Regulating the Unregulated: The Beginning of the End of a Laissez-Faire Era of the Crypto WIld West

The crypto market has been left largely unregulated on a global scale for over a decade. 1 Recently, multiple jurisdictions are aligning efforts to tame the increasingly volatile crypto “Wild West” as evidenced by the influx of forthcoming legislations, consultations between operators and regulators, and regulatory crackdowns. 2 A cross-comparative analysis of the regulatory framework in the United States, the European Union, and Korea indicates that the proposed scopes of legislations cover an expansive breadth of assets. However, there are further needs for supplementary regulations following the enactment of the newly proposed regulations to close certain critical gaps that remain unaddressed as well as elucidating the scope of application with respect to ancillary and incidental actors in the industry. Moreover, this piece notes that while cross-border and cross-sector crypto transactions require a globally coordinated enforcement scheme, regulatory initiatives should concurrently reflect the unique business and regulatory climates and practices of each jurisdiction

Domain Adaptation for Time Series Under Feature and Label Shifts

Unsupervised domain adaptation (UDA) enables the transfer of models trained on source domains to unlabeled target domains. However, transferring complex time series models presents challenges due to the dynamic temporal structure variations across domains. This leads to feature shifts in the time and frequency representations. Additionally, the label distributions of tasks in the source and target domains can differ significantly, posing difficulties in addressing label shifts and recognizing labels unique to the target domain. Effectively transferring complex time series models remains a formidable problem. We present Raincoat, the first model for both closed-set and universal domain adaptation on complex time series. Raincoat addresses feature and label shifts by considering both temporal and frequency features, aligning them across domains, and correcting for misalignments to facilitate the detection of private labels. Additionally, Raincoat improves transferability by identifying label shifts in target domains. Our experiments with 5 datasets and 13 state-of-the-art UDA methods demonstrate that Raincoat can improve transfer learning performance by up to 16.33% and can handle both closed-set and universal domain adaptation.Comment: Accepted by ICML 2023; 29 pages (14 pages main paper + 15 pages supplementary materials). Code: see https://github.com/mims-harvard/Raincoa

Boosting Multi-Core Reachability Performance with Shared Hash Tables

This paper focuses on data structures for multi-core reachability, which is a key component in model checking algorithms and other verification methods. A cornerstone of an efficient solution is the storage of visited states. In related work, static partitioning of the state space was combined with thread-local storage and resulted in reasonable speedups, but left open whether improvements are possible. In this paper, we present a scaling solution for shared state storage which is based on a lockless hash table implementation. The solution is specifically designed for the cache architecture of modern CPUs. Because model checking algorithms impose loose requirements on the hash table operations, their design can be streamlined substantially compared to related work on lockless hash tables. Still, an implementation of the hash table presented here has dozens of sensitive performance parameters (bucket size, cache line size, data layout, probing sequence, etc.). We analyzed their impact and compared the resulting speedups with related tools. Our implementation outperforms two state-of-the-art multi-core model checkers (SPIN and DiVinE) by a substantial margin, while placing fewer constraints on the load balancing and search algorithms.Comment: preliminary repor

Forecasting financial asset price movements using convolutional neutral networks – application to the U.S. financial services sector and comparisson across industries

This thesis explores the applicability of CNNs as a price movement forecasting tool for ETFs, using a technical analysis approach and three different image encoding techniques. After developing a general methodology, the thesis focuses on the application to the U.S. financial services sector. Subsequently, the research draws comparisons to results obtained for other U.S. sector ETFs using the same model approach. Overall results show that the CNN models, while proving some potential and exceeding a random model in accuracy, show significant weaknesses for all industries in predicting Buy and Sell signals. Addressing these weaknesses, limitations of the approach are explored to suggest methods for model performance improvements

Applicability to a pre-design tool of analytical models on the impact of composite laminates

Development of composite materials has caused that innovative features and capabilities are being sought for their use in new technology applications. Catastrophic events, like high velocity impacts, are likely to appear in elds where composite materials are being lately applied. Lightweight headgear for modern military ground forces, structural shielding for a knew orbiter spacecraft, or blades intended for high-e ciency wind turbines, are just some examples. The dynamic response of composite materials has become an essential knowledge. New set of tools need to be developed in order to spread this knowledge and decrease design cost. As compared to experimental testing and numerical methods, analytical models stand out as the cheapest and fastest way to provide preliminary results that successfully assist early design stages. The main goal of this project relies on laying the basics for the development of a predesign tool on the implementation of this latter type of models. This tool will support the early design of composite material structural components likely subjected to impact situations. During the last three decades, a great number of analytical models have being developed intended to theoretically predict the behavior under impact of composite materials. The present work covers the basics ndings on the eld. Two models, based on the impact over carbon ber, and glass and polymer ber composites, are selected and thoroughly described in order to evaluate their applicability into a pre-design tool. Description of both models are provided with the needed background and theoretical foundations. In order to evaluate the applicability of analytical models, a simple requirements-based method is presented. It is based on three broad requirements: good predictions, broad applicability and event sensitivity. Both analytical models are evaluated against these requirements. Results of this evaluation show how the rst model (carbon ber model) reproduces one speci c case with accuracy. In spite of this good agreement, there is no possibility to reproduce further environments. This makes the model non optimal to be implemented into a design tool. The second model (glass and polymer ber model) appears to successfully meet the three established requirements. Finally, some guidelines in the development of future analytical models under the method here described, and some possible paths to be followed in the implementation of the pre-design tool, are presented.Ingeniería Industria

Value Creation through Co-Opetition in Service Networks

Well-defined interfaces and standardization allow for the composition of single Web services into value-added complex services. Such complex Web Services are increasingly traded via agile marketplaces, facilitating flexible recombination of service modules to meet heterogeneous customer demands. In order to coordinate participants, this work introduces a mechanism design approach - the co-opetition mechanism - that is tailored to requirements imposed by a networked and co-opetitive environment