8,648 research outputs found
Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction
We study property prediction for crystal materials. A crystal structure
consists of a minimal unit cell that is repeated infinitely in 3D space. How to
accurately represent such repetitive structures in machine learning models
remains unresolved. Current methods construct graphs by establishing edges only
between nearby nodes, thereby failing to faithfully capture infinite repeating
patterns and distant interatomic interactions. In this work, we propose several
innovations to overcome these limitations. First, we propose to model
physics-principled interatomic potentials directly instead of only using
distances as in many existing methods. These potentials include the Coulomb
potential, London dispersion potential, and Pauli repulsion potential. Second,
we model the complete set of potentials among all atoms, instead of only
between nearby atoms as in existing methods. This is enabled by our
approximations of infinite potential summations with provable error bounds. We
further develop efficient algorithms to compute the approximations. Finally, we
propose to incorporate our computations of complete interatomic potentials into
message passing neural networks for representation learning. We perform
experiments on the JARVIS and Materials Project benchmarks for evaluation.
Results show that the use of interatomic potentials and complete interatomic
potentials leads to consistent performance improvements with reasonable
computational costs. Our code is publicly available as part of the AIRS library
(https://github.com/divelab/AIRS)
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
Meso-scale FDM material layout design strategies under manufacturability constraints and fracture conditions
In the manufacturability-driven design (MDD) perspective, manufacturability of the product or system is the most important of the design requirements. In addition to being able to ensure that complex designs (e.g., topology optimization) are manufacturable with a given process or process family, MDD also helps mechanical designers to take advantage of unique process-material effects generated during manufacturing. One of the most recognizable examples of this comes from the scanning-type family of additive manufacturing (AM) processes; the most notable and familiar member of this family is the fused deposition modeling (FDM) or fused filament fabrication (FFF) process. This process works by selectively depositing uniform, approximately isotropic beads or elements of molten thermoplastic material (typically structural engineering plastics) in a series of pre-specified traces to build each layer of the part. There are many interesting 2-D and 3-D mechanical design problems that can be explored by designing the layout of these elements. The resulting structured, hierarchical material (which is both manufacturable and customized layer-by-layer within the limits of the process and material) can be defined as a manufacturing process-driven structured material (MPDSM). This dissertation explores several practical methods for designing these element layouts for 2-D and 3-D meso-scale mechanical problems, focusing ultimately on design-for-fracture. Three different fracture conditions are explored: (1) cases where a crack must be prevented or stopped, (2) cases where the crack must be encouraged or accelerated, and (3) cases where cracks must grow in a simple pre-determined pattern. Several new design tools, including a mapping method for the FDM manufacturability constraints, three major literature reviews, the collection, organization, and analysis of several large (qualitative and quantitative) multi-scale datasets on the fracture behavior of FDM-processed materials, some new experimental equipment, and the refinement of a fast and simple g-code generator based on commercially-available software, were developed and refined to support the design of MPDSMs under fracture conditions. The refined design method and rules were experimentally validated using a series of case studies (involving both design and physical testing of the designs) at the end of the dissertation. Finally, a simple design guide for practicing engineers who are not experts in advanced solid mechanics nor process-tailored materials was developed from the results of this project.U of I OnlyAuthor's request
Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse
This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses.
This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups.
In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in usersā speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018ā6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena
Neural Architecture Search: Insights from 1000 Papers
In the past decade, advances in deep learning have resulted in breakthroughs
in a variety of areas, including computer vision, natural language
understanding, speech recognition, and reinforcement learning. Specialized,
high-performing neural architectures are crucial to the success of deep
learning in these areas. Neural architecture search (NAS), the process of
automating the design of neural architectures for a given task, is an
inevitable next step in automating machine learning and has already outpaced
the best human-designed architectures on many tasks. In the past few years,
research in NAS has been progressing rapidly, with over 1000 papers released
since 2020 (Deng and Lindauer, 2021). In this survey, we provide an organized
and comprehensive guide to neural architecture search. We give a taxonomy of
search spaces, algorithms, and speedup techniques, and we discuss resources
such as benchmarks, best practices, other surveys, and open-source libraries
Nonparametric Two-Sample Test for Networks Using Joint Graphon Estimation
This paper focuses on the comparison of networks on the basis of statistical
inference. For that purpose, we rely on smooth graphon models as a
nonparametric modeling strategy that is able to capture complex structural
patterns. The graphon itself can be viewed more broadly as density or intensity
function on networks, making the model a natural choice for comparison
purposes. Extending graphon estimation towards modeling multiple networks
simultaneously consequently provides substantial information about the
(dis-)similarity between networks. Fitting such a joint model - which can be
accomplished by applying an EM-type algorithm - provides a joint graphon
estimate plus a corresponding prediction of the node positions for each
network. In particular, it entails a generalized network alignment, where
nearby nodes play similar structural roles in their respective domains. Given
that, we construct a chi-squared test on equivalence of network structures.
Simulation studies and real-world examples support the applicability of our
network comparison strategy.Comment: 25 pages, 6 figure
Co-occurrence of macroplastics, microplastics, and legacy and emerging plasticisers in UK soils
Despite a theoretical link between plastic and plasticiser occurrence in the terrestrial environment, there are few empirical studies of the relationship between these contaminants in soils. We carried out a field study to assess the cooccurrence of plastic waste, and legacy and emerging plasticisers in UK soils (n = 19) from various land uses (woodlands, urban roadsides, urban parklands, landfill-associated). Surface plastics and soil microplastics were quantified and characterised using ATR-FTIR and Ī¼-FTIR. Eight legacy (phthalate) and three emerging (adipate, citrate, trimellitate) plasticisers were quantified using GCāMS. Surface plastics were found at higher prevalence at landfillassociated and urban roadside sites, with levels significantly (2 orders of magnitude) greater than in woodlands. Microplastics were detected in landfill-associated (mean 12.3 particles gā1 dw), urban roadside (17.3 particles gā1dw) and urban parkland (15.7 particles gā1 dw) soils, but not in woodland soils. The most commonly detected polymers were polyethene, polypropene and polystyrene. Mean āplasticiser concentration in urban roadside soils (3111 ng gā1 dw) was significantly higher than in woodlands (134 ng gā1 dw). No significant difference was found between landfill-associated (318 ng gā1 dw) and urban parkland (193 ng gā1 dw) soils and woodlands. Di-n-butyl phthalate (94.7% detection frequency) and the emerging plasticiser trioctyl trimellitate (89.5%) were the most commonly detected plasticisers, with diethylhexyl phthalate (493 ng gā1 dw) and di-iso-decyl phthalate (96.7 ng gā1
dw) present at the highest concentrations. āplasticiser concentrations were significantly correlated with surface plastic (R2 = 0.23), but not with soil microplastic concentrations. Whilst plastic litter seems a fundamental source of plasticisers in soils, mechanisms such as airborne transport from source areas may be as important. Based on the data from this study, phthalates remain the dominant plasticisers in soils, but emerging plasticisers are already widespread, as reflected by their presence in all land uses studied
Hierarchical Quadratic Random Forest Classifier
In this paper, we proposed a hierarchical quadratic random forest classifier
for classifying multiresolution samples extracted from multichannel data. This
forest incorporated a penalized multivariate linear discriminant in each of its
decision nodes and processed squared features to realize quadratic decision
boundaries in the original feature space. The penalized discriminant was based
on a multiclass sparse discriminant analysis and the penalization was based on
a group Lasso regularizer which was an intermediate between the Lasso and the
ridge regularizer. The classification probabilities estimated by this forest
and the features learned by its decision nodes could be used standalone or
foster graph-based classifiers
Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNN
Learning the physical simulation on large-scale meshes with flat Graph Neural
Networks (GNNs) and stacking Message Passings (MPs) is challenging due to the
scaling complexity w.r.t. the number of nodes and over-smoothing. There has
been growing interest in the community to introduce \textit{multi-scale}
structures to GNNs for physical simulation. However, current state-of-the-art
methods are limited by their reliance on the labor-intensive drawing of coarser
meshes or building coarser levels based on spatial proximity, which can
introduce wrong edges across geometry boundaries. Inspired by the bipartite
graph determination, we propose a novel pooling strategy, \textit{bi-stride} to
tackle the aforementioned limitations. Bi-stride pools nodes on every other
frontier of the breadth-first search (BFS), without the need for the manual
drawing of coarser meshes and avoiding the wrong edges by spatial proximity.
Additionally, it enables a one-MP scheme per level and non-parametrized pooling
and unpooling by interpolations, resembling U-Nets, which significantly reduces
computational costs. Experiments show that the proposed framework,
\textit{BSMS-GNN}, significantly outperforms existing methods in terms of both
accuracy and computational efficiency in representative physical simulations.Comment: Updates summary: * update to the accepted version ICM
Acoustic modelling, data augmentation and feature extraction for in-pipe machine learning applications
Gathering measurements from infrastructure, private premises, and harsh environments can be difficult and expensive. From this perspective, the development of
new machine learning algorithms is strongly affected by the availability of training
and test data. We focus on audio archives for in-pipe events. Although several
examples of pipe-related applications can be found in the literature, datasets of
audio/vibration recordings are much scarcer, and the only references found relate
to leakage detection and characterisation. Therefore, this work proposes a methodology to relieve the burden of data collection for acoustic events in deployed pipes.
The aim is to maximise the yield of small sets of real recordings and demonstrate
how to extract effective features for machine learning. The methodology developed
requires the preliminary creation of a soundbank of audio samples gathered with
simple weak annotations. For practical reasons, the case study is given by a range
of appliances, fittings, and fixtures connected to pipes in domestic environments.
The source recordings are low-reverberated audio signals enhanced through a
bespoke spectral filter and containing the desired audio fingerprints. The soundbank is then processed to create an arbitrary number of synthetic augmented
observations. The data augmentation improves the quality and the quantity of
the metadata and automatically creates strong and accurate annotations that
are both machine and human-readable. Besides, the implemented processing
chain allows precise control of properties such as signal-to-noise ratio, duration
of the events, and the number of overlapping events. The inter-class variability
is expanded by recombining source audio blocks and adding simulated artificial
reverberation obtained through an acoustic model developed for the purpose.
Finally, the dataset is synthesised to guarantee separability and balance. A few
signal representations are optimised to maximise the classification performance,
and the results are reported as a benchmark for future developments. The contribution to the existing knowledge concerns several aspects of the processing chain
implemented. A novel quasi-analytic acoustic model is introduced to simulate
in-pipe reverberations, adopting a three-layer architecture particularly convenient
for batch processing. The first layer includes two algorithms: one for the numerical
calculation of the axial wavenumbers and one for the separation of the modes. The
latter, in particular, provides a workaround for a problem not explicitly treated in the
literature and related to the modal non-orthogonality given by the solid-liquid interface in the analysed domain. A set of results for different waveguides is reported
to compare the dispersive behaviour against different mechanical configurations.
Two more novel solutions are also included in the second layer of the model and
concern the integration of the acoustic sources. Specifically, the amplitudes of the
non-orthogonal modal potentials are obtained using either a distance minimisation
objective function or by solving an analytical decoupling problem. In both cases,
results show that sources sufficiently smooth can be approximated with a limited
number of modes keeping the error below 1%. The last layer proposes a bespoke
approach for the integration of the acoustic model into the synthesiser as a reverberation simulator. Additional elements of novelty relate to the other blocks of the
audio synthesiser. The statistical spectral filter, for instance, is a batch-processing
solution for the attenuation of the background noise of the source recordings. The
signal-to-noise ratio analysis for both moderate and high noise levels indicates
a clear improvement of several decibels against the closest filter example in the
literature. The recombination of the audio blocks and the system of fully tracked
annotations are also novel extensions of similar approaches recently adopted in
other contexts. Moreover, a bespoke synthesis strategy is proposed to guarantee
separable and balanced datasets. The last contribution concerns the extraction
of convenient sets of audio features. Elements of novelty are introduced for the
optimisation of the filter banks of the mel-frequency cepstral coefficients and the
scattering wavelet transform. In particular, compared to the respective standard
definitions, the average F-score performance of the optimised features is roughly
6% higher in the first case and 2.5% higher for the latter. Finally, the soundbank,
the synthetic dataset, and the fundamental blocks of the software library developed
are publicly available for further research
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