Evaluating Transformer and LSTM Frameworks for Prediction in Ungauged Basins

Watershed networks exhibit convergent topologies in which multiple tributaries merge into downstream channels,integrating diverse upstream hydrological processes. In ungauged basins, the absence of direct observations increases uncertainty and limits the ability to anticipate extreme events. This study evaluates whether an encoder-only Transformer provides an advantage over an LSTM for upstream streamflow inference under limited hydrologic information, using retrospective simulations from the NOAA National Water Model (NWM). Across both upstream-only and combined configurations, the LSTM showed stronger overall performance than the Transformer model across the two configurations. Incorporating downstream information further boosted performance for all models, increasing median NNSE by more than 60%. Rather than treating this as a leaderboard-style comparison, we interpret the experiments as a test of architectural inductive bias for hydrologic sequence inference. The results indicate that recurrent memory remains better aligned with this upstream reconstruction task than an encoder-only Transformer, while downstream hydrologic context provides a strong auxiliary constraint that substantially improves prediction skill across architectures

On the Evaluation of Spiking Neural Network Configurations for Network Intrusion Detection

Network intrusion detection is a core component of modern cybersecurity infrastructure, yet the deep learning models that dominate the field are computationally demanding, motivating interest in lightweight alternatives suited to edge and neuromorphic deployment. Spiking Neural Networks (SNNs) are therefore a natural candidate, but their design space, spanning the choice of neuron model and spike encoding scheme, remains poorly characterized for intrusion detection. We bridge this gap by using a controlled ablation study using 9 neurons coupled with 3 spike encoding schemes, making 27 variants, all implemented on snntorch evaluated over raw inputs with limited preprocessing on four benchmark datasets (NSL KDD, KDDCup99, CIC-IDS2017, and CTU-13) with 5 seeds. We find that spike encoding scheme is a better determinant for detection quality than the neuron model, where rate and delta spike encodings perform worse than latency encoding over the sweep. The LeakyParallel neuron with latency encoding performed the best overall, averaging at 92.11% accuracy and 0.80 macro- F1 at a rate of 2.01% false positives averaged over all 4 datasets, with accuracy close to perfect for CIC-IDS2017 and CTU-13, and also performed the fastest on inference. These results highlight the potential of SNNs as a viable alternative to traditional methods of intrusion detection when considering low-latency or resource-constrained deployments.