2,086 research outputs found
Essays on Panel Data Prediction Models
Forward-looking analysis is valuable for policymakers as they need effective strategies to mitigate imminent risks and potential challenges. Panel data sets contain time series information over a number of cross-sectional units and are known to have superior predictive abilities in comparison to time series only models. This PhD thesis develops novel panel data methods to contribute to the advancement of short-term forecasting and nowcasting of macroeconomic and environmental variables. The two most important highlights of this thesis are the use of cross-sectional dependence in panel data forecasting and to allow for timely predictions and ‘nowcasts’.Although panel data models have been found to provide better predictions in many empirical scenarios, forecasting applications so far have not included cross-sectional dependence. On the other hand, cross-sectional dependence is well-recognised in large panels and has been explicitly modelled in previous causal studies. A substantial portion of this thesis is devoted to developing cross-sectional dependence in panel models suited to diverse empirical scenarios. The second important aspect of this work is to integrate the asynchronous release schedules of data within and across panel units into the panel models. Most of the thesis emphasises the pseudo-real-time predictions with efforts to estimate the model on the data that has been released at the time of predictions, thus trying to replicate the realistic circumstances of delayed data releases.Linear, quantile and non-linear panel models are developed to predict a range of targets both in terms of their meaning and method of measurement. Linear models include panel mixed-frequency vector-autoregression and bridge equation set-ups which predict GDP growth, inflation and CO2 emissions. Panel quantile regressions and latent variable discrete choice models predict growth-at-risk and extreme episodes of cross-border capital flows, respectively. The datasets include both international cross-country panels as well as regional subnational panels. Depending on the nature of the model and the prediction targets, different precision criteria evaluate the accuracy of the models in out-of-sample settings. The generated predictions beat respective standard benchmarks in a more timely fashion
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
Current and Future Challenges in Knowledge Representation and Reasoning
Knowledge Representation and Reasoning is a central, longstanding, and active
area of Artificial Intelligence. Over the years it has evolved significantly;
more recently it has been challenged and complemented by research in areas such
as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl
Perspectives workshop was held on Knowledge Representation and Reasoning. The
goal of the workshop was to describe the state of the art in the field,
including its relation with other areas, its shortcomings and strengths,
together with recommendations for future progress. We developed this manifesto
based on the presentations, panels, working groups, and discussions that took
place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge
Representation: its origins, goals, milestones, and current foci; its relation
to other disciplines, especially to Artificial Intelligence; and on its
challenges, along with key priorities for the next decade
A stochastic hybrid control law for a localization task based on active sensing
openA localization task by a mobile agent is considered, where the sensing and the motion actions are performed exclusively with respect to each other, meaning that sensing the target is not available during the agent movement. To formalize this scenario the system is modeled on the 1D line and a control law with a timer and a logic variable, to allow switching between the operating modes of sensing the target and moving towards it, is designed. A Stochastic Hybrid System in standard form is obtained, satisfying the Stochastic Hybrid Basic conditions, and the overall closed-loop system behavior is then studied.
An extension to the 2D plane is also proposed and its behavior is analyzed. Finally, the 2D control law is tested for a simulated Search & Rescue task in an unknown indoor environment under some simplifying hypotheses
Reshaping Higher Education for a Post-COVID-19 World: Lessons Learned and Moving Forward
No abstract available
A Comprehensive Review of Data-Driven Co-Speech Gesture Generation
Gestures that accompany speech are an essential part of natural and efficient
embodied human communication. The automatic generation of such co-speech
gestures is a long-standing problem in computer animation and is considered an
enabling technology in film, games, virtual social spaces, and for interaction
with social robots. The problem is made challenging by the idiosyncratic and
non-periodic nature of human co-speech gesture motion, and by the great
diversity of communicative functions that gestures encompass. Gesture
generation has seen surging interest recently, owing to the emergence of more
and larger datasets of human gesture motion, combined with strides in
deep-learning-based generative models, that benefit from the growing
availability of data. This review article summarizes co-speech gesture
generation research, with a particular focus on deep generative models. First,
we articulate the theory describing human gesticulation and how it complements
speech. Next, we briefly discuss rule-based and classical statistical gesture
synthesis, before delving into deep learning approaches. We employ the choice
of input modalities as an organizing principle, examining systems that generate
gestures from audio, text, and non-linguistic input. We also chronicle the
evolution of the related training data sets in terms of size, diversity, motion
quality, and collection method. Finally, we identify key research challenges in
gesture generation, including data availability and quality; producing
human-like motion; grounding the gesture in the co-occurring speech in
interaction with other speakers, and in the environment; performing gesture
evaluation; and integration of gesture synthesis into applications. We
highlight recent approaches to tackling the various key challenges, as well as
the limitations of these approaches, and point toward areas of future
development.Comment: Accepted for EUROGRAPHICS 202
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