27 research outputs found
Meta-Reinforcement Learning for Timely and Energy-efficient Data Collection in Solar-powered UAV-assisted IoT Networks
Unmanned aerial vehicles (UAVs) have the potential to greatly aid Internet of
Things (IoT) networks in mission-critical data collection, thanks to their
flexibility and cost-effectiveness. However, challenges arise due to the UAV's
limited onboard energy and the unpredictable status updates from sensor nodes
(SNs), which impact the freshness of collected data. In this paper, we
investigate the energy-efficient and timely data collection in IoT networks
through the use of a solar-powered UAV. Each SN generates status updates at
stochastic intervals, while the UAV collects and subsequently transmits these
status updates to a central data center. Furthermore, the UAV harnesses solar
energy from the environment to maintain its energy level above a predetermined
threshold. To minimize both the average age of information (AoI) for SNs and
the energy consumption of the UAV, we jointly optimize the UAV trajectory, SN
scheduling, and offloading strategy. Then, we formulate this problem as a
Markov decision process (MDP) and propose a meta-reinforcement learning
algorithm to enhance the generalization capability. Specifically, the
compound-action deep reinforcement learning (CADRL) algorithm is proposed to
handle the discrete decisions related to SN scheduling and the UAV's offloading
policy, as well as the continuous control of UAV flight. Moreover, we
incorporate meta-learning into CADRL to improve the adaptability of the learned
policy to new tasks. To validate the effectiveness of our proposed algorithms,
we conduct extensive simulations and demonstrate their superiority over other
baseline algorithms
Routing with QoS Information Aggregation in Hierarchical Networks
Abstract-In this paper, we consider the problem of routing with two additive constraints in the hierarchical networks, such as the Internet. In order for scalability, the supported QoS information in the hierarchical networks has to be aggregated. We propose a novel method for aggregating the QoS information. To the best of our knowledge, our approach is the first study to use the area-minimization optimization, the de facto optimization problem of the QoS information aggregation. We use a set of real numbers to approximate the supported QoS between different domains. The size of the set is predefined so that advertisement overhead and the space requirement will not grow exponentially as the network size grows. The simulation results show that the proposed method outperforms the existing methods
The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens
Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.Peer reviewe
The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens
BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.</p
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Estimating Treatment Effect under Additive Hazards Models with High-dimensional Covariates
Estimating causal effects for survival outcomes in the high-dimensional
setting is an extremely important topic for many biomedical applications as
well as areas of social sciences. We propose a new orthogonal score method for
treatment effect estimation and inference that results in asymptotically valid
confidence intervals assuming only good estimation properties of the hazard
outcome model and the conditional probability of treatment. This guarantee
allows us to provide valid inference for the conditional treatment effect under
the high-dimensional additive hazards model under considerably more generality
than existing approaches. In addition, we develop a new Hazards Difference
(HDi), estimator. We showcase that our approach has double-robustness
properties in high dimensions: with cross-fitting, the HDi estimate is
consistent under a wide variety of treatment assignment models; the HDi
estimate is also consistent when the hazards model is misspecified and instead
the true data generating mechanism follows a partially linear additive hazards
model. We further develop a novel sparsity doubly robust result, where either
the outcome or the treatment model can be a fully dense high-dimensional model.
We apply our methods to study the treatment effect of radical prostatectomy
versus conservative management for prostate cancer patients using the
SEER-Medicare Linked Data
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Fine-Gray competing risks model with high-dimensional covariates: estimation and Inference
The purpose of this paper is to construct confidence intervals for the
regression coefficients in the Fine-Gray model for competing risks data with
random censoring, where the number of covariates can be larger than the sample
size. Despite strong motivation from biomedical applications, a
high-dimensional Fine-Gray model has attracted relatively little attention
among the methodological or theoretical literature. We fill in this gap by
developing confidence intervals based on a one-step bias-correction for a
regularized estimation. We develop a theoretical framework for the partial
likelihood, which does not have independent and identically distributed entries
and therefore presents many technical challenges. We also study the
approximation error from the weighting scheme under random censoring for
competing risks and establish new concentration results for time-dependent
processes. In addition to the theoretical results and algorithms, we present
extensive numerical experiments and an application to a study of non-cancer
mortality among prostate cancer patients using the linked Medicare-SEER data