California Institute of Technology

Chromacode, Inc.

Yaser Said Abu-Mostafa

Carlos Roberto Gonzalez

Aditya Rajagopal

Alaina Ann Brinley Rajagopal

Dominic Yurk

Harvey L Kasdan

Christopher Macdonald

Paul Flook

Raymond Jimenez

Joseph Sill

Xubo Song

Some implementations of the disclosure describe a blood pressure measurement apparatus and method that enable continuous, non-invasive blood pressure measurement using sound and ultrasound transducers. In one implementation, a blood pressure measurement device includes: a first transducer configured to emit multiple soundwaves having multiple frequencies, the soundwaves configured to cause a blood vessel of a subject to vibrate; a second transducer configured to capture one or more ultrasound images of the blood vessel; and a processing device configured to: determine, based on the one or more ultrasound images, a wall thickness, a radius, and a resonant frequency of the blood vessel; and calculate, based on the wall thickness, the radius, and the resonant frequency, a blood pressure of the subject.

Blood pressure measurement apparatus and methods of use thereof

The present disclosure provides methods and compositions for the multiplexed detection of multiple analytes from a sample. Analytes may be nucleic acid analytes. Detection of analytes may comprise contacting one or more sample subsets with hybridization probes, thereby generating one or more cumulative signal measurements capable of detecting the presence of absence of a plurality of analytes.

Formulations and signal encoding and decoding methods for massively multiplexed biochemical assays

Technologies are generally described for methods and systems effective to determine a weight benefit associated with application of weights to training data in a machine learning environment. In an example, a device may determine a first function based on the training data, where the training data includes training inputs and training labels. The device may determine a second function based on weighted training data, which is based on application of weights to the training data. The device may determine a third function based on target data, where the target data is generated based on a target function. The target data may include target labels different from the training labels. The device may determine a fourth function based on weighted target data, which is a result of application of weights to the target data. The device may determine the weight benefit based on the first, second, third, and fourth functions.

Weight benefit evaluator for training data

Technologies are generally described for methods and systems in a machine learning environment. In some examples, a method may include retrieving training data from a memory. The training data may include training inputs and training labels. The methods may further include determining a set of datasets based on the training inputs. The methods may further include determining a set of out of sample errors based on the training inputs and based on test data. Each out of sample error may correspond to a respective dataset in the set of datasets. The methods may further include generating alternative distribution data based on the set of out of sample errors. The alternative distribution data may be used to determine weights to be applied to the training data.

Alternative training distribution data in machine learning

Technologies are generally described for systems, devices and methods relating to determining weights in a machine learning environment. In some examples, a training distribution of training data may be identified, information about a test distribution of test data, and a coordinate of the training data and the test data may be identified. Differences between the test distribution and the training distribution may be determined, for the coordinate. A weight importance parameter may be identified, for the coordinate. A processor may calculate weights based on the differences, and based on the weight importance parameter. The weights may be adapted to cause the training distribution to conform to the test distribution at a degree of conformance. The degree of conformance may be based on the weight importance parameter.

Generation of weights in machine learning

Technologies are generally described for systems, devices and methods relating to a machine learning environment. In some examples, a processor may identify a training distribution of a training data. The processor may identify information about a test distribution of a test data. The processor may identify a coordinate of the training data and the test data. The processor may determine, for the coordinate, differences between the test distribution and the training distribution. The processor may determine weights based on the differences. The weights may be adapted to cause the training distribution to conform to the test distribution when the weights are applied to the training distribution.

Weight generation in machine learning

Analysis of white blood cells is carried out by first analyzing the sample using image processing to obtain a no-context estimate of each cell's content. Then, a post processing operation is carried out to refine the estimate to include information about neighboring white blood cells. The estimate is refined to take into account the identity of the neighboring cells.

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Pasadena, CA, United States of America

Yaser Said Abu-Mostafa