42,383 research outputs found
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
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
Dissecting the genome-wide evolution and function of R2R3-MYB transcription factor family in Rosa chinensis
Rosa chinensis, an important ancestor species of Rosa hybrida, the most popular ornamental plant species worldwide, produces flowers with diverse colors and fragrances. The R2R3-MYB transcription factor family controls a wide variety of plant-specific metabolic processes, especially phenylpropanoid metabolism. Despite their importance for the ornamental value of flowers, the evolution of R2R3-MYB genes in plants has not been comprehensively characterized. In this study, 121 predicted R2R3-MYB gene sequences were identified in the rose genome. Additionally, a phylogenomic synteny network (synnet) was applied for the R2R3-MYB gene families in 35 complete plant genomes. We also analyzed the R2R3-MYB genes regarding their genomic locations, Ka/Ks ratio, encoded conserved motifs, and spatiotemporal expression. Our results indicated that R2R3-MYBs have multiple synteny clusters. The RcMYB114a gene was included in the Rosaceae-specific Cluster 54, with independent evolutionary patterns. On the basis of these results and an analysis of RcMYB114a-overexpressing tobacco leaf samples, we predicted that RcMYB114a functions in the phenylpropanoid pathway. We clarified the relationship between R2R3-MYB gene evolution and function from a new perspective. Our study data may be relevant for elucidating the regulation of floral metabolism in roses at the transcript level
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