Recent progress in random metric theory and its applications to conditional risk measures

The purpose of this paper is to give a selective survey on recent progress in random metric theory and its applications to conditional risk measures. This paper includes eight sections. Section 1 is a longer introduction, which gives a brief introduction to random metric theory, risk measures and conditional risk measures. Section 2 gives the central framework in random metric theory, topological structures, important examples, the notions of a random conjugate space and the Hahn-Banach theorems for random linear functionals. Section 3 gives several important representation theorems for random conjugate spaces. Section 4 gives characterizations for a complete random normed module to be random reflexive. Section 5 gives hyperplane separation theorems currently available in random locally convex modules. Section 6 gives the theory of random duality with respect to the locally $L^{0}-$convex topology and in particular a characterization for a locally $L^{0}-$convex module to be $L^{0}-$pre$-$barreled. Section 7 gives some basic results on $L^{0}-$convex analysis together with some applications to conditional risk measures. Finally, Section 8 is devoted to extensions of conditional convex risk measures, which shows that every representable $L^{\infty}-$type of conditional convex risk measure and every continuous $L^{p}-$type of convex conditional risk measure ($1\leq p<+\infty$) can be extended to an $L^{\infty}_{\cal F}({\cal E})-$type of $\sigma_{\epsilon,\lambda}(L^{\infty}_{\cal F}({\cal E}), L^{1}_{\cal F}({\cal E}))-$lower semicontinuous conditional convex risk measure and an $L^{p}_{\cal F}({\cal E})-$type of ${\cal T}_{\epsilon,\lambda}-$continuous conditional convex risk measure ($1\leq p<+\infty$), respectively.Comment: 37 page

Nuclear spin relaxation rates in two-leg spin ladders

Using the transfer-matrix DMRG method, we study the nuclear spin relaxation rate 1/T_1 in the two-leg s=1/2 ladder as function of the inter-chain (J_{\perp}) and intra-chain (J_{|}) couplings. In particular, we separate the q_y=0 and \pi contributions and show that the later contribute significantly to the copper relaxation rate ^{63}(1/T_1) in the experimentally relevant coupling and temperature range. We compare our results to both theoretical predictions and experimental measures on ladder materials.Comment: Few modifications from the previous version 4 pages, 5 figures, accepted for publication in PR

Topological String Defect Formation During the Chiral Phase Transition

We extend and generalize the seminal work of Brandenberger, Huang and Zhang on the formation of strings during chiral phase transitions(berger) and discuss the formation of abelian and non-abelian topological strings during such transitions in the early Universe and in the high energy heavy-ion collisions. Chiral symmetry as well as deconfinement are restored in the core of these defects. Formation of a dense network of string defects is likely to play an important role in the dynamics following the chiral phase transition. We speculate that such a network can give rise to non-azimuthal distribution of transverse energy in heavy-ion collisions.Comment: 10 pages, 4 figures, minor correction

Neural Collaborative Filtering

In recent years, deep neural networks have yielded immense success on speech recognition, computer vision and natural language processing. However, the exploration of deep neural networks on recommender systems has received relatively less scrutiny. In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -- collaborative filtering -- on the basis of implicit feedback. Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics. When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items. By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering. NCF is generic and can express and generalize matrix factorization under its framework. To supercharge NCF modelling with non-linearities, we propose to leverage a multi-layer perceptron to learn the user-item interaction function. Extensive experiments on two real-world datasets show significant improvements of our proposed NCF framework over the state-of-the-art methods. Empirical evidence shows that using deeper layers of neural networks offers better recommendation performance.Comment: 10 pages, 7 figure

Two-Level Systems in Evaporated Amorphous Silicon

In $e$-beam evaporated amorphous silicon ($a$-Si), the densities of two-level systems (TLS), $n_{0}$ and $\overline{P}$, determined from specific heat $C$ and internal friction $Q^{-1}$ measurements, respectively, have been shown to vary by over three orders of magnitude. Here we show that $n_{0}$ and $\overline{P}$ are proportional to each other with a constant of proportionality that is consistent with the measurement time dependence proposed by Black and Halperin and does not require the introduction of additional anomalous TLS. However, $n_{0}$ and $\overline{P}$ depend strongly on the atomic density of the film ($n_{\rm Si}$) which depends on both film thickness and growth temperature suggesting that the $a$-Si structure is heterogeneous with nanovoids or other lower density regions forming in a dense amorphous network. A review of literature data shows that this atomic density dependence is not unique to $a$-Si. These findings suggest that TLS are not intrinsic to an amorphous network but require a heterogeneous structure to form

Relativistic cosmological perturbation scheme on a general background: scalar perturbations for irrotational dust

In standard perturbation approaches and N-body simulations, inhomogeneities are described to evolve on a predefined background cosmology, commonly taken as the homogeneous-isotropic solutions of Einstein's field equations (Friedmann-Lema\^itre-Robertson-Walker (FLRW) cosmologies). In order to make physical sense, this background cosmology must provide a reasonable description of the effective, i.e. spatially averaged, evolution of structure inhomogeneities also in the nonlinear regime. Guided by the insights that (i) the average over an inhomogeneous distribution of matter and geometry is in general not given by a homogeneous solution of general relativity, and that (ii) the class of FLRW cosmologies is not only locally but also globally gravitationally unstable in relevant cases, we here develop a perturbation approach that describes the evolution of inhomogeneities on a general background being defined by the spatially averaged evolution equations. This physical background interacts with the formation of structures. We derive and discuss the resulting perturbation scheme for the matter model `irrotational dust' in the Lagrangian picture, restricting our attention to scalar perturbations.Comment: 18 pages. Matches published version in CQ

Experimental observation of an enhanced anisotropic magnetoresistance in non-local configuration

We compare non-local magnetoresistance measurements in multi-terminal Ni nanostructures with corresponding local experiments. In both configurations, the measured voltages show the characteristic features of anisotropic magnetoresistance (AMR). However, the magnitude of the non-local AMR signal is up to one order of magnitude larger than its local counterpart. Moreover, the non-local AMR increases with increasing degree of non-locality, i.e., with the separation between the region of the main current flow and the voltage measurement region. All experimental observations can be consistently modeled in terms of current spreading in a non-isotropic conductor. Our results show that current spreading can significantly enhance the magnetoresistance signal in non-local experiments