Egocentric Video Task Translation

Different video understanding tasks are typically treated in isolation, and even with distinct types of curated data (e.g., classifying sports in one dataset, tracking animals in another). However, in wearable cameras, the immersive egocentric perspective of a person engaging with the world around them presents an interconnected web of video understanding tasks -- hand-object manipulations, navigation in the space, or human-human interactions -- that unfold continuously, driven by the person's goals. We argue that this calls for a much more unified approach. We propose EgoTask Translation (EgoT2), which takes a collection of models optimized on separate tasks and learns to translate their outputs for improved performance on any or all of them at once. Unlike traditional transfer or multi-task learning, EgoT2's flipped design entails separate task-specific backbones and a task translator shared across all tasks, which captures synergies between even heterogeneous tasks and mitigates task competition. Demonstrating our model on a wide array of video tasks from Ego4D, we show its advantages over existing transfer paradigms and achieve top-ranked results on four of the Ego4D 2022 benchmark challenges.Comment: Project page: https://vision.cs.utexas.edu/projects/egot2

Method and apparatus for managing quality of service

A system that incorporates teachings of the present disclosure may include, for example, obtaining regression coefficients that quantify a relationship between premises feedback and first network and premises performance indicators, obtaining second network performance indicators for the network elements, obtaining second premises performance indicators for the customer premises equipment, and predicting customer complaints by applying the obtained regression coefficients to at least the second network performance indicators and the second premises performance indicators. Other embodiments are disclosed.Board of Regents, University of Texas Syste

Terahertz Spectroscopy as a Tool to Investigate Guest-Host Interactions in Porous Materials

Porous materials are becoming increasingly popular because they are highly functional, readily produced, economically efficient. There are numerous applications involving porous materials, such as catalysis, gas separation and water purification. However, in order to more effectively use porous materials in advanced applications, it is critical that accurate information related to host-guest interactions be well-understood with atomic-level precision. In this work, two applications of porous materials are investigated, focusing on the nature and dynamics of guest molecules within the porous solid. In the first case, the stabilization of active pharmaceutical ingredients through interactions with mesoporous silica was explored. In the study, the guest-host interaction is characterized experimentally through analysis of the thermodynamics associated with monolayer formation on the porous silica. Using solid-state density functional theory (DFT). The binding energy, molecular conformations, and atomic dynamics were all obtained. The results show that loading the drug molecules into porous silica did increase the stability of the amorphous state by allowing the drug molecules to maximize favorable intermolecular interactions, while simultaneously resulting in a more highly strand intramolecular conformation, revealing the complex energetic interplay between conformational strain and stabilization from external sources. In a second application, the gas-capture and sequestration phenomena in porous materials was studied in hydroquinone (HQ) clathrate materials. A series of gases were studied, including to Noble gases (He, Ne, Ar, Kr, Xe, Rn) as well as carbon dioxide (CO2). By using the terahertz spectroscopy, the dynamics of the gas molecule – porous HQ interaction were uncovered as a function of temperature and pressure. Solid-state DFT simulations help to determine and assign the terahertz spectra of the interaction mechanism. The results also indicate that porous HQ adsorbs the guest gas molecules selectively based on the various energetic forces present within the pores

Assessing the Performance of Density Functional Theory Methods on the Prediction of Low-Frequency Vibrational Spectra

The low-frequency (terahertz) dynamics of condensed phase materials provide valuable insight into numerous bulk phenomena. However, the assignment and interpretation of experimental results requires computational methods due to the complex mode-types that depend on weak intermolecular forces. Solid-state density functional theory has been used in this regard with great success, yet the selection of specific computational parameters, namely the chosen basis set and density functional, has a profound influence on the accuracy of predicted spectra. In this work, the role of these two parameters is investigated in a series of organic molecular crystals, in order to assess the ability of various methods to reproduce intermolecular forces, and subsequently experimental terahertz spectra. Specifically, naphthalene, oxalic acid, and thymine were chosen based on the varied intermolecular interactions present in each material. The results highlight that unconstrained geometry optimizations can be used as an initial proxy for the accuracy of interatomic forces, with errors in the calculated geometries indicative of subsequent errors in the calculated low-frequency vibrational spectra, providing a powerful metric for the validation of theoretical results. Finally, the origins of the observed shortcomings are analyzed, providing a basic framework for further studies on related materials