Degradation of transistor parameter values due to Negative Bias Temperature Instability (NBTI) has emerged as a major reliability problem in current and future technology generations. NBTI Aging of a Static Random Access Memory (SRAM) cell leads to a lower noise margin, thereby increasing the failure rate. The register file, which consists of an array of SRAM cells, can suffer from data loss, leading to a system failure. In this work, we study the source of NBTI stress in an architecture and physical register file. Based on our study, we modified the register file structure to reduce the NBTI degradation and improve the overall system reliability. Having evaluated new register file structures, we find that our techniques substantially improve reliability of the register files. The new register files have small overhead, while in some cases they provide saving in area and power.

In a controlled lab experiment, we investigate hypothetical biases in the value of time by comparing stated preference (SP) and revealed preference (RP) values attached to unexpected waiting times. The SP and RP choice sets are identical in terms of design with the only difference being that the RP choices have real consequences in terms of unexpected waiting times and monetary incentives. We find a substantial hypothetical bias with the average SP value of time being only 71% of the corresponding RP value. The bias is mainly driven by participants who have scheduling constraints during the time of the unexpected wait. Scheduling constraints are taken into account to a much lesser extent in the SP setting than in the RP setting, presumably because only in the latter, the consequences of ignoring them are costly. We find evidence that this effect is stronger for persons with relatively low cognitive ability.

The purpose of this study is to examine the use of ERP and outputs by six decision-makers in one SME manufacturing organization and provide artifacts targeted to improve their pricing decisions. Through elaborated action design research, we collect data to diagnosis decision-makers concerns and identify decision making biases and errors. Using insights and collaboration, we design, implement and evaluate seven artifacts targeted to minimize four biases identified – overconfidence bias, optimistic bias, planning fallacy and representativeness. The data collected during the diagnosis phase reveals that concerns fell into three primary themes: data, human interfaces, and cognitive bias. The seven combined artifacts implemented have a positive impact minimizing bias in this organization. This research reveals how artifacts such as policies, procedures, processes, reports and system modules help SME decision-makers mitigate cognitive biases and errors. Additionally, this study confirms that the eADR process can be an effective means of implementing incremental changes, evaluating impacts and increasing engagement in this environment. Limitations of this study include concurrent introduction of artifacts, single SME organization and embedded nature of the researcher.

Consistency modeling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of classification or clustering on a training set was often found very different from the same operations on a testing set. Here, the issue is addressed as a consistency problem. In practice, the inconsistency of microarray datasets prevents many typical gene selection methods working properly for cancer diagnosis and prognosis. In an attempt to deal with this problem, a new concept of performance-based consistency is proposed in this thesis.An interesting finding in our previous experiments is that by using a proper set of informative genes, we significantly improved the consistency characteristic of microarray data. Therefore, how to select genes in terms of consistency modelling becomes an interesting topic. Many previously published gene selection methods perform well in the cancer diagnosis domain, but questions are raised because of the irreproducibility of experimental results. Motivated by this, two new gene selection methods based on the proposed performance-based consistency concept, GAGSc (Genetic Algorithm Gene Selection method in terms of consistency) and LOOLSc (Leave-one-out Least-Square bound method with consistency measurement) were developed in this study with the purpose of identifying a set of informative genes for achieving replicable results of microarray data analysis.The proposed consistency concept was investigated on eight benchmark microarray and proteomic datasets. The experimental results show that the different microarray datasets have different consistency characteristics, and that better consistency can lead to an unbiased and reproducible outcome with good disease prediction accuracy.As an implementation of the proposed performance-based consistency, GAGSc and LOOLSc are capable of providing a small set of informative genes. Comparing with those traditional gene selection methods without using consistency measurement, GAGSc and LOOLSc can provide more accurate classification results. More importantly, GAGSc and LOOLSc have demonstrated that gene selection, with the proposed consistency measurement, is able to enhance the reproducibility in microarray diagnosis experiments.

Systemic lupus erythematosus (SLE) is a relatively rare autoimmune disease with no known aetiology or cure. In addition to numerous physical symptoms, those living with SLE have also been shown to experience significant emotional and psychosocial difficulties. There has been little psychological research into SLE despite the rapidly increasing interest in health psychology and quality of life issues over the last two decades. One such issue that has commanded particular attention is that of cognitive bias in individuals with chronic pain and/or chronic illness. Cognitive bias toward illness-related information is theorised to indicate the presence of an illness self-schema, and is a valuable tool of investigation as it permits access to a level of cognitive structure that is inaccessible via self-report instruments. The primary focus of the present study is to investigate recall bias for pain- and illness-related words in SLE patients. This bias is explored relative to the recall of neutral words and depression-related words, and also relative to the responses of rheumatoid arthritis (RA) patients and healthy controls. Two hypotheses are proposed: firstly, that bias is related to disease activity; and secondly, that bias is related to the combination of illness and depression. The findings provide support for the second hypothesis, with the additional caveat that the nature of the pain/illness stimuli used is important in determining the presence of cognitive bias. No recall bias for illness-related words as a whole was found in any of the groups, nor was there evidence of a recall bias in the SLE and RA patients when they were divided according to depression status. However, when the illness words were examined separately according to �sensory pain� and �disability-related� words, a clear bias for disability words was found in the depressed patient group. It is concluded that there is a relationship between depression in chronically ill individuals, and the way in which such individuals process disability-related words. In accordance with the schema-enmeshment model (Pincus & Morley, 2001), it is suggested that both a pain-schema and an illness-schema exist, and it is when these two schemas become enmeshed with the self-schema that depression occurs in chronic pain/chronically ill patients. The cognitive bias assessment paradigm adopted in this study-one that is typically used in similar investigations-is lengthy, requires sophisticated equipment and can be difficult to interpret on an individual level. The present study investigates the relationship between cognitive biases in SLE patients and a recently-developed task, PRISM, which appears to symbolise the enmeshment of illness-, pain- and self-schemas. Analyses confirmed that recall of negative illness words was the only independent predictor of PRISM scores. This suggests that PRISM, a quick and easy task to administer, may have considerable usefulness as a clinical tool to assess information relevant to the enmeshment of illness- and self-schema. A greater understanding of schema and the processing styles of chronically ill patients will allow for more effective psychological treatment such that quality of life can be improved.

<p>Att ha attityder grundade i rationella tankar uppfattas som mer önskvärd</p><p>än att ha attityder som är grundade i externa faktorer. Människor är</p><p>benägna att uppfatta individer som håller med dem som mer rationella</p><p>och mindre externt influerade i sina preferenser än individer som inte</p><p>håller med. Construal Level Theory förutsätter högre psykologisk</p><p>distans för med sig ökad bias. Högstatusindivider har en högre</p><p>psykologisk distans vilket leder till ökad bias. I studien undersöks hur</p><p>psykologisk distans och individens benägenhet att tolka världen naivt,</p><p>utifrån eget perspektiv, inverkar på mellangruppsattributioner.</p><p>Resultaten visar att deltagarna skattar preferenserna av de som har</p><p>samma åsikt i en fråga som mer rationella än dem som har motsatt åsikt,</p><p>och att maktposition leder till ökad bias. Studien har betydelse för</p><p>förståelsen om hur gruppmedlemmar interagerar med varandra, samt</p><p>hur oreflekterat innehavande av makt och status kan leda till felaktiga</p><p>tankemönster, vilka i sin tur kan leda till felaktiga beslut.</p>

<p>Control and diagnosis of complex systems demand accurate knowledge of certain quantities to be able to control the system efficiently and also to detect small errors. Physical sensors are expensive and some quantities are hard or even impossible to measure with physical sensors. This has made model-based estimation an attractive alternative.</p><p>Model-based estimators are sensitive to errors in the model and since the model complexity needs to be kept low, the accuracy of the models becomes limited. Further, modeling is hard and time consuming and it is desirable to design robust estimators based on existing models. An experimental investigation shows that the model deficiencies in engine applications often are stationary errors while the dynamics of the engine is well described by the model equations. This together with fairly frequent appearance of sensor offsets have led to a demand for systematic ways of handling stationary errors, also called bias, in both models and sensors.</p><p>In the thesis systematic design methods for reducing bias in estimators are developed. The methods utilize a default model and measurement data. In the first method, a low order description of the model deficiencies is estimated from the default model and measurement data, resulting in an automatic model augmentation. The idea is then to use the augmented model for estimator design, yielding reduced stationary estimation errors compared to an estimator based on the default model. Three main results are: a characterization of possible model augmentations from observability perspectives, an analysis of what augmentations that are possible to estimate from measurement data, and a robustness analysis with respect to noise and model uncertainty.</p><p>An important step is how the bias is modeled, and two ways of describing the bias are introduced. The first is a random walk and the second is a parameterization of the bias. The latter can be viewed as an extension of the first and utilizes a parameterized function that describes the bias as a function of the operating point of the system. The parameters, rather than the bias, are now modeled as random walks, which eliminates the trade-off between noise suppression in the parameter convergence and rapid change of the offset in transients. This is achieved by storing information about the bias in different operating points. A direct application for the parameterized bias is the adaptation algorithms that are commonly used in engine control systems.</p><p>The methods are applied to measurement data from a heavy duty diesel engine. A first order model augmentation is found for a third order model and by modeling the bias as a random walk, an estimation error reduction of 50\,\% is achieved for a European transient cycle. By instead letting a parameterized function describe the bias, simulation results indicate similar, or better, improvements and increased robustness.</p>

<p>Investors in the financial market are supposed to hold diversified portfolios to minimize their risk adjusted for expected return. However, several researchers have pointed out that most investors are over weighted in their home market. This means that most diversification happens in terms of choosing stocks in the home market which means that further possible diversification through international diversification is unused. One can therefore expect that foreign investors have preferences for securities with specific characteristics once they go abroad. An earlier study of the Swedish stock market over the years 1993-1997 has shown that foreign investors, in greater extent than domestic investors, have a preference for large firms, firms paying low dividend and firms with low leverage. With the steep up-turn of the Swedish stock market before the millennium and the down-turn in year 2000 in mind, this study examine whether the investment patterns between 1996 and 2005 are consistent with the results of earlier investigations. In general the results are consistent with earlier investigations. However, this study also shows that foreign investors seem to be more interested in choosing securities with relatively high fundamental value and lower level of leverage during market down-turns.</p>

Experimental willingness to pay (WTP) studies can be classified as hypothetical or non-hypothetical. In a hypothetical study, such as conjoint analysis, a subject does not need to make a real economic commitment. In contrast, in a non-hypothetical task such as in experimental auctions, a subject may need to actually buy the product. Subjects in hypothetical studies tend to overstate their true WTP. Consequently, researchers need to correct hypothetical values to obtain reliable WTP estimates. Recently, incentive-aligned and cheap talk approaches have been proposed as ways to correct for hypothetical bias. In a hypothetical task, a cheap talk script explicitly reminds the subject about the hypothetical nature of the task and its expected consequences. In an incentive-aligned task (non-hypothetical), subjects are randomly selected to physically buy the product. The objective of our study is to assess and compare the reduction of hypothetical bias in consumers’ willingness to pay for novel products by applying a generic, short, and neutral cheap talk script in a retail setting. To accomplish this objective, we employ non-hypothetical, hypothetical, and hypothetical with cheap talk treatments in our experimental design. We conducted our experimental retail study using conjoint analysis and open-ended elicitation mechanisms, utilizing Becker DeGroot Marshak (BDM) mechanism for the incentive-aligned treatments. Consistently in both elicitation mechanisms, using seemingly unrelated and random-effect Tobit techniques, we find that our cheap talk script is effective in eliminating the hypothetical bias. As expected, the hypothetical WTP values are significantly higher than the non-hypothetical values but the hypothetical values with cheap talk are not significantly different from incentive-aligned or non-hypothetical estimates. In addition, we find that open-ended estimates are significantly higher than conjoint analysis estimates and that emotions and familiarity can have significant impacts on WTP estimates.

This study investigates whether market learning explains the absence of the accrual anomaly in recent years by examining three conditions associated with the presence of the anomaly in prior research: (i) a differential relation between future earnings and cash flows versus accruals, (ii) incorrect weighting of cash flows and accruals by investors when predicting earnings, and (iii) association of earnings forecast errors with returns. All of these conditions are widely documented in the anomaly period. In the no-anomaly period, I continue to find a differential relation of cash flows and accruals with future earnings. However, investors appear to correctly weight accruals and cash flows in their earnings predictions implicit in beginning-of-year security prices, consistent with learning. This study also investigates whether improvements in analyst forecasts contribute to investor learning and the absence of the anomaly. The association between analyst optimism and accruals is weaker in the no-anomaly period, but is still statistically significant. Furthermore, the anomaly ended simultaneously for firms followed by analysts and for non-followed firms, suggesting that improvements in analyst forecasts alone cannot account for improved market efficiency with respect to accruals. The results suggest that the anomaly was similar for firms held by institutional investors and for firms with no institutional holdings before the discovery of the anomaly while the anomaly ended sooner for held firms than for non-held firms after the discovery of the anomaly, consistent with the conjecture that arbitrage by institutional investors reduce the anomaly. Overall, the findings are consistent with market learning and suggest that improvement in investors' interpretation of accruals after the discovery of the anomaly explains the end of the anomaly. This improvement in investor learning is not due to changes in analysts' forecasting behavior, however.