Abstract: A system and method for health state estimation. In some embodiments, the method includes receiving a first measurement of a subject, the first measurement being a first tissue spectrum of the subject; and generating, using a machine learning inference process based on the first measurement, an estimate of an aspect of the health state of the subject.

Abstract: A method for determining a calibration function includes: calculating a first distance, between a distribution of target spectra and a comparison distribution of spectra; calibrating the distribution of target spectra with a first preliminary calibration function to form a first distribution of calibrated target spectra; calculating a second distance, between the first distribution of calibrated target spectra and the comparison distribution of spectra; determining that the second distance is less than the first distance; and setting the calibration function equal to the first preliminary calibration function.

Abstract: An example private processing pipeline may include: a masked decryption unit to perform a masked decryption operation transforming input data into masked decrypted data; a masked functional unit to produce a masked result by performing a masked operation on the masked decrypted data; and a masked encryption unit to perform a masked encryption operation transforming the masked result into an encrypted result.

Abstract: Embodiments of the present disclosure are directed toward techniques and apparatus comprising at least one layer of an ONN that includes an optical matrix multiplier provided in a semiconductor substrate to receive a plurality of optical signal inputs and to linearly transform the plurality of optical signal inputs into a plurality of optical signal outputs. The optical matrix multiplier comprises one or more 2×2 unitary optical matrices optically interconnected to implement a singular value decomposition (SVD) of a matrix, and a nonlinear optical device coupled with the optical matrix multiplier in the semiconductor substrate, to receive the optical signal outputs and to provide an optical output that is generated in a nonlinear manner in response to the optical signal outputs of the optical matrix multiplier reaching saturation or attenuation. Additional embodiments may be described and claimed.

Abstract: Embodiments of the present disclosure are directed toward techniques and apparatus comprising at least one layer of an ONN that includes an optical matrix multiplier provided in a semiconductor substrate to receive a plurality of optical signal inputs and to linearly transform the plurality of optical signal inputs into a plurality of optical signal outputs. The optical matrix multiplier comprises one or more 2×2 unitary optical matrices optically interconnected to implement a singular value decomposition (SVD) of a matrix, and a nonlinear optical device coupled with the optical matrix multiplier in the semiconductor substrate, to receive the optical signal outputs and to provide an optical output that is generated in a nonlinear manner in response to the optical signal outputs of the optical matrix multiplier reaching saturation or attenuation. Additional embodiments may be described and claimed.

Abstract: Embodiments of the present disclosure are directed toward techniques and configurations for a photonics integrated circuit (IC) for an optical neural network (ONN). In embodiments, the photonics IC includes monolithically optoelectronic components in a single semiconductor substrate including a combination of one or more of integrated array of light sources, a plurality of optical modulators, an optical unitary matrix multiplier, non-linear optical amplifiers or attenuators, and a plurality of photodetectors. In embodiments, the optical unitary matrix multiplier comprises a plurality of 2×2 unitary optical matrices optically interconnected, wherein each 2×2 unitary optical matrix comprises a plurality of phase shifters. In embodiments, each 2×2 unitary optical matrix is to phase shift, split, and/or combine one or more of the optical signal inputs. Other embodiments may be described and/or claimed.

Abstract: Embodiments of the present disclosure are directed toward techniques and apparatus comprising at least one layer of an ONN that includes an optical matrix multiplier provided in a semiconductor substrate to receive a plurality of optical signal inputs and to linearly transform the plurality of optical signal inputs into a plurality of optical signal outputs. The optical matrix multiplier comprises one or more 2×2 unitary optical matrices optically interconnected to implement a singular value decomposition (SVD) of a matrix, and a nonlinear optical device coupled with the optical matrix multiplier in the semiconductor substrate, to receive the optical signal outputs and to provide an optical output that is generated in a nonlinear manner in response to the optical signal outputs of the optical matrix multiplier reaching saturation or attenuation. Additional embodiments may be described and claimed.

Abstract: Techniques and configurations for an optical neural network (ONN) with layers of optical matrix multipliers and an optical nonlinearity function are described herein. The techniques provide for programmable matrix multipliers, allowing for a partitioned use of a part of a matrix as needed, for computation efficiency. The techniques provide for multiple pass-through the same optical matrix die on the same photonic integrated circuit (PIC) chip and for connecting multiple layers of the ONN and running through them in sequence. The techniques further provide for scaling the ONN to different sizes. Additional embodiments may be described and claimed.

Abstract: An example private processing pipeline may include: a masked decryption unit to perform a masked decryption operation transforming input data into masked decrypted data; a masked functional unit to produce a masked result by performing a masked operation on the masked decrypted data; and a masked encryption unit to perform a masked encryption operation transforming the masked result into an encrypted result.

Abstract: Various systems and methods of dynamically filtering depth estimates to generate a volumetric map of a three-dimensional (3-D) environment having an adjustable maximum depth include obtaining sensor output including depth estimates and pose estimates in a robotic vehicle, detecting a condition that corresponds to an error level in the pose estimates, filtering the depth estimates obtained from the sensor output based on the detected condition, and generating the volumetric map of the 3-D environment using the filtered depth estimates. Filtering depth estimates obtained from the sensor output based on the detected condition may include adjusting a maximum depth parameter for generating the volumetric map. Further embodiments include a robotic vehicle and/or a computing device within a robotic vehicle including a processor configured with processor-executable instructions for controlling the maximum depth of a volumetric map of a 3-D environment.

Abstract: The invention is used to shape noise in time domain and frequency domain coding schemes. The method advantageously uses a noise weighting filter based on a filterbank with variable gains. A method is presented for decoding an encoded signal based on received side information and on a masking matrix derived from the masking properties of speech. In particular, the encoded signal is separated into subband signal components, each of which is multiplied by a corresponding gain value based on the masking matrix. These multiplied subband signal components are then combined to produce a decoded signal.

Abstract: The invention is used to shape noise in time domain and frequency domain coding schemes. The method advantageously uses a noise weighting filter based on a filterbank with variable gains. A method is presented for computing the gains in the noise weighting filterbank with filter parameters derived from the masking properties of speech. Illustrative embodiments of the method in various coding schemes are illustrated.