This paper addresses the problem of autonomous UAV search missions, where a UAV must locate specific Entities of Interest (EOIs) within a time limit, based on brief descriptions in large, hazard-prone environments with keep-out zones. The UAV must perceive, reason, and make decisions with limited and uncertain information. We propose NEUSIS, a compositional neuro-symbolic system designed for interpretable UAV search and navigation in realistic scenarios. NEUSIS integrates neuro-symbolic visual perception, reasoning, and grounding (GRiD) to process raw sensory inputs, maintains a probabilistic world model for environment representation, and uses a hierarchical planning component (SNaC) for efficient path planning. Experimental results from simulated urban search missions using AirSim and Unreal Engine show that NEUSIS outperforms a state-of-the-art (SOTA) vision-language model and a SOTA search planning model in success rate, search efficiency, and 3D localization. These results demonstrate the effectiveness of our compositional neuro-symbolic approach in handling complex, real-world scenarios, making it a promising solution for autonomous UAV systems in search missions.

The performance of diagnostic Computer-Aided Design (CAD) systems for retinal diseases depends on the quality of the retinal images being screened. Thus, many studies have been developed to evaluate and assess the quality of such retinal images. However, most of them did not investigate the relationship between the accuracy of the developed models and the quality of the visualization of interpretability methods for distinguishing between gradable and non-gradable retinal images. Consequently, this paper presents a novel framework called FGR-Net to automatically assess and interpret underlying fundus image quality by merging an autoencoder network with a classifier network. The FGR-Net model also provides an interpretable quality assessment through visualizations. In particular, FGR-Net uses a deep autoencoder to reconstruct the input image in order to extract the visual characteristics of the input fundus images based on self-supervised learning. The extracted features by the autoencoder are then fed into a deep classifier network to distinguish between gradable and ungradable fundus images. FGR-Net is evaluated with different interpretability methods, which indicates that the autoencoder is a key factor in forcing the classifier to focus on the relevant structures of the fundus images, such as the fovea, optic disk, and prominent blood vessels. Additionally, the interpretability methods can provide visual feedback for ophthalmologists to understand how our model evaluates the quality of fundus images. The experimental results showed the superiority of FGR-Net over the state-of-the-art quality assessment methods, with an accuracy of 89% and an F1-score of 87%.

Autonomous mobile robots are increasingly employed in pedestrian-rich environments where safe navigation and appropriate human interaction are crucial. While Deep Reinforcement Learning (DRL) enables socially integrated robot behavior, challenges persist in novel or perturbed scenarios to indicate when and why the policy is uncertain. Unknown uncertainty in decision-making can lead to collisions or human discomfort and is one reason why safe and risk-aware navigation is still an open problem. This work introduces a novel approach that integrates aleatoric, epistemic, and predictive uncertainty estimation into a DRL-based navigation framework for uncertainty estimates in decision-making. We, therefore, incorporate Observation-Dependent Variance (ODV) and dropout into the Proximal Policy Optimization (PPO) algorithm. For different types of perturbations, we compare the ability of Deep Ensembles and Monte-Carlo Dropout (MC-Dropout) to estimate the uncertainties of the policy. In uncertain decision-making situations, we propose to change the robot's social behavior to conservative collision avoidance. The results show that the ODV-PPO algorithm converges faster with better generalization and disentangles the aleatoric and epistemic uncertainties. In addition, the MC-Dropout approach is more sensitive to perturbations and capable to correlate the uncertainty type to the perturbation type better. With the proposed safe action selection scheme, the robot can navigate in perturbed environments with fewer collisions.

LiDAR is one of the most commonly adopted sensors for simultaneous localization and mapping (SLAM) and map-based global localization. SLAM and map-based localization are crucial for the independent operation of autonomous systems, especially when external signals such as GNSS are unavailable or unreliable. While state-of-the-art (SOTA) LiDAR SLAM systems could achieve 0.5% (i.e., 0.5m per 100m) of errors and map-based localization could achieve centimeter-level global localization, it is still unclear how robust they are under various common LiDAR data corruptions. In this work, we extensively evaluated five SOTA LiDAR-based localization systems under 18 common scene-level LiDAR point cloud data (PCD) corruptions. We found that the robustness of LiDAR-based localization varies significantly depending on the category. For SLAM, hand-crafted methods are in general robust against most types of corruption, while being extremely vulnerable (up to +80% errors) to a specific corruption. Learning-based methods are vulnerable to most types of corruptions. For map-based global localization, we found that the SOTA is resistant to all applied corruptions. Finally, we found that simple Bilateral Filter denoising effectively eliminates noise-based corruption but is not helpful in density-based corruption. Re-training is more effective in defending learning-based SLAM against all types of corruption.

Structural network embedding is a crucial step in enabling effective downstream tasks for complex systems that aims to project a network into a lower-dimensional space while preserving similarities among nodes. We introduce a simple and efficient embedding technique based on approximate variants of equitable partitions. The approximation consists in introducing a user-tunable tolerance parameter relaxing the otherwise strict condition for exact equitable partitions that can be hardly found in real-world networks. We exploit a relationship between equitable partitions and equivalence relations for Markov chains and ordinary differential equations to develop a partition refinement algorithm for computing an approximate equitable partition in polynomial time. We compare our method against state-of-the-art embedding techniques on benchmark networks. We report comparable -- when not superior -- performance for visualization, classification, and regression tasks at a cost between one and three orders of magnitude smaller using a prototype implementation, enabling the embedding of large-scale networks which could not be efficiently handled by most of the competing techniques.

Deep learning provides powerful methods to impute structured information from large-scale, unstructured text and image datasets. For example, economists might wish to detect the presence of economic activity in satellite images, or to measure the topics or entities mentioned in social media, the congressional record, or firm filings. This review introduces deep neural networks, covering methods such as classifiers, regression models, generative AI, and embedding models. Applications include classification, document digitization, record linkage, and methods for data exploration in massive scale text and image corpora. When suitable methods are used, deep learning models can be cheap to tune and can scale affordably to problems involving millions or billions of data points.. The review is accompanied by a companion website, EconDL, with user-friendly demo notebooks, software resources, and a knowledge base that provides technical details and additional applications.

Studying and building datasets for dialogue tasks is both expensive and time-consuming due to the need to recruit, train, and collect data from study participants. In response, much recent work has sought to use large language models (LLMs) to simulate both human-human and human-LLM interactions, as they have been shown to generate convincingly human-like text in many settings. However, to what extent do LLM-based simulations \textit{actually} reflect human dialogues? In this work, we answer this question by generating a large-scale dataset of 100,000 paired LLM-LLM and human-LLM dialogues from the WildChat dataset and quantifying how well the LLM simulations align with their human counterparts. Overall, we find relatively low alignment between simulations and human interactions, demonstrating a systematic divergence along the multiple textual properties, including style and content. Further, in comparisons of English, Chinese, and Russian dialogues, we find that models perform similarly. Our results suggest that LLMs generally perform better when the human themself writes in a way that is more similar to the LLM's own style.

Recent advances in the field of machine learning have led to new ways for mobile robots to acquire advanced navigational capabilities. However, these learning-based methods raise the possibility that learned navigation behaviors may not align with the intentions and preferences of people, a problem known as value misalignment. To mitigate this risk, this dissertation aims to answer the question: "How can we use machine learning methods to align the navigational behaviors of autonomous mobile robots with human intentions and preferences?" First, this dissertation addresses this question by introducing a new approach to learning navigation behaviors by imitating human-provided demonstrations of the intended navigation task. This contribution allows mobile robots to acquire autonomous visual navigation capabilities through imitation, using a novel objective function that encourages the agent to align with the human's navigation objectives and penalizes misalignment. Second, this dissertation introduces two algorithms to enhance terrain-aware off-road navigation for mobile robots by learning visual terrain awareness in a self-supervised manner. This contribution enables mobile robots to respect a human operator's preferences for navigating different terrains in urban outdoor environments, while extrapolating these preferences to visually novel terrains by leveraging multi-modal representations. Finally, in the context of robot navigation in human-occupied environments, this dissertation introduces a dataset and an algorithm for robot navigation in a socially compliant manner in both indoor and outdoor environments. In summary, the contributions in this dissertation take significant steps toward addressing the value alignment problem in autonomous navigation, enabling mobile robots to navigate autonomously with objectives that align with human intentions and preferences.

Pre-trained large language models(LLMs) have attracted increasing attention in biomedical domains due to their success in natural language processing. However, the complex traits and heterogeneity of multi-sources genomics data pose significant challenges when adapting these models to the bioinformatics and biomedical field. To address these challenges, we present GP-GPT, the first specialized large language model for genetic-phenotype knowledge representation and genomics relation analysis. Our model is fine-tuned in two stages on a comprehensive corpus composed of over 3,000,000 terms in genomics, proteomics, and medical genetics, derived from multiple large-scale validated datasets and scientific publications. GP-GPT demonstrates proficiency in accurately retrieving medical genetics information and performing common genomics analysis tasks, such as genomics information retrieval and relationship determination. Comparative experiments across domain-specific tasks reveal that GP-GPT outperforms state-of-the-art LLMs, including Llama2, Llama3 and GPT-4. These results highlight GP-GPT's potential to enhance genetic disease relation research and facilitate accurate and efficient analysis in the fields of genomics and medical genetics. Our investigation demonstrated the subtle changes of bio-factor entities' representations in the GP-GPT, which suggested the opportunities for the application of LLMs to advancing gene-phenotype research.

The DARPA Subterranean Challenge is leading the development of robots capable of mapping underground mines and tunnels up to 8km in length and identify objects and people. Developing these autonomous abilities paves the way for future planetary cave and surface exploration missions. The Co-STAR team, competing in this challenge, is developing a hybrid aerial-ground vehicle, known as the Rollocopter. The current design of this vehicle is a drone with wheels attached. This allows for the vehicle to roll, actuated by the propellers, and fly only when necessary, hence benefiting from the reduced power consumption of the ground mode and the enhanced mobility of the aerial mode. This thesis focuses on the development and increased robustness of the local planning architecture for the Rollocopter. The first development of thesis is a local planner capable of collision avoidance. The local planning node provides the basic functionality required for the vehicle to navigate autonomously. The next stage was augmenting this with the ability to plan more reliably without localisation. This was then integrated with a hybrid mobility mode capable of rolling and flying to exploit power and mobility benefits of the respective configurations. A traversability analysis algorithm as well as determining the terrain that the vehicle is able to traverse is in the late stages of development for informing the decisions of the hybrid planner. A simulator was developed to test the planning algorithms and improve the robustness of the vehicle to different environments. The results presented in this thesis are related to the mobility of the rollocopter and the range of environments that the vehicle is capable of traversing. Videos are included in which the vehicle successfully navigates through dust-ridden tunnels, horizontal mazes, and areas with rough terrain.