Lateral transshipments within an inventory system are stock movements between locations of the same echelon. These transshipments can be conducted periodically at predetermined points in time to ...proactively redistribute stock, or they can be used reactively as a method of meeting demand which cannot be satisfied from stock on hand. The elements of an inventory system considered, e.g. size, cost structures and service level definition, all influence the best method of transshipping. Models of many different systems have been considered. This paper provides a literature review which categorizes the research to date on lateral transshipments, so that these differences can be understood and gaps within the literature can be identified.
Currently, 3D object detection is a research hotspot in the field of computer vision. In this paper, we have observed that the commonly used set abstraction module retains excessive irrelevant ...background information during downsampling, impacting object detection precision. To address this, we propose a mixed sampling method. During point feature extraction, we integrate semantic features into the sampling process, guiding the set abstraction module to sample foreground points. In order to leverage the high-quality 3D proposals generated in the first stage, we have developed a virtual point pooling module aimed at acquiring the features of these proposals. This module facilitates the capture of more comprehensive and resilient ROI features. Experimental results on the KITTI test set show a 3.51% higher Average Precision (AP) compared to the PointRCNN baseline, particularly for moderately challenging car classes, highlighting the effectiveness of our approach.
•Proposes mixed sampling to enhance 3D object detection precision.•Integrates semantic features for focused foreground point sampling.•Introduces a module for robust feature extraction from high-quality 3D proposals.•Achieves 3.51% higher AP than PointRCNN on KITTI test set, proving effectiveness.
The pooling layer is a layer used in Convolutional Neural Networks (CNN) that takes the output feature map of the previous convolutional layer and reduces the feature maps to smaller sizes. ...Furthermore, in CNN the pooling layer is one of the layers that determines the success of the model. The pooling layer, reduces the spatial dimension of a CNN, greatly reducing the learning time and computational cost of the model. The most common pooling methods are maximum and average pooling. Due to the fact that the pooling strategy reduces the amount of feature maps and model parameters, it is crucial to preserve the dominant information. In this study, a cost-effective new pooling method approach is proposed. The proposed pooling method is used by calculating the weighted average of the dominant features. The proposed pooling model has been developed to address the shortcomings of maximum pooling and average pooling. The proposed new Avg-TopK pooling model takes the pixels with the highest interaction as much as the specified K number and averages them. In this study, the performances of several pooling strategies for gray and RGB picture classification in 3 different datasets were compared and analyzed in depth. Extensive experiments have demonstrated that the Avg-TopK pooling method achieves significantly higher image classification accuracy than conventional pooling methods. It has been observed that using the AVG-TopK method in transfer learning models leads to much more successful results. Furthermore, studies in the literature have compared based on the performance metrics and it has been seen that the proposed method produces more successful outcomes. In research conducted on datasets using this method, the accuracy achieved for the CIFAR-10 dataset was 6.28% and 16.62% according to the maximum pooling and the average pooling, respectively. For the CIFAR-100 dataset, the accuracy rate increased by 7.76% compared to the maximum pooling and by 25% compared to the average pooling.
The healthcare systems in Scandinavia inform nationwide registers and the Scandinavian populations are increasingly combined in research. We aimed to compare Norway (NO), Sweden (SE), and Denmark ...(DK) regarding sociodemographic factors and healthcare.
In this cross-sectional study, we analyzed aggregated data from the nationwide Scandinavian registers. We calculated country-specific statistics on sociodemographic factors and healthcare use (general practitioner visits, admissions to somatic hospitals, and use of medicines).
In 2018, population were 5,295,619 (NO), 10,120,242 (SE), and 5,781,190 (DK). The populations were comparable regarding sex, age, education, and income distribution. Overall, medication use was comparable, while there was more variation in hospital admissions and general practitioner visits. For example, per 1000 inhabitants, 703 (NO), 665 (SE), and 711 (DK) individuals redeemed a prescription, whereas there were 215 (NO), 134 (SE), and 228 (DK) somatic hospital admissions per 1000 inhabitants. General practitioner contacts per 1000 inhabitants were 7082 in DK and 5773 in NO (-data from SE).
The Scandinavian countries are comparable regarding aggregate-level sociodemographic factors and medication use. Variations are noted in healthcare utilisation as measured by visits to general practitioners and admissions to hospitals. This variation should be considered when comparing data from the Scandinavian countries.
Two architectures that generalize convolutional neural networks (CNNs) for the processing of signals supported on graphs are introduced. We start with the selection graph neural network (GNN), which ...replaces linear time invariant filters with linear shift invariant graph filters to generate convolutional features and reinterprets pooling as a possibly nonlinear subsampling stage where nearby nodes pool their information in a set of preselected sample nodes. A key component of the architecture is to remember the position of sampled nodes to permit computation of convolutional features at deeper layers. The second architecture, dubbed aggregation GNN, diffuses the signal through the graph and stores the sequence of diffused components observed by a designated node. This procedure effectively aggregates all components into a stream of information having temporal structure to which the convolution and pooling stages of regular CNNs can be applied. A multinode version of aggregation GNNs is further introduced for operation in large-scale graphs. An important property of selection and aggregation GNNs is that they reduce to conventional CNNs when particularized to time signals reinterpreted as graph signals in a circulant graph. Comparative numerical analyses are performed in a source localization application over synthetic and real-world networks. Performance is also evaluated for an authorship attribution problem and text category classification. Multinode aggregation GNNs are consistently the best-performing GNN architecture.
The technology-enabled ride-pooling (RP) is designed as an on-demand feeder service to connect remote areas to transit terminals (or activity centers). We propose the so-called “hold-dispatch” ...operation strategy, which imposes a target number of shared rides (termed the ride-pooling size) for each vehicle to enhance RP’s transportation efficiency. Analytical models are formulated at the planning level to estimate the costs of the RP operator and the patrons. Accordingly, the design problem is constructed to minimize the total system cost concerning the system layout (i.e., in terms of service zone partitioning), resource deployment (i.e., fleet size), and operational decisions (i.e., RP size). The proposed models admit spatial heterogeneity arising from the non-uniformity of demand distributions and service locations, and can furnish heterogeneous designs. Closed-form formulas for the optimal zoning and fleet size are developed, which unveil fundamental insights regarding the impacts of key operating factors (e.g., demand density and distance to the terminal). Extensive numerical experiments demonstrate (i) the effectiveness of heterogeneous service designs and (ii) the advantage of the proposed RP service with hold-dispatch strategy over alternative designs studied in the literature, i.e., RP with a “quick-dispatch” strategy and flexible-route transit, in a wide range of operating scenarios. These findings can assist transportation network companies and transit agencies in successfully integrating RP and transit services.
•Formulated analytic models for designing ride-pooling as an on-demand feeder to terminals.•The proposed model admits spatially non-uniform demand and accordingly furnishes heterogeneous designs.•Closed-form solutions are derived with new insights into the optimal designs.•The ride-pooling system outperforms the flexible-route transit with up to 6% cost saving.
We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine consists of an ...encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network 1 . The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature map(s). Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling. This eliminates the need for learning to upsample. The upsampled maps are sparse and are then convolved with trainable filters to produce dense feature maps. We compare our proposed architecture with the widely adopted FCN 2 and also with the well known DeepLab-LargeFOV 3 , DeconvNet 4 architectures. This comparison reveals the memory versus accuracy trade-off involved in achieving good segmentation performance. SegNet was primarily motivated by scene understanding applications. Hence, it is designed to be efficient both in terms of memory and computational time during inference. It is also significantly smaller in the number of trainable parameters than other competing architectures and can be trained end-to-end using stochastic gradient descent. We also performed a controlled benchmark of SegNet and other architectures on both road scenes and SUN RGB-D indoor scene segmentation tasks. These quantitative assessments show that SegNet provides good performance with competitive inference time and most efficient inference memory-wise as compared to other architectures. We also provide a Caffe implementation of SegNet and a web demo at http://mi.eng.cam.ac.uk/projects/segnet/.
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems. Existing methods heavily rely on the expert-designed networks, which may lead to a sub-optimal solution for FAS task. ...Here we propose the first FAS method based on neural architecture search (NAS), called NAS-FAS, to discover the well-suited task-aware networks. Unlike previous NAS works mainly focus on developing efficient search strategies in generic object classification, we pay more attention to study the search spaces for FAS task. The challenges of utilizing NAS for FAS are in two folds: the networks searched on 1) a specific acquisition condition might perform poorly in unseen conditions, and 2) particular spoofing attacks might generalize badly for unseen attacks. To overcome these two issues, we develop a novel search space consisting of central difference convolution and pooling operators. Moreover, an efficient static-dynamic representation is exploited for fully mining the FAS-aware spatio-temporal discrepancy. Besides, we propose Domain/Type-aware Meta-NAS, which leverages cross-domain/type knowledge for robust searching. Finally, in order to evaluate the NAS transferability for cross datasets and unknown attack types, we release a large-scale 3D mask dataset, namely CASIA-SURF 3DMask, for supporting the new 'cross-dataset cross-type' testing protocol. Experiments demonstrate that the proposed NAS-FAS achieves state-of-the-art performance on nine FAS benchmark datasets with four testing protocols.
Power Normalizations ( PN ) are useful non-linear operators which tackle feature imbalances in classification problems. We study PNs in the deep learning setup via a novel PN layer pooling feature ...maps. Our layer combines the feature vectors and their respective spatial locations in the feature maps produced by the last convolutional layer of CNN into a positive definite matrix with second-order statistics to which PN operators are applied, forming so-called Second-order Pooling ( SOP ). As the main goal of this paper is to study Power Normalizations, we investigate the role and meaning of MaxExp and Gamma, two popular PN functions. To this end, we provide probabilistic interpretations of such element-wise operators and discover surrogates with well-behaved derivatives for end-to-end training. Furthermore, we look at the spectral applicability of MaxExp and Gamma by studying Spectral Power Normalizations ( SPN ). We show that SPN on the autocorrelation/covariance matrix and the Heat Diffusion Process (HDP) on a graph Laplacian matrix are closely related, thus sharing their properties. Such a finding leads us to the culmination of our work, a fast spectral MaxExp which is a variant of HDP for covariances/autocorrelation matrices. We evaluate our ideas on fine-grained recognition, scene recognition, and material classification, as well as in few-shot learning and graph classification.
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