Miracle-dz/UniFAR-FTUs huggingface dataset:
COVARIANCE MATRICES AND SKEWNESS: MODELING AND APPLICATIONS IN FINANCE

(Bayesian correlation estimation, shadow prior, portfolio selection with higher moments) Abstract This Ph.D. dissertation is concerned with using model based and computation intensive statistical approaches to gain insight into substantive issues in finance related topics. It addresses the use of posterior (predictive) simulation for Bayesian inference in high dimensional and analytically intractable models. It consists of three studies. One study focuses on the issue of covariance estimation. I propose prior probability models for variance-covariance matrices. The proposed models address two important issues. First, the models allow a researcher to represent substantive prior information about the strength of correlations among a set of variables. Second, even in the absence of such substantive prior information, it provides increased flexibility. This is achieved by including a clustering mechanism in the prior probability model. Clustering is with respect to variables or with respect to pairs of variables. This leads to shrinkage towards a mixture structure implied by the clustering, instead of towards a diagonal structure as is commonly done. With departure from the standard MLE or inverse-Wishart prior there are many computational difficulties that arise in modeling covariances. A second study follows from the computational challenges that arise in the co-variance estimation study. Because of the requirement that the correlation matrix be positive (semi-) definite, the high dimensional space of the correlation matrix is analytically intractable. Calculating the normalizing constant is then left to numerical techniques. I examine a method for estimating the constant, and also an argument for ignoring it in certain cases. An additional strategy called the " shadow prior " is explored by introducing an additional level of hierarchy into the model. A third study follows the foundational work of Markowitz's portfolio selection in iv 1952. Issues that he pointed out but didn't address include parameter uncertainty, utility optimization, and inclusion of higher moments. I build upon his foundation by incorporating a skew-normal model. Using Bayesian methods, higher moments and parameter uncertainty can be accommodated in a natural way in the portfolio selection process. Preference over portfolios is framed in terms of utility function maximization, which are optimized using Bayesian methods. Findings suggest that skewness is important for distinguishing between 'good' and 'bad' variance.
A Taxonomy of Consumer Purchase Strategies in a Promotion Intensive Environment

This paper proposes a taxonomy of consumer purchase strategies based on household decisions about which brand to purchase, how much, and when to purchase in a promotion intensive environment. We infer decision rules at the household level from supermarket scanner panel data and then cluster the inferred choice routes in order to discover the major purchasing strategies used in our population of interest. Subsequently, we test for the distribution of these purchase strategies in our customer population. Results add to our knowledge of consumer behavior in response to promotions.
A Phase I/II Study of GTI-2040 Plus Docetaxel as Second-Line Treatment in Advanced Non-small Cell Lung Cancer: A Study of the PMH Phase II Consortium

Introduction: GTI-2040, an antisense oligonucleotide, targets the ribonucleotide reductase R2 subunit, critical for DNA synthesis. This study determined the recommended phase II dose (RP2D) of docetaxel plus GTI-2040, toxicity, and response rate in advanced non-small cell lung cancer (NSCLC). Patients and Methods: Advanced solid tumor patients, preferably with platinum-treated NSCLC, performance status 0 to 2, no symptomatic central nervous system metastases, adequate organ and bone marrow function, and ≥1 prior chemotherapy regimen were treated with escalating doses of GTI-2040 given by 14-day continuous intravenous infusion (CVI) plus docetaxel every 21 days. Results: Twenty-nine patients were treated, (24 NSCLC, 3 hormone-refractory prostate cancer, 1 head and neck, and 1 small cell lung cancer). GTI-2040 5 mg/kg as CVI for 14 days plus docetaxel 75 mg/m2 intravenously every 21days was determined as the RP2D. Dose-limiting toxicity was not seen. Two patients at RP2D developed grade 4/5 febrile neutropenia. One prostate specific antigen response was seen in phase I, but no objective tumor responses in the NSCLC patients. Median time to progression was 3.4 months, 3.2 months in the NSCLC patients treated at RP2D. Conclusions: Activity of the combination at RP2D, GTI-2040 5 mg/kg/d × 14 days by CVI plus docetaxel 75 mg/m2 does not seem superior to docetaxel alone in previously treated NSCLC.
Role of the N-Terminal Zinc Finger of Human Immunodeficiency Virus Type 1 Nucleocapsid Protein in Virus Structure and Replication

ABSTRACT Nucleocapsid protein (NCp7) of human immunodeficiency virus type 1 is found covering the genomic RNA in the interior of the viral particle. It is a highly basic protein with two zinc fingers of the form CX2CX4HX4C which exhibit strong affinity for a zinc cation. To study the structure-function relationship of the N-terminal zinc finger of NCp7, this domain was either deleted or changed to CX2CX4CX4C. We examined virus formation and structure as well as proviral DNA synthesis. Our data show that these two NC mutations result in the formation of particles with an abnormal core morphology and impair the end of proviral DNA synthesis, leading to noninfectious viruses.
Do security and privacy policies in B2B and B2C e-commerce differ? A comparative study using content analysis

ABSTRACT Security and privacy policies address consumer concerns related to security and privacy in e-commerce websites. As these policies represent only the vendor’s perspective, often there exists a mismatch between the stated and desired policy. Based on transaction cost theory, we speculate that business-to-business (B2B) and business-to-consumer (B2C) e-commerce customers use their transaction cost savings in order to obtain varying levels of security and privacy. These differences are bound to be reflected in the security and privacy policies of e-commerce companies. Therefore, in this paper, we perform a comparative content analysis of the security and privacy policies in B2C and B2B e-commerce. Results show that B2B vendors are more concerned about security than their B2C counterparts, while B2C vendors are anxious about intimacy and restriction privacy. Our findings have important implications for e-commerce consumers and vendors as individual and corporate consumers have varying concerns while transacting online. Individual consumers are concerned about maintaining security and intimacy privacy, whereas corporate users are anxious about regulatory issues. Therefore, B2C vendors should incorporate stringent measures dedicated to confidentiality and protection of consumer data as well as enhance intimacy privacy in their security policies, while their B2B counterparts should focus on enhancing restriction privacy.
Multisector Approaches to Societal Issues Management

The social justice emphasis of the 1960s is much less evident in recent research and theory. Much of the scholarship in the business and society field has focused primarily on the firm’s perspective—the firm’s issues, the firm’s stakeholders, and the firm’s actions given the two—so that it is sometimes difficult for scholars to grasp the big picture or to help corporations learn how to approach big, messy societal problems. It is likely that societal problems such as poverty, urban decay, racial and gender discrimination, and environmental destruction are so gargantuan that they require the efforts of many individuals, groups, and organizations from every social institution—business, government, and nonprofit/community—to attempt to remedy them. Unfortunately, given the intricacies of social problems, a focus on the firm’s actions merely provides one perspective on addressing these problems. Business and society scholars need to better understand the motivation, process, and impact of the firm’s participation in addressing problems at
Analysis of data security in Cloud Computing

Cloud Computing is one of the increasing technology that is connected with Grid Computing, Utility Computing, Distributed Computing. In today's world, securing of data plays a vital role. In the computing environment, data privacy and data security is popular in fields like government, industry, and business for the future development. These are inter-related to both hardware and software. Hence, this paper analyses the data security solutions in Cloud Computing.
Perforin- and Granzyme-Mediated Cytotoxic Effector Functions Are Essential for Protection against Francisella tularensis following Vaccination by the Defined F. tularensis subsp. novicida ΔfopC Vaccine Strain

ABSTRACT A licensed vaccine against Francisella tularensis is currently not available. Two Francisella tularensis subsp. novicida (herein referred to by its earlier name, Francisella novicida) attenuated strains, the ΔiglB and ΔfopC strains, have previously been evaluated as potential vaccine candidates against pneumonic tularemia in experimental animals. F. novicida ΔiglB, a Francisella pathogenicity island (FPI) mutant, is deficient in phagosomal escape and intracellular growth, whereas F. novicida ΔfopC, lacking the outer membrane lipoprotein FopC, which is required for evasion of gamma interferon (IFN-γ)-mediated signaling, is able to escape and replicate in the cytosol. To dissect the difference in protective immune mechanisms conferred by these two vaccine strains, we examined the efficacy of the F. novicida ΔiglB and ΔfopC mutants against pulmonary live-vaccine-strain (LVS) challenge and found that both strains provided comparable protection in wild-type, major histocompatibility complex class I (MHC I) knockout, and MHC II knockout mice. However, F. novicida ΔfopC-vaccinated but not F. novicida ΔiglB-vaccinated perforin-deficient mice were more susceptible and exhibited greater bacterial burdens than similarly vaccinated wild-type mice. Moreover, perforin produced by natural killer (NK) cells and release of granzyme contributed to inhibition of LVS replication within macrophages. This NK cell-mediated LVS inhibition was enhanced with anti-F. novicida ΔfopC immune serum, suggesting antibody-dependent cell-mediated cytotoxicity (ADCC) in F. novicida ΔfopC-mediated protection. Overall, this study provides additional immunological insight into the basis for protection conferred by live attenuated F. novicida strains with different phenotypes and supports further investigation of this organism as a vaccine platform for tularemia.
CLIMATE CHANGE AND OVERLAPPING GENERATIONS

This paper examines the interplay between discounting and the distribution of welfare between generations in formulating climate change response strategies. The analysis shows that one can understand Nordhaus's (1994) standard representative agent model for climate policy analysis as a reduced form of an overlapping generations model that embodies more realistic demographic assumptions. In this setting, alternative Pareto efficient allocations may be supported as competitive equilibria given appropriate sets of income transfers between generations. Numerical simulations establish that increased intergenerational transfers entail reduced monetary discount rates and increased rates of greenhouse gas emissions abatement. Short-run policy choices are highly sensitive to normative judgments concerning the relative weight attached to the welfare of future generations.
A GROWTH MODEL FOR ACADEMIC PROGRAM LIFE CYCLE (APLC): A THEORETICAL AND EMPIRIRCAL ANALYSIS

Academic program life cycle concept states each program’s life flows through several stages: introduction, growth, maturity, and decline. A mixed-influence diffusion growth model is fitted to enrolment data on academic programs to analyze the factors determining progress of academic programs through their life cycles. The regression analysis yield reasonable parameter estimates, including magnitude of enrolment peaks and duration of stages and describes growth patterns of academic programs very well. The results indicate that key factors accounting for progress of academic programs through life cycle stages are external information and word-ofmouth communication (social and behavioural factors as well as economic factors). The model’s application for analyzing market dynamics and long-range forecasting is demonstrated. Introduction: Recent challenges and competition in the higher education sector have created a situation where postsecondary institutions are viewed as producers and their academic programs are viewed as products; and students are perceived as end-users or customers in the education marketplace. In the past, postsecondary institutions sprang up on the assumption that: “if you build, they will come. ” This assumption is no longer sustainable. In recent years, it has been claimed that colleges and universities have been characterized by the “market ethos”, which is said to involve the transformation of educational values into business values (Karabel, 2005; Tuchman, 2009). Colleges and universities see the purpose of education in more practical terms, such as preparing students for jobs (workforce development) (Tuchman, 2009). More importantly, according to Tuchman (2009), colleges and universities perceive knowledge and job preparation as commodities whose transmission is purchased by student customers (and their parents), when they pay tuition, room, board, and other fees. According to this perception, education has become a market transaction in which potential buyers (students and their parents) must be actively sought after and motivated with incentives to buy into an institution’s market offerings. Like businesses, academic institutions require innovative marketing strategies in order to survive and grow in the highly competitive postsecondary market. These marketing strategies depend on the stage of each academic program in its life cycle. The academic program life cycle (APLC) is a powerful marketing tool. Effective program marketing requires that deans, directors and program managers understand how to introduce new academic
Microbial and metabolic interactions between the gastrointestinal tract and Clostridium difficile infection

Antibiotics disturb the gastrointestinal tract microbiota and in turn reduce colonization resistance against Clostridium difficile. The mechanism for this loss of colonization resistance is still unknown but likely reflects structural (microbial) and functional (metabolic) changes to the gastrointestinal tract. Members of the gut microbial community shape intestinal metabolism that provides nutrients and ultimately supports host immunity. This review will discuss how antibiotics alter the structure of the gut microbiota and how this impacts bacterial metabolism in the gut. It will also explore the chemical requirements for C. difficile germination, growth, toxin production and sporulation. Many of the metabolites that influence C. difficile physiology are products of gut microbial metabolism including bile acids, carbohydrates and amino acids. To restore colonization resistance against C. difficile after antibiotics a targeted approach restoring both the structure and function of the gastrointestinal tract is needed.
Adaptive alpha-trimmed mean filters based on asymptotic variance minimization

Alpha-trimmed mean filters are widely used for the restoration of signals and images corrupted by additive symmetric noise. They are preferred when the pdf tail length is between that of Gaussian and Laplacian distributions. The design problem of these filters is nothing but selecting its only parameter, /spl alpha/, optimally for a given noise type. In this paper, we present an adaptive alpha-trimmed mean filter, which selects /spl alpha/ that minimizes the sample asymptotic variance estimate, V/sub n/, of the alpha-trimmed mean estimator. This is an extension of Jaeckel's proposal of optimal alpha-trimmed mean estimators to signal and image filtering. The simulations that use moderate sample sizes to compute V/sub n/ produce promising results.
Glucose Clamp Algorithms and Insulin Time-Action Profiles

Motivation: Most current insulin pumps include an insulin-on-board (IOB) feature to help subjects avoid problems associated with “insulin stacking. ” In addition, many control algorithms proposed for a closed-loop artificial pancreas make use of IOB to reduce the probability of hypoglycemic events that often occur due to the integral action of the controller. The IOB curves are generated from the pharmacodynamic (time-activity profiles) actions of subcutaneous insulin, which are obtained from glycemic clamp studies. Methods: Glycemic clamp algorithms are reviewed and in silico studies are performed to analyze the effect of glucose meter bias and noise on glycemic control and the manipulated glucose infusion rates. The glucose infusion rates are used to obtain insulin time-activity profiles, which are then used to generate IOB curves. Results: A model-based, three-step-ahead controller is shown to be equivalent to a proportional-integral control algorithm with time-delay compensation. A systematic glucose meter bias of +6 mg/dl results in a decrease in the glucose area under the curve of 3% but no change in the IOB profiles. Conclusions: Based on these preliminary simulation studies, a substantial amount of glucose meter bias and noise during a glycemic clamp can be tolerated with little net effect on the IOB curves. It is suggested that handheld glucose meters can therefore be used in clamp studies if the measurements are filtered (averaged) before processing by the control algorithm. Clinical studies are needed to confirm these preliminary results.
The stabilisation problem: the case of New Zealand

This paper examines stabilisation bias - the difference between the inferior macroeconomic outcomes attained with discretionary monetary policy relative to the ideal that could be attained with commitment policy. The paper works within the linear-quadratic framework and represents the monetary policy problem for the central bank as setting the interest rate in order to minimise an explicit loss function for macroeconomic variables. The government's problem is one of "optimal negotiation", whereby the government, representing society, joins with the central bank to search for the optimal set of loss function parameters to be embedded in a contract with the central bank. The framework, due to Rogoff (1985), is usefully applied to the case of New Zealand where recent Policy Target Agreements - contracts between the government and the central bank - are interpreted as representing society's preferences between inflation, output and other dimensions of macroeconomic stability. Within the context of an estimated, small open economy model, a sizeable stabilisation bias is found for New Zealand. Substantial reductions in the stabilisation bias can be achieved by strategic optimal delegation behaviour on the part of the government. It transpires that the weight the central bank should have on the variance of the output gap is considerably lower than the weight society places on the variance of the output gap.
Robust Coded Cooperation Based on Multi-Dimensional Spatially-Coupled Repeat-Accumulate Codes

In this paper, we propose a new cooperative protocol called multi- dimensional spatially-coupled repeat-accumulate coded cooperation (MD-SC-RA-CC), which is even more robust against burst errors over block Rayleigh fading channels. Our approach utilizes both time- and user-domain to transmit every packets over as many statistically independent channels as possible. To this end, we investigate the design of multi-dimensional spatially-coupled repeat-accumulate codes for MD-RA-CC, which has few short loops in the corresponding protograph. Numerical results confirm that our proposed MD-SC-RA-CC exhibits the superior decoding performance to conventional coded cooperation especially at high signal-to-noise power ratio (SNR) region.
Supraagonistic, bispecific single‐chain antibody purified from the serum of cloned, transgenic cows induces T‐cell‐mediated killing of glioblastoma cells in vitro and in vivo

Here we characterize the antitumor activity of a recombinant bispecific single‐chain antibody isolated from the serum of cloned transgenic cows. The antibody, termed r28M, is directed to a melanoma‐associated proteoglycan, also expressed on glioblastoma cells, and to human CD28. Bound to tumor cells, r28M induced exceedingly efficient supraagonistic T‐cell activation via the CD28 molecule without an additional stimulus via the TCR/CD3 complex. In vitro, T cells and NK cells contributed to tumor cell killing after r28M‐mediated activation of peripheral blood mononuclear cells. However, NK activity depended on T‐cell‐derived cytokines. In vivo, r28M markedly inhibited the growth of human glioblastoma cells in nude mice. The serum half‐life of the protein after i.v. injection was approximately 6 hr. Thus, r28M is unique not only in inducing supraagonistic CD28‐mediated T‐cell activation against tumor cells in vitro and in vivo, it also meets 2 additional requirements that are critical for clinical application: a relatively long serum half‐life and the possibility of obtaining large amounts of active material from cloned transgenic livestock. © 2005 Wiley‐Liss, Inc.
Statistical and Scaling Analyses of Neural Network Soil Property Inputs/Outputs at an Arizona Field Site

Analyses of flow and transport in the shallow subsurface require information about spatial and statistical distributions of soil hydraulic properties (water content and permeability, their dependence on capillary pressure) as functions of scale and direction. Measuring these properties is relatively difficult, time consuming and costly. It is generally much easier, faster and less expensive to collect and describe the makeup of soil samples in terms of textural composition (e.g. per cent sand, silt, clay and organic matter), bulk density and other such pedological attributes. Over the last two decades soil scientists have developed a set of tools, known collectively as pedotransfer functions (PTFs), to help translate information about the spatial distribution of pedological indicators into corresponding information about soil hydraulic properties. One of the most successful PTFs is the nonlinear Rosetta neural network model developed by one of us. Among remaining open questions are the extents to which spatial and statistical distributions of Rosetta hydraulic property outputs, and their scaling behavior, reflect those of Rosetta pedological inputs. We address the last question by applying Rosetta, coupled with a novel statistical scaling analysis recently proposed by three of us, to soil sample data from an experimental site in southern Arizona, USA.
Capital Structure and Managerial Compensation: The Effects of Remuneration Seniority

We show that the relative seniority of debt and managerial compensation has important implications on the design of remuneration contracts.Whereas the traditional literature assumes that debt is senior to remuneration, we show that this is frequently not the case according to bankruptcy regulation and as observed in practice.We theoretically show that including risky debt changes the incentive to provide the manager with stronger performance-related incentives ("contract substitution" effect). If managerial compensation has priority over the debt claims, higher leverage produces lower powerincentive schemes (lower bonuses) and a higher base salary.With junior compensation, we expect more emphasis on pay-for-performance incentives.The empirical findings are in line with the regime of remuneration seniority as the base salary is significantly higher and the performance bonus is lower in financially distressed firms.