“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

For many years, laser-driven ion acceleration, mainly proton acceleration, has been proposed and a number of proof-of-principle experiments have been carried out with lasers whose pulse duration was in the nanosecond range. In the 1990s, ion acceleration in a relativistic plasma was demonstrated with ultra-short pulse lasers based on the chirped pulse amplification technique which can provide not only picosecond or femtosecond laser pulse duration, but simultaneously ultra-high peak power of terawatt to petawatt levels. Starting from the year 2000, several groups demonstrated low transverse emittance, tens of MeV proton beams with a conversion efficiency of up to several percent. The laser-accelerated particle beams have a duration of the order of a few picoseconds at the source, an ultra-high peak current and a broad energy spectrum, which make them suitable for many, including several unique, applications. This paper reviews, firstly, the historical background including the early laser-matter interaction studies on energetic ion acceleration relevant to inertial confinement fusion. Secondly, we describe several implemented and proposed mechanisms of proton and/or ion acceleration driven by ultra-short high-intensity lasers. We pay special attention to relatively simple models of several acceleration regimes. The models connect the laser, plasma and proton/ion beam parameters, predicting important features, such as energy spectral shape, optimum conditions and scalings under these conditions for maximum ion energy, conversion efficiency, etc. The models also suggest possible ways to manipulate the proton/ion beams by tailoring the target and irradiation conditions. Thirdly, we review experimental results on proton/ion acceleration, starting with the description of driving lasers. We list experimental results and show general trends of parameter dependences and compare them with the theoretical predictions and simulations. The fourth topic includes a review of scientific, industrial and medical applications of laser-driven proton or ion sources, some of which have already been established, while the others are yet to be demonstrated. In most applications, the laser-driven ion sources are complementary to the conventional accelerators, exhibiting significantly different properties. Finally, we summarize the paper.

https://iopscience.iop.org/article/10.1088/0034-4885/75/5/056401/pdf

Review of laser-driven ion sources and their applications.

Optical imaging relies on the ability to illuminate an object, collect and analyse the light it scatters or transmits. Propagation through complex media such as biological tissues was so far believed to degrade the attainable depth, as well as the resolution for imaging, because of multiple scattering. This is why such media are usually considered opaque. Recently, we demonstrated that it is possible to measure the complex mesoscopic optical transmission channels that allow light to traverse through such an opaque medium. Here, we show that we can optimally exploit those channels to coherently transmit and recover an arbitrary image with a high fidelity, independently of the complexity of the propagation.

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Image transmission through an opaque material.

The recovery of objects obscured by scattering is an important goal in imaging and has been approached by exploiting, for example, coherence properties, ballistic photons or penetrating wavelengths. Common methods use scattered light transmitted through an occluding material, although these fail if the occluder is opaque. Light is scattered not only by transmission through objects, but also by multiple reflection from diffuse surfaces in a scene. This reflected light contains information about the scene that becomes mixed by the diffuse reflections before reaching the image sensor. This mixing is difficult to decode using traditional cameras. Here we report the combination of a time-of-flight technique and computational reconstruction algorithms to untangle image information mixed by diffuse reflection. We demonstrate a three-dimensional range camera able to look around a corner using diffusely reflected light that achieves sub-millimetre depth precision and centimetre lateral precision over 40 cm×40 cm×40 cm of hidden space. An important goal in optics is to image objects hidden by turbid media, although line-of-sight techniques fail when the obscuring medium becomes opaque. Veltenet al. use ultrafast imaging techniques to recover three-dimensional shapes of non-line-of-sight objects after reflection from diffuse surfaces.

/pdf/recovering-three-dimensional-shape-around-a-corner-using-3j2ptae4fl.pdf

Recovering three-dimensional shape around a corner using ultrafast time-of-flight imaging.

Active learning (AL) attempts to maximize the performance gain of the model by marking the fewest samples. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize massive parameters, so that the model learns how to extract high-quality features. In recent years, due to the rapid development of internet technology, we are in an era of information torrents and we have massive amounts of data. In this way, DL has aroused strong interest of researchers and has been rapidly developed. Compared with DL, researchers have relatively low interest in AL. This is mainly because before the rise of DL, traditional machine learning requires relatively few labeled samples. Therefore, early AL is difficult to reflect the value it deserves. Although DL has made breakthroughs in various fields, most of this success is due to the publicity of the large number of existing annotation datasets. However, the acquisition of a large number of high-quality annotated datasets consumes a lot of manpower, which is not allowed in some fields that require high expertise, especially in the fields of speech recognition, information extraction, medical images, etc. Therefore, AL has gradually received due attention. A natural idea is whether AL can be used to reduce the cost of sample annotations, while retaining the powerful learning capabilities of DL. Therefore, deep active learning (DAL) has emerged. Although the related research has been quite abundant, it lacks a comprehensive survey of DAL. This article is to fill this gap, we provide a formal classification method for the existing work, and a comprehensive and systematic overview. In addition, we also analyzed and summarized the development of DAL from the perspective of application. Finally, we discussed the confusion and problems in DAL, and gave some possible development directions for DAL.

A Survey of Deep Active Learning

From night vision and objects overwhelmed by sunlight to jammed signals and those that are purposely encrypted, detecting low-level or hidden signals is a fundamental problem in imaging. Here, we develop and exploit a new type of stochastic resonance, in which nonlinear coupling allows signals to grow at the expense of noise, to recover noise-hidden images propagating in a self-focusing medium. The growth rate is derived analytically by treating the signal–noise interaction as a photonic beam–plasma instability and matches experimentally measured resonances in coupling strength, noise statistics and modal content of the signal. This is the first observation of nonlinear intensity exchange between coherent and spatially incoherent light and the first demonstration of spatial coherence resonance for a dynamically evolving signal. The results suggest a general method of reconstructing images through seeded instability and confirm information limits predicted, but not yet observed, in nonlinear communications systems. By exploiting stochastic resonance — in which nonlinear coupling allows signals to grow at the expense of noise — scientists show that they can recover noise-hidden images propagating in a self-focusing medium. The findings pave the way for a variety of nonlinear instability-driven imaging techniques.

Nonlinear self-filtering of noisy images via dynamical stochastic resonance

Particle-in-cell simulations show that the inhomogeneity scale of the plasma produced in the interaction of high-power laser radiation with gas targets is of fundamental importance for ion acceleration. In a plasma slab with sharp boundaries, the quasistatic magnetic field and the associated electron vortex structure produced by fast electron beams both expand along the slab boundary in a direction perpendicular to the plasma density gradient, forming an extended region with a quasistatic electric field, in which the ions are accelerated. In a plasma with a smooth density distribution, the dipole magnetic field can propagate toward the lower plasma density in the propagation direction of the laser pulse. In this case, the electron density in an electric current filament at the axis of the magnetic dipole decreases to values at which the charge quasineutrality condition fails to hold. In electric fields generated by this process, the ions are accelerated to energies substantially higher than those characteristic of plasma configurations with sharp boundaries.

Ion acceleration in a dipole vortex in a laser plasma corona

We demonstrate an all-optical bump-on-tail instability by considering the nonlinear interaction of two partially coherent spatial beams. For weak wave coupling, we observe momentum transfer with no variation in intensity. For strong wave coupling, modulations appear in intensity and evidence appears for wave (Langmuir) collapse at large scales. Borrowing plasma language, these limits represent regimes of weak and strong spatial optical turbulence. In both limits, the internal spectral energy redistribution is observed by recording and reconstructing a hologram of the evolving dynamics. The results are universal and can appear in any wave-kinetic system with short-wave-long-wave coupling.

Observation of all-optical bump-on-tail instability.

Anomaly detection is the problem of recognizing abnormal inputs based on the seen examples of normal data. Despite recent advances of deep learning in recognizing image anomalies, these methods still prove incapable of handling complex images, such as those encountered in the medical domain. Barely visible abnormalities in chest X-rays or metastases in lymph nodes on the scans of the pathology slides resemble normal images and are very difficult to detect. To address this problem, we introduce a new powerful method of image anomaly detection. It relies on the classical autoencoder approach with a re-designed training pipeline to handle high-resolution, complex images, and a robust way of computing an image abnormality score. We revisit the very problem statement of fully unsupervised anomaly detection, where no abnormal examples are provided during the model setup. We propose to relax this unrealistic assumption by using a very small number of anomalies of confined variability merely to initiate the search of hyperparameters of the model. We evaluate our solution on two medical datasets containing radiology and digital pathology images, where the state-of-the-art anomaly detection models, originally devised for natural image benchmarks, fail to perform sufficiently well. The proposed approach suggests a new baseline for anomaly detection in medical image analysis tasks.

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Anomaly Detection in Medical Imaging With Deep Perceptual Autoencoders

We experimentally demonstrate diffraction from a straight edge in a medium with self-focusing nonlinearity Diffraction into the shadow region is suppressed with increasing nonlinearity, but mode coupling leads to excitations and traveling waves on the high-intensity side Theoretically, we interpret these modulations as spatially dispersive shock waves with negative pressure

Dmitry V. Dylov

Papers

Nonlinear self-filtering of noisy images via dynamical stochastic resonance

Ion acceleration in a dipole vortex in a laser plasma corona

Observation of all-optical bump-on-tail instability.

Anomaly Detection in Medical Imaging With Deep Perceptual Autoencoders

Diffraction from an edge in a self-focusing medium.