14,763 research outputs found
Cross-Modal Health State Estimation
Individuals create and consume more diverse data about themselves today than
any time in history. Sources of this data include wearable devices, images,
social media, geospatial information and more. A tremendous opportunity rests
within cross-modal data analysis that leverages existing domain knowledge
methods to understand and guide human health. Especially in chronic diseases,
current medical practice uses a combination of sparse hospital based biological
metrics (blood tests, expensive imaging, etc.) to understand the evolving
health status of an individual. Future health systems must integrate data
created at the individual level to better understand health status perpetually,
especially in a cybernetic framework. In this work we fuse multiple user
created and open source data streams along with established biomedical domain
knowledge to give two types of quantitative state estimates of cardiovascular
health. First, we use wearable devices to calculate cardiorespiratory fitness
(CRF), a known quantitative leading predictor of heart disease which is not
routinely collected in clinical settings. Second, we estimate inherent genetic
traits, living environmental risks, circadian rhythm, and biological metrics
from a diverse dataset. Our experimental results on 24 subjects demonstrate how
multi-modal data can provide personalized health insight. Understanding the
dynamic nature of health status will pave the way for better health based
recommendation engines, better clinical decision making and positive lifestyle
changes.Comment: Accepted to ACM Multimedia 2018 Conference - Brave New Ideas, Seoul,
Korea, ACM ISBN 978-1-4503-5665-7/18/1
Benefit-Cost Analysis for Transportation Planning and Public Policy: Towards Multimodal Demand Modeling
This report examines existing methods of benefit-cost analysis (BCA) in two areas, transportation policy and transportation planning, and suggests ways of modifying these methods to account for travel within a multimodal system. Although the planning and policy contexts differ substantially, this report shows how important multimodal impacts can be incorporated into both by using basic econometric techniques and even simpler rule-of-thumb methods. Case studies in transportation planning focus on the California Department of Transportation (Caltrans), but benchmark California’s competencies by exploring methods used by other states and local governments. The report concludes with a list and discussion of recommendations for improving transportation planning models and methods. These will have immediate use to decision makers at Caltrans and other state DOTs as they consider directions for developing new planning capabilities. This project also identifies areas, and lays groundwork, for future research. Finally, by fitting the planning models into the broader context of transportation policy, this report will serve as a resource for students and others who wish to better understand BCA and its use in practice
Continuous Health Interface Event Retrieval
Knowing the state of our health at every moment in time is critical for
advances in health science. Using data obtained outside an episodic clinical
setting is the first step towards building a continuous health estimation
system. In this paper, we explore a system that allows users to combine events
and data streams from different sources to retrieve complex biological events,
such as cardiovascular volume overload. These complex events, which have been
explored in biomedical literature and which we call interface events, have a
direct causal impact on relevant biological systems. They are the interface
through which the lifestyle events influence our health. We retrieve the
interface events from existing events and data streams by encoding domain
knowledge using an event operator language.Comment: ACM International Conference on Multimedia Retrieval 2020 (ICMR
2020), held in Dublin, Ireland from June 8-11, 202
Are road transportation investments in line with demand projections? A gravity-based analysis for Turkey
This is the post-print version of the article which has been published and is available at the link below.In this research, an integrated gravity-based model was built, and a scenario analysis was conducted to project the demand levels for routes related to the highway projects suggested in TINA-Turkey. The gravity-based model was used to perform a disaggregated analysis to estimate the demand levels that will occur on the routes which are planned to be improved in specific regions of Turkey from now until 2020. During the scenario development phase for these gravity-based models, the growth rate of Turkey's GDP, as estimated by the World Bank from now until 2017, was used as the baseline scenario. Besides, it is assumed that the gross value added (GVA) of the origin and destination regions of the selected routes will show a pattern similar to GDP growth rates. Based on the estimated GDP values, and the projected GVA growth rates, the demand for each selected route was projected and found that the demand level for some of these road projects is expected to be very low, and hence additional measures would be needed to make these investments worthwhile
M2* - mobility to anywhere, an IoT aggregation service platform
This work addresses the problem of the creation of a central integrated platform to collect and manipulate mobility data and sensor data towards the creation of useful information for users in their mobility process. This is an academic work towards a framework for mobility process, where that manipulate can create useful information for users, public transportation operators and authorities, energy and water real time consumption.info:eu-repo/semantics/acceptedVersio
Measuring Air Quality via Multimodal AI and Satellite Imagery
Climate change may be classified as the most important environmental problem
that the Earth is currently facing, and affects all living species on Earth.
Given that air-quality monitoring stations are typically ground-based their
abilities to detect pollutant distributions are often restricted to wide areas.
Satellites however have the potential for studying the atmosphere at large; the
European Space Agency (ESA) Copernicus project satellite, "Sentinel-5P" is a
newly launched satellite capable of measuring a variety of pollutant
information with publicly available data outputs. This paper seeks to create a
multi-modal machine learning model for predicting air-quality metrics where
monitoring stations do not exist. The inputs of this model will include a
fusion of ground measurements and satellite data with the goal of highlighting
pollutant distribution and motivating change in societal and industrial
behaviors. A new dataset of European pollution monitoring station measurements
is created with features including from
the ESA Copernicus project. This dataset is used to train a multi-modal ML
model, Air Quality Network (AQNet) capable of fusing these various types of
data sources to output predictions of various pollutants. These predictions are
then aggregated to create an "air-quality index" that could be used to compare
air quality over different regions. Three pollutants, NO, O, and
PM, are predicted successfully by AQNet and the network was found to be
useful compared to a model only using satellite imagery. It was also found that
the addition of supporting data improves predictions. When testing the
developed AQNet on out-of-sample data of the UK and Ireland, we obtain
satisfactory estimates though on average pollution metrics were roughly
overestimated by around 20\%.Comment: 14 pages, 9 figures, 4 table
A philosophical context for methods to estimate origin-destination trip matrices using link counts.
This paper creates a philosophical structure for classifying methods which estimate origin-destination matrices using link counts. It is claimed that the motivation for doing so is to help real-life transport planners use matrix estimation methods effectively, especially in terms of trading-off observational data with prior subjective input (typically referred to as 'professional judgement'). The paper lists a number of applications that require such methods, differentiating between relatively simple and highly complex applications. It is argued that a sound philosophical perspective is particularly important for estimating trip matrices in the latter type of application. As a result of this argument, a classification structure is built up through using concepts of realism, subjectivity, empiricism and rationalism. Emphasis is put on the fact that, in typical transport planning applications, none of these concepts is useful in its extreme form. The structure is then used to make a review of methods for estimating trip matrices using link counts, covering material published over the past 30 years. The paper concludes by making recommendations, both philosophical and methodological, concerning both practical applications and further research
Automatic Methodology for Multi-modal Trip Generation with Roadside LiDAR
Transportation planning based on historical data and methods has major limitations. Trip data canbe useful to increase the transportation safety of the specific sites and the process and programming
purposes. One of the challenges in this regard is data collecting to gain an accurate analysis of land
use development. The previous methods of data gathering such as human observational data
counting and automatic methods like pneumatic tubes and video camera suffers some limitations
that affect the accuracy of trip analysis which cause over mitigating or set some wrong rules and
regulations. Light Detection and Ranging (LiDAR) sensing is a powerful tool that has been vastly
used for mapping, safety, and medical applications. [1] Also, its application in transportation has
drawn attention in recent years. However, LiDAR sense is yet to be further explored in trip
generation. This study is an initial attempt to: 1) perform a LiDAR-based trip generation data
gathering for a local area in midtown, Reno, and 2) analyze the resulting data based on the GIS
software to develop a systematic plan for the case study and beyond
Developing a Series of AI Challenges for the United States Department of the Air Force
Through a series of federal initiatives and orders, the U.S. Government has
been making a concerted effort to ensure American leadership in AI. These broad
strategy documents have influenced organizations such as the United States
Department of the Air Force (DAF). The DAF-MIT AI Accelerator is an initiative
between the DAF and MIT to bridge the gap between AI researchers and DAF
mission requirements. Several projects supported by the DAF-MIT AI Accelerator
are developing public challenge problems that address numerous Federal AI
research priorities. These challenges target priorities by making large,
AI-ready datasets publicly available, incentivizing open-source solutions, and
creating a demand signal for dual use technologies that can stimulate further
research. In this article, we describe these public challenges being developed
and how their application contributes to scientific advances
- …