Abstract: A method of detecting alpha particles may include providing a layer of a scintillating slurry comprising a granular scintillating material in water onto a measurement surface of a sample; positioning the sample having the scintillating slurry within a detection chamber of detection apparatus; detecting photons produced by the granular scintillating material in the scintillating slurry when the granular scintillating material is excited by ionizing alpha radiation emitted by alpha particles within the sample using a photon detector and generating a corresponding output signal; and removing the sample and the scintillating slurry from the detection chamber.

Abstract: This disclosure relates to the embedding of visual objects into the image content of a video by a visual embed specialist, whilst maintaining the security of the video. A low-resolution version of the video content is sent by the video owner to the specialist for analysis to identify parts of the video that are suitable for visual object insertion. A high resolution version of those identified parts of the video is then sent to the specialist for visual object insertion. The specialist may then return the modified parts of the video and the content owner create a final version of the high-resolution video by replacing the relevant parts of the high-resolution video with the modified parts.

Abstract: The present disclosure relates to a computer implemented method, computer program and apparatus for classifying object insertion opportunities in a video by identifying at least one object insertion opportunity in a scene of the video, identifying a focus of attention in the scene, determining a proximity value for each of the at least one object insertion opportunity based at least in part on the object insertion opportunity and the focus of attention, wherein the proximity value is indicative of a distance between the respective at least one object insertion opportunity and the focus of attention in the scene, and classifying each of the at least one object insertion opportunity based at least in part on the proximity value determined for each respective at least one object insertion opportunity.

Abstract: The present disclosure relates to a video content discovery apparatus, system, method and computer program. In one aspect of the disclosure there is provided a video content discovery module configured to receive a content query, retrieve, from one or more information sources, text that relates to the content query, process the retrieved text, at least in part using Natural Language Processing, to transform the content query to a set comprising one or more video content descriptors, and identify one or more video segments of a plurality of available video segments, using the one or more video content descriptors.

Abstract: This disclosure relates to the embedding of visual objects into the image content of a video by a visual embed specialist, whilst maintaining the security of the video. A low-resolution version of the video content is sent by the video owner to the specialist for analysis to identify parts of the video that are suitable for visual object insertion. A high resolution version of those identified parts of the video is then sent to the specialist for visual object insertion. The specialist may then return the modified parts of the video and the content owner create a final version of the high-resolution video by replacing the relevant parts of the high-resolution video with the modified parts.

Abstract: This disclosure relates to the embedding of visual objects into the image content of a video by a visual embed specialist, whilst maintaining the security of the video. A low-resolution version of the video content is sent by the video owner to the specialist for analysis to identify parts of the video that are suitable for visual object insertion. A high resolution version of those identified parts of the video is then sent to the specialist for visual object insertion. The specialist may then return the modified parts of the video and the content owner create a final version of the high-resolution video by replacing the relevant parts of the high-resolution video with the modified parts.

Abstract: Systems and methods for large-scale geospatial mosaic generation with image processing in a cloud computing environment. The approaches described herein specifically leverage scalable cloud computing features to facilitate highly parallel, granular image processing. Front-end image processing techniques allow for generation of a user interface that may provide automated material selection with human operator refinement. The user interface may be a web-based design that provides browse version images at lower resolutions to improve performance of the user interface and generating mosaic recipe. In turn, the mosaic recipe may be provided to back-end image processing that coordinates strip-level jobs and tile-level jobs and highly parallel fashion scalable cloud computing nodes. In turn, very large-scale mosaic images may be generated from geospatial images in computationally and cost-effective manner.

Abstract: Aspects of the present disclosure aim to improve upon methods and systems for the incorporation of additional material into source video data. In particular, the method of the present disclosure may use a pre-existing corpus of source video data to produce, test and refine a prediction model for enabling the prediction of the characteristics of placement opportunities. The model may be created using video analysis techniques which obtain metadata regarding placement opportunities and also through the identification of categorical characteristics relating to the source video which may be provided as metadata with the source video, or obtaining through image processing techniques described below. Using the model, the method and system may then be used to create a prediction of insertion zone characteristics for projects for which source video is not yet available, but for which information corresponding to the identified categorical characteristics is known.

Abstract: This disclosure relates to the embedding of visual objects into the image content of a video by a visual embed specialist, whilst maintaining the security of the video. A low-resolution version of the video content is sent by the video owner to the specialist for analysis to identify parts of the video that are suitable for visual object insertion. A high resolution version of those identified parts of the video is then sent to the specialist for visual object insertion. The specialist may then return the modified parts of the video and the content owner create a final version of the high-resolution video by replacing the relevant parts of the high-resolution video with the modified parts.

Abstract: The present disclosure relates to a computer implemented method, computer program and apparatus for classifying object insertion opportunities in a video by identifying at least one object insertion opportunity in a scene of the video, identifying a focus of attention in the scene, determining a proximity value for each of the at least one object insertion opportunity based at least in part on the object insertion opportunity and the focus of attention, wherein the proximity value is indicative of a distance between the respective at least one object insertion opportunity and the focus of attention in the scene, and classifying each of the at least one object insertion opportunity based at least in part on the proximity value determined for each respective at least one object insertion opportunity.

Abstract: Aspects of the present disclosure aim to improve upon methods and systems for the incorporation of additional material into source video data. In particular, the method of the present disclosure may use a pre-existing corpus of source video data to produce, test and refine a prediction model for enabling the prediction of the characteristics of placement opportunities. The model may be created using video analysis techniques which obtain metadata regarding placement opportunities and also through the identification of categorical characteristics relating to the source video which may be provided as metadata with the source video, or obtaining through image processing techniques described below. Using the model, the method and system may then be used to create a prediction of insertion zone characteristics for projects for which source video is not yet available, but for which information corresponding to the identified categorical characteristics is known.

Abstract: The method disclosure provides methods, systems and computer programs for identification of candidate video insertion object types using machine learning. Machine learning is used for at least part of the processing of the image contents of a plurality of frames of a scene of a source video. The processing includes identification of a candidate insertion zone for the insertion of an object into the image content of at least some of the plurality of frames and determination of an insertion zone descriptor for the identified candidate insertion zone, the insertion zone descriptor comprising a candidate object type indicative of a type of object that is suitable for insertion into the candidate insertion zone.

Abstract: Aspects of the present disclosure aim to improve upon methods and systems for the incorporation of additional material into source video data. In particular, the method of the present disclosure may use a pre-existing corpus of source video data to produce, test and refine a prediction model for enabling the prediction of the characteristics of placement opportunities. The model may be created using video analysis techniques which obtain metadata regarding placement opportunities and also through the identification of categorical characteristics relating to the source video which may be provided as metadata with the source video, or obtaining through image processing techniques described below. Using the model, the method and system may then be used to create a prediction of insertion zone characteristics for projects for which source video is not yet available, but for which information corresponding to the identified categorical characteristics is known.

Abstract: The method disclosure provides methods, systems and computer programs for identification of candidate video insertion object types using machine learning. Machine learning is used for at least part of the processing of the image contents of a plurality of frames of a scene of a source video. The processing includes identification of a candidate insertion zone for the insertion of an object into the image content of at least some of the plurality of frames and determination of an insertion zone descriptor for the identified candidate insertion zone, the insertion zone descriptor comprising a candidate object type indicative of a type of object that is suitable for insertion into the candidate insertion zone.

Abstract: Aspects of the present disclosure aim to improve upon methods and systems for the incorporation of additional material into source video data. In particular, the method of the present disclosure may use a pre-existing corpus of source video data to produce, test and refine a prediction model for enabling the prediction of the characteristics of placement opportunities. The model may be created using video analysis techniques which obtain metadata regarding placement opportunities and also through the identification of categorical characteristics relating to the source video which may be provided as metadata with the source video, or obtaining through image processing techniques described below. Using the model, the method and system may then be used to create a prediction of insertion zone characteristics for projects for which source video is not yet available, but for which information corresponding to the identified categorical characteristics is known.

Abstract: A flexible spacer body has two opposing faces adapted to engage the inner surfaces of glazing structures to define an insulating glazing unit. The spacer body may be desiccated polymeric foam such as a silicone foam rubber or EPDM. An adhesive capable of bonding the spacer body to the glazing structure is carried by both of the faces. The adhesive may be from about 0.050 mm to about 1.524 mm thick. The adhesive material also has the properties of low argon gas and low moisture permeability. The adhesive comprises polymers where butyl rubber and/or polyisobutylene polymers together make up the majority of the polymers. The adhesive may also comprise other materials as needed to make it pressure sensitive and to impart a water resistant bond to glass glazing structures. The space assembly may include additional materials to secure the adhesive to the spacer body.

Abstract: A method of providing lock-based access to nodes in a concurrent linked list includes providing a plurality of striped lock objects. Each striped lock object is configured to lock at least one of the nodes in the concurrent linked list. An index is computed based on a value stored in a first node to be accessed in the concurrent linked list. A first one of the striped lock objects is identified based on the computed index. The first striped lock object is acquired, thereby locking and providing protected access to the first node.

Abstract: A method of providing lock-based access to nodes in a concurrent linked list includes providing a plurality of striped lock objects. Each striped lock object is configured to lock at least one of the nodes in the concurrent linked list. An index is computed based on a value stored in a first node to be accessed in the concurrent linked list. A first one of the striped lock objects is identified based on the computed index. The first striped lock object is acquired, thereby locking and providing protected access to the first node.

Abstract: The majority of such software attacks exploit software vulnerabilities or flaws to write data to unintended locations. For example, control-data attacks exploit buffer overflows or other vulnerabilities to overwrite a return address in the stack, a function pointer, or some other piece of control data. Non-control-data attacks exploit similar vulnerabilities to overwrite security critical data without subverting the intended control flow in the program. We describe a method for securing software against both control-data and non-control-data attacks. A static analysis is carried out to determine data flow information for a software program. Data-flow tracking instructions are formed in order to track data flow during execution or emulation of that software. Also, checking instructions are formed to check the tracked data flow against the static analysis results and thereby identify potential attacks or errors. Optional optimisations are described to reduce the resulting additional overheads.

Abstract: A flexible spacer body has two opposing faces adapted to engage the inner surfaces of glazing structures to define an insulating glazing unit. The spacer body may be desiccated polymeric foam such as a silicone foam rubber or EPDM. An adhesive capable of bonding the spacer body to the glazing structure is carried by both of the faces. The adhesive may be from about 0.050 mm to about 1.524 mm thick. The adhesive material also has the properties of low argon gas and low moisture permeability. The adhesive comprises polymers where butyl rubber and/or polyisobutylene polymers together make up the majority of the polymers. The adhesive may also comprise other materials as needed to make it pressure sensitive and to impart a water resistant bond to glass glazing structures. The space assembly may include additional materials to secure the adhesive to the spacer body.