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1. Rigidity percolation and geometric information in floppy origami

   Siheng Chen, L. Mahadevan (April 2019, Proceedings of the National Academy of Sciences of the United States of America)

   Origami structures with a large number of excess folds are capable of storing distinguishable geometric states that are energetically equivalent. As the number of excess folds is reduced, the system has fewer equivalent states and can eventually become rigid. We quantify this transition from a floppy to a rigid state as a function of the presence of folding constraints in a classic origami tessellation, Miura-ori. We show that in a fully triangulated Miura-ori that is maximally floppy, adding constraints via the elimination of diagonal folds in the quads decreases the number of degrees of freedom in the system, first linearly and then nonlinearly. In the nonlinear regime, mechanical cooperativity sets in via a redundancy in the assignment of constraints, and the degrees of freedom depend on constraint density in a scale- invariant manner. A percolation transition in the redundancy in the constraints as a function of constraint density suggests how excess folds in an origami structure can be used to store geometric information in a scale-invariant way. 

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2. The effect of particle geometry on squirming in a heterogeneous medium

   https://doi.org/10.1017/jfm.2024.347  

   Demir, E.; van Gogh, B.; Palaniappan, D.; Nganguia, H. (May 2024, Journal of Fluid Mechanics)

   Biological microorganisms and artificial micro-swimmers often locomote in heterogeneous viscous environments consisting of networks of obstacles embedded into viscous fluid media. In this work, we use the squirmer model and present a numerical investigation of the effects of shape on swimming in a heterogeneous medium. Specifically, we analyse the microorganism's propulsion speed as well as its energetic cost and swimming efficiency. The analysis allows us to probe the general characteristics of swimming in a heterogeneous viscous environment in comparison with the case of a purely viscous fluid. We found that a spheroidal microorganism always propels faster, expends less energy and is more efficient than a spherical microorganism in either a homogeneous fluid or a heterogeneous medium. Moreover, we determined that above a critical eccentricity, a spheroidal microorganism in a heterogeneous medium can swim faster than a spherical microorganism in a homogeneous fluid. Based on an analysis of the forces acting on the squirmer, we offer an explanation for the decrease in the squirmer's speed observed in heterogeneous media compared with homogeneous fluids. 

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3. Adaptive Beamforming Using Scattering From a Drone Swarm

   https://doi.org/10.1109/WMCS49442.2020.9172335  

   Egarguin, Neil J.; Jackson, David R.; Onofrei, Daniel; Leclerc, Julien; Becker, Aaron (May 2020, 2020 IEEE Texas Symposium on Wireless and Microwave Circuits and Systems (WMCS))

   null (Ed.)

   There may be situations where a direct line of sight between a transmitter and a receiver is blocked. In such a situation it may be possible to transmit a signal upward from a transmitter to a swarm of drones, each of which carries a scattering object. By positioning each drone properly, the scattered signal from the drones can add coherently in a given direction, forming a beam in that direction. The altitude of each drone is used as a degree of freedom in order to change the phase of the signal scattered by the drone. For a given set of horizontal drone positions, the drone altitudes can be determined to produce a main beam in a given direction. The drone positions can also be optimized to focus a beam in a given direction while producing pattern nulls in other prescribed directions with very small sidelobes. 

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4. A Phone in a Basket Looks Like a Knife in a Cup: Role-Filler Independence in Visual Processing

   https://doi.org/10.1162/opmi_a_00146  

   Hafri, Alon; Bonner, Michael F; Landau, Barbara; Firestone, Chaz (January 2024, Open Mind)

   Abstract When a piece of fruit is in a bowl, and the bowl is on a table, we appreciate not only the individual objects and their features, but also the relations containment and support, which abstract away from the particular objects involved. Independent representation of roles (e.g., containers vs. supporters) and “fillers” of those roles (e.g., bowls vs. cups, tables vs. chairs) is a core principle of language and higher-level reasoning. But does such role-filler independence also arise in automatic visual processing? Here, we show that it does, by exploring a surprising error that such independence can produce. In four experiments, participants saw a stream of images containing different objects arranged in force-dynamic relations—e.g., a phone contained in a basket, a marker resting on a garbage can, or a knife sitting in a cup. Participants had to respond to a single target image (e.g., a phone in a basket) within a stream of distractors presented under time constraints. Surprisingly, even though participants completed this task quickly and accurately, they false-alarmed more often to images matching the target’s relational category than to those that did not—even when those images involved completely different objects. In other words, participants searching for a phone in a basket were more likely to mistakenly respond to a knife in a cup than to a marker on a garbage can. Follow-up experiments ruled out strategic responses and also controlled for various confounding image features. We suggest that visual processing represents relations abstractly, in ways that separate roles from fillers. 

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5. Navigating mobile robots to target in near shortest time using reinforcement learning with spiking neural networks

   Mahadevuni, A; Li, P (May 2017, Proceedings of ... International Joint Conference on Neural Networks)

   The autonomous navigation of mobile robots in unknown environments is of great interest in mobile robotics. This article discusses a new strategy to navigate to a known target location in an unknown environment using a combination of the “go-to-goal” approach and reinforcement learning with biologically realistic spiking neural networks. While the “goto-goal” approach itself might lead to a solution for most environments, the added neural reinforcement learning in this work results in a strategy that takes the robot from a starting position to a target location in a near shortest possible time. To achieve the goal, we propose a reinforcement learning approach based on spiking neural networks. The presented biologically motivated delayed reward mechanism using eligibility traces results in a greedy approach that leads the robot to the target in a close to shortest possible time. 

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