Distribution of Caustic-Crossing Intervals for Galactic Binary-Lens Microlensing Events

Detection of caustic crossings of binary-lens gravitational microlensing events is important because by detecting them one can obtain useful information both about the lens and source star. In this paper, we compute the distribution of the intervals between two successive caustic crossings, $f(t_{\rm cc})$, for Galactic bulge binary-lens events to investigate the observational strategy for the optimal detection and resolution of caustic crossings. From this computation, we find that the distribution is highly skewed toward short $t_{\rm cc}$ and peaks at $t_{\rm cc}\sim 1.5$ days. For the maximal detection of caustic crossings, therefore, prompt initiation of followup observations for intensive monitoring of events will be important. We estimate that under the strategy of the current followup observations with a second caustic-crossing preparation time of $\sim 2$ days, the fraction of events with resolvable caustic crossing is $\sim 80$. We find that if the followup observations can be initiated within 1 day after the first caustic crossing by adopting more aggressive observational strategies, the detection rate can be improved into $\sim 90$.Comment: total 6 pages, including 5 Figures and no Table, submitted to MNRA

Do Financial Analysts Facilitate Investors’ Assessment Of Earnings?: Evidence From The Korean Stock Market

This paper seeks to enhance our understanding of financial analysts in assisting market investors’ use of accounting earnings in the Korean stock market. We examine whether stock returns differentially reflect earnings information for firms with analyst coverage. We propose that the role of analysts as external monitors as well as information intermediaries enhances the market investors’ valuation of earnings. We find that market valuation of earnings is higher for firms with analyst following. Furthermore, market investors’ valuation of earnings increases (or decreases) with the number of analysts (or with the dispersion of analysts’ forecasts). This suggests that the beneficial effect of analysts arises through the quantity and quality of analysts’ information. This study contributes to the literature by investigating the important role of analysts in emerging market

Treatment effect analysis of early reemployment bonus program : panel MLE and mode-based semiparametric estimator for interval truncation

We use Korean data to find the ef fects of Early Reemployment Bonus (ERB) on unemployment duration; ERB is a bonus that the eligible unemployed receive if they find a job before their unemployment insurance benefit expires. A naive approach would be comparing the ERB receiving group with the non-receiving group, but the ERB receipt is partly determined by the unemployment duration itself (thus, an endogeneity problem). Interestingly, there were many individuals who did not receive the ERB despite being fully eligible, and this is attributed to being unaware of the ERB scheme. Taking this as a ‘pseudo randomization’, we construct treatment and control groups using only the eligible. Our data set is an unbalanced panel with the response variable interval-truncated due to eligibility requirement of the ERB. We propose a panel random-effect MLE and a semiparametric ‘mode-based’ estimator for the interval-truncated response. Our empirical finding is that the effect varies much, depending on individual characteristics. As for the mean effects, whereas the MLE indicates large duration-shortening effects, the semiparametric estimator shows much weaker and mostly insignificant effects.info:eu-repo/semantics/publishedVersio

FiFo: Fishbone Forwarding in Massive IoT Networks

Massive Internet of Things (IoT) networks have a wide range of applications, including but not limited to the rapid delivery of emergency and disaster messages. Although various benchmark algorithms have been developed to date for message delivery in such applications, they pose several practical challenges such as insufficient network coverage and/or highly redundant transmissions to expand the coverage area, resulting in considerable energy consumption for each IoT device. To overcome this problem, we first characterize a new performance metric, forwarding efficiency, which is defined as the ratio of the coverage probability to the average number of transmissions per device, to evaluate the data dissemination performance more appropriately. Then, we propose a novel and effective forwarding method, fishbone forwarding (FiFo), which aims to improve the forwarding efficiency with acceptable computational complexity. Our FiFo method completes two tasks: 1) it clusters devices based on the unweighted pair group method with the arithmetic average; and 2) it creates the main axis and sub axes of each cluster using both the expectation-maximization algorithm for the Gaussian mixture model and principal component analysis. We demonstrate the superiority of FiFo by using a real-world dataset. Through intensive and comprehensive simulations, we show that the proposed FiFo method outperforms benchmark algorithms in terms of the forwarding efficiency.Comment: 13 pages, 16 figures, 5 tables; to appear in the IEEE Internet of Things Journal (Please cite our journal version that will appear in an upcoming issue.

An efficient downlink beamforming scheme for FDD/SDMA systems

Without channel information of the downlink, the base station can generate downlink beam pattern using the weight vector used for the uplink. In the frequency division duplex system, however, it may result in significant performance degradation due to the carrier frequency offset between the uplink and downlink. To resolve this problem, we propose an efficient downlink beamforming algorithm based on a least square method with constraints. We also consider the control of null depth to obtain a desired signal to interference power ratio. Simulation results show that the proposed scheme can sufficiently reduce the interference from other users, improving the BER performance in the downlink

Materialization of single multicomposite nanowire: entrapment of ZnO nanoparticles in polyaniline nanowire

We present materialization of single multicomposite nanowire (SMNW)-entrapped ZnO nanoparticles (NPs) via an electrochemical growth method, which is a newly developed fabrication method to grow a single nanowire between a pair of pre-patterned electrodes. Entrapment of ZnO NPs was controlled via different conditions of SMNW fabrication such as an applied potential and mixture ratio of NPs and aniline solution. The controlled concentration of ZnO NP results in changes in the physical properties of the SMNWs, as shown in transmission electron microscopy images. Furthermore, the electrical conductivity and elasticity of SMNWs show improvement over those of pure polyaniline nanowire. The new nano-multicomposite material showed synergistic effects on mechanical and electrical properties, with logarithmical change and saturation increasing ZnO NP concentration