Data-efficient Neuroevolution with Kernel-Based Surrogate Models

Surrogate-assistance approaches have long been used in computationally expensive domains to improve the data-efficiency of optimization algorithms. Neuroevolution, however, has so far resisted the application of these techniques because it requires the surrogate model to make fitness predictions based on variable topologies, instead of a vector of parameters. Our main insight is that we can sidestep this problem by using kernel-based surrogate models, which require only the definition of a distance measure between individuals. Our second insight is that the well-established Neuroevolution of Augmenting Topologies (NEAT) algorithm provides a computationally efficient distance measure between dissimilar networks in the form of "compatibility distance", initially designed to maintain topological diversity. Combining these two ideas, we introduce a surrogate-assisted neuroevolution algorithm that combines NEAT and a surrogate model built using a compatibility distance kernel. We demonstrate the data-efficiency of this new algorithm on the low dimensional cart-pole swing-up problem, as well as the higher dimensional half-cheetah running task. In both tasks the surrogate-assisted variant achieves the same or better results with several times fewer function evaluations as the original NEAT.Comment: In GECCO 201

Some Natural History Notes on the Brooding Behavior and Social System of Two Oklahoma Skinks, Plestiodon fasciatus and Plestiodon obtusirostris

The purpose of this study was to quantify the social and reproductive behavior of Plestiodon fasciatus and P. obtusirostris. We conducted laboratory experiments with brooding behavior and field experiments to test for mate-guarding and territoriality. To determine the use of space by both species, we conducted a mark-recapture study. We constructed two permanent 1-ha trapping grids of can pitfall traps and cover-boards, with an inter-trap distance of 10 m. One was in a mixed woodland-grassland habitat and one in a grassland habitat. We manipulated the hydric environment to determine parental behavior of brooding female P. obtusirostris. We size-matched male P. fasciatus and P. obtusirostris for dyadic encounters with and without females and both on and off home ranges in order to determine social behavior. Change in hydric conditions did not induce female P. obtusirostris to move eggs to more suitable nest sites in our experiments. Plestiodon fasciatus exhibited behavior associated with mate-guarding. Plestiodon obtusirostris did not display behavior associated with territoriality, and our experiment examining mate-guarding calls for a more intensive study

Some Natural History Notes on the Brooding Behavior and Social System of Two Oklahoma Skinks, Plestiodon fasciatus and Plestiodon obtusirostris

The purpose of this study was to quantify the social and reproductive behavior of Plestiodon fasciatus and P. obtusirostris. We conducted laboratory experiments with brooding behavior and field experiments to test for mate-guarding and territoriality. To determine the use of space by both species, we conducted a mark-recapture study. We constructed two permanent 1-ha trapping grids of can pitfall traps and cover-boards, with an inter-trap distance of 10 m. One was in a mixed woodland-grassland habitat and one in a grassland habitat. We manipulated the hydric environment to determine parental behavior of brooding female P. obtusirostris. We size-matched male P. fasciatus and P. obtusirostris for dyadic encounters with and without females and both on and off home ranges in order to determine social behavior. Change in hydric conditions did not induce female P. obtusirostris to move eggs to more suitable nest sites in our experiments. Plestiodon fasciatus exhibited behavior associated with mate-guarding. Plestiodon obtusirostris did not display behavior associated with territoriality, and our experiment examining mate-guarding calls for a more intensive study

High Energy Photon-Photon Collisions at a Linear Collider

High intensity back-scattered laser beams will allow the efficient conversion of a substantial fraction of the incident lepton energy into high energy photons, thus significantly extending the physics capabilities of an electron-electron or electron-positron linear collider. The annihilation of two photons produces C=+ final states in virtually all angular momentum states. The annihilation of polarized photons into the Higgs boson determines its fundamental two-photon coupling as well as determining its parity. Other novel two-photon processes include the two-photon production of charged lepton pairs, vector boson pairs, as well as supersymmetric squark and slepton pairs and Higgstrahlung. The one-loop box diagram leads to the production of pairs of neutral particles. High energy photon-photon collisions can also provide a remarkably background-free laboratory for studying possibly anomalous $W W$ collisions and annihilation. In the case of QCD, each photon can materialize as a quark anti-quark pair which interact via multiple gluon exchange. The diffractive channels in photon-photon collisions allow a novel look at the QCD pomeron and odderon. Odderon exchange can be identified by looking at the heavy quark asymmetry. In the case of electron-photon collisions, one can measure the photon structure functions and its various components. Exclusive hadron production processes in photon-photon collisions test QCD at the amplitude level and measure the hadron distribution amplitudes which control exclusive semi-leptonic and two-body hadronic B-decays.Comment: Invited talk, presented at the 5th International Workshop On Electron-Electron Interactions At TeV Energies, Santa Cruz, California, 12-14 December 200

Coevolution of Generative Adversarial Networks

Generative adversarial networks (GAN) became a hot topic, presenting impressive results in the field of computer vision. However, there are still open problems with the GAN model, such as the training stability and the hand-design of architectures. Neuroevolution is a technique that can be used to provide the automatic design of network architectures even in large search spaces as in deep neural networks. Therefore, this project proposes COEGAN, a model that combines neuroevolution and coevolution in the coordination of the GAN training algorithm. The proposal uses the adversarial characteristic between the generator and discriminator components to design an algorithm using coevolution techniques. Our proposal was evaluated in the MNIST dataset. The results suggest the improvement of the training stability and the automatic discovery of efficient network architectures for GANs. Our model also partially solves the mode collapse problem.Comment: Published in EvoApplications 201

Ideals of Quasi-Symmetric Functions and Super-Covariant Polynomials for S_n

The aim of this work is to study the quotient ring R_n of the ring Q[x_1,...,x_n] over the ideal J_n generated by non-constant homogeneous quasi-symmetric functions. We prove here that the dimension of R_n is given by C_n, the n-th Catalan number. This is also the dimension of the space SH_n of super-covariant polynomials, that is defined as the orthogonal complement of J_n with respect to a given scalar product. We construct a basis for R_n whose elements are naturally indexed by Dyck paths. This allows us to understand the Hilbert series of SH_n in terms of number of Dyck paths with a given number of factors.Comment: LaTeX, 3 figures, 12 page

Occlusion Handler Density Networks for 3D Multimodal Joint Location of Hand Pose Hypothesis

Predicting the pose parameters during the hand pose estimation (HPE) process is an ill-posed challenge. This is due to severe self-occluded joints of the hand. The existing approaches for predicting pose parameters of the hand, utilize a single-value mapping of an input image to generate final pose output. This way makes it difficult to handle occlusion especially when it comes from the multimodal pose hypothesis. This paper introduces an effective method of handling multimodal joint occlusion using the negative log-likelihood of a multimodal mixture-of-Gaussians through a hybrid hierarchical mixture density network (HHMDN). The proposed approach generates multiple feasible hypotheses of 3D poses with visibility, unimodal and multimodal distribution units to locate joint visibility. The visible features are extracted and fed into the Convolutional Neural Networks (CNN) layer of the HHMDN for feature learning. Finally, the effectiveness of the proposed method is proved on ICVL, NYU, and BigHand public hand pose datasets. The imperative results show that the proposed method in this paper is effective as it achieves a visibility error of 30.3mm, which is less error compared to many state-of-the-art approaches that use different distributions of visible and occluded joints

Financial factor influence on scaling and memory of trading volume in stock market

We study the daily trading volume volatility of 17,197 stocks in the U.S. stock markets during the period 1989--2008 and analyze the time return intervals $\tau$ between volume volatilities above a given threshold q. For different thresholds q, the probability density function P_q(\tau) scales with mean interval as P_q(\tau)=^{-1}f(\tau/) and the tails of the scaling function can be well approximated by a power-law f(x)~x^{-\gamma}. We also study the relation between the form of the distribution function P_q(\tau) and several financial factors: stock lifetime, market capitalization, volume, and trading value. We find a systematic tendency of P_q(\tau) associated with these factors, suggesting a multi-scaling feature in the volume return intervals. We analyze the conditional probability P_q(\tau|\tau_0) for $\tau$ following a certain interval \tau_0, and find that P_q(\tau|\tau_0) depends on \tau_0 such that immediately following a short/long return interval a second short/long return interval tends to occur. We also find indications that there is a long-term correlation in the daily volume volatility. We compare our results to those found earlier for price volatility.Comment: 17 pages, 6 figure

Semirelativistic Potential Model for Heavy Quarkonia

The c¯c, b¯b, and t¯t spectra are investigated with the use of a semirelativistic potential model described in an earlier paper. Results for the energy levels, leptonic widths, and E1 transition widths are compared with the experimental data for c¯c, and b¯b, and predicted for t¯t. We also find that the quark-antiquark interaction can best be described by a quasistatic rather than a momentum-dependent potential, and propose a theoretical justification for this surprising conclusion

Comment on Spin-Dependent Forces in Heavy-Quark Systems

A Comment on the Letter by Y. J. Ng et al., Phys. Rev. Lett. 55, 916 (1985)