771 research outputs found
A Fast and Map-Free Model for Trajectory Prediction in Traffics
To handle the two shortcomings of existing methods, (i)nearly all models rely
on high-definition (HD) maps, yet the map information is not always available
in real traffic scenes and HD map-building is expensive and time-consuming and
(ii) existing models usually focus on improving prediction accuracy at the
expense of reducing computing efficiency, yet the efficiency is crucial for
various real applications, this paper proposes an efficient trajectory
prediction model that is not dependent on traffic maps. The core idea of our
model is encoding single-agent's spatial-temporal information in the first
stage and exploring multi-agents' spatial-temporal interactions in the second
stage. By comprehensively utilizing attention mechanism, LSTM, graph
convolution network and temporal transformer in the two stages, our model is
able to learn rich dynamic and interaction information of all agents. Our model
achieves the highest performance when comparing with existing map-free methods
and also exceeds most map-based state-of-the-art methods on the Argoverse
dataset. In addition, our model also exhibits a faster inference speed than the
baseline methods.Comment: 7 pages, 3 figure
Multiobjective Particle Swarm Optimization Based on PAM and Uniform Design
In MOPSO (multiobjective particle swarm optimization), to maintain or increase the diversity of the swarm and help an algorithm to jump out of the local optimal solution, PAM (Partitioning Around Medoid) clustering algorithm and uniform design are respectively introduced to maintain the diversity of Pareto optimal solutions and the uniformity of the selected Pareto optimal solutions. In this paper, a novel algorithm, the multiobjective particle swarm optimization based on PAM and uniform design, is proposed. The differences between the proposed algorithm and the others lie in that PAM and uniform design are firstly introduced to MOPSO. The experimental results performing on several test problems illustrate that the proposed algorithm is efficient
Exploiting Contextual Information for Prosodic Event Detection Using Auto-Context
Prosody and prosodic boundaries carry significant information regarding linguistics and paralinguistics and are important aspects of speech. In the field of prosodic event detection, many local acoustic features have been investigated; however, contextual information has not yet been thoroughly exploited. The most difficult aspect of this lies in learning the long-distance contextual dependencies effectively and efficiently. To address this problem, we introduce the use of an algorithm called auto-context. In this algorithm, a classifier is first trained based on a set of local acoustic features, after which the generated probabilities are used along with the local features as contextual information to train new classifiers. By iteratively using updated probabilities as the contextual information, the algorithm can accurately model contextual dependencies and improve classification ability. The advantages of this method include its flexible structure and the ability of capturing contextual relationships. When using the auto-context algorithm based on support vector machine, we can improve the detection accuracy by about 3% and F-score by more than 7% on both two-way and four-way pitch accent detections in combination with the acoustic context. For boundary detection, the accuracy improvement is about 1% and the F-score improvement reaches 12%. The new algorithm outperforms conditional random fields, especially on boundary detection in terms of F-score. It also outperforms an n-gram language model on the task of pitch accent detection
Dimethylbis(3-methylsulfanyl-1,2,4-thiadiazole-5-thiolato)tin(IV)
In the title compound, [Sn(CH3)2(C3H3N2S3)2], the SnIV atom is coordinated within a C2N2S2 donor set that defines a skew-trapezoidal bipyramidal geometry in which the methyl groups lie over the weakly coordinated N atoms. Two independent molecules comprise the asymmetric unit, each of which lies on a mirror plane that passes through the C2Sn unit
Virtual Accessory Try-On via Keypoint Hallucination
The virtual try-on task refers to fitting the clothes from one image onto
another portrait image. In this paper, we focus on virtual accessory try-on,
which fits accessory (e.g., glasses, ties) onto a face or portrait image.
Unlike clothing try-on, which relies on human silhouette as guidance, accessory
try-on warps the accessory into an appropriate location and shape to generate a
plausible composite image. In contrast to previous try-on methods that treat
foreground (i.e., accessories) and background (i.e., human faces or bodies)
equally, we propose a background-oriented network to utilize the prior
knowledge of human bodies and accessories. Specifically, our approach learns
the human body priors and hallucinates the target locations of specified
foreground keypoints in the background. Then our approach will inject
foreground information with accessory priors into the background UNet. Based on
the hallucinated target locations, the warping parameters are calculated to
warp the foreground. Moreover, this background-oriented network can also easily
incorporate auxiliary human face/body semantic segmentation supervision to
further boost performance. Experiments conducted on STRAT dataset validate the
effectiveness of our proposed method
Wetland Degradation and Ecological Restoration
Wetlands are among the most important ecosystems on earth and functioned as the “kidneys” of the earth, which play an important role in maintaining ecological service functions. However, with the rapid growth in human populations, wetlands worldwide are suffering from serious degradation or loss as affected by wetland pollution, wetland reclamation, civilization and land use changes, and so forth. Wetland degradation has potential influences on human health, biodiversity, regional climate, and regional ecological security. Therefore, it is an urgent task to recover these degraded wetlands. In recent years, wetland protection, restoration, and its reasonable exploitation have been paid much more attention to by most governments and researchers. Moreover, wetland restoration has become the frontier fields of wetlands science, which has been listed as one of important themes in these recent international wetlands and ecological conferences. Understanding wetland degradation processes can contribute to better effective wetland restoration. Therefore, we organized this special issue on “wetland degradation and ecological restoration.” The objective of this special issue is to emphasize the effects of human activities on wetland ecosystems, the relationships between soil, water, and plant in wetlands, and wetland restoration issues and applications
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