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1. Modeling Cosmogenic Nuclides in Transiently Evolving Topography and Chemically Weathering Soils

   https://doi.org/10.1029/2023JF007201  

   Reed, Miles M; Ferrier, Ken L; Perron, J Taylor (October 2023, Journal of Geophysical Research: Earth Surface)

   Abstract Terrestrial cosmogenic nuclides (TCN) are widely employed to infer denudation rates in mountainous landscapes. The calculation of an inferred denudation rate (D_inf) from TCN concentrations is typically performed under the assumptions that denudation rates were steady during TCN accumulation and that soil chemical weathering negligibly impacted soil mineral abundances. In many landscapes, however, denudation rates were not steady and soil composition was significantly impacted by chemical weathering, which complicates interpretation of TCN concentrations. We present a landscape evolution model that computes transient changes in topography, soil thickness, soil mineralogy, and soil TCN concentrations. We used this model to investigate TCN responses in transient landscapes by imposing idealized perturbations in tectonically (rock uplift rate) and climatically sensitive parameters (soil production efficiency, hillslope transport efficiency, and mineral dissolution rate) on initially steady‐state landscapes. These experiments revealed key insights about TCN responses in transient landscapes. (a) Accounting for soil chemical erosion is necessary to accurately calculateD_inf. (b) Responses ofD_infto tectonic perturbations differ from those to climatic perturbations, suggesting that spatial and temporal patterns inD_infare signatures of perturbation type and magnitude. (c) If soil chemical erosion is accounted for, basin‐averagedD_infinferred from TCN in stream sediment closely tracks actual basin‐averaged denudation rate, showing thatD_infis a reasonable proxy for actual denudation rate, even in many transient landscapes. (d) Response times ofD_infto perturbations increase with hillslope length, implying that response times should be sensitive to the climatic, biological, and lithologic processes that control hillslope length. 

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2. Tracking of Dynamical Processes with Model Switching Using Temporal Convolutional Networks

   https://doi.org/10.1109/AERO50100.2021.9438450  

   Grootveld, Arick; Bugayev, Vlad I.; Lackey, Leah; Klein, Andrew G.; Vedula, Kirty; Brown, D. Richard (March 2021, 2021 IEEE Aerospace Conference)

   This paper considers the problem of tracking and predicting dynamical processes with model switching. The classical approach to this problem has been to use an interacting multiple model (IMM) which uses multiple Kalman filters and an auxiliary system to estimate the posterior probability of each model given the observations. More recently, data-driven approaches such as recurrent neural networks (RNNs) have been used for tracking and prediction in a variety of settings. An advantage of data-driven approaches like the RNN is that they can be trained to provide good performance even when the underlying dynamic models are unknown. This paper studies the use of temporal convolutional networks (TCNs) in this setting since TCNs are also data-driven but have certain structural advantages over RNNs. Numerical simulations demonstrate that a TCN matches or exceeds the performance of an IMM and other classical tracking methods in two specific settings with model switching: (i) a Gilbert-Elliott burst noise communication channel that switches between two different modes, each modeled as a linear system, and (ii) a maneuvering target tracking scenario where the target switches between a linear constant velocity mode and a nonlinear coordinated turn mode. In particular, the results show that the TCN tends to identify a mode switch as fast or faster than an IMM and that, in some cases, the TCN can perform almost as well as an omniscient Kalman filter with perfect knowledge of the current mode of the dynamical system. 

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3. Estimating human joint moments unifies exoskeleton control, reducing user effort

   https://doi.org/10.1126/scirobotics.adi8852  

   Molinaro, Dean D; Kang, Inseung; Young, Aaron J (March 2024, Science Robotics)

   Robotic lower-limb exoskeletons can augment human mobility, but current systems require extensive, context-specific considerations, limiting their real-world viability. Here, we present a unified exoskeleton control framework that autonomously adapts assistance on the basis of instantaneous user joint moment estimates from a temporal convolutional network (TCN). When deployed on our hip exoskeleton, the TCN achieved an average root mean square error of 0.142 newton-meters per kilogram across 35 ambulatory conditions without any user-specific calibration. Further, the unified controller significantly reduced user metabolic cost and lower-limb positive work during level-ground and incline walking compared with walking without wearing the exoskeleton. This advancement bridges the gap between in-lab exoskeleton technology and real-world human ambulation, making exoskeleton control technology viable for a broad community. 

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4. An Approach to Construct Technological Convergence Networks Across Different IPC Hierarchies and Identify Key Technology Fields

   https://doi.org/10.1109/TEM.2021.3120709  

   Luan, Chunjuan; Deng, Siming; Porter, Alan L.; Song, Bowen (October 2021, IEEE Transactions on Engineering Management)

   Technological convergence network (TCN) is an effective method to identify the advancement of technology convergence. However, the previous TCN investigations are limited to a single level of IPC (abbreviation of International Patent Classification) rather than different IPC hierarchies, which can only provide decision support for policy-makers with one dimension instead of various ones. In this study, we propose a new approach to construct TCNs across different IPC hierarchies based on technology co-classification analysis, and further identify key technology fields by employing the indicator of betweenness centrality (BC) in the TCNs from any IPC hierarchy. This study makes two important contributions. First, theoretically, our study is to contribute to understanding the advancement of technological convergence from various IPC hierarchies, rather than a single IPC level. Second, methodologically, the new approach we propose can benefit decision-makers serving at various levels of technology management agencies. We conclude possible implications and future directions. 

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5. The Performance of Sequential Deep Learning Models in Detecting Phishing Websites Using Contextual Features of URLs

   Gopali, S; Namin, A; Abri, F; Jones, K (May 2024, ACM SAC '24: Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing)

   Cyber attacks continue to pose significant threats to individuals and organizations, stealing sensitive data such as personally identifiable information, financial information, and login credentials. Hence, detecting malicious websites before they cause any harm is critical to preventing fraud and monetary loss. To address the increasing number of phishing attacks, protective mechanisms must be highly responsive, adaptive, and scalable. Fortunately, advances in the field of machine learning, coupled with access to vast amounts of data, have led to the adoption of various deep learning models for timely detection of these cyber crimes. This study focuses on the detection of phishing websites using deep learning models such as Multi-Head Attention, Temporal Convolutional Network (TCN), BI-LSTM, and LSTM where URLs of the phishing websites are treated as a sequence. The results demonstrate that Multi-Head Attention and BI-LSTM model outperform some other deep learning-based algorithms such as TCN and LSTM in producing better precision, recall, and F1-scores. 

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