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This study introduces a novel computational framework for Robust Topology Optimization (RTO) considering imprecise random field parameters. Unlike the worst-case approach, the present method provides upper and lower bounds for the mean and standard deviation of compliance as well as the optimized topological layouts of a structure for various scenarios. In the proposed approach, the imprecise random field variables are determined utilizing parameterized p-boxes with different confidence intervals. The Karhunen-Lo\`eve (K-L) expansion is extended to provide a spectral description of the imprecise random field. The linear superposition method in conjunction with a linear combination of orthogonal functions is employed to obtain explicit mathematical expressions for the first and second order statistical moments of the structural compliance. Then, an interval sensitivity analysis is carried out, applying the Orthogonal Similarity Transformation (OST) method with the boundaries of each of the intermediate variable searched efficiently at every iteration using a Combinatorial Approach (CA). Finally, the validity, accuracy, and applicability of the work are rigorously checked by comparing the outputs of the proposed approach with those obtained using the particle swarm optimization (PSO) and Quasi-Monte-Carlo Simulation (QMCS) methods. Three different numerical examples with imprecise random field loads are presented to show the effectiveness and feasibility of the study.

In this paper, we introduce the online and streaming MAP inference and learning problems for Non-symmetric Determinantal Point Processes (NDPPs) where data points arrive in an arbitrary order and the algorithms are constrained to use a single-pass over the data as well as sub-linear memory. The online setting has an additional requirement of maintaining a valid solution at any point in time. For solving these new problems, we propose algorithms with theoretical guarantees, evaluate them on several real-world datasets, and show that they give comparable performance to state-of-the-art offline algorithms that store the entire data in memory and take multiple passes over it.

Contrastive learning has achieved remarkable success on various high-level tasks, but there are fewer contrastive learning-based methods proposed for low-level tasks. It is challenging to adopt vanilla contrastive learning technologies proposed for high-level visual tasks to low-level image restoration problems straightly. Because the acquired high-level global visual representations are insufficient for low-level tasks requiring rich texture and context information. In this paper, we investigate the contrastive learning-based single image super-resolution from two perspectives: positive and negative sample construction and feature embedding. The existing methods take naive sample construction approaches (e.g., considering the low-quality input as a negative sample and the ground truth as a positive sample) and adopt a prior model (e.g., pre-trained VGG model) to obtain the feature embedding. To this end, we propose a practical contrastive learning framework for SISR, named PCL-SR. We involve the generation of many informative positive and hard negative samples in frequency space. Instead of utilizing an additional pre-trained network, we design a simple but effective embedding network inherited from the discriminator network which is more task-friendly. Compared with existing benchmark methods, we re-train them by our proposed PCL-SR framework and achieve superior performance. Extensive experiments have been conducted to show the effectiveness and technical contributions of our proposed PCL-SR thorough ablation studies. The code and pre-trained models can be found at https://github.com/Aitical/PCL-SISR., Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems

A fully-sampled and hitherto highest resolution and sensitivity observation of neutral hydrogen (HI) in the Leo Triplet (NGC 3628, M 65/NGC 3623, and M 66/NGC 3627) reveals six HI structures beyond the three galaxies. We present detailed results of the morphologies and kinematics of these structures, which can be used for future simulations. In particular, we detect a two-arm structure in the plume of NGC 3628 for the first time, which can be explained by a tidal interaction model. The optical counterpart of the plume is mainly associated with the southern arm. The connecting part (base) of the plume (directed eastwards) with NGC 3628 is located at the blueshifted (western) side of NGC 3628. Two bases appear to be associated with the two arms of the plume. A clump with reversed velocity gradient (relative to the velocity gradient of M 66) and a newly detected tail, i.e. M 66SE, is found in the southeast of M 66. We suspect that M 66SE represents gas from NGC 3628 which was captured by M 66 in the recent interaction between the two galaxies. Meanwhile gas is falling toward M 66, resulting in features already previously observed in the southeastern part of M 66, e.g. large line widths and double peaks. An upside-down `Y'-shaped HI gas component (M 65S) is detected in the south of M 65 which suggests that M 65 may also have been involved in the interaction. We strongly encourage modern hydrodynamical simulations of this interacting group of galaxies to reveal the origin of the gaseous debris surrounding all three galaxies., Comment: 21 pages, 12 figures, accepted for publication in A&A

We present a domain- and user-preference-agnostic approach to detect highlightable excerpts from human-centric videos. Our method works on the graph-based representation of multiple observable human-centric modalities in the videos, such as poses and faces. We use an autoencoder network equipped with spatial-temporal graph convolutions to detect human activities and interactions based on these modalities. We train our network to map the activity- and interaction-based latent structural representations of the different modalities to per-frame highlight scores based on the representativeness of the frames. We use these scores to compute which frames to highlight and stitch contiguous frames to produce the excerpts. We train our network on the large-scale AVA-Kinetics action dataset and evaluate it on four benchmark video highlight datasets: DSH, TVSum, PHD2, and SumMe. We observe a 4-12% improvement in the mean average precision of matching the human-annotated highlights over state-of-the-art methods in these datasets, without requiring any user-provided preferences or dataset-specific fine-tuning., Comment: 10 pages, 5 figures, 5 tables. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021