A multidimensional spatial lag panel data model with spatial moving average nested random effects errors

This paper focuses on a three-dimensional model that combines two different types of spatial interaction effects, i.e. endogenous interaction effects via a spatial lag on the dependent variable and interaction effects among the disturbances via a spatial moving average (SMA) nested random effects errors. A three-stage procedure is proposed to estimate the parameters. In a first stage, the spatial lag panel data model is estimated using an instrumental variable (IV) estimator. In a second stage, a generalized moments (GM) approach is developed to estimate the SMA parameter and the variance components of the disturbance process using IV residuals from the first stage. In a third stage, to purge the equation of the specific structure of the disturbances a Cochrane–Orcutt-type transformation is applied combined with the IV principle. This leads to the GM spatial IV estimator and the regression parameter estimates. Monte Carlo simulations show that our estimators are not very different in terms of root mean square error from those produced by maximum likelihood. The approach is applied to European Union regional employment data for regions nested within countries

Transfer of spectral weight across the gap of Sr2IrO4 induced by La doping

We study with Angle Resolved PhotoElectron Spectroscopy (ARPES) the evolution of the electronic structure of Sr2IrO4, when holes or electrons are introduced, through Rh or La substitutions. At low dopings, the added carriers occupy the first available states, at bottom or top of the gap, revealing an anisotropic gap of 0.7eV in good agreement with STM measurements. At further doping, we observe a reduction of the gap and a transfer of spectral weight across the gap, although the quasiparticle weight remains very small. We discuss the origin of the in-gap spectral weight as a local distribution of gap values

CO2 streams containing associated components—A review of the thermodynamic and geochemical properties and assessment of some reactive transport codes

AbstractModelling of the impact on storage of “ CO2-associated components” has rarely been addressed so far. This review, performed within the European research project CO2ReMoVe, exposes a selection of CO2 streams compositions coming from thermal power plants emissions and those injected in pilot sites part of the CO2ReMoVe project. It highlights the lack of data coming from laboratory experiments to describe properly the physical properties of some relevant gas mixtures. The geochemical impact of only 2 components (SO2 and H2S) is evidenced by some geochemical studies. Concerning the numerical modelling, four reactive transport codes (PHREEQC, SCALE2000, TOUGHREACT and COORES) were assessed. Actual limitations lie mainly in the capacity of calculating the physical properties of the whole set of gases (CO2–O2–SO2–N2–Ar–NOx–H2S–COS–CO–H2–HCl–NH3–CH4–C2H6–H2O). The new data acquired within on-going French projects will complete the knowledge of such complex gas mixtures behaviour

A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays

We introduce the IBM Analog Hardware Acceleration Kit, a new and first of a kind open source toolkit to simulate analog crossbar arrays in a convenient fashion from within PyTorch (freely available at https://github.com/IBM/aihwkit). The toolkit is under active development and is centered around the concept of an "analog tile" which captures the computations performed on a crossbar array. Analog tiles are building blocks that can be used to extend existing network modules with analog components and compose arbitrary artificial neural networks (ANNs) using the flexibility of the PyTorch framework. Analog tiles can be conveniently configured to emulate a plethora of different analog hardware characteristics and their non-idealities, such as device-to-device and cycle-to-cycle variations, resistive device response curves, and weight and output noise. Additionally, the toolkit makes it possible to design custom unit cell configurations and to use advanced analog optimization algorithms such as Tiki-Taka. Moreover, the backward and update behavior can be set to "ideal" to enable hardware-aware training features for chips that target inference acceleration only. To evaluate the inference accuracy of such chips over time, we provide statistical programming noise and drift models calibrated on phase-change memory hardware. Our new toolkit is fully GPU accelerated and can be used to conveniently estimate the impact of material properties and non-idealities of future analog technology on the accuracy for arbitrary ANNs.Comment: Submitted to AICAS202

Effect of grain orientation and magnesium doping on β-tricalcium phosphate resorption behavior

The efficiency of calcium phosphate (CaP) bone substitutes can be improved by tuning their resorption rate. The influence of both crystal orientation and ion doping on resorption is here investigated for beta-tricalcium phosphate (β-TCP). Non-doped and Mg-doped (1 and 6 mol%) sintered β-TCP samples were immersed in acidic solution (pH 4.4) to mimic the environmental conditions found underneath active osteoclasts. The surfaces of β-TCP samples were observed after acid-etching and compared to surfaces after osteoclastic resorption assays. β-TCP grains exhibited similar patterns with characteristic intra-crystalline pillars after acid-etching and after cell-mediated resorption. Electron BackScatter Diffraction analyses, coupled with Scanning Electron Microscopy, Inductively Coupled Plasma–Mass Spectrometry and X-Ray Diffraction, demonstrated the influence of both grain orientation and doping on the process and kinetics of resorption. Grains with c-axis nearly perpendicular to the surface were preferentially etched in non-doped β-TCP samples, whereas all grains with simple axis (a, b or c) nearly normal to the surface were etched in 6 mol% Mg-doped samples. In addition, both the dissolution rate and the percentage of etched surface were lower in Mg-doped specimens. Finally, the alignment direction of the intra-crystalline pillars was correlated with the preferential direction for dissolution. Statement of significance: The present work focuses on the resorption behavior of calcium phosphate bioceramics. A simple and cost-effective alternative to osteoclast culture was implemented to identify which material features drive resorption. For the first time, it was demonstrated that crystal orientation, measured by Electron Backscatter Diffraction, is the discriminating factor between grains, which resorbed first, and grains, which resorbed slower. It also elucidated how resorption kinetics can be tuned by doping β-tricalcium phosphate with ions of interest. Doping with magnesium impacted lattice parameters. Therefore, the crystal orientations, which preferentially resorbed, changed, explaining the solubility decrease. These important findings pave the way for the design of optimized bone graft substitutes with tailored resorption kinetics

Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference

Analog In-Memory Computing (AIMC) is a promising approach to reduce the latency and energy consumption of Deep Neural Network (DNN) inference and training. However, the noisy and non-linear device characteristics, and the non-ideal peripheral circuitry in AIMC chips, require adapting DNNs to be deployed on such hardware to achieve equivalent accuracy to digital computing. In this tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github.com/IBM/aihwkit. The AIHWKit is a Python library that simulates inference and training of DNNs using AIMC. We present an in-depth description of the AIHWKit design, functionality, and best practices to properly perform inference and training. We also present an overview of the Analog AI Cloud Composer, that provides the benefits of using the AIHWKit simulation platform in a fully managed cloud setting. Finally, we show examples on how users can expand and customize AIHWKit for their own needs. This tutorial is accompanied by comprehensive Jupyter Notebook code examples that can be run using AIHWKit, which can be downloaded from https://github.com/IBM/aihwkit/tree/master/notebooks/tutorial

Effect of Geological Heterogeneities on Reservoir Storage Capacity and migration of CO 2 Plume in a Deep Saline Fractured Carbonate Aquifer

In a reservoir characterization study of the Hontomín deep saline aquifer, the impact of geological heterogeneities on reservoir storage capacity and the migration of the CO2 plume is explored. This work presents, for the first time, very long-term (up to 200 years) simulations of CO2 injection into the naturally fractured Sopeña Formation, of the lower Jurassic age, at Hontomín. CO2 injection was simulated as a dual permeability case with Eclipse compositional software. The matrix permeability of the carbonate reservoir is quite low (0.5 mD) and thus fluid flow through the fractures dominates. The reservoir is dissected by eight normal faults which limited its southeast extension and divided it into several segments. The effect of geological heterogeneities was tested through scenario-based modeling and variation of parameters characterizing heterogeneity within realistic limits based on other similar formations. This modeling approach worked well in Hontomín where the database is completely scarce. The plume migration, the reservoir storage capacity, and pressure, were each influenced in diverse ways by incorporating particular types of heterogeneities. The effect of matrix heterogeneities on reservoir storage capacity was substantial (by factors up to ~2.8×), compared to the plume migration. As the reservoir matrix permeability heterogeneity increased, the reservoir storage capacity markedly decreased, whilst an increase in porosity heterogeneity significantly increased it. The vertical gas migration in the homogeneous base case was relatively larger compared to the heterogeneous cases, and gas accumulated underneath the caprock via hydrodynamic trapping. It was also observed that, in heterogeneous cases, gas saturation in rock layers from top to bottom was relatively high compared to the base case, for which most of the gas was stored in the topmost layer. In contrast, the impact on storage capacity and plume movement of matrix vertical to horizontal permeability ratio in the fractured carbonate reservoir was small. The impact of the transmissibility of faults on reservoir pressure was only observed when the CO2 plume reached their vicinity

Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators

Analog in-memory computing (AIMC) -- a promising approach for energy-efficient acceleration of deep learning workloads -- computes matrix-vector multiplications (MVMs) but only approximately, due to nonidealities that often are non-deterministic or nonlinear. This can adversely impact the achievable deep neural network (DNN) inference accuracy as compared to a conventional floating point (FP) implementation. While retraining has previously been suggested to improve robustness, prior work has explored only a few DNN topologies, using disparate and overly simplified AIMC hardware models. Here, we use hardware-aware (HWA) training to systematically examine the accuracy of AIMC for multiple common artificial intelligence (AI) workloads across multiple DNN topologies, and investigate sensitivity and robustness to a broad set of nonidealities. By introducing a new and highly realistic AIMC crossbar-model, we improve significantly on earlier retraining approaches. We show that many large-scale DNNs of various topologies, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, can in fact be successfully retrained to show iso-accuracy on AIMC. Our results further suggest that AIMC nonidealities that add noise to the inputs or outputs, not the weights, have the largest impact on DNN accuracy, and that RNNs are particularly robust to all nonidealities.Comment: 35 pages, 7 figures, 5 table

Regional age structure, human capital and innovation - is demographic ageing increasing regional disparities?

Demographic change is expected to affect labour markets in very different ways on a regional scale. The objective of this paper is to explore the spatio-temporal patterns of recent distributional changes in the workers age structure, innovation output and skill composition for German regions by conducting an Exploratory Space-Time Data Analysis (ESTDA). Beside commonly used tools, we apply newly developed approaches which allow investigating the space-time dynamics of the spatial distributions. We include an analysis of the joint distributional dynamics of the patenting variable with the remaining interest variables. Overall, we find strong clustering tendencies for the demographic variables and innovation that constitute a great divide across German regions. The detected clusters partly evolve over time and suggest a demographic polarization trend among regions that may further reinforce the observed innovation divide in the future

Classic and spatial shift-share analysis of state-level employment change in Brazil

This paper combines classic and spatial shift-share decompositions of 1981 to 2006 employment change across the 27 states of Brazil. The classic shift-share method shows higher employment growth rates for underdeveloped regions that are due to an advantageous industry-mix and also due to additional job creation, commonly referred to as the competitive effect. Alternative decompositions proposed in the literature do not change this broad conclusion. Further examination employing exploratory spatial data analysis (ESDA) shows spatial correlation of both the industry-mix and the competitive effects. Considering that until the 1960s economic activities were more concentrated in southern regions of Brazil than they are nowadays, these results support beta convergence theories but also find evidence of agglomeration effects. Additionally, a very simple spatial decomposition is proposed that accounts for the spatially-weighted growth of surrounding states. Favourable growth in northern and centre-western states is basically associated with those states’ strengths in potential spatial spillover effect and in spatial competitive effect