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At least 253 records · Page 14

Structure‐Aware Representation Learning for Effective Performance Prediction

ABSTRACT Application performance is a function of several unknowns stemming from the interactions between the application, runtime, OS, and underlying hardware, making it challenging to model performance using deep learning techniques, especially without a large labeled dataset. Collecting such labeled longitudinal datasets can take weeks. Intuitively, developers could save analysis time during code development by taking a comparative approach between multiple applications. However, the unknown dynamic interactions between applications and execution environments make it difficult for deep learning‐based models to predict the performance of new applications. In this paper, we address these problems by presenting a labeled dataset for the community and taking a comparative analysis approach to explore the source code differences between different correct implementations of the same problem. This paper assesses the feasibility of using purely static information, for example, Abstract Syntax Tree (AST), of applications to predict performance change based on code structure. We evaluate several deep learning‐based representation learning techniques for source code and propose an architecture for the tree‐based Long Short‐Term Memory (LSTM) models to discover latent representations for a source code's hierarchical structure. We demonstrate that our proposed architecture enables feed‐forward predictive models to predict change in performance using source code with up to 84% accuracy.

Ramadan, Tarek [Department of Computer Science Tex↗

Li iontronics in single-crystalline T-Nb2O5 thin films with vertical ionic transport channels

Abstract The niobium oxide polymorph T -Nb 2 O 5 has been extensively investigated in its bulk form especially for applications in fast-charging batteries and electrochemical (pseudo)capacitors. Its crystal structure, which has two-dimensional (2D) layers with very low steric hindrance, allows for fast Li-ion migration. However, since its discovery in 1941, the growth of single-crystalline thin films and its electronic applications have not yet been realized, probably due to its large orthorhombic unit cell along with the existence of many polymorphs. Here we demonstrate the epitaxial growth of single-crystalline T -Nb 2 O 5 thin films, critically with the ionic transport channels oriented perpendicular to the film’s surface. These vertical 2D channels enable fast Li-ion migration, which we show gives rise to a colossal insulator–metal transition, where the resistivity drops by 11 orders of magnitude due to the population of the initially empty Nb 4 d 0 states by electrons. Moreover, we reveal multiple unexplored phase transitions with distinct crystal and electronic structures over a wide range of Li-ion concentrations by comprehensive in situ experiments and theoretical calculations, which allow for the reversible and repeatable manipulation of these phases and their distinct electronic properties. This work paves the way for the exploration of novel thin films with ionic channels and their potential applications.

Chemistry↗

Initial Process Planning of a Hybrid Multi-Tasking Platform

Abstract Applications of hybrid technology are expanding from refurbishment and repair to low quantity, specialty part production, which are staple characteristics in medical implant, energy, and aerospace industry sectors, among others. This expansion has led to the development of the Mazak VC-500A/5X AM HWD, a wire fed laser cladding unit equipped with a standard 5 axis CNC. This unit is capable of building near net geometry of complex medium to large parts within a profitable timeframe, due to its comparatively high rate of deposition to that of a powder fed hybrid system. In this study, deposition and machining capabilities of the VC-500A/5X AM HWD are assessed through the production of three different test geometries by different process plans. Production of these test geometries is supported by an open loop sensor package primarily for monitoring machine health, data collection, and machine operator aid. The viability of extended deposition followed by machining is evaluated against a more cyclical strategy of reoccurring deposition and machining operations. Lastly, common defects in as-built geometries are evaluated and addressed through revisions to original process plans and toolpaths, indicating the need for continued innovation in hybrid manufacturing specific CAM/CAD software, as well as closed loop machine monitoring and quality control.

DeWitte, Lisa N.↗

Impact of biochar amendments on soil water and plant uptake dynamics under different cropping systems

Abstract Application of biochar amendments in agricultural systems has received much attention in recent years. In this study, we assess the 5‐year impacts of biochar application on soil water and plant interactions for an irrigated fresh market tomato ( Solanum lycopersicum ) and a rainfed pasture ( Poaceae ) cropping system. In particular, we focus on three varieties of locally produced biochar from agricultural waste materials—almond shell, walnut shell, and almond pruning residues that are pyrolyzed using a mobile pyrolysis unit. We used the soil hydrological model HYDRUS‐1D to explicitly track seasonal and annual soil water fluxes through changes in water retention, drainage, evaporation, and plant water uptake under biochar application. Modeling results show that the application of biochar at 5% increased soil water availability within the top 20 cm for a rainfed system, irrespective of biochar amendment type. This is clearly indicative of higher plant water uptake and greater water use efficiency (WUE) under biochar application. In contrast, a similar biochar amendment for the irrigated system did not affect WUE, instead reducing seasonal soil evaporation loss and thereby reducing irrigation demand. In both cropping systems, year‐to‐year variability in precipitation significantly impacted the total amount of water saved under biochar application with certain amendments retaining more water than others. Given that biochar application increased water retention irrespective of cropping systems, we further used a simple approach to determine yield trade‐off, if any, between control and biochar treatments. Our economic balance clearly demonstrates that the water saved by amending soil with biochar does not offset the yield disparity if compensated with carbon credits and therefore, application of biochar should be actively considered for both its direct and indirect benefits to potential greenhouse gas mitigation (e.g., diverting orchard waste from open burning), water savings, and soil health.

54 ENVIRONMENTAL SCIENCES↗

Automated Symbolic Upscaling: 2. Model Generation for Extended Applicability Regimes

Abstract In this second part of the two paper series, we detail an algorithmic procedure for systematically implementing the generalized closure form strategy presented in Part 1. This strategy extends the applicability of homogenized models with respect to classical homogenization theory, as demonstrated in Part 1 where upscaled models are rigorously derived in moderately reactive physical regimes. After encoding the algorithm into Symbolica, an automated upscaling framework, we upscale two reactive mass transport problems and numerically validate the resulting nonlinear homogenized models by showing the absolute error estimates predicted by homogenization theory are satisfied. In both problems, nontrivial closure forms and closure problems are automatically formulated using the encoded strategy with no human interaction, nor prior knowledge regarding the closure required for the systems. We hope these demonstrations spark further interest in automated analytical frameworks for multiscale modeling, as such capabilities are invaluable for generating rigorous multiscale models of complex phenomena in porous media.

Pietrzyk, Kyle↗

Automated Symbolic Upscaling: 1. Model Generation for Extended Applicability Regimes

Abstract In porous media theory, upscaling techniques are fundamental to deriving rigorous Darcy‐scale models for flow and reactive transport in subsurface systems. Due to limitations in classical techniques, a number of ad hoc approaches have been proposed to address physical regimes in which reactive time scales are similar to, or faster than, diffusive time scales. In Part 1 of this two part series, we present a strategy for expanding the applicability of classical homogenization theory by generalizing the assumed closure form. We detail the implementation of this strategy on two reactive mass transport problems with moderately reactive physics. The strategy produces nontrivial homogenized models with emergent terms and effective parameters that couple reactive, diffusive, and advective transport. The differences in equation forms between the macroscopic and pore‐scale descriptions advise caution to further studies where the forms of macroscopic equations are assumed, as opposed to rigorously derived. Numerical validation is provided for each problem to show that the error estimates of homogenization theory are satisfied, and to justify the implemented strategy. In Part 2, the presented strategy is automated using symbolic computing to expedite its implementation.

Pietrzyk, Kyle↗

Hybrid Oscillator-Qubit Quantum Processors: Instruction Set Architectures, Abstract Machine Models, and Applications

This tutorial offers a pedagogical guide to hybrid quantum processors that integrate discrete-variable (DV) qubits and continuous-variable (CV) oscillators. Aimed at computer scientists, engineers, and physicists, it provides an overview of the experimental, algorithmic, and architectural aspects of this novel and rapidly developing hardware model. Experimental realizations of this model include superconducting, trapped-ion, and neutral-atom platforms. By combining DV and CV components, hybrid oscillator-qubit processors enable a powerful new paradigm that offers complementary strengths for quantum control, error correction, computation, and simulation. Working toward the goal of a full-stack system connecting applications to CV-DV hardware, we define and formulate abstract machine models and instruction set architectures. These essential abstractions enable codesign of hardware and software, and resource estimation for exploring the potential of current and future hardware for computational and simulation tasks. Using these abstractions, we present both new and existing examples that illustrate the benefits of hybrid CV-DV processors relative to traditional DV-only hardware in computation as well as quantum simulation of physical models. Examples include algorithms for transferring states between DV and CV systems, performing the quantum Fourier transform, and simulation of lattice gauge theories. Relative to qubit-only hardware, the bosonic degrees of freedom natively available in hybrid architectures can substantially reduce the circuit complexity of simulations for physical models containing bosons. A key technique is the extension of quantum signal processing ideas to CV-DV systems. This work is intended to serve as a timely and comprehensive guide to this relatively unexplored yet promising approach to quantum computation and to provide a road map to guide future development.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Density functional theory of material design: fundamentals and applications—II

Abstract This is the second and the final part of the review on density functional theory (DFT), referred to as DFT-II. In the first review, DFT-I, we have discussed wavefunction-based methods, their complexity, and basics of density functional theory. In DFT-II, we focus on fundamentals of DFT and their implications for the betterment of the theory. We start our presentation with the exact DFT results followed by the concept of exchange-correlation (xc) or Fermi-Coulomb hole and its relationship with xc energy functional. We also provide the exact conditions for the xc-hole, xc-energy and xc-potential along with their physical interpretation. Next, we describe the extension of DFT for non-integer number of electrons, the piecewise linearity of total energy and discontinuity of chemical potential at integer particle numbers, and derivative discontinuity of the xc potential, which has consequences on fundamental gap of solids. After that, we present how one obtains more accurate xc energy functionals by going beyond the LDA. We discuss the gradient expansion approximation (GEA), generalized gradient approximation (GGA), and hybrid functional approaches to designing better xc energy functionals that give accurate total energies. However, these functionals fail to predict properties like the ionization potential and the band gap. Thus, we next describe different methods of modelling these potentials and results of their application for calculation of the band gaps of different solids to highlight accuracy of different xc potentials. Finally, we conclude with a glimpse on orbital-free density functional theory and the machine learning approach.

36 MATERIALS SCIENCE↗