In this manuscript, we propose a novel framework of computational ghost imaging, i.e., ghost imaging using deep learning (GIDL). With a set of images reconstructed using traditional GI and the corresponding ground-truth counterparts, a deep neural network was trained so that it can learn the sensing model and increase the quality image reconstruction. Moreover, detailed comparisons between the image reconstructed using deep learning and compressive sensing shows that the proposed GIDL has a much better performance in extremely low sampling rate. Numerical simulations and optical experiments were carried out for the demonstration of the proposed GIDL.

Most of the neural networks proposed so far for computational imaging (CI) in optics employ a supervised training strategy, and thus need a large training set to optimize their weights and biases. Setting aside the requirements of environmental and system stability during many hours of data acquisition, in many practical applications, it is unlikely to be possible to obtain sufficient numbers of ground-truth images for training. Here, we propose to overcome this limitation by incorporating into a conventional deep neural network a complete physical model that represents the process of image formation. The most significant advantage of the resulting physics-enhanced deep neural network (PhysenNet) is that it can be used without training beforehand, thus eliminating the need for tens of thousands of labeled data. We take single-beam phase imaging as an example for demonstration. We experimentally show that one needs only to feed PhysenNet a single diffraction pattern of a phase object, and it can automatically optimize the network and eventually produce the object phase through the interplay between the neural network and the physical model. This opens up a new paradigm of neural network design, in which the concept of incorporating a physical model into a neural network can be generalized to solve many other CI problems.

In Ref. 1, Schubert et al. [Phys. Rev. Research 1, 032004 (2019)] reported measurements of the isothermal magnetoresistance of Fe-and Ni-substituted YbRh2Si2, based on which they raised questions about the Kondo destruction description for the magnetic field-induced quantum critical point (QCP) of pristine YbRh2Si2. Here we make three points. Firstly, as shown by studies on pristine YbRh2Si2 in Paschen et al. and Friedemann et al., isothermal crossed-field and single-field Hall effect measurements are necessary to ascertain the evolution of the Fermi surface across this QCP. Because Schubert et al. did not carry out such measurements, their results on Fe-and Ni-substituted YbRh2Si2 cannot be used to assess the validity of the Kondo destruction picture neither for substituted nor for pristine YbRh2Si2. Secondly, when referring to the data of Friedemann et al. on the isothermal crossover of YbRh2Si2, they did not recognize the implications of the crossover width, quantified by the full width at half maximum (FWHM), being linear in temperature, with zero offset, over about 1.5 decades in temperature, from 30 mK to 1 K. Finally, in claiming deviations of Hall crossover FWHM data of Friedemann et al. from the above linear-in-T dependence they neglected the error bars of these measurements and discarded some of the data points. The claims of Schubert et al. are thus not supported by data, neither previously published nor new (Ref. 1). As such they cannot invalidate the evidence that has been reported for Kondo destruction quantum criticality in YbRh2Si2.Quantum criticality is a topic of considerable interest for a variety of strongly correlated electron systems, with antiferromagnetic heavy fermion systems representing a prototype. From extensive experimental measurements across QCPs of several heavy fermion metals, a variety of properties are found 2-16 to be inconsistent with spin-density-wave quantum criticality [17][18][19] , which is based on Landau's framework of order-parameter fluctuations. Instead, they support Kondo destruction quantum criticality 20-22 , which goes beyond the Landau framework through a critical destruction of the static Kondo entanglement. In particular, across the magnetic field-induced QCP in YbRh 2 Si 2 , the linear-response Hall coefficient determined from a crossed-field Hall measurement 3,5 , along with single-field Hall effect 3,5 , magnetoresistance 3,5 , and thermodynamic properties 4 , provided evidence for an extra energy scale, T * , in the T -B plane. This energy scale goes to zero as the QCP is approached from the non-magnetic side. Isothermal magnetotransport and thermodynamic properties undergo a rapid crossover across the T * -line, which extrapolates to a jump in the T = 0 limit, across generations of YbRh 2 Si 2 samples. These properties are in contrast with the po-larization crossover scenario 1 .Recently, Schubert et al. 1 studied the magnetoresistance of Fe-and Ni-substituted YbRh 2 Si 2 . Primarily based on the isothermal behavior of the magnetoresistance in these dope...

It is well known that in-line digital holography (DH) makes use of the full pixel count in forming the holographic imaging. But it usually requires phase-shifting or phase retrieval techniques to remove the zero-order and twin-image terms, resulting in the so-called two-step reconstruction process, i.e., phase recovery and focusing. Here, we propose a one-step end-to-end learning-based method for in-line holography reconstruction, namely, the eHoloNet, which can reconstruct the object wavefront directly from a single-shot in-line digital hologram. In addition, the proposed learning-based DH technique has strong robustness to the change of optical path difference between reference beam and object light and does not require the reference beam to be a plane or spherical wave.

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The problem of imaging through thick scattering media is encountered in many disciplines of science, ranging from mesoscopic physics to astronomy. Photons become diffusive after propagating through a scattering medium with an optical thickness of over 10 times the scattering mean free path. As a result, no image but only noise-like patterns can be directly formed. We propose a hybrid neural network for computational imaging through such thick scattering media, demonstrating the reconstruction of image information from various targets hidden behind a white polystyrene slab of 3 mm in thickness or 13.4 times the scattering mean free path. We also demonstrate that the target image can be retrieved with acceptable quality from a very small fraction of its scattered pattern, suggesting that the speckle pattern produced in this way is highly redundant. This leads to a profound question of how the information of the target being encoded into the speckle is to be addressed in future studies.

The search of efficient amine-functionalized materials for direct air capture (DAC) of CO2 is highly attractive to achieve negative emissions. Here we report a new family of class 1 adsorbents...

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