A Normalizing Flow-Based Bidirectional Mapping Residual Network for Unsupervised Defect Detection

Unsupervised methods based on density representation have shown their abilities in anomaly detection,but detection performance still needs to be improved.Specifically,approaches using normalizing flows can accurately evaluate sample distributions,mapping normal features to the normal distribution and anomalous features outside it.Consequently,this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network(NF-BMR).It utilizes pre-trained Convolutional Neural Networks(CNN)and normalizing flows to construct discriminative source and target domain feature spaces.Additionally,to better learn feature information in both domain spaces,we propose the Bidirectional Mapping Residual Network(BMR),which maps sample features to these two spaces for anomaly detection.The two detection spaces effectively complement each other’s deficiencies and provide a comprehensive feature evaluation from two perspectives,which leads to the improvement of detection performance.Comparative experimental results on the MVTec AD and DAGM datasets against the Bidirectional Pre-trained Feature Mapping Network(B-PFM)and other state-of-the-art methods demonstrate that the proposed approach achieves superior performance.On the MVTec AD dataset,NF-BMR achieves an average AUROC of 98.7%for all 15 categories.Especially,it achieves 100%optimal detection performance in five categories.On the DAGM dataset,the average AUROC across ten categories is 98.7%,which is very close to supervised methods.

Depth-Guided Vision Transformer With Normalizing Flows for Monocular 3D Object Detection

Monocular 3D object detection is challenging due to the lack of accurate depth information.Some methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input to augment the RGB images.Depth-based methods attempt to convert estimated depth maps to pseudo-LiDAR and then use LiDAR-based object detectors or focus on the perspective of image and depth fusion learning.However,they demonstrate limited performance and efficiency as a result of depth inaccuracy and complex fusion mode with convolutions.Different from these approaches,our proposed depth-guided vision transformer with a normalizing flows(NF-DVT)network uses normalizing flows to build priors in depth maps to achieve more accurate depth information.Then we develop a novel Swin-Transformer-based backbone with a fusion module to process RGB image patches and depth map patches with two separate branches and fuse them using cross-attention to exchange information with each other.Furthermore,with the help of pixel-wise relative depth values in depth maps,we develop new relative position embeddings in the cross-attention mechanism to capture more accurate sequence ordering of input tokens.Our method is the first Swin-Transformer-based backbone architecture for monocular 3D object detection.The experimental results on the KITTI and the challenging Waymo Open datasets show the effectiveness of our proposed method and superior performance over previous counterparts.

Effect of normalizing cooling process on microstructure and precipitates in low-temperature silicon steel

Microstructure, precipitate and magnetic characteristic of fmal products with different normalizing cooling processes for Fe-3.2%Si low-temperature hot-rolled grain-oriented silicon steel were analyzed and compared with the hot-rolled plate by optical microscopy （OM）, transmission electron microscopy （TEM）, and energy dispersive spectrometry （EDS）. The results show that, the surface microstructure is uniform, the proportion of recrystallization in matrix increases, and the banding textures are narrowed; the precipitates, whose quantity in normalized plate is more than that in hot-rolled plate greatly, are mainly A1N, MnS, composite precipitates （Cu,Mn）S and so on. Normalizing technology with a temperature of 1120 ℃, holding for 3 min, and a two-stage cooling is a most advantaged method to obtain oriented silicon steel with sharper Goss texture and higher magnetic properties, owing to the uniform surface microstructures and the obvious inhomogeneity of microstructures along the thickness. The normalizing technology with the two-stage cooling is the optimum process, which can generate more fine precipitates dispersed over the matrix, and be beneficial for finished products to get higher magnetic properties.

Comparison of up ladder type and terraced type normalizing heat treatments of heavy cylinder

Because of the mixed grain and coarse grain structure, the long heat treatment cycle and large energy conservation in the heavy cylinder heat treatment process, the up ladder type and terraced type normalizing heat treatment of heavy cylinder after rolling were put forward. The microstructure and mechanical properties of 2.25Cr1Mo0.25 V steel after the up ladder type normalizing, terraced type normalizing and isothermal type normalizing were studied. Experimental results show that： 1） For the grain refinement, the twice terraced type normalizing is better than the up ladder type and isothermal type normalizing, and the average grain size is 18 μm; 2） The yield strength, tensile strength and-30℃ charpy impact energy after twice terraced type normalizing are 681 MPa, 768 MPa and 181 J, respectively, and the mechanical properties are better than those of the up ladder type and isothermal type normalizing; 3） Compared with the isothermal type normalizing, the holding time of terraced type normalizing can be shortened by 30%, which greatly reduces the energy consumption.

Effect of Normalizing and Hardening on Mechanical Properties of Spring

This study was carried out to investigate the effect of heat treatment (Normalizing and Hardening) on the mechanical properties of springs. The springs were made from mild steel rod having a diameter of 6 mm, a total of 15 springs were made. The springs were then subjected to various heat treatment processes which included;normalizing, hardening and tempering. The heat treated springs were then subjected to various test in other to determine their mechanical properties, these included;impact toughness test, hardness test and tension test. The normalized spring had more strength, was harder and was much tougher than both the annealed and as received springs. The water quenched springs were the hardest of all the heat treated springs, were very brittle and had the lowest percentage elongation. Their strength was also lower than that of the normalized and as received springs. The water quenched and tempered springs had better mechanical properties required for spring making, they had the optimum combination of hardness, strength and toughness when compared with the other heat treated springs.

Robust adaptive output stabilization using dynamic normalizing signal

For a class of nonlinear systems with dynamic uncertainties, robust adaptive stabilization problem is considered in this paper. Firstly, by introducing an observer, an augmented system is obtained. Based on the system, we construct an exp-ISpS Lyapunov function for the unmodeled dynamics, prove that the unmodeled dynamics is exp-ISpS, and then obtain a dynamic normalizing signal to counteract the dynamic disturbances. By the backstepping technique, an adaptive controller is given, it is proved that all the signals in the adaptive control system are globally uniformly ultimately bounded, and the output can be regulated to the origin with any prescribed accuracy. A simulation example further demonstrates the efficiency of the control scheme.

Performance Study of an FFH/BFSK Self-Normalizing Receiver with Multitone Jamming's Frequency Offsets

This study investigates how frequency offsets of multitone jamming affect the fast frequencyhopped binary frequency shift keying （FFH/BFSK） self-normalizing （SNZ） receiver under additive white Gaussian noise （AWGN）. The average bit-error-rate （BER） expressions of the FFH/BFSK SNZ receiver and the average BER expressions of an FFH/BFSK spread-spectrum （SS） communication system with frequency offsets of multitone jamming for the sake of understanding the simulation results better. Simulation results show that BER performance of the FFH/BFSK SNZ receiver with diversity under the worst case multitone jamming （MTJ） and AWGN suffers from multitone jamming＇s frequency offsets when the jamming power is moderate, which is validated by several simulations with different frequency offsets configured in multitone jamming. Therefore, an FFH/BFSK SNZ receiver under multitone jamming can be combated with the help of frequency offsets of multitone jamming.

Normalizing Relationships

AS diplomatic relations were restored between China and the Islamic Republic of the Gambia in March,China’s Foreign Minister Wang Yi called it a“historic moment”for the two countries.Wang met with his Gambian counterpart Neneh Mac Douall-Gaye in Beijing to sign a joint communique on March 17.

OpenNAU:An open-source platform for normalizing,analyzing,and visualizing cancer untargeted metabolomics data

Objective:As an important part of metabolomics analysis,untargeted metabolomics has become a powerful tool in the study of tumor mechanisms and the discovery of metabolic markers with high-throughput spectrometric data which also poses great challenges to data analysis,from the extraction of raw data to the identification of differential metabolites.To date,a large number of analytical tools and processes have been developed and constructed to serve untargeted metabolomics research.The different selection of analytical tools and parameter settings lead to varied results of untargeted metabolomics data.Our goal is to establish an easily operated platform and obtain a repeatable analysis result.Methods:We used the R language basic environment to construct the preprocessing system of the original data and the LAMP(Linux+Apache+MySQL+PHP)architecture to build a cloud mass spectrum data analysis system.Results:An open-source analysis software for untargeted metabolomics data(openNAU)was constructed.It includes the extraction of raw mass data and quality control for the identification of differential metabolic ion peaks.A reference metabolomics database based on public databases was also constructed.Conclusions:A complete analysis system platform for untargeted metabolomics was established.This platform provides a complete template interface for the addition and updating of the analysis process,so we can finish complex analyses of untargeted metabolomics with simple human-computer interactions.The source code can be downloaded from https://github.com/zjuRong/openNAU.

New formulas for normalizing photon-added(-subtracted) two-mode squeezed thermal states

For the first time,we derive the compact forms of normalization factors for photon-added（-subtracted） two-mode squeezed thermal states by using the P-representation and the integration within an ordered product of operators（IWOP） technique.It is found that these two factors are related to the Jacobi polynomials.In addition,some new relationships for Jacobi polynomials are presented.

Normalizing Book Citations in Google Scholar: A Hybrid Cited-side Citing-side Method

Purpose: To design and test a method for normalizing book citations in Google Scholar.Design/methodology/approach: A hybrid citing-side, cited-side normalization method was developed and this was tested on a sample of 285 research monographs. The results were analyzed and conclusions drawn.Findings: The method was technically feasible but required extensive manual intervention because of the poor quality of the Google Scholar data. Research limitations: The sample of books was limited and also all were from one discipline —business and management. Also, the method has only been tested on Google Scholar, it would be useful to test it on Web of Science or Scopus.Practical limitations: Google Scholar is a poor source of data although it does cover a much wider range citation sources that other databases. Originality/value: This is the first method that has been developed specifically for normalizing books which have so far not been able to be normalized.

Efficient parameter inference for gravitational wave signals in the presence of transient noises using temporal and time-spectral fusion normalizing flow

Glitches represent a category of non-Gaussian and transient noise that frequently intersects with gravitational wave(GW)signals,thereby exerting a notable impact on the processing of GW data.The inference of GW parameters,crucial for GW astronomy research,is particularly susceptible to such interference.In this study,we pioneer the utilization of a temporal and time-spectral fusion normalizing flow for likelihood-free inference of GW parameters,seamlessly integrating the high temporal resolution of the time domain with the frequency separation characteristics of both time and frequency domains.Remarkably,our findings indicate that the accuracy of this inference method is comparable to that of traditional non-glitch sampling techniques.Furthermore,our approach exhibits a greater efficiency,boasting processing times on the order of milliseconds.In conclusion,the application of a normalizing flow emerges as pivotal in handling GW signals affected by transient noises,offering a promising avenue for enhancing the field of GW astronomy research.

Pretreating and normalizing metabolomics data for statistical analysis

Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological samples.Metabolomics is emerging as a powerful tool generally for pre-cision medicine.Particularly,integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and disease.However,metabo-lomics data are very complicated.Preprocessing/pretreating and normalizing procedures on metabolomics data are usually required before statistical analysis.In this review article,we comprehensively review various methods that are used to preprocess and pretreat metabolo-mics data,including MS-based data and NMR-based data preprocessing,dealing with zero and/or missing values and detecting outliers,data normalization,data centering and scaling,data transformation.We discuss the advantages and limitations of each method.The choice for a suitable preprocessing method is determined by the biological hypothesis,the characteristics of the data set,and the selected statistical data analysis method.We then provide the perspective of their applications in the microbiome and metabolome research.

Effects of Normalizing Annealing on Grain-oriented Silicon Steel

The grain-oriented silicon steel is a kind of important magnetic materials with low iron loss and high induc tion. Hot hand normalizing annealing is an important process which influences the microstructure and the development of the inhibitors. The effects of different annealing temperatures and cooling conditions on the inhibitors and microstructures of normalizing annealing band were investigated. The microstructure and different kinds of the inhibitors, i. e. , A1N, AIN＋Cu, S＋MnS, and TiN, were discovered. The result shows that a suitable cooling condition leads to more nano scale inhibitors and uniform microstructure of the normalizing annealing band and consequently results in better magnetic properties.

Precipitation Behavior of V-N Microalloyed Steels during Normalizing

The precipitation behavior of V-N microalloyed steel during normalizing process was studied by physicochemical phase analysis and transmission electron microscopy（TEM）. The effect of precipitation behavior on mechanical properties was investigated by theoretical calculations. The results showed that 32.9% of V（C,N） precipitates remained undissolved in the austenite during the soaking step of the normalizing process. These precipitates prevented the growth of the austenite grains. During the subsequent cooling process, the dissolved V（C,N） re-precipitated and played a role in precipitation strengthening. The undissolved V（C,N） induced intragranular ferrite nucleation and refined the ferrite grains. Consequently, compared with hot-rolled steel, the normalized steel exhibited increased grain-refining strengthening but diminished precipitation strengthening, leading to an improvement of the impact energy at the expense of about 40 MPa yield strength.

A Normalizing Field Flow Induced Two-Stage Stochastic Homogenization Method for Random Composite Materials

The traditional stochastic homogenization method can obtain homogenized solutions of elliptic problems with stationary random coefficients.However,many random composite materials in scientific and engineering computing do not satisfy the stationary assumption.To overcome the difficulty,we propose a normalizing field flow induced two-stage stochastic homogenization method to efficiently solve the random elliptic problem with non-stationary coefficients.By applying the two-stage stochastic homogenization method,the original elliptic equation with random and fast oscillatory coefficients is approximated as an equivalent elliptic equation,where the equivalent coefficients are obtained by solving a set of cell problems.Without the stationary assumption,the number of cell problems is large and the corresponding computational cost is high.To improve the efficiency,we apply the normalizing field flow model to learn a reference Gaussian field for the random equivalent coefficients based on a small amount of data,which is obtained by solving the cell problems with the finite element method.Numerical results demonstrate that the newly proposed method is efficient and accurate in tackling high dimensional partial differential equations in composite materials with complex random microstructures.

Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow

In this work,we propose an adaptive learning approach based on temporal normalizing flows for solving time-dependent Fokker-Planck(TFP)equations.It is well known that solutions of such equations are probability density functions,and thus our approach relies on modelling the target solutions with the temporal normalizing flows.The temporal normalizing flow is then trained based on the TFP loss function,without requiring any labeled data.Being a machine learning scheme,the proposed approach is mesh-free and can be easily applied to high dimensional problems.We present a variety of test problems to show the effectiveness of the learning approach.

Normalized Solutions of Nonlinear Choquard Equations with Nonconstant Potential

In this paper,we mainly focus on a type of nonlinear Choquard equations with nonconstant potential.Under appropriate hypotheses on potential function and nonlinear terms,we prove that the above Choquard equation with prescribed 2-norm has some normalized solutions by introducing variational methods.

A Sufficient Condition for a Smash Product as a Transfinite Left Free Normalizing Extension and Its Application

In this paper,a sufficient condition is given under which the smash product A#H is a transfinite left.free normalizing extension of an algebra A.Moreover,the result is applied to a skew semigroup ring,a skew group ring and the quantum group U_q(sl(2))such that some properties are shown.

TanshinoneⅡA improves distribution and anti-tumor efficacy of pegylated liposomal doxorubicin via normalizing the structure and function of tumor vasculature in hepa1-6 hepatoma mice model

OBJECTIVE: To investigate whether the Tandistribution and anti-tumor Ⅱef A could improve the ficacy of Pegylated Liposomal Doxorubicin(PLD) via normalizing the structure and function of vasculature in Hepa1-6 hepatoma mice model.METHODS: Hepa1-6 hepatoma-bearing mice were treated with TanⅡA for 14 d. Distribution and anti-tumor efficacy of PLD, and the structure and function of the tumor vasculature were evaluated using various techniques.RESULTS: TanⅡ A significantly reduced the micro-vessel density(MVD). After Tan vascular walls were betteⅡr s A treatment,the tumor tructured, as the increased coverage of the pericytes and the promoted contact of the basement membrane and endothelial cell. Functional tests showed that tumor hypoxia was improved and the exudation amount of Evans blue in the parenchyma of the tumor decreased. In addition, mice treated with TanA had greater PLD penetration distance intratumoⅡrally. Furthermore, combined therapy of Tanibited tumor growth.ⅡA and PLD significantly inhCONCLUSION: This study suggests that Tanasculature andⅡ h A helps normalizing the tumor vas therapeutic potential in increasing the distribution of chemotherapy drug in the tumor.