We introduce a new method for clustering based on Cluster Catch Digraphs (CCDs). The new method addresses the limitations of RK-CCDs by employing a new variant of spatial randomness test that employs the nearest neighbor distance (NND) instead of the Ripley's K function used by RK-CCDs. We conduct a comprehensive Monte Carlo analysis to assess the performance of our method, considering factors such as dimensionality, data set size, number of clusters, cluster volumes, and inter-cluster distance. Our method is particularly effective for high-dimensional data sets, comparable to or outperforming KS-CCDs and RK-CCDs that rely on a KS-type statistic or the Ripley's K function. We also evaluate our methods using real and complex data sets, comparing them to well-known clustering methods. Again, our methods exhibit competitive performance, producing high-quality clusters with desirable properties. Keywords: Graph-based clustering, Cluster catch digraphs, High-dimensional data, The nearest neighbor distance, Spatial randomness test

Understanding the spatial distribution of palms within tropical forests is essential for effective ecological monitoring, conservation strategies, and the sustainable integration of natural forest products into local and global supply chains. However, the analysis of remotely sensed data in these environments faces significant challenges, such as overlapping palm and tree crowns, uneven shading across the canopy surface, and the heterogeneous nature of the forest landscapes, which often affect the performance of palm detection and segmentation algorithms. To overcome these issues, we introduce PalmDSNet, a deep learning framework for real-time detection, segmentation, and counting of canopy palms. Additionally, we employ a bimodal reproduction algorithm that simulates palm spatial propagation to further enhance the understanding of these point patterns using PalmDSNet's results. We used UAV-captured imagery to create orthomosaics from 21 sites across western Ecuadorian tropical forests, covering a gradient from the everwet Choc\'o forests near Colombia to the drier forests of southwestern Ecuador. Expert annotations were used to create a comprehensive dataset, including 7,356 bounding boxes on image patches and 7,603 palm centers across five orthomosaics, encompassing a total area of 449 hectares. By combining PalmDSNet with the bimodal reproduction algorithm, which optimizes parameters for both local and global spatial variability, we effectively simulate the spatial distribution of palms in diverse and dense tropical environments, validating its utility for advanced applications in tropical forest monitoring and remote sensing analysis.

AI-supported programming has taken giant leaps with tools such as Meta's Llama and openAI's chatGPT. These are examples of stochastic sources of programs and have already greatly influenced how we produce code and teach programming. If we consider input to such models as a stochastic source, a natural question is, what is the relation between the input and the output distributions, between the chatGPT prompt and the resulting program? In this paper, we will show how the relation between random Python programs generated from chatGPT can be described geometrically and topologically using Tree-edit distances between the program's syntax trees and without explicit modeling of the underlying space. A popular approach to studying high-dimensional samples in a metric space is to use low-dimensional embedding using, e.g., multidimensional scaling. Such methods imply errors depending on the data and dimension of the embedding space. In this article, we propose to restrict such projection methods to purely visualization purposes and instead use geometric summary statistics, methods from spatial point statistics, and topological data analysis to characterize the configurations of random programs that do not rely on embedding approximations. To demonstrate their usefulness, we compare two publicly available models: ChatGPT-4 and TinyLlama, on a simple problem related to image processing. Application areas include understanding how questions should be asked to obtain useful programs; measuring how consistently a given large language model answers; and comparing the different large language models as a programming assistant. Finally, we speculate that our approach may in the future give new insights into the structure of programming languages.

We introduce the manifold density function, which is an intrinsic method to validate manifold learning techniques. Our approach adapts and extends Ripley's $K$-function, and categorizes in an unsupervised setting the extent to which an output of a manifold learning algorithm captures the structure of a latent manifold. Our manifold density function generalizes to broad classes of Riemannian manifolds. In particular, we extend the manifold density function to general two-manifolds using the Gauss-Bonnet theorem, and demonstrate that the manifold density function for hypersurfaces is well approximated using the first Laplacian eigenvalue. We prove desirable convergence and robustness properties.

Amazon today launched SageMaker Reinforcement Learning (RL) Kubeflow Components, a toolkit supporting the company's AWS RoboMaker service for orchestrating robotics workflows. Amazon says that the goal is to make it faster to experiment and manage robotics workloads from perception to controls and optimization, and to create end-to-end solutions without having to rebuild them each time. Robots are being used more widely for purposes that are increasing in sophistication, like assembly, picking and packing, last-mile delivery, environmental monitoring, search and rescue, and assisted surgery. In China, Oxford Economics anticipates 12.5 million manufacturing jobs will become automated, while in the U.S., McKinsey projects that machines will take upwards of 30% of such jobs. As for reinforcement learning, it's an emerging AI technique that can help develop solutions for the kinds of problems that are increasingly cropping up in robotics.

Clustering is one of the most challenging tasks in machine learning and pattern recognition, and perhaps, discovering the exact number of clusters of an unlabelled data set is the leading one. Many clustering methods find the clusters (or hidden classes) and the number of these clusters simultaneously (Frey and Dueck, 2007; Sajana et al., 2016). Although there exist methods for validating and comparing the quality of a partitioning of a data set, algorithms that provide the (estimated) number of clusters without any input parameter are still appealing. However, such methods or algorithms rely on other parameters viewed as the intensity, i.e. expected number of objects in a unit area. The value of the intensity parameter works as a threshold, and if the local intensity of the data set exceeds the threshold, it may indicate the existence of a possible cluster. However, the choice of such parameters is often a difficult task since different values of such parameters may drastically change the result of the algorithm. We use unsupervised adaptations of a family of vertex random digraphs, namely class cover catch digraphs (CCCDs), that showed relatively good performance in statistical pattern classification (Manukyan and Ceyhan, 2016; Priebe et al., 2003a). Unsupervised versions of CCCDs are called cluster catch digraphs (CCDs) (DeVinney, 2003; Marchette, 2004). Primarily, CCDs use statistics that require an intensity parameter to be specified or estimated.

The blocks world is a classic toy domain that has long been used to build and test spatial reasoning systems. Despite its relative simplicity, tackling this domain in its full complexity requires the agent to exhibit a rich set of functional capabilities, ranging from vision to natural language understanding. There is currently a resurgence of interest in solving problems in such limited domains using modern techniques. In this work we tackle spatial question answering in a holistic way, using a vision system, speech input and output mediated by an animated avatar, a dialogue system that robustly interprets spatial queries, and a constraint solver that derives answers based on 3-D spatial modeling. The contributions of this work include a semantic parser that maps spatial questions into logical forms consistent with a general approach to meaning representation, a dialog manager based on a schema representation, and a constraint solver for spatial questions that provides answers in agreement with human perception. These and other components are integrated into a multi-modal human-computer interaction pipeline.

SpaceX's new Crew Dragon capsule has arrived at the International Space Station, acing its second milestone in just over a day. No one was on board the capsule launched on Saturday on its first test flight, only an instrumented dummy. But the three station astronauts had front-row seats as the Dragon neatly docked on Sunday morning and became the first American-made, designed-for-crew spacecraft to pull up in eight years. If the six-day demo goes well, SpaceX could launch two astronauts this summer under NASA's commercial crew programme. Both astronauts were at SpaceX Mission Control in California, observing all the action.

SpaceX's new crew capsule arrived at the International Space Station on Sunday, acing its second milestone in just over a day. No one was aboard the Dragon capsule launched Saturday on its first test flight, only an instrumented dummy. But the three station astronauts had front-row seats as the sleek, white vessel neatly docked and became the first American-made, designed-for-crew spacecraft to pull up in eight years. TV cameras on Dragon as well as the space station provided stunning views of one another throughout the rendezvous. If the six-day demo goes well, SpaceX could launch two astronauts this summer under NASA's commercial crew program.

Once upon a time, the Consumer Electronics Show was about personal gadgets: fancy phones, curving televisions, college-educated refrigerators. But in recent years cars have become the stars, and the auto industry has taken its place in Vegas. This week at CES, Ford CEO Jim Hackett gave a keynote talk about the future of urban mobility. Toyota CEO Akio Toyoda took a selfie with audience members after rolling out the carmaker's adorable autonomous shuttle concept. Chinese Tesla competitors sprang up to great fanfare.