Competition Between Antiferromagnetic Order and Spin-Liquid Behavior in the Two-Dimensional Periodic Anderson Model at Half-Filling

We study the two-dimensional periodic Anderson model at half-filling using quantum Monte Carlo (QMC) techniques. The ground state undergoes a magnetic order-disorder transition as a function of the effective exchange coupling between the conduction and localized bands. Low-lying spin and charge excitations are determined using the maximum entropy method to analytically continue the QMC data. At finite temperature we find a competition between the Kondo effect and antiferromagnetic order which develops in the localized band through Ruderman-Kittel-Kasuya-Yosida interactions.Comment: Revtex 3.0, 10 pages + 5 figures, UCSBTH-94-2

A new set of BXD recombinant inbred lines from advanced intercross populations in mice

BACKGROUND: Recombinant inbred (RI) strains are an important resource for mapping complex traits in many species. While large RI panels are available for Arabidopsis, maize, C. elegans, and Drosophila, mouse RI panels typically consist of fewer than 30 lines. This is a severe constraint on the power and precision of mapping efforts and greatly hampers analysis of epistatic interactions. RESULTS: In order to address these limitations and to provide the community with a more effective collaborative RI mapping panel we generated new BXD RI strains from two independent advanced intercrosses (AI) between C57BL/6J (B6) and DBA/2J (D2) progenitor strains. Progeny were intercrossed for 9 to 14 generations before initiating inbreeding, which is still ongoing for some strains. Since this AI base population is highly recombinant, the 46 advanced recombinant inbred (ARI) strains incorporate approximately twice as many recombinations as standard RI strains, a fraction of which are inevitably shared by descent. When combined with the existing BXD RI strains, the merged BXD strain set triples the number of previously available unique recombinations and quadruples the total number of recombinations in the BXD background. CONCLUSION: The combined BXD strain set is the largest mouse RI mapping panel. It is a powerful tool for collaborative analysis of quantitative traits and gene function that will be especially useful to study variation in transcriptome and proteome data sets under multiple environments. Additional strains also extend the value of the extensive phenotypic characterization of the previously available strains. A final advantage of expanding the BXD strain set is that both progenitors have been sequenced, and approximately 1.8 million SNPs have been characterized. This provides unprecedented power in screening candidate genes and can reduce the effective length of QTL intervals. It also makes it possible to reverse standard mapping strategies and to explore downstream effects of known sequence variants

Spin-fluctuations in the quarter-filled Hubbard ring : significances to LiV2_2O4_4

Using the quantum Monte Carlo method, we investigate the spin dynamics of itinerant electrons in the one-dimensional Hubbard system. Based on the model calculation, we have studied the spin-fluctuations in the quarter-filled metallic Hubbard ring, which is aimed at the vanadium ring or chain defined along corner-sharing tetrahedra of LiV$_2$O$_4$, and found the dramatic changes of magnetic responses and spin-fluctuation characteristics with the temperature. Such results can explain the central findings in the recent neutron scattering experiment for LiV$_2$O$_4$.Comment: 5 pages, 3 figure

Hadronic Spectral Functions in Lattice QCD

QCD spectral functions of hadrons in the pseudo-scalar and vector channels are extracted from lattice Monte Carlo data of the imaginary time Green's functions. The maximum entropy method works well for this purpose, and the resonance and continuum structures in the spectra are obtained in addition to the ground state peaks.Comment: 4 pages, 3 eps-figures, revtex (minor modifications in the text and an added reference). To appear in Physical Review D Rapid Communication

AI Researchers, Video Games Are Your Friends!

If you are an artificial intelligence researcher, you should look to video games as ideal testbeds for the work you do. If you are a video game developer, you should look to AI for the technology that makes completely new types of games possible. This chapter lays out the case for both of these propositions. It asks the question "what can video games do for AI", and discusses how in particular general video game playing is the ideal testbed for artificial general intelligence research. It then asks the question "what can AI do for video games", and lays out a vision for what video games might look like if we had significantly more advanced AI at our disposal. The chapter is based on my keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad audience.Comment: in Studies in Computational Intelligence Studies in Computational Intelligence, Volume 669 2017. Springe

The future of human nature: a symposium on the promises and challenges of the revolutions in genomics and computer science, April 10, 11, and 12, 2003

This repository item contains a single issue of the Pardee Conference Series, a publication series that began publishing in 2006 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future. This was the Center's Symposium on the Promises and Challenges of the Revolutions in Genomics and Computer Science took place during April 10, 11, and 12, 2003. Co-organized by Charles DeLisi and Kenneth Lewes; sponsored by Boston University, the Frederick S. Pardee Center for the Study of the Longer-Range Future.This conference focused on scientific and technological advances in genetics, computer science, and their convergence during the next 35 to 250 years. In particular, it focused on directed evolution, the futures it allows, the shape of society in those futures, and the robustness of human nature against technological change at the level of individuals, groups, and societies. It is taken as a premise that biotechnology and computer science will mature and will reinforce one another. During the period of interest, human cloning, germ-line genetic engineering, and an array of reproductive technologies will become feasible and safe. Early in this period, we can reasonably expect the processing power of a laptop computer to exceed the collective processing power of every human brain on the planet; later in the period human/machine interfaces will begin to emerge. Whether such technologies will take hold is not known. But if they do, human evolution is likely to proceed at a greatly accelerated rate; human nature as we know it may change markedly, if it does not disappear altogether, and new intelligent species may well be created

The future of human nature: a symposium on the promises and challenges of the revolutions in genomics and computer science, April 10, 11, and 12, 2003

This repository item contains a single issue of the Pardee Conference Series, a publication series that began publishing in 2006 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future. This was the Center's Symposium on the Promises and Challenges of the Revolutions in Genomics and Computer Science took place during April 10, 11, and 12, 2003. Co-organized by Charles DeLisi and Kenneth Lewes; sponsored by Boston University, the Frederick S. Pardee Center for the Study of the Longer-Range Future.This conference focused on scientific and technological advances in genetics, computer science, and their convergence during the next 35 to 250 years. In particular, it focused on directed evolution, the futures it allows, the shape of society in those futures, and the robustness of human nature against technological change at the level of individuals, groups, and societies. It is taken as a premise that biotechnology and computer science will mature and will reinforce one another. During the period of interest, human cloning, germ-line genetic engineering, and an array of reproductive technologies will become feasible and safe. Early in this period, we can reasonably expect the processing power of a laptop computer to exceed the collective processing power of every human brain on the planet; later in the period human/machine interfaces will begin to emerge. Whether such technologies will take hold is not known. But if they do, human evolution is likely to proceed at a greatly accelerated rate; human nature as we know it may change markedly, if it does not disappear altogether, and new intelligent species may well be created

Privacy Risks of Securing Machine Learning Models against Adversarial Examples

The arms race between attacks and defenses for machine learning models has come to a forefront in recent years, in both the security community and the privacy community. However, one big limitation of previous research is that the security domain and the privacy domain have typically been considered separately. It is thus unclear whether the defense methods in one domain will have any unexpected impact on the other domain. In this paper, we take a step towards resolving this limitation by combining the two domains. In particular, we measure the success of membership inference attacks against six state-of-the-art defense methods that mitigate the risk of adversarial examples (i.e., evasion attacks). Membership inference attacks determine whether or not an individual data record has been part of a model's training set. The accuracy of such attacks reflects the information leakage of training algorithms about individual members of the training set. Adversarial defense methods against adversarial examples influence the model's decision boundaries such that model predictions remain unchanged for a small area around each input. However, this objective is optimized on training data. Thus, individual data records in the training set have a significant influence on robust models. This makes the models more vulnerable to inference attacks. To perform the membership inference attacks, we leverage the existing inference methods that exploit model predictions. We also propose two new inference methods that exploit structural properties of robust models on adversarially perturbed data. Our experimental evaluation demonstrates that compared with the natural training (undefended) approach, adversarial defense methods can indeed increase the target model's risk against membership inference attacks.Comment: ACM CCS 2019, code is available at https://github.com/inspire-group/privacy-vs-robustnes

Optical absorption and single-particle excitations in the 2D Holstein t-J model

To discuss the interplay of electronic and lattice degrees of freedom in systems with strong Coulomb correlations we have performed an extensive numerical study of the two-dimensional Holstein t-J model. The model describes the interaction of holes, doped in a quantum antiferromagnet, with a dispersionsless optical phonon mode. We apply finite-lattice Lanczos diagonalization, combined with a well-controlled phonon Hilbert space truncation, to the Hamiltonian. The focus is on the dynamical properties. In particular we have evaluated the single-particle spectral function and the optical conductivity for characteristic hole-phonon couplings, spin exchange interactions and phonon frequencies. The results are used to analyze the formation of hole polarons in great detail. Links with experiments on layered perovskites are made. Supplementary we compare the Chebyshev recursion and maximum entropy algorithms, used for calculating spectral functions, with standard Lanczos methods.Comment: 32 pages, 12 figures, submitted to Phys. Rev.

Universal Conductivity in the Two dimensional Boson Hubbard Model

We use Quantum Monte Carlo to evaluate the conductivity $\sigma$ of the 2--dimensional disordered boson Hubbard model at the superfluid-bose glass phase boundary. At the critical point for particle density $\rho=0.5$, we find $\sigma_{c}=(0.45 \pm 0.07) \sigma_{Q}$, where $\sigma_{Q}= e_{*}^{2} / h$ from a finite size scaling analysis of the superfluid density. We obtain $\sigma_{c}=(0.47 \pm 0.08) \sigma_{Q}$ from a direct calculation of the current--current correlation function. Simulations at the critical points for other particle densities, $\rho=0.75$ and $1.0$, give similar values for $\sigma$. We discuss possible origins of the difference in this value from that recently obtained by other numerical approaches.Comment: 20 pages, figures available upon request. Tex with jnl3.tex and reforder.tex macros. cond-mat/yymmnn