A semi-supervised approach using label propagation to support citation screening.

Citation screening, an integral process within systematic reviews that identifies citations relevant to the underlying research question, is a time-consuming and resource-intensive task. During the screening task, analysts manually assign a label to each citation, to designate whether a citation is eligible for inclusion in the review. Recently, several studies have explored the use of active learning in text classification to reduce the human workload involved in the screening task. However, existing approaches require a significant amount of manually labelled citations for the text classification to achieve a robust performance. In this paper, we propose a semi-supervised method that identifies relevant citations as early as possible in the screening process by exploiting the pairwise similarities between labelled and unlabelled citations to improve the classification performance without additional manual labelling effort. Our approach is based on the hypothesis that similar citations share the same label (e.g., if one citation should be included, then other similar citations should be included also). To calculate the similarity between labelled and unlabelled citations we investigate two different feature spaces, namely a bag-of-words and a spectral embedding based on the bag-of-words. The semi-supervised method propagates the classification codes of manually labelled citations to neighbouring unlabelled citations in the feature space. The automatically labelled citations are combined with the manually labelled citations to form an augmented training set. For evaluation purposes, we apply our method to reviews from clinical and public health. The results show that our semi-supervised method with label propagation achieves statistically significant improvements over two state-of-the-art active learning approaches across both clinical and public health reviews.

Classification of spent reactor fuel for nuclear forensics.

In this paper we demonstrate the use of pattern recognition and machine learning techniques to determine the reactor type from which spent reactor fuel has originated. This has been done using the isotopic and elemental measurements of the sample and proves to be very useful in the field of nuclear forensics. Nuclear materials contain many variables (impurities and isotopes) that are very difficult to consider individually. A method that considers all material parameters simultaneously is advantageous. Currently the field of nuclear forensics focuses on the analysis of key material properties to determine details about the materials processing history, for example, utilizing known half-lives of isotopes can determine when the material was last processed (Stanley, F. E. J. Anal. At. Spectrom. 2012, 27, 1821; Varga, Z.; Wallenius, M.; Mayer, K.; Keegan, E.; Millet, S. Anal. Chem. 2009, 81, 8327-8334). However, it has been demonstrated that multivariate statistical analysis of isotopic concentrations can complement these method and are able to make use of a greater level of information through dimensionality reduction techniques (Robel, M.; Kristo, M. J. J. Environ. Radioact. 2008, 99, 1789-1797; Robel, M.; Kristo, M. J.; Heller, M. A. Nuclear Forensic Inferences Using Iterative Multidimensional Statistics. In Proceedings of the Institute of Nuclear Materials Management 50th Annual Meeting, Tucson, AZ, July 2009; 12 pages; Nicolaou, G. J. Environ. Radioact. 2006, 86, 313-318; Pajo, L.; Mayer, K.; Koch, L. Fresenius' J. Anal. Chem. 2001, 371, 348-352). There has been some success in using such multidimensional statistical methods to determine details about the history of spent reactor fuel (Robel, M.; Kristo, M. J. J. Environ. Radioact. 2008, 99, 1789-1797). Here, we aim to expand on these findings by pursuing more robust dimensionality reduction techniques based on manifold embedding which are able to better capture the intrinsic data set information. Furthermore, we demonstrate the use of a number of classification algorithms to reliably determine the reactor type in which a spent fuel material has been irradiated. A number of these classification techniques are novel applications in nuclear forensics and expand on the existing knowledge in this field by creating a reliable and robust classification model. The results from this analysis show that our techniques have been very successful and further ascertain the excellent potential of these techniques in the field of nuclear forensics at least with regard to spent reactor fuel.

EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction With Exogenous Variables.

While exogenous variables have a major impact on performance improvement in time series analysis, interseries correlation and time dependence among them are rarely considered in the present continuous methods. The dynamical systems of multivariate time series could be modeled with complex unknown partial differential equations (PDEs) which play a prominent role in many disciplines of science and engineering. In this article, we propose a continuous-time model for arbitrary-step prediction to learn an unknown PDE system in multivariate time series whose governing equations are parameterized by self-attention and gated recurrent neural networks. The proposed model, exogenous-guided PDE network (EgPDE-Net), takes account of the relationships among the exogenous variables and their effects on the target series. Importantly, the model can be reduced into a regularized ordinary differential equation (ODE) problem with specially designed regularization guidance, which makes the PDE problem tractable to obtain numerical solutions and feasible to predict multiple future values of the target series at arbitrary time points. Extensive experiments demonstrate that our proposed model could achieve competitive accuracy over strong baselines: on average, it outperforms the best baseline by reducing 9.85% on RMSE and 13.98% on MAE for arbitrary-step prediction.

Generalized image outpainting with U-transformer.

In this paper, we develop a novel transformer-based generative adversarial neural network called U-Transformer for generalized image outpainting problems. Different from most present image outpainting methods conducting horizontal extrapolation, our generalized image outpainting could extrapolate visual context all-side around a given image with plausible structure and details even for complicated scenery, building, and art images. Specifically, we design a generator as an encoder-to-decoder structure embedded with the popular Swin Transformer blocks. As such, our novel neural network can better cope with image long-range dependencies which are crucially important for generalized image outpainting. We propose additionally a U-shaped structure and multi-view Temporal Spatial Predictor (TSP) module to reinforce image self-reconstruction as well as unknown-part prediction smoothly and realistically. By adjusting the predicting step in the TSP module in the testing stage, we can generate arbitrary outpainting size given the input sub-image. We experimentally demonstrate that our proposed method could produce visually appealing results for generalized image outpainting against the state-of-the-art image outpainting approaches.

Densest plane group packings of regular polygons.

Packings of regular convex polygons (n-gons) that are sufficiently dense have been studied extensively in the context of modeling physical and biological systems as well as discrete and computational geometry. Former results were mainly regarding densest lattice or double-lattice configurations. Here we consider all two-dimensional crystallographic symmetry groups (plane groups) by restricting the configuration space of the general packing problem of congruent copies of a compact subset of the two-dimensional Euclidean space to particular isomorphism classes of the discrete group of isometries. We formulate the plane group packing problem as a nonlinear constrained optimization problem. By means of the Entropic Trust Region Packing Algorithm that approximately solves this problem, we examine some known and unknown densest packings of various n-gons in all 17 plane groups and state conjectures about common symmetries of the densest plane group packings for every n-gon.

Discriminative Triad Matching and Reconstruction for Weakly Referring Expression Grounding.

In this paper, we are tackling the weakly-supervised referring expression grounding task, for the localization of a referent object in an image according to a query sentence, where the mapping between image regions and queries are not available during the training stage. In traditional methods, an object region that best matches the referring expression is picked out, and then the query sentence is reconstructed from the selected region, where the reconstruction difference serves as the loss for back-propagation. The existing methods, however, conduct both the matching and the reconstruction approximately as they ignore the fact that the matching correctness is unknown. To overcome this limitation, a discriminative triad is designed here as the basis to the solution, through which a query can be converted into one or multiple discriminative triads in a very scalable way. Based on the discriminative triad, we further propose the triad-level matching and reconstruction modules which are lightweight yet effective for the weakly-supervised training, making it three times lighter and faster than the previous state-of-the-art methods. One important merit of our work is its superior performance despite the simple and neat design. Specifically, the proposed method achieves a new state-of-the-art accuracy when evaluated on RefCOCO (39.21 percent), RefCOCO+ (39.18 percent) and RefCOCOg (43.24 percent) datasets, that is 4.17, 4.08 and 7.8 percent higher than the previous one, respectively. The code is available at https://github.com/insomnia94/DTWREG.

An Interpretable Deep Architecture for Similarity Learning Built Upon Hierarchical Concepts.

In general, development of adequately complex mathematical models, such as deep neural networks, can be an effective way to improve the accuracy of learning models. However, this is achieved at the cost of reduced post-hoc model interpretability, because what is learned by the model can become less intelligible and tractable to humans as the model complexity increases. In this paper, we target a similarity learning task in the context of image retrieval, with a focus on the model interpretability issue. An effective similarity neural network (SNN) is proposed to offer not only to seek robust retrieval performance but also to achieve satisfactory post-hoc interpretability. The network is designed by linking the neuron architecture with the organization of a concept tree and by formulating neuron operations to pass similarity information between concepts. Various ways of understanding and visualizing what is learned by the SNN neurons are proposed. We also exhaustively evaluate the proposed approach using a number of relevant datasets against a number of state-of-the-art approaches to demonstrate the effectiveness of the proposed network. Our results show that the proposed approach can offer superior performance when compared against state-of-the-art approaches. Neuron visualization results are demonstrated to support the understanding of the trained neurons.

Activity identification using body-mounted sensors--a review of classification techniques.

With the advent of miniaturized sensing technology, which can be body-worn, it is now possible to collect and store data on different aspects of human movement under the conditions of free living. This technology has the potential to be used in automated activity profiling systems which produce a continuous record of activity patterns over extended periods of time. Such activity profiling systems are dependent on classification algorithms which can effectively interpret body-worn sensor data and identify different activities. This article reviews the different techniques which have been used to classify normal activities and/or identify falls from body-worn sensor data. The review is structured according to the different analytical techniques and illustrates the variety of approaches which have previously been applied in this field. Although significant progress has been made in this important area, there is still significant scope for further work, particularly in the application of advanced classification techniques to problems involving many different activities.

Predicting aspiration after hemispheric stroke from timing measures of oropharyngeal bolus flow and laryngeal closure.

Deglutitive aspiration is common after stroke, affecting up to 50% of patients and predisposing them to pneumonia, yet it is virtually impossible to predict those patients at greatest risk. The aim of this study was to develop a robust predictive model for aspiration after stroke. Swallowing was assessed by digital videofluoroscopy (VF) in 90 patients following hemispheric stroke. Lesion characteristics were determined by computerized tomography (CT) brain scan using the Alberta Stroke Programme Early CT Score (ASPECTS). Aspiration severity was measured using a validated penetration-aspiration scale. The probability of aspiration was then determined from measures of swallowing pathophysiology and lesion location by discriminant analysis. Aspiration was observed in 47 (52%) patients, yet despite disrupted swallowing physiology, intrasubject aspiration scores were variable. The best discriminant model combined pharyngeal transit time, swallow response time, and laryngeal closure duration to predict 73.11% of those aspirating (sensitivity = 66.54, specificity = 80.22, p > 0.001). The addition of lesion location did not add anything further to the predictive model. We conclude that the pathophysiology of poststroke aspiration is multifactorial but in most cases can be predicted by three key swallowing measurements. These measurements, if translatable into clinical bedside evaluation, may assist with the development of novel measurement and intervention techniques to detect and treat poststroke aspiration.

Movement variability in stroke patients and controls performing two upper limb functional tasks: a new assessment methodology.

BACKGROUND: In the evaluation of upper limb impairment post stroke there remains a gap between detailed kinematic analyses with expensive motion capturing systems and common clinical assessment tests. In particular, although many clinical tests evaluate the performance of functional tasks, metrics to characterise upper limb kinematics are generally not applicable to such tasks and very limited in scope. This paper reports on a novel, user-friendly methodology that allows for the assessment of both signal magnitude and timing variability in upper limb movement trajectories during functional task performance. In order to demonstrate the technique, we report on a study in which the variability in timing and signal magnitude of data collected during the performance of two functional tasks is compared between a group of subjects with stroke and a group of individually matched control subjects. METHODS: We employ dynamic time warping for curve registration to quantify two aspects of movement variability: 1) variability of the timing of the accelerometer signals' characteristics and 2) variability of the signals' magnitude. Six stroke patients and six matched controls performed several trials of a unilateral ('drinking') and a bilateral ('moving a plate') functional task on two different days, approximately 1 month apart. Group differences for the two variability metrics were investigated on both days. RESULTS: For 'drinking from a glass' significant group differences were obtained on both days for the timing variability of the acceleration signals' characteristics (p = 0.002 and p = 0.008 for test and retest, respectively); all stroke patients showed increased signal timing variability as compared to their corresponding control subject. 'Moving a plate' provided less distinct group differences. CONCLUSION: This initial application establishes that movement variability metrics, as determined by our methodology, appear different in stroke patients as compared to matched controls during unilateral task performance ('drinking'). Use of a user-friendly, inexpensive accelerometer makes this methodology feasible for routine clinical evaluations. We are encouraged to perform larger studies to further investigate the metrics' usefulness when quantifying levels of impairment.

Multivoxel fMRI analysis of color tuning in human primary visual cortex.

We use multivoxel pattern analysis (MVPA) to study the spatial clustering of color-selective neurons in the human brain. Our main objective was to investigate whether MVPA reveals the spatial arrangements of color-selective neurons in human primary visual cortex (V1). We measured the distributed fMRI activation patterns for different color stimuli (Experiment 1: cardinal colors (to which the LGN is known to be tuned), Experiment 2: perceptual hues) in V1. Our two main findings were that (i) cone-opponent cardinal color modulations produce highly reproducible patterns of activity in V1, but these were not unique to each color. This suggests that V1 neurons with tuning characteristics similar to those found in LGN are not spatially clustered. (ii) Unique activation patterns for perceptual hues in V1 support current evidence for a spatially clustered hue map. We believe that our work is the first to show evidence of spatial clustering of neurons with similar color preferences in human V1.

Pedobarographic statistical parametric mapping (pSPM): a pixel-level approach to foot pressure image analysis.

Traditional pedobarographic analyses conduct statistical tests on single pressure values extracted from discrete anatomical regions, a process which yields a low-resolution view of the continuous foot-ground interaction and which can involve substantial user interaction for region definition. Using image processing techniques derived from a cerebral imaging methodology called 'statistical parametric mapping' (SPM), we describe a fully automatic method that requires no anatomical assumptions or region definitions and that generates high-resolution continuous statistical maps across the entire plantar foot surface. Here, we demonstrate both pedobarographic SPM (pSPM) and its robustness to arbitrary foot postures by producing statistical maps for a sample of nine healthy young adults walking: normally, with everted feet, and with inverted feet. After spatially smoothing pedobarographic images, within-subjects (WS) and between-subjects (BS) registration were performed using an optimal rigid body transformation and an optimum affine transformation, respectively. Statistical tests were performed over all 742 foot pixels of the 270 registered images using a linear mass-univariate model and the resulting SPMs were compared qualitatively with results obtained using a traditional ten-region technique. SPMs were found to provide a qualitatively improved view of pedobarographic changes, but the more important finding was that regional pedobarographic statistics can misrepresent the trends of their constituent pixels and thus potentially lead to misinterpretations of foot function. Since pSPM is fully non-interactive, is robust to arbitrary foot posture, and provides rapid and easily interpretable results, it appears to be a suitable alternative to regionalization for routine pedobarographic analyses in both laboratory and clinic.

A comparison of seven methods of within-subjects rigid-body pedobarographic image registration.

Image registration, the process of transforming images such that homologous structures optimally overlap, provides the pre-processing foundation for pixel-level functional image analysis. The purpose of this study was to compare the performances of seven methods of within-subjects pedobarographic image registration: (1) manual, (2) principal axes, (3) centre of pressure trajectory, (4) mean squared error, (5) probability-weighted variance, (6) mutual information, and (7) exclusive OR. We assumed that foot-contact geometry changes were negligibly small trial-to-trial and thus that a rigid-body transformation could yield optimum registration performance. Thirty image pairs were randomly selected from our laboratory database and were registered using each method. To compensate for inter-rater variability, the mean registration parameters across 10 raters were taken as representative of manual registration. Registration performance was assessed using four dissimilarity metrics (#4-7 above). One-way MANOVA found significant differences between the methods (p<0.001). Bonferroni post-hoc tests revealed that the centre of pressure method performed the poorest (p<0.001) and that the principal axes method tended to perform more poorly than remaining methods (p<0.070). Average manual registration was not different from the remaining methods (p=1.000). The results suggest that a variety of linear registration methods are appropriate for within-subjects pedobarographic images, and that manual image registration is a viable alternative to algorithmic registration when parameters are averaged across raters. The latter finding, in particular, may be useful for cases of image peculiarities resulting from outlier trials or from experimental manipulations that induce substantial changes in contact area or pressure profile geometry.

New insights into the plantar pressure correlates of walking speed using pedobarographic statistical parametric mapping (pSPM).

This study investigates the relation between walking speed and the distribution of peak plantar pressure and compares a traditional ten-region subsampling (10RS) technique with a new technique: pedobarographic statistical parametric mapping (pSPM). Adapted from cerebral fMRI methodology, pSPM is a digital image processing technique that registers foot pressure images such that homologous structures optimally overlap, thereby enabling statistical tests to be conducted at the pixel level. Following previous experimental protocols, we collected pedobarographic records from 10 subjects walking at three different speeds: slow, normal, and fast. Walking speed was recorded and correlated with the peak pressures extracted from the 10 regions, and subsequently with the peak pixel data extracted after pSPM preprocessing. Both methods revealed significant positive correlation between peak plantar pressure and walking speed over the rearfoot and distal forefoot after Bonferroni correction for multiple comparisons. The 10RS analysis found positive correlation in the midfoot and medial proximal forefoot, but the pixel data exhibited significant negative correlation throughout these regions (p<5x10(-5)). Comparing the statistical maps from the two approaches shows that subsampling may conflate pressure differences evident in pixel-level data, obscuring or even reversing statistical trends. The negative correlation observed in the midfoot implies reduced longitudinal arch collapse with higher walking speeds. We infer that this results from pre- or early-stance phase muscle activity and speculate that preferred walking speed reflects, in part, a balance between the energy required to tighten the longitudinal arch and the apparent propulsive benefits of the stiffened arch.

Density-driven generalized regression neural networks (DD-GRNN) for function approximation.

This paper proposes a new nonparametric regression method, based on the combination of generalized regression neural networks (GRNNs), density-dependent multiple kernel bandwidths, and regularization. The presented model is generic and substitutes the very large number of bandwidths with a much smaller number of trainable weights that control the regression model. It depends on sets of extracted data density features which reflect the density properties and distribution irregularities of the training data sets. We provide an efficient initialization scheme and a second-order algorithm to train the model, as well as an overfitting control mechanism based on Bayesian regularization. Numerical results show that the proposed network manages to reduce significantly the computational demands of having individual bandwidths, while at the same time, provides competitive function approximation accuracy in relation to existing methods.

Generalized regression neural networks with multiple-bandwidth sharing and hybrid optimization.

This paper proposes a novel algorithm for function approximation that extends the standard generalized regression neural network. Instead of a single bandwidth for all the kernels, we employ a multiple-bandwidth configuration. However, unlike previous works that use clustering of the training data for the reduction of the number of bandwidths, we propose a distinct scheme that manages a dramatic bandwidth reduction while preserving the required model complexity. In this scheme, the algorithm partitions the training patterns to groups, where all patterns within each group share the same bandwidth. Grouping relies on the analysis of the local nearest neighbor distance information around the patterns and the principal component analysis with fuzzy clustering. Furthermore, we use a hybrid optimization procedure combining a very efficient variant of the particle swarm optimizer and a quasi-Newton method for global optimization and locally optimal fine-tuning of the network bandwidths. Training is based on the minimization of a flexible adaptation of the leave-one-out validation error that enhances the network generalization. We test the proposed algorithm with real and synthetic datasets, and results show that it exhibits competitive regression performance compared to other techniques.

Unsupervised t-Distributed Video Hashing and Its Deep Hashing Extension.

In this paper, a novel unsupervised hashing algorithm, referred to as t-USMVH, and its extension to unsupervised deep hashing, referred to as t-UDH, are proposed to support large-scale video-to-video retrieval. To improve robustness of the unsupervised learning, the t-USMVH combines multiple types of feature representations and effectively fuses them by examining a continuous relevance score based on a Gaussian estimation over pairwise distances, and also a discrete neighbor score based on the cardinality of reciprocal neighbors. To reduce sensitivity to scale changes for mapping objects that are far apart from each other, Student t-distribution is used to estimate the similarity between the relaxed hash code vectors for keyframes. This results in more accurate preservation of the desired unsupervised similarity structure in the hash code space. By adapting the corresponding optimization objective and constructing the hash mapping function via a deep neural network, we develop a robust unsupervised training strategy for a deep hashing network. The efficiency and effectiveness of the proposed methods are evaluated on two public video collections via comparisons against multiple classical and the state-of-the-art methods.

Raman spectroscopy of uranium compounds and the use of multivariate analysis for visualization and classification.

Raman spectroscopy was used on 95 samples comprising mainly of uranium ore concentrates as well as some UF4 and UO2 samples, in order to classify uranium compounds for nuclear forensic purposes, for the first time. This technique was selected as it is non-destructive and rapid. The spectra obtained from 9 different classes of chemical compounds were subjected to multivariate data analysis such as principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA) and Fisher Discriminant Analysis (FDA). These classes were ammonium diuranate (ADU), sodium diuranate (SDU), ammonium uranyl carbonate (AUC), uranyl hydroxide (UH), UO2, UO3, UO4, U3O8 and UF4. Unsupervised PCA of full spectra shows fairly good distinction among the classes with some overlaps observed with ADU and UH. These overlaps are also reflected in the poorer specificities determined by PLS-DA. Higher values of sensitivities and specificities of remaining compounds were obtained. Supervised FDA based on reduced dataset of only 40 variables shows similar results to that of PCA but with closer clustering of ADU, UH, SDU, AUC. As a rapid and non-destructive technique, Raman spectroscopy is useful and complements existing techniques in multi-faceted nuclear forensics.

Automated detection of instantaneous gait events using time frequency analysis and manifold embedding.

Accelerometry is a widely used sensing modality in human biomechanics due to its portability, non-invasiveness, and accuracy. However, difficulties lie in signal variability and interpretation in relation to biomechanical events. In walking, heel strike and toe off are primary gait events where robust and accurate detection is essential for gait-related applications. This paper describes a novel and generic event detection algorithm applicable to signals from tri-axial accelerometers placed on the foot, ankle, shank or waist. Data from healthy subjects undergoing multiple walking trials on flat and inclined, as well as smooth and tactile paving surfaces is acquired for experimentation. The benchmark timings at which heel strike and toe off occur, are determined using kinematic data recorded from a motion capture system. The algorithm extracts features from each of the acceleration signals using a continuous wavelet transform over a wide range of scales. A locality preserving embedding method is then applied to reduce the high dimensionality caused by the multiple scales while preserving salient features for classification. A simple Gaussian mixture model is then trained to classify each of the time samples into heel strike, toe off or no event categories. Results show good detection and temporal accuracies for different sensor locations and different walking terrains.

Adaptive data embedding framework for multiclass classification.

The objective of this paper is the design of an engine for the automatic generation of supervised manifold embedding models. It proposes a modular and adaptive data embedding framework for classification, referred to as DEFC, which realizes in different stages including initial data preprocessing, relation feature generation and embedding computation. For the computation of embeddings, the concepts of friend closeness and enemy dispersion are introduced, to better control at local level the relative positions of the intraclass and interclass data samples. These are shown to be general cases of the global information setup utilized in the Fisher criterion, and are employed for the construction of different optimization templates to drive the DEFC model generation. For model identification, we use a simple but effective bilevel evolutionary optimization, which searches for the optimal model and its best model parameters. The effectiveness of DEFC is demonstrated with experiments using noisy synthetic datasets possessing nonlinear distributions and real-world datasets from different application fields.