Assisted Identification over Modulo-Additive Noise Channels.

The gain in the identification capacity afforded by a rate-limited description of the noise sequence corrupting a modulo-additive noise channel is studied. Both the classical Ahlswede-Dueck version and the Ahlswede-Cai-Ning-Zhang version, which does not allow for missed identifications, are studied. Irrespective of whether the description is provided to the receiver, to the transmitter, or to both, the two capacities coincide and both equal the helper-assisted Shannon capacity.

All-optical image identification with programmable matrix transformation.

An optical neural network is proposed and demonstrated with programmable matrix transformation and nonlinear activation function of photodetection (square-law detection). Based on discrete phase-coherent spatial modes, the dimensionality of programmable optical matrix operations is 30∼37, which is implemented by spatial light modulators. With this architecture, all-optical classification tasks of handwritten digits, objects and depth images are performed. The accuracy values of 85.0% and 81.0% are experimentally evaluated for MNIST (Modified National Institute of Standards and Technology) digit and MNIST fashion tasks, respectively. Due to the parallel nature of matrix multiplication, the processing speed of our proposed architecture is potentially as high as 7.4∼74 T FLOPs per second (with 10∼100 GHz detector).

Automated meal detection from continuous glucose monitor data through simulation and explanation.

BACKGROUND: Artificial pancreas systems aim to reduce the burden of type 1 diabetes by automating insulin dosing. These systems link a continuous glucose monitor (CGM) and insulin pump with a control algorithm, but require users to announce meals, without which the system can only react to the rise in blood glucose. OBJECTIVE: We investigate whether CGM data can be used to automatically infer meals in daily life even in the presence of physical activity, which can raise or lower blood glucose. MATERIALS AND METHODS: We propose a novel meal detection algorithm that combines simulations with CGM, insulin pump, and heart rate monitor data. When observed and predicted glucose differ, our algorithm uses simulations to test whether a meal may explain this difference. We evaluated our method on simulated data and real-world data from individuals with type 1 diabetes. RESULTS: In simulated data, we detected meals earlier and with higher accuracy than was found in prior work (25.7 minutes, 1.2 g error; compared with 48.3 minutes, 17.2 g error). In real-world data, we discovered a larger number of plausible meals than was found in prior work (30 meals, 76.7% accepted; compared with 33 meals, 39.4% accepted). DISCUSSION: Prior research attempted meal detection from CGM, but had delays and lower accuracy in real data or did not allow for physical activity. Our approach can be used to improve insulin dosing in an artificial pancreas and trigger reminders for missed meal boluses. CONCLUSIONS: We demonstrate that meal information can be robustly inferred from CGM and body-worn sensor data, even in challenging environments of daily life.