A locally-connected neural network for fingerprint recognition
Fingerprint recognition is typically done by matching graphs or templates of extracted minutia from whole fingerprint images.
neural network pattern recognition fingerprint↗
SEARCH · Engineering Papers
Results for “pattern recognition”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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The three-dimensional structure and recognition mechanism of Manduca sexta peptidoglycan recognition protein-1
Peptidoglycan recognition proteins (PGRPs) recognize bacteria through their unique cell wall constituent, peptidoglycans (PGs). PGRPs are conserved from insects to mammals and all function in antibacterial defense. In the tobacco hornworm Manduca sexta, PGRP1 and microbe binding protein (MBP) interact with PGs and hemolymph protease-14 precursor (proHP14) to yield active HP14. HP14 triggers a serine protease network that produces active phenoloxidase (PO), Spätzle, and other cytokines to stimulate immune responses. PGRP1 binds preferentially to diaminopimelic acid (DAP)-PGs of Gram-negative bacteria and Gram-positive Bacillus and Clostridium species than Lys-PGs of other Gram-positive bacteria. In this study, we synthesized DAP- and Lys-muramyl pentapeptide (MPP) and monitored their associations with M. sexta PGRP1 by surface plasmon resonance. The K d values (0.57 μM for DAP-MPP and 45.6 μM for Lys-MPP) agree with the differential recognition of DAP- and Lys-PGs. To reveal its structural basis, we produced the PGRP1 in insect cells and determined its structure at a resolution of 2.1 Å. The protein adopts a fold similar to those from other PGRPs with a classical L-shaped PG-binding groove. A unique loop lining the shallow groove suggests a different ligand-binding mechanism. In summary, this study provided new insights into the PG recognition by PGRPs, a critical first step that initiates the serine protease cascade.
59 BASIC BIOLOGICAL SCIENCES↗
Recognition of Stochastic System States for Continuous-Discrete Observations with Sliding Memory
The paper describes the problem of finding the likelihood ratio for specific problem of stochastic system recognition in continuous time to member functions within continuous-discrete time, which depend not only on current, but also on arbitrary numbers of previous non-observable process values.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Capture of an Object on the Basis of Tactile Surface Recognition
A method is proposed for the capture of an object on the basis of fuzzy information regarding the form and physical characteristics of its surface. Exponential or robust filters may be employed. Decisions rules are formulated regarding the tactile recognition of target points on the surface. The condition for successful capture of the object is presented. An algorithm is proposed for the formulation of a target point on the motion plane of active capture elements.
42 ENGINEERING↗
The leucine-rich repeats in allelic barley MLA immune receptors define specificity towards sequence-unrelated powdery mildew avirulence effectors with a predicted common RNase-like fold
Nucleotide-binding domain leucine-rich repeat-containing receptors (NLRs) in plants can detect avirulence (AVR) effectors of pathogenic microbes. The Mildew locus a (Mla) NLR gene has been shown to confer resistance against diverse fungal pathogens in cereal crops. In barley, Mla has undergone allelic diversification in the host population and confers isolate-specific immunity against the powdery mildew-causing fungal pathogen Blumeria graminis forma specialis hordei (Bgh). We previously isolated the Bgh effectors AVR A1 , AVR A7 , AVR A9 , AVR A13 , and allelic AVR A10 /AVR A22 , which are recognized by matching MLA1, MLA7, MLA9, MLA13, MLA10 and MLA22, respectively. Here, we extend our knowledge of the Bgh effector repertoire by isolating the AVR A6 effector, which belongs to the family of catalytically inactive RNase-Like Proteins expressed in Haustoria (RALPHs). Using structural prediction, we also identified RNase-like folds in AVR A1 , AVR A7 , AVR A10 /AVR A22 , and AVR A13 , suggesting that allelic MLA recognition specificities could detect structurally related avirulence effectors. To better understand the mechanism underlying the recognition of effectors by MLAs, we deployed chimeric MLA1 and MLA6, as well as chimeric MLA10 and MLA22 receptors in plant co-expression assays, which showed that the recognition specificity for AVR A1 and AVR A6 as well as allelic AVR A10 and AVR A22 is largely determined by the receptors’ C-terminal leucine-rich repeats (LRRs). The design of avirulence effector hybrids allowed us to identify four specific AVR A10 and five specific AVR A22 aa residues that are necessary to confer MLA10- and MLA22-specific recognition, respectively. This suggests that the MLA LRR mediates isolate-specific recognition of structurally related AVR A effectors. Thus, functional diversification of multi-allelic MLA receptors may be driven by a common structural effector scaffold, which could be facilitated by proliferation of the RALPH effector family in the pathogen genome.
59 BASIC BIOLOGICAL SCIENCES↗
Block segmentation in feature space for realtime object detection in high granularity images
Computer vision has applications in object detection, image recognition and classification, and object tracking. One of the challenges of computer vision is the presence of useful information at multiple distance scales. Filtering techniques may sacrifice details at small scales in order to prioritize the analysis of large-scale features of the image. We present a strategy for coarse-graining multidimensional data while maintaining fine-grained detail for subsequent analysis. The algorithm is based on fixed-size block segmentation in the feature space. We apply this strategy to solve the long-standing challenge of detecting particle trajectories at the Large Hadron Collider in real time.
Computer vision↗
A Convolutional Neural Network Neutrino Event Classifier
Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Feature Extraction and Selection Strategies for Automated Target Recognition
Several feature extraction and selection methods for an existing automatic target recognition (ATR) system using JPLs Grayscale Optical Correlator (GOC) and Optimal Trade-Off Maximum Average Correlation Height (OT-MACH) filter were tested using MATLAB. The ATR system is composed of three stages: a cursory region of-interest (ROI) search using the GOC and OT-MACH filter, a feature extraction and selection stage, and a final classification stage. Feature extraction and selection concerns transforming potential target data into more useful forms as well as selecting important subsets of that data which may aide in detection and classification. The strategies tested were built around two popular extraction methods: Principal Component Analysis (PCA) and Independent Component Analysis (ICA). Performance was measured based on the classification accuracy and free-response receiver operating characteristic (FROC) output of a support vector machine(SVM) and a neural net (NN) classifier.
computer vision↗
Semi-supervised permutation invariant particle-level anomaly detection
The development of analysis methods to distinguish potential beyond the Standard Model phenomena in a model-agnostic way can significantly enhance the discovery reach in collider experiments. However, the typical machine learning (ML) algorithms employed for this task require fixed length and ordered inputs that break the natural permutation invariance in collision events. To address this, a semi-supervised anomaly detection tool is presented that takes a variable number of particle-level inputs and leverages a signal model to encode this information into a permutation invariant, event-level representation via supervised training with a Particle Flow Network (PFN). Data events are then encoded into this representation and given as input to an autoencoder for unsupervised ANomaly deTEction on particLe flOw latent sPacE (ANTELOPE), classifying anomalous events based on a low-level and permutation invariant input modeling. Performance of the ANTELOPE architecture is evaluated on simulated samples of hadronic processes in a high energy collider experiment, showing good capability to distinguish disparate models of new physics.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
A Common Tracking Software Project
Abstract The reconstruction of the trajectories of charged particles, or track reconstruction, is a key computational challenge for particle and nuclear physics experiments. While the tuning of track reconstruction algorithms can depend strongly on details of the detector geometry, the algorithms currently in use by experiments share many common features. At the same time, the intense environment of the High-Luminosity LHC accelerator and other future experiments is expected to put even greater computational stress on track reconstruction software, motivating the development of more performant algorithms. We present here A Common Tracking Software (ACTS) toolkit, which draws on the experience with track reconstruction algorithms in the ATLAS experiment and presents them in an experiment-independent and framework-independent toolkit. It provides a set of high-level track reconstruction tools which are agnostic to the details of the detection technologies and magnetic field configuration and tested for strict thread-safety to support multi-threaded event processing. We discuss the conceptual design and technical implementation of ACTS, selected applications and performance of ACTS, and the lessons learned.
97 MATHEMATICS AND COMPUTING↗
Charged particle tracking with quantum annealing optimization
Abstract At the High Luminosity Large Hadron Collider (HL-LHC), traditional track reconstruction techniques that are critical for physics analysis will need to be upgraded to scale with track density. Quantum annealing has shown promise in its ability to solve combinatorial optimization problems amidst an ongoing effort to establish evidence of a quantum speedup. As a step towards exploiting such potential speedup, we investigate a track reconstruction approach by adapting the existing geometric Denby-Peterson (Hopfield) network method to the quantum annealing framework for HL-LHC conditions. We develop additional techniques to embed the problem onto existing and near-term quantum annealing hardware. Results using simulated annealing and quantum annealing with the D-Wave 2X system on the TrackML open dataset are presented, demonstrating the successful application of a quantum annealing algorithm to the track reconstruction challenge. We find that combinatorial optimization problems can effectively reconstruct tracks, suggesting possible applications for fast hardware-specific implementations at the HL-LHC while leaving open the possibility of a quantum speedup for tracking.
97 MATHEMATICS AND COMPUTING↗
Analyzing acoustic emission data to identify cracking modes in cement paste using an artificial neural network
This research is focused on the identification of cracking mechanisms for cement paste using acoustic emission data, recorded from compression and notched four-point bending tests. A procedure is developed for analyzing the data by employing an agglomerative hierarchical clustering method, an artificial neural network, and a ray-tracing source location algorithm. An agglomerative hierarchical clustering method is utilized to cluster the AE data from a compression test using frequency-dependent features. A neural network is trained using the compression test data and applied to the AE data emitted during the four-point bending test. The clustered data from the four-point bending test is localized using a ray-tracing algorithm. Based on the occurrence and locations of the clustered events and signal feature analyses, potential cracking mechanisms are identified and assigned.
36 MATERIALS SCIENCE↗
RU-net for automatic characterization of TRISO fuel cross sections
During irradiation, phenomena such as kernel swelling and buffer densification may impact the performance of tristructural isotropic (TRISO) particle fuel. Post-irradiation microscopy is often used to identify these irradiation-induced morphologic changes. However, each fuel compact generally contains thousands of TRISO particles. Manually performing the work to get statistical information on these phenomena is cumbersome and subjective. Here, to reduce the subjectivity inherent in that process and to accelerate data analysis, we used convolutional neural networks (CNNs) to automatically segment cross-sectional images of microscopic TRISO layers. CNNs are a class of machine-learning algorithms specifically designed for processing structured grid data. They have gained popularity in recent years due to their remarkable performance in various computer vision tasks, including image classification, object detection, and image segmentation. In this research, we generated a large irradiated TRISO layer dataset with more than 2,000 microscopic images of cross-sectional TRISO particles and the corresponding annotated images. Based on these annotated images, we used different CNNs to automatically segment different TRISO layers. These CNNs include RU-Net (developed in this study), as well as three existing architectures: U-Net, Residual Network (ResNet), and Attention U-Net. The preliminary results show that the model based on RU-Net performs best in terms of Intersection over Union (IoU). Using CNN models, we can expedite the analysis of TRISO particle cross sections, significantly reducing the manual labor involved and improving the objectivity of the segmentation results.
11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Performance of the LHCb muon detector in Run 3
In Run 3 of the LHC, the instantaneous luminosity at the LHCb interaction point has been increased by a factor of five, from to $\mathscr{L}$ = 4 x 10 32 cm -2 s -2 to $\mathscr{L}$ = 2 x 10 33 cm -2 s -1 . Several hardware interventions, including a complete overhaul of the readout electronics, have been carried out on the muon detector. The muon identification algorithms in the software trigger were improved with the aim of ensuring Run 2 performance under a higher particle rate. The operation and calibration of the upgraded muon detector are presented. The muon detection efficiency and muon identification performance are evaluated on data calibration samples collected during the year 2024. A muon identification efficiency above 90% with sub-percent hadron misidentification probability is achieved by exploiting the pattern of hits in the muon detector.
Gaseous detectors↗
Discovering State-Parameter Mappings in Subsurface Models Using Generative Adversarial Networks
A fundamental problem in geophysical modeling is related to the identification and approximation of causal structures among physical processes. However, resolving the bidirectional mappings between physical parameters and model state variables (i.e., solving the forward and inverse problems) is challenging, especially when parameter dimensionality is high. Deep learning has opened a new door toward knowledge representation and complex pattern identification. In particular, the recently introduced generative adversarial networks (GANs) hold strong promises in learning cross-domain mappings for image translation. This study presents a state-parameter identification GAN (SPID-GAN) for simultaneously learning bidirectional mappings between a high-dimensional parameter space and the corresponding model state space. SPID-GAN is demonstrated using a series of representative problems from subsurface flow modeling. Results show that SPID-GAN achieves satisfactory performance in identifying the bidirectional state-parameter mappings, providing a new deep-learning-based, knowledge representation paradigm for a wide array of complex geophysical problems.
58 GEOSCIENCES↗
Development of human cGAS-specific small-molecule inhibitors for repression of dsDNA-triggered interferon expression
Cyclic GMP-AMP synthase (cGAS) is the primary sensor for aberrant intracellular dsDNA producing the cyclic dinucleotide cGAMP, a second messenger initiating cytokine production in subsets of myeloid lineage cell types. Therefore, inhibition of the enzyme cGAS may act anti-inflammatory. Here we report the discovery of human-cGAS-specific small-molecule inhibitors by high-throughput screening and the targeted medicinal chemistry optimization for two molecular scaffolds. Lead compounds from one scaffold co-crystallize with human cGAS and occupy the ATP- and GTP-binding active site. The specificity and potency of these drug candidates is further documented in human myeloid cells including primary macrophages. These novel cGAS inhibitors with cell-based activity will serve as probes into cGAS-dependent innate immune pathways and warrant future pharmacological studies for treatment of cGAS-dependent inflammatory diseases.
59 BASIC BIOLOGICAL SCIENCES↗
Topological background discrimination in the PandaX-III neutrinoless double beta decay experiment
The PandaX-III experiment plans to search for neutrinoless double beta decay (0ν ββ ) of 136 Xe in the China JinPing underground Laboratory (CJPL). The experiment will use a high pressure gaseous time projection chamber (TPC) to register both the energy and the electron track topology of an event. This article is devoted to demonstrate our particular detector setup capabilities for the identification of 0ν ββ and the consequent background reduction. As software tool we use REST, a framework developed for the reconstruction and simulation of TPC-based detector systems. We study the potential for background reduction by introducing appropriate parameters based on the properties of 0ν ββ events. We exploit for the first time not only the energy density of the electron track-ends, but also the electron scattering angles produced by an electron near the end of its trajectory. To implement this, we have added new algorithms for detector signal and track processing inside REST. Their assessment shows that background can be reduced by about 7 orders of magnitude while keeping 0ν ββ efficiency above 20% for the PandaX-III baseline readout scheme, a two-dimensional 3 mm pitch stripped readout. More generally, we use the potential of REST to handle 2D/3D data to assess the impact on signal-to-background significance at different detector granularities, and to validate the PandaX-III baseline choice. Finally, we demonstrate the additional potential to discriminate surface background events generated at the readout plane in the absence of to, by making use of event parameters related with the diffusion of electrons.
Majorana↗
Performance of photon reconstruction and identification with the CMS detector in proton-proton collisions at √s = 8 TeV
A description is provided of the performance of the CMS detector for photon reconstruction and identification in proton-proton collisions at a centre-of-mass energy of 8 TeV at the CERN LHC. Details are given on the reconstruction of photons from energy deposits in the electromagnetic calorimeter (ECAL) and the extraction of photon energy estimates. Furthermore, the reconstruction of electron tracks from photons that convert to electrons in the CMS tracker is also described, as is the optimization of the photon energy reconstruction and its accurate modelling in simulation, in the analysis of the Higgs boson decay into two photons. In the barrel section of the ECAL, an energy resolution of about 1% is achieved for unconverted or late-converting photons from H → γγ decays. Furthermore, different photon identification methods are discussed and their corresponding selection efficiencies in data are compared with those found in simulated events.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗