Recommender Systems with Characterized Social Regularization

Social recommendation, which utilizes social relations to enhance recommender systems, has been gaining increasing attention recently with the rapid development of online social network. Existing social recommendation methods are based on the fact that users preference or decision is influenced by their social friends' behaviors. However, they assume that the influences of social relation are always the same, which violates the fact that users are likely to share preference on diverse products with different friends. In this paper, we present a novel CSR (short for Characterized Social Regularization) model by designing a universal regularization term for modeling variable social influence. Our proposed model can be applied to both explicit and implicit iteration. Extensive experiments on a real-world dataset demonstrate that CSR significantly outperforms state-of-the-art social recommendation methods.Comment: to appear in CIKM 201

Unpacking merger jets: a Bayesian analysis of GW170817, GW190425 and electromagnetic observations of short gamma-ray bursts

We present a novel fully Bayesian analysis to constrain short gamma-ray burst (sGRB) jet structures associated with cocoon, wide-angle, and simple top-hat jet models, as well as the binary neutron star (BNS) merger rate. These constraints are made given the distance and inclination information from GW170817, observed flux of GRB 170817A, observed rate of sGRBs detected by Swift, and the neutron star merger rate inferred from LIGO's first and second observing runs. A separate analysis is conducted where a fitted sGRB luminosity function is included to provide further constraints. The jet structure models are further constrained using the observation of GW190425, and we find that the assumption that it produced a GRB 170817–like sGRB which went undetected due to the jet geometry is consistent with previous observations. We find and quantify evidence for low-luminosity and wide-angle jet structuring in the sGRB population, independently from afterglow observations, with log Bayes factors of 0.45–0.55 for such models when compared to a classical top-hat jet. Slight evidence is found for a Gaussian jet structure model over all others when the fitted luminosity function is provided, producing log Bayes factors of 0.25–0.9 ± 0.05 when compared to the other models. However, without considering GW190425 or the fitted luminosity function, the evidence favors a cocoon-like model with log Bayes factors of 0.14 ± 0.05 over the Gaussian jet structure. We provide new constraints to the BNS merger rates of 1–1300 Gpc⁻³ yr⁻¹ or 2–680 Gpc⁻³ yr⁻¹ when a fitted luminosity function is assumed

Rapid Generation of Kilonova Light Curves Using Conditional Variational Autoencoder

The discovery of the optical counterpart, along with the gravitational waves from GW170817, of the first binary neutron star merger, opened up a new era for multi-messenger astrophysics. Combining the GW data with the optical counterpart, also known as AT2017gfo, classified as a kilonova, has revealed the nature of compact binary merging systems by extracting enriched information about the total binary mass, the mass ratio, the system geometry, and the equation of state. Even though the detection of kilonova brought about a revolution in the domain of multi-messenger astronomy, since there has been only one kilonova from a gravitational wave detected binary neutron star merger event so far, this limits the exact understanding of the origin and propagation of the kilonova. Here, we use a conditional variational autoencoder trained on light curve data from two kilonova models having different temporal lengths, and consequently, generate kilonova light curves rapidly based on physical parameters of our choice with good accuracy. Once trained, the time scale for light curve generation is of the order of a few milliseconds, thus speeding up generating light curves by $1000$ times compared to the simulation. The mean squared error between the generated and original light curves is typically $0.015$ with a maximum of $0.08$ for each set of considered physical parameter; while having a maximum of $\approx0.6$ error across the whole parameter space. Hence, implementing this technique provides fast and reliably accurate results.Comment: 19 pages, 7 figures (3 additional figures in appendix), accepted to Ap

Pengembangan Suplemen Pembelajaran Fisika Gelombang Elektromagnetik Cahaya Sebagai Partikel Memanfaatkan Virtual Laboratorium

This research has been done to make a supplement for physics learning about light electromagnetic wave as a particle using virtual laboratory. The population of this research was the second year science-students at SMA Muhammadiyah 1 Metro. This development is begun by needs analysis, then identification of resource which is the background of this developmental research. The next step is, identifying the product specification then developing products which contained a tutorial book for teacher and a work sheet for student (LKS). The material and design expert test result is that those products were approved. The external test resulted by users show that the LKS was attractive, very easy to use, and useful. It also was effective to be used as a learning resource because 80% of students reached the passing grade.Telah dilakukan penelitian untuk mengembangkan suplemen pembelajaran fisika gelombang elektromagnetik cahaya sebagai partikel dengan memanfaatkan virtual laboratorium. Populasi penelitian pengembangan ini adalah siswa kelas XI IPA di SMA Muhammadiyah 1 Metro. Pengembangan ini diawali dengan analisis kebutuhan, kemudian identifikasi sumber daya yang melatar belakangi pengembangan. Langkah selanjutnya identifikasi spesifikasi produk yang dilanjutkan dengan mengembangkan produk berupa LKS untuk siswa dan buku panduan untuk guru. Hasil uji internal oleh ahli materi dan ahli desain menyatakan produk yang dikembangkan layak digunakan sebagai media pembelajaran. Hasil uji eksternal oleh pengguna menunjukkan kualitas media pembelajaran menarik, sangat mudah digunakan, dan bermanfaat serta efektif digunakan sebagai media pembelajaran dengan presentase hasil belajar sebesar 80% siswa telah memenuhi KKM

A Bayesian Inference Framework for Gamma-ray Burst Afterglow Properties

In the field of multi-messenger astronomy, Bayesian inference is commonly adopted to compare the compatibility of models given the observed data. However, to describe a physical system like neutron star mergers and their associated gamma-ray burst (GRB) events, usually more than ten physical parameters are incorporated in the model. With such a complex model, likelihood evaluation for each Monte Carlo sampling point becomes a massive task and requires a significant amount of computational power. In this work, we perform quick parameter estimation on simulated GRB X-ray light curves using an interpolated physical GRB model. This is achieved by generating a grid of GRB afterglow light curves across the parameter space and replacing the likelihood with a simple interpolation function in the high-dimensional grid that stores all light curves. This framework, compared to the original method, leads to a ∼90× speedup per likelihood estimation. It will allow us to explore different jet models and enable fast model comparison in the future

Inclination estimates from off-axis GRB afterglow modelling

For gravitational wave (GW) detected neutron star mergers, one of the leading candidates for electromagnetic (EM) counterparts is the afterglow from an ultra-relativistic jet. Where this afterglow is observed, it will likely be viewed off-axis, such as the afterglow following GW170817/GRB 170817A. The temporal behaviour of an off-axis observed GRB afterglow can be used to reveal the lateral jet structure, and statistical model fits can put constraints on the various model free-parameters. Amongst these parameters is the inclination of the system to the line of sight. Along with the GW detection, the afterglow modelling provides the best constraint on the inclination to the line-of-sight and can improve the estimates of cosmological parameters, for example, the Hubble constant, from GW-EM events. However, modelling of the afterglow depends on the assumed jet structure and—often overlooked—the effects of lateral spreading. Here we show how the inclusion of lateral spreading in the afterglow models can affect the estimated inclination of GW-EM events

Rapid generation of kilonova light curves using conditional variational autoencoder

The discovery of the optical counterpart, along with the gravitational waves (GWs) from GW170817, of the first binary neutron star merger has opened up a new era for multimessenger astrophysics. Combining the GW data with the optical counterpart, also known as AT 2017gfo and classified as a kilonova, has revealed the nature of compact binary merging systems by extracting enriched information about the total binary mass, the mass ratio, the system geometry, and the equation of state. Even though the detection of kilonovae has brought about a revolution in the domain of multimessenger astronomy, there has been only one kilonova from a GW-detected binary neutron star merger event confirmed so far, and this limits the exact understanding of the origin and propagation of the kilonova. Here, we use a conditional variational autoencoder (CVAE) trained on light-curve data from two kilonova models having different temporal lengths, and consequently, generate kilonova light curves rapidly based on physical parameters of our choice with good accuracy. Once the CVAE is trained, the timescale for light-curve generation is of the order of a few milliseconds, which is a speedup of the generation of light curves by 1000 times as compared to the simulation. The mean squared error between the generated and original light curves is typically 0.015 with a maximum of 0.08 for each set of considered physical parameters, while having a maximum of ≈0.6 error across the whole parameter space. Hence, implementing this technique provides fast and reliably accurate results