The technology disclosed corrects inter-cluster intensity profile variation for improved base calling on a cluster-by-cluster basis. The technology disclosed accesses current intensity data and historic intensity data of a target cluster, where the current intensity data is for a current sequencing cycle and the historic intensity data is for one or more preceding sequencing cycles. A first accumulated intensity correction parameter is determined by accumulating distribution intensities measured for the target cluster at the current and preceding sequencing cycles. A second accumulated intensity correction parameter is determined by accumulating intensity errors measured for the target cluster at the current and preceding sequencing cycles. Based on the first and second accumulated intensity correction parameters, next intensity data for a next sequencing cycle is corrected to generate corrected next intensity data, which is used to base call the target cluster at the next sequencing cycle.

The technology disclosed generates variation correction coefficients on a cluster-by-cluster basis to correct inter-cluster intensity profile variation for improved base calling. An amplification coefficient corrects scale variation. Channel-specific offset coefficients correct shift variation along respective intensity channels. The variation correction coefficients for a target cluster are generated based on combining analysis of historic intensity data generated for the target cluster at preceding sequencing cycles of a sequencing run with analysis of current intensity data generated for the target cluster at a current sequencing cycle of the sequencing run. The variation correction coefficients are then used to correct next intensity data generated for the target cluster at a next sequencing cycle of the sequencing run. The corrected next intensity data is then used to base call the target cluster at the next sequencing cycle.

In one embodiment, a set of feature vectors can be derived from any biometric data, and then using a deep neural network (“DNN”) on those one-way homomorphic encryptions (i.e., each biometrics' feature vector) can determine matches or execute searches on encrypted data. Each biometrics' feature vector can then be stored and/or used in conjunction with respective classifications, for use in subsequent comparisons without fear of compromising the original biometric data. In various embodiments, the original biometric data is discarded responsive to generating the encrypted values. In another embodiment, the homomorphic encryption enables computations and comparisons on cypher text without decryption. This improves security over conventional approaches. Searching biometrics in the clear on any system, represents a significant security vulnerability. In various examples described herein, only the one-way encrypted biometric data is available on a given device. Various embodiments restrict execution to occur on encrypted biometrics for any matching or searching.

Systems, methods, and computer program products for associating a media content clip(s) with other media content clip(s) having a different modality by determining first embedding vectors of media content items of a first modality, receiving a media content clip of a second modality, determining a second embedding vector of the media content clip of the second modality, ranking the first embedding vectors based on a distance between the embedding vectors and the second embedding vector, and selecting one or more of the media content items of the first modality based on the ranking, thereby pairing media content clips based on emotion.

A processor-implemented method of implementing an attention mechanism in a neural network includes obtaining key-value coupling data determined based on an operation between new key data determined using a first nonlinear transformation for key data of an attention layer, and value data of the attention layer corresponding to the key data; determining new query data by applying a second nonlinear transformation to query data corresponding to input data of the attention layer; and determining output data of the attention layer based on an operation between the new query data and the key-value coupling data.

Techniques are described for using computing devices to perform automated operations for identifying building floor plans that have attributes satisfying target criteria and for subsequently using the identified floor plans in further automated manners. In at least some situations, the identification of such building floor plans is based on generating and using adjacency graphs generated for the floor plans that represent inter-connections between rooms and other attributes of the buildings, and in some cases is further based on generating and using embedding vectors that concisely represent the information of the adjacency graphs. Information about such identified building floor plans may be used in various automated manners, including for controlling navigation of devices (e.g., autonomous vehicles), for display on client devices in corresponding graphical user interfaces, for further analysis to identify shared and/or aggregate characteristics, etc.

Methods and servers for storing data associated with users and digital items of a recommendation system having access to non-distributed and distributed storages. The server trains a model based for generating first user and item embeddings. The server stores (i) the first user embeddings in the non-distributed storage, and (ii) the first item embeddings in the distributed storage. The server re-trains the model for generating second user and item embeddings. The server stores (i) the second user embeddings in the non-distributed storage in addition to the first user embeddings, and (ii) second item embeddings in the distributed storage instead of the respective first item embeddings by replacing the respective first item embeddings. When the second item embeddings are stored on each node of the distributed storage, the server removes the first user embeddings associated with the first value from the non-distributed storage.

Systems, methods, and computer program products for associating a media content clip(s) with other media content clip(s) having a different modality by determining first embedding vectors of media content items of a first modality, receiving a media content clip of a second modality, determining a second embedding vector of the media content clip of the second modality, ranking the first embedding vectors based on a distance between the embedding vectors and the second embedding vector, and selecting one or more of the media content items of the first modality based on the ranking, thereby pairing media content clips based on emotion.

A method for analyzing an anatomical structure of a patient may include the steps of receiving volumetric scan data representative of one or more features of an anatomical structure; mapping the features to a node tree diagram; and displaying the node tree diagram. The features can comprise branching points, pathways connecting the branching points, and location data of the branching points and pathways. The node tree diagram may comprise a plurality of nodes and branches representing the branching points and pathways in the anatomical structure, respectively. The plurality of nodes may comprise a root node representing a root branching point as well as additional nodes representing additional branching points. Additionally, the node tree diagram may comprise a first set of one or more regions, wherein each region encompasses a respective portion of the node tree diagram and is representative of a defined portion of the anatomical structure.

Embodiments of a method and system described herein enable capture of video data streams from multiple, different video data source devices and the processing of the video data streams. The video data streams are merged such that various data protocols can all be processed with the same worker processors on different types of operating systems, which are typically distributed. In an embodiment the multiple video data sources comprises at least one mobile device executing a video sensing application that produces a video data stream for processing by video analysis worker processes. The processes include automatically detecting moving objects in a video data stream, and further tracking and analyzing the moving objects.