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High connectivity of artificial neural network chip-embodiments combined with currently emerging 3-dimensionally stacked multichip modules for real-time applications of target classification require a scrutiny for low power technology insertion.
Using a recent classification of the solar wind at 1 AU into its principal components (slow solar wind, high-speed streams, and coronal mass ejections (CMEs) for 1972-2000, we show that the monthly-averaged galactic cosmic ray intensity is anti-correlated with the percentage of time that the Earth is imbedded in CME flows. We suggest that this correlation results primarily from a CME related change in the tail of the distribution function of hourly-averaged values of the solar wind magnetic field (B) between solar minimum and solar maximum. The number of high-B (square proper subset 10 nT) values increases by a factor of approx. 3 from minimum to maximum (from 5% of all hours to 17%), with about two-thirds of this increase due to CMEs. On an hour-to-hour basis, average changes of cosmic ray intensity at Earth become negative for solar wind magnetic field values square proper subset 10 nT.
Exploring relationships between material structures and desired properties for material discovery and synthesis requires mining machine-readable databases and linking experimental data with sufficiently rich annotations. There are vast gaps both in these data sources and in the connections between them. We present a provenance-based data management and analysis framework that enables capturing, persisting, and reanalyzing experimental data from the BNL National Synchrotron Light Source II (NSLS-II). Our system leverages NSLS-II Bluesky for experimental data acquisition, captures analysis parameters, and enriches the search space with results from the scientific literature. We describe in detail the challenges and benefits of data-driven approaches when comparing computational and experimental data, such as the multiple possible scenarios of interest in often ill-posed inverse problems and missing data. Our infrastructure utilizes the NSLS-II event model and includes discovery mechanisms across multiple beamlines. A text and data mining module using natural language processing classification techniques extracts relevant information from the literature that is added to data sources. We describe a provenance acquisition and analysis rerun workflow based on a directed acyclic graph that keeps track of process and data provenance and allows to replay analysis.
A genetic algorithm is developed and applied to optimize hyperparameters of convolutional recursively determined dual neural network-Gaussian process (NNGP) kernels. As a specific application of the combined GPNN-GA algorithm, it is applied to image classification in publicly available data of Hyper Suprime-Cam Subaru Strategic Program. Matthews correlation coefficient is calculated based on results of binary star-galaxy classification and used as a fitting function of the GA module of the algorithm. The simulation results confirm significant improvement of the classification accuracy with optimized hyperparameters.
Information processing systems, reasoning modules, and reasoning system design methods are described. According to one aspect, an information processing system includes working memory comprising a semantic graph which comprises a plurality of abstractions, wherein the abstractions individually include an individual which is defined according to an ontology and a reasoning system comprising a plurality of reasoning modules which are configured to process different abstractions of the semantic graph, wherein a first of the reasoning modules is configured to process a plurality of abstractions which include individuals of a first classification type of the ontology and a second of the reasoning modules is configured to process a plurality of abstractions which include individuals of a second classification type of the ontology, wherein the first and second classification types are different.
Information processing systems, reasoning modules, and reasoning system design methods are described. According to one aspect, an information processing system includes working memory comprising a semantic graph which comprises a plurality of abstractions, wherein the abstractions individually include an individual which is defined according to an ontology and a reasoning system comprising a plurality of reasoning modules which are configured to process different abstractions of the semantic graph, wherein a first of the reasoning modules is configured to process a plurality of abstractions which include individuals of a first classification type of the ontology and a second of the reasoning modules is configured to process a plurality of abstractions which include individuals of a second classification type of the ontology, wherein the first and second classification types are different.
Information processing systems, reasoning modules, and reasoning system design methods are described. According to one aspect, an information processing system includes working memory comprising a semantic graph which comprises a plurality of abstractions, wherein the abstractions individually include an individual which is defined according to an ontology and a reasoning system comprising a plurality of reasoning modules which are configured to process different abstractions of the semantic graph, wherein a first of the reasoning modules is configured to process a plurality of abstractions which include individuals of a first classification type of the ontology and a second of the reasoning modules is configured to process a plurality of abstractions which include individuals of a second classification type of the ontology, wherein the first and second classification types are different.
This paper presents the results of research activities aimed at creation of a principally new LRW distilling treatment method. The new process is based on the instantaneous evaporation method widely used in distillation units. The main difference of the proposed process is that the vapor condensation is conducted without using heat exchangers in practically ideal mode by way of direct contacting in a vapor-liquid system. This process is conducted in a specially designed ejector unit in supersonic mode. Further recuperation of excess heat of vaporization is carried out in a standard heat exchanger. Such an arrangement of the process, together with use of the barometric height principle, allows to carry out LRW evaporation under low temperatures, which enables to use excess heat from NPS for heating initial LRW. Thermal calculations and model experiments have revealed that, in this case, the expenditure of energy for LRW treatment by distilling will not exceed 3 kilowatt-hour/m{sup 3}, which is comparable with the reverse-osmosis desalination method. Besides, the proposed devices are 4 to 5 times less metal-intensive than standard evaporation units. These devices are also characterized by versatility. Experiments have revealed that the new method can be used for evaporation of practically any types of LRW, including those containing a considerable amount of oil products. Owing to arrangement of the evaporation process at low temperatures, the new devices are not sensitive to 'scale formation'. This is why, they can be used for concentrating brines of up to 500-600 g/l. New types of such evaporating devices can be required both for LRW treatment processes at nuclear-power plants under design and for treating 'non-standard' LRW with complex physicochemical and radionuclide composition resulting from the disaster at the Fukushima I Nuclear Power Plant.) As a result of accidents at nuclear energy objects, as it has recently happened at NPP 'Fukushima-1', personnel faces the necessity to take emergency measures and to use marine water for cooling of reactor zone in contravention of the technological regulations. In these cases significant amount of liquid radioactive wastes of complex physicochemical composition is being generated, the purification of which by traditional methods is close to impossible. According to the practice of elimination of the accident after-effects at NPP 'Fukushima' there are still no technical means for the efficient purification of liquid radioactive wastes of complex composition like marine water from radionuclides. Therefore development of state-of-the-art highly efficient facilities capable of fast and safe purification of big amounts of liquid radioactive wastes of complex physicochemical composition from radionuclides turns to be utterly topical problem. Cesium radionuclides, being extremely dangerous for the environment, present over 90% of total radioactivity contained in liquid radioactive wastes left as a result of accidents at nuclear power objects. For the purpose of radiation accidents aftereffects liquidation VNIIHT proposes to create a plant for LRW reprocessing, consisting of 4 major technological modules: Module of LRW pretreatment to remove mechanical and organic impurities including oil products; Module of sorption purification of LWR by means of selective inorganic sorbents; Module of reverse osmotic purification and desalination; Module of deep evaporation of LRW concentrates. The first free modules are based on completed technological and designing concepts implemented by VNIIHT in the framework of LLRW Project in the period of 2000-2001 in Russia for comprehensive treatment of LWR of atomic fleet. These industrial plants proved to be highly efficient and secure during their long operation life. Module of deep evaporation is a new technological development. It will ensure conduction of evaporation and purification of LRW of different physicochemical composition, including those containing hardness salts, resulted in generation of LRW concentrate 300-600 g/l. The method is based on utilization of supersonic ejector for intensification of thermal physic processes and performance of evaporation in brine recycling mode. All proposed technological solutions are totally based on patented Russian developments. Proposed work will allow to construct modular plants, which will be totally prepared for efficient purification of any types of liquid radioactive wastes from radionuclides in case of force majeure. According to proposed scheme concentration level of cesium radionuclides in safe-for-storage form will make up not less than 5000. With respect to purification from cesium radionuclides of liquid radioactive wastes stored at NPP 'Fukushima' about 10 t of inorganic sorbents, loaded in 160 protective filter-containers, will be required for solving this problem. The amount of secondary wastes will be reduced approximately in 5 times in comparison with traditional schemes, applied in purification of secondary LRW of Fukushima-1 by Areva (France) and Kurion (USA) companies. All units of modular plants will be constructed and manufactured as totally automated, providing their twenty-four-hour safe operation. Modular design will ensure efficiency and let optimize the costs of secondary LRW treatment. In order to ensure off-line operation in emergency conditions the plant should be equipped with auxiliary modules: energy and ventilation ones. Under normal conditions these modules can be stored in 'mothballed' condition at special warehouses under the authority of federal bodies. It will be reasonable to choose required transport facilities, the most suitable for transportation of modules to target destination beforehand, using vessel classification list.
How the interactions between cortices through a specific white matter pathway change during cognitive processing in patients with epilepsy remains unclear. Here, we used surface-based structural connectivity analysis to examine the change in structural connectivity with Broca’s area/the right Broca’s homologue in the lateral temporal and inferior parietal cortices through the arcuate fasciculus (AF) in 17 patients with left temporal lobe epilepsy (TLE) compared with 17 healthy controls. Then, we investigated its functional relevance to the changes in task-related responses and task-modulated functional connectivity with Broca’s area/the right Broca’s homologue during a semantic classification task of a single word. The structural connectivity through the AF pathway and task-modulated functional connectivity with Broca’s area decreased in the left midtemporal cortex. Furthermore, task-related response decreased in the left mid temporal cortex that overlapped with the region showing a decrease in the structural connectivity. In contrast, the region showing an increase in the structural connectivity through the AF overlapped with the regions showing an increase in task-modulated functional connectivity in the left inferior parietal cortex. These structural and functional changes in the overlapping regions were correlated. The results suggest that the change in the structural connectivity through the left frontal–temporal AF pathway underlies the altered functional networks between the frontal and temporal cortices during the language-related processing in patients with left TLE. The left frontal–parietal AF pathway might be employed to connect anterior and posterior brain regions during language processing and compensate for the compromised left frontal–temporal AF pathway.
Average 430-MHz pulse profiles and, where possible, modulation indices and pulse-nulling fractions are computed for 28 pulsars. Morphological classifications are determined for most of the pulsars. It is found that core emission components tend to have lower modulation indices than conal components, and that pulsars having only a core component never exhibit pulse pulling. PSR 1612 + 07 is shown to undergo mode changes.
Engineering grain boundary (GB) strain provides a promising pathway to tune the catalytic properties of nanocrystals. However, structural heterogeneity from random grain orientation and geometry has limited clear structure–property correlations. Here, we utilize a multigrain Co3O4/Mn3O4 core/shell nanocrystal platform as a model system to systematically investigate how geometric misfit strain at GBs serves as catalytically active sites for the oxygen reduction reaction. Through precise subnanometer-level control over grain morphology and by integrating multiscale electronic structure characterization, we identify the electronic structural signature of GB defects and establish a direct correlation between localized strain fields and modified electronic states. Strain modulation at GBs alters the eg orbital energy levels, with elongation along the z-axis combined with shear strain stabilizing the eg states, in contrast to the destabilization observed under pure shear strain. This stabilization mechanism enhances the electrocatalytic activity and selectivity of strained GBs compared with strain-relaxed grain surfaces. Furthermore, we reveal that GBs exhibit a radial strain gradient, producing a spatial energy shift that further modulates local electronic structures, as resolved through the classification of electron energy loss spectroscopy data. Together, these findings demonstrate that geometric misfit strain enables precise tuning of grain geometry and the resulting electronic structures, offering a robust strategy for engineering next-generation nanocatalysts.
In this report the unsteady, nonlinear magnetization dynamics induced by spin injection in an easy-plane ferromagnetic channel subject to an external magnetic field are studied analytically. Leveraging a dispersive hydrodynamic description, the Landau-Lifshitz equation is recast in terms of hydrodynamic-like variables for the magnetization's perpendicular component (spin density) and azimuthal phase gradient (fluid velocity). Spin injection acts as a moving piston that generates nonlinear, dynamical spin textures in the ferromagnetic channel with downstream quiescent spin density set by the external field. In contrast to the classical problem of a piston accelerating a compressible gas, here, variable spin injection and field lead to a rich variety of nonlinear wave phenomena from oscillatory spin shocks to solitons and rarefaction (expansion) waves. A full classification of solutions is provided using nonlinear wave modulation theory by identifying two key aspects of the fluid-like dynamics: subsonic/supersonic conditions and convex/nonconvex hydrodynamic flux. Familiar waveforms from the classical piston problem such as rarefaction waves and shocks manifest in their spin-based counterparts as smooth and highly oscillatory transitions, respectively, between two distinct magnetic states. The spin shock is an example of a dispersive shock wave, which arises in many physical systems. New features without a gas dynamics counterpart include composite wave complexes with “contact” spin shocks and rarefactions. Magnetic supersonic conditions lead to two pronounced piston edge behaviors including a stationary soliton and an oscillatory wave train. These coherent wave structures have physical implications for the generation of high frequency spin waves from pulsed injection and persistent, stable stationary and/or propagating solitons in the presence of magnetic damping. The analytical results are favorably compared with numerical simulations.
EDGE is a bioinformatics platform developed in 2016 by researchers at Los Alamos National Laboratory (LANL) to facilitate the analysis of next-generation sequencing data by researchers with varying levels of experience in bioinformatics (Li et al., 2017). Users with single-end, paired-end or long-read sequencing data can provide their reads as input to EDGE and select the combination of workflows to run that are most useful for their research (e.g., quality control of reads, genome assembly, or the taxonomic classification of input reads). Table 1 summarizes the modules available in EDGE. EDGE is available as a web platform at https://edgebioinformatics.org, as installable source code maintained on GitHub under a GPLv3 license, and as a publicly hosted Docker image.
The distributions and relationships derived from the change data collected during the development of a medium scale satellite software project show that meaningful results can be obtained which allow an insight into software traits and the environment in which it is developed. Modified and new modules were shown to behave similarly. An abstract classification scheme for errors which allows a better understanding of the overall traits of a software project is also shown. Finally, various size and complexity metrics are examined with respect to errors detected within the software yielding some interesting results.
The distributions and relationships derived from the change data collected during the development of a medium scale satellite software project show that meaningful results can be obtained which allow an insight into software traits and the environment in which it is developed. Modified and new modules were shown to behave similarly. An abstract classification scheme for errors which allows a better understanding of the overall traits of a software project is also shown. Finally, various size and complexity metrics are examined with respect to errors detected within the software yielding some interesting results.
Satellite imagery often exhibits large spatial extent areas that encompass object classes with considerable variability. This often limits large-scale model generalization with machine learning algorithms. Notably, acquisition conditions, including dates, sensor position, lighting condition, and sensor types, often translate into class distribution shifts introducing complex nonlinear factors and hamper the potential impact of machine learning classifiers. Here, this article investigates the challenge of exploiting satellite images using convolutional neural networks (CNN) for settlement classification where the class distribution shifts are significant. We present a large-scale human settlement mapping workflow based-off multiple modules to adapt a pretrained CNN to address the negative impact of distribution shift on classification performance. To extend a locally trained classifier onto large spatial extents areas we introduce several submodules: First, a human-in-the-loop element for relabeling of misclassified target domain samples to generate representative examples for model adaptation; second, an efficient hashing module to minimize redundancy and noisy samples from the mass-selected examples; and third, a novel relevance ranking module to minimize the dominance of source example on the target domain. The workflow presents a novel and practical approach to achieve large-scale domain adaptation with binary classifiers that are based-off CNN features. Experimental evaluations are conducted on areas of interest that encompass various image characteristics, including multisensors, multitemporal, and multiangular conditions. Domain adaptation is assessed on source–target pairs through the transfer loss and transfer ratio metrics to illustrate the utility of the workflow.
With the proliferation of LiDAR sensors and 3-D vision cameras, 3-D point cloud analysis has attracted significant attention in recent years. In this article, we propose a novel 3-D point cloud learning network, referred to as dynamic point feature aggregation network (DPFA-Net), by selectively performing the neighborhood feature aggregation (FA) with dynamic pooling and an attention mechanism. DPFA-Net has two variants for semantic segmentation and classification of 3-D point clouds. As the core module of the DPFA-Net, we propose an FA layer, in which features of the dynamic neighborhood of each point are aggregated via a self-attention mechanism. In contrast to other segmentation models, which aggregate features from fixed neighborhoods, our approach can aggregate features from different neighbors in different layers providing a more selective and broader view to the query points and focusing more on the relevant features in a local neighborhood. In addition, to further improve the performance of semantic segmentation, we exploit the background–foreground (BF) information and present two novel approaches, namely, two-stage BF-Net and BF regularization. Experimental results show that the proposed DPFA-Net achieves the state-of-the-art overall accuracy score of 89.22% for semantic segmentation on the Stanford large-scale 3-D Indoor Spaces (S3DIS) dataset and provides consistently satisfactory performance across different tasks of semantic segmentation, part segmentation, and 3-D object classification. Furthermore, our model achieves 93.1% accuracy on the ModelNet40 dataset and provides a mean shape intersection-over-union (IoU) value of 85.5% for part segmentation on the ShapeNet-Part dataset. It is a also computationally more efficient compared to other methods.