Abstract: A system can comprise a communications antenna comprising a material that is configured to change shape in response to being stimulated with an external stimulus. The system can comprise an antenna performance detector that is configured to detect a measure of performance of the communications antenna. The system can comprise a distortion correction component that is configured to receive an indication of the measure of performance, determine an amount of distortion of the shape of the communications antenna based on the indication of the measure of performance, based on the amount of distortion, determine an amount of the external stimulus with which to stimulate the communications antenna, and selectively apply the amount of the external stimulus to the communications antenna to change the shape of the communications antenna.

Abstract: Methods and systems for detecting data drift while performing anomaly detection in a distributed environment are disclosed. To perform anomaly detection, a system may include an anomaly detector and one or more data collectors. The anomaly detector may detect anomalies in data obtained from one or more of the data collectors using a continuous inference model. To detect data drifts in data from the one or more data collectors, the anomaly detector may also detect anomalies in data obtained from one or more data detectors using a quantized inference model. The output of the continuous inference model may be compared to the output of the quantized inference model to determine whether the continuous inference model has adapted to data drift over time through re-training. Following anomaly detection and/or data drift detection, the data may be discarded to remove the data from the anomaly detector.

Abstract: Methods and systems for anomaly detection in a distributed environment are disclosed. To manage anomaly detection, a system may include an anomaly detector and one or more data collectors. The anomaly detector may detect anomalies in data and classify the anomalies based on magnitudes of anomalies using an inference model. Different magnitudes of anomalies may be keyed to different action sets in response to the presence of anomalies in data. To perform anomaly detection, the inference model may require re-training. Data collected from the one or more data collectors may be used to re-train the inference model as needed. Following anomaly detection and/or inference model re-training, the data may be discarded to remove the data from the anomaly detector.

Abstract: Methods and systems for anomaly detection in a distributed environment are disclosed. To manage anomaly detection, a system may include an anomaly detector and one or more data collectors. The anomaly detector may detect anomalies in data obtained from one or more of the data collectors using an inference model and an anomaly threshold. The anomaly threshold may be determined by fitting a normal distribution to output of the inference model when the inference model is exercised across an input range of the inference model. The anomaly threshold may correspond to a portion of the normal distribution. To perform anomaly detection, the inference model may require periodic re-training. Data collected from the one or more data collectors may be used to re-train the inference model as needed. Following anomaly detection and/or inference model re-training, the data may be discarded to remove the data from the anomaly detector.

Abstract: Methods and systems for anomaly detection in a distributed system are disclosed. To manage anomaly detection, a system may include an anomaly detector and one or more data collectors. The anomaly detector may detect anomalies in data obtained from one or more of the data collectors using an inference model. The inference model may be an autoencoder trained to reconstruct data that is intended to match input data to an extent considered acceptable by the system. To accurately perform anomaly detection, the inference model may require re-training. Data collected from the one or more data collectors may be used to re-train the inference model as needed. Following anomaly detection and/or inference model re-training, the data may be discarded to remove the data from the anomaly detector.

Abstract: Methods and systems for anomaly detection in a distributed environment are disclosed. To manage anomaly detection, a system may include an anomaly detector and one or more data collectors. The anomaly detector may detect anomalies in data obtained from one or more of the data collectors using an inference model. To perform anomaly detection, the inference model may require periodic re-training. Data collected from the one or more data collectors may be used to re-train the inference model as needed. Following anomaly detection and/or inference model re-training, the data may be discarded to remove the data from the anomaly detector.

Abstract: One example method includes accessing an appearance history of a person, the appearance history including information concerning an appearance of a person at a particular time, generating, based on the appearance history, a forecast that comprises a probability that the person will appear again at some future point in time, determining that the probability meets or exceeds a threshold, and updating a high probability group database to include a facial image of a face of the person.

Abstract: A system can establish a primary file system on a block array of computer storage, wherein the block array comprises a range of addresses, wherein the primary file system is configured to address the range of addresses. The system can establish a shadowed file system on the block array, wherein the shadowed file system is configured to access portions of the block array that are unused by the primary file system. The system can, in response to receiving a request to write data to the primary file system, and in response to determining that an amount of the block array utilized by the primary file system is full, transfer a first portion of the block array utilized by the shadowed file system to the primary file system.

Abstract: Methods and systems for identifying areas of interest in an image and management of images are disclosed. To manage identification of areas of interest in an image, subject matter expert driven processes may be used to identify the areas of interest. The identified areas of interest may be used to maintain a database usable to guide subsequent use of the images. The database may associate image segments of the images with various landmarks and/or area of interest. The associations may be used to limit the quantity of image segments read from storage during subsequent use of the images.

Abstract: Methods and systems for operating biosystem on a chip are disclosed. To operate biosystem on a chip based systems, causal mechanisms may be identified based on previous operations performed by the biosystem chip based systems. The causal mechanisms may be used to develop new operation plans, refine existing operation plans, and/or develop new biosystem on a chip architecture. The causal mechanism may be derived from a causal graph that includes nodes representing unknown causal mechanisms. Data regarding previous operations in combination with the causal graph may be used to learn the unknown causal mechanisms.

Abstract: Methods and systems for storing and managing biosystem on a chip data disclosed. To store the data, operation data from the biosystem on a chip may be stored in a database. To manage use of the stored data, a graph representation of the biosystem on a chip. The nodes of the graph representation may correspond to the architectural features of the biosystem on a chip. The edges between the nodes may be based on whether the corresponding architectural features are in communication with one another. Each of the nodes may be associated with pointers that point to portions of the operation data in the database that is relevant to the architecture feature associated with the respective node.

Abstract: Methods and systems for identifying areas of interest in an image and management of images are disclosed. To manage identification of areas of interest in an image, subject matter expert driven processes may be used to identify the areas of interest. The identified areas of interest may be used to establish plans to guide subsequent use of the image. The identified areas of interest may also be used to establish plans to cache portions of the image to speed subsequent use of the image.

Abstract: Methods and systems for obtaining synthesized images are disclosed. To obtain synthesized images, an imaging system may obtain areas of interest associated with an image. The areas of interest may be keyed to various processes and/or services. A downstream consumer of the services may retrieve the areas of interest to perform further analysis. In order to more easily interpret the areas of interest, the areas of interest may be displayed to the downstream consumer as part of a synthesized image. The synthesized image may include the areas of interest and a synthesized environment. The synthesized environment may be intended to emulate an environment in which the downstream consumer may expect to find the areas of interest. By doing so, the downstream consumer may be able to understand and contextualize the areas of interest more easily than if viewing the areas of interest alone.

Abstract: Methods and systems for managing storage of data are disclosed. To manage storage of data, images may be stored across a number of storages that provide varying levels of storage performance and have correspondingly varying costs for storing data. To store the images across the storages, the images may be segmented into image segments and a likelihood of each of the image segments being used in the future may be identified. The image segments that are more likely to be used in the future may be stored in higher performance storages while the image segments that are less likely to be used in the future may be stored in lower performance storages. To identify the likelihood of each of the image segments being used in the future, the image segments may be classified based on their membership in one or more areas of interest within the images.

Abstract: Methods and systems for operating biosystem on a chip are disclosed. To operate biosystem on a chip based systems, likely faults in the operation of the system may be predicted. Algorithms usable to mitigate the predicted likely faults may be identified, and ranked based on a level of impact that access to data reelecting the operation of the system may have on the utility of the algorithms. Higher ranked algorithms may be deployed for execution during operation of the system to lower latency locations while lower ranked algorithms may be deployed for execution to higher latency locations. The lower latency locations may include computing resources that are local to the biosystem on a chip, but that may be limited in quantity.

Abstract: Methods and systems for identifying areas of interest in an image are disclosed. To manage identification of areas of interest in an image, a trained inference model may generate inferences based on the pixels of the image. The inferences may include areas of interest that contributed to the generation of the inferences. Some areas of interest may be highly relevant to the inferences and may be classified as primary areas of interest. Auxiliary areas of interest may also be identified using a trained inference model. Auxiliary areas of interest may be obtained by calculating gradients for each pixel that contributed to the identification of the primary areas of interest. By rank ordering the pixels, pixels with the highest contribution to the identification of the primary areas of interest may be identified. Proximate groupings of these pixels may be classified as auxiliary areas of interest in the image.

Abstract: Methods and systems for identifying areas of interest in an image and management of images are disclosed. To manage identification of areas of interest in an image, subject matter expert driven processes may be used to identify the areas of interest. The identified areas of interest may be used to maintain a database usable to guide subsequent use of the images. The database may associate image segments of the images with various landmarks and/or area of interest. The associations may be used to limit the quantity of image segments read from storage during subsequent use of the images.

Abstract: Methods and systems for inference generation are disclosed. To manage inference generation, a system may include an inference model manager and any number of data processing systems. The inference model manager may represent an inference model as a bipartite graph in order to obtain portions of the inference model. Each portion of the inference model may be distributed to one data processing system so that the data processing systems may collectively generate inferences usable by a downstream consumer. Portions of the inference model may be obtained so that each portion matches the available computing resources of a data processing system throughout the distributed environment. In addition, the portions may be obtained in order to reduce inter-data processing system communications during execution of the inference model.

Abstract: Methods and systems for inference generation are disclosed. To manage inference generation, a system may include an inference model manager and any number of data processing systems. The inference model manager may represent an inference model as a bipartite graph. To obtain portions of the inference model, the bipartite graph may be partitioned into portions and the portions may undergo an optimization process. The optimization process may include adding and/or competing for nodes of the bipartite graph in order to increase the stability of the portion with respect to the available computing resources of a corresponding data processing system. The optimization process may continue until all portions achieve stability in a way that reduces necessary communications between the data processing systems. Each portion of the inference model may be distributed to one data processing system so that the data processing systems may collectively generate inferences usable by a downstream consumer.

Abstract: Methods and systems for seamlessly changing over between inference models are disclosed. The inference models may be distributed across multiple data processing systems. Provide a seamless changeover, updated inference models and original inference models may be managed in accordance with an update framework. The update framework may ensure that the original inference model continues to operate until all of the portions of the updated inference model are in place and ready to operate. During the update process, the update framework may ensure that redundancy goals continue to be met so that failures of some of the data processing systems are not be fatal to continued operation of at least one of the inference models, such as the original or updated inference model.