Large Background Research Articles

Quantify total activity by volume-of-interest expansion with clinical SPECT/CT systems, a phantom study.

Quantitative measurements of activity in SPECT are important for radioisotope therapy planning and disease diagnosis. The aim of this manuscript is to develop a robust method to quantify the total activity in a volume-of-interest (VOI) of different quantitative SPECT reconstructions and validate the estimation accuracy. We customized an IEC body phantom using 3D printing technology and made six sphere inserts of 1-6cm in diameter with at least 3cm separation. The activity concentration within the spheres was in the range of patient lesion/organ activity. The background activity was then increased from zero to a sphere/background activity concentration of 8:1, 4:1, and 2:1. SPECT data were acquired with Philips Brightview and GE Discovery 670 SPECT/computed tomography (CT) systems under clinical acquisition protocols. Quantitative SPECT images were reconstructed with Hermes SUV-SPECT (both Philips and GE data) and GE Q.Metrix (GE data only). The quantitative SPECT reconstructions are iterative with scatter, CT attenuation correction, and resolution recovery. We quantified the total activity by expanding the sphere VOI to include a spill-out region. Background correction was applied by sampling a region outside the spill-out region. The true fractions (TFs) (total activity/true activity) were measured for all six spheres for all SPECT acquisitions. The TF is close to 100% for 2-6cm spheres for zero background, 8:1 and 4:1 sphere/background activity ratios. The TF was found to be unreliable for the 1-cm sphere because of the limit of phantom design. TF accuracy for 2:1 sphere/background ratio was degraded due to significantly large background, inadequate scatter correction and detector count loss. The results demonstrated that the proposed quantification method is accurate for objects of different sizes in currently clinical quantitative reconstruction and has the potential for improving the accuracy for therapeutic treatment planning or radiation dosimetry calculations.

Detecting the chiral magnetic effect via deep learning

The search of chiral magnetic effect (CME) in heavy-ion collisions has attracted long-term attentions. Multiple observables have been proposed but all suffer from obstacles due to large background contaminations. In this Letter, we construct an observable-independent CME-meter based on a deep convolutional neural network. After trained over data set generated by a multiphase transport model, the CME-meter shows high accuracy in recognizing the CME-featured charge separation from the final-state pion spectra. It also exhibits remarkable robustness to diverse conditions including different collision energies, centralities, and elliptic flow backgrounds. In a transfer learning manner, the CME-meter is validated in isobaric collision systems, showing good transferability among different colliding systems. Based on variational approaches, we utilize the DeepDream method to derive the most responsive CME-spectra that demonstrates the physical contents the machine learns.

Glioblastoma-derived extracellular vesicle subpopulations following 5-aminolevulinic acid treatment bear diagnostic implications.

Liquid biopsy is a minimally invasive alternative to surgical biopsy, encompassing different analytes including extracellular vesicles (EVs), circulating tumour cells (CTCs), circulating tumour DNA (ctDNA), proteins, and metabolites. EVs are released by virtually all cells, but at a higher rate by faster cycling, malignant cells. They encapsulate cargo native to the originating cell and can thus provide a window into the tumour landscape. EVs are often analysed in bulk which hinders the analysis of rare, tumour-specific EV subpopulations from the large host EV background. Here, we fractionated EV subpopulations in vitro and in vivo and characterized their phenotype and generic cargo. We used 5-aminolevulinic acid (5-ALA) to induce release of endogenously fluorescent tumour-specific EVs (EVPpIX ). Analysis of five different subpopulations (EVPpIX , EVCD63 , EVCD9 , EVEGFR , EVCFDA ) from glioblastoma (GBM) cell lines revealed unique transcriptome profiles, with the EVPpIX transcriptome demonstrating closer alignment to tumorigenic processes over the other subpopulations. Similarly, isolation of tumour-specific EVs from GBM patient plasma showed enrichment in GBM-associated genes, when compared to bulk EVs from plasma. We propose that fractionation of EV populations facilitates detection and isolation of tumour-specific EVs for disease monitoring.

Design and Testing of a Prototype Eddy Current Actuated Valve for the ITER Shattered Pellet Injection System

Reliably mitigating disruptions is essential for ITER to meet its long-term operational research plan without damage to the in-vessel components. Currently, the shattered pellet injection (SPI) technique is the most effective radiator of thermal energy and has been chosen for the baseline disruption mitigation system (DMS) for ITER. The SPI process uses cryogenic temperatures to desublimate material into the barrel of a pipe gun forming a solid cylindrical pellet. Pellets for ITER will initially be hydrogen and hydrogen–neon mixtures. Once formed, pellets are dislodged and accelerated using high-pressure gas (40–60 bar) delivered by a fast-opening valve. The solenoid valves currently used for SPI experiments will not operate in an ITER environment due to the large background magnetic field. An ITER prototype fast-opening valve, called a flyer plate valve (FPV), has been designed and has undergone a wide range of testing. The FPV operates by pulsing current through a pancake coil that is closely coupled with a “flyer plate.” The flyer plate is an aluminum plate in which eddy currents are generated creating a repulsive force from the pancake coil. The force generated in the flyer plate rapidly lifts the valve tip off the seat and delivers a pulse of gas to the rear of the pellet, breaking it free from the barrel and accelerating the pellet downstream to its intended target. The design of the valve has been iterated on over the lifetime of this project, as the DMS for ITER shifted from massive gas injection (MGI) to SPI. The most recent design has been tested, and operational ranges have been mapped. The valve must survive 3000+ cycles in an ITER-like magnetic field. The principal functional requirement of this valve is to reliably dislodge and accelerate hydrogen (or H–Ne mixture) pellets into ITER. The valve was mated with an ITER SPI test stand and has been shown to be capable of launching pellets reliably. The valve and power supply design will be discussed in this article, along with the various testing setups used to determine the feasibility of this valve for use on ITER.

A learning-based crack defect detection and 3D localization framework for automated fluorescent magnetic particle inspection

With the rapid development of deep learning, target detection and segmentation in dirty backgrounds have been readily available. Fluorescent magnetic particle inspection (MPI) based on such technology will be a promising alternative for automated crack defect inspection. Most previous studies in MPI have focused only on crack detection. Instead, we frame it as a crack 3D localization problem, since cracks on non-machined surfaces of metal parts need to be polished and re-inspected, which relies on their 3D positions. Although good results were obtained in defect detection, it is still challenging to perform pixel-level segmentation of micro-cracks from the large background to obtain crack 2D pixels for 3D reconstruction. This paper proposes a two-stage convolutional neural network (CNN) method for metal parts crack defect detection and segmentation at the image-pixel level. The first stage detects and crops the potential cracks to a small area, and the second stage can learn the context of cracks in the detected patches. A window-based stereo matching method is then used to find matching pixels of cracks and to map crack image plane points to the 3D world points. We also illustrate the entire system’s model deployment and signaling work to apply these methods. Both computational and experimental results based on the system are presented for validation. The training precision of target detection reaches 96.3%, its average precision reaches 85.4%, and the average precision reaches 98.3% when the Intersection-over-Union (IoU) threshold is 0.5. The Dice score reaches 94% in pixel-level segmentation, and the average precision is 99.3% when the probability threshold is set to 0.5. The corresponding efficiency reaches 19 FPS and 18 FPS, and the mean absolute errors of 3D coordinates of reconstructed crack defects are all within 1 mm in X-, Y- and Z- directions.

Application of Machine Learning Algorithms for Searching BSM Higgs Bosons Decaying to a Pair of Bottom Quarks

Although the discovery of the 125 GeV Higgs boson confirms the Higgs mechanism of the Standard Model (SM), many theories beyond the SM have been introduced to address several phenomena yet to be explained by the SM. For instance, the 2-Higgs Doublet Models is the simplest extension of the SM Higgs sector and predicting the existence of additional Higgs bosons at different states. The aim of this study is to search for machine learning (ML) algorithms which have been widely used in High Energy Physics. This will improve the sensitivity of the search for BSM Higgs bosons produced in association with a bottom quark () that then decays into a pair of bottom quarks (); the predominant decay channel of the Higgs boson, though, buried by a large multi-jet background process. In this study, we train 2 different ML algorithms: Tree-based models and Neural Networks, to classify signal and background events collected by the Compact Muon Solenoid detector from proton-proton collisions at 13 TeV. The evaluation metrics are calculated to provide classification efficiencies from different models. The results show that the classification of signal and background processes can be improved using ML techniques. Neural Networks reported the highest AUC score of 0.951 which is comparable with Adaptive Boosting model, while Decision Trees (DTs) and Random Forest models slightly underperformed by 2 - 3 %. We therefore can make use of the trained models as signal vs background classifiers to perform further statistical analysis searches for BSM Higgs bosons. GRAPHICAL ABSTRACT

Enhanced Chiral Sensing at the Few-Molecule Level Using Negative Index Metamaterial Plasmonic Nanocuvettes.

Chirality is a fundamental property of biological molecules and some pharmaceutical molecules. Chiral molecules have a pair of chiral isomers (enantiomers) with opposite handedness. Although both enantiomers of the same molecule have identical chemical and physical properties, one enantiomer may be toxic to living organisms while the other one is harmless. The detection of these enantiomers is done using their small differential absorption between right and left circularly polarized light, known as circular dichroism (CD). Considering the macroscopic size of these molecules, combined with their small differential absorption, the obtained CD signal is very small, imposing a severe limitation on the minimal concentration that can be detected. Chiral plasmonic and metamaterial structures have been used to enhance the sensitivity of CD measurements by orders of magnitude through chiral density hot spots (super chiral fields). However, the large background signal due to these structures' intrinsic chirality limits the effectiveness of these methods. Contrary to absorption-based chiral sensing measurements (CD), fluorescence detection circular dichroism (FDCD) sensing can greatly improve chiral measurement sensitivity, down to the ultimate limit of a few and even a single chiral molecule. Like differential absorption, differential fluorescence also produces a weak signal at the few-chiral-molecule limit. However, here we demonstrate a negative-index metamaterial (NIM) cavity that acts as a "plasmonic nanocuvette" with globally enhanced volume super chiral fields. Moreover, the achiral structure of the plasmonic nanocuvette allows for completely background-free chiral sensing. We show that with NIM-cavity-enhanced FDCD, we can detect as low as a few tens of chiral molecules, well within the zeptomole range.

Careful least squares active noise control with no prior secondary path model

SummaryIn feedforward active noise and vibration control (ANVC) or active noise control (ANC), with online secondary path modeling, fast converging overall modeling algorithms may give high modeling errors. These errors are due to the highly correlated inputs, namely the reference and antinoise signals. These high modeling errors can result in poor performance if ignored. However, it is possible to consider these modeling errors in the control filter design, resulting in a careful controller and good performance, as proposed in this work. Careful control is a dual controller feature, which is the solution to the optimal control problem, but this has not been proposed before in the context of ANC. The modeling error covariance matrix (Sigma(n)) is available in the Kalman filter. However, this requires the knowledge of the time‐varying measurement noise power q(n) and state noise. Instead, this work uses a different approach in which the corresponding linear system is solved in a non‐recursive manner. The resulting maximum error is used for a worst‐case estimate of q(n), and, as it turns out, this can be used to get a worst‐case estimate of Sigma(n). A lower control filter update frequency and other optimizations keep computational complexity similar to other approaches. The resulting algorithm has very few easily selected parameters, good performance, does not require any prior secondary path estimate, and adapts to slow and sudden changes in the physical paths without loss of performance or noise overshoots. However, performance can be impaired by large background noise levels.

Single-beam integrated hybrid optical pumping spin exchange relaxation free magnetometer for biomedical applications

An ingenious approach to accomplish the high signal strengthen and relatively homogeneous spin polarization has been presented in a hybrid optical pumping spin-exchange-relaxation-free atomic magnetometer only utilizing single-beam configuration. We have experimentally demonstrated an approximately three-fold enhancement of the output signal at the optimal spin polarization by optically pumping the thin vapor due to the same spin evolution behavior of the two different kinds of vapor atoms. Eventually, a measuring sensitivity of 30 fT/Hz1/2 was achieved combined with the homemade differential detection system for attenuating large background offset and suppressing optical power noise. This scheme provides a prospect for the development of ultra-highly sensitive and chip-scale atomic magnetometer for the applications that desire both high signal-to-noise ratio and uniform spin polarization, such as magnetocardiography and magnetoencephalography.

A Weyl semimetal WTe2/GaAs 2D/3D Schottky diode with high rectification ratio and unique photocurrent behavior

Since the discovery of Dirac semimetal graphene, two-dimensional (2D) Weyl semimetals (WSMs) have been widely used in low-energy photon detection, polarization imaging, and other systems due to their rich physical characteristics, such as unique nonlinear optical structure, topological nontrivial electronic structure, thickness-tunable bandgap, high electric conductivity, and so on. However, it is difficult to detect the photocurrent signal at room temperature because of its large intrinsic background current. Fortunately, the fabrication of a van der Waals (vdW) heterojunction based on WSM can effectively suppress the background current, greatly extend the detection range, improve the light absorption efficiency, and increase the response speed. Herein, the 2D type-II WSM 1T′-WTe2/bulk GaAs vdW vertical Schottky diode is investigated. Benefiting from the lateral built-in electric field of 260 meV and zero-bandgap structure of 52 nm 1T′-WTe2, it delivers a rectifying ratio over 103 and can respond to the wavelength range of 400–1100 nm. Particularly, when the light power density is 0.02 mW/cm2, the maximum photoresponsivity (R) and specific detectivity (D*) under 808 nm are 298 mA/W and 1.70 × 1012 Jones, respectively. Meanwhile, the Ilight/Idark ratio and response time are 103 and 520/540 μs, respectively. Moreover, an abnormal negative response behavior can be observed with thin WTe2 (11 nm) under 1064 nm illumination because of the open surface bandgap. It is suggested that such 2D WTe2/GaAs mixed-dimensional vdW structure can be extended to other WSM/3D semiconductor junctions and used in fast response and wide broadband spectrum photodetectors' arrays.

Accurate detection of organophosphorus pesticides based on covalent organic framework nanofiber with a turn-on strategy

In this work, an electroactive COFTab-Dva nanofiber with abundant vinyl group was prepared and used to fabricate electrochemical biosensors for O,O-dimethyl-O-2,2-dichlorovinylphosphate (DDVP), an organophosphorus pesticide. The COFTab-Dva nanofiber with large surface area and plentiful vinyl group facilitate the immobilization of AChE and enrich the thiocholine. Thanks to that, a low potential turn-on DDVP electrochemical sensor with [Ru(bpy)3]2+ as the detection probe was proposed for the first time. Meanwhile, a ratiometric DDVP sensor with the oxidation peak of thiocholine as response signal, the oxidation peak of COFTab-Dva as reference signal, and a low potential turn-off electrochemical sensor with oxidation peak of [Fe(CN)6]3− as response signal were proposed as the contrast experiment. The performances of these sensors were investigated and compared in detail. The results reveal the low potential turn-on DDVP sensor with [Ru(bpy)3]2+ as the detection probe can not only effectively improve the selectivity, but also avoid the influence of large background current, providing a new and effective method for pesticide detection.

Detector optimization to reduce the cosmogenic neutron backgrounds in the TAO experiment

Short-baseline reactor antineutrino experiments with shallow overburden usually have large cosmogenic neutron backgrounds. The Taishan Antineutrino Observatory (TAO) is a ton-level liquid scintillator detector located at about 30 m from a core of the Taishan Nuclear Power Plant. It will measure the reactor antineutrino spectrum with high precision and high energy resolution to provide a reference spectrum for JUNO and other reactor antineutrino experiments, and provide a benchmark measurement to test nuclear databases. Background is one of the critical concerns of TAO since the overburden is just 10 meter-water-equivalent. The cosmogenic neutron background was estimated to be ∼10% of signals [1]. With detailed Monte Carlo simulations, we propose several measures in this work to reduce the neutron backgrounds, including doping Gadolinium in the buffer liquid, adding a polyethylene layer above the bottom lead shield, and optimization of the veto strategy. With these improvements, the neutron background-to-signal ratio can be reduced to ∼2%, and might be further suppressed with pulse shape discrimination.

Experimental and computational insights into the anomalous thermal expansion of (NH4)ReO4

The temperature dependence of the structure and the ground state properties of scheelite-type NH4ReO4 have been studied using neutron powder diffraction (NPD) and Density Functional Theory (DFT), respectively. Despite the large incoherent background in the experimental NPD, associated with the presence of hydrogen, accurate and precise structural parameters were obtained. Comparison of the results of the NPD and DFT studies shows that the observed anomalous thermal contraction in NH4ReO4 is a consequence of thermally induced rotational disorder of the NH4 groups. Comparing the experimentally determined and optimized structures reveals deformation of the NH4 tetrahedra that is responsible for the unusual tetragonal distortion of this material. The Raman spectra of NH4ReO4 is presented and the modes are assigned based on the DFT calculations.

An estimate of dibaryon production in the process of at anda facility* *Supported by the National Natural Science Foundation of China (11475192), the Sino-German CRC 110 "Symmetries and the Emergence of Structure in QCD" project by NSFC (12070131001), the Key Research Program of Frontier Sciences, CAS (Y7292610K1), and the National Key Research and Development Program of China (2020YFA0406300), IHEP Innovation

Although was first observed by WASA@COSY, its existence has yet to be further confirmed in different types of processes at other facilities. In this work, the possible production of the single dibaryon state in the process of in a future experiment at the anda facility is estimated. Following the method used in our previous study (Chin. Phys. C 46, 023105), a phenomenological Lagrangian approach is employed to study the single production. Based on the conclusions obtained by the non-relativistic constituent quark model, the cross section of the reaction via the intermediate state is estimated, which is in the order of . It is shown that the dominant contribution comes from the diagram with the subprocess. However, it is difficult to measure owing to the large background. Further, although the cross section of the diagram with the subprocess is small, i.e., only approximately of the total cross section or even smaller, the corresponding number of events is still sufficiently large and can be measured at anda because the outgoing and come from the same source .

Functional lightweight polystyrene@polydopamine nanoparticle for high-performance ELISA

Nanoparticles are usually used as carrier to load more antibody and enzyme to improve the sensitivity of enzyme-linked immunosorbent assay (ELISA). However, limited by high density and complicated modification procedure, the traditional nanoparticles such as Au nanoparticles (AuNPs) usually induce large background signal and poor reproducibility in ELISA. In this work, functional lightweigh nanoparticle polystyrene@polydopamine (PS@PDA) was prepared and induced as the carrier of detection antibody and horseradish peroxidase (HRP) to form PS@PDA@Ab2/HRP biojungates. The appropriate density (close to water) and good hydropilicity ensure the good dispersion of PS@PDA@Ab2/HRP in solution, preventing the physical sedimention and decreasing the background signal even though the bioconjugate's size is close to 200 nm. The large surface area and abundant active group from PDA facilitate the loading of detection antibody and HRP, improving the loading efficiency and stability of biojungates. Based on it, taking interleukin-17A (IL-17A, a biomarker of psoriasis) as the detection target, we developed a PS@PDA-based sandwich ELISA, achieving a sensitive dynamic range from 0.3 to 80 pg/mL and a detection limit of 0.2 pg/mL. Furthermore, the contents of IL-17A were assayed successfully in 10-fold diluted serum samples from psoriasis patients. Compared with those commercial or AuNP-based ELISA, our PS@PDA-based ELISA method exhibits higher sensitivity, lower background interference, and higher stability, which will significantly improve the application of ELISA in the low-abundant biomolecule assays.

SIM-MFR: Spatial interactions mechanisms based multi-feature representation for background modeling

Moving object detection is frequently used as a springboard for advanced computer vision analysis in complex scenes. Nevertheless, due to unstable changes in the background, most existing background model hardly maintain superior performance. To this concern, we propose a novel pixel-level background model that has three innovations. First, we introduce K-means to directly model the spatiotemporal dependencies between pixels. These dependencies are exploited to discover static core information in the high-frequency changing spatial domain, resulting in excellent property in dynamic backgrounds. Besides, the notion of complementarity is taken as a feature selection criterion. In multi-feature model, the ability to supervise each other between features is important in the ambiguity challenges, e.g., shadow. Finally, feature models recommend each other in the update mechanism, and the diffusion rate of effective information in each feature model can be maximized by finding the best candidate feature. By virtue of this mechanism, model can be updated efficiently when large background migration occurs, e.g., PTZ. Experimental results on some standard benchmarks show that SIM-MFR can achieve promising performance compared to some state-of-the-art approaches.

Research on the On-orbit Background of the Hard X-Ray Imager Onboard ASO-S

The space environment background of various particle fluxes of the Hard X-ray Imager (HXI), one of the payloads of the Advanced Space-based Solar Observatory (ASO-S) spacecraft, is investigated and presented. Different approaches are used to obtain the input information on various space environment particles (protons, alpha particles, electrons, positrons, neutrons, and photons). Some special regions (SAA and radiation belt) are also taken into account. The findings indicate that electrons are the primary background source in the radiation belt. Due to the large background flux generated by electrons, HXI cannot effectively observe solar flares in the radiation belt. Outside the radiation belt, primary protons and albedo photons are the main sources of background at low and high magnetic latitudes respectively. The statistical analysis of the flare and background spectra shows that the errors of the flare energy spectrum observation are mainly concentrated in the high energy band, and the detector still has a certain spectrum observation capability for flares of C-class and below in the low energy band of the non-radiation belt. The imaging observation of flares of C-class and below is significantly affected by the accuracy of background subtraction. The energy band with the best signal-to-noise ratio is from 10 to 50 keV, which can be used to monitor the formation and class of flares.

Analytical Techniques for Developing Argumentative Writing in STEM: A Pilot Study

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> Demonstrates how to use experiential learning (EL) to improve argumentative writing. Presents the design and development of a natural language processing (NLP) application for aiding instructors in providing feedback on student essays. Discusses how EL combined with automated support provides an analytical approach to improving written-communication skills. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</i> High-quality, timely, feedback is an effective way to improve students’ writing. However, large class sizes and limited instructor backgrounds often make formative feedback impossible. Recent trends, including lowering entry requirements, have added to these challenges. Assistive technologies for implementing inclusive education provide viable solutions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Research Questions:</i> 1) How and why can EL be used to develop argumentative writing skills in university STEM students? 2) How can technologies be developed to support using EL in teaching writing? and 3) How might the holistic impact of using such analytic techniques be evaluated? <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methodology:</i> Participants in an EL project were assigned two essays in sequence. They were given instructions on making good arguments and shown how to use an analytic rubric to maximize their scores. The essays were hand scored by tutors who provided scores for each dimension of the rubric. Subsequently, the content and argumentation of the essays were analyzed using NLP techniques to obtain independent scores. Qualitative data were also collected. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Findings:</i> The project produced transformative writing experiences for the participants. It showed how analytical techniques help improve writing skills and how relevant automated instructor assistance can be developed using NLP technologies.

Three-Dimensional Atomically Resolved Analytical Imaging with a Field Ion Microscope

Abstract Atom probe tomography (APT) helps elucidate the link between the nanoscale chemical variations and physical properties, but it has a limited structural resolution. Field ion microscopy (FIM), a predecessor technique to APT, is capable of attaining atomic resolution along certain sets of crystallographic planes albeit at the expense of elemental identification. We demonstrate how two commercially available atom probe instruments, one with a straight flight path and one fitted with a reflectron lens, can be used to acquire time-of-flight mass spectrometry data concomitant with a FIM experiment. We outline various experimental protocols making the use of temporal and spatial correlations to best discriminate field-evaporated signals from the large field-ionized background signal, demonstrating an unsophisticated yet efficient data mining strategy to provide this discrimination. We discuss the remaining experimental challenges that need to be addressed, notably concerned with accurate detection and identification of individual field-evaporated ions contained within the high field-ionized flux that contributes to a FIM image. Our hybrid experimental approach can, in principle, exhibit true atomic resolution with elemental discrimination capabilities, neither of which atom probe nor FIM can individually fully deliver—thereby making this new approach, here broadly termed analytical field ion microscopy (aFIM), unique.

Kernelized dynamic convolution routing in spatial and channel interaction for attentive concrete defect recognition

Image/video based defect recognition is a crucial task in automating visual inspection of concrete structures. Although some progress has been made to automatically recognize defects in concrete structural images, significant challenges still exist. In this work, we propose a deep convolutional neural network architecture that embeds novel spatial-channel interaction based concurrent attention (SCA) for multi-target, multi-class defect recognition. SCA module stems from novel kernel salient feature (KSF) encoder that captures higher-order features with robust discriminative representation of concrete defects. KSF encoder incorporates kernelized convolution followed by dynamic routing operation to be used as the primary building block. The proposed CSDNet architecture is able to apportion higher weightage in defective regions while suppressing large background area (healthy region), thereby improving recognition performance of overlapping defects in concrete structures. Experimental results and ablation study on four large benchmark datasets show consistent superiority of our proposed network as compared to current state-of-the-art methodologies. This end-to-end trainable architecture can be augmented with unmanned aerial vehicles (UAVs) to monitor health of massive infrastructures to leverage high concrete defect recognition performance.