Optimizations for Wave-Controlled Metasurface-Based Reconfigurable Intelligent Surfaces

As the number of devices that require wireless access increases and the environment becomes more complex, Reconfigurable Intelligent Surfaces (RIS) -- structures that employ metasurfaces with tunable phase shifts -- provide the means to passively reflect incoming electromagnetic signals towards desired directions and away from others, where direct reception of the signal from the transmitter base station may not always be possible. This allows for a wider dynamic coverage of the environment while reducing the need to place more base station antennas for wireless communications. One difficulty with the implementation of RIS is its control -- to create the programmable phase shifts, each RIS element needs to have its own reflection coefficient determined by varying its impedance. This can be done by applying a specific voltage bias to a varactor diode connected to the element. Though, this becomes an issue when more RIS elements are added and controlled individually, as the wiring and configuration of the system become more complex. This research builds upon the idea of tuning the RIS elements using standing waves on transmission lines on the back of the RIS structure, and sampling the voltages using dedicated circuitry at each varactor diode location to create the controlled phase shifts. Different methods to sample the voltages by using envelope detectors or sample-and-hold circuits are explored. For each implementation, various analytical and nonlinear optimization algorithms are proposed to achieve desired radiation patterns both when steering power towards multiple intended receiver directions, and when forming nulls in other directions. The performances of these algorithms are analyzed through simulations, verifying their feasibility in practical RIS settings where convergence speed and accurate radiation patterns are critical. Finally, the scalability of wave-controlled RIS structures is analyzed with options to use machine learning algorithms to adapt the RIS to the environment and minimize the number of calculations required over time to generate desired beam patterns

FOREIGN AID AND TERRORISM: When is Aid Effective in Reducing Terror?

This research examines the effectiveness of foreign aid in reducing terrorism. To uncover the circumstances in which aid is more likely to decrease terrorism, I examine total and sector-specific aid along with twenty-seven indicators of socio-economic and political grievances that aid seeks to redress. The overall expectation is that sectoral aid targeted at addressing relevant needs in aid-recipient countries is more likely to impact terrorism negatively. To test this expectation, I conduct a cross-national, longitudinal analysis of 190 countries and territories over a twenty-year period, from 1990 to 2010. The results, reported in eleven negative binomial, dynamic regression models, largely confirm that certain types of sectoral aid become statistically significant negative predictors of terrorism when addressed at specific socio-economic or political grievances. Examples of sectoral aid exercising a negative impact on terrorism include education aid spent on tertiary school enrollment and on public spending on education, social services aid assisting with research and development expenditures, governance aid geared toward strengthening state control of corruption as well as twelve additional instances when sectoral aid targeted at specific needs is found to correlate negatively with terrorism in a statistically significant way. Theoretically and empirically, this dissertation bridges the current divide between studies examining the effects of aggregate, total aid on poverty and conflict and research focusing on disaggregated, sectoral aid and its impact on terrorist incidents. In addition to integrating and testing both types of aid within the same theoretical framework, this study adds a new parameter to the current scholarship on aid and terrorism by including a wide variety of societal and governmental level grievances and testing their influence on the impact that aid exercises on terrorism

Multivariate Public Key Cryptosystem from Sidon Spaces

A Sidon space is a subspace of an extension field over a base field in which the product of any two elements can be factored uniquely, up to constants. This paper proposes a new public-key cryptosystem of the multivariate type which is based on Sidon spaces, and has the potential to remain secure even if quantum supremacy is attained. This system, whose security relies on the hardness of the well-known MinRank problem, is shown to be resilient to several straightforward algebraic attacks. In particular, it is proved that the two popular attacks on the MinRank problem, the kernel attack, and the minor attack, succeed only with exponentially small probability. The system is implemented in software, and its hardness is demonstrated experimentally.Comment: Appeared in Public-Key Cryptography - PKC 2021, 24th IACR International Conference on Practice and Theory of Public Key Cryptograph

Managerial Overconfidence and Corporate Policies

Miscalibration is a standard measure of overconfidence in both psychology and economics. Although it is often used in lab experiments, there is scarcity of evidence about its effects in practice. We test whether top corporate executives are miscalibrated, and whether their miscalibration impacts investment behavior. Over six years, we collect a unique panel of nearly 7,000 observations of probability distributions provided by top financial executives regarding the stock market. Financial executives are miscalibrated: realized market returns are within the executives' 80% confidence intervals only 38% of the time. We show that companies with overconfident CFOs use lower discount rates to value cash flows, and that they invest more, use more debt, are less likely to pay dividends, are more likely to repurchase shares, and they use proportionally more long-term, as opposed to short-term, debt. The pervasive effect of this miscalibration suggests that the effect of overconfidence should be explicitly modeled when analyzing corporate decision-making.

Do Hedge Funds Manipulate Stock Prices?

We find evidence of significant price manipulation at the stock level by hedge funds on critical reporting dates. Stocks in the top quartile by hedge fund holdings exhibit abnormal returns of 30 basis points in the last day of the month and a reversal of 25 basis points in the following day. Using intraday data, we show that a significant part of the return is earned during the last minutes of the last day of the month, at an increasing rate towards the closing bell. This evidence is consistent with hedge funds’ incentive to inflate their monthly performance by buying stocks that they hold in their portfolios. Higher manipulations occur with funds that have higher incentives to improve their ranking relative to their peers and a lower cost of doing so.