From Laboratory to Juryroom: A Review of Experiments on Jury Decision-Making

http://deepblue.lib.umich.edu/bitstream/2027.42/50911/1/136.pd

The Use of Responsibility Rules in Jury Decision-Making

http://deepblue.lib.umich.edu/bitstream/2027.42/50905/1/130.pd

Pycnogonids (Arthropoda: Pycnogonida) of Portofino, Ligurian Sea (North-Western Mediterranean Sea)

Pycnogonida collected monthly from September 2017 to August 2018 in the Portofino Marine Protected Area at 0\u20135 m depth were studied. A total of 499 specimens were collected, 457 of which were identified to species level. These were classified as belonging to 10 species: Achelia echinata*, Ascorhynchus castelli, Neotrygaeus communis*, Tanystylum conirostre*, Anoplodactylus angulatus, A. petiolatus, A. pygmaeus*, A. virescens, Callipallene phantoma and C. tiberi*. For five dominant species (those marked with an asterisk) the annual phenology was outlined. Four hundred and seventeen additional specimens, collected from the same area and depth range mainly during the 1970s and 1980s were identified to species level for completeness of information, leading to the addition of Pycnogonum pusillum and Endeis spinosa

BERT's sentiment score for portfolio optimization: a fine-tuned views in Black and Litterman model

In financial markets, sentiment analysis on natural language sentences can improve forecasting. Many investors rely on information extracted from newspapers or their feelings. Therefore, this information is expressed in their language. Sentiment analysis models classify sentences (or entire texts) with their polarity (positive, negative, or neutral) and derive a sentiment score. In this paper, we use this sentiment (polarity) score to improve the forecasting of stocks and use it as a new ‘‘view’’ in the Black and Litterman model. This score is related to various events (both positive and negative) that have affected some stocks. The sentences used to determine the scores are taken from articles published in Financial Times (an international financial newspaper). To improve the forecast using this average sentiment score, we use a Monte Carlo method to generate a series of possible paths for several trading hours after the article was published to discretize (or approximate) the Wiener measure, which is applied to the paths and returning an exact price as results. Finally, we use the price determined in this way to calculate a yield to be used as views in a new type of ‘‘dynamic’’ portfolio optimization, based on hourly prices. We compare the results by applying the views obtained, disregarding the sentiment and leaving the initial portfolio unchanged

Neural Network Contribute to Reverse Cryptographic Processes in Bitcoin Systems: attention on SHA256

Bitcoin is a digital currency created in January 2009 following the housing market crash that promises lower transaction fees than traditional online payment mechanisms. Though each bitcoin transaction is recorded in a public log, the names of buyers and sellers are never revealed. While that keeps bitcoin users' transactions private, it also lets them buy or sell anything without easily tracing it back to them. Bitcoin is based on cryptographic evidence, which therefore does not suffer from the weakness present in a model based on trust in guarantee authorities. The use of cryptography is of crucial importance in the Bitcoin system. In addition to maintaining data secrecy, in the case of Bitcoin, cryptography is used to make it impossible for anyone to spend money from another user's wallet. In our paper, we develop the idea that it is possible to reverse the cryptography process based on hash functions (one-way) through Machine Translation with neural networks. Assuming this hypothesis is true and considering some quantistic algorithms to decrypt certain types of hash functions, we will highlight their effects on the Bitcoin system