Staying up-to-date with current medical research can be a challenge for doctors and other medi- cal decision-makers. Systematic reviews are one of the key tools that doctors use to stay informed. These are meta-analyses of all the relevant research with the intention of answering one specific ques- tion within the healthcare domain. Cochrane pro- duces systematic reviews of medical research that are globally considered as a gold standard for high- quality healthcare information. However, because of the high volume of papers published and the fact that Cochrane’s review authors are volunteers, it can take up to three years to write and publish one of these reviews. Our research focuses on speeding up this process. We propose a hybrid human-AI system to establish the topical area of a newly pub- lished paper faster, easing the process of searching for papers to include in a review.
Conflict resolution practitioners consistently struggle with access to structured armed conflict data, a dataset already rife with uncertainty, inconsistency, and politicization. Due to the lack of a standardized approach to collating conflict data, publicly available armed conflict datasets often require manipulation depending upon the needs of end users. Transformation of armed conflict data tends to be a manual, time consuming task that nonprofits with limited budgets struggle to keep up with. In this paper, we explore the use of a deep natural language processing (NLP) model to aid the transformation of armed conflict data for conflict analysis. Our model drastically reduces the time spent on manual data transformations and improves armed conflict event classification by identifying multiple incidence types. This minimizes the human supervision cost and allows nonprofits to access a broader range of conflict data sources to reduce reporting bias. Thus our model contributes to the incorporation of technology in the peace building and conflict resolution sector.
Detection of burn marks due to wildfires in inaccessible rain forests is important for various disaster management and ecological studies. The fragmented nature of arable landscapes and diverse
cropping patterns often thwart the precise mapping
of burn scars. Recent advances in remote-sensing
and availability of multimodal data offer a viable
solution to this mapping problem. However, the
task to segment burn marks is difficult because of
its indistinguishably with similar looking land patterns, severe fragmented nature of burn marks and
partially labelled noisy datasets.
In this work we present AmazonNET – a convolutional based network that allows extracting of burn
patters from multimodal remote sensing images.
The network consists of UNet- a well-known encoder decoder type of architecture with skip connections. The proposed framework utilises stacked
RGB-NIR channels to segment burn scars from the
pastures by training on a new weakly labelled noisy
dataset from Amazonia.
Our model illustrates superior performance by correctly identifying partially labelled burn scars and
rejecting incorrectly labelled samples, demonstrating our approach as one of the first to effectively
utilise deep learning based segmentation models in
multimodal burn scar identification.
Within the relatively busy area of fair machine learning that has been dominated by classification fairness research, fairness in clustering has started to see some recent attention. In this position paper, we assess the existing work in fair clustering and observe that there are several directions that are yet to be explored, and postulate that the state-of-the- art in fair clustering has been quite parochial in out- look. We posit that widening the normative prin- ciples to target for, characterizing shortfalls where the target cannot be achieved fully, and making use of knowledge of downstream processes can significantly widen the scope of research in fair clustering research. At a time when clustering and unsupervised learning are being increasingly used to make and influence decisions that matter significantly to human lives, we believe that widening the ambit of fair clustering is of immense significance.
As disasters such as earthquakes and floods be- come more frequent and detrimental, it is increas- ingly important that water infrastructure resilience be strategically enhanced to support post-disaster functionality and recovery. In this paper, we focus on the problem of strategically building seismic- resilient pipe networks to ensure direct water sup- ply to critical customers and certain proximity to water sources for residential areas, which we for- malize as the Steiner network problem with cov- erage constraints. We present an efficient mixed- integer linear program encoding to solve the prob- lem. We also investigate the problem of planning partial network installments to maximize efficiency over time and propose an effective sequential plan- ning algorithm to solve it. We evaluate our algo- rithms on synthetic water networks and apply them to a case study on a water service zone in Los An- geles, which demonstrate the effectiveness of our methods for large-scale real-world applications.
The goal of the proposed project is to transform a large transportation hub into a smart and accessible hub (SA T-Hub), with minimal infrastructure change. The societal need is significant, especially impactful for people in great need, such as those who are blind and visually impaired (BVI) or with Autism Spectrum Disorder (ASD), as well as those unfamiliar with metropolitan areas. With our inter- disciplinary background in urban systems, sensing, AI and data analytics, accessibility, and paratransit and assistive services, our solution is a hu- man-centric system approach that integrates facility modeling, mobile navigation, and user interface designs. We leverage several transportation facili- ties in the heart of New York City and throughout the State of New Jersey as testbeds for ensuring the relevance of the research and a smooth transition to real world applications.
There is widespread acceptance that data from earth observation satellites, combined with artificial intelligence, have the potential to play an important role to enable the quantification of the United Nations Sustainable Development Indicators (SDIs). However, building workflows that allow accurate and timely measurement of the SDIs from sub-national to global scales is proving challenging. We discuss a research program that aims to develop techniques to meet these challenges and help provide member states of the UN with effective methods of monitoring progress towards meeting the goals of the 2030 Agenda for Sustainable Development.
Background: The COVID-19 outbreak has already caused significant mortality worldwide. As the epidemic accelerates, understanding the transmission dynamics of COVID-19 is crucial to informing national and regional policies. We develop an individual-level model for SARS-CoV2 transmission which accounts for location-dependent distributions of age and household structure. We apply our model to Hubei, China and Lombardy, Italy to analyze the impact of demographic structure on estimates for key parameters such as the rate of documentation and the reproduction number r0 for COVID-19 cases. We also assess the effectiveness of potential policies ranging from physical distancing to sheltering in place in Lombardy.
Methods: Our study develops a stochastic, agent-based model for SARS-CoV2 spread. A key feature of the model is the inclusion of population-specific demographic structure, such as the distributions of age, household structure, contact across age groups, and comorbidities. We use prior estimates of these demographic features to instantiate our model for two locations: Hubei, China and Lombardy, Italy. Furthermore, we utilize the data on the number of reported deaths due to COVID-19 in both locations to estimate parameters describing location-specific variation in the transmissibility and fatality of the disease (for reasons beyond demography). The range of the parameters in our model that are consistent with reported data are used to construct plausible ranges for r0 and the rate of documentation in each location. Finally, we analyze potential policy responses in the context of Lombardy. Our analysis traces out the trade-off between adoption of physical distancing across the entire population and policies that encourage members of a specific age group to shelter at home.
Results: Our estimates for r0 are comparable to the rest of the literature, with a range of 2.11–2.27 for Hubei and 2.50-3.20 for Lombardy, suggesting higher rates of transmission in the latter. Scenarios where the case fatality rates are higher in Lombardy than Hubei by a factor of 1-5 times appear plausible given the data (even after accounting for differences in age and comorbidity distributions). We estimate the rate at which symptomatic cases are documented to be at 10.3-19.2% in Hubei and 1.2-8% in Lombardy, indicating that the number of undocumented cases may be even higher than has previously been estimated. Evaluation of potential policies suggests that encouraging a single age group to shelter in place is insufficient to control the epidemic by itself, but that targeted "salutary sheltering" by even 50% of a single age group has a substantial impact when combined with adoption of physical distancing by the rest of the population.
Background: On March 24, India ordered a 3-week nationwide lockdown in an effort to control the spread of COVID-19. While the lockdown has been effective, our model suggests that completely ending the lockdown after three weeks could have considerable adverse public health ramifications. We extend our individual-level model for COVID-19 transmission  to study the disease dynamics in India at the state level for Maharashtra and Uttar Pradesh to estimate the effect of further lockdown policies in each region. Specifically, we test policies which alternate between total lockdown and simple physical distancing to find "middle ground" policies that can provide social and economic relief as well as salutary population-level health effects.
Methods: We use an agent-based SEIR model that uses population-specific age distribution, household structure, contact patterns, and comorbidity rates to perform tailored simulations for each region. The model is first calibrated to each region using publicly available COVID-19 death data, then implemented to simulate a range of policies. We also compute the basic reproduction number R0 and case documentation rate for both regions.
Results: After the initial lockdown, our simulations demonstrate that even policies that enforce strict physical distancing while returning to normal activity could lead to widespread outbreaks in both states. However, "middle ground" policies that alternate weekly between total lockdown and physical distancing may lead to much lower rates of infection while simultaneously permitting some return to normalcy.
Controlling recurrent infectious diseases is a vital yet complicated problem in global health. During the long period of time from patients becoming infected to finally seeking treatment, their close contacts are exposed and vulnerable to the disease they carry. Active screening (or case finding) methods seek to actively discover undiagnosed cases by screening contacts of known infected people to reduce the spread of the disease. Existing practice of active screening methods often screen all contacts of an infected person, requiring a large budget. In cooperation with a research institute in India, we develop a model of the active screening problem and present a software agent, REMEDY. This agent assists maximizing effectiveness of active screening under real world budgetary constraints and limited contact information. Our contributions are: (1) A new approach to modeling multi-round network-based screening/contact tracing under uncertainty and proof of its NP-hardness; (2) Two novel algorithms, Full- and Fast-REMEDY. Full-REMEDY considers the effect of future actions and provides high solution quality, whereas Fast-REMEDY scales linearly in the size of the network; (3) Evaluation of Full- and Fast-REMEDY on several real-world datasets which emulate human contact to show that they control diseases better than the baselines. We also show that the software agent is robust to errors in estimates of disease parameters, and incomplete information of the contact network. Our software agent is currently under review before deployment as a means to improve the efficiency of district-wise active screening for tuberculosis in India.