Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Comparison and Consumer Behavior: When Feeling Richer or Poorer Than Others Is More Important Than Being So1

The present study investigates how households’ social comparisons of their economic situation affect purchase decisions. In structured telephone interviews, participants (n = 109) answered questions about purchases of durable goods and groceries. In line with the hypothesis, social comparisons had an effect on purchase decisions of durable goods when controlling for actual economic situation. Households that considered themselves to be worse off economically than others reported fewer purchases of durable goods, perceived the impact on their economy of their latest purchase to be greater, and planned purchases more carefully than did households that considered themselves better off economically than others. Also in line with the hypothesis, for purchases of groceries, households’ actual economic situation was more important than social comparisons. With limited economic resources, households prioritize and make decisions to satisfy their needs and wants. Households’ income and wealth set boundaries for how much they can consume. When making purchases, households therefore frequently raise the question of whether or not they can afford the purchase. How do households know what they can afford? Certainly, income and wealth constitute one basis for such an evaluation. Another basis is what other products and services households need or want to buy using the same limited resources. In the present study, we assume that still another basis is how households evaluate their own economic situation in relation to other households. The purpose is thus to shed light on how purchases of durable goods and groceries are influenced by households’ evaluations of their own economic situation in comparison with that of others.
Teacher Research and University Institutional Review Boards

The author combines a literature review with a theoretical analysis of the interface between teacher researchers and Institutional Review Boards in higher education. Maintaining that teacher researchers are “creators of knowledge” (Castle, 2006, p. 2), the article explores the lack of fit between insider research with an emic design and the requirements of IRBs, which are set up for objective, predetermined, outsider research. The article concludes with a list of questions for teacher researchers to use in self-interrogation regarding ethics including: basic questions about research purpose and participants, more complex questions about the research, and ongoing questions designed for examination over time, such as “What is my place in terms of power? My students' place? ” Also included are considerations (questions) for IRBs to use in evaluating teacher research proposals.
The Complexity of Optimal Auction Design

This dissertation provides a complexity-theoretic critique of Myerson's theorem, one of Mechanism Design's crown jewels, for which Myerson was awarded the 2007 Nobel Memorial Prize in Economic Sciences. This theorem gives a remarkably crisp solution to the problem faced by a monopolist wishing to sell a single item to a number of interested, rational bidders, whose valuations for the item are distributed independently according to some given distributions; the monopolist's goal is to design an auction that will maximize her expected revenue, while at the same time incentivizing the bidders to bid their true value for the item. Myerson solves this problem of designing the revenue-maximizing auction, through an elegant transformation of the valuation space, and a reduction to the problem of designing the social welfare-maximizing auction (i.e. allocating the item to the bidder who values it the most). This latter problem is well understood, and it admits a deterministic (i.e. the auctioneer does not have to flip any coins) and simple solution: the Vickrey (or second-price) auction. In the present dissertation we explore the trade-offs between the plausibility of this result and its tractability: First, we consider what happens as we shift away from the simple setting of Myerson to more complex settings, and, in particular, to the case of bidders with arbitrarily correlated valuations. Is a characterization as crisp and elegant as Myerson's still possible? In Chapter 2 we provide a negative answer: we show that, for three or more bidders, the problem of computing a deterministic, ex-post incentive compatible and individually rational auction that maximizes revenue is NP-complete –in fact, inapproximable. Even for the case of two bidders, where, as we show, the revenue-maximizing auction is easy to compute, it admits nonetheless no obvious natural interpretation a-la Myerson. Then, motivated by the subtle interplay between social welfare- and revenue-maximizing auctions implied by Myerson's theorem, we study the trade-off between those two objectives for various types of auctions. We show that, as one moves from the least plausible auction format to the most plausible one, the problem of reconciling revenue and welfare becomes less and less tractable. Indeed, if one is willing to settle for randomized solutions, then auctions that fare well with respect to both objectives simultaneously are possible, as shown by Myerson and Satterthwaite. For deterministic auctions on the other hand, we show in Chapter 3 that it is NP-hard to exactly compute the optimal trade-off (Pareto) curve between those two objectives. On the positive side, we show how this curve can be approximated within arbitrary precision for some settings of interest. Finally, when one is only allowed to use variants of the simple Vickrey auction, we show in Chapter 4 that there exist auctions that achieve constant factor approximations of the optimal revenue and social welfare simultaneously.
Please Scroll down for Article Iie Transactions Fully Sequential Selection Procedures with Control Variates Fully Sequential Selection Procedures with Control Variates

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. Fully sequential selection procedures have been developed in the field of stochastic simulation to find the simulated system with the best expected performance when the number of alternatives is finite. Kim and Nelson proposed the KN procedure to allow for unknown and unequal variances and the use of common random numbers. KN approximates the raw sum of differences between observations from two systems as a Brownian motion process with drift and uses a triangular continuation region to decide the stopping time of the selection process. In this paper new fully sequential selection procedures are derived that employ a more effective sum of differences, which is called a controlled sum. Two provably valid procedures and an approximate procedure are described. Empirical results and a realistic illustration are provided to compare the efficiency of these procedures with other procedures that solve the same problem. [Supplemental materials are available for this article. Go to the publisher's online edition of IIE Transactions for the following supplemental resources: Proofs and guidelines to choose appropriate parameters. ]
Zebrafish Tyrosine Hydroxylase 2 Gene Encodes Tryptophan Hydroxylase*

Background: Zebrafish tyrosine hydroxylase 2 (th2) has been considered as a marker of dopaminergic (DA) neurons in the investigation of DA neuron development and the pathological mechanism of Parkinson disease. Results: th2 is required for the synthesis of serotonin in vivo and has tryptophan hydroxylase activity in vitro. Conclusion: th2 should be considered as a marker gene of serotonergic neurons. Significance: This result facilitates the elucidation of zebrafish neural circuitry. The primary pathological hallmark of Parkinson disease (PD) is the profound loss of dopaminergic neurons in the substantia nigra pars compacta. To facilitate the understanding of the underling mechanism of PD, several zebrafish PD models have been generated to recapitulate the characteristics of dopaminergic (DA) neuron loss. In zebrafish studies, tyrosine hydroxylase 1 (th1) has been frequently used as a molecular marker of DA neurons. However, th1 also labels norepinephrine and epinephrine neurons. Recently, a homologue of th1, named tyrosine hydroxylase 2 (th2), was identified based on the sequence homology and subsequently used as a novel marker of DA neurons. In this study, we present evidence that th2 co-localizes with serotonin in the ventral diencephalon and caudal hypothalamus in zebrafish embryos. In addition, knockdown of th2 reduces the level of serotonin in the corresponding th2-positive neurons. This phenotype can be rescued by both zebrafish th2 and mouse tryptophan hydroxylase 1 (Tph1) mRNA as well as by 5-hydroxytryptophan, the product of tryptophan hydroxylase. Moreover, the purified Th2 protein has tryptophan hydroxylase activity comparable with that of the mouse TPH1 protein in vitro. Based on these in vivo and in vitro results, we conclude that th2 is a gene encoding for tryptophan hydroxylase and should be used as a marker gene of serotonergic neurons.
Evolution of a Pathogen: A Comparative Genomics Analysis Identifies a Genetic Pathway to Pathogenesis in Acinetobacter

Acinetobacter baumannii is an emergent and global nosocomial pathogen. In addition to A. baumannii, other Acinetobacter species, especially those in the Acinetobacter calcoaceticus-baumannii (Acb) complex, have also been associated with serious human infection. Although mechanisms of attachment, persistence on abiotic surfaces, and pathogenesis in A. baumannii have been identified, the genetic mechanisms that explain the emergence of A. baumannii as the most widespread and virulent Acinetobacter species are not fully understood. Recent whole genome sequencing has provided insight into the phylogenetic structure of the genus Acinetobacter. However, a global comparison of genomic features between Acinetobacter spp. has not been described in the literature. In this study, 136 Acinetobacter genomes, including 67 sequenced in this study, were compared to identify the acquisition and loss of genes in the expansion of the Acinetobacter genus. A whole genome phylogeny confirmed that A. baumannii is a monophyletic clade and that the larger Acb complex is also a well-supported monophyletic group. The whole genome phylogeny provided the framework for a global genomic comparison based on a blast score ratio (BSR) analysis. The BSR analysis demonstrated that specific genes have been both lost and acquired in the evolution of A. baumannii. In addition, several genes associated with A. baumannii pathogenesis were found to be more conserved in the Acb complex, and especially in A. baumannii, than in other Acinetobacter genomes; until recently, a global analysis of the distribution and conservation of virulence factors across the genus was not possible. The results demonstrate that the acquisition of specific virulence factors has likely contributed to the widespread persistence and virulence of A. baumannii. The identification of novel features associated with transcriptional regulation and acquired by clades in the Acb complex presents targets for better understanding the evolution of pathogenesis and virulence in the expansion of the genus.
Achieving energy efficient and secure communication in wireless sensor networks

Wireless sensor networks (WSN) are increasingly become viable solutions to many challenging problems and will successively be deployed in many areas in the future such as in environmental monitoring, business, and military applications. The huge challenge in WSN is due to inherent resource and computing constraints. Because the sensor nodes are battery powered, increasing the autonomous lifetime of a WSN is a challenging optimization problem. Transmission of data is one of the most energy expensive tasks a node undertakes - using data compression to reduce the number of bits sent reduces energy expended for transmission. Data compression which highly reduces the communication overhead by aggregating and compressing data packets is performed at intermediate sensor nodes. However, deploying new technology, without security in mind has often proved to be unreasonably dangerous. The problem of securing these networks emerges more and more as a hot topic. Symmetric key cryptography is commonly seen as infeasible on such networks. Public key cryptography has its own key distribution problem. In contrast to this prejudice this paper presents a method to increase the lifetime of a WSN by minimizing the energy cost of transporting information from a set of sources nodes to the sink nodes and for achieving security we have used a new public-key encryption technology called identity-based encryption (IBE) which allows to calculate a public key directly from a user's identity. By calculating public keys instead of generating them randomly, many of the difficulties that make encryption technology difficult to deploy and maintain are eliminated, making encrypted communications much easier to implement than in the past
Decreasing Impact of SLA Violations:A Proactive Resource Allocation Approachfor Cloud Computing Environments

User satisfaction as a significant antecedent to user loyalty has been highlighted by many researchers in market based literatures. SLA violation as an important factor can decrease users' satisfaction level. The amount of this decrease depends on user's characteristics. Some of these characteristics are related to QoS requirements and announced to service provider through SLAs. But some of them are unknown for service provider and selfish users are not interested to reveal them truly. Most the works in literature ignore considering such characteristics and treat users just based on SLA parameters. So, two users with different characteristics but similar SLAs have equal importance for the service provider. In this paper, we use two user's hidden characteristics, named willingness to pay for service and willingness to pay for certainty, to present a new proactive resource allocation approach with aim of decreasing impact of SLA violations. New methods based on learning automaton for estimation of these characteristics are provided as well. To validate our approach we conducted some numerical simulations in critical situations. The results confirm that our approach has ability to improve users' satisfaction level that cause to gain in profitability.
Improving Voltage Assignment by Outlier Detection and Incremental Placement

Design for low power has become a key requirement in today's SoC design, especially for mobile applications. Multi-Vdd is an effective method to reduce both leakage and dynamic power. In a multi-Vdd design, cells of different supply voltages are often grouped into a small number of voltage islands, in order to avoid complex power supply system and excessive amount of level shifters. Recently, Wu et al. (2005) proposed an elegant algorithm for voltage island grouping based on the physical proximity of the critical cells in a post- placement voltage assignment, and Wu et al. (2006) proposed an efficient algorithm for voltage assignment which not only meets timing but also forms good proximity of the critical cells. However, due to insufficient slack, a few isolated critical cells (called outliers) may still exist in the resulting voltage assignment, causing disproportionately expensive penalty to the final voltage island grouping. In this paper, we propose a novel approach to improve the voltage assignment by automatic outlier detection followed by incremental placement. The outlier detection is based on a modified algorithm for the facility location problem. The incremental placement is guided by setting proper constraints on the paths containing the detected outliers, such that the outliers can be eliminated later. Our experiments on industry designs show that our algorithm leads to 12% - 54% improvement in the final voltage island grouping, with quick turn around time.
New directions in IoT privacy using attribute-based authentication

The Internet of Things (IoT) is a ubiquitous system that incorporates not only the current Internet of computers, but also smart objects and sensors. IoT technologies often rely on centralised architectures that follow the current business models. This makes efficient data collection and processing possible, which can be beneficial from a business perspective, but has many ramifications for users privacy. As communication within the IoT happens among many devices from various contexts, they need to authenticate each other to know that they talk to the intended party. Authentication, typically including identification, is the proof of identity information. However, transactions linked to the same identifier are traceable, and ultimately make people also traceable, hence their privacy is threatened. We propose a framework to counter this problem. We argue that applying attribute-based (AB) authentication in the context of IoT empowers users to maintain control over what data their devices disclose. At the same time AB authentication provides the possibility of data minimisation and unlinkability of user transactions. Therefore, this approach improves substantially user privacy in the IoT.
Preanalytic and analytic sources of variations in C-reactive protein measurement: implications for cardiovascular disease risk assessment.

BACKGROUND C-reactive protein (CRP) is a widely recognized indicator of inflammation and is known to play an important role in atherogenesis. Recent prospective studies have demonstrated that increased CRP concentrations within the reference interval are a strong predictor of myocardial infarction, stroke, sudden cardiac death, and peripheral vascular disease in apparently healthy adults. On the basis of available evidence, the American Heart Association and the CDC have issued guidelines for the utility of CRP in the primary prevention of coronary heart disease and in patients with stable coronary disease or acute coronary syndromes. Nevertheless, there remains considerable work to optimize the utility of this marker for risk assessment. ISSUES Most traditional CRP tests designed to monitor acute and chronic inflammation have inadequate sensitivity for risk stratification of coronary disease. Thus, manufacturers have had to develop tests with higher sensitivity. Because an individual's CRP concentration will be interpreted according to fixed cut-points, issues related to the preanalytic and analytic components of CRP measurement must be considered and standardized where possible to avoid potential misclassification of cardiovascular risk. CONCLUSIONS Efforts to define performance criteria for high-sensitivity CRP applications coupled with growing awareness of the physiologic aspects of CRP most likely will lead to refinements in standardization, improved performance in quality-assessment schemes, and enhanced risk prediction.
Towards a knowledge federation of linked patient registries

Rare disease patient registries are now an essential tool for all clinical stakeholders. These systems' features aim to improve patient treatments by collecting comprehensive electronic patient records. Understanding these data is a vital step towards personalized medicine. Yet, the growing number of disease-specific patient registries brings new challenges for life sciences developers. These systems are closed data silos, with independent formats and data models. As they were built with security and privacy in mind, available tools lack comprehensive data access mechanisms, thus making data sharing a complex process. However, exchanging knowledge is essential to a better understanding of studied diseases. To tackle these challenges we introduce a semantic web-based architecture to connect distributed and heterogeneous registries. This enables the federation of knowledge between multiple independent environments. The semantic web paradigm enhances the ways we deal with data, optimising how we can create, infer and publish knowledge. Hence, we adopt these modern standards to deploy patient registry add ons. These can extract anonymised data and elevate them to a knowledge-oriented format, common to all registries. The outcome is a unique semantic layer, connecting miscellaneous registries, which we access using federated querying. Ultimately, this strategy empowers an holistic view through connected registries, enabling state-of-the-art semantic data sharing and access.
Implementing fast recovery for register alias table in out-of-order processors

Register renaming is an indispensable technique to cope with false data dependencies in out-of-order processors. The critical component for performing register renaming is a register alias table (RAT), which maintains the mappings between architecture and physical registers. Unfortunately, a potential misprediction may seriously slower the processor execution, because new instructions are not allowed to be renamed until the RAT has been restored to the previous correct status. Therefore, a fast RAT recovery is necessary to sustain high performance. As instruction windows size increases, the traditional recovery mechanisms such as using retirement map table and history buffer become too slow. In order to instantly restore RAT from a misprediction, the embedded checkpoint method is introduced but too costly due to creating a large number of checkpoints. In this paper, we propose a selective checkpoint policy which is based on branch confidence and decides when to assign a new checkpoint. Experimental results show that for a 2048-entry large instruction window, our proposal only uses 8 checkpoints to implement fast RAT recovery, reducing the misprediction overhead to just 3% of the ideal model.
Cities after socialism : urban and regional change and conflict in post-socialist societies

List of Contributors. Preface. 1. Cities in the Transition: Michael Harloe. 2. Structural Change and Boundary Instability: Gregory Andrusz. 3. The Socialist City: David Smith. 4. Urbanisation under Socialism: Georgy Enyedi. 5. Privatisation and its Discontents: Property Rights in Land and Housing in the transition in Eastern Europe: Peter Marcuse. 6. Housing Privatisation in the Former Soviet Bloc to 1995: Raymond J. Struyk. 7. From the Socialist to the Capitalist City - Experiences from Germany: Hartmut Haussermann. 8. Environmental and Housing Movements in Cities after Socialism: The Cases of Budapest and Moscow: Chris Pickvance. 9. A New Movement in an Ideological Vacuum: Nationalism in Eastern Europe: Klaus von Beyme. 10. Cities Under Socialism: and After: Ivan Szelenyi. Bibliography. Index.