LCMamNet: A Lightweight Cross-scale Mamba Network for Infrared Small Target Detection

Infrared small target detection (IRSTD) is important for low-altitude perception, unmanned-system warning, and security monitoring. However, weak targets in infrared imagery usually occupy only a few pixels and are easily submerged by cloud clutter, ground edges, and bright noise, making it difficult for lightweight segmentation-based methods to preserve local target structures while suppressing background interference. To address these challenges, we propose LCMamNet, a lightweight cross-scale Mamba network that progressively enhances local target structures, interacts cross-scale context in a latent space, and restores spatial details with background suppression. Specifically, a compact hierarchical encoder with cross-shaped directional bottleneck residual (CDBR) blocks strengthens direction-sensitive target structures under a small computation budget. A latent dense cross-scale fusion (LDCF) module then performs dense all-level interaction through bidirectional Mamba modeling and reorganizes the interacted features into stable hierarchical semantics. Finally, a progressive decoder selectively recovers shallow spatial details while suppressing irrelevant background textures. Extensive experiments on IRSTD-1k, NUAA-SIRST, and NUDT-SIRST show that the proposed network achieves mIoU scores of 71.25\%, 79.60\%, and 95.58\%, respectively, with only 1.175M parameters and 6.91 GFLOPs. It also runs with a mean inference latency of 6.62 ms, and deployment results on an NVIDIA Jetson Orin NX 16G SUPER further demonstrate its practical potential for real-time edge inference. The code and checkpoints are publicly available at https://github.com/Haoyu096/LCMamNet.

A Gaussian Process Model of Cross-Category Dynamics in Brand Choice

Understanding individual customers' sensitivities to prices, promotions, brand, and other aspects of the marketing mix is fundamental to a wide swath of marketing problems, including targeting and pricing. Companies that operate across many product categories have a unique opportunity, insofar as they can use purchasing data from one category to augment their insights in another. Such cross-category insights are especially crucial in situations where purchasing data may be rich in one category, and scarce in another. An important aspect of how consumers behave across categories is dynamics: preferences are not stable over time, and changes in individual-level preference parameters in one category may be indicative of changes in other categories, especially if those changes are driven by external factors. Yet, despite the rich history of modeling cross-category preferences, the marketing literature lacks a framework that flexibly accounts for \textit{correlated dynamics}, or the cross-category interlinkages of individual-level sensitivity dynamics. In this work, we propose such a framework, leveraging individual-level, latent, multi-output Gaussian processes to build a nonparametric Bayesian choice model that allows information sharing of preference parameters across customers, time, and categories. We apply our model to grocery purchase data, and show that our model detects interesting dynamics of customers' price sensitivities across multiple categories. Managerially, we show that capturing correlated dynamics yields substantial predictive gains, relative to benchmarks. Moreover, we find that capturing correlated dynamics can have implications for understanding changes in consumers preferences over time, and developing targeted marketing strategies based on those dynamics.