bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research on application of model ensemble in sports image classification based on environmental information
Authors: Cui Jiacheng 
Published: November 2023

Abstract
In recent years, a deep convolutional neural network has been widely used in the field of image classification. However, training a satisfactory network is very arduous, not only to tune the hyper-parameters in the network but also to avoid the overfitting problem caused by a deep neural network. Another point is that it is difficult for a neural network to learn subtle details without human annotation. Therefore this paper proposed a preferable classification algorithm for sports classification tasks that combines the deep neural network with the object detection algorithm to obtain the prediction result. In this paper, the author compared the differences between classification directly using neural networks and a modified model ensemble approach to classification from a holistic perspective, as well as elaborating the advantages and disadvantages of the two approaches. The conclusion shows that the use of the improved model ensemble classification algorithm performs better than the direct use of neural networks and also achieves a high degree of accuracy in the test set.
Research advanced in the visual SLAM methods under indoor environment
Authors: Lu Zhengsheng , Wang Jinchang , Zhang Zelin , Zhu Conghua 
Published: November 2023

Abstract
Simultaneous localization and mapping (SLAM) have been a hotspot study topic in the computer vision community these days, which aims to locate the agent’s position and perceive the surrounding environment to build a map. In Visual SLAM, the camera is the external sensor used, creating an environment map while locating itself. Compared with radar and other rangefinders, the visual sensor is usually tiny and has low power consumption, which has been the mainstream framework in SLAM research task. This paper starts from the system overview of visual SLAM, introduces the landmark achievements and research hotspots of visual SLAM, and discusses the key issues and three research methods based on visual SLAM in indoor environment. Meanwhile, the applications of visual SLAM in dynamic scenes and large indoor environments are listed. Finally, the trend of its future development is prospected.
On the convergence acceleration of vanilla Generative Adversarial Network
Authors: Si Hang 
Published: November 2023

Abstract
Generative Adversarial Network (GAN) is very successful and widely used in both academic and industrial fields. It is inspired by game theory where two models, a generator and a critic, compete with each other, learn from each other, and make each other stronger and stronger. Most improvements focused on stability and there are already many different models such as deep convolutional GAN (DCGAN) and Wassertein GAN (WGAN) that have succeeded. But the speed of the convergence during training process is also an essential ability that needs to be enhanced. In this paper, a decreasing noise is added to the MNIST data and these new data are delivered to the discriminator of the vanilla GAN to accelerate the training process. The result shows that this change could accelerate the convergence and not affect the results’ quality a lot. And this improvement should be general enough to apply to the other models.
Comprehensive study and analysis for StarGAN and StarGAN-v2 in the task of image generation
Authors: Cai Yuanan , Li Yepeng , Zhang Zhenyu 
Published: November 2023

Abstract
With the continuous development of deep learning and artificial intelligence industry, more and more neural networks have been developed. Among them, one typical network widely used is StarGAN. It solves the problem of requiring multiple generators and discriminators for multiple domains and style transitions. In this article, we will analyze and explore StarGAN v1 and StarGAN v2. including StarGAN’s development history, advantages and disadvantages in the application, etc. At the same time, we will compare StarGAN with other neural networks such as CycleGAN and MSGAN through some discrimination criteria, and clearly demonstrate the influence of different networks on the generation of image data sets through quantitative and qualitative analysis. In addition, we also found a representative code on some of the actual uses of StarGAN for hair replacement through online sources. Since this code may be used to confuse the use of StarGAN v1 and v2, we will also improve this code in the article and put forward suggestions for changes. In this paper, we carried out the specific analysis of StarGAN’s loss function based on the existing data and put forward some relevant insights. By taking the data we searched for StarGAN, it can be found that StarGAN could use as few resources as possible to achieve clearer and more varied results during the image generation task
Explore the role and emphasis of K-Means, Decision Tree and Distance Based algorithms in data exception detection
Authors: Chen Xiaotian 
Published: November 2023

Abstract
K-Means, Decision Tree and Distance-Based algorithms are 3 important ways of classifying data. These three algorithms have different methods and focus on data classification. Therefore, they are always applied into different scenarios. K-Means algorithm is to divide three-dimensional data or two-dimensional data into several clusters to facilitate subsequent data processing and analysis. Decision Tree is based on the “tree” structure to make decisions. It is an important classification and regression method in data mining technology. It is a prediction analysis model expressed in the form of tree structure (including binary tree and multi tree). When it comes to the Distance-Based algorithm, it is a common anomaly detection method applicable to various data domains. It defines outliers based on the nearest neighbor distance. The purpose of this paper is to explore the kernel of three anomaly detection algorithms through an example of data anomaly detection. Therefore, in this paper, all three algorithms’ commonalities and differences will be discussed and illustrated through a case of data exception handling.
Researches advanced in the SLAM for dynamic environment
Authors: Li Ying 
Published: November 2023

Abstract
Simultaneous Localization and Mapping (SLAM) has always been a popular topic in the computer vision community, as it seeks to forecast the location of agents and use sensors to detect their surroundings in order to construct maps and perform navigation. The majority of SLAM algorithms assume that objects in the environment are immobile or in slow motion, whereas the SLAM system in a dynamic world remains an unanswered question. When constructing a 3D point cloud map by sequentially accumulating scanning data, dynamic objects typically leave undesirable traces in the map. The traces of these dynamic items operate as impediments, hindering the positioning and navigation performance of mobile vehicles. In this work, we discuss the most recent advancements in SLAM in a dynamic context. Specifically, this paper examines three characteristics of the most significant negative effects that the dynamic environment has on SLAM. Then, according to the dynamic degree, solutions are presented for various dynamic items, including the design concept, basic framework, advantages, and disadvantages. We conclude by discussing the existing research difficulties in the subject of dynamic SLAM and its projected development path.
Simulations of quantum dynamics with fermionic phase-space representations using numerical matrix factorizations as stochastic gauges
Authors: Rousse François , Fasi Massimiliano , Dmytryshyn Andrii , Gulliksson Mårten , Ögren Magnus 
Published: November 2023

Abstract
The Gaussian phase-space representation can be used to implement quantum dynamics for fermionic particles numerically. To improve numerical results, we explore the use of dynamical diffusion gauges in such implementations. This is achieved by benchmarking quantum dynamics of few-body systems against independent exact solutions. A diffusion gauge is implemented here as a so-called noise-matrix, which satisfies a matrix equation defined by the corresponding Fokker–Planck equation of the phase-space representation. For the physical systems with fermionic particles considered here, the numerical evaluation of the new diffusion gauges allows us to double the practical simulation time, compared with hitherto known analytic noise-matrices. This development may have far reaching consequences for future quantum dynamical simulations of many-body systems.
Comparative evaluation of alternatives for management of wood wastes by using multi-criteria decision tools
Authors: Sayın Fatma Ece , Topaloğlu Gülay , Ozbay Bilge , Ozbay Ismail 
Published: November 2023

Abstract
In Turkey, the wood industry is a prevailing sector generating economically valuable by-products such as sawdust, wood chips and wood shavings. The utilization of these materials supports an important contribution to the revival of the economy. Selection of the best management alternative considering economic and environmental factors synchronously requires the application of statistical decision-making methods. The significance of this study is to determine the suitability of promising alternatives for the utilization of wood wastes (production of pellet, panel, cemented wood composites and pyrolysis) by using multi-criteria decision-making methods (PROMETHEE and FPROMETHEE). Super Decision Program was used to evaluate the importance of the selected criteria (environmental impact, operation risk, installation cost, applicability, operation cost and market competition) based on data obtained from the 9-scale questionnaire. In PROMETHEE and FPROMETHEE applications, both equally weighted and calculated importance of criteria was considered. According to results of AHP, installation cost was the most important criterion with 31% ratio. Results of the study showed that pellet production was the best alternative in all applications with the highest-ranking values. Wood-based panel production was followed by pellet production; however, it was seen that the production of cemented wood composites and pyrolysis are not suitable for the utilization of wood wastes.
Soil oribatid mite community structure and its relationship with environmental factors in different agricultural land-use types in Shibing Karst
Authors: Gong Tong , Yin Xiumei , Chen Hu 
Published: November 2023

Abstract
The karst world natural heritage site is characterized by fragility due to severe rocky desertification. Buffer zone functions as the ecological barrier of the core area of the heritage site, and can play an ecological filtering role in the core area. Therefore, buffer zones are of great ecological significance. In order to reveal the community characteristics of soil oribatid mites in different agricultural land-use in the buffer zone of Shibing Karst World Natural Heritage and their relationship with environmental factors, soil oribatid mites in the buffer zone of corn field (CF), golden pear garden (GP) and tobacco field (TF) were collected in September 2021. A total of 1220 oribatid mites belonging to 79 genera and 41 families were found in the study. Our key results include (1) The family number, genus number, individual number, and individual density of GP are higher (2) Scheloribates is the dominant group, 22 genera such as Oppiella, Tectocepheus, and Xylobates are common groups, and 56 genera such as Neoribates and Protoribates are rare groups. (3) The Diversity index of GP is higher than that of other cultivated land types. Community similarity analysis shows that the similarity between GP and CF is high, but all habitats are at a medium level of dissimilarity. (4) Analysis of the ecological group of oribatid mites shows that Poronota is more abundant than Macropylina and Gymnonota. (5) The Diversity index, richness index, family and genus level of oribatid mites were significantly positively correlated with soil humidity (SH) and the number of genera, individuals and individual density were significantly positively correlated with total phosphorus (TP). Redundancy analysis indicates that total phosphorus (TP), total potassium (TK), and soil organic matter (SOM) are the main environmental factors affecting the distribution of soil oribatid mites communities. Based on the above research results, it can be seen that there are differences in the community structure of soil oribatid mites in different agricultural land-use in the study area. The use of dominant genera of mites can preliminarily indicate the soil ecological environment of different agricultural land-use. The research results provide basic data for the biodiversity protection of soil animals in different agricultural land-use in the buffer zone of Shibing Karst World Natural Heritage.
Artificial intelligent identification of apatite fission tracks based on machine learning
Authors: Ren Zuoting , Li Shichao , Xiao Perry , Yang Xiaopeng , Wang Hongtao 
Published: November 2023

Abstract
Over the past half century, apatite fission track (AFT) thermochronometry has been widely used in the studies of thermal histories of Earth’s uppermost crust. The acquired thermal histories in turn can be used to quantify many geologic processes such as erosion, sedimentary burial, and tectonic deformation. However, the current practice of acquiring AFT data has major limitations due to the use of traditional microscopes by human operators, which is slow and error-prone. This study uses the local binary pattern feature based on the OpenCV cascade classifier and the faster region-based convolutional neural network model based on the TensorFlow Object Detection API, these two methods offer a means for the rapid identification and measurement of apatite fission tracks, leading to significant improvements in the efficiency and accuracy of track counting. We employed a training dataset consisting of 50 spontaneous fission track images and 65 Durango standard samples as training data for both techniques. Subsequently, the performance of these methods was evaluated using additional 10 spontaneous fission track images and 15 Durango standard samples, which resulted in higher Precision, Recall, and F1-Score values. Through these illustrative examples, we have effectively demonstrated the higher accuracy of these newly developed methods in identifying apatite fission tracks. This suggests their potential for widespread applications in future apatite fission track research.
Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps
Authors: Dai Xin , Wu Longlong , Yoo Shinjae , Liu Qun 
Published: November 2023

Abstract
Interpretation of cryo-electron microscopy (cryo-EM) maps requires building and fitting 3D atomic models of biological molecules. AlphaFold-predicted models generate initial 3D coordinates; however, model inaccuracy and conformational heterogeneity often necessitate labor-intensive manual model building and fitting into cryo-EM maps. In this work, we designed a protein model-building workflow, which combines a deep-learning cryo-EM map feature enhancement tool, CryoFEM (Cryo-EM Feature Enhancement Model) and AlphaFold. A benchmark test using 36 cryo-EM maps shows that CryoFEM achieves state-of-the-art performance in optimizing the Fourier Shell Correlations between the maps and the ground truth models. Furthermore, in a subset of 17 datasets where the initial AlphaFold predictions are less accurate, the workflow significantly improves their model accuracy. Our work demonstrates that the integration of modern deep learning image enhancement and AlphaFold may lead to automated model building and fitting for the atomistic interpretation of cryo-EM maps.
CENTRE: a gradient boosting algorithm for Cell-type-specific ENhancer-Target pREdiction
Authors: Rapakoulia Trisevgeni , Lopez Ruiz De Vargas Sara , Omgba Persia Akbari , Laupert Verena , Ulitsky Igor , Vingron Martin 
Published: November 2023

Abstract

Motivation
Identifying target promoters of active enhancers is a crucial step for realizing gene regulation and deciphering phenotypes and diseases. Up to now, several computational methods were developed to predict enhancer gene interactions, but they require either many epigenomic and transcriptomic experimental assays to generate cell-type (CT)-specific predictions or a single experiment applied to a large cohort of CTs to extract correlations between activities of regulatory elements. Thus, inferring CT-specific enhancer gene interactions in unstudied or poorly annotated CTs becomes a laborious and costly task.

Results
Here, we aim to infer CT-specific enhancer target interactions, using minimal experimental input. We introduce Cell-specific ENhancer Target pREdiction (CENTRE), a machine learning framework that predicts enhancer target interactions in a CT-specific manner, using only gene expression and ChIP-seq data for three histone modifications for the CT of interest. CENTRE exploits the wealth of available datasets and extracts cell-type agnostic statistics to complement the CT-specific information. CENTRE is thoroughly tested across many datasets and CTs and achieves equivalent or superior performance than existing algorithms that require massive experimental data.

Availability and implementation
CENTRE’s open-source code is available at GitHub via https://github.com/slrvv/CENTRE.
Genetic model of UBA5 deficiency highlights the involvement of both peripheral and central nervous systems and identifies widespread mitochondrial abnormalities
Authors: Serrano Rita J , Oorschot Viola , Palipana Dashika , Calcinotto Vanessa , Sonntag Carmen , Ramm Georg , Bryson-Richardson Robert J 
Published: November 2023

Abstract
Variants in UBA5 have been reported to cause neurological disease with impaired motor function, developmental delay, intellectual disability and brain pathology as recurrent clinical manifestations. UBA5 encodes a ubiquitin-activating-like enzyme that activates ufmylation, a post-translational ubiquitin-like modification pathway, which has been implicated in neurodevelopment and neuronal survival. The reason behind the variation in severity and clinical manifestations in affected individuals and the signal transduction pathways regulated by ufmylation that compromise the nervous system remains unknown. Zebrafish have emerged as a powerful model to study neurodegenerative disease due to its amenability for in vivo analysis of muscle and neuronal tissues, high-throughput examination of motor function and rapid embryonic development allowing an examination of disease progression. Using clustered regularly interspaced short palindromic repeats-associated protein 9 genome editing, we developed and characterized zebrafish mutant models to investigate disease pathophysiology. uba5 mutant zebrafish showed a significantly impaired motor function accompanied by delayed growth and reduced lifespan, reproducing key phenotypes observed in affected individuals. Our study demonstrates the suitability of zebrafish to study the pathophysiology of UBA5-related disease and as a powerful tool to identify pathways that could reduce disease progression. Furthermore, uba5 mutants exhibited widespread mitochondrial damage in both the nervous system and the skeletal muscle, suggesting that a perturbation of mitochondrial function may contribute to disease pathology.
Electric vehicle battery capacity degradation and health estimation using machine-learning techniques: a review
Authors: Das Kaushik , Kumar Roushan 
Published: November 2023

Abstract
Lithium-ion batteries have an essential characteristic in consumer electronics applications and electric mobility. However, predicting their lifetime performance is a difficult task due to the impact of operating and environmental conditions. Additionally, state-of-health (SOH) and remaining-useful-life (RUL) predictions have developed into crucial components of the energy management system for lifetime prediction to guarantee the best possible performance. Due to the non-linear behaviour of the health prediction of electric vehicle batteries, the assessment of SOH and RUL has therefore become a core research challenge for both business and academics. This paper introduces a comprehensive analysis of the application of machine learning in the domain of electric vehicle battery management, emphasizing state prediction and ageing prognostics. The objective is to provide comprehensive information about the evaluation, categorization and multiple machine-learning algorithms for predicting the SOH and RUL. Additionally, lithium-ion battery behaviour, the SOH estimation approach, key findings, advantages, challenges and potential of the battery management system for different state estimations are discussed. The study identifies the common challenges encountered in traditional battery management and provides a summary of how machine learning can be employed to address these challenges.
A pragmatic retrofitting approach to enhancing the thermal, energy and economic performance of an educational building: a case study in Malaysia
Authors: Alamin Etaib , Kamaruzaman Natrah , Kamar Haslinda Mohamed 
Published: November 2023

Abstract
Building retrofit procedures play a crucial role in improving the energy performance and economic indicators of a building. In this context, an energy audit is typically recommended, but it is seldom used as a comprehensive approach due to the complexity and associated costs. This article aims to conduct a holistic energy audit approach for a university building in Malaysia, with the objective of diagnosing energy efficiency deficiencies, identifying areas of energy waste and proposing practical retrofit measures accordingly. The approach involved multiple stages, including measurements, surveys and simulation work. Eight energy-saving measures were proposed, targeting improvements in envelope elements, cooling and lighting systems, and operation and control. The Design-Builder software was utilized for energy simulation, assessing the annual energy savings. Economic evaluation indices, such as net present value and simple payback period, were used to assess the economic feasibility of the measures. The results demonstrated significant potential for energy reduction, with each measure achieving annual energy reductions ranging from 2% to 18%, and a cumulative impact of 41% on annual energy consumption when combined. The investment payback period for the energy-saving measures varied from 0.8 to 8.9 years, with a payback period of 3.9 years for the combined energy-saving measures. Furthermore, the net present value was positive, indicating the economic feasibility of investing in the proposed energy-saving measures. These findings provide valuable energy-saving opportunities that can be applied to improve similar buildings on the university campus.