Training AI models requires ingesting massive amounts of training data. We define Parametric Matching (PM), a grammar-based compression technique that parses structured text into abstract syntax trees, replaces format patterns with compact tokens, and moves numerical parameters into homogeneous streams of tokens that can additionally be bit compressed using LZMA. The results are input streams that are equivalent to the original interleaved text and data in formats such as SVG, 3D-models such as OBJ, PDF, but highly compressed and much more amenable to processing. PM has many advantages. Loading data is much faster (a factor of 20 for 3D models, and 100 to 200 times faster to load). Just as important, by preprocessing the semantics, ambiguous cases can be resolved once, and can be turned into embedding vectors more efficiently, and potentially more accurately. Our experiments with SVG, G-Code, OBJ and PDF files show large compression. The results in Table 1 show that the PM algorithm can create data objects that are not only more compressible but far faster to load because the binary format can be instantly used, in this case to load OpenGL and render without parsing ASCII data. When compressing a binary object that is half the size of the original ASCII representation, by definition LZMA takes half the time to compress because there are fewer bytes to process.

The increasing demand for real-time analysis in video streaming has driven significant advancements in object detection and motion prediction. This paper presents SkelAI, an innovative application that combines YOLOv8, OpenCV, OpenAI API, and our own innovative algorithms to achieve real-time object detection and medial axis skeletonization tailored explicitly for live video streaming environments. In addition, SkelAI integrates AI-generated image capabilities through the DALL-E 3 model, enabling the extraction of skeletons from synthetic content that simulates streaming scenarios. The application supports exporting skeleton data in PyTorch-compatible formats, facilitating the training of sequence predicting deep learning models. Comprehensive evaluations demonstrate SkelAI’s enhanced accuracy, efficiency, and versatility compared to existing tools, underscoring its potential applications in digital animation, biomechanical research and robotics, human-computer interaction, and video compression within streaming platforms.

Modern general-purpose compressors can compress a wide variety of files but do not achieve high compression ratios on files that contain short sequences of delimiters with interleaved numeric data and generally with interleaved data where each sequence is not well correlated to the previous bytes. We demonstrate Parametric Matching (PM), which vastly improves the compression of various structured languages, including PDF, SVG, and G-code files. By de-interleaving and coalescing delimiters and storing data as delta-encoded, discretized binary, compressions of a factor of 10 or more are possible. A Python prototype compresses files to a binary representation, which is then compressed using Lempel-Ziv-Markov (LZMA) to efficiently store the binary tokens in a minimal number of bits. Table 1 shows a ratio of 6 for PDF files containing only text, which are first parsed, and recompressed using PM. For SVG, we demonstrate a factor of 8 to 10 for files including a randomized spiral and a US county map. For the G-code, we compressed the Statue of Liberty, demonstrating that even when the layers are different, a high degree of compression can be achieved. Times are all less than 250ms, even in our Python prototype.

This paper addresses the problems of automatically planning autonomous underwater vehicle (AUV) paths which best exploit complex current data, from computational estuarine model forecasts, while also avoiding obstacles. In particular we examine the possibilities for a novel type of AUV mission deployment in fast flowing tidal river regions which experience bi-directional current flow. These environments are interesting in that, by choosing an appropriate path in space and time, an AUV may both bypass adverse currents which are too fast to be overcome by the vehicle's motors and also exploit favorable currents to achieve far greater speeds than the motors could otherwise provide, while substantially saving energy. The AUV can "ride" currents both up and down the river, enabling extended monitoring of otherwise energy-exhausting, fast flow environments. The paper discusses suitable path parameterizations, cost functions and optimization techniques which enable optimal AUV paths to be efficiently generated. These paths take maximum advantage of the river currents in order to minimize energy expenditure, journey time and other cost parameters. The resulting path planner can automatically suggest useful alternative mission start and end times and locations to those specified by the user. Examples are presented for navigation in a simple simulation of the fast flowing Hudson River waters around Manhattan.

The paper focuses on the design of remote sensing and detecting systems for the underwater environment around estuary areas. The behavior of estuary water conditions is studied and the environmental impacts on the performance of underwater sensing and detecting systems are investigated. Sensor coverage variations are determined in particular. Environmental data is extracted from the NYHOPS ocean model with periodic calibration from fixed CTD stations in the Hudson River. The result shows that careful planning is required for sensor placement in an estuary area.