Conductance correlations in a mesoscopic spin glass wire : a numerical Landauer study

In this letter we study the coherent electronic transport through a metallic nanowire with magnetic impurities. The spins of these impurities are considered as frozen to mimic a low temperature spin glass phase. The transport properties of the wire are derived from a numerical Landauer technique which provides the conductance of the wire as a function of the disorder configuration. We show that the correlation of conductance between two spin configurations provides a measure of the correlation between these spin configurations. This correlation corresponds to the mean field overlap in the absence of any spatial order between the spin configurations. Moreover, we find that these conductance correlations are sensitive to the spatial order between the two spin configurations, i.e whether the spin ?ips between them occur in a compact region or not

Universal metallic and insulating properties of one dimensional Anderson Localization : a numerical Landauer study

We present results on the Anderson localization in a quasi one-dimensional metallic wire in the presence of magnetic impurities. We focus within the same numerical analysis on both the universal localized and metallic regimes, and we study the evolution of these universal properties as the strength of the magnetic disorder is varied. For this purpose, we use a numerical Landauer approach, and derive the scattering matrix of the wire from electron's Green's function obtained from a recursive algorithm

First-order logic learning in artificial neural networks

Artificial Neural Networks have previously been applied in neuro-symbolic learning to learn ground logic program rules. However, there are few results of learning relations using neuro-symbolic learning. This paper presents the system PAN, which can learn relations. The inputs to PAN are one or more atoms, representing the conditions of a logic rule, and the output is the conclusion of the rule. The symbolic inputs may include functional terms of arbitrary depth and arity, and the output may include terms constructed from the input functors. Symbolic inputs are encoded as an integer using an invertible encoding function, which is used in reverse to extract the output terms. The main advance of this system is a convention to allow construction of Artificial Neural Networks able to learn rules with the same power of expression as first order definite clauses. The system is tested on three examples and the results are discussed

Boosting gets full Attention for Relational Learning

More often than not in benchmark supervised ML, tabular data is flat, i.e. consists of a single $m \times d$ (rows, columns) file, but cases abound in the real world where observations are described by a set of tables with structural relationships. Neural nets-based deep models are a classical fit to incorporate general topological dependence among description features (pixels, words, etc.), but their suboptimality to tree-based models on tabular data is still well documented. In this paper, we introduce an attention mechanism for structured data that blends well with tree-based models in the training context of (gradient) boosting. Each aggregated model is a tree whose training involves two steps: first, simple tabular models are learned descending tables in a top-down fashion with boosting's class residuals on tables' features. Second, what has been learned progresses back bottom-up via attention and aggregation mechanisms, progressively crafting new features that complete at the end the set of observation features over which a single tree is learned, boosting's iteration clock is incremented and new class residuals are computed. Experiments on simulated and real-world domains display the competitiveness of our method against a state of the art containing both tree-based and neural nets-based models

Effect of relative humidity on the evaporation of a colloidal solution droplet

International audienceBackground The deposition of uniform layers of colloids on a solid surface is a major challenge for several industrial processes such as glass surface treatment and creating optical filters. One strategy involves the deposition of the colloids behind a contact line that recedes due to hydrodynamic reasons and evaporation (drying). The interaction between deposition, evaporation and hydrodynamics is a complex matter. We need to get a better understanding of the mechanisms at the contact line and the role they play in coating an organized deposit [1]

Space-resolved dynamic light scattering within a millimetric drop: from Brownian diffusion to the swelling of hydrogel beads

We present a novel dynamic light scattering setup to probe, with time and space resolution, the microscopic dynamics of soft matter systems confined within millimeter-sized spherical drops. By using an ad-hoc optical layout, we tackle the challenges raised by refraction effects due to the unconventional shape of the samples. We first validate the setup by investigating the dynamics of a suspension of Brownian particles. The dynamics measured at different positions in the drop, and hence different scattering angles, are found to be in excellent agreement with those obtained for the same sample in a conventional light scattering setup. We then demonstrate the setup capabilities by investigating a bead made of a polymer hydrogel undergoing swelling. The gel microscopic dynamics exhibit a space dependence that strongly varies with time elapsed since the beginning of swelling. Initially, the dynamics in the periphery of the bead are much faster than in the core, indicative of non-uniform swelling. As the swelling proceeds, the dynamics slow down and become more spatially homogeneous. By comparing the experimental results to numerical and analytical calculations for the dynamics of a homogeneous, purely elastic sphere undergoing swelling, we establish that the mean square displacement of the gel strands deviates from the affine motion inferred from the macroscopic deformation, evolving from fast diffusive-like dynamics at the onset of swelling to slower, yet supradiffusive, rearrangements at later stages