Visual Control of Altitude in Flying Drosophila

Unlike creatures that walk, flying animals need to control their horizontal motion as well as their height above the ground. Research on insects, the first animals to evolve flight, has revealed several visual reflexes that are used to govern horizontal course. For example, insects orient toward prominent vertical features in their environment [1], [2], [3], [4] and [5] and generate compensatory reactions to both rotations [6] and [7] and translations [1], [8], [9], [10] and [11] of the visual world. Insects also avoid impending collisions by veering away from visual expansion [9], [12], [13] and [14]. In contrast to this extensive understanding of the visual reflexes that regulate horizontal course, the sensory-motor mechanisms that animals use to control altitude are poorly understood. Using a 3D virtual reality environment, we found that Drosophila utilize three reflexes—edge tracking, wide-field stabilization, and expansion avoidance—to control altitude. By implementing a dynamic visual clamp, we found that flies do not regulate altitude by maintaining a fixed value of optic flow beneath them, as suggested by a recent model [15]. The results identify a means by which insects determine their absolute height above the ground and uncover a remarkable correspondence between the sensory-motor algorithms used to regulate motion in the horizontal and vertical domains

Fast Uncertainty Estimation for Deep Learning Based Optical Flow

We present a novel approach to reduce the processing time required to derive the estimation uncertainty map in deep learning-based optical flow determination methods. Without uncertainty aware reasoning, the optical flow model, especially when it is used for mission critical fields such as robotics and aerospace, can cause catastrophic failures. Although several approaches such as the ones based on Bayesian neural networks have been proposed to handle this issue, they are computationally expensive. Thus, to speed up the processing time, our approach applies a generative model, which is trained by input images and an uncertainty map derived through a Bayesian approach. By using synthetically generated images of spacecraft, we demonstrate that the trained generative model can produce the uncertainty map 100∼700 times faster than the conventional uncertainty estimation method used for training the generative model itself. We also show that the quality of uncertainty map derived by the generative model is close to that of the original uncertainty map. By applying the proposed approach, the deep learning model operated in real-time can avoid disastrous failures by considering the uncertainty as well as achieving better performance removing uncertain portions of the prediction result

Fast Uncertainty Estimation for Deep Learning Based Optical Flow

We present a novel approach to reduce the processing time required to derive the estimation uncertainty map in deep learning-based optical flow determination methods. Without uncertainty aware reasoning, the optical flow model, especially when it is used for mission critical fields such as robotics and aerospace, can cause catastrophic failures. Although several approaches such as the ones based on Bayesian neural networks have been proposed to handle this issue, they are computationally expensive. Thus, to speed up the processing time, our approach applies a generative model, which is trained by input images and an uncertainty map derived through a Bayesian approach. By using synthetically generated images of spacecraft, we demonstrate that the trained generative model can produce the uncertainty map 100∼700 times faster than the conventional uncertainty estimation method used for training the generative model itself. We also show that the quality of uncertainty map derived by the generative model is close to that of the original uncertainty map. By applying the proposed approach, the deep learning model operated in real-time can avoid disastrous failures by considering the uncertainty as well as achieving better performance removing uncertain portions of the prediction result

Distance Dependence of the Energy Transfer Rate From a Single Semiconductor Nanostructure to Graphene

The near-field Coulomb interaction between a nano-emitter and a graphene monolayer results in strong F\"orster-type resonant energy transfer and subsequent fluorescence quenching. Here, we investigate the distance dependence of the energy transfer rate from individual, i) zero-dimensional CdSe/CdS nanocrystals and ii) two-dimensional CdSe/CdS/ZnS nanoplatelets to a graphene monolayer. For increasing distances $d$, the energy transfer rate from individual nanocrystals to graphene decays as $1/d^4$. In contrast, the distance dependence of the energy transfer rate from a two-dimensional nanoplatelet to graphene deviates from a simple power law, but is well described by a theoretical model, which considers a thermal distribution of free excitons in a two-dimensional quantum well. Our results show that accurate distance measurements can be performed at the single particle level using graphene-based molecular rulers and that energy transfer allows probing dimensionality effects at the nanoscale.Comment: Main text (+ 5 figures) and Supporting Information (+ 7 figures

Image-based Early Detection System for Wildfires

Wildfires are a disastrous phenomenon which cause damage to land, loss of property, air pollution, and even loss of human life. Due to the warmer and drier conditions created by climate change, more severe and uncontrollable wildfires are expected to occur in the coming years. This could lead to a global wildfire crisis and have dire consequences on our planet. Hence, it has become imperative to use technology to help prevent the spread of wildfires. One way to prevent the spread of wildfires before they become too large is to perform early detection i.e, detecting the smoke before the actual fire starts. In this paper, we present our Wildfire Detection and Alert System which use machine learning to detect wildfire smoke with a high degree of accuracy and can send immediate alerts to users. Our technology is currently being used in the USA to monitor data coming in from hundreds of cameras daily. We show that our system has a high true detection rate and a low false detection rate. Our performance evaluation study also shows that on an average our system detects wildfire smoke faster than an actual person.Comment: Published in Tackling Climate Change with Machine Learning workshop, Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022

Kidney and Kidney Tumor Segmentation Using Two- stage Convolutional Neural Network

Kidney tumor is typically diagnosed using computed tomography (CT) imaging by investigating geometric features of kidney tumor. For a reliable diagnosis and treatment planning, kidney tumor quantification is necessary. However, manual segmentation by human requires time and expertise. In addition, inter/intra variability of segmentation results can lead to suboptimal decision. In this study, we propose the two-stage segmentation method using 2.5D and 3D convolutional neural network for kidney and kidney tumor delineation. The two stage model was trained with multi-task loss for pixel-wise cross-entropy loss function for segmentation task and mean square error function for regression task. Experimental results confirm that the proposed method effectively segments kidney and kidney tumor

First-time comparison between NO2 vertical columns from GEMS and Pandora measurements

The Geostationary Environmental Monitoring Spectrometer (GEMS) is a UV&ndash;visible spectrometer onboard the GEO-KOMPSAT-2B satellite launched into geostationary orbit in February 2020. To evaluate GEMS NO2 column data, comparison was carried out using NO2 vertical column density (VCD) measured using direct-sunlight observations by the Pandora spectrometer system at four sites in Seosan, South Korea, during November 2020 to January 2021. Correlation coefficients between GEMS and Pandora NO2 data at four sites ranged from 0.35 to 0.48, with root mean square errors (RMSEs) from 4.7 &times; 1015 molec. cm-2 to 5.5 &times; 1015 molec. cm-2 for cloud fraction (CF) &lt; 0.7. Higher correlation coefficients of 0.62&ndash;0.78 with lower RMSEs from 3.3 &times; 1015 molec. cm-2 to 4.3 &times; 1015 molec. cm-2 were found with CF &lt; 0.3, indicating the higher sensitivity of GEMS to atmospheric NO2 in less-cloudy conditions. Overall, GEMS NO2 column data tend to be lower than those of Pandora due to differences in representative spatial coverage, with a large negative bias under high-CF conditions. With correction for horizontal representativeness in Pandora measurement coverage, the correlation coefficients range from 0.69 to 0.81 with RMSEs from 3.2 &times; 1015 molec. cm-2 to 4.9 &times; 1015 molec. cm-2 were achieved for CF &lt; 0.3, showing the better correlation with the correction than that without the correction.</p

First-time comparison between NO2 vertical columns from Geostationary Environmental Monitoring Spectrometer (GEMS) and Pandora measurements

The Geostationary Environmental Monitoring Spectrometer (GEMS) is a UV-visible (UV-Vis) spectrometer on board the GEO-KOMPSAT-2B (Geostationary Korea Multi-Purpose Satellite 2B) satellite launched into a geostationary orbit in February 2020. To evaluate the GEMS NO2 total column data, a comparison was carried out using the NO2 vertical column density (VCD) that measured direct sunlight using the Pandora spectrometer system at four sites in Seosan, South Korea, from November 2020 to January 2021. Correlation coefficients between GEMS and Pandora NO2 data at four sites ranged from 0.35 to 0.48, with root mean square errors (RMSEs) from 4.7×1015 to 5.5×1015 molec. cm−2 for a cloud fraction (CF) &lt;0.7. Higher correlation coefficients of 0.62–0.78 with lower RMSEs from 3.3×1015 to 5.0×1015 molec. cm−2 were found with CF &lt;0.3, indicating the higher sensitivity of GEMS to atmospheric NO2 in less cloudy conditions. Overall, the GEMS NO2 total column data tended to be lower than the Pandora data, owing to differences in the representative spatial coverage, with a large negative bias under high CF conditions. With a correction for horizontal representativeness in the Pandora measurement coverage, correlation coefficients ranging from 0.69 to 0.81, with RMSEs from 3.2×1015 to 4.9×1015 molec. cm−2, were achieved for CF &lt;0.3, showing a better correlation with the correction than without the correction.</p

Hyaline Vascular-Type Castleman Disease Presenting as an Esophageal Submucosal Tumor: Case Report

Castleman disease is a relatively rare disorder of lymphoid tissue that involves the gastrointestinal tract in a variety of clinical and pathologic manifestations. A submucosal location has never been described in the medical literature. We report a case of esophageal Castleman disease involving thesubmucosal layer in a 62-year-old man, which was confirmed on pathology. Esophagography and CT demonstrated an intramural tumor, and a leiomyoma or leiomyosarcoma was suspected based on the known incidence of such tumors

Measurement of Neutrino-Electron Scattering Cross-Section with a CsI(Tl) Scintillating Crystal Array at the Kuo-Sheng Nuclear Power Reactor

The $\bar{\nu}_{e}-e^{-}$ elastic scattering cross-section was measured with a CsI(Tl) scintillating crystal array having a total mass of 187kg. The detector was exposed to an average reactor $\bar{\nu}_{e}$ flux of $\rm{6.4\times 10^{12} ~ cm^{-2}s^{-1}}$ at the Kuo-Sheng Nuclear Power Station. The experimental design, conceptual merits, detector hardware, data analysis and background understanding of the experiment are presented. Using 29882/7369 kg-days of Reactor ON/OFF data, the Standard Model(SM) electroweak interaction was probed at the squared 4-momentum transfer range of $\rm{Q^2 \sim 3 \times 10^{-6} ~ GeV^2}$. The ratio of experimental to SM cross-sections of $\xi =[ 1.08 \pm 0.21(stat)\pm 0.16(sys)]$ was measured. Constraints on the electroweak parameters $(g_V , g_A)$ were placed, corresponding to a weak mixing angle measurement of \s2tw = 0.251 \pm 0.031({\it stat}) \pm 0.024({\it sys}) . Destructive interference in the SM \nuebar -e process was verified. Bounds on anomalous neutrino electromagnetic properties were placed: neutrino magnetic moment at \mu_{\nuebar}< 2.2 \times 10^{-10} \mu_{\rm B} and the neutrino charge radius at -2.1 \times 10^{-32} ~{\rm cm^{2}} < \nuchrad < 3.3 \times 10^{-32} ~{\rm cm^{2}}, both at 90% confidence level.Comment: 18 Figures, 7 Tables; published version as V2 with minor revision from V