DEVCOM Army Research Laboratory

Topological data analysis (TDA) has exploded as a tool for analyzing and making sense of high dimensional datasets across a variety of fields. Mapper is a tool from TDA that captures low-dimensional structure from high-dimensional data, precisely the approach needed to capture relevant information from high-dimensional neural time series. Electrical potential scalp recording, or electroencephalography (EEG), is routinely used in clinical applications and research studies thanks to its noninvasive nature, relatively inexpensive equipment, and high temporal resolution. But, it is prone to contamination, exhibits low spatial resolution, and has a non-stationary nature. Thus, it requires advanced signal processing and mathematical analysis methods for tasks requiring unsupervised brain state clustering. We introduce MapperEEG, an approach to unsupervised brain state clustering that uses tools from classical EEG analysis combined with Mapper to cluster and connect brain states. We show that MapperEEG can serve as a clustering algorithm in the spectral domain and provide additional information about the underlying brain state connectivity in a tapping task. Additionally, we use a go/no-go shooting task to explore how MapperEEG can still provide insight into the underlying structure and clusters of brain states even when it and other clustering methods fail. We demonstrate that it outperforms six other clustering algorithms such as hierarchical clustering, Hidden Markov Models, and basic autoencoders on identifying states in a tapping task. MapperEEG offers a novel and effective approach to analyzing EEG data, showing promise for brain state clustering and analysis.

Detecting wildebeest from very-high-resolution (VHR) satellite imagery enables large-area population monitoring in a single acquisition, avoiding aircraft-induced disturbance and reducing sampling bias caused by transect-based surveys. However, wildebeest appear as extremely small objects in satellite images, and direct application of classical object detectors (e.g., YOLO-style detectors) often yields poor performance. In particular, high-density aggregation areas suffer from severe missed detections due to scale mismatch and limitations in post-processing for densely packed small objects.To address these challenges, we develop a targeted detection solution that integrates (1) an adaptive sliding-window strategy to better capture local context under varying density conditions, (2) resolution–detector adaptation to mitigate scale mismatch between object size and detector design, and (3) improved post-processing modules, including an enhanced non-maximum suppression (NMS) tailored for dense small-object scenarios. We evaluate the proposed framework using WorldView-2 and WorldView-3 imagery over the Serengeti acquired in 2022 and 2023. The overall F1-score improves from 0.727 to 0.770 in 2022 and from 0.682 to 0.756 in 2023. Notably, in high-density areas in 2022, the F1-score increases from 0.330 to 0.821, demonstrating that our approach effectively reduces missed detections in dense small-object scenarios that commonly lead to substantial omissions in traditional pipelines.Beyond wildebeest monitoring, our results highlight a generalizable pathway for adapting classical detectors to dense small-object detection in VHR satellite imagery, where objects are tiny and crowded.

The human visual system adapts to statistical regularities in the environment to facilitate visual processing. While laboratory-based tasks make clear distinctions between how task-relevant and task-irrelevant visual information can guide this adaptation, such discretization is rarely available in the real world. As such, it remains unclear exactly what information the visual system tracks to flexibly adapt to a given task. The current study used a massive visual search dataset from the mobile game Airport Scanner. Effects of exposure over a range of more task-relevant (e.g., target presence) to less task-relevant (e.g., background context) features were analyzed in an omnibus model to predict response times in both target-present and target-absent trials. As in previous work (Kramer et al., Journal of Experimental Psychology: General,151 (8), 1854, 2022), increased exposure to target-present trials significantly sped up the detection of targets and slowed the rejection of target-absent trials. Exposure to salient distractors reduced response times for target-present trials, potentially as a result of learned distractor suppression (Gaspelin & Luck, Trends in cognitive sciences,22 (1), 79-92, 2018) or increased familiarity (Mruczek & Sheinberg, Perception & psychophysics,67 (6), 1016-1031, 2005), but had no effect on target-absent trials. Exposure to background information decreased response times in both target-present and target-absent trials, with notable interactions between target and background exposure. Specifically, the effect of background information was more pronounced when target exposure was low, suggesting that less task-relevant context information is more likely to be tracked in the absence of more task-relevant information, namely, the presentation of targets. The findings highlight the importance of considering multiple sources of exposure in visual search tasks and demonstrate the value of large datasets in quantifying their complex interactions.

ABSTRACT Previous NMR 1 J (C13,H) –s correlations exhibited only marginal degrees of success. Presented here is a new such correlation where it is proposed NMR coupling data is passed by H–C–H and H–H pathways as suggested by the theoretical results of Provasi et al. Used where the current best values of the 1 J (C13,H)'s and molecular structure data for the hydrocarbons methane ( 1 ), ethane ( 2 ), ethene ( 3 ), benzene ( 4 ), propadiene ( 5 ), and ethyne ( 6 ). Simple equations for calculation of s character and bond angles are presented. The correlation has been extended to the case of a fluorine substituent. How the s character frameworks of molecules can be mapped out is demonstrated and compared to natural bond orbital (NBO) analysis estimations from density functional theory calculations. The definitions and calculation of the parameters in the master equation, 1 J (C13,H) = A s + n B + m C + o D + E + F , are explained in detail.

Abstract Technologies such as multimaterial printing have enabled the increasing integration of sensing, actuation and other responsiveness into 3D printed parts and opened up a wide range of new applications. In this short review, we focus on several new and emerging approaches for stimuli-responsive materials from ferroelectret and shape memory behaviors of conventional printable polymers to novel polyurethane-acrylate elastomers and ferroelectric composites for printing electromechanical actuators. We will conclude with a discussion of applications of novel applications of stimuli-responsive 3D printed polymers from 4D printing to soft robotics, along with a perspective on future trends in the field. Graphical abstract