Multi-dimensional modulation codes for fading channel

Some new codes are presented which have good performance on Rician fading channel with small decoding complexities. A new M-way partition chain is proposed for the L x MPSK (L less than or equal to M) signal set which maximizes the intra-set distance of each subset at each partition level. Based on this partition chain, a class of asymptotical optimum codes was found. For M = 4, these codes have both large symbol distances and product distances. Multi-level coding scheme allows to construct a code by hand such that the code meets some desired parameters, e.g., symbol distance, product distance, etc. In design of a multi-level code, all factors were considered which affect the performance and complexity of the code, such as, the decoding scheme, decoding complexity, and performance under the decoding scheme, e.g., if the multi-stage decoding scheme is used, the performance degradation due to the suboptimum decoding is taken into consideration. The performance for most of the presented codes was simulated on Rayleigh fading channel, and the results show that these codes have good performance with small decoding complexities

Identifying the Predictors for Financial Crisis Using Gibbs Sampler

The Asian financial crisis broke out in Thailand in July 1997, and rapidly spread throughout the neighboring countries. An important question then arises? Is it possible to predict next financial crisis? If yes, then what are the predictors? The answer lies in combined usage of economic theory and econometric methods. By using the economic theory, one can locate possible potential crisis predictors whereas appropriate econometric models can pinpoint effective ones. In this paper we suggest using the Stochastic Search Variable Selection (SSVS) developed by George and McCulloch (1993) to identify the crisis predictors. As is suggested by the name, SSVS stochastically searches for practically significant variables. Each variable coefficient is assumed to come from a mixture of two normal variates with respectively large and small variances. For the former case, this variable is considered as insignificant and should be excluded from the model whereas for the latter, this variable is significant and should be included in the model. SSVS is not affected by the ordering of the candidate variables and is particularly effective when the sample size is much smaller than the number of all possible models. By employing SSVS method, we conclude that annual growth rate of money supply, $M_2$, and the ratio of government debt to GDP are promising predictors for financial crisis. It is worth mentioning that the frequently mentioned factors, such as ratio of total foreign reserve to GDP and the ratio of current deficit to GDP are not selected by our analysis. Our empirical analysis implies that monetary and fiscal policy play a crucial role in exploring the Asian financial crisis.early warning

Identifying the Predictors for Financial Crisis Using Gibbs Sampler

The Asian financial crisis broke out in Thailand in July 1997, and rapidly spread throughout the neighboring countries. An important question then arises? Is it possible to predict next financial crisis? If yes, then what are the predictors? The answer lies in combined usage of economic theory and econometric methods. By using the economic theory, one can locate possible potential crisis predictors whereas appropriate econometric models can pinpoint effective ones. In this paper we suggest using the Stochastic Search Variable Selection (SSVS) developed by George and McCulloch (1993) to identify the crisis predictors. As is suggested by the name, SSVS stochastically searches for practically significant variables. Each variable coefficient is assumed to come from a mixture of two normal variates with respectively large and small variances. For the former case, this variable is considered as insignificant and should be excluded from the model whereas for the latter, this variable is significant and should be included in the model. SSVS is not affected by the ordering of the candidate variables and is particularly effective when the sample size is much smaller than the number of all possible models. By employing SSVS method, we conclude that annual growth rate of money supply, $M_2$, and the ratio of government debt to GDP are promising predictors for financial crisis. It is worth mentioning that the frequently mentioned factors, such as ratio of total foreign reserve to GDP and the ratio of current deficit to GDP are not selected by our analysis. Our empirical analysis implies that monetary and fiscal policy play a crucial role in exploring the Asian financial crisis.Financial crisis, early warning

Lifshitz spacetimes, solitons, and generalized BTZ black holes in quantum gravity at a Lifshitz point

In this paper, we study static vacuum solutions of quantum gravity at a fixed Lifshitz point in (2+1) dimensions, and present all the diagonal solutions in closed forms in the infrared limit. The exact solutions represent spacetimes with very rich structures: they can represent generalized BTZ black holes, Lifshitz space-times or Lifshitz solitons, in which the spacetimes are free of any kind of space-time singularities, depending on the choices of the free parameters of the solutions. We also find several classes of exact static non-diagonal solutions, which represent similar space-time structures as those given in the diagonal case. The relevance of these solutions to the non-relativistic Lifshitz-type gauge/gravity duality is discussed.Comment: revtex4, 5 figures. Typos are correcte

On the nexus between energy efficiency, financial inclusion and environment: Evidence from emerging seven economies using novel research methods

Emerging seven (E7) are some of the rising economies in the world and are expected to be economically strengthened in the coming few decades due to rapid economic growth. Besides, financial inclusion and globalization are also rising in these economies, which compel them to adopt energy saving techniques to lower carbon (CO2) emissions in the region. This study aims to explore the influence of these variables on CO2 emissions in E7 economies over the period from 2004 to 2019. Various panel econometric approaches reveal that all the variables are stationary at first difference. Also, the long-run cointegration association exists between them. The non-normal distribution of data leads to the adoption of the panel quantile estimator for the long run estimations across three quantiles (i.e., q0.25, q0.50, and q0.75). The empirical findings illustrate that energy efficiency is negatively associated to CO2 emissions in all the quantiles. However, financial inclusion, economic growth, globalization, and composite risk index are the prominent factors of CO2 emissions. Such factors are the primary reasons for environmental degradation in the region. The estimated panel causality test results confirm the feedback effect for the variables except for globalization, which runs toward CO2 emissions. Based on findings, this study suggests policies regarding the encouragement of energy efficiency and alteration of economic growth from non-renewable energy sources to renewables. Devotion of financial inclusion towards green finance and green bonds promotion and reducing composite risk to promote environmental sustainability

Annotating Protein Functional Residues by Coupling High-Throughput Fitness Profile and Homologous-Structure Analysis.

Identification and annotation of functional residues are fundamental questions in protein sequence analysis. Sequence and structure conservation provides valuable information to tackle these questions. It is, however, limited by the incomplete sampling of sequence space in natural evolution. Moreover, proteins often have multiple functions, with overlapping sequences that present challenges to accurate annotation of the exact functions of individual residues by conservation-based methods. Using the influenza A virus PB1 protein as an example, we developed a method to systematically identify and annotate functional residues. We used saturation mutagenesis and high-throughput sequencing to measure the replication capacity of single nucleotide mutations across the entire PB1 protein. After predicting protein stability upon mutations, we identified functional PB1 residues that are essential for viral replication. To further annotate the functional residues important to the canonical or noncanonical functions of viral RNA-dependent RNA polymerase (vRdRp), we performed a homologous-structure analysis with 16 different vRdRp structures. We achieved high sensitivity in annotating the known canonical polymerase functional residues. Moreover, we identified a cluster of noncanonical functional residues located in the loop region of the PB1 β-ribbon. We further demonstrated that these residues were important for PB1 protein nuclear import through the interaction with Ran-binding protein 5. In summary, we developed a systematic and sensitive method to identify and annotate functional residues that are not restrained by sequence conservation. Importantly, this method is generally applicable to other proteins about which homologous-structure information is available.ImportanceTo fully comprehend the diverse functions of a protein, it is essential to understand the functionality of individual residues. Current methods are highly dependent on evolutionary sequence conservation, which is usually limited by sampling size. Sequence conservation-based methods are further confounded by structural constraints and multifunctionality of proteins. Here we present a method that can systematically identify and annotate functional residues of a given protein. We used a high-throughput functional profiling platform to identify essential residues. Coupling it with homologous-structure comparison, we were able to annotate multiple functions of proteins. We demonstrated the method with the PB1 protein of influenza A virus and identified novel functional residues in addition to its canonical function as an RNA-dependent RNA polymerase. Not limited to virology, this method is generally applicable to other proteins that can be functionally selected and about which homologous-structure information is available