Conventional Representation Research Articles

Mining opinions from instructor evaluation reviews: A deep learning approach

AbstractStudent evaluations of teaching (SET) provides potentially essential source of information to achieve educational quality objectives of higher educational institutions. The findings can be utilized as a measure of teaching effectiveness and they may aid the administrative decision‐making process. The purpose of our research is to establish an efficient sentiment classification scheme on instructor evaluation reviews by pursuing the paradigm of deep learning. Deep learning is a recent research direction of machine learning, which seeks to identify a classification scheme with higher predictive performance based on multiple layers of nonlinear information processing. In this study, we present a recurrent neural network (RNN) based model for opinion mining on instructor evaluation reviews. We analyze a corpus containing 154,000 such reviews, with the use of conventional machine learning algorithms, ensemble learning methods, and deep learning architectures. In the empirical analysis, three conventional text representation schemes (namely, term‐presence, term‐frequency [TF], and TF‐inverse document frequency schemes) and four word embedding schemes (namely, word2vec, global vector [GloVe], fastText, and LDA2Vec) have been taken into consideration. The predictive performance of supervised machine learning methods (such as, Naïve Bayes, support vector machines, logistic regression, K‐nearest neighbor, and random forest) and three ensemble learning methods have been examined on word embedding schemes. The extensive empirical analysis indicates that deep learning‐based architectures outperform the conventional machine learning classifiers for the task of sentiment classification on instructor reviews. For the RNN with attention mechanism in conjunction with GloVe word embedding scheme‐based representation a classification accuracy of 98.29% has been obtained.

A joint network optimization framework to predict clinical severity from resting state functional MRI data

We propose a novel optimization framework to predict clinical severity from resting state fMRI (rs-fMRI) data. Our model consists of two coupled terms. The first term decomposes the correlation matrices into a sparse set of representative subnetworks that define a network manifold. These subnetworks are modeled as rank-one outer-products which correspond to the elemental patterns of co-activation across the brain; the subnetworks are combined via patient-specific non-negative coefficients. The second term is a linear regression model that uses the patient-specific coefficients to predict a measure of clinical severity. We validate our framework on two separate datasets in a ten fold cross validation setting. The first is a cohort of fifty-eight patients diagnosed with Autism Spectrum Disorder (ASD). The second dataset consists of sixty three patients from a publicly available ASD database. Our method outperforms standard semi-supervised frameworks, which employ conventional graph theoretic and statistical representation learning techniques to relate the rs-fMRI correlations to behavior. In contrast, our joint network optimization framework exploits the structure of the rs-fMRI correlation matrices to simultaneously capture group level effects and patient heterogeneity. Finally, we demonstrate that our proposed framework robustly identifies clinically relevant networks characteristic of ASD.

Designing of Gabor filters for spectro-temporal feature extraction to improve the performance of ASR system

Existing automatic speech recognition (ASR) system uses the spectral or temporal features of speech. The performance of such systems is still poor compared to the human perception of hearing, especially in noisy environments. This paper concentrates on the extraction of spectro-temporal features based on physiological and psychoacoustically inspired approaches. Here, two dimensional Gabor filters are used to estimate the spectro-temporal features from time–frequency representation of uttered speech signals. The Gabor filters are designed using the concept of constant Q factor. It is found that human perception system maintains approximately constant Q in its frequency response along the chain of its filter bank. Constant Q analysis ensures that the Gabor filters occupy a set of geometrically spaced spectral and temporal bins. Time–frequency representation of speech signal is a key ingredient for Gabor based feature extraction method. For time–frequency mapping, Gammatonegram is adopted instead of conventional spectrogram representations. The performance of the ASR system with the proposed feature set is experimentally validated using AURORA2 noisy digit database. Under clean training; the proposed features obtained a relative improvement of about 50% in word error rate (WER) compared to Mel frequency cepstral coefficients (MFCC) features. A relative improvement of 23% in WER is also obtained compared with that of existing spectro-temporal feature extraction methods. Further analysis is carried out on TIMIT corrupted with noise samples taken from the NOISEX-92 database. The experimental verification proves the robustness of proposed features in building a robust acoustic model for the ASR system.

Refocusing of Moving Targets Based on Low-Bit Quantized SAR Data via Parametric Quantized Iterative Hard Thresholding

Low-bit quantization of echo improves storage and leads to more efficient downlink transmission of spaceborne synthetic aperture radar (SAR) systems. In this paper, a new parametric quantized iterative hard thresholding (PQIHT) algorithm is proposed to refocus the images of moving targets with low-bit quantized SAR data, based on the combination of quantized iterative hard thresholding (QIHT) and the parametric sparse representation. The blurred and quantization-error-involved subimage of the region of interest (ROI) containing the moving target is represented in a sparse fashion through an adaptive parametric dictionary. The QIHT with a pruned searching method is performed for efficiently estimating the motion-adaptive parameter inside the dictionary, refocusing the ROI image and suppressing the quantization-induced error in an iterative way. Different from the conventional QIHT algorithm with a fixed dictionary that can only represent stationary targets, the proposed method exploits a parametric dictionary with a parameter related to target motion status, which is capable of adaptively representing the radar echo from a moving target with unknown motion status and, therefore, is suitable for moving target refocusing. Simulations and experiments on real GF-3 satellite SAR data demonstrate that, compared with the conventional parametric sparse representation framework for moving target refocusing based on purely precise data, the proposed algorithm can provide satisfactory quality of moving target refocusing with remarkably reduced data volume.

A Robust Pansharpening Algorithm Based on Convolutional Sparse Coding for Spatial Enhancement

Pansharpening (PS) is a prominent remote sensing image fusion technique. It yields high-resolution multispectral (HRMS) images, which are imperative for the applications, such as recognition and detection. The PS methods based on conventional sparse representation induce blurring effects and are unable to preserve the essential spatial details in the fused outcome. In this article, to overcome these drawbacks, a robust fusion scheme is proposed based on convolutional sparse coding (CSC). The source images are decomposed into its constituent texture and cartoon components. The sparse coefficient maps are acquired from texture components by adapting CSC. Texture components are fused using activity level measurement, whereas averaging mechanism is used to fuse the cartoon components. The HRMS image is reconstructed by combining the fused components in proportion to the gradient information. Impact of number of filters on quality metrics estimation is analyzed. Comprehensive experiments are performed on the images acquired from distinct sensors. The proposed method is evaluated in terms of visual analysis and the quantitative metrics with reduced-scale and full-scale experiments. Extensive evaluations manifest the capability of the proposed method of maintaining the balanced tradeoff and retaining the desired spatial and spectral details.

Low-rank quaternion approximation for color image processing.

Low-rank matrix approximation (LRMA)-based methods have made a great success for grayscale image processing. When handling color images, LRMA either restores each color channel independently using the monochromatic model or processes the concatenation of three color channels using the concatenation model. However, these two schemes may not make full use of the high correlation among RGB channels. To address this issue, we propose a novel low-rank quaternion approximation (LRQA) model. It contains two major components: first, instead of modeling a color image pixel as a scalar in conventional sparse representation and LRMA-based methods, the color image is encoded as a pure quaternion matrix, such that the cross-channel correlation of color channels can be well exploited; second, LRQA imposes the low-rank constraint on the constructed quaternion matrix. To better estimate the singular values of the underlying low-rank quaternion matrix from its noisy observation, a general model for LRQA is proposed based on several nonconvex functions. Extensive evaluations for color image denoising and inpainting tasks verify that LRQA achieves better performance over several state-of-the-art sparse representation and LRMA-based methods in terms of both quantitative metrics and visual quality.

A new sparse representation framework for compressed sensing MRI

Compressed sensing based Magnetic Resonance imaging (MRI) via sparse representation (or transform) has recently attracted broad interest. The tight frame (TF)-based sparse representation is a promising approach in compressed sensing MRI. However, the conventional TF-based sparse representation is difficult to utilize the sparsity of the whole image. Since the whole image usually has different structure textures and a kind of tight frame can only represent a particular kind of ground object, how to reconstruct high-quality of magnetic resonance (MR) image is a challenge. In this work, we propose a new sparse representation framework, which fuses the double tight frame (DTF) into the mixed-norm regularization for MR image reconstruction from undersampled k-space data. In this framework, MR image is decomposed into smooth and nonsmooth regions. For the smooth regions, the wavelet TF-based weighted L1-norm regularization is developed to reconstruct piecewise-smooth information of image. For nonsmooth regions, we introduce the curvelet TF-based robust L1,a-norm regularization with the parameter to preserve the edge structural details and texture. To estimate the reasonable parameter, an adaptive parameter selection scheme is designed in robust L1,a-norm regularization. Experimental results demonstrate that the proposed method can achieve the best image reconstruction results when compared with other existing methods in terms of quantitative metrics and visual effect.

Energy-Efficient Time-Domain Vector-by-Matrix Multiplier for Neurocomputing and Beyond

We propose an extremely energy-efficient mixed-signal ${N} \times {N}$ vector-by-matrix multiplication (VMM) in a time domain. Multi-bit inputs/outputs are represented with time-encoded digital signals, while multi-bit matrix weights are realized with adjustable current sources, e.g., transistors biased in subthreshold regime. The major advantage of the proposed approach over other types of mixed-signal implementations is very compact peripheral circuits, which would be essential for achieving high energy efficiency and speed at the system level. As a case study, we have designed a multilayer perceptron, based on two layers of 10 $ \times $ 10 four-quadrant multipliers, in 55-nm process with embedded NOR flash memory technology, which allows for compact implementation of adjustable current sources. Our analysis, based on memory cell measurements, shows that >6 bit operation can be ensured for larger ( ${N} >$ 50) VMMs. Post-layout estimates for 55-nm 6-bit VMM, which take into account the impact of PVT variations, noise, and overhead of I/O circuitry for converting between conventional digital and time domain representations, show ~7 fJ/Op for ${N} >$ 500. The energy efficiency can be further improved to POp/J regime for more optimal and aggressive designs.

Victorians Undone: Tales of the Flesh in the Age of Decorum, by Kathryn Hughes

Reviewed by: Victorians Undone: Tales of the Flesh in the Age of Decorum by Kathryn Hughes Hilary Fraser (bio) Victorians Undone: Tales of the Flesh in the Age of Decorum, by Kathryn Hughes; pp. xviii + 414. Baltimore: Johns Hopkins University Press, 2018, $29.95, $19.95 paper. Kathryn Hughes opens her deeply researched and wonderfully entertaining new study, Victorians Undone: Tales of the Flesh in the Age of Decorum, with the vividly realized scene of Thomas Carlyle's first encounter with Samuel Taylor Coleridge. Her dual focus on the imagined contrasting physiques, features, and demeanors of these two eminent writers of different generations—the "spare, springy" young Carlyle, hewn from his native granite and birch, confronting the "damp, spongy apparition" of the drug-addled Coleridge—and on the insistent, sometimes unsparing corporeality of Carlyle's historiographic method (not least in his representations of Coleridge), neatly introduces her own distinctive methodology (ix). Carlyle's ambition to conjure a past inhabited by people "with colour in their cheeks, with passions in their stomach, and the idioms, features and vitalities of very men" is brilliantly brought to life in Hughes's own animated account of the Victorians' obsessive and often tortured relationship with their bodies (Carlyle qtd. in Hughes x). The close-up physicality of the newly urbanized nineteenth century, when "other people's sneezes, bums, elbows, smells, snores, farts and breathy whistles were, quite literally, in your face," she argues, sharpened awareness of the body's unruliness and caprice (xi). As a way of managing a subject that could itself become unruly, Hughes structures her discussion around a selection of body parts suffered or enjoyed by their owners that seem peculiarly eloquent of broader Victorian preoccupations. Chapters on, for example, Charles Darwin's astonishingly rampant beard, George Eliot's over-developed hand, and Fanny Cornforth's plumply kissable mouth, bring focus and specificity to a wide-ranging discussion of attitudes toward sexuality, class, gender, race, and generally what it felt like to be human in the nineteenth century. Always running alongside the theme of the embodied Victorians, though, is her attention to the ways in which their physical lives have been represented and misrepresented by subsequent historians, as Coleridge's was by Carlyle. This is a book that is as interested in the historical and critical afterlives of Darwin's beard and Cornforth's mouth as it is in the material existence of their ungovernable originals. The resonances of the book's title beautifully encapsulate its intelligence and range. Victorians Undone conjures the buttoned-up Victorians even as it undoes the myths of [End Page 539] their legendary prudishness. It conveys the ways in which they were at once uptight and undone, discombobulated by their bodies. They are dissected into body parts and reassembled into fully rounded inhabitants of an age of which we now only have fugitive material traces that help us distinguish the myths from the historically true: hairs collected from Darwin's unpruned beard; paintings and photographs of Cornforth's mouth at different stages of her colorful life; and the glove that once fitted Eliot's contested right hand and (spoiler alert) reveals that it was not, as she and others after her claimed, enlarged by her early work in the family dairy. Hughes undoes conventional representations of the Victorians and connects us with them anew, alert to the pastness of the past, but also to its continuities with the present. The book ends with a chapter on the murdered girl who was the eponymous source of our modern phrase "sweet Fanny Adams" (287). Her name is now truncated to "sweet F. A.," cut down in a way that calls to mind her decapitated head and dismembered and unremembered body (365). Hughes wears her learning lightly. The book is often very funny, drawing on anecdote and gossip, quoting from private letters and journals, recounting scandals, but it is always for a serious purpose. She goes deep into the archives to piece together what actually happened, and she thinks about how and why myths develop. The explanation for some of these frankly odd stories sometimes has to do with the gate-keepers, usually family, who want to eradicate anything disreputable...

DOCUMENTATION, ANALYSIS AND REPRESENTATION OF MODERNIST HERITAGE THROUGH BUILDING INFORMATION MODELING

Abstract. The paper is focused on research activities on documentation, analysis and representation of Modernist Heritage in Latin America. The research is part of a broad project developed by the Department of Architecture of the University of Ferrara following the main aim of documentation for knowledge, enhancement and preservation of cultural heritage. In addition to several on site projects developed in cooperation with local institutions, mainly in Brazil, and based on historical analysis, documentation, survey, modelling and diagnostic assessment, the research has then opened up to an in-depth study of Modernist architecture. The field of investigation has been extended to Latin America, through the analysis of the main architectures of the iconic figures of Modernism, in order to collect a database of models as support for different research avenues. The methodological process is based on the “survey of the project”, an analysis of documentation as the starting point for the representation in BIM environment from which to extract conventional representations and different levels of interpretation.

Transient feature self-enhancement via shift-invariant manifold sparse learning for rolling bearing health diagnosis

Transient feature caused by local defect is crucial for bearing health diagnosis and sparse learning has been widely used to enhance the desired features with the noise interference removed. In order to overcome the difficulties of conventional sparse approaches in dictionary construction and the limitations of sparse representation results, by considering the merits of time-frequency manifold (TFM) learning, a new self-enhancement method, shift-invariant manifold sparse learning (SI-MSL), is proposed in an adaptive way. Different from the conventional time-domain sparse methods, the envelope signal is isolated and self-rebuilt with the latent shift-invariant structure enhanced. First, the intrinsic manifold modes of a given signal can be adaptively mined by establishing TFM learning on a local segment, and the amplitude-phase separation technique is then applied to complete shift-invariant sparse on the raw envelope signal. Second, based on the reinforcement learning of shift-invariant manifold mode for the whole signal, a novel self-enhancement of bearing can be achieved by solving a convex optimization problem. Finally, by utilizing phase preserve and a series of inverse transforms, the transient feature of the given signal can be self-enhanced in an efficient way. Specially, envelope spectral entropy is used to adaptively output the optimal manifold basis function from the local TFM signature. Different from conventional sparse representation, this method utilizes feature mining of TFM learning to ensure more accurate sparse expression from dictionary construction to solution, providing more accurate diagnostic results. Experimental and comparison results show that the proposed method can achieve a good effect in interference suppression with the transient feature enhancement in a self-learning process, which is benefit for an accurate and intelligent rolling bearing health diagnosis.

Nonlinear finite element formulation based on invariant-free hyperelasticity for orthotropic materials

The conventional representation of isotropic hyperelastic strain energy densities as functions of scalar invariants of finite deformation tensors does not naturally extend to the field of anisotropic mechanics. Formulating an invariant-free representation of the strain energy function, fourth-order Orthotropic Lamé tensors define the constitutive law whilst naturally collapsing to the transversely isotropic and fully isotropic case where necessary simply as a by-product of known material symmetries. In this study, a simple linear isoparametric hexahedral finite element capable of describing anisotropic invariant-free hyperelasticity is presented. Careful conversion of the fourth-order tensor operations present in the strain energy function to computational arrays then applying to the principle of virtual work generates a weak formulation for finite element analyses. The finite element is then applicable to materials of any degree of anisotropy or compressibility and is particularly useful for predicting highly nonlinear responses such as the stiffening of fibrous biological tissues. A discussion of simple shear experimentation and modelling follows, as well as remarks on modelling nearly-incompressible materials.

El panorama gráfico arquitectónico de los ochenta. Arquitecturas de escape y dibujos de resistencia

<p>Durante los años ochenta del siglo pasado, se dieron las condiciones para reivindicar un pensamiento crítico capaz de extender el discurso de la arquitectura a la sociedad. Desde algunas escuelas de arquitectura que apostaron por una arquitectura más diversa y culturalmente accesible al gran público, se procuró el acceso a su producción gráfica mediante iniciativas de difusión que incluían publicaciones, exposiciones y otros eventos. A través de un dibujo más lúdico que había incorporado elementos de la publicidad o la cultura pop, una nueva generación de arquitectos, pretendían expresar su desacuerdo con el corsé estético impuesto por los postulados de la modernidad. En su diversidad, estas propuestas gráficas poseen una serie de elementos comunes: la transgresión de los sistemas de representación convencionales, la inclusión de elementos dinámicos mediante la incorporación de escenas al más puro estilo cinematográfico, y el deseo de introducir narraciones paralelas al discurso arquitectónico.</p>

How Conventional Visual Representations of Time-Frequency Analyses Bias Our Perception of EEG/MEG Signals and What to Do About It.

Time-frequency decompositions of the EEG/MEG have become such a familiar part of the cognitive neuroscience landscape over the past two decades that their appearance no longer seems remarkable. But to those of us who laboured in the days when the signal analysis toolbox contained Fourier analysis, event-related potentials and not much else, the arrival of time-frequency decompositions was little short of revolutionary. With their introduction, complex information about both the timing and frequency of changes in the EEG/MEG could be presented in the visually attractive format of time-frequency plots (TFPs). Like maps, with time on the abscissa, frequency on the ordinate and a colour or grey scale to indicate the amplitude or power at each time-frequency location, TFPs provide a convenient and efficient way to represent a large amount of detailed information in an easily digestible format and, for that, they are to be commended. Yet, despite all these benefits, it is my contention that TFPs, in the format most commonly seen in journal articles and at conferences, systematically distort and bias our perception of the EEG/MEG signals that they are supposed to help us understand. Specifically, my contention is that TFPs are biased by the use of linear frequency scales. Linear frequency scales distort our perception of the EEG/MEG signal by placing far too much emphasis on the high frequency components of the signal, where there is very little energy, and far too little emphasis on the lower frequencies where the biggest changes are seen. This disproportionate focus on high frequencies confers a degree of significance to the gamma band that is not justified by the evidence.

Learning Domain-Independent Deep Representations by Mutual Information Minimization.

Domain transfer learning aims to learn common data representations from a source domain and a target domain so that the source domain data can help the classification of the target domain. Conventional transfer representation learning imposes the distributions of source and target domain representations to be similar, which heavily relies on the characterization of the distributions of domains and the distribution matching criteria. In this paper, we proposed a novel framework for domain transfer representation learning. Our motive is to make the learned representations of data points independent from the domains which they belong to. In other words, from an optimal cross-domain representation of a data point, it is difficult to tell which domain it is from. In this way, the learned representations can be generalized to different domains. To measure the dependency between the representations and the corresponding domain which the data points belong to, we propose to use the mutual information between the representations and the domain-belonging indicators. By minimizing such mutual information, we learn the representations which are independent from domains. We build a classwise deep convolutional network model as a representation model and maximize the margin of each data point of the corresponding class, which is defined over the intraclass and interclass neighborhood. To learn the parameters of the model, we construct a unified minimization problem where the margins are maximized while the representation-domain mutual information is minimized. In this way, we learn representations which are not only discriminate but also independent from domains. An iterative algorithm based on the Adam optimization method is proposed to solve the minimization to learn the classwise deep model parameters and the cross-domain representations simultaneously. Extensive experiments over benchmark datasets show its effectiveness and advantage over existing domain transfer learning methods.

Between Zero Dark Thirty and Camp X-Ray: Changing Gender Dynamics of Contemporary American War Film

War films play a substantial role in American society by cultivating an understanding of military politics. I argue that the use of different narrative strategies encouraging spectator identification within the US American war films establishes the political backbone of the genre. This paper concentrates on the transformation of the genre’s conventional gender representation in connection with American military politics. After providing a brief history of female integration into the US military, it offers a comparative analysis of Zero Dark Thirty (2012) and Camp X-Ray (2014).

Towards a user preference model for interior lighting, Part 3: An alternative model

In Parts 1 and 2 of this work, an experiment was described in which subjects assessed their visual impressions of scene brightness (B), visual clarity (VC), colour preference (CP) and scene preference (SP) in a real room. In this room, the horizontal illuminance ( Ev), the correlated colour temperature (CCT) and the level of chroma enhancement caused by the spectrum of the light source (Δ C*) were changed systematically. In the present Part 3, these mean subjective B, VC, CP and SP scale values are re-analysed in terms of an alternative model based on a different set of independent variables: CCT, Δ C* and the circadian stimulus (CS). Contour map diagrams resulting from the new modelling equations are shown and compared with the conventional Kruithof-type representation.

Lightning Strike to a Tall Grounded Object: Part 1. Circuit Modeling With Direction Dependence

This paper proposes a circuit model of a lightning strike to a tall grounded object. The model can consider the direction-dependent attenuation characteristic of current propagating along with a tall grounded object as well as frequency-dependent characteristics of lightning channel and tower impedances, in contrast to the conventional lossless transmission line representations. Hence, the model removes the use of an unrealistic high grounding impedance for approximately expressing the significant current attenuation propagating upward along with the tall object. Measured lightning currents at a height of about 500 m of the 634-m-high Tokyo Skytree are accurately reproduced with the proposed model, which assumes a constant lightning channel impedance with a current source that generates a current waveform decaying with time, or a frequency (time) dependent lightning channel impedance with a step-like voltage source. The proposed model can also be used for lightning electromagnetic pulse calculations.

The Impact of Cloud Representation on the Sub-Seasonal Forecasts of Atmospheric Teleconnections and Preferred Circulation Regimes in the Northern Hemisphere

The impact of cloud representation on the simulation of mid-latitude recurrent large-scale flows and forecast skill of mid-latitude atmospheric teleconnections is evaluated using the Community Climate System Model, version 4 (CCSM4), and the super-parameterized CCSM4 (SP-CCSM4). Patterns of low-level atmospheric circulation anomalies and convection associated with the Madden–Julian oscillation (MJO) are affected by the method used for the representation of cloud processes. The configuration of the model using super-parameterization for the representation of cloud processes produces MJO-related patterns that agree better with observations than the configuration of the model using a conventional cloud parameterization scheme. The recurrent circulation regimes of the mid-latitudes are also sensitive to the representation of cloud processes. In the North Atlantic sector, the inability of CCSM4 to simulate the Scandinavian blocking regime is corrected in the super-parameterized version of the model. In the North Pacific sector, the strength of the clustering (measured by a variance ratio) is too large in CCSM4 compared with observations and SP-CCSM4. The SP-CCSM4 model has better forecast skill for the MJO amplitude and phase than the model with conventional representation of moist convective processes. In turn, the improved forecast skill of the super-parameterized model results in better forecast skill for mid-latitude teleconnections in 500 hPa geopotential height anomalies forced by the MJO convection.

Deciphering the Relations between Pore Structure and Adsorption Behavior in Metal-Organic Frameworks: Unexpected Lessons from Argon Adsorption on Copper-Benzene-1,3,5-tricarboxylate.

Consistent adsorption characterization of metal-organic frameworks (MOFs) is imperative for their wider adoption in industry and practical applications. Current approaches are based on the conventional intuitive representation of MOF pore space as a regular network of pore compartments (cages and channels), adsorption in which occurs independently according to their geometric dimensions. Here, we demonstrate that this conventional approach is unable to describe even qualitatively the shape of Ar adsorption isotherms on hydrated and dehydrated Cu-BTC structures, one of the most well-known MOF materials. A combination of geometric characterization of MOF crystallographic structure, molecular simulation, and virtual visualization of the adsorption process reveals that the filling of the adjacent pore compartments proceeds in parallel in a complex cooperative fashion. The proposed synergistic approach helps us to understand the relations between pore structure geometric and chemical features and adsorption behavior, laying down a foundation for improved methods for MOF characterization.