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Register a new userA simple solution of the proton spin crisis and supersymmetry crisis
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Vladan Pankovic
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In this work we suggest a simple theoretical model of the proton able to effectively solve proton spin crisis. Within domain of applicability of this simple model proton consists only of two u quarks and one d quarks (two of which have spin opposite to proton and one identical to proton) and one neutral vector phi meson (with spin two times larger than proton spin and directed identically to proton spin). This model is in full agreement not only with existing DIS experiments, but also with spin and electric charge conservation as well as in a satisfactory agreement with rest mass-energy conservation (since phi meson mass is close to proton rest mass). Our model opens an interesting possibility of the solution of the quarks and leptons families problem (proton is not an absolutely non-strange particle, but only a particle with almost totally effectively hidden strange). Also we suggest a possible first step toward the solution of the supersymmetry crisis using so-called superexclusion principle. According to this principle usual particles (electron, neutrino,...) can exist actually and virtually, while their supersymmetric partners, s-particles (selectron, neutralino, ...) can exist (super)exclusively virtually but not actually.
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The proton spin crisis remains an unsolved problem in particle physics. The spin and angular momentum of the partons inside the proton are non-perturbative quantities in QCD which cannot be calculated by using the perturbative QCD (pQCD). In this paper we present the lattice QCD formulation to study the proton spin crisis. We derive the non-perturbative formula of the spin and angular momentum of the partons inside the proton from the first principle in QCD which can be calculated by using the lattice QCD method.
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Classification of crisis events, such as natural disasters, terrorist attacks and pandemics, is a crucial task to create early signals and inform relevant parties for spontaneous actions to reduce overall damage. Despite crisis such as natural disasters can be predicted by professional institutions, certain events are first signaled by civilians, such as the recent COVID-19 pandemics. Social media platforms such as Twitter often exposes firsthand signals on such crises through high volume information exchange over half a billion tweets posted daily. Prior works proposed various crisis embeddings and classification using conventional Machine Learning and Neural Network models. However, none of the works perform crisis embedding and classification using state of the art attention-based deep neural networks models, such as Transformers and document-level contextual embeddings. This work proposes CrisisBERT, an end-to-end transformer-based model for two crisis classification tasks, namely crisis detection and crisis recognition, which shows promising results across accuracy and f1 scores. The proposed model also demonstrates superior robustness over benchmark, as it shows marginal performance compromise while extending from 6 to 36 events with only 51.4% additional data points. We also proposed Crisis2Vec, an attention-based, document-level contextual embedding architecture for crisis embedding, which achieve better performance than conventional crisis embedding methods such as Word2Vec and GloVe. To the best of our knowledge, our works are first to propose using transformer-based crisis classification and document-level contextual crisis embedding in the literature.
We model the spreading of a crisis by constructing a global economic network and applying the Susceptible-Infected-Recovered (SIR) epidemic model with a variable probability of infection. The probability of infection depends on the strength of economic relations between the pair of countries, and the strength of the target country. It is expected that a crisis which originates in a large country, such as the USA, has the potential to spread globally, like the recent crisis. Surprisingly we show that also countries with much lower GDP, such as Belgium, are able to initiate a global crisis. Using the {it k}-shell decomposition method to quantify the spreading power (of a node), we obtain a measure of ``centrality as a spreader of each country in the economic network. We thus rank the different countries according to the shell they belong to, and find the 12 most central countries. These countries are the most likely to spread a crisis globally. Of these 12 only six are large economies, while the other six are medium/small ones, a result that could not have been otherwise anticipated. Furthermore, we use our model to predict the crisis spreading potential of countries belonging to different shells according to the crisis magnitude.
The Global Financial Crisis of 2008, caused by the accumulation of excessive financial risk, inspired Satoshi Nakamoto to create Bitcoin. Now, more than ten years later, Decentralized Finance (DeFi), a peer-to-peer financial paradigm which leverages blockchain-based smart contracts to ensure its integrity and security, contains over 702m USD of capital as of April 15th, 2020. As this ecosystem develops, it is at risk of the very sort of financial meltdown it is supposed to be preventing. In this paper we explore how design weaknesses and price fluctuations in DeFi protocols could lead to a DeFi crisis. We focus on DeFi lending protocols as they currently constitute most of the DeFi ecosystem with a 76% market share by capital as of April 15th, 2020. First, we demonstrate the feasibility of attacking Makers governance design to take full control of the protocol, the largest DeFi protocol by market share, which would have allowed the theft of 0.5bn USD of collateral and the minting of an unlimited supply of DAI tokens. In doing so, we present a novel strategy utilizing so-called flash loans that would have in principle allowed the execution of the governance attack in just two transactions and without the need to lock any assets. Approximately two weeks after we disclosed the attack details, Maker modified the governance parameters mitigating the attack vectors. Second, we turn to a central component of financial risk in DeFi lending protocols. Inspired by stress-testing as performed by central banks, we develop a stress-testing framework for a stylized DeFi lending protocol, focusing our attention on the impact of a drying-up of liquidity on protocol solvency. Based on our parameters, we find that with sufficiently illiquidity a lending protocol with a total debt of 400m USD could become undercollateralized within 19 days.
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