Abstract: A method for recognizing long-range activities in videos includes segmenting an input video stream to generate multiple frame sets. For each of the frame sets, a frame with a highest likelihood of including one or more actions of a set of predefined actions is identified regardless of its order in the frame set. A global representation of the input stream is generated based on pooled representations of the identified frames. A long-range activity in the video stream is classified based on the global representation.

Abstract: A method for classifying a human-object interaction includes identifying a human-object interaction in the input. Context features of the input are identified. Each identified context feature is compared with the identified human-object interaction. An importance of the identified context feature is determined for the identified human-object interaction. The context feature is fused with the identified human-object interaction when the importance is greater than a threshold.

Abstract: A method for predicting formation permeability by measuring diffusional tortuosity in several directions by pulse gradient NMR. The method comprises evaluating an anisotropic diffusion coefficient by pulsed gradient NMR, determining diffusional tortuosity from the restricted diffusion data, supplementing the NMR results with resistivity and sonic logging data, measuring anisotropic tortuosity and porosity by resistivity and sonic data and combining all components in a single fitting model. The 11-coefficient model is trained to recognize the true values of permeability by comparing the real oil permeabilities measured. in a library of oil-carrying rock cores with the NMR, resistivity and sonic correlates The fitting coefficients are extracted by minimizing the discrepancy between the laboratory measured permeabilities and the predicted values combining all rapid logging information components with the agreement-maximizing weights.

Abstract: A method for predicting formation permeability by measuring diffusional tortuosity in several directions by pulse gradient NMR. The method comprises evaluating an anisotropic diffusion coefficient by pulsed gradient NMR, determining diffusional tortuosity from the restricted diffusion data, supplementing the NMR results with resistivity and sonic logging data, measuring anisotropic tortuosity and porosity by resistivity and sonic data and combining all components in a single fitting model. The 11-coefficient model is trained to recognize the true values of permeability by comparing the real oil permeabilities measured in a library of oil-carrying rock cores with the NMR, resistivity and sonic correlates. The fitting coefficients are extracted by minimizing the discrepancy between the laboratory measured permeabilities and the predicted values combining all rapid logging information components with the agreement-maximizing weights.

Abstract: A method for predicting formation permeability by measuring diffusional tortuosity in several directions by pulse gradient NMR. The method comprises evaluating an anisotropic diffusion coefficient by pulsed gradient NMR, determining diffusional tortuosity from the restricted diffusion data, supplementing the NMR results with resistivity and sonic logging data, measuring anisotropic tortuosity and porosity by resistivity and sonic data and combining all components in a single fitting model. The 11-coefficient model is trained to recognize the true values of permeability by comparing the real oil permeabilities measured in a library of oil-carrying rock cores with the NMR, resistivity and sonic correlates. The fitting coefficients are extracted by minimizing the discrepancy between the laboratory measured permeabilities and the predicted values combining all rapid logging information components with the agreement-maximizing weights.

Abstract: A method for predicting formation permeability by measuring diffusional tortuosity in several directions by pulse gradient NMR. The method comprises evaluating an anisotropic diffusion coefficient by pulsed gradient NMR, determining diffusional tortuosity from the restricted diffusion data, supplementing the NMR results with resistivity and sonic logging data, measuring anisotropic tortuosity and porosity by resistivity and sonic data and combining all components in a single fitting model. The 11-coefficient model is trained to recognize the true values of permeability by comparing the real oil permeabilities measured in a library of oil-carrying rock cores with the NMR, resistivity and sonic correlates. The fitting coefficients are extracted by minimizing the discrepancy between the laboratory measured permeabilities and the predicted values combining all rapid logging information components with the agreement-maximizing weights.

Abstract: A method for predicting formation permeability by measuring diffusional tortuosity in several directions by pulse gradient NMR. The method comprises evaluating an anisotropic diffusion coefficient by pulsed gradient NMR, determining diffusional tortuosity from the restricted diffusion data, supplementing the NMR results with resistivity and sonic logging data, measuring anisotropic tortuosity and porosity by resistivity and sonic data and combining all components in a single fitting model. The 11-coefficient model is trained to recognize the true values of permeability by comparing the real oil permeabilities measured in a library of oil-carrying rock cores with the NMR, resistivity and sonic correlates. The fitting coefficients are extracted by minimizing the discrepancy between the laboratory measured permeabilities and the predicted values combining all rapid logging information components with the agreement-maximizing weights.

Abstract: The medical diagnostic system comprises a deep neural network model trained with catheter hardness data, lesion hardness data, and operation time to complete catheter treatment; an irradiation energy emitting device to calculate irradiation energy of a patient; a control unit to identify a plurality of lesions from irradiation energy data; and a catheter to insert into an artery in the patient's arm, wherein the catheter tip is positioned to at least the patient's aortailiac bifurcation, wherein a therapeutic catheter is introduced into a catheter lumen and the therapeutic catheter tip is projected from the catheter tip thereby the harder lesion is initially treated, and the therapeutic catheter tip of the therapeutic catheter is projected from the catheter tip to treat the softer lesion.

Abstract: A method for classifying subject activities in videos includes learning latent (previously generated) concepts that are analogous to nodes of a graph to be generated for an activity in a video. The method also includes receiving video segments of the video. A similarity between the video segments and the previously generated concepts is measured to obtain segment representations as a weighted set of latent concepts. The method further includes determining a relationship between the segment representations and their transitioning pattern over time to determine a reduced set of nodes and/or edges for the graph. The graph of the activity in the video represented by the video segments is generated based on the reduced set of nodes and/or edges. The nodes of the graph are represented by the latent concepts. Subject activities in the video are classified based on the graph.

Abstract: A fracture geometry mapping method includes determining a value of a diffusive tortuosity in a first direction in a first rock sample from a subterranean formation with one or more hardware processors; determining a value of a diffusive tortuosity in a second direction in the first rock sample from the subterranean formation with the one or more hardware processors, the second direction orthogonal to the first direction in the first rock sample; determining a value of a diffusive tortuosity in third direction in the first rock sample from the subterranean formation with the one or more hardware processors, the third direction orthogonal to both the first direction and the second direction in the first rock sample; comparing the values of the diffusive tortuosities in the in the first direction, the second direction, and the third direction; and based on the comparison, generating a first fracture network map of the subterranean formation, the first fracture network map including a first plurality of anisotrop

Abstract: Examples disclosed herein involve a computing system configured to (i) obtain first image data captured by a first camera of a vehicle during a given period of operation of the vehicle, (ii) obtain second image data captured by a second camera of the vehicle during the given period of operation, (iii) based on the obtained first and second image data, determine (a) a candidate extrinsics transformation between the first camera and the second camera and (b) a candidate time offset between the first camera and the second camera, and (iv) based on (a) the candidate extrinsics transformation and (b) the candidate time offset, apply optimization to determine a combination of (a) an extrinsics transformation and (b) a time offset that minimizes a reprojection error in the first image data, where the reprojection error is defined based on a representation of at least one landmark that is included in both the first and second image data.

Abstract: Examples disclosed herein involve a computing system configured to (i) obtain first sensor data captured by a first sensor of a vehicle during a given period of operation of the vehicle (ii) obtain second sensor data captured by a second sensor of the vehicle during the given period of operation of the vehicle, (iii) based on the first sensor data, localize the first sensor within a first coordinate frame of a first map layer, (iv) based on the second sensor data, localize the second sensor within a second coordinate frame of a second map layer, (v) based on a known transformation between the first coordinate frame and the second coordinate frame, determine respective poses for the first sensor and the second sensor in a common coordinate frame, and (vi) determine (a) a translation and (b) a rotation between the respective poses for the first and second sensors in the common coordinate frame.

Abstract: A method of making an array of aligned hafnium oxide nanotubes is provided. The method includes generating a first reactant gas from a first solution comprising a first hafnium precursor dissolved in a first solvent. Directing the flow of the first reactant gas over a substrate to form a seed layer that comprises particles of hafnium oxide. The method further includes generating a second reactant gas from a second solution comprising a second hafnium precursor dissolved in a second solvent. Directing the flow of the second reactant gas over the seed layer to form the array of aligned hafnium oxide nanotubes substantially perpendicular on a surface of the substrate. A method of using the array of aligned hafnium oxide tubes for detection of toxic gases in a gas sample is also provided.

Abstract: A method for assessing an optimal acid injection rate in the process of hydrocarbon formation stimulation. The method comprises evaluating an anisotropic diffusion coefficient by pulsed gradient NMR, introduction of a semi-empirical correction based on comparison of the downhole conditions with the library of laboratory experiments where such corrections were measured, extrapolation of the library data to the real downhole conditions. The improved values of the diffusion coefficients are applied in determining wormhole regime conditions that are optimal in terms of acid consumption per a unit of stimulated yield of the hydrocarbon.

Abstract: A fracture geometry mapping method includes determining a value of a diffusive tortuosity in a first direction in a first rock sample from a subterranean formation with one or more hardware processors; determining a value of a diffusive tortuosity in a second direction in the first rock sample from the subterranean formation with the one or more hardware processors, the second direction orthogonal to the first direction in the first rock sample; determining a value of a diffusive tortuosity in third direction in the first rock sample from the subterranean formation with the one or more hardware processors, the third direction orthogonal to both the first direction and the second direction in the first rock sample; comparing the values of the diffusive tortuosities in the in the first direction, the second direction, and the third direction; and based on the comparison, generating a first fracture network map of the subterranean formation, the first fracture network map including a first plurality of anisotrop

Abstract: A method for predicting formation permeability by measuring diffusional tortuosity in several directions by pulse gradient NMR. The method comprises evaluating an anisotropic diffusion coefficient by pulsed gradient NMR, determining diffusional tortuosity from the restricted diffusion data, supplementing the NMR results with resistivity and sonic logging data, measuring anisotropic tortuosity and porosity by resistivity and sonic data and combining all components in a single fitting model. The 11-coefficient model is trained to recognize the true values of permeability by comparing the real oil permeabilities measured in a library of oil-carrying rock cores with the NMR, resistivity and sonic correlates. The fitting coefficients are extracted by minimizing the discrepancy between the laboratory measured permeabilities and the predicted values combining all rapid logging information components with the agreement-maximizing weights.

Abstract: A method for predicting formation permeability by measuring diffusional tortuosity in several directions by pulse gradient NMR. The method comprises evaluating an anisotropic diffusion coefficient by pulsed gradient NMR, determining diffusional tortuosity from the restricted diffusion data, supplementing the NMR results with resistivity and sonic logging data, measuring anisotropic tortuosity and porosity by resistivity and sonic data and combining all components in a single fitting model. The 11-coefficient model is trained to recognize the true values of permeability by comparing the real oil permeabilities measured in a library of oil-carrying rock cores with the NMR, resistivity and sonic correlates. The fitting coefficients are extracted by minimizing the discrepancy between the laboratory measured permeabilities and the predicted values combining all rapid logging information components with the agreement-maximizing weights.

Abstract: A fluorescent nanocomposite which includes a thallium doped gadolinium chalcogenide having formula TlxGd1-xY, wherein x is 0.01 to 0.1, and Y is selected from the group consisting of S, Se, or Te, and a benzothiazolium salt bound to a surface of the thallium doped gadolinium chalcogenide. A method of detecting antimony ions in a fluid sample whereby the fluid sample is contacted with the fluorescent nanocomposite to form a mixture, and a fluorescence emission profile of the mixture is measured to determine a presence or absence of antimony ions in the fluid sample, wherein a reduction in intensity of a fluorescence emissions peak associated with the fluorescent nanocomposite indicates the presence of antimony ions in the fluid sample.

Abstract: Examples disclosed herein may involve a computing system that is operable to (i) generate a local map portion of a geographical environment based on sensor data captured by a device, wherein the local map portion comprises local map structure data generated using one or more map structure generation methods, (ii) determine a transformation of the local map structure data relative to existing map structure data of an existing map based on common visible features between the local map structure data and the existing map structure data, wherein the existing map structure data is aligned to a global coordinate system and is predetermined from a plurality of previously-generated map structure data, and (iii) determine a localization of the device within the global coordinate system using the determined transformation.

Abstract: Examples disclosed herein may involve a computing system that is operable to (i) generate first structure data from one or more first image data, wherein the first structure data comprises one or more visible features captured in the one or more first image data, (ii) generate further structure data from one or more further image data, wherein the further structure data comprises one or more visible features captured in the one or more further image data, (iii) determine pose constraints for the further structure data based on common visible features, (iv) determine a transformation of the further structure data relative to the first structure data using the determined pose constraints, and (v) generate combined structure data using the determined transformation to fuse the further structure data and the first structure data.