, but that has a lot to do with their pace, which is 29th in the league. The Lakers, on the other hand, play at the fastest pace in the league. Dallas ranks 23rd in field goal percentage and is 29th defending small forwards according to props.cash. Dallas will be without forward Dorian Finney-Smith, the team’s best perimeter defender, once again, which will make it tough to slow down James.
The stage is for James to have another big performance on Christmas.
Bet this LeBron prop Instantly With QuickSlip at FanDuel!A research programme and report exploring the future of green infrastructure in the design and function of cities. Cities Alive highlights the social, economic and environmental benefits of green infrastructure, including case studies of best practice from across the globe.
The Garden Bridge © Arup / Heatherwick Studios
Urban landscapes are increasingly being designed to support efficiency, community health, climate adaptation, space security, resource production, and economic development. Our cities are facing an uncertain future, which may include underground roads and automated transport, permeable pavements and water roofs, vertical farming and even glowing trees.
Cities Alive: Rethinking green infrastructure shows how the creation of a linked ‘city ecosystem’ that encompasses parks and open spaces; urban trees, streets, squares; woodland and waterways can help create healthier, safer and more prosperous cities. To realise this vision, green infrastructure has to now play a more influential role in the planning and design of cities and urban environments.
Supported by the Landscape Institute and Royal Botanical Gardens, Kew, Cities Alive names five key requirements, which, if implemented, would have significant implications for the future design of cities.
- Recognising ‘urban green’ as more than an aesthetic consideration
- Making landscapes work harder through a multi-functional design approach
- Designing creatively to deliver a green city ecosystem
- Utilising advances in technology to measure the value nature delivers through ecosystem services
- Maintaining an integrated approach to delivery that better links and connects policy
Cities Alive: Rethinking green infrastructure is a product of collaboration between the Landscape Architecture and Foresight + Research + Innovation teams at Arup and has involved a wide range of specialists within the firm.<reponame>greathouse/tidewater
package greenmoonsoftware.tidewater.plugins.docker

import com.github.dockerjava.api.DockerClient
import com.github.dockerjava.core.DockerClientBuilder
import com.github.dockerjava.core.DockerClientConfig
import com.google.common.net.HostAndPort

import static com.google.common.base.Optional.fromNullable
import static com.google.common.base.Strings.isNullOrEmpty
import static java.lang.System.getProperty

class Client {
    static final String DEFAULT_UNIX_ENDPOINT = "unix:///var/run/docker.sock"
    static final String DEFAULT_HOST = "localhost"
    static final int DEFAULT_PORT = 2375

    static DockerClient dockerClient(String uri, String certPath) {
        def config = DockerClientConfig.createDefaultConfigBuilder()
               .withUri(buildUri(uri, certPath))
               .withDockerCertPath(certPath)
               .build();
        return DockerClientBuilder.getInstance(config).build()
    }

    private static String buildUri(String uri, String certPath) {
        def endpoint = fromNullable(uri).or(defaultEndpoint())
        if (endpoint.startsWith('unix://')) {
            return endpoint
        }
        else {
            def stripped = endpoint.replaceAll('.*://', '')
            def hostAndPort = HostAndPort.fromString(stripped)
            def hostText = hostAndPort.getHostText()
            def scheme = certPath == null? 'http' : 'https'

            def port = hostAndPort.getPortOrDefault(DEFAULT_PORT)
            def address = isNullOrEmpty(hostText)? DEFAULT_HOST : hostText
            return "$scheme://$address:$port"
        }
    }

    private static String defaultEndpoint() {
        if (getProperty("os.name").equalsIgnoreCase("linux")) {
            return DEFAULT_UNIX_ENDPOINT
        } else {
            return DEFAULT_HOST + ":" + DEFAULT_PORT
        }
    }
}
At the generation time, the model constructs the semantic parse tree by recursively inserting the predicted non-terminal labels at the predicted positions until termination.
To recover the three-dimensional HSIs from two-dimensional compressive images, we present dual-camera equipment with a physics-informed self-supervising CNN method based on a coded aperture snapshot spectral imaging system.
To better regularize the LDCT denoising model, this paper proposes a novel method, termed DU-GAN, which leverages U-Net based discriminators in the GANs framework to learn both global and local difference between the denoised and normal-dose images in both image and gradient domains.
In order to address the challenge that the transferabilities of different sensors are different, we propose SALIENCE (unsupervised user adaptation model for multiple wearable sensors based human activity recognition) model.
Teamwork is cooperative, participative and power sharing.
We experiment with a newly created evaluation dataset, Politi-Prov, based on fact-checking articles from \url{www. politifact. com}; our experimental results show that our solution leads to a significant improvement over baselines.
How to explore corner cases as efficiently and thoroughly as possible has long been one of the top concerns in the context of deep reinforcement learning (DeepRL) autonomous driving.
With this goal in mind, we propose PV-SOD, a new task that aims to segment salient objects from panoramic videos.
Co-training, extended from self-training, is one of the frameworks for semi-supervised learning.
DCVAE conducts feature synthesis via pairing two Conditional Variational AutoEncoders (CVAEs) with the same seed but different modality conditions in a dizygotic symbiosis manner.
Moreover, our model is more stable for training in a non-adversarial manner, compared to other adversarial based novelty detection methods.
In this paper, we study the Salient Object Ranking (SOR) task, which manages to assign a ranking order of each detected object according to its visual saliency.
Offline experiments and mechanical Turk based evaluations show that it is possible to rewrite bland questions in a more detailed and attractive way to increase the response rate, and images can be helpful.
Both components of our graph induction solution are evaluated in experiments, demonstrating that our models outperform a state-of-the-art text generator significantly.
We first take into consideration all the linguistic information embedded in the past layers and then take a further step to engage the future information which is originally inaccessible for predictions.
In this paper, we conduct in-depth comparative studies to understand the use of natural language description for schema in dialog state tracking.
Knowledge facts are typically represented by relational triples, while we observe that some commonsense facts are represented by the triples whose forms are inconsistent with the expression of language.
This interest has also been developed in the field of the medical domain where researchers are focusing on building a dialog system in the medical domain.
However, 360{\deg} video SHD has been seldom discussed in the computer vision community due to a lack of datasets with large-scale omnidirectional videos and rich annotations.
Magnetic resonance imaging (MRI) acquisition, reconstruction, and segmentation are usually processed independently in the conventional practice of MRI workflow.
Behavior of deep neural networks can be inconsistent between different versions.
In the past few years, numerous deep learning methods have been proposed to address the task of segmenting salient objects from RGB images.
Time Delay Neural Networks (TDNNs) are widely used in both DNN-HMM based hybrid speech recognition systems and recent end-to-end systems.
Cooperative driving at signal-free intersections, which aims to improve driving safety and efficiency for connected and automated vehicles, has attracted increasing interest in recent years.
We propose a novel Synergistic Attention Network (SA-Net) to address the light field salient object detection by establishing a synergistic effect between multi-modal features with advanced attention mechanisms.
We propose a provably convergent method, called Efficient Learned Descent Algorithm (ELDA), for low-dose CT (LDCT) reconstruction.
Cooperative driving at isolated intersections attracted great interest and had been well discussed in recent years.
Twitter serves as a data source for many Natural Language Processing (NLP) tasks.
Twitter can be viewed as a data source for Natural Language Processing (NLP) tasks.
In this paper, we introduce the homotopy equivalence relation (HER) to make the neural networks learn the data distribution from a high-dimension manifold.
Recent advance on neural network architecture search (NAS) has proved that the network architecture has a dramatic effect on the model performance, which indicates that current network architectures for LDCT may be sub-optimal.
Traditional statistics forbids use of test data (a. k. a.
Constraining our measurement to the Standard Model expectation of lepton universality ($R=9. 75$), we find the more precise results $\cal B(D_s^+\to \tau^+\nu_\tau) = (5. 22\pm0. 10\pm 0. 14)\times10^{-2}$ and $A_{\it