CHARLES: A C++ fixed-point library for Photonic-Aware Neural Networks.

In this paper we present CHARLES (C++ pHotonic Aware neuRaL nEtworkS), a C++ library aimed at providing a flexible tool to simulate the behavior of Photonic-Aware Neural Network (PANN). PANNs are neural network architectures aware of the constraints due to the underlying photonic hardware, mostly in terms of low equivalent precision of the computations. For this reason, CHARLES exploits fixed-point computations for inference, while it supports both floating-point and fixed-point numerical formats for training. In this way, we can compare the effects due to the quantization in the inference phase when the training phase is performed on a classical floating-point model and on a model exploiting high-precision fixed-point numbers. To validate CHARLES and identify the most suited numerical format for PANN training, we report the simulation results obtained considering three datasets: Iris, MNIST, and Fashion-MNIST. Fixed-training is shown to outperform floating-training when executing inference on bitwidths suitable for photonic implementation. Indeed, performing the training phase in the floating-point domain and then quantizing to lower bitwidths results in a very high accuracy loss. Instead, when fixed-point numbers are exploited in the training phase, the accuracy loss due to quantization to lower bitwidths is significantly reduced. In particular, we show that for Iris dataset, fixed-training achieves a performance similar to floating-training. Fixed-training allows to obtain an accuracy of 90.4% and 68.1% with the MNIST and Fashion-MNIST datasets using only 6 bits, while the floating-training reaches an accuracy of just 25.4% and 50.0% when exploiting the same bitwidths.

Risk-Taking Behaviors of Adult Bedridden Patients in Neurosurgery: What Could/Should We Do?

Risk-taking behaviors of adult bedridden patients in neurosurgery are frequent, however little analyzed. We aimed to estimate from the literature and our clinical experience the incidence of the different clinical pictures. Risk-taking behaviors seem to be more frequent than reported. They are often minor, but they can lead to death, irrespective of the prescription of physical or chemical constraints. We also aimed to contextualize the risks, and to describe the means reducing the consequences for the patients. Two main conditions were identified, the loss of awareness of risk-taking behaviors by the patient, and uncontrolled body motions. Besides, current experience feedback analyses and new non-exclusive technological solutions could limit the complications, while improving prevention with wearable systems, neighborhood sensors, or room monitoring and service robots. Further research is mandatory to develop efficient and reliable systems avoiding complications and saving lives. Ethical and legal issues must also be accounted for, notably concerning the privacy of patients and caregivers.