Controllable linear π\pi-phase modulation in a thermal atom vapor without diffraction or absorption

A scheme is proposed to achieve substantial controllable phase modulation for a probe field propagating through a thermal atomic vapor in double-$\Lambda$ configuration. The phase modulation is based on the linear susceptibility of the probe field, paraxial diffraction is eliminated by exploiting the thermal motion of atoms, and residual absorption is compensated via an incoherent pump field. As a result, a strong controllable uniform phase modulation without paraxial diffraction is achieved essentially independent of the spatial profile or the intensity of the probe field. This phase shift can be controlled via the intensities of the control or the incoherent pump fields. A possible proof-of-principle experiment in alkali atoms is discussed.Comment: 10 pages, 7 figure

Nonlocal nonlinear response of thermal Rydberg atoms and modulational instability in absorptive nonlinear media

Nonlinear and nonlocal effects are discussed in the interaction of laser fields with thermal Rydberg atoms in electromagnetically induced transparency configuration. We show that under the crucial approximation that the time variation in the dipole-dipole interactions due to atomic motions can be neglected in an ensemble average, an analytical form can be obtained for the nonlocal nonlinear atomic response of the thermal medium, and study it for different parameter cases. We further propose a generalized model to describe the modulational instability (MI) in absorptive nonlinear media, in order to understand the propagation dynamics in the thermal Rydberg medium. Interestingly, this model predicts that at short propagation distances, each wave component exhibits the MI effect in absorptive nonlinear media, unlike in the purely dispersive case.Comment: 15 pages, 11 figure

Flexible Models for Heterogeneous Biomedical Data

With the development of biomedical sensing techniques and data storage, machine learning has been widely applied to many healthcare applications from the abundance of data resources. However, biomedical data, from real-world applications, has the nature of heterogeneity, and this heterogeneity has not been comprehensively considered and successfully addressed. The heterogeneity in biomedical data includes the various data distributions, the irregularly sampled timeseries data, the variation in the time domain, and other heterogeneous factors such as uncertain labeling. These different types of heterogeneity can happen individually or simultaneously, and sometimes a type of heterogeneity can trigger another one, for instance, a patient’s health condition changed over time, and the doctors made adjustments to the measurements and treatments which causes the irregular feature sampling. Facing the challenge of heterogeneous data, a generalized may have decent performance on average, but fails in certain cases, which should not be ignored in the clinic. In addition, when building individual models for each group of homogeneous data, the training data can become limited, even with a large data size in total. For example, there are a great number of medications, but each of them may not have enough data. The limitation of the generalized models and the possible shortage of training data make the data heterogeneity a very challenging problem to address. Therefore, flexible models are demanded for the various types of heterogeneous biomedical data in real-world applications. This dissertation investigates data heterogeneity and builds flexible models in biomedical data by focusing on different levels of heterogeneity: different types of heterogeneity happening individually, multi-source simultaneous heterogeneity, multiple data modalities on the same task, and clinical translation of data heterogeneity. We start by building different adaptive models for each individual heterogeneity on a certain type of biomedical data, focusing on time series, and then addressing a more complex situation of simultaneous heterogeneity. Next, the problem setting is extended from time-series data only to multiple data modalities, and finally, we introduce a clinical translation model trying to understand the data heterogeneity. Based on the focus on the heterogeneity in each type of data, transfer learning, adversarial training, and meta-learning techniques are proposed and applied to build adaptive models

Coherent control and manipulation of classical or quantum light via nonlocal effects

The thesis is devoted to the theoretical studies of coherent control and manipulation of classical or quantum light via nonlocal effects. At the classical level, controllable light propagation dynamics in the paraxial regime is investigated. The specific type of nonlocal linear effects induced in thermal atomic vapor is explored to achieve diffraction-less and lossless propagation, uniform phase modulation, and frequency conversion with diffractionless image duplication for laser beams with arbitrarily encoded spatial profiles. Next, the study is extended to investigate propagation dynamics in the presence of nonlocal nonlinear effects generated in thermal interacting Rydberg atoms, which mainly reveals simultaneous competition between the nonlocal nonlinear absorption and the modulational instability for each wave component. Moreover, parity-time (PT) sym- metric dynamics in cold Rydberg atoms are exploited, and it is shown that a phase transition from unbroken to broken PT symmetry can be induced by nonlocal nonlinear effects. At the quantum level, it is further proposed to test the quantum nonlocality of single x-ray photons in a system where very weak x-ray pulses interact with 57 Fe nuclei in a thin cavity, such that a Bell-like inequality in the single-photon version is violated. All these proposals are feasible in current experimental settings