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DOE OSTI2022

Effectiveness of Deep Learning Trained on SynthCity Data for Urban Point-Cloud Classification

3D object recognition is one of the most popular areas of study in computer vision. Many of the more recent algorithms focus on indoor point clouds, classifying 3D geometric objects, and segmenting outdoor 3D scenes. One of the challenges of the classification pipeline is finding adequate and accurate training data. Hence, this article seeks to evaluate the accuracy of a synthetically generated data set called SynthCity, tested on two mobile laser-scan data sets. Varying levels of noise were applied to the training data to reflect varying levels of noise in different scanners. The chosen deep-learning algorithm was Kernel Point Convolution, a convolutional neural network that uses kernel points in Euclidean space for convolution weights.

Geology↗

DOE OSTI2018

ODDS: Real-Time Object Detection Using Depth Sensors on Embedded GPUs

Detecting objects that are carried when someone enters or exits a room is very useful for a wide range of smart building applications including safety, security, and energy efficiency. While there has been a significant amount of work on object recognition using large-scale RGB image datasets, RGB cameras are too privacy invasive in many smart building applications and they work poorly in the dark. Additionally, deep object detection networks require powerful and expensive GPUs. We propose a novel system that we call ODDS (Object Detector using a Depth Sensor) that can detect objects in real-time using only raw depth data on an embedded GPU, e.g., NVIDIA Jetson TX1. Hence, our solution is significantly less privacy invasive (even if the sensor is compromised) and less expensive, while maintaining a comparable accuracy with state of the art solutions. Specifically, we resort to training a deep convolutional neural network using raw depth images, with curriculum based learning to improve accuracy by considering the complexity and imbalance in object classes and developing a sparse coding based technique that speeds up the system ~2× with minimal loss of accuracy. Based on a complete implementation and real-world evaluation, we see ODDS achieve 80.14% mean average precision in object detection in real-time (5-6 FPS) on a Jetson TX1.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1973

Robot Vision

The operation of a system is described that is built both to model the vision of primate animals, including man, and serve as a pre-prototype of possible object recognition system. It was employed in a series of experiments to determine the practicability of matching left and right images of a scene to determine the range and form of objects. The experiments started with computer generated random-dot stereograms as inputs and progressed through random square stereograms to a real scene. The major problems were the elimination of spurious matches, between the left and right views, and the interpretation of ambiguous regions, on the left side of an object that can be viewed only by the left camera, and on the right side of an object that can be viewed only by the right camera.

Sutro, L. L.↗

DOE OSTI2021

Photon-Sparse, Poisson Light-Sheet Microscopy

Light-sheet microscopy has revolutionized bioimaging by enabling approximately an order of magnitude reduction in specimen irradiance compared to confocal imaging. Here, we introduce a light-sheet imaging system that enables an additional order of magnitude reduction in specimen irradiance by operating at the Poisson limit. To operate at this limit, we integrated classical illumination with single-photon detection and wavelet-based image reconstruction. This integration enabled brightness quantification and object recognition from fewer than one detected photon per image pixel, corresponding to more than 10-fold lower irradiance levels than modern systems. We demonstrate how such photon-sparse imaging can eradicate photobleaching and enable both dim and bright object imaging, thus, further enhancing the related gains of light-sheet microscopy.

60 APPLIED LIFE SCIENCES↗

NASA NTRS1991

A color-coded vision scheme for robotics

Most vision systems for robotic applications rely entirely on the extraction of information from gray-level images. Humans, however, regularly depend on color to discriminate between objects. Therefore, the inclusion of color in a robot vision system seems a natural extension of the existing gray-level capabilities. A method for robot object recognition using a color-coding classification scheme is discussed. The scheme is based on an algebraic system in which a two-dimensional color image is represented as a polynomial of two variables. The system is then used to find the color contour of objects. In a controlled environment, such as that of the in-orbit space station, a particular class of objects can thus be quickly recognized by its color.

Johnson, Kelley Tina↗

NASA NTRS1989

Connectionist model-based stereo vision for telerobotics

Autonomous stereo vision for range measurement could greatly enhance the performance of telerobotic systems. Stereo vision could be a key component for autonomous object recognition and localization, thus enabling the system to perform low-level tasks, and allowing a human operator to perform a supervisory role. The central difficulty in stereo vision is the ambiguity in matching corresponding points in the left and right images. However, if one has a priori knowledge of the characteristics of the objects in the scene, as is often the case in telerobotics, a model-based approach can be taken. Researchers describe how matching ambiguities can be resolved by ensuring that the resulting three-dimensional points are consistent with surface models of the expected objects. A four-layer neural network hierarchy is used in which surface models of increasing complexity are represented in successive layers. These models are represented using a connectionist scheme called parameter networks, in which a parametrized object (for example, a planar patch p=f(h,m sub x, m sub y) is represented by a collection of processing units, each of which corresponds to a distinct combination of parameter values. The activity level of each unit in the parameter network can be thought of as representing the confidence with which the hypothesis represented by that unit is believed. Weights in the network are set so as to implement gradient descent in an energy function.

Hoff, William↗

DOE OSTI2019

Design and Synthesis of Selective Phosphodiesterase 4D (PDE4D) Allosteric Inhibitors for the Treatment of Fragile X Syndrome and Other Brain Disorders

Novel pyridine- and pyrimidine-based allosteric inhibitors are reported that achieve PDE4D subtype selectivity through recognition of a single amino acid difference on a key regulatory domain, known as UCR2, that opens and closes over the catalytic site for cAMP hydrolysis. The design and optimization of lead compounds was based on iterative analysis of X-ray crystal structures combined with metabolite identification. Selectivity for the activated, dimeric form of PDE4D provided potent memory enhancing effects in a mouse model of novel object recognition with improved tolerability and reduced vascular toxicity over earlier PDE4 inhibitors that lack subtype selectivity. The lead compound, 28 (BPN14770), has entered midstage, human phase 2 clinical trials for the treatment of Fragile X Syndrome.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Bringing Different Views Together: A Hybrid Cooperative Perception Framework for Connected Autonomous Vehicles

Cooperative perception will be essential for connected autonomous vehicles to enhance object recognition and optimize path planning by sending data information about the surrounding environment. However, an inherent challenge in existing systems is the high bandwidth cost of transmitting information in real-time, which restricts cooperative perception’s practicality. Here, this work presents a hybrid cooperative perception fusion framework aimed at mitigating this issue by optimizing data transmission according to available bandwidth or through data reduction techniques. Our methods ensure that vehicles can rapidly transmit high-confidence data without overwhelming the network. Experimental results indicate that our methodology substantially diminishes data transmission sizes while maintaining object detection accuracy. For cooperative perception in autonomous vehicle systems, our approach provides a scalable and effective way to get past the bandwidth barrier.

Carrillo, Dominic [Univ. of North Texas, Denton, T↗

DOE OSTI2016

Cross-Domain Recognition by Identifying Joint Subspaces of Source Domain and Target Domain

This paper introduces here a new method to solve the cross-domain recognition problem. Different from the traditional domain adaption methods which rely on a global domain shift for all classes between the source and target domains, the proposed method is more flexible to capture individual class variations across domains. By adopting a natural and widely used assumption that the data samples from the same class should lay on an intrinsic low-dimensional subspace, even if they come from different domains, the proposed method circumvents the limitation of the global domain shift, and solves the cross-domain recognition by finding the joint subspaces of the source and target domains. Specifically, given labeled samples in the source domain, we construct a subspace for each of the classes. Then we construct subspaces in the target domain, called anchor subspaces, by collecting unlabeled samples that are close to each other and are highly likely to belong to the same class. The corresponding class label is then assigned by minimizing a cost function which reflects the overlap and topological structure consistency between subspaces across the source and target domains, and within the anchor subspaces, respectively. We further combine the anchor subspaces to the corresponding source subspaces to construct the joint subspaces. Subsequently, one-versus-rest support vector machine classifiers are trained using the data samples belonging to the same joint subspaces and applied to unlabeled data in the target domain. We evaluate the proposed method on two widely used datasets: 1) object recognition dataset for computer vision tasks and 2) sentiment classification dataset for natural language processing tasks. Comparison results demonstrate that the proposed method outperforms the comparison methods on both datasets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Hypomyelination and cognitive impairment in mice lacking CD133 (Prominin-1)

The CD133 antigen, also known as prominin-1, is a glycoprotein that specifically localizes to plasma membrane protrusions. The precise function of CD133 remains unknown, but it is expressed in various progenitor cells including those derived from the neural and hematopoietic system, as well as different tissues. In the adult mouse brain, CD133 is highly expressed in white matter. Here, we performed immunohistochemical staining and electron microscopy to demonstrate that mice lacking CD133 (CD133{sup −/−}) exhibit decreased myelin in the corpus callosum, the largest white matter tract in the brain. Hypomyelination in CD133{sup −/−} mice was associated with fewer oligodendrocyte progenitor cells and mature oligodendrocytes. Behavioral analyses revealed that significantly impaired object recognition memory and altered Y-maze performance by CD133{sup −/−} mice compared with wild-type mice, suggesting perturbed cognitive performance. These results suggest that CD133 regulates myelination and understanding the underlying molecular mechanisms may guide the development of novel therapeutic strategies for diseases characterized by myelin deficiency.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Changes in Imaging and Cognition in Juvenile Rats After Whole-Brain Irradiation

Purpose: In pediatric cancer survivors treated with whole-brain irradiation (WBI), long-term cognitive deficits and morbidity develop that are poorly understood and for which there is no treatment. We describe similar cognitive defects in juvenile WBI rats and correlate them with alterations in diffusion tensor imaging and magnetic resonance spectroscopy (MRS) during brain development. Methods and Materials: Juvenile Fischer rats received clinically relevant fractionated doses of WBI or a high-dose exposure. Diffusion tensor imaging and MRS were performed at the time of WBI and during the subacute (3-month) and late (6-month) phases, before behavioral testing. Results: Fractional anisotropy in the splenium of the corpus callosum increased steadily over the study period, reflecting brain development. WBI did not alter the subacute response, but thereafter there was no further increase in fractional anisotropy, especially in the high-dose group. Similarly, the ratios of various MRS metabolites to creatine increased over the study period, and in general, the most significant changes after WBI were during the late phase and with the higher dose. The most dramatic changes observed were in glutamine-creatine ratios that failed to increase normally between 3 and 6 months after either radiation dose. WBI did not affect the ambulatory response to novel open field testing in the subacute phase, but locomotor habituation was impaired and anxiety-like behaviors increased. As for cognitive measures, the most dramatic impairments were in novel object recognition late after either dose of WBI. Conclusions: The developing brains of juvenile rats given clinically relevant fractionated doses of WBI show few abnormalities in the subacute phase but marked late cognitive alterations that may be linked with perturbed MRS signals measured in the corpus callosum. This pathomimetic phenotype of clinically relevant cranial irradiation effects may be useful for modeling, mechanistic evaluations, and testing of mitigation approaches.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2024

Non-DNA radiosensitive targets that initiate persistent behavioral deficits in rats exposed to space radiation

Predicting future CNS risks for astronauts during deep-space missions will rely substantially on ground-based rodent data with space-relevant ions and behaviors. For rats, the accumulated evidence indicates that less densely ionizing radiation, such as 4 He and 12 C ions, induce behavior deficits at lower doses than densely ionizing ions, such as 48 Ti and 56 Fe. However, this observation conflicts with standard somatic radiobiology, in which densely ionizing ions are generally more effective than less densely ionizing ions, and where the DNA/nucleus is the accepted target for radiation-induced tumorigenesis, cytogenetic aberrations, genetic mutations, and reproductive cell death. To gain deeper insight into the subcellular nature of the radiation targets for behavior risks, we compared the effects of dose, fluence, and linear energy transfer (LET) of 4 He and 56 Fe particles using existing datasets for four distinct behavioral outcomes in rats: elevated plus maze (EPM-anxiety), novel object recognition (NOR-memory), operant responding (OR-response to environmental stimuli), and attentional set-shifting (ATSET-cognitive flexibility). We confirmed that less densely ionizing particles (except protons) showed ~100-fold lower threshold doses than densely ionizing particles for behavioral deficits (0.1–1 cGy for 4 He vs. 15–100 cGy for 56 Fe). However, when analyzed by fluence the behavioral responses converged, indicating that 4 He and 56 Fe were equally effective on a per-track basis. When analyzed by LET, there were ~100-fold differences in the LET for maximum effectiveness for behavioral deficits and DNA endpoints (~1 vs ~100 keV/μm, respectively). These unique features of radiation-induced behavioral deficits (high sensitivity to particles in the 1-keV/μm range, insensitivity to protons in the 0.2 keV/μm range, and isofluence dependence for particles with LET>1 keV/μm) provide evidence in support of a new hypothesis of sub-micron sized radiosensitive targets for behavioral effects consistent with the thickness of plasma membranes and/or small subcellular structures, smaller than a whole synapse. Like our behavior findings, mouse immature oocyte killing which is known to have a plasma membrane target was also better explained by fluence, rather than dose. In contrast, fluence analyses for DNA/nuclear endpoints in somatic cells (e.g., tumor induction, chromosome aberrations) showed opposite results, suggesting that behavior targets are not DNA. Our findings raise questions regarding the identity of subcellular targets and the multi-cellular functional unit for behavior risks, low-dose susceptibility, and generalizability from rat to other species and astronauts.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2017

Bisphenol A impairs the memory function and glutamatergic homeostasis in a sex-dependent manner in mice: Beneficial effects of diphenyl diselenide

Bisphenol A (BPA) is a compound integrated in commodities, which consequently increases the human exposure to this toxicant. The deleterious effects of BPA exposure during periods of brain development have been documented mainly concerning the impairment in memory functions. Diphenyl diselenide (PhSe){sub 2}, an organoselenium compound, shows protective/restorative effects against memory deficits in experimental models. Thus, this study investigated the effects of (PhSe){sub 2} on the memory impairments induced by BPA exposure to male and female mice and the possible involvement of glutamatergic system in these effects. Three-week-old male and female Swiss mice received BPA (5 mg/kg), intragastrically, from 21st to 60th postnatal day. After, the animals were intragastrically treated with (PhSe){sub 2} (1 mg/kg) during seven days. The mice performed the behavioral memory tests and the [{sup 3}H] glutamate uptake and NMDA receptor subunits (2A and 2B) analyses were carried out in the hippocampus and cerebral cortex of mice. The results demonstrated that the BPA exposure induced impairment of object recognition memory in both sexes. However, it caused impairments in spatial memory in female and in the passive avoidance memory in male mice. Besides, BPA caused a decrease in the [{sup 3}H] glutamate uptake and NMDA receptor subunit levels in the cortical and hippocampal regions depending on the sex. Treatment with (PhSe){sub 2} reversed in a sex-independent manner the behavioral impairments and molecular alterations. In conclusion, BPA had a negative effect in different memory types as well as in the glutamatergic parameters in a sex-dependent manner and (PhSe){sub 2} treatment was effective against these alterations. - Highlights: • BPA exposure induced memory impairment in a sex dependent manner in mice. • (PhSe){sub 2} reversed memory impairments caused by BPA exposure in mice. • (PhSe){sub 2} modulated hippocampal and cortical [{sup 3}H]glutamate uptake in mice. • (PhSe){sub 2} restored the hippocampal and cortical NMDA receptor subunits levels.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Perovskite neural trees

Trees are used by animals, humans and machines to classify information and make decisions. Natural tree structures displayed by synapses of the brain involves potentiation and depression capable of branching and is essential for survival and learning. Demonstration of such features in synthetic matter is challenging due to the need to host a complex energy landscape capable of learning, memory and electrical interrogation. We report experimental realization of tree-like conductance states at room temperature in strongly correlated perovskite nickelates by modulating proton distribution under high speed electric pulses. This demonstration represents physical realization of ultrametric trees, a concept from number theory applied to the study of spin glasses in physics that inspired early neural network theory dating almost forty years ago. We apply the tree-like memory features in spiking neural networks to demonstrate high fidelity object recognition, and in future can open new directions for neuromorphic computing and artificial intelligence.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Anti-distortion bioinspired camera with an inhomogeneous photo-pixel array

The bioinspired camera, comprising a single lens and a curved image sensor—a photodiode array on a curved surface—, was born of flexible electronics. Its economical build lends itself well to space-constrained machine vision applications. The curved sensor, much akin to the retina, helps image focusing, but the curvature also creates a problem of image distortion, which can undermine machine vision tasks such as object recognition. Here we report an anti-distortion single-lens camera, where 4096 silicon photodiodes arrayed on a curved surface in a nonuniform pattern assimilated to the distorting optics are the key to anti-distortion engineering. That is, the photo-pixel distribution pattern itself is warped in the same manner as images are warped, which correctively reverses distortion. Acquired images feature no appreciable distortion across a 120° horizontal view, as confirmed by their neural-network recognition accuracies. This distortion correction via photo-pixel array reconfiguration is a form of in-sensor computing.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2020

Deep transfer learning for star cluster classification: I. application to the PHANGS– HST survey

ABSTRACT We present the results of a proof-of-concept experiment that demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in Hubble Space Telescope(HST) ultraviolet-optical imaging of nearby spiral galaxies ($D\lesssim 20\, \textrm{Mpc}$) in the Physics at High Angular Resolution in Nearby GalaxieS (PHANGS)–HST survey. Given the relatively small nature of existing, human-labelled star cluster samples, we transfer the knowledge of state-of-the-art neural network models for real-object recognition to classify star clusters candidates into four morphological classes. We perform a series of experiments to determine the dependence of classification performance on neural network architecture (ResNet18 and VGG19-BN), training data sets curated by either a single expert or three astronomers, and the size of the images used for training. We find that the overall classification accuracies are not significantly affected by these choices. The networks are used to classify star cluster candidates in the PHANGS–HST galaxy NGC 1559, which was not included in the training samples. The resulting prediction accuracies are 70 per cent, 40 per cent, 40–50 per cent, and 50–70 per cent for class 1, 2, 3 star clusters, and class 4 non-clusters, respectively. This performance is competitive with consistency achieved in previously published human and automated quantitative classification of star cluster candidate samples (70–80 per cent, 40–50 per cent, 40–50 per cent, and 60–70 per cent). The methods introduced herein lay the foundations to automate classification for star clusters at scale, and exhibit the need to prepare a standardized data set of human-labelled star cluster classifications, agreed upon by a full range of experts in the field, to further improve the performance of the networks introduced in this study.

Wei, Wei↗

DOE OSTI2017

pnnl/safekit

Some of the most pressing machine learning applications such as cyber security and object recognition lack enough ground-truth training data to build a classifier. We solve this problem by using unsupervised deep learning techniques to determine when data were anomalous or deviated from the norm. Traditional methods require either specific context dependent expertise to construct models, or prior examples of events and large amounts of well-labeled data to perform machine learning. Some cutting-edge deep learning approaches have been used to characterize complex events, but these have not been applied to the streaming environment and require a great deal of data. We demonstrated the use of an autoencoder and relevant featurization techniques to both learn a feature space and then identify anomalous portions without the aid of labeled data or domain knowledge. In this way, we demonstrated the detection of anomalous features without domain knowledge or tagged examples, and delivered these features to users.

Central, PNNL Developer↗

DOE OSTI2024

Radiation-Induced Noise Resilience of Neuromorphic Architectures

Neuromorphic event-based networks use asynchronous time-dependent information to extract features from input data that can allow for edge-based distributed applications such as object recognition. The noise resilience properties of such networks, especially in the context of space applications, are yet to be explored. In this paper, we use the hierarchy of time surfaces (HOTS) algorithm, which is one of the neuromorphic algorithms, to understand the least and most resilient modules in a neuromorphic network. The HOTS algorithm relies on the computing of time surfaces that maps the temporal delays between neighboring pixels into normalized features that involve many computations that are also found in other neuromorphic networks such as exponential decays, distance computations, etcetera. We implemented HOTS on a Digilent PYNQ board with a Xilinx Zynq 7020 system on a chip, and we subjected the boards running the HOTS network inference to neutron radiation at the Los Alamos Neutron Science Center. Furthermore, we used simulation models from our previous similar experiments on the event-based sensor to create a neutron induced noise model to quantify the effect of this noise on the overall performance of the network. This experiment provides the preliminary measurements of the reliability of the HOTS algorithm and proposes methods to create a more reliable HOTS architecture in future spacecraft missions.

Engineering↗