Successive Convexification of Non-Convex Optimal Control Problems and Its Convergence Properties

This paper presents an algorithm to solve non-convex optimal control problems, where non-convexity can arise from nonlinear dynamics, and non-convex state and control constraints. This paper assumes that the state and control constraints are already convex or convexified, the proposed algorithm convexifies the nonlinear dynamics, via a linearization, in a successive manner. Thus at each succession, a convex optimal control subproblem is solved. Since the dynamics are linearized and other constraints are convex, after a discretization, the subproblem can be expressed as a finite dimensional convex programming subproblem. Since convex optimization problems can be solved very efficiently, especially with custom solvers, this subproblem can be solved in time-critical applications, such as real-time path planning for autonomous vehicles. Several safe-guarding techniques are incorporated into the algorithm, namely virtual control and trust regions, which add another layer of algorithmic robustness. A convergence analysis is presented in continuous- time setting. By doing so, our convergence results will be independent from any numerical schemes used for discretization. Numerical simulations are performed for an illustrative trajectory optimization example.Comment: Updates: corrected wordings for LICQ. This is the full version. A brief version of this paper is published in 2016 IEEE 55th Conference on Decision and Control (CDC). http://ieeexplore.ieee.org/document/7798816

More comprehensive facial inversion for more effective expression recognition

Facial expression recognition (FER) plays a significant role in the ubiquitous application of computer vision. We revisit this problem with a new perspective on whether it can acquire useful representations that improve FER performance in the image generation process, and propose a novel generative method based on the image inversion mechanism for the FER task, termed Inversion FER (IFER). Particularly, we devise a novel Adversarial Style Inversion Transformer (ASIT) towards IFER to comprehensively extract features of generated facial images. In addition, ASIT is equipped with an image inversion discriminator that measures the cosine similarity of semantic features between source and generated images, constrained by a distribution alignment loss. Finally, we introduce a feature modulation module to fuse the structural code and latent codes from ASIT for the subsequent FER work. We extensively evaluate ASIT on facial datasets such as FFHQ and CelebA-HQ, showing that our approach achieves state-of-the-art facial inversion performance. IFER also achieves competitive results in facial expression recognition datasets such as RAF-DB, SFEW and AffectNet. The code and models are available at https://github.com/Talented-Q/IFER-master

Successive Convexification of Non-convex Optimal Control Problems: Theory and Applications

Thesis (Ph.D.)--University of Washington, 2021The topic of this dissertation centers around Successive Convexification, a family of iterative algorithms designed to solve non-convex constrained optimal control problems. This document begins with an introduction to optimal control and finite-dimensional optimization in Chapter 2. It then presents the main algorithm within the Successive Convexification framework, SCvx, in Chapter 3. SCvx is a general-purpose solver that can handle problems with nonlinear system dynamics and non-convex state and control constraints. Analytical and numerical results are presented to demonstrate its convergence properties, including global convergence, strong convergence and superlinear convergence rate. SCvx-fast, a specialized version of SCvx is introduced next in Chapter 4 to handle systems with simpler dynamics and convex keep-out zones type of constraints commonly seen in quadrotor obstacle avoidance problems. It has new features such as a project-and-convexify step, removes the smoothness assumption, and does not rely on the trust-region updating mechanism. As a result, more aggressive steps can be taken and thus convergence occurs in much fewer iterations.With SCvx or SCvx-fast as the central pillar for on-board trajectory planning, we can build a fully autonomous system by further integrating i) a computer-vision-based perception unit and ii) Signal-Temporal-Logic (STL)-based mission specifications. Chapter 5 explores these directions as aerospace applications of Successive Convexification

Improvements to Self-Supervised Representation Learning for Masked Image Modeling

This paper explores improvements to the masked image modeling (MIM) paradigm. The MIM paradigm enables the model to learn the main object features of the image by masking the input image and predicting the masked part by the unmasked part. We found the following three main directions for MIM to be improved. First, since both encoders and decoders contribute to representation learning, MIM uses only encoders for downstream tasks, which ignores the impact of decoders on representation learning. Although the MIM paradigm already employs small decoders with asymmetric structures, we believe that continued reduction of decoder parameters is beneficial to improve the representational learning capability of the encoder . Second, MIM solves the image prediction task by training the encoder and decoder together , and does not design a separate task for the encoder . To further enhance the performance of the encoder when performing downstream tasks, we designed the encoder for the tasks of comparative learning and token position prediction. Third, since the input image may contain background and other objects, and the proportion of each object in the image varies, reconstructing the tokens related to the background or to other objects is not meaningful for MIM to understand the main object representations. Therefore we use ContrastiveCrop to crop the input image so that the input image contains as much as possible only the main objects. Based on the above three improvements to MIM, we propose a new model, Contrastive Masked AutoEncoders (CMAE). We achieved a Top-1 accuracy of 65.84% on tinyimagenet using the ViT-B backbone, which is +2.89 outperforming the MAE of competing methods when all conditions are equal. Code will be made available

Medical supervised masked autoencoders: Crafting a better masking strategy and efficient fine-tuning schedule for medical image classification

Masked autoencoders (MAEs) have displayed significant potential in the classification and semantic segmentation of medical images in the last year. Due to the high similarity of human tissues, even slight changes in medical images may represent diseased tissues, necessitating fine-grained inspection to pinpoint diseased tissues. The random masking strategy of MAEs is likely to result in areas of lesions being overlooked by the model. At the same time, inconsistencies between the pre-training and fine-tuning phases impede the performance and efficiency of MAE in medical image classification. To address these issues, we propose a medical supervised masked autoencoder (MSMAE) in this paper. In the pre-training phase, MSMAE precisely masks medical images via the attention maps obtained from supervised training, contributing to the representation learning of human tissue in the lesion area. During the fine-tuning phase, MSMAE is also driven by attention to the accurate masking of medical images. This improves the computational efficiency of the MSMAE while increasing the difficulty of fine-tuning, which indirectly improves the quality of MSMAE medical diagnosis. Extensive experiments demonstrate that MSMAE achieves state-of-the-art performance in case with three official medical datasets for various diseases. Meanwhile, transfer learning for MSMAE also demonstrates the great potential of our approach for medical semantic segmentation tasks. Moreover, the MSMAE accelerates the inference time in the fine-tuning phase by 11.2% and reduces the number of floating-point operations (FLOPs) by 74.08% compared to a traditional MAE

A Network Pharmacology Approach to Investigate the Active Compounds and Mechanisms of Musk for Ischemic Stroke

Objectives. This study aims to study the material basis and effective mechanism of musk for ischemic stroke (IS) based on the network pharmacology approach. Methods. We collected the chemical components and target gene of musk from the BATMAN-TCM analytical platform and identified ischemic stroke-related targets from the following databases: DisGeNET, NCBI-Gene, HPO, OMIM, DrugBank, and TTD. The targets of musk and IS were uploaded to the String database to construct the protein-protein interaction (PPI) network, and then, the key targets were analyzed by topological methods. At last, the function biological process and signaling pathways of key targets were carried out by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and cluster analysis by using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) server and Metascape platform. Results. A total of 29 active compounds involving 1081 predicted targets were identified in musk and there were 1104 IS-related targets. And 88 key targets of musk for IS were obtained including AKT1, MAPK1/3, TP53, TNF, SRC, FOS, CASP3, JUN, NOS3, and IL1B. The GO and KEGG enrichment analysis suggested that these key targets are mainly involved in multiple pathways which participated in TNF signaling pathway, estrogen signaling pathway, prolactin signaling pathway, neurotrophin signaling pathway, T-cell receptor signaling pathway, cAMP signaling pathway, FoxO signaling pathway, and HIF1 signaling pathway. Conclusion. This study revealed that the effective mechanisms of musk against IS would be associated with the regulation of apoptosis, inflammatory response, and gene transcription

dataset for re-estimating SOC change rates with new approach

Here are the three datasets showing how re-estimating SOC change rates with the newly proposed approaches based on equivalent mineral-matter volume and what are the differences compared to those with the previously approaches based on equivalent soil volume and equivalent mass of soil or mineral-matter

Sequential glycosylations at the multibasic cleavage site of SARS-CoV-2 spike protein regulate viral activity

Abstract The multibasic furin cleavage site at the S1/S2 boundary of the spike protein is a hallmark of SARS-CoV-2 and plays a crucial role in viral infection. However, the mechanism underlying furin activation and its regulation remain poorly understood. Here, we show that GalNAc-T3 and T7 jointly initiate clustered O-glycosylations in the furin cleavage site of the SARS-CoV-2 spike protein, which inhibit furin processing, suppress the incorporation of the spike protein into virus-like-particles and affect viral infection. Mechanistic analysis reveals that the assembly of the spike protein into virus-like particles relies on interactions between the furin-cleaved spike protein and the membrane protein of SARS-CoV-2, suggesting a possible mechanism for furin activation. Interestingly, mutations in the spike protein of the alpha and delta variants of the virus confer resistance against glycosylation by GalNAc-T3 and T7. In the omicron variant, additional mutations reverse this resistance, making the spike protein susceptible to glycosylation in vitro and sensitive to GalNAc-T3 and T7 expression in human lung cells. Our findings highlight the role of glycosylation as a defense mechanism employed by host cells against SARS-CoV-2 and shed light on the evolutionary interplay between the host and the virus