How to Train Your Dragon: Tamed Warping Network for Semantic Video Segmentation

Real-time semantic segmentation on high-resolution videos is challenging due to the strict requirements of speed. Recent approaches have utilized the inter-frame continuity to reduce redundant computation by warping the feature maps across adjacent frames, greatly speeding up the inference phase. However, their accuracy drops significantly owing to the imprecise motion estimation and error accumulation. In this paper, we propose to introduce a simple and effective correction stage right after the warping stage to form a framework named Tamed Warping Network (TWNet), aiming to improve the accuracy and robustness of warping-based models. The experimental results on the Cityscapes dataset show that with the correction, the accuracy (mIoU) significantly increases from 67.3% to 71.6%, and the speed edges down from 65.5 FPS to 61.8 FPS. For non-rigid categories such as "human" and "object", the improvements of IoU are even higher than 18 percentage points

Downhole microseismic monitoring of hydraulic fracturing: a full-waveform approach for complete moment tensor inversion and stress estimation

Downhole microseismics has gained in popularity in recent years as a way to characterize hydraulic fracturing sources and to estimate in-situ stress state. Conventional approaches only utilize part of the information contained in the microseismic waveforms such as the P/S amplitude ratio and/or P first motion polarity to determine the microearthquake focal mechanisms and infer stress state. Thus, additional con-straints like double-couple assumption must be made to stabilize the inversion for conventional methods. The situation becomes even worse for downhole monitoring where only limited azimuthal coverage is available. In this study, we have developed a full-waveform based approach to invert for complete moment tensor. We use the discrete wavenumber integration approach as the fast forward modeling tool to calculate the synthetic waveforms for one-dimensional layered velocity models. By matching full three-component waveforms across the array, a stable moment tensor solution can be obtained without imposing additional constraints. We also derive the source radius from the far-field displacement spectrum with the Madariaga’s model and determine the stress drop afterwards. We test our method on a downhole microseismic dataset from hydraulic fracturing treatments in East Texas. The result indicates the existence of the isotropic component in some events. A clear difference is observed that non-double-couple events tend to have smaller stress drops, which is consistent with other studies. The derived fracture plane direction also agrees with that derived from multiple event location.Halliburton Company (Pinnacle)Song, Fuxian; Toksoz, M. Nafi; Li, Junlu

Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation

Travel time estimation is one of the core tasks for the development of intelligent transportation systems. Most previous works model the road segments or intersections separately by learning their spatio-temporal characteristics to estimate travel time. However, due to the continuous alternations of the road segments and intersections in a path, the dynamic features are supposed to be coupled and interactive. Therefore, modeling one of them limits further improvement in accuracy of estimating travel time. To address the above problems, a novel graph-based deep learning framework for travel time estimation is proposed in this paper, namely Spatio-Temporal Dual Graph Neural Networks (STDGNN). Specifically, we first establish the node-wise and edge-wise graphs to respectively characterize the adjacency relations of intersections and that of road segments. In order to extract the joint spatio-temporal correlations of the intersections and road segments, we adopt the spatio-temporal dual graph learning approach that incorporates multiple spatial-temporal dual graph learning modules with multi-scale network architectures for capturing multi-level spatial-temporal information from the dual graph. Finally, we employ the multi-task learning approach to estimate the travel time of a given whole route, each road segment and intersection simultaneously. We conduct extensive experiments to evaluate our proposed model on three real-world trajectory datasets, and the experimental results show that STDGNN significantly outperforms several state-of-art baselines

Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction

Accurate traffic prediction is a challenging task in intelligent transportation systems because of the complex spatio-temporal dependencies in transportation networks. Many existing works utilize sophisticated temporal modeling approaches to incorporate with graph convolution networks (GCNs) for capturing short-term and long-term spatio-temporal dependencies. However, these separated modules with complicated designs could restrict effectiveness and efficiency of spatio-temporal representation learning. Furthermore, most previous works adopt the fixed graph construction methods to characterize the global spatio-temporal relations, which limits the learning capability of the model for different time periods and even different data scenarios. To overcome these limitations, we propose an automated dilated spatio-temporal synchronous graph network, named Auto-DSTSGN for traffic prediction. Specifically, we design an automated dilated spatio-temporal synchronous graph (Auto-DSTSG) module to capture the short-term and long-term spatio-temporal correlations by stacking deeper layers with dilation factors in an increasing order. Further, we propose a graph structure search approach to automatically construct the spatio-temporal synchronous graph that can adapt to different data scenarios. Extensive experiments on four real-world datasets demonstrate that our model can achieve about 10% improvements compared with the state-of-art methods. Source codes are available at https://github.com/jinguangyin/Auto-DSTSGN

Modeling and Analyzing Operational Decision-Making Synchronization of C2 Organization in Complex Environment

In order to improve capability of operational decision-making synchronization (ODMS) in command and control (C2) organization, the paper puts forward that ODMS is the negotiation process of situation cognition with three phases about “situation cognition, situation interaction and decision-making synchronization” in complex environment, and then the model and strategies of ODMS are given in quantity. Firstly, measure indexes of three steps above are given in the paper based on the time consumed in negotiation, and three patterns are proposed for negotiating timely in high quality during situation interaction. Secondly, the ODMS model with two stages in continuous changing situation is put forward in the paper, and ODMS strategies are analyzed within environment influence and time restriction. Thirdly, simulation cases are given to validate the process of ODMS under different continuous changing situations the results of this model are better than the other previous models to fulfill the actual restrictions, and the process of ODMS can be adjusted more reasonable for improving the capability of ODMS. Then we discuss the case and summarize the influence factors of ODMS in the C2 organization as organization structure, shared information resources, negotiation patterns, and allocation of decision rights

Co-expression of apoptin (VP3) and antibacterial peptide cecropin B mutant (ABPS1) genes induce higher rate of apoptosis in HepG2 and A375 cell lines

The antibacterial peptide cecropin B mutant (ABPS1) gene has a broad range of antibacterial and  antiproliferative properties. Apoptin (VP3), a chicken anaemia virus-encoded protein is known to induce  apoptosis in human transformed cells. To explore drug combination in human tumor cells, apoptin and ABPS1 eukaryotic expression vector pIRES2-EGFP-apoptin and pIRES2-EGFP-ABPS1 were constructed and their expression effect individually and in combinations were studied in HepG2 and A375 cells. The vector pIRES2-EGFP-ABPS1 and pIRES2-EGFP-apoptin were transfected into tumor cells HepG2 and A375 by the  lipofectamine-mediated DNA transfection procedure. At 48 h post transfection, the apoptotic rate obtained by flow cytometry and the morphological changes under light and scanning electron microscope of tumor cells  were significant. In contrast, the microvilli on the surface of the control cells were disrupted, decreased and even disappeared. The cell membrane was injured and intracellular substances leaked out. Furthermore, our  results indicate that the apoptotic rates of apoptin (27.32% in HepG2 and 9.34% in A375 cells), were higher  than ABPS1 (23.79% in HepG2 and 8.33% in A375 cells). Moreover, the co-expression of Apoptin and ABPS1  showed higher apoptotic rates which were 27.66 and 10.33% in HepG2 and A375 cells respectively. However, the apoptotic rates obtained in HepG2 cells treated with apoptin and apoptin and ABPS1 together were closely  similar, but, not in A375 cells. Therefore, the results of the present study showed that the combination of  Apoptin and ABPS1 has synergistic effect in HepG2 and A375 cell lines.Keys words: Apoptin, ABPS1, apoptosis, co-expression, HepG2, A375

Performance Analysis and Optimal Allocation of Layered Defense M/M/N Queueing Systems

One important mission of strategic defense is to develop an integrated layered Ballistic Missile Defense System (BMDS). Motivated by the queueing theory, we presented a work for the representation, modeling, performance simulation, and channels optimal allocation of the layered BMDS M/M/N queueing systems. Firstly, in order to simulate the process of defense and to study the Defense Effectiveness (DE), we modeled and simulated the M/M/N queueing system of layered BMDS. Specifically, we proposed the M/M/N/N and M/M/N/C queueing model for short defense depth and long defense depth, respectively; single target channel and multiple target channels were distinguished in each model. Secondly, we considered the problem of assigning limited target channels to incoming targets, we illustrated how to allocate channels for achieving the best DE, and we also proposed a novel and robust search algorithm for obtaining the minimum channel requirements across a set of neighborhoods. Simultaneously, we presented examples of optimal allocation problems under different constraints. Thirdly, several simulation examples verified the effectiveness of the proposed queueing models. This work may help to understand the rules of queueing process and to provide optimal configuration suggestions for defense decision-making

Rapid detection of grass carp reovirus type 1 using RPA-based test strips combined with CRISPR Cas13a system

IntroductionDue to the existence of grass carp reovirus (GCRV), grass carp hemorrhagic disease occurs frequently, and its high pathogenicity and infectivity are great challenges to the aquaculture industry. As a highly pathogenic pathogen, the outbreak of hemorrhagic disease often causes tremendous economic losses. Therefore, it is important to rapidly and accurately detect GCRV on site to control timely.MethodsIn this study, recombinant enzyme amplification (RPA) combined with clustered regularly interspaced short palindromic repeats (CRISPR)/Cas13a system was employed to establish a method to detect the vp7 gene of grass carp reovirus type 1. This method can be adopted for judging the results by collecting fluorescence signal, ultraviolet excitation visual fluorescence and test strip.ResultsCombined with the RPA amplification experiment, the detection limit of the RPA-CRISPR method can reach 7.2 × 101 copies/μL of vp7 gene per reaction, and the detection process can be completed within 1 h. In addition, this method had no cross-reaction with the other 11 common aquatic pathogens. Then, the performance of the RPA-CRISPR/Cas13a detection method was evaluated by comparing it with the real-time fluorescence quantitative PCR detection method of clinical samples. The results of RPA-CRISPR/Cas13a detection were shown to be in consistence with the results obtained from the real-time fluorescence quantitative PCR detection. The coincidence rate of this method with 26 GCRV clinical samples was 92.31%.DiscussionIn summary, this method has high sensitivity, specificity and on-site practicability for detecting GCRV type 1, and has great application potential in on-site GCRV monitoring