Relevant bibliographies by topics / Stance prediction

Academic literature on the topic 'Stance prediction'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Stance prediction"

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LI, Yang, and Rui QI. "Heterogeneous Graph Contrastive Learning for Stance Prediction." IEICE Transactions on Information and Systems E105.D, no. 10 (October 1, 2022): 1790–98. http://dx.doi.org/10.1587/transinf.2022edp7065.

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Kamble, Aditya, Prathamesh Badgujar, Anuj Kadam, Dhruv Shah, and A. J. Kadam. "Stance Prediction of Tweets on Farmers Protests in India." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 4288–95. http://dx.doi.org/10.22214/ijraset.2022.43070.

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Abstract: Protests are an integral part of democracy and are a vital tool for the general public to convey their demands and/or discontentment to the ruling government. As voters return to term with any new rules, there are an increasing range of protests everywhere in the world for numerous socio-political reasons. With the advancement of technology, there has additionally been an exponential rise within the use of social media for the exchange of data and ideas. During this research, knowledge was gathered from the web site “twitter.com”, regarding farmers’ protest to know the feelings that the public shared on a global level. Sadly now since the Farm Laws are repealed, we have a tendency to aim to use this knowledge to know the general public stance on these laws, and whether or not it affected the government’s decision. This paper proposes a stance prediction deep learning model achieved after fine tuning the well known ULMFiT (Universal Language Model Fine-tuning) model by Howard and Ruder. Categories to be classified into are For (F), Against (A) and Neutral (N). Proposed model achieved an F1 score of 0.67 on our training and test data, which is essentially a labeled subset of the actual data. Keywords: Dataset, ULMFiT, deep learning, text classification, Language Model (LM)
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Huang, Kuo-Yu, Hen-Hsen Huang, and Hsin-Hsi Chen. "HARGAN: Heterogeneous Argument Attention Network for Persuasiveness Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (May 18, 2021): 13045–54. http://dx.doi.org/10.1609/aaai.v35i14.17542.

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Argument structure elaborates the relation among claims and premises. Previous works in persuasiveness prediction do not consider this relation in their architectures. To take argument structure information into account, this paper proposes an approach to persuasiveness prediction with a novel graph-based neural network model, called heterogeneous argument attention network (HARGAN). By jointly training on the persuasiveness and stance of the replies, our model achieves the state-of-the-art performance on the ChangeMyView (CMV) dataset for the persuasiveness prediction task. Experimental results show that the graph setting enables our model to aggregate information across multiple paragraphs effectively. In the meanwhile, our stance prediction auxiliary task enables our model to identify the viewpoint of each party, and helps our model perform better on the persuasiveness prediction.
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Simaki, Vasiliki, Carita Paradis, and Andreas Kerren. "A two-step procedure to identify lexical elements of stance constructions in discourse from political blogs." Corpora 14, no. 3 (November 2019): 379–405. http://dx.doi.org/10.3366/cor.2019.0179.

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The Brexit Blog Corpus (bbc) is a collection of texts extracted from political blogs, which, in a recent study, was annotated according to a cognitive–functional stance framework by two independent annotators (Annotator A and B) using semantic criteria ( Simaki et al., 2017 ). The goal was to label the stance or stances taken based on the overall meaning of a set of utterances. The annotators were not instructed to identify the lexical forms that were used to express the stances. In this study, we make use of those stance-labelled utterances as a springboard to approach stance-taking in text from the opposite point of view, namely from how stance is realised through language. Our aim is to provide a description of the specific lexical elements used to express six stance categories (i.e., contrariety, hypotheticality, necessity, prediction, source of knowledge and uncertainty). To this end, we followed a two-step experimental procedure. First, we performed a quantitative analysis of the stance-labelled utterances in order to identify the lexical realisations of each stance category. Second, we carried out a meta-annotation of the data. Annotator B was instructed to single out the actual lexical forms of the constructions that triggered his semantic stance category decisions. This meta-annotation procedure made it possible for us to sift out the most salient lexical realisations of the constructions of each of the six category types on the basis of the qualitative assessments made by Annotator B. We then compared the results of the quantitative and the qualitative approaches, and we present a list of shared stance expressions for each stance category type.
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Bhati, Piali, Theodore C. K. Cheung, Gobika Sithamparanathan, and Mark A. Schmuckler. "Striking a balance in sports: the interrelation between children's sports experience, body size, and posture." AIMS Neuroscience 9, no. 2 (2022): 288–302. http://dx.doi.org/10.3934/neuroscience.2022016.

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<abstract> <p>This study investigated the relation between sports participation, body size, and postural control in children between 3 and 11 years of age. To explore this question, children's body sway was measured across multisensory conditions manipulating visual input (the presence versus absence of visual information) and proprioceptive input (varying stance widths), with postural sway in these conditions then related to reports of children's sports participation, and anthropometric measures. Corroborating well-known findings, postural sway was systematically influenced by multisensory factors, with the removal of visual information and narrower stance widths decreasing postural stability. Of more novelty, postural sway in the most stable stance, but without vision, was significantly predicted by measures of sports participation and body size variables, with these factors contributing independently to this prediction. Moreover, the impact on postural sway of having visual input, relative to removing visual input in unstable stances, was significantly predicted by sports participation in activities stressing changing stances and bases of support (e.g., dance, martial arts). Generally, these findings support multisensory and dynamic systems theories of perceptual-motor behavior, and also support sports specificity effects in assessments of the relation between posture and sports.</p> </abstract>
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Simaki, Vasiliki, Eleni Seitanidi, and Carita Paradis. "Evaluating stance annotation of Twitter data." Research in Corpus Linguistics 11, no. 1 (2022): 53–80. http://dx.doi.org/10.32714/ricl.11.01.03.

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Taking stance towards any topic, event or idea is a common phenomenon on Twitter and social media in general. Twitter users express their opinions about different matters and assess other people’s opinions in various discursive ways. The identification and analysis of the linguistic ways that people use to take different stances leads to a better understanding of the language and user behaviour on Twitter. Stance is a multidimensional concept involving a broad range of related notions such as modality, evaluation and sentiment. In this study, we annotate data from Twitter using six notional stance categories ––contrariety, hypotheticality, necessity, prediction, source of knowledge and uncertainty––¬¬ following a comprehensive annotation protocol including inter-coder reliability measurements. The relatively low agreement between annotators highlighted the challenges that the task entailed, which made us question the inter-annotator agreement score as a reliable measurement of annotation quality of notional categories. The nature of the data, the difficulty of the stance annotation task and the type of stance categories are discussed, and potential solutions are suggested
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Ahmad, Muhammad, Muhammad Asim Mahmood, and Ammara Farukh. "Use of Modals as Stance Markers: A Corpus-Based Study on Pakistani English Newspaper Editorials." Asia Pacific Media Educator 30, no. 1 (June 2020): 108–25. http://dx.doi.org/10.1177/1326365x20945424.

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This study analyses the use of modals as stance markers in newspaper editorials. Corpora of the study comprised of 500 editorials published in Pakistani English newspapers, that is, The Daily Dawn and The Daily News (250 editorials from each newspaper) which were analysed with the help of AntConc 3.4.4.0. Results show Pakistani editorial writers use all types of modals (i.e., prediction, possibility, necessity, modal adverbs, reporting verbs, knowledge verbs and generic phrases). The editorial writers use these modals to mark personal stance while commenting, reporting or informing about the state of affairs. Among these modals the use of prediction markers is the most frequent which indicates that prediction is a characteristic feature as well as function in the said newspaper editorials. Results also reveal that there is a difference in the use of modal adverbs in the editorials of both newspapers. In addition, the results reveal that the editorial writers of The Daily Dawn make less use of prediction markers as compared to the editorial writers of The Daily News. The study creates awareness of the stance of editorial writers’ and how it might affect readers’ opinions.
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Wang, Heyuan, Tengjiao Wang, and Yi Li. "Incorporating Expert-Based Investment Opinion Signals in Stock Prediction: A Deep Learning Framework." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 971–78. http://dx.doi.org/10.1609/aaai.v34i01.5445.

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Investment messages published on social media platforms are highly valuable for stock prediction. Most previous work regards overall message sentiments as forecast indicators and relies on shallow features (bag-of-words, noun phrases, etc.) to determine the investment opinion signals. These methods neither capture the time-sensitive and target-aware characteristics of stock investment reviews, nor consider the impact of investor's reliability. In this study, we provide an in-depth analysis of public stock reviews and their application in stock movement prediction. Specifically, we propose a novel framework which includes the following three key components: time-sensitive and target-aware investment stance detection, expert-based dynamic stance aggregation, and stock movement prediction. We first introduce our stance detection model named MFN, which learns the representation of each review by integrating multi-view textual features and extended knowledge in financial domain to distill bullish/bearish investment opinions. Then we show how to identify the validity of each review, and enhance stock movement prediction by incorporating expert-based aggregated opinion signals. Experiments on real datasets show our framework can effectively improve the performance of both investment opinion mining and individual stock forecasting.
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Aftab, Zohaib, and Rizwan Shad. "Estimation of gait parameters using leg velocity for amputee population." PLOS ONE 17, no. 5 (May 13, 2022): e0266726. http://dx.doi.org/10.1371/journal.pone.0266726.

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Quantification of key gait parameters plays an important role in assessing gait deficits in clinical research. Gait parameter estimation using lower-limb kinematics (mainly leg velocity data) has shown promise but lacks validation for the amputee population. The aim of this study is to assess the accuracy of lower-leg angular velocity to predict key gait events (toe-off and heel strike) and associated temporal parameters for the amputee population. An open data set of reflexive markers during treadmill walking from 10 subjects with unilateral transfemoral amputation was used. A rule-based dual-minima algorithm was developed to detect the landmarks in the shank velocity signal indicating toe-off and heel strike events. Four temporal gait parameters were also estimated (step time, stride time, stance and swing duration). These predictions were compared against the force platform data for 3000 walking cycles from 239 walking trials. Considerable accuracy was achieved for the HS event as well as for step and stride timings, with mean errors ranging from 0 to -13ms. The TO prediction exhibited a larger error with its mean ranging from 35-81ms. The algorithm consistently predicted the TO earlier than the actual event, resulting in prediction errors in stance and swing timings. Significant differences were found between the prediction for sound and prosthetic legs, with better TO accuracy on the prosthetic side. The prediction accuracy also appeared to improve with the subjects’ mobility level (K-level). In conclusion, the leg velocity profile, coupled with the dual-minima algorithm, can predict temporal parameters for the transfemoral amputee population with varying degrees of accuracy.
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Su, Binbin, and Elena M. Gutierrez-Farewik. "Gait Trajectory and Gait Phase Prediction Based on an LSTM Network." Sensors 20, no. 24 (December 12, 2020): 7127. http://dx.doi.org/10.3390/s20247127.

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Lower body segment trajectory and gait phase prediction is crucial for the control of assistance-as-needed robotic devices, such as exoskeletons. In order for a powered exoskeleton with phase-based control to determine and provide proper assistance to the wearer during gait, we propose an approach to predict segment trajectories up to 200 ms ahead (angular velocity of the thigh, shank and foot segments) and five gait phases (loading response, mid-stance, terminal stance, preswing and swing), based on collected data from inertial measurement units placed on the thighs, shanks, and feet. The approach we propose is a long-short term memory (LSTM)-based network, a modified version of recurrent neural networks, which can learn order dependence in sequence prediction problems. The algorithm proposed has a weighted discount loss function that places more weight in predicting the next three to five time frames but also contributes to an overall prediction performance for up to 10 time frames. The LSTM model was designed to learn lower limb segment trajectories using training samples and was tested for generalization across participants. All predicted trajectories were strongly correlated with the measured trajectories, with correlation coefficients greater than 0.98. The proposed LSTM approach can also accurately predict the five gait phases, particularly swing phase with 95% accuracy in inter-subject implementation. The ability of the LSTM network to predict future gait trajectories and gait phases can be applied in designing exoskeleton controllers that can better compensate for system delays to smooth the transition between gait phases.
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Dissertations / Theses on the topic "Stance prediction"

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Ruggeri, Federico. "Predizione della struttura di un argomento con feature di stance classification." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/15009/.

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Nell'ultimo decennio i settori di ricerca nell'ambito dell'elaborazione automatica del linguaggio naturale (NLP) hanno acquisito sempre maggiore interesse, in concomitanza con l'introduzione continua di strumenti di machine learning innovativi. In generale, seppur sia comunque presente un'ambizione ideologica, l'interesse principale è riconducibile ad un piano prettamente applicativo, di natura sociale o politica, dove l'introduzione di nuove informazioni può apportare un notevole miglioramento in molti processi di interesse reale e contribuire all'indagine di nuove tendenze e/o conflitti. Il potenziale riscontrato nei presenti ambiti di ricerca ha attirato l'attenzione di aziende di notevole portata, al punto da finanziare progetti ambiziosi volti a rispondere a necessità emergenti proprie di molteplici realtà di diversa natura. A partire da queste premesse, il presente elaborato si pone come obiettivo la definizione di un modello atto a individuare punti di affinità tra due specifiche attività di recente interesse, denominate stance classification e argument structure prediction. I primi due capitoli introducono rispettivamente i concetti di stance classification e argumentation mining, soffermandosi, in particolar modo, sulle tecniche e le metodologie impiegate. Il terzo capitolo affronta il tema della definizione di uno strumento di classificazione comparabile con lo stato dell'arte nell'ambito della stance classification. Il quarto capitolo si interessa principalmente dell'individuazione di un legame tra i due settori di ricerca presi in esame, per poi mettere a confronto alcune tecniche specifiche attualmente impiegate per l'argument structure prediction con le principali adottate nell'ambito della stance classification. Infine, nel quinto e ultimo capitolo viene proposto un nuovo corpus per la sperimentazione degli stessi approcci impiegati nel capitolo precedente, con particolare attenzione a proporre degli opportuni test di confronto e di verifica.
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Wojatzki, Michael Maximilian [Verfasser], and Torsten [Akademischer Betreuer] Zesch. "Computer-assisted understanding of stance in social media : formalizations, data creation, and prediction models / Michael Maximilian Wojatzki ; Betreuer: Torsten Zesch." Duisburg, 2019. http://d-nb.info/1177681471/34.

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LeeTiernan, Scott. "Modeling and predicting stable response variation across situations /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/9076.

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Bingham, Jeffrey Thomas. "A framework to quantify neuromechanical contributions to stable standing balance: Modeling predictions and experimental observations." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/52161.

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Interactions between the neural and musculoskeletal systems are a prerequisite for the production of robust movement. In spite of this, the neural control and musculoskeletal structure underlying biological movements are typically studied independently, with little attention paid to how changes in one may affect the other. Understanding these interactions may be critical to improving current rehabilitation technologies and therapy methods. As an example, balance disorders are multifactorial in nature and identifying whether biomechanical or neural changes are the source of instability remains an unanswered question. I have used a combined experimental and modeling approach to understand neural and biomechanical interactions governing human balance control. I developed a simple four-bar linkage model with delayed feedback to investigate frontal-plane standing balance. Using methods from time-delay systems I present evidence from this model that biomechanical structure is important for behavioral function and show that neural control and biomechanical structure co-vary for stable human balance. Predictions from the model were tested experimentally to dissociate the effects of inertia and postural configuration on balance. In addition, I applied robust control methods to solve the difficult problem of comparing the relative performance between neuromechanical systems that differ in parameter values and predicted a common mechanism to explain changes in neural control across biomechanical contexts. In the future, the analytical tools and simulation methods I have developed can be generalized to investigate changes in neuromechanical interactions of various deficits in biomechanics (ACL rupture, amputation) and neural control (Parkinson's disease, stroke). Furthermore, this approach can be used to explain how neural control and biomechanical structure relate to the diversity of animal form and function, as well as suggest biomimetic control policies for robotics.
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Chevalier, Samuel Chapman. "Inference, estimation, and prediction for stable operation of modern electric power systems." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130842.

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Thesis: Ph. D. in Mechanical Engineering and Computation, Massachusetts Institute of Technology, Department of Mechanical Engineering, February, 2021
Cataloged from the official PDF of thesis.
Includes bibliographical references (pages 261-277).
To keep pace with social-ecological disruptions and technological progressions, electrical power systems must continually adapt. In order to address the stability-related challenges associated with these adaptations, this thesis develops a set of analytically rigorous yet practically oriented methods for ensuring the continued stability of modern power systems. By leveraging inference, estimation, and predictive modeling techniques, the proposed methods capitalize on the unprecedented amount of real time data emerging from modernizing smart grids. For each method, we provide simulated test results from IEEE benchmark systems. Newly deployed Phasor Measurement Units (PMUs) are observing the presence of detrimental low frequency forced oscillations (FOs) in transmission grid networks. To begin this thesis, we address the problem of locating the unknown sources of these FOs.
To perform source identification, we develop an equivalent circuit transformation which leverages suitably constructed transfer functions of grid elements. Since FO sources appear in this equivalent circuit as independent current injections, a Bayesian framework is applied to locate the most probable source of these injections. Subsequently, we use our equivalent circuit to perform a systematic investigation of energy-based source identification methods. We further leverage this equivalent circuit transformation by developing "plug-and-play" stability standards for microgrid networks that contain uncertain loading configurations. As converter-based technology declines in cost, microgrids are becoming an increasingly feasible option for expanding grid access. Via homotopic parameterization of the instability drivers in these tightly regulated systems, we identify a family of rotational functions which ensure that no eigenmodes can be driven unstable.
Any component which satisfies the resulting standards can be safely added to the network, thus allowing for plug-and-play operability. High-fidelity linearized models are needed to perform both FO source identification and microgrid stability certification. Furthermore, as loss of inertia and real-time observability of grid assets accelerate in tandem, real-time linearized modeling is becoming an increasingly useful tool for grid operators. Accordingly, we develop tools for performing real-time predictive modeling of low frequency power system dynamics in the presence of ambient perturbations. Using PMU data, we develop a black-box modeling procedure, known as Real-Time Vector Fitting (RTVF), that takes explicit account for initial state decay and concurrently active input signals. We then outline a proposed extension, known as stochastic-RTVF, that accounts for the corrupting effects of unobservable stochastic inputs.
The surrogate modeling utilized by vector fitting can also be applied to the steady state power flow problem. Due to an unprecedented deployment of distributed energy resources, operational uncertainty in electrical distribution networks is increasing dramatically. To address this challenge, we develop methodology for speeding up probabilistic power flow and state estimation routines in distribution networks. We do so by exploiting the inherently low-rank nature of the voltage profile in these systems. The associated algorithms dynamically generate a low-dimensional subspace which is used to construct a projection-based reduced order model (ROM) of the full nonlinear system. Future system solves using this ROM are highly efficient.
by Samuel Chapman Chevalier.
Ph. D. in Mechanical Engineering and Computation
Ph.D.inMechanicalEngineeringandComputation Massachusetts Institute of Technology, Department of Mechanical Engineering
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Handl, Tomáš. "Algoritmus Vivaldi pro nalezení pozice stanice v Internetu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2009. http://www.nusl.cz/ntk/nusl-218110.

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Diploma thesis deals with usage of artificial coordinate systems used for localization of a station on the internet and prediction of delay between the stations. There are described and compared basic properties of centralized and decentralized algorithms providing station localization on the internet and RTT prediction. More in depth are presented main representatives of both types of algorithms such as GNP, IDMAPS or Lighthouse. Central part of thesis is aimed at getting to know Vivaldi distributed algorithm. Basic principle of the algorithm for constant and variable time step, using two dimensional coordinate system with 3rd parameter height, is here outlined. Further more implementation of this algorithm as a library Vivaldi-lib in the environment of Java is implemented. Part of the thesis are simulations of behaviour of this algorithm for both variations realized on artificial networks and data obtained from PlanetLab experimental network, using simulation created program VIVALDIMONITOR.
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Wangerin, Spencer D. "Development and validation of a human knee joint finite element model for tissue stress and strain predictions during exercise." DigitalCommons@CalPoly, 2013. https://digitalcommons.calpoly.edu/theses/1129.

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Osteoarthritis (OA) is a degenerative condition of cartilage and is the leading cost of disability in the United States. Motion analysis experiments in combination with knee-joint finite element (FE) analysis may be used to identify exercises that maintain knee-joint osteochondral (OC) loading at safe levels for patients at high-risk for knee OA, individuals with modest OC defects, or patients rehabilitating after surgical interventions. Therefore, a detailed total knee-joint FE model was developed by modifying open-source knee-joint geometries in order to predict OC tissue stress and strain during the stance phase of gait. The model was partially validated for predicting the timing and locations of maximum contact parameters (contact pressure, contact area, and principal Green-Lagrangian strain), but over-estimated contact parameters compared with both published in vivo studies and other FE analyses of the stance phase of gait. This suggests that the model geometry and kinematic boundary conditions utilized in this FE model are appropriate, but limitations in the material properties used, as well as potentially the loading boundary conditions represent primary areas for improvement.
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Verde, Joshua A. "Lake Powell Food Web Structure: Predicting Effects of Quagga Mussel." BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6702.

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Food webs in aquatic ecosystems can be dramatically altered by invasive species. Quagga mussels are prevalent invaders that compete with existing species and disrupt nutrient cycling. In 2012, the Quagga Mussel (Dreissena rostriformus bugensis) was introduced into Lake Powell and is expected to move throughout the reservoir in the near future. Stable isotope analysis is a powerful tool for characterizing food webs and trophic interactions. To predict the long-term effects of Quagga Mussels, we used stable isotope analysis of primary producers, primary consumers, prey fish species, and predator fish species in Lake Powell to determine food web structure. Quagga Mussel are positioned to disrupt the pelagic arm of the food web by interfering with the link between phytoplankton and herbivorous zooplankton. This will likely have negative impacts on pelagic fish such as striped bass (Morone saxatilis) and threadfin shad (Dorosoma petenense). Quagga Mussel may also boost benthic productivity in the littoral zone by diverting nutrients from the water column to the benthos. This may have positive impacts on littoral fishes such as largemouth bass (Micropterus salmoides), smallmouth bass (Micropterus dolomieu), bluegill (Lepomis macrochirus), and green sunfish (Lepomis cyanellus).
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Oh, Yunje. "Prediction of steady state response in dynamic mode atomic force microscopy and its applications in nano-metrology." The Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=osu1135222817.

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Hopf, Konstantin [Verfasser], and Thorsten [Akademischer Betreuer] Staake. "Predictive Analytics for Energy Efficiency and Energy Retailing / Konstantin Hopf ; Betreuer: Thorsten Staake." Bamberg : University of Bamberg Press, 2019. http://d-nb.info/1191183580/34.

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Books on the topic "Stance prediction"

1

Koning, A. V. de. Finite element analyses of stable crack growth in thin sheet material. Amsterdam: National Aerospace Laboratory, 1985.

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2

Estrella, Arturo. How stable is the predictive power of the yield curve?: Evidence from Germany and the United States. [New York, N.Y.]: Federal Reserve Bank of New York, 2000.

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Chistyakova, Guzel, Lyudmila Ustyantseva, Irina Remizova, Vladislav Ryumin, and Svetlana Bychkova. CHILDREN WITH EXTREMELY LOW BODY WEIGHT: CLINICAL CHARACTERISTICS, FUNCTIONAL STATE OF THE IMMUNE SYSTEM, PATHOGENETIC MECHANISMS OF THE FORMATION OF NEONATAL PATHOLOGY. au: AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/monography_62061e70cc4ed1.46611016.

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The purpose of the monograph, which contains a modern view of the problem of adaptation of children with extremely low body weight, is to provide a wide range of doctors with basic information about the clinical picture, functional activity of innate and adaptive immunity, prognostic criteria of postnatal pathology, based on their own research. The specific features of the immunological reactivity of premature infants of various gestational ages who have developed bronchopulmonary dysplasia (BPD) and retinopathy of newborns (RN) from the moment of birth and after reaching postconceptional age (37-40 weeks) are described separately. The mechanisms of their implementation with the participation of factors of innate and adaptive immunity are considered in detail. Methods for early prediction of BPD and RN with the determination of an integral indicator and an algorithm for the management of premature infants with a high risk of postnatal complications at the stage of early rehabilitation are proposed. The information provided makes it possible to personify the treatment, preventive and rehabilitation measures in premature babies. The monograph is intended for obstetricians-gynecologists, neonatologists, pediatricians, allergists-immunologists, doctors of other specialties, residents, students of the system of continuing medical education. This work was done with financial support from the Ministry of Education and Science, grant of the President of the Russian Federation No. MK-1140.2020.7.
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Zawidzki, Tadeusz. The Many Roles of the Intentional Stance. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199367511.003.0003.

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Can the intentional stance play all of the roles Dennett claims that it must play? There is reason for skepticism about the suitability of the intentional stance as an analysis of mature, person-level, intentional concepts. In part this is because of the dynamic and socially situated structure of our interpersonal practices. In part this is because folk-ascriptions of mentality are often guided by regulative concerns with impression management and identity construction. But scientific practice often relies on intentional states that are characterized in terms of their predictive and explanatory roles; and most humans employ tacit cognitive resources with a similar character when they make quick and efficient behavioral anticipations. In light of these considerations, it is unlikely a single set of explanatory norms will be operative in practices of quotidian interpretation, scientific explanation, and philosophical naturalization.
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Stache: A novel cache architecture using predictive prefetch. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1992.

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Barbaree, Howard E., and Robert A. Prentky. Risk assessment of sex offenders. Edited by Teela Sanders. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190213633.013.21.

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This essay discusses the assessment of recidivism risk in sex offenders. It begins with definitions of critical terms and concepts. A number of approaches to risk assessment are described. Validated risk instruments are reviewed, with a focus on their reliability and accuracy in predicting recidivism. Actuarial assessment of risk is described as a two-stage process. In the first stage, offenders are assessed and assigned to a risk level or stratum. In the second stage, the probability of risk over a follow-up period is estimated based on the offender’s risk ranking. The essay discusses calibration in the context of Bayes’ theorem, which reveals critically important realities involving base rates and the use of currently available standardization samples in determining a final estimate of recidivism likelihood. The essay concludes with a glimpse into the future of risk assessment and predictions about the next stage in evidence-based risk assessment of sex offenders.
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7

C, Newman J., and Langley Research Center, eds. Prediction of stable tearing of 2024-T3 aluminum alloy using the crack-tip opening angle approach. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1993.

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C, Newman J., and Langley Research Center, eds. Prediction of stable tearing of 2024-T3 aluminum alloy using the crack-tip opening angle approach. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1993.

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National Aeronautics and Space Administration (NASA) Staff. Prediction of Stable Tearing of 2024-T3 Aluminum Alloy Using the Crack-Tip Opening Angle Approach. Independently Published, 2018.

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Lemons, Don S. Drawing Physics. The MIT Press, 2018. http://dx.doi.org/10.7551/mitpress/9780262035903.001.0001.

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Drawing Physics is a collection of 51 essays each one organized around a simple, informative, line drawing that conveys a key idea in the history of physics. The essays, each approximately 1000 words long, are chronologically ordered from Thales, who around 600 BCE explained and used the principles of triangulation, to Peter Higgs, who received the Nobel Prize in 2012 for his prediction of the Higgs boson. The essays expand on the science conveyed in each drawing and place that science in a broader cultural context. The essays are grouped into five sections: Antiquity, Middle Ages, Early Modern Period, Nineteenth Century, and Twentieth Century and Beyond. Each essay stands alone and requires no background in physics or mathematics.
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Book chapters on the topic "Stance prediction"

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Lozhnikov, Nikita, Leon Derczynski, and Manuel Mazzara. "Stance Prediction for Russian: Data and Analysis." In Advances in Intelligent Systems and Computing, 176–86. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14687-0_16.

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Rivadulla, Andrés. "Prediction and Explanation by Theoretical Models: An Instrumentalist Stance." In Logic, Epistemology, and the Unity of Science, 235–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65802-1_11.

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Elzanfaly, Doaa S., Zeyad Radwan, and Nermin Abdelhakim Othman. "User Stance Detection and Prediction Considering Most Frequent Interactions." In Artificial Intelligence and Online Engineering, 421–33. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17091-1_43.

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Price, Sarah L., and Louise S. Price. "Computational Polymorph Prediction." In Solid State Characterization of Pharmaceuticals, 427–50. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470656792.ch12.

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Ellyin, Fernand. "Constitutive laws for transient and stable behaviour of inelastic solids." In Fatigue Damage, Crack Growth and Life Prediction, 205–77. Dordrecht: Springer Netherlands, 1997. http://dx.doi.org/10.1007/978-94-009-1509-1_6.

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Wellers, Matthias, and Heinrich Rake. "Nonlinear Model Predictive Control Based on Stable Wiener and Hammerstein Models." In Nonlinear Model Predictive Control, 357–66. Basel: Birkhäuser Basel, 2000. http://dx.doi.org/10.1007/978-3-0348-8407-5_20.

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Gottipati, Swapna, Minghui Qiu, Liu Yang, Feida Zhu, and Jing Jiang. "Predicting User’s Political Party Using Ideological Stances." In Lecture Notes in Computer Science, 177–91. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03260-3_16.

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Schwan, Constanze, and Wolfram Schenck. "Visual Movement Prediction for Stable Grasp Point Detection." In Proceedings of the 21st EANN (Engineering Applications of Neural Networks) 2020 Conference, 70–81. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48791-1_5.

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Zheng, Jian, Xin Hua Ni, and Zhan Jun Yao. "Stiffness Prediction of Nano-Fibers Composite Ceramics." In Solid State Phenomena, 1171–74. Stafa: Trans Tech Publications Ltd., 2007. http://dx.doi.org/10.4028/3-908451-30-2.1171.

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Bazan, Jan G., Stanislawa Bazan-Socha, Sylwia Buregwa-Czuma, Przemyslaw Wiktor Pardel, and Barbara Sokolowska. "Prediction of Coronary Arteriosclerosis in Stable Coronary Heart Disease." In Communications in Computer and Information Science, 550–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31715-6_58.

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Conference papers on the topic "Stance prediction"

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Padnekar, S. Meena, G. Santhosh Kumar, and P. Deepak. "BiLSTM-Autoencoder Architecture for Stance Prediction." In 2020 International Conference on Data Science and Engineering (ICDSE). IEEE, 2020. http://dx.doi.org/10.1109/icdse50459.2020.9310133.

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Fang, Wei, Moin Nadeem, Mitra Mohtarami, and James Glass. "Neural Multi-Task Learning for Stance Prediction." In Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER). Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/d19-6603.

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Mayfield, Elijah, and Alan Black. "Stance Classification, Outcome Prediction, and Impact Assessment:." In Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-2108.

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Sun, Yizhou. "User Stance Prediction via Online Behavior Mining." In the 26th International Conference. New York, New York, USA: ACM Press, 2017. http://dx.doi.org/10.1145/3041021.3051144.

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Khouja, Jude. "Stance Prediction and Claim Verification: An Arabic Perspective." In Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER). Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.fever-1.2.

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Hosseinia, Marjan, Eduard Dragut, and Arjun Mukherjee. "Stance Prediction for Contemporary Issues: Data and Experiments." In Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.socialnlp-1.5.

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Darwish, Kareem, Walid Magdy, and Tahar Zanouda. "Improved Stance Prediction in a User Similarity Feature Space." In ASONAM '17: Advances in Social Networks Analysis and Mining 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3110025.3110112.

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Nix, Stephanie, Kana Koishi, Hirokazu Madokoro, Takashi K. Saito, and Kazuhito Sato. "Prediction of Dangerous Pedestrians using Depth and Stance Estimation." In 2022 22nd International Conference on Control, Automation and Systems (ICCAS). IEEE, 2022. http://dx.doi.org/10.23919/iccas55662.2022.10003829.

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Graells-Garrido, Eduardo, Ricardo Baeza-Yates, and Mounia Lalmas. "Every Colour You Are: Stance Prediction and Turnaround in Controversial Issues." In WebSci '20: 12th ACM Conference on Web Science. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394231.3397907.

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Qiu, Minghui, Yanchuan Sim, Noah A. Smith, and Jing Jiang. "Modeling User Arguments, Interactions, and Attributes for Stance Prediction in Online Debate Forums." In Proceedings of the 2015 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611974010.96.

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Reports on the topic "Stance prediction"

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Cambanis, S., and A. G. Miamee. On Prediction of Harmonizable Stable Processes. Fort Belvoir, VA: Defense Technical Information Center, July 1985. http://dx.doi.org/10.21236/ada161412.

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Соловйов, В. М., and В. В. Соловйова. Моделювання мультиплексних мереж. Видавець Ткачук О.В., 2016. http://dx.doi.org/10.31812/0564/1253.

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From the standpoint of interdisciplinary self-organization theories and synergetics analyzes current approaches to modeling socio-economic systems. It is shown that the complex network paradigm is the foundation on which to build predictive models of complex systems. We consider two algorithms to transform time series or a set of time series to the network: recurrent and graph visibility. For the received network designed dynamic spectral, topological and multiplex measures of complexity. For example, the daily values the stock indices show that most of the complexity measures behaving in a characteristic way in time periods that characterize the different phases of the behavior and state of the stock market. This fact encouraged to use monitoring and prediction of critical and crisis states in socio-economic systems.
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Lee, Kab Soo, Hwayong Kim, Seong-An Hong, and Hee Chun Lim. Prediction of temperature profile in MCFC stack. Office of Scientific and Technical Information (OSTI), December 1996. http://dx.doi.org/10.2172/460260.

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Rockwood, D. L., B. Yang, and K. W. Outcalt. Stand-yield prediction for managed Ocala sand pine. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station, 1997. http://dx.doi.org/10.2737/srs-rp-003.

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Rockwood, D. L., B. Yang, and K. W. Outcalt. Stand-yield prediction for managed Ocala sand pine. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station, 1997. http://dx.doi.org/10.2737/srs-rp-3.

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Milliff, Ralph F., Andrew M. Moore, and Hernan G. Arango. Ocean State Estimation and Prediction in the Intra-Americas Seas. Fort Belvoir, VA: Defense Technical Information Center, September 2006. http://dx.doi.org/10.21236/ada630983.

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Соловйов, Володимир Миколайович, Vladimir Saptsin, and Dmitry Chabanenko. Prediction of financial time series with the technology of high-order Markov chains. AGSOE, March 2009. http://dx.doi.org/10.31812/0564/1131.

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In this research the technology of complex Markov chains, i.e. Markov chains with a memory is applied to forecast the financial time-series. The high-order Markov chains can be simplified to first-order ones by generalizing the states in Markov chains. Considering the *generalized state* as the sequence of states makes a possibility to model high-order Markov chains like first-order ones. The adaptive method of defining the states is proposed, it is concerned with the statistic properties of price returns. The algorithm of prediction includes the next steps: (1) Generate the hierarchical set of time discretizations; (2) Reducing the discretiza- tion of initial data and doing prediction at the every time-level (3) Recurrent conjunction of prediction series of different discretizations in a single time-series. The hierarchy of time discretizations gives a possibility to review long-memory properties of the series without increasing the order of the Markov chains, to make prediction on the different frequencies of the series. The technology is tested on several time-series, including: EUR/USD Forex course, the World’s indices, including Dow Jones, S&P 500, RTS, PFTS and other.
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Piterbarg, L. I. Statistical and Stochastic Problems in Ocean Modeling and Prediction, Stage II. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada626590.

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Seale, Maria, Natàlia Garcia-Reyero, R. Salter, and Alicia Ruvinsky. An epigenetic modeling approach for adaptive prognostics of engineered systems. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41282.

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Prognostics and health management (PHM) frameworks are widely used in engineered systems, such as manufacturing equipment, aircraft, and vehicles, to improve reliability, maintainability, and safety. Prognostic information for impending failures and remaining useful life is essential to inform decision-making by enabling cost versus risk estimates of maintenance actions. These estimates are generally provided by physics-based or data-driven models developed on historical information. Although current models provide some predictive capabilities, the ability to represent individualized dynamic factors that affect system health is limited. To address these shortcomings, we examine the biological phenomenon of epigenetics. Epigenetics provides insight into how environmental factors affect genetic expression in an organism, providing system health information that can be useful for predictions of future state. The means by which environmental factors influence epigenetic modifications leading to observable traits can be correlated to circumstances affecting system health. In this paper, we investigate the general parallels between the biological effects of epigenetic changes on cellular DNA to the influences leading to either system degradation and compromise, or improved system health. We also review a variety of epigenetic computational models and concepts, and present a general modeling framework to support adaptive system prognostics.
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Alchanatis, Victor, Stephen W. Searcy, Moshe Meron, W. Lee, G. Y. Li, and A. Ben Porath. Prediction of Nitrogen Stress Using Reflectance Techniques. United States Department of Agriculture, November 2001. http://dx.doi.org/10.32747/2001.7580664.bard.

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Commercial agriculture has come under increasing pressure to reduce nitrogen fertilizer inputs in order to minimize potential nonpoint source pollution of ground and surface waters. This has resulted in increased interest in site specific fertilizer management. One way to solve pollution problems would be to determine crop nutrient needs in real time, using remote detection, and regulating fertilizer dispensed by an applicator. By detecting actual plant needs, only the additional nitrogen necessary to optimize production would be supplied. This research aimed to develop techniques for real time assessment of nitrogen status of corn using a mobile sensor with the potential to regulate nitrogen application based on data from that sensor. Specifically, the research first attempted to determine the system parameters necessary to optimize reflectance spectra of corn plants as a function of growth stage, chlorophyll and nitrogen status. In addition to that, an adaptable, multispectral sensor and the signal processing algorithm to provide real time, in-field assessment of corn nitrogen status was developed. Spectral characteristics of corn leaves reflectance were investigated in order to estimate the nitrogen status of the plants, using a commercial laboratory spectrometer. Statistical models relating leaf N and reflectance spectra were developed for both greenhouse and field plots. A basis was established for assessing nitrogen status using spectral reflectance from plant canopies. The combined effect of variety and N treatment was studied by measuring the reflectance of three varieties of different leaf characteristic color and five different N treatments. The variety effect on the reflectance at 552 nm was not significant (a = 0.01), while canonical discriminant analysis showed promising results for distinguishing different variety and N treatment, using spectral reflectance. Ambient illumination was found inappropriate for reliable, one-beam spectral reflectance measurement of the plants canopy due to the strong spectral lines of sunlight. Therefore, artificial light was consequently used. For in-field N status measurement, a dark chamber was constructed, to include the sensor, along with artificial illumination. Two different approaches were tested (i) use of spatially scattered artificial light, and (ii) use of collimated artificial light beam. It was found that the collimated beam along with a proper design of the sensor-beam geometry yielded the best results in terms of reducing the noise due to variable background, and maintaining the same distance from the sensor to the sample point of the canopy. A multispectral sensor assembly, based on a linear variable filter was designed, constructed and tested. The sensor assembly combined two sensors to cover the range of 400 to 1100 nm, a mounting frame, and a field data acquisition system. Using the mobile dark chamber and the developed sensor, as well as an off-the-shelf sensor, in- field nitrogen status of the plants canopy was measured. Statistical analysis of the acquired in-field data showed that the nitrogen status of the com leaves can be predicted with a SEP (Standard Error of Prediction) of 0.27%. The stage of maturity of the crop affected the relationship between the reflectance spectrum and the nitrogen status of the leaves. Specifically, the best prediction results were obtained when a separate model was used for each maturity stage. In-field assessment of the nitrogen status of corn leaves was successfully carried out by non contact measurement of the reflectance spectrum. This technology is now mature to be incorporated in field implements for on-line control of fertilizer application.
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