Conjuring the Moon

Conjuring The Moon wrestles with the question of why we as women still submit to norms created by men who can\u27t possibly understand our reality. Why should we support ideologies that claim to represent us while actively working against us? Why should we conform to a system that positions us as inessential Other? The speaker of this book aspires to liberate herself from such burdens. Conjuring the Moon encapsulates one woman\u27s search for the feminine divine within herself, her religion, and her environment; but as empowering as this search may be, it remains inextricably connected to her social and historical role as inessential Other. Narrated primarily from a first-person point-of-view, a consistent, evolving I, inspired by myself, the poems in this book present a woman longing for feminine divinity in a world that reduces her status to inessential Other

Number Representation With Varying Number of Bits

In a computer, usually, all real numbers are stored by using the same number of bits: usually, 8 bytes, i.e., 64 bits. This amount of bits enables us to represent numbers with high accuracy -- up to 19 decimal digits. However, in most cases -- whether we process measurement results or whether we process expert-generated membership degrees -- we do not need that accuracy, so most bits are wasted. To save space, it is therefore reasonable to consider representations with varying number of bits. This would save space used for representing numbers themselves, but we would also need to store information about the length of each number. In view of this, the first natural question is whether a varying-length representation can lead to a drastic decrease in needed computer space. Another natural question is related to the fact that while potentially, allowing number of bits which is not proportional to 8 bits per byte will save even more space, this would require a drastic change in computer architecture, since the current architecture is based on bytes. So will going from bytes to bits be worth it -- will it save much space? In this paper, we provide answers to both questions

Warmth of the Welcome: Immigration and Local Housing Returns

We study the effect of immigration on home values in the U.S. Applying a county-level instrument for immigration, we find that immigration increases local house price appreciation and decreases its within-county spatial dispersion. Our estimates suggest that, on average, a one percentage point in- crease in the immigrant share of the local population raises house price appreciation by approximately 7 percent and reduces the dispersion of housing return within a county by about 1.5 percentage points. We also show that such effects are strikingly heterogeneous across counties and appear to be deter- mined by local culture. Using several proxies for attitudes toward immigration at the county level, we find that immigration boosts housing returns and limits its spatial dispersion only in counties with residents who are younger, more educated, and less racially biased. Our findings highlight that the effect of immigration on home values (or the lack thereof) is highly contingent on natives’ attitudes toward immigrants

Update From Aristotle to Newton, from Sets to Fuzzy Sets, and from Sigmoid to ReLU: What Do All These Transitions Have in Common?

In this paper, we show that there is a -- somewhat unexpected -- common trend behind several seemingly unrelated historic transitions: from Aristotelian physics to modern (Newton\u27s) approach, from crisp sets (such as intervals) to fuzzy sets, and from traditional neural networks, with close-to-step-function sigmoid activation functions to modern successful deep neural networks that use a completely different ReLU activation function. In all these cases, the main idea of the corresponding transition can be explained, in mathematical terms, as going from the first order to second order differential equations

Why Bernstein Polynomials: Yet Another Explanation

In many computational situations -- in particular, in computations under interval or fuzzy uncertainty -- it is convenient to approximate a function by a polynomial. Usually, a polynomial is represented by coefficients at its monomials. However, in many cases, it turns out more efficient to represent a general polynomial by using a different basis -- of so-called Bernstein polynomials. In this paper, we provide a new explanation for the computational efficiency of this basis

Unleashing the Power of ChatGPT in Finance Research: Opportunities and Challenges

Natural language processing (NLP) technologies, such as ChatGPT, are revolutionizing various fields, including finance research. This article explores the multifaceted potential of ChatGPT as a transformative tool for finance researchers, highlighting the benefits, challenges, and novel insights it can offer to facilitate the research. We demonstrate applications in coding support, theoretical derivation, research idea assistance, and professional editing. A comparison of ChatGPT-3.5, ChatGPT-4, and Microsoft Bing reveals unique features and applicability. By discussing pitfalls and ethical concerns, we encourage responsible AI adoption and a comprehensive understanding of advanced NLP’s impact on finance research and practice

How to Gauge Inequality and Fairness: A Complete Description of All Decomposable Versions of Theil Index

In general, in statistics, the most widely used way to describe the difference between different elements of a sample if by using standard deviation. This characteristic has a nice property of being decomposable: e.g., to compute the mean and standard deviation of the income overall the whole US, it is sufficient to compute the number of people, mean, and standard deviation over each state; this state-by-state information is sufficient to uniquely reconstruct the overall standard deviation. However, e.g., for gauging income inequality, standard deviation is not very adequate: it provides too much weight to outliers like billionaires, and thus, does not provide us with a good understanding of how unequal are incomes of the majority of folks. For this purpose, Theil introduced decomposable modifications of the standard deviation that is now called Theil indices. Crudely speaking, these indices are based on using logarithm instead of the square. Other researchers found other another decomposable modifications that use power law. In this paper, we provide a complete description of all decomposable versions of the Theil index. Specifically, we prove that the currently known functions are the only one for which the corresponding versions of the Theil index are decomposable -- so no other decomposable versions are possible. A similar result was previously proven under the additional assumption of linearity; our proof shows that this result is also true in the general case, without assuming linearity

Assessing the Impact of Informal Sector Employment on Young Less-Educated Workers

In this paper, we develop a search and matching model that allows for two important channels through which participation in the informal sector may benefit young less- educated workers: (i) human capital accumulation, and (ii) employer screening. We calibrate our model using the ENOE, a Mexican household survey on income and labor dynamics. Using our calibrated model, we shed light on many unobservable characteristics of the Mexican labor market for young less-educated workers, most notably the di↵ering hiring standards for informal and formal jobs. Specifically, hiring standards for these workers are found to be substantially higher for formal versus informal positions, making these workers naturally flow from unemployment to the informal sector where they can gain skills and reduce the uncertainty about their abilities. We also conduct counterfactual policy experiments to assess how labor market reforms designed to limit the employment share of the informal sector impact young less-educated workers. While our results favor reducing regulations in the formal sector due to the policy’s positive effect on aggregate employment, the policy is still found to impede the development of skills within this vulnerable segment of the labor market