The Sparse Abstract Machine

We propose the Sparse Abstract Machine (SAM), an abstract machine model for targeting sparse tensor algebra to reconfigurable and fixed-function spatial dataflow accelerators. SAM defines a streaming dataflow abstraction with sparse primitives that encompass a large space of scheduled tensor algebra expressions. SAM dataflow graphs naturally separate tensor formats from algorithms and are expressive enough to incorporate arbitrary iteration orderings and many hardware-specific optimizations. We also present Custard, a compiler from a high-level language to SAM that demonstrates SAM's usefulness as an intermediate representation. We automatically bind from SAM to a streaming dataflow simulator. We evaluate the generality and extensibility of SAM, explore the performance space of sparse tensor algebra optimizations using SAM, and show SAM's ability to represent dataflow hardware.Comment: 18 pages, 17 figures, 3 table

The effects of high versus low talker variability and individual aptitude on phonetic training of Mandarin lexical tones

High variability (HV) training has been found to be more effective than low variability (LV) training when learning various non-native phonetic contrasts. However, little research has considered whether this applies to the learning of tone contrasts. The only two relevant studies suggested that the effect of HV training depends on the perceptual aptitude of participants (Perrachione et al., 2011; Sadakata & McQueen, 2014). The present study extends these findings by examining the interaction between individual aptitude and input variability using natural, meaningful second language input (both previous studies used pseudowords). A total of 60 English speakers took part in an eight session phonetic training paradigm. They were assigned to high/low/high-blocked variability training groups and learned real Mandarin tones and words. Individual aptitude was measured following previous work. Learning was measured using one discrimination task, one identification task and two production tasks. All tasks assessed generalization. All groups improved in both the production and perception of tones which transferred to untrained voices and items, demonstrating the effectiveness of training despite the increased complexity compared with previous research. Although the LV group exhibited an advantage with the training stimuli, there was no evidence for a benefit of high-variability in any of the tests of generalisation. Moreover, although aptitude significantly predicted performance in discrimination, identification and training tasks, no interaction between individual aptitude and variability was revealed. Additional Bayes Factor analyses indicated substantial evidence for the null for the hypotheses of a benefit of high-variability in generalisation, however the evidence regarding the interaction was ambiguous. We discuss these results in light of previous findings

Automating System Configuration

The increasing complexity of modern configurable systems makes it critical to improve the level of automation in the process of system configuration. Such automation can also improve the agility of the development cycle, allowing for rapid and automated integration of decoupled workflows. In this paper, we present a new framework for automated configuration of systems representable as state machines. The framework leverages model checking and satisfiability modulo theories (SMT) and can be applied to any application domain representable using SMT formulas. Our approach can also be applied modularly, improving its scalability. Furthermore, we show how optimization can be used to produce configurations that are best according to some metric and also more likely to be understandable to humans. We showcase this framework and its flexibility by using it to configure a CGRA memory tile for various image processing applications.2103111

Fundamental Insight into Humid CO2 Uptake in Direct Air Capture Nanocomposites Using Fluorescence and Portable NMR Relaxometry

Direct air capture (DAC) technology is being explored as a pathway for reducing greenhouse gas emissions through the efficient removal of CO2 from the atmosphere. However, there remains a knowledge gap regarding structure-property-performance factors that impact the behavior of these systems in diverse, real-world environments. In aminopolymer-based DAC systems, gas diffusion is tightly coupled with polymer mobility, which is in turn affected by a large matrix of variables, including interactions with the pore wall of the support, nanoconfinement, the presence of co-adsorbates (moisture), and electrostatic crosslinks that develop as a function of CO2 chemisorption. Higher throughput, benchtop techniques for studying and understanding mobility in these systems would lead to more rapid advances in the field. Here, we demonstrate the value of a fluorescence technique for monitoring polymer mobility within nanocomposite capture materials as a function of CO2 and water adsorption in a series of humidified polyethylenimine-Al2O3 composite materials. The approach allows us to correlate changes in mobility with CO2 adsorption kinetics as a function of relative humidity. We further couple this information with NMR relaxometry data attained using a portable single-sided magnetic resonance device, and we employ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) to correlate the formation of different relative amounts of carbamates and carbonates with the environmental conditions. These results provide a blueprint for using benchtop techniques to promote fundamental understanding in DAC systems that can in turn enable more efficient operation in real-world conditions

Sampling density and date along with species selection influence spatial representation of tree-ring reconstructions

Our understanding of the natural variability of hydroclimate before the instrumental period (ca. 1900 CE in the United States) is largely dependent on tree-ring-based reconstructions. Large-scale soil moisture reconstructions from a network of tree-ring chronologies have greatly improved our understanding of the spatial and temporal variability in hydroclimate conditions, particularly extremes of both drought and pluvial (wet) events. However, certain regions within these large-scale network reconstructions in the US are modeled by few tree-ring chronologies. Further, many of the chronologies currently publicly available on the International Tree-Ring Data Bank (ITRDB) were collected in the 1980s and 1990s, and thus our understanding of the sensitivity of radial growth to soil moisture in the US is based on a period that experienced multiple extremely severe droughts and neglects the impacts of recent, rapid global change. In this study, we expanded the tree-ring network of the Ohio River valley in the US, a region with sparse coverage. We used a total of 72 chronologies across 15 species to examine how increasing the density of the tree-ring network influences the representation of reconstructing the Palmer Meteorological Drought Index (PMDI). Further, we tested how the sampling date and therefore the calibration period influenced the reconstruction models by creating reconstructions that ended in the year 1980 and compared them to reconstructions ending in 2010 from the same chronologies. We found that in- creasing the density of the tree-ring network resulted in reconstructed values that better matched the spatial variability of instrumentally recorded droughts and, to a lesser extent, pluvials. By extending the calibration period to 2010 compared to 1980, the sensitivity of tree rings to PMDI decreased in the southern portion of our region where severe drought conditions have been absent over recent decades. We emphasize the need of building a high-density tree-ring network to better represent the spatial variability of past droughts and pluvials. Further, chronologies on the ITRDB need updating regularly to better understand how the sensitivity of tree rings to climate may vary through time.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Sampling Density and Date Along With Species Selection Influence Spatial Representation of Tree-Ring Reconstructions

© 2020 Royal Society of Chemistry. All rights reserved. Our understanding of the natural variability of hydroclimate before the instrumental period (ca. 1900 CE in the United States) is largely dependent on tree-ring-based reconstructions. Large-scale soil moisture reconstructions from a network of tree-ring chronologies have greatly improved our understanding of the spatial and temporal variability in hydroclimate conditions, particularly extremes of both drought and pluvial (wet) events. However, certain regions within these large-scale network reconstructions in the US are modeled by few tree-ring chronologies. Further, many of the chronologies currently publicly available on the International Tree-Ring Data Bank (ITRDB) were collected in the 1980s and 1990s, and thus our understanding of the sensitivity of radial growth to soil moisture in the US is based on a period that experienced multiple extremely severe droughts and neglects the impacts of recent, rapid global change. In this study, we expanded the tree-ring network of the Ohio River valley in the US, a region with sparse coverage. We used a total of 72 chronologies across 15 species to examine how increasing the density of the tree-ring network influences the representation of reconstructing the Palmer Meteorological Drought Index (PMDI). Further, we tested how the sampling date and therefore the calibration period influenced the reconstruction models by creating reconstructions that ended in the year 1980 and compared them to reconstructions ending in 2010 from the same chronologies. We found that increasing the density of the tree-ring network resulted in reconstructed values that better matched the spatial variability of instrumentally recorded droughts and, to a lesser extent, pluvials. By extending the calibration period to 2010 compared to 1980, the sensitivity of tree rings to PMDI decreased in the southern portion of our region where severe drought conditions have been absent over recent decades.We emphasize the need of building a high-density tree-ring network to better represent the spatial variability of past droughts and pluvials. Further, chronologies on the ITRDB need updating regularly to better understand how the sensitivity of tree rings to climate may vary through time