ConstGCN: Constrained Transmission-based Graph Convolutional Networks for Document-level Relation Extraction

Document-level relation extraction with graph neural networks faces a fundamental graph construction gap between training and inference - the golden graph structure only available during training, which causes that most methods adopt heuristic or syntactic rules to construct a prior graph as a pseudo proxy. In this paper, we propose $\textbf{ConstGCN}$, a novel graph convolutional network which performs knowledge-based information propagation between entities along with all specific relation spaces without any prior graph construction. Specifically, it updates the entity representation by aggregating information from all other entities along with each relation space, thus modeling the relation-aware spatial information. To control the information flow passing through the indeterminate relation spaces, we propose to constrain the propagation using transmitting scores learned from the Noise Contrastive Estimation between fact triples. Experimental results show that our method outperforms the previous state-of-the-art (SOTA) approaches on the DocRE dataset

Visual Working Memory Capacity Does Not Modulate the Feature-Based Information Filtering in Visual Working Memory

Background: The limited capacity of visual working memory (VWM) requires us to select the task relevant information and filter out the irrelevant information efficiently. Previous studies showed that the individual differences in VWM capacity dramatically influenced the way we filtered out the distracters displayed in distinct spatial-locations: low-capacity individuals were poorer at filtering them out than the high-capacity ones. However, when the target and distracting information pertain to the same object (i.e., multiple-featured object), whether the VWM capacity modulates the featurebased filtering remains unknown. Methodology/Principal Findings: We explored this issue mainly based on one of our recent studies, in which we asked the participants to remember three colors of colored-shapes or colored-landolt-Cs while using two types of task irrelevant information. We found that the irrelevant high-discriminable information could not be filtered out during the extraction of VWM but the irrelevant fine-grained information could be. We added 8 extra participants to the original 16 participants and then split the overall 24 participants into low- and high-VWM capacity groups. We found that regardless of the VWM capacity, the irrelevant high-discriminable information was selected into VWM, whereas the irrelevant fine-grained information was filtered out. The latter finding was further corroborated in a second experiment in which the participants were required to remember one colored-landolt-C and a more strict control was exerted over the VWM capacity

A continuously benchmarked and crowdsourced challenge for rapid development and evaluation of models to predict COVID-19 diagnosis and hospitalization

Importance: Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic. Objectives: To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups. Design, Setting, and Participants: This diagnostic and prognostic study operated a continuous, crowdsourced challenge using a model-to-data approach to securely enable the use of regularly updated COVID-19 patient data from the University of Washington by participants from May 6 to December 23, 2020. A postchallenge analysis was conducted from December 24, 2020, to April 7, 2021, to assess the generalizability of models on the cumulative data set as well as subgroups stratified by age, sex, race, and time of COVID-19 test. By December 23, 2020, this challenge engaged 482 participants from 90 teams and 7 countries. Main Outcomes and Measures: Machine learning algorithms used patient data and output a score that represented the probability of patients receiving a positive COVID-19 test result or being hospitalized within 21 days after receiving a positive COVID-19 test result. Algorithms were evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) scores. Ensemble models aggregating models from the top challenge teams were developed and evaluated. Results: In the analysis using the cumulative data set, the best performance for COVID-19 diagnosis prediction was an AUROC of 0.776 (95% CI, 0.775-0.777) and an AUPRC of 0.297, and for hospitalization prediction, an AUROC of 0.796 (95% CI, 0.794-0.798) and an AUPRC of 0.188. Analysis on top models submitting to the challenge showed consistently better model performance on the female group than the male group. Among all age groups, the best performance was obtained for the 25- to 49-year age group, and the worst performance was obtained for the group aged 17 years or younger. Conclusions and Relevance: In this diagnostic and prognostic study, models submitted by citizen scientists achieved high performance for the prediction of COVID-19 testing and hospitalization outcomes. Evaluation of challenge models on demographic subgroups and prospective data revealed performance discrepancies, providing insights into the potential bias and limitations in the models

A framework for future national pediatric pandemic respiratory disease severity triage: The HHS pediatric COVID-19 data challenge

Abstract Introduction: With persistent incidence, incomplete vaccination rates, confounding respiratory illnesses, and few therapeutic interventions available, COVID-19 continues to be a burden on the pediatric population. During a surge, it is difficult for hospitals to direct limited healthcare resources effectively. While the overwhelming majority of pediatric infections are mild, there have been life-threatening exceptions that illuminated the need to proactively identify pediatric patients at risk of severe COVID-19 and other respiratory infectious diseases. However, a nationwide capability for developing validated computational tools to identify pediatric patients at risk using real-world data does not exist. Methods: HHS ASPR BARDA sought, through the power of competition in a challenge, to create computational models to address two clinically important questions using the National COVID Cohort Collaborative: (1) Of pediatric patients who test positive for COVID-19 in an outpatient setting, who are at risk for hospitalization? (2) Of pediatric patients who test positive for COVID-19 and are hospitalized, who are at risk for needing mechanical ventilation or cardiovascular interventions? Results: This challenge was the first, multi-agency, coordinated computational challenge carried out by the federal government as a response to a public health emergency. Fifty-five computational models were evaluated across both tasks and two winners and three honorable mentions were selected. Conclusion: This challenge serves as a framework for how the government, research communities, and large data repositories can be brought together to source solutions when resources are strapped during a pandemic

An Ultrahigh Sensitivity Acetone Sensor Enhanced by Light Illumination

Au:SmFe0.9Zn0.1O3 is synthesized by a sol-gel method and annealed at 750 °C. Through XRD, SEM and XPS analysis methods, the microstructure of the material has been observed. The average particle size is about 50 nm. The sensor shows a high sensitivity toward acetone vapor. As the relative humidity increases, the resistance and sensitivity of the sensor decline. To obtain a low optimum operating temperature, light illumination with different wavelengths has been introduced. The sensitivity toward acetone is improved at lower operating temperature when the sensor is irradiated by light. The smaller the wavelengths, the better the sensitivity of the sensor. Compared with other gases, the sensor shows excellent selectivity to acetone vapor, with better sensitivity, selectivity and stability when under light illumination

Intentional commitment through an internalized theory of mind: Acting in the eyes of an imagined observer

The ancient Greek hero Ulysses chose to bind himself to resist the temptation of Sirens, highlighting the fact that humans may voluntarily sacrifice their freedom of choice to achieve committed goals. In this work, we propose a computational model for such commitment under the framework of Bayesian Theory of Mind. The model is based on the idea that even when alone, humans act to better demonstrate their intentions to an imagined third-party observer (ITO) censoring their actions. Our model successfully captures the Ulysses-constraint of freedom, as the freedom confuses the ITO’s inference of their intention. We further show that, trajectories generated both by human actors and actors modeled with ITO censorship are easy to interpret both in the eyes of an actual human ob- server and an ITO. The results demonstrate that under conflict- ing desires, humans achieve commitment by spontaneously censoring their actions with an internalized theory of mind

UV Light Illumination Can Improve the Sensing Properties of LaFeO3 to Acetone Vapor

The synthesized LaFeO3 nanocrystalline sensor powders show positive response to sensing acetone vapor at 200 °C. The responses to acetone vapor (at 0.5, 1, 2, 5, 10 ppm) are 1.18, 1.22, 1.89, 3.2 and 7.83. To make the sensor operate at a lower optimum temperature, UV light illumination 365 nm is performed. Response of the sensor has a larger improvement under 365 nm UV light illumination than without it. The responses to acetone vapor (at 0.5, 1, 2, 5, 10 ppm) are 1.37, 1.85, 3.16, 8.32 and 14.1. Furthermore, the optimum operating temperature is reduced to 170 °C. As the relative humidity increases, the resistance and sensitivity of sensor are reduced. The sensor shows good selectivity toward acetone when compared with other gases. Since the detection of ultralow concentrations of acetone vapor is possible, the sensor can be used to preliminarily judge diabetes in the general public, as a high concentration of acetone is exhaled in breath of diabetic patients. The sensor shows a good stability, which is further enhanced under UV light illumination. The sensor shows better stability when under 365 nm UV light illumination. Whether under light illumination or not. The LaFeO3 material shows good performance as a sensor when exposed to acetone vapor

Intention beyond Desire: Humans Spontaneously Commit to Future Actions

It is an ancient insight that human actions are driven by desires. Yet it misses one mental representation, intention, with which agents regulate conflicting desires by committing to an admissible plan. Here we demonstrate four behavioral signatures of intention only observed in humans: disruption resistance as sticking with a plan despite setbacks; exclusiveness as avoiding paths with temptations of re-planning; deliberation as the gradual emergence of a commitment plan; temporal leap as forming future plans before finishing the current one. Humans were compared against an optimal model formulated as Markov Decision Process (MDP), who acts only to maximize expected future rewards. Conflicting desires are defined as a reward function returning positive rewards for multiple states. It showed none of the behavioral signatures of intention. These results reveal that humans regulate conflicting desires with intentions, which directly drive actions

Perceptual Grouping for War and Peace

In a ground war, achieving peace is challenging even when both sides suffer. Here we demonstrate that when humans jointly viewed a battlefield as perceptually grouped by colors, a factor irrelevant to the rules of land-war games, they waged fewer wars and accumulated more wealth due to the perceptual border’s constraint on their aggression. This facilitation of peace arose from perceptual grouping that serves as a visual common sense shared by humans, as a mismatched perceptual grouping between players failed to limit war. Moreover, it relies on the geopolitical principles that make ground wars destructive and disappeared when these principles were violated. Together, we show that perceptual grouping limits human ground war as it is a mutually acknowledged “rallying point” where expectations of peace converge

Exploring an Imagined “We” in Human Collective Hunting: Joint Commitment within Shared Intentionality

Human collaboration often involves a decision to pursue one out of multiple comparable goals, in which case it is challenging to remain committed to the same goal collectively. Philosophical theories as well as empirical evidence from developmental psychology suggest that humans, having shared intentionality as an underlying cognitive structure, may be able to form joint commitment in pursuing a collective goal without communication. By conducting experiments in a real-time cooperative hunting game that heavily relies on visual perception, we demonstrated that humans established and maintained robust cooperation with high-quality hunting, even with a large number of potential targets. Additionally, we showed that a Bayesian imagined “We” (IW) model within a joint commitment framework, could capture humans’ robustness in resisting alternative targets with relatively high quality of hunting. This poses a contrast with a Reward Sharing (RS) model that, despite performing proficiently in pursuing a single goal, mostly exhibited low-quality hunting and whose teaming fell apart as available targets increased. In a hybrid team simulation experiment, the IW model could better mimic the intentions of human hunters compared to the RS model. Together, the success of the persevered group commitment in humans suggests that shared intentionality is a pivotal element in human cooperation. Moreover, the similarity between the performance of humans and the IW model sheds light on the computational formulation of shared intentionality and further advances our understanding of the nature of cooperation