Lightweight Cross-Modal Multispectral Pedestrian Detection Based on Spatial Reweighted Attention Mechanism

Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the weighted feature data across different modalities, thereby reducing the interference of multi-modal features. Subsequently, lightweight modules with depthwise separable convolution are incorporated to reduce the model’s parameter count and computational load through channel-wise and point-wise convolutions. The network model algorithm proposed in this paper was experimentally validated on the publicly available KAIST dataset and compared with other existing methods. The experimental results demonstrate that our approach achieves favorable performance in various complex environments, affirming the effectiveness of the multispectral pedestrian detection technology proposed in this paper.

New regularization method and iteratively reweighted algorithm for sparse vector recovery

Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design an iterative algorithm,namely the iteratively reweighted algorithm(IR-algorithm),for efficiently computing the sparse solutions to the proposed regularization model.The convergence of the IR-algorithm and the setting of the regularization parameters are analyzed at length.Finally,we present numerical examples to illustrate the features of the new regularization and algorithm.

APPLICATION OF LEAST MEDIAN OF SQUARED ORTHOGONAL DISTANCE (LMD) AND LMD BASED REWEIGHTED LEAST SQUARES (RLS) METHODS ON THE STOCK RECRUITMENT RELATIONSHIP

Analysis of stock recruitment (SR) data is most often done by fitting various SR relationship curves to the data. Fish population dynamics data often have stochastic variations and measurement errors, which usually result in a biased regression analysis. This paper presents a robust regression method, least median of squared orthogonal distance (LMD), which is insensitive to abnormal values in the dependent and independent variables in a regression analysis. Outliers that have significantly different variance from the rest of the data can be identified in a residual analysis. Then, the least squares (LS) method is applied to the SR data with defined outliers being down weighted. The application of LMD and LMD based Reweighted Least Squares (RLS) method to simulated and real fisheries SR data is explored.

Evaluating gravity gradient components based on a reweighted inversion method

In gravity gradient inversion,to choose an appropriate component combination is very important,that needs to understand the function of each component of gravity gradient in the inversion.In this paper,based on the previous research on the characteristics of gravity gradient components,we propose a reweighted inversion method to evaluate the influence of single gravity gradient component on the inversion resolution The proposed method only adopts the misfit function of the regularized equation and introduce a depth weighting function to overcome skin effect produced in gravity gradient inversion.A comparison between different inversion results was undertaken to verify the influence of the depth weighting function on the inversion result resolution.To avoid the premise of introducing prior information,we select the depth weighting function based on the sensitivity matrix.The inversion results using the single-prism model and the complex model show that the influence of different components on the resolution of inversion results is different in different directions,however,the inversion results based on two kind of models with adding different levels of random noise are basically consistent with the results of inversion without noises.Finally,the method was applied to real data from the Vinton salt dome,Louisiana,USA.

Iterative Reweighted <i>l</i>₁Penalty Regression Approach for Line Spectral Estimation

In this paper, we proposed an iterative reweighted l1?penalty regression approach to solve the line spectral estimation problem. In each iteration process, we first use the ideal of Bayesian lasso to update the sparse vectors;the derivative of the penalty function forms the regularization parameter. We choose the anti-trigonometric function as a penalty function to approximate the?l0? norm. Then we use the gradient descent method to update the dictionary parameters. The theoretical analysis and simulation results demonstrate the effectiveness of the method and show that the proposed algorithm outperforms other state-of-the-art methods for many practical cases.

Continuous Iteratively Reweighted Least Squares Algorithm for Solving Linear Models by Convex Relaxation

In this paper, we present continuous iteratively reweighted least squares algorithm (CIRLS) for solving the linear models problem by convex relaxation, and prove the convergence of this algorithm. Under some conditions, we give an error bound for the algorithm. In addition, the numerical result shows the efficiency of the algorithm.

A Reweighted Total Variation Algorithm with the Alternating Direction Method for Computed Tomography

A variety of alternating direction methods have been proposed for solving a class of optimization problems. The applications in computed tomography (CT) perform well in image reconstruction. The reweighted schemes were applied in l1-norm and total variation minimization for signal and image recovery to improve the convergence of algorithms. In this paper, we present a reweighted total variation algorithm using the alternating direction method (ADM) for image reconstruction in CT. The numerical experiments for ADM demonstrate that adding reweighted strategy reduces the computation time effectively and improves the quality of reconstructed images as well.

Continuous Mixed p-norm Control Scheme with Reweighted L_(0) norm Variable Step Size for Mitigating Power Quality Problems of Grid Coupled Solar PV Systems

In this paper,the performance of a two-stage three-phase grid coupled solar photovoltaic generating system(SPVGS)is analyzed by using a novel reweighted Lo norm variable step size continuous mixed p-norm(RLo-VSSCMPN)of a voltage source inverter(VSI)control scheme.The efficacy of the system is determined by considering unbalanced grid voltage,DC offset,voltage sag and swell,unbalanced load and variations in solar insolation.RLo-VSSCMPN is used for inverter control and it ex-tracts fundamental components of load current for generating the reference grid current with a faster convergence rate and lesser steady state oscillations.With the proposed control,harmonics in the grid current follows the IEEE-519 norm and the performance is also satisfactory under varying environmental/load conditions.The power generated from SPvGS is transferred optimally using a DC-DC boost converter utilizing the incremental conductance(INC)maximum power point technique.The proposed system is simulated using MATLAB/Simulink 2018a and test results are verified experimentally using dSPACE1202 in the laboratory to ensure the validity of a novel proposed robust RLo-VSSCMPN.Index Terms-INC maximum power point tracker,power quality,reweighted LoVSSCMPN algorithm,solar PV generating system,total harmonic distortion,voltage source inverter.

Software Defect Prediction Method Based on Stable Learning

The purpose of software defect prediction is to identify defect-prone code modules to assist software quality assurance teams with the appropriate allocation of resources and labor.In previous software defect prediction studies,transfer learning was effective in solving the problem of inconsistent project data distribution.However,target projects often lack sufficient data,which affects the performance of the transfer learning model.In addition,the presence of uncorrelated features between projects can decrease the prediction accuracy of the transfer learning model.To address these problems,this article propose a software defect prediction method based on stable learning(SDP-SL)that combines code visualization techniques and residual networks.This method first transforms code files into code images using code visualization techniques and then constructs a defect prediction model based on these code images.During the model training process,target project data are not required as prior knowledge.Following the principles of stable learning,this paper dynamically adjusted the weights of source project samples to eliminate dependencies between features,thereby capturing the“invariance mechanism”within the data.This approach explores the genuine relationship between code defect features and labels,thereby enhancing defect prediction performance.To evaluate the performance of SDP-SL,this article conducted comparative experiments on 10 open-source projects in the PROMISE dataset.The experimental results demonstrated that in terms of the F-measure,the proposed SDP-SL method outperformed other within-project defect prediction methods by 2.11%-44.03%.In cross-project defect prediction,the SDP-SL method provided an improvement of 5.89%-25.46% in prediction performance compared to other cross-project defect prediction methods.Therefore,SDP-SL can effectively enhance within-and cross-project defect predictions.

Rapid fatty acids detection of vegetable oils by Raman spectroscopy based on competitive adaptive reweighted sampling coupled with support vector regression

Objectives:The composition and content of fatty acids are critical indicators of vegetable oil quality.To overcome the drawbacks of traditional detection methods,Raman spectroscopy was investigated for the fast determination of the fatty acids composition of oil.Materials and Methods:Rapeseed and soybean oil at different depths of the oil tank at different storage times were collected and an eighth-degree polynomial function was used to fit the Raman spectrum.Then,the multivariate scattering correction,standard normal variable transformation(SNV),and Savitzky–Golay convolution smoothing methods were compared.Results:Polynomial fitting combined with SNV was found to be the optimal pretreatment method.Characteristic wavelengths were selected by competitive adaptive reweighted sampling.For monounsaturated fatty acids(MUFAs),polyunsaturated fatty acids(PUFAs),and saturated fatty acids(SFAs),44,75,and 92 characteristic wavelengths of rapeseed oil,and 60,114,and 60 characteristic wavelengths of soybean oil were extracted.Support vector regression was used to establish the prediction model.The R^(2)values of the prediction results of MUFAs,PUFAs,and SFAs for rapeseed oil were 0.9670,0.9568,and 0.9553,and the root mean square error(RMSE)values were 0.0273,0.0326,and 0.0340,respectively.The R^(2)values of the prediction results of fatty acids for soybean oil were respectively 0.9414,0.9562,and 0.9422,and RMSE values were 0.0460,0.0378,and 0.0548,respectively.A good correlation coefficient and small RMSE value were obtained,indicating the results to be highly accurate and reliable.Conclusions:Raman spectroscopy,based on competitive adaptive reweighted sampling coupled with support vector regression,can rapidly and accurately analyze the fatty acid composition of vegetable oil.

Threshold reweighted Nadaraya-Watson estimation of jump-diffusion models

In this paper,we propose a new method to estimate the diffusion function in the jump-diffusion model.First,a threshold reweighted Nadaraya-Watson-type estimator is introduced.Then,we establish asymptotic normality for the estimator and conduct Monte Carlo simulations through two examples to verify the better finite-sampling properties.Finally,our estimator is demonstrated through the actual data of the Shanghai Interbank Offered Rate in China.

Instance Reweighting Adversarial Training Based on Confused Label

Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks,which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger weights.The probability margin(PM)method is a promising approach to continuously and path-independently mea-suring such closeness between the example and decision boundary.However,the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories,where the latter is closer to multi-classification decision boundaries and is supported to be more critical in our observation.To tackle this problem,this paper proposed an improved PM criterion,called confused-label-based PM(CL-PM),to measure the closeness mentioned above and reweight adversarial examples during training.Specifi-cally,a confused label(CL)is defined as the label whose prediction probability is greater than that of the ground truth label given a specific adversarial example.Instead of considering the discrepancy between the probability of the true label and the probability of the most misclassified label as the PM method does,we evaluate the closeness by accumulating the probability differences of all the CLs and ground truth label.CL-PM shares a negative correlation with data vulnerability:data with larger/smaller CL-PM is safer/riskier and should have a smaller/larger weight.Experiments demonstrated that CL-PM is more reliable in indicating the closeness regarding multiple misclassified categories,and reweighting adversarial training based on CL-PM outperformed state-of-the-art counterparts.

A new method of searching for concealed Au deposits by using the spectrum of arid desert plant species

With the increase of exploration depth,it is more and more difficult to find Au deposits.Due to the limitation of time and cost,traditional geological exploration methods are becoming increasingly difficult to be effectively applied.Thus,new methods and ideas are urgently needed.This study assessed the feasibility and effectiveness of using hyperspectral technology to prospect for hidden Au deposits.For this purpose,48 plant(Seriphidium terrae-albae)and soil(aeolian gravel desert soil)samples were first collected along a sampling line that traverses an Au mineralization alteration zone(Aketasi mining region in an arid region of China)and were used to obtain soil Au contents by a chemical analysis method and the reflectance spectra of plants obtained with an Analytical Spectral Device(ASD)FieldSpec3 spectrometer.Then,the corresponding relationship between the soil Au content anomaly and concealed Au deposits was investigated.Additionally,the characteristic bands were selected from plant spectra using four different methods,namely,genetic algorithm(GA),stepwise regression analysis(STE),competitive adaptive reweighted sampling(CARS),and correlation coefficient method(CC),and were then input into the partial least squares(PLS)method to construct a model for estimating the soil Au content.Finally,the quantitative relationship between the soil Au content and the 15 different plant transformation spectra was established using the PLS method.The results were compared with those of a model based on the full spectrum.The results obtained in this study indicate that the location of concealed Au deposits can be predicted based on soil geochemical anomaly information,and it is feasible and effective to use the full plant spectrum and PLS method to estimate the Au content in the soil.The cross-validated coefficient of determination(R2)and the ratio of the performance to deviation(RPD)between the predicted value and the measured value reached the maximum of 0.8218 and 2.37,respectively,with a minimum value of 6.56μg/kg for the root-mean-squared error(RMSE)in the full spectrum model.However,in the process of modeling,it is crucial to select the appropriate transformation spectrum as the input parameter for the PLS method.Compared with the GA,STE,and CC methods,CARS was the superior characteristic band screening method based on the accuracy and complexity of the model.When modeling with characteristic bands,the highest accuracy,R2 of 0.8016,RMSE of 7.07μg/kg,and RPD of 2.20 were obtained when 56 characteristic bands were selected from the transformed spectra(1/lnR)'(where it represents the first derivative of the reciprocal of the logarithmic spectrum)of sampled plants using the CARS method and were input into the PLS method to construct an inversion model of the Au content in the soil.Thus,characteristic bands can replace the full spectrum when constructing a model for estimating the soil Au content.Finally,this study proposes a method of using plant spectra to find concealed Au deposits,which may have promising application prospects because of its simplicity and rapidity.

Application of Hyperspectral Imaging Technology in Rapid Detection of Preservative in Milk

To ensure the quality and safety of pure milk,detection method of typical preservative-potassium sorbate in milk was researched in this paper.Hyperspectral imaging technology was applied to realize rapid detection.Influence factors for hyperspectral data collection for milk samples were firstly researched,including height of sample,bottom color and sample filled up container or not.Pretreatment methods and variable selection algorithms were applied into original spectral data.Rapid detection models were built based on support vector machine method(SVM).Finally,standard normalized variable(SNV)-competitive adaptive reweighted sampling(CARS)and SVM model was chosen in this paper.The accuracies of calibration set and testing set were 0.97 and 0.97,respectively.Kappa coefficient of the model was 0.93.It could be seen that hyperspectral imaging technology could be used to detect for potassium sorbate in milk.Meanwhile,it also provided methodological supports for the rapid detection of other preservatives in milk.

A System of Simultaneous Equations (SEM) for the Study of the Effectiveness of the Japanese Monetary Policy

In this paper, the authors study the effectiveness of the Japanese monetary policy set by the Bank of Japan (BOJ) to contrast the three major crises that the country has experienced since the second half of the 90s: that of the lost decade, that of 2008 and that of the Covid-19 pandemic. To this end, they use a particular type of mathematical-statistical model that is widely applied today in the economic field, namely a simultaneous equation model (SEM). This simultaneous equation model is estimated through an Iteratively reweighted least squares (IRLS) using quarterly historical series in the sample period Q1 1994 - Q2 2020. All data are in real terms. The results, appropriately compared with those of other authors, suggest that the monetary policy has a (limited) impact only on the interbank market. The fiscal policy, instead, has a greater ability to influence the money supply, the private consumption and the inflation expectations.

Numerical Studies of the Generalized <i>l</i>₁Greedy Algorithm for Sparse Signals

The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results showed that this algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in reconstructing these medical images. In this paper the effectiveness of the generalized l1 greedy algorithm in finding random sparse signals from underdetermined linear systems is investigated. A series of numerical experiments demonstrate that the generalized l1 greedy algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in the successful recovery of randomly generated Gaussian sparse signals from data generated by Gaussian random matrices. In particular, the generalized l1 greedy algorithm performs extraordinarily well in recovering random sparse signals with nonzero small entries. The stability of the generalized l1 greedy algorithm with respect to its parameters and the impact of noise on the recovery of Gaussian sparse signals are also studied.

方阱链状分子临界性质的Monte Carlo模拟

在巨正则系综下对阱宽为λ=1.5,链长分别为4、8、16的方阱链状流体实施Monte Carlo模拟,采用建立在完整标度基础上的无偏的Q-参数方法,通过histogram reweighting技术以及有限尺寸标度理论得到了热力学极限下该系列流体的临界温度和临界密度.模拟结果表明,方阱链流体的临界温度随着链长的增加而升高.并且不同链长方阱流体的临界温度均低于已报道的结果.由于本文所采用的完整标度的无偏性,我们估计的临界点更加准确.并且流体的临界温度与链长之间的关系与Flory-Huggins理论相一致.我们还预测了无限链长方阱流体的临界温度,比已有结果略高.

成本引导学习的少数类分类算法设计

为提高不均衡样本中正类的检测率,提出了基于Reweighted思想与成本引导的MisC_S算法.算法赋予样本错分成本参数,迭代产生的多个分类器按加权错误率最小做最优选择,样本权重按分类结果做更新.实验选取六组数据,按R指标进行比较。MisC_S算法较其他算法,R值总体得到了一定幅度的提升,且在Credit_2和Credit_3数据集上提升明显.实验结果表明,MisC_S算法在提高不均衡样本的正类检测率上较有效.

Effects of Kampo medicine hangebyakujutsutemmato on persistent postural-perceptual dizziness:A retrospective pilot study

BACKGROUND Persistent postural-perceptual dizziness(PPPD)is a functional disorder,typically preceded by acute vestibular disorders.It is characterized by a shift in processing spatial orientation information,to favor visual over vestibular and somatosensory inputs,and a failure of higher cortical mechanisms.To date,no therapies for PPPD have been approved.Kampo medicine hangebyakujutsutemmato(HBT)has been reported to alleviate disturbances of equilibrium.We hypothesized that HBT would be a beneficial treatment for PPPD.AIM To examine the efficacy of HBT for the treatment of PPPD.METHODS Patients with PPPD were enrolled and divided into two groups:The HBT group(n=24)and the non-HBT group(n=14).The participants completed questionnaire surveys[Niigata PPPD questionnaire(NPQ),dizziness handicap inventory,hospital anxiety and depression scale(HADS),orthostatic dysregulation questionnaire,pittsburg sleep quality index(PSQI),and motion sickness scores]before and after HBT treatment.Additionally,to identify HBT responders,multivariate regression analysis was performed using the results of the ques-tionnaire surveys and equilibrium tests;including stabilometry,and caloric,vestibular evoked myogenic response,and head-up tilt tests.RESULTS Thirty-eight outpatients were included in this study,of which 14 patients(3 men,11 women;mean age,63.5±15.9 years)received treatment without HBT,and 24(1 man,23 women;mean age,58.2±18.7 years)received combination treatment with HBT.Following HBT treatment,NPQ scores decreased significantly(baseline 40.1±10.0 vs 2 mo 24.6±17.7,P<0.001).No statistically significant changes were observed in the NPQ scores in the non-HBT group(baseline 38.6±12.2 vs 2 mo 39.4±14.4,P=0.92).Multivariable regression analysis revealed that the results of stabilometry(P=0.02)and the caloric(P=0.03),and head-up tilt tests(P<0.001),HADS(P=0.003),and PSQI(P=0.01)were associated with HBT responsiveness in PPPD patients.CONCLUSION HBT may be an effective adjunct therapy for PPPD.Patients with autonomic dysfunction,unstable balance,semicircular canal paresis,anxiety,and poor sleep quality may be high responders to HBT.

The construction of general basis functions in reweighting ensemble dynamics simulations: Reproduce equilibrium distribution in complex systems from multiple short simulation trajectories

Ensemble simulations, which use multiple short independent trajectories from dispersive initial conformations, rather than a single long trajectory as used in traditional simulations, are expected to sample complex systems such as biomolecules much more efficiently. The re-weighted ensemble dynamics（RED） is designed to combine these short trajectories to reconstruct the global equilibrium distribution. In the RED, a number of conformational functions, named as basis functions,are applied to relate these trajectories to each other, then a detailed-balance-based linear equation is built, whose solution provides the weights of these trajectories in equilibrium distribution. Thus, the sufficient and efficient selection of basis functions is critical to the practical application of RED. Here, we review and present a few possible ways to generally construct basis functions for applying the RED in complex molecular systems. Especially, for systems with less priori knowledge, we could generally use the root mean squared deviation（RMSD） among conformations to split the whole conformational space into a set of cells, then use the RMSD-based-cell functions as basis functions. We demonstrate the application of the RED in typical systems, including a two-dimensional toy model, the lattice Potts model, and a short peptide system. The results indicate that the RED with the constructions of basis functions not only more efficiently sample the complex systems, but also provide a general way to understand the metastable structure of conformational space.