Sparse Signal Recovery under Poisson Statistics

We are motivated by problems that arise in a number of applications such as Online Marketing and explosives detection, where the observations are usually modeled using Poisson statistics. We model each observation as a Poisson random variable whose mean is a sparse linear superposition of known patterns. Unlike many conventional problems observations here are not identically distributed since they are associated with different sensing modalities. We analyze the performance of a Maximum Likelihood (ML) decoder, which for our Poisson setting involves a non-linear optimization but yet is computationally tractable. We derive fundamental sample complexity bounds for sparse recovery when the measurements are contaminated with Poisson noise. In contrast to the least-squares linear regression setting with Gaussian noise, we observe that in addition to sparsity, the scale of the parameters also fundamentally impacts sample complexity. We introduce a novel notion of Restricted Likelihood Perturbation (RLP), to jointly account for scale and sparsity. We derive sample complexity bounds for $\ell_1$ regularized ML estimators in terms of RLP and further specialize these results for deterministic and random sensing matrix designs.Comment: 13 pages, 11 figures, 2 tables, submitted to IEEE Transactions on Signal Processin

PENGARUH KULIAH ONLINE TERHADAP MOTIVASI BELAJAR MAHASISWA DI DAERAH JABODETABEK SELAMA MASA PANDEMIK COVID-19

 This study aims to determine whether online lectures have a positive and significant effect on student learning motivation at tertiary institutions in the Greater Jakarta area (Jakarta-Bogor-Depok-Tangerang-Bekasi) during the covid-19 pandemic. Data collection used a questionnaire in the form of google form which was distributed to 200 respondents. The data analysis method used is descriptive analysis and simple linear regression analysis. Based on the results of the descriptive analysis of the research variables, it was found that basically online lectures were not as effective and efficient as face-to-face lectures, online lectures during the Covid-19 pandemic did not run as optimally and as optimally as face-to-face lectures. Meanwhile, based on the results of simple linear regression analysis, online lectures have a positive effect on student learning motivation at tertiary institutions in the Jabodetabek area.Penelitian ini bertujuan untuk mengetahui apakah kuliah online berpengaruh positif dan signifikan terhadap motivasi belajar mahasiswa pada perguruan tinggi di daerah Jabodetabek (Jakarta-Bogor-Depok-Tangerang-Bekasi) selama masa pandemik covid-19. Pengumpulan data menggunakan angket berupa google form yang disebarkan kepada 200 responden. Metode analisis data yang digunakan adalah analisis deskriptif dan analisis regresi linear sederhana. Berdasarkan hasil analisis deskriptif variabel penelitian diperoleh hasil bahwa pada dasarnya kuliah online tidak seefektif dan seefisien kuliah tatap muka, kuliah online di masa pandemik covid-19 ini tidak berjalan seoptimal dan semaksimal kuliah tatap muka. Sedangkan berdasarkan hasil analisis regresi linear sederhana menunjukkan kuliah online berpengaruh positif terhadap motivasi belajar mahasiswa pada perguruan tinggi di daerah Jabodetabek

Pengaruh Trust Terhadap Keputusan Beli Produk Tiff Body

The shift of the Indonesian lifestyle has become online oriented has a huge impact on e-commerce business's popularity. This study aims to determine the influence of trust on to purchase decision of Tiff Body's product. This research uses the quantitative methodology and data collection method to use an online questionnaire with a purposive sampling technique distributed to 100 respondents who have been shopping at Tiff Body. After the data is analyzed using Simple Linear Regression with IBM SPSS Statistics 25 program, the result shows that trust influences purchase decisions

Improved Dynamic Regret of Distributed Online Multiple Frank-Wolfe Convex Optimization

In this paper, we consider a distributed online convex optimization problem over a time-varying multi-agent network. The goal of this network is to minimize a global loss function through local computation and communication with neighbors. To effectively handle the optimization problem with a high-dimensional and structural constraint set, we develop a distributed online multiple Frank-Wolfe algorithm to avoid the expensive computational cost of projection operation. The dynamic regret bounds are established as $\mathcal{O}(T^{1-\gamma}+H_T)$ with the linear oracle number $\mathcal{O} (T^{1+\gamma})$, which depends on the horizon (total iteration number) $T$, the function variation $H_T$, and the tuning parameter $0<\gamma<1$. In particular, when the prior knowledge of $H_T$ and $T$ is available, the bound can be enhanced to $\mathcal{O} (1+H_T)$. Moreover, we illustrate the significant advantages of the multiple iteration technique and reveal a trade-off between dynamic regret bound, computational cost, and communication cost. Finally, the performance of our algorithm is verified and compared through the distributed online ridge regression problems with two constraint sets

Pengaruh Penggunaan Celebrity Instagram Endorser Terhadap Keputusan Pembelian Pada Bisnis Online Di Kota Bandung

In the online era like today, Indonesians prefer to buy and sell products through social media. Instagram is widely used to promote products. Consumer purchasing decisions are very important to be reviewed by the company because it will have an impact on the survival of the company. One important aspect is the Instagram celebrity endorser. The purpose of this study was to determine the effect of using celebrity instagram endorser on purchasing decisions in online businesses in Bandung. The place of research was conducted at Mayoutfit Bandung. This research is a quantitative descriptive study with simple linear regression analysis. The data collection technique is through the distribution of questionnaires distributed to Mayoutfit consumers who use Instagram. The data analysis technique is done by validity and reliability test, descriptive test, classic assumption test, normality test, simple linear regression test, hypothesis test and test coefficient of determination. This study shows the influence of Instagram celebrity endorser on consumer purchasing decisions. Suggestions for future research and practical suggestions will be presented further. Keywords: Celebrity endorser, Instagram, Onlineshop, Purchase Decisio

SERVICE QUALITY AND ONLINE CUSTOMER RATING ON F&B PURCHASE DECISIONS

This study aims to determine how the influence of service quality and online customer rating on food and beverage product purchasing decisions using the Grabfood application. This research data is sourced and collected from questionnaires distributed to respondents (consumers Grabfood application). The number of respondents in this study amounted to 100 respondents in Makassar. This type of research is a quantitative research. The research method uses multiple linear regression analysis using SPSS software to process the data. The results showed that Service Quality had a positive and significant effect on Purchase Decisions, Online Customer Rating had a positive and significant effect on Purchase Decisions, Service Quality and Online Customer Rating simultaneously had a positive and significant effect on Purchase Decisions

Pengaruh Media Pembelajaran Daring terhadap Hasil Belajar PWPB Kelas XII RPL SMK N 1 Bukit Sundi

This study aims to determine the effect of online learning (X) on learning outcomes in web programming and mobile devices (Y) subjects at SMK N 1 Bukit Sundi. This study uses quantitative research. The data collection technique uses a questionnaire which is distributed to students. The population in this study were all class XII students majoring in RPL SMK N 1 Bukit Sundi totaling 40 students. The number of samples in this study were 40 students. Data analysis used simple linear regression analysis through calculations with the help of the IBM SPSS Statistics 26 program. The results showed that "The influence of online learning has a positive effect on learning outcomes of PWPB subjects at SMK N 1 Bukit Sundi". Linear Regression Equation The simple linear equation Y = 33.258 + 0.701X is obtained. From the results of the analysis, tcount is 4.284 &gt; ttable value is 2.024 or Ho is rejected. Thus "Online Learning has a positive effect on Learning Outcomes of Class XII RPL PWPB subjects". This can be seen in the coefficient table of the tcount and the p-value is less than 0.05. The coefficient of determination is as big as in the table above, namely the value of R Square = 0.326 = 32.6%, this means that the variation in the learning outcomes variable is PWPB subject

PENGARUH PEMBELAJARAN DARING TERHADAP HASIL BELAJAR SISWA KELAS XI IPS MATA PELAJARAN GEOGRAFI SMA NEGERI 2 PONTIANAK

This research aims to determine the effect of online learning onstudents’ learning results in XI Grades of IPS at SMA Negeri 2Pontianak. The research method that used in this research is ex postfacto with a quantitative approach. The population that used in thisresearch were all students of XI Grades of IPS at SMA Negeri 2Pontianak which consisted of 185 students. The sample in this researchwas 65 students using a simple random sampling technique. Datacollection techniques used in this research were questionnaires anddocumentation. The results of this research showed that there was asignificant influence between online learning on students’ learningresults. Online learning results with an average score of 51.87 andstudents’ learning results with an average score of 56.35. Prerequisiteanalysis of online learning normality test results is 0.124 and learningresults are 0.280>0.05 can be declared normally distributed. Theresults of the linearity test are 0.302 > 0.05, so it can be stated that alinear in online learning on students’ learning results. Hypothesistesting, the results of simple regression analysis showed a sig score of0.001<0.05, a simple regression test can be said to predict variable Xin other words there is an effect of variable X on variable Y. Thesimple linear regression equation is Y = 22.58 + 0.651 X. The resultsof the determination test that the R2 of the online learning variable is0.163, which means that the online learning variable contributes16.3% to the effect of learning results