Reasoning with Streamed Uncertain Information from Unreliable Sources

Humans or intelligent software agents are increasingly faced with the challenge of making decisions based on large volumes of streaming information from diverse sources. Decision makers must process the observed information by inferring additional information, estimating its reliability, and orienting it for decision making. Processing streaming trust framework, when fact is getting created and inferred is a process in online mode and our paper works effciently in online mode. In online mode, someone initiates a query and gets an output based on the query. In this paper we have mainly shown that unstructured reports from unreliable and heterogeneous sources are processed to generate structured information in Controlled English. Uncertainty in the information is modelled using Subjective Logic that allows statistical inference over uncertain information. Trustworthiness of information is modelled and conflicts are resolved before fusion. This process is totally undertaken on streaming information resulting in new facts being inferred from incoming information which immediately goes through trust assessment framework and trust is propagated to the inferred fact. In this paper, we propose a comprehensive framework where unstructured reports are streamed from heterogeneous and potentially untrustworthy information sources. These reports are processed to extract valuable uncertain information, which is represented using Controlled Natural Language and Subjective Logic. Additional information is inferred using deduction and abduction operations over subjective opinions derived from the reports. Before fusing extracted and inferred opinions, the framework estimates trustworthiness of these opinions, detects conflicts between them, and resolve these conflicts by analysing evidence about the reliability of their sources. Lastly, we describe an implementation of the framework using International Technology Alliance (ITA) assets (Information Fabric Services and Controlled English Fact Store) and present an experimental evaluation that quantifies the efficiency with respect to accuracy and overhead of the proposed framework

Pay for Performance from Future Fund Flows: The Case of Private Equity

Lifetime incomes of private equity general partners are affected by their current funds’ performance through both carried interest profit sharing provisions, and also by the effect of the current fund’s performance on general partners’ abilities to raise capital for future funds. We present a learning-based framework for estimating the market-based pay for performance arising from future fundraising. For the typical first-time private equity fund, we estimate that implicit pay for performance from expected future fundraising is approximately the same order of magnitude as the explicit pay for performance general partners receive from carried interest in their current fund, implying that the performance-sensitive component of general partner revenue is about twice as large as commonly discussed. Consistent with the learning framework, we find that implicit pay for performance is stronger when managerial abilities are more scalable and weaker when current performance contains less new information about ability. Specifically, implicit pay for performance is stronger for buyout funds compared to venture capital funds, and declines in the sequence of a partnership’s funds. Our framework can be adapted to estimate implicit pay for performance in other asset management settings in which future fund flows and compensation depend on current performance.Private equity; Venture capital; Fundraising; Compensation; Incentives

Corneal Diseases in Cats: A Retrospective Study of 477 Cases (2015-2020)

Corneal diseases are common in cats. If not diagnosed and treated in time, they can cause vision loss or even eye loss.This retrospective study aimed to introduce corneal disorders in cats, briefly explaining the therapeutic management of these disorders, and exploring the possibility of breed, age, and sex predisposition. In the study, a total of 477 cats, referred to the clinics of Istanbul University-Cerrahpasa, Faculty of Veterinary Medicine, and Department of Surgery between 2015-2020 with ophthalmological complaints and diagnosed with and treated for corneal disorders, were retrospectively evaluated. The most commonly encountered corneal disease was corneal ulcers (n=208, 43.60%), followed in descending order by corneal sequestrum (n=71, 14.8%), and corneal opacities (n=57, 11.9%) due to infection-associated symblepharon. Overall prevalence rates of ulcerative keratitis and non-ulcerative keratitis were 59.6% and 35.9%, respectively, in the study’s entire cat population. The congenital corneal diseases, such as persistent pupillary membrane (PPM) and corneal opacity due to endothelial dystrophy and acquired corneal disorders, such as corneal degeneration, scarring, and endothelial degeneration, were less frequently monitored conditions. In this study, it was seen that some corneal diseases in cats are more common in cats of certain breeds and ages, and corneal diseases are diseases that can be treated with early diagnosis. It has been noted that certain diseases are of infectious origin and are more likely to be treatable conditions

Club Deals in Leveraged Buyouts

We analyze the pricing and characteristics of club deal leveraged buyouts (LBOs)—those in which two or more private equity partnerships jointly conduct an LBO. Using a comprehensive sample of completed LBOs of U.S. publicly traded targets conducted by prominent private equity firms, we find that target shareholders receive approximately 10% less of pre-bid firm equity value, or roughly 40% lower premiums, in club deals compared to sole-sponsored LBOs. This result is concentrated before 2006 and in target firms with low institutional ownership. These results are robust to controls for target and deal characteristics, including size, Q, measures of risk, and time and industry fixed effects. We find little support for benign motivations for club deals based on capital constraints, diversification motives, or the ability of clubs to obtain favorable debt amounts or prices, but it is possible that the lower pricing of club deals is an inadvertent byproduct of an unobserved benign motivation for club formation

Effective transfer entropy approach to information flow between exchange rates and stock markets

We investigate the strength and direction of information flow between exchange rates and stock prices in several emerging countries by the novel concept of effective transfer entropy (an alternative non-linear causality measure) with symbolic encoding methodology. Analysis shows that before the 2008 crisis, only low level interaction exists between these two variables and exchange rates dominate stock prices in general. During crisis, strong bidirectional interaction arises. In the post-crisis period, the strong interaction continues to exist and in general stock prices dominate exchange rates. © 2014 Elsevier Ltd. All rights reserved

The development of Bitcoin futures : exploring the interactions between cryptocurrency derivatives

We utilise a high-frequency analysis to investigate the period surrounding the establishment of two new futures contracts based on the performance of Bitcoin. Our analysis shows that there have been significant pricing effects sourced from both fraudulent and regulatory unease within the industry. While analysing breakpoints in efficiency, we verify the view that Bitcoin futures dominate price discovery relative to spot markets. However, we add to this research by finding that CBOE futures are found to be the leading source of informational flow when compared directly to their CME equivalent

Misclassification Risk and Uncertainty Quantification in Deep Classifiers

In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a classifier’s predictions and reduce the likelihood of acting on erroneous predictions. The second is a novel way to train the classifier such that erroneous classifications are biased towards less risky categories. We combine these two approaches in a principled way. While doing this, we extend evidential deep learning with pignistic probabilities, which are used to quantify uncertainty of classification predictions and model rational decision making under uncertainty.We evaluate the performance of our approach on several image classification tasks. We demonstrate that our approach allows to (i) incorporate misclassification cost while training deep classifiers, (ii) accurately quantify the uncertainty of classification predictions, and (iii) simultaneously learn how to make classification decisions to minimize expected cost of classification errors

Prima facie reasons to question enclosed intellectual property regimes and favor open-source regimes for germplasm

In principle, intellectual property protections (IPPs) promote and protect important but costly investment in research and development. However, the empirical reality of IPPs has often gone without critical evaluation, and the potential of alternative approaches to lend equal or greater support for useful innovation is rarely considered. In this paper, we review the mounting evidence that the global intellectual property regime (IPR) for germplasm has been neither necessary nor sufficient to generate socially beneficial improvements in crop plants and maintain agrobiodiversity. Instead, based on our analysis, the dominant global IPR appears to have contributed to consolidation in the seed industry while failing to genuinely engage with the potential of alternatives to support social goods such as food security, adaptability, and resilience. The dominant IPR also constrains collaborative and cumulative plant breeding processes that are built upon the work of countless farmers past and present. Given the likely limits of current IPR, we propose that social goods in agriculture may be better supported by alternative approaches, warranting a rapid move away from the dominant single-dimensional focus on encouraging innovation through ensuring monopoly profits to IPP holders

Analysis of cross-correlations between financial markets after the 2008 crisis

We analyze the cross-correlation matrix C of the index returns of the main financial markets after the 2008 crisis using methods of random matrix theory. We test the eigenvalues of C for universal properties of random matrices and find that the majority of the cross-correlation coefficients arise from randomness. We show that the eigenvector of the largest deviating eigenvalue of C represents a global market itself. We reveal that high volatility of financial markets is observed at the same times with high correlations between them which lowers the risk diversification potential even if one constructs a widely internationally diversified portfolio of stocks. We identify and compare the connection and cluster structure of markets before and after the crisis using minimal spanning and ultrametric hierarchical trees. We find that after the crisis, the co-movement degree of the markets increases. We also highlight the key financial markets of pre and post crisis using main centrality measures and analyze the changes. We repeat the study using rank correlation and compare the differences. Further implications are discussed. © 2013 Elsevier B.V. All rights reserved

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes