MULTIMODAL LEARNING FOR AUDIO AND VISUAL PROCESSING

The world contains vast amounts of information which can be sensed and captured in a variety of ways and formats. Virtual environments also lend themselves to endless possibilities and diversity of data. Often our experiences draw from these separate but complementary parts which can be combined in a way to provide a comprehensive representation of the events. Multimodal learning focuses on these types of combinations. By fusing multiple modalities, multimodal learning can improve results beyond individual mode performance. However, many of today’s state-of-the-art techniques in computer vision, robotics, and machine learning rely solely or primarily on visual inputs even when the visual data is obtained from video where corresponding audio may also be readily available to augment learning. Vision only approaches can experience challenges in cases of highly reflective, transparent, or occluded objects and scenes where, if used alone or in conjunction with, audio may improve task performance. To address these challenges, this thesis explores coupling multimodal information to enhance task performance through learning-based methods for audio and visual processing using real and synthetic data. Physically-based graphics pipelines can naturally be extended for audio and visual synthetic data generation. To enhance the rigid body sound synthesis pipeline for objects containing a liquid, I used an added mass operator for fluid-structure coupling as a pre-processing step. My method is fast and practical for use in interactive 3D systems where live sound synthesis is desired. By fusing audio and visual data from real and synthetic videos, we also demonstrate enhanced processing and performance for object classification, tracking, and reconstruction tasks. As has been shown in visual question and answering and other related work, multiple modalities have the ability to complement one another and outperform single modality systems. To the best of my knowledge, I introduced the first use of audio-visual neural networks to analyze liquid pouring sequences by classifying their weight, liquid, and receiving container. Prior work often required predefined source weights or visual data. My contribution was to use the sound from a pouring sequence—a liquid being poured into a target container- to train a multimodal convolutional neural networks (CNNs) that fuses mel-scaled spectrograms as audio inputs with corresponding visual data based on video images. I described the first use of an audio-visual neural network for tracking tabletop sized objects and enhancing visual object trackers. Like object detection of reflective surfaces, object trackers can also run into challenges when objects collide, occlude, appear similar, or come close to one another. By using the impact sounds of the objects during collision, my audio-visual object tracking (AVOT) neural network can correct trackers that drift from their original objects that were assigned before collision. Reflective and textureless surfaces not only are difficult to detect and classify, they are also often poorly reconstructed and filled with depth discontinuities and holes. I proposed the first use of an audiovisual method that uses the reflections of sound to aid in geometry and audio reconstruction, referred to as ”Echoreconstruction”. The mobile phone prototype emits pulsed audio, while recording video for RGBbased 3D reconstruction and audio-visual classification. Reflected sound and images from the video are input into our audio (EchoCNN-A) and audio-visual (EchoCNN-AV) convolutional neural networks for surface and sound source detection, depth estimation, and material classification. EchoCNN inferences from these classifications enhance scene 3D reconstructions containing open spaces and reflective surfaces by depth filtering, inpainting, and placement of unmixed sound sources in the scene. In addition to enhancing scene reconstructions, I proposed a multimodal single- and multi-frame reconstruction LSTM autoencoder for 3D reconstructions using audio-visual inputs. Our neural network produces high-quality 3D reconstructions using voxel representation. It is the first audio-visual reconstruction neural network for 3D geometry and material representation. Contributions of this thesis include new neural network designs, new enhancements to real and synthetic audio-visual datasets, and prototypes that demonstrate audio and audio-augmented performance for sound synthesis, inference, and reconstruction.Doctor of Philosoph

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The Science Performance of JWST as Characterized in Commissioning

This paper characterizes the actual science performance of the James Webb Space Telescope (JWST), as determined from the six month commissioning period. We summarize the performance of the spacecraft, telescope, science instruments, and ground system, with an emphasis on differences from pre-launch expectations. Commissioning has made clear that JWST is fully capable of achieving the discoveries for which it was built. Moreover, almost across the board, the science performance of JWST is better than expected; in most cases, JWST will go deeper faster than expected. The telescope and instrument suite have demonstrated the sensitivity, stability, image quality, and spectral range that are necessary to transform our understanding of the cosmos through observations spanning from near-earth asteroids to the most distant galaxies.Comment: 5th version as accepted to PASP; 31 pages, 18 figures; https://iopscience.iop.org/article/10.1088/1538-3873/acb29

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements