Abstract: A technology is described for automating deployment of a machine learning model. An example method may include receiving, via a graphical user interface, credentials for connecting to a data store containing a plurality of datasets and connecting to the data store using the credentials. A selection of a target metric to predict using the machine learning model can be received, via the graphical user interface, and datasets included in the plurality of datasets that correlate to the target metric can be identified by analyzing the datasets to identify an association between the target metric and data contained within the datasets. The datasets can be input to the machine learning model to train the machine learning model to generate predictions of the target metric, and the machine learning model can be deployed to computing resources in a service provider environment to generate predictions associated with the target metric.

Abstract: A method and apparatus are disclosed for forming an image signal by receiving a flux of photons at a convex photodetector such as a hemispherical photodetector. The convex photodetector includes a plurality of photosensors. Each photosensor has a different orientation with respect to a propagation vector of the flux of photons. The photosensors generate a respective plurality of intensity signals. Each of the intensity signals is related to the respective orientation of the photosensor that generates it. The intensity signals are received by a signal processor, such as a digital signal processor, which uses the intensity signals to compute an image signal related to the intensity signals and thereby produce a focused output image.

Abstract: A method and apparatus are disclosed for forming an image signal by receiving a flux of photons at a convex photodetector such as a hemispherical photodetector. The convex photodetector includes a plurality of photosensors. Each photosensor has a different orientation with respect to a propagation vector of the flux of photons. The photosensors generate a respective plurality of intensity signals. Each of the intensity signals is related to the respective orientation of the photosensor that generates it. The intensity signals are received by a signal processor, such as a digital signal processor, which uses the intensity signals to compute an image signal related to the intensity signals and thereby produce a focused output image.

Abstract: A method and apparatus are disclosed for forming an image signal by receiving a flux of photons at a convex photodetector such as a hemispherical photodetector. The convex photodetector includes a plurality of photosensors. Each photosensor has a different orientation with respect to a propagation vector of the flux of photons. The photosensors generate a respective plurality of intensity signals. Each of the intensity signals is related to the respective orientation of the photosensor that generates it. The intensity signals are received by a signal processor, such as a digital signal processor, which uses the intensity signals to compute an image signal related to the intensity signals and thereby produce a focused output image.

Abstract: A method and apparatus are disclosed for forming an image signal by receiving a flux of photons at a convex photodetector such as a hemispherical photodetector. The convex photodetector includes a plurality of photosensors. Each photosensor has a different orientation with respect to a propagation vector of the flux of photons. The photosensors generate a respective plurality of intensity signals. Each of the intensity signals is related to the respective orientation of the photosensor that generates it. The intensity signals are received by a signal processor, such as a digital signal processor, which uses the intensity signals to compute an image signal related to the intensity signals and thereby produce a focused output image.

Abstract: There is disclosed herein an invention which relates to the fields of genetic engineering, microbiology, and computer science, that allows a user, whether they be a molecular biologist or a clinical diagnostician, to calculate and design extremely specific oligonucleotide probes for DNA and mRNA hybridization procedures. The probes designed with this invention may be used for medical diagnostic kits, DNA identification, and potentially continuous monitoring of metabolic processes in human beings. The key features design oligonucleotide probes based on the GenBank database of DNA and mRNA sequences and examine candidate probes for specificity or commonality with respect to a user-selected experimental preparation. Two models are available: a Mismatch Model, that employs hashing and continuous seed filtration, and an H-Site Model, that analyzes candidate probes for their binding specificity relative to some known set of mRNA or DNA sequences.