Hasil untuk "Information theory"

M. Abkemeier

S. Boucheron, G. Lugosi, P. Massart

Yazhen Wang

Quantum computation and quantum information are of great current interest in computer science, mathematics, physical sciences and engineering. They will likely lead to a new wave of technological innovations in communication, computation and cryptography. As the theory of quantum physics is fundamentally stochastic, randomness and uncertainty are deeply rooted in quantum computation, quantum simulation and quantum information. Consequently quantum algorithms are random in nature, and quantum simulation utilizes Monte Carlo techniques extensively. Thus statistics can play an important role in quantum computation and quantum simulation, which in turn offer great potential to revolutionize computational statistics. While only pseudo-random numbers can be generated by classical computers, quantum computers are able to produce genuine random numbers; quantum computers can exponentially or quadratically speed up median evaluation, Monte Carlo integration and Markov chain simulation. This paper gives a brief review on quantum computation, quantum simulation and quantum information. We introduce the basic concepts of quantum computation and quantum simulation and present quantum algorithms that are known to be much faster than the available classic algorithms. We provide a statistical framework for the analysis of quantum algorithms and quantum simulation.

Geoffrey G. Parker, Marshall W. Van Alstyne

Nikolay Raychev, I. Chuang

J. Shin, Sang Joon Kim

K. Burnham, David R. Anderson

P. M. Woodward

J. Walls, G. Widmeyer, O. E. Sawy

G. Tononi

BackgroundConsciousness poses two main problems. The first is understanding the conditions that determine to what extent a system has conscious experience. For instance, why is our consciousness generated by certain parts of our brain, such as the thalamocortical system, and not by other parts, such as the cerebellum? And why are we conscious during wakefulness and much less so during dreamless sleep? The second problem is understanding the conditions that determine what kind of consciousness a system has. For example, why do specific parts of the brain contribute specific qualities to our conscious experience, such as vision and audition?Presentation of the hypothesisThis paper presents a theory about what consciousness is and how it can be measured. According to the theory, consciousness corresponds to the capacity of a system to integrate information. This claim is motivated by two key phenomenological properties of consciousness: differentiation – the availability of a very large number of conscious experiences; and integration – the unity of each such experience. The theory states that the quantity of consciousness available to a system can be measured as the Φ value of a complex of elements. Φ is the amount of causally effective information that can be integrated across the informational weakest link of a subset of elements. A complex is a subset of elements with Φ>0 that is not part of a subset of higher Φ. The theory also claims that the quality of consciousness is determined by the informational relationships among the elements of a complex, which are specified by the values of effective information among them. Finally, each particular conscious experience is specified by the value, at any given time, of the variables mediating informational interactions among the elements of a complex.Testing the hypothesisThe information integration theory accounts, in a principled manner, for several neurobiological observations concerning consciousness. As shown here, these include the association of consciousness with certain neural systems rather than with others; the fact that neural processes underlying consciousness can influence or be influenced by neural processes that remain unconscious; the reduction of consciousness during dreamless sleep and generalized seizures; and the time requirements on neural interactions that support consciousness.Implications of the hypothesisThe theory entails that consciousness is a fundamental quantity, that it is graded, that it is present in infants and animals, and that it should be possible to build conscious artifacts.

B. C. Brookes

David N. Lee

J. Bettman

A. Bandura

V. Venkatesh, Michael G. Morris, G. Davis et al.

R. Yeung

韓 太舜, H. Koga

J. Chod, Evgeny Lyandres

This paper develops a theory of financing of entrepreneurial ventures via crypto tokens, which is not limited to platform-based ventures. We compare token financing with traditional equity financing, focusing on agency problems and information asymmetry frictions associated with the two financing methods, as well as on risk sharing between entrepreneurs and investors. Token financing introduces an agency problem not present under equity financing (underproduction), while mitigating an agency problem often associated with equity financing (entrepreneurial effort underprovision). Our theory abstracts from all institutional and potentially transient differences between tokens and equity and is based on a single intrinsic characteristic of tokens: they represent claims to a venture’s output. We show that tokens are likely to dominate equity for ventures developing goods or services that involve low marginal production costs, those for which entrepreneurial effort is crucial, and/or those with relatively low payoff volatility. In addition, tokens can have an advantage over equity in signaling venture quality to outside investors. This paper was accepted by Kay Giesecke, finance.

Nayoung Ryoo, Ji Yong Park, Chunghwee Lee et al.

Abstract Background Subjective cognitive decline (SCD) has been recognized as a preclinical stage of Alzheimer’s disease. However, the identification of early functional brain changes remains challenging. This study investigated the functional brain changes in SCD using longitudinal EEG and evaluate the feasibility of EEG features as scalable biomarkers for identifying amyloid burden and cognitive decline using an interpretable machine learning framework. Methods We analyzed 120 individuals with SCD enrolled in a multicenter prospective cohort (the CoSCo study) at baseline and after a 2-year follow-up. Participants were classified as amyloid-positive (A + SCD) or amyloid-negative (A − SCD). Spectral power and graph theory-based network analyses were conducted. Also, we trained machine learning classifiers to distinguish between the groups and interpreted the predictions of classifiers using SHAP. Results At both baseline and follow-up, the A + SCD group exhibited elevated low-frequency (delta and theta) activity and reduced alpha activity compared to the A − SCD group. The EEG-based classifiers distinguished A + SCD from A-SCD individuals with high performance, outperforming a classifier based on demographic data. The results of SHAP analysis confirmed the importance and relative contribution of selected EEG features. Conclusions Longitudinal EEG, when combined with interpretable machine learning, can detect and track the functional alterations of brain related to amyloid pathology in preclinical AD. Our findings support the feasibility of EEG as a non-invasive, scalable, and sensitive biomarker for risk stratification, before overt cognitive impairment emerges. Trial registration This study was registered at the Clinical Research Information Service (CRIS) (cris.nih.go.kr/cris; # KCT0003397, Registration Date: December 21, 2018).

Selfira Salsabilla

Main Purpose - This study aims to analyze the influence of labor market considerations, subjective norms, self-efficacy-technical skills, and outcome expectations on the intention to become a tax consultant. Method - This study uses a quantitative method with the Structural Equation Modeling (SEM) approach. The research sample consisted of 170 students of the UII Tax Accounting study program. Main Findings - The results of the study indicate that students’ intention to become tax consultants is more influenced by subjective norms and outcome expectations. However, no influence of labor market considerations and self-efficacy of technical skills on the intention to become a tax consultant was found. Theory and Practical Implications - The results of the study show that the surrounding environment is a factor that influences becoming a tax consultant. Therefore, academics need to provide adequate information about the role of the current tax consultant profession. In addition, the results of this study can be a basis for companies that want to recruit tax consultants to provide compensation and rewards that are in accordance with their work. Novelty - This study elaborates on the Theory of Planned Behavior (TPB) and Social Cognitive Career Theory (SCCT) to explore students’ intentions to become tax consultants.