@article{1700,
abstract = {We use the dual boson approach to reveal the phase diagram of the Fermi-Hubbard model with long-range dipole-dipole interactions. By using a large-scale finite-temperature calculation on a 64×64 square lattice we demonstrate the existence of a novel phase, possessing an "ultralong-range" order. The fingerprint of this phase - the density correlation function - features a nontrivial behavior on a scale of tens of lattice sites. We study the properties and the stability of the ultralong-range-ordered phase, and show that it is accessible in modern experiments with ultracold polar molecules and magnetic atoms.},
author = {Van Loon, Erik and Katsnelson, Mikhail and Lemeshko, Mikhail},
journal = {Physical Review B},
number = {8},
publisher = {American Physical Society},
title = {{Ultralong-range order in the Fermi-Hubbard model with long-range interactions}},
doi = {10.1103/PhysRevB.92.081106},
volume = {92},
year = {2015},
}
@article{1701,
abstract = {The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increasing numbers of spikes are less probable, but, with more spikes, the number of possible patterns increases. This tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics. We construct this relationship for populations of up to N = 160 neurons in a small patch of the vertebrate retina, using a combination of direct and model-based analyses of experiments on the response of this network to naturalistic movies. We see signs of a thermodynamic limit, where the entropy per neuron approaches a smooth function of the energy per neuron as N increases. The form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point. We suggest further tests of criticality, and give a brief discussion of its functional significance. },
author = {Tkacik, Gasper and Mora, Thierry and Marre, Olivier and Amodei, Dario and Palmer, Stephanie and Berry Ii, Michael and Bialek, William},
journal = {PNAS},
number = {37},
pages = {11508 -- 11513},
publisher = {National Academy of Sciences},
title = {{Thermodynamics and signatures of criticality in a network of neurons}},
doi = {10.1073/pnas.1514188112},
volume = {112},
year = {2015},
}
@article{1704,
abstract = {Given a convex function (Formula presented.) and two hermitian matrices A and B, Lewin and Sabin study in (Lett Math Phys 104:691–705, 2014) the relative entropy defined by (Formula presented.). Among other things, they prove that the so-defined quantity is monotone if and only if (Formula presented.) is operator monotone. The monotonicity is then used to properly define (Formula presented.) for bounded self-adjoint operators acting on an infinite-dimensional Hilbert space by a limiting procedure. More precisely, for an increasing sequence of finite-dimensional projections (Formula presented.) with (Formula presented.) strongly, the limit (Formula presented.) is shown to exist and to be independent of the sequence of projections (Formula presented.). The question whether this sequence converges to its "obvious" limit, namely (Formula presented.), has been left open. We answer this question in principle affirmatively and show that (Formula presented.). If the operators A and B are regular enough, that is (A − B), (Formula presented.) and (Formula presented.) are trace-class, the identity (Formula presented.) holds.},
author = {Deuchert, Andreas and Hainzl, Christian and Seiringer, Robert},
journal = {Letters in Mathematical Physics},
number = {10},
pages = {1449 -- 1466},
publisher = {Springer},
title = {{Note on a family of monotone quantum relative entropies}},
doi = {10.1007/s11005-015-0787-5},
volume = {105},
year = {2015},
}
@inproceedings{1706,
abstract = {We consider a problem of learning kernels for use in SVM classification in the multi-task and lifelong scenarios and provide generalization bounds on the error of a large margin classifier. Our results show that, under mild conditions on the family of kernels used for learning, solving several related tasks simultaneously is beneficial over single task learning. In particular, as the number of observed tasks grows, assuming that in the considered family of kernels there exists one that yields low approximation error on all tasks, the overhead associated with learning such a kernel vanishes and the complexity converges to that of learning when this good kernel is given to the learner.},
author = {Pentina, Anastasia and Ben David, Shai},
location = {Banff, AB, Canada},
pages = {194 -- 208},
publisher = {Springer},
title = {{Multi-task and lifelong learning of kernels}},
doi = {10.1007/978-3-319-24486-0_13},
volume = {9355},
year = {2015},
}
@article{1709,
abstract = {The competition for resources among cells, individuals or species is a fundamental characteristic of evolution. Biological all-pay auctions have been used to model situations where multiple individuals compete for a single resource. However, in many situations multiple resources with various values exist and single reward auctions are not applicable. We generalize the model to multiple rewards and study the evolution of strategies. In biological all-pay auctions the bid of an individual corresponds to its strategy and is equivalent to its payment in the auction. The decreasingly ordered rewards are distributed according to the decreasingly ordered bids of the participating individuals. The reproductive success of an individual is proportional to its fitness given by the sum of the rewards won minus its payments. Hence, successful bidding strategies spread in the population. We find that the results for the multiple reward case are very different from the single reward case. While the mixed strategy equilibrium in the single reward case with more than two players consists of mostly low-bidding individuals, we show that the equilibrium can convert to many high-bidding individuals and a few low-bidding individuals in the multiple reward case. Some reward values lead to a specialization among the individuals where one subpopulation competes for the rewards and the other subpopulation largely avoids costly competitions. Whether the mixed strategy equilibrium is an evolutionarily stable strategy (ESS) depends on the specific values of the rewards.},
author = {Reiter, Johannes and Kanodia, Ayush and Gupta, Raghav and Nowak, Martin and Chatterjee, Krishnendu},
journal = {Proceedings of the Royal Society of London Series B Biological Sciences},
number = {1812},
publisher = {Royal Society},
title = {{Biological auctions with multiple rewards}},
doi = {10.1098/rspb.2015.1041},
volume = {282},
year = {2015},
}