Enhanced GPS Measurements Simulation for Space-oriented Navigation System Design

AbstractAt the stage of preliminary scheme and algorithm design for spaceborne navigation systems, a precise and high-fidelity software global positioning system (GPS) simulator is a necessary and feasible testing facility in laboratory environments, with consideration of the tradeoffs where possible. This article presents a software GPS measurements simulator on the L1 C/A code and carrier signal for space-oriented navigation system design. The simulator, coded in MATLAB language, generates both C/A code pseudorange and carrier phase measurements. Mathematical models in the Earth centered inertial (ECI) frame are formulated to simulate the GPS constellation and to generate GPS measurements. A series of efficient measures are investigated and utilized to rationalize the enhanced simulator, in terms of ephemeris data selection, space ionospheric model and range rate calculation, etc. Such an enhanced simulator has been facilitating our current work for designing a space integrated GPS/inertial navigation system (INS) navigation system. Consequently, it will promote our future research on space-oriented navigation system

Gauss Newton method for solving variational problems of PDEs with neural network discretizaitons

The numerical solution of differential equations using machine learning-based approaches has gained significant popularity. Neural network-based discretization has emerged as a powerful tool for solving differential equations by parameterizing a set of functions. Various approaches, such as the deep Ritz method and physics-informed neural networks, have been developed for numerical solutions. Training algorithms, including gradient descent and greedy algorithms, have been proposed to solve the resulting optimization problems. In this paper, we focus on the variational formulation of the problem and propose a Gauss- Newton method for computing the numerical solution. We provide a comprehensive analysis of the superlinear convergence properties of this method, along with a discussion on semi-regular zeros of the vanishing gradient. Numerical examples are presented to demonstrate the efficiency of the proposed Gauss-Newton method

An efficient greedy training algorithm for neural networks and applications in PDEs

Recently, neural networks have been widely applied for solving partial differential equations. However, the resulting optimization problem brings many challenges for current training algorithms. This manifests itself in the fact that the convergence order that has been proven theoretically cannot be obtained numerically. In this paper, we develop a novel greedy training algorithm for solving PDEs which builds the neural network architecture adaptively. It is the first training algorithm that observes the convergence order of neural networks numerically. This innovative algorithm is tested on several benchmark examples in both 1D and 2D to confirm its efficiency and robustness

Ambiguity Function Method Scheme for Aircraft Attitude Sensor Utilising GPS/GLONASS Carrier Phase Measurement

When the receivers of GPS, GLONASS, COMPASS and other such systems are equipped with multiple antennas, they can give attitude information. Based on the difference carrier phase equations established in local level frame (LLF), a new algorithm is presented to resolve aircraft attitude determination problems in real-time. Presuming that the cycle integer ambiguity is known, the measurement equations have attitude analytical resolutions using single difference (SD) equations of two navigation satellites in-view. Similar with SD process, the doubledifference (DD) measurements are established and analysed. In addition, the SD and DD algorithms are capable of reducing the integer search space into some discrete point space and then the ambiguity function method (AFM) resolves the ambiguity function within the point solutions space. Therefore the procedures have very low computation, thus saving time. The hardware architecture has been realised using multiple  GPS/GLONASS OEMs. The experimental results have demonstrated that the proposed approach is effective and can satisfy the requirement of real-time application in cases of GPS, and combined GPS, and GLONASS.Defence Science Journal, 2009, 59(5), pp.466-470, DOI:http://dx.doi.org/10.14429/dsj.59.154

Hybrid Distillation: Connecting Masked Autoencoders with Contrastive Learners

Representation learning has been evolving from traditional supervised training to Contrastive Learning (CL) and Masked Image Modeling (MIM). Previous works have demonstrated their pros and cons in specific scenarios, i.e., CL and supervised pre-training excel at capturing longer-range global patterns and enabling better feature discrimination, while MIM can introduce more local and diverse attention across all transformer layers. In this paper, we explore how to obtain a model that combines their strengths. We start by examining previous feature distillation and mask feature reconstruction methods and identify their limitations. We find that their increasing diversity mainly derives from the asymmetric designs, but these designs may in turn compromise the discrimination ability. In order to better obtain both discrimination and diversity, we propose a simple but effective Hybrid Distillation strategy, which utilizes both the supervised/CL teacher and the MIM teacher to jointly guide the student model. Hybrid Distill imitates the token relations of the MIM teacher to alleviate attention collapse, as well as distills the feature maps of the supervised/CL teacher to enable discrimination. Furthermore, a progressive redundant token masking strategy is also utilized to reduce the distilling costs and avoid falling into local optima. Experiment results prove that Hybrid Distill can achieve superior performance on different benchmarks

AiluRus: A Scalable ViT Framework for Dense Prediction

Vision transformers (ViTs) have emerged as a prevalent architecture for vision tasks owing to their impressive performance. However, when it comes to handling long token sequences, especially in dense prediction tasks that require high-resolution input, the complexity of ViTs increases significantly. Notably, dense prediction tasks, such as semantic segmentation or object detection, emphasize more on the contours or shapes of objects, while the texture inside objects is less informative. Motivated by this observation, we propose to apply adaptive resolution for different regions in the image according to their importance. Specifically, at the intermediate layer of the ViT, we utilize a spatial-aware density-based clustering algorithm to select representative tokens from the token sequence. Once the representative tokens are determined, we proceed to merge other tokens into their closest representative token. Consequently, semantic similar tokens are merged together to form low-resolution regions, while semantic irrelevant tokens are preserved independently as high-resolution regions. This strategy effectively reduces the number of tokens, allowing subsequent layers to handle a reduced token sequence and achieve acceleration. We evaluate our proposed method on three different datasets and observe promising performance. For example, the "Segmenter ViT-L" model can be accelerated by 48% FPS without fine-tuning, while maintaining the performance. Additionally, our method can be applied to accelerate fine-tuning as well. Experimental results demonstrate that we can save 52% training time while accelerating 2.46 times FPS with only a 0.09% performance drop. The code is available at https://github.com/caddyless/ailurus/tree/main.Comment: Accepted by NeurIPS 202

Manipulating Multiple Order Parameters via Oxygen Vacancies: The case of Eu0.5Ba0.5TiO3-{\delta}

Controlling functionalities, such as magnetism or ferroelectricity, by means of oxygen vacancies (VO) is a key issue for the future development of transition metal oxides. Progress in this field is currently addressed through VO variations and their impact on mainly one order parameter. Here we reveal a new mechanism for tuning both magnetism and ferroelectricity simultaneously by using VO. Combined experimental and density-functional theory studies of Eu0.5Ba0.5TiO3-{\delta}, we demonstrate that oxygen vacancies create Ti3+ 3d1 defect states, mediating the ferromagnetic coupling between the localized Eu 4f7 spins, and increase an off-center displacement of Ti ions, enhancing the ferroelectric Curie temperature. The dual function of Ti sites also promises a magnetoelectric coupling in the Eu0.5Ba0.5TiO3-{\delta}.Comment: Accepted by Physical Review B, 201

Precancerous Stem Cells Have the Potential for both Benign and Malignant Differentiation

Cancer stem cells (CSCs) have been identified in hematopoietic and solid tumors. However, their precursors—namely, precancerous stem cells (pCSCs) —have not been characterized. Here we experimentally define the pCSCs that have the potential for both benign and malignant differentiation, depending on environmental cues. While clonal pCSCs can develop into various types of tissue cells in immunocompetent mice without developing into cancer, they often develop, however, into leukemic or solid cancers composed of various types of cancer cells in immunodeficient mice. The progress of the pCSCs to cancers is associated with the up-regulation of c-kit and Sca-1, as well as with lineage markers. Mechanistically, the pCSCs are regulated by the PIWI/AGO family gene called piwil2. Our results provide clear evidence that a single clone of pCSCs has the potential for both benign and malignant differentiation, depending on the environmental cues. We anticipate pCSCs to be a novel target for the early detection, prevention, and therapy of cancers