This paper describes an approach to largescale modeling of sentiment analysis for the social sciences. The goal is to model relations between nation states through social media. Many cross-disciplinary applications of NLP involve making predictions (such as predicting political elections), but this paper instead focuses on a model that is applicable to broader analysis. Do citizens express opinions in line with their home country's formal relations? When opinions diverge over time, what is the cause and can social media serve to detect these changes? We describe several learning algorithms to study how the populace of a country discusses foreign nations on Twitter, ranging from state-of-theart contextual sentiment analysis to some required practical learners that filter irrelevant tweets. We evaluate on standard sentiment evaluations, but we also show strong correlations with two public opinion polls and current international alliance relationships. We conclude with some political science use cases.
A series of tetraphenyl meso-substituted porphyrins with either p-amino or p-carboxy substituents has been studied as dyes for standard dye-sensitized solar cells (DSSCs). The porphyrins with greater numbers of amino groups generally show greater efficiency, primarily due to higher photocurrents; open-circuit voltage and fill factors are comparable. The most efficient sensitizer was the trans disubstituted zinc porphyrin, with an overall solar energy conversion efficiency of 5.66%, slightly higher than the triamino zinc porphyrin at 5.18%. The improved efficiency is attributed to the well known push–pull effect in porphyrinsensitized DSSCs with electrondonating groups opposite to the electronwithdrawing anchoring (carboxy) groups. It is suggested that porphyrin derivatives with cis diaminophenyl groups show somewhat diminished efficiency due to difficulties with regeneration of the dye from its oxidized form.
To interpolate a supersparse polynomial with integer coefficients, two alternative approaches are the Prony-based "big prime" technique, which acts over a single large finite field, or the more recently-proposed "small primes" technique, which reduces the unknown sparse polynomial to many low-degree dense polynomials. While the latter technique has not yet reached the same theoretical efficiency as Prony-based methods, it has an obvious potential for parallelization. We present a heuristic "small primes" interpolation algorithm and report on a low-level C implementation using FLINT and MPI.
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