Role requiring'No experience data provided' months of experience in None Role requiring'No experience data provided' months of experience in None Events in recent years have made us all too familiar with the havoc that natural disasters can wreak, and the increasing frequency and intensity with which they are occurring. Despite record levels of losses, conventional methods of risk modeling continue to paint at best an incomplete picture of these threats. While AI alone may not be able to thwart these disasters, it can help us become more prepared for them, and ultimately that will lead to better outcomes. As a Senior Data Scientist – Computer Vision, you are comfortable and excited to work closely with the engineering team to build the best AI tech possible. You will scale the development of top-tier models by using diverse data sources to provide strong insights and maximize the impact of our company efforts.

This paper addresses the problem of developing an algorithm for autonomous ship landing of vertical take-off and landing (VTOL) capable unmanned aerial vehicles (UAVs), using only a monocular camera in the UAV for tracking and localization. Ship landing is a challenging task due to the small landing space, six degrees of freedom ship deck motion, limited visual references for localization, and adversarial environmental conditions such as wind gusts. We first develop a computer vision algorithm which estimates the relative position of the UAV with respect to a horizon reference bar on the landing platform using the image stream from a monocular vision camera on the UAV. Our approach is motivated by the actual ship landing procedure followed by the Navy helicopter pilots in tracking the horizon reference bar as a visual cue. We then develop a robust reinforcement learning (RL) algorithm for controlling the UAV towards the landing platform even in the presence of adversarial environmental conditions such as wind gusts. We demonstrate the superior performance of our algorithm compared to a benchmark nonlinear PID control approach, both in the simulation experiments using the Gazebo environment and in the real-world setting using a Parrot ANAFI quad-rotor and sub-scale ship platform undergoing 6 degrees of freedom (DOF) deck motion.

The central idea involves automating the Navy helicopter ship landing procedure where the pilot utilizes the ship as the visual reference for long-range tracking; however, refers to a standardized visual cue installed on most Navy ships called the "horizon bar" for the final approach and landing phases. This idea is implemented using a uniquely designed nonlinear controller integrated with machine vision. The vision system utilizes machine learning based object detection for long-range ship tracking and classical computer vision for the estimation of aircraft relative position and orientation utilizing the horizon bar during the final approach and landing phases. The nonlinear controller operates based on the information estimated by the vision system and has demonstrated robust tracking performance even in the presence of uncertainties. The developed autonomous ship landing system was implemented on a quad-rotor UAV equipped with an onboard camera, and approach and landing were successfully demonstrated on a moving deck, which imitates realistic ship deck motions. Extensive simulations and flight tests were conducted to demonstrate vertical landing safety, tracking capability, and landing accuracy. The video of the real-world experiments and demonstrations is available at this URL.

Spatial-temporal Map (STMap)-based methods have shown great potential to process high-angle videos for vehicle trajectory reconstruction, which can meet the needs of various data-driven modeling and imitation learning applications. In this paper, we developed Spatial-Temporal Deep Embedding (STDE) model that imposes parity constraints at both pixel and instance levels to generate instance-aware embeddings for vehicle stripe segmentation on STMap. At pixel level, each pixel was encoded with its 8-neighbor pixels at different ranges, and this encoding is subsequently used to guide a neural network to learn the embedding mechanism. At the instance level, a discriminative loss function is designed to pull pixels belonging to the same instance closer and separate the mean value of different instances far apart in the embedding space. The output of the spatial-temporal affinity is then optimized by the mutex-watershed algorithm to obtain final clustering results. Based on segmentation metrics, our model outperformed five other baselines that have been used for STMap processing and shows robustness under the influence of shadows, static noises, and overlapping. The designed model is applied to process all public NGSIM US-101 videos to generate complete vehicle trajectories, indicating a good scalability and adaptability. Last but not least, the strengths of the scanline method with STDE and future directions were discussed. Code, STMap dataset and video trajectory are made publicly available in the online repository. GitHub Link: shorturl.at/jklT0.

Data quality is the key factor for the development of trustworthy AI in healthcare. A large volume of curated datasets with controlled confounding factors can help improve the accuracy, robustness and privacy of downstream AI algorithms. However, access to good quality datasets is limited by the technical difficulty of data acquisition and large-scale sharing of healthcare data is hindered by strict ethical restrictions. Data synthesis algorithms, which generate data with a similar distribution as real clinical data, can serve as a potential solution to address the scarcity of good quality data during the development of trustworthy AI. However, state-of-the-art data synthesis algorithms, especially deep learning algorithms, focus more on imaging data while neglecting the synthesis of non-imaging healthcare data, including clinical measurements, medical signals and waveforms, and electronic healthcare records (EHRs). Thus, in this paper, we will review the synthesis algorithms, particularly for non-imaging medical data, with the aim of providing trustworthy AI in this domain. This tutorial-styled review paper will provide comprehensive descriptions of non-imaging medical data synthesis on aspects including algorithms, evaluations, limitations and future research directions.

Lung cancer is the leading cause of cancer related mortality by a significant margin. While new technologies, such as image segmentation, have been paramount to improved detection and earlier diagnoses, there are still significant challenges in treating the disease. In particular, despite an increased number of curative resections, many postoperative patients still develop recurrent lesions. Consequently, there is a significant need for prognostic tools that can more accurately predict a patient's risk for recurrence. In this paper, we explore the use of convolutional neural networks (CNNs) for the segmentation and recurrence risk prediction of lung tumors that are present in preoperative computed tomography (CT) images. First, expanding upon recent progress in medical image segmentation, a residual U-Net is used to localize and characterize each nodule. Then, the identified tumors are passed to a second CNN for recurrence risk prediction. The system's final results are produced with a random forest classifier that synthesizes the predictions of the second network with clinical attributes. The segmentation stage uses the LIDC-IDRI dataset and achieves a dice score of 70.3%. The recurrence risk stage uses the NLST dataset from the National Cancer institute and achieves an AUC of 73.0%. Our proposed framework demonstrates that first, automated nodule segmentation methods can generalize to enable pipelines for a wide range of multitask systems and second, that deep learning and image processing have the potential to improve current prognostic tools. To the best of our knowledge, it is the first fully automated segmentation and recurrence risk prediction system.

This paper presents a method to control a manipulator system grasping a rigid-body payload so that the motion of the combined system in consequence of externally applied forces to be the same as another free-floating rigid-body (with different inertial properties). This allows zero-g emulation of a scaled spacecraft prototype under the test in a 1-g laboratory environment. The controller consisting of motion feedback and force/moment feedback adjusts the motion of the test spacecraft so as to match that of the flight spacecraft, even if the latter has flexible appendages (such as solar panels) and the former is rigid. The stability of the overall system is analytically investigated, and the results show that the system remains stable provided that the inertial properties of two spacecraft are different and that an upperbound on the norm of the inertia ratio of the payload to manipulator is respected. Important practical issues such as calibration and sensitivity analysis to sensor noise and quantization are also presented.

Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others. Deep learning methods are particularly promising for modeling many remote sensing tasks given the success of deep neural networks in similar computer vision tasks and the sheer volume of remotely sensed imagery available. However, the variance in data collection methods and handling of geospatial metadata make the application of deep learning methodology to remotely sensed data nontrivial. For example, satellite imagery often includes additional spectral bands beyond red, green, and blue and must be joined to other geospatial data sources that can have differing coordinate systems, bounds, and resolutions. To help realize the potential of deep learning for remote sensing applications, we introduce TorchGeo, a Python library for integrating geospatial data into the PyTorch deep learning ecosystem. TorchGeo provides data loaders for a variety of benchmark datasets, composable datasets for generic geospatial data sources, samplers for geospatial data, and transforms that work with multispectral imagery. TorchGeo is also the first library to provide pre-trained models for multispectral satellite imagery (e.g., models that use all bands from the Sentinel-2 satellites), allowing for advances in transfer learning on downstream remote sensing tasks with limited labeled data. We use TorchGeo to create reproducible benchmark results on existing datasets and benchmark our proposed method for preprocessing geospatial imagery on the fly. TorchGeo is open source and available on GitHub: https://github.com/microsoft/torchgeo.

The way people communicate has evolved and diversified dramatically over the last two years. Gone are the days when email and phone calls were the primary forms of communication for many. In both personal and professional settings, people increasingly rely on messaging apps, social media and video conferencing. Meanwhile, many federal agencies are just beginning to address digitizing their paper records. M-19-21 compels all federal agencies to make their records fully electronic by December 2022.

I'm a musician and serial entrepreneur, was trained as classical pianist, and studied for a master's in music and philosophy. As a business executive, I have over 20 years of experience, leading several innovations from inception and product strategy to development and product management, to commercialization in global markets. My track record includes closing successful deals with global vendors, mobile operators, and content companies, as well as winning global awards and prizes. Across the past five years, Amit Sternberg has been driving innovation around music, health and technology. Sternberg's newest company, Rubato, is currently in its seed stage.