Enhanced low-energy spin dynamics with diffusive character in the iron-based superconductor (La0.87Ca0.13)FePO: Analogy with high Tc cuprates (A short note)

In a recent NMR investigation of the iron-based superconductor (La0.87Ca0.13)FePO [Phys. Rev. Lett. 101, 077006 (2008)] Y. Nakai et al. reported an anomalous behavior of the nuclear spin-lattice relaxation of 31P nuclei in the superconducting state: The relaxation rate 1/T1 strongly depends on the measurement frequency and its T dependence does not show the typical decrease expected for the superconducting state. In this short note, we point out that these two observations bear similarity with the situation is some of the high Tc cuprates.Comment: To appear in J. Phys. Soc. Jpn. (Short Note

Building Machines That Learn and Think Like People

Recent progress in artificial intelligence (AI) has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn, and how they learn it. Specifically, we argue that these machines should (a) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (b) ground learning in intuitive theories of physics and psychology, to support and enrich the knowledge that is learned; and (c) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes towards these goals that can combine the strengths of recent neural network advances with more structured cognitive models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary proposals (until Nov. 22, 2016). https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar

Field Induced Magnetic Ordering and Single-ion Anisotropy in the Quasi-1D Haldane Chain Compound SrNi2V2O8: A Single Crystal investigation

Field-induced magnetic ordering in the Haldane chain compound SrNi$_{2}$V$_{2}$O$_{8}$ and effect of anisotropy have been investigated using single crystals. Static susceptibility, inelastic neutron scattering, high-field magnetization, and low temperature heat-capacity studies confirm a non-magnetic spin-singlet ground state and a gap between the singlet ground state and triplet excited states. The intra-chain exchange interaction is estimated to be $J \sim 8.9{\pm}$0.1 meV. Splitting of the dispersions into two modes with minimum energies 1.57 and 2.58 meV confirms the existence of single-ion anisotropy $D(S^z){^2}$. The value of {\it D} is estimated to be $-0.51{\pm}0.01$ meV and the easy axis is found to be along the crystallographic {\it c}-axis. Field-induced magnetic ordering has been found with two critical fields [$\mu_0H_c^{\perp c} = 12.0{\pm}$0.2 T and $\mu_0H_c^{\parallel c} = 20.8{\pm}$0.5 T at 4.2 K]. Field-induced three-dimensional magnetic ordering above the critical fields is evident from the heat-capacity, susceptibility, and high-field magnetization study. The Phase diagram in the {\it H-T} plane has been obtained from the high-field magnetization. The observed results are discussed in the light of theoretical predictions as well as earlier experimental reports on Haldane chain compounds

Discovering Structure by Learning Sparse Graphs

Systems of concepts such as colors, animals, cities, and artifacts are richly structured, and people discover the structure of these domains throughout a lifetime of experience. Discovering structure can be formalized as probabilistic inference about the organization of entities, and previous work has operationalized learning as selection amongst specific candidate hypotheses such as rings, trees, chains, grids, etc. defined by graph grammars (Kemp & Tenenbaum, 2008). While this model makes discrete choices from a limited set, humans appear to entertain an unlimited range of hypotheses, many without an obvious grammatical description. In this paper, we approach structure discovery as optimization in a continuous space of all possible structures, while encouraging structures to be sparsely connected. When reasoning about animals and cities, the sparse model achieves performance equivalent to more structured approaches. We also explore a large domain of 1000 concepts with broad semantic coverage and no simple structure

Gravitational Collapse of Dust with a Cosmological Constant

The recent analysis of Markovic and Shapiro on the effect of a cosmological constant on the evolution of a spherically symmetric homogeneous dust ball is extended to include the inhomogeneous and degenerate cases. The histories are shown by way of effective potential and Penrose-Carter diagrams.Comment: 2 pages, 2 figures (png), revtex. To appear in Phys. Rev.

ReLEx: Regularisation for Linear Extrapolation in Neural Networks with Rectified Linear Units

Despite the great success of neural networks in recent years, they are not providing useful extrapolation. In regression tasks, the popular Rectified Linear Units do enable unbounded linear extrapolation by neural networks, but their extrapolation behaviour varies widely and is largely independent of the training data. Our goal is instead to continue the local linear trend at the margin of the training data. Here we introduce ReLEx, a regularising method composed of a set of loss terms design to achieve this goal and reduce the variance of the extrapolation. We present a ReLEx implementation for single input, single output, and single hidden layer feed-forward networks. Our results demonstrate that ReLEx has little cost in terms of standard learning, i.e. interpolation, but enables controlled univariate linear extrapolation with ReLU neural networks

Acceleration of particles by rotating black holes: near-horizon geometry and kinematics

Nowadays, the effect of infinite energy in the centre of mass frame due to near-horizon collisions attracts much attention.We show generality of the effect combining two seemingly completely different approaches based on properties of a particle with respect to its local light cone and calculating its velocity in the locally nonrotaing frame directly. In doing so, we do not assume that particles move along geodesics. Usually, a particle reaches a horizon having the velocity equals that of light. However, there is also case of "critical" particles for which this is not so. It is just the pair of usual and critical particles that leads to the effect under discussion. The similar analysis is carried out for massless particles. Then, critical particles are distinguishable due to the finiteness of local frequency. Thus, both approach based on geometrical and kinematic properties of particles moving near the horizon, reveal the universal character of the effect.Comment: 8 page

Resolving the Structure of Cold Dark Matter Halos

We examine the effects of mass resolution and force softening on the density profiles of cold dark matter halos that form within cosmological N-body simulations. As we increase the mass and force resolution, we resolve progenitor halos that collapse at higher redshifts and have very high densities. At our highest resolution we have nearly 3 million particles within the virial radius, several orders of magnitude more than previously used and we can resolve more than one thousand surviving dark matter halos within this single virialised system. The halo profiles become steeper in the central regions and we may not have achieved convergence to a unique slope within the inner 10% of the virialised region. Results from two very high resolution halo simulations yield steep inner density profiles, $\rho(r)\sim r^{-1.4}$. The abundance and properties of arcs formed within this potential will be different from calculations based on lower resolution simulations. The kinematics of disks within such a steep potential may prove problematic for the CDM model when compared with the observed properties of halos on galactic scales.Comment: Final version, to be published in the ApJLetter