7,295 research outputs found
Location Reference Recognition from Texts: A Survey and Comparison
A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of its specific applications is still missing. Further, there is a lack of a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matchingâbased, statistical learning-âbased, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27Â most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references worldwide. Results from this thorough evaluation can help inform future methodological developments and can help guide the selection of proper approaches based on application needs
Political Hierarchy of Opening-Up Policy and Chinaâs Carbon Reduction: Empirical Research Based on Spatial Regression Discontinuity
This paper constructs a counterfactual framework based on the opening-up policies of provinces in the eastern coastal region. It analyzes the role of the political hierarchy of the opening-up policy in Chinaâs carbon reduction at the county level by using Spatial Regression Discontinuity, and the data used are from 1997 to 2017. The study found the following: (1) The improvement of the political hierarchy of the opening-up policy is negatively related to the carbon reduction, which has significantly boosted the carbon emission of counties in the eastern coastal areas of China. (2) The impact on border counties is more significant, and there is an obvious boundary effect. In terms of net carbon emissions, the political-hierarchy difference has a significant impact only in the area adjacent to the border. (3) There is strong heterogeneity among provinces, showing the boundary jump effect and boundary depression effect. (4) The political-hierarchy differences are significantly related to the regional carbon reduction by changing policy intensity, resulting in fiscal subsidies effects and gradient transfer effects. The location selection for the implementation of the opening-up policy significantly impacted the carbon reductions
Improving diagnostic procedures for epilepsy through automated recording and analysis of patientsâ history
Transient loss of consciousness (TLOC) is a time-limited state of profound cognitive impairment characterised by amnesia, abnormal motor control, loss of responsiveness, a short duration and complete recovery. Most instances of TLOC are caused by one of three health conditions: epilepsy, functional (dissociative) seizures (FDS), or syncope. There is often a delay before the correct diagnosis is made and 10-20% of individuals initially receive an incorrect diagnosis. Clinical decision tools based on the endorsement of TLOC symptom lists have been limited to distinguishing between two causes of TLOC. The Initial Paroxysmal Event Profile (iPEP) has shown promise but was demonstrated to have greater accuracy in distinguishing between syncope and epilepsy or FDS than between epilepsy and FDS. The objective of this thesis was to investigate whether interactional, linguistic, and communicative differences in how people with epilepsy and people with FDS describe their experiences of TLOC can improve the predictive performance of the iPEP. An online web application was designed that collected information about TLOC symptoms and medical history from patients and witnesses using a binary questionnaire and verbal interaction with a virtual agent. We explored potential methods of automatically detecting these communicative differences, whether the differences were present during an interaction with a VA, to what extent these automatically detectable communicative differences improve the performance of the iPEP, and the acceptability of the application from the perspective of patients and witnesses. The two feature sets that were applied to previous doctor-patient interactions, features designed to measure formulation effort or detect semantic differences between the two groups, were able to predict the diagnosis with an accuracy of 71% and 81%, respectively. Individuals with epilepsy or FDS provided descriptions of TLOC to the VA that were qualitatively like those observed in previous research. Both feature sets were effective predictors of the diagnosis when applied to the web application recordings (85.7% and 85.7%). Overall, the accuracy of machine learning models trained for the threeway classification between epilepsy, FDS, and syncope using the iPEP responses from patients that were collected through the web application was worse than the performance observed in previous research (65.8% vs 78.3%), but the performance was increased by the inclusion of features extracted from the spoken descriptions on TLOC (85.5%). Finally, most participants who provided feedback reported that the online application was acceptable. These findings suggest that it is feasible to differentiate between people with epilepsy and people with FDS using an automated analysis of spoken seizure descriptions. Furthermore, incorporating these features into a clinical decision tool for TLOC can improve the predictive performance by improving the differential diagnosis between these two health conditions. Future research should use the feedback to improve the design of the application and increase perceived acceptability of the approach
Eating Behavior In-The-Wild and Its Relationship to Mental Well-Being
The motivation for eating is beyond survival. Eating serves as means for socializing, exploring cultures, etc. Computing researchers have developed various eating detection technologies that can leverage passive sensors available on smart devices to automatically infer when and, to some extent, what an individual is eating. However, despite their significance in eating literature, crucial contextual information such as meal company, type of food, location of meals, the motivation of eating episodes, the timing of meals, etc., are difficult to detect through passive means. More importantly, the applications of currently developed automated eating detection systems are limited.
My dissertation addresses several of these challenges by combining the strengths of passive sensing technologies and EMAs (Ecological Momentary Assessment). EMAs are a widely adopted tool used across a variety of disciplines that can gather in-situ information about individual experiences. In my dissertation, I demonstrate the relationship between various eating contexts and the mental well-being of college students and information workers through naturalistic studies.
The contributions of my dissertation are four-fold. First, I develop a real-time meal detection system that can detect meal-level episodes and trigger EMAs to gather contextual data about oneâs eating episode. Second, I deploy this system in a college student population to understand their eating behavior during day-to-day life and investigate the relationship of these eating behaviors with various mental well-being outcomes. Third, based on the limitations of passive sensing systems to detect short and sporadic chewing episodes present in snacking, I develop a snacking detection system and operationalize the definition of snacking in this thesis. Finally, I investigate the causal relationship between stress levels experienced by remote information workers during their workdays and its effect on lunchtime. This dissertation situates the findings in an interdisciplinary context, including ubiquitous computing, psychology, and nutrition.Ph.D
Geoarchaeological Investigations of Late Pleistocene Physical Environments and Impacts of Prehistoric Foragers on the Ecosystem in Northern Malawi and Austria
A growing body of research shows that not only did environmental changes play an important role in human evolution, but humans in turn have impacted ecosystems and landscape evolution since the Late Pleistocene. This thesis presents collaborative work on Late Pleistocene open-air sites in the Karonga District of northern Malawi, in which new aspects of forager behavior came to light through the reconstruction of physical environments. My work has helped recognize that late Middle Stone Age (MSA) activity and tool production occurred in locally more open riparian environments within evergreen gallery forest, surrounded by a regional vegetation dominated by miombo woodlands and savanna. Additionally, MSA hunter-gatherers exploited the confluence of river and wetland areas along the shores of Lake Malawi, which likely served as important corridors for the dispersal of biota. By comparing data from the archaeological investigations with lake core records, we were able to identify effects of anthropogenic burning on vegetation structures and sedimentation in the region as early as 80 thousand years ago. These findings not only proved it possible to uncover early impacts of human activity on the ecosystem, but also emphasize the importance of fire in the lives of early foragers.
Publications contained within this dissertation:
A. Wright, D.K., Thompson, J.C., Schilt, F.C., Cohen, A., Choi, J-H., Mercader, J., Nightingale, S., Miller, C.E., Mentzer, S.M., Walde, D., Welling, M., and Gomani-Chindebvu, E. âApproaches to Middle Stone Age landscape archaeology in tropical Africaâ. Special issue Geoarchaeology of the Tropics of Journal of Archaeological Science 77:64-77. http://dx.doi.org/10.1016/j.jas.2016.01.014
B. Schilt, F.C., Verpoorte, A., Antl, W. âMicromorphology of an Upper Paleolithic cultural layer at Grub-Kranawetberg, Austriaâ. Journal of Archaeological Science: Reports 14:152-162. http://dx.doi.org/10.1016/j.jasrep.2017.05.041
C. Nightingale, S., Schilt, F.C., Thompson, J.C., Wright, D.K., Forman, S., Mercader, J., Moss, P., Clarke, S. Itambu, M., Gomani-Chindebvu, E., Welling, M. Late Middle Stone Age Behavior and Environments at Chaminade I (Karonga, Malawi). Journal of Paleolithic Archaeology 2-3:258-397. https://doi.org/10.1007/s41982-019-00035-3
D. Thompson, J.C.*, Wright, D.K.*, Ivory, S.J.*, Choi, J-H., Nightingale, S., Mackay, A., Schilt, F.C., OtĂĄrola-Castillo, E., Mercader, J., Forman, S.L., Pietsch, T., Cohen, A.S., Arrowsmith, J.R., Welling, M., Davis, J., Schiery, B., Kaliba, P., Malijani, O., Blome, M.W., OâDriscoll, C., Mentzer, S.M., Miller, C., Heo, S., Choi, J., Tembo, J., Mapemba, F., Simengwa, D., and Gomani-Chindebvu, E. âEarly human impacts and ecosystem reorganization in southern-central Africaâ. Science Advances 7(19): eabf9776. *equal contribution https://doi.org/10.1126/sciadv.abf9776
E. Schilt, F.C., Miller, C.M., Wright, D.K., Mentzer, S.M., Mercader, J., Moss, Choi, J.-H., Siljedal, G., Clarke, S., Mwambwiga, A., Thomas, K., Barbieri, A., Kaliba, P., Gomani-Chindebvu, E., Thompson, J.C. âHunter-gatherer environments at the Late Pleistocene sites of Bruce and Mwanganda´s Village, northern Malawiâ. Quaternary Science Reviews 292: 107638. https://www.sciencedirect.com/science/article/pii/S0277379122002694 [untranslated
Carbon-Free Power
There is a new world order in electrical energy production. Solar and wind power are established as the low-cost leaders. However, these energy sources are highly variable and electrical power is needed 24/7. Alternative sources must fill the gaps, but only a few are both economical and carbon-free or -neutral.
This book presents one alternative: small modular nuclear reactors (SMRs). The authors describe the technology, including its safety and economic aspects, and assess its fit with other carbon-free energy sources, storage solutions, and industrial opportunities. They also explain the challenges with SMRs, including public acceptance.
The purpose of the book is to help readers consider these relatively new reactors as part of an appropriate energy mix for the future and, ultimately, to make their own judgments on the merits of the arguments for SMRs.Publishe
Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring
Artificially intelligent perception is increasingly present in the lives of
every one of us. Vehicles are no exception, (...) In the near future, pattern
recognition will have an even stronger role in vehicles, as self-driving cars
will require automated ways to understand what is happening around (and within)
them and act accordingly. (...) This doctoral work focused on advancing
in-vehicle sensing through the research of novel computer vision and pattern
recognition methodologies for both biometrics and wellbeing monitoring. The
main focus has been on electrocardiogram (ECG) biometrics, a trait well-known
for its potential for seamless driver monitoring. Major efforts were devoted to
achieving improved performance in identification and identity verification in
off-the-person scenarios, well-known for increased noise and variability. Here,
end-to-end deep learning ECG biometric solutions were proposed and important
topics were addressed such as cross-database and long-term performance,
waveform relevance through explainability, and interlead conversion. Face
biometrics, a natural complement to the ECG in seamless unconstrained
scenarios, was also studied in this work. The open challenges of masked face
recognition and interpretability in biometrics were tackled in an effort to
evolve towards algorithms that are more transparent, trustworthy, and robust to
significant occlusions. Within the topic of wellbeing monitoring, improved
solutions to multimodal emotion recognition in groups of people and
activity/violence recognition in in-vehicle scenarios were proposed. At last,
we also proposed a novel way to learn template security within end-to-end
models, dismissing additional separate encryption processes, and a
self-supervised learning approach tailored to sequential data, in order to
ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022
to the University of Port
Multiphase flow measurement and data analytic based on multi-modal sensors
Accurate multiphase flow measurement is crucial in the energy industry. Over
the past decades, separation of the multiphase flow into single-phase flows has
been a standard method for measuring multiphase flowrate. However, in-situ, non-invasive, and real-time imaging and measuring the key parameters of multiphase
flows remain a long-standing challenge. To tackle the challenge, this thesis first
explores the feasibility of performing time-difference and frequency-difference imaging
of multiphase flows with complex-valued electrical capacitance tomography (CVECT).
The multiple measurement vector (MMV) model-based CVECT imaging algorithm
is proposed to reconstruct conductivity and permittivity distribution simultaneously,
and the alternating direction method of multipliers (ADMM) is applied to solve
the multi-frequency image reconstruction problem. The proposed multiphase flow
imaging approach is verified and benchmarked with widely adopted tomographic
image reconstruction algorithms. Another focus of this thesis is multiphase flowrate
estimation based on low-cost, multi-modal sensors. Machine learning (ML) has
recently emerged as a powerful tool to deal with time series sensing data from multi-modal sensors. This thesis investigates three prevailing machine learning methods,
i.e., deep neural network (DNN), support vector machine (SVM), and convolutional
neural network (CNN), to estimate the flowrate of oil/gas/water three-phase flows
based on the Venturi tube. The improvement of CNN with the combination of long-short term memory machine (LSTM) is made and a temporal convolution network
(TCN) model is introduced to analyse the collected time series sensing data from the
Venturi tube installed in a pilot-scale multiphase flow facility. Furthermore, a multi-modal approach for multiphase flowrate measurement is developed by combining
the Venturi tube and a dual-plane ECT sensor. An improved TCN model is built
to predict the multiphase flowrate with various data pre-processing methods. The
results provide guidance on data pre-processing methods for multiphase flowrate
measurement and suggest that the proposed combination of low-cost flow sensing
techniques and machine learning can effectively translate the time series sensing
data to achieve satisfactory flowrate measurement under various flow conditions
Student Movements, Politics, and Policy in Chile, 2001 â 2012
Chile has frequently been touted as an economic miracle, the âJaguar of Latin Americaâ. Boasting the strongest economy in South America, due to severe neoliberal economic structural adjustments made under the dictatorship of General Augusto Pinochet, it has long been held up as the perfect exemplar of economic growth and stability, as well as the poster child for the effectiveness of neoliberal economics. After the re-establishment of democracy in 1990, political conditions improved as well; the country enjoyed a decade of stability and peace under its first two democratically-elected governments.
Yet, beginning approximately ten years after the transition to democracy, Chilean students began engaging in massive waves of protest. Discontent grew, and students manifested in larger numbers and for longer periods of time with each successive cycle of mobilization, eventually culminating in the âsocial explosionâ of 2019. This dissertation examines three cycles of student mobilization in Chile; the Mochilazo (2001), the RevoluciĂłn PengĂźina (2006), and the Invierno Chileno (2011), seeking to explain the effects the protests had on public policy, laws, and political institutions in the country. It delves into how the students were able to enlarge both the number of participants and their claims with each successive cycle; their repertoires of contention; their interactions with government officials; their framing and messages; and what changes occurred as a result of each cycle. A combination of the joint-effect model and Felix Kolbâs framework are used to analyze the effects of social mobilization.
Guided by the state-movement intersection model, Marco Giugniâs joint-effect model, and Felix Kolbâs framework for analyzing the impact of social movements, I find that the students were able to affect numerous changes in each cycle of mobilization, enlarging their claims and numbers each time via transferred knowledge from previous cycles. Chilean students have come to be regarded as important political actors in the political system, and have evolved their claims to demand massive structural changes to both the political and economic systems in the country
- âŚ