Stress and frequency optimization of prismatic sandwich beams with joints: Performance improvements through topology optimization

Prismatic sandwich panels fabricated from metals offer a compelling alternative to more traditional panels across diverse industries, primarily due to their superior strength-to-weight ratio. Although several core types were proposed in the past, further improvements in performance could be achieved by devising the topology of the core through a topology optimization framework, which is explored in this article for the first time. Another novelty is the inclusion of joints between the sandwich beams and its surroundings in the analysis and optimization. Stress is minimized under uniform pressure loading on the beams and natural frequency maximized using the Method of Moving Asymptotes. The results are compared with X-core, Y-core, corrugated-core, and web-core sandwich beams, a few conventional prismatic sandwich types, which are optimized using a prominent global evolutionary algorithm. Manufacturing requirements are considered through practical limitations on the design variables. It is shown that structures produced by topology optimization outperform the conventional sandwich beams by up to 44% at intermediate to high mass levels, where volume fraction is between 0.2 and 0.4, but often through increased topological complexity. The new core topologies bear a certain resemblance with the conventional core types, underscoring engineering ingenuity that went into their development over the years. The topology of the optimized joints differs from the conventional joint. The results also show some limitations of the topology optimization framework, for example that it does not offer better-performing beams for volume fractions below 0.2.Comment: 17 pages, 3 tables, 19 figures, journal pape

Recursive Cascaded Networks for Unsupervised Medical Image Registration

We present recursive cascaded networks, a general architecture that enables learning deep cascades, for deformable image registration. The proposed architecture is simple in design and can be built on any base network. The moving image is warped successively by each cascade and finally aligned to the fixed image; this procedure is recursive in a way that every cascade learns to perform a progressive deformation for the current warped image. The entire system is end-to-end and jointly trained in an unsupervised manner. In addition, enabled by the recursive architecture, one cascade can be iteratively applied for multiple times during testing, which approaches a better fit between each of the image pairs. We evaluate our method on 3D medical images, where deformable registration is most commonly applied. We demonstrate that recursive cascaded networks achieve consistent, significant gains and outperform state-of-the-art methods. The performance reveals an increasing trend as long as more cascades are trained, while the limit is not observed. Code is available at https://github.com/microsoft/Recursive-Cascaded-Networks.Comment: Accepted to ICCV 201

MaskFlownet: Asymmetric Feature Matching with Learnable Occlusion Mask

Feature warping is a core technique in optical flow estimation; however, the ambiguity caused by occluded areas during warping is a major problem that remains unsolved. In this paper, we propose an asymmetric occlusion-aware feature matching module, which can learn a rough occlusion mask that filters useless (occluded) areas immediately after feature warping without any explicit supervision. The proposed module can be easily integrated into end-to-end network architectures and enjoys performance gains while introducing negligible computational cost. The learned occlusion mask can be further fed into a subsequent network cascade with dual feature pyramids with which we achieve state-of-the-art performance. At the time of submission, our method, called MaskFlownet, surpasses all published optical flow methods on the MPI Sintel, KITTI 2012 and 2015 benchmarks. Code is available at https://github.com/microsoft/MaskFlownet.Comment: CVPR 2020 (Oral

GFlowCausal: Generative Flow Networks for Causal Discovery

Causal discovery aims to uncover causal structure among a set of variables. Score-based approaches mainly focus on searching for the best Directed Acyclic Graph (DAG) based on a predefined score function. However, most of them are not applicable on a large scale due to the limited searchability. Inspired by the active learning in generative flow networks, we propose a novel approach to learning a DAG from observational data called GFlowCausal. It converts the graph search problem to a generation problem, in which direct edges are added gradually. GFlowCausal aims to learn the best policy to generate high-reward DAGs by sequential actions with probabilities proportional to predefined rewards. We propose a plug-and-play module based on transitive closure to ensure efficient sampling. Theoretical analysis shows that this module could guarantee acyclicity properties effectively and the consistency between final states and fully-connected graphs. We conduct extensive experiments on both synthetic and real datasets, and results show the proposed approach to be superior and also performs well in a large-scale setting

Improved Conditional Generative Adversarial Net Classification For Spoken Language Recognition

Recent research on generative adversarial nets (GAN) for language identification (LID) has shown promising results. In this paper, we further exploit the latent abilities of GAN networks to firstly combine them with deep neural network (DNN)-based i-vector approaches and then to improve the LID model using conditional generative adversarial net (cGAN) classification. First, phoneme dependent deep bottleneck features (DBF) combined with output posteriors of a pre-trained DNN for automatic speech recognition (ASR) are used to extract i-vectors in the normal way. These i-vectors are then classified using cGAN, and we show an effective method within the cGAN to optimize parameters by combining both language identification and verification signals as supervision. Results show firstly that cGAN methods can significantly outperform DBF DNN i-vector methods where 49-dimensional i-vectors are used, but not where 600-dimensional vectors are used. Secondly, training a cGAN discriminator network for direct classification has further benefit for low dimensional i-vectors as well as short utterances with high dimensional i-vectors. However, incorporating a dedicated discriminator network output layer for classification and optimizing both classification and verification loss brings benefits in all test cases

Implications for the Explosion Mechanism of Type Ia Supernovae from their Late-time Spectra

Late-time spectra of Type Ia supernovae (SNe Ia) are important in clarifying the physics of their explosions, as they provide key clues to the inner structure of the exploding white dwarfs. We examined late-time optical spectra of 36 SNe Ia, including five from our own project (SNe 2019np, 2019ein, 2021hpr, 2021wuf, and 2022hrs), with phase coverage of $\sim 200$ to $\sim 400$ days after maximum light. At this late phase, the outer ejecta have become transparent and the features of inner iron-group elements emerge in the spectra. Based on multicomponent Gaussian fits and reasonable choices for the pseudocontinuum around Ni and Fe emission features, we get reliable estimates of the Ni to Fe ratio, which is sensitive to the explosion models of SNe Ia. Our results show that the majority (about 67%) of our SNe Ia are more consistent with the sub-Chandrasekhar-mass (i.e., double-detonation) model, although they could be affected by evolutionary or ionisation effects. Moreover, we find that the Si II $\lambda$6355 velocity measured around the time of maximum light tends to increase with the Ni to Fe ratio for the subsample with either redshifted or blueshifted nebular velocities, suggesting that progenitor metallicity might play an important role in accounting for the observed velocity diversity of SNe Ia.Comment: 20 pages, 10 figures, accepted for publication in MNRA

Effects of natural covers on soil evaporation of the shelterbelt along the Tarim Desert Highway

The control of soil evaporation is one of important approaches to save water. The artificially simulated evaporation experiments have been conducted in the hinterland of the Taklimakan Desert to reveal the effects of the natural covers on the soil evaporation of the Tarim Desert Highway shelterbelt as well as provide some insights in the efficient utilization of water resources and optimization of irrigation systems. The results showed that (1) All the covers, including the sand deposit, the salt crust, the litter, the sand-litter mixed layer and so on, can significantly inhibit the soil water evaporation. Specifically, the daily evaporation, the total evaporation, and the evaporation rate in covered sands were much smaller than that of sands without cover. The cover inhibition effects increased with the cover thickness. Particularly, the soil evaporation of the covered sands was less affected by external and internal factors than that of the bare sands. Moreover, the variation of daily evaporation of covered sands was smaller than that of bare sands. The cumulative evaporation varied linearly with time in the covered sands whereas it varied logarithmically in the bare sands. In addition, the soil evaporation in the bare sands showed significantly different characteristics in the early and late stages of the evaporation. (2) All the covers exhibited the significant inhibiting effect on the soil evaporation, and the inhibition efficiency increased logarithmically with the cover thickness. However, as the cover thickness was above a certain value, the increase in the inhibition efficiency was slow. Particularly, at a cover thickness of 2 cm, there was no obvious difference in the inhibition efficiency among all kinds of covers. The maximum inhibition efficiency as calculated from the daily evaporation on the first day of irrigation was: sand-litter mixed layer (79.92%) > litter layer (78.96%) > salt crust (75.58%) > sand bed (74.11%), whereas the average inhibiting efficiency as calculated from the cumulative soil evaporation at the end of an irrigation cycle (the fourth day) was: salt crust (67.78%) > sand-litter mixed layer (66.72%) > sand deposit (63.28%) > litter layer (61.74%)