that our input is more specific to help get a high accuracy. The gestures are consists of two type, first is static gesture and second is dynamic gestures. These two types of gestures have a different characteristic, the static gestures use each single frame as an input while the dynamic gestures require a sequence of frame as an input. We use a different method to handle each type of gestures, CNN is used for static gestures and 3DCNN is used for dynamic gestures. The proposed method provides a recognition rate of up to 92% and average speed is up to 30 FPS
Supporting Diagnosis and Treatment of Scoliosis: Using Augmented Reality to Calculate 3D Spine Models in Real-Time - ARScoliosis

Scoliosis is a complex spinal disorder that mostly affects adolescents and younger population. To avoid and reduce harmful radiation techniques in adolescent idiopathic scoliosis investigation and follow-ups, a novel non-invasive approach is highly recommended and needed in clinical practice. In this paper ARScoliosis application to diagnose, visualize and document spinal condition and particularly adolescent idiopathic scoliosis in real time is presented. This ARScoliosis application is developed in Unity 3D, a game engine, and allows non-invasive quantification of posture and spinal deformity using recently available non-ionizing wide-angle depth sensor Microsoft Azure Kinect DK as a diagnostic medium. Parametric and scalable 3D model of the spine is registered to the internal spinal alignment extracted from the anatomical landmarks and joints in real time and visible over the specific patient. It is envisioned that ARScoliosis will support monitoring of scoliosis after applied therapy and include more diagnostic parameters, after final testing and validation against standard X-Ray imaging modalities.
Joint Multiclass Classification of the subjects of Alzheimer’s and Parkinson’s Diseases through Neuroimaging Modalities and Convolutional Neural Networks

Alzheimer’s disease (AD) is the most widespread type of dementia defined by an accumulation of amyloid-$\beta$ proteins, the formation of tau plaques as well as the loss of neurons. On the other hand, Parkinson’s disease (PD) is defined by the loss of dopaminergic neurons in the substantia nigra pars compacta within the midbrain. AD and PD are affecting millions of elderly people worldwide which highlights the need for their early diagnosis for the welfare of subjects diagnosed with these neurodegenerative disorders. A large number of autopsy confirmed PD subjects have sufficient postmortem plaque and tangle pathology to meet criteria for a second diagnosis of AD. Neuroimaging modalities such as Positron Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT) are routinely used by clinicians to diagnose the initial stages of these two diseases. Together with these neuroimaging modalities, deep learning techniques are widely used in medical settings and have the potential to aid clinicians in the early diagnosis of these two diseases. In this work, we deployed a 3D Convolutional Neural Network (CNN) forjoint feature extraction and multiclass classification of both AD and PD brain images in the spatial and frequency domains using PET and SPECT imaging modalities discriminating between AD, PD and Normal Control (NC) classes. We used Discrete Cosine Transform (DCT) as the frequency domain method and deployed random weak Gaussian blurring and random zooming in/out augmentation methods in both spatial and frequency domains. Based on our experiments and deployment of cross-validation approach for optimal hyperparameters selection, we found the performance of AD/NC(SPECT)/PD classification with random weak Gaussian blurred augmentation in the spatial domain to be the best and that of AD/NC(SPECT)/PD classification with combined augmentations in the frequency domain to be the worst while spatial domain methods outperformed their frequency domain counterparts.
PaKman: A Scalable Algorithm for Generating Genomic Contigs on Distributed Memory Machines

De novo genome assembly is a fundamental problem in the field of bioinformatics, that aims to assemble the DNA sequence of an unknown genome from numerous short DNA fragments (aka reads) obtained from it. With the advent of high-throughput sequencing technologies, billions of reads can be generated in a matter of hours, necessitating efficient parallelization of the assembly process. While multiple parallel solutions have been proposed in the past, conducting a large-scale assembly at scale remains a challenging problem because of the inherent complexities associated with data movement, and irregular access footprints of memory and I/O operations. In this article, we present a novel algorithm, called PaKman, to address the problem of performing large-scale genome assemblies on a distributed memory parallel computer. Our approach focuses on improving performance through a combination of novel data structures and algorithmic strategies for reducing the communication and I/O footprint during the assembly process. PaKman presents a solution for the two most time-consuming phases in the full genome assembly pipeline, namely, k-mer counting and contig generation. A key aspect of our algorithm is its graph data structure (PaK-Graph), which comprises fat nodes (or what we call “macro-nodes”) that reduce the communication burden during contig generation. We present an extensive performance and qualitative evaluation of our algorithm across a wide range of genomes (varying in both size and species group), including comparisons to other state-of-the-art parallel assemblers. Our results demonstrate the ability to achieve near-linear speedups on up to 16K cores (tested) on the NERSC Cori supercomputer; perform better than or comparable to other state-of-the-art distributed memory and shared memory tools in terms of performance while delivering comparable (if not better) quality; and reduce time to solution significantly. For instance, PaKman is able to generate a high-quality set of assembled contigs for complex genomes such as the human and bread wheat genomes in under a minute on 16K cores. In addition, PaKman was able to successfully process a 3.1 TB simulated dataset of one of the largest known genomes (to date)-Ambystoma mexicanum (the axolotl), in just over 200 seconds on 16K cores.
Biological Processes underlying the Reinforcing Primary Qi Delivered by Ginseng

Ginseng is an important herbal medicine in traditional Chinese medicine (TCM) with therapeutic properties of “reinforcing primary qi” and “pulse restoring and blocking depletion” that are mentioned in both TCM textbooks and the Pharmacopoeia of the People’s Republic of China. However, the underlying mechanisms are still under exploration. In this study, based on evidenced bioactive compounds and targeted genes/proteins, top-level functional annotation towards gene ontology biological processes was deployed and then validated with transcript microarray data. As a result, a kernel candidate regulation network was constructed towards the therapeutic properties of ginseng identified in TCM. Functional annotation indicated three associated biological processes e.g. (1) cell killing, (2) rhythmic process, and (3) growth. In short, through regulating the three processes, the therapeutic properties of “reinforcing primary qi” and “pulse restoring and blocking depletion” identified in TCM can be roughly elaborated and validated.
Wind Power Short-Term Forecasting Model Based on the Hierarchical Output Power and Poisson Re-Sampling Random Forest Algorithm

Under the background of big data, the use of massive online data to improve the real-time characteristics and reliability of wind power prediction and to reduce the impact of wind farms on the power grid makes the power supply and demand balance important problems to solve. This paper provides a new solution for short-term wind power forecasting to address these problems. In this paper, an improved random forest short-term prediction model based on the hierarchical output power is proposed, and it is used to forecast the power output of a real wind farm located in Northwest China. First, a chi-square test is adopted to discretize the power data to divide the large-scale training data and remove abnormal data. The novelty of this study is the establishment of a classification model with the output wind power as the classification target and the use of Poisson re-sampling to replace the bootstrap method of the random forest, that is, to improve the training speed of the random forest algorithm. The results indicate that the proposed technique can estimate the output wind power with an MSE of 0.0232, and the comparison illustrates the effectiveness and superiority of the proposed method.
Cadmium Mobility with Persulfate Chemical Oxidation: Effects of Soil Properties and Activation Methods

AbstractThe potential effects of persulfate in situ chemical oxidation (ISCO) on metals and on long-term groundwater quality is an important issue because of the potential risks to public health and to the environment. Application of persulfate and its activation amendments to soil and groundwater can lead to complex interactions, altering geochemical conditions and processes, thereby affecting metals mobility. The mobility of cadmium sorbed on three soils varying in physical–chemical properties was investigated after chemical oxidation by unactivated and activated persulfate using mixed batch reactors. Each of the three soils was treated with two different concentrations of persulfate, 2.1 mM and 42 mM, and different activation amendments that included Fe(II), Fe(II)-EDTA, and high pH. Resulting solutions were analyzed for soluble cadmium after 48 h and 6 months. Results show that cadmium mobilization varied with the persulfate activation method and the soil type. The maximum amount of cadmium was mobili.
Retrieval of Scientific Documents Based on HFS and BERT

When retrieving scientific documents with mathematical expressions as the main content, both mathematical expressions and their contextual text features require consideration. However, mathematical expressions are different from texts in terms of grammar and semantics. Thus, integrating the above features and realizing scientific document retrieval is difficult. In this study, a retrieval method of scientific documents based on HFS (Hesitation Fuzzy Sets) and BERT (Bidirectional Encoder Representations from Transformer) is proposed. This method is realized through utilizing the advantages of HFS in multi-attribute decision making and BERT in context-dependent similarity calculation. By analyzing mathematical expressions and calculating the membership degree of symbolic multi-attributes, the similarity of mathematical expressions can be obtained, which can improve the accuracy of mathematical expression recall. With the extraction of the text of the expression context, BERT is used to calculate the context similarity. Then, the recalled technical documents are sorted according to the similarity of context, and the final retrieval result can be obtained. Experiments were carried out on 10,372 Chinese and 11,770 English scientific documents in the NTCIR extended data set. The average value of MAP_<inline-formula> <tex-math notation="LaTeX">$k (k=10)$ </tex-math></inline-formula> for the recall results of scientific documents was 74.13%. The average <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>DCG (<inline-formula> <tex-math notation="LaTeX">$n=10$ </tex-math></inline-formula>) for the ranking of scientific documents was 86.04%.
Circular Economy Analysis. Ranking of Energy Resources from Waste

The aim of the circular economy is to maximize the value of a product during its use, using secondary raw materials to create new products. This research focus on the possibilities of using waste as an energy resource - both the aspects of waste recovery, from the point of view of reducing the amount of landfilled waste, and the extraction and further efficient use of landfill gas. In this case the TOPSIS analysis method was used, five alternatives were selected - waste as an energy resource, which was evaluated based on four criteria: technological, economic, social, as well as environmental and climate aspects. The results of ranking energy recovery methods from waste using the TOPSIS method clearly show that the most optimal of the selected alternatives is the use of biodegradable (organic) waste for biogas production. This opens up opportunities for biofuels to be applied in road transport.
Multi-factory Job Shop Scheduling With Due Date Objective

The existing literature on distributed scheduling mainly focuses on the performance measures makespan, total completion time, or costs. Due date related objectives that are gaining in importance in the industry have not been considered to a similar extent. In this contribution, we present a model formulation of the distributed job shop scheduling problem with due date consideration and present adapted greedy heuristics as well as a genetic algorithm to solve large problem instances. Computational experiments are carried out to assess the performance of the model and the algorithms. The heuristics and metaheuristics show promising results in reasonable computation times, with the genetic algorithm outperforming the other heuristics. The results indicate that managers should consider incorporating due date related objectives in the decision-making process of production planning and scheduling in distributed manufacturing.
Dimensionality Reduction of Volterra Kernels by Tensor Decomposition using Higher-Order SVD

The paper proposes a practical method for a significant dimensionality reduction of Volterra kernels, defining a discrete nonlinear model of a signal by Volterra series of higher order. In system identification of Volterra series, the Volterra kernels and nonlinear inputs of the system can be described by super-symmetrical tensors. The reduction of their dimensionality is obtained by a tensor decomposition technique called Higher Order Singular Value Decomposition (HOSVD). The main contribution of the paper is a cascade learning algorithm for the system identification based on residuals of least squares minimization. Numerical examples for Volterra system of order four are used to illustrate the approach.
Locally Manifold Non-negative Matrix Factorization Based on Centroid for scRNA-seq Data Analysis

The rapid development of single cell RNA sequencing (scRNA-seq) has made it possible to study the association between cells and genes at molecular resolution. When the follow-up analysis is carried out, it is often difficult to extract the cell information in high-dimensional space because of the high gene dimension in single-cell sequencing, which leads to inaccurate results in the follow-up analysis. To solve the problem, we propose a method called locally manifold non-negative matrix factorization based on centroid for scRNA-seq data analysis (MNMFC). MNMFC is a similarity modeling scheme based on locally manifold, which can map cell association in high dimensional space. Through similarity learning based on locally manifold and non-negative matrix decomposition (NMF) algorithm, the data in high-dimensional space can be mapped to low-dimensional space, which provides help for downstream clustering analysis. The performance of the model was validated experimentally on 10 scRNA-seq datasets. Compared with other nine advanced single-cell clustering methods, whether it is a comprehensive analysis or an individual analysis of the dataset, MNMFC has achieved encouraging results.
Fusion Prediction Model of Atmospheric Pollutant Based on Self-Organized Feature

In response to the new situation and new needs of international industrial modernization transformation, machine learning provides important data basis for monitoring air pollution in industrial parks and can control exhaust emissions in industrial production on a human basis. In all kinds of systems, the time series obtained in practice are generally non-stationary, noisy and complicated data influenced by multiple factors. In this paper, the EMD-FUSION model based on the self-organizing model is proposed. First, the complex original data were decomposed into empirical mode decomposition (EMD). Based on the volatility and periodicity of each intrinsic mode function (IMF), the three subs modal time series with similar change rates were obtained; Secondly, use a back propagation (BP) to predict simpler sub-modal time series data, while the more complicated sub-mode timing series data of the two groups were predicted by the gated recurrent unit (GRU) network. Finally, the prediction results of three sub-series are added and fused to obtain the prediction data of the original data. Through experiments on the atmospheric data of the industrial park provided by Taiyuan environmental monitoring center station, the correctness of the model is verified.
Towards Plug-and-Play Converter-Interfaced DC Microgrids

This paper focuses on the development of plug-and-play voltage control mechanisms for DC microgrids with flexible structures. To this end, we present a voltage control approach that guarantees stable operation and satisfactory performance of DC microgrids under arbitrary interconnection of distributed generation (DG) units. The local voltage controllers are solutions of loop-shaping convex optimization problems. The proposed voltage control framework is applied to a case study of a multiple-DG DC microgrid in MATLAB/Simscape Electrical environment. The simulation results demonstrate the feasibility of the proposed voltage control approach and its viability for DC microgrids.
A Tractable Mean Field Game Model for the Analysis of Crowd Evacuation Dynamics

A fixed time horizon evacuation model for a large population of confined agents is proposed within the mean field games framework. Unlike previously proposed models relying on simulations or partial differential equation analysis, the proposed model remains linear in the individual agent dynamics and quadratic in the cost functions, ultimately dictating the agents’ motion. We use negative definite matrices in the cost function to reflect congestion effect during evacuations. This leads to Riccati equations that generically display a finite escape time. Therefore, we develop an existence theory for the infinite population mean field game based equilibrium dynamics, and establish its Nash property for a large but finite population of agents. Simulation results illustrating the numerical behavior of the model are presented with stress effect and different social interaction scenarios such as congestion and crowd following behavior.
The Role of Millimeter-Wave Technologies in 5G/6G Wireless Communications

Ever since the deployment of the first-generation of mobile telecommunications, wireless communication technology has evolved at a dramatically fast pace over the past four decades. The upcoming fifth-generation (5G) holds a great promise in providing an ultra-fast data rate, a very low latency, and a significantly improved spectral efficiency by exploiting the millimeter-wave spectrum for the first time in mobile communication infrastructures. In the years beyond 2030, newly emerged data-hungry applications and the greatly expanded wireless network will call for the sixth-generation (6G) communication that represents a significant upgrade from the 5G network – covering almost the entire surface of the earth and the near outer space. In both the 5G and future 6G networks, millimeter-wave technologies will play an important role in accomplishing the envisioned network performance and communication tasks. In this paper, the relevant millimeter-wave enabling technologies are reviewed: they include the recent developments on the system architectures of active beamforming arrays, beamforming integrated circuits, antennas for base stations and user terminals, system measurement and calibration, and channel characterization. The requirements of each part for future 6G communications are also briefly discussed.
NEXT & FEXT Prediction using Hybrid 2D-3D Solver for Fast Electromagnetic Analysis

The excessive time and memory requirement for a 3D full-wave solver is a challenge in simulation for signal integrity. This paper presents a hybrid 2D-3D electromagnetic full-wave simulator based on automated segmentation of layout. The novelty of this work is primarily in the classification of segments of layout as 2D or 3D segments using corner points and analysis of the electric field profile. An appropriate error metric is defined and is used in the process of this classification. Finally, cascading studies are performed using different port-structures for concatenating segments to get the overall response. Numerical results for a differential pair is presented with near-end crosstalk (NEXT) and far-end crosstalk (FEXT) to illustrate the time versus accuracy merits.