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  1. From the start, the airline industry has remarkably connected countries all over the world through rapid long-distance transportation, helping people overcome geographic barriers. Consequently, this has ushered in substantial economic growth, both nationally and internationally. The airline industry produces vast amounts of data, capturing a diverse set of information about their operations, including data related to passengers, freight, flights, and much more. Analyzing air travel data can advance the understanding of airline market dynamics, allowing companies to provide customized, efficient, and safe transportation services. Due to big data challenges in such a complex environment, the benefits of drawing insights from the air travel data in the airline industry have not yet been fully explored. This article aims to survey various components and corresponding proposed data analysis methodologies that have been identified as essential to the inner workings of the airline industry. We introduce existing data sources commonly used in the papers surveyed and summarize their availability. Finally, we discuss several potential research directions to better harness airline data in the future. We anticipate this study to be used as a comprehensive reference for both members of the airline industry and academic scholars with an interest in airline research.
    Free, publicly-accessible full text available November 30, 2023
  2. Abstract Maternal-to-filial nutrition transfer is central to grain development and yield. nitrate transporter 1/peptide transporter (NRT1-PTR)-type transporters typically transport nitrate, peptides, and ions. Here, we report the identification of a maize (Zea mays) NRT1-PTR-type transporter that transports sucrose and glucose. The activity of this sugar transporter, named Sucrose and Glucose Carrier 1 (SUGCAR1), was systematically verified by tracer-labeled sugar uptake and serial electrophysiological studies including two-electrode voltage-clamp, non-invasive microelectrode ion flux estimation assays in Xenopus laevis oocytes and patch clamping in HEK293T cells. ZmSUGCAR1 is specifically expressed in the basal endosperm transfer layer and loss-of-function mutation of ZmSUGCAR1 caused significantly decreased sucrose and glucose contents and subsequent shrinkage of maize kernels. Notably, the ZmSUGCAR1 orthologs SbSUGCAR1 (from Sorghum bicolor) and TaSUGCAR1 (from Triticum aestivum) displayed similar sugar transport activities in oocytes, supporting the functional conservation of SUGCAR1 in closely related cereal species. Thus, the discovery of ZmSUGCAR1 uncovers a type of sugar transporter essential for grain development and opens potential avenues for genetic improvement of seed-filling and yield in maize and other grain crops.
    Free, publicly-accessible full text available September 1, 2023
  3. Abstract The diversity of the Madden-Julian Oscillation (MJO) in terms of its maximum intensity, zonal extent and phase speed was explored using a cluster analysis method. The zonal extent is found to be significantly correlated to the phase speed. A longer zonal extent is often associated with a faster phase speed. The diversities of zonal extent and speed are connected with distinctive interannual sea surface temperature anomaly (SSTA) distributions and associated moisture and circulation patterns over the equatorial Pacific. An El Niño–like background SSTA leads to enhanced precipitation over the central Pacific, allowing a stronger vertically overturning circulation to the east of the MJO. This promotes both a larger east-west asymmetry of column-integrated moist static energy (MSE) tendency and a greater boundary-layer moisture leading, serving as potential causes of the faster phase speed. The El Niño–like SSTA also favors the MJOs intruding further into the Pacific, causing a larger zonal extent. The intensity diversity is associated with the interannual SSTA over the Maritime Continent and background moisture condition over the tropical Indian Ocean. An observed warm SSTA over the Maritime Continent excites a local Walker cell with a subsidence over the Indian Ocean, which could decrease the background moisture, weakeningmore »the MJO intensity. The intensity difference between strong and weak events would be amplified due to distinct intensity growth speed. The faster intensity growth of a strong MJO is attributed to a greater longwave radiative heating and a greater surface latent heat flux, as both of which contribute to a greater total time change rate of the column-integrated MSE.« less
  4. Immersive Learning Environments (ILEs) developed in Virtual and Augmented Reality (VR/AR) are a novel pro- fessional training platform. An ILE can facilitate an Adaptive Learning System (ALS), which has proven beneficial to the learning process. However, there is no existing AI-ready ILE that facilitates collecting multimedia multimodal data from the environment and users for training AI models, nor allows for the learning contents and complex learning process to be dynamically adapted by an ALS. This paper proposes a novel multimedia system in VR/AR to dynamically build ILEs for a wide range of use-cases, based on a description language for the generalizable ILE structure. It will detail users’ paths and conditions for completing learning activities, and a content adaptation algorithm to update the ILE at runtime. Human and AI systems can customize the environment based on user learning metrics. Results show that this framework is efficient and low- overhead, suggesting a path to simplifying and democratizing the ILE development without introducing bloat. Index Terms—virtual reality, augmented reality, content generation, immersive learning, 3D environments
  5. In the robust submodular partitioning problem, we aim to allocate a set of items into m blocks, so that the evaluation of the minimum block according to a submodular function is maximized. Robust submodular partitioning promotes the diversity of every block in the partition. It has many applications in machine learning, e.g., partitioning data for distributed training so that the gradients computed on every block are consistent. We study an extension of the robust submodular partition problem with additional constraints (e.g., cardinality, multiple matroids, and/or knapsack) on every block. For example, when partitioning data for distributed training, we can add a constraint that the number of samples of each class is the same in each partition block, ensuring data balance. We present two classes of algorithms, i.e., Min-Block Greedy based algorithms (with an ⌦(1/m) bound), and Round-Robin Greedy based algorithms (with a constant bound) and show that under various constraints, they still have good approximation guarantees. Interestingly, while normally the latter runs in only weakly polynomial time, we show that using the two together yields strongly polynomial running time while preserving the approximation guarantee. Lastly, we apply the algorithms on a real-world machine learning data partitioning problem showing good results.
  6. Modern manufacturing processes are in a state of flux, as they adapt to increasing demand for flexible and self-configuring production. This poses challenges for training workers to rapidly master new machine operations and processes, i.e. machine tasks. Conventional in-person training is effective but requires time and effort of experts for each worker trained and not scalable. Recorded tutorials, such as video-based or augmented reality (AR), permit more efficient scaling. However, unlike in-person tutoring, existing recorded tutorials lack the ability to adapt to workers’ diverse experiences and learning behaviors. We present AdapTutAR, an adaptive task tutoring system that enables experts to record machine task tutorials via embodied demonstration and train learners with different AR tutoring contents adapting to each user’s characteristics. The adaptation is achieved by continually monitoring learners’ tutorial-following status and adjusting the tutoring content on-the-fly and in-situ. The results of our user study evaluation have demonstrated that our adaptive system is more effective and preferable than the non-adaptive one.
  7. Data collected from real-world environments often contain multiple objects, scenes, and activities. In comparison to single-label problems, where each data sample only defines one concept, multi-label problems allow the co-existence of multiple concepts. To exploit the rich semantic information in real-world data, multi-label classification has seen many applications in a variety of domains. The traditional approaches to multi-label problems tend to have the side effects of increased memory usage, slow model inference speed, and most importantly the under-utilization of the dependency across concepts. In this paper, we adopt multi-task learning to address these challenges. Multi-task learning treats the learning of each concept as a separate job, while at the same time leverages the shared representations among all tasks. We also propose a dynamic task balancing method to automatically adjust the task weight distribution by taking both sample-level and task-level learning complexities into consideration. Our framework is evaluated on a disaster video dataset and the performance is compared with several state-of-the-art multi-label and multi-task learning techniques. The results demonstrate the effectiveness and supremacy of our approach.
  8. Recognition of human behavior plays an important role in context-aware applications. However, it is still a challenge for end-users to build personalized applications that accurately recognize their own activities. Therefore, we present CAPturAR, an in-situ programming tool that supports users to rapidly author context-aware applications by referring to their previous activities. We customize an AR head-mounted device with multiple camera systems that allow for non-intrusive capturing of user's daily activities. During authoring, we reconstruct the captured data in AR with an animated avatar and use virtual icons to represent the surrounding environment. With our visual programming interface, users create human-centered rules for the applications and experience them instantly in AR. We further demonstrate four use cases enabled by CAPturAR. Also, we verify the effectiveness of the AR-HMD and the authoring workflow with a system evaluation using our prototype. Moreover, we conduct a remote user study in an AR simulator to evaluate the usability.