bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hybrid feature selection model based on machine learning and knowledge graph
Authors: Peng Xiaoqing , Shuai Yong , Gan Yaxi , Chen Yaokai 
Published: November 2021

Abstract
Aiming at the problem that the current feature selection algorithm can not adapt to both supervised learning data and unsupervised learning data, and had poor feature interpretability, this paper proposed a hybrid feature selection model based on machine learning and knowledge graph. By the idea of hybridization, this model used supervised learning algorithms, unsupervised learning algorithms and knowledge graph technology to model from the perspective of data features and text features. Firstly, the data-based feature weights were obtained through the machine learning model, and then the text-based weights were obtained by using the knowledge graph technology, and the weight sets are combined to obtain a feature matrix with good explanatory properties that meets both the data and text features. Finally, the case analysis proves that the method proposed in this paper has good effects and interpretability.
Research on Neural Style Transfer
Authors: Liu Xueyuan 
Published: November 2021

Abstract
The process of using CNN (Convolutional Neural Network) to blend the contents of a picture with different styles is called neural style transfer (NST). The purpose of this paper is to introduce current progress of NST, and introduce in detail the classification of the main NST algorithms based on deep learning, and make qualitative and quantitative comparisons of different algorithms, and then analyze the application prospects of image style migration in related fields, and finally summarize the existing problems and future research directions of NST.
Application of 3D vision intelligent calibration and imaging technology for industrial robots
Authors: Yang Rui , Mo Qinglong , Li Yuhong , Liu Yizhi , Hu Ruihan 
Published: November 2021

Abstract
During modern flexible lean manufacturing, flexible operation of irregular and complex workpieces with different specifications and arbitrary placement is an essential ability of industrial robots, while it cannot be met by traditional clamping methods. Vision technology brings flexibility and convenience to industrial robots, but the common two-dimensional technology only involves three degrees of freedom (plane displacement and rotation), which hinders the positioning of arbitrarily placing workpieces (often six degrees of freedom) and disorderly sorting. In addition, for typical visual tasks in industrial environments like defect detection, accurate distinguishing of such defects as pits and scratches is challenging under two-dimensional plane imaging. The introduction of three-dimensional information provides an effective solution to this problem. Thus, in the face of increasingly complex, flexible, intelligent and personalized manufacturing needs, the acquisition and processing of 3D visual information are of much importance.
Breast cancer X-ray image staging: based on efficient net with multi-scale fusion and cbam attention
Authors: Li Runyi , Wang Sen , Wang Zizhou , Zhang Lei 
Published: November 2021

Abstract
With ever-progressing development period, image classification algorithms based on deep learning have shown good performance on some large datasets. In the development of classification algorithms, many proposals related to attention mechanism have greatly improved the accuracy of the model, and at the same time increased the interpretability of the network structure. However, on medical image data, the performance of the classification algorithm is not as expected, and the reason is that the fine-grained image data differs little among all classes, resulting that the knowledge domain is also hard to learn for models. We (1) proposed the Efficientnet model based on the cbam attention mechanism, and added a multi-scale fusion method; (2) applied the model to the breast cancer medical image data set, and completed the breast cancer classification task with high accuracy (Phase I, Phase II, Phase III, etc.); (3) Compared with other existing image classification algorithms, our method has the highest accuracy, thus the researchers conclude that EfficientNet with CBAM and multi-scale fusion will improve the classification performance. This result is helpful for deeper research on medical image processing and breast cancer staging.
Hypergraph Neural Networks with Attention Mechanism for Session-based Recommendation
Authors: Ding Meirong , Lin Xiaokang , Zeng Biqing , Chai Yuan 
Published: November 2021

Abstract
The session-based recommendation task is designed to predict the behavior of the current session at the next moment based on multiple anonymous sessions. Due to the lack of user information in the session, the traditional recommendation model cannot be used directly to model the interest of specific users. In this paper, a session recommendation model based on hypergraph neural networks and attention mechanism (HGNNA) is proposed. Firstly, the features of items are learned by constructing hypergraph neural networks, then the conversation information is aggregated by self-attention mechanism, and finally the information among similar sessions is aggregated by graph attention networks. The hypergraph neural networks can capture the correlation between items, the self-attention mechanism can show the interest of the current session, and the graph attention networks can find the interest pattern between similar sessions, so that the representation vector of the session includes the information of the items in the session, other items outside the session and other sessions. In the experiments on two datasets, Yoochoose1/4 and Diginetica, the recommendation effect of HGNNA is higher than that of other relevant methods, especially in the P@20, which is improved by 0.69 and 1.40 respectively.
Dangerous goods identification based on multi-channel neural network
Authors: Wei XiaoTian , Hao ZiQiang , Du Bo 
Published: November 2021

Abstract
In the current society, there is an increasing demand for dangerous goods identification technology in X-ray images, but at the current stage, most of the identification of dangerous goods in X-ray images still relies on artificial eye recognition. In order to solve this problem, this paper proposes A method for automatically and intelligently identifying dangerous goods in X-ray images based on the transformation of the convolutional neural network. By adding multi-channel convolution and normalization to the convolutional neural network, the target features are extracted to achieve automatic detection of dangerous goods. The purpose of better identification. In the simulation experiment, using the public data set and self-built data set in the X-ray security inspection field, the accuracy of the identification of dangerous goods in the X-ray image was obtained more satisfactory results than the traditional dangerous goods identification. The improved Alex Net network’ s testing accuracy on contraband knives and guns is 8.53% and 11.6% higher than the training accuracy of the original Alex Net network.
Design of stepper motor driver based on STM32
Authors: Liu Haowen , Li Ripeng 
Published: November 2021

Abstract
In the field of industrial control, stepping has been widely used as an actuator. For this article, based on STM32F103 as the control core, the functions of start and stop, forward rotation, reverse rotation and speed regulation of the stepping motor are realized through four buttons. The system uses L293D to drive a stepper motor, LED0 and LED1 two status lights display the motor’s forward and reverse rotation status, and the digital tube displays the current stepper motor speed. The whole system includes L293D module, button module, status light module and speed display module. The whole system is modeled, theoretically analyzed and simulated based on the drive control principle of the stepper motor, and the precise and stable control performance of the system under different occasions is further verified through experiments.
Path Aggregation Model for Attributed Network Embedding with a Smart Random Walk
Authors: Xiao Xiang , Zhang Kang , Qiu Shuang , Liu Wei 
Published: November 2021

Abstract
Network embedding has attracted a surge of attention recently. In this field, how to preserve high-order proximity has long been a difficult task. Graph convolutional network (GCN) and random walk-based approaches can preserve high-order proximity to a certain extent. However, they partially concentrate on the aggregation process and sampling process respectively. Path aggregation methods combine the merits of GCN and random walk, and thus can preserve more high-order information and achieve better performance. However, path aggregation framework has not been applied in attributed network embedding yet. In this paper, we propose a path aggregation model for attributed network embedding, with two main contributions. First, we claim that there always exists implicit edge weight in networks, and design a tweaked random walk algorithm to sample paths accordingly. Second, we propose a path aggregation framework dealing with both nodes and attributes. Extensive experimental results show that our proposal outperforms the cutting-edge baselines on downstream tasks, such as node clustering, node classification, and link prediction.
Effects of temperature anneal cycling on a cryogenically proton irradiated CCD
Authors: Parsons S. , Buggey T. , Holland A. , Sembay S. , Randall G. , Hetherington O. , Yeoman D. , Hall D. , Verhoeve P. , Soman M. 
Published: November 2021

Abstract
Throughout a typical Earth orbit a satellite is constantly bombarded by radiation with trapped and solar protons being of particular concern as they gradually damage the focal plane devices throughout the mission and degrade their performance. To understand the impact the damage has on CCDs and how it varies with their thermal history a proton radiation campaign has been carried out using a CCD280. The CCD is irradiated at 153 K and gradually warmed to 188 K in 5 K increments with Fe55 X-ray, dark current and trap pumping images taken at 153 K after each anneal step.
The results show that despite the trap landscape changing throughout the anneal it has little impact on parallel charge transfer inefficiency. This is thought to be because most traps are unaffected and a lot of those that do anneal only move from the continuum between distinct trap species and into a nearby divacancy trap “peak” whose emission time constant is similar enough to still impact the CTI.
In terms of using a CCD280 or similar devices in a mission the CTI being unaffected by thermal annealing up to 188 K means that any CTI correction needed as the radiation damage builds up does not have to take into account the thermal history of the focal plane. However, it is possible that a significant amount of annealing will occur at temperatures greater than 188 K and care should be taken when a mission is operating in this range to gather accurate pre-flight data.
Using precipitation sensitivity to temperature to adjust projected global runoff
Authors: Chai Yuanfang , Berghuijs Wouter R , Naudts Kim , Janssen Thomas A J , Yao Yue , Dolman Han 
Published: November 2021

Abstract
Climate change affects the water cycle. Despite the improved accuracy of simulations of historical temperature, precipitation and runoff in the latest Coupled Model Intercomparison Project Phase 6 (CMIP6), the uncertainty of the future sensitivity of global runoff to temperature remains large. Here, we identify a statistical relationship at the global scale between the sensitivity of precipitation to temperature change (1979–2014) and the sensitivity of runoff to temperature change (2015–2100). We use this relation to constrain future runoff sensitivity estimates. Our statistical relationship only slightly reduces the uncertainty range of future runoff sensitivities (order 10% reduction). However, more importantly, it raises the expected global runoff sensitivity to background global warming by 36%–104% compared to estimates taken directly from the CMIP6 model ensemble. The constrained sensitivities also indicate a shift towards globally more wet conditions and less dry conditions.
Carbon and ecohydrological priorities in managing woody encroachment: UAV perspective 63 years after a control treatment
Authors: Sankey Temuulen Ts , Leonard Jackson , Moore Margaret M , Sankey Joel B , Belmonte Adam 
Published: November 2021

Abstract
Woody encroachment, including both woody species expansion and density increase, is a globally observed phenomenon that deteriorates arid and semi-arid rangeland health, biodiversity, and ecosystem services. Mechanical and chemical control treatments are commonly performed to reduce woody cover and restore ecohydrologic function. While the immediate impacts of woody control treatments are well documented in short-term studies, treatment impacts at decadal scales are not commonly studied. Using a controlled herbicide treatment from 1954 in the Sierra Ancha Experimental Forest in central Arizona, USA, we quantify woody encroachment and associated aboveground carbon accumulation in treated and untreated watersheds. Woody encroachment and aboveground carbon are estimated using high resolution multispectral images and photogrammetric data from a fixed-wing unmanned aerial vehicle (UAV). We then combine the contemporary UAV image-derived estimates with historical records from immediately before and after the treatment to consider long-term trends in woody vegetation cover, aboveground carbon, water yield, and sedimentation. Our results indicate that the treatment has had a lasting impact. More than six decades later, woody cover in two treated watersheds are still significantly lower compared to two control watersheds, even though woody cover increased in all four drainages. Aboveground woody carbon in the treated watersheds is approximately one half that accumulated in the control watersheds. The historical records indicate that herbicide treatment also increased water yield and reduced annual sedimentation. Given the sustained reduction in woody cover and aboveground woody biomass in treated watersheds, we infer that the herbicide treatment has had similarly long lasting impacts on ecohydrological function. Land managers can consider legacy impacts from control treatments to better balance carbon and ecohydrological consequences of woody encroachment and treatment activities.
Trade-offs between efficiency, equality and equity in restoration for flood protection
Authors: Villarreal-Rosas Jaramar , Vogl Adrian L , Sonter Laura J , Possingham Hugh P , Rhodes Jonathan R 
Published: November 2021

Abstract
Conservation decision-makers and practitioners increasingly strive for efficient and equitable outcomes for people and nature. However, environmental management programs commonly benefit some groups of people more than others, and very little is known about how efforts to promote equality (i.e. even distributions) and equity (i.e. proportional distributions) trade-off against efficiency (i.e. total net outcome per dollar spent). Based on a case study in the Brigalow Belt Bioregion, Australia, we quantified trade-offs between equality, equity, and efficiency in planning for flood protection. We considered optimal restoration strategies that allocate a fixed budget (a) evenly among beneficiary sectors (i.e. seeking equality among urban residents, rural communities, and the food sector), (b) evenly among local government areas (LGAs) within the Brigalow Belt (i.e. seeking spatial equality), and (c) preferentially to areas of highest socioeconomic disadvantage (i.e. seeking equity). We assessed equality using the Gini coefficient, and equity using an index of socioeconomic disadvantage. At an AUD10M budget, evenly distributing the budget among beneficiary sectors was 80% less efficient than ignoring beneficiary groups, and did not improve equality in the distribution of flood protection among beneficiary sectors. Evenly distributing the budget among LGAs ensured restoration in four areas that were otherwise ignored, with a modest reduction in efficiency (12%–25%). Directing flood protection to areas of highest socioeconomic disadvantage did not result in additional reductions in efficiency, and captured areas of high disadvantage for the rural and urban sectors that were missed otherwise. We show here that different ways of targeting equity and equality lead to quite different trade-offs with efficiency. Our approach can be used to guide transparent negotiations between beneficiaries and other stakeholders involved in a planning process.
Unsupervised learning of anomalous diffusion data: an anomaly detection approach
Authors: Muñoz-Gil Gorka , Guigo i Corominas Guillem , Lewenstein Maciej 
Published: November 2021

Abstract
The characterization of diffusion processes is a keystone in our understanding of a variety of physical phenomena. Many of these deviate from Brownian motion, giving rise to anomalous diffusion. Various theoretical models exists nowadays to describe such processes, but their application to experimental setups is often challenging, due to the stochastic nature of the phenomena and the difficulty to harness reliable data. The latter often consists on short and noisy trajectories, which are hard to characterize with usual statistical approaches. In recent years, we have witnessed an impressive effort to bridge theory and experiments by means of supervised machine learning techniques, with astonishing results. In this work, we explore the use of unsupervised methods in anomalous diffusion data. We show that the main diffusion characteristics can be learnt without the need of any labelling of the data. We use such method to discriminate between anomalous diffusion models and extract their physical parameters. Moreover, we explore the feasibility of finding novel types of diffusion, in this case represented by compositions of existing diffusion models. At last, we showcase the use of the method in experimental data and demonstrate its advantages for cases where supervised learning is not applicable.
Tunable coupling of terahertz Dirac plasmons and phonons in transition-metal dichalcogenide-based van der Waals heterostructures
Authors: Lavor I R , Chaves Andrey , Peeters F M , Van Duppen B 
Published: November 2021

Abstract
Dirac plasmons in graphene hybridize with phonons of transition metal dichalcogenides (TMDs) when the materials are combined in so-called van der Waals heterostructures (vdWh), thus forming surface plasmon-phonon polaritons (SPPPs). The extend to which these modes are coupled depends on the TMD composition and structure, but also on the plasmons’ properties. By performing realistic simulations that account for the contribution of each layer of the vdWh separately, we calculate how the strength of plasmon-phonon coupling depends on the number and composition of TMD layers, on the graphene Fermi energy and the specific phonon mode. From this, we present a semiclassical theory that is capable of capturing all relevant characteristics of the SPPPs. We find that it is possible to realize both strong and ultra-strong coupling regimes by tuning graphene’s Fermi energy and changing TMD layer number.
Photoelectric and magnetic properties of boron nitride nanosheets with turbostratic structure and oxygen doping
Authors: Cao Chaochao , Yang Jingwen , Yan Song , Bai Wenjuan , Ma Yuanhui , Xue Yanming , Tang Chengchun 
Published: November 2021

Abstract
Turbostratic and oxygen doping (3.7 atom %) hexagonal boron nitride nanosheets (TO-BNNSs) with abundant defect sites, were synthesized by pyrolyzing the mixture of melamine cyanurate and boric acid. Systematic analyses reveal a highly disordered structures and covalent oxygen-doping in the TO-BNNSs. These features endow the product with increased unpaired electrons, localized charge asymmetry and spin polarization. While compared with bulk h-BN, the optical bandgap of TO-BNNSs drop down to ∼5.2 from ∼5.7 eV, dielectric constant raised from ∼2.1 to ∼2.4, the saturation magnetic moment increased from ∼0.011 to ∼0.033 emu g−1, and the coercivity enlarged from ∼73.56 to ∼367.39 Oe. These results suggest that h-BN materials with turbostratic structure and heteroatom-doping have extensive application prospect in the fields of nanoscale optics, electronics and magnetics.
High carrier mobility in single-crystal PtSe2 grown by molecular beam epitaxy on ZnO(0001)
Authors: Bonell Frédéric , Marty Alain , Vergnaud Céline , Consonni Vincent , Okuno Hanako , Ouerghi Abdelkarim , Boukari Hervé , Jamet Matthieu 
Published: November 2021

Abstract
PtSe2 is attracting considerable attention as a high mobility two-dimensional material with envisioned applications in microelectronics, photodetection and spintronics. The growth of high quality PtSe2 on insulating substrates with wafer-scale uniformity is a prerequisite for electronic transport investigations and practical use in devices. Here, we report the growth of highly oriented few-layers PtSe2 on ZnO(0001) by molecular beam epitaxy. The crystalline structure of the films is characterized with electron and x-ray diffraction, atomic force microscopy and transmission electron microscopy. The comparison with PtSe2 layers grown on graphene, sapphire, mica, SiO2 and Pt(111) shows that among insulating substrates, ZnO(0001) yields films of superior structural quality. Hall measurements performed on epitaxial ZnO/PtSe2 with five monolayers of PtSe2 show a clear semiconducting behaviour and a high mobility in excess of 200 cm2 V−1 s−1 at room temperature and up to 447 cm2 V−1 s−1 at low temperature.
Constitutive modelling with a novel two-step optimization for an Al-Zn-Mg-Cu alloy and its application in FEA
Authors: Wu Daoxiang , Long Shuai , Wang Shuyan , Li Shi-Shan , Zhou Yu-Ting 
Published: November 2021

Abstract
The modified Johnson-Cook constitutive model was developed for describing the flow behavior of Al-7.8Zn-1.65Mg-2.0Cu (wt%) alloy based on the flow curves in the temperature range of 300 °C∼450 °C and strain rate range of 0.01 s−1 ∼ 10 s−1 which were obtained by isothermal compression tests conducted on a Gleeble-3500 isothermal simulator. A two-step optimization method was proposed to optimize the prediction precision according to the evaluation of average absolute relative error (AARE). By using a traversal procedure for calculating the model under different reference conditions, this evaluator was found varying in the range of 4.1837% ∼ 11.105%, revealing the great influence of reference condition on the precision, then the reference condition optimization (RCO) was conducted. Genetic algorithm (GA) was introduced as the second step of the two-step optimization (TSO) to optimize the material constants of the model, which furtherly improved the precision by reducing the AARE-value to 3.801%. The models before and after optimization were written into subroutines for the software DEFORM and the compression tests were investigated through finite element analysis (FEA). The simulated results (forming load and temperature rise) revealed that the model after TSO has the highest agreement with the experimental.