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1. Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference

   Awan, Usaid ; Morucci, Marco ; Orlandi, Vittorio ; Roy, Sudeepa ; Rudin, Cynthia ; Volfovsky, Alexander ( January 2020 , Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics)

   null (Ed.)

   We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network, and units that share edges can potentially influence each others’ outcomes. Traditional treatment effect estimators for randomized experiments are biased and error prone in this setting. Our method matches units almost exactly on counts of unique subgraphs within their neighborhood graphs. The matches that we construct are interpretable and high-quality. Our method can be extended easily to accommodate additional unit-level covariate information. We show empirically that our method performs better than other existing methodologies for this problem, while producing meaningful, interpretable results. 

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2. Slip-Spring and Kink Dynamics Models for Fast Extensional Flow of Entangled Polymeric Fluids

   https://doi.org/10.3390/polym11030465  

   Moghadam, Soroush ; Saha Dalal, Indranil ; Larson, Ronald ( March 2019 , Polymers)

   We combine a slip-spring model with an ‘entangled kink dynamics’ (EKD) model for strong uniaxial extensional flows (with Rouse Weissenberg number W i R ≫ 1 ) of long ( M w > 1   Mkg / mol for polystyrene) entangled polymers in solutions and melts. The slip-spring model captures the dynamics up to the formation of a ‘kinked’ or folded state, while the kink dynamics simulation tracks the dynamics from that point forward to complete extension. We show that a single-chain slip-spring model using affine motion of the slip-spring anchor points produces unrealistically high tension near the center of the chain once the Hencky strain exceeds around unity or so, exceeding the maximum tension that a chain entangled with a second chain is able to support. This unrealistic tension is alleviated by pairing the slip links on one chain with those on a second chain, and allowing some of the large tension on one of the two to be transferred to the second chain, producing non-affine motion of each. This explicit pairing of entanglements mimics the entanglement pairing also used in the EKD model, and allows the slip spring simulations to be carried out to strains high enough for the EKD model to become valid. We show that results nearly equivalent to those from paired chains are obtained in a single-chain slip-spring simulation by simply specifying that the tension in a slip spring cannot exceed the theoretical maximum value of ζ ′ ϵ ˙ L 2 / 8 where ζ ′ , ϵ ˙ and L are the friction per unit length, strain rate and contour length of the chain, respectively. The effects of constraint release (CR) and regeneration of entanglements is also studied and found to have little effect on the chain statistics up to the formation of the kinked state. The resulting hybrid model provides a fast, simple, simulation method to study the response of high molecular weight ( M w > 1   Mkg / mol ) polymers in fast flows ( W i R ≫ 1 ), where conventional simulation techniques are less applicable due to computational cost. 

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3. Self-relevance effects and label choice: Strong variations in label-matching performance due to non-self-relevant factors

   https://doi.org/10.3758/s13414-017-1307-8  

   Wade, Gregory L. ; Vickery, Timothy J. ( April 2017 , Attention, Perception, & Psychophysics)

   Merely associating one’s self with a stimulus may be enough to enhance performance in a label-matching paradigm (Sui, He, & Humphreys, 2012), implying prioritized processing of self-relevant stimuli. For instance, labeling a square as SELF and a circle as OTHER yields speeded performance when verifying square-SELF compared with circle-OTHER label matches. The precise causes of such effects are unclear. We propose that prioritized processing of label-matches can occur for reasons other than self-relevance. Here, we employ the label-matching paradigm to show similar benefits for non-self-relevant labels (SNAKE, FROG, and GREG) over a frequently employed, non-self-relevant control label (OTHER). These benefits suggest the possibility that self-relevance effects in the label-matching paradigm may be confounded with other properties of labels that lead to relative performance benefits, such as concreteness. The size of self-relevance effects may be overestimated in prior work employing the label-matching paradigm, which calls for greater care in the choice of control labels to determine the true magnitude of self-relevance effects. Our results additionally indicate the possibility of a powerful effect of concreteness (and related properties) on associative memory performance. 

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4. Design Considerations and Robustness to Parameter Uncertainty in Wire-Wrapped Cam Mechanisms

   https://doi.org/10.1115/1.4056600  

   Johnston, Garrison L. ; Orekhov, Andrew L. ; Simaan, Nabil ( February 2024 , Journal of Mechanisms and Robotics)

   Collaborative robots must simultaneously be safe enough to operate in close proximity to human operators and powerful enough to assist users in industrial tasks such as lifting heavy equipment. The requirement for safety necessitates that collaborative robots are designed with low-powered actuators. However, some industrial tasks may require the robot to have high payload capacity and/or long reach. For collaborative robot designs to be successful, they must find ways of addressing these conflicting design requirements. One promising strategy for navigating this tradeoff is through the use of static balancing mechanisms to offset the robot’s self-weight, thus enabling the selection of low-powered actuators. In this paper, we introduce a novel, two degrees-of-freedom static balancing mechanism based on spring-loaded, wire-wrapped cams. We also present an optimization-based cam design method that guarantees the cams stay convex, ensures the springs stay below their extensions limits, and minimizes sensitivity to unmodeled deviations from the nominal spring constant. Additionally, we present a model of the effect of friction between the wire and the cam. Lastly, we show experimentally that the torque generated by the cam mechanism matches the torque predicted in our modeling approach. Our results also suggest that the effects of wire-cam friction are significant for non-circular cams.

    

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5. Self-Stabilizing Task Allocation in Spite of Noise

   https://doi.org/10.1145/3350755.3400226  

   Dornhaus, A.R. ; Lynch, N.A ; Mallmann-Trenn, F. ; Pajak, D. ; Radeva, T. ( January 2020 , 32nd ACM Symposium on Parallelism in Algorithms and Architectures (SPAA))

   We study the problem of distributed task allocation by workers in an ant colony in a setting of limited capabilities and noisy environment feedback. We assume that each task has a demand that should be satisfied but not exceeded, i.e., there is an optimal number of ants that should be working on this task at a given time. The goal is to assign a near-optimal number of workers to each task in a distributed manner without explicit access to the value of the demand nor to the number of ants working on the task. We seek to answer the question of how the quality of task allocation depends on the accuracy of assessing by the ants whether too many (overload) or not enough (lack) ants are currently working on a given task. In our model, each ant receives a binary feedback that depends on the deficit, defined as the difference between the demand and the current number of workers in the task. The feedback is modeled as a random variable that takes values lack or overload with probability given by a sigmoid function of the deficit. The higher the overload or lack of workers for a task, the more likely it is that an ant receives the correct feedback from this task; the closer the deficit is to zero, the less reliable the feedback becomes. Each ant receives the feedback independently about one chosen task. We measure the performance of task allocation algorithms using the notion of inaccuracy, defined as the number of steps in which the deficit of some task is beyond certain threshold. We propose a simple, constant-memory, self-stabilizing, distributed algorithm that converges from any initial assignment to a near-optimal assignment under noisy feedback and keeps the deficit small for all tasks in almost every step. We also prove a lower bound for any constant-memory algorithm, which matches, up to a constant factor, the accuracy achieved by our algorithm. 

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