搜索结果：Perseverance

The check-in service is often provided as an incentive system by online learning platforms to help users establish a learning routine and achieve accomplishment. However, according to the questionnaire conducted in this study, 82.5% of users of online English learning platforms that feature a check-in service have failed to maintain the daily check-in behavior for long-term language learning, mainly by reason of demotivation, forgetfulness, boredom, and insufficient time. As a language learner, I have an empirical experience in maintaining a record of over 4,000 daily check-ins on China's leading online English learning platform of Shanbay. In the meantime, I have been constantly exploring a practical solution to help cultivate perseverance for other users to follow through the learning routine. In this paper, I systematically introduce this practical solution, the GILT method, and its instructions. The experience and solution for perseverance development are based on Shanbay, but they can be applied to other learning platforms for different purposes.

Alignment research focuses on making individual AI systems reliable. Human institutions achieve reliable collective behaviour differently: they mitigate the risk posed by misaligned individuals through organisational structure. Multi-agent AI systems should follow this institutional model using compartmentalisation and adversarial review to achieve reliable outcomes through architectural design rather than assuming individual alignment. We demonstrate this approach through the Perseverance Composition Engine, a multi-agent system for document composition. The Composer drafts text, the Corroborator verifies factual substantiation with full source access, and the Critic evaluates argumentative quality without access to sources: information asymmetry enforced by system architecture. This creates layered verification: the Corroborator detects unsupported claims, whilst the Critic independently assesses coherence and completeness. Observations from 474 composition tasks (discrete cycles of drafting, verification, and evaluation) exhibit patterns consistent with the institutional hypothesis. When assigned impossible tasks requiring fabricated content, this iteration enabled progression f

In this work, we propose the use of Ground Penetrating Radar (GPR) for rover localization on Mars. Precise pose estimation is an important task for mobile robots exploring planetary surfaces, as they operate in GPS-denied environments. Although visual odometry provides accurate localization, it is computationally expensive and can fail in dim or high-contrast lighting. Wheel encoders can also provide odometry estimation, but are prone to slipping on the sandy terrain encountered on Mars. Although traditionally a scientific surveying sensor, GPR has been used on Earth for terrain classification and localization through subsurface feature matching. The Perseverance rover and the upcoming ExoMars rover have GPR sensors already equipped to aid in the search of water and mineral resources. We propose to leverage GPR to aid in Mars rover localization. Specifically, we develop a novel GPR-based deep learning model that predicts 1D relative pose translation. We fuse our GPR pose prediction method with inertial and wheel encoder data in a filtering framework to output rover localization. We perform experiments in a Mars analog environment and demonstrate that our GPR-based displacement pred

ProtoSpace is a custom JPL-built platform to help scientists and engineers visualize their CAD models collaboratively in augmented reality (AR) and on the web in 3D. In addition to this main use case, ProtoSpace has been used throughout the entire spacecraft mission lifecycle and beyond: ventilator design and assembly; providing AR-based instructions to astronauts in-training; educating the next generation on the process of spacecraft design; etc. ProtoSpace has been used for a decade by NASA missions-including Mars Perseverance, Europa Clipper, NISAR, SPHEREx, CAL, and Mars Sample Return-to reduce cost and risk by helping engineers and scientists fix problems earlier through reducing miscommunication and helping people understand the spatial context of their spacecraft in the appropriate physical context more quickly. This paper will explore how ProtoSpace came to be, define the system architecture and overview-including HoloLens and 3D web clients, the ProtoSpace server, and the CAD model optimizer-and dive into the use cases, spin-offs, and lessons learned that led to 10 years of success at NASA's Jet Propulsion Laboratory.

This study demonstrates a novel use of the U-Net architecture in the field of semantic segmentation to detect landforms using preprocessed satellite imagery. The study applies the U-Net model for effective feature extraction by using Convolutional Neural Network (CNN) segmentation techniques. Dropout is strategically used for regularization to improve the model's perseverance, and the Adam optimizer is used for effective training. The study thoroughly assesses the performance of the U-Net architecture utilizing a large sample of preprocessed satellite topographical images. The model excels in semantic segmentation tasks, displaying high-resolution outputs, quick feature extraction, and flexibility to a wide range of applications. The findings highlight the U-Net architecture's substantial contribution to the advancement of machine learning and image processing technologies. The U-Net approach, which emphasizes pixel-wise categorization and comprehensive segmentation map production, is helpful in practical applications such as autonomous driving, disaster management, and land use planning. This study not only investigates the complexities of U-Net architecture for semantic segmentat

This study explores the relationship between textual features and Information Engagement (IE) on digital platforms. It highlights the impact of computational linguistics and analytics on user interaction. The READ model is introduced to quantify key predictors like representativeness, ease of use, affect, and distribution, which forecast engagement levels. The model's effectiveness is validated through AB testing and randomized trials, showing strong predictive performance in participation (accuracy: 0.94), perception (accuracy: 0.85), perseverance (accuracy: 0.81), and overall IE (accuracy: 0.97). While participation metrics are strong, perception and perseverance show slightly lower recall and F1-scores, indicating some challenges. The study demonstrates that modifying text based on the READ model's insights leads to significant improvements. For example, increasing representativeness and positive affect boosts selection rates by 11 percent, raises evaluation averages from 3.98 to 4.46, and improves retention rates by 11 percent. These findings highlight the importance of linguistic factors in IE, providing a framework for enhancing digital text engagement. The research offers pr

School dropout is a serious problem in distance learning, where early detection is crucial for effective intervention and student perseverance. Predicting student dropout using available educational data is a widely researched topic in learning analytics. Our partner's distance learning platform highlights the importance of integrating diverse data sources, including socio-demographic data, behavioral data, and sentiment analysis, to accurately predict dropout risks. In this paper, we introduce a novel model that combines sentiment analysis of student comments using the Bidirectional Encoder Representations from Transformers (BERT) model with socio-demographic and behavioral data analyzed through Extreme Gradient Boosting (XGBoost). We fine-tuned BERT on student comments to capture nuanced sentiments, which were then merged with key features selected using feature importance techniques in XGBoost. Our model was tested on unseen data from the next academic year, achieving an accuracy of 84\%, compared to 82\% for the baseline model. Additionally, the model demonstrated superior performance in other metrics, such as precision and F1-score. The proposed method could be a vital tool in

We present a study of atmospheric disturbances at Jezero Crater, Mars, using ground-based measurements of surface pressure by the Perseverance rover in combination with orbital images from the Mars Express and Mars Reconnaissance Orbiter missions. The study starts at Ls $\sim$ 13.3° in MY36 (March 6th, 2021) and extends up to Ls $\sim$ 30.3° in MY37 (February 28th, 2023). We focus on the characterization of the major atmospheric phenomena at synoptic and planetary-scales. These are the thermal tides (measured up to the sixth component), long-period pressure oscillations (periods > 1 sol), the Aphelion Cloud Belt, and the occasional development of regional dust storms over Jezero. We present the seasonal evolution of the amplitudes and phases of the thermal tides and their relation with the atmospheric dust content (optical depth). Three regional dust storms and one polar storm extending over Jezero produced an increase in the diurnal and semidiurnal amplitudes but resulted in inverse responses in their phases. We show that the primary regular wave activity is due to baroclinic disturbances with periods of 2-4 sols and amplitudes $\sim$ 1-15 Pa increasing with dust content, in go

In this paper, we address the task of characterizing the chemical composition of planetary surfaces using convolutional neural networks (CNNs). Specifically, we seek to predict the multi-oxide weights of rock samples based on spectroscopic data collected under Martian conditions. We frame this problem as a multi-target regression task and propose a novel regularization method based on f-divergence. The f-divergence regularization is designed to constrain the distributional discrepancy between predictions and noisy targets. This regularizer serves a dual purpose: on the one hand, it mitigates overfitting by enforcing a constraint on the distributional difference between predictions and noisy targets. On the other hand, it acts as an auxiliary loss function, penalizing the neural network when the divergence between the predicted and target distributions becomes too large. To enable backpropagation during neural network training, we develop a differentiable f-divergence and incorporate it into the f-divergence regularization, making the network training feasible. We conduct experiments using spectra collected in a Mars-like environment by the remote-sensing instruments aboard the Curi

Late-stage Ca-sulfate-filled fractures are common on Mars. Notably, the Shenandoah formation in the western edge of Jezero crater preserves a variety of Ca-sulfate minerals in the fine-grained siliciclastic rocks explored by the Perseverance rover. However, the depositional environment and timing of the formation of these sulfates is unknown. To address this outstanding problem, we developed a new technique to map the crystal textures of these sulfates in situ at two stratigraphically similar locations in the Shenandoah formation, allowing us to constrain the burial depth and paleoenvironment at the time of their deposition. Our results suggest that some Ca-sulfate analyzed was formed at a burial depth greater than 80m, whereas Ca-sulfates present at another outcrop likely precipitated in a shallow-subsurface environment. These results indicate that samples collected for potential return to Earth at the two studied locations capture two different times and distinct chemical conditions in the depositional history of the Shenandoah formation providing multiple opportunities to evaluate surface and subsurface habitability.