Using Machine Learning for Building Multivariate IPR Models From High Frequency Streaming Data

2020 ◽  
Author(s):  
Mario Antonio del Pino Fiorillo
Keyword(s):  
Machine Learning ◽  
High Frequency ◽  
Streaming Data

Predicting high‐frequency variation in stream solute concentrations with water quality sensors and machine learning

2020 ◽  
Author(s):  
Mark B. Green ◽  
Linda H. Pardo ◽  
Scott W. Bailey ◽  
John L. Campbell ◽  
William H. McDowell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
High Frequency ◽  
Frequency Variation ◽  
Water Quality Sensors

Machine Learning and Speed in High-Frequency Trading

2021 ◽  
Author(s):  
Jasmina Arifovic ◽  
Xue-Zhong 'Tony' He ◽  
Lijian Wei
Keyword(s):  
Machine Learning ◽  
High Frequency ◽  
High Frequency Trading

Acquisition/Processing: Machine learning-based deblending: Dispersed source array data example

2021 ◽  
Vol 40 (10) ◽  
pp. 759-767
Author(s):  
Rolf H. Baardman ◽  
Rob F. Hegge
Keyword(s):  
Machine Learning ◽  
High Frequency ◽  
Low Frequency ◽  
Seismic Interpretation ◽  
Salt Dome ◽  
Training Data ◽  
Array Data ◽  
Seismic Processing ◽  
Processing Machine ◽  
Source Array

Machine learning (ML) has proven its value in the seismic industry with successful implementations in areas of seismic interpretation such as fault and salt dome detection and velocity picking. The field of seismic processing research also is shifting toward ML applications in areas such as tomography, demultiple, and interpolation. Here, a supervised ML deblending algorithm is illustrated on a dispersed source array (DSA) data example in which both high- and low-frequency vibrators were deployed simultaneously. Training data pairs of blended and corresponding unblended data were constructed from conventional (unblended) data from another survey. From this training data, the method can automatically learn a deblending operator that is used to deblend for both the low- and the high-frequency vibrators of the DSA data. The results obtained on the DSA data are encouraging and show that the ML deblending method can offer a good performing, less user-intensive alternative to existing deblending methods.

scholarly journals Localizing spontaneous memory reprocessing during human sleep

2021 ◽  
Author(s):  
Lea Himmer ◽  
Zoé Bürger ◽  
Leonie Fresz ◽  
Janina Maschke ◽  
Lore Wagner ◽  
...  
Keyword(s):  
Machine Learning ◽  
Memory Consolidation ◽  
High Frequency ◽  
Gamma Oscillations ◽  
Gamma Activity ◽  
Related Activity ◽  
Memory Processing ◽  
Slow Oscillations ◽  
Parallel Memory ◽  
Human Sleep

Reactivation of newly acquired memories during sleep across hippocampal and neocortical systems is proposed to underlie systems memory consolidation. Here, we investigate spontaneous memory reprocessing during sleep by applying machine learning to source space-transformed magnetoencephalographic data in a two-step exploratory and confirmatory study design. We decode memory-related activity from slow oscillations in hippocampus, frontal cortex and precuneus, indicating parallel memory processing during sleep. Moreover, we show complementary roles of hippocampus and neocortex: while gamma activity indicated memory reprocessing in hippocampus, delta and theta frequencies allowed decoding of memory in neocortex. Neocortex and hippocampus were linked through coherent activity and modulation of high-frequency gamma oscillations by theta, a dynamic similar to memory processing during wakefulness. Overall, we noninvasively demonstrate localized, coordinated memory reprocessing in human sleep.

scholarly journals TRUSTED MACHINES? MACHINE LEARNING, MORE-THAN-HUMAN SPEED AND DEAD LABOR IN PLATFORM CAPITALISM

2019 ◽  
Vol 2019 ◽  
Author(s):  
Sy Taffel
Keyword(s):  
Machine Learning ◽  
User Interfaces ◽  
High Frequency ◽  
Social Inequalities ◽  
Creative Industries ◽  
Binary Opposition ◽  
Media Archaeology ◽  
For Profit ◽  
Software Studies ◽  
Human Labor

Decision making machines are today ‘trusted’ to perform or assist with a rapidly expanding array of tasks. Indeed, many contemporary industries could not now function without them. Nevertheless, this trust in and reliance upon digital automation is far from unproblematic. This paper combines insights drawn from qualitative research with creative industries professionals, with approaches derived from software studies and media archaeology to critically interrogate three ways that digital automation is currently employed and accompanying questions that relate to trust. Firstly, digital automation is examined as a way of saving time and/or reducing human labor, such as when programmers use automated build tools or graphical user interfaces. Secondly, automation enables new types of behavior by operating at more-than-human speeds, as exemplified by high-frequency trading algorithms. Finally, the mode of digital automation associated with machine learning attempts to both predict and influence human behaviors, as epitomized by personalization algorithms within social media and search engines. While creative machines are increasingly trusted to underpin industries, culture and society, we should at least query the desirability of increasing dependence on these technologies as they are currently employed. These for-profit, corporate-controlled tools performatively reproduce a neoliberal worldview. Discussing misplaced trust in digital automation frequently conjures an imagined binary opposition between humans and machines, however, this reductive fantasy conceals the far more concrete conflict between differing technocultural assemblages composed of humans and machines. Across the examples explored in this talk, what emerges are numerous ways in which creative machines are used to perpetuate social inequalities.  

scholarly journals Significant Impact of Improved Machine Learning Algorithm in The Processes of Large Data Sets

2020 ◽  
pp. 458-467
Author(s):  
Virendra Tiwari ◽  
Balendra Garg ◽  
Uday Prakash Sharma
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Dynamic Environment ◽  
Large Data ◽  
Machine Learning Algorithms ◽  
Streaming Data ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm ◽  
Learning Mechanisms

The machine learning algorithms are capable of managing multi-dimensional data under the dynamic environment. Despite its so many vital features, there are some challenges to overcome. The machine learning algorithms still requires some additional mechanisms or procedures for predicting a large number of new classes with managing privacy. The deficiencies show the reliable use of a machine learning algorithm relies on human experts because raw data may complicate the learning process which may generate inaccurate results. So the interpretation of outcomes with expertise in machine learning mechanisms is a significant challenge in the machine learning algorithm. The machine learning technique suffers from the issue of high dimensionality, adaptability, distributed computing, scalability, the streaming data, and the duplicity. The main issue of the machine learning algorithm is found its vulnerability to manage errors. Furthermore, machine learning techniques are also found to lack variability. This paper studies how can be reduced the computational complexity of machine learning algorithms by finding how to make predictions using an improved algorithm.

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