Ab initio holography

We apply the quantum renormalization group to construct a holographic dual for the U(N) vector model for complex bosons defined on a lattice. The bulk geometry becomes dynamical as the hopping amplitudes which determine connectivity of space are promoted to quantum variables. In the large N limit, the full bulk equations of motion for the dynamical hopping fields are numerically solved for finite systems. From finite size scaling, we show that different phases exhibit distinct geometric features in the bulk. In the insulating phase, the space gets fragmented into isolated islands deep inside the bulk, exhibiting ultra-locality. In the superfluid phase, the bulk exhibits a horizon beyond which the geometry becomes non-local. Right at the horizon, the hopping fields decay with a universal power-law in coordinate distance between sites, while they decay in slower power-laws with continuously varying exponents inside the horizon. At the critical point, the bulk exhibits a local geometry whose characteristic length scale diverges asymptotically in the IR limit.Comment: 44+11 pages, many figures, added how to extract critical exponent from bulk (Fig. 13), other minor change

Fractionalized topological insulators from frustrated spin models in three dimensions

We present a theory of three dimensional fractionalized topological insulators in the form of U(1) spin liquids with gapped fermionic spinons in the bulk and topologically protected gapless spinon surface states. Starting from a spin-1/2 model on a pyrochlore lattice, with frustrated antiferromagnetic and ferromagnetic exchange interactions, we show that decomposition of the latter interactions, within slave-fermion representation of the spins, can naturally give rise to an emergent spin-orbit coupling for the spinons. This stabilizes a fractionalized topological insulators which also have bulk bond spin-nematic order. Finally, we describe the low energy properties of these states.Comment: 10 page

Microscopic origin of bipolar resistive switching of nanoscale titanium oxide thin films

We report a direct observation of the microscopic origin of the bipolar resistive switching behavior in nanoscale titanium oxide films. Through a high-resolution transmission electron microscopy, an analytical TEM technique using energy-filtering transmission electron microscopy and an in situ x-ray photoelectron spectroscopy, we demonstrated that the oxygen ions piled up at top interface by an oxidation-reduction reaction between the titanium oxide layer and the top Al metal electrode. We also found that the drift of oxygen ions during the on/off switching induced the bipolar resistive switching in the titanium oxide thin films.Comment: 10 pages, 4 figure

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash???Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application

Monitoring Coastal Chlorophyll-a Concentrations in Coastal Areas Using Machine Learning Models

Harmful algal blooms have negatively affected the aquaculture industry and aquatic ecosystems globally. Remote sensing using satellite sensor systems has been applied on large spatial scales with high temporal resolutions for effective monitoring of harmful algal blooms in coastal waters. However, oceanic color satellites have limitations, such as low spatial resolution of sensor systems and the optical complexity of coastal waters. In this study, bands 1 to 4, obtained from Landsat-8 Operational Land Imager satellite images, were used to evaluate the performance of empirical ocean chlorophyll algorithms using machine learning techniques. Artificial neural network and support vector machine techniques were used to develop an optimal chlorophyll-a model. Four-band, four-band-ratio, and mixed reflectance datasets were tested to select the appropriate input dataset for estimating chlorophyll-a concentration using the two machine learning models. While the ocean chlorophyll algorithm application on Landsat-8 Operational Land Imager showed relatively low performance, the machine learning methods showed improved performance during both the training and validation steps. The artificial neural network and support vector machine demonstrated a similar level of prediction accuracy. Overall, the support vector machine showed slightly superior performance to that of the artificial neural network during the validation step. This study provides practical information about effective monitoring systems for coastal algal blooms

Sensitivity Analysis and Optimization of a Radiative Transfer Numerical Model for Turbid Lake Water

Remote sensing can detect and map algal blooms. The HydroLight (Sequoia Scientific Inc., Bellevue, Washington, DC, USA) model generates the reflectance profiles of various water bodies. However, the influence of model parameters has rarely been investigated for inland water. Moreover, the simulation time of the HydroLight model increases as the amount of input data increases, which limits the practicality of the HydroLight model. This study developed a graphical user interface (GUI) software for the sensitivity analysis of the HydroLight model through multiple executions. The GUI software stably performed parameter sensitivity analysis and substantially reduced the simulation time by up to 92%. The GUI software results for lake water show that the backscattering ratio was the most important parameter for estimating vertical reflectance profiles. Based on the sensitivity analysis results, parameter calibration of the HydroLight model was performed. The reflectance profiles obtained using the optimized parameters agreed with observed profiles, with R-2 values of over 0.98. Thus, a strong relationship between the backscattering coefficient and the observed cyanobacteria genera cells was identified

The ice composition in the disk around V883 Ori revealed by its stellar outburst

Complex organic molecules (COMs), which are the seeds of prebiotic material and precursors of amino acids and sugars, form in the icy mantles of circumstellar dust grains but cannot be detected remotely unless they are heated and released to the gas phase. Around solar-mass stars, water and COMs only sublimate in the inner few au of the disk, making them extremely difficult to spatially resolve and study. Sudden increases in the luminosity of the central star will quickly expand the sublimation front (so-called snow line) to larger radii, as seen previously in the FU Ori outburst of the young star V883 Ori. In this paper, we take advantage of the rapid increase in disk temperature of V883 Ori to detect and analyze five different COMs, methanol, acetone, acetonitrile, acetaldehyde, and methyl formate, in spatially-resolved submillimeter observations. The COMs abundances in V883 Ori is in reasonable agreement with cometary values. This result suggests that outbursting young stars can provide a special opportunity to study the ice composition of material directly related to planet formation.Comment: Published in Nature Astronom

Deep Learning for Simulating Harmful Algal Blooms Using Ocean Numerical Model

In several countries, the public health and fishery industries have suffered from harmful algal blooms (HABs) that have escalated to become a global issue. Though computational modeling offers an effective means to understand and mitigate the adverse effects of HABs, it is challenging to design models that adequately reflect the complexity of HAB dynamics. This paper presents a method involving the application of deep learning to an ocean model for simulating blooms of Alexandrium catenella. The classification and regression convolutional neural network (CNN) models are used for simulating the blooms. The classification CNN determines the bloom initiation while the regression CNN estimates the bloom density. GoogleNet and Resnet 101 are identified as the best structures for the classification and regression CNNs, respectively. The corresponding accuracy and root means square error values are determined as 96.8% and 1.20 [log(cells L-1)], respectively. The results obtained in this study reveal the simulated distribution to follow the Alexandrium catenella bloom. Moreover, Grad-CAM identifies that the salinity and temperature contributed to the initiation of the bloom whereas NH4-N influenced the growth of the bloom.</p&gt

Association between dyslipidemia and asthma in children: a systematic review and multicenter cohort study using a common data model

Background The association between dyslipidemia and asthma in children remains unclear. Purpose This study investigated the association between dyslipidemia and cholesterol levels in children. Methods A systematic literature review was performed to identify studies investigating the association between dyslipidemia and asthma in children. The PubMed database was searched for articles published from January 2000–March 2022. Data from a cohort study using electronic health records from 5 hospitals, converted to the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM), were used to identify the association between total cholesterol (TC) levels and asthma in children. This cohort study used the Cox proportional hazards model to examine hazard ratio (HR) of asthma after propensity score matching, and included an aggregate meta-analysis of HR. Results We examined 11 studies reporting an association between dyslipidemia and asthma in children. Most were cross-sectional; however, their results were inconsistent. In OMOP-CDM multicenter analysis, the high TC (>170 mg/dL) group included 29,038 children, while the normal TC (≤170 mg/dL) group included 88,823 children including all hospital datasets. In a meta-analysis of this multicenter cohort, a significant association was found between high TC levels and later development of asthma in children <15 years of age (pooled HR, 1.30; 95% confidence interval, 1.12–1.52). Conclusion Elevated TC levels in children may be associated with asthma