Network Topologies That Can Achieve Dual Function of Adaptation and Noise Attenuation.

Many signaling systems execute adaptation under circumstances that require noise attenuation. Here, we identify an intrinsic trade-off existing between sensitivity and noise attenuation in the three-node networks. We demonstrate that although fine-tuning timescales in three-node adaptive networks can partially mediate this trade-off in this context, it prolongs adaptation time and imposes unrealistic parameter constraints. By contrast, four-node networks can effectively decouple adaptation and noise attenuation to achieve dual function without a trade-off, provided that these functions are executed sequentially. We illustrate ideas in seven biological examples, including Dictyostelium discoideum chemotaxis and the p53 signaling network and find that adaptive networks are often associated with a noise attenuation module. Our approach may be applicable to finding network design principles for other dual and multiple functions

Transcranial photoacoustic tomography of the monkey brain

A photoacoustic tomography (PAT) system using a virtual point ultrasonic transducer was developed for transcranial imaging of monkey brains. The virtual point transducer provided a 10 times greater field-of-view (FOV) than finiteaperture unfocused transducers, which enables large primate imaging. The cerebral cortex of a monkey brain was accurately mapped transcranially, through up to two skulls ranging from 4 to 8 mm in thickness. The mass density and speed of sound distributions of the skull were estimated from adjunct X-ray CT image data and utilized with a timereversal algorithm to mitigate artifacts in the reconstructed image due to acoustic aberration. The oxygenation saturation (sO_2) in blood phantoms through a monkey skull was also imaged and quantified, with results consistent with measurements by a gas analyzer. The oxygenation saturation (sO_2) in blood phantoms through a monkey skull was also imaged and quantified, with results consistent with measurements by a gas analyzer. Our experimental results demonstrate that PAT can overcome the optical and ultrasound attenuation of a relatively thick skull, and the imaging aberration caused by skull can be corrected to a great extent

Photoacoustic computed tomography correcting for heterogeneity and attenuation

We report an investigation of image reconstruction in photoacoustic tomography for objects that possess heterogeneous material and acoustic attenuation distributions. When the object contains materials, such as bone and soft-tissue, that are modeled using power law attenuation models with distinct exponents, we demonstrate that the effects of acoustic attenuation due to the most strongly attenuating material can be compensated for if the attenuation of the other less attenuating material(s) are neglected. Experiments with phantom objects are presented to validated our findings

Measuring Quantum Entanglement from Local Information by Machine Learning

Entanglement is a key property in the development of quantum technologies and in the study of quantum many-body simulations. However, entanglement measurement typically requires quantum full-state tomography (FST). Here we present a neural network-assisted protocol for measuring entanglement in equilibrium and non-equilibrium states of local Hamiltonians. Instead of FST, it can learn comprehensive entanglement quantities from single-qubit or two-qubit Pauli measurements, such as R\'enyi entropy, partially-transposed (PT) moments, and coherence. It is also exciting that our neural network is able to learn the future entanglement dynamics using only single-qubit traces from the previous time. In addition, we perform experiments using a nuclear spin quantum processor and train an adoptive neural network to study entanglement in the ground and dynamical states of a one-dimensional spin chain. Quantum phase transitions (QPT) are revealed by measuring static entanglement in ground states, and the entanglement dynamics beyond measurement time is accurately estimated in dynamical states. These precise results validate our neural network. Our work will have a wide range of applications in quantum many-body systems, from quantum phase transitions to intriguing non-equilibrium phenomena such as quantum thermalization.Comment: 5 pages, 4 figures. All comments are welcom