Communicating through motion in dance and animal groups

This study explores principles of motion based communication in animal and human group behavior. It develops models of cooperative control that involve communication through actions aimed at a shared objective. Moreover, it aims at understanding the collective motion in multi-agent models towards a desired objective which requires interaction with the environment. In conducting a formal study of these problems, first we investigate the leader-follower interaction in a dance performance. Here, the prototype model is salsa. Salsa is of interest because it is a structured interaction between a leader (usually a male dancer) and a follower (usually a female dancer). Success in a salsa performance depends on how effectively the dance partners communicate with each other using hand, arm and body motion. We construct a mathematical framework in terms of a Dance Motion Description Language (DMDL). This provides a way to specify control protocols for dance moves and to represent every performance as sequences of letters and corresponding motion signals. An enhanced form of salsa (intermediate level) is discussed in which the constraints on the motion transitions are described by simple rules suggested by topological knot theory. It is shown that the proficiency hierarchy in dance is effectively captured by proposed complexity metrics. In order to investigate the group behavior of animals that are reacting to environmental features, we have analyzed a large data set derived from 3-d video recordings of groups of Myotis velifer emerging from a cave. A detailed statistical analysis of large numbers of trajectories indicates that within certain bounds of animal diversity, there appear to be common characteristics of the animals' reactions to features in a clearly defined flight corridor near the mouth of the cave. A set of vision-based motion control primitives is proposed and shown to be effective in synthesizing bat-like flight paths near groups of obstacles. A comparison of synthesized paths and actual bat motions culled from our data set suggests that motions are not based purely on reactions to environmental features. Spatial memory and reactions to the movement of other bats may also play a role. It is argued that most bats employ a hybrid navigation strategy that combines reactions to nearby obstacles and other visual features with some combination of spatial memory and reactions to the motions of other bats

Batched Sparse Codes

Network coding can significantly improve the transmission rate of communication networks with packet loss compared with routing. However, using network coding usually incurs high computational and storage costs in the network devices and terminals. For example, some network coding schemes require the computational and/or storage capacities of an intermediate network node to increase linearly with the number of packets for transmission, making such schemes difficult to be implemented in a router-like device that has only constant computational and storage capacities. In this paper, we introduce BATched Sparse code (BATS code), which enables a digital fountain approach to resolve the above issue. BATS code is a coding scheme that consists of an outer code and an inner code. The outer code is a matrix generation of a fountain code. It works with the inner code that comprises random linear coding at the intermediate network nodes. BATS codes preserve such desirable properties of fountain codes as ratelessness and low encoding/decoding complexity. The computational and storage capacities of the intermediate network nodes required for applying BATS codes are independent of the number of packets for transmission. Almost capacity-achieving BATS code schemes are devised for unicast networks, two-way relay networks, tree networks, a class of three-layer networks, and the butterfly network. For general networks, under different optimization criteria, guaranteed decoding rates for the receiving nodes can be obtained.Comment: 51 pages, 12 figures, submitted to IEEE Transactions on Information Theor

Classifying and completing word analogies by machine learning

Analogical proportions are statements of the form ‘a is to b as c is to d’, formally denoted a:b::c:d. They are the basis of analogical reasoning which is often considered as an essential ingredient of human intelligence. For this reason, recognizing analogies in natural language has long been a research focus within the Natural Language Processing (NLP) community. With the emergence of word embedding models, a lot of progress has been made in NLP, essentially assuming that a word analogy like man:king::woman:queen is an instance of a parallelogram within the underlying vector space. In this paper, we depart from this assumption to adopt a machine learning approach, i.e., learning a substitute of the parallelogram model. To achieve our goal, we first review the formal modeling of analogical proportions, highlighting the properties which are useful from a machine learning perspective. For instance, the postulates supposed to govern such proportions entail that when a:b::c:d holds, then seven permutations of a,b,c,d still constitute valid analogies. From a machine learning perspective, this provides guidelines to build training sets of positive and negative examples. Taking into account these properties for augmenting the set of positive and negative examples, we first implement word analogy classifiers using various machine learning techniques, then we approximate by regression an analogy completion function, i.e., a way to compute the missing word when we have the three other ones. Using a GloVe embedding, classifiers show very high accuracy when recognizing analogies, improving state of the art on word analogy classification. Also, the regression processes usually lead to much more successful analogy completion than the ones derived from the parallelogram assumption. © 202

Active pitch control of an oscillating foil with biologically-inspired boundary layer feedback

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 75-76).We present a high-fidelity numerical study of a two-dimensional °apping airfoil, ad- dressing the hypothesis that boundary layer feedback control can enable improved performance in flapping light. To this end, we model a novel biologically-inspired feedback controller which adjusts wing motion in response to the flow-induced bending load experienced by sensory hairs mounted on the wing. Such hairs have been observed on bats, and biological studies suggest that an associated feedback controller may play an important role in enabling bats' well-known mastery of light. The coupled °uid and structural equations of our model are solved numerically by a Discontinuous Galerkin finite element method, combined with an Arbitrary Lagrangian-Eulerian (ALE) formulation to account for airfoil motion. Feedback control is deed by a simple proportional-derivative (PD) control law relating hair sensor feedback to an applied torque at the pivot point of the wing. We also include a torsional spring at the pivot point to model passive aeroelasticity, following prior work by Israeli [5]. Our results show that hair sensors are well-suited for detecting flow separation, and sensors placed near the leading edge enable better light performance than sensors placed near the trailing edge. We compute a "performance envelope" for a purely passive flapping airfoil, and demonstrate that our active feedback controller enables improvements of up to 5% in propulsive efficiency. We also present gust alleviation experiments, where we find that an optimal PD controller reduces lift deviation by 33% compared to a spring-only airfoil. Mechanisms for these performance improvements are discussed. Our findings suggest that boundary layer feedback control may plausibly contribute to the outstanding °ight abilities of bats, and may also provide valuable clues for designing robust and maneuverable Micro Air Vehicles (MAVs).by Hemant Kumar Chaurasia.S.M

Novel Discretization Schemes for the Numerical Simulation of Membrane Dynamics

Motivated by the demands of simulating flapping wings of Micro Air Vehicles, novel numerical methods were developed and evaluated for the dynamic simulation of membranes. For linear membranes, a mixed-form time-continuous Galerkin method was employed using trilinear space-time elements, and the entire space-time domain was discretized and solved simultaneously. For geometrically nonlinear membranes, the model incorporated two new schemes that were independently developed and evaluated. Time marching was performed using quintic Hermite polynomials uniquely determined by end-point jerk constraints. The single-step, implicit scheme was significantly more accurate than the most common Newmark schemes. For a simple harmonic oscillator, the scheme was found to be symplectic, frequency-preserving, and conditionally stable. Time step size was limited by accuracy requirements rather than stability. The spatial discretization scheme employed a staggered grid, grouping of nonlinear terms, and polygon shape functions in a strong-form point collocation formulation. Validation against existing experimental data showed the method to be accurate until hyperelastic effects dominate

An Application of Gaussian Process Modeling for High-order Accurate Adaptive Mesh Refinement Prolongation

We present a new polynomial-free prolongation scheme for Adaptive Mesh Refinement (AMR) simulations of compressible and incompressible computational fluid dynamics. The new method is constructed using a multi-dimensional kernel-based Gaussian Process (GP) prolongation model. The formulation for this scheme was inspired by the GP methods introduced by A. Reyes et al. (A New Class of High-Order Methods for Fluid Dynamics Simulation using Gaussian Process Modeling, Journal of Scientific Computing, 76 (2017), 443-480; A variable high-order shock-capturing finite difference method with GP-WENO, Journal of Computational Physics, 381 (2019), 189-217). In this paper, we extend the previous GP interpolations and reconstructions to a new GP-based AMR prolongation method that delivers a high-order accurate prolongation of data from coarse to fine grids on AMR grid hierarchies. In compressible flow simulations special care is necessary to handle shocks and discontinuities in a stable manner. To meet this, we utilize the shock handling strategy using the GP-based smoothness indicators developed in the previous GP work by A. Reyes et al. We demonstrate the efficacy of the GP-AMR method in a series of testsuite problems using the AMReX library, in which the GP-AMR method has been implemented