Abstract--Continuous human motion understanding remains a core challenge in computer vision due to its high dimensionality and inherent redundancy. Efficient compression and representation are crucial for analyzing complex motion dynamics. In this work, we introduce an adversarially-refined VQ-GAN framework with dense motion tokenization for compressing spatio-temporal heatmaps while preserving the fine-grained traces of human motion. Our approach combines dense motion tokenization with adversarial refinement, which eliminates reconstruction artifacts like motion smearing and temporal misalignment observed in non-adversarial baselines. Our experiments on the CMU Panoptic dataset [7] provide conclusive evidence of our method's superiority, outperforming the dV AE baseline by 9.31% SSIM and reducing temporal instability by 37.1%. Furthermore, our dense tokenization strategy enables a novel analysis of motion complexity, revealing that 2D motion can be optimally represented with a compact 128-token vocabulary, while 3D motion's complexity demands a much larger 1024-token codebook for faithful reconstruction. These results establish practical deployment feasibility across diverse motion analysis applications.

Deployable structures inspired by origami have provided lightweight, compact, and reconfigurable solutions for various robotic and architectural applications. However, creating an integrated structural system that can effectively balance the competing requirements of high packing efficiency, simple deployment, and precise morphing into multiple load-bearing configurations remains a significant challenge. This study introduces a new class of hyper-Yoshimura origami, which exhibits a wide range of kinematically admissible and locally metastable states, including newly discovered symmetric "self-packing" and asymmetric "pop-out" states. This metastability is achieved by breaking a design rule of Yoshimura origami that has been in place for many decades. To this end, this study derives a new set of mathematically rigorous design rules and geometric formulations. Based on this, forward and inverse kinematic strategies are developed to stack hyper-Yoshimura modules into deployable booms that can approximate complex 3D shapes. Finally, this study showcases the potential of hyper-Yoshimura with a meter-scale pop-up cellphone charging station deployed at our university's bus transit station, along with a 3D-printed, scaled prototype of a space crane that can function as an object manipulator, solar tracking device, or high-load-bearing structure. These results establish hyper-Yoshimura as a promising platform for deployable and adaptable robotic systems in both terrestrial and space environments.

Handling missing values is a common challenge in biostatistical analyses, typically addressed by imputation methods. We propose a novel, fast, and easy-to-use imputation method called missing value imputation with adversarial random forests (MissARF), based on generative machine learning, that provides both single and multiple imputation. MissARF employs adversarial random forest (ARF) for density estimation and data synthesis. To impute a missing value of an observation, we condition on the non-missing values and sample from the estimated conditional distribution generated by ARF. Our experiments demonstrate that MissARF performs comparably to state-of-the-art single and multiple imputation methods in terms of imputation quality and fast runtime with no additional costs for multiple imputation.

As the data volume of astronomical imaging surveys rapidly increases, traditional methods for image anomaly detection, such as visual inspection by human experts, are becoming impractical. We introduce a machine-learning-based approach to detect poor-quality exposures in large imaging surveys, with a focus on the DECam Legacy Survey (DECaLS) in regions of low extinction (i.e., $E(B-V)<0.04$). Our semi-supervised pipeline integrates a vision transformer (ViT), trained via self-supervised learning (SSL), with a k-Nearest Neighbor (kNN) classifier. We train and validate our pipeline using a small set of labeled exposures observed by surveys with the Dark Energy Camera (DECam). A clustering-space analysis of where our pipeline places images labeled in ``good'' and ``bad'' categories suggests that our approach can efficiently and accurately determine the quality of exposures. Applied to new imaging being reduced for DECaLS Data Release 11, our pipeline identifies 780 problematic exposures, which we subsequently verify through visual inspection. Being highly efficient and adaptable, our method offers a scalable solution for quality control in other large imaging surveys.

Distributed systems frequently encounter consistency violation faults (cvfs), where nodes operate on outdated or inaccurate data, adversely affecting convergence and overall system performance. This study presents a machine learning-based approach for analyzing the impact of CVFs, using Dijkstra's Token Ring problem as a case study. By computing program transition ranks and their corresponding effects, the proposed method quantifies the influence of cvfs on system behavior. To address the state space explosion encountered in larger graphs, two models are implemented: a Feedforward Neural Network (FNN) and a distributed neural network leveraging TensorFlow's \texttt{tf.distribute} API. These models are trained on datasets generated from smaller graphs (3 to 10 nodes) to predict parameters essential for determining rank effects. Experimental results demonstrate promising performance, with a test loss of 4.39 and a mean absolute error of 1.5. Although distributed training on a CPU did not yield significant speed improvements over a single-device setup, the findings suggest that scalability could be enhanced through the use of advanced hardware accelerators such as GPUs or TPUs.

Agent-based Transformers have been widely adopted in recent reinforcement learning advances due to their demonstrated ability to solve complex tasks. However, the high computational complexity of Transformers often results in significant energy consumption, limiting their deployment in real-world autonomous systems. Spiking neural networks (SNNs), with their biologically inspired structure, offer an energy-efficient alternative for machine learning. In this paper, a novel Spike-Transformer Reinforcement Learning (STRL) algorithm that combines the energy efficiency of SNNs with the powerful decision-making capabilities of reinforcement learning is developed. Specifically, an SNN using multi-step Leaky Integrate-and-Fire (LIF) neurons and attention mechanisms capable of processing spatio-temporal patterns over multiple time steps is designed. The architecture is further enhanced with state, action, and reward encodings to create a Transformer-like structure optimized for reinforcement learning tasks. Comprehensive numerical experiments conducted on state-of-the-art benchmarks demonstrate that the proposed SNN Transformer achieves significantly improved policy performance compared to conventional agent-based Transformers. With both enhanced energy efficiency and policy optimality, this work highlights a promising direction for deploying bio-inspired, low-cost machine learning models in complex real-world decision-making scenarios.

This research explores the use of Natural Language Processing (NLP) techniques to locate geological resources, with a specific focus on industrial minerals. By using word embeddings trained with the GloVe model, we extract semantic relationships between target keywords and a corpus of geological texts. The text is filtered to retain only words with geographical significance, such as city names, which are then ranked by their cosine similarity to the target keyword. Dimensional reduction techniques, including Principal Component Analysis (PCA), Autoencoder, Variational Autoencoder (VAE), and VAE with Long Short-Term Memory (VAE-LSTM), are applied to enhance feature extraction and improve the accuracy of semantic relations. For benchmarking, we calculate the proximity between the ten cities most semantically related to the target keyword and identified mine locations using the haversine equation. The results demonstrate that combining NLP with dimensional reduction techniques provides meaningful insights into the spatial distribution of natural resources. Although the result shows to be in the same region as the supposed location, the accuracy has room for improvement.

Large Language Models (LLMs) have demonstrated great promise in generating code, especially when used inside an evolutionary computation framework to iteratively optimize the generated algorithms. However, in some cases they fail to generate competitive algorithms or the code optimization stalls, and we are left with no recourse because of a lack of understanding of the generation process and generated codes. We present a novel approach to mitigate this problem by enabling users to analyze the generated codes inside the evolutionary process and how they evolve over repeated prompting of the LLM. We show results for three benchmark problem classes and demonstrate novel insights. In particular, LLMs tend to generate more complex code with repeated prompting, but additional complexity can hurt algorithmic performance in some cases. Different LLMs have different coding ``styles'' and generated code tends to be dissimilar to other LLMs. These two findings suggest that using different LLMs inside the code evolution frameworks might produce higher performing code than using only one LLM.

This work presents the first large-scale neutral benchmark experiment focused on single-event, right-censored, low-dimensional survival data. Benchmark experiments are essential in methodological research to scientifically compare new and existing model classes through proper empirical evaluation. Existing benchmarks in the survival literature are often narrow in scope, focusing, for example, on high-dimensional data. Additionally, they may lack appropriate tuning or evaluation procedures, or are qualitative reviews, rather than quantitative comparisons. This comprehensive study aims to fill the gap by neutrally evaluating a broad range of methods and providing generalizable conclusions. We benchmark 18 models, ranging from classical statistical approaches to many common machine learning methods, on 32 publicly available datasets. The benchmark tunes for both a discrimination measure and a proper scoring rule to assess performance in different settings. Evaluating on 8 survival metrics, we assess discrimination, calibration, and overall predictive performance of the tested models. Using discrimination measures, we find that no method significantly outperforms the Cox model. However, (tuned) Accelerated Failure Time models were able to achieve significantly better results with respect to overall predictive performance as measured by the right-censored log-likelihood. Machine learning methods that performed comparably well include Oblique Random Survival Forests under discrimination, and Cox-based likelihood-boosting under overall predictive performance. We conclude that for predictive purposes in the standard survival analysis setting of low-dimensional, right-censored data, the Cox Proportional Hazards model remains a simple and robust method, sufficient for practitioners.

The recent growth in renewable energy development in the United States has been accompanied by a simultaneous surge in renewable energy siting ordinances. These zoning laws play a critical role in dictating the placement of wind and solar resources that are critical for achieving low-carbon energy futures. In this context, efficient access to and management of siting ordinance data becomes imperative. The National Renewable Energy Laboratory (NREL) recently introduced a public wind and solar siting database to fill this need. This paper presents a method for harnessing Large Language Models (LLMs) to automate the extraction of these siting ordinances from legal documents, enabling this database to maintain accurate up-to-date information in the rapidly changing energy policy landscape. A novel contribution of this research is the integration of a decision tree framework with LLMs. Our results show that this approach is 85 to 90% accurate with outputs that can be used directly in downstream quantitative modeling. We discuss opportunities to use this work to support similar large-scale policy research in the energy sector. By unlocking new efficiencies in the extraction and analysis of legal documents using LLMs, this study enables a path forward for automated large-scale energy policy research.