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Objectives: The objective of this study was to assess knowledge, attitudes, and behaviors surrounding healthcare-related mobile phone use and text messaging among persons at risk for or infected with tuberculosis (TB) or the human immunodeficiency virus (HIV). Methods: An anonymous survey was conducted in three groups of subjects: (1) HIV-infected persons attending an HIV clinic; (2) persons with latent TB infection at a public health clinic; and (3) persons presenting for TB, HIV, and syphilis screening at a community screening site. Results: Three hundred fifteen (n = 315) persons responded to the survey, of whom 241 (76.5%) owned a cell phone. Cell phone owners were younger and more educated than nonowners. Transportation difficulty and forgetting appointments were cited as significant barriers by 34.2% and 39.5% of respondents, respectively. Fifty-six percent of subjects felt it would be acceptable to receive text message appointment reminders, and 33% felt that text message reminders to take medications would be acceptable. Younger age and cell phone ownership were significantly associated with acceptance of text message reminders. Black and Hispanic subjects were more likely to feel that text message reminders for appointments or medications were helpful than White subjects. Further, Black and Hispanic subjects, as well as subjects with lower educational attainment, were more receptive to healthcare-related educational text messages. Conclusions: Cell phones and text messaging were prevalent among our subjects attending HIV and TB clinics, and subjects were generally receptive to text messaging for healthcare-related communication. Interventions that explore the potential for text messaging to improve clinic attendance, medication adherence, and health knowledge should be explored.

In this paper, we study the task of embodied interactive learning for object detection. Given a set of environments (and some labeling budget), our goal is to learn an object detector by having an agent select what data to obtain labels for. How should an exploration policy decide which trajectory should be labeled? One possibility is to use a trained object detector's failure cases as an external reward. However, this will require labeling millions of frames required for training RL policies, which is infeasible. Instead, we explore a self-supervised approach for training our exploration policy by introducing a notion of semantic curiosity. Our semantic curiosity policy is based on a simple observation -the detection outputs should be consistent. Therefore, our semantic curiosity rewards trajectories with inconsistent labeling behavior and encourages the exploration policy to explore such areas. The exploration policy trained via semantic curiosity generalizes to novel scenes and helps train an object detector that outperforms baselines trained with other possible alternatives such as random exploration, prediction-error curiosity, and coverage-maximizing exploration.

BackgroundMass spectrometry (MS) has evolved to become the primary high throughput tool for proteomics based biomarker discovery. Until now, multiple challenges in protein MS data analysis remain: large-scale and complex data set management; MS peak identification, indexing; and high dimensional peak differential analysis with the concurrent statistical tests based false discovery rate (FDR). “Turnkey” solutions are needed for biomarker investigations to rapidly process MS data sets to identify statistically significant peaks for subsequent validation.FindingsHere we present an efficient and effective solution, which provides experimental biologists easy access to “cloud” computing capabilities to analyze MS data. The web portal can be accessed at http://transmed.stanford.edu/ssa/.ConclusionsPresented web application supplies large scale MS data online uploading and analysis with a simple user interface. This bioinformatic tool will facilitate the discovery of the potential protein biomarkers using MS.

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Explainable AI (XAI) research has been booming, but the question "To whom are we making AI explainable?" is yet to gain sufficient attention. Not much of XAI is comprehensible to non-AI experts, who nonetheless, are the primary audience and major stakeholders of deployed AI systems in practice. The gap is glaring: what is considered "explained" to AI-experts versus non-experts are very different in practical scenarios. Hence, this gap produced two distinct cultures of expectations, goals, and forms of XAI in real-life AI deployments [41]. We advocate that it is critical to develop XAI methods for non-technical audiences. We then present a real-life case study, where AI experts provided non-technical explanations of AI decisions to non-technical stakeholders, and completed a successful deployment in a highly regulated industry. We then synthesize lessons learned from the case, and share a list of suggestions for AI experts to consider when explaining AI decisions to non-technical stakeholders.

We describe a technique for structured prediction, based on canonical correlation analysis. Our learning algorithm finds two projections for the input and the output spaces that aim at projecting a given input and its correct output into points close to each other. We demonstrate our technique on a language-vision problem, namely the problem of giving a textual description to an "abstract scene".