Forecasting Singapore economic growth with mixed-frequency data

In this paper we intend to forecast the economic growth of Singapore by employing mixed frequency data. This study is motivated by the following observations: macroeconomic variables are the important indicators of the economic performance, but they are normally available at low frequencies, e.g. quarterly for GDP and monthly for inflation. In contrast, the financial variables such as stock returns are available at high frequency, and often the asset prices are forward-looking and believed to contain useful information about future economic developments (Stock and Watson 2003). It is therefore an interesting question to raise whether or not one can use the high-frequency financial variables to better estimate and forecast the macroeconomic variables. Using mixed-frequency data in forecasting is clearly against the conventional forecasting models which generally require data with the same frequency. Time-aggregating, such as averaging, of the high frequency data is usually practiced to match the sampling rate of lower frequency data. But time-aggregation always leads to loss of individual timing information that might be important for forecasting. Hence, finding a suitable method to handle the high frequency data is a crucial task for every forecaster dealing with mixed frequency data. We employ the Mixed Data Sampling (MIDAS) regression model introduced by Ghysels, et al. (2004). MIDAS regressions are essentially tightly parameterized, highly parsimonious regressions that deal with mixed frequency data. It is designed to find a balance between retaining the individual timing information of the high frequency data and reducing the number of parameters that need to be estimated. It is believed to have better estimating and forecasting ability than many other conventional models. A number of studies adopted MIDAS models to forecast quarterly series using monthly or daily financial data, mostly from the US (Anthony 2007; Clements and Galvão 2009). Singapore is a small open economy, and vulnerable to the global economic conditions. Although its stock market is not comparable with that of the US in term of capitalization, the Singapore stock market performance is believed to be highly correlated with its real macroeconomic variable and contains important information for economic forecasting. In this paper, we forecast one-quarter-ahead Singapore GDP growth rate using Singapore stock market return sampled at various high frequencies. We investigate the forecasting performance from three models: a Mixed Data Sampling (MIDAS) regression model, a direct regression model on high frequency data and a time-averaging regression model. Our results show that MIDAS regression using high frequency stock return data produces better forecast of GDP growth rate than the other two models. Best forecasting performance is achieved using weekly stock return. The forecasting result is further improved by performing intra-period forecasting

Proactive edge caching in content-centric networks with massive dynamic content requests

Edge computing is a promising infrastructure evolution to reduce traffic loads and support low-latency communications. Furthermore, content-centric networks provide a natural solution to cache contents at edge nodes. However, it is a challenge for edge nodes to handle massive and highly dynamic content requests by users, and if without an efficient content caching strategy, the edge nodes will encounter high traffic load and latency due to increasing retrieval from content providers. This paper formulates a proactive edge caching problem to minimize the content retrieval cost at edge nodes. We exploit the inherent content caching and request aggregation mechanism in the content-centric networks to jointly minimize traffic load and content retrieval delay cost generated by the massive and dynamic content requests. We develop a Q-learning algorithm, which is an online optimal caching strategy, as it is adaptable to dynamic content popularity and content request intensity, and derive the long-term minimization of the content retrieval cost. Simulation results illustrate that the proposed algorithm can achieve a lower content retrieval cost compared with several baseline caching schemes

Quantifying uncertainty of taxonomic placement in DNA barcoding and metabarcoding

A crucial step in the use of DNA markers for biodiversity surveys is the assignment of Linnaean taxonomies (species, genus, etc.) to sequence reads. This allows the use of all the information known based on the taxonomic names. Taxonomic placement of DNA barcoding sequences is inherently probabilistic because DNA sequences contain errors, because there is natural variation among sequences within a species, and because reference data bases are incomplete and can have false annotations. However, most existing bioinformatics methods for taxonomic placement either exclude uncertainty, or quantify it using metrics other than probability. In this paper we evaluate the performance of the recently proposed probabilistic taxonomic placement method PROTAX by applying it to both annotated reference sequence data as well as to unknown environmental data. Our four case studies include contrasting taxonomic groups (fungi, bacteria, mammals and insects), variation in the length and quality of the barcoding sequences (from individually Sanger-sequenced sequences to short Illumina reads), variation in the structures and sizes of the taxonomies (800–130 000 species) and variation in the completeness of the reference data bases (representing 15–100% of known species). Our results demonstrate that PROTAX yields essentially unbiased probabilities of taxonomic placement, which means its quantification of species identification uncertainty is reliable. As expected, the accuracy of taxonomic placement increases with increasing coverage of taxonomic and reference sequence data bases, and with increasing ratio of genetic variation among taxonomic levels over within taxonomic levels. We conclude that reliable species-level identification from environmental samples is still challenging and that neglecting identification uncertainty can lead to spurious inference. A key aim for future research is the completion of taxonomic and reference sequence data bases and making these two types of data compatible

Cooling Properties of Cloudy Bag Strange Stars

As the chiral symmetry is widely recognized as an important driver of the strong interaction dynamics, current strange stars models based on MIT bag models do not obey such symmetry. We investigate properties of bare strange stars using the Cloudy Bag Model, in which a pion cloud coupled to the quark-confining bag is introduced such that chiral symmetry is conserved. We find that in this model the decay of pions is a very efficient cooling way. In fact it can carry out most the thermal energy in a few milliseconds and directly convert them into 100MeV photons via pion decay. This may be a very efficient $\gamma$-ray burst mechanism. Furthermore, the cooling behavior may provide a possible way to distinguish a compact object between a neutron star, MIT strange star and Cloudy Bag strange star in observations.Comment: 23 pages, 14 figures, accepted by Astroparticle Physics, abstract appeared here has been shortene

Efficient magneto-optical trapping of Yb atoms with a violet laser diode

We report the first efficient trapping of rare-earth Yb atoms with a high-power violet laser diode (LD). An injection-locked violet LD with a 25 mW frequency-stabilized output was used for the magneto-optical trapping (MOT) of fermionic as well as bosonic Yb isotopes. A typical number of $4\times 10^6$ atoms for $^{174}$Yb with a trap density of $\sim 1\times10^8/$cm$^3$ was obtained. A 10 mW violet external-cavity LD (ECLD) was used for the one-dimensional (1D) slowing of an effusive Yb atomic beam without a Zeeman slower resulting in a 35-fold increase in the number of trapped atoms. The overall characteristics of our compact violet MOT, e.g., the loss time of 1 s, the loading time of 400 ms, and the cloud temperature of 0.7 mK, are comparable to those in previously reported violet Yb MOTs, yet with a greatly reduced cost and complexity of the experiment.Comment: 5 pages, 3 figures, 1 table, Phys. Rev. A (to be published

Dissociation cross sections of ground-state and excited charmonia with light mesons in the quark model

We present numerical results for the dissociation cross sections of ground-state, orbitally- and radially-excited charmonia in collisions with light mesons. Our results are derived using the nonrelativistic quark model, so all parameters are determined by fits to the experimental meson spectrum. Examples of dissociation into both exclusive and inclusive final states are considered. The dissociation cross sections of several C=(+) charmonia may be of considerable importance for the study of heavy ion collisions, since these states are expected to be produced more copiously than the J/psi. The relative importance of the productions of ground-state and orbitally-excited charmed mesons in a pion-charmonium collision is demonstrated through the $\sqrt {s}$-dependent charmonium dissociation cross sections.Comment: 9 pages, 8 figure

Circulating Levels of Adipocyte and Epidermal Fatty Acid–Binding Proteins in Relation to Nephropathy Staging and Macrovascular Complications in Type 2 Diabetic Patients

OBJECTIVE—To investigate the relationships of serum adipocyte fatty acid–binding protein (A-FABP) and epidermal fatty acid–binding protein (E-FABP) with renal dysfunction and macrovascular complications in type 2 diabetic patients

Temperature dependence of current self-oscillations and electric field domains in sequential tunneling doped superlattices

We examine how the current--voltage characteristics of a doped weakly coupled superlattice depends on temperature. The drift velocity of a discrete drift model of sequential tunneling in a doped GaAs/AlAs superlattice is calculated as a function of temperature. Numerical simulations and theoretical arguments show that increasing temperature favors the appearance of current self-oscillations at the expense of static electric field domain formation. Our findings agree with available experimental evidence.Comment: 7 pages, 5 figure

Mass measurements of neutron-deficient Y, Zr, and Nb isotopes and their impact on rp and νp nucleosynthesis processes

© 2018 The Authors. Published by Elsevier B.V. This manuscript is made available under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International licence (CC BY-NC-ND 4.0). For further details please see: https://creativecommons.org/licenses/by-nc-nd/4.0/Using isochronous mass spectrometry at the experimental storage ring CSRe in Lanzhou, the masses of 82Zr and 84Nb were measured for the first time with an uncertainty of ∼10 keV, and the masses of 79Y, 81Zr, and 83Nb were re-determined with a higher precision. The latter are significantly less bound than their literature values. Our new and accurate masses remove the irregularities of the mass surface in this region of the nuclear chart. Our results do not support the predicted island of pronounced low α separation energies for neutron-deficient Mo and Tc isotopes, making the formation of Zr–Nb cycle in the rp-process unlikely. The new proton separation energy of 83Nb was determined to be 490(400) keV smaller than that in the Atomic Mass Evaluation 2012. This partly removes the overproduction of the p-nucleus 84Sr relative to the neutron-deficient molybdenum isotopes in the previous νp-process simulations.Peer reviewe