Second Repeating FRB 180814.J0422+73: Ten-year Fermi-LAT Upper Limits and Implications

The second repeating fast radio burst source, FRB 180814.J0422+73, was detected recently by the CHIME collaboration. We use the ten-year Fermi Large Area Telescope archival data to place a flux upper limit in the energy range of 100 MeV−10 GeV at the position of the source, which is ~1.1 × 10−11 erg cm−2 s−1 for a six-month time bin on average, and ~2.4 × 10−12 erg cm−2 s−1 for the entire ten-year time span. For the maximum redshift of z = 0.11, the ten-year upper limit of luminosity is ~7.3 × 1043 erg s−1. We utilize these upper limits to constrain the fast radio burst (FRB) progenitor and central engine. For the rotation-powered young magnetar model, the upper limits can pose constraints on the allowed parameter space for the initial rotational period and surface magnetic field of the magnetar. We also place significant constraints on the kinetic energy of a relativistic external shock wave, ruling out the possibility that there existed a gamma-ray burst (GRB) beaming toward Earth during the past ten years as the progenitor of the repeater. The case of an off-beam GRB is also constrained if the viewing angle is not much greater than the jet opening angle. All of these constraints are more stringent if FRB 180814.J0422+73 is at a closer distance

Simultaneous Feature and Body-Part Learning for Real-Time Robot Awareness of Human Behaviors

Robot awareness of human actions is an essential research problem in robotics with many important real-world applications, including human-robot collaboration and teaming. Over the past few years, depth sensors have become a standard device widely used by intelligent robots for 3D perception, which can also offer human skeletal data in 3D space. Several methods based on skeletal data were designed to enable robot awareness of human actions with satisfactory accuracy. However, previous methods treated all body parts and features equally important, without the capability to identify discriminative body parts and features. In this paper, we propose a novel simultaneous Feature And Body-part Learning (FABL) approach that simultaneously identifies discriminative body parts and features, and efficiently integrates all available information together to enable real-time robot awareness of human behaviors. We formulate FABL as a regression-like optimization problem with structured sparsity-inducing norms to model interrelationships of body parts and features. We also develop an optimization algorithm to solve the formulated problem, which possesses a theoretical guarantee to find the optimal solution. To evaluate FABL, three experiments were performed using public benchmark datasets, including the MSR Action3D and CAD-60 datasets, as well as a Baxter robot in practical assistive living applications. Experimental results show that our FABL approach obtains a high recognition accuracy with a processing speed of the order-of-magnitude of 10e4 Hz, which makes FABL a promising method to enable real-time robot awareness of human behaviors in practical robotics applications.Comment: 8 pages, 6 figures, accepted by ICRA'1

The Discovery of Timescale-Dependent Color Variability of Quasars

Quasars are variable on timescales from days to years in UV/optical, and generally appear bluer while they brighten. The physics behind the variations in fluxes and colors remains unclear. Using SDSS g and r band photometric monitoring data of quasars in Stripe 82, we find that although the flux variation amplitude increases with timescale, the color variability exhibits opposite behavior. The color variability of quasars is prominent at timescales as short as ~ 10 days, but gradually reduces toward timescales up to years. In other words, the variable emission at shorter timescales is bluer than that at longer timescales. This timescale dependence is clearly and consistently detected at all redshifts from z = 0 to 3.5, thus can not be due to contaminations to broadband photometry from emission lines which do not respond to fast continuum variations. The discovery directly rules out the possibility that simply attributes the color variability to contamination from a non-variable redder component, such as the host galaxy. It can not be interpreted as changes in global accretion rate either. The thermal accretion disk fluctuation model is favored, in the sense that fluctuations in the inner hotter region of the disk are responsible for short term variations, while longer term and stronger variations are expected from larger and cooler disk region. An interesting implication is that one can use quasar variations at different timescales to probe disk emission at different radii.Comment: Accepted for publication in ApJ, 22 pages, 5 figure

Scale-Adaptive Group Optimization for Social Activity Planning

Studies have shown that each person is more inclined to enjoy a group activity when 1) she is interested in the activity, and 2) many friends with the same interest join it as well. Nevertheless, even with the interest and social tightness information available in online social networks, nowadays many social group activities still need to be coordinated manually. In this paper, therefore, we first formulate a new problem, named Participant Selection for Group Activity (PSGA), to decide the group size and select proper participants so that the sum of personal interests and social tightness of the participants in the group is maximized, while the activity cost is also carefully examined. To solve the problem, we design a new randomized algorithm, named Budget-Aware Randomized Group Selection (BARGS), to optimally allocate the computation budgets for effective selection of the group size and participants, and we prove that BARGS can acquire the solution with a guaranteed performance bound. The proposed algorithm was implemented in Facebook, and experimental results demonstrate that social groups generated by the proposed algorithm significantly outperform the baseline solutions.Comment: 20 pages. arXiv admin note: substantial text overlap with arXiv:1305.150

The Effects of Herding Cues and Social Connectivity on Online Group-based Smoking Cessation

With the development of mobile technologies, online group-based intervention has emerged as a novel approach to promote smoking cessation. However, there has been limited research on how features in online group-based interventions contribute to shaping behavior change. Using unique data from a mobile smoking cessation platform, we investigate how herding cues and the use of @mentions affect members’ commitment to smoking cessation. Our results show that herding cues indicating a higher rate of peer adherence to abstinence check-in, and more connections facilitated through @mentions are associated with reduced likelihood of quitting the online smoking cessation group. Further, we found the effects vary among individuals, depending on their prior group experiences and failure reasons. Our findings have implications for the design of mobile cessation apps, indicating the need to develop different strategies tailored to effectively retain distinct user groups based on their characteristics