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In this paper, we propose a new deep learning-based method for estimating room layout given a pair of 360◦ panoramas. Our system, called Position-aware Stereo Merging Network or PSMNet, is an end-to-end joint layout-pose estimator. PSMNet consists of a Stereo Pano Pose (SP2) transformer and a novel Cross-Perspective Projection (CP2) layer. The stereo-view SP2 transformer is used to implicitly infer correspondences between views, and can handle noisy poses. The pose-aware CP2 layer is designed to render features from the adjacent view to the anchor (reference) view, in order to perform view fusion and estimate the visible layout. Our experiments and analysis validate our method, which significantly outperforms the state-of-the-art layout estimators, especially for large and complex room spaces. 

Meditation, a mental and physical exercise which helps to focus attention and reduce stress has gained more popularity in recent years. However, meditation requires a concerted effort and regular practice. To explore the feasibility of using Augmented Reality (AR) Devices to assist in meditating, we recruited ten subjects to perform a five-minute meditation task integrated into AR devices. Heart Rate, Heart Rate Variability, and skin conductance response (SCR) are analyzed based on an Electrocardiogram (ECG), Electrodermal activity to monitor the physiological changes during and after a meditation session. Additionally, participants filled out surveys containing the Perceived Stress Questionnaire (PSQ), a clinically validated survey designed to evaluate stress levels before and after meditation to analyze the change in stress levels. Finally, we found significant differences in Heart Rate and Mean SCR Recovery Time for participants between the three study procedure periods (before, during, and after guided meditation). 

Volatile chemical products (VCPs) and other non-combustion-related sourceshave become important for urban air quality, and bottom-up calculationsreport emissions of a variety of functionalized compounds that remainunderstudied and uncertain in emissions estimates. Using a new instrumentalconfiguration, we present online measurements of oxygenated organiccompounds in a US megacity over a 10 d wintertime sampling period, whenbiogenic sources and photochemistry were less active. Measurements wereconducted at a rooftop observatory in upper Manhattan, New York City, USAusing a Vocus chemical ionization time-of-flight mass spectrometer, withammonium (NH4+) as the reagent ion operating at 1 Hz. The range ofobservations spanned volatile, intermediate-volatility, and semi-volatileorganic compounds, with targeted analyses of ∼150 ions, whoselikely assignments included a range of functionalized compound classes suchas glycols, glycol ethers, acetates, acids, alcohols, acrylates, esters,ethanolamines, and ketones that are found in various consumer, commercial,and industrial products. Their concentrations varied as a function of winddirection, with enhancements over the highly populated areas of the Bronx,Manhattan, and parts of New Jersey, and included abundant concentrations ofacetates, acrylates, ethylene glycol, and other commonly used oxygenatedcompounds. The results provide top-down constraints on wintertime emissionsof these oxygenated and functionalized compounds, with ratios to commonanthropogenic marker compounds and comparisons of their relative abundancesto two regionally resolved emissions inventories used in urban air qualitymodels.