scholarly journals We’re Allison Mccartney and Brittany Harris, data reporters and engineers on the Bloomberg News Graphics team. We worked on the 2016 and 2018 election cycles, and have been focused for the past year (at least!) on our data-driven coverage of the 2020 U.S. election. Ask Us Anything!

The Winnower ◽  
2020 ◽  
Author(s):  
bloomberg ◽  
r/Science
Keyword(s):  
Data Driven ◽  
The Past ◽  
Election Cycles

scholarly journals Data-driven control of complex networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Giacomo Baggio ◽  
Danielle S. Bassett ◽  
Fabio Pasqualetti
Keyword(s):  
Complex Networks ◽  
Network Dynamics ◽  
Noisy Data ◽  
Data Driven ◽  
Control Algorithms ◽  
Optimal Controls ◽  
Network Systems ◽  
The Past ◽  
Efficient Control ◽  
Finite Set

AbstractOur ability to manipulate the behavior of complex networks depends on the design of efficient control algorithms and, critically, on the availability of an accurate and tractable model of the network dynamics. While the design of control algorithms for network systems has seen notable advances in the past few years, knowledge of the network dynamics is a ubiquitous assumption that is difficult to satisfy in practice. In this paper we overcome this limitation, and develop a data-driven framework to control a complex network optimally and without any knowledge of the network dynamics. Our optimal controls are constructed using a finite set of data, where the unknown network is stimulated with arbitrary and possibly random inputs. Although our controls are provably correct for networks with linear dynamics, we also characterize their performance against noisy data and in the presence of nonlinear dynamics, as they arise in power grid and brain networks.

Data-driven modeling decadal-to-centennial ENSO variability and its response to external forcing

2021 ◽  
Author(s):  
Aleksei Seleznev ◽  
Dmitry Mukhin ◽  
Andrey Gavrilov ◽  
Alexander Feigin
Keyword(s):  
Co2 Concentration ◽  
Data Driven ◽  
Time Interval ◽  
Solar Forcing ◽  
Russian Science ◽  
Dynamical Mechanism ◽  
Enso Variability ◽  
The Past ◽  
Sst Variability ◽  
Data Driven Modeling

<p>We investigate the decadal-to-centennial ENSO variability based on nonlinear data-driven stochastic modeling. We construct data-driven model of yearly Niño-3.4 indices reconstructed from paleoclimate proxies based on three different sea-surface temperature (SST) databases at the time interval from 1150 to 1995 [1]. The data-driven model is forced by the solar activity and CO2 concentration signals. We find the persistent antiphasing relationship between the solar forcing and Niño-3.4 SST on the bicentennial time scale. The dynamical mechanism of such a response is discussed.</p><p>The work was supported by the Russian Science Foundation (Grant No. 20-62-46056)</p><p>1. Emile-Geay, J., Cobb, K. M., Mann, M. E., & Wittenberg, A. T. (2013). Estimating Central Equatorial Pacific SST Variability over the Past Millennium. Part II: Reconstructions and Implications, Journal of Climate, 26(7), 2329-2352.</p>

The vigorous development of data driven astronomy education and public outreach (DAEPO)

2019 ◽  
Vol 15 (S367) ◽  
pp. 199-209
Author(s):  
Shanshan Li ◽  
Chenzhou Cui ◽  
Cuilan Qiao ◽  
Dongwei Fan ◽  
Changhua Li ◽  
...  
Keyword(s):  
Best Practices ◽  
Rapid Evolution ◽  
Internet Technology ◽  
Data Driven ◽  
Public Outreach ◽  
Continuous Change ◽  
Astronomy Education ◽  
The Past ◽  
Education And Public Outreach ◽  
Science Educators

AbstractAstronomy education and public outreach (EPO) is one of the important part of the future development of astronomy. During the past few years, as the rapid evolution of Internet and the continuous change of policy, the breeding environment of science EPO keep improving and the number of related projects show a booming trend. EPO is no longer just a matter of to teachers and science educators but also attracted the attention of professional astronomers. Among all activates of astronomy EPO, the data driven astronomy education and public outreach (abbreviated as DAEPO) is special and important. It benefits from the development of Big Data and Internet technology and is full of flexibility and diversity. We will present the history, definition, best practices and prospective development of DAEPO for better understanding this active field.

Against generalisation: Data-driven decisions need context to be human-compatible

2021 ◽  
pp. 026638212110619
Author(s):  
Sharon Richardson
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Theoretical Frameworks ◽  
The Past ◽  
Real World Applications ◽  
Contextual Framework ◽  
Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

scholarly journals Comparative analysis of molecular representations in prediction of cancer drug combination synergy and sensitivity

2021 ◽  
Author(s):  
Bulat Zagidullin ◽  
Ziyan Wang ◽  
Yuanfang Guan ◽  
Esa Pitkänen ◽  
Jing Tang
Keyword(s):  
Drug Combination ◽  
High Throughput Screening ◽  
Data Driven ◽  
Cancer Drug ◽  
Molecular Fingerprints ◽  
Rule Based ◽  
Drug Synergy ◽  
The Past ◽  
Optimal Representation ◽  
Preclinical Drug Development

Application of machine and deep learning (ML/DL) methods in drug discovery and cancer research has gained a considerable amount of attention in the past years. As the field grows, it becomes crucial to systematically evaluate the performance of novel DL solutions in relation to established techniques. To this end we compare rule-based and data-driven molecular representations in prediction of drug combination sensitivity and drug synergy scores using standardized results of 14 high throughput screening studies, comprising 64,200 unique combinations of 4,153 molecules tested in 112 cancer cell lines. We evaluate the clustering performance of molecular fingerprints and quantify their similarity by adapting Centred Kernel Alignment metric. Our work demonstrates that in order to identify an optimal representation type it is necessary to supplement quantitative benchmark results with qualitative considerations, such as model interpretability and robustness, which may vary between and throughout preclinical drug development projects.

scholarly journals Technoversal Leader: Triumphant Leader of the Technological Era

2020 ◽  
Vol 23 (1) ◽  
pp. 440
Author(s):  
Hakan Kapucu
Keyword(s):  
Significant Role ◽  
Scientific Knowledge ◽  
New World ◽  
World Order ◽  
Digital Transformation ◽  
Business Leaders ◽  
Data Driven ◽  
Critical Component ◽  
New World Order ◽  
The Past

The new world order reminds disruptions and turmoil. Exponentially-developing technology plays a significant role in causing these radical changes. These rapidly-changing conditions affect leaders with all humans. As scientific knowledge, digital transformation, technology is a backbone at the point that humanity has reached. Thus, it has become a critical component, which affects leader behaviors and the skillset expected from them. In this context, this article introduces a new leader who distinguishes from other styles. This distinction arises from the skills that leaders must adopt in the future are different than the past, from the reality of the earth’s being on the edge of collapse, business leaders’ being obliged to act upon it. And along with these specific behaviors, the leaders’ having data-driven mindsets, being technology adept.

A Means to an End

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Alberto Prieto
Keyword(s):  
Wealth Distribution ◽  
Roman History ◽  
Data Driven ◽  
Scientific Methods ◽  
The Past ◽  
The Future ◽  
Ancient Slavery

Can data-driven approaches help researchers reconstruct Roman history? Scientific methods are now being used to reexamine ancient slavery, wealth distribution, health, and the costs of trade. Such approaches are demonstrated in The Science of Roman History: Biology, Climate, and the Future of the Past, edited by Walter Scheidel. But Alberto Prieto finds not enough of the book’s data to be Roman.

Effective Statistical Methods for Big Data Analytics

2017 ◽  
pp. 280-299 ◽  
Author(s):  
Cheng Meng ◽  
Ye Wang ◽  
Xinlian Zhang ◽  
Abhyuday Mandal ◽  
Wenxuan Zhong ◽  
...  
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Knowledge Discovery ◽  
Statistical Methods ◽  
Large Scale ◽  
Big Data Analytics ◽  
Divide And Conquer ◽  
Data Driven ◽  
The Past ◽  
Large Scale Dataset

With advances in technologies in the past decade, the amount of data generated and recorded has grown enormously in virtually all fields of industry and science. This extraordinary amount of data provides unprecedented opportunities for data-driven decision-making and knowledge discovery. However, the task of analyzing such large-scale dataset poses significant challenges and calls for innovative statistical methods specifically designed for faster speed and higher efficiency. In this chapter, we review currently available methods for big data, with a focus on the subsampling methods using statistical leveraging and divide and conquer methods.

scholarly journals From pre-emption to slowness: Assessing the contrasting temporalities of data-driven predictive policing

2020 ◽  
Vol 22 (9) ◽  
pp. 1528-1544
Author(s):  
Mark Andrejevic ◽  
Lina Dencik ◽  
Emiliano Treré
Keyword(s):  
Data Processing ◽  
Predictive Analytics ◽  
Data Driven ◽  
Temporal Shift ◽  
Predictive Policing ◽  
Social Actors ◽  
The Past ◽  
Automated Data Processing ◽  
Institutional Contexts ◽  
Conditions Of Possibility

Debates on the temporal shift associated with digitalization often stress notions of speed and acceleration. With the advent of big data and predictive analytics, the time-compressing features of digitalization are compounded within a distinct operative logic: that of pre-emption. The temporality of pre-emption attempts to project the past into a simulated future that can be acted upon in the present; a temporality of pure imminence. Yet, inherently paradoxical, pre-emption is marked by myriads of contrasts and frictions as it is caught between the supposedly all-encompassing knowledge of the data-processing ‘Machine’, and the daily reality of decision-making practices by relevant social actors. In this article, we explore the contrasting temporalities of automated data processing and predictive analytics, using policing as an illustrative example. Drawing on insights from two cases of predictive policing systems that have been implemented among UK police forces, we highlight the prevalence of counter-temporalities as predictive analytics is situated in institutional contexts and consider the conditions of possibility for agency and deliberation. Analysing these temporal tensions in relation to ‘slowness’ as a mode of resistance, the contextual examination of predictive policing advanced in the article provides a contribution to the formation of a deeper awareness of the politics of time in automated data processing; one that may serve to counter the imperative of pre-emption that, taken to the limit, seeks to foreclose the time for politics, action and life.

scholarly journals A Novel Method for Early Gear Pitting Fault Diagnosis Using Stacked SAE and GBRBM

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 758 ◽  
Author(s):  
Jialin Li ◽  
Xueyi Li ◽  
David He ◽  
Yongzhi Qu
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Continuous Time ◽  
Data Driven ◽  
Vibration Signals ◽  
Vibration Data ◽  
The Past ◽  
Diagnosis Accuracy ◽  
Novel Method ◽  
Faults Diagnosis

Research on data-driven fault diagnosis methods has received much attention in recent years. The deep belief network (DBN) is a commonly used deep learning method for fault diagnosis. In the past, when people used DBN to diagnose gear pitting faults, it was found that the diagnosis result was not good with continuous time domain vibration signals as direct inputs into DBN. Therefore, most researchers extracted features from time domain vibration signals as inputs into DBN. However, it is desirable to use raw vibration signals as direct inputs to achieve good fault diagnosis results. Therefore, this paper proposes a novel method by stacking spare autoencoder (SAE) and Gauss-Binary restricted Boltzmann machine (GBRBM) for early gear pitting faults diagnosis with raw vibration signals as direct inputs. The SAE layer is used to compress the raw vibration data and the GBRBM layer is used to effectively process continuous time domain vibration signals. Vibration signals of seven early gear pitting faults collected from a gear test rig are used to validate the proposed method. The validation results show that the proposed method maintains a good diagnosis performance under different working conditions and gives higher diagnosis accuracy compared to other traditional methods.

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