Lasing without inversion in three-level systems : self-pulsing in the cascade schemes

Lasing without inversion (LWI) in specific models of closed three-level systems is analyzed in terms of nonlinear dynamics. From a linear stability analysis of the trivial nonlasing solution of the homogeneously broadened systems with on-resonance driving and laser fields, we find that, near lasing threshold, resonant closed Λ and V schemes yield continuous-wave LWI while resonant cascade schemes can give rise to self-pulsing LWI. The origin of this different behavior is discussed. For parameters of a real cascade system in atomic 138Ba we check numerically that the self-pulsing solution is stable in a broad range of nonzero detunings. It is shown that the self-pulsing emission can still be observed when the typical residual Doppler broadening of an atomic beam is taken into account

Predicting Short-term MCI-to-AD Progression Using Imaging, CSF, Genetic Factors, Cognitive Resilience, and Demographics.

In the Alzheimer’s disease (AD) continuum, the prodromal state of mild cognitive impairment (MCI) precedes AD dementia and identifying MCI individuals at risk of progression is important for clinical management. Our goal was to develop generalizable multivariate models that integrate highdimensional data (multimodal neuroimaging and cerebrospinal fuid biomarkers, genetic factors, and measures of cognitive resilience) for identifcation of MCI individuals who progress to AD within 3 years. Our main fndings were i) we were able to build generalizable models with clinically relevant accuracy (~93%) for identifying MCI individuals who progress to AD within 3 years; ii) markers of AD pathophysiology (amyloid, tau, neuronal injury) accounted for large shares of the variance in predicting progression; iii) our methodology allowed us to discover that expression of CR1 (complement receptor 1), an AD susceptibility gene involved in immune pathways, uniquely added independent predictive value. This work highlights the value of optimized machine learning approaches for analyzing multimodal patient information for making predictive assessments

Phase-dependent spectra in a driven two-level atom

We propose a method to observe phase-dependent spectra in resonance fluorescence, employing a two-level atom driven by a strong coherent field and a weak, amplitude-fluctuating field. The spectra are similar to those which occur in a squeezed vacuum, but avoid the problem of achieving squeezing over a $4\pi$ solid angle. The system shows other interesting features, such as pronounced gain without population inversion.Comment: 4 pages and 4 figures. Submitted to Phys. Rev. Let

Challenges and Opportunities with Causal Discovery Algorithms: Application to Alzheimer’s Pathophysiology

© 2020, The Author(s). Causal Structure Discovery (CSD) is the problem of identifying causal relationships from large quantities of data through computational methods. With the limited ability of traditional association-based computational methods to discover causal relationships, CSD methodologies are gaining popularity. The goal of the study was to systematically examine whether (i) CSD methods can discover the known causal relationships from observational clinical data and (ii) to offer guidance to accurately discover known causal relationships. We used Alzheimer’s disease (AD), a complex progressive disease, as a model because the well-established evidence provides a “gold-standard” causal graph for evaluation. We evaluated two CSD methods, Fast Causal Inference (FCI) and Fast Greedy Equivalence Search (FGES) in their ability to discover this structure from data collected by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We used structural equation models (which is not designed for CSD) as control. We applied these methods under three scenarios defined by increasing amounts of background knowledge provided to the methods. The methods were evaluated by comparing the resulting causal relationships with the “gold standard” graph that was constructed from literature. Dedicated CSD methods managed to discover graphs that nearly coincided with the gold standard. For best results, CSD algorithms should be used with longitudinal data providing as much prior knowledge as possible

Excluding ionospherically unsafe satellite geometries in GBAS CAT-I

We show the results of the implementation of a preliminary algorithm for excluding ionospherically unsafe satellite geometries in Ground-Based Augmentation Systems Category I. Minimum knowledge of the ionospheric treat model is assumed and the assistance of the code-carrier divergence monitor is not considered. All the satellites in view above 5◦ in elevation are included in the computations. The inflation of the standard deviation of the vertical ionospheric gradient implements the exclusion. Full availability remains for a typical day in the site of La Plata Airport.VIII Workshop Procesamiento de Señales y Sistemas de Tiempo Real (WPSTR).Red de Universidades con Carreras en Informática (RedUNCI

Narrowing of EIT resonance in a Doppler Broadened Medium

We derive an analytic expression for the linewidth of EIT resonance in a Doppler broadened system. It is shown here that for relatively low intensity of the driving field the EIT linewidth is proportional to the square root of intensity and is independent of the Doppler width, similar to the laser induced line narrowing effect by Feld and Javan. In the limit of high intensity we recover the usual power broadening case where EIT linewidth is proportional to the intensity and inversely proportional to the Doppler width.Comment: 4 pages, 2 figure

Out of distribution detection for intra-operative functional imaging

Multispectral optical imaging is becoming a key tool in the operating room. Recent research has shown that machine learning algorithms can be used to convert pixel-wise reflectance measurements to tissue parameters, such as oxygenation. However, the accuracy of these algorithms can only be guaranteed if the spectra acquired during surgery match the ones seen during training. It is therefore of great interest to detect so-called out of distribution (OoD) spectra to prevent the algorithm from presenting spurious results. In this paper we present an information theory based approach to OoD detection based on the widely applicable information criterion (WAIC). Our work builds upon recent methodology related to invertible neural networks (INN). Specifically, we make use of an ensemble of INNs as we need their tractable Jacobians in order to compute the WAIC. Comprehensive experiments with in silico, and in vivo multispectral imaging data indicate that our approach is well-suited for OoD detection. Our method could thus be an important step towards reliable functional imaging in the operating room.Comment: The final authenticated version is available online at https://doi.org/10.1007/978-3-030-32689-0_

An Augmented Interface to Display Industrial Robot Faults

Technology advancement is changing the way industrial factories have to face an increasingly complex and competitive market. The fourth industrial revolution (known as industry 4.0) is also changing how human workers have to carry out tasks and actions. In fact, it is no longer impossible to think of a scenario in which human operators and industrial robots work side-by-side, sharing the same environment and tools. To realize a safe work environment, workers should trust robots as well as they trust human operators. Such goal is indeed complex to achieve, especially when workers are under stress conditions, such as when a fault occurs and the human operators are no longer able to understand what is happening in the industrial manipulator. Indeed, Augmented Reality (AR) can help workers to visualize in real-time robots’ faults. This paper proposes an augmented system that assists human workers to recognize and visualize errors, improving their awareness of the system. The system has been tested using both an AR see-through device and a smartphone