bluuebunny/crossref_metadata_2025_split huggingface dataset:
From Transcendence to Kitsch: Have We Lost Faith?
Authors: Castelli Alberto 
Published: November 2023

AbstractThe role played by the artist and the nature of art are two of the most enduring and controversial themes in Western philosophy. Indeed, if we were to trace the history of aesthetics, it seems evident that the role of the artist and the effects of art have been called into question as history crosses over into another age. My concern in this study has to do with the function of art and the artist throughout Western history, which is not so much an emancipatory movement as it is a crisis of transcendence. Likewise, the secular intellectual dogmatism that, in our age, treats literary texts as open documents of linguistics analysis without consideration for the author’s intention is less a democratic endeavor than it is a crisis of religious belief.
Estimating phase transition of perturbed
J1−J2
Heisenberg quantum chain in mixtures of ground and first excited states
Authors: Mondal Sayan , Biswas George , Ghoshal Ahana , Biswas Anindya , Sen Ujjwal 
Published: November 2023

Abstract
We show that the nearest neighbour entanglement in a mixture of ground and first excited states—a subjacent state—of the

J
1

−

J
2

Heisenberg quantum spin chain can be used as an order parameter to detect the phase transition of the chain from a gapless spin fluid to a gapped dimer phase. We study the effectiveness of the order parameter for varying relative mixing probabilities between the ground and first excited states in the subjacent state for different system sizes, and extrapolate the results to the thermodynamic limit. We observe that the nearest neighbour concurrence can play a role of a good order parameter even if the system is in the ground state, but with a small finite probability of leaking into the first excited state. Moreover, we apply the order parameter of the subjacent state to investigate the response to separate introductions of anisotropy and of glassy disorder on the phase diagram of the model, and analyse the corresponding finite-size scale exponents and the emergent tricritical point in the former case. The anisotropic

J
1

−

J
2

chain has a richer phase diagram which is also clearly visible by using the same order parameter.
Unmanned Aerial Vehicles Communication Interference Detection Based on SSA-BP Neural Network
Authors: Li Xiangkun , Guo Bin , Guo Qianrui , Ma Weijiao 
Published: November 2023

Abstract
This paper proposes a method for detecting communication interference in unmanned aerial vehicles (UAVs). First, we train the BP neural network with signal data from interference attacks, and then continuously optimize the BP neural network using the sparrow search algorithm (SSA). After iterations, we obtain the final interference detection model. With this detection model, we can detect whether there are malicious interference signals in the drone flight environment and evaluate the detection model using the detection rate. Finally, our detection model has an accuracy of up to 93.64%.
Satellite image super-resolution reconstruction based on ACGAN and dual-channel dense residual network
Authors: Wei Zikang , Lin Meiling 
Published: November 2023

Abstract
For solving the problem of simple information image super-resolution reconstruction, this paper proposes a method based on ACGAN and dual-channel dense residual networks. Firstly, the different information represented by the images is classified to form different types of datasets. Focusing on image feature information, ACGAN is used to learn image feature information to generate images with this kind of feature information in this type of dataset so as to achieve image enhancement. Secondly, the study designed and proposed a dual-channel dense residual network to train the enhanced image dataset and achieve image super-resolution reconstruction. Experiments show that the method proposed in this paper not only can obtain higher reconstruction image quality than other methods but also obtain higher PSNR and SSIM than others. From this point of view, the application of this technology will have a far-reaching impact on the research of image super-resolution reconstruction with simple and sparse feature information.
An evaluation method with convolutional white-box models for image attribution explainers
Authors: Hu Tan , Wu Tieru 
Published: November 2023

Abstract
Research in the explanation of models has developed rapidly in recent years. However, research in the evaluation of explainers is still limited, with most evaluation strategies centered around perturbation. This approach may produce incorrect evaluations when the relationship between features gets complicated. In this research, we present a ground-truth-based evaluation method for feature attribution explainers on image tasks. We design three perspectives to evaluate. The input perspective evaluates whether the explainers accurately represent the inputs that the model detects. The feature perspective evaluates whether the explainers capture the features important in decisions. The user perspective evaluates the reliability that the user derives from the data. Then, using the traditional white-box model, we extract the ground truth corresponding to the three perspectives and provide an example to demonstrate the procedure. To acquire the results of the image attribution explainer, we also reconstruct the traditional white-box model into the convolutional network white-box model. Our method provides an a priori benchmark that is not affected by the explainer. The experiments show that we may use the evaluation method for different tasks and extend it to natural datasets, which offers a flexible and low-cost evaluation strategy.
Optimization of Robotic Arm Grasping through Fractional-Order Deep Deterministic Policy Gradient Algorithm
Authors: Geng Hui , Hu Qi , Wang Zhe 
Published: November 2023

Abstract
With the rapid development of robotics technology, robotic arm grasping has gained significant attention in the fields of automation and artificial intelligence. In this study, we propose a fractional-order deep deterministic policy gradient (DDPG) algorithm for optimizing robotic arm grasping tasks. Traditional machine learning algorithms face challenges in handling continuous action spaces, while the DDPG algorithm effectively addresses this issue. In this research, we first review the background and challenges of robotic arm grasping and provide an overview of the application of traditional reinforcement learning algorithms in grasping tasks. Subsequently, we introduce the principles and fundamental ideas of the DDPG algorithm in detail, discussing its potential for optimizing robotic arm grasping. To further enhance the performance of robotic arm grasping, we propose an improved approach based on fractional-order control. Fractional-order control exhibits unique advantages in environmental dynamics modeling and grasp posture optimization, enhancing the robustness and adaptability of robotic arm grasping. Through a series of experiments, we validate the effectiveness and superiority of the fractional-order DDPG algorithm in robotic arm grasping tasks. Our algorithm achieves significant improvements in grasping success rate and stability compared to traditional methods. The experimental results demonstrate that the fractional-order DDPG algorithm is better equipped to handle control challenges in continuous action spaces and optimize the performance of robotic arm grasping tasks.
Research on Intelligent Recommendation of Maintenance Scheme based on Multi-modal Data Learning
Authors: Li Jiaqi , Tan Chengle , Zhou Qunhao , Cheng Qing 
Published: November 2023

Abstract
In view of the unavoidable degradation and damage to the performance and stability of military, medical, transportation and industrial equipment in the process of daily use, especially after long-term operation or shutdown, and the maintenance personnel cannot be timely in place, an intelligent recommendation technology or program for remote maintenance of equipment based on multi-modal data learning is studied. Multi-modal data such as text, pictures and videos are integrated. Based on image recognition and learning of multi-modal data, intelligent recommendation of remote maintenance solutions can be realized when maintenance personnel cannot arrive in time.
Efficient and Lightweight Spatial Attention-Based Shallow CNNs with Superior Performance for Concrete Surface Crack Detection
Authors: Dai Dikang , Xia Peiwen 
Published: November 2023

Abstract
To solve the problems of high training cost and low time efficiency when detecting concrete surface crack defects, this paper proposes a concrete surface crack detection method based on shallow CNNs. First, an image dataset of concrete surface cracks is constructed and preprocessing operations are performed on the dataset. Then, a shallow CNNs model for concrete surface crack detection is constructed, hyperparameters of the network model are set, spatial attention mechanism is introduced, and the dataset is input to the model for training. Finally, the results are analyzed and evaluated, and compared with the mainstream deep learning models InceptionV3 and Resnet50. Experimental results demonstrate that our proposed method achieves a 99.56% accuracy rate for detecting cracks on concrete surfaces, while also offering higher time efficiency and lower training costs. Given the significantly higher training costs associated with mainstream deep learning models such as InceptionV3 and ResNet50, our proposed method holds promise for future applications in industrial production processes and the intelligent field of construction quality inspection. By effectively, automatically, and accurately identifying cracks on concrete surfaces, it can reduce the risk of catastrophic failures.
Overcoming Catastrophic Forgetting with Detail-Degradation Decoupling Networks for Super-Resolution
Authors: Jiang Yichun , Liu Yunqing , Zhan Weida 
Published: November 2023

Abstract
Super-resolution reconstruction is a crucial technology that can significantly enhance image resolution and reconstruct high-frequency details. Existing methods are fine-tuned based on the application target during their usage, which enables them to be applied to new data domains. Nevertheless, the extent to which the fine-tuned model can generalize to the original data domain remains unclear, and therefore, additional research is warranted in this particular aspect. To solve the above problems, we analyzed the forgetting phenomenon of super-resolution methods under different degradation kernels, scenarios, and modalities. Then, we introduced Detail-Degradation Decoupling Networks, which separate the inverse process of degradation and the process of recovering high-frequency details in super-resolution. During the incremental learning process, the network parameters can be adjusted based on the task’s characteristics. Our experimental results demonstrate that our approach can significantly reduce catastrophic forgetting in super-resolution reconstruction and enhance the network’s ability to generalize and remain robust.
Crop Water Requirement Prediction Method Based on EEMD-Attention-LSTM Model
Authors: Ma Yuanzheng , Lv Bing , Wang Yuanfa , Shi Changyu 
Published: November 2023

Abstract
Crop water demand prediction is an important part of Precision agriculture. Due to the nonlinear relationship between input variables (weather data, soil moisture, and crop type) and output variables (crop water demand), it is difficult to accurately predict crop water demand. This article proposes a method for predicting crop water demand based on the EEMD Attention LSTM model. The model combines the ensemble empirical mode decomposition (EEMD), attention mechanism (Attention), and Long short-term memory (LSTM) neural networks to capture the changes in different scales of input variables. It selectively focuses on the relevant parts of the input sequence when making predictions. The performance of the EEMD attention LSTM model was evaluated on the historical crop water demand data set and compared with other prediction models such as RNN, LSTM, and EMD-LSTM. It shows that the model is superior to other methods in terms of Mean squared error and mean absolute error. When the relationship between input variables and output variables is nonlinear, the EEMD-attention-LSTM model is a powerful tool for crop water demand prediction.
Gear Transmission System Based on Cloud Platform
Authors: Lu Daixing , He Jintao , Shen Ye 
Published: November 2023

Abstract
Based on the Amazon cloud platform, the development and application scenarios of industrial software for gear transmission systems are introduced. Taking the geometric dimension calculation transmission with involute cylindrical gear as an example, the design idea, development process and system architecture design of the transmission design software based on the cloud platform are expounded. Results show that the developed software of transmission system design based on the cloud platform can meet the requirements of accurate calculation, fast response and operation.
Small Target Detection Algorithm Based on Wavelet Domain Features and Weighted Sample Allocation Strategy
Authors: Tian Chunxin , Chen Xujun , Li Huxiao 
Published: November 2023

Abstract
Small target detection using UAV aerial photography has emerged as a popular research topic. The resolution of small target images is low and the background is complex. In this paper, utilizing a single-stage target detection algorithm ATSS as its foundation, we propose a small target network detection model SDW-Net that combines deformation convolution with wavelet domain feature enhancement by weighting the samples from both classification and regression tasks, and combining the strong local feature representation ability of discrete wavelet transform. Firstly, the original features are obtained by using discrete wavelet transform, and the weighted wavelet features are obtained through the deformable convolution block and the spatial channel attention unit; secondly, the weighted features of the regular domain are obtained through the inverse wavelet trans-form; finally, the original feature and the wavelet features are fused to enrich the details of the small target image and sent to the detection head for output. According to the experimental findings, when compared to the ATSS algorithm, the proposed detection model achieved an average increase in prediction accuracy of 7.2%, and a recall rate increase of 3.7%, and the problem of missing detection and false detection can be alleviated, which can provide a new technical solution for small-sized target detection scenarios.
Multi-modal Physiological Signal Fusion for Emotion Classification: A Multi-Head Attention Approach
Authors: Bai Xuemei , Tan Jiaqi , Hu Hanping , Zhang Chenjie , Gu Dongbing 
Published: November 2023

Abstract
In this essay, a model-level fusion technique of multi-modal physiological signals using Multi-Head Attention is studied. A framework that utilizes multi-model physiological signals for the task of emotion classification is proposed. First, the GCRNN model, which combines the Graph Convolutional Network (GCN) and the Long and Short Term Memory (LSTM), captures the unique features of electroencephalogram (EEG) signals. The spatial and temporal information that makes up impulses from the EEG can be captured precisely by such a technique. The CCRNN model, which combines the Convolutional Neural Network (CNN) integrated with the Channel-wise Attention and the LSTM, is used for peripheral physiological signals. The model can extract useful features from peripheral physiological signals and automatically learn to weigh the importance of various channels. Finally, Multi-head Attention is employed to fuse the output of the GCRNN and CCRNN methods. The Multi-head Attention can automatically learn the relevance and importance of different modal signals and weigh them accordingly. Emotion classification is implemented by adding a component of Softmax to map what the model produced to discrete emotion categories. The DEAP dataset was utilized in this study for experimental verification, and the results indicate that the method using multi-modal physiological signal fusion is substantially greater in precision than the technique using simply EEG signals. Additionally, the Multi-head Attention fusion method performs better than previous fusion techniques.
Multi target tracking algorithm based on anchor free detection
Authors: Zhu Xinli , Kou Tingting , Si Mingzhi , Wang Shuai 
Published: November 2023

Abstract
In order to alleviate the problems of frequent identity label switching caused by mutual occlusion between targets and missed detection caused by small target sizes in multi-target tracking, this paper proposes a multi-target tracking algorithm based on anchor-free detection. This algorithm first uses YOLOX for detection and embeds the ReID algorithm to enhance pedestrian recognition. Then, it uses the DeepSORT algorithm for tracking and correcting abnormal trajectory algorithms for occluded targets. Finally, experiments are conducted on both the MOT16 dataset and the self-made dataset. This algorithm has higher tracking accuracy than other algorithms, and its tracking performance for small targets has also been effectively improved.
Inter-satellite Routing Study for LEO Constellations Based on Orbit Prediction
Authors: Lei Chaowen , Wang Tao , Wang Junrui , Cheng Shiqi , Wang Zhijun 
Published: November 2023

Abstract
Due to the periodic motion of the LEO constellation network, finding the shortest delay path between two satellites in a dynamic network is a challenge for LEO constellations to perform inter-satellite communication. In this paper, we first use the two-body model for orbit prediction to fit the trajectories of satellites in the future period and construct the network topology of the LEO constellation in the future period. Finally, the shortest delay path algorithm based on a time aggregation graph is designed. Simulation results show that the shortest delay path algorithm designed in this paper has good performance in terms of both end-to-end delay and packet loss rate of satellite networks.
Lower Socioeconomic Status, Psychological Distress, and Self-reported Hypertension: A Longitudinal Moderated Mediation Analysis
Authors: Zhang Liying , Dailey Rhonda , Brook Robert D , Opara Ijeoma Nnodim , Liu Xianchen , Carbone Jason T , Levy Phillip D 
Published: November 2023

Abstract

BACKGROUND
Lower socioeconomic status (SES) has been associated with hypertension; however, the mediators and moderators of this association remain understudied. We examined the mediation effect of psychological distress on the link between lower SES and self-reported hypertension and the racial and sex moderation effects.

METHODS
We analyzed the data collected from 2009 to 2019 among adults from the Panel Study of Income Dynamics (PSID). Lower SES was defined as one of 3 indicators: education ≤12 years, unemployed, or individual annual income &lt;$27,800. Psychological distress was assessed using the Kessler K6 scale. Cox proportional hazard regression was conducted. Mediation analyses were performed using the PROCESS macro.

RESULTS
In the sample of heads of family who did not have self-reported hypertension in 2009 (N = 6,214), the mean age was 41 years, 30.6% were female, 32.9% were African American. The cumulative incidence of self-reported hypertension was 29.8% between 2009 and 2019. Cox proportional hazard regression analysis showed that after controlling for covariates, lower SES (score &gt; 0 vs. score = 0) was associated with self-reported hypertension (hazard ratio = 1.27, 95% confidence interval = 1.14–1.42). SES had indirect effect on self-reported hypertension through psychological distress and the indirect effect (0.02 in females, 0.01 in males, P &lt; 0.05) was moderated by sex but not by race.

CONCLUSIONS
The association of SES and self-reported hypertension was mediated by psychological distress and sex moderated the mediation effect. Interventions focused on reducing contributors to SES and psychological stress should be considered to reduce hypertension risk.
Examining DNA breathing with pyDNA-EPBD
Authors: Kabir Anowarul , Bhattarai Manish , Rasmussen Kim Ø , Shehu Amarda , Usheva Anny , Bishop Alan R , Alexandrov Boian 
Published: November 2023

Abstract

Motivation
The two strands of the DNA double helix locally and spontaneously separate and recombine in living cells due to the inherent thermal DNA motion. This dynamics results in transient openings in the double helix and is referred to as “DNA breathing” or “DNA bubbles.” The propensity to form local transient openings is important in a wide range of biological processes, such as transcription, replication, and transcription factors binding. However, the modeling and computer simulation of these phenomena, have remained a challenge due to the complex interplay of numerous factors, such as, temperature, salt content, DNA sequence, hydrogen bonding, base stacking, and others.

Results
We present pyDNA-EPBD, a parallel software implementation of the Extended Peyrard-Bishop-Dauxois (EPBD) nonlinear DNA model that allows us to describe some features of DNA dynamics in detail. The pyDNA-EPBD generates genomic scale profiles of average base-pair openings, base flipping probability, DNA bubble probability, and calculations of the characteristically dynamic length indicating the number of base pairs statistically significantly affected by a single point mutation using the Markov Chain Monte Carlo algorithm.

Availability and implementation
pyDNA-EPBD is supported across most operating systems and is freely available at https://github.com/lanl/pyDNA_EPBD. Extensive documentation can be found at https://lanl.github.io/pyDNA_EPBD/.
Multilayer analysis of dynamic network reconfiguration in pediatric posttraumatic stress disorder
Authors: Suo Xueling , Lan Huan , Zuo Chao , Chen Li , Qin Kun , Li Lingjiang , Kemp Graham J , Wang Song , Gong Qiyong 
Published: November 2023

Abstract
Neuroimage studies have reported functional connectome abnormalities in posttraumatic stress disorder (PTSD), especially in adults. However, these studies often treated the brain as a static network, and time-variance of connectome topology in pediatric posttraumatic stress disorder remain unclear. To explore case-control differences in dynamic connectome topology, resting-state functional magnetic resonance imaging data were acquired from 24 treatment-naïve non-comorbid pediatric posttraumatic stress disorder patients and 24 demographically matched trauma-exposed non-posttraumatic stress disorder controls. A graph-theoretic analysis was applied to construct time-varying modular structure of whole-brain networks by maximizing the multilayer modularity. Network switching rate at the global, subnetwork, and nodal levels were calculated and compared between posttraumatic stress disorder and trauma-exposed non-posttraumatic stress disorder groups, and their associations with posttraumatic stress disorder symptom severity and sex interactions were explored. At the global level, individuals with posttraumatic stress disorder exhibited significantly lower network switching rates compared to trauma-exposed non-posttraumatic stress disorder controls. This difference was mainly involved in default-mode and dorsal attention subnetworks, as well as in inferior temporal and parietal brain nodes. Posttraumatic stress disorder symptom severity was negatively correlated with switching rate in the global network and default mode network. No significant differences were observed in the interaction between diagnosis and sex/age. Pediatric posttraumatic stress disorder is associated with dynamic reconfiguration of brain networks, which may provide insights into the biological basis of this disorder.