Incumbent Competition, Decision-Making, and Policy Choice

In the first paper, two politicians decide whether to follow what they believe the public wants or choose the option that secures their private gain. The public only rewards a politician when a policy is implemented, or an action that coincides with the public decision is chosen. Politicians with good decision-making abilities, under sufficiently high policy rewards and moderate private benefit, take the action that generates a public benefit, implementing the popular policy. Politicians with very poor decision-making abilities, give sufficiently high policy rewards, choose to implement a policy regardless of what the public want. Only popular policies are passed for salient issues.In the second paper, two incumbents each decide on an action to maximize their popularity. The choices are made in consideration of their beliefs on the popular and socially optimal choices. The paper looks at the types of policies passed for both salient and non-salient issues given different levels of clarity on public opinion. For salient issues, a divided public is better than a united but ill-informed one. For non-salient issues, policies are always passed when public opinion is clear, while politicians diverge strategically under low policy payoffs when public opinion is unclear.The third paper considers a model of lobbying where two opposing lobbyists vye for the support of a legislator with uncertain preferences. When uncertainty on legislator preference is low, lobbyists bid aggressively. When uncertainty is high, lobbyists bid conservatively. When the degree of uncertainty is moderate, we find asymmetric equilibria where one lobbyist chooses to either bid conservatively or aggressively, and the other just enough to ensure that the average bid is equal to the legislator’s integrity threshold

Combining NLP, speech recognition, and indexing. An AI-based learning assistant for higher education

This paper presents the ongoing development of HAnS (Hochschul-Assistenz-System), an Intelligent Tutoring System (ITS) designed to support self-directed digital learning in higher education. Initiated by twelve collaborating German universities and research institutes, HAnS is developed 2021–2025 with the goal of utilizing artificial intelligence (AI) and Big Data in academic settings to enhance technology-based learning. The system employs AI for speech recognition and the indexing of existing learning resources, enabling users to search and compile these materials based on various parameters. Here, we provide an overview of the project, showcasing how iterative design and development processes contribute to innovative educational research in the evolving field of AI-based ITS in higher education. Notwithstanding the potential of HAnS, we also deliberate upon the challenges associated with ensuring a suitable dataset for training the AI, refining complex algorithms for personalization, and maintaining data privacy. (DIPF/Orig.)In diesem Beitrag wird die laufende Entwicklung von HAnS (Hochschul-Assistenz-System) vorgestellt, einem Intelligenten Tutoring-System (ITS), das selbstgesteuertes digitales Lernen in der Hochschulbildung unterstützen soll. HAnS wurde von zwölf deutschen Hochschulen und Forschungsinstituten initiiert und wird 2021-2025 mit dem Ziel entwickelt, künstliche Intelligenz (KI) und Big Data im akademischen Umfeld zu nutzen, um technologiebasiertes Lernen zu verbessern. Das System nutzt KI für die Spracherkennung und die Indizierung vorhandener Lernressourcen und ermöglicht es den Nutzern, diese Materialien auf der Grundlage verschiedener Parameter zu suchen und zusammenzustellen. Hier geben wir einen Überblick über das Projekt und zeigen, wie iterative Design- und Entwicklungsprozesse zu innovativer Bildungsforschung auf dem sich entwickelnden Gebiet der KI-basierten ITS in der Hochschulbildung beitragen. Ungeachtet des Potenzials von HAnS gehen wir auch auf die Herausforderungen ein, die mit der Sicherstellung eines geeigneten Datensatzes für das Training der KI, der Verfeinerung komplexer Algorithmen für die Personalisierung und der Wahrung des Datenschutzes verbunden sind. (Autor

Tax Risk, Corporate Governance, and the Valuation of Tax Avoidance Across Philippine Firms: How Do Investors Value Corporate Tax Avoidance?

Tax avoidance has traditionally been thought to enhance firm value because it generates cash savings for reinvestment or distribution to shareholders. More recent literature, however, suggests that tax avoidance valuation may not be so simple. Desai and Dharmapala (2009) introduced the “agency perspective” on tax avoidance, arguing that investors consider the risk of tax avoidance as opening opportunities for managers to extract rents from their firms. Positive tax avoidance value would therefore be conditional on good corporate governance quality. Drake et al. (2017) introduced yet another dimension—tax risk—to the valuation of tax avoidance, arguing that tax avoidance that comes with less variability in tax outcomes (i.e., comes with lower tax risk) should be preferred to those that come with more because investors prefer stable earnings over risky earnings. This policy brief discusses our findings on how public investors in the Philippines value corporate tax avoidance in the contexts of tax risk and corporate governance quality, and policies that can be implemented to enhance firm transparency, increase tax revenues, and raise firm valuations

Crowdsourcing genomic analyses of ash and ash dieback – power to the people

Ash dieback is a devastating fungal disease of ash trees that has swept across Europe and recently reached the UK. This emergent pathogen has received little study in the past and its effect threatens to overwhelm the ash population. In response to this we have produced some initial genomics datasets and taken the unusual step of releasing them to the scientific community for analysis without first performing our own. In this manner we hope to ‘crowdsource’ analyses and bring the expertise of the community to bear on this problem as quickly as possible. Our data has been released through our website at oadb.tsl.ac.uk and a public GitHub repository

ZZ-boson polarization as a model-discrimination analyzer

Determining the spin of any new particle is critical in identifying the true theory among various extensions of the Standard Model (SM). The degree of $Z$-boson polarization in any two-body decay process $A\to B Z$ is sensitive to the spin assignments of two new particles $A$ and $B$. Considering all possible spin-0, 1/2 and 1 combinations in a renormalizable field theory, we demonstrate that $Z$-boson polarization can indeed play a role of a decisive and universal analyzer in distinguishing the different spin assignments.Comment: 10 pages, 3 figures, 1 tabl

The Ctf18 RFC-like complex positions yeast telomeres but does not specify their replication time

Peer reviewedPreprin

Analysis of the prostate cancer cell line LNCaP transcriptome using a sequencing-by-synthesis approach

BACKGROUND: High throughput sequencing-by-synthesis is an emerging technology that allows the rapid production of millions of bases of data. Although the sequence reads are short, they can readily be used for re-sequencing. By re-sequencing the mRNA products of a cell, one may rapidly discover polymorphisms and splice variants particular to that cell. RESULTS: We present the utility of massively parallel sequencing by synthesis for profiling the transcriptome of a human prostate cancer cell-line, LNCaP, that has been treated with the synthetic androgen, R1881. Through the generation of approximately 20 megabases (MB) of EST data, we detect transcription from over 10,000 gene loci, 25 previously undescribed alternative splicing events involving known exons, and over 1,500 high quality single nucleotide discrepancies with the reference human sequence. Further, we map nearly 10,000 ESTs to positions on the genome where no transcription is currently predicted to occur. We also characterize various obstacles with using sequencing by synthesis for transcriptome analysis and propose solutions to these problems. CONCLUSION: The use of high-throughput sequencing-by-synthesis methods for transcript profiling allows the specific and sensitive detection of many of a cell's transcripts, and also allows the discovery of high quality base discrepancies, and alternative splice variants. Thus, this technology may provide an effective means of understanding various disease states, discovering novel targets for disease treatment, and discovery of novel transcripts