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Being an emotionally unaffected investor: evidence from Bitcoin
No full textAs one of the most prominent cryptocurrencies, Bitcoin has been at the forefront of a major revolution in the financial and technological sectors. This study utilizes data from social media to extract the emotional tendencies of investors in the Bitcoin market and analyze differences in investor behavior under various emotional features. We find that when investors exhibit reluctance (such as Sadness and Fear) to buy Bitcoin, it is the opportune moment to invest and achieve returns higher than expected. Conversely, when the emotional tone of investors becomes positive (such as Joy and Love), indicating a tendency to invest, we choose to avoid investing. Our research has also revealed that such emotional cues can assist in better predicting returns in the Bitcoin market. Analyzing market emotions contributes to a deeper understanding of market fluctuations and investor behavior. Our findings help stakeholders recognize the role of subjective emotions in the market and provide them with prudent investment advice: avoid relying excessively on the feelings of others, as this may trigger investment losse
Predictive norm optimal iterative learning control for high-performance formation control problem
No full textThis paper develops a predictive optimisationbased iterative learning control (ILC) strategy for the highperformance formation control problem in networked dynamical systems working repetitively. It avoids the need for exact model information in traditional methods and achieves high performance via a predictive framework incorporating a unique performance index that integrates both immediate and future performance. The proposed framework guarantees geometric convergence of the formation error norm to zero and is capable of handling both heterogeneous and non-minimum phase systems. A distributed implementation of the framework is developed using the Alternating Direction Method of Multipliers to guarantee the framework’s scalability for largescale networks. Rigorous convergence analysis and numerical examples are provided to confirm its effectiveness
Data-driven model predictive control for continuous-time systems
No full textWe present some preliminary ideas on a data-driven Model Predictive Control framework for continuous-time systems. We use Chebyshev polynomial orthogonal bases to represent system trajectories and subsequently develop a data-driven continuous-time version of the classical Model Predictive Control algorithm. We investigate the effects of the parameters in our framework with two numerical examples and draw comparison to model-driven MPC schemes
Unimodular JT gravity and de Sitter quantum cosmology
No full textIn this work, we show that a gauge-theoretic description of Jackiw-Teitelboim (JT) gravity naturally yields a Henneaux-Teitelboim (HT) unimodular gravity via a central extension of its isometry group, valid for both flat and curved two-dimensional spacetimes. HT gravity introduces a unimodular time canonically conjugate to the cosmological constant, serving as a physical time in quantum cosmology. By studying the mini-superspace reduction of HT2 gravity, the Wheeler-DeWitt equation becomes a Schrödinger-like equation, giving a consistent and unitary quantum theory. Analysis of the wavefunction's probability density reveals a quantum distribution for the scale factor a, offering a quantum perspective on the expansion and contraction of the universe. In this perspective, the possibility of reaching the singular point a=0 signals that topology change could occur. Finally, we give a consistent quantum description of unimodular time that aligns seamlessly with Page-Wootters formulation of quantum mechanics, where quantum correlations between unimodular time and JT gravity are studied in HT2 quantum cosmology.<br/
Bias correction in multiple systems estimation
No full textIf part of a population is hidden but two or more samples are available that each cover parts of this population, multiple systems estimation can be applied to estimate the size of this population. A problem is that these estimates suffer from finite-sample bias that can be substantial in case of a small sample or a small population size. This problem was recognized by Chapman, who derived his essentially unbiased Chapman-estimator for two samples. Because more than two samples may be required to correct for sample dependence, we propose a Generalized Chapman-estimator that can be applied with any number of samples. In a Monte Carlo experiment, this new estimator shows hardly any bias and has smaller standard errors than competing bias-reduced estimators. It is also compared to the usual maximum likelihood estimates for the case of estimating the number of homeless people in the Netherlands, where it shows notably different outcomes
Nietzsche on art as the good will to appearance
No full textNietzsche makes a number of remarks that suggest that he thinks that art and truth are antithetical – indeed that he thinks that the value of art lies in its falsification of aspects of the world that would otherwise prove unbearable. ‘Truth is ugly,’ he says: ‘We possess art lest we perish of the truth.’ But the argument of the present paper is that the falsification reading is unsustainable, and that if we attend to the notion of ‘appearance’ rather more attentively than Nietzsche himself always did, we can (a) read him as defending a plausible account of the relation between art and truth, rather than an unsustainable one, (b) recast the passages that have encouraged the falsification reading so that they lend support to the reading suggested here, and (c) show how the resultant account squares with, and indeed reinforces, Nietzsche's perspectivism.</p
First joint absorption and T<sub>e</sub>-based metallicity measured in a GRB host galaxy at z = 4.28 using <i>JWST</i>/NIRSpec
No full textWe present the first gamma-ray burst (GRB) host galaxy with a measured absorption line and electron temperature (Te) basedmetallicity, using the temperature sensitive [O III]λ4363 auroral line detected in the JWST/NIRSpec spectrum of the host ofGRB 050505 at redshift z = 4.28. We find that the metallicity of the cold interstellar gas, derived from the absorption lines inthe GRB afterglow, of 12 + log(O/H) ∼ 7.7 is in reasonable agreement with the temperature-based emission line metallicityin the warm gas of the GRB host galaxy, which has values of 12 + log(O/H) = 7.80±0.19 and 7.96±0.21 for two commonindicators. When using strong emission line diagnostics appropriate for high-z galaxies and sensitive to ionization parameter, wefind good agreement between the strong emission line metallicity and the other two methods. Our results imply that, for the hostof GRB050505, mixing between the warm and the cold interstellar medium along the line of sight to the GRB is efficient, andthat GRB afterglow absorption lines can be a reliable tracer of the metallicity of the galaxy. If confirmed with a large sample,this suggest that metallicities determined via GRB afterglow spectroscopy can be used to trace cosmic chemical evolution to theearliest cosmic epochs and in galaxies far too faint for emission line spectroscopy, even for JWST
Funding the Arts: Politics, Economics and Their Interplay in Public Policy, Andrew Pinnock (2024)
No full textUsing machine-assisted topic analysis to expedite thematic analysis of free-text data: Exemplar investigation of factors influencing health behaviours and wellbeing during the COVID-19 pandemic
No full textObjectives: investigate the use of machine learning to expedite thematic analysis of qualitative data concerning factors that influenced health behaviours and wellbeing during the COVID-19 pandemic.Design: qualitative investigation using Machine-Assisted Topic Analysis (MATA) of free-text data collected from a prospective cohort.Methods: free-text survey data (2177 responses from 762 participants) of influences on health behaviours and wellbeing were collected among UK participants recruited online, using Qualtrics at 3, 6, 12 and 24 months after the COVID-19 pandemic started. MATA, which employs structural topic modelling (STM), was used (in R) to discern latent topics within the responses. Two researchers independently labelled topics and collaboratively organized them into themes, with ‘sense checking’ from two additional researchers. Plots and rankings were generated, showing change in topic prevalence by time. Total researcher time to complete analysis was collated.Results: fifteen STM-generated topics were labelled and integrated into six themes: the influences of and impacts on (1) health behaviours, (2) physical health (3) mood and (4) how these interacted, partly moderated by (5) external influences of control and (6) reflections on wellbeing and personal growth. Topic prevalence varied meaningfully over time, aligning with changes in the pandemic context. Themes were generated (excluding write-up) with 20 h combined researcher time.Conclusions: MATA shows promise as a resource-saving method for thematic analysis of large qualitative datasets whilst maintaining researcher control and insight. Findings show the interconnection between health behaviours, physical health and wellbeing over the pandemic, and the influence of control and reflective processes
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