Miracle-dz/UniFAR-FTUs huggingface dataset:
Accounting disclosure, stock price synchronicity and stock crash risk: An emerging-market perspective

Purpose - – This study aims to examine the effects of firms’ accounting disclosure policies on stock price synchronicity and stock crash risk, using a sample including 13 emerging markets. Furthermore, this research investigates how these relationships are affected by country-level investor protection and firm-level governance rankings. Design/methodology/approach - – This paper uses accounting disclosure measures constructed based on survey questions by Credit Lyonnais Securities Asia (2001, CLSA). The accounting disclosure measure is used to explain the two dependent variables, stock price synchronicity and stock crash risk. The stock price synchronicity measure is defined as the logistic transformation of Findings - – This paper finds that firms’ stocks are less synchronized with the entire market and have less crash risk if firms have superior accounting disclosure policies. These results suggest that the cost to collect firm-specific information may be decreased for investors if firms are more transparent. Thus, these firms’ stocks have more firm-specific information content. These results also suggest that management is less likely to hide some negative information and release such negative information suddenly in the future if firms have higher levels of accounting disclosure. Thus, these firms’ stocks are less likely to crash. In addition, the influences of firms’ accounting disclosure policies on stock price synchronicity and crash risk are more significant for firms with superior country-level investor protection and firm-level governance rankings. These results imply that external investors place more value on accounting disclosure by well-governed firms because firms with superior governance standards are less likely to intentionally disclose misleading information. Thus, these firms’ stocks can incorporate more firm-specific information and have less crash risk. Originality/value - – The current study is the first to show that the effects of accounting disclosure on stock price synchronicity and crash risk are more pronounced for firms with superior country-level investor protection and firm-level governance standards. Thus, this research extends the literature by providing a comprehensive picture of the influences of accounting disclosure on stock markets. In addition, the existing literature (Chen
Theory and Observations of Type I X-Ray Bursts from Neutron Stars

I review our understanding of the thermonuclear instabilities on accreting neutron stars that produce Type I X-Ray bursts. I emphasize those observational and theoretical aspects that should interest the broad audience of this meeting. The easily accessible timescales of the bursts (durations of tens of seconds and recurrence times of hours to days) allow for a very stringent comparison to theory. The largest discrepancy (which was found with EXOSAT observations) is the accretion rate dependence of the Type I burst properties. Bursts become less frequent and energetic as the global accretion rate increases, just the opposite of what the spherical theory predicts. I present a resolution of this issue by taking seriously the observed dependence of the burning area on the global accretion rate, which implies that as the accretion rate increases, the accretion rate per unit area decreases. This resurrects the unsolved problem of knowing where the freshly accreted material accumulates on the star, equally relevant to the likely signs of rotation during the bursts summarized by Swank at this meeting. I close by highlighting the Type I bursts from GS 1826-238 that were found with BeppoSAX and RXTE. Their energetics, recurrence times and temporal profiles clearly indicate that hydrogen is being burned during these bursts, most likely by the rapid-proton (rp) process.
On optimal survivability design under a scheduled traffic model in wavelength-routed optical mesh networks

We study the optimal survivability design problem under a scheduled traffic model in wavelength convertible WDM optical mesh networks. In this model, a set of demands is given, and the setup time and teardown time of a demand are known in advance. We formulate the joint routing and wavelength assignment problems as integer linear programs that maximally exploit network resource reuse in both space and time. Moreover, we propose efficient heuristic algorithms to solve large survivable routing and wavelength assignment problems under the scheduled traffic model. Our simulation results indicate that the optimization of resource sharing in space and time enabled by our connection-holding-time-aware protection schemes can achieve significantly better resource utilization than schemes that are holding time unaware. In addition, the proposed heuristic algorithms are shown to be indeed very effective
Multivariate Gaussian Random Fields Using Systems of Stochastic Partial Differential Equations

In this paper a new approach for constructing multivariate Gaussian random fields (GRFs) using systems of stochastic partial differential equations (SPDEs) has been introduced and applied to simulated data and real data. By solving a system of SPDEs, we can construct multivariate GRFs. On the theoretical side, the notorious requirement of non-negative definiteness for the covariance matrix of the GRF is satisfied since the constructed covariance matrices with this approach are automatically symmetric positive definite. Using the approximate stochastic weak solutions to the systems of SPDEs, multivariate GRFs are represented by multivariate Gaussian Markov random fields (GMRFs) with sparse precision matrices. Therefore, on the computational side, the sparse structures make it possible to use numerical algorithms for sparse matrices to do fast sampling from the random fields and statistical inference. Therefore, the big-n problem can also be partially resolved for these models. These models out-preform existing multivariate GRF models on a commonly used real dataset.
A proximal splitting approach to regularized distributed adaptive estimation in diffusion networks

We propose a proximal splitting approach to regularized distributed estimation over networks employing diffusion adaptation strategies. Playing a central role in the proposed framework is the so-called proximity operator, which is a generalization of the convex projection mapping, that enables us to handle convex regularization terms efficiently. The diffusion algorithms developed using the proximal formalism endow networks with new learning abilities and open up possibilities for enhancing performance of the networks by utilizing more general convex penalties. We present performance analysis of the proposed method and provide simulations to demonstrate its feasibility in recovering sparse signals.
Decreased Serum Level of miR-146a as Sign of Chronic Inflammation in Type 2 Diabetic Patients

Background There is increasing evidence that chronic inflammation is an important determinant in insulin resistance and in the pathogenesis of type 2 diabetes (T2D). MicroRNAs constitute a newly discovered system of cell regulation and in particular two microRNAs (miR-146a and miR-155) have been described as regulators and biomarkers of inflammation. Aim To determine a putative association between the levels of miR-146a and miR-155 in serum of T2D patients, clinical parameters and serological indicators of inflammation. Methods We performed quantitative Real Time PCR (qPCR) of microRNAs from serum (56 Ecuadorian T2D ambulatory patients and 40 non-diabetic controls). In addition, we evaluated T2D-related serum cytokines.chemokines and growth factors using a commercially available multi-analyte cytometric bead array system. We correlated outcomes to clinical parameters, including BMI, HbA1c and lipid state. Results The Ecuadorian non-diabetic controls appeared as overweight (BMI>25: patients 85%, controls 82.5%) and as dyslipidemic (hypercholesterolemia: patients 60.7%, controls 67.5%) as the patients. The serum levels of miR-146a were significantly reduced in T2D patients as compared to these non-diabetic, but obese/dyslipidemic control group (mean patients 0.61, mean controls set at 1; p = 0.042), those of miR-155 were normal. The serum levels of both microRNAs correlated to each other (r = 0.478; p<0.001) and to leptin levels. The microRNAs did not correlate to BMI, glycemia and dyslipidemia. From the tested cytokines, chemokines and growth factors, we found IL-8 and HGF significantly raised in T2D patients versus non-diabetic controls (p = 0.011 and 0.023 respectively). Conclusions This study shows decreased serum anti-inflammatory miR-146a, increased pro-inflammatory IL-8 and increased HGF (a vascular/insular repair factor) as discriminating markers of failure of glucose control occurring on the background of obesity and dyslipidemia.
Unconscious Bias and the Limits of Director Independence

Corporate directors make difficult decisions: How much should we pay our CEO? Should we permit a lawsuit against a fellow director? Should we sell the company? Directors are legally obligated to decide in good faith based on the business merits of the issue rather than extraneous considerations and influences. Naturally, some directors may have preferences, or even biases: Our CEO, my colleague and friend, deserves a lot; The company should not sue my fellow board member; We should not sell, because after all, I would like to remain a board member. But the courts presume that independent directors either do not have these preferences or can make decisions without being affected by them. Similarly, independent directors acting in good faith are likely to believe that they are either unbiased or have overcome their biases. Based on a synthesis of more than two decades of social psychology research, this article argues that frequently the courts’ presumption and the directors’ belief will be wrong. First, directors are likely to have preferences, even though they sometimes will not be consciously aware of them. Second, regardless of directors’ good faith, unconscious and, to a significant extent, uncontrollable cognitive processes will prevent the directors’ decisions from being unaffected by their preferences. Given this serious flaw in the conception of independent directors’ decision-making ability, the Article briefly evaluates several legal and procedural solutions, including heightened judicial scrutiny, expanded roles for other decision makers, and changed decision-making processes.
Predictive Techniques and Methods for Decision Support in Situations with Poor Data Quality

Today, decision support systems based on predictive modeling are becoming more common, since organizations often collect more data than decision makers can handle manually. Predictive models are used to find potentially valuable patterns in the data, or to predict the outcome of some event. There are numerous predictive techniques, ranging from simple techniques such as linear regression, to complex powerful ones like artificial neural networks. Complex models usually obtain better predictive performance, but are opaque and thus cannot be used to explain predictions or discovered patterns. The design choice of which predictive technique to use becomes even harder since no technique outperforms all others over a large set of problems. It is even difficult to find the best parameter values for a specific technique, since these settings also are problem dependent. One way to simplify this vital decision is to combine several models, possibly created with different settings and techniques, into an ensemble. Ensembles are known to be more robust and powerful than individual models, and ensemble diversity can be used to estimate the uncertainty associated with each prediction.In real-world data mining projects, data is often imprecise, contain uncertainties or is missing important values, making it impossible to create models with sufficient performance for fully automated systems. In these cases, predictions need to be manually analyzed and adjusted. Here, opaque models like ensembles have a disadvantage, since the analysis requires understandable models. To overcome this deficiency of opaque models, researchers have developed rule extraction techniques that try to extract comprehensible rules from opaque models, while retaining sufficient accuracy.This thesis suggests a straightforward but comprehensive method for predictive modeling in situations with poor data quality. First, ensembles are used for the actual modeling, since they are powerful, robust and require few design choices. Next, ensemble uncertainty estimations pinpoint predictions that need special attention from a decision maker. Finally, rule extraction is performed to support the analysis of uncertain predictions. Using this method, ensembles can be used for predictive modeling, in spite of their opacity and sometimes insufficient global performance, while the involvement of a decision maker is minimized.The main contributions of this thesis are three novel techniques that enhance the performance of the purposed method. The first technique deals with ensemble uncertainty estimation and is based on a successful approach often used in weather forecasting. The other two are improvements of a rule extraction technique, resulting in increased comprehensibility and more accurate uncertainty estimations.
ATM networks and cash usage

This article deals with the issue of how the market structure in banking affects the choice of the means of payment. In particular, the demand for cash is analysed from this point of view. The analysis is based on a simple spatial transactions model in which banks’ optimization problem is solved. The solution quite clearly shows that monopoly banks have an incentive to restrict the number of ATMs to a minimum. More generally, the number of ATMs depends on competitiveness in the banking sector. The predictions of the theoretical analysis are tested using a panel data from 20 Organization for Economic Co-operation and Development (OECD) countries for the period 1988 to 2003. Empirical analysis shows that there is a strong and robust relationship between the number of ATM networks and the number of ATMs (in relation to populations). Moreover, it can be shown that the demand for cash depends on the number of ATMs, ATM networks and the popularity of other means of payment. Thus, the use of cash can be pretty well explained in the transaction demand framework assuming that the market structure and technical environment is properly controlled.
Trade and Poverty in South Africa: Lessons and Policy Recommendations

Abstract South Africa engaged in extensive tariff liberalisation during the 1990s with the aim of improving growth, employment and national welfare. However, the benefits of trade reform do not accrue automatically and equally to all households or communities and in some cases poverty and unemployment may rise. This paper reviews the research conducted for the South Africa Trade and Poverty Research Project under the Southern Africa Labour Development Unit. We find that trade liberalisation in South Africa is an important source of welfare gain for the economy as a whole and has contributed towards productivity gains at the enterprise level and lower costs of living for poor households. However, the effects are not uniform across households and industries: poor households are both winners and losers in the process. This makes it difficult to measure the net impact of liberalisation on the poor. While we argue that the net effect of liberalisation has been positive, tariff liberalisation alone is not sufficient to reduce unemployment and poverty especially amongst the unskilled and rural poor.
Recidivism in the child protection system: identifying children at greatest risk of reabuse among those remaining in the home.

OBJECTIVE To identify risk clusters that are associated with higher or lower risk of new abuse reports (rereports) and substantiated rereports (reabuse) in children who remain in the home after an abuse report. DESIGN A 5-year prospective cohort study. SETTING National Survey of Child and Adolescent Well-Being. PARTICIPANTS Children reported to the child protection system for child abuse. MAIN EXPOSURE Remaining in the home after an abuse report. MAIN OUTCOME MEASURE Incidence of rereports and reabuse. RESULTS A total of 2578 children remained in the home following an abuse report, and 44% were rereported within the follow-up period. In bivariate analyses, children with behavior problems (49% vs 38%), caregivers with an abuse history (33% vs 16%) or a child welfare history (38% vs 25%), and families with an annual income lower than $20 000 (70% vs 60%) were more likely to be rereported. Forty-five percent of rereports were substantiated reabuse, but 2 risk clusters had a higher incidence: (1) the cluster with a substantiated index report, having a caregiver without parenting class, non-African American race/ethnicity, and caregiver younger than 41.5 years (54%); and (2) the cluster with a substantiated index report, a caregiver with parenting class, and child age younger than 8.5 years (60%). The lowest risk group for reabuse had a substantiated index report, a caregiver without parenting class, non-African American race/ethnicity, and a caregiver 41.5 years or older (26%). CONCLUSIONS Among children remaining in the home following an abuse report, specific risk groups have higher and lower incidence of rereports and reabuse. These risk-group categories may be useful to child protection services and others in identifying at-risk children and making decisions about placement and services.
Eyewitness accuracy and confidence: within- versus between-subjects correlations.

Previous researchers using between-subjects comparisons have found eyewitness confidence and accuracy to be only negligibly correlated. In this study, we examined the predictive power of confidence in within-subject terms. Ninety-six subjects answered, and made confidence ratings for, a series of questions about a crime they witnessed. The average between-subjects and within-subject accuracy-confidence correlations were comparably low: r = . 14 (p less than .001) and r = . 17 (p less than .001), respectively. Confidence is neither a useful predictor of the accuracy of a particular witness nor of the accuracy of particular statements made by the same witness. Another possible predictor of accuracy, response latency, correlated only negligibly with accuracy (r = -.09 within subjects), but more strongly with confidence (r = -.27 within subjects). This pattern was obtained for both between-subjects and within-subject comparisons. The theoretical and practical implications of these results are discussed.
IDENTIFICATION OF FLUID ON OPTICAL COHERENCE TOMOGRAPHY BY TREATING OPHTHALMOLOGISTS VERSUS A READING CENTER IN THE COMPARISON OF AGE-RELATED MACULAR DEGENERATION TREATMENTS TRIALS

Purpose: To examine treatment decisions by ophthalmologists versus reading center fluid identification from optical coherence tomography in Comparison of Age-Related Macular Degeneration Treatments Trials (CATT). Methods: Fluid in 6,210 optical coherence tomography scans (598 patients) in “as needed treatment” arm of CATT Year 1 was compared with ophthalmologist's treatment: positive fluid agreement (PFA, fluid+, treatment+) and positive fluid discrepancy (PFD, fluid+, treatment−), negative fluid agreement (fluid−, treatment−) and negative fluid discrepancy (fluid−, treatment+). For PFDs, fluid location and visual acuity were characterized. Results: Treatment and reading center fluid determination agreed in 72.1% (53.0% PFA, 19.1% negative fluid agreement) and disagreed in 27.9% (25.7% PFD, 2.2% negative fluid discrepancy) of visits, with no discrepancies for 20.9% of patients. Compared with PFA, PFD occurred more commonly with lower total foveal thickness (mean ± SD: 265 ± 103 PFD, 366 ± 151 &mgr;m PFA), presence of intraretinal fluid only, smaller fluid areas (PFA areas greater than twice those of PFD, P < 0.001), and greater decrease in retinal and lesion thickness. Mean acuities before, at, and after PFD were 65.8, 66.9, and 66.3 letters. Conclusion: Treatment decisions by ophthalmologists matched reading center fluid determination in the majority of visits. More pronounced response to treatment and smaller foci of fluid likely contributed to PFD. Positive fluid discrepancy did not have substantial impact on subsequent visual acuity.
Application of a modified sequential organ failure assessment score to critically ill patients

The purpose of the present study was to explore the usefulness of the Mexican sequential organ failure assessment (MEXSOFA) score for assessing the risk of mortality for critically ill patients in the ICU. A total of 232 consecutive patients admitted to an ICU were included in the study. The MEXSOFA was calculated using the original SOFA scoring system with two modifications: the PaO2/FiO2 ratio was replaced with the SpO2/FiO2 ratio, and the evaluation of neurologic dysfunction was excluded. The ICU mortality rate was 20.2%. Patients with an initial MEXSOFA score of 9 points or less calculated during the first 24 h after admission to the ICU had a mortality rate of 14.8%, while those with an initial MEXSOFA score of 10 points or more had a mortality rate of 40%. The MEXSOFA score at 48 h was also associated with mortality: patients with a score of 9 points or less had a mortality rate of 14.1%, while those with a score of 10 points or more had a mortality rate of 50%. In a multivariate analysis, only the MEXSOFA score at 48 h was an independent predictor for in-ICU death with an OR = 1.35 (95%CI = 1.14-1.59, P < 0.001). The SOFA and MEXSOFA scores calculated 24 h after admission to the ICU demonstrated a good level of discrimination for predicting the in-ICU mortality risk in critically ill patients. The MEXSOFA score at 48 h was an independent predictor of death; with each 1-point increase, the odds of death increased by 35%.
Isotonic Optimization in Tariff Construction

Abstract The problem of the best fit to set ideal values under general inequality order restrictions is examined for asymmetric, quadratic, absolute, and Chebyshev norms. Special solution procedures are given in terms of network flow algorithms over a network associated with the given isotonic order relations, and the nature of the optimal solutions is characterized for the different norms. The model is formulated in terms of finding an optimal insurance rate structure over given risk classes for which a desired pattern of tariffs can be specified. The suitability of different norms is considered in the context of corporate profitability, and the relationship to a simple rate relativities model is described.
Turbo-BLAST for wireless communications: theory and experiments

Turbo-BLAST is a novel multitransmit multireceive (MTMR) antenna scheme for high-throughput wireless communications. It exploits the following ideas: the Bell Labs layered space time (BLAST) architecture; random layered space-time (RLST) coding scheme by using independent block codes and random space-time interleaving; sub-optimal turbo-like receiver that performs iterative decoding of the RLST codes and estimation of the channel matrix in an iterative and, most important, simple fashion. The net result is a new transceiver that is not only computationally efficient compared with the optimal maximum likelihood decoder, but it also yields a probability of error performance that is orders of magnitude smaller than traditional BLAST schemes for the same operating conditions. This paper also presents experimental results using real-life indoor channel measurements demonstrating the high-spectral efficiency of turbo-BLAST.