66 research outputs found
Data Citation: Giving Credit where Credit is Due
No full textAn increasing amount of information is being published in structured databases and retrieved using queries, raising the question of how query results should be cited. Since there are a large number of possible queries over a database, one strategy is to specify citations to a small set of frequent queries-citation views-and use these to construct citations to other "general" queries. We present three approaches to implementing citation views and describe alternative policies for the joint, alternate and aggregated use of citation views. Extensive experiments using both synthetic and realistic citation views and queries show the tradeoffs between the approaches in terms of the time to generate citations, as well as the size of the resulting citation. They also show that the choice of policy has a huge effect both on performance and size, leading to useful guidelines for what policies to use and how to specify citation views
sj-doc-1-tam-10.1177_17588359231225035 – Supplemental material for Reclassification of RAS/BRAF allele mutations predicts the survival benefit of triplet chemotherapy in metastatic colorectal cancer
No full textSupplemental material, sj-doc-1-tam-10.1177_17588359231225035 for Reclassification of RAS/BRAF allele mutations predicts the survival benefit of triplet chemotherapy in metastatic colorectal cancer by Xiang Zhang, Haizhong Ma, Yinjun He, Wenguang He, Nan Chen, Yandong Li, Weixiang Zhong, Guosheng Wu, Xile Zhou, Hanju Hua, Feng Ye, Hui Cai and Weiqin Jiang in Therapeutic Advances in Medical Oncology</p
sj-doc-2-tam-10.1177_17588359231225035 – Supplemental material for Reclassification of RAS/BRAF allele mutations predicts the survival benefit of triplet chemotherapy in metastatic colorectal cancer
No full textSupplemental material, sj-doc-2-tam-10.1177_17588359231225035 for Reclassification of RAS/BRAF allele mutations predicts the survival benefit of triplet chemotherapy in metastatic colorectal cancer by Xiang Zhang, Haizhong Ma, Yinjun He, Wenguang He, Nan Chen, Yandong Li, Weixiang Zhong, Guosheng Wu, Xile Zhou, Hanju Hua, Feng Ye, Hui Cai and Weiqin Jiang in Therapeutic Advances in Medical Oncology</p
Editorial: Dams and wetland biodiversity: Impacts and mitigating measures
No full textTo meet energy, water and transportation needs, an incredible amount of dams have been constructed around the world. For example, only in the Yangtze River's watershed of China, over 50,000 dams were built since 1950 (Nilsson et al., 2005; Wu et al., 2019). Dams could contribute to energy and water supply, and flood protection, but they also affect aquatic ecosystems by alteration of hydrologic regime and fragmentation (Barbarossa et al., 2020). While about 50% of the river around the world is currently changed by dam, this percentage is expected to increase to 93% because of the pending construction of about 3,700 major hydropower dams (Grill et al., 2015).
The construction and operation of dams has extensively altered global freshwater wetland ecosystems, which represent biodiversity hotspots around the world and play a crucial part in protection of biodiversity (Wu et al., 2019). Freshwater wetlands cover about 0.8% of Earth's surface, but host an excessively high diversity of species (Barbarossa et al., 2020). Freshwater wetlands provided habitat for about one fifth of species (particularly the endangered and endemic species) and one third of vertebrate species in the world (Wu et al., 2019).
The aim of this Research Topic is to gather the latest research addressing the critical issue of the impact of construction, operation and removal of dams on biodiversity, with a particular focus on mitigating measures. We are convinced that the studies in this field are an essential condition for biodiversity conservation and ecological restoration in freshwater wetlands. This collection of seven papers is our humble contribution to achieve this target.To meet energy, water and transportation needs, an incredible amount of dams have been constructed around the world. For example, only in the Yangtze River's watershed of China, over 50,000 dams were built since 1950 (Nilsson et al., 2005; Wu et al., 2019). Dams could contribute to energy and water supply, and flood protection, but they also affect aquatic ecosystems by alteration of hydrologic regime and fragmentation (Barbarossa et al., 2020). While about 50% of the river around the world is currently changed by dam, this percentage is expected to increase to 93% because of the pending construction of about 3,700 major hydropower dams (Grill et al., 2015).
The construction and operation of dams has extensively altered global freshwater wetland ecosystems, which represent biodiversity hotspots around the world and play a crucial part in protection of biodiversity (Wu et al., 2019). Freshwater wetlands cover about 0.8% of Earth's surface, but host an excessively high diversity of species (Barbarossa et al., 2020). Freshwater wetlands provided habitat for about one fifth of species (particularly the endangered and endemic species) and one third of vertebrate species in the world (Wu et al., 2019).
The aim of this Research Topic is to gather the latest research addressing the critical issue of the impact of construction, operation and removal of dams on biodiversity, with a particular focus on mitigating measures. We are convinced that the studies in this field are an essential condition for biodiversity conservation and ecological restoration in freshwater wetlands. This collection of seven papers is our humble contribution to achieve this target.HW was financially supported by the Natural Science Foundation of Hunan Province (2021JJ40601) and the Scientific Research Foundation of Hunan Provincial Education Department (20B005).HW was financially supported by the Natural Science Foundation of Hunan Province (2021JJ40601) and the Scientific Research Foundation of Hunan Provincial Education Department (20B005)
Towards the Efficient Use of Fine-Grained Provenance in Data Science Applications
No full textRecent years have witnessed increased demand for users to be able to interpret the results of data science pipelines, locate erroneous data items in the input, evaluate the importance of individual input data items, and acknowledge the contributions of data curators. Such applications often involve the use of the provenance at a fine-grained level, and require very fast response time. To address this issue, my goal is to expedite the use of fine-grained provenance in applications within both the database and machine learning domains, which are ubiquitous in contemporary data science pipelines. In applications from the database domain, I focus on the problem of data citation and provide two different types of solutions, Rewriting-based solutions and Provenance-based solutions, to generate fine-grained citations to database query results by implicitly or explicitly leveraging provenance information. In applications from the ML domain, the first considers the problem of incrementally updating ML models after the deletions of a small subset of training samples. This is critical for understanding the importance of individual training samples to ML models, especially in online pipelines. For this problem, I provide two solutions, PrIU and DeltaGrad, to incrementally update ML models constructed by SGD/GD methods, which utilize provenance information collected during the training phase on the full dataset before the deletion requests. The second application from the ML domain that I focus on is to explore how to clean label uncertainties located in the ML training dataset in a more efficient and cheaper manner. To address this problem, I proposed a solution, CHEF, to reduce the cost and the overhead at each phase of the label cleaning pipeline and maintain the overall model performance simultaneously. I also propose initial ideas for how to remove some assumptions used in these solutions to extend them to more general scenarios
Dynamic redundancy scaling for client load dynamics
No full textThis paper presents the mechanism and implementation of dynamically redun-
dancy scaling for client load dynamics. The scaling is about the control of max-
imum and minimum counter of replicas which provide identical service in a dis-
tributed computer networks. This mechanism is managed under a Distributed Au-
tonomous Replication Management (DARM) framework which is of autonomous
fault treatment supporting and builds upon Spread group communication system.
DARM, like the other existing fault tolerance frameworks, has good solution on
fault detecting and toleration aspects; apart from that, DARM is set to always keep
recovering a faulty service in a active manner and the recovery is done by generating
new replica of the same service in another machine which locates within the same
network. The objective of DARM, which is focus more on the improvement of
dependability of the system, makes DARM novel among all similar frameworks.
The objective of this dynamical redundancy scaling mechanism built on DARM
is to actively and efficiently control the maximum and minimum counter of the
replicas in an appropriate state. The maximum and minimum counter will di-
rectly effect on the identical-service-providing replicas within a distributed net-
work. When a host is observed with higher CPU load value than a preset thresh-
old, new replicas will be automatically created; similarly, when a host is viewed
with lower CPU load, it will be considered to kill if it has not achieved the least
replica number that required among the whole network.
This scheme allows system vary the replicas number according to the situation
of the service loading all over the network: A new replica can be added to share the
burden of whole; an existing replica can be removed to avoid unnecessary resource
consuming. The advantages of this schema are: (i) comparing with static control,
a dynamic one is more practice-oriented; it has better
exibility for the realistic
service loading in a network; (ii) possibility of host crashed due to high loading
on top of it is reduced, the principle behind this mechanism is to always balance
the existing loading over all active hosts. The approach has been evaluated as an efficient mechanism support to the current DARM framework.
A C implementation of this dynamic control mechanism has been accomplished
and introduced in this paper, as well as its testing evaluation. The performance is
positive and effective
Metal Bridging for Directing and Accelerating Electron Transfer as Exemplified by Harnessing the Reactivity of AIBN
No full textTorchQL: A Programming Framework for Integrity Constraints in Machine Learning
No full textFinding errors in machine learning applications requires a thorough exploration of their behavior over data. Existing approaches used by practitioners are often ad-hoc and lack the abstractions needed to scale this process. We present TorchQL, a programming framework to evaluate and improve the correctness of machine learning applications. TorchQL allows users to write queries to specify and check integrity constraints over machine learning models and datasets. It seamlessly integrates relational algebra with functional programming to allow for highly expressive queries using only eight intuitive operators. We evaluate TorchQL on diverse use-cases including finding critical temporal inconsistencies in objects detected across video frames in autonomous driving, finding data imputation errors in time-series medical records, finding data labeling errors in real-world images, and evaluating biases and constraining outputs of language models. Our experiments show that TorchQL enables up to 13x faster query executions than baselines like Pandas and MongoDB, and up to 40% shorter queries than native Python. We also conduct a user study and find that TorchQL is natural enough for developers familiar with Python to specify complex integrity constraints
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