| 31. |
- Pham, Tuan, Professor, 1962-, et al.
(författare)
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Tensor decomposition of non-EEG physiological signals for visualization and recognition of human stress
- 2019
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Ingår i: ICBBT 2019: 2019 11TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL TECHNOLOGY. - New York : ACM Publications. - 9781450362313 ; , s. 132-136
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Konferensbidrag (refereegranskat)abstract
- Recognition of physical and mental responses to stress is important for the purpose of stress management to reduce its negative effects in health. Wearable technology, mainly using electroencephalogram (EEG), provides information such as tracking fitness activity, disease detection, and neurological states of individuals. However, the recording of EEG signals from a wearable device is inconvenient. This study introduces the application of tensor decomposition of non-EEG data for visualizing and tracking neurological status with implication to human stress recognition. Results obtained from testing the proposed method using a PhyioNet database show visualizations that can well separate four groups of neurological statuses obtained from twenty healthy subjects, and very high up to 100% classification of the neurological statuses. The investigation suggests the potential application of tensor decomposition for the analysis of physiological measurements collected from multiple sensors. The proposed study can significantly contribute to the advancement of wearable technology for human stress monitoring
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| 32. |
- Purdum, Josiah N., et al.
(författare)
-
Time-series and Phase-curve Photometry of the Episodically Active Asteroid (6478) Gault in a Quiescent State Using APO, GROWTH, P200, and ZTF
- 2021
-
Ingår i: Astrophysical Journal Letters. - : American Astronomical Society. - 2041-8205 .- 2041-8213. ; 911:2
-
Tidskriftsartikel (refereegranskat)abstract
- We observed the episodically active asteroid (6478) Gault in 2020 with multiple telescopes in Asia and North America and found that it is no longer active after its recent outbursts at the end of 2018 and the start of 2019. The inactivity during this apparition allowed us to measure the absolute magnitude of Gault of H ( r ) = 14.63 +/- 0.02, G ( r ) = 0.21 +/- 0.02 from our secular phase-curve observations. In addition, we were able to constrain Gault's rotation period using time-series photometric lightcurves taken over 17 hr on multiple days in 2020 August, September, and October. The photometric lightcurves have a repeating less than or similar to 0.05 mag feature suggesting that (6478) Gault has a rotation period of similar to 2.5 hr and may have a semispherical or top-like shape, much like the near-Earth asteroids Ryugu and Bennu. The rotation period of similar to 2.5 hr is near the expected critical rotation period for an asteroid with the physical properties of (6478) Gault, suggesting that its activity observed over multiple epochs is due to surface mass shedding from its fast rotation spin-up by the Yarkovsky-O'Keefe-Radzievskii-Paddack effect.
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| 33. |
- Raghavan, Maanasa, et al.
(författare)
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Genomic evidence for the Pleistocene and recent population history of Native Americans
- 2015
-
Ingår i: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 349:6250
-
Tidskriftsartikel (refereegranskat)abstract
- Howand when the Americas were populated remains contentious. Using ancient and modern genome-wide data, we found that the ancestors of all present-day Native Americans, including Athabascans and Amerindians, entered the Americas as a single migration wave from Siberia no earlier than 23 thousand years ago (ka) and after no more than an 8000-year isolation period in Beringia. After their arrival to the Americas, ancestral Native Americans diversified into two basal genetic branches around 13 ka, one that is now dispersed across North and South America and the other restricted to North America. Subsequent gene flow resulted in some Native Americans sharing ancestry with present-day East Asians (including Siberians) and, more distantly, Australo-Melanesians. Putative "Paleoamerican" relict populations, including the historical Mexican Pericues and South American Fuego-Patagonians, are not directly related to modern Australo-Melanesians as suggested by the Paleoamerican Model.
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| 34. |
- Rodriguez Déniz, Héctor, et al.
(författare)
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A multilayered block network model to forecast large dynamic transportation graphs : An application to US air transport
- 2022
-
Ingår i: Transportation Research Part C. - Oxford, United Kingdom : Elsevier. - 0968-090X .- 1879-2359. ; 137
-
Tidskriftsartikel (refereegranskat)abstract
- Dynamic transportation networks have been analyzed for years by means of static graph-based indicators in order to study the temporal evolution of relevant network components, and to reveal complex dependencies that would not be easily detected by a direct inspection of the data. This paper presents a state-of-the-art probabilistic latent network model to forecast multilayer dynamic graphs that are increasingly common in transportation and proposes a community-based extension to reduce the computational burden. Flexible time series analysis is obtained by modeling the probability of edges between vertices through latent Gaussian processes. The models and Bayesian inference are illustrated on a sample of 10-year data from four major airlines within the US air transportation system. Results show how the estimated latent parameters from the models are related to the airlines’ connectivity dynamics, and their ability to project the multilayer graph into the future for out-of-sample full network forecasts, while stochastic blockmodeling allows for the identification of relevant communities. Reliable network predictions would allow policy-makers to better understand the dynamics of the transport system, and help in their planning on e.g. route development, or the deployment of new regulations.
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| 35. |
- Rodriguez Déniz, Héctor, 1982-
(författare)
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Bayesian Models for Spatiotemporal Data from Transportation Networks
- 2023
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Urbanization has caused a historical transformation at a global scale, and humanity is moving towards a fully connected society where cities will concentrate population, infrastructure and economic activity. A key element in the cities’ infrastructure is the transportation system, as it facilitates the mobility of people and goods. Transportation systems are constantly generating data from, e.g., GPS, sensors and cameras, and the statistical modeling is challenging due to the complex structure and dynamics of the system, and the inherent uncertainty. In this thesis, we develop Bayesian models with applications to transportation. We specifically focus on models that can be trained on spatiotemporal data coming from transport networks to make predictions on, e.g., bus delays or the actual network topology. Special attention has been given to model scalability issues and uncertainty quantification. We have used real-world data from transportation systems in every study to keep a balance between statistical rigor, novelty, and applicability. The thesis consists of four papers. The first study presents a state-of-the-art probabilistic latent network model to forecast multilayer dynamic graphs. The model uses stochastic blockmodeling to reduce the computational burden, and is illustrated on a sample of 10-year data from four major airlines within the US air transportation system. In the second paper, we develop a robust model for real-time bus travel time prediction that departs from Gaussian assumptions by using Student-t errors, and show how Bayesian inference naturally allows for predictive uncertainty quantification in a highly stochastic environment. Experiments are performed using data from high-frequency buses in Stockholm, Sweden. The third paper shows the potential of multi-output Gaussian processes to tackle network-wide travel time prediction in an urban area. We develop a responsive online model based on a coregionalized covariance and test its accuracy on real data from GPS-equipped taxis. Finally, we propose a novel regularization strategy for the vector autoregressive model that is based on a graphical spike-and-slab prior, and present a case study with real airline delay data to assess its predictive performance and analyze network patterns related to the propagation of delays across airports.
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| 36. |
- Rodriguez Déniz, Héctor, et al.
(författare)
-
Classifying airports according to their hub dimensions: an application to the US domestic network
- 2013
-
Ingår i: Journal of Transport Geography. - : Elsevier. - 0966-6923 .- 1873-1236. ; 33, s. 188-195
-
Tidskriftsartikel (refereegranskat)abstract
- Government agencies classify airports for different purposes, including the allocation of public funding for capacity developments. In a context of hub classification, determining the contribution of each airport to the national network in terms of the two dimensions of hubbing -i.e., traffic generation and connectivity- is a key aspect. In this regard, the choice of an appropriate connectivity indicator is still an unresolved issue. This paper adapts the well-known flow centrality indicator to an air transport context and develops a novel measure of airport connectivity. An application to the US domestic network is provided, using quarterly data on passenger demand to perform a detailed time-series analysis of airport connectivity patterns between 1993 and 2012. The flow centrality indicator is then used to define an alternative airport classification method within the context of the Federal Aviation Administration’s National Plan of Integrated Airport Systems (NPIASs). Results show that there is potential for improving the existing airport typology by explicitly separating connectivity and traffic generation as classification criteria.
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| 37. |
- Rodriguez Déniz, Héctor, et al.
(författare)
-
Robust Real-Time Delay Predictions in a Network of High-Frequency Urban Buses
- 2022
-
Ingår i: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 23:9, s. 16304-16317
-
Tidskriftsartikel (refereegranskat)abstract
- Providing transport users and operators with accurate forecasts on travel times is challenging due to a highly stochastic traffic environment. Public transport users are particularly sensitive to unexpected waiting times, which negatively affect their perception on the system's reliability. In this paper we develop a robust model for real-time bus travel time prediction that departs from Gaussian assumptions by using Student-t errors. The proposed approach uses spatiotemporal characteristics from the route and previous bus trips to model short-term effects, and date/time variables and Gaussian processes for long-run forecasts. The model allows for flexible modeling of mean, variance and kurtosis spaces. We propose algorithms for Bayesian inference and for computing probabilistic forecast distributions. Experiments are performed using data from high-frequency buses in Stockholm, Sweden. Results show that Student-t models outperform Gaussian ones in terms of log-posterior predictive power to forecast bus delays at specific stops, which reveals the importance of accounting for predictive uncertainty in model selection. Estimated Student-t regressions capture typical temporal variability between within-day hours and different weekdays. Strong spatiotemporal effects are detected for incoming buses from immediately previous stops, which is in line with many recently developed models. We finally show how Bayesian inference naturally allows for predictive uncertainty quantification, e.g. by returning the predictive probability that the delay of an incoming bus exceeds a given threshold.
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| 38. |
- Rodriguez-Deniz, Hector, et al.
(författare)
-
Robust Real-Time Delay Predictions in a Network of High-Frequency Urban Buses
- 2022
-
Ingår i: IEEE transactions on intelligent transportation systems (Print). - 1524-9050 .- 1558-0016. ; 23:9, s. 16304-16317
-
Tidskriftsartikel (refereegranskat)abstract
- Providing transport users and operators with accurate forecasts on travel times is challenging due to a highly stochastic traffic environment. Public transport users are particularly sensitive to unexpected waiting times, which negatively affect their perception on the system's reliability. In this paper we develop a robust model for real-time bus travel time prediction that departs from Gaussian assumptions by using Student-t errors. The proposed approach uses spatiotemporal characteristics from the route and previous bus trips to model short-term effects, and date/time variables and Gaussian processes for long-run forecasts. The model allows for flexible modeling of mean, variance and kurtosis spaces. We propose algorithms for Bayesian inference and for computing probabilistic forecast distributions. Experiments are performed using data from high-frequency buses in Stockholm, Sweden. Results show that Student-t models outperform Gaussian ones in terms of log-posterior predictive power to forecast bus delays at specific stops, which reveals the importance of accounting for predictive uncertainty in model selection. Estimated Student-t regressions capture typical temporal variability between within-day hours and different weekdays. Strong spatiotemporal effects are detected for incoming buses from immediately previous stops, which is in line with many recently developed models. We finally show how Bayesian inference naturally allows for predictive uncertainty quantification, e.g. by returning the predictive probability that the delay of an incoming bus exceeds a given threshold.
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| 39. |
- Rodriguez-Deniz, Hector, et al.
(författare)
-
Urban Network Travel Time Prediction via Online Multi-Output Gaussian Process Regression
- 2017
-
Ingår i: 2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC). - : IEEE. - 9781538615263
-
Konferensbidrag (refereegranskat)abstract
- The paper explores the potential of Multi-Output Gaussian Processes to tackle network-wide travel time prediction in an urban area. Forecasting in this context is challenging due to the complexity of the traffic network, noisy data and unexpected events. We build on recent methods to develop an online model that can be trained in seconds by relying on prior network dependences through a coregionalized covariance. The accuracy of the proposed model outperforms historical means and other simpler methods on a network of 47 streets in Stockholm, by using probe data from GPS-equipped taxis. Results show how traffic speeds are dependent on the historical correlations, and how prediction accuracy can be improved by relying on prior information while using a very limited amount of current-day observations, which allows for the development of models with low estimation times and high responsiveness.
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| 40. |
- Rodriguez, Ivan D., et al.
(författare)
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FOND Planning with Explicit Fairness Assumptions
- 2022
-
Ingår i: The journal of artificial intelligence research. - : AI Access Foundation ; AAAI Press. - 1076-9757 .- 1943-5037. ; 74, s. 887-916
-
Tidskriftsartikel (refereegranskat)abstract
- We consider the problem of reaching a propositional goal condition in fully-observable non deterministic (FOND) planning under a general class of fairness assumptions that are given explicitly. The fairness assumptions are of the form A/B and say that state trajectories that contain infinite occurrences of an action a from A in a state s and finite occurrence of actions from B, must also contain infinite occurrences of action a in s followed by each one of its possible outcomes. The infinite trajectories that violate this condition are deemed as unfair, and the solutions are policies for which all the fair trajectories reach a goal state. We show that strong and strong-cyclic FOND planning, as well as QNP planning, a planning model introduced recently for generalized planning, are all special cases of FOND planning with fairness assumptions of this form which can also be combined. FOND+ planning, as this form of planning is called, combines the syntax of FOND planning with some of the versatility of LTL for expressing fairness constraints. A sound and complete FOND+ planner is implemented by reducing FOND+ planning to answer set programs, and its performance is evaluated in comparison with FOND and QNP planners, and LTL synthesis tools. Two other FOND+ planners are introduced as well which are more scalable but are not complete.
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