Template Classes Research Articles

Geometric and electronic structures of monolayer potassium fullerides on Si(111)-√3×√3-Ag

Metal-terminated silicon surfaces are a specific class of templates for growth of fulleride ultrathin films. Using scanning tunneling microscope and spectroscope, we investigate the geometric and electronic structures of KxC60 monolayers on Si(111)-√3×√3-Ag with x gradually increased to 2. Besides long-range ordered KC60 and K2C60 monolayers, a fraction-stoichiometric fulleride, K4/3C60 monolayer, is newly discovered. All these fulleride monolayers open an energy gap at the Fermi level owing to electronic correlations and Jahn-Teller distortion. The gap of the even-stoichiometric fulleride, K2C60, is prominently softened by partial electron doping from the Si(111)-√3×√3-Ag surface.

Ionic Covalent Organic Frameworks in Adsorption and Catalysis

AbstractThe ion extraction and electro/photo catalysis are promising methods to address environmental and energy issues. Covalent organic frameworks (COFs) are a class of promising template to construct absorbents and catalysts because of their stable frameworks, high surface areas, controllable pore environments, and well‐defined catalytic sites. Among them, ionic COFs as unique class of crystalline porous materials, with charges in the frameworks or along the pore walls, have shown different properties and resulting performance in these applications with those from charge‐neutral COFs. In this review, current research progress based on the ionic COFs for ion extraction and energy conversion, including cationic/anionic materials and electro/photo catalysis is reviewed in terms of the synthesis strategy, modification methods, mechanisms of adsorption and catalysis, as well as applications. Finally, we demonstrated the current challenges and future development of ionic COFs in design strategies and applications.

Ionic Covalent Organic Frameworks in Adsorption and Catalysis.

The ion extraction and electro/photo catalysis are promising methods to address environmental and energy issues. Covalent organic frameworks (COFs) are a class of promising template to construct absorbents and catalysts because of their stable frameworks, high surface areas, controllable pore environments, and well-defined catalytic sites. Among them, ionic COFs as unique class of crystalline porous materials, with charges in the frameworks or along the pore walls, have shown different properties and resulting performance in these applications with those from charge-neutral COFs. In this review, current research progress based on the ionic COFs for ion extraction and energy conversion, including cationic/anionic materials and electro/photo catalysis is reviewed in terms of the synthesis strategy, modification methods, mechanisms of adsorption and catalysis, as well as applications. Finally, we demonstrated the current challenges and future development of ionic COFs in design strategies and applications.

BINO: Automatic recognition of inline binary functions from template classes

In this paper, we propose BINO, a static analysis approach that relieves reverse engineers from the challenging task of recognizing library functions that have been inlined. BINO recognizes inline calls of methods of C++ template classes (even with unknown data types). We do this through a binary fingerprinting and matching approach. Our fingerprint model captures syntactic and semantic features of an assembly function, along with its Control-Flow Graph structure. Using these fingerprints and subgraph isomorphism, it recognizes inline method calls in a target binary. BINO automates the fingerprints generation phase by parsing the source code of the template classes and automatically building appropriate binaries with representative inline calls of said methods. We evaluate BINO by performing experiments on a dataset of 555 GitHub C++ projects containing 10,600 inline functions, exploring several optimization levels that allow the compiler to inline function calls. We show that our approach can recognize inline function calls to the most used methods of well-known template classes with an F1-Score up to 63% with the -O2, -O3, and -Ofast optimizations levels.

CNOK: A C++ Glauber model code for single-nucleon knockout reactions

A C++ program package was developed to calculate parallel momentum distributions (including the stripping mechanism and the diffractive dissociation mechanism) of the heavy residue (core) in single-nucleon knockout reactions induced by intermediate-energy (around and more than tens of MeV per nucleon) beams of stable and radioactive atomic nuclei. The program implements the Glauber reaction model that is based on the eikonal approximation and the sudden approximation when dealing with the scattering process, and uses a t-ρ-ρ method to build the projectile-target scattering optical potential, where the nucleonic densities of the core and the target are needed as input. The radial wavefunction of the valence nucleon is solved by a built-in bound-wave solver driven by a globally convergent search engine for optimized potential parameters of the valence nucleon to reproduce user-specified separation energies and root-mean-square (rms) radii of the valence nucleon. The YAML file format is adopted to facilitate user input. The program is expected to especially serve the interpretation of data analysis results of single-nucleon knockout from radioactive ion beams (RIBs), where the experimental practitioners may be more conversant with C++. Program summaryProgram Title: CNOKCPC Library link to program files:https://doi.org/10.17632/dmffpbjhsh.1Developer's repository link:https://gitee.com/asiarabbit/cnokLicensing provisions: GPLv3Programming language: C++Nature of problem: This program calculates the single-particle reaction cross sections of single-nucleon removal from stable and radioactive nuclei impinging on a light composite (i.e. non-hydrogen, e.g. carbon and beryllium) target at intermediate energies (around and more than tens of MeV per nucleon), and the parallel momentum (stripping+diffractive dissociation) distributions of the core, for a pure valence configuration (bound core state+valence nucleon single-particle orbit).Solution method: The bound radial wavefunction of the valence nucleon in a Woods-Saxon (WS) potential is solved following the method of shooting to a fitting point, where the wavefunction is integrated with Runge-Kutta method from 0 and some large distance r∞ to a matching point rm near the nuclear surface. The process is iterated to optimize the eigenvalue Enlj by joining the two waves at rm “smoothly”. Most integrals are evaluated with Simpson's rules, while some with Romberg integration. Searching in the (V0,r0) space of the central WS potential to reproduce the user-specified separation energy and rms radius of the valence nucleon is done via a globally convergent Newton-Raphson root-finding routine for nonlinear systems of equations.Additional comments including restrictions and unusual features: User input is read in with YAML parser (yaml-cpp). Global parameters are readily accessed and uniformly modified in a singleton class. General algorithms (integration, interpolation, minimization, and root-finding routines) are implemented with C++ template class or functors, which adapt to different data types and/or function types. Various batch modes are wrapped in the program package to save user time. For instance, in one run, with the batch mode, one can calculate inclusive single-nucleon removal cross section involving many valence configurations, the relevant inclusive core momentum distribution, and similarly for a series of projectile+target combinations in study of systematics, as is frequently encountered in the study of quenching of single-particle-orbit occupancies (spectroscopic factors) in single-nucleon knockout reactions. Unit tests with Catch are widely deployed to facilitate debugging. C++11 support is required for compilers.

An Optimized Framework for Matrix Factorization on the New Sunway Many-core Platform

Matrix factorization functions are used in many areas and often play an important role in the overall performance of the applications. In the LAPACK library, matrix factorization functions are implemented with blocked factorization algorithm, shifting most of the workload to the high-performance Level-3 BLAS functions. But the non-blocked part, the panel factorization, becomes the performance bottleneck, especially for small- and medium-size matrices that are the common cases in many real applications. On the new Sunway many-core platform, the performance bottleneck of panel factorization can be alleviated by keeping the panel in the LDM for the panel factorization. Therefore, we propose a new framework for implementing matrix factorization functions on the new Sunway many-core platform, facilitating the in-LDM panel factorization. The framework provides a template class with wrapper functions, which integrates inter-CPE communication for the Level-1 and Level-2 BLAS functions with flexible interfaces and can accommodate different partitioning schemes. With the framework, writing panel factorization code with data residing in the LDM space can be done with much higher productivity. We implemented three functions ( dgetrf , dgeqrf , and dpotrf ) based on the framework and compared our work with a CPE_BLAS version, which uses the original LAPACK implementation linked with optimized BLAS library that runs on the CPE mesh. Using the most favorable partitioning, the panel factorization part achieves speedup of up to 26.3, 19.1, and 18.2 for the three matrix factorization functions. For the whole function, our implementation is based on a carefully tuned recursion framework, and we added specific optimization to some subroutines used in the factorization functions. Overall, we obtained average speedup of 9.76 on dgetrf , 10.12 on dgeqrf , and 4.16 on dpotrf , compared to the CPE_BLAS version. Based on the current template class, our work can be extended to support more categories of linear algebra functions.

Human induced pluripotent stem cell formation and morphology prediction during reprogramming with time-lapse bright-field microscopy images using deep learning methods

Background and objectiveHuman induced pluripotent stem cells (hiPSCs) represent an ideal source for patient specific cell-based regenerative medicine; however, efficiency of hiPSC formation from reprogramming cells is low. We use several deep-learning results from time-lapse brightfield microscopy images during culture, to early detect the cells potentially reprogramming into hiPSCs and predict the colony morphology of these cells for improving efficiency of culturing a new hiPSC line. MethodsSets of time-lapse bright-field images are taken to track reprogramming process of CD34+ cells biologically identified as just beginning reprogramming. Prior the experiment, 9 classes of templates with distinct cell features clipped from microscopy images at various reprogramming stages are used to train a CNN model. The CNN is then used to classify a microscopy image as probability images of these classes. Probability images of some class are used to train a densely connected convolutional network for extracting regions of this class on a microscopy image. A U-net is trained to segment cells on the time-lapse images in early reprogramming stage during culture. The segmented cells are classified by the extracted regions to count various types of cells appearing in the early reprogramming stage for predicting the identified cells potentially forming hiPSCs. The probability images of hiPSC classes are also used to train a spatiotemporal RNN for predicting the future hiPSC colony morphology of the potential cells. ResultsExperimental results show the prediction (before 7 days after of beginning of the reprogramming) achieved 0.8 accuracy, and 66% of the identified cells under different culture conditions, predicted as forming, finally formed hiPSCs. The predicted hiPSC images and extracted colonies on the images show the prediction for future 1.5 days achieved high accuracy of hiPSC colony areas and image similarity. ConclusionsOur study proposes a method using several deep learning models to efficiently select the reprogramming cells possibly forming hiPSCs and to predict the shapes of growing hiPSC colonies. The proposed method is expected to improve the efficiency when establishing a new hiPSC line culture.

Ionic Liquid‐Incorporated Metal‐Organic Framework with High Magnesium Ion Conductivity for Quasi‐Solid‐State Magnesium Batteries

AbstractMagnesium batteries are promising candidates for post‐lithium energy storage systems due to their low cost, high volumetric energy density, and low risk of dendrite formation. This study reports a new magnesium ion conducting ionogel electrolyte based on a Metal‐Organic Framework (MOF) structure (UiO‐66) impregnated with an ionic liquid, magnesium bis[(trifluoromethyl)sulfonyl]imide in 1‐ethyl‐3‐methylimidazolium bis[(trifluoromethyl)sulfonyl]imide. Comparably high conductivity of 5.7×10−5 S cm−1 can be achieved at room temperature. By employing the prepared MOF‐ionogel electrolyte, a reversible quasi‐solid‐state magnesium battery (QSSMB) is reported. Surface analysis unveils the possible origin of the large overpotential of magnesium plating and stripping. The findings suggest that MOF‐based materials are a promising class of ionogel electrolyte templates for QSSMBs. The results on the magnesium anode will be useful to define optimization strategies for magnesium metal anodes in SSMBs.

Safe gauge-string correspondence

Safe theories are quantum field theories whose continuum limit is defined by a non-Gaussian ultraviolet fixed point when the ultraviolet cutoff is removed. They constitute an important set in the space of quantum field theories. Here we develop the ‘safe’ gauge-string correspondence program according to which d-dimensional safe gauge theories, admitting the 't Hooft-Veneziano limit, are holographically dual to (d+1)-dimensional safe noncritical string theories on asymptotically anti-de Sitter space. We provide evidences for this correspondence on a class of safe templates that engage fermion and scalar matter fields into gauge, Yukawa and Higgs self-interactions. Safe theories can feature in the infrared both a weak coupling phase and a strong coupling phase on either side of the ultraviolet fixed point. They correspond respectively to dilaton and warp factors taking domain-wall or Liouville-wall profiles on asymptotically anti-de Sitter space. We argue that four-dimensional N=4 super Yang-Mills theories and all known interacting (super)conformal field theories are nonperturbative limit situations of safe gauge theories. The weak coupling phase provides a solvable holographic renormalization group flow while the strong coupling infrared phase provides a runaway-free alternative to holographic QCD.

Flexible skill-based control for robot cells in manufacturing.

Decreasing batch sizes lead to an increasing demand for flexible automation systems in manufacturing industries. Robot cells are one solution for automating manufacturing tasks more flexibly. Besides the ongoing unifications in the hardware components, the controllers are still programmed application specifically and non-uniform. Only specialized experts can reconfigure and reprogram the controllers when process changes occur. To provide a more flexible control, this paper presents a new method for programming flexible skill-based controls for robot cells. In comparison to the common programming in logic controllers, operators independently adapt and expand the automated process sequence without modifying the controller code. For a high flexibility, the paper summarizes the software requirements in terms of an extensibility, flexible usability, configurability, and reusability of the control. Therefore, the skill-based control introduces a modularization of the assets in the control and parameterizable skills as abstract template class methodically. An orchestration system is used to call the skills with the corresponding parameter set and combine them into automated process sequences. A mobile flexible robot cell is used for the validation of the skill-based control architecture. Finally, the main benefits and limitations of the concept are discussed and future challenges of flexible skill-based controls for robot cells are provided.

SAPO-34 crystallized using novel pyridinium template as highly active catalyst for synthesis of ethyl levulinate biofuel

Silicoaluminophosphate Number 34 (SAPO-34) crystallized using novel N-heterocyclic organic template—1-propylpyridinium hydroxide ([PPy]OH)—as an excellent catalyst for the production of ethyl levulinate biofuel is reported. First, the time-dependent crystallization study of SAPO-34 using various spectroscopy, microscopy and analytical techniques is performed and it reveals that the precursor undergoes several important crystallization steps, namely dissolution of reactants (induction), nucleation and crystal growth of SAPO-34, before it transforms into a more metastable SAPO-36 crystalline phase. The resulting SAPO-34 solid (Si0.198Al0.475P0.327O2) exhibits high porosity (SBET = 673 m2/g, VTot = 0.27 cm3/g) and high Si content (Si/(Si + Al + P) = 0.198) which contribute to high surface acidity (2.52 mmol/g) as confirmed by the NH3-TPD analysis. The SAPO-34 catalyst shows 93.4% conversion to ethyl levulinate via esterification of levulinic acid with ethanol just within 20 min at 190 °C under non-microwave instant heating, which is considered very fast as compared to other systems. In addition, high catalyst recyclability is observed even after fifth cycle of reaction, thus offering another promising pathway for crystallizing zeolites using this new class of template that are beneficial for catalytic biofuel upgrading process.

RGE++: A C++ library to solve renormalisation group equations in quantum field theory

In recent years three-, four- and five-loop beta functions have been computed for various phenomenologically interesting models. However, most of these results have not been implemented in easy to use software packages. RGE++ bridges this gap by providing a flexible, template-based, C++ library to solve renormalisation group equations. Furthermore, we implement the available beta functions for the Standard Model, the minimal supersymmetric extension of the Standard Model and two-Higgs-doublet models, as well as right-handed neutrino extensions of the former two. Program summaryProgram Title:RGE++CPC Library link to program files:https://doi.org/10.17632/c9xmrzg46b.1Developer's repository link:www.github.com/Herren/RGEppLicensing provisions: MITProgramming language:C++Nature of problem: Values of coupling constants in dimensionally regularized quantum field theories depend on the chosen renormalization scale μ. The change of coupling constants w.r.t a change of μ is encoded in coupled first-order differential equations. RGE++ provides routines for solving these differential equations in the Standard Model and selected models beyond it. Furthermore, it provides templates for the implementation of additional models.Solution method:RGE++ is implemented as a set of C++ template classes. To numerically solve the differential equations, we employ Eigen3 [1] and odeint [2]. Furthermore, various additional C++ routines are implemented to extract physical quantities from Yukawa matrices.Additional comments including restrictions and unusual features: For non-physical choices of the coupling constants Landau poles might occur.

Cathelicidin-DM is an Antimicrobial Peptide from Duttaphrynus melanostictus and Has Wound-Healing Therapeutic Potential.

Antimicrobial peptides (AMPs) are a class of templates with application potential for drug development. Amphibians are important sources of AMPs. Duttaphrynus melanostictus is the main source of traditional Chinese medicine “Chansu”, which has anti-infection effect while without a clear mechanism. This study aimed to find the cathelicidin peptide in D. melanostictus and then investigate the activity in vivo and in vitro, and an AMP-encoding gene (cathelicidin-DM, GenBank: KJ820824.1) was obtained from the constructed cDNA library of D. melanostictus. The MIC test and SYTOX Green uptake were used for the evaluation of the bactericidal capacity and mechanisms. The serum stability tests were used for the evaluation of the application potential. The skin wound infection model and in vivo imaging were used for in vitro application of possibility evaluation. The results showed that cathelicidin-DM was a 37 amino acid AMP with good bactericidal ability, which was similar to melittin: both can kill bacteria within 15 min. Moreover, cathelicidin-DM exhibits good therapeutic potential in the mouse wound infection model, and it can be enriched to the site of infection for treatment. Thus, cathelicidin-DM could be a new template for antimicrobial drug development given its good antibacterial activity in vivo and in vitro.

Diblock Copolymer Core–Shell Nanoparticles as Template for Mesoporous Carbons: Independent Tuning of Pore Size and Pore Wall Thickness

Latex templating using core-shell particles represents a unique opportunity to design mesoporous carbons with a high level of control on textural properties. This new class of organic colloid templates is synthesized by polymerization-induced self-assembly (PISA) in which a solvophilic poly(hydroxyethyl acrylate) (PHEA) homopolymer is chain extended with a solvophobic polystyrene (PS) via a photomediated reversible-addition-fragmentation-transfer (RAFT) polymerization. The resultant PHEA-b-PS diblock copolymer nanoparticles exhibit a PS core stabilized by a PHEA shell, with two blocks characterized by a low molecular weight dispersity (1.1-1.3) and an adjustable degree of polymerization (DP). The core-shell structured nanoparticles are used as soft template for the formation of mesostructured carbons from phloroglucinol and glyoxylic acid in methanol solution. A micro- and mesostructured cellular foam is obtained having uniform, interconnected, and narrowly distributed mesopores ranging between 15 and 30 nm in diameter, a specific surface area up to 719 m2 g-1, and a total pore volume of (0.4-1.3) cm3 g-1. The mesopore size can be controlled by adjusting the diameter of the PS core (16-29 nm), while the wall thickness can be tailored independently by varying the size of the solvated PHEA shell (5-25 nm). An increase of PHEA block's DP from 25 to 85 gradually extends the stabilizing shell dimension, thus increasing the wall thickness up to 10 nm, and causing the shift from interconnected to isolated mesopores. By comparison, much thinner walls (2-3 nm) are obtained with conventional latex templates such as polystyrene nanoparticles or colloidal silica. Decreasing PHEA DP to 17 induces the formation of copolymer vesicles that can be used as template to create mesoporous carbons with nonspherical mesopores.

VarRhoTurbVOF: A new set of volume of fluid solvers for turbulent isothermal multiphase flows in OpenFOAM

The volume of fluid (VOF) method is a popular approach for multiphase flow modeling. The open-source computational fluid dynamics (CFD) software, OpenFOAM, implements a variety of VOF-based solvers and provides users a wide range of turbulence models. Since isothermal multiphase flows under the VOF framework belong to the variable-density incompressible flow category, the isothermal VOF-based solvers in OpenFOAM fail to use the correct turbulence models. varRhoTurbVOF is designed to solve this issue and with the hope to replace all the corresponding existing solvers in the future. With the object-oriented paradigm, varRhoTurbVOF guarantees the usability, reusability and maintainability of the codes. Aside from turbulence modeling, all other features in the original solvers are preserved in varRhoTurbVOF. Program summaryProgram Title: varRhoTurbVOFProgram Files doi:http://dx.doi.org/10.17632/4t8z8vzyvs.1Licensing provisions: GPLv3Programming language: C++Supplementary material:http://dx.doi.org/10.17632/7mp25kyb4p.4Nature of problem: Under the VOF framework, the flow of the isothermal mixture belongs to the variable-density incompressible flow category. For such flows, VOF-based solvers of OpenFOAM fail to construct the correct governing equations for turbulence modeling. varRhoTurbVOF contains a set of newly designed VOF-based solvers which could use the desired governing equations for turbulence quantities.Solution method: varRhoTurbVOF creates a new class for variable-density incompressible turbulence models, which allows reusing the existing turbulence model template classes. A set of VOF-based solvers are then created to be able to construct variable-density incompressible turbulence models.

Silver Chlorobromide Nanocubes: A Class of Reactive Templates for Synthesizing Nanoplates and Nanocages of Silver Thiolates

ABSTRACTUniform silver chlorobromide nanocubes have been used to regulate the availability of an extremely low concentration of Ag+ that react with different thiol molecules to form silver thiolate nanoplates with high controllability. The sacrificial silver chlorobromide nanocubes also serve as templates to provide surface nucleation sites for forming the silver thiolate nanoplates, which grow against the surfaces of the nanocubes to organize into nanocages upon the consumption of the entire nanocubes. The precise regulation of the extremely low concentration of Ag+ prevents the uncontrolled fast reaction with thiol molecules and the formation of irregular silver thiolate particles. This method represents a versatile strategy to control the reaction rate of fast reactions involving in the synthesis of colloidal nanoparticles, thus enabling the synthesis of uniform nanoparticles with desirable parameters.

Automatic target recognition of synthetic aperture radar images via gaussian mixture modeling of target outlines

A synthetic aperture radar (SAR) target recognition method is proposed in this study based on target outlines. To model the uncertainties of the target outline points caused by noises, extraction errors, etc., the Gaussian mixture model (GMM) is adopted. Afterwards, the distance between two target outlines is measured by the L2 distance between their corresponding GMMs. GMM can robustly describe the possible deformations of the target outline. Therefore, the defined distance measure in this study can better evaluate the inner correlations and differences between different classes using target outlines. By comparing the target outline of the test sample with different template classes based on the designed distance measure, the target label is decided to be the class with the minimum distance. Finally, experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) dataset for performance evaluation. According to the experimental results, the proposed method achieves a high recognition accuracy of 98.34% in the 10-class recognition problem under the standard operating condition (SOC). In addition, the robustness of the proposed method is also superior over other methods under some typical extended operating conditions (EOCs) including configuration variations, depression angle variance, and noise corruption.

Modeling conformational flexibility of kinases in inactive states.

Kinase structures in the inactive “DFG‐out” state provide a wealth of druggable binding site variants. The conformational plasticity of this state can be mainly described by different conformations of binding site‐forming elements such as DFG motif, A‐loop, P‐loop, and αC‐helix. Compared to DFG‐in structures, DFG‐out structures are largely underrepresented in the Protein Data Bank (PDB). Thus, structure‐based drug design efforts for DFG‐out inhibitors may benefit from an efficient approach to generate an ensemble of DFG‐out structures. Accordingly, the presented modeling pipeline systematically generates homology models of kinases in several DFG‐out conformations based on a sophisticated creation of template structures that represent the major states of the flexible structural elements. Eighteen template classes were initially selected from all available kinase structures in the PDB and subsequently employed for modeling the entire kinome in different DFG‐out variants by fusing individual structural elements to multiple chimeric template structures. Molecular dynamics simulations revealed that conformational transitions between the different DFG‐out states generally do not occur within trajectories of a few hundred nanoseconds length. This underlines the benefits of the presented homology modeling pipeline to generate relevant conformations of “DFG‐out” kinase structures for subsequent in silico screening or binding site analysis studies.

Alkali-treated starches as a new class of templates for CaCO3 spherulite formation: Experimental and theoretical studies

Biomineralization is the study of minerals produced by biomaterials or living organisms, and it has attracted considerable attention in industry and fundamental research. Here, for the first time, the roles of alkali-treated mung bean (MB) and cassava (CS) starches as templates for calcium carbonate (CaCO3) spherulite formation were investigated by bulk rheology and molecular dynamics (MD) simulations. The rheological results, including yield stress, flow behavior index, consistency coefficient, and hysteresis area of alkali-treated MB and CS starch suspensions containing Ca2+, revealed that the Ca2+ induced the folding of starch chains via Ca2+ crosslinking, leading to aggregation of starch–Ca2+ complexes. After the starch films were fabricated from the suspensions, CaCO3 spherulites were observed inside the films. The size and number of the spherulites increased with increasing concentration of Ca2+ in the suspensions as a result of an increase in nucleation sites needed for spherulite growth. Moreover, to gain a deeper understanding of the interactions between Ca2+ and starch and of the formation of the nucleation sites for spherulite growth, MD simulations were performed. Our findings pave the way for the design of new biomaterial-based templates, which can efficiently control crystal shape and size of CaCO3.

Target Recognition of Synthetic Aperture Radar Images Based on Matching and Similarity Evaluation Between Binary Regions

This work uses the binary target region for synthetic aperture radar (SAR) automatic target recognition (ATR). Due to the differences of physical sizes and target shapes, the region residuals among the same classes and those between different targets are distributed in different manners. The Euclidean distance transform is then performed on the region residuals to further enhance such differences, which is beneficial for correctly discriminating different targets. Based on the results, a similarity measure is formed according to the distribution characteristics of the region residuals. In addition, the designed similarity measure considers the possible variations of the target region caused by the nuisance conditions like noise corruption, partial occlusion, etc. Owing to its robustness and comprehensiveness, the similarity measure is applied to target recognition by comparing the test sample with different kinds of template classes. Experiments are undertaken on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset under standard operating condition (SOC) and some representative extended operating conditions (EOCs), i.e., configuration variants, depression angle variation, noise corruption, resolution variation and partial occlusion. Moreover, the proposed method is examined under reduced training size and possible azimuth estimation errors for a comprehensive evaluation. The experimental results demonstrate the superiority of the proposed method in comparison with several baseline algorithms in SAR ATR.