24 research outputs found
Diversification Across Mining Pools: Optimal Mining Strategies under PoW
Mining is a central operation of all proof-of-work (PoW) based
cryptocurrencies. The vast majority of miners today participate in "mining
pools" instead of "solo mining" in order to lower risk and achieve a more
steady income. However, this rise of participation in mining pools negatively
affects the decentralization levels of most cryptocurrencies. In this work, we
look into mining pools from the point of view of a miner: We present an
analytical model and implement a computational tool that allows miners to
optimally distribute their computational power over multiple pools and PoW
cryptocurrencies (i.e. build a mining portfolio), taking into account their
risk aversion levels. Our tool allows miners to maximize their risk-adjusted
earnings by diversifying across multiple mining pools which enhances PoW
decentralization. Finally, we run an experiment in Bitcoin historical data and
demonstrate that a miner diversifying over multiple pools, as instructed by our
model/tool, receives a higher overall Sharpe ratio (i.e. average excess reward
over its standard deviation/volatility).Comment: 13 pages, 16 figures. Presented at WEIS 201
Text-based Emotion Aware Recommender
We apply the concept of users' emotion vectors (UVECs) and movies' emotion
vectors (MVECs) as building components of Emotion Aware Recommender System. We
built a comparative platform that consists of five recommenders based on
content-based and collaborative filtering algorithms. We employed a Tweets
Affective Classifier to classify movies' emotion profiles through movie
overviews. We construct MVECs from the movie emotion profiles. We track users'
movie watching history to formulate UVECs by taking the average of all the
MVECs from all the movies a user has watched. With the MVECs, we built an
Emotion Aware Recommender as one of the comparative platforms' algorithms. We
evaluated the top-N recommendation lists generated by these Recommenders and
found the top-N list of Emotion Aware Recommender showed serendipity
recommendations.Comment: 13 pages, 8 tables, International Conference on Natural Language
Computing and AI (NLCAI2020) July25-26, London, United Kingdo
Global convergence of a primal-dual interior-point method for nonlinear programming
Many recent convergence results obtained for primal-dual interior-point methods for nonlinear programming, use assumptions of the boundedness of generated iterates. In this paper we replace such assumptions by new assumptions on the NLP problem, develop a modification of a primal-dual interior-point method implemented in software package LOQO and analyze convergence of the new method from any initial guess
Investigating Functional Roles for Positive Feedback and Cellular Heterogeneity in the Type I Interferon Response to Viral Infection
Secretion of type I interferons (IFN) by infected cells mediates protection against many viruses, but prolonged or excessive type I IFN secretion can lead to immune pathology. A proper type I IFN response must therefore maintain a balance between protection and excessive IFN secretion. It has been widely noted that the type I IFN response is driven by positive feedback and is heterogeneous, with only a fraction of infected cells upregulating IFN expression even in clonal cell lines, but the functional roles of feedback and heterogeneity in balancing protection and excessive IFN secretion are not clear. To investigate the functional roles for feedback and heterogeneity, we constructed a mathematical model coupling IFN and viral dynamics that extends existing mathematical models by accounting for feedback and heterogeneity. We fit our model to five existing datasets, reflecting different experimental systems. Fitting across datasets allowed us to compare the IFN response across the systems and suggested different signatures of feedback and heterogeneity in the different systems. Further, through numerical experiments, we generated hypotheses of functional roles for IFN feedback and heterogeneity consistent with our mathematical model. We hypothesize an inherent tradeoff in the IFN response: a positive feedback loop prevents excessive IFN secretion, but also makes the IFN response vulnerable to viral antagonism. We hypothesize that cellular heterogeneity of the IFN response functions to protect the feedback loop from viral antagonism. Verification of our hypotheses will require further experimental studies. Our work provides a basis for analyzing the type I IFN response across systems