Francis A. Gatsi

Aydin Wells, Francis A. Gatsi, Aaron Striegel et al.

Protein structure classification (PSC) uses supervised learning to predict a protein's CATH/SCOP(e) class from the protein's sequence or 3D structural feature(s). We already modeled 3D structures as (static) protein structure networks (PSNs), demonstrating the competitiveness of PSN-based features to sequence or direct (i.e. non-network) 3D structural features in the PSC task. More recently, we demonstrated the power of features extracted from dynamic PSNs over features extracted from static PSNs (and thus by transitivity over sequence and direct 3D structural features) in the same task. That dynamic PSN approach used traditional machine learning (ML), combining manual (pre-engineered) features with an off-the-shelf classifier. Here, we evaluate whether automatic deep learning (DL) from the dynamic PSNs yields improvements. Our evaluation on 72 datasets spanning ~44,000 CATH- or SCOPe-labeled dynamic PSNs reveals that in terms of PSC accuracy, traditional ML and DL are (close to) tied for a large majority of the datasets, while DL is on average 10+ times slower. We are the first to evaluate traditional ML vs. DL in the dynamic PSN-based PSC task.

Joshua Roy Palathinkal, Hardani Ismu Nabil, Muhammad Iqbal Rochman et al.

The recently deployed 6 GHz spectrum in the U.S. utilizes distinct power categories, with the latest proposed "Geofenced Variable Power" (GVP) category permitting indoor and outdoor operations without continuous Automated Frequency Coordination (AFC) by relying instead on local databases of exclusion zones. Consequently, the safe operation of GVP devices depends entirely on reliable GNSS localization to respect these geofences. However, GNSS accuracy is highly variable and significantly degrades in environments like urban canyons or indoors. This paper presents the first comprehensive empirical study evaluating GNSS reliability specifically for GVP compliance. Utilizing the SigCap Android application, we document and compare GNSS accuracy across an extensive array of real-world conditions, encompassing urban versus suburban landscapes, varying mobility states (stationary, walking, driving), and indoor versus outdoor settings. The results demonstrate that while device hardware causes variations in GNSS accuracy, the operational environment is the primary driver of error. Indoor settings and dense urban areas consistently degrade localization. Moreover, outdoor positions adjacent to buildings often surprisingly produce significant inaccuracies, even near low-elevation structures. We further analyze the contribution of different GNSS constellations to device positioning and show that satellites from non-U.S.-licensed constellations-although currently used in a substantial portion of location fixes-are not permitted for regulatory geolocation under FCC requirements.