Probing the Emission States of PSR J1107−5907

The emission from PSR J1107−5907 is erratic. Sometimes the radio pulse is undetectable, at other times the pulsed emission is weak, and for short durations the emission can be very bright. In order to improve our understanding of these state changes, we have identified archival data sets from the Parkes radio telescope in which the bright emission is present, and find that the emission never switches from the bright state to the weak state, but instead always transitions to the "off" state. Previous work had suggested the identification of the "off" state as an extreme manifestation of the weak state. However, the connection between the "off" and bright emission reported here suggests that the emission can be interpreted as undergoing only two emission states: a "bursting" state consisting of both bright pulses and nulls, and the weak emission state

Flux density measurements for 32 pulsars in the 20 cm band

Flux density measurements provide fundamental observational parameters that describe a pulsar. In the current pulsar catalogue, 27% of radio pulsars have no flux density measurement in the 20 cm observing band. Here, we present the first measurements of the flux densities in this band for 32 pulsars observed using the Parkes radio telescope and provide updated pulse profiles for these pulsars. We have used both archival and new observations to make these measurements. Various schemes exist for measuring flux densities. We show how the flux densities measured vary between these methods and how the presence of radio-frequency-interference will bias flux density measurementsComment: Accepted by RA

Lane Detection Based on Machine Learning Algorithm

In order to improve accuracy and robustness of the lane detection in complex conditions, such as the shadows and illumination changing, a novel detection algorithm was proposed based on machine learning. After pretreatment, a set of haar-like filters were used to calculate the eigenvalue in the gray image f(x,y) and edge e(x,y). Then these features were trained by using improved boosting algorithm and the final class function g(x) was obtained, which was used to judge whether the point x belonging to the lane or not. To avoid the over fitting in traditional boosting, Fisher discriminant analysis was used to initialize the weights of samples. After testing by many road in all conditions, it showed that this algorithm had good robustness and real-time to recognize the lane in all challenging conditions. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.3923

Systematic Investigation on the Structure-Property Relationship in Isotactic Polypropylene Films Processed via Cast Film Extrusion

The effect of cast film extrusion processing conditions, such as the chill-roll temperature, temperature of the melt, and line speed, on the structure of different isotactic polypropylene homo- and random copolymers has been investigated by means of Small- and Wide-Angle X-ray Scattering (SAXS and WAXS) and correlated to stiffness and haze. Stiffness and transparency have been found to be strongly dependent on the temperature of the chill-roll. Interestingly, line speed has been found to affect the total crystallinity when the chill-roll temperature is increased, while an overall minor effect of the melt temperature was found for all cast films. The polymer characteristics, defined by the catalyst nature and comonomer content, affect the final material performance, with the single-site catalyzed grades performing better in both mechanics and optics. Haze levels were found to correlate with the mesophase content rather than to α-crystallinity and to be dependent on the domain size for all grades. The remarkably low haze levels reached by the single-site grade with higher isotacticity can arise from high nucleation rate and orientational effects, which ultimately yield smaller and smoother scattering domains

Deep Graph Neural Networks via Flexible Subgraph Aggregation

Graph neural networks (GNNs), a type of neural network that can learn from graph-structured data and learn the representation of nodes through aggregating neighborhood information, have shown superior performance in various downstream tasks. However, it is known that the performance of GNNs degrades gradually as the number of layers increases. In this paper, we evaluate the expressive power of GNNs from the perspective of subgraph aggregation. We reveal the potential cause of performance degradation for traditional deep GNNs, i.e., aggregated subgraph overlap, and we theoretically illustrate the fact that previous residual-based GNNs exploit the aggregation results of 1 to $k$ hop subgraphs to improve the effectiveness. Further, we find that the utilization of different subgraphs by previous models is often inflexible. Based on this, we propose a sampling-based node-level residual module (SNR) that can achieve a more flexible utilization of different hops of subgraph aggregation by introducing node-level parameters sampled from a learnable distribution. Extensive experiments show that the performance of GNNs with our proposed SNR module outperform a comprehensive set of baselines

Advances in 3D Generation: A Survey

Generating 3D models lies at the core of computer graphics and has been the focus of decades of research. With the emergence of advanced neural representations and generative models, the field of 3D content generation is developing rapidly, enabling the creation of increasingly high-quality and diverse 3D models. The rapid growth of this field makes it difficult to stay abreast of all recent developments. In this survey, we aim to introduce the fundamental methodologies of 3D generation methods and establish a structured roadmap, encompassing 3D representation, generation methods, datasets, and corresponding applications. Specifically, we introduce the 3D representations that serve as the backbone for 3D generation. Furthermore, we provide a comprehensive overview of the rapidly growing literature on generation methods, categorized by the type of algorithmic paradigms, including feedforward generation, optimization-based generation, procedural generation, and generative novel view synthesis. Lastly, we discuss available datasets, applications, and open challenges. We hope this survey will help readers explore this exciting topic and foster further advancements in the field of 3D content generation.Comment: 33 pages, 12 figure

Spatial and temporal characteristics of dryness/wetness for grapevine in the Northeast of China between 1981-2020

The Northeast of China has a marked continental monsoon climate characterized by dry and wet hazards that have destructive impacts on wine grape yields and quality. The purpose of this study was to analyze the spatiotemporal characteristics of dryness/wetness of grapevines in the wine region of northeast China from 1981 to 2020. The Crop Water Surplus and Deficit Index (CWSDI) was used to characterize the dryness/wetness using meteorological data collected at 15 meteorological stations located in or near the wine region of northeast China from 1981–2020. Results showed that the multi-year average precipitation could satisfy the water requirement of grapevine with the average CWSDI of 43% (Bud burst), 35% (Shoot growth), 40% (Flowering), 73% (Berry development), 24% (Maturation) and 56% (Full growing stage) respectively for grapevine. Most growing stages experienced a wetting trend and varied discontinuously with the abrupt change in years. The drought-stricken areas were smaller than wet-stricken areas for each growing stage, especially for berry development and full growing stages. The drought and wet characteristics were stage-specific during the grapevine growth period. The precipitation, CWSDI, wet frequency, and wet risk increased from northwest to southeast for each growing stage, while crop evapotranspiration (ETc), drought frequency and drought risk showed the opposite characteristics. The drought risk was lower than wet risk in the Northeast wine region. These results can be used to develop strategies for mitigating and adapting dryness/wetness events in the wine regions of northeast China

Reconstruction of primary vertices at the ATLAS experiment in Run 1 proton–proton collisions at the LHC

This paper presents the method and performance of primary vertex reconstruction in proton–proton collision data recorded by the ATLAS experiment during Run 1 of the LHC. The studies presented focus on data taken during 2012 at a centre-of-mass energy of √s=8 TeV. The performance has been measured as a function of the number of interactions per bunch crossing over a wide range, from one to seventy. The measurement of the position and size of the luminous region and its use as a constraint to improve the primary vertex resolution are discussed. A longitudinal vertex position resolution of about 30μm is achieved for events with high multiplicity of reconstructed tracks. The transverse position resolution is better than 20μm and is dominated by the precision on the size of the luminous region. An analytical model is proposed to describe the primary vertex reconstruction efficiency as a function of the number of interactions per bunch crossing and of the longitudinal size of the luminous region. Agreement between the data and the predictions of this model is better than 3% up to seventy interactions per bunch crossing