A speculative relation between the cosmological constant and the Planck mass

We propose the relation $M_\Lambda \sim (M_{Pl} M_U)^{1/2}$ where $M_\Lambda$, $M_{Pl},$ and $M_U$ denote the mass scale associated with the cosmological constant, the gravitational interaction, and the size of the universe respectively.Comment: 3 page

Message in the Sky

We argue that the cosmic microwave background (CMB) provides a stupendous opportunity for the Creator of universe our (assuming one exists) to have sent a message to its occupants, using known physics. Our work does not support the Intelligent Design movement in any way whatsoever, but asks, and attempts to answer, the entirely scientific question of what the medium and message might be IF there was actually a message. The medium for the message is unique. We elaborate on this observation, noting that it requires only careful adjustment of the fundamental Lagrangian, but no direct intervention in the subsequent evolution of the universe.Comment: 3 pages, revtex; to appear in Mod.Phys.Lett.

PDF turbulence modeling and DNS

The problem of time discontinuity (or jump condition) in the coalescence/dispersion (C/D) mixing model is addressed in probability density function (pdf). A C/D mixing model continuous in time is introduced. With the continuous mixing model, the process of chemical reaction can be fully coupled with mixing. In the case of homogeneous turbulence decay, the new model predicts a pdf very close to a Gaussian distribution, with finite higher moments also close to that of a Gaussian distribution. Results from the continuous mixing model are compared with both experimental data and numerical results from conventional C/D models. The effect of Coriolis forces on compressible homogeneous turbulence is studied using direct numerical simulation (DNS). The numerical method used in this study is an eight order compact difference scheme. Contrary to the conclusions reached by previous DNS studies on incompressible isotropic turbulence, the present results show that the Coriolis force increases the dissipation rate of turbulent kinetic energy, and that anisotropy develops as the Coriolis force increases. The Taylor-Proudman theory does apply since the derivatives in the direction of the rotation axis vanishes rapidly. A closer analysis reveals that the dissipation rate of the incompressible component of the turbulent kinetic energy indeed decreases with a higher rotation rate, consistent with incompressible flow simulations (Bardina), while the dissipation rate of the compressible part increases; the net gain is positive. Inertial waves are observed in the simulation results

Calculating and understanding the value of any type of match evidence when there are potential testing errors

It is well known that Bayes’ theorem (with likelihood ratios) can be used to calculate the impact of evidence, such as a ‘match’ of some feature of a person. Typically the feature of interest is the DNA profile, but the method applies in principle to any feature of a person or object, including not just DNA, fingerprints, or footprints, but also more basic features such as skin colour, height, hair colour or even name. Notwithstanding concerns about the extensiveness of databases of such features, a serious challenge to the use of Bayes in such legal contexts is that its standard formulaic representations are not readily understandable to non-statisticians. Attempts to get round this problem usually involve representations based around some variation of an event tree. While this approach works well in explaining the most trivial instance of Bayes’ theorem (involving a single hypothesis and a single piece of evidence) it does not scale up to realistic situations. In particular, even with a single piece of match evidence, if we wish to incorporate the possibility that there are potential errors (both false positives and false negatives) introduced at any stage in the investigative process, matters become very complex. As a result we have observed expert witnesses (in different areas of speciality) routinely ignore the possibility of errors when presenting their evidence. To counter this, we produce what we believe is the first full probabilistic solution of the simple case of generic match evidence incorporating both classes of testing errors. Unfortunately, the resultant event tree solution is too complex for intuitive comprehension. And, crucially, the event tree also fails to represent the causal information that underpins the argument. In contrast, we also present a simple-to-construct graphical Bayesian Network (BN) solution that automatically performs the calculations and may also be intuitively simpler to understand. Although there have been multiple previous applications of BNs for analysing forensic evidence—including very detailed models for the DNA matching problem, these models have not widely penetrated the expert witness community. Nor have they addressed the basic generic match problem incorporating the two types of testing error. Hence we believe our basic BN solution provides an important mechanism for convincing experts—and eventually the legal community—that it is possible to rigorously analyse and communicate the full impact of match evidence on a case, in the presence of possible error

Summertime evaluation of REFAME over the Unites States for near real-time high resolution precipitation estimation

Precipitation is the key input for hydrometeorological modeling and applications. In many regions of the world, including populated areas, ground-based measurement of precipitation (whether from radar or rain gauge) is either sparse in time and space or nonexistent. Therefore, high-resolution satellite-based precipitation products are recognized as critical data sources, especially for rapidly-evolving hydrometeorological events such as flash floods which primarily occur during summer/warm seasons. As " proof of concept" , a recently proposed algorithm called Rain Estimation using Forward Adjusted-advection of Microwave Estimates (REFAME) and its variation REFAMEgeo are evaluated over the contiguous United States during summers of 2009 and 2011. Both methods are originally designed for near real-time high resolution precipitation estimation from remotely sensed data. High-resolution Q2 (ground radar) precipitation data, in conjunction with two operational near real-time satellite-based precipitation products (PERSIANN, PERSIANN-CCS) are used as evaluation reference and for comparison. The study is performed at half-hour temporal resolution and at a range of spatial resolutions (0.08-, 0.25-, 0.5-, and 1-degree latitude/longitude). The statistical analyses suggest that REFAMEgeo performs favorably among the studied products in terms of capturing both spatial coverage and intensity of precipitation at near real-time with the temporal resolution offered by geostationary satellites. With respect to volume precipitation, REFAMEgeo together with REFAME demonstrates slight overestimation of intense precipitation and underestimation of light precipitation events. Compared to REFAME, It is observed that REFAMEgeo maintains stable performance, even when the amount of accessible microwave (MW) overpasses is limited. Based on the encouraging outcome of this study which was intended as " proof of concept" , further testing for other seasons and data-rich regions is the next logical step. Upon confirmation of the relative reliability of the algorithm, it is reasonable to recommend the use of its precipitation estimates for data-sparse regions of the world. © 2012 Elsevier B.V

"Does the Appointment of the Outside Director Increase Firm Value? The Evidence from Taiwan"

We examine the stock market reaction to the announcement of outside director appointments in Taiwan. We employ a sample of 58 outside director announcements made by Taiwan Stock Exchange listed firms during the period 1 January, 1999 to 30 June, 2003. Using this data, we can test some important hypotheses regarding the role of outside directors in conjunction with other conditions for corporate performance in affecting the stock market reactions. Our empirical findings indicate that there exists a significantly positive reaction to the announcements. The cumulative abnormal returns ---one indicator of stock market reaction measured by using the methodology of market model based event study --- reached 4.776%. We also find that the abnormal returns are positive and higher with respect to each of the following characteristics: poorer prior corporate performance, the CEO as chairman of the board, larger free cash flow and a higher degree of information asymmetry. Further, we find that the announcement effect is decreasing as number of outside directors increases. Our findings are different from existing literature, for instance, those of Lin, Pope and Young (2003) and Rosenstein and Wyatt (1990) mainly because the outside director appointment is not mandatory in Taiwan. This suggests that the announcement effects could be different across countries. The appointment appears to be more beneficial for a country with poor corporate governance mechanisms.

The Basu measure as an indicator of conditional conservatism: Evidence from U.K. earnings components

Following the work of Basu in 1997, the excess of the sensitivity of accounting earnings to negative share return over its sensitivity to positive share return (the Basu coefficient) has been interpreted as an indicator of conditional accounting conservatism. Although this interpretation is supported by substantial evidence that the Basu coefficient is associated with likely demands for conservatism, concerns have arisen that it may reflect factors not directly related to conservatism, and that this may adversely affect its validity as an indicator of that phenomenon. We argue that evidence on the validity of the Basu coefficient as an indicator of conditional conservatism can be obtained by disaggregating earnings into components, classifying those components by whether or not they are likely to be affected by conditional conservatism, and examining whether the Basu coefficient arises primarily from components likely to be affected by conditional conservatism. We implement this procedure for UK firms reporting under FRS 3: Reporting Financial Performance from 1992 to 2004. Although a substantial proportion of the Basu coefficient emanates from cash flow from operating and investing activities (CFOI), which cannot directly reflect accounting conservatism, its incidence across other components of earnings is predominantly within those components likely to be affected by conditional conservatism. Also, although the bias documented by Patatoukas and Thomas in 2009 is present in all of our aggregate earnings measures, it is heavily concentrated in the CFOI component of earnings and largely absent from components classified as likely to be affected by conditional conservatism. With the important caveat that researchers should test the robustness of their results to the exclusion of the element of the Basu coefficient due to cash flows, our findings are consistent with the conditional conservatism interpretation of the coefficient

Improving Precipitation Estimation Using Convolutional Neural Network

Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. We introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation-related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation-related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation-related parameterization schemes using a data-driven approach