Bregman Proximal Gradient Algorithm with Extrapolation for a class of Nonconvex Nonsmooth Minimization Problems

In this paper, we consider an accelerated method for solving nonconvex and nonsmooth minimization problems. We propose a Bregman Proximal Gradient algorithm with extrapolation(BPGe). This algorithm extends and accelerates the Bregman Proximal Gradient algorithm (BPG), which circumvents the restrictive global Lipschitz gradient continuity assumption needed in Proximal Gradient algorithms (PG). The BPGe algorithm has higher generality than the recently introduced Proximal Gradient algorithm with extrapolation(PGe), and besides, due to the extrapolation step, BPGe converges faster than BPG algorithm. Analyzing the convergence, we prove that any limit point of the sequence generated by BPGe is a stationary point of the problem by choosing parameters properly. Besides, assuming Kurdyka-{\'L}ojasiewicz property, we prove the whole sequences generated by BPGe converges to a stationary point. Finally, to illustrate the potential of the new method BPGe, we apply it to two important practical problems that arise in many fundamental applications (and that not satisfy global Lipschitz gradient continuity assumption): Poisson linear inverse problems and quadratic inverse problems. In the tests the accelerated BPGe algorithm shows faster convergence results, giving an interesting new algorithm.Comment: Preprint submitted for publication, February 14, 201

Masked Autoencoders in 3D Point Cloud Representation Learning

Transformer-based Self-supervised Representation Learning methods learn generic features from unlabeled datasets for providing useful network initialization parameters for downstream tasks. Recently, self-supervised learning based upon masking local surface patches for 3D point cloud data has been under-explored. In this paper, we propose masked Autoencoders in 3D point cloud representation learning (abbreviated as MAE3D), a novel autoencoding paradigm for self-supervised learning. We first split the input point cloud into patches and mask a portion of them, then use our Patch Embedding Module to extract the features of unmasked patches. Secondly, we employ patch-wise MAE3D Transformers to learn both local features of point cloud patches and high-level contextual relationships between patches and complete the latent representations of masked patches. We use our Point Cloud Reconstruction Module with multi-task loss to complete the incomplete point cloud as a result. We conduct self-supervised pre-training on ShapeNet55 with the point cloud completion pre-text task and fine-tune the pre-trained model on ModelNet40 and ScanObjectNN (PB\_T50\_RS, the hardest variant). Comprehensive experiments demonstrate that the local features extracted by our MAE3D from point cloud patches are beneficial for downstream classification tasks, soundly outperforming state-of-the-art methods ($93.4\%$ and $86.2\%$ classification accuracy, respectively).Comment: Accepted to IEEE Transactions on Multimedi

Bregman Proximal Gradient Algorithm with Extrapolation for a Class of Nonconvex Nonsmooth Minimization Problems

In this paper, we consider an accelerated method for solving nonconvex and nonsmooth minimization problems. We propose a Bregman Proximal Gradient algorithm with extrapolation (BPGe). This algorithm extends and accelerates the Bregman Proximal Gradient algorithm (BPG), which circumvents the restrictive global Lipschitz gradient continuity assumption needed in Proximal Gradient algorithms (PG). The BPGe algorithm has a greater generality than the recently introduced Proximal Gradient algorithm with extrapolation (PGe) and, in addition, due to the extrapolation step, BPGe converges faster than the BPG algorithm. Analyzing the convergence, we prove that any limit point of the sequence generated by BPGe is a stationary point of the problem by choosing the parameters properly. Besides, assuming Kurdyka-Lojasiewicz property, we prove that all the sequences generated by BPGe converge to a stationary point. Finally, to illustrate the potential of the new method BPGe, we apply it to two important practical problems that arise in many fundamental applications (and that not satisfy global Lipschitz gradient continuity assumption): Poisson linear inverse problems and quadratic inverse problems. In the tests the accelerated BPGe algorithm shows faster convergence results, giving an interesting new algorithm

Regulation of Migration in Mythimna separata (Walker) in China: A Review Integrating Environmental, Physiological, Hormonal, Genetic, and Molecular Factors

Each year the Mythimna separate (Walker), undertakes a seasonal, long-distance, multigeneration roundtrip migration between southern and northern China. Despite its regularity, the decision to migrate is facultative, and is controlled by environmental, physiological, hormonal, genetic, and molecular factors. Migrants take off on days 1 or 2 after eclosion, although the preoviposition period lasts ≈7 d. The trade-offs among the competing physiological demands of migration and reproduction are coordinated in M. separata by the “oogenesis-flight syndrome.” Larvae that experience temperatures above or below certain thresholds accompanied by appropriate humidity, short photoperiod, poor nutrition, and moderate density tend to develop into migrants. However, there is a short window of sensitivity within 24 h after adult eclosion when migrants can be induced to switch to reproductive residents if they encounter extreme environmental factors including starvation, low temperature and long photoperiod. Juvenile hormone (JH) titer is low before migration but high titers are associated with termination of migratory behavior and the switch to reproduction. Early release of JH by the corpora allata in environmentally stressed 1-d old adults, otherwise destined by larval conditions to be migrants, switches them to residents. Offspring inherit parental additive genetic effects governing migratory behavior. However, they also retain flexibility in expression of both flight and reproductive life history traits. The insect neuropeptide, allatotropin, which activates corpora allata to synthesize JH, controls adult flight and reproduction. Future research directions to better understand regulation of migration in this species are discussed

Comparison of Reproductive and Flight Capacity of Loxostege sticticalis (Lepidoptera: Pyralidae), Developing From Diapause and Non-Diapause Larvae

The beet webworm, Loxostege sticticalis (L.) (Lepidoptera: Pyralidae), uses both diapause and migration as life history strategies. To determine the role of diapause plays in the population dynamics of L. sticticalis, the reproductive and flight potentials of adults originating from diapause and nondiapause larvae were investigated under controlled laboratory conditions. Preoviposition period, lifetime fecundity, and daily egg production of females originating from diapause larvae were not significantly different from those originating from nondiapause larvae, showing that diapause has no significant effect on reproductive capacity when adults are provided with an adequate carbohydrate source. However, females that developed from diapause larvae lived significantly longer than those from nondiapause larvae. Flight capacity, including flight duration, distance and velocity of 3-d-old adults were all significantly greater in adults originating from diapause larvae than those from nondiapause larvae. L. sticticalisadults developing from diapause larvae tended to have more extreme values of longest flight duration and furthest flight distance than those from nondiapause larvae. Together, these results suggest that long-distance flight potential of L. sticticalis is greater after larval diapause than after direct development to adulthood. However, there were no significant differences between sexes within the two categories of moths in terms of total flight duration, total flight distance, flight velocity, and longest flight duration