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1. Generating the Gopher’s Grounds: Form, Function, Order, and Alignment

   Zhao, Jiayi ; Kamath, Anshul ; Grisanti, Nick ; Montanez, George D ( January 2023 , Agents and Artificial Intelligence. ICAART 2022. Lecture Notes in Computer Science (vol 13786))

   Rocha, A.P. (Ed.)

   Previous work has shown that artificial agents with the ability to discern function from structure (intention perception) in simple combinatorial machines possess a survival advantage over those that cannot. We seek to examine the strength of the relationship between structure and function in these cases. To do so, we use genetic algorithms to generate simple combinatorial machines (in this case, traps for artificial gophers). Specifically, we generate traps both with and without structure and function, and examine the correlation between trap coherence and lethality, the capacity of genetic algorithms to generate lethal and coherent traps, and the information resources necessary for genetic algorithms to create traps with specified traits. We then use the traps generated by the genetic algorithms to see if artificial agents with intention perception still possess a survival advantage over those that do not. Our findings are two-fold. First, we find that coherence (structure) is much harder to achieve than lethality (function) and that optimizing for one does not beget the other. Second, we find that agents with intention perception do not possess strong survival advantages when faced with traps generated by a genetic algorithm. 

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2. Generating the Gopher’s Grounds: Form, Function, Order, and Alignment

   Zhao, Jiayi ; Kamath, Anshul ; Grisanti, Nick ; Montanez, George D ( January 2023 , Agents and Artificial Intelligence. ICAART 2022. Lecture Notes in Computer Science (vol 13786))

   Rocha, A.P. ; Steels, L. ; van den Herik, J. (Ed.)

   Previous work has shown that artificial agents with the ability to discern function from structure (intention perception) in simple combinatorial machines possess a survival advantage over those that cannot. We seek to examine the strength of the relationship between structure and function in these cases. To do so, we use genetic algorithms to generate simple combinatorial machines (in this case, traps for artificial gophers). Specifically, we generate traps both with and without structure and function, and examine the correlation between trap coherence and lethality, the capacity of genetic algorithms to generate lethal and coherent traps, and the information resources necessary for genetic algorithms to create traps with specified traits. We then use the traps generated by the genetic algorithms to see if artificial agents with intention perception still possess a survival advantage over those that do not. Our findings are two-fold. First, we find that coherence (structure) is much harder to achieve than lethality (function) and that optimizing for one does not beget the other. Second, we find that agents with intention perception do not possess strong survival advantages when faced with traps generated by a genetic algorithm. 

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3. Predicting gene structure changes resulting from genetic variants via exon definition features

   https://doi.org/10.1093/bioinformatics/bty324  

   Majoros, William H. ; Holt, Carson ; Campbell, Michael S. ; Ware, Doreen ; Yandell, Mark ; Reddy, Timothy E. ; Birol, ed., Inanc ( April 2018 , Bioinformatics)

   Genetic variation that disrupts gene function by altering gene splicing between individuals can substantially influence traits and disease. In those cases, accurately predicting the effects of genetic variation on splicing can be highly valuable for investigating the mechanisms underlying those traits and diseases. While methods have been developed to generate high quality computational predictions of gene structures in reference genomes, the same methods perform poorly when used to predict the potentially deleterious effects of genetic changes that alter gene splicing between individuals. Underlying that discrepancy in predictive ability are the common assumptions by reference gene finding algorithms that genes are conserved, well-formed and produce functional proteins.

   We describe a probabilistic approach for predicting recent changes to gene structure that may or may not conserve function. The model is applicable to both coding and non-coding genes, and can be trained on existing gene annotations without requiring curated examples of aberrant splicing. We apply this model to the problem of predicting altered splicing patterns in the genomes of individual humans, and we demonstrate that performing gene-structure prediction without relying on conserved coding features is feasible. The model predicts an unexpected abundance of variants that create de novo splice sites, an observation supported by both simulations and empirical data from RNA-seq experiments. While these de novo splice variants are commonly misinterpreted by other tools as coding or non-coding variants of little or no effect, we find that in some cases they can have large effects on splicing activity and protein products and we propose that they may commonly act as cryptic factors in disease.

   The software is available from geneprediction.org/SGRF.

   Supplementary information is available at Bioinformatics online.

    

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4. Bayesian Seismic Refraction Inversion for Critical Zone Science and Near‐Surface Applications

   https://doi.org/10.1029/2020GC009172  

   Huang, Mong‐Han ; Hudson‐Rasmussen, Berit ; Burdick, Scott ; Lekic, Vedran ; Nelson, Mariel D. ; Fauria, Kristen E. ; Schmerr, Nicholas ( May 2021 , Geochemistry, Geophysics, Geosystems)

   The critical zone (CZ) is the region of the Earth’s surface that extends from the bottom of the weathered bedrock to the tree canopy and is important because of its ability to store water and support ecosystems. A growing number of studies use active source shallow seismic refraction to explore and define the size and structure of the CZ across landscapes. However, measurement uncertainty and model resolution at depth are generally not evaluated, which makes the identification and interpretation of CZ features inconclusive. To reliably resolve seismic velocity with depth, we implement a Transdimensional Hierarchical Bayesian (THB) framework with reversible‐jump Markov Chain Monte Carlo to generate samples from the posterior distribution of velocity structures. We also perform 2D synthetic tests to explore how well THB traveltime inversion can resolve different subsurface velocity structures. We find that THB recovers both sharp changes in velocity as well as gradual velocity increases with depth. Furthermore, we explore the velocity structure in a series of ridge‐valley systems in northern California. The posterior velocity model shows an increasing thickness of low velocity material from channels to ridgetops along a transect parallel to bedding strike, implying a deeper weathering zone below ridgetops and hillslopes than below channels. The THB method enhances the ability to reliably image CZ structure, and the model uncertainty estimates it yields provides an objective way to interpret deep CZ structure. The method can be applied across other near‐surface studies, especially in the presence of significant surface topography.

    

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5. Nonlinear, Low-Energy-Actuator-Prioritizing Control Allocation for Winged eVTOL UAVs

   https://doi.org/10.1109/IETC54973.2022.9796842  

   Peterson, Mason B. ; Beard, Randal W. ; Willis, Jacob B. ( May 2022 , Nonlinear, Low-Energy-Actuator-Prioritizing Control Allocation for Winged eVTOL UAVs)

   Winged eVTOL aircraft’s ability to generate aerodynamic lift with wings and to create upward thrust with upward-facing rotors makes these vehicles capable of the kind of versatile flight needed in urban environments. Because of these vehicles’ aerodynamic complexities and their unique methods of producing thrusts and torques, control allocation is needed to determine how to distribute force and torque efforts across the aircraft’s actuators. However, current control allocation methods fail to properly represent the actuators’ complex dynamics and are unable to harness the full potential of these over-actuated vehicles. Current shortcomings include modeling rotors as linear effectors while the wide range of airspeeds experienced by eVTOL aircraft leads to significant nonlinearities in the thrust and torque achieved by each rotor. This means linear control allocation methods may consistently fail to produce desired thrusts and torques, which can inhibit the vehicle from tracking a trajectory at best, and at worst can cause the vehicle to stall and lose control. Additionally, current control allocation methods are often unable to prioritize low-energy actuators resulting in shorter battery life. We present a nonlinear control allocation method that considers a nonlinear rotor model, allows for prioritization of low-energy control surfaces over rotors, and reliably accounts for actuator saturation. Simulation results show a 90% reduction in high-airspeed trajectory tracking position error from a typical, linear least-squares pseudoinverse control allocation method while maintaining comparable energy use. 

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