Swarm robotics systems have the potential to transform warfighting in urban environments, but until now have not seen large-scale field testing. We present the Rapid Integration Swarming Ecosystem (RISE), a platform for future multi-agent research and deployment. RISE enables rapid integration of third-party swarm tactics and behaviors, which was demonstrated using both physical and simulated swarms. Our physical testbed is composed of more than 250 networked heterogeneous agents and has been extensively tested in mock warfare scenarios at five urban combat training ranges. RISE implements live, virtual, constructive simulation capabilities to allow the use of both virtual and physical agents simultaneously, while our "fluid fidelity" simulation enables adaptive scaling between low and high fidelity simulation levels based on dynamic runtime requirements. Both virtual and physical agents are controlled with a unified gesture-based interface that enables a greater than 150:1 agent-to-operator ratio. Through this interface, we enable efficient swarm-based mission execution. RISE translates mission needs to robot actuation with rapid tactic integration, a reliable testbed, and efficient operation.

Accurate predictions of crop yield and crop price provide valuable inputs for decision making by various stakeholders in agriculture: farmers, consumers, retailers, wholesalers, dealers, and the Government. Some of these decisions have far-reaching implications for the economic well-being of farmers, ensuring food security, stability of supplies, breeding of seeds, and for economic planning in general. This paper focuses on the problem of crop price prediction. There are several factors affecting crop prices. These include the expected yield, expected demand, export projections, import decisions, supply chain factors, weather conditions, geospatial factors, unanticipated events such as a pandemic or a flood, etc. Compounding this is the fact that the data that are available in many emerging economies about historical crop prices and crop price variations have several issues such as missing values, outliers, and even data entry errors. Accurate prediction of crop prices is therefore a grand challenge problem but at the same time an important one to help secure the economic prosperity of farmers. This paper focuses on how geospatial dependencies can be harnessed to obtain improved accuracy in predictions of crop prices.

With increased reliance on Internet based technologies, cyberattacks compromising users' sensitive data are becoming more prevalent. The scale and frequency of these attacks are escalating rapidly, affecting systems and devices connected to the Internet. The traditional defense mechanisms may not be sufficiently equipped to handle the complex and ever-changing new threats. The significant breakthroughs in the machine learning methods including deep learning, had attracted interests from the cybersecurity research community for further enhancements in the existing anomaly detection methods. Unfortunately, collecting labelled anomaly data for all new evolving and sophisticated attacks is not practical. Training and tuning the machine learning model for anomaly detection using only a handful of labelled data samples is a pragmatic approach. Therefore, few-shot weakly supervised anomaly detection is an encouraging research direction. In this paper, we propose an enhancement to an existing few-shot weakly-supervised deep learning anomaly detection framework. This framework incorporates data augmentation, representation learning and ordinal regression. We then evaluated and showed the performance of our implemented framework on three benchmark datasets: NSL-KDD, CIC-IDS2018, and TON_IoT.

Unmanned air vehicles often produce significant noise from their propulsion systems. Using this broadband signal as "acoustic illumination" for an auxiliary sensing system could make vehicles more robust at a minimal cost. We present an acoustic beamforming-based algorithm that estimates object-relative distance with a small two-microphone array using the generated propulsion system noise of a vehicle. We demonstrate this approach in several closed-loop distance feedback control tests with a mounted quad-rotor vehicle in a noisy environment and show accurate object-relative distance estimates more than 2x further than the baseline channel-based approach. We conclude that this approach is robust to several practical vehicle and noise situations and shows promise for use in more complex operating environments.

The accuracy and complexity of machine learning algorithms based on kernel optimization are determined by the set of kernels over which they are able to optimize. An ideal set of kernels should: admit a linear parameterization (for tractability); be dense in the set of all kernels (for robustness); be universal (for accuracy). Recently, a framework was proposed for using positive matrices to parameterize a class of positive semi-separable kernels. Although this class can be shown to meet all three criteria, previous algorithms for optimization of such kernels were limited to classification and furthermore relied on computationally complex Semidefinite Programming (SDP) algorithms. In this paper, we pose the problem of learning semiseparable kernels as a minimax optimization problem and propose a SVD-QCQP primal-dual algorithm which dramatically reduces the computational complexity as compared with previous SDP-based approaches. Furthermore, we provide an efficient implementation of this algorithm for both classification and regression -- an implementation which enables us to solve problems with 100 features and up to 30,000 datums. Finally, when applied to benchmark data, the algorithm demonstrates the potential for significant improvement in accuracy over typical (but non-convex) approaches such as Neural Nets and Random Forest with similar or better computation time.

The robustness of a deep classifier can be characterized by its margins: the decision boundary's distances to natural data points. However, it is unclear whether existing robust training methods effectively increase the margin for each vulnerable point during training. To understand this, we propose a continuous-time framework for quantifying the relative speed of the decision boundary with respect to each individual point. Through visualizing the moving speed of the decision boundary under Adversarial Training, one of the most effective robust training algorithms, a surprising moving-behavior is revealed: the decision boundary moves away from some vulnerable points but simultaneously moves closer to others, decreasing their margins. To alleviate these conflicting dynamics of the decision boundary, we propose Dynamics-aware Robust Training (DyART), which encourages the decision boundary to engage in movement that prioritizes increasing smaller margins. In contrast to prior works, DyART directly operates on the margins rather than their indirect approximations, allowing for more targeted and effective robustness improvement. Experiments on the CIFAR-10 and Tiny-ImageNet datasets verify that DyART alleviates the conflicting dynamics of the decision boundary and obtains improved robustness under various perturbation sizes compared to the state-of-the-art defenses. Our code is available at https://github.com/Yuancheng-Xu/Dynamics-Aware-Robust-Training.

Time elapsed till an event of interest is often modeled using the survival analysis methodology, which estimates a survival score based on the input features. There is a resurgence of interest in developing more accurate prediction models for time-to-event prediction in personalized healthcare using modern tools such as neural networks. Higher quality features and more frequent observations improve the predictions for a patient, however, the impact of including a patient's geographic location-based public health statistics on individual predictions has not been studied. This paper proposes a complementary improvement to survival analysis models by incorporating public health statistics in the input features. We show that including geographic location-based public health information results in a statistically significant improvement in the concordance index evaluated on the Surveillance, Epidemiology, and End Results (SEER) dataset containing nationwide cancer incidence data. The improvement holds for both the standard Cox proportional hazards model and the state-of-the-art Deep Survival Machines model. Our results indicate the utility of geographic location-based public health features in survival analysis.

Artificial Intelligence (AI) has huge impact on our daily lives with applications such as voice assistants, facial recognition, chatbots, autonomously driving cars, etc. Natural Language Processing (NLP) is a cross-discipline of AI and Linguistics, dedicated to study the understanding of the text. This is a very challenging area due to unstructured nature of the language, with many ambiguous and corner cases. In this thesis we address a very specific area of NLP that involves the understanding of entities (e.g., names of people, organizations, locations) in text. First, we introduce a radically different, entity-centric view of the information in text. We argue that instead of using individual mentions in text to understand their meaning, we should build applications that would work in terms of entity concepts. Next, we present a more detailed model on how the entity-centric approach can be used for the entity linking task. In our work, we show that this task can be improved by considering performing entity linking at the coreference cluster level rather than each of the mentions individually. In our next work, we further study how information from Knowledge Base entities can be integrated into text. Finally, we analyze the evolution of the entities from the evolving temporal perspective.

These narratives distract from what we call the "pathologies of scale" that become more entrenched every day: large-scale AI models are still largely controlled by Big Tech firms because of the enormous computing and data resources they require, and also present well-documented concerns around discrimination, privacy and security vulnerabilities, and negative environmental impacts. Large-scale AI models like Large Language Models (LLMs) have received the most hype, and fear-mongering, over the past year. "Opinion You Can Have the Blue Pill or the Red Pill, and We're Out of Blue Pills." Greg Noone, "'Foundation models' may be the future of AI. They're also deeply flawed," Tech Monitor, November 11, 2021 (updated February 9, 2023); Dan McQuillan, "We Come to Bury ChatGPT, Not to Praise It," danmcquillan.org,

A European spacecraft has blasted off on a quest to explore Jupiter and three of its ice-encrusted moons. Dubbed Juice, the robotic explorer set off on an eight-year journey Friday from French Guiana in South America, launching atop an Ariane rocket. Juice is taking a long, roundabout route. It should reach Jupiter in 2031 and spend three years buzzing Callisto, Europa and Ganymede. Then it will attempt to enter orbit around Ganymede, our solar system's largest moon. The three moons are believed to harbor underground oceans, where sea life could exist. If underground seas are confirmed, ice picks and a submarine could be next up.