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Background: Rising demands for traditional postpartum depression (PPD) treatment options (e.g., psychiatry), especially in the context of the COVID-19 pandemic, are increasingly difficult to meet. ...

The COVID-19 pandemic is causing mass disruption to our daily lives. We integrate mobility data from mobile devices and area-level data to study the walking patterns of 1.62 million anonymous users in 10 metropolitan areas in the United States. The data covers the period from mid-February 2020 (pre-lockdown) to late June 2020 (easing of lockdown restrictions). We detect when users were walking, distance walked and time of the walk, and classify each walk as recreational or utilitarian. Our results reveal dramatic declines in walking, particularly utilitarian walking, while recreational walking has recovered and even surpassed pre-pandemic levels. Our findings also demonstrate important social patterns, widening existing inequalities in walking behavior. COVID-19 response measures have a larger impact on walking behavior for those from low-income areas and high use of public transportation. Provision of equal opportunities to support walking is key to opening up our society and economy., Mobility restrictions implemented to reduce the spread of COVID-19 have significantly impacted walking behavior. In this study, the authors integrated mobility data from mobile devices and area-level data to study the walking patterns of 1.62 million anonymous users in 10 US metropolitan areas.

Tourism has been an increasingly significant contributor to the economy, society, and environment. Policy-making and research on tourism traditionally rely on surveys and economic datasets, which are based on small samples and depict tourism dynamics at a low granularity. Anonymous call detail record (CDR) is a novel source of data with enormous potential in areas of high societal value: epidemics, poverty, and urban development. This study demonstrates the added value of CDR in event tourism, especially for the analysis and evaluation of marketing strategies, event operations, and the externalities at the local and national levels. To achieve this aim, we formalize 14 indicators in high spatial and temporal resolutions to measure both the positive and the negative impacts of the touristic events. We exemplify the use of these indicators in a tourism country, Andorra, on 22 high-impact events including sports competitions, cultural performances, and music festivals. We analyze these touristic events using the large-scale CDR data across 2 years. Our approach serves as a prescriptive and a diagnostic tool with mobile phone data and opens up future directions for tourism analytics.

Customer patronage behavior has been widely studied in market share modeling contexts, which is an essential step toward estimating retail sales and finding new store locations in a competitive setting. Existing studies have conducted surveys to estimate merchants' market share and factors of attractiveness to use in various proposed mathematical models. Recent trends in Big Data analysis allow us to better understand human behavior and decision making, potentially leading to location models with more realistic assumptions. In this article, we propose a novel approach for validating the Huff gravity market share model, using a large-scale transactional dataset that describes customer patronage behavior at a regional level. Although the Huff model has been well studied and widely used in the context of sales estimation, competitive facility location, and demand allocation, this article is the first in validating the Huff model with a real dataset. Our approach helps to easily apply the model in different regions and with different merchant categories. Experimental results show that the Huff model fits well when modeling customer shopping behavior for a number of shopping categories, including grocery stores, clothing stores, gas stations, and restaurants. We also conduct regression analysis to show that certain features such as gender diversity and marital status diversity lead to stronger validation of the Huff model. We believe we provide strong evidence, with the help of real-world data, that gravity-based market share models are viable assumptions for retail sales estimation and competitive facility location models.

We examine why trust change occurs when potential users first encounter news about a specific technology. We propose personal perceptions and three cognitive outcomes—attention, sensemaking, and th...

The world is blazing with change and digital innovation is fuelling the fire. Process management can help channel the heat into useful work. Unfortunately, research on digital innovation and process management has been conducted by separate communities operating under orthogonal assumptions. We argue that a synthesis of assumptions is required to bring these streams of research together. We offer suggestions for how these assumptions can be updated to facilitate a convergent conversation between the two research streams. We also suggest ways that methodologies from each stream could benefit the other. Together with the three exemplar empirical studies included in the special issue on business process management and digital innovation, we develop a broader foundation for reinventing research on business process management in a world ablaze with digital innovation.

Patient-reported outcome (PRO) measures play an important role in clinical care. Currently, a broad-spectrum, validated PRO measure suitable for all dermatology patients, as part of clinical care, does not exist. Patient-reported Outcome Measures Information System (PROMIS) measures track specific domain outcomes across all diseases. To assess the relevance and utility of a computer-adaptive health assessment consisting of three PROMIS domains in routine dermatologic care. This retrospective study evaluated a PROMIS health assessment, consisting of three computer-adaptive test domains (pain interference, anxiety, and depression), administered as part of routine clinical care in three dermatology clinics at an academic medical center. The primary objective was to identify clinically significant associations between high PROMIS domain scores (i.e., t score > 55) and dermatologic disease, as well as change in PROMIS domain scores in response to treatment. The majority of patients who initiated the assessment completed all domains (88.7%). In patients with atopic dermatitis, acne, hidradenitis suppurativa, and psoriasis, high PROMIS scores correlated with clinically relevant outcomes, such as severe disease, unsuccessful treatment, uncontrolled disease, and the presence of a mental health condition. PROMIS Pain Interference, anxiety and depression identified patients with severe disease, unsuccessful treatment regimens, poorly-controlled disease, and/or mental health comorbidities for multiple skin conditions. Further utilization of PROMIS domains in routine clinical care will promote patient-centered care and improve quality of care.

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens' privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens' "personal data stores", to be shared separately and selectively, voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates - if and when they want, for specific aims - with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society., Revised text. Additional authors

ImportanceExcessive consumption of fast food (FF) is associated with chronic disease. Population-level research on FF outlet visits is now possible with mobility data, however its usefulness as an indicator of FF intake and diet-related disease must be established.ObjectiveInvestigate whether FF outlet visits from mobility data are indicators of self-reported FF intake, obesity, and diabetes, and compared with self-reported intake, equivalent or better indicators of obesity and diabetes.Design, Setting, and ParticipantsA secondary analysis of data from a representative sample of 8,036 adult residents of Los Angeles County (LAC) from the 2011 Los Angeles County Health Survey (LACHS), and mobility data representing all geolocations between October 2016 - March 2017 of 243,644 anonymous and opted-in smartphone users in LAC.Main Outcomes and MeasuresMain outcomes were self-reported FF intake frequency (never, infrequent, moderate, frequent), obesity, and diabetes from LACHS. FF outlet visits were computed as the temporal frequency of FF visits (FF visits/time) and the ratio of visits to FF over all food outlets (FF visits/food), summarized over smartphone users in a neighborhood, scaled from 0-10, and linked to LACHS respondents by census tract.ResultsThe analytic sample included 5,447 LACHS respondents and 234,995 smartphone users with 14,498,850 visits to food outlets. FF outlet visits were significantly associated with self-reported FF intake (reference: never) for both FF visits/time (infrequent: odds ratio [OR], 1.13; 95% CI, 1.06-1.20; frequent: OR, 1.35; 95% CI, 1.28-1.42) and FF visits/food (infrequent: OR, 1.12; 95% CI, 1.06-1.17; frequent: OR, 1.28; 95% CI, 1.22-1.33). FF outlet visits were significantly associated with obesity (FF visits/time: adjusted OR [AOR], 1.16; 95% CI, 1.12-1.21; FF visits/food: AOR, 1.13; 95% CI, 1.10-1.17) and diabetes (FF visits/time: AOR, 1.15; 95% CI, 1.09-1.21; FF visits/food: AOR, 1.11; 95% CI, 1.07-1.16), adjusted for sociodemographic factors. Models of the association between FF outlet visits and obesity or diabetes had better fits than between self-reported FF intake and obesity or diabetes.Conclusions and relevanceThis study illustrates that population-scale mobility data provide useful, passively-collected indicators of FF intake and diet-related disease within large, diverse urban populations that may be better than self-report intake.Key PointsQuestionDo visits to fast food outlets observed in mobility data provide meaningful measures of fast food intake, and when compared with self-reported intake, equivalent or better indicators of diet-related disease?FindingsIn this cross-sectional Los Angeles County study from a survey of 8,036 adults and mobility data from 243,644 smartphone users with 14.5 million food outlet visits, neighborhood-level features representing visits to fast food outlets were significantly associated with self-reported fast food intake, significantly associated with obesity and diabetes, and were a better predictor of these diseases than self-reported fast food intake.MeaningMeasures of food behaviors observed in population-scale mobility data can provide meaningful indicators of food intake and diet-related diseases, and could complement existing dietary surveillance methods.

AI is turning up everywhere, but when people try to use it to as a tool help make better decisions it often stumbles…users reject it, rely too much on it, and so forth. Not only is this a problem for AI-as-tool applications, it is increasingly clear that AI without human oversight is prone to bad mistakes, typically because the AI has such a narrow view of the world and can't tell when it is violating norms or when the context has changed. As a consequence, AI-automation is getting serious pushback from citizens and lawmakers. What are we to do in order to integrate AI tools into human group decision making?

Global financial crises have led to the understanding that classical econometric models are limited in comprehending financial markets in extreme conditions, partially since they disregarded complex interactions within the system. Consequently, in recent years research efforts have been directed towards modeling the structure and dynamics of the underlying networks of financial ecosystems. However, difficulties in acquiring fine-grained empirical financial data, due to regulatory limitations, intellectual property and privacy control, still hinder the application of network analysis to financial markets. In this paper we study the trading of cryptocurrency tokens on top of the Ethereum Blockchain, which is the largest publicly available financial data source that has a granularity of individual trades and users, and which provides a rare opportunity to analyze and model financial behavior in an evolving market from its inception. This quickly developing economy is comprised of tens of thousands of different financial assets with an aggregated valuation of more than 500 Billion USD and typical daily volume of 30 Billion USD, and manifests highly volatile dynamics when viewed using classic market measures. However, by applying network theory methods we demonstrate clear structural properties and converging dynamics, indicating that this ecosystem functions as a single coherent financial market. These results suggest that a better understanding of traditional markets could become possible through the analysis of fine-grained, abundant and publicly available data of cryptomarkets.

Crowdsourcing has become an indispensable tool in the behavioral sciences. Often, the “crowd” is considered a black box for gathering impersonal but generalizable data. Researchers sometimes seem to forget that crowdworkers are people with social contexts, unique personalities, and lives. To test this possibility, we measure how crowdworkers ( N = 2,337, preregistered) share a monetary endowment in a Dictator Game with another Mechanical Turk (MTurk) worker, a worker from another crowdworking platform, or a randomly selected stranger. Results indicate preferential in-group treatment for MTurk workers in particular and for crowdworkers in general. Cooperation levels from typical anonymous economic games on MTurk are not a good proxy for anonymous interactions and may generalize most readily only to the intragroup context.

Current theory in routine dynamics focuses on patterning (Feldman 2016) as a mechanism for stability and change in routines. We define patterning as the process of adding, removing, or reinforcing paths in the narrative network that describes an organizational routine. Patterning is a hybrid mechanism that can be driven by any of the four change motors (teleology, dialectic, lifecycle, or evolution). Through patterning, routines change and adapt over time. In this chapter, the idea of organizational routines is illustrated with examples from videogame development. The authors suggest that narrative networks provide a way to see routine dynamics as network dynamics and to analyze routines and organizational change from a fresh point of view.

Invariant approaches have been remarkably successful in tackling the problem of domain generalization, where the objective is to perform inference on data distributions different from those used in training. In our work, we investigate whether it is possible to leverage domain information from the unseen test samples themselves. We propose a domain-adaptive approach consisting of two steps: a) we first learn a discriminative domain embedding from unsupervised training examples, and b) use this domain embedding as supplementary information to build a domain-adaptive model, that takes both the input as well as its domain into account while making predictions. For unseen domains, our method simply uses few unlabelled test examples to construct the domain embedding. This enables adaptive classification on any unseen domain. Our approach achieves state-of-the-art performance on various domain generalization benchmarks. In addition, we introduce the first real-world, large-scale domain generalization benchmark, Geo-YFCC, containing 1.1M samples over 40 training, 7 validation, and 15 test domains, orders of magnitude larger than prior work. We show that the existing approaches either do not scale to this dataset or underperform compared to the simple baseline of training a model on the union of data from all training domains. In contrast, our approach achieves a significant improvement., Comment: To appear as an oral presentation in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021. v2 corrects double printing of appendix

A critical question relevant to the increasing importance of crowd-sourced-based finance is how to optimize collective information processing and decision-making. Here, we investigate an often under-studied aspect of the performance of online traders: beyond focusing on just accuracy, what gives rise to the trade-off between risk and accuracy at the collective level? Answers to this question will lead to designing and deploying more effective crowd-sourced financial platforms and to minimizing issues stemming from risk such as implied volatility. To investigate this trade-off, we conducted a large online Wisdom of the Crowd study where 2037 participants predicted the prices of real financial assets (S&amp, P 500, WTI Oil and Gold prices). Using the data collected, we modeled the belief update process of participants using models inspired by Bayesian models of cognition. We show that subsets of predictions chosen based on their belief update strategies lie on a Pareto frontier between accuracy and risk, mediated by social learning. We also observe that social learning led to superior accuracy during one of our rounds that occurred during the high market uncertainty of the Brexit vote.

In the United States (US), low-income workers are being pushed away from city centers where the cost of living is high. The effects of such changes on labor mobility and housing price have been explored in the literature. However, few studies have focused on the occupations and specific skills that identify the most susceptible workers. For example, it has become increasingly challenging to fill the service sector jobs in the San Francisco (SF) Bay Area because appropriately skilled workers cannot afford the growing cost of living within commuting distance. With this example in mind, how does a neighborhood's skill composition change as a result of higher housing prices? Are there certain skill sets that are being pushed to the geographical periphery of a city despite their essentialness to the city's economy? Our study focuses on the impact of housing prices with a granular view of skills compositions to answer the following question: Has the density of cognitive skill workers been increasing in a gentrified area? We hypothesize that, over time, low-skilled workers are pushed away from downtown or areas where high-skill establishments thrive. Our preliminary results show that high-level cognitive skills are getting closer to the city center indicating adaptation to the increase of median housing prices as opposed to low-level physical skills that got further away. We examined tracts that the literature indicates as gentrified areas and found a pattern in which there is a temporal increase in median housing prices and the number of business establishments coupled with an increase in the percentage of skilled cognitive workers.

We extend previous studies on the impact of masks on COVID-19 outcomes by investigating an unprecedented breadth and depth of health outcomes, geographical resolutions, types of mask mandates, early versus later waves and controlling for other government interventions, mobility testing rate and weather. We show that mask mandates are associated with a statistically significant decrease in new cases (-3.55 per 100K), deaths (-0.13 per 100K), and the proportion of hospital admissions (-2.38 percentage points) up to 40 days after the introduction of mask mandates both at the state and county level. These effects are large, corresponding to 14% of the highest recorded number of cases, 13% of deaths, and 7% of admission proportion. We also find that mask mandates are linked to a 23.4 percentage point increase in mask adherence in four diverse states. Given the recent lifting of mandates, we estimate that the ending of mask mandates in these states is associated with a decrease of -3.19 percentage points in mask adherence and 12 per 100K (13% of the highest recorded number) of daily new cases with no significant effect on hospitalizations and deaths. Lastly, using a large novel survey dataset of 847 thousand responses in 69 countries, we introduce the novel results that community mask adherence and community attitudes towards masks are associated with a reduction in COVID-19 cases and deaths. Our results have policy implications for reinforcing the need to maintain and encourage mask-wearing by the public, especially in light of some states starting to remove their mask mandates.

We extend previous studies on the impact of masks on COVID-19 outcomes by investigating an unprecedented breadth and depth of health outcomes, geographical resolutions, types of mask mandates, early versus later waves and controlling for other government interventions, mobility testing rate and weather. We show that mask mandates are associated with a statistically significant decrease in new cases (-3.55 per 100K), deaths (-0.13 per 100K), and the proportion of hospital admissions (-2.38 percentage points) up to 40 days after the introduction of mask mandates both at the state and county level. These effects are large, corresponding to 14% of the highest recorded number of cases, 13% of deaths, and 7% of admission proportion. We also find that mask mandates are linked to a 23.4 percentage point increase in mask adherence in four diverse states. Lastly, using a large novel survey dataset of almost half a million people in 68 countries, we introduce the novel results that community mask adherence and community attitudes towards masks are associated with a reduction in COVID-19 cases and deaths. Our results have policy implications for reinforcing the need to maintain and encourage mask-wearing by the public, especially in light of some states starting to remove their mask mandates.

BACKGROUNDDiabetes and hypertension are among top public health priorities, particularly in low and middle-income countries where their health and socioeconomic impact is exacerbated by the quality and accessibility of health care. Moreover, their connection with severe or deadly COVID-19 illness has further increased their societal relevance. Tools for early detection of these chronic diseases enable interventions to prevent high-impact complications, such as loss of sight and kidney failure. Similarly, prognostic tools for COVID-19 help stratify the population to prioritize protection and vaccination of high-risk groups, optimize medical resources and tests, and raise public awareness.METHODSWe developed and validated state-of-the-art risk models for the presence of undiagnosed diabetes, hypertension, visual complications associated with diabetes and hypertension, and the risk of severe COVID-19 illness (if infected). The models were estimated using modern methods from the field of statistical learning (e.g., gradient boosting trees), and were trained on publicly available data containing health and socioeconomic information representative of the Mexican population. Lastly, we assembled a short integrated questionnaire and deployed a free online tool for massifying access to risk assessment.RESULTSOur results show substantial improvements in accuracy and algorithmic equity (balance of accuracy across population subgroups), compared to established benchmarks. In particular, the models: i) reached state-of-the-art sensitivity and specificity rates of 90% and 56% (0.83 AUC) for diabetes, 80% and 64% (0.79 AUC) for hypertension, 90% and 56% (0.84 AUC) for visual diminution as a complication, and 90% and 60% (0.84 AUC) for development of severe COVID disease; and ii) achieved substantially higher equity in sensitivity across gender, indigenous/non-indigenous, and regional populations. In addition, the most relevant features used by the models were in line with risk factors commonly identified by previous studies. Finally, the online platform was deployed and made accessible to the public on a massive scale.CONCLUSIONSThe use of large databases representative of the Mexican population, coupled with modern statistical learning methods, allowed the development of risk models with state-of-the-art accuracy and equity for two of the most relevant chronic diseases, their eye complications, and COVID-19 severity. These tools can have a meaningful impact on democratizing early detection, enabling large-scale preventive strategies in low-resource health systems, increasing public awareness, and ultimately raising social well-being.

Data sharing efforts to allow underserved groups and organizations to overcome the concentration of power in our data landscape. A few special organizations, due to their data monopolies and resources, are able to decide which problems to solve and how to solve them. But even though data sharing creates a counterbalancing democratizing force, it must nevertheless be approached cautiously. Underserved organizations and groups must navigate difficult barriers related to technological complexity and legal risk. To examine what those common barriers are, one type of data sharing effort—data trusts—are examined, specifically the reports commenting on that effort. To address these practical issues, data governance technologies have a large role to play in democratizing data trusts safely and in a trustworthy manner. Yet technology is far from a silver bullet. It is dangerous to rely upon it. But technology that is no-code, flexible, and secure can help more responsibly operate data trusts. This type of technology helps innovators put relationships at the center of their efforts.

How does the visual design of digital platforms impact user behavior and the resulting environment? A body of work suggests that introducing social signals to content can increase both the inequality and unpredictability of its success, but has only been shown in the context of music listening. To further examine the effect of social influence on media popularity, we extend this research to the context of algorithmically-generated images by re-adapting Salganik et al's Music Lab experiment. On a digital platform where participants discover and curate AI-generated hybrid animals, we randomly assign both the knowledge of other participants' behavior and the visual presentation of the information. We successfully replicate the Music Lab's findings in the context of images, whereby social influence leads to an unpredictable winner-take-all market. However, we also find that social influence can lead to the emergence of local cultural trends that diverge from the status quo and are ultimately more diverse. We discuss the implications of these results for platform designers and animal conservation efforts., Comment: 18 pages, to appear in CSCW October 2021

Background: The automated screening of patients at risk of developing diabetic retinopathy represents an opportunity to improve their midterm outcome and lower the public expenditure associated with direct and indirect costs of common sight-threatening complications of diabetes. Objective: This study aimed to develop and evaluate the performance of an automated deep learning–based system to classify retinal fundus images as referable and nonreferable diabetic retinopathy cases, from international and Mexican patients. In particular, we aimed to evaluate the performance of the automated retina image analysis (ARIA) system under an independent scheme (ie, only ARIA screening) and 2 assistive schemes (ie, hybrid ARIA plus ophthalmologist screening), using a web-based platform for remote image analysis to determine and compare the sensibility and specificity of the 3 schemes. Methods: A randomized controlled experiment was performed where 17 ophthalmologists were asked to classify a series of retinal fundus images under 3 different conditions. The conditions were to (1) screen the fundus image by themselves (solo); (2) screen the fundus image after exposure to the retina image classification of the ARIA system (ARIA answer); and (3) screen the fundus image after exposure to the classification of the ARIA system, as well as its level of confidence and an attention map highlighting the most important areas of interest in the image according to the ARIA system (ARIA explanation). The ophthalmologists’ classification in each condition and the result from the ARIA system were compared against a gold standard generated by consulting and aggregating the opinion of 3 retina specialists for each fundus image. Results: The ARIA system was able to classify referable vs nonreferable cases with an area under the receiver operating characteristic curve of 98%, a sensitivity of 95.1%, and a specificity of 91.5% for international patient cases. There was an area under the receiver operating characteristic curve of 98.3%, a sensitivity of 95.2%, and a specificity of 90% for Mexican patient cases. The ARIA system performance was more successful than the average performance of the 17 ophthalmologists enrolled in the study. Additionally, the results suggest that the ARIA system can be useful as an assistive tool, as sensitivity was significantly higher in the experimental condition where ophthalmologists were exposed to the ARIA system’s answer prior to their own classification (93.3%), compared with the sensitivity of the condition where participants assessed the images independently (87.3%; P=.05). Conclusions: These results demonstrate that both independent and assistive use cases of the ARIA system present, for Latin American countries such as Mexico, a substantial opportunity toward expanding the monitoring capacity for the early detection of diabetes-related blindness.

Introduction: Pre-school orthoptic vision screening (POVS) was implemented by the Scottish government and is a standardised assessment to promote early detection of visual problems in children. The target conditions are amblyopia, refractive errors and strabismus. We present the preliminary findings for the first three years of the screening program. Methods: The data from POVS was collected retrospectively. The data includes screening years 2013 to 2016 inclusive. Data was collected from each health board in Scotland. We report the coverage, referral rate, true positives and positive predictive values. Results: A total of 167,962 children were due to have vision screening over the 3 screening years included in this paper. This figure does not include the children that opted out of the eye test (mean opt-out rate 1.8%) and children that already attend the hospital eye service (mean already attend rate 3.1%). The POVS program had a mean coverage of 85.5%, ranging from 63.7% to 94.8% between health boards. Over the 3 year screening period, the mean referral rate was found to be 17.9%. The mean true positive rate was 88.9%, and the mean positive predictive value was 86.9%. Conclusion: The Scottish data set on pre-school orthoptic vision screening has shown excellent mean coverage. A consistently high true positive rate over the three screening years demonstrates it is a sensitive screening program, which is essential for the detection of visual problems in children.

BackgroundThe automated screening of patients at risk of developing diabetic retinopathy (DR) represents an opportunity to improve their mid-term outcome, and lower the public expenditure associated with direct and indirect costs of common sight-threatening complications of diabetes.ObjectiveIn the present study, we aim at developing and evaluating the performance of an automated deep learning–based system to classify retinal fundus images from international and Mexican patients, as referable and non-referable DR cases. In particular, we study the performance of the automated retina image analysis (ARIA) system under an independent scheme (i.e. only ARIA screening) and twoassistiveschemes (i.e., hybrid ARIA + ophthalmologist screening), using a web-based platform for remote image analysis.MethodsWe ran a randomized controlled experiment where 17 ophthalmologists were asked to classify a series of retinal fundus images under three different conditions: 1) screening the fundus image by themselves (solo), 2) screening the fundus image after being exposed to the opinion of the ARIA system (ARIA answer), and 3) screening the fundus image after being exposed to the opinion of the ARIA system, as well as its level of confidence and an attention map highlighting the most important areas of interest in the image according to the ARIA system (ARIA explanation). The ophthalmologists’ opinion in each condition and the opinion of the ARIA system were compared against agold standardgenerated by consulting and aggregating the opinion of three retina specialists for each fundus image.ResultsThe ARIA system was able to classify referable vs. non-referable cases with an area under the Receiver Operating Characteristic curve (AUROC), sensitivity, and specificity of 98%, 95.1% and 91.5% respectively, for international patient-cases; and an AUROC, sensitivity, and specificity of 98.3%, 95.2%, and 90% respectively for Mexican patient-cases. The results achieved on Mexican patient-cases outperformed the average performance of the 17 ophthalmologist participants of the study. We also find that the ARIA system can be useful as an assistive tool, as significant sensitivity improvements were observed in the experimental condition where participants were exposed to the answer of theARIA systemas a second opinion (93.3%), compared to the sensitivity of the condition where participants assessed the images independently (87.3%).ConclusionsThese results demonstrate that both use cases of ARIA systems,independentandassistive, present a substantial opportunity for Latin American countries like Mexico towards an efficient expansion of monitoring capacity for the early detection of diabetes-related blindness.

Indwelling silicone valves called voice prostheses (VPs) are the gold standard for speech rehabilitation in patients with laryngeal cancer following total laryngectomy. Reported VP lifespans amongst these patients are highly variable but when devices fail patients experience loss of voice and an increase risk of chest infection. Early failure of VP is a current clinical concern that is associated with regular hospital visits, reduced quality of life and associated medical cost. Poly-microbial biofilms comprised of both bacterial and fungal microorganisms readily colonize VPs and are linked to loss of device performance and its early failure in addition to providing a reservoir for potential infection. Our detailed analysis of poly-microbial biofilm composition on 159 early failing VPs from 48 total laryngectomy patients confirmed Candida albicans as the predominant fungal species and Staphylococcus aureus as the most common bacterial colonizer within our patient cohort. Using a combination of microbiological analysis, patient data and a high-throughput antifungal test assay mimicking in vivo conditions we established an evidence based precision antifungal treatment approach to VP management. Our approach has allowed us to implement a personalized VP management pathway, which increases device in situ lifespan by an average of 270%. Our study represents a significant step forward in both our understanding of the cause of VP failure and a new effective treatment pathway that offers tangible benefit to patients.

Business process management is a prolific field of research and an area of strong industrial uptake with roots in both management science and information systems engineering. Traditionally, business process management has largely been utilized in an inward-looking way with the aim to improve operations, eliminate waste, and increase efficiency. Recent developments around digital innovation challenge conventional ideas of process reengineering with a strong emphasis on the external market and exploration. In this talk, we will discuss the complementarity of BPM and digital innovation.

The new coronavirus disease 2019 (COVID-19) has required the implementation of severe mobility restrictions and social distancing measures worldwide. While these measures have been proven effective in abating the epidemic in several countries, it is important to estimate the effectiveness of testing and tracing strategies to avoid a potential second wave of the COVID-19 epidemic. We integrate highly detailed (anonymized, privacy-enhanced) mobility data from mobile devices, with census and demographic data to build a detailed agent-based model to describe the transmission dynamics of SARS-CoV-2 in the Boston metropolitan area. We find that enforcing strict social distancing followed by a policy based on a robust level of testing, contact-tracing and household quarantine, could keep the disease at a level that does not exceed the capacity of the health care system. Assuming the identification of 50% of the symptomatic infections, and the tracing of 40% of their contacts and households, which corresponds to about 9% of individuals quarantined, the ensuing reduction in transmission allows the reopening of economic activities while attaining a manageable impact on the health care system. Our results show that a response system based on enhanced testing and contact tracing can play a major role in relaxing social distancing interventions in the absence of herd immunity against SARS-CoV-2.

In order for VASPs to fulfill the regulatory requirements from the FATF and the Travel Rule, VASPs need access to truthful information regarding originators, beneficiaries and other VASPs involved in a virtual asset transfer instance. Additionally, in seeking data regarding subjects (individuals or organizations) VASPs are faced with privacy regulations such as the GDPR and CCPA. In this paper we a propose privacy-preserving claims issuance model that carries indicators of the provenance of the data and the algorithms used to derive the claim or assertion. This allows VASPs to obtain originator and beneficiary information without necessarily having access to the private data about these entities. Secondly we propose the use of a consortium trust network arrangement for VASPs to exchange signed claims about subjects and their public-key information or certificate., 4 figures

Understanding the mechanisms of network formation is central in social network analysis. Network formation has been studied in many research fields with their different focuses; for example, network embedding algorithms in machine learning literature consider broad heterogeneity among agents while the social sciences emphasize the interpretability of link formation mechanisms. Here we propose a social network formation model that integrates methods in multiple disciplines and retain both heterogeneity and interpretability. We represent each agent by an “endowment vector” that encapsulates their features and use game-theoretical methods to model the utility of link formation. After applying machine learning methods, we further analyze our model by examining micro- and macro- level properties of social networks as most agent-based models do. Our work contributes to the literature on network formation by combining the methods in game theory, agent-based modeling, machine learning, and computational sociology., Complex networks can be a useful tool to investigate problems in social science. Here the authors use game theory to establish a network model and then use a machine learning approach to characterize the role of nodes within a social network.

Participants in cryptocurrency markets are in constant communication with each other about the latest coins and news releases. Do these conversations build hype through the contagiousness of excitement, help the community process information, or play some other role? Using a novel dataset from a major cryptocurrency forum, we conduct an exploratory study of the characteristics of online discussion around cryptocurrencies. Through a regression analysis, we find that coins with more information available and higher levels of technical innovation are associated with higher quality discussion. People who talk about "serious" coins tend to participate in discussion displaying signatures of collective intelligence and information processing, while people who talk about "less serious" coins tend to display signatures of hype and naïvety. Interviews with experienced forum members also confirm these quantitative findings. These results highlight the varied roles of discussion in the cryptocurrency ecosystem and suggest that discussion of serious coins may be oriented towards earnest, perhaps more accurate, attempts at discovering which coins are likely to succeed.

Customer retention is crucial in a variety of businesses as acquiring new customers is often more costly than keeping the current ones. As a consequence, churn prediction has attracted great attention from both the business and academic worlds. Traditional efforts in the financial domain mainly focus on domain specific variables such as product ownership or service usage aggregation, however, without considering dynamic behavioral patterns of customers’ financial transactions. In this paper, we attempt to fill in this gap by investigating the spatio-temporal patterns and entropy of choices underlying the customers’ financial decisions, and their relations to customer churning activities. Inspired by previous works in the emerging field of computational social science, we built a prediction model based on spatio-temporal and choice behavioral traits using individual transaction records. Our results show that proposed dynamic behavioral models could predict churn decisions significantly better than traditionally considered factors such as demographic-based features, and that this effect remains consistent across multiple data sets and various churn definitions. We further study the relative importance of the various behavioral features in churn prediction, and how the predictive power varies across different demographic groups. More generally, the proposed features can also be applied to churn prediction in other domains where spatio-temporal behavioral data are available. Keywords: Churn prediction, Customer behavior, Spatio-temporal patterns, Credit card data

PURPOSE: Despite advances in medical technology, radiation dermatitis occurs in 95% of patients receiving radiation therapy (RT) for cancer. Currently, there is no standard and effective treatment for the prevention or control of radiation dermatitis. The goal of the study was to determine the efficacy of oral curcumin, one of the biologically active components in turmeric, at reducing radiation dermatitis severity (RDS) at the end of RT, using the RDS scale, compared to placebo. METHODS: This was a multisite, randomized, double-blinded, placebo-controlled trial of 686 breast cancer patients. Patients took four 500 mg capsules of placebo or curcumin three times daily throughout their prescribed course of RT until one week post-RT. RESULTS: A total of 686 patients were included in the final analyses (87.5% white females, mean age = 58). Linear mixed model analyses demonstrated that curcumin did not reduce radiation dermatitis severity at the end of RT compared to placebo (B (95% CI) =0.044 (−0.101, 0.188), p=0.552). Fewer curcumin patients with RDS > 3.0 suggested a trend toward reduced severity (7.4% vs. 12.9%, p=0.082). Patient-reported changes in pain, symptoms, and quality of life were not statistically significant between arms. CONCLUSIONS: Oral curcumin did not significantly reduce radiation dermatitis severity compared to placebo. The skin rating variation and broad eligibility criteria could not account for the undetectable therapeutic effect. An objective measure for radiation dermatitis severity and further exploration for an effective treatment for radiation dermatitis is warranted.

Bees play a key role in pollination of crops and in diverse ecosystems. There have been multiple reports in recent years illustrating bee population declines worldwide. The search for more accurate forecast models can aid both in the understanding of the regular behavior and the adverse situations that may occur with the bees. It also may lead to better management and utilization of bees as pollinators. We address an investigation with Recurrent Neural Networks in the task of forecasting bees’ level of activity taking into account previous values of level of activity and environmental data such as temperature, solar irradiance and barometric pressure. We also show how different input time windows, algorithms of attribute selection and correlation analysis can help improve the accuracy of our model.

Recommender systems (RS) are ubiquitous in digital space. This paper develops a deep learning-based approach to address three practical challenges in RS: complex structures of high-dimensional data, noise in relational information, and the black-box nature of machine learning algorithms. Our method—Multi-GraphGraph Attention Network (MG-GAT)—learns latent user and business representations by aggregating a diverse set of information from neighbors of each user (business) on a neighbor importance graph. MG-GAT out-performs state-of-the-art deep learning models in the recommendation task using two large-scale datasets collected from Yelp and four other standard datasets in RS. The improved performance highlights MG-GAT’s advantage in incorporating multi-modal features in a principled manner. The features importance, neighbor importance graph, and latent representations reveal business insights on predictive features and explainable characteristics of business and users. Moreover, the learned neighbor importance graph can be used in a variety of management applications, such as targeting customers, promoting new businesses, and designing information acquisition strategies. Our paper presents a quintessential big data application of deep learning models in management while providing interpretability essential for real-world decision-making.

Social behaviours emerge from the exchange of information among individuals—constrained by and reciprocally influencing the structure of information flows. The Internet radically transformed communication by democratizing broadcast capabilities and enabling easy and borderless formation of new acquaintances. However, actual information flows are heterogeneous and confined to self-organized echo-chambers. Of central importance to the future of society is understanding how existing physical segregation affects online social fragmentation. Here, we show that the virtual space is a reflection of the geographical space where physical interactions and proximity-based social learning are the main transmitters of ideas. We show that online interactions are segregated by income just as physical interactions are, and that physical separation reflects polarized behaviours beyond culture or politics. Our analysis is consistent with theoretical concepts suggesting polarization is associated with social exposure that reinforces within-group homogenization and between-group differentiation, and they together promote social fragmentation in mirrored physical and virtual spaces.

The Data for Refugees (D4R) Challenge resulted in many insights related to the movement patterns of the Syrian refugees within Turkey. In this chapter, we summarize some of the important findings, and suggest policy recommendations for the main areas of the challenge. These recommendations are sometimes broad suggestions, as the policy interventions involve many factors that are difficult to take into account. We give examples of such issues to help policy-makers.

Today’s smartphones allow for a wide range of “big data” measurement, for example, ecological momentary assessment (EMA), whereby behaviours are repeatedly assessed within a person’s natural environment. With this type of data, we can better understand – and predict – risk for behavioral and health issues and opportunities for (self-monitoring) interventions. In this mixed-methods feasibility study, through convenience sampling we collected data from 32 participants (aged 16–24) over a period of three months. To gain more insight into the app experiences of youth with mental health problems, we interviewed a subsample of 10 adolescents who received psycthological treatment. The results from this feasibility study indicate that emojis) can be used to identify positive and negative feelings, and individual pattern analyses of emojis may be useful for clinical purposes. While adolescents receiving mental health care are positive about future applications, these findings also highlight some caveats, such as possible drawback of inaccurate representation and incorrect predictions of emotional states. Therefore, at this stage, the app should always be combined with professional counseling. Results from this small pilot study warrant replication with studies of substantially larger sample size.

Gifts are important instruments for forming bonds in interpersonal relationships. Our study analyzes the phenomenon of gift contagion in online groups. Gift contagion encourages social bonds of prompting further gifts; it may also promote group interaction and solidarity. Using data on 36 million online red packet gifts on China's social site WeChat, we leverage a natural experimental design to identify the social contagion of gift giving in online groups. Our natural experiment is enabled by the randomization of the gift amount allocation algorithm on WeChat, which addresses the common challenge of causal identifications in observational data. Our study provides evidence of gift contagion: on average, receiving one additional dollar causes a recipient to send 18 cents back to the group within the subsequent 24 hours. Decomposing this effect, we find that it is mainly driven by the extensive margin -- more recipients are triggered to send red packets. Moreover, we find that this effect is stronger for "luckiest draw" recipients, suggesting the presence of a group norm regarding the next red packet sender. Finally, we investigate the moderating effects of group- and individual-level social network characteristics on gift contagion as well as the causal impact of receiving gifts on group network structure. Our study has implications for promoting group dynamics and designing marketing strategies for product adoption., 33 pages

PURPOSE: Although topical agents are often provided during radiation therapy, there is limited consensus and evidence for their use prophylactically to prevent or reduce radiation dermatitis. METHODS: This was a multi-site, randomized, placebo-controlled, blinded study of 191 breast cancer patients to compare the prophylactic effectiveness of three topical agents (Curcumin, HPR Plus™, and Placebo) for reducing radiation dermatitis and associated pain. Patients applied the topical agent to their skin in the radiation area site three times daily starting the first day of radiation therapy (RT) until 1 week after RT completion. RESULTS: Of the 191 randomized patients, 171 patients were included in the final analyses (87.5% white females, mean age = 58 (range = 36–88)). Mean radiation dermatitis severity (RDS) scores did not significantly differ between study arms (Curcumin = 2.68 [2.49, 2.86]; HPR Plus™ = 2.64 [2.45, 2.82]; Placebo = 2.63 [2.44, 2.83];p = 0.929). Logistic regression analyses showed that increased breast field separation positively correlated with increased radiation dermatitis severity (p = 0.018). In patients with high breast field separation (≥ 25 cm), RDS scores (Curcumin = 2.70 [2.21, 3.19]; HPR Plus™ = 3.57 [3.16, 4.00]; Placebo = 2.95 [2.60, 3.30];p = 0.024) and pain scores (Curcumin = 0.52 [− 0.28, 1.33]; HPR Plus™ = 0.55 [− 0.19, 1.30]; Placebo = 1.73 [0.97, 2.50]; p = 0.046) significantly differed at the end of RT. CONCLUSIONS: Although there were no significant effects of the treatment groups on the overall population, our exploratory subgroup analysis suggests that prophylactic treatment with topical curcumin may be effective for minimizing skin reactions and pain for patients with high breast separation (≥ 25 cm) who may have the worst skin reactions.

Modern cities are growing ecosystems that face new challenges due to the increasing population demands. One of the many problems they face nowadays is waste management, which has become a pressing issue requiring new solutions. Swarm robotics systems have been attracting an increasing amount of attention in the past years and they are expected to become one of the main driving factors for innovation in the field of robotics. The research presented in this paper explores the feasibility of a swarm robotics system in an urban environment. By using bio-inspired foraging methods such as multi-place foraging and stigmergy-based navigation, a swarm of robots is able to improve the efficiency and autonomy of the urban waste management system in a realistic scenario. To achieve this, a diverse set of simulation experiments was conducted using real-world GIS data and implementing different garbage collection scenarios driven by robot swarms. Results presented in this research show that the proposed system outperforms current approaches. Moreover, results not only show the efficiency of our solution, but also give insights about how to design and customize these systems., SCOPUS: cp.p, info:eu-repo/semantics/published

With the rise of artificial intelligence (AI), we see renewed interest in algorithms that underlie all digital work. The aim of this symposium is to gather our different understandings of algorithm...

Objective : Characterize Reflectance confocal microscopy’s use and curriculum in residency programs. Methods : Observational nine-question survey sent to the Association of Dermatology Professors (APD). Results: Seventy responses were collected with a faculty response rate of 6.6% (29/439). Fifty-four percent of responses indicated RCM is not taught or learned in a meaningful capacity. If RCM is included within curriculum, teaching occurs on average