Auxiliary Model Research Articles

Iterative QML estimation for asymmetric stochastic volatility models

The paper illustrates a new procedure for estimating asymmetric stochastic volatility models. These models shape the asymmetric effect of negative and positive financial returns on the expected volatility, behaviour often observed in the stock prices, and known as “leverage effect”. The procedure is based on the iterative application of the quasi-maximum likelihood (QML) method and is proposed as an alternative to the procedure presented by Harvey and Shephard in 1996 and based on the application of the QML method on a modified auxiliary model. The estimation results generally converge to constant values after a few iterations. The volatility predictor provided by the new method is conceptually similar to the EGARCH predictor and different from the predictor of the other procedure. A simulation study shows that the iterative QML method provides parameter estimators with RMSEs decreasing as series length increases. The distribution of the estimates is approximately normal, and the approximation improves as the series size increases. Empirical applications of the method provide results similar to ones of the method known in literature. However, the two methods provide two different predictors and smoothers of volatility, which should be compared on a case-by-case basis.

Deep Learning for Perfusion Cerebral Blood Flow (CBF) and Volume (CBV) Predictions and Diagnostics

Dynamic susceptibility contrast magnetic resonance perfusion (DSC-MRP) is a non-invasive imaging technique for hemodynamic measurements. Various perfusion parameters, such as cerebral blood volume (CBV) and cerebral blood flow (CBF), can be derived from DSC-MRP, hence this non-invasive imaging protocol is widely used clinically for the diagnosis and assessment of intracranial pathologies. Currently, most institutions use commercially available software to compute the perfusion parametric maps. However, these conventional methods often have limitations, such as being time-consuming and sensitive to user input, which can lead to inconsistent results; this highlights the need for a more robust and efficient approach like deep learning. Using the relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) perfusion maps generated by FDA-approved software, we trained a multistage deep learning model. The model, featuring a combination of a 1D convolutional neural network (CNN) and a 2D U-Net encoder-decoder network, processes each 4D MRP dataset by integrating temporal and spatial features of the brain for voxel-wise perfusion parameters prediction. An auxiliary model, with similar architecture, but trained with truncated datasets that had fewer time-points, was designed to explore the contribution of temporal features. Both qualitatively and quantitatively evaluated, deep learning-generated rCBV and rCBF maps showcased effective integration of temporal and spatial data, producing comprehensive predictions for the entire brain volume. Our deep learning model provides a robust and efficient approach for calculating perfusion parameters, demonstrating comparable performance to FDA-approved commercial software, and potentially mitigating the challenges inherent to traditional techniques.

Regional choroidal thickness estimation from color fundus images based on convolutional neural networks

PurposeThis study aims to estimate the regional choroidal thickness from color fundus images from convolutional neural networks in different network structures and task learning models. Method1276 color fundus photos and their corresponding choroidal thickness values from healthy subjects were obtained from the Topcon DRI Triton optical coherence tomography machine. Initially, ten commonly used convolutional neural networks were deployed to identify the most accurate model, which was subsequently selected for further training. This selected model was then employed in combination with single-, multiple-, and auxiliary-task training models to predict the average and sub-region choroidal thickness in both ETDRS (Early Treatment Diabetic Retinopathy Study) grids and 100-grid subregions. The values of mean absolute error and coefficient of determination (R2) were involved to evaluate the models' performance. ResultsEfficientnet-b0 network outperformed other networks with the lowest mean absolute error value (25.61 μm) and highest R2 (0.7817) in average choroidal thickness. Incorporating diopter spherical, anterior chamber depth, and lens thickness as auxiliary tasks improved predicted accuracy (p-value = 6.39×10−44, 2.72×10−38, 1.15×10−36 respectively). For ETDRS regional choroidal thickness estimation, multi-task model achieved better results than single task model (lowest mean absolute error = 31.10 μm vs. 33.20 μm). The multi-task training also can simultaneously predict the choroidal thickness of 100 grids with a minimum mean absolute error of 33.86 μm. ConclusionsEfficientnet-b0, in combination with multi-task and auxiliary task models, achieve high accuracy in estimating average and regional macular choroidal thickness directly from color fundus photographs.

Parameter estimation of multivariable Wiener nonlinear systems by the improved particle swarm optimization and coupling identification

This paper investigates the parameter estimation of multivariable Wiener nonlinear systems. To solve the inconsistency problem of the parameter vector and the parameter matrix, the coupling identification concept is applied. Combined with particle swarm optimization (PSO) and an auxiliary model, the partially coupled improved particle swarm optimization (PC-IPSO) method is proposed. In this algorithm, the adaptive feedback inertia weight is improved to accelerate the convergence speed, and the retirement update mechanism is introduced to improve the optimization ability of the basic PSO algorithm. To verify the performance of PC-IPSO, we also derive a multivariable improved PSO (M-IPSO) method for comparison. The computational complexity analysis shows that the PC-IPSO algorithm requires less computational resources than the M-IPSO algorithm. Then, the convergence of the improved PSO method is analyzed. The simulation results indicate that the PC-IPSO method has a faster convergence speed and higher identification accuracy than the M-IPSO and several existing state-of-the-art methods for multivariable Wiener system identification.

GFNS: An OpenGL-based tool for shield tunneling simulation in 3D complex stratum

Shield tunneling under complex geological conditions may lead to uneven surface settlement, thereby posing risks of damaging adjacent structures. This paper introduces the Geological Model for Numerical Simulation (GFNS) software, which employs OpenGL to implement a 3D geological model. The software incorporates an advanced geological stratum meshing algorithm based on conditional statements, facilitating the swift construction of numerical models for shield tunneling in intricate geological formations. In this study, a numerical model for shield tunneling in complex geological conditions was developed using GFNS. The research investigated potential risk levels to adjacent structures due to shield tunneling through alternating soft and hard geological strata along the tunnel's longitudinal axis. Moreover, the influence of shield excavation position and construction parameters on surface settlement was analyzed. Finally, a comparison was conducted with the traditional parallel geological modeling approach. The results revealed that simulating shield tunneling while considering complex geological conditions demonstrates a stronger correlation with the laws of stratum settlement. Thus, GFNS offers an auxiliary modeling method for simulating shield tunneling under complex geological conditions.

Towards Efficient Battery Electric Bus Operations: A Novel Energy Forecasting Framework

As the adoption of battery electric buses (BEBs) in public transportation systems grows, the need for precise energy consumption forecasting becomes increasingly important. Accurate predictions are essential for optimizing routes, charging schedules, and ensuring adequate operational range. This paper introduces an innovative forecasting methodology that combines a propulsion and auxiliary energy model with a novel concept, the environment generator. This approach addresses the primary challenge in electric bus energy forecasting: estimating future environmental conditions, such as weather, passenger load, and traffic patterns, which significantly impact energy demand. The environment generator plays a crucial role by providing the energy models with realistic input data. This study validates various models with different levels of model complexity against real-world operational data from a case study of over one year with 16 electric buses in Göttingen, Germany. Our analysis thoroughly examines influencing factors on energy consumption, like altitude, temperature, passenger load, and driving patterns. In order to comprehensively understand energy demands under varying operational conditions, the methodology integrates data-driven models and physical simulations into a modular and highly accurate energy predictor. The results demonstrate the effectiveness of our approach in providing more accurate energy consumption forecasts, which is essential for efficient electric bus fleet management. This research contributes to the growing body of knowledge in electric vehicle energy prediction and offers practical insights for transit authorities and operators in optimizing electric bus operations.

Effective Combination of 3D-DenseNet's Artificial Intelligence Technology and Gallbladder Cancer Diagnosis Model

Gallbladder cancer is the most common malignant tumor in the biliary system. It has the characteristics of low early diagnosis rate, strong invasiveness and high lymphatic metastasis rate. In recent years, with the rapid development of artificial intelligence technology, relevant technologies based on machine learning and deep learning algorithms have been applied to the diagnosis and treatment of malignant tumors, prognosis assessment and medical image processing, bringing revolutionary changes to the diagnosis and treatment mode of malignant tumors. At present, artificial intelligence technology has been preliminarily studied in the early screening and diagnosis of gallbladder cancer, preoperative lymph node status assessment, intraoperative lymph node dissection, surgical treatment and prognosis assessment, showing certain clinical value. In this paper, in order to assist clinical diagnosis of gallbladder cancer, an improved 3D-DenseNet was used to establish an assisted diagnosis model of gallbladder cancer based on enhanced CT images of patients. Firstly, multiple arterial CT images of patients were converted into 3D images, and the regions of interest were cut out using the gallbladder area marked by doctors. Then, the traditional Dense Net network is optimized, the Dropout mechanism and Soft max loss function are improved, and the cross-entropy function is replaced by Focal loss in the output part for imbalance correction, so as to establish the auxiliary diagnosis model of gallbladder cancer.

The Nesterov accelerated gradient algorithm for Auto-Regressive Exogenous models with random lost measurements: Interpolation method and auxiliary model method

For ARX models with random lost measurements, this article proposes an interpolation and auxiliary model based Nesterov accelerated gradient algorithm. This algorithm utilizes the interpolation and auxiliary model methods to fill in lost measurements. The parameters are estimated with better accuracy based on the interpolated input and predicted output data. Two simulation examples are presented to verify the effective of the proposed algorithm.

Computer user profiles in early adolescence and digitally assessed mathematics: A latent class analysis

This study aimed at exploring the pattern of computer usage among early adolescents in Italy, about one year before the spread of the COVID-19 pandemic. The study also aimed at developing and testing models on the associations between socio-demographic characteristics, computer-user profiles, and mathematics achievement. A three-step estimation of Latent Class Analysis models and auxiliary variable models was applied to data collected on a sample of 1155 students attending grade 8 in Italy in the school year 2018–19. Four subpopulations were uncovered, which differ both with regards to their activities with computers (desktop, laptop, and tablet computers) and the likelihood of being daily users. Students’ socioeconomic and cultural background and gender predicted their pattern of computer usage, which in turn predicted digitally assessed mathematics. The main results suggested that excessive and almost exclusive use of computers for entertainment and communication purposes may depict an “at-risk" profile in young adolescents, with possible negative consequences for their learning achievements. This profile was more likely to be found among students from more disadvantageous backgrounds and males, at least before the COVID-19 pandemic started.

A game theory-based pricing model for ancillary services in electricity markets

Abstract Amidst the ongoing evolution and substantial reforms within the electric power market, the development of an auxiliary service pricing model grounded in game theory emerges as crucial. This study delineates the construction of an electricity auxiliary service pricing model, utilizing a dual mechanism approach: a cooperative game-based electricity price formation mechanism and a Stackelberg game-based time-sharing pricing mechanism. Furthermore, it incorporates demand response technology to conduct a detailed analysis of optimization results and the applicability of the proposed electricity service pricing model. The pricing scheme designated as Scheme 4, derived from the proposed model, demonstrates notable superiority in terms of economic efficiency and environmental sustainability when juxtaposed with three alternative schemes. Specifically, Scheme 4 yields a net profit of $13,267.6, achieves clean energy utilization amounting to 89.67 MWh, and minimizes wind abandonment to 17.35 MWh, outperforming all other considered scenarios in these metrics. Operational analysis reveals that the model's execution time varies between 15 and 50 seconds across different sample sizes, exhibiting minimal fluctuations. Additionally, the Monte Carlo simulations consistently produce values inferior to the objective function value of the developed model, with the discrepancy narrowing from 38 to 20, indicating the model's robust adaptability.

Auxiliary Model and Key-Term Separation-Based Identification of Hammerstein OE System With Missing Outputs and Outliers

Auxiliary Model and Key-Term Separation-Based Identification of Hammerstein OE System With Missing Outputs and Outliers

Storm‐Time Characteristics of Ionospheric Model (MSAP) Based on Multi‐Algorithm Fusion

AbstractGeomagnetic storms induce ionospheric disturbances, affecting short‐wave radio communication systems. Accurate ionospheric total electron content (TEC) prediction is vital for accurately describing the short‐wave radio environment of the ionosphere. We use the Multi‐Step Auxiliary Prediction (MSAP) model, a deep learning algorithm, to forecast TEC during geomagnetic storms. The MSAP model integrates Bi‐LSTM networks, an auxiliary model, and convolutional processes for spatiotemporal modeling. Our validation shows the MSAP model outperforms the IRI‐2016 model in predicting global TEC for the next 6 days in the test set. We assess its performance during 116 geomagnetic storm events, considering storm intensity, solar activity, month, and Universal Time (UT). The MSAP model exhibits a weak correlation with storm intensity and a strong correlation with solar activity. Monthly variation displays similar strong correlations in root mean square error (RMSE) and R2 for both models. For UT variation, the other metrics exhibit a weak correlation with the number of Global Navigation Satellite System stations, except for the RMSE of the MSAP and IRI‐2016 models.

Using novel deep learning models for rapid and efficient assistance in monkeypox screening from skin images.

Monkeypox, a communicable disease instigated by the monkeypox virus, transmits through direct contact with infectious skin lesions or mucosal blisters, posing severe complications such as pneumonia, encephalitis, and even fatality. Traditional clinical diagnostics, heavily reliant on the discerning judgment of clinical experts, are both time-consuming and labor-intensive, with inherent infection risks, underscoring the critical need for automated, efficient auxiliary diagnostic models. In response, we have developed a deep learning classification model augmented by self-attention mechanisms and feature pyramid integration, employing attentional strategies to amalgamate image features across varying scales and assimilating a priori knowledge from the VGG model to selectively capture salient features. Aiming to enhance task performance and model generalizability, we incorporated different components into the baseline model in a series of ablation studies, revealing the contribution of each component to overall model efficacy. In comparison with state-of-the-art deep learning models, our proposed model achieved the highest accuracy and precision, marking a 6% improvement over the second-best model. The results from ablation experiments corroborate the effectiveness of individual module components in enhancing model performance. Our method for diagnosing monkeypox demonstrates improved diagnostic precision and extends the reach of medical services in resource-constrained settings.

Deep Learning-Empowered Clinical Big Data Analytics in Healthcare Digital Twins.

With the rapid development of information technology, great changes have taken place in the way of managing, analyzing, and using data in all walks of life. Using deep learning algorithm for data analysis in the field of medicine can improve the accuracy of disease recognition. The purpose is to realize the intelligent medical service mode of sharing medical resources among many people under the dilemma of limited medical resources. Firstly, the Digital Twins module in the Deep Learning algorithm is used to establish the medical care and disease auxiliary diagnosis model. With the help of the digital visualization model of Internet of Things technology, data is collected at the client and server. Based on the improved Random Forest algorithm, the demand analysis and target function design of the medical and health care system are carried out. Based on data analysis, the medical and health care system is designed using the improved algorithm. The results show that the intelligent medical service platform can collect and analyze the clinical trial data of patients. The accuracy of improved ReliefF & Wrapper Random Forest (RW-RF) for sepsis disease recognition can reach about 98%, and the accuracy of algorithm for disease recognition is also more than 80%, which can provide better technical support for disease recognition and medical care services. It provides a solution and experimental reference for the practical problem of scarce medical resources.

Analysis of Talent Cultivation Mode of Network and New Media Communication Majors in Colleges and Universities under the Background of Big Data

Abstract In college education, the introduction of data analysis to quantitatively analyze students’ knowledge has become a new trend. The education of network and new media communication majors can also use big data to assist. To this end, this paper uses knowledge tracking technology to construct an auxiliary teaching model for network and new media communication majors in colleges and universities. A new cross-feature formula is used to incorporate many elements of students’ answers into the feature calculation to improve the operation effect of DKVMN model and avoid the complex operation brought by dimensionality reduction. Introducing the cross formula of guessing rate and error rate refreshment based on cognitive diagnostic model. The judgment level of the model is tested using a public dataset after the model has been constructed and put into practical use in a university’s network and new media communication department. Seventy-three students in the department had a good grasp of the knowledge points and six had an excellent understanding. The adjusted means of the experimental and control groups are 93.102 and 82.596 respectively, and there is a significant difference between the two groups of students’ academic performance (p<0.05). The model in this paper can accurately determine the mastery of students’ knowledge points and provide a development direction based on data analysis for teaching network and new media communication majors in colleges and universities.

Knowledge Distillation-Based Domain-Invariant Representation Learning for Domain Generalization

Domain generalization (DG) aims to generalize the knowledge learned from multiple source domains to unseen target domains. Existing DG techniques can be subsumed under two broad categories, i.e., domain-invariant representation learning and domain manipulation. Nevertheless, it is extremely difficult to explicitly augment or generate the unseen target data. And when source domain variety increases, developing a domain-invariant model by simply aligning more domain-specific information becomes more challenging. In this paper, we propose a simple yet effective method for domain generalization, named Knowledge Distillation based Domain-invariant Representation Learning (KDDRL), that learns domain-invariant representation while encouraging the model to maintain domain-specific features, which recently turned out to be effective for domain generalization. To this end, our method incorporates multiple auxiliary student models and one student leader model to perform a two-stage distillation. In the first-stage distillation, each domain-specific auxiliary student treats the ensemble of other auxiliary students' predictions as a target, which helps to excavate the domain-invariant representation. Also, we present an error removal module to prevent the transfer of faulty information by eliminating incorrect predictions compared to the true labels. In the second-stage distillation, the student leader model with domain-specific features combines the domain-invariant representation learned from the group of auxiliary students to make the final prediction. Extensive experiments and in-depth analysis on popular DG benchmark datasets demonstrate that our KDDRL significantly outperforms the current state-of-the-art methods.

Accelerated Gradient Descent Algorithm for Systems With Missing Input Data Using Inverse Auxiliary Model

Accelerated Gradient Descent Algorithm for Systems With Missing Input Data Using Inverse Auxiliary Model

Didactic Resources for Counting Rectangular Trapezoids

Didactic resources for counting rectangular trapezoids are presented. Examples of tasks for counting rectangular trapezoids, the made and printed 3D auxiliary models for them are given. Variants for using manipulatives made of EVA material to help mathematics education are considered. The resources are suitable for use in mathematics, information technology and computer modeling classes, when working in STEM centers. They are also useful for setting and solving the tasks under consideration by people with visual impairments.

Filtering-based gradient joint identification algorithms for nonlinear fractional-order models with colored noises

The main focus is to realize the on-line parameter estimation of the nonlinear fractional-order model with colored noises in this paper. Firstly, an auxiliary model gradient descent algorithm is derived to synchronously produce the estimates of the parameters, including the fractional-order. In order to decrease the noise interference, the filtering-based gradient descent estimation framework by constructing a linear filter provides a feasible method. Furthermore, the forgetting factor is introduced to the proposed filtering-based algorithm for the sake of improving the convergence rate. Comparative simulation results demonstrate the proposed effectiveness and high approximation accuracy of the proposed algorithms.

Interpretable multidisease diagnosis and label noise detection based on a matching network and self-paced learning

With the extensive use of information systems in hospitals, a large quantity of electronic medical record data has been accumulated, which makes it possible to train clinical decision support systems based on the data. However, electronic medical records are written by doctors of different levels, which easily introduces label noise into the datasets. The lack of interpretability of current auxiliary diagnosis methods is another problem.To address these challenges, we introduce a matching network based on medical guidelines and build an auxiliary diagnosis model based on self-paced learning. The matching network based on guidelines can provide medical knowledge beyond medical records and a certain degree of interpretability. Additionally, self-paced learning can help the model identify the label noise and prevent the model from being misled. The experiments show that our method outperforms the baselines in a Chinese medical multi-disease diagnosis dataset and the MIMIC-III dataset and has good performance in the label noise detection task.