Abstract One of the major features of financial reporting is conservatism. Accounting conservatism is traditionally defined by the adage “anticipate no profit, but anticipate all losses.” Accounting conservatism is manifested in two general but distinct ways. First, conservatism can be unconditional, meaning that the book value of net assets is understated due to predetermined accounting practices (e.g. immediate expensing of research and development expenditures as incurred). Second, conservatism can be conditional, meaning that the book value of assets is written down under sufficiently adverse circumstances, but not up under favorable circumstances (e.g. goodwill impairment rules). This dissertation focuses only on conditional conservatism. The purpose of this dissertation is to increase our understanding of conditional conservatism through three inter-related essays. These essays seek to answer the following research questions: (1) Are standard measures of conditional conservatism affected by the asymmetry in cash flows? (2) How does “bad news” contribute to the persistence of accruals and cash flows? (3) Do firms manage fair value based goodwill write-offs under Statement of Financial Accounting Standards No. 142 (SFAS 142)? (4) What are the economic consequences of SFAS 142 goodwill write-offs? Collectively, the empirical results of this dissertation further our understanding of the determinants and implications of conditional conservatism. The first essay demonstrates that the asymmetry in cash flows biases standard measures of conditional conservatism. The second and third essays are one of the first to assess conservatism using an individual accrual account, namely, SFAS 142 goodwill write-offs. The second essay examines the reliability of goodwill write-offs, while the third essay provides evidence on the economic consequences of goodwill write-offs. The findings of these two essays are important for the debate on whether fair value measurements in financial statements are appropriate.

In this paper limit theorems for the conditional distributions of linear test statistics are proved. The assertions are conditioned by the sigma-field of permutation symmetric sets. Limit theorems are proved both for the conditional distributions under the hypothesis of randomness and under general contiguous alternatives with independent but not identically distributed observations. The proofs are based on results on limit theorems for exchangeable random variables by Strasser and Weber. The limit theorems under contiguous alternatives are consequences of an LAN-result for likelihood ratios of symmetrized product measures. The results of the paper have implications for statistical applications. By example it is shown that minimum variance partitions which are defined by observed data (e.g. by LVQ) lead to asymptotically optimal adaptive tests for the k-sample problem. As another application it is shown that conditional k-sample tests which are based on data-driven partitions lead to simple confidence sets which can be used for the simultaneous analysis of linear contrasts. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Psychologists interested in the study of language find that people are faster at making decisions about words that are related than they are at making decisions about words that are not related – an effect called semantic priming. This phenomenon has largely only been document in laboratory settings using natural languages as contest and real words as stimuli. The current study explores the relation between the semantic priming effect and a laboratory procedure designed to give rise to performances that can be described as linguistic. Six adult participants learned to partition a collection of eight stimuli into two sets of four stimuli. Following this, the subjects showed the semantic priming effect within a set of stimuli but not across sets. These data suggest that it may be possible to study linguistic phenomenon in laboratory-based procedures allowing better control and the ability to ask very precise questions about linguistic functioning.

The present study sought to identify the effects that derived relational training had on simulated gambling behavior with slot machines. Prior to conducting any relational training procedures, participants' relational responses in each contextual condition was probed. Each of the four backgrounds/conditions (red, blue, green, yellow) were presented with various stimuli from differing stimulus classes. Twenty trials were presented with no feedback for correct responding. Upon completion of the probes, pretest preference assessments were administered with blue and red slot machines being concurrently available for twenty trials then green and yellow slot machines doing the same. Following the pretest preference assessment, relational training began, here stimuli from each of the stimulus classes were presented on screen for three of the contextual conditions with the blue contextual condition left out to assess for possible derived relational responses. After completion, the second set of relational probes and posttest preference assessments began to assess if relational training altered the response allocation of slot machines and accuracy of responding during probes. After relational training, all participants showed an increase in response allocation to the red slot machine and a decrease to the blue. The yellow slot machine was selected increasingly with three participants while green was selected more often with one individual, one staying at the same number, and two selecting it less than in the pretest.

The concept of unconditional self-love is fundamental to many theoretical perspectives in social science and is referred to periodically in the literature. This study addressed the problem that scientific literature refers to unconditional self-love, but does not clearly define it, differentiate it from other types of feelings that comprise self-esteem, measure it, or even attempt to substantiate its existence. The purpose of this study was to define, describe, and measure unconditional self-love so that it may be used in the treatment of problems caused by low self-esteem. To achieve this purpose, the concept of self-love was defined in relation to self-liking, separated from the other components of self-esteem, and operationalized through the creation of the Unconditional Self-Liking (USL) model and the Unconditional Self-Liking (USL) scale. The USL scale is a self-report questionnaire that simultaneously measures two variables in relation to one another. This was done using the intersect of the variables of personal success and self-liking as a measure of unconditionality across nine areas of self-identity. The USL model allows for the measurement and description of four primary types of self-liking: (a) conditional self-likers who like themselves only when they succeed, (b) unconditional self-dislikers who dislike themselves even when they succeed, (c) conditional self-dislikers who dislike themselves when they fail, and (d) unconditional self-likers who like themselves even when they fail. The USL scale was administered to a convenience sample of 164 undergraduate university students who also completed the Modified Barrett-Lennard Relationship Inventory (BLRI) and the 10-question Rosenberg Self-Esteem (RSE) scale. The subjects' responses on the three scales were compared. Major findings indicated that individual levels of self-liking varied between areas of self-identity. Moreover, the level of self-liking was not necessarily dependent on their level of success. Each of the corresponding measures of the three instruments showed positive correlations, except the measures of unconditionality. Finally, each of the four primary patterns of self-liking described by the USL model characterized some subjects. Findings support the concept of unconditional self- liking, which suggests that high levels of self-liking arc not limited to only the most capable, intelligent, talented, or attractive people.

No description available.

No description available.

In response to cardiac cell death from an injury, zebrafish, as opposed to mammals, are able to regenerate new heart cells without significant scar tissue. Heart attacks, a leading cause of death in the United States, leave behind substantial scar tissue that weakens the heart and leads to a greater chance of a repeated cardiac event. Many genes and major molecular pathways are highly conserved from fish all the way to humans; thus, understanding how the regenerative process works in zebrafish may provide insight into potential therapies for heart attacks in humans. However, we must first understand how heart regeneration occurs in zebrafish at the molecular level. From the time of injury to a zebrafish heart through the completion of regeneration, we want to build a regulatory network showing which genes are up- or down-regulated and how they are interconnected. Transcription factors, such as tcf21 and tbx20, bind to regulatory elements of DNA and can either upregulate or downregulate nearby genes. To build this gene regulatory network, scientists use a technique called ChIP-seq that can determine where in the genome these transcription factors bind. Nearby genes are potential targets of their regulation, and we can validate these enhancers by testing differences in expression using a fluorescent protein reporter construct. ChIP-seq requires high quality antibodies capable of specifically recognizing the transcription factor of interest. These are rarely available. Because each different antibody that is used requires validation and optimization for ChIP-seq, it is not easy to scale up the collection of data for different transcription factors. One way to get around these problems is to express a tagged version of the transcription factor. The tag is recognizable by the same antibody; however, expressing the tagged transcription factor in this manner almost inevitably results in higher than normal levels of expression, leading to false positives in the ChIP-seq data. Using CRISPR/Cas9 technology to target and modify specific sequences in the genome, we developed a novel method to add an epitope tag to these transcription factors at their endogenous loci. This allows us to run ChIP-seq experiments with the transcription factor at physiological levels of expression. We can also use the same antibody to eliminate repeated validation and optimization steps. We have successfully tagged two genes that may be involved in heart regeneration, tcf21 and tbx20. tcf21 is expressed in the developing epicardium and is required for the proper development of the branchial arches. tbx20 is expressed in the cardiomyocytes and is required for the proper development of the heart, and it has also been shown to be upregulated in response to injury in the zebrafish. With tbx20, we have performed a successful ChIP-seq experiment and have tested several promising target genes. It is difficult to test if either tcf21 or tbx20 is required for regeneration, as both of these genes are essential for development and mutants do not survive. The solution to this problem is to engineer the gene so that it can be turned off at a specific time and in a specific cell type. A common method of this in the mouse model utilizes the Cre/loxP system: two loxP sites flank a required segment of a gene, and the introduction of the enzyme Cre deletes the DNA between them. Until CRISPRs this was not feasible in zebrafish, which lacked an efficient method of targeted modification in the genome. We adapted our method for integrating epitope tags to add the two loxP sites in the genome. We have made and tested a fully conditional mutant for tbx20, and we have put in the first of the two loxP sites for tcf21. / Biology

On-Demand Mobility (ODM) is a concept to address congestion problems. Using electric aircraft and vertical take-off with limited landing (VTOL) capabilities, the ODM concept offers on demand transportation service between designated landing sites at a fraction of driving time. The purpose of this research is to estimate the potential ODM demand and understand the challenges of introducing ODM using the Northern California region (including major cities like San Francisco, Sacramento, and San Jose) as an area of study and a second, less rigorous analysis for the Washington-Baltimore region. A conditional logit model was developed to estimate mode choice behavior and to estimate ODM demand; presenting automobile and public transportation as the two competing modes to ODM. There are significant challenges associated with the service including ability to operate in bad weather, vehicle operating cost, siting and cost of landing sites, and overall public acceptance of small, remotely operated aircraft. Nine scenarios were run varying the input for a base fare, landing fare, cost per-passenger-mile, auto operational costs, and ingress (waiting) times. The results yielded sensitivity of demand to all these parameters and especially showed a great difference in demand when auto costs were decreased from the standard American Automobile Association (AAA) cost per mile to a likely, future auto operating cost. The challenge that aerospace engineers face is designing an aircraft capable of achieving lower operational costs. The results showed that in order for the ODM to be a competitive mode, the cost per passenger-mile should be kept at $1. / Master of Science

The R package coin implements a unified approach to permutation tests providing a huge class of independence tests for nominal, ordered, numeric, and censored data as well as multivariate data at mixed scales. Based on a rich and exible conceptual framework that embeds different permutation test procedures into a common theory, a computational framework is established in coin that likewise embeds the corresponding R functionality in a common S4 class structure with associated generic functions. As a consequence, the computational tools in coin inherit the exibility of the underlying theory and conditional inference functions for important special cases can be set up easily. Conditional versions of classical tests|such as tests for location and scale problems in two or more samples, independence in two- or three-way contingency tables, or association problems for censored, ordered categorical or multivariate data|can easily be implemented as special cases using this computational toolbox by choosing appropriate transformations of the observations. The paper gives a detailed exposition of both the internal structure of the package and the provided user interfaces along with examples on how to extend the implemented functionality. (authors' abstract)