Learning Continuous Network Emerging Dynamics from Scarce Observations via Data-Adaptive Stochastic Processes

Learning network dynamics from the empirical structure and spatio-temporal observation data is crucial to revealing the interaction mechanisms of complex networks in a wide range of domains. However, most existing methods only aim at learning network dynamic behaviors generated by a specific ordinary differential equation instance, resulting in ineffectiveness for new ones, and generally require dense observations. The observed data, especially from network emerging dynamics, are usually difficult to obtain, which brings trouble to model learning. Therefore, how to learn accurate network dynamics with sparse, irregularly-sampled, partial, and noisy observations remains a fundamental challenge. We introduce Neural ODE Processes for Network Dynamics (NDP4ND), a new class of stochastic processes governed by stochastic data-adaptive network dynamics, to overcome the challenge and learn continuous network dynamics from scarce observations. Intensive experiments conducted on various network dynamics in ecological population evolution, phototaxis movement, brain activity, epidemic spreading, and real-world empirical systems, demonstrate that the proposed method has excellent data adaptability and computational efficiency, and can adapt to unseen network emerging dynamics, producing accurate interpolation and extrapolation with reducing the ratio of required observation data to only about 6\% and improving the learning speed for new dynamics by three orders of magnitude.Comment: preprin

The Green Bank North Celestial Cap Pulsar Survey. III. 45 New Pulsar Timing Solutions

We provide timing solutions for 45 radio pulsars discovered by the Robert C. Byrd Green Bank Telescope. These pulsars were found in the Green Bank North Celestial Cap pulsar survey, an all-GBT-sky survey being carried out at a frequency of 350 MHz. We include pulsar timing data from the Green Bank Telescope and Low Frequency Array. Our sample includes five fully recycled millisecond pulsars (MSPs, three of which are in a binary system), a new relativistic double neutron star system, an intermediate-mass binary pulsar, a mode-changing pulsar, a 138 ms pulsar with a very low magnetic field, and several nulling pulsars. We have measured two post-Keplerian parameters and thus the masses of both objects in the double neutron star system. We also report a tentative companion mass measurement via Shapiro delay in a binary MSP. Two of the MSPs can be timed with high precision and have been included in pulsar timing arrays being used to search for low-frequency gravitational waves, while a third MSP is a member of the black widow class of binaries. Proper motion is measurable in five pulsars, and we provide an estimate of their space velocity. We report on an optical counterpart to a new black widow system and provide constraints on the optical counterparts to other binary MSPs. We also present a preliminary analysis of nulling pulsars in our sample. These results demonstrate the scientific return of long timing campaigns on pulsars of all types

Cost-aware Graph Generation: A Deep Bayesian Optimization Approach

Graph-structured data is ubiquitous throughout the natural and social sciences, ranging from complex drug molecules to artificial neural networks. Evaluating their functional properties, e.g., drug effectiveness and prediction accuracy, is usually costly in terms of time, money, energy, or environment, becoming a bottleneck for the graph generation task. In this work, from the perspective of saving cost, we propose a novel Cost-Aware Graph Generation (CAGG) framework to generate graphs with optimal properties at as low cost as possible. By introducing a robust Bayesian graph neural network as the surrogate model and a goal-oriented training scheme for the generation model, the CAGG can approach the real expensive evaluation function and generate search space close to the optimal property, to avoid unnecessary evaluations. Intensive experiments conducted on two challenging real-world applications, including molecular discovery and neural architecture search, demonstrate its effectiveness and applicability. The results show that it can generate the optimal graphs and reduce the evaluation costs significantly compared to the state-of-the-art

A Multi-Objective Compounded Local Mobile Cloud Architecture Using Priority Queues to Process Multiple Jobs.

As a result of the greatly increased use of mobile devices, the disadvantages of portable devices have gradually begun to emerge. To solve these problems, the use of mobile cloud computing assisted by cloud data centers has been proposed. However, cloud data centers are always very far from the mobile requesters. In this paper, we propose an improved multi-objective local mobile cloud model: Compounded Local Mobile Cloud Architecture with Dynamic Priority Queues (LMCpri). This new architecture could briefly store jobs that arrive simultaneously at the cloudlet in different priority positions according to the result of auction processing, and then execute partitioning tasks on capable helpers. In the Scheduling Module, NSGA-II is employed as the scheduling algorithm to shorten processing time and decrease requester cost relative to PSO and sequential scheduling. The simulation results show that the number of iteration times that is defined to 30 is the best choice of the system. In addition, comparing with LMCque, LMCpri is able to effectively accommodate a requester who would like his job to be executed in advance and shorten execution time. Finally, we make a comparing experiment between LMCpri and cloud assisting architecture, and the results reveal that LMCpri presents a better performance advantage than cloud assisting architecture

Finite-Frequency Fault Detection for Two-Dimensional Roesser Systems

Rotating radio transients (RRATs) are sporadically emitting pulsars which are detected only through single pulse search. Detecting these single pulses in RRATs observation with high detection accuracy is a challenge due to the background noise. It is better to conduct the single pulse detection directly on the raw time-frequency observation than on the de-dispersed data, because de-dispersion process takes very intensive computation. In this paper, we propose to accomplish this idea by treating two-dimensional (2D) time-frequency data as images and develop a curvelet based denoising approach after studying the characteristics of the RRATs pulses and the noise. The denoising approach estimates the range of curvature (orientations) and width (scales) that describe the RRATs pulses and reconstructs cleaner images from the selected orientations and scales. The proposed denoising approach does not require prior knowledge of exact dispersion measures (DM) value. In addition, a framework of detecting the single pulses from the time-frequency data, named HOG-SVM, is also proposed to further evaluate the curvelet based denoising approach. Compared with the other four denoising approaches, the proposed curvelet based method leads to better detection results, with detection accuracy being increased to 98.7% by HOG-SVM

Research on the RRB+ Tree for Resource Reservation

Part 2: Session 2: Cloud Resource ManagementInternational audienceThe performance of the data structure has a significant impact on the overall performance of the advance resource reservation in the distributed computing. Because the query and update operations of the B+ tree are of high efficiency, so this paper proposes a B+ tree structure suitable for resource reservation the RRB+ tree. Also, we design and implement the corresponding algorithms of query, insertion and deletion. Different with the B+ tree that insert and delete one key word at a time, the RRB+ tree insert one reservation request and delete one tree node every time. The RRB+ tree is of a higher precision of expression. With the fixed reservation admission control algorithm and the same rate of acceptance, the experimental results show that the RRB+ tree is easier to operate for the complex and changing network environment, and have a higher utilization of storage space

Global Features are All You Need for Image Retrieval and Reranking

Utilizing a two-stage paradigm comprising of coarse image retrieval and precise reranking, a well-established image retrieval system is formed. It has been widely accepted for long time that local feature is imperative to the subsequent stage - reranking, but this requires sizeable storage and computing capacities. We, for the first time, propose an image retrieval paradigm leveraging global feature only to enable accurate and lightweight image retrieval for both coarse retrieval and reranking, thus the name - SuperGlobal. It consists of several plug-in modules that can be easily integrated into an already trained model, for both coarse retrieval and reranking stage. This series of approaches is inspired by the investigation into Generalized Mean (GeM) Pooling. Possessing these tools, we strive to defy the notion that local feature is essential for a high-performance image retrieval paradigm. Extensive experiments demonstrate substantial improvements compared to the state of the art in standard benchmarks. Notably, on the Revisited Oxford (ROxford)+1M Hard dataset, our single-stage results improve by 8.2% absolute, while our two-stage version gain reaches 3.7% with a strong 7568X speedup. Furthermore, when the full SuperGlobal is compared with the current single-stage state-of-the-art method, we achieve roughly 17% improvement with a minimal 0.005% time overhead. Code: https://github.com/ShihaoShao-GH/SuperGlobal.Comment: Accepted to ICCV 202

Power consumption of requester cost items.

<p>Power consumption of requester cost items.</p