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Researchers using instrumental variables to investigate the effects of ordered treatments (e.g., years of education, months of healthcare coverage) often recode treatment into a binary indicator for any exposure (e.g., any college, any healthcare coverage). The resulting estimand is difficult to interpret unless the instruments only shift compliers from no treatment to some positive quantity and not from some treatment to more -- i.e., there are extensive margin compliers only (EMCO). When EMCO holds, recoded endogenous variables capture a weighted average of treatment effects across complier groups that can be partially unbundled into each group's treated and untreated means. Invoking EMCO along with the standard Local Average Treatment Effect assumptions is equivalent to assuming choices are determined by a simple two-factor selection model in which agents first decide whether to participate in treatment at all and then decide how much. The instruments must only impact relative utility in the first step. Although EMCO constrains unobserved counterfactual choices, it places testable restrictions on the joint distribution of outcomes, treatments, and instruments.

Reliable image geolocation is crucial for several applications, ranging from social media geo-tagging to fake news detection. State-of-the-art geolocation methods surpass human performance on the task of geolocation estimation from images. However, no method assesses the suitability of an image for this task, which results in unreliable and erroneous estimations for images containing no geolocation clues. In this paper, we define the task of image localizability, i.e. suitability of an image for geolocation, and propose a selective prediction methodology to address the task. In particular, we propose two novel selection functions that leverage the output probability distributions of geolocation models to infer localizability at different scales. Our selection functions are benchmarked against the most widely used selective prediction baselines, outperforming them in all cases. By abstaining from predicting non-localizable images, we improve geolocation accuracy from 27.8% to 70.5% at the city-scale, and thus make current geolocation models reliable for real-world applications.

Randomized controlled trials are considered the gold standard to evaluate the treatment effect (estimand) for efficacy and safety. According to the recent International Council on Harmonisation (ICH)-E9 addendum (R1), intercurrent events (ICEs) need to be considered when defining an estimand, and principal stratum is one of the five strategies to handle ICEs. Qu et al. (2020, Statistics in Biopharmaceutical Research 12:1-18) proposed estimators for the adherer average causal effect (AdACE) for estimating the treatment difference for those who adhere to one or both treatments based on the causal-inference framework, and demonstrated the consistency of those estimators; however, this method requires complex custom programming related to high-dimensional numeric integrations. In this article, we implemented the AdACE estimators using multiple imputation (MI) and constructs CI through bootstrapping. A simulation study showed that the MI-based estimators provided consistent estimators with the nominal coverage probabilities of CIs for the treatment difference for the adherent populations of interest. As an illustrative example, the new method was applied to data from a real clinical trial comparing 2 types of basal insulin for patients with type 1 diabetes.

COVID-19 pandemic is an ongoing global pandemic which has caused unprecedented disruptions in the public health sector and global economy. The virus, SARS-CoV-2 is responsible for the rapid transmission of coronavirus disease. Due to its contagious nature, the virus can easily infect an unprotected and exposed individual from mild to severe symptoms. The study of the virus effects on pregnant mothers and neonatal is now a concerning issue globally among civilians and public health workers considering how the virus will affect the mother and the neonates health. This paper aims to develop a predictive model to estimate the possibility of death for a COVID-diagnosed mother based on documented symptoms: dyspnea, cough, rhinorrhea, arthralgia, and the diagnosis of pneumonia. The machine learning models that have been used in our study are support vector machine, decision tree, random forest, gradient boosting, and artificial neural network. The models have provided impressive results and can accurately predict the mortality of pregnant mothers with a given input.The precision rate for 3 models(ANN, Gradient Boost, Random Forest) is 100% The highest accuracy score(Gradient Boosting,ANN) is 95%,highest recall(Support Vector Machine) is 92.75% and highest f1 score(Gradient Boosting,ANN) is 94.66%. Due to the accuracy of the model, pregnant mother can expect immediate medical treatment based on their possibility of death due to the virus. The model can be utilized by health workers globally to list down emergency patients, which can ultimately reduce the death rate of COVID-19 diagnosed pregnant mothers.

This work presents a proposal for a wireless sensor network for participatory sensing, with IoT sensing devices developed especially for monitoring and predicting air quality, as alternatives of high cost meteorological stations. The system, called pmSensing, aims to measure particulate material. A validation is done by comparing the data collected by the prototype with data from stations. The comparison shows that the results are close, which can enable low-cost solutions to the problem. The system still presents a predictive analysis using recurrent neural networks, in this case the LSTM-RNN, where the predictions presented high accuracy in relation to the real data.

Modern cars technologies are evolving quickly. They collect a variety of personal data and treat it on behalf of the car manufacturer to improve the drivers' experience. The precise terms of such a treatment are stated within the privacy policies accepted by the user when buying a car or through the infotainment system when it is first started. This paper uses a double lens to assess people's privacy while they drive a car. The first approach is objective and studies the readability of privacy policies that comes with cars. We analyse the privacy policies of twelve car brands and apply well-known readability indices to evaluate the extent to which privacy policies are comprehensible by all drivers. The second approach targets drivers' opinions to extrapolate their privacy concerns and trust perceptions. We design a questionnaire to collect the opinions of 88 participants and draw essential statistics about them. Our combined findings indicate that privacy is insufficiently understood at present as an issue deriving from driving a car, hence future technologies should be tailored to make people more aware of the issue and to enable them to express their preferences.

With the progressive commoditization of modeling capabilities, data-centric AI recognizes that what happens before and after training becomes crucial for real-world deployments. Following the intuition behind Model Cards, we propose DAG Cards as a form of documentation encompassing the tenets of a data-centric point of view. We argue that Machine Learning pipelines (rather than models) are the most appropriate level of documentation for many practical use cases, and we share with the community an open implementation to generate cards from code.

Deep learning models on graphs have achieved remarkable performance in various graph analysis tasks, e.g., node classification, link prediction and graph clustering. However, they expose uncertainty and unreliability against the well-designed inputs, i.e., adversarial examples. Accordingly, various studies have emerged for both attack and defense addressed in different graph analysis tasks, leading to the arms race in graph adversarial learning. For instance, the attacker has poisoning and evasion attack, and the defense group correspondingly has preprocessing- and adversarial- based methods. Despite the booming works, there still lacks a unified problem definition and a comprehensive review. To bridge this gap, we investigate and summarize the existing works on graph adversarial learning tasks systemically. Specifically, we survey and unify the existing works w.r.t. attack and defense in graph analysis tasks, and give proper definitions and taxonomies at the same time. Besides, we emphasize the importance of related evaluation metrics, and investigate and summarize them comprehensively. Hopefully, our works can serve as a reference for the relevant researchers, thus providing assistance for their studies. More details of our works are available at //github.com/gitgiter/Graph-Adversarial-Learning.

In this work, we compare three different modeling approaches for the scores of soccer matches with regard to their predictive performances based on all matches from the four previous FIFA World Cups 2002 - 2014: Poisson regression models, random forests and ranking methods. While the former two are based on the teams' covariate information, the latter method estimates adequate ability parameters that reflect the current strength of the teams best. Within this comparison the best-performing prediction methods on the training data turn out to be the ranking methods and the random forests. However, we show that by combining the random forest with the team ability parameters from the ranking methods as an additional covariate we can improve the predictive power substantially. Finally, this combination of methods is chosen as the final model and based on its estimates, the FIFA World Cup 2018 is simulated repeatedly and winning probabilities are obtained for all teams. The model slightly favors Spain before the defending champion Germany. Additionally, we provide survival probabilities for all teams and at all tournament stages as well as the most probable tournament outcome.

Background: Social media has the capacity to afford the healthcare industry with valuable feedback from patients who reveal and express their medical decision-making process, as well as self-reported quality of life indicators both during and post treatment. In prior work, [Crannell et. al.], we have studied an active cancer patient population on Twitter and compiled a set of tweets describing their experience with this disease. We refer to these online public testimonies as "Invisible Patient Reported Outcomes" (iPROs), because they carry relevant indicators, yet are difficult to capture by conventional means of self-report. Methods: Our present study aims to identify tweets related to the patient experience as an additional informative tool for monitoring public health. Using Twitter's public streaming API, we compiled over 5.3 million "breast cancer" related tweets spanning September 2016 until mid December 2017. We combined supervised machine learning methods with natural language processing to sift tweets relevant to breast cancer patient experiences. We analyzed a sample of 845 breast cancer patient and survivor accounts, responsible for over 48,000 posts. We investigated tweet content with a hedonometric sentiment analysis to quantitatively extract emotionally charged topics. Results: We found that positive experiences were shared regarding patient treatment, raising support, and spreading awareness. Further discussions related to healthcare were prevalent and largely negative focusing on fear of political legislation that could result in loss of coverage. Conclusions: Social media can provide a positive outlet for patients to discuss their needs and concerns regarding their healthcare coverage and treatment needs. Capturing iPROs from online communication can help inform healthcare professionals and lead to more connected and personalized treatment regimens.