bluuebunny/crossref_metadata_2025 huggingface dataset:
Rapid scan white light two-dimensional electronic spectroscopy with 100 kHz shot-to-shot detection
Authors: Thomas Asha S. , Bhat Vivek N. , Tiwari Vivek
Published: December 2023

We demonstrate an approach to two-dimensional electronic spectroscopy (2DES) that combines the benefits of shot-to-shot detection at high-repetition rates with the simplicity of a broadband white light continuum input and conventional optical elements to generate phase-locked pump pulse pairs. We demonstrate this through mutual synchronization between the laser repetition rate, the acousto-optical deflector, the pump delay stage, and the CCD line camera, which allows for rapid scanning of pump optical delay synchronously with the laser repetition rate, while the delay stage is moved at a constant velocity. The resulting shot-to-shot detection scheme is repetition rate scalable and only limited by the CCD line rate and the maximum stage velocity. Using this approach, we demonstrate the measurement of an averaged 2DES absorptive spectrum in as much as 1.2 s of continuous sample exposure per 2D spectrum. We achieve a signal-to-noise ratio of 6.8 for optical densities down to 0.05 with 11.6 s of averaging at 100 kHz laser repetition rate. Combining rapid scanning of mechanical delay lines with shot-to-shot detection as demonstrated here provides a viable alternative to acousto-optic pulse shaping approaches that is repetition-rate scalable, has comparable throughput and sensitivity, and minimizes sample exposure per 2D spectrum with promising micro-spectroscopy applications.
Excessive nucleic acid R-loops induce mitochondria-dependent epithelial cell necroptosis and drive spontaneous intestinal inflammation
Authors: Yang Xu , Li Guilin , Lou Pengbo , Zhang Mingxin , Yao Kai , Xiao Jintao , Chen Yiqian , Xu Jiuzhi , Tian Shengyuan , Deng Min , Pan Yuwei , Li Mengzhen , Wu Xi , Liu Ruiqi , Shi Xiaojing , Tian Yuhua , Yu Lu , Ke Hao , Jiao Baowei , Cong Yingzi , Plikus Maksim V. , Liu Xiaowei , Yu Zhengquan , Lv Cong
Published: December 2023

Oxidative stress, which can be activated by a variety of environmental risk factors, has been implicated as an important pathogenic factor for inflammatory bowel disease (IBD). However, how oxidative stress drives IBD onset remains elusive. Here, we found that oxidative stress was strongly activated in inflamed tissues from both ulcerative colitis patients and Crohn’s disease patients, and it caused nuclear-to-cytosolic TDP-43 transport and a reduction in the TDP-43 protein level. To investigate the function of TDP-43 in IBD, we inducibly deleted exons 2 to 3 of Tardbp (encoding Tdp-43) in mouse intestinal epithelium, which disrupted its nuclear localization and RNA-processing function. The deletion gave rise to spontaneous intestinal inflammation by inducing epithelial cell necroptosis. Suppression of the necroptotic pathway with deletion of Mlkl or the RIP1 inhibitor Nec-1 rescued colitis phenotypes. Mechanistically, disruption of nuclear TDP-43 caused excessive R-loop accumulation, which triggered DNA damage and genome instability and thereby induced PARP1 hyperactivation, leading to subsequent NAD
+
depletion and ATP loss, consequently activating mitochondrion-dependent necroptosis in intestinal epithelial cells. Importantly, restoration of cellular NAD
+
levels with NAD
+
or NMN supplementation, as well as suppression of ALKBH7, an α-ketoglutarate dioxygenase in mitochondria, rescued TDP-43 deficiency-induced cell death and intestinal inflammation. Furthermore, TDP-43 protein levels were significantly inversely correlated with γ-H2A.X and p-MLKL levels in clinical IBD samples, suggesting the clinical relevance of TDP-43 deficiency-induced mitochondrion-dependent necroptosis. Taken together, these findings identify a unique pathogenic mechanism that links oxidative stress to intestinal inflammation and provide a potent and valid strategy for IBD intervention.
A meticulous covariance adaptive Kalman filter for satellite attitude estimation
Authors: Xiao Yang , Jiang Tao , Fan Guo-Wei , Zhang Liu , Gao Yu , Zhang Le
Published: December 2023

Aiming at the problems of model errors, non-Gaussian noise and measurement anomaly in the spacecraft attitude estimation system, this article proposes an improved adaptive filtering method based on covariance matching, which solves the problems of simultaneous dynamics model error and measurement model error in the attitude estimation system, and at the same time, effectively reduces the effects of non-Gaussian noise and large outlier situations occurring in the vector measurement sensor. Firstly, an adaptive filtering algorithm based on the innovation sequence estimation covariance is investigated under the framework of multiplicative extended Kalman filter (MEKF), which is used to correct process noise covariance, then the Sage–Husa adaptive Kalman filtering (SHAKF) method is combined to correct the measurement noise covariance, and finally the meticulous covariance adaptive multiplicative extended Kalman filter is designed. The proposed algorithm uses both innovation and SHAKF methods to correct the two covariance matrices simultaneously. Several attitude estimation simulation scenarios are set up to simulate the proposed algorithm in the presence of model errors, non-Gaussian noise, and large outlier. The simulation results demonstrate that the proposed algorithm outperforms the conventional algorithms in terms of estimation accuracy and robustness.
Mechanistic modelling of relative biological effectiveness of carbon ion beams and comparison with experiments
Authors: Feng Haonan , Li Weiguang , Zhang Yibao , Chang Cheng , Hua Ling , Feng Yiwen , Lai Youfang , Geng LiSheng
Published: December 2023

Objective. Relative biological effectiveness (RBE) plays a vital role in carbon ion radiotherapy, which is a promising treatment method for reducing toxic effects on normal tissues and improving treatment efficacy. It is important to have an effective and precise way of obtaining RBE values to support clinical decisions. A method of calculating RBE from a mechanistic perspective is reported. Approach. Ratio of dose to obtain the same number of double strand breaks (DSBs) between different radiation types was used to evaluate RBE. Package gMicroMC was used to simulate DSB yields. The DSB inductions were then analyzed to calculate RBE. The RBE values were compared with experimental results. Main results. Furusawa's experiment yielded RBE values of 1.27, 2.22, 3.00 and 3.37 for carbon ion beam with dose-averaged LET of 30.3 keV μm−1, 54.5 keV μm−1, 88 keV μm−1 and 137 keV μm−1, respectively. RBE values computed from gMicroMC simulations were 1.75, 2.22, 2.87 and 2.97. When it came to a more sophisticated carbon ion beam with 6 cm spread-out Bragg peak, RBE values were 1.61, 1.63, 2.19 and 2.36 for proximal, middle, distal and distal end part, respectively. Values simulated by gMicroMC were 1.50, 1.87, 2.19 and 2.34. The simulated results were in reasonable agreement with the experimental data. Significance. As a mechanistic way for the evaluation of RBE for carbon ion radiotherapy by combining the macroscopic simulation of energy spectrum and microscopic simulation of DNA damages, this work provides a promising tool for RBE calculation supporting clinical applications such as treatment planning.
Equivalent-time-active-cavitation-imaging enables vascular-resolution blood-brain-barrier-opening-therapy planning
Authors: Desmarais Samuel , Ramos-Palacios Gerardo , Porée Jonathan , Lee Stephen A , Leconte Alexis , Sadikot Abbas F , Provost Jean
Published: December 2023

Objective. Linking cavitation and anatomy was found to be important for predictable outcomes in focused-ultrasound blood-brain-barrier-opening and requires high resolution cavitation mapping. However, cavitation mapping techniques for planning and monitoring of therapeutic procedures either (1) do not leverage the full resolution capabilities of ultrasound imaging or (2) place constraints on the length of the therapeutic pulse. This study aimed to develop a high-resolution technique that could resolve vascular anatomy in the cavitation map. Approach. Herein, we develop BandPass-sampled-equivalent-time-active-cavitation-imaging (BP-ETACI), derived from bandpass sampling and dual-frequency contrast imaging at 12.5 MHz to produce cavitation maps prior and during blood–brain barrier opening with long therapeutic bursts using a 1.5 MHz focused transducer in the brain of C57BL/6 mice. Main results. The BP-ETACI cavitation maps were found to correlate with the vascular anatomy in ultrasound localization microscopy vascular maps and in histological sections. Cavitation maps produced from non-blood-brain-barrier disrupting doses showed the same cavitation-bearing vasculature as maps produced over entire blood-brain-barrier opening procedures, allowing use for (1) monitoring focused-ultrasound blood-brain-barrier-opening (FUS-BBBO), but also for (2) therapy planning and target verification. Significance. BP-ETACI is versatile, created high resolution cavitation maps in the mouse brain and is easily translatable to existing FUS-BBBO experiments. As such, it provides a means to further study cavitation phenomena in FUS-BBBO.
Low-cost and simple fabrication of micro-mask/micro-channels on different substrates based on water-soluble fiber poly (vinyl alcohol) (PVA)
Authors: Liu Ruifang , Yin Chaoyi , Shamim Sara , Wu Zhengting , Ba Long
Published: December 2023

Here we report an approach to fabricate micro-mask/micro-channels using water-soluble fiber poly (vinyl alcohol) (PVA). The scheme is a simple, environmentally friendly, and low-cost technology, that can realize the preparation of micro-mask/micro-channels on different substrates. Scanning electron microscope (SEM) and optical microscope (OM) are used to observe surface morphology and measured fiber diameter. We showed that a variety of micro-mask/micro-channels can be easily controlled by changing the swelling temperature and time of the PVA fiber. The swelling temperature was varied from 25 °C to 50 °C, the diameter of the fiber increased by 9.5 μm, and the diameter and volume swelling rates reached 59.38% and 154%, respectively. Meanwhile, with the increase in swelling time, the diameter and volume swelling rate showed the same increasing trend. Additionally, we used the PVA fiber as a micro-mask to prepare the micro-channel on the WSe2 surface and verified the feasibility of the PVA fiber to prepare micro-mask/micro-channels by testing the electrical properties of the WSe2 sample. Finally, we also verified the feasibility of the above methods to fabricate micro-mask/micro-channels on different substrates such as conductive fibers, PDMS, and WSe2/SiO2Si, and proposed to realize nano-scale patterning through direct electrospinning. Therefore, it is expected to be more widely promoted in the future.
VOSTN: Variational One-shot Transformer Network for Pedestrian Trajectory Prediction
Authors: Wang Jinyu , Sang Haifeng , Chen Wangxing , Zhao Zishan
Published: December 2023

The accurate and reliable prediction of pedestrian future trajectories is of crucial significance for ensuring the safe navigation of autonomous driving systems. This paper introduces a novel approach called the Variational One-Shot Transformer Network (VOSTN) for the prediction of future trajectories within the 2D on-board domain. VOSTN presents an innovative method called the One-Shot Generation Block, which aims to generate queries simultaneously in order to predict future trajectories in a parallel manner. The utilization of the parallel prediction effectively addresses the issue of error accumulation resulting from autoregression, improves the efficiency of inference time, and enhances the precision of long-term trajectory prediction. And the cross-attention module investigates the inter-relationship between trajectory and ego-motion. Given the inherent stochasticity of pedestrian movement, we employ the Conditional Variational AutoEncoder to forecast available multimodal trajectories. Experimental results demonstrate that our model effectively exploits the information associated with trajectory and ego-motion, leading to the acquisition of more comprehensive feature representations. Moreover, our model outperforms the performance of existing methods on the two 2D on-board domain datasets. Our deterministic/multimodal prediction models show a reduction in the bounding box center final displacement error by 8% / 9% and 0.7% / 3% on PIE and JAAD, respectively, when compared to the most optimal baseline.
The analysis on time transfer of GPS/Galileo /BDS PPP with integer ambiguity resolution
Authors: Qin Weijin , Yang Haiyan , Zhang Zhe , Wei Pei , Yang Xuhai
Published: December 2023

GPS precise point positioning (PPP) approach has been considered for achieving time transfer for a long time. By virtue of GPS/Galileo/BDS FCB products, PPP model has the possibilities for changing phase ambiguities from ‘float’ value to ‘integer’ value. In this study, PPP time/frequency transfer model has been presented and performance of seven links equipped with Hydrogen Masers (H-Masers) and cesium atomic clocks are compared in static and kinematic modes. With partial ambiguity resolution (PAR) enabled, in contrast to GPS, results show that multi-GNSS’s fixing rate is much higher and TTFF(Time To First Fixing) is much shorter. It is verified that the fixing rate and TTFF has nothing to do with the atomic clock type but has strong correlation with the quality of observation. We find that frequency stability of time link is seriously dependent on the type of atomic clock. As far as H-Masers, it has reached the order of 1E-16/1E-15 at the averaging time of 122880 s, respectively. As far as Cesium clock, it has reached the order of 1E-15/1E-14 at the averaging time of 122880 s, respectively. For H-Maser, the long-term frequency stabilities of integer PPP (IPPP) have been improved by roughly 3% at the static mode and 4% at the kinematic mode on average, respectively. For positioning, compared to PPP solutions, the stabilities of the IPPP coordinates are improved after an averaging time of 7680 s in static or kinematic mode.
Tribological behavior of electroless Ni-B ternary and quaternary coatings with inclusion of W and Mo
Authors: Barman Manik , Barman Tapan Kumar , Sahoo Prasanta
Published: December 2023

Electroless nickel coatings help to tailor requisite surface properties in materials for use in various applications. The aim of the present study is to examine the tribological behavior of electroless nickel-boron coatings incorporated with Mo and W. The coated layers are developed over pin samples with various bath compositions. The coated specimens are characterised to assess their basic features. The tribological tests are carried out with the help of a pin-on-disc tribo-tester under dry conditions. The coating characteristics are determined to comprehend the importance of tungsten as well as molybdenum concentration in electroless nickel-boron coatings. In the as-deposited state, all the coated specimens displayed a surface morphology resembling cauliflower. Moreover, the roughness of these coated surfaces in their as-deposited state showed an increase corresponding to the concentration of the bath parameter, resulting in a higher coefficient of friction (COF). The addition of Mo into Ni-B matrix promotes the coarse grained and cracked surface morphology leading to highest friction coefficient value of as-deposited Ni-B-Mo coatings. The highest surface roughness of Ni-B-Mo coatings also leads to the highest specific wear rate due to breaking and crushing of roughness peaks. The present study reveals the effect of incorporation of W and Mo in electroless Ni-B coatings to control the friction and wear characteristics of the specimens. This will help in deciding the proper electroless bath compositions for specific tribological applications.
Effect of uniaxial compressive strain on the thermoelectric properties of two-dimensional HfNF
Authors: Chang Wen-Li , Zhang Zi-Meng , He Xin-Huan , Zhang Ji-Long , Wei Xiao-Ping , Tao Xiaoma
Published: December 2023

Two-dimensional transition metal nitride halides have shown promise in thermoelectric applications due to their low dimensionality, excellent electron transfer properties, and quantum confinement of carriers. This study focuses on investigating the impact of uniaxial compressive strain on the stability, electronic and thermoelectric properties of monolayer HfNF through first-principles calculations. The research findings reveal that the semiconductor properties of monolayer HfNF remain unchanged under various strain conditions. Furthermore, the thermoelectric properties of monolayer HfNF materials are examined using Slack model and the Boltzmann transport theory under different strain conditions. The findings indicate that applying uniaxial compressive strains at temperatures of 500 K, 700 K, and 900 K increase the Seebeck coefficients of n-type and p-type HfNF, resulting in an enhanced power factor for the material. Specifically, the power factor of p-type HfNF under uniaxial compressive strain increased by 83%, with the ZT value reaching 2.01 at 900 K, which is approximately 40% higher than the ZT value without strain. These results suggest that strain can be utilized as a modulation method to enhance the thermoelectric properties of materials. Moreover, the study suggests that two-dimensional HfNF holds great promise for thermoelectric applications when subjected to uniaxial compressive strain.
Consequences of Rényi entropy on the thermal geometries and Hawking evaporation of topological dyonic dilaton black hole
Authors: Yasir Muhammad , Tiecheng Xia , Usman Muhammad , Chaudhary Shahid
Published: December 2023

The thermodynamics of black holes (BHs) has had a profound impact on theoretical physics, providing insight into the nature of gravity, the quantum structure of spacetime and the fundamental laws governing the Universe. In this study, we investigate thermal geometries and Hawking evaporation of the recently proposed topological dyonic dilaton BH in anti-de Sitter (AdS) space. We consider Rényi entropy and obtain the relations for pressure, heat capacity and Gibbs free energy and observe that the Rényi parameter and dilaton field play a vital role in the phase transition and stability of the BH. Moreover, we use Weinhold, Ruppeiner and Hendi Panahiyah Eslam Momennia models to evaluate the scalar curvature of the BH and find out that the divergence points of the scalar curvature coincides with the zero of specific heat. Finally, using Stefan–Boltzmann law, we determine that the BH without a dilaton field evaporates far more quickly compared to the dilaton BH in AdS space.
The modeling non-sequential double ionization of helium atom under high-intensity femtosecond laser pulses with shielding charge approximation
Authors: Zakavi Marjan , Sabaeian Mohammad
Published: December 2023

In this study, we successfully extracted the ‘knee structure’ for non-sequential double ionization (NSDI) in the helium atom. To achieve this, for the first time, we solved the time-dependent Schrödinger equation in three dimensions for the helium atom, utilizing the shielding charge approximation. Our findings corroborate prior observations by Wanget al[Wang and Eberly, Phys. Rev. Lett.105, 083001 (2010)], demonstrating that NSDI occurs within a narrower time window in circular polarization compared to linear polarization. As a result, the yield of linear polarization was higher than that of circular polarization, aligning with the previously reported results. Notably, in the case of circular polarization, the time window further narrows with increasing intensity, attributed to a decrease in the time-of-flight.
Variation in the Flaring Potential of Different Sunspot Groups During Different Phases of Solar Cycles 23 and 24
Authors: Singh Abha , Chaudhari Anurag , Sharma Gyaneshwar , Singh A. K.
Published: December 2023

In this present study, we have analyzed different types of X-ray solar flares (C, M, and X classes) coming out from different classes of sunspot groups (SSGs). The data which we have taken under this study cover the duration of 24 yr from 1996 to 2019. During this, we observed a total of 15015 flares (8417 in SC-23 and 6598 in SC-24) emitted from a total of 33780 active regions (21746 in SC-23 and 12034 in SC-24) with sunspot only. We defined the flaring potential or flare-production potential as the ratio of the total number of flares produced from a particular type of SSG to the total number of the same-class SSGs observed on the solar surface. Here we studied yearly changes in the flaring potential of different McIntosh class groups of sunspots in different phases of SC-23 and 24. In addition, we investigated yearly variations in the potential of producing flares by different SSGs (A, B, C, D, E, F, and H) during different phases (ascending, maximum, descending, and minimum) of SC-23 and 24. These are our findings: (1) D, E, and F SSGs have the potential of producing flares ≥8 times greater than A, B, C and H SSGs; (2) The larger and more complex D, E, and F SSGs produced nearly 80% of flares in SC-23 and 24; (3) The A, B, C and H SSGs, which are smaller and simpler, produced only 20% of flares in SC-23 and 24; (4) The biggest and most complex SSGs of F-class have flaring potential 1.996 and 3.443 per SSG in SC-23 and 24, respectively. (5) The potential for producing flares in each SSG is higher in SC-24 than in SC-23, although SC-24 is a weaker cycle than SC-23. (6) The alterations in the number of flares (C+M+X) show different time profiles than the alterations in sunspot numbers during SC-23 and 24, with several peaks. (7) The SSGs of C, D, E, and H-class have the highest flaring potential in the descending phase of both SC-23 and 24. (8) F-class SSGs have the highest flaring potential in the descending phase of SC-23 but also in the maximum phase of SC-24.
Fundamental ray tracing project based on OpenGL with acceleration
Authors: You Zhenghao
Published: December 2023

In 3D computer graphics, ray tracing is an essential technique for modeling light transport for use in a wide variety of rendering algorithms for generating digital images. And it is also an important subject for computer science students who want to discover more in Computer Graphics and image rendering. In this project the author implemented a fundamental light tracing system based on C++ in visual studio. It is able to render a delicate image with an input of a nnf model file, and output a ppm file, as a final assignment of a student computer graphics research program. This paper is divided into two chapters. The first chapter is about some fundamental theories that are critical to light tracing. The second chapter is about detailed design and implementation process of the program. And the final chapter is about some actual problems and effective solutions.
Using VoteNet to detect beverage bottles
Authors: Ji Haocheng
Published: December 2023

When using point cloud to detect indoor objects, the collected point cloud often has a lot of noise, such as points that are too far away, background points that have nothing to do with the objects to be detected, which will have a bad impact on the detection. In order to solve these problems, a method is proposed to preprocess the collected original point cloud before training and prediction. First, the points that are too far away should be eliminated. Second, the background such as the table should be eliminated as much as possible. Then, the coordinate conversion is required for the point cloud. Finally, VoteNet is used to train these processed point clouds to obtain the model, which is then used for prediction. The experimental results show that the model is good when using these processed point cloud data for training and prediction. In addition, this paper proposes to obtain point cloud by scanning objects, and then process the obtained point cloud to expand the dataset. The significance of this study is using the collected point cloud data for final training and prediction and expanding the dataset without using 3D cameras.
Virtual flight-based design and simulation of a quadrotor UAV
Authors: Guo Haoxu
Published: December 2023

Lately, Quadrotor UAVs control the attitude and position of the vehicle by adjusting four motor speeds to vary the rotor speed and achieve changes in lift. A quadrotor UAV is a six-degree-of-freedom vertical elevator. As drone technology matures, the demand for drones is increasing and virtual drone control technology offers new ways to skillfully control drone flight techniques, with multiple scenarios virtually minimising the variety of situations encountered in real-world situations. At the same time, the gradual maturation of 5G technology opens up even more possibilities for how drones can be controlled.
Learning to self-adaptive robust dehazing network for video detection in drainage pipeline
Authors: Qiao Jingyi , Li Ce , Tang Zhengyan , Huang Yingjie
Published: December 2023

With the continuous urbanization and expansion of the built-up area of cities, the requirement for regular maintenance of urban underground drainage pipeline will be further increased. However, the drainage pipeline environment is humid and the water quality conditions are complex, which leads to the existence of uneven concentration of haze in the images captured by CCTV (closed-circuit television). To address these problems, this paper suggests a self-adaptive robust dehazing network for video detection in drainage pipeline. The method extracts feature in the encoder by self-adaptive module and fully multi-scale feature alignment module, and the dehazed image is gradually recovered in the enhanced decoder after the feature recovery module. The results of the experiments indicate that this method significantly improves the robustness and generalization ability of model dehazing by capturing global hazy features at multi-scale resolution, and it is an effective method for applying to drainage pipeline dehazing.
SewerOD: A visual sewer disease detection dataset for machine learning
Authors: Wei Wei , Li Ce , Li Shuo , Chen Zheng , Yang Feng
Published: December 2023

The underground sewer pipeline is an essential urban infrastructure that undertakes the vital responsibility of discharging sewage. The location and type of diseases in the pipeline often need to be checked manually by inspectors, which cannot be completed efficiently due to labor costs and time requirements. With the development of computer vision, the use of detection technology to maintain sewer pipelines has extremely high research value. However, image data of pipelines are often regarded as commercial secrets, and these studies are greatly limited due to the scarcity of open-source pipeline disease datasets. To solve this problem, we present a public large-scale object detection dataset for sewer disease detection named SewerOD in this work. The dataset contains about 47K images, annotated by professional researchers, and includes two of the most widespread structural disease types: Corrosion and Crack. Our dataset is available at https://github.com/SewerOD.
Comparison of machine learning algorithms and feature visualization analysis for diabetes risk prediction
Authors: Chen Siyuan
Published: December 2023

There are already 537 million people suffering from diabetes worldwide, according to the research data released by the International Diabetes Federation in 2022. It is a frequent and pervasive condition that has overtaken cancer as the second leading cause of death from modern diseases and is second only to it in terms of the harm it causes to the human body. The excessive morbidity, mortality, shortened life expectancy, and economic and other costs associated with it make it an important condition that plagues people. This study selected the Diabetes Health Indicators Dataset from the Kaggle database based on machine learning algorithm prediction models in predicting the risk of diabetes. 70% of the data were randomly chosen from the presence of high blood pressure (High BP), high cholesterol (High Chol), body mass index (BMI), smoking status (Smoker), and the risk of diabetes, smoking status (Smoker), health status, age, education, income, and gender as independent variables, and whether or not they had diabetes as dependent variables, and built diabetes prediction models based on five traditional machine algorithms such as random forests, logistic regression, decision trees, Support Vector Machine (SVM), XGBoost, and deep neural networks (DNN). The models were then validated with 30% of the samples. Three indicators, Accuracy, AUC, and Recall, were compared and analyzed, and DNN was found to have a high prediction accuracy (75.49%) and recall rate (79%).
Expansion of the CT-scans image set based on the pretrained DCGAN for improving the performance of the CNN
Authors: Cheng Ruoxi
Published: December 2023

Diagnosis of COVID-19 based on CT scans images using artificial intelligence can massively reduce medical manpower, time, and resources and reduce misdiagnosis rate. However, there are few relevant image sets publicly available at present, which is not conducive to the training of models such as classification. Transfer learning has been extensively considered in image classification, but less utilized in image generation. This work attempts to expand this dataset with discriminators using Deep Convolutional Generative Adversarial Network (DCGAN) models of Imagenet pre-training models and verify the significance of pre-training models for generating image veracity. A DCGAN model using the Imagenet pre-training model (i.e. resnet18) and a DCGAN network without the pre-train model generated 100 positive and 100 negative images to extend the original dataset, respectively. These images were classified using a Convolutional Neural Network (CNN) binary classifier, and the accuracy improved from 82.67% to 85.33% after adding the pre-training model. Furthermore, the comparison shown by the Gradcam visualization shows that the discriminator with the pre-training model can better capture the key details of the images. This experiment shows that even though Imagenet and lung images are totally uncorrelated, there are still some features that can be transferred. It also demonstrates that Imagenet pre-training models can improve the quality of GAN-generated images, extending the application of migration transfer learning.
Research on signal processing of upper limbs of three gestures
Authors: Yang Rongkai , Jiang Jiajun , Jiang Zhaoling , Zhu Jiaming
Published: December 2023

There are a large number of disabled people in the world whose lives are seriously affected by the lack of upper limbs. Research on related prostheses is crucial to making these individuals’ lives as convenient as those of regular people. The association between the acts that took place and the relevant data waveforms are determined in this investigation. To lessen the impact of fluctuations in the data itself, EMG signals are obtained using the five channels’ worth of data. By examining the related EMG data, preprocessing, feature extraction, and the construction of two CNN models are used to categories three gestures (scissors, rock, and cloth). Finally, the classification accuracy and loss indicate that the 2-D CNN model is better by comparison with the 1-D CNN model in 3 gesture classification. These correlation results show that the control of the prosthetic limb can realize the completion of various actions through the analysis of the corresponding data and transmission to the entity.
End to end multifunctional autoencoder-based classification and denoising network
Authors: Zhou Mingze
Published: December 2023

Brain tumours are one of the common diseases in human beings. Currently, brain Nuclear Magnetic Resonance (MRI) is the main means of detecting brain diseases. There are many works related to the classification and noise reduction of brain MRI images, but there are few works that combine these two parts simultaneously, i.e., noise reduction processing and classification of brain images at the same time. In this work, these two parts of work will be combined together, extracting the features of brain MRI images through an encoder composed of convolutional neural networks, using the features to classify the images, and then using the features to reduce the noise of the images to produce a low-noise image. According to the model, this work also proposes a new loss function. The new loss function is composed by adjusting the weights of the loss functions related to the classification and denoising tasks. This paper tries three different ways of weight assignment, and the experimental results show that the dynamic parameter assignment approach achieves the best results for image classification, but none of the three approaches achieves acceptable results for noise reduction.
Traffic sign detection based on classic visual recognition models
Authors: Yang Jirui
Published: December 2023

This paper aims to assess the effectiveness of various object detection high-level architectures, including Faster R-CNN, R-FCN, SSD, and YOLO, in recognizing traffic signs. Since traffic sign recognition is a critical element of driver-assistance and autonomous driving systems, this study explores how five distinct architectures for feature extracting (ResNet V1 50, ResNet V1 101, Inception V2, Inception-ResNet-V2, and Darknet-19) can enhance the performance of these meta-architectures. To evaluate the models, the authors fine-tuned a pre-trained object recognition algorithm on the German Traffic Sign Detection Benchmark (GTSDB) dataset. While the Faster R-CNN Inception Resnet v2 proved to be the most accurate among the models, the unbalanced distribution of mandatory, prohibitory, and danger signs in the GTSDB dataset may have caused biased results in object detection. Overall, the result obtained from this study can help improve the accuracy of driver-assistance and autonomous driving systems by providing insights into the performance of various object detection models in recognizing traffic signs.
YOLO sparse training and model pruning for street view house numbers recognition
Authors: Zhang Ruohao , Lu Yijie , Song Zhengfei
Published: December 2023

This paper proposes a YOLO (You Only Look Once) sparse training and model pruning technique for recognizing house numbers in street view images. YOLO is a popular object detection algorithm that has achieved state-of-the-art performance in various computer vision tasks. However, its large model size and computational complexity limit its deployment on resource-constrained devices such as smartphones and embedded systems. To address this issue, we use a sparse training technique that trains YOLO with L1 norm regularization to encourage the network to learn sparse representations. This results in a significant reduction in the number of parameters and computation without sacrificing accuracy. Furthermore, we apply a model pruning technique to the sparse-trained model to reduce the model size and computation. We evaluate our proposed method on the SVHN (Street View House Numbers) dataset. We show that it performs comparably to the original YOLO model while reducing the model size by 5% and the computation time by 7%. Overall, our proposed YOLO sparse training and model pruning technique provides an effective solution for deploying YOLO-based object detection models on resource-constrained devices.
Performance analysis of parallel programming models for C++
Authors: Zeng Guang
Published: December 2023

As multicore processors become more common in today’s computing systems and parallel programming models are enriched, programmers must consider how to choose the appropriate and parallel programming model when writing parallel code. The purpose of this paper is to compare and analyze the performance gap between different C++ parallel programming models, such as C++ standard library threads, OpenMP and Pthreads, in terms of matrix operations. The experiments use different libraries to implement matrix multiplication separately and then analyze their performance. The experimental data show that the data size has a significant impact on the performance of the different models. For very small matrices of size magnitude less than or close to the number of threads, the performance of parallel implementations is much lower than that of serial implementations. For small matrices with magnitudes larger than the number of threads, the C++ standard library threads outperforms Pthreads and OpenMP due to its lightweight thread performance on relatively small matrices. pthreads shows the best performance on very large matrices due to its fine-grained control over thread management, communication, and synchronization operations. openMP’s is not as stable as the other two libraries, especially for smaller matrices. This paper provides a comparative analysis that can help programmers choose the most appropriate library for their specific computational needs.
Enhanced stability of aerial refueling process of UAV based on fuzzy PID
Authors: Liu Yuhang
Published: December 2023

This essay aims to study UAV(Unmanned Aerial Vehicle) aerial refueling technology, and by understanding the control analysis of aerial UAV refueling by comparing multiple R&D(Research And Development) teams, a more practical and feasible method is obtained, that is, through the discussion and design of UAV motion modeling, UAV trajectory tracking control system, fuzzy PID(proportion integral derivative) controller design, etc., the purpose of UAV aerial refueling is finally achieved, and the time of UAV is increased to enhance the stability and durability of UAV in air operation.
Prediction model of pet rescue outcome based on LightGBM and Bayesian optimisation
Authors: Chen Lin
Published: December 2023

Due to owner abandoning, pets getting lost, and irresponsible breeding, the number of strays is rising steadily as more individuals opt to have pets. Stray animals are not only in a precarious position, but they also wreak havoc on the lives of humans. To reduce the number of stray animals and enhance the pet rescue mechanism, this paper develops a prediction model of pet rescue outcome based on LightGBM (Light Gradient Boosting Machine). The model selects data features using the Pearson correlation coefficient, employs SMOTE (Synthetic Minority Oversampling Technique) to balance the data, and then uses LightGBM modelling to predict the outcome of each pet, such as adoption, transfer, death, etc. Simultaneously, the Bayesian algorithm is used to optimise the model’s hyper-parameters in order to generate the final model. Comparing the prediction results of SVM, Random Forest, and BP neural network models with those of the model based on LightGBM, the experimental results on the real animal centre dataset reveal that the model based on LightGBM has a superior predict performance, and the Bayesian optimisation effect is evident. In addition, the paper assesses the importance ranking of model features to aid animals in animal shelters more effectively.
Transformer-based visual question answering model comparison
Authors: He Zhicheng , Li Yuanzhi , Zhang Dingming
Published: December 2023

The potential use of visual question and answer (VQA) models in fields including robotics, autonomous driving, and health care has recently attracted a lot of attention. However, the development of the multi-modular learning VL (Vision-Language Learning) model itself in the “pre-training + fine-tuning” mode is challenged under the rapid development of large-scale language models. Therefore, optimizing the value and feasibility of the single-task model itself is also worth thinking more cautiously. With both model principles, structures, and experimental contrast analysed in this paper, the two excellent models, LXMERT and UNITER, are evaluated to demonstrate the reasons for performance differences on specific downstream tasks VQA deeply. In addition, this paper provides insight into the possibility of further model optimization in the future for multi-modular tasks, especially VQA. In comparison to UNITER, the findings reveal greater accuracy in the training set and verification set from beginning for LXMERT, which indicates that the better model architecture and pre-training method is where LXMERT’s higher adaptability probably comes from. However, the UNITER, which has fewer parameters and smaller architecture, can also achieve fine results after fine-tuning. This paper highlights the differences in accuracy shown by existing Transformers-based VQA models in local test sets and explores the possibility of optimizing VQA systems through fine-tuning.
Combining feature pyramid and CycleGAN for image generation
Authors: Li Yuqi , Lu Jiren , Meng Xiangyu
Published: December 2023

Image generation has always been a hot topic in computer vision community, which aims to learn the data distribution from a give image dataset and then generates new images obeying this distribution. Thanks to the rapid development of convolutional neural networks, breakthroughs have been made in the accuracy and speed of image generation. Currently, image generation is mainly based on the framework for generating countermeasures networks. However, limited by the quality of features, the generated images still have problems such as edge blurring, which restricts large-scale practical applications. In this paper, we propose an image generation algorithm that combines feature pyramid network (FPN) and CycleGAN. Specifically, the FPN containing two upsampling and feature fusion operations is added after the residual blocks and before the decoder of the CycleGAN generator, which can help the generator produce more detailed images and learn from features at different scales. Qualitative and quantitative experimental results show that the CycleGAN model with FPN outperforms the original CycleGAN model in terms of image generation.
Review on data imputation methods in machine learning
Authors: Xue Jianing
Published: December 2023

Data is an important element in the analysis of machine learning. It is usually measured based on observations and is also an indispensable element in training a model. Good preparation of data helps enhance the performance of analysis and is able to deliver reliable final results. However, lots of factors influence the dataset and some lead to the loss of some data. When some portion of the data is missing, it causes biases in the final prediction outcomes. In order to minimize the consequences of missing data, several data imputation methods are established to solve the problem. This paper will first talk about some basic concepts about missing data. In the following sections, the paper will present several popular data imputation methods, including complete case analysis, single imputation, and multiple imputations. Applications of some methods will be presented to see how they can be used in real analysis situations. Finally, the paper will talk about the limits of current data imputation methods.
Study on video key frame extraction in different scenes based on optical flow
Authors: Dong Jinshan
Published: December 2023

Key frame extraction is an important component of video analysis, and has gradually become a research hotspot in the computer vision community in recent years. Early key frame extraction algorithms were mostly based on fixed time intervals, and their accuracy could not meet practical application requirements. Thanks to the rapid development of machine learning technology, key frame extraction algorithms based on image quality, motion analysis, and deep learning are gradually becoming mature. Although the above methods significantly improve the accuracy of key frame extraction, few work pay attention to the extraction effect in different scenarios. In this article, selecting the key frame extraction algorithm based on optical flow method, we compared the key frame extraction algorithm based on optical flow method in detail for different video scenarios. Extensive experimental results demonstrate the robuteness of key frame extraction algorithm based on optical flow.
Automatic food recognition based on efficientnet and ResNet
Authors: Liu Yizhe
Published: December 2023

Automatic Food recognitionbased image is an emerging topic, which aims to extract the feature of given food image and then predict its category. Compared with the ordinary image recognition task, the food recognition is more challenging due to the high similarity between subcategories and the high inter-class variability of specific category. Early food image recognition methods are mainly based on manual features such as color, shape, texture and other information, which cannot meet actual application requirements limited by insufficient feature expression capabilities. Thanks to the rapid development of convolutional neural network, the methods based on deep learning boost the recognition accuracy and speed significantly. In this paper, we construct the food recognition models based on the EfficientNet and ResNet, respectively. Extensive experiments have been conducted on Food-101 dataset. All the results show the effectiveness of our models, which can recognize various foods accurately.
Improvements in adverse drug reaction prediction
Authors: Zhu Wanyu , Wang Yusen , Ma Yuzhou , Wang Peirui , Cui Tingyue
Published: December 2023

This report investigates prediction on adverse drug reactions (ADR) with kernel and imbalance data mechanisms. The hypothesis is that different types of kernel lead to different prediction results, which suggests deciding the best-fit kernel might be a critical way of improving prediction accuracy. Besides, it was also hypothesized that edge cases in real-life setting would cause imbalance in the dataset, thus further causing inaccuracy in prediction. Similarly, attempting to add class weight to various machine learning models could also be a way to improve prediction accuracy. Hence, these hypotheses are being explored in this study.
A study on the crucial indicators for breast cancer detection using machine learning algorithm
Authors: Li Shutai
Published: December 2023

Since breast cancer is the most serious disease affecting women, early detection comes as a priority. The Wisconsin Breast Cancer Dataset (WBCD), which was retrieved from the UCI database, has been applied in numerous studies in recent years to help with the definitive diagnosis. Machine learning (ML) algorithms, such as K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and Neural Network (NN) can be used to attain the upshot. Although these algorithms make predictions well, advantages cannot overshadow drawbacks because the outcome is circumstantial by the peculiar dataset itself and cannot draw a direct conclusion reflecting the deeper issue. To implement ML skills to figure out the factors that influence the prediction most in a statistical dimension, this paper uses the dataset above, compares five methods, and chooses three best classifiers: KNN, RF, and SVM. After selection, the author eliminates every single variable each time to get the accuracy, and compares them with the full model’s accuracy. Having controlled variables, it can be informed that Clump Thickness and Bare Nuclei are the factors that matter most.