Lattice instabilities in hexagonal NiSi: A NiAs prototype structure

We report a first-principles study of the hexagonal NiSi phase with the B81 strukturbericht designation. This structure, reported by Föll Philos. Mag. A 45, 31 1982, d’Heurle J. Appl. Phys. 55, 4208 1984, and Dai Appl. Phys. Lett. 75, 2214 1999, is actually only observed during annealing of Ni films on 111 silicon crystals. We discuss, in this paper, about its structural, energetic, vibrational, electronic, and elastic properties, computed by means of the density-functional and density-functional perturbative theory within the spinpolarized Perdew-Burke-Ernzerhof functional. Two configurations with this crystallographic structure have been studied, noted h-NiSi and h-SiNi in the following. We show that theoretical and experimental lattice parameters are not compatible for both systems. A large discrepancy 8–10 % is evidenced, much larger than both experimental and simulation accuracies obtained for others Ni-Si systems. Moreover the vibrational spectra of h-NiSi and h-SiNi present both soft modes, indicating that in their ground states these systems are dynamically unstable. Using a band folding approach, we have analyzed modes for h-NiSi on a supercell, permitting us to identify eigenvectors associated to these instabilities. We have then relaxed the cell in accordance to these eigenvectors, and a final structure is thus proposed. To understand the mechanism at the origin of these negative frequencies in h-NiSi, electronic states around the Fermi level have been plotted, and we identify in the Fermi-surface potential nesting vectors, suggesting that an electron-phonon coupling mechanism could be at the origin of the instability. Whereas the ground state of “h-NiSi” seems not to be associated to the B81 system, we show that a stress in the basal plane could induce an increasein the c axis, restoring the agreement with experimental data

First-principles study of nickel-silicides ordered phases

We present a study of nickel-silicides ordered alloys by means of first-principles calculations. Emphasis was put on the phases (low and high temperatures) identified in the binary phase diagram, namely: Ni3Si-β1, -β2, and -β3, Ni31Si12-γ, Ni2Si-δ, -θ, Ni3Si2-ɛ, NiSi-MnP and NiSi2-α. In addition, some common structures are computed for information: L12, D03 and D022. The simulations reproduce with a high accuracy lattice parameters and formation energies of main experimental structures, except for β2 and β3. Our results clarify the crystallographic nature of the γ structure, and the comparison of experimental Raman spectra and vibrational calculations will help experimentalists to identify without ambiguity NiSi3 structures

Stochastic Flips on Two-letter Words

This paper introduces a simple Markov process inspired by the problem of quasicrystal growth. It acts over two-letter words by randomly performing \emph{flips}, a local transformation which exchanges two consecutive different letters. More precisely, only the flips which do not increase the number of pairs of consecutive identical letters are allowed. Fixed-points of such a process thus perfectly alternate different letters. We show that the expected number of flips to converge towards a fixed-point is bounded by $O(n^3)$ in the worst-case and by $O(n^{5/2}\ln{n})$ in the average-case, where $n$ denotes the length of the initial word.Comment: ANALCO'1

Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions

One of the most challenging problems in computational advertising is the prediction of click-through and conversion rates for bidding in online advertising auctions. An unaddressed problem in previous approaches is the existence of highly non-uniform misprediction costs. While for model evaluation these costs have been taken into account through recently proposed business-aware offline metrics -- such as the Utility metric which measures the impact on advertiser profit -- this is not the case when training the models themselves. In this paper, to bridge the gap, we formally analyze the relationship between optimizing the Utility metric and the log loss, which is considered as one of the state-of-the-art approaches in conversion modeling. Our analysis motivates the idea of weighting the log loss with the business value of the predicted outcome. We present and analyze a new cost weighting scheme and show that significant gains in offline and online performance can be achieved.Comment: First version of the paper was presented at NIPS 2015 Workshop on E-Commerce: https://sites.google.com/site/nips15ecommerce/papers Third version of the paper will be presented at AdKDD 2017 Workshop: adkdd17.wixsite.com/adkddtargetad201

Spatio-temporal Learning with Arrays of Analog Nanosynapses

Emerging nanodevices such as resistive memories are being considered for hardware realizations of a variety of artificial neural networks (ANNs), including highly promising online variants of the learning approaches known as reservoir computing (RC) and the extreme learning machine (ELM). We propose an RC/ELM inspired learning system built with nanosynapses that performs both on-chip projection and regression operations. To address time-dynamic tasks, the hidden neurons of our system perform spatio-temporal integration and can be further enhanced with variable sampling or multiple activation windows. We detail the system and show its use in conjunction with a highly analog nanosynapse device on a standard task with intrinsic timing dynamics- the TI-46 battery of spoken digits. The system achieves nearly perfect (99%) accuracy at sufficient hidden layer size, which compares favorably with software results. In addition, the model is extended to a larger dataset, the MNIST database of handwritten digits. By translating the database into the time domain and using variable integration windows, up to 95% classification accuracy is achieved. In addition to an intrinsically low-power programming style, the proposed architecture learns very quickly and can easily be converted into a spiking system with negligible loss in performance- all features that confer significant energy efficiency.Comment: 6 pages, 3 figures. Presented at 2017 IEEE/ACM Symposium on Nanoscale architectures (NANOARCH

GraphStream: A Tool for bridging the gap between Complex Systems and Dynamic Graphs

The notion of complex systems is common to many domains, from Biology to Economy, Computer Science, Physics, etc. Often, these systems are made of sets of entities moving in an evolving environment. One of their major characteristics is the emergence of some global properties stemmed from local interactions between the entities themselves and between the entities and the environment. The structure of these systems as sets of interacting entities leads researchers to model them as graphs. However, their understanding requires most often to consider the dynamics of their evolution. It is indeed not relevant to study some properties out of any temporal consideration. Thus, dynamic graphs seem to be a very suitable model for investigating the emergence and the conservation of some properties. GraphStream is a Java-based library whose main purpose is to help researchers and developers in their daily tasks of dynamic problem modeling and of classical graph management tasks: creation, processing, display, etc. It may also be used, and is indeed already used, for teaching purpose. GraphStream relies on an event-based engine allowing several event sources. Events may be included in the core of the application, read from a file or received from an event handler

Toward Optimal Run Racing: Application to Deep Learning Calibration

This paper aims at one-shot learning of deep neural nets, where a highly parallel setting is considered to address the algorithm calibration problem - selecting the best neural architecture and learning hyper-parameter values depending on the dataset at hand. The notoriously expensive calibration problem is optimally reduced by detecting and early stopping non-optimal runs. The theoretical contribution regards the optimality guarantees within the multiple hypothesis testing framework. Experimentations on the Cifar10, PTB and Wiki benchmarks demonstrate the relevance of the approach with a principled and consistent improvement on the state of the art with no extra hyper-parameter

Comparative study of metallic silicide–germanide orthorhombic MnP systems

We present a comparative study of the structural, energetic, electronic and elastic properties of MX type MnP systems (where X D Si or Ge, and M D Pt, Pd or Ni) using first-principles calculations. The optimized ground state properties of these systems are in excellent agreement with the experimental values. A detailed comparative study of the elastic properties of polycrystalline structures is also presented. We analyze the relationship between the composition and the properties of the systems. Finally, we present the properties of NiSi1 x Gex alloys. We show that these properties depend linearly on the Ge content of the alloy. This work has important consequences for semiconductor devices in which silicides, germanides and alloys thereof are used as contact materials