Publication Library
Experiments in Finance A survey of historical trends
Description: Experiments complement other methods in identifying causal relationships and measuring behavioral deviations from theoretical predictions. While the experimental method has long been central in many scientific disciplines, it was almost nonexistent in finance until the 1980s. To survey the development of experiments in finance, we compiled a comprehensive account of experimental studies published in the Journal of Finance, Journal of Financial Economics, Review of Financial Studies, Review of Finance, Journal of Quantitative and Financial Analysis, and Journal of Banking and Finance, and experimental finance studies published in the top 5 journals in economics. With this novel dataset, we identified historical trends in experimental finance. Since the first experiments were published in finance journals in the 1980s, especially in the last 20 years, the share of experimental publications in these journals has markedly increased. In this article, we report trends toward descriptive, individual decision, and field experiments.
Created At: 14 December 2024
Updated At: 14 December 2024
Topological Data Analysis and Machine Learning Theory
Description: Topological Data Analysis and Machine Learning Theory
Created At: 14 December 2024
Updated At: 14 December 2024
Topological Data Analysis
Description: Scientific data is often in the form of a finite set of noisy points, sampled from an unknown space, and embedded in a high-dimensional space. Topological data analysis focuses on recovering the topology of the sampled space. In this chapter, we look at methods for constructing combinatorial representations of point sets, as well as theories and algorithms for effective computation of robust topological invariants. Throughout, we maintain a computational view by applying our techniques to a dataset representing the conformation space of a small molecule.
Created At: 14 December 2024
Updated At: 14 December 2024
Transition to Post-Quantum Cryptography Standards
Description: This report describes NIST’s expected approach to transitioning from quantum-vulnerable cryptographic algorithms to post-quantum digital signature algorithms and key-establishment schemes. It identifies existing quantum-vulnerable cryptographic standards and the quantum resistant standards to which information technology products and services will need to transition. It is intended to foster engagement with industry, standards organizations, and relevant agencies to facilitate and accelerate the adoption of post-quantum cryptography.
Created At: 14 December 2024
Updated At: 14 December 2024
Adversarial Machine Learning A Taxonomy and Terminology of Attacks and Mitigations
Description: This NIST Trustworthy and Responsible AI report develops a taxonomy of concepts and defnes terminology in the feld of adversarial machine learning (AML). The taxonomy is built on surveying the AML literature and is arranged in a conceptual hierarchy that includes key types of ML methods and lifecycle stages of attack, attacker goals and objectives, and attacker capabilities and knowledge of the learning process. The report also provides corresponding methods for mitigating and managing the consequences of attacks and points out relevant open challenges to take into account in the lifecycle of AI systems. The terminology used in the report is consistent with the literature on AML and is complemented by a glossary that defnes key terms associated with the security of AI systems and is intended to assist non-expert readers. Taken together, the taxonomy and terminology are meant to inform other standards and future practice guides for assessing and managing the security of AI systems, by establishing a common language and understanding of the rapidly developing AML landscape.
Created At: 14 December 2024
Updated At: 14 December 2024