Joint inversion of Rayleigh wave phase velocity and ellipticity using USArray: Constraining velocity and density structure in the upper crust

Rayleigh wave ellipticity, or H/V ratio, observed on the surface is particularly sensitive to shallow earth structure. In this study, we jointly invert measurements of Rayleigh wave H/V ratio and phase velocity between 24–100 and 8–100 sec period, respectively, for crust and upper mantle structure beneath more than 1000 USArray stations covering the western United States. Upper crustal structure, in particular, is better constrained by the joint inversion compared to inversions based on phase velocities alone. In addition to imaging Vs structure, we show that the joint inversion can be used to constrain Vp/Vs and density in the upper crust. New images of uppermost crustal structure (<3 km depth) are in excellent agreement with known surface features, with pronounced low Vs, low density, and high Vp/Vs anomalies imaged in the locations of several major sedimentary basins including the Williston, Powder River, Green River, Denver, and San Juan basins. These results demonstrate not only the consistency of broadband H/V ratios and phase velocity measurements, but also that their complementary sensitivities have the potential to resolve density and Vp/Vs variations

Diffraction-limited Fabry-Perot Cavity in the Near Concentric Regime

Nearly concentric optical cavities can be used to prepare optical fields with a very small mode volume. We implement an anaclastic design of a such a cavity that significantly simplifies mode matching to the fundamental cavity mode. The cavity is shown to have diffraction-limited performance for a mode volume of $\approx10^4\lambda^3$. This is in sharp contrast with the behavior of cavities with plano-concave mirrors, where aberrations significantly increase the losses in the fundamental mode. We estimate the related cavity QED parameters and show that the proposed cavity design allows for strong coupling without a need for high finesse or small physical cavity volume.Comment: 6 pages, 7 figure

DETERMINANTS OF FINANCIAL INCLUSION (FI) IN NIGERIAN ECONOMY

In this study, which covered the 16-year period from 2006 to 2021, the factors of financial inclusion (FI) in the Nigerian economy were empirically explored. As suggested in the model, after controlling for deposit interest rate and inflation rate, GDP per capita income, domestic credit to the private sector (% of GDP), broad money supply (% of GDP), number of commercial banks network, and age dependency ratio were all suggested as the determinants of FI. Data for the study were taken from the Central Bank of Nigeria Statistical Bulletin (2021), World Bank Data (2021), and International Monetary Fund report (2020), and were estimated using the ordinary least square approach with the aid of Econometric Views version 9.0. The study found that GDPPC, PSC/GDP NBRA, ADEPR and INFR all have a positive statistically insignificant effect on FI. This stance implies that FI in Nigeria is marginal. However, the number of commercial banks network and the age dependence ratio have a negative statistically negligible impact on FI whereas the M2/GDP and DEIR have a positive statistically significant impact. Therefore, the analysis draws the conclusion that the only significant factors of FI in Nigeria are M2/GDP and DEIR. In light of this, we advise all deposit money banks to make sure their goods and services are alluring since doing so will encourage the Nigerian people to save more. Keywords: FI, Deposit, Interest Rate, Inflation Rate and Credit to Private Sector

CORPORATE GOVERNANCE (CG) AND ITS IMPLICATION ON PERFORMANCE OF DEPOSIT MONEY BANKS (DMBs) IN NIGERIA

This study examined the effects of corporate governance (CG) on performance of deposit money banks (DMBs) in Nigeria between the periods 2012-2021 (10years). The specific objective of the study is to examine the relationship between the measures of CG [Board Size (BS), Board Independence (BI), Board Gender Diversity (BGD), Audit Committee Size (ACS) and Audit Committee Independence (ACS)] and [performance of DMBs proxy with Returns on Equity (ROE)]. The data for the study would be gotten from the annual reports and accounts of the 10 DMBs on the basis of the variables under study. The type of relationship between the independent and dependent variables will be determined using descriptive statistics and correlation analysis, and the method of data analysis chosen was a multiple regression analysis using the OLS method by E-VIEW 9.0. DMBs that wish to enhance their FP, especially post covid-19, should try to reduce their BS to plus or minus ten, but with strict adherence to the regulatory guideline. This will ensure that the yearly huge expense on this is reduced and efficiency is guaranteed. This will have more positive influence on corporate performance. Keywords: Corporate Governance, Size, Gender, Board and Performance

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

3-D crustal structure of the western United States: application of Rayleigh-wave ellipticity extracted from noise cross-correlations

We present a new 3-D seismic model of the western United States crust derived from a joint inversion of Rayleigh-wave phase velocity and ellipticity measurements using periods from 8 to 100 s. Improved constraints on upper-crustal structure result from use of short-period Rayleigh-wave ellipticity, or Rayleigh-wave H/V (horizontal to vertical) amplitude ratios, measurements determined using multicomponent ambient noise cross-correlations. To retain the amplitude ratio information between vertical and horizontal components, for each station, we perform daily noise pre-processing (temporal normalization and spectrum whitening) simultaneously for all three components. For each station pair, amplitude measurements between cross-correlations of different components (radial–radial, radial–vertical, vertical–radial and vertical–vertical) are then used to determine the Rayleigh-wave H/V ratios at the two station locations. We use all EarthScope/USArray Tranportable Array data available between 2007 January and 2011 June to determine the Rayleigh-wave H/V ratios and their uncertainties at all station locations and construct new Rayleigh-wave H/V ratio maps in the western United States between periods of 8 and 24 s. Combined with previous longer period earthquake Rayleigh-wave H/V ratio measurements and Rayleigh-wave phase velocity measurements from both ambient noise and earthquakes, we invert for a new 3-D crustal and upper-mantle model in the western United States. Correlation between the inverted model and known geological features at all depths suggests good resolution in five crustal layers. Use of short-period Rayleigh-wave H/V ratio measurements based on noise cross-correlation enables resolution of distinct near surface features such as the Columbia River Basalt flows, which overlie a thick sedimentary basin

Profit Maximization with Sufficient Customer Satisfactions

In many commercial campaigns, we observe that there exists a tradeoff between the number of customers satisfied by the company and the profit gained. Merely satisfying as many customers as possible or maximizing the profit is not desirable. To this end, in this article, we propose a new problem called k - &lt;underline&gt;S&lt;/underline&gt;atisfiability &lt;underline&gt;A&lt;/underline&gt;ssignment for &lt;underline&gt;M&lt;/underline&gt;aximizing the &lt;underline&gt;P&lt;/underline&gt;rofit ( k -SAMP), where k is a user parameter and a non-negative integer. Given a set P of products and a set O of customers, k -SAMP is to find an assignment between P and O such that at least k customers are satisfied in the assignment and the profit incurred by this assignment is maximized. Although we find that this problem is closely related to two classic computer science problems, namely maximum weight matching and maximum matching, the techniques developed for these classic problems cannot be adapted to our k -SAMP problem. In this work, we design a novel algorithm called Adjust for the k -SAMP problem. Given an assignment A , Adjust iteratively increases the profit of A by adjusting some appropriate matches in A while keeping at least k customers satisfied in A . We prove that Adjust returns a global optimum. Extensive experiments were conducted that verified the efficiency of Adjust . </jats:p

Amplification and Attenuation across USArray using Ambient Noise Wavefront Tracking

As seismic traveltime tomography continues to be refined using data from the vast USArray data set, it is advantageous to also exploit the amplitude information carried by seismic waves. We use ambient noise cross correlation to make observations of surface wave amplification and attenuation at shorter periods (8–32 s) than can be observed with only traditional teleseismic earthquake sources. We show that the wavefront tracking approach can be successfully applied to ambient noise correlations, yielding results quite similar to those from earthquake observations at periods of overlap. This consistency indicates that the wavefront tracking approach is viable for use with ambient noise correlations, despite concerns of the inhomogeneous and unknown distribution of noise sources. The resulting amplification and attenuation maps correlate well with known tectonic and crustal structure; at the shortest periods, our amplification and attenuation maps correlate well with surface geology and known sedimentary basins, while our longest period amplitudes are controlled by crustal thickness and begin to probe upper mantle materials. These amplification and attenuation observations are sensitive to crustal materials in different ways than traveltime observations and may be used to better constrain temperature or density variations. We also value them as an independent means of describing the lateral variability of observed Rayleigh wave amplitudes without the need for 3-D tomographic inversions

Investigating Graph Embedding Methods for Cross-Platform Binary Code Similarity Detection

IoT devices are increasingly present, both in the industry and in consumer markets, but their security remains weak, which leads to an unprecedented number of attacks against them. In order to reduce the attack surface, one approach is to analyze the binary code of these devices to early detect whether they contain potential security vulnerabilities. More specifically, knowing some vulnerable function, we can determine whether the firmware of an IoT device contains some security flaw by searching for this function. However, searching for similar vulnerable functions is in general challenging due to the fact that the source code is often not openly available and that it can be compiled for different architectures, using different compilers and compilation settings. In order to handle these varying settings, we can compare the similarity between the graph embeddings derived from the binary functions. In this paper, inspired by the recent advances in deep learning, we propose a new method – GESS (graph embeddings for similarity search) – to derive graph embeddings, and we compare it with various state-of-the-art methods. Our empirical evaluation shows that GESS reaches an AUC of 0.979, thereby outperforming the best known approach. Furthermore, for a fixed low false positive rate, GESS provides a true positive rate (or recall) about 36% higher than the best previous approach. Finally, for a large search space, GESS provides a recall between 50% and 60% higher than the best previous approach