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Calculus rules of the generalized concave Kurdyka-Łojasiewicz property
[article]
2021
arXiv
pre-print
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In this paper, we propose several calculus rules for the generalized concave Kurdyka-\L ojasiewicz (KL) property, which generalize Li and Pong's results for KL exponents. The optimal concave desingularizing function has various forms and may be nondifferentiable. Our calculus rules do not assume desingularizing functions to have any specific form nor differentiable, while the known results do. Several examples are also given to show that our calculus rules are applicable to a broader class of functions than the known ones.
arXiv:2110.03795v1
fatcat:qm7szcoy6zemzav625b6cpdavi
Tuftsin: a natural molecule against SARS-CoV-2 infection
[article]
2022
bioRxiv
pre-print
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Coronavirus disease 2019 (COVID-19) continuously proceeds despite the application of a variety of vaccines. It is still urgent to find effective ways to treat COVID-19. Recent studies indicate that NRP1, an important receptor of the natural peptide tuftsin, facilitates SARS-CoV-2 infection. Importantly, tuftsin is a natural human molecule released from IgG. Here, we found 91 overlapping genes between tuftsin targets and COVID-19-associated genes. Bioinformatics analyses indicated that tuftsin
doi:10.1101/2022.01.10.475746
fatcat:rfbrsd6yjjdixfob6mgoxkqrfi
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... uld also target ACE2 and exert some immune-related functions to treat COVID-19. Using surface plasmon resonance (SPR) analysis, we confirmed that tuftsin can bind ACE2 and NRP1 directly. Moreover, tuftsin effectively impairs the binding of SARS-CoV-2 S1 to ACE2. Thus, tuftsin is an attractive drug against COVID-19. And tuftsin as natural immunostimulating peptide in human, we speculate that tuftsin may has crucial roles in asymptomatic carriers or mild cases of COVID-19.
Program Repair: Automated vs. Manual
[article]
2022
arXiv
pre-print
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Various automated program repair (APR) techniques have been proposed to fix bugs automatically in the last decade. Although recent researches have made significant progress on the effectiveness and efficiency, it is still unclear how APR techniques perform with human intervention in a real debugging scenario. To bridge this gap, we conduct an extensive study to compare three state-of-the-art APR tools with manual program repair, and further investigate whether the assistance of APR tools (i.e.,
arXiv:2203.05166v1
fatcat:z5vvbvzt6zd33is6iewszedgfq
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... repair reports) can improve manual program repair. To that end, we recruit 20 participants for a controlled experiment, resulting in a total of 160 manual repair tasks and a questionnaire survey. The experiment reveals several notable observations that (1) manual program repair may be influenced by the frequency of repair actions sometimes; (2) APR tools are more efficient in terms of debugging time, while manual program repair tends to generate a correct patch with fewer attempts; (3) APR tools can further improve manual program repair regarding the number of correctly-fixed bugs, while there exists a negative impact on the patch correctness; (4) participants are used to consuming more time to identify incorrect patches, while they are still misguided easily; (5) participants are positive about the tools' repair performance, while they generally lack confidence about the usability in practice. Besides, we provide some guidelines for improving the usability of APR tools (e.g., the misleading information in reports and the observation of feedback).
TransFollower: Long-Sequence Car-Following Trajectory Prediction through Transformer
[article]
2022
arXiv
pre-print
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Car-following refers to a control process in which the following vehicle (FV) tries to keep a safe distance between itself and the lead vehicle (LV) by adjusting its acceleration in response to the actions of the vehicle ahead. The corresponding car-following models, which describe how one vehicle follows another vehicle in the traffic flow, form the cornerstone for microscopic traffic simulation and intelligent vehicle development. One major motivation of car-following models is to replicate
arXiv:2202.03183v1
fatcat:laiv5hp2rvcvxoep2avcjgotga
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... man drivers' longitudinal driving trajectories. To model the long-term dependency of future actions on historical driving situations, we developed a long-sequence car-following trajectory prediction model based on the attention-based Transformer model. The model follows a general format of encoder-decoder architecture. The encoder takes historical speed and spacing data as inputs and forms a mixed representation of historical driving context using multi-head self-attention. The decoder takes the future LV speed profile as input and outputs the predicted future FV speed profile in a generative way (instead of an auto-regressive way, avoiding compounding errors). Through cross-attention between encoder and decoder, the decoder learns to build a connection between historical driving and future LV speed, based on which a prediction of future FV speed can be obtained. We train and test our model with 112,597 real-world car-following events extracted from the Shanghai Naturalistic Driving Study (SH-NDS). Results show that the model outperforms the traditional intelligent driver model (IDM), a fully connected neural network model, and a long short-term memory (LSTM) based model in terms of long-sequence trajectory prediction accuracy. We also visualized the self-attention and cross-attention heatmaps to explain how the model derives its predictions.
Assessing Different Feature Sets' Effects on Land Cover Classification in Complex Surface-Mined Landscapes by ZiYuan-3 Satellite Imagery
2017
Remote Sensing
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In this study, data such as various feature sets derived from ZiYuan-3 stereo satellite imagery, a feature subset resulting from a feature selection (FS) procedure, training data polygons, and test sample ...
First, based on our former study [14] , the feature sets derived from ZiYuan-3 stereo satellite imagery (ZY-3), the feature subset resulting from a FS procedure, the training data polygons, and the test ...
ZiYuan-3 fused true color image (R, Red; G, Green; B, Blue)
Figure 2 . 2 Location of training and test samples and the red band of ZiYuan-3 fused image. ...
doi:10.3390/rs10010023
fatcat:csmpf4udznggdh7famdx5kqfc4
LeaveNow: A Social Network-based Smart Evacuation System for Disaster Management
[article]
2016
arXiv
pre-print
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The importance of timely response to natural disasters and evacuating affected people to safe areas is paramount to save lives. Emergency services are often handicapped by the amount of rescue resources at their disposal. We present a system that leverages the power of a social network forming new connections among people based on real-time location and expands the rescue resources pool by adding private sector cars. We also introduce a car-sharing algorithm to identify safe routes in an
arXiv:1610.02869v1
fatcat:w4cmgyspx5fjxkm2odjfkywdeu
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... cy with the aim of minimizing evacuation time, maximizing pick-up of people without cars, and avoiding traffic congestion.
SAS: A Simple, Accurate and Scalable Node Classification Algorithm
[article]
2021
arXiv
pre-print
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Graph neural networks have achieved state-of-the-art accuracy for graph node classification. However, GNNs are difficult to scale to large graphs, for example frequently encountering out-of-memory errors on even moderate size graphs. Recent works have sought to address this problem using a two-stage approach, which first aggregates data along graph edges, then trains a classifier without using additional graph information. These methods can run on much larger graphs and are orders of magnitude
arXiv:2104.09120v1
fatcat:xibedesnxjhipojfdjqi5sdubu
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... aster than GNNs, but achieve lower classification accuracy. We propose a novel two-stage algorithm based on a simple but effective observation: we should first train a classifier then aggregate, rather than the other way around. We show our algorithm is faster and can handle larger graphs than existing two-stage algorithms, while achieving comparable or higher accuracy than popular GNNs. We also present a theoretical basis to explain our algorithm's improved accuracy, by giving a synthetic nonlinear dataset in which performing aggregation before classification actually decreases accuracy compared to doing classification alone, while our classify then aggregate approach substantially improves accuracy compared to classification alone.
Personalized Context-Aware Multi-Modal Transportation Recommendation
[article]
2019
arXiv
pre-print
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AUTHOR CONTRIBUTIONS The authors confirm contribution to the paper as follows: study conception and design: Hu, Zhu, Wang; data preprocess: Zhu, Hu, Yang, Pu; analysis and interpretation of results: Hu ...
arXiv:1910.12601v1
fatcat:ausvi6tjafa2zjnncnwuwvcpua
Multimodal and Multi-Model Deep Fusion for Fine Classification of Regional Complex Landscape Areas Using ZiYuan-3 Imagery
2019
Remote Sensing
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First, low-level and multimodal spectral–spatial and topographic features derived from ZiYuan-3 imagery were extracted and fused. The features were then input into a DBN for deep feature learning. ...
RGB: ZiYuan-3 true color image with red, green, and blue bands. NIRRG: ZiYuan-3 false color image with near-infrared, red, and green bands. DTM: digital terrain model. DBN: deep belief network. ...
Figure 1 . 1 ZiYuan-3 fused imagery and location of the study area and field samples [12] . ...
doi:10.3390/rs11222716
fatcat:bqogaykynzarxpjbkyxh6y5jka
Decentralized Traffic Management Strategies for Sensor-Enabled Cars
[article]
2009
arXiv
pre-print
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Traffic Congestions and accidents are major concerns in today's transportation systems. This thesis investigates how to optimize traffic flow on highways, in particular for merging situations such as intersections where a ramp leads onto the highway. In our work, cars are equipped with sensors that can detect distance to neighboring cars, and communicate their velocity and acceleration readings with one another. Sensor-enabled cars can locally exchange sensed information about the traffic and
arXiv:0906.3424v1
fatcat:5rixotcfezceba2x4a56bzuyge
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... apt their behavior much earlier than regular cars. We propose proactive algorithms for merging different streams of sensor-enabled cars into a single stream. A proactive merging algorithm decouples the decision point from the actual merging point. Sensor-enabled cars allow us to decide where and when a car merges before it arrives at the actual merging point. This leads to a significant improvement in traffic flow as velocities can be adjusted appropriately. We compare proactive merging algorithms against the conventional priority-based merging algorithm in a controlled simulation environment. Experiment results show that proactive merging algorithms outperform the priority-based merging algorithm in terms of flow and delay.
Recent Accelerating Glacier Mass Loss of the Geladandong Mountain, Inner Tibetan Plateau, Estimated from ZiYuan-3 and TanDEM-X Measurements
2020
Remote Sensing
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Here, we estimated the recent glacier mass change of the Geladandong mountain, by differencing the digital elevation models (DEMs) produced from ZiYuan-3 images and TanDEM-X data. ...
doi:10.3390/rs12030472
fatcat:6mph5f4y2jcvvevu6bhzv4njga
Graph Markov Network for Traffic Forecasting with Missing Data
[article]
2019
arXiv
pre-print
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Traffic forecasting is a classical task for traffic management and it plays an important role in intelligent transportation systems. However, since traffic data are mostly collected by traffic sensors or probe vehicles, sensor failures and the lack of probe vehicles will inevitably result in missing values in the collected raw data for some specific links in the traffic network. Although missing values can be imputed, existing data imputation methods normally need long-term historical traffic
arXiv:1912.05457v1
fatcat:6i47zxumafhphjdy7bx7537xb4
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... ate data. As for short-term traffic forecasting, especially under edge computing and online prediction scenarios, traffic forecasting models with the capability of handling missing values are needed. In this study, we consider the traffic network as a graph and define the transition between network-wide traffic states at consecutive time steps as a graph Markov process. In this way, missing traffic states can be inferred step by step and the spatial-temporal relationships among the roadway links can be Incorporated. Based on the graph Markov process, we propose a new neural network architecture for spatial-temporal data forecasting, i.e. the graph Markov network (GMN). By incorporating the spectral graph convolution operation, we also propose a spectral graph Markov network (SGMN). The proposed models are compared with baseline models and tested on three real-world traffic state datasets with various missing rates. Experimental results show that the proposed GMN and SGMN can achieve superior prediction performance in terms of both accuracy and efficiency. Besides, the proposed models' parameters, weights, and predicted results are comprehensively analyzed and visualized.
Gait Recognition via Disentangled Representation Learning
[article]
2019
arXiv
pre-print
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Gait, the walking pattern of individuals, is one of the most important biometrics modalities. Most of the existing gait recognition methods take silhouettes or articulated body models as the gait features. These methods suffer from degraded recognition performance when handling confounding variables, such as clothing, carrying and view angle. To remedy this issue, we propose a novel AutoEncoder framework to explicitly disentangle pose and appearance features from RGB imagery and the LSTM-based
arXiv:1904.04925v1
fatcat:ylxwc2b3mzh47c7gy3rp57up4i
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... ntegration of pose features over time produces the gait feature. In addition, we collect a Frontal-View Gait (FVG) dataset to focus on gait recognition from frontal-view walking, which is a challenging problem since it contains minimal gait cues compared to other views. FVG also includes other important variations, e.g., walking speed, carrying, and clothing. With extensive experiments on CASIA-B, USF and FVG datasets, our method demonstrates superior performance to the state of the arts quantitatively, the ability of feature disentanglement qualitatively, and promising computational efficiency.
Bacteriophage-based nanoprobes for rapid bacteria separation
2015
Nanoscale
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A nanoscale bacteriophage-modified magnetic nanoprobe is developed for the low-cost and efficient separation of bacteria from liquid samples.
doi:10.1039/c5nr03779d
pmid:26315848
fatcat:md2b2lk6gzejbaiwrshxpdgtf4
OPTIMIZING PHASE COMPRESSION FOR TRANSIT SIGNAL PRIORITY AT ISOLATED INTERSECTIONS
2017
Transport
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That is, the phase in which a bus arrives with priority request will be selected and compressed to provide time for the request (Balke et al. 2000; Ma, Yang 2007; Li, Zhang 2012; Ahmed 2014; Wang et al ...
related studies mainly concentrate on several representative topics: -TSP strategies logic development for an isolated intersection (Balke et al. 2000; Dion, Hellinga 2002; Polgár et al. 2013; Ahmed 2014; Wang ...
doi:10.3846/16484142.2017.1345787
fatcat:ptrkyibauvegpib6bvl23le25u
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