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Efficient and accurate cyclone detection is essential for avoiding or reducing severe damage in coastal areas. However, traditional detection methods fail to provide accurate results with efficiency. To respond, we propose a convolutional-based cyclone detection framework, EasyRP-R-CNN, focusing on improving efficiency while minimizing the loss of accuracy. Our detection method utilizes satellite cloud images in the visible light band to improve detection accuracy. We designed a new Region of Interest (ROI) selection mechanism, named Easy Region Proposal, and a scale-based ROI grouping module to avoid classifying undersized ROIs. Experimental results demonstrate that our method exhibits satisfactory detection accuracy for cyclones of various intensities, with improved detection efficiency. [ABSTRACT FROM AUTHOR]

Air pollution data provides important information on air quality, which can be used to assess the impact of atmospheric pollution on human health, the environment, and the economy, as well as to develop corresponding policies and measures to reduce pollutant emissions and improve air quality. In this paper, we propose a novel approach to improve the resolution of meteorological data via masked image modeling (PMIM) to generate high-resolution air pollution data. In order to apply the image masking modeling to process air pollution data, we convert the data format and use radial basis function visualization to generate smooth distribution maps of air pollution data. To generate high-resolution air pollution data, we design several different masking strategies and use the masked image modeling to simulate the reconstruction process from low-resolution grid data to high-resolution grid data, obtaining the reconstructed high-resolution grid images. Finally, we use the mapping relationship between the pixel colors of the reconstructed images and the air pollution data to generate high-resolution air pollution concentration data. In order to verify the effectiveness of the proposed method, we conduct comparative experiments using different masking strategies and test air pollution data of different resolutions. The results show that our method has good applicability and effectiveness in different situations. [ABSTRACT FROM AUTHOR]

Introduction: Although there is now a consensus on asparaginase-based chemotherapy regimens in the treatment of advanced-stage extranodal natural killer/T-cell lymphomas (ENKTCLs), patient survival in the real-world setting is still not optimistic according to previous literature reports, and the optimal chemotherapeutic regimens and integration of different therapeutic methods under the concept of combined-modality treatment still need to be further explored and verified. Methods: Newly diagnosed stage Ⅲ/Ⅳ ENKTCL patients from Chinese National Cancer Center in the last two decades were retrospectively collected and analyzed. Overall survival (OS) and progression-free survival (PFS) were determined as primary endpoints. Log-rank tests and Cox proportional hazard models were performed to test for survival differences between subgroups and examine the univariable and multivariable associations. Results: The study included 83 newly diagnosed stage Ⅲ/Ⅳ ENKTCL patients and reported a median OS of 26.07 months and an estimated 5-year OS of 41.3% with a median follow-up of 82.13 months. First-line asparaginase-based regimens compared to non-asparaginase-based regimens significantly prolonged PFS (p = 0.007; HR = 0.48, p = 0.020) and showed a tendency to improve OS (p = 0.064; HR = 0.74, p = 0.359). Gemcitabine-based regimens also exhibited a trend toward improved PFS (p = 0.048; HR = 0.59, p = 0.164) and OS (p = 0.008; HR = 0.67, p = 0.282) compared to non-gemcitabine-based ones. The asparaginase and gemcitabine combinations yielded a 5-year OS of 55.0% and led to significantly superior PFS (p = 0.020; HR = 0.40, p = 0.022) and slightly better OS (p = 0.054; HR = 0.79, p = 0.495) compared to the remaining regimens. First-line combined-modality treatment integrating chemotherapy and radiotherapy improved PFS (p = 0.051) and OS (p = 0.036) compared to chemotherapy alone. Four autologous hematopoietic stem cell transplantation recipients reached a median OS of 58.34 months. Conclusion: Asparaginase and gemcitabine alone brought a favorable impact on PFS and OS; and the asparaginase and gemcitabine combination chemotherapy yielded the optimal efficacy, response duration, and survival outcomes. Combined-modality treatment including potent chemotherapy supplemented by radiotherapy and/or consolidative transplantation could improve prognosis in newly diagnosed advanced-stage ENKTCLs. [ABSTRACT FROM AUTHOR]

The present study aimed to investigate the clinical features, prognosis, and treatment of advanced-stage non-nasal type extranodal natural killer/T-cell lymphoma (ENKTCL). This real-world study retrospectively reviewed 56 newly diagnosed advanced-stage non-nasal type ENKTCL patients from two large-scale Chinese cancer centers in the last 10–15 years and screened 139 newly diagnosed advanced-stage nasal type ENKTCLs admitted during the same period for comparison. The non-nasal type ENKTCLs exhibited significantly higher Ki-67 expression levels compared to nasal type disease (P = 0.011). With a median follow-up duration of 75.03 months, the non-nasal group showed slightly inferior survival outcomes without statistically significant differences compared to the nasal group (median overall survival (OS): 14.57 vs. 21.53 months, 5-year OS: 28.0% vs. 38.5%, P = 0.120). Eastern Cooperative Oncology Group (ECOG) score ≥ 2 (hazard ratio (HR) = 2.18, P = 0.039) and lactic dehydrogenase (LDH) elevation (HR = 2.44, P = 0.012) were significantly correlated with worse OS in the non-nasal group. First-line gemcitabine-based chemotherapy regimens showed a trend toward slightly improved efficacy and survival outcomes compared to non-gemcitabine-based ones in the present cohort of non-nasal ENKTCLs (objective response rate: 91.7% vs. 63.6%, P = 0.144; complete response rate: 50.0% vs. 33.3%, P = 0.502; median progression-free survival: 10.43 vs. 3.40 months, P = 0.106; median OS: 25.13 vs. 9.30 months, P = 0.125), which requires further validation in larger sample size studies. Advanced-stage non-nasal type patients could achieve comparable prognosis with nasal cases after rational therapy. The modified nomogram-revised index (including age, ECOG score, and LDH) and modified international prognostic index (including age, ECOG score, LDH, and number of extranodal involvement) functioned effectively for prognostic stratification in non-nasal type ENKTCLs. [ABSTRACT FROM AUTHOR]

The study investigated the treatment and prognosis of advanced‐stage extranodal natural killer/T‐cell lymphoma (ENKTL). With a median follow‐up of 75.03 months, the median overall survival (mOS) for the 195 newly diagnosed stage III/IV ENKTL patients was 19.43 months, and estimated 1‐, 2‐, 3‐ and 5‐year OS were 59.5%, 46.3%, 41.8% and 35.1%, respectively. Chemotherapy (CT) + radiotherapy (RT) compared to CT alone (P =.007), and hematopoietic stem cell transplantation (HSCT) compared to non‐HSCT (P <.001), both improved OS. For patients ≤60 years and ineligible for HSCT, other therapies with complete remission led to comparable OS (P =.141). Nine patients ever treated with chidamide achieved a median progression‐free survival (mPFS) and mOS of 53.63 (range, 3.47‐92.33) and 54.80 (range, 5.50‐95.70) months, and four with chidamide maintenance therapy (MT) achieved a mPFS and mOS of 55.83 (range, 53.27‐92.33) and 60.65 (range, 53.70‐95.70) months, possibly providing an alternative option for non‐HSCT patients. Non‐anthracycline (ANT)‐ compared to ANT‐, asparaginase (Aspa)‐ compared to non‐Aspa‐ and gemcitabine (Gem)‐ compared to non‐Gem‐based regimens, prolonged PFS (P =.031; P =.005; P =.009) and OS (P =.010; P =.086; P =.003), respectively. Multivariate analysis demonstrated that Gem‐based regimens improved PFS (HR = 0.691, P =.061) and OS (HR = 0.624, P =.037). Gem + Aspa combinations slightly improved PFS and OS compared to regimens containing Gem or Aspa alone (P > 0.05). First‐line "intensive therapy," including CT (particularly Gem + Aspa regimens), RT, HSCT and alternative chidamide MT, was proposed and could improve long‐term survival for advanced‐stage ENKTLs. Ongoing prospective clinical studies may shed further light on the value of chidamide MT. [ABSTRACT FROM AUTHOR]

AST3-2 is the second of the three Antarctic Survey Telescopes, aimed at wide-field time-domain optical astronomy. It is located at Dome A, Antarctica, which is by many measures the best optical astronomy site on the Earth's surface. Here we present the data from the AST3-2 automatic survey in 2016 and the photometry results. The median 5σ limiting magnitude in i -band is 17.8 mag and the light-curve precision is 4 mmag for bright stars. The data release includes photometry for over 7 million stars, from which over 3500 variable stars were detected, with 70 of them newly discovered. We classify these new variables into different types by combining their light-curve features with stellar properties from surveys such as StarHorse. [ABSTRACT FROM AUTHOR]

Cross-matching operation, which is to find corresponding data for the same celestial object or region from multiple catalogues, is indispensable to astronomical data analysis and research. Due to the large amount of astronomical catalogues generated by the ongoing and next-generation large-scale sky surveys, the time complexity of the cross-matching is increasing dramatically. Heterogeneous computing environments provide a theoretical possibility to accelerate the cross-matching, but the performance advantages of heterogeneous computing resources have not been fully utilized. To meet the challenge of cross-matching for substantial increasing amount of astronomical observation data, this paper proposes H eterogeneous-computing-enabled L arge C atalogue C ross-matcher (HLC2), a high-performance cross-matching framework based on spherical position deviation on CPU-GPU heterogeneous computing platforms. It supports scalable and flexible cross-matching and can be directly applied to the fusion of large astronomical catalogues from survey missions and astronomical data centres. A performance estimation model is proposed to locate the performance bottlenecks and guide the optimizations. A two-level partitioning strategy is designed to generate an optimized data placement according to the positions of celestial objects to increase throughput. To make HLC2 a more adaptive solution, the architecture-aware task splitting, thread parallelization, and concurrent scheduling strategies are designed and integrated. Moreover, a novel quad-direction strategy is proposed for the boundary problem to effectively balance performance and completeness. We have experimentally evaluated HLC2 using public released catalogue data. Experiments demonstrate that HLC2 scales well on different sizes of catalogues and the cross-matching speed is significantly improved compared to the state-of-the-art cross-matchers. [ABSTRACT FROM AUTHOR]

Light-curve data are one of the most important data sources in time domain astronomy research. With the advancement of observation facilities and the continuous accumulation of observation data, and considering the analysis needs of large sample data sets, software or tools based on new technologies, especially artificial intelligence (AI), will be indispensable for light-curve analysis. The light-curve analysis tool designed by an individual will follow its own defined data structure, which will make the tools designed and developed by different individuals incompatible. A unified light-curve data model will be able to solve this compatibility problem, similar to the traditional flexible image transport system (FITS) file format. This paper proposes a light-curve data model named TSCat, designs and implements a data storage engine. The TSCat data model defines the basic metadata and format required for storing optical curve data following the international virtual observatory alliance (IVOA) data format specification system. The TSCat storage engine implements basic operations such as importing, storing, and accessing light-curve data. The function and performance of the TSCat storage engine are evaluated through the actual observation data. The experimental results show that the TSCat data model is complete enough to support the analysis of light curves. TSCat will help support the standardization of the data access level of software and tools in the field of light-curve analysis, and provide a new reference for the specification system of astronomical scientific data formats. [ABSTRACT FROM AUTHOR]

In the fields of traffic management, traffic health, and vehicle safety, vehicle speed prediction is an important research topic. The greater the difference between vehicle speed and average vehicle speed, or the more discrete the vehicle speed distribution, the higher the accident rate. This paper proposes a vehicle speed prediction method based on adaptive KF (Kalman filtering) in the ARMA (Autoregressive Moving Average) environment to address the problem of high-speed moving vehicle speed prediction. The ARMA theory is used to model the prediction of speed time series. The contribution rate of each coefficient representing the original time series is different after fitting the original time series with the ARMA model, so each coefficient must be given a certain weight. Multisource traffic data fusion and interval speed prediction are carried out on the basis of few-shot data preprocessing and traffic state division, according to different traffic states. The speed prediction accuracy is very high, according to the algorithm verification results. [ABSTRACT FROM AUTHOR]

With the explosive increase of big data in industry and academic fields, it is important to apply large-scale data processing systems to analyze Big Data. Arguably, Spark is the state-of-the-art in large-scale data computing systems nowadays, due to its good properties including generality, fault tolerance, high performance of in-memory data processing, and scalability. Spark adopts a flexible Resident Distributed Dataset (RDD) programming model with a set of provided transformation and action operators whose operating functions can be customized by users according to their applications. It is originally positioned as a fast and general data processing system. A large body of research efforts have been made to make it more efficient (faster) and general by considering various circumstances since its introduction. In this survey, we aim to have a thorough review of various kinds of optimization techniques on the generality and performance improvement of Spark. We introduce Spark programming model and computing system, discuss the pros and cons of Spark, and have an investigation and classification of various solving techniques in the literature. Moreover, we also introduce various data management and processing systems, machine learning algorithms and applications supported by Spark. Finally, we make a discussion on the open issues and challenges for large-scale in-memory data processing with Spark. [ABSTRACT FROM AUTHOR]

Graphics Processing Units (GPUs) have evolved into powerful accelerators for the development of Convolutional Neural Network (CNN) models. Most existing GPU-based frameworks adopt a kernel-based execution approach and only focus on optimizing individual kernels for better performance and resource utilization. With this approach, kernels involved will be launched sequentially, which may result in the underutilization of GPU resources due to the limited optimization space of a single kernel. In this paper, we propose an efficient software parallelization framework, called HGP4CNN, to accelerate the training of CNN models by considering the characteristics of workloads from both the same layer and adjacent layers as well as new GPU features such as concurrent kernel execution. In the intra-layer level, to achieve a better training performance of a single network layer, we design a novel model-based lightweight parallelization module to make better use of the concurrent kernel execution feature on modern GPUs. An asynchronous resource tracker is used to collect kernels' information at runtime, and a kernel analyzer is devised to calculate the number of kernels that can be dispatched concurrently. Moreover, to avoid consuming too many CPU threads or process resources, we integrate a runtime scheduler module for kernel launch and a pool-based stream manager for GPU work queue management. While in the inter-layer level, we present a pipeline execution strategy to overlap the processing of workloads from adjacent layers. To determine the number of samples to be processed by a single pipeline stage, the analysis result from the intra-layer module is considered. In the end, we implement a prototype of the proposed framework with Caffe, a well-known deep learning framework and conduct experiments with four off-the-shelf CNN models on three NVIDIA GPUs. Results show that HGP4CNN can be exploited to achieve better performance over the original implementation and keep the convergence property of networks. We can achieve a speedup of up to 6.X for a single convolutional layer and 2.X for multiple layers within pipelines of a network model. [ABSTRACT FROM AUTHOR]

The improvement of observation capabilities has expanded the scale of new data available for time domain astronomy research, and the accumulation of observational data continues to accelerate. However, traditional data analysis methods are difficult to fully tap the potential scientific value of all data. Therefore, in the current and future research on light curve analysis, it is inevitable to use artificial intelligence (AI) technology to assist in data analysis in order to obtain as many candidates as possible with scientific research goals. This survey reviews important developments in light curve analysis over the past years, summarizes the basic concepts in machine learning and their applications in light curve analysis and concludes perspectives and challenges for light curve analysis in the near future. The full exploration of light curves of variable celestial objects relies heavily on new techniques derived from promotion of machine learning and deep learning in the astronomical big data era. This article is categorized under:Technologies > Machine LearningTechnologies > Artificial Intelligence [ABSTRACT FROM AUTHOR]

Monoclinic Lu2WO6 undergoes diphase-to-perovskite BiLuWO6 transition via selective occupancy of Bi in three Lu sites. The transformation mechanism, process, and structure stabilities are revealed by variable cell nudged elastic band method, video, and phonon spectrum. Lattice transition brings about photogenerated charge separation in BiLuWO6. This is verified by indirect band gap transition, high electron migration rate, weak exciton binding energy, large photocurrent response, and small impedance. The electron–hole life time is elongated to produce abundant superoxide and hydroxyl radicals for the degradation of rhodamine B and phenol molecules. Bi–O antibonding states serve as immediate energy levels to change the recombination path, inducing 340 nm excitation band and 510 nm green light emission of Lu2WO6. Furthermore, multicolor emission of 1 at% Bi3+ + RE3+ (RE = Sm/Eu/Dy)-codoped Lu2WO6 is acquired via synergistic modification of the Bi–O antibonding state and RE3+ 4f states. Thus, the photogenerated charge motion in Lu2WO6 is tuned to expand application fields. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth analysis and discussion of the key issues of quality management, evaluation, and detection contained in big data for automotive application scenarios. A generalized big data quality management model and programming framework are proposed, and a series of data quality detection and repair interfaces are built to express the processing semantics of various data quality issues. Through this data quality management model and detection and repair interfaces, users can quickly build custom data quality detection and repair tasks for different data quality requirements. To improve the operational efficiency of complex data quality management algorithms in large-scale data scenarios, corresponding parallelization algorithms are studied and implemented for detection and repair algorithms with long computation time, including priority-based multiconditional function-dependent detection and repair algorithms, entity detection, and extraction algorithms based on semantic information and chunking techniques, and plain Bayesian-based missing value filling algorithms, and this paper proposes a data validity evaluation algorithm and enhances the validity of the original data in practical applications by adding temporal weights, and finally it passed the experimental validation. Through the comprehensive detection process of data importance, network busyness, duration of transmission process, and failure situation, the efficiency has been increased by 20%, and an adaptive data integrity detection method based on random algorithm and encryption algorithm is designed. After experimental verification, this method can effectively detect the integrity of the data transmission process and improve the application of data value, and the final effect is increased by 30.5%. [ABSTRACT FROM AUTHOR]

Gridding operation, which is to map non-uniform data samples on to a uniformly distributed grid, is one of the key steps in radio astronomical data reduction process. One of the main bottlenecks of gridding is the poor computing performance, and a typical solution for such performance issue is the implementation of multicore CPU platforms. Although such a method could usually achieve good results, in many cases, the performance of gridding is still restricted to an extent due to the limitations of CPU, since the main workload of gridding is a combination of a large number of single instruction, multidata stream operations, which is more suitable for GPU, rather than CPU implementations. To meet the challenge of massive data gridding for the modern large single-dish radio telescopes, e.g. the Five-hundred-meter Aperture Spherical radio Telescope, inspired by existing multicore CPU gridding algorithms such as Cygrid, here we present an easy-to-install, high-performance, and open-source convolutional gridding framework , HCGrid , in CPU-GPU heterogeneous platforms. It optimizes data search by employing multithreading on CPU, and accelerates the convolution process by utilizing massive parallelization of GPU. In order to make HCGrid a more adaptive solution, we also propose the strategies of thread organization and coarsening, as well as optimal parameter settings under various GPU architectures. A thorough analysis of computing time and performance gain with several GPU parallel optimization strategies show that it can lead to excellent performance in hybrid computing environments. [ABSTRACT FROM AUTHOR]

The nonlinear spectral broadening light source was used as a partially coherent carrier source with high repetition frequency to carry out experimental research on high-speed information transmission in atmospheric turbulence channels. A partially coherent beams source with nonlinear spectral broadening was obtained by pumping a highly nonlinear fiber with a picosecond fiber laser, and the repetition frequency could reach 10 GHz. The 10 Gbit/s pulse high-speed digital modulation was used and transmitted through a simulated atmospheric turbulence channel, the signal-to-noise ratio of the coherent optical carrier and partially coherent optical carrier before and after the turbulent channel transmission is reduced from 16.83 dB and 11.83 dB to 4.31 dB and 5.64 dB, respectively. In the simulated atmospheric turbulence channel at ΔT=230°C, when the bit error rate is 3.8x10-3, the sensitivity of partially coherent optical carrier links reaches -27.8 dBm, which is 1.6 dB higher than that of coherent optical carriers. [ABSTRACT FROM AUTHOR]

Time series data of celestial objects are commonly used to study valuable and unexpected objects such as extrasolar planets and supernova in time domain astronomy. Due to the rapid growth of data volume, traditional manual methods are becoming extremely hard and infeasible for continuously analysing accumulated observation data. To meet such demands, we designed and implemented a special tool named AstroCatR that can efficiently and flexibly reconstruct time series data from large-scale astronomical catalogues. AstroCatR can load original catalogue data from Flexible Image Transport System (FITS) files or data bases, match each item to determine which object it belongs to, and finally produce time series data sets. To support the high-performance parallel processing of large-scale data sets, AstroCatR uses the extract-transform-load (ETL) pre-processing module to create sky zone files and balance the workload. The matching module uses the overlapped indexing method and an in-memory reference table to improve accuracy and performance. The output of AstroCatR can be stored in CSV files or be transformed other into formats as needed. Simultaneously, the module-based software architecture ensures the flexibility and scalability of AstroCatR. We evaluated AstroCatR with actual observation data from The three Antarctic Survey Telescopes (AST3). The experiments demonstrate that AstroCatR can efficiently and flexibly reconstruct all time series data by setting relevant parameters and configuration files. Furthermore, the tool is approximately 3× faster than methods using relational data base management systems at matching massive catalogues. [ABSTRACT FROM AUTHOR]

We experimentally demonstrate a 2- $\mu \text{m}$ free-space data transmission system in a simulated atmospheric turbulent device with an actively mode-locked holmium-doped fiber laser. The central wavelength can be adjusted from 2035.6 nm to 2050.2 nm with optional repetition rates of 1.01 GHz and 2.02 GHz, respectively, and the pulse is stabilized by adding a fiber Fabry-Perot filter in the laser cavity. The pulse sequence can be modulated by a digital signal and transmitted in a tunable simulated atmospheric turbulent channel. Compared with back-to-back (BTB), the optical signal-to-noise ratio (OSNR) of 2- $\mu \text{m}$ laser carrier reduces from 15.04 dB to 6.67 dB under the condition of $\text{C}_{\mathrm {n}}^{2}=5.71 \times 10^{-16}\text{m}^{-2/3}$ , and the received power jitter increases from ±0.04 dB to ±0.74 dB. The sensitivity of the 2- $\mu \text{m}$ data transmission system is −19.52 dBm. In addition, we compare the average receiving power standard deviation and power penalty between the 2- $\mu \text{m}$ laser and a 1.55- $\mu \text{m}$ laser under the same propagation conditions. The experimental results demonstrate that the 2- $\mu \text{m}$ laser communication system realizes superior performance in a weak turbulent channel. [ABSTRACT FROM AUTHOR]

Radio frequency interference (RFI) detection and excision are key steps in the data-processing pipeline of the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Because of its high sensitivity and large data rate, FAST requires more accurate and efficient RFI flagging methods than its counterparts. In the last decades, approaches based upon artificial intelligence (AI), such as codes using convolutional neural networks (CNNs), have been proposed to identify RFI more reliably and efficiently. However, RFI flagging of FAST data with such methods has often proved to be erroneous, with further manual inspections required. In addition, network construction as well as preparation of training data sets for effective RFI flagging has imposed significant additional workloads. Therefore, rapid deployment and adjustment of AI approaches for different observations is impractical to implement with existing algorithms. To overcome such problems, we propose a model called RFI-Net. With the input of raw data without any processing, RFI-Net can detect RFI automatically, producing corresponding masks without any alteration of the original data. Experiments with RFI-Net using simulated astronomical data show that our model has outperformed existing methods in terms of both precision and recall. Besides, compared with other models, our method can obtain the same relative accuracy with fewer training data, thus reducing the effort and time required to prepare the training data set. Further, the training process of RFI-Net can be accelerated, with overfittings being minimized, compared with other CNN codes. The performance of RFI-Net has also been evaluated with observing data obtained by FAST and the Bleien Observatory. Our results demonstrate the ability of RFI-Net to accurately identify RFI with fine-grained, high-precision masks that required no further modification. [ABSTRACT FROM AUTHOR]

Over the past year, the discovery of the first electromagnetic counterparts to sources of gravitational waves and high-energy neutrinos has brought us to the new era of "multi-messenger" astrophysics (MMA). These new events provide deep insights into astrophysics, meanwhile, lead to the development of astronomical data fusion techniques. Each messenger carries information from different observation process and fusing all messengers can therefore provide the best understanding of the celestial objects' attributes and expose various astrophysical phenomena. Over the decades, for the full exploitation of multi-band astronomical data, advanced unified platforms and data fusion techniques have been developed and become a research topic of interest for the astronomical community. In this paper, we present a systematic review of the popular and state-of-the-art fusion methods and platforms in different formats of astronomical data, aiming to give a comprehensive introduction to the current progress in this field; at the same time, we put forward insightful prospects for future study of multi-messenger data fusion, hoping to provide new thoughts for researchers in MMA era. [ABSTRACT FROM AUTHOR]

Smith–Waterman algorithm (SW) is a popular dynamic programming algorithm widely used in bioinformatics for local biological sequence alignment. Due to the O (n 2) high time and space complexity of SW and growing size of biological data, it is crucial to accelerate SW for high performance. In view of the GPU high efficiency in science computation, many existing studies (e.g., CUDAlign, CUDASW++) speedup SW with GPU. However, the strong data dependency makes SW communication intensive, and the previous works fail to fully leverage the heterogeneous capabilities of the GPU machine for either the neglect of the CPU ability or the low bandwidth of PCI-e. In this paper, we propose ASW, which aims at accelerating SW algorithm with accelerated processing unit (APU), a heterogeneous processor integrates CPU and GPU in a single die and share the same memory. This coupled CPU–GPU architecture is more suitable for frequent data exchanging due to the elimination of PCI-e bus. For the full utilization of both CPU and GPU in APU system, ASW partitions the whole SW matrix into blocks and dynamically dispatches each block to CPU and GPU for the concurrent execution. A DAG-based dynamic scheduling method is presented to dispatch the workload automatically. Moreover, we also design a time cost model to determine the partition granularity in the matrix division phase. We have evaluated ASW on AMD A12 platform and our results show that ASW achieves a good performance of 7.2 GCUPS (gigacells update per second). [ABSTRACT FROM AUTHOR]

Many current computing systems such as clouds and supercomputers charge users for their resource usages. A user’s demand is often changing over time, indicating that it is difficult to keep the high resource utilization all the time for cost efficiency. Resource sharing is a classical and effective approach for high resource utilization. In view of the heterogeneous resource demands of users’ workloads, multi-resource allocation fairness is a must for resource sharing in such pay-as-you-use computing systems. However, we find that, existing multi-resource fair policies such as Dominant Resource Fairness (DRF), implemented in currently popular resource management systems such as Apache YARN [4] and Mesos [23] , are not suitable for the pay-as-you-use computing systems. We show that this is because of their memoryless characteristic that can cause the following problems in the pay-as-you-use computing systems: 1). users can get resource benefits by cheating; 2). users might not be able to get the total amount of resources that they are entitled to in terms of their resource contributions. In this paper, we propose a new policy called H-MRF, which generalizes DRF and Asset Fairness with the long-term notion. We show that it can address these problems and is suitable for pay-as-you-use computing systems. We have implemented it into YARN by developing a prototype called MRYARN. Finally, we evaluate H-MRF using both testbed and simulated experiments. The experimental results show that there are about $1.1\sim 1.5$ sharing benefit degrees and $1.2\times \sim 1.8\times$ performance improvement for users with H-MRF, better than existing fair schedulers. [ABSTRACT FROM AUTHOR]

A cost-efficient schedule for the Five-hundred-meter Aperture Spherical radio Telescope (FAST) requires to maximize the number of observable proposals and the overall scientific priority, and minimize the overall slew-cost generated by telescope shifting, while taking into account the constraints including the astronomical objects visibility, user-defined observable times, avoiding Radio Frequency Interference (RFI). In this contribution, first we solve the problem of maximizing the number of observable proposals and scientific priority by modeling it as a Minimum Cost Maximum Flow (MCMF) problem. The optimal schedule can be found by any MCMF solution algorithm. Then, for minimizing the slew-cost of the generated schedule, we devise a maximally-matchable edges detection-based method to reduce the problem size, and propose a backtracking algorithm to find the perfect matching with minimum slew-cost. Experiments on a real dataset from NASA/IPAC Extragalactic Database (NED) show that, the proposed scheduler can increase the usage of available times with high scientific priority and reduce the slew-cost significantly in a very short time. [ABSTRACT FROM AUTHOR]

Cost savings have become a significant challenge in the management of data centers. In this paper, we show that, besides energy consumption, service level agreement (SLA) violations also severely degrade the cost-efficiency of data centers. We present online VM placement algorithms for increasing cloud provider’s revenue. First, First-Fit and Harmonic algorithm are devised for VM placement without considering migrations. Both algorithms get the same performance in the worst-case analysis, and equal to the lower bound of the competitive ratio. However, Harmonic algorithm could create more revenue than First-Fit by more than 10 percent when job arriving rate is greater than 1.0. Second, we formulate an optimization problem of maximizing revenue from VM migration, and prove it as NP-Hard by a reduction from 3-Partition problem. Therefore, we propose two heuristics: Least-Reliable-First (LRF) and Decreased-Density-Greedy (DDG). Experiments demonstrate that DDG yields more revenue than LRF when migration cost is low, yet leads to losses when SLA penalty is low or job arriving rate is high, due to the large number of migrations. Finally, we compare the four algorithms above with algorithms adopted in Openstack using a real trace, and find that the results are consistent with the ones using synthetic data. [ABSTRACT FROM PUBLISHER]

Astronomy has always been, and will continue to be, a data-based science, and astronomers nowadays are faced with increasingly massive datasets, one key problem of which is to efficiently retrieve the desired cup of data from the ocean. AQUAdexIM, an innovative spatial indexing and querying method, performs highly efficient on-the-fly queries under users' request to search for Time Series Images from existing observation data on the server side and only return the desired FITS images to users, so users no longer need to download entire datasets to their local machines, which will only become more and more impractical as the data size keeps increasing. Moreover, AQUAdexIM manages to keep a very low storage space overhead and its specially designed in-memory index structure enables it to search for Time Series Images of a given area of the sky 10 times faster than using Redis, a state-of-the-art in-memory database. [ABSTRACT FROM AUTHOR]

Astronomy cloud computing environment is a cyber-Infrastructure for Astronomy Research initiated by Chinese Virtual Observatory (China-VO) under funding support from NDRC (National Development and Reform commission) and CAS (Chinese Academy of Sciences). Based on virtualization technology, astronomy cloud computing environment was designed and implemented by China-VO team. It consists of five distributed nodes across the mainland of China. Astronomer can get compuitng and storage resource in this cloud computing environment. Through this environments, astronomer can easily search and analyze astronomical data collected by different telescopes and data centers , and avoid the large scale dataset transportation. [ABSTRACT FROM PUBLISHER]