This paper presents the results of the wet lab information extraction task at WNUT 2020. This task consisted of two sub tasks: (1) a Named Entity Recognition (NER) task with 13 participants and (2) a Relation Extracti...
Life events are noteworthy moments that we often share on social media. However, how such online disclosures of life events impact our mental wellbeing is largely unknown. This study examines the effects of these disc...
Life events are noteworthy moments that we often share on social media. However, how such online disclosures of life events impact our mental wellbeing is largely unknown. This study examines the effects of these disclosures using data from 236 participants. Regression models reveal that individual differences and event attributes significantly influence wellbeing. Through a quasi-experimental design, we find that sharing life events on Facebook positively impacts wellbeing by increasing positive affect and sleep quality while reducing negative affect, stress, and anxiety. Notably, disclosing negative events shows the strongest improvement in wellbeing, suggesting a protective effect of social media. Additionally, life event disclosures elicit more reactions and comments than other Facebook posts, with negative events receiving more comments but fewer reactions than non-negative life event disclosures. These findings offer insights into the complex relationship between online disclosures and wellbeing, contributing to theoretical understanding and practical strategies for enhancing online experiences and supporting mental health.
Researchers in human-centered computing have surfaced a feminist ethic of care in interaction with technologies, in data collection, and in data work. Drawing on two years of ethnographic fieldwork, we consider how de...
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Autonomous systems that operate around humans will likely always rely on kill switches that stop their execution and allow them to be remote-controlled for the safety of humans or to prevent damage to the system. It i...
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Identifying predictive covariates, which forecast individual treatment effectiveness, is crucial for decision-making across different disciplines such as personalized medicine. These covariates, referred to as biomark...
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ISBN:
(数字)9798331510831
ISBN:
(纸本)9798331510848
Identifying predictive covariates, which forecast individual treatment effectiveness, is crucial for decision-making across different disciplines such as personalized medicine. These covariates, referred to as biomarkers, are extracted from pretreatment data, often within randomized controlled trials, and should be distinguished from prognostic biomarkers, which are independent of treatment assignment. Our study focuses on discovering predictive imaging biomarkers, specific image features, by leveraging pretreatment images to uncover new causal relationships. Unlike laborintensive approaches relying on handcrafted features prone to bias, we present a novel task of directly learning predictive features from images. We propose an evaluation protocol to assess a model's ability to identify predictive imaging biomarkers and differentiate them from purely prognostic ones by employing statistical testing and a comprehensive analysis of image feature attribution. We explore the suitability of deep learning models originally developed for estimating the conditional average treatment effect (CATE) for this task, which have been assessed primarily for their precision of CATE estimation while overlooking the evaluation of imaging biomarker discovery. Our proof-of-concept analysis demonstrates the feasibility and potential of our approach in discovering and validating predictive imaging biomarkers from synthetic outcomes and real-world image datasets. Our code is available at https://***/MIC-DKFZ/predictive_image_biomarker_analysis.
Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we can not only form a decision on the spot, bu...
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STABLE-BASELINES3 provides open-source implementations of deep reinforcement learning (RL) algorithms in Python. The implementations have been benchmarked against reference codebases, and automated unit tests cover 95...
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STABLE-BASELINES3 provides open-source implementations of deep reinforcement learning (RL) algorithms in Python. The implementations have been benchmarked against reference codebases, and automated unit tests cover 95% of the code. The algorithms follow a consistent interface and are accompanied by extensive documentation, making it simple to train and compare different RL algorithms.
The lack of large-scale, labeled data sets impedes progress in developing robust and generalized predictive models for on-body sensor-based human activity recognition (HAR). Labeled data in human activity recognition ...
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We address the problem of whole-body person recognition in unconstrained environments. This problem arises in surveillance scenarios such as those in the IARPA Biometric Recognition and Identification at Altitude and ...
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Maker culture is on the rise in healthcare with the adoption of consumer-grade fabrication technologies. However, little is known about the activities and resources involved in prototyping medical devices to improve p...
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