Regularization Method Research Articles

The Transfer Dehydrogenation Method Enables a Family of High Crystalline Benzimidazole‐linked Cu (II)‐phthalocyanine‐based Covalent Organic Frameworks Films

AbstractHeterocycle‐linked phthalocyanine‐based COFs with close‐packed π–π conjugated structures are a kind of material with intrinsic electrical conductivity, and they are considered to be candidates for photoelectrical devices. Previous studies have revealed their applications for energy storage, gas sensors, and field‐effect transistors. However, their potential application in photodetector is still not fully studied. The main difficulty is preparing high‐quality films. In our study, we found that our newly designed benzimidazole‐linked Cu (II)‐phthalocyanine‐based COFs (BICuPc‐COFs) film can hardly formed with a regular aerobic oxidation method. Therefore, we developed a transfer dehydrogenation method with N‐benzylideneaniline (BA) as a mild reagent. With this in hand, we successfully prepared a family of high crystalline BICuPc‐COFs powders and films. Furthermore, both of these new BICuPc‐COFs films showed high electrical conductivity (0.022–0.218 S/m), higher than most of the reported COFs materials. Due to the broad absorption and high conductivity of BICuPc‐COFs, synaptic devices with small source‐drain voltage (VDS=1 V) were fabricated with response light from visible to near‐infrared. Based on these findings, we expect this study will provide a new perspective for the application of conducting heterocycle‐linked COFs in synaptic devices.

The Transfer Dehydrogenation Method Enables a Family of High Crystalline Benzimidazole-linked Cu (II)-phthalocyanine-based Covalent Organic Frameworks Films.

Heterocycle-linked phthalocyanine-based COFs with close-packed π-π conjugated structures are a kind of material with intrinsic electrical conductivity, and they are considered to be candidates for photoelectrical devices. Previous studies have revealed their applications for energy storage, gas sensors, and field-effect transistors. However, their potential application in photodetector is still not fully studied. The main difficulty is preparing high-quality films. In our study, we found that our newly designed benzimidazole-linked Cu (II)-phthalocyanine-based COFs (BICuPc-COFs) film can hardly formed with a regular aerobic oxidation method. Therefore, we developed a transfer dehydrogenation method with N-benzylideneaniline (BA) as a mild reagent. With this in hand, we successfully prepared a family of high crystalline BICuPc-COFs powders and films. Furthermore, both of these new BICuPc-COFs films showed high electrical conductivity (0.022-0.218 S/m), higher than most of the reported COFs materials. Due to the broad absorption and high conductivity of BICuPc-COFs, synaptic devices with small source-drain voltage (VDS=1 V) were fabricated with response light from visible to near-infrared. Based on these findings, we expect this study will provide a new perspective for the application of conducting heterocycle-linked COFs in synaptic devices.

Exploratory Data Analysis Based Design and Implementation of Student Stress Detection Using Machine Learning Approaches

The administration of stress is fundamental in perceiving the degrees of stress that can prevent our own and social prosperity. As per the World Wellbeing Association, roughly one of every four people experience pressure related mental issues, prompting mental and financial issues, unfortunate working environment connections, and even self-destruction in serious cases. Guiding is a vital asset to assist people with adapting to pressure. While stress can't be totally kept away from, preventive measures can help with overseeing feelings of anxiety. At the moment, only experts in medicine and physiology can tell if someone is stressed or not. Nonetheless, the conventional technique for distinguishing pressure in view of self-revealed replies from people is untrustworthy. Using physiological signals to automate the detection of stress levels, a more accurate and objective strategy for reducing health risks and enhancing society's well-being is available. A significant social contribution that can improve people's lives is the detection of stress levels. The IT business has presented new advancements and items that guide in the recognition of feelings of anxiety in workers, which is basic in upgrading their presentation. Albeit a few associations offer emotional well-being plans for their workers, the issue stays testing to make due. The machine is subjected to exploratory data analysis (EDA) in order to determine its accuracy. EDA is utilized on dataset so individual boundaries will be broke down with exactness and plotted in graph.The organization of stress is central in seeing the levels of pressure that can forestall our own and social success. According to the World Prosperity Affiliation, around one of each and every four individuals experience pressure related mental issues, provoking mental and monetary issues, lamentable work space associations, and, surprisingly, implosion in serious cases. Directing is an essential resource for help individuals with adjusting to pressure. While stress can't be completely avoided, preventive measures can assist with supervising sensations of nervousness. At this time, only specialists in physiology and medicine can determine whether a person is stressed or not. Regardless, the regular method for recognizing tension considering self-uncovered answers from individuals is conniving. Utilizing physiological signs to robotize the discovery of feelings of anxiety, a more precise and objective procedure for decreasing wellbeing chances and upgrading society's prosperity is accessible. A critical social commitment that can further develop individuals' lives is the location of feelings of anxiety. The IT business has introduced new progressions and things that aide in the acknowledgment of sensations of nervousness in laborers, which is fundamental in redesigning their show. Yet a couple of affiliations offer close to home prosperity plans for their laborers, the issue stays testing to make due. The machine is exposed to exploratory information examination (EDA) to decide its precision. EDA is used on dataset so individual limits will be destitute down with precision and plotted in chart.

Predicting anomalies in computer networks using autoencoder-based representation learning

<span>Recent improvements in the internet of things (IoT), cloud services, and network data variety have increased the demand for complex anomaly detection algorithms in network intrusion detection systems (IDSs) capable of dealing with sophisticated network threats. Academics are interested in deep and machine learning (ML) breakthroughs because they have the potential to address complex challenges such as zero-day attacks. In comparison to firewalls, IDS are the initial line of network security. This study suggests merging supervised and unsupervised learning in identification systems IDS. Support vector machine (SVM) is an anomaly-based classification classifier. Deep autoencoder (DAE) lowers dimensionality. DAE are compared to principal component analysis (PCA) in this study, and hyper-parameters for F-1 micro score and balanced accuracy are specified. We have an uneven set of data classes. precision-recall curves, average precision (AP) score, train-test times, t-SNE, grid search, and L1/L2 regularization methods are used. KDDTrain+ and KDDTest+ datasets will be used in our model. For classification and performance, the DAE+SVM neural network technique is successful. Autoencoders outperformed linear PCA in terms of capturing valuable input attributes using t-SNE to embed high dimensional inputs on a two-dimensional plane. Our neural system outperforms solo SVM and PCA encoded SVM in multi-class scenarios.</span>

DFT and TDDFT exploration on electronic transitions and bonding aspect of DPA and PTDC ligated transition metal complexes.

In this study, we have investigated the structure, reactivity, bonding, and electronic transitions ofDPAandPDTC along with theirNi-Zncomplexes usingDFT/TD-DFT methods. The energy gap between the frontier orbitals was computed to understand the reactivity pattern of the ligands and metal complexes. From the energies of FMO's, the global reactivity descriptors such as electron affinity, ionization potential, hardness (η), softness (S), chemical potential (μ), electronegativity (χ), and electrophilicity index (ω) have been calculated. The complexes show a strong NLO properties due to easily polarization as indicated by the narrow HOMO-LUMO gap. The polarizability and hyperpolarizabilities of the complexes indicate that they are good candidates for NLO materials. Molecular electrostatic potential (MEP) maps identified electrophilic and nucleophilic sites on the surfaces of the complexes. TDDFT and NBO analyses provided insights into electronic transitions, bonding, and stabilizing interactions within the studied complexes. DPA and PDTC exhibited largerHOMO-LUMO gapsand more negative electrostatic potentials compared to their metal complexes suggesting the higher reactivity. Ligands (DPA and PDTC) had absorption spectra in the range of250nm to 285nm while their complexes spanned250nm to 870nm. These bands offer valuable information on electronic transitions, charge transfer and optical behavior. This work enhances our understanding of the electronic structure and optical properties of these complexes. Gaussian16 program was used for the optimization of all the compounds. B3LYP functional in combination with basis sets, such as LanL2DZ for Zn, Ni and Cu while 6-311G** for other atoms like C, H, O, N, and S was used. Natural bond orbital (NBO) analysis is carried out to find out how the filled orbital of one sub-system interacts with the empty orbital of another sub-system. The ORCA software is used for computing spectral features along with the zeroth order regular approximation method (ZORA) to observe its relativistic effects. TD-DFT study is carried out to calculate the excitation energy by using B3LYP functional.

Inverse finite element method with energy-based regularization for deformation reconstruction and structural health monitoring

Distributed Fiber Optical Sensing (DFOS) can be used to perform compact, low-invasive and large-scale strain measurements, providing an important tool for strain-based deformation reconstruction and Structural Health Monitoring (SHM). However, most current deformation reconstruction methods are difficult to apply to the sparse uniaxial strain measurement conditions associated with DFOS, especially for structures with complex geometries. This work introduces an energy-based regularization method, i.e., a priori assumption that the actual deformation state is close to the state with the minimum deformation energy under the premise of satisfying the strain constraints, into inverse finite element method to perform load-independently deformation reconstruction under the condition of sparse uniaxial strain measurement. The Inverse Finite Element Method with Energy-based Regularization (iFEM-EBR) can naturally construct the DFOS-based deformation reconstruction model with quasi-forward finite element analysis without introducing additional mathematical processing. The sensitivity of this method to structural characteristics, such as material parameters, makes it a unique prospect in SHM.

Cross-domain information fusion and personalized recommendation in artificial intelligence recommendation system based on mathematical matrix decomposition

Given the challenges of inter-domain information fusion and data sparsity in collaborative filtering algorithms, this paper proposes a cross-domain information fusion matrix decomposition algorithm to enhance the accuracy of personalized recommendations in artificial intelligence recommendation systems. The study begins by collecting Douban movie rating data and social network information. To ensure data integrity, Levenshtein distance detection is employed to remove duplicate scores, while natural language processing technology is utilized to extract keywords and topic information from social texts. Additionally, graph convolutional networks are utilized to convert user relationships into feature vectors, and a unique thermal coding method is used to convert discrete user and movie information into binary matrices. To prevent overfitting, the Ridge regularization method is introduced to gradually optimize potential feature vectors. Weighted average and feature connection techniques are then applied to integrate features from different fields. Moreover, the paper combines the item-based collaborative filtering algorithm with merged user characteristics to generate personalized recommendation lists.In the experimental stage, the paper conducts cross-domain information fusion optimization on four mainstream mathematical matrix decomposition algorithms: alternating least squares method, non-negative matrix decomposition, singular value decomposition, and latent factor model (LFM). It compares these algorithms with the non-fused approach. The results indicate a significant improvement in score accuracy, with mean absolute error and root mean squared error reduced by 12.8% and 13.2% respectively across the four algorithms. Additionally, when k = 10, the average F1 score reaches 0.97, and the ranking accuracy coverage of the LFM algorithm increases by 54.2%. Overall, the mathematical matrix decomposition algorithm combined with cross-domain information fusion demonstrates clear advantages in accuracy, prediction performance, recommendation diversity, and ranking quality, and improves the accuracy and diversity of the recommendation system. By effectively addressing collaborative filtering challenges through the integration of diverse techniques, it significantly surpasses traditional models in recommendation accuracy and variety.

ЕФЕКТИВНЕ РОЗВ’ЯЗУВАННЯ МУЛЬТИМОДАЛЬНИХ ОПТИМІЗАЦІЙНИХ ЗАДАЧ

In this work, we consider multimodal optimization problems. Such problems contain many local extrema. We can say that most practical problems are multimodal. In particular, discrete optimization problems with Boolean and integer variables can easily be transformed into multimodal problems with continuous variables. Multimodal optimization problems can be of small or large dimensions in which the number of variables to be determined is hundreds or thousands of variables. Libraries of test and applied problems have been created to test the effectiveness of new global optimization methods. The author suggests separating problems with unknown optimal solutions in these tests. Then the best method for solving multimodal optimization problems will be the one that will allow obtaining better solutions in most such problems. Currently, this criterion is satisfied only by the exact quadratic regularization method developed by the author. This is confirmed by significant computational experiments on existing tests and applied multimodal optimization problems.

Hybrid regularization inspired by total variation and deep denoiser prior for image restoration

Image restoration is a fundamental problem in computer vision, with the goal of restoring high-quality images from degraded low-quality observation images. However, the ill-posedness of restoration problem often leads to artifacts in the results. It inspires us to study the combination of prior, effectively restrict the solution space and improving the quality of the restored image. In this paper, a novel hybrid regularization method for image restoration is proposed, which adopts both internal and external image priors into a unified framework. Specifically, a new hybrid regularization model is designed. The TV model and a deep image denoiser are inserted into the restoration model using the plug-and-play framework, protecting image details while implicitly using external information for deep denoising. Moreover, in order to make the proposed hybrid regularization operable, an adaptive parameter method is proposed to adaptively balance the TV model and learned model in iteration. Experiments show that the proposed method surpasses the performance of existing image restoration techniques.

Regularity method and large deviation principles for the Erdős–Rényi hypergraph

Regularity method and large deviation principles for the Erdős–Rényi hypergraph

A dynamic light scattering inversion method based on regularization matrix reconstruction for flowing aerosol measurement.

Tikhonov regularization, or truncated singular value decomposition (TSVD), is usually used for dynamic light scattering (DLS) inversion of particles in suspension. The Tikhonov regularization method uses a regularization matrix to modify all singular values in the kernel matrix. The modification of large singular values cannot effectively reduce the variance of the estimated values but may introduce bias in the solution, resulting in poor disturbance resistance in the inversion results. The TSVD method, on the other hand, truncates all small singular values, which leads to the loss of particle size information during the inversion process. The shortcomings of the two methods mentioned above do not have a significant impact on the inversion of high signal-to-noise ratio data. However, compared to the classical DLS particle size inversion for non-flowing suspended particles, the DLS inversion of flowing aerosols is more significantly affected by noise, and the extraction of particle size information is more difficult due to the effect of flow velocity, resulting in worse inversion results with increasing aerosol flow velocity for both methods. To improve the accuracy of the particle size distribution (PSD) of flowing aerosols, we introduced a kernel matrix into the regularization matrix, and based on the principles of the two methods, the spectral information of the kernel matrix was utilized to make the modification of small singular values by the regularization matrix. Correspondingly, weak or no modification was made according to the values of large singular values to reduce the introduction of bias. The inversion results of simulated and measured data indicate that the reconstruction of the regularization matrix improves the anti-disturbance performance and avoids the loss of particle size information during the regularization inversion process, thereby significantly improving the PSD accuracy, which is affected by the dual effects of flow velocity and noise in the DLS measurement of flowing particles. The peak error and distribution error of the inversion results by reconstructing the regularization matrix are lower than those of Tikhonov regularization.

Inverse Analysis of Strata in Seepage Field Based on Regularization Method and Geostatistics Theory

During the construction of underground engineering, the prediction of groundwater distribution and rock body permeability is essential for evaluating the safety of the project and guiding subsequent design and construction. This article proposes an objective function that solves an underdetermined inverse analysis problem based on the least-squares theory and regularization method and uses geostatistics theory and the variogram function to describe the spatial characteristics of the actual engineering system. It also establishes an optimization model of the analysis stratum seepage field and puts forward the method of using on-site test observation data to solve the stratum penetration coefficient. Relying on the foundation pit project of the Lingshanwei Station of Qingdao Metro, the on-site pumping and packer permeability test was conducted for different strata venues in the foundation pit, and the on-site water-head observation value was obtained. Physical detection of the influence area of foundation pit excavation confirms the correctness of the model from the region and verifies the accuracy of the model on the value through the on-site pumping test. Results show that the accuracy of the use of this objective function to solve the underdetermined inverse problem is above 85%, which proves the effectiveness of the method. The stratigraphic geological information obtained by the inverse analysis model provides an important basis for engineering design and security construction.

A Regularization Method for Quasivariational Inequalities

A Regularization Method for Quasivariational Inequalities

Nonstationary asymptotical regularization method with convex constraints for linear ill-posed problems

We investigate the method of nonstationary asymptotical regularization for solving linear ill-posed problems in Hilbert spaces. This method introduces the convex constraints that are proper lower semicontinuous and allowed to be non-smooth, therefore can be used for sparsity and discontinuity reconstruction. Under some suitable conditions , the convergence and regularity of the proposed method are established. Under the discretion of Runge–Kutta method, different iteration modes can be deduced for numerical implementation. The numerical results of iteration modes under one-stage explicit Euler, one-stage implicit Euler and two-stage explicit Runge–Kutta are presented to illustrate the efficiency of the proposed method.

SIMULTANEOUS INVERSION OF THE SOURCE TERM AND INITIAL VALUE OF THE TIME FRACTIONAL DIFFUSION EQUATION

In this paper, the problem we investigate is to simultaneously identify the source term and initial value of the time fractional diffusion equation. This problem is ill-posed, i.e., the solution (if exists) does not depend on the measurable data. We give the conditional stability result under the a-priori bound assumption for the exact solution. The modified Tikhonov regularization method is used to solve this problem, and under the a-priori and the a-posteriori selection rule for the regularization parameter, the convergence error estimations for this method are obtained. Finally, numerical example is given to prove the effectiveness of this regularization method.

MCSSME: Multi-Task Contrastive Learning for Semi-supervised Singing Melody Extraction from Polyphonic Music

Singing melody extraction is an important task in the field of music information retrieval (MIR). The development of data-driven models for this task have achieved great successes. However, the existing models have two major limitations: firstly, most of the existing singing melody extraction models have formulated this task as a pixel-level prediction task. The lack of labeling data has limited the model for further improvements. Secondly, the generalization of the existing models are prone to be disturbed by the music genres. To address the issues mentioned above, in this paper, we propose a multi-Task contrastive learning framework for semi-supervised singing melody extraction, termed as MCSSME. Specifically, to deal with data scarcity limitation, we propose a self-consistency regularization (SCR) method to train the model on the unlabeled data. Transformations are applied to the raw signal of polyphonic music, which makes the network to improve its representation capability via recognizing the transformations. We further propose a novel multi-task learning (MTL) approach to jointly learn singing melody extraction and classification of transformed data. To deal with generalization limitation, we also propose a contrastive embedding learning, which strengthens the intra-class compactness and inter-class separability. To improve the generalization on different music genres, we also propose a domain classification method to learn task-dependent features by mapping data from different music genres to shared subspace. MCSSME evaluates on a set of well-known public melody extraction datasets with promising performances. The experimental results demonstrate the effectiveness of the MCSSME framework for singing melody extraction from polyphonic music using very limited labeled data scenarios.

An Implicit Trust Region Approach to Behavior Regularized Offline Reinforcement Learning

We revisit behavior regularization, a popular approach to mitigate the extrapolation error in offline reinforcement learning (RL), showing that current behavior regularization may suffer from unstable learning and hinder policy improvement. Motivated by this, a novel reward shaping-based behavior regularization method is proposed, where the log-probability ratio between the learned policy and the behavior policy is monitored during learning. We show that this is equivalent to an implicit but computationally lightweight trust region mechanism, which is beneficial to mitigate the influence of estimation errors of the value function, leading to more stable performance improvement. Empirical results on the popular D4RL benchmark verify the effectiveness of the presented method with promising performance compared with some state-of-the-art offline RL algorithms.

Impartial Adversarial Distillation: Addressing Biased Data-Free Knowledge Distillation via Adaptive Constrained Optimization

Data-Free Knowledge Distillation (DFKD) enables knowledge transfer from a pretrained teacher to a light-weighted student without original training data. Existing works are limited by a strong assumption that samples used to pretrain the teacher model are balanced, which is, however, unrealistic for many real-world tasks. In this work, we investigated a pragmatic yet under-explored problem: how to perform DFKD from a teacher model pretrained from imbalanced data. We observe a seemingly counter-intuitive phenomenon, i.e., adversarial DFKD algorithms favour minority classes, while causing a disastrous impact on majority classes. We theoretically prove that a biased teacher could cause severe disparity on different groups of synthetic data in adversarial distillation, which further exacerbates the mode collapse of a generator and consequently degenerates the overall accuracy of a distilled student model. To tackle this problem, we propose a class-adaptive regularization method, aiming to encourage impartial representation learning of a generator among different classes under a constrained learning formulation. We devise a primal-dual algorithm to solve the target optimization problem. Through extensive experiments, we show that our method mitigates the biased learning of majority classes in DFKD and improves the overall performance compared with baselines. Code will be available at https://github.com/ldpbuaa/ipad.

Iterative Regularization with k-support Norm: An Important Complement to Sparse Recovery

Sparse recovery is ubiquitous in machine learning and signal processing. Due to the NP-hard nature of sparse recovery, existing methods are known to suffer either from restrictive (or even unknown) applicability conditions, or high computational cost. Recently, iterative regularization methods have emerged as a promising fast approach because they can achieve sparse recovery in one pass through early stopping, rather than the tedious grid-search used in the traditional methods. However, most of those iterative methods are based on the l1 norm which requires restrictive applicability conditions and could fail in many cases. Therefore, achieving sparse recovery with iterative regularization methods under a wider range of conditions has yet to be further explored. To address this issue, we propose a novel iterative regularization algorithm, IRKSN, based on the k-support norm regularizer rather than the l1 norm. We provide conditions for sparse recovery with IRKSN, and compare them with traditional conditions for recovery with l1 norm regularizers. Additionally, we give an early stopping bound on the model error of IRKSN with explicit constants, achieving the standard linear rate for sparse recovery. Finally, we illustrate the applicability of our algorithm on several experiments, including a support recovery experiment with a correlated design matrix.

An Efficient Segmentation and Classification of Brain Tumor Detection using Deep Learning

In medical diagnostics, the identification of brain tumors is a crucial endeavour that frequently necessitates the careful review of intricate imaging data. With the development of deep learning methods, especially convolutional neural networks (CNNs), automated brain tumor detection has become a viable way to help radiologists diagnose patients accurately and quickly. This study provides an extensive overview of current developments in deep learning approaches for brain tumor identification. The suggested techniques usually start with preprocessing medical images, like CT or MRI scans, and then use CNN architectures to extract features. Different CNN architectures, such as U-Net and conventional CNNs, have been used to extract discriminative characteristics from brain pictures. The performance of brain tumor detection systems has also been improved using transfer learning techniques, which make use of pre-trained models on sizable datasets. These results are especially encouraging when there is a shortage of training data. Additionally, research has been done on using ensemble learning approaches to strengthen the generality and durability of brain tumor detection models. By combining several base models into one prediction, these methods improve overall performance and lower the likelihood of overfitting. Furthermore, the incorporation of sophisticated regularisation methods such batch normalisation and dropout has enhanced the deep learning models' capacity for generalisation in brain tumor identification tasks. Moreover, the use of deep learning models in practical clinical environments demands the resolution of issues of uncertainty estimation and model interpretability