Interpretable Representation Research Articles

Kernelized sparse hashing for scalable image retrieval

Recently, hashing has been widely applied to large scale image retrieval applications due to its appealing query speed and low storage cost. The key idea of hashing is to learn a hash function that maps high dimensional data into compact binary codes while preserving the similarity structure in the original feature space. In this paper, we propose a new method called the Kernelized Sparse Hashing, which generates sparse hash codes with ℓ1 and non-negative regularizations. Compared to traditional hashing methods, our method only activates a small number of relevant bits on the hash code and hence provides a more compact and interpretable representation of data. Moreover, the kernel trick is introduced to capture the nonlinear similarity of features, and the local geometrical structure of data is explicitly considered in our method to improve the retrieval accuracy. Extensive experiments on three large-scale image datasets demonstrate the superior performance of our proposed method over the examined state-of-the-art techniques.

Scalable and Interpretable Data Representation for High-Dimensional, Complex Data

The majority of machine learning research has been focused on building models and inference techniques with sound mathematical properties and cutting edge performance. Little attention has been devoted to the development of data representation that can be used to improve a user's ability to interpret the data and machine learning models to solve real-world problems. In this paper, we quantitatively and qualitatively evaluate an efficient, accurate and scalable feature-compression method using latent Dirichlet allocation for discrete data. This representation can effectively communicate the characteristics of high-dimensional, complex data points. We show that the improvement of a user's interpretability through the use of a topic modeling-based compression technique is statistically significant, according to a number of metrics, when compared with other representations. Also, we find that this representation is scalable --- it maintains alignment with human classification accuracy as an increasing number of data points are shown. In addition, the learned topic layer can semantically deliver meaningful information to users that could potentially aid human reasoning about data characteristics in connection with compressed topic space.

Optimisation of the partial napping approach for the successful capturing of mouthfeel differentiation between brandy products

Partial napping has been validated as a suitable sensory profiling method for brandy evaluation. However, it was found that, compared to conventional profiling, very little useful information could be extracted on brandy mouthfeel when it was evaluated as part of overall in-mouth perceptions. This study aimed to optimise the partial napping method to improve information output on the mouthfeel of brandies. Panellists’ proficiency in visual, aroma and in-mouth evaluation of brandies were scrutinised after which three partial napping protocols were tested to identify the most effective solution for the successful capturing of mouthfeel differences between brandies. The results showed that panellists were equally efficient in aroma and in-mouth evaluations, but that in-mouth perception (defined as retronasal flavour, basic taste and mouthfeel) was not a useful construct as it did not contribute to the product configuration that could be obtained with visual and colour assessments alone. Instructing panellists to ignore retronasal flavour delivered more useful results. Using dark glasses and nose-clips to eliminate visual, aroma and retronasal flavour perceptions were not necessary to obtain a reliable and interpretable representation of the mouthfeel differences between brandies. Clear glasses and written instructions were sufficient to generate useful mouthfeel information under conditions more representative of the consumer product experience.

DETERMINATION OF INDIVIDUAL FEATURES OF BALANCE RETAINING IN PATIENTS WITH CEREBRAL PALSY USING STABILOMETRY TESTING

The practical aim of the research is to develop a methodology for assessing the functional state of the musculoskeletal system in the case of complex human pathologies and associated injuries with the objective distinction of interdependent anatomical and functional disorders in the osteoarticular system, nervous and muscular subsystems, identification pathology severity in the separate subsystems and the degree of influence for each of them on the movement organization as a whole. In patients with cerebral palsy the neurological movement disturbance is a priority component, differentiation of which may by difficult due to lack of tools to evaluate objectively and display the features of functioning nervous system in normal and pathological conditions. The authors propose a set of interval indicators of stabilometric study and method to use it providing the more qualitative consideration individual characteristics and the formation of clinically interpretable representation of a visual results image as compared with the standard vector and frequency indicators.

An integrated virtual geographic environmental simulation framework: a case study of flood disaster simulation

Dynamic flood disaster simulation is an emerging and promising technology significantly useful in urban planning, risk assessment, and integrated decision support systems. It is still an important issue to integrate the large assets such as dynamic observational data, numerical flood simulation models, geographic information technologies, and computing resources into a unified framework. For the intended end user, it is also a holistic solution to create computer interpretable representations and gain insightful understanding of the dynamic disaster processes, the complex impacts, and interactions of disaster factors. In particular, it is still difficult to access and join harmonized data, processing algorithms, and models that are provided by different environmental information infrastructures. In this paper, we demonstrate a virtual geographic environments-based integrated environmental simulation framework for flood disaster management based on the notion of interlinked resources, which is capable of automated accumulating and manipulating of sensor data, creating dynamic geo-analysis and three-dimensional visualizations of ongoing geo-process, and updating the contents of simulation models representing the real environment. The prototype system is evaluated by applying it as a proof of concept to integrate in situ weather observations, numerical weather and flood disaster simulation models, visualization, and analysis of the real time flood event. Case applications indicate that the developed framework can be adopted for use by decision-makers for short-term planning and control since the resulting simulation and visualization are completely based on the latest status of environment.

Understanding the personhood of Deaf people with dementia: Methodological issues

This article concerns Deaf people in the United Kingdom, who use sign language, who have a formal diagnosis of dementia and who have participated in interviews in British Sign Language (BSL) about their experience of living with dementia. We address the methodological challenges involved in enabling culturally meaningful participation in circumstances where the non-verbal is not equivalent to the non-linguistic. We demonstrate the use of interpretative narrative representation of data for purposes of cultural brokering. We explore the contribution of Deaf people's experiences and the analysis of their visual, spatial narratives to debates about personhood and the embodied self in dementia studies. Finally, we consider the significance of the situational as cultural in relation to holistic interpretation of narrative.

On Equivalence of FIS and ELM for Interpretable Rule-Based Knowledge Representation.

This paper presents a fuzzy extreme learning machine (F-ELM) that embeds fuzzy membership functions and rules into the hidden layer of extreme learning machine (ELM). Similar to the concept of ELM that employed the random initialization technique, three parameters of F-ELM are randomly assigned. They are the standard deviation of the membership functions, matrix-C (rule-combination matrix), and matrix-D [don't care (DC) matrix]. Fuzzy if-then rules are formulated by the rule-combination Matrix of F-ELM, and a DC approach is adopted to minimize the number of input attributes in the rules. Furthermore, F-ELM utilizes the output weights of the ELM to form the target class and confidence factor for each of the rules. This is to indicate that the corresponding consequent parameters are determined analytically. The operations of F-ELM are equivalent to a fuzzy inference system. Several benchmark data sets and a real world fault detection and diagnosis problem have been used to empirically evaluate the efficacy of the proposed F-ELM in handling pattern classification tasks. The results show that the accuracy rates of F-ELM are comparable (if not superior) to ELM with distinctive ability of providing explicit knowledge in the form of interpretable rule base.

Conceptual graph operations for formal visual reasoning in the medical domain

ObjectiveConceptual graphs (CGs) are used to represent clinical guidelines because they support visual reasoning with a logical background, making them a potentially valuable representation for guidelines. Materials and methodsConceptual graph formalism has an essential and basic component: a formal vocabulary that drives all of the other mechanisms, notably specialization and projection. The graph's theoretical operations, such as projection, rules, derivation, constraints, probabilities and uncertainty, support diagrammatic reasoning. ResultsA conceptual graph's graphical user interface includes a multilingual vocabulary management, some query and decision-making facilities and visual graph representations that are simple and interesting for user interactions. The described proposition using the Conceptual Graph user interface (CoGui) improves the performance of the actors in the diagnostic context of heart failure with preserved ejection fraction. DiscussionCGs capture the essential features of the medical processes underlying clinical reasoning. CGs are indeed useful as a way for the physician to represent guidelines, and well-defined semantic representations allow users to have a maximal understanding of the knowledge reasoning process. ConclusionCG operations of visual representations that uncover some of the actual complexities of clinicians’ reasoning have been tested in clinical guideline comprehension and used to translate text and diagrammatic guidelines into computer interpretable representations.

Hierarchical Adaptive Regression Kernels for Regression With Functional Predictors

We propose a new method for regression using a parsimonious and scientifically interpretable representation of functional predictors. Our approach is designed for data that exhibit features such as spikes, dips, and plateaus whose frequency, location, size, and shape varies stochastically across subjects. We propose Bayesian inference of the joint functional and exposure models, and give a method for efficient computation. We contrast our approach with existing state-of-the-art methods for regression with functional predictors, and show that our method is more effective and efficient for data that include features occurring at varying locations. We apply our methodology to a large and complex dataset from the Sleep Heart Health Study, to quantify the association between sleep characteristics and health outcomes. Software and technical appendices are provided in the online supplementary materials.

Fuzzy rules for describing subgroups from Influenza A virus using a multi-objective evolutionary algorithm

Extraction of biologically-meaningful knowledge is one of the important and challenging tasks in bioinformatics, in particular computational analysis of DNA and protein sequences, in order to identify biological function(s) and behaviour(s) of newly-extracted sequences. Computational intelligence techniques in corporation with sequence-driven features have been applied to tackle the problem and help classify different functional classes of the sequences. In order to study this problem, subgroup discovery algorithms together with a signal processing-based feature extraction method are applied, where the sequences are represented as a signal. The applicability of this method has been studied through four different Neuraminidase genes of Influenza A subtypes, H1N1, H2N2, H3N2 and H5N1. The results yielded not only higher predictive accuracy over these four classes of the proteins but also interpretable rule-based representation of the descriptive model with a significantly reduced feature set driven by means of the signal processing method. Subgroup discovery technique based on evolutionary fuzzy systems is expected to open new areas of research in bioinformatics and further help identify and understand more focused therapeutic protein targets.

Geometric steerable medial maps

To provide more intuitive and easily interpretable representations of complex shapes/organs, medial manifolds should reach a compromise between simplicity in geometry and capability of restoring the anatomy/shape of the organ/volume. Existing morphological methods show excellent results when applied to 2D objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds from a theoretical and a practical point of view. First, we introduce a continuous operator for accurate and efficient computation of medial structures of arbitrary dimension. Second, we present a validation protocol for assessing the suitability of medial surfaces for anatomical representation in medical applications. We evaluate quantitatively the performance of our method with respect to existing approaches and show its higher performance for medical imaging applications in terms of medial simplicity and capability of reconstructing the anatomical volume.

Stylisation and representation in subtitles: can less be more?

This article considers film dialogues and interlingual subtitles from the point of view of linguistic and cultural representation, and revisits from that perspective the question of loss, as a platform for considering alternative views on the topic and broader theoretical issues. The cross-cultural pragmatics perspective and focus on viewers’ reactions that dealing with representation entails cast the question of loss in a different light and opens up avenues for alternative modes of analysis. They make room for subtitles to be construed as producing their own systems of multimodal textual representation and modes of interpretation, and for their text to be recognised as having a greater expressive and representational potential than face values might suggest. This is the argument, informed by Fowler's Theory of Mode (1991, 2000), that is taken up in the paper, and harnessed to the review of examples or observations from recent studies on subtitles, and complementary evidence from dubbing. The capacity of subtitles to produce insights into the cultures and languages represented is of particular interest, and has wider implications for the culturally instrumental functions of subtitles and translation strategies.

Where are possible worlds? (arguments for ReALIS)

We follow Pollard (2007) in assuming that the mainstream Kripke/Montagueinspired possible-worlds semantics is “a framework known to have dubious foundations” (primarily because of the granularity problem), and “worlds are constructed from propositions […], and not the other way around”. We intend to work out this approach in a DRT-based framework, called ReALIS, in order to account for phenomena concerning referent accessibility, at the same time. We claim that our system offers a general solution to problems of intensional identity, and it is devoid of DRT’s “extra level” problem—by embedding discourse representations in the world model, not directly but as parts of the representations of interpreters’ minds, i.e., their (permanently changing) information states/“internal worlds”. Hence, there is simply no intensionality in ReALIS as interpreters’ “worldlets” (in description of their brains within the entire model of the universe) carry all kinds of information (BDI, guesswork, dream) typically “entrusted to” possible worlds.

An integrated data collection and analysis framework for remote monitoring and planning of construction operations

Recent advances in data collection and operations analysis techniques have facilitated the process of designing, analyzing, planning, and controlling of engineering processes. Mathematical tools such as graphical models, scheduling techniques, operations research, and simulation have enabled engineers to create models that represent activities, resources, and the environment under which a project is taking place. Traditionally, most simulation paradigms use static or historical data to create computer interpretable representations of real engineering systems. The suitability of this approach for modeling construction operations, however, has always been a challenge since most construction projects are unique in nature as every project is different in design, specifications, methods, and standards. Due to the dynamic nature and complexity of most construction operations, there is a significant need for a methodology that combines the capabilities of traditional modeling of engineering systems and real time field data collection. This paper presents the requirements and applicability of a data-driven modeling framework capable of collecting and manipulating real time field data from construction equipment, creating dynamic 3D visualizations of ongoing engineering activities, and updating the contents of a discrete event simulation model representing the real engineering system. The developed framework can be adopted for use by project decision-makers for short-term project planning and control since the resulting simulation and visualization are completely based on the latest status of project entities.

Fast Inference for the Latent Space Network Model Using a Case-Control Approximate Likelihood

Network models are widely used in social sciences and genome sciences. The latent space model proposed by Hoff et al. (2002), and extended by Handcock et al. (2007) to incorporate clustering, provides a visually interpretable model-based spatial representation of relational data and takes account of several intrinsic network properties. Due to the structure of the likelihood function of the latent space model, the computational cost is of order O(N 2), where N is the number of nodes. This makes it infeasible for large networks. In this article, we propose an approximation of the log-likelihood function. We adapt the case-control idea from epidemiology and construct a case-control log-likelihood, which is an unbiased estimator of the log-full likelihood. Replacing the full likelihood by the case-control likelihood in the Markov chain Monte Carlo estimation of the latent space model reduces the computational time from O(N 2) to O(N), making it feasible for large networks. We evaluate its performance using simulated and real data. We fit the model to a large protein–protein interaction data using the case-control likelihood and use the model fitted link probabilities to identify false positive links. Supplemental materials are available online.

How far can one compress digital fault records? Analysis of a matching pursuit-based algorithm

In this paper, we verify how far electric disturbance signals can be compressed without compromising the analysis of encoded fault records. A recently proposed compression algorithm, referred to as Damped Sinusoidal Matching Pursuit (DSMP) has the remarkable feature of obtaining both compact and physically interpretable representations. However, for fault analysis applications, one is primarily interested in how accurate can be the analysis performed on compressed signals, instead of evaluating mean-squared error figures. Unlike previous works in digital fault records compression, the performance of the DSMP compression method is evaluated using a protocol based on fault analysis procedures commonly performed by expert engineers. This protocol is applied for comparing the results obtained in the analysis of both uncompressed records and their compressed versions at different compression ratios. The results show that the DSMP is a reliable compression system since it achieves high compression ratios (6.4:1) without causing fault analysis misinterpretation.

Archetypal analysis for machine learning and data mining

Archetypal analysis (aa) proposed by Cutler and Breiman (1994) [7] estimates the principal convex hull (pch) of a data set. As such aa favors features that constitute representative ‘corners’ of the data, i.e., distinct aspects or archetypes. We currently show that aa enjoys the interpretability of clustering – without being limited to hard assignment and the uniqueness of svd – without being limited to orthogonal representations. In order to do large scale aa, we derive an efficient algorithm based on projected gradient as well as an initialization procedure we denote FurthestSum that is inspired by the FurthestFirst approach widely used for k-means (Hochbaum and Shmoys, 1985 [14]). We generalize the aa procedure to kernel-aa in order to extract the principal convex hull in potential infinite Hilbert spaces and derive a relaxation of aa when the archetypes cannot be represented as convex combinations of the observed data. We further demonstrate that the aa model is relevant for feature extraction and dimensionality reduction for a large variety of machine learning problems taken from computer vision, neuroimaging, chemistry, text mining and collaborative filtering leading to highly interpretable representations of the dynamics in the data. Matlab code for the derived algorithms is available for download from www.mortenmorup.dk.

SPATIAL ANALYSIS OF THE APTITUDE TO LATE MATERNITY ON THE ISLAND OF SARDINIA

This study examines local heterogeneity in the aptitude of Sardinian mothers towards late reproduction, and explores its temporal persistence and association with both post-reproductive longevity and propensity to consanguineous marriage. Data on women's fertility from 1961 and birth records for 1980-1996 from Vital Statistics were analysed by means of the following indicators: the incidence of old mothers at last childbirth, female mortality (1980-2001) at 80 years of age and over and the proportion of consanguineous marriages (1930-1969). A variable kernel-smoothing method was used to create interpretable representations of the true spatial structure of the indicators, and to highlight areas of higher than expected intensity. In particular, an area of reproductive and post-reproductive longevity was identified where the traits combine with a higher tendency to relatedness. Intriguingly, this area corresponds approximately to the geographically and historically well defined central-eastern zone, which was the refuge of Sardinians during past invasions, and overlaps the Ogliastra region, which has been widely studied for its genetic homogeneity.

Swarm Intelligence for Non-Negative Matrix Factorization

The Non-negative Matrix Factorization (NMF) is a special low-rank approximation which allows for an additive parts-based and interpretable representation of the data. This article presents efforts to improve the convergence, approximation quality, and classification accuracy of NMF using five different meta-heuristics based on swarm intelligence. Several properties of the NMF objective function motivate the utilization of meta-heuristics: this function is non-convex, discontinuous, and may possess many local minima. The proposed optimization strategies are two-fold: On the one hand, a new initialization strategy for NMF is presented in order to initialize the NMF factors prior to the factorization; on the other hand, an iterative update strategy is proposed, which improves the accuracy per runtime for the multiplicative update NMF algorithm. The success of the proposed optimization strategies are shown by applying them on synthetic data and data sets coming from the areas of spam filtering/email classification, and evaluate them also in their application context. Experimental results show that both optimization strategies are able to improve NMF in terms of faster convergence, lower approximation error, and better classification accuracy. Especially the initialization strategy leads to significant reductions of the runtime per accuracy ratio for both, the NMF approximation as well as the classification results achieved with NMF.

Characterizing sensitivity and uncertainty in a multiscale model of a complex coral reef system

Abstract Sensitivity and uncertainty are intrinsic properties of ecological models, and their characterization is an important step in the modelling process. We use a spatially explicit multi-scale model of a coral reef system to explore four aspects of model sensitivity and uncertainty: (i) sensitivity to initial conditions; (ii) sensitivity to parameter values; (iii) sensitivity to spatio-temporal resolution; and (iv) the effects of uncertainty about spatio-temporal variability of ecological processes on the shape of distributions of model predictions. We use reef community composition, visualized in multivariate space, as a response variable. This approach provides an easily interpretable representation of changes in reef community composition under different parameter conditions and spatio-temporal resolutions. It is also a useful means to visualize distributions of model outcomes under differing assumptions about the nature of variability in ecological processes in the real world. Our results indicate that reef state and recovery trajectories are particularly sensitive to parameters determining coral growth and mortality rates. Variability in model outcomes depends on assumptions about the way parameters vary in space and time, and is greater at local scales than at subregional and regional scales. We highlight the fact that predictions based on the kind of model presented here are likely to be more robust for subregions and regions than for particular reef localities.