scholarly journals Mapping the physics research space: a machine learning approach

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Matteo Chinazzi ◽  
Bruno Gonçalves ◽  
Qian Zhang ◽  
Alessandro Vespignani
Keyword(s):  
Machine Learning ◽  
Scientific Knowledge ◽  
Urban Areas ◽  
Dimensional Space ◽  
Research Capacity ◽  
Scientific Production ◽  
Learning Approach ◽  
Research Topics ◽  
Machine Learning Approach ◽  
Over Time

Abstract Scientific discoveries do not occur in vacuum but rather by connecting existing pieces of knowledge in new and creative ways. Mapping the relation and structure of scientific knowledge is therefore central to our understanding of the dynamics of scientific production. Here we introduce a new approach to generate scientific knowledge maps based on a machine learning approach that, starting from the observed publication patterns of authors, generates an N-dimensional space where it is possible to measure the similarity or distance between different research topics and knowledge domains. We provide an implementation of the proposed approach that considers the American Physical Society publications database and generates a map of the research space in Physics that characterizes the relation among research topics over time. We use this map to measure two indicators, the research capacity fingerprint and the knowledge density, to profile the research activity in physical sciences of more than 400 urban areas across the world. We show that these indicators can be used to analyze and predict the evolution over time of the research capacity and specialization of specific geographical areas. Furthermore we provide an extensive analysis of the relation between socio-economic development indicators and the ability to produce new knowledge for 67 countries, as measured by our approach, highlighting some key correlates of scientific production capacity. The proposed approach is scalable to very large datasets and can be extended to study other disciplines and research areas without having to rely on ad-hoc science classification schemes.

scholarly journals Redewiedergabe – Schritte zur automatischen Erkennung

2019 ◽  
Vol 47 (1) ◽  
pp. 216-248
Author(s):  
Annelen Brunner
Keyword(s):  
Machine Learning ◽  
Automatic Detection ◽  
Learning Approach ◽  
Manual Annotation ◽  
Narrative Texts ◽  
Rule Based ◽  
Machine Learning Approach ◽  
Different Types ◽  
Short Narrative ◽  
Over Time

Abstract This contribution presents a quantitative approach to speech, thought and writing representation (ST&WR) and steps towards its automatic detection. Automatic detection is necessary for studying ST&WR in a large number of texts and thus identifying developments in form and usage over time and in different types of texts. The contribution summarizes results of a pilot study: First, it describes the manual annotation of a corpus of short narrative texts in relation to linguistic descriptions of ST&WR. Then, two different techniques of automatic detection – a rule-based and a machine learning approach – are described and compared. Evaluation of the results shows success with automatic detection, especially for direct and indirect ST&WR.

scholarly journals A Machine Learning Approach to Reduce Dimensional Space in Large Datasets

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 148181-148192
Author(s):  
Rafael Munoz Terol ◽  
Alejandro Reina Reina ◽  
Saber Ziaei ◽  
David Gil
Keyword(s):  
Machine Learning ◽  
Dimensional Space ◽  
Large Datasets ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals The antibody response to SARS-CoV-2 increases over 5 months in patients with anosmia/dysgeusia

2021 ◽  
Author(s):  
Riccardo Levi ◽  
Leonardo Ubaldi ◽  
Chiara Pozzi ◽  
Giovanni Angelotti ◽  
Maria Teresa Sandri ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regression Analysis ◽  
Antibody Response ◽  
Multivariate Analyses ◽  
Multivariate Logistic Regression Analysis ◽  
Learning Approach ◽  
Multivariate Logistic Regression ◽  
Healthcare Facilities ◽  
Machine Learning Approach ◽  
Over Time

AbstractThe factors involved in the persistence of antibodies to SARS-CoV-2 are unknown. We evaluated the antibody response to SARS-CoV-2 in personnel from 10 healthcare facilities and its association with individuals’ characteristics and COVID-19 symptoms in an observational study. We enrolled 4735 subjects (corresponding to 80% of all personnel) over a period of 5 months when the spreading of the virus was drastically reduced. For each participant, we determined the rate of antibody increase or decrease over time in relation to 93 features analyzed in univariate and multivariate analyses through a machine learning approach. In individuals positive for IgG (≥ 12 AU/mL) at the beginning of the study, we found an increase [p= 0.0002] in antibody response in symptomatic subjects, particularly with anosmia/dysgeusia (OR 2.75, 95% CI 1.753 – 4.301), in a multivariate logistic regression analysis. This may be linked to the persistence of SARS-CoV-2 in the olfactory bulb.

scholarly journals Novel criteria to classify ARDS severity using a machine learning approach

Critical Care ◽  
2021 ◽  
Vol 25 (1) ◽  
Author(s):  
Mohammed Sayed ◽  
David Riaño ◽  
Jesús Villar
Keyword(s):  
Machine Learning ◽  
Distress Syndrome ◽  
Learning Approach ◽  
The Novel ◽  
Arterial Oxygenation ◽  
Positive End Expiratory Pressure ◽  
Machine Learning Approach ◽  
Mimic Iii ◽  
Over Time ◽  
Better Than

Abstract Background Usually, arterial oxygenation in patients with the acute respiratory distress syndrome (ARDS) improves substantially by increasing the level of positive end-expiratory pressure (PEEP). Herein, we are proposing a novel variable [PaO2/(FiO2xPEEP) or P/FPE] for PEEP ≥ 5 to address Berlin’s definition gap for ARDS severity by using machine learning (ML) approaches. Methods We examined P/FPE values delimiting the boundaries of mild, moderate, and severe ARDS. We applied ML to predict ARDS severity after onset over time by comparing current Berlin PaO2/FiO2 criteria with P/FPE under three different scenarios. We extracted clinical data from the first 3 ICU days after ARDS onset (N = 2738, 1519, and 1341 patients, respectively) from MIMIC-III database according to Berlin criteria for severity. Then, we used the multicenter database eICU (2014–2015) and extracted data from the first 3 ICU days after ARDS onset (N = 5153, 2981, and 2326 patients, respectively). Disease progression in each database was tracked along those 3 ICU days to assess ARDS severity. Three robust ML classification techniques were implemented using Python 3.7 (LightGBM, RF, and XGBoost) for predicting ARDS severity over time. Results P/FPE ratio outperformed PaO2/FiO2 ratio in all ML models for predicting ARDS severity after onset over time (MIMIC-III: AUC 0.711–0.788 and CORR 0.376–0.566; eICU: AUC 0.734–0.873 and CORR 0.511–0.745). Conclusions The novel P/FPE ratio to assess ARDS severity after onset over time is markedly better than current PaO2/FiO2 criteria. The use of P/FPE could help to manage ARDS patients with a more precise therapeutic regimen for each ARDS category of severity.

scholarly journals Tracking conflict and emotions with a computational qualitative discourse analytic support approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251186
Author(s):  
Nikodem Rybak ◽  
Daniel J. Angus
Keyword(s):  
Machine Learning ◽  
Emotional State ◽  
Learning Approach ◽  
Emotional Information ◽  
Machine Learning Approach ◽  
Depth Study ◽  
Over Time ◽  
Qualitative Analyses

Accurate inferences of the emotional state of conversation participants can be critical in shaping analysis and interpretation of conversational exchanges. In qualitative analyses of discourse, most labelling of the perceived emotional state of conversation participants is performed by hand, and is limited to selected moments where an analyst may believe that emotional information is valuable for interpretation. This reliance on manual labelling processes can have implications for repeatability and objectivity, both in terms of accuracy, but also in terms of changes in emotional state that might go unnoticed. In this paper we introduce a qualitative discourse analytic support method intended to support the labelling of emotional state of conversational participants over time. We demonstrate the utility of the technique using a suite of well-studied broadcast interviews, taking a particular focus on identifying instances of inter-speaker conflict. Our findings indicate that this two-step machine learning approach can help decode how moments of conflict arise, sustain, and are resolved through the mapping of emotion over time. We show how such a method can provide useful evidence of the change in emotional state by interlocutors which could be useful to prompt and support further in-depth study.

scholarly journals Beyond modularity: Fine-scale mechanisms and rules for brain network reconfiguration

2017 ◽  
Author(s):  
Ankit N. Khambhati ◽  
Marcelo G. Mattar ◽  
Danielle S. Bassett
Keyword(s):  
Machine Learning ◽  
Brain Network ◽  
Modular Organization ◽  
Functional Networks ◽  
Learning Approach ◽  
Functional Brain ◽  
Functional Interactions ◽  
Functional Brain Network ◽  
Machine Learning Approach ◽  
Over Time

AbstractThe human brain is in constant flux, as distinct areas engage in transient communication to support basic behaviors as well as complex cognition. The collection of interactions between cortical and subcortical areas forms a functional brain network whose topology evolves with time. Despite the nontrivial dynamics that are germaine to this networked system, experimental evidence demonstrates that functional interactions organize into putative brain systems that facilitate different facets of cognitive computation. We hypothesize that such dynamic functional networks are organized around a set of rules that constrain their spatial architecture – which brain regions may functionally interact – and their temporal architecture – how these interactions fluctuate over time. To objectively uncover these organizing principles, we apply an unsupervised machine learning approach called nonnegative matrix factorization to time-evolving, resting state functional networks in 20 healthy subjects. This machine-learning approach automatically clusters temporally co-varying functional interactions into subgraphs that represent putative topological modes of dynamic functional architecture. We find that subgraphs are stratified based on both the underlying modular organization and the topographical distance of their strongest interactions: while many subgraphs are largely contained within modules, others span between modules and are expressed differently over time. The relationship between dynamic subgraphs and modular architecture is further highlighted by the ability of time-varying subgraph expression to explain inter-individual differences in module reorganization. Collectively, these results point to the critical role subgraphs play in constraining the topography and topology of functional brain networks. More broadly, this machine learning approach opens a new door for understanding the architecture of dynamic functional networks during both task and rest states, and for probing alterations of that architecture in disease.

scholarly journals Feeling anxious? Perceiving anxiety in tweets using machine learning

2019 ◽  
Author(s):  
Dritjon Gruda ◽  
Souleiman Hasan
Keyword(s):  
Machine Learning ◽  
Trait Anxiety ◽  
State Anxiety ◽  
Social Engagement ◽  
Measurement Tool ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Reverse Relationship ◽  
The Individual ◽  
Over Time

This study provides a predictive measurement tool to examine perceived anxiety from a longitudinal perspective, using a non-intrusive machine learning approach to scale human rating of anxiety in microblogs. Results suggest that our chosen machine learning approach depicts perceived user state-anxiety fluctuations over time, as well as mean trait anxiety. We further find a reverse relationship between perceived anxiety and outcomes such as social engagement and popularity. Implications on the individual, organizational, and societal levels are discussed.

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