139 research outputs found

    Optimal control of nonlinear partially-unknown systems with unsymmetrical input constraints and its applications to the optimal UAV circumnavigation problem

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    Aimed at solving the optimal control problem for nonlinear systems with unsymmetrical input constraints, we present an online adaptive approach for partially unknown control systems/dynamics. The designed algorithm converges online to the optimal control solution without the knowledge of the internal system dynamics. The optimality of the obtained control policy and the stability for the closed-loop dynamic optimality are proved theoretically. The proposed method greatly relaxes the assumption on the form of the internal dynamics and input constraints in previous works. Besides, the control design framework proposed in this paper offers a new approach to solve the optimal circumnavigation problem involving a moving target for a fixed-wing unmanned aerial vehicle (UAV). The control performance of our method is compared with that of the existing circumnavigation control law in a numerical simulation and the simulation results validate the effectiveness of our algorithm

    BEVBert: Multimodal Map Pre-training for Language-guided Navigation

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    Large-scale pre-training has shown promising results on the vision-and-language navigation (VLN) task. However, most existing pre-training methods employ discrete panoramas to learn visual-textual associations. This requires the model to implicitly correlate incomplete, duplicate observations within the panoramas, which may impair an agent's spatial understanding. Thus, we propose a new map-based pre-training paradigm that is spatial-aware for use in VLN. Concretely, we build a local metric map to explicitly aggregate incomplete observations and remove duplicates, while modeling navigation dependency in a global topological map. This hybrid design can balance the demand of VLN for both short-term reasoning and long-term planning. Then, based on the hybrid map, we devise a pre-training framework to learn a multimodal map representation, which enhances spatial-aware cross-modal reasoning thereby facilitating the language-guided navigation goal. Extensive experiments demonstrate the effectiveness of the map-based pre-training route for VLN, and the proposed method achieves state-of-the-art on four VLN benchmarks.Comment: ICCV 2023, project page: https://github.com/MarSaKi/VLN-BEVBer

    Self-Supervised Scene Dynamic Recovery from Rolling Shutter Images and Events

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    Scene Dynamic Recovery (SDR) by inverting distorted Rolling Shutter (RS) images to an undistorted high frame-rate Global Shutter (GS) video is a severely ill-posed problem, particularly when prior knowledge about camera/object motions is unavailable. Commonly used artificial assumptions on motion linearity and data-specific characteristics, regarding the temporal dynamics information embedded in the RS scanlines, are prone to producing sub-optimal solutions in real-world scenarios. To address this challenge, we propose an event-based RS2GS framework within a self-supervised learning paradigm that leverages the extremely high temporal resolution of event cameras to provide accurate inter/intra-frame information. % In this paper, we propose to leverage the event camera to provide inter/intra-frame information as the emitted events have an extremely high temporal resolution and learn an event-based RS2GS network within a self-supervised learning framework, where real-world events and RS images can be exploited to alleviate the performance degradation caused by the domain gap between the synthesized and real data. Specifically, an Event-based Inter/intra-frame Compensator (E-IC) is proposed to predict the per-pixel dynamic between arbitrary time intervals, including the temporal transition and spatial translation. Exploring connections in terms of RS-RS, RS-GS, and GS-RS, we explicitly formulate mutual constraints with the proposed E-IC, resulting in supervisions without ground-truth GS images. Extensive evaluations over synthetic and real datasets demonstrate that the proposed method achieves state-of-the-art and shows remarkable performance for event-based RS2GS inversion in real-world scenarios. The dataset and code are available at https://w3un.github.io/selfunroll/

    Multi-level characteristics recognition of cancer core therapeutic targets and drug screening for a broader patient population

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    Introduction: Target therapy for cancer cell mutation has brought attention to several challenges in clinical applications, including limited therapeutic targets, less patient benefits, and susceptibility to acquired due to their clear biological mechanisms and high specificity in targeting cancers with specific mutations. However, the identification of truly lethal synthetic lethal therapeutic targets for cancer cells remains uncommon, primarily due to compensatory mechanisms.Methods: In our pursuit of core therapeutic targets (CTTs) that exhibit extensive synthetic lethality in cancer and the corresponding potential drugs, we have developed a machine-learning model that utilizes multiple levels and dimensions of cancer characterization. This is achieved through the consideration of the transcriptional and post-transcriptional regulation of cancer-specific genes and the construction of a model that integrates statistics and machine learning. The model incorporates statistics such as Wilcoxon and Pearson, as well as random forest. Through WGCNA and network analysis, we identify hub genes in the SL network that serve as CTTs. Additionally, we establish regulatory networks for non-coding RNA (ncRNA) and drug-target interactions.Results: Our model has uncovered 7277 potential SL interactions, while WGCNA has identified 13 gene modules. Through network analysis, we have identified 30 CTTs with the highest degree in these modules. Based on these CTTs, we have constructed networks for ncRNA regulation and drug targets. Furthermore, by applying the same process to lung cancer and renal cell carcinoma, we have identified corresponding CTTs and potential therapeutic drugs. We have also analyzed common therapeutic targets among all three cancers.Discussion: The results of our study have broad applicability across various dimensions and histological data, as our model identifies potential therapeutic targets by learning multidimensional complex features from known synthetic lethal gene pairs. The incorporation of statistical screening and network analysis further enhances the confidence in these potential targets. Our approach provides novel theoretical insights and methodological support for the identification of CTTs and drugs in diverse types of cancer

    Structural Based Screening of Antiandrogen Targeting Activation Function-2 Binding Site

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    Androgen receptor (AR) plays a critical role in the development and progression of prostate cancer (PCa). Current antiandrogen therapies induce resistant mutations at the hormone binding pocket (HBP) that convert the activity of these agents from antagonist to agonist. Thus, there is a high unmet medical need for the development of novel antiandrogens which circumvent mutation-based resistance. Herein, through the analysis of AR structures with ligands binding to the activation function-2 (AF2) site, we built a combined pharmacophore model. In silico screening and the subsequent biological evaluation lead to the discovery of the novel lead compound IMB-A6 that binds to the AF2 site, which inhibits the activity of either wild-type (WT) or resistance mutated ARs. Our work demonstrates structure-based drug design is an efficient strategy to discover new antiandrogens, and provides a new class of small molecular antiandrogens for the development of novel treatment agents against PCa

    Cancer-associated fibroblast infiltration in osteosarcoma: the discrepancy in subtypes pathways and immunosuppression

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    Introduction: Osteosarcoma (OS), the primary malignant bone tumor, has a low survival rate for recurrent patients. Latest reports indicated that cancer-associated fibroblasts (CAFs) were the main component of tumor microenvironment, and would generate a variable role in the progression of tumors. However, the role of CAFs is still few known in osteosarcoma.Methods: The processed RNA-seq data and the corresponding clinical and molecular information were retrieved from the Cancer Genome Atlas Program (TCGA) database and processed data of tumor tissue was obtained from Gene Expression Omnibus (GEO) database. Xcell method was used in data processing, and Gene set variation analysis (GSVA) was used to calculates enrichment scores. Nomogram was constructed to evaluate prognostic power of the predictive model. And the construction of risk scores and assessment of prognostic predictive were based on the LASSO model.Results: This study classified Cancer Genome Atlas (TCGA) cohort into high and low CAFs infiltrate phenotype with different CAFs infiltration enrichment scores. Then TOP 9 genes were screened as prognostic signatures among 2,488 differentially expressed genes between the two groups. Key prognostic molecules were CGREF1, CORT and RHBDL2 and the risk score formula is: Risk-score = CGREF1*0.004 + CORT*0.004 + RHBDL2*0.002. The signatures were validated to be independent prognostic factors to predict tumor prognosis with single-factor COX and multi-factor COX regression analyses and Norton chart. The risk score expression of risk score model genes could predict the drug resistance, and significant differences could be found between the high and low scoring groups for 17-AAG, AZD6244, PD-0325901 and Sorafenib.Discussion: To sum up, this article validated the prediction role of CAF infiltration in the prognosis of OS, which might shed light on the treatment of OS

    The TP53-Related Signature Predicts Immune Cell Infiltration, Therapeutic Response, and Prognosis in Patients With Esophageal Carcinoma

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    TP53 mutation (TP53MUT) is one of the most common gene mutations and frequently occurs in many cancers, especially esophageal carcinoma (ESCA), and it correlates with clinical prognostic outcomes. Nevertheless, the mechanisms by which TP53MUT regulates the correlation between ESCA and prognosis have not been sufficiently studied. Here, in the current research, we constructed a TP53MUT-related signature to predict the prognosis of patients with esophageal cancer and successfully verified this model in patients in the TP53 mutant group, esophageal squamous cell carcinoma group, and adenocarcinoma group. The risk scores proved to be better independent prognostic factors than clinical features, and prognostic features were combined with other clinical features to establish a convincing nomogram to predict overall survival from 1 to 3 years. In addition, we further predicted the tumor immune cell infiltration, chemical drugs, and immunotherapy responses between the high-risk group and low risk group. Finally, the gene expression of the seven-gene signature (AP002478.1, BHLHA15, FFAR2, IGFBP1, KCTD8, PHYHD1, and SLC26A9) can provide personalized prognosis prediction and insights into new treatments
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