scholarly journals Using neural networks to mine text and predict metabolic traits for thousands of microbes

2020 ◽  
Author(s):  
Timothy J. Hackmann
Keyword(s):  
Neural Networks ◽  
Phylogenetic Trees ◽  
Chemical Compounds ◽  
Easy Access ◽  
Proof Of Concept ◽  
Metabolic Traits ◽  
Access Information ◽  
Written Descriptions ◽  
Extract Information ◽  
The Way

AbstractMicrobes can metabolize more chemical compounds than any other group of organisms. As a result, their metabolism is of interest to investigators across biology. Despite the interest, information on metabolism of specific microbes is hard to access. Information is buried in text of books and journals, and investigators have no easy way to extract it out. Here we investigate if neural networks can extract out this information and predict metabolic traits. For proof of concept, we predicted two traits: whether microbes carry one type of metabolism (fermentation) or produce one metabolite (acetate). We collected written descriptions of 7,021 species of bacteria and archaea from Bergey’s Manual. We read the descriptions and manually identified (labeled) which species were fermentative or produced acetate. We then trained neural networks to predict these labels. In total, we identified 2,364 species as fermentative, and 1,009 species as also producing acetate. Neural networks could predict which species were fermentative with 97.3% accuracy. Accuracy was even higher (98.6%) when predicting species also producing acetate. We used these predictions to draw phylogenetic trees of species with these traits. The resulting trees were close to the actual trees (drawn using labels). Previous counts of fermentative species are 4-fold lower than our own. For acetate-producing species, they are 100-fold lower. This undercounting confirms past difficulty in extracting metabolic traits from text. Our approach with neural networks can extract information efficiently and accurately. It paves the way for putting more metabolic traits into databases, providing easy access of information by investigators.

scholarly journals Using neural networks to mine text and predict metabolic traits for thousands of microbes

2021 ◽  
Vol 17 (3) ◽  
pp. e1008757
Author(s):  
Timothy J. Hackmann ◽  
Bo Zhang
Keyword(s):  
Neural Networks ◽  
Phylogenetic Trees ◽  
Chemical Compounds ◽  
Easy Access ◽  
Proof Of Concept ◽  
Metabolic Traits ◽  
Access Information ◽  
Written Descriptions ◽  
Extract Information ◽  
The Way

Microbes can metabolize more chemical compounds than any other group of organisms. As a result, their metabolism is of interest to investigators across biology. Despite the interest, information on metabolism of specific microbes is hard to access. Information is buried in text of books and journals, and investigators have no easy way to extract it out. Here we investigate if neural networks can extract out this information and predict metabolic traits. For proof of concept, we predicted two traits: whether microbes carry one type of metabolism (fermentation) or produce one metabolite (acetate). We collected written descriptions of 7,021 species of bacteria and archaea from Bergey’s Manual. We read the descriptions and manually identified (labeled) which species were fermentative or produced acetate. We then trained neural networks to predict these labels. In total, we identified 2,364 species as fermentative, and 1,009 species as also producing acetate. Neural networks could predict which species were fermentative with 97.3% accuracy. Accuracy was even higher (98.6%) when predicting species also producing acetate. Phylogenetic trees of species and their traits confirmed that predictions were accurate. Our approach with neural networks can extract information efficiently and accurately. It paves the way for putting more metabolic traits into databases, providing easy access of information to investigators.

scholarly journals Minimal Modifications of Deep Neural Networks using Verification

10.29007/699q ◽  
2020 ◽  
Author(s):  
Ben Goldberger ◽  
Guy Katz ◽  
Yossi Adi ◽  
Joseph Keshet
Keyword(s):  
Neural Networks ◽  
Life Cycle ◽  
Complex Systems ◽  
Real World ◽  
Deep Neural Networks ◽  
Proof Of Concept ◽  
Recent Advances ◽  
The Way

Deep neural networks (DNNs) are revolutionizing the way complex systems are de- signed, developed and maintained. As part of the life cycle of DNN-based systems, there is often a need to modify a DNN in subtle ways that affect certain aspects of its behav- ior, while leaving other aspects of its behavior unchanged (e.g., if a bug is discovered and needs to be fixed, without altering other functionality). Unfortunately, retraining a DNN is often difficult and expensive, and may produce a new DNN that is quite different from the original. We leverage recent advances in DNN verification and propose a technique for modifying a DNN according to certain requirements, in a way that is provably minimal, does not require any retraining, and is thus less likely to affect other aspects of the DNN’s behavior. Using a proof-of-concept implementation, we demonstrate the usefulness and potential of our approach in addressing two real-world needs: (i) measuring the resilience of DNN watermarking schemes; and (ii) bug repair in already-trained DNNs.

Teacher digital competence: bringing education to the next level

2020 ◽  
Vol 11 (1) ◽  
pp. 22-26
Author(s):  
S.V. Tsymbal ◽  
Keyword(s):  
Deep Learning ◽  
Learning Experiences ◽  
Digital Competence ◽  
Digital Revolution ◽  
Access Information ◽  
Set Up ◽  
Insight Into ◽  
The Way

The digital revolution has transformed the way people access information, communicate and learn. It is teachers' responsibility to set up environments and opportunities for deep learning experiences that can uncover and boost learners’ capacities. Twentyfirst century competences can be seen as necessary to navigate contemporary and future life, shaped by technology that changes workplaces and lifestyles. This study explores the concept of digital competence and provide insight into the European Framework for the Digital Competence of Educators.

Cut Flowers Being Sold as Impulse Items: A Look at Factors Affecting Impulse Product Merchandising

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 558c-558
Author(s):  
Jennifer B. Neujahr ◽  
Karen L.B. Gast
Keyword(s):  
Consumer Behavior ◽  
Marketing Strategies ◽  
Easy Access ◽  
Future Research ◽  
Grocery Stores ◽  
Cut Flowers ◽  
Factors Affecting ◽  
New Type ◽  
The 1950S ◽  
The Way

Consumer behavior research seems to play an big role in determining the wants and needs of an industry. This research helps to shape the way we market to the consumers and helps make marketing strategies more effective. In the 1950s grocery stores began to sell horticulture products in order to alleviate the growers' surplus. Supermarkets now have seem to found their niche in this market due to the fact that they can influence their consumers to buy their flowers right along with their bread, and get all of their shopping done at once. This new type of sale, commonly referred to as the impulse sale, can relate directly to how well the store is merchandised and maintained. A study was conducted at a local supermarket, to determine the following: good locations for impulse sales items, special conditions affecting impulse sales items, and what types of things could affect demand for impulse items. It was discovered that certain locations make better sales than other locations. Locations that were front and center and allowed easy access to seeing the mixed flower bouquet without having to touch it yielded the best results. The variables used to show a change in demand showed little to some variability and has raised some questions which may be used to conduct future research.

scholarly journals Sensuator: A Hybrid Sensor–Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks

Actuators ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 30
Author(s):  
Pornthep Preechayasomboon ◽  
Eric Rombokas
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Linear Models ◽  
Open Loop ◽  
Proof Of Concept ◽  
State Estimator ◽  
Loop Control ◽  
Practical Applications ◽  
Soft Actuator ◽  
The Cost

Soft robotic actuators are now being used in practical applications; however, they are often limited to open-loop control that relies on the inherent compliance of the actuator. Achieving human-like manipulation and grasping with soft robotic actuators requires at least some form of sensing, which often comes at the cost of complex fabrication and purposefully built sensor structures. In this paper, we utilize the actuating fluid itself as a sensing medium to achieve high-fidelity proprioception in a soft actuator. As our sensors are somewhat unstructured, their readings are difficult to interpret using linear models. We therefore present a proof of concept of a method for deriving the pose of the soft actuator using recurrent neural networks. We present the experimental setup and our learned state estimator to show that our method is viable for achieving proprioception and is also robust to common sensor failures.

scholarly journals Augmenting Around-Device Interaction by Geomagnetic Field Built-in Sensor Utilization

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3087
Author(s):  
Sandi Ljubic ◽  
Franko Hržić ◽  
Alen Salkanovic ◽  
Ivan Štajduhar
Keyword(s):  
Magnetic Field ◽  
Neural Networks ◽  
Curve Fitting ◽  
Absolute Error ◽  
Interaction Space ◽  
Text Entry ◽  
Proof Of Concept ◽  
Field Sensor ◽  
Device Interaction ◽  
Input Techniques

In this paper, we investigate the possibilities for augmenting interaction around the mobile device, with the aim of enabling input techniques that do not rely on typical touch-based gestures. The presented research focuses on utilizing a built-in magnetic field sensor, whose readouts are intentionally affected by moving a strong permanent magnet around a smartphone device. Different approaches for supporting magnet-based Around-Device Interaction are applied, including magnetic field fingerprinting, curve-fitting modeling, and machine learning. We implemented the corresponding proof-of-concept applications that incorporate magnet-based interaction. Namely, text entry is achieved by discrete positioning of the magnet within a keyboard mockup, and free-move pointing is enabled by monitoring the magnet’s continuous movement in real-time. The related solutions successfully expand both the interaction language and the interaction space in front of the device without altering its hardware or involving sophisticated peripherals. A controlled experiment was conducted to evaluate the provided text entry method initially. The obtained results were promising (text entry speed of nine words per minute) and served as a motivation for implementing new interaction modalities. The use of neural networks has shown to be a better approach than curve fitting to support free-move pointing. We demonstrate how neural networks with a very small number of input parameters can be used to provide highly usable pointing with an acceptable level of error (mean absolute error of 3 mm for pointer position on the smartphone display).

scholarly journals Using Convolutional Neural Networks to Map Houses Suitable for Electric Vehicle Home Charging

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 135-149
Author(s):  
James Flynn ◽  
Cinzia Giannetti
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Urban Areas ◽  
Proof Of Concept ◽  
Major Step ◽  
Dominant Form ◽  
Residential Properties ◽  
The Uk ◽  
Ev Charging ◽  
Unique Dataset

With Electric Vehicles (EV) emerging as the dominant form of green transport in the UK, it is critical that we better understand existing infrastructures in place to support the uptake of these vehicles. In this multi-disciplinary paper, we demonstrate a novel end-to-end workflow using deep learning to perform automated surveys of urban areas to identify residential properties suitable for EV charging. A unique dataset comprised of open source Google Street View images was used to train and compare three deep neural networks and represents the first attempt to classify residential driveways from streetscape imagery. We demonstrate the full system workflow on two urban areas and achieve accuracies of 87.2% and 89.3% respectively. This proof of concept demonstrates a promising new application of deep learning in the field of remote sensing, geospatial analysis, and urban planning, as well as a major step towards fully autonomous artificially intelligent surveying techniques of the built environment.

scholarly journals Point configurations, phylogenetic trees, and dissimilarity vectors

2021 ◽  
Vol 118 (12) ◽  
pp. e2021244118
Author(s):  
Alessio Caminata ◽  
Noah Giansiracusa ◽  
Han-Bom Moon ◽  
Luca Schaffler
Keyword(s):  
Phylogenetic Trees ◽  
Geometric Interpretation ◽  
Explicit Characterization ◽  
Point Configurations ◽  
Definition Of ◽  
Tropical Basis ◽  
The Way

In 2004, Pachter and Speyer introduced the higher dissimilarity maps for phylogenetic trees and asked two important questions about their relation to the tropical Grassmannian. Multiple authors, using independent methods, answered affirmatively the first of these questions, showing that dissimilarity vectors lie on the tropical Grassmannian, but the second question, whether the set of dissimilarity vectors forms a tropical subvariety, remained opened. We resolve this question by showing that the tropical balancing condition fails. However, by replacing the definition of the dissimilarity map with a weighted variant, we show that weighted dissimilarity vectors form a tropical subvariety of the tropical Grassmannian in exactly the way that Pachter and Speyer envisioned. Moreover, we provide a geometric interpretation in terms of configurations of points on rational normal curves and construct a finite tropical basis that yields an explicit characterization of weighted dissimilarity vectors.

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