There is active debate over whether to consider patient race and ethnicity when estimating disease risk. By accounting for race and ethnicity, it is possible to improve the accuracy of risk predictions, but there is concern that their use may encourage a racialized view of medicine. In diabetes risk models, despite substantial gains in statistical accuracy from using race and ethnicity, the gains in clinical utility are surprisingly modest. These modest clinical gains stem from two empirical patterns: first, the vast majority of individuals receive the same screening recommendation regardless of whether race or ethnicity are included in risk models; and second, for those who do receive different screening recommendations, the difference in utility between screening and not screening is relatively small. Our results are based on broad statistical principles, and so are likely to generalize to many other risk-based clinical decisions.
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We prove a tight upper bound on the variance of the priority sampling method (aka sequential Poisson sampling). Our proof is significantly shorter and simpler than the original proof given by Mario Szegedy at STOC 2006, which resolved a conjecture by Duffield, Lund, and Thorup.
Automated driving has the potential to revolutionize personal, public, and freight mobility. Besides the enormous challenge of perception, i.e. accurately perceiving the environment using available sensor data, automated driving comprises planning a safe, comfortable, and efficient motion trajectory. To promote safety and progress, many works rely on modules that predict the future motion of surrounding traffic. Modular automated driving systems commonly handle prediction and planning as sequential separate tasks. While this accounts for the influence of surrounding traffic on the ego-vehicle, it fails to anticipate the reactions of traffic participants to the ego-vehicle's behavior. Recent works suggest that integrating prediction and planning in an interdependent joint step is necessary to achieve safe, efficient, and comfortable driving. While various models implement such integrated systems, a comprehensive overview and theoretical understanding of different principles are lacking. We systematically review state-of-the-art deep learning-based prediction, planning, and integrated prediction and planning models. Different facets of the integration ranging from model architecture and model design to behavioral aspects are considered and related to each other. Moreover, we discuss the implications, strengths, and limitations of different integration methods. By pointing out research gaps, describing relevant future challenges, and highlighting trends in the research field, we identify promising directions for future research.
Classic BFT consensus protocols guarantee safety and liveness for all clients if fewer than one-third of replicas are faulty. However, in applications such as high-value payments, some clients may want to prioritize safety over liveness. Flexible consensus allows each client to opt for a higher safety resilience, albeit at the expense of reduced liveness resilience. We present the first construction that allows optimal safety--liveness tradeoff for every client simultaneously. This construction is modular and is realized as an add-on applied on top of an existing consensus protocol. The add-on consists of an additional round of voting and permanent locking done by the replicas, to sidestep a sub-optimal quorum-intersection-based constraint present in previous solutions. We adapt our construction to the existing Ethereum protocol to derive optimal flexible confirmation rules that clients can adopt unilaterally without requiring system-wide changes. This is possible because existing Ethereum protocol features can double as the extra voting and locking. We demonstrate an implementation using Ethereum's consensus API.
The primary method for identifying mental disorders automatically has traditionally involved using binary classifiers. These classifiers are trained using behavioral data obtained from an interview setup. In this training process, data from individuals with the specific disorder under consideration are categorized as the positive class, while data from all other participants constitute the negative class. In practice, it is widely recognized that certain mental disorders share similar symptoms, causing the collected behavioral data to encompass a variety of attributes associated with multiple disorders. Consequently, attributes linked to the targeted mental disorder might also be present within the negative class. This data impurity may lead to sub-optimal training of the classifier for a mental disorder of interest. In this study, we investigate this hypothesis in the context of major depressive disorder (MDD) and post-traumatic stress disorder detection (PTSD). The results show that upon removal of such data impurity, MDD and PTSD detection performances are significantly improved.
Informal reasoning ability is the ability to reason based on common sense, experience, and intuition.Humans use informal reasoning every day to extract the most influential elements for their decision-making from a large amount of life-like information.With the rapid development of language models, the realization of general artificial intelligence has emerged with hope. Given the outstanding informal reasoning ability of humans, how much informal reasoning ability language models have has not been well studied by scholars.In order to explore the gap between humans and language models in informal reasoning ability, this paper constructs a Detective Reasoning Benchmark, which is an assembly of 1,200 questions gathered from accessible online resources, aims at evaluating the model's informal reasoning ability in real-life context.Considering the improvement of the model's informal reasoning ability restricted by the lack of benchmark, we further propose a Self-Question Prompt Framework that mimics human thinking to enhance the model's informal reasoning ability.The goals of self-question are to find key elements, deeply investigate the connections between these elements, encourage the relationship between each element and the problem, and finally, require the model to reasonably answer the problem.The experimental results show that human performance greatly outperforms the SoTA Language Models in Detective Reasoning Benchmark.Besides, Self-Question is proven to be the most effective prompt engineering in improving GPT-4's informal reasoning ability, but it still does not even surpass the lowest score made by human participants.Upon acceptance of the paper, the source code for the benchmark will be made publicly accessible.
Exchanging data while ensuring non-repudiation is a challenge, especially if no trusted third party exists. Blockchain promises to provide many of the required guarantees, which is why it has been used in many non-repudiable data exchange protocols. Specifically, some authors propose to append data to blockchain to achieve non-repudiation of receipt. In this position paper, we show that this approach is insufficient. While appending data to blockchain can guarantee non-repudiation of origin in some cases, it is not sufficient for non-repudiation of receipt. For confidential data, we find a catch-22 that makes it impossible. For non-confidential data, meanwhile, plausible deniability remains. We discuss potential solutions and suggest smart contracts as a promising approach.
Heart failure is a debilitating condition that affects millions of people worldwide and has a significant impact on their quality of life and mortality rates. An objective assessment of cardiac pressures remains an important method for the diagnosis and treatment prognostication for patients with heart failure. Although cardiac catheterization is the gold standard for estimating central hemodynamic pressures, it is an invasive procedure that carries inherent risks, making it a potentially dangerous procedure for some patients. Approaches that leverage non-invasive signals - such as electrocardiogram (ECG) - have the promise to make the routine estimation of cardiac pressures feasible in both inpatient and outpatient settings. Prior models trained to estimate intracardiac pressures (e.g., mean pulmonary capillary wedge pressure (mPCWP)) in a supervised fashion have shown good discriminatory ability but have been limited to the labeled dataset from the heart failure cohort. To address this issue and build a robust representation, we apply deep metric learning (DML) and propose a novel self-supervised DML with distance-based mining that improves the performance of a model with limited labels. We use a dataset that contains over 5.4 million ECGs without concomitant central pressure labels to pre-train a self-supervised DML model which showed improved classification of elevated mPCWP compared to self-supervised contrastive baselines. Additionally, the supervised DML model that is using ECGs with access to 8,172 mPCWP labels demonstrated significantly better performance on the mPCWP regression task compared to the supervised baseline. Moreover, our data suggest that DML yields models that are performant across patient subgroups, even when some patient subgroups are under-represented in the dataset. Our code is available at //github.com/mandiehyewon/ssldml
Automated driving functions (ADFs) have become increasingly popular in recent years. However, their safety must be assured. Thus, the verification and validation of these functions is still an important open issue in research and development. To achieve this efficiently, scenario-based testing has been established as a valuable methodology among researchers, industry, as well as authorities. Simulations are a powerful way to test those scenarios reproducibly. In this paper, we propose a method to automatically test a set of scenarios in many variations. In contrast to related approaches, those variations are not applied to traffic participants around the ADF, but to the road network to show that parameters regarding the road topology also influence the performance of such an ADF. We present a continuous tool chain to set up scenarios, variate them, run simulations and finally, evaluate the performance with a set of key performance indicators (KPIs).
The concept of causality plays an important role in human cognition . In the past few decades, causal inference has been well developed in many fields, such as computer science, medicine, economics, and education. With the advancement of deep learning techniques, it has been increasingly used in causal inference against counterfactual data. Typically, deep causal models map the characteristics of covariates to a representation space and then design various objective optimization functions to estimate counterfactual data unbiasedly based on the different optimization methods. This paper focuses on the survey of the deep causal models, and its core contributions are as follows: 1) we provide relevant metrics under multiple treatments and continuous-dose treatment; 2) we incorporate a comprehensive overview of deep causal models from both temporal development and method classification perspectives; 3) we assist a detailed and comprehensive classification and analysis of relevant datasets and source code.
Australia is a leading AI nation with strong allies and partnerships. Australia has prioritised robotics, AI, and autonomous systems to develop sovereign capability for the military. Australia commits to Article 36 reviews of all new means and methods of warfare to ensure weapons and weapons systems are operated within acceptable systems of control. Additionally, Australia has undergone significant reviews of the risks of AI to human rights and within intelligence organisations and has committed to producing ethics guidelines and frameworks in Security and Defence. Australia is committed to OECD's values-based principles for the responsible stewardship of trustworthy AI as well as adopting a set of National AI ethics principles. While Australia has not adopted an AI governance framework specifically for Defence; Defence Science has published 'A Method for Ethical AI in Defence' (MEAID) technical report which includes a framework and pragmatic tools for managing ethical and legal risks for military applications of AI.
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