scholarly journals Pollution Source Identification for River Chemical Spills by Modular-Bayesian Approach: A Retrospective Study on the ‘Landmark’ Spill Incident in China

Hydrology ◽  
2019 ◽  
Vol 6 (3) ◽  
pp. 74 ◽  
Author(s):  
Jiping Jiang ◽  
Yasong Chen ◽  
Baoyu Wang
Keyword(s):  
Bayesian Approach ◽  
Large Scale ◽  
Real Life ◽  
Pollution Source ◽  
Source Inversion ◽  
Source Information ◽  
Retrospective Case ◽  
Songhua River ◽  
Chemical Spills ◽  
River Chemical Spills

It is important to identify source information after a river chemical spill incident occurs. Among various source inversion approaches, a Bayesian-based framework is able to directly characterize inverse uncertainty using a probability distribution and has recently become of interest. However, the literature has not reported its application to actual spill incidents, and many aspects in practical use have not yet been clearly illustrated, e.g., feasibility for large scale pollution incidents, algorithm parameters, and likelihood functions. This work deduced a complete modular-Bayesian approach for river chemical spills, which combined variance assumptions on a pollutant concentration time series with Adaptive-Metropolis sampling. A retrospective case study was conducted based on the ‘landmark’ spill incident in China, the Songhua River nitrobenzene spill of 2005. The results show that release mass, place, and moment were identified with biases of −26.9%, −7.9%, and 16.9%, respectively. Inverse uncertainty statistics were also quantified for each source parameter. Performance, uncertainty sources, and future work are discussed. This study provides an important real-life case to demonstrate the usefulness of the modular-Bayesian approach in practice and provides valuable references for the setting of parameters for the sampling algorithm and variance assumptions.

scholarly journals The emission reduction effect of environmental information disclosure: a Chinese city perspective

2021 ◽  
Author(s):  
Weihua Yu ◽  
Xin Jin
Keyword(s):  
Information Disclosure ◽  
Large Scale ◽  
Pollution Source ◽  
Environmental Information ◽  
Source Information ◽  
Treatment Information ◽  
Environmental Information Disclosure ◽  
The Difference ◽  
Reduction Effect

Abstract To address the problem of air pollution caused by industrialization and urbanization, China has issued various types of policies related to environmental protection. However, policies regarding environmental information disclosure have long-term advantages in reducing information asymmetry and improving regulatory efficiency, and are selected as the research objects of this paper. Using the "Environmental Information Disclosure Measures (Trial)" in 2008 as a quasi-natural experiment, our analysis utilizing the difference-in-differences method shows that (1) as the quality of environmental information disclosure increase, the production and emission of sulfur dioxide decrease, and this relationship is robust to different specifications; (2) a possible channel composed of green patents and the pollution source information and treatment information (PITI) disclosure index has a same-direction moderating effect on the reduction of pollutants; and (3) our findings are particularly pronounced in subsamples of large-scale and resource-intensive cities. Overall, this paper reveals micro evidence on the effects of environmental information disclosure policy on the reduction of pollutants, thus providing timely insights for the further improvement and implementation of the policy.

scholarly journals Neural methods for effective, efficient, and exposure-aware information retrieval

2021 ◽  
Vol 55 (1) ◽  
pp. 1-2
Author(s):  
Bhaskar Mitra
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Large Scale ◽  
Web Search ◽  
Real Life ◽  
Inverted Index ◽  
Information Need ◽  
Product Model ◽  
Performance Improvements ◽  
Deep Model

Neural networks with deep architectures have demonstrated significant performance improvements in computer vision, speech recognition, and natural language processing. The challenges in information retrieval (IR), however, are different from these other application areas. A common form of IR involves ranking of documents---or short passages---in response to keyword-based queries. Effective IR systems must deal with query-document vocabulary mismatch problem, by modeling relationships between different query and document terms and how they indicate relevance. Models should also consider lexical matches when the query contains rare terms---such as a person's name or a product model number---not seen during training, and to avoid retrieving semantically related but irrelevant results. In many real-life IR tasks, the retrieval involves extremely large collections---such as the document index of a commercial Web search engine---containing billions of documents. Efficient IR methods should take advantage of specialized IR data structures, such as inverted index, to efficiently retrieve from large collections. Given an information need, the IR system also mediates how much exposure an information artifact receives by deciding whether it should be displayed, and where it should be positioned, among other results. Exposure-aware IR systems may optimize for additional objectives, besides relevance, such as parity of exposure for retrieved items and content publishers. In this thesis, we present novel neural architectures and methods motivated by the specific needs and challenges of IR tasks. We ground our contributions with a detailed survey of the growing body of neural IR literature [Mitra and Craswell, 2018]. Our key contribution towards improving the effectiveness of deep ranking models is developing the Duet principle [Mitra et al., 2017] which emphasizes the importance of incorporating evidence based on both patterns of exact term matches and similarities between learned latent representations of query and document. To efficiently retrieve from large collections, we develop a framework to incorporate query term independence [Mitra et al., 2019] into any arbitrary deep model that enables large-scale precomputation and the use of inverted index for fast retrieval. In the context of stochastic ranking, we further develop optimization strategies for exposure-based objectives [Diaz et al., 2020]. Finally, this dissertation also summarizes our contributions towards benchmarking neural IR models in the presence of large training datasets [Craswell et al., 2019] and explores the application of neural methods to other IR tasks, such as query auto-completion.

scholarly journals Multi-GPU approach to global induction of classification trees for large-scale data mining

2021 ◽  
Author(s):  
Krzysztof Jurczuk ◽  
Marcin Czajkowski ◽  
Marek Kretowski
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Real Life ◽  
Population Based ◽  
Tree Structure ◽  
Global Approach ◽  
Data Parallel ◽  
Large Scale Data ◽  
The Impact ◽  
Scale Data

AbstractThis paper concerns the evolutionary induction of decision trees (DT) for large-scale data. Such a global approach is one of the alternatives to the top-down inducers. It searches for the tree structure and tests simultaneously and thus gives improvements in the prediction and size of resulting classifiers in many situations. However, it is the population-based and iterative approach that can be too computationally demanding to apply for big data mining directly. The paper demonstrates that this barrier can be overcome by smart distributed/parallel processing. Moreover, we ask the question whether the global approach can truly compete with the greedy systems for large-scale data. For this purpose, we propose a novel multi-GPU approach. It incorporates the knowledge of global DT induction and evolutionary algorithm parallelization together with efficient utilization of memory and computing GPU’s resources. The searches for the tree structure and tests are performed simultaneously on a CPU, while the fitness calculations are delegated to GPUs. Data-parallel decomposition strategy and CUDA framework are applied. Experimental validation is performed on both artificial and real-life datasets. In both cases, the obtained acceleration is very satisfactory. The solution is able to process even billions of instances in a few hours on a single workstation equipped with 4 GPUs. The impact of data characteristics (size and dimension) on convergence and speedup of the evolutionary search is also shown. When the number of GPUs grows, nearly linear scalability is observed what suggests that data size boundaries for evolutionary DT mining are fading.

scholarly journals Robots for Elderly Care in the Home: A Landscape Analysis and Co-Design Toolkit

2021 ◽  
Author(s):  
Gianluca Bardaro ◽  
Alessio Antonini ◽  
Enrico Motta
Keyword(s):  
Large Scale ◽  
Daily Life ◽  
Elderly Care ◽  
Real Life ◽  
Robotic Platform ◽  
Holistic View ◽  
Personal Assistants ◽  
Healthcare Interventions ◽  
The Impact ◽  
The Eu

AbstractOver the last two decades, several deployments of robots for in-house assistance of older adults have been trialled. However, these solutions are mostly prototypes and remain unused in real-life scenarios. In this work, we review the historical and current landscape of the field, to try and understand why robots have yet to succeed as personal assistants in daily life. Our analysis focuses on two complementary aspects: the capabilities of the physical platform and the logic of the deployment. The former analysis shows regularities in hardware configurations and functionalities, leading to the definition of a set of six application-level capabilities (exploration, identification, remote control, communication, manipulation, and digital situatedness). The latter focuses on the impact of robots on the daily life of users and categorises the deployment of robots for healthcare interventions using three types of services: support, mitigation, and response. Our investigation reveals that the value of healthcare interventions is limited by a stagnation of functionalities and a disconnection between the robotic platform and the design of the intervention. To address this issue, we propose a novel co-design toolkit, which uses an ecological framework for robot interventions in the healthcare domain. Our approach connects robot capabilities with known geriatric factors, to create a holistic view encompassing both the physical platform and the logic of the deployment. As a case study-based validation, we discuss the use of the toolkit in the pre-design of the robotic platform for an pilot intervention, part of the EU large-scale pilot of the EU H2020 GATEKEEPER project.

scholarly journals Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks

2021 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Christos Makris ◽  
Georgios Pispirigos
Keyword(s):  
Social Networks ◽  
Graph Partitioning ◽  
Large Scale ◽  
Real Life ◽  
Information Networks ◽  
Digital Marketing ◽  
Partitioning Problems ◽  
Iterative Solutions ◽  
Community Extraction ◽  
Stability And Accuracy

Nowadays, due to the extensive use of information networks in a broad range of fields, e.g., bio-informatics, sociology, digital marketing, computer science, etc., graph theory applications have attracted significant scientific interest. Due to its apparent abstraction, community detection has become one of the most thoroughly studied graph partitioning problems. However, the existing algorithms principally propose iterative solutions of high polynomial order that repetitively require exhaustive analysis. These methods can undoubtedly be considered resource-wise overdemanding, unscalable, and inapplicable in big data graphs, such as today’s social networks. In this article, a novel, near-linear, and highly scalable community prediction methodology is introduced. Specifically, using a distributed, stacking-based model, which is built on plain network topology characteristics of bootstrap sampled subgraphs, the underlined community hierarchy of any given social network is efficiently extracted in spite of its size and density. The effectiveness of the proposed methodology has diligently been examined on numerous real-life social networks and proven superior to various similar approaches in terms of performance, stability, and accuracy.

scholarly journals Synchronization in 5G networks: a hybrid Bayesian approach toward clock offset/skew estimation and its impact on localization

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Meysam Goodarzi ◽  
Darko Cvetkovski ◽  
Nebojsa Maletic ◽  
Jesús Gutiérrez ◽  
Eckhard Grass
Keyword(s):  
Bayesian Approach ◽  
Large Scale ◽  
Hybrid Approach ◽  
Position Estimation ◽  
5G Networks ◽  
Clock Skew ◽  
Depth Analysis ◽  
The Impact ◽  
Mean Square Errors ◽  
Clock Offset

AbstractClock synchronization has always been a major challenge when designing wireless networks. This work focuses on tackling the time synchronization problem in 5G networks by adopting a hybrid Bayesian approach for clock offset and skew estimation. Furthermore, we provide an in-depth analysis of the impact of the proposed approach on a synchronization-sensitive service, i.e., localization. Specifically, we expose the substantial benefit of belief propagation (BP) running on factor graphs (FGs) in achieving precise network-wide synchronization. Moreover, we take advantage of Bayesian recursive filtering (BRF) to mitigate the time-stamping error in pairwise synchronization. Finally, we reveal the merit of hybrid synchronization by dividing a large-scale network into local synchronization domains and applying the most suitable synchronization algorithm (BP- or BRF-based) on each domain. The performance of the hybrid approach is then evaluated in terms of the root mean square errors (RMSEs) of the clock offset, clock skew, and the position estimation. According to the simulations, in spite of the simplifications in the hybrid approach, RMSEs of clock offset, clock skew, and position estimation remain below 10 ns, 1 ppm, and 1.5 m, respectively.

scholarly journals ALVIC versus the Internet: Redesigning a Networked Virtual Environment Architecture

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
Peter Quax ◽  
Jeroen Dierckx ◽  
Bart Cornelissen ◽  
Wim Lamotte
Keyword(s):  
Virtual Environment ◽  
Large Scale ◽  
Virtual Communities ◽  
Real Life ◽  
Point Of View ◽  
The Internet ◽  
Generic Framework ◽  
Short Period ◽  
Networked Virtual Environment ◽  
Near Future

The explosive growth of the number of applications based on networked virtual environment technology, both games and virtual communities, shows that these types of applications have become commonplace in a short period of time. However, from a research point of view, the inherent weaknesses in their architectures are quickly exposed. The Architecture for Large-Scale Virtual Interactive Communities (ALVICs) was originally developed to serve as a generic framework to deploy networked virtual environment applications on the Internet. While it has been shown to effectively scale to the numbers originally put forward, our findings have shown that, on a real-life network, such as the Internet, several drawbacks will not be overcome in the near future. It is, therefore, that we have recently started with the development of ALVIC-NG, which, while incorporating the findings from our previous research, makes several improvements on the original version, making it suitable for deployment on the Internet as it exists today.

scholarly journals Phenotypic discordance between primary and metastatic breast cancer in the large-scale real-life multicenter French ESME cohort

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Thomas Grinda ◽  
Natacha Joyon ◽  
Amélie Lusque ◽  
Sarah Lefèvre ◽  
Laurent Arnould ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Metastatic Breast Cancer ◽  
Large Scale ◽  
Tumour Progression ◽  
Primary Tumour ◽  
Multivariable Analysis ◽  
Real Life ◽  
Metastatic Breast ◽  
Her2 Status ◽  
Risk Of Death

AbstractExpression of hormone receptor (HR) for estrogens (ER) and progesterone (PR) and HER2 remains the cornerstone to define the therapeutic strategy for breast cancer patients. We aimed to compare phenotypic profiles between matched primary and metastatic breast cancer (MBC) in the ESME database, a National real-life multicenter cohort of MBC patients. Patients with results available on both primary tumour and metastatic disease within 6 months of MBC diagnosis and before any tumour progression were eligible for the main analysis. Among the 16,703 patients included in the database, 1677 (10.0%) had available biopsy results at MBC diagnosis and on matched primary tumour. The change rate of either HR or HER2 was 27.0%. Global HR status changed (from positive = either ER or PR positive, to negative = both negative; and reverse) in 14.2% of the cases (expression loss in 72.5% and gain in 27.5%). HER2 status changed in 7.8% (amplification loss in 45.2%). The discordance rate appeared similar across different biopsy sites. Metastasis to bone, HER2+ and RH+/HER2- subtypes and previous adjuvant endocrine therapy, but not relapse interval were associated with an HR discordance in multivariable analysis. Loss of HR status was significantly associated with a risk of death (HR adjusted = 1.51, p = 0.002) while gain of HR and HER2 discordance was not. In conclusion, discordance of HR and HER2 expression between primary and metastatic breast cancer cannot be neglected. In addition, HR loss is associated with worse survival. Sampling metastatic sites is essential for treatment adjustment.

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