Hasil untuk "q-fin.ST"

K. Sørensen, S. van den Broucke, J. Pelikan et al.

BackgroundSeveral measurement tools have been developed to measure health literacy. The tools vary in their approach and design, but few have focused on comprehensive health literacy in populations. This paper describes the design and development of the European Health Literacy Survey Questionnaire (HLS-EU-Q), an innovative, comprehensive tool to measure health literacy in populations.MethodsBased on a conceptual model and definition, the process involved item development, pre-testing, field-testing, external consultation, plain language check, and translation from English to Bulgarian, Dutch, German, Greek, Polish, and Spanish.ResultsThe development process resulted in the HLS-EU-Q, which entailed two sections, a core health literacy section and a section on determinants and outcomes associated to health literacy. The health literacy section included 47 items addressing self-reported difficulties in accessing, understanding, appraising and applying information in tasks concerning decisions making in healthcare, disease prevention, and health promotion. The second section included items related to, health behaviour, health status, health service use, community participation, socio-demographic and socio-economic factors.ConclusionsBy illuminating the detailed steps in the design and development process of the HLS-EU-Q, it is the aim to provide a deeper understanding of its purpose, its capability and its limitations for others using the tool. By stimulating a wide application it is the vision that HLS-EU-Q will be validated in more countries to enhance the understanding of health literacy in different populations.

Anderson Cs, E. Heeley, Y. Huang et al.

G. Aad, T. Abajyan, B. Abbott et al.

H. Srivastava, Choi Junesang

M. Aguilar, G. Alberti, B. Alpat et al.

M. Adams, S. Celniker, R. Holt et al.

Q. Fu, H. Saltsburg, M. Flytzani-Stephanopoulos

Steven R. Brown

Steven R. Brown

This primer serves two functions: (1) It is a simplified introduction to Q methodology, covering the topics of concourse, Q samples, Q sorting, correlation, factor analysis, theoretical rotation, factor scores, and factor interpretation. (2) It also illustrates different conceptions of Q methodology by taking the concept of "Q methodology" as the subject matter of the study. The factor results show how current understandings about Q are traceable to debates among Stephenson, Burt, and others in the 1930s, '40s, and '50s.

M. Aguilar, D. Aisa, B. Alpat et al.

A precise measurement of the proton flux in primary cosmic rays with rigidity (momentum/charge) from 1 GV to 1.8 TV is presented based on 300 million events. Knowledge of the rigidity dependence of the proton flux is important in understanding the origin, acceleration, and propagation of cosmic rays. We present the detailed variation with rigidity of the flux spectral index for the first time. The spectral index progressively hardens at high rigidities.

L. Kriston, I. Scholl, L. Hölzel et al.

Simon Watts, P. Stenner

K. Svore, Alan Geller, M. Troyer et al.

Quantum computing exploits quantum phenomena such as superposition and entanglement to realize a form of parallelism that is not available to traditional computing. It offers the potential of significant computational speed-ups in quantum chemistry, materials science, cryptography, and machine learning. The dominant approach to programming quantum computers is to provide an existing high-level language with libraries that allow for the expression of quantum programs. This approach can permit computations that are meaningless in a quantum context; prohibits succint expression of interaction between classical and quantum logic; and does not provide important constructs that are required for quantum programming. We present Q#, a quantum-focused domain-specific language explicitly designed to correctly, clearly and completely express quantum algorithms. Q# provides a type system; a tightly constrained environment to safely interleave classical and quantum computations; specialized syntax; symbolic code manipulation to automatically generate correct transformations of quantum operations; and powerful functional constructs which aid composition.

C. Westin, H. Knutsson, O. Pasternak et al.

Jonathan A Stefely, D. Pagliarini

Marcin Wątorek, Marija Bezbradica, Martin Crane et al.

Based on the cryptocurrency market dynamics, this study presents a general methodology for analyzing evolving correlation structures in complex systems using the $q$-dependent detrended cross-correlation coefficient ρ(q,s). By extending traditional metrics, this approach captures correlations at varying fluctuation amplitudes and time scales. The method employs $q$-dependent minimum spanning trees ($q$MSTs) to visualize evolving network structures. Using minute-by-minute exchange rate data for 140 cryptocurrencies on Binance (Jan 2021-Oct 2024), a rolling window analysis reveals significant shifts in $q$MSTs, notably around April 2022 during the Terra/Luna crash. Initially centralized around Bitcoin (BTC), the network later decentralized, with Ethereum (ETH) and others gaining prominence. Spectral analysis confirms BTC's declining dominance and increased diversification among assets. A key finding is that medium-scale fluctuations exhibit stronger correlations than large-scale ones, with $q$MSTs based on the latter being more decentralized. Properly exploiting such facts may offer the possibility of a more flexible optimal portfolio construction. Distance metrics highlight that major disruptions amplify correlation differences, leading to fully decentralized structures during crashes. These results demonstrate $q$MSTs' effectiveness in uncovering fluctuation-dependent correlations, with potential applications beyond finance, including biology, social and other complex systems.

Tony Wang, Kyle Feinstein, Sheryl Chen

We study how a central bank should dynamically set short-term nominal interest rates to stabilize inflation and unemployment when macroeconomic relationships are uncertain and time-varying. We model monetary policy as a sequential decision-making problem where the central bank observes macroeconomic conditions quarterly and chooses interest rate adjustments. Using publicly accessible historical Federal Reserve Economic Data (FRED), we construct a linear-Gaussian transition model and implement a discrete-action Markov Decision Process with a quadratic loss reward function. We chose to compare nine different reinforcement learning style approaches against Taylor Rule and naive baselines, including tabular Q-learning variants, SARSA, Actor-Critic, Deep Q-Networks, Bayesian Q-learning with uncertainty quantification, and POMDP formulations with partial observability. Notably, despite its simplicity, standard tabular Q-learning achieved the best performance (-615.13 +- 309.58 mean return), outperforming both enhanced RL methods and traditional policy rules. Our results suggest that while sophisticated RL techniques show promise for monetary policy applications, simpler approaches may be more robust in this domain, highlighting important challenges in applying modern RL to macroeconomic policy.

Florian Kittelmann, Pavel Sulimov, Kurt Stockinger

Classical and learned query optimizers (LQOs) use cardinality estimations as one of the critical inputs for query planning. Thus, accurately predicting the cardinality of arbitrary queries plays a vital role in query optimization. A recent boom in novel deep learning methods stimulated not only the rise of LQOs but also contributed to the appearance of learned cardinality estimators (LCEs). However, the majority of them are based on classical neural networks, ignoring that multivariate correlations between attributes across different tables could be naturally represented via entanglements in quantum circuits. In this paper, we introduce QardEst - Quantum Cardinality Estimator - a novel quantum neural network approach to estimate the cardinality of join queries. Our experiments conducted with a similar number of trainable parameters suggest that quantum neural networks executed on a quantum simulator outperform classical neural networks in terms of mean squared error as well as the q-error.

S. Hüttel, Sebastian Hess

The scientific production system is crucial in how global challenges are addressed. However, scholars have recently begun to voice concerns about structural inefficiencies within the system, as highlighted, for example, by the replication crisis, the p-value debate and various forms of publication bias. Most suggested remedies tend to address only partial aspects of the system's inefficiencies, but there is currently no unifying agenda in favour of an overall transformation of the system. Based on a critical review of the current scientific system and an exploratory pilot study about the state of student training, we argue that a unifying agenda is urgently needed, particularly given the emergence of artificial intelligence (AI) as a tool in scientific writing and the research discovery process. Without appropriate responses from academia, this trend may even compound current issues around credibility due to limited replicability and ritual-based statistical practice, while amplifying all forms of existing biases. Naïve openness in the science system alone is unlikely to lead to major improvements. We contribute to the debate and call for a system reform by identifying key elements in the definition of transformation pathways towards open, democratic and conscious learning, teaching, reviewing and publishing supported by openly maintained AI tools. Roles and incentives within the review process will have to adapt and be strengthened in relation to those that apply to authors. Scientists will have to write less, learn differently and review more in the future, but need to be trained better in and for AI even today.

Sunil Mithas, R. Rust