Flexible Representation Research Articles

Multimodality Image Fusion Based on Quantum Wavelet Transform and Sum-Modified-Laplacian Rule

The parallelism and entanglement characteristics of quantum computation greatly improve the efficiency of image processing tasks. With the sharp increase of data size and requirement of real-time processing in image fusion application, rapid implementation using quantum computation will become the inexorable trend. A novel multimodality image fusion algorithm based on quantum wavelet transform (QWT) and proposed quantum version of sum-modified-laplacian (SML) rule is designed in this paper. The source digital images are firstly represented by flexible representation of quantum image (FRQI) model, and then the quantum form images are transformed with QWT to capture salient features of source images. The quantum version of SML rule is proposed to fuse wavelet coefficients, which has higher efficiency and runs faster than its classical counterpart. The final fused image is obtained by using inverse quantum wavelet transform. The simulations and theoretical analysis verify that the proposed algorithm is effective in the fusion of multimodality images.

Dual Quantum Audio Watermarking Schemes Based on Quantum Discrete Cosine Transform

The study explores the implementation of watermarking schemes for quantum audio signals based on quantum Discrete Cosine Transform (qDCT). Dual Quantum Audio Watermarking (QAW) schemes, i.e. QAW-I and QAW-II, are proposed by utilizing the property of strong energy compaction of the qDCT. The qDCT transforms the host quantum audio signal which is encoded as an FRQA (Flexible Representation for Quantum Audio) content from time to frequency domain. To retain the high imperceptibility of the dual QAW schemes, the quantum watermark is embedded into the high-frequency portions of host quantum audio signal after the qDCT operation. The circuit networks to execute the embedding and extraction procedures of QAW-I and QAW-II schemes are designed. In addition, simulation-based experiments are conducted to demonstrate their implementation and conclude their advantages in terms of imperceptibility and robustness.

Face recognition using nonnegative matrix factorization with fractional power inner product kernel

Kernel nonnegative matrix factorization (KNMF) algorithms have been widely used to extract features for face recognition. The choice of kernel function is vital to facial feature extraction. The polynomial kernel function has been commonly used in KNMF. The power of the polynomial kernel is required to be a positive integer, thereby ensuring that the kernel generates a positive semi-definite matrix. In this paper, we investigate a new type of inner-product kernel that has a fractional power. The new kernel offers us flexibility in data representation as the power can be any positive real number. Based on the fractional power inner-product kernel, we present a novel KNMF algorithm called fractional power inner-product KNMF (FPKNMF). The FPKNMF algorithm is theoretically and experimentally validated to be convergent. The experimental results confirm that our algorithm exhibits a performance superior to the state-of-the-art methods in terms of facial representation and recognition accuracy.

Machine Learning Reveals Missing Edges and Putative Interaction Mechanisms in Microbial Ecosystem Networks.

Microbes affect each other's growth in multiple, often elusive, ways. The ensuing interdependencies form complex networks, believed to reflect taxonomic composition as well as community-level functional properties and dynamics. The elucidation of these networks is often pursued by measuring pairwise interactions in coculture experiments. However, the combinatorial complexity precludes an exhaustive experimental analysis of pairwise interactions, even for moderately sized microbial communities. Here, we used a machine learning random forest approach to address this challenge. In particular, we show how partial knowledge of a microbial interaction network, combined with trait-level representations of individual microbial species, can provide accurate inference of missing edges in the network and putative mechanisms underlying the interactions. We applied our algorithm to three case studies: an experimentally mapped network of interactions between auxotrophic Escherichia coli strains, a community of soil microbes, and a large in silico network of metabolic interdependencies between 100 human gut-associated bacteria. For this last case, 5% of the network was sufficient to predict the remaining 95% with 80% accuracy, and the mechanistic hypotheses produced by the algorithm accurately reflected known metabolic exchanges. Our approach, broadly applicable to any microbial or other ecological network, may drive the discovery of new interactions and new molecular mechanisms, both for therapeutic interventions involving natural communities and for the rational design of synthetic consortia. IMPORTANCE Different organisms in a microbial community may drastically affect each other's growth phenotypes, significantly affecting the community dynamics, with important implications for human and environmental health. Novel culturing methods and the decreasing costs of sequencing will gradually enable high-throughput measurements of pairwise interactions in systematic coculturing studies. However, a thorough characterization of all interactions that occur within a microbial community is greatly limited both by the combinatorial complexity of possible assortments and by the limited biological insight that interaction measurements typically provide without laborious specific follow-ups. Here, we show how a simple and flexible formal representation of microbial pairs can be used for the classification of interactions via machine learning. The approach we propose predicts with high accuracy the outcome of yet-to-be performed experiments and generates testable hypotheses about the mechanisms of specific interactions.

Low-Resolution Place and Response Learning Capacities in Down Syndrome.

Down syndrome (DS), the most common genetic cause of intellectual disability, results from the partial or complete triplication of chromosome 21. Individuals with DS are impaired at using a high-resolution, allocentric spatial representation to learn and remember discrete locations in a controlled environment. Here, we assessed the capacity of individuals with DS to perform low-resolution spatial learning, depending on two competing memory systems: (1) the place learning system, which depends on the hippocampus and creates flexible relational representations of the environment; and (2) the response learning system, which depends on the striatum and creates fixed stimulus–response representations of behavioral actions. Individuals with DS exhibited a preservation of the low-resolution spatial learning capacities subserved by these two systems. In place learning, although the average performance of individuals with DS was lower than that of typically developing (TD) mental age (MA)-matched children and TD young adults, the number of individuals with DS performing above chance level did not differ from TD children. In response learning, the average performance of individuals with DS was lower than that of TD adults, but it did not differ from that of TD children. Moreover, the number of individuals with DS performing above chance level did not differ from TD adults, and was higher than that of TD children. In sum, whereas low-resolution place learning appears relatively preserved in individuals with DS, response learning appears facilitated. Our findings are consistent with the hypothesis that the neural pathways supporting low-resolution place learning and response learning are relatively preserved in DS.

Children's Mental Models of Prenatal Development.

Children's thinking about prenatal development requires reasoning about change that cannot be observed directly. How do children gain knowledge about this topic? Do children have mental models or is their knowledge fragmented? In Experiment 1, results of a forced-choice questionnaire about prenatal development (6- to 13-year-olds; N = 317) indicated that children do have a variety of coherent, grade-related, theories about early shape of the fetus, but not about bodily functions. Coherence of the mental models was enhanced by a preceding generative task. Children's mental models were in agreement with reasoning about natural transformations (Rosengren et al., 1991) and constraints in representational flexibility (Karmiloff-Smith, 1992). In Experiment 2, an open-question interview was administered (6- to 12-year-old children; N = 38). The interview resulted in grade-unrelated, incoherent responses. This study contributes to a deeper understanding of naïve biology and to the effects of different methodologies being used in the area of mental models.

Conjunctive coding in an evolved spiking model of retrosplenial cortex.

Retrosplenial cortex (RSC) is an association cortex supporting spatial navigation and memory. However, critical issues remain concerning the forms by which its ensemble spiking patterns register spatial relationships that are difficult for experimental techniques to fully address. We therefore applied an evolutionary algorithmic optimization technique to create spiking neural network models that matched electrophysiologically observed spiking dynamics in rat RSC neuronal ensembles. Virtual experiments conducted on the evolved networks revealed a mixed selectivity coding capability that was not built into the optimization method, but instead emerged as a consequence of replicating biological firing patterns. The experiments reveal several important outcomes of mixed selectivity that may subserve flexible navigation and spatial representation: (a) robustness to loss of specific inputs, (b) immediate and stable encoding of novel routes and route locations, (c) automatic resolution of input variable conflicts, and (d) dynamic coding that allows rapid adaptation to changing task demands without retraining. These findings suggest that biological retrosplenial cortex can generate unique, first-trial, conjunctive encodings of spatial positions and actions that can be used by downstream brain regions for navigation and path integration. Moreover, these results are consistent with the proposed role for the RSC in the transformation of representations between reference frames and navigation strategy deployment. Finally, the specific modeling framework used for evolving synthetic retrosplenial networks represents an important advance for computational modeling by which synthetic neural networks can encapsulate, describe, and predict the behavior of neural circuits at multiple levels of function. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Optimal Piecewise Linear Regression Algorithm for QSAR Modelling.

Quantitative Structure-Activity Relationship (QSAR) models have been successfully applied to lead optimisation, virtual screening and other areas of drug discovery over the years. Recent studies, however, have focused on the development of models that are predictive but often not interpretable. In this article, we propose the application of a piecewise linear regression algorithm, OPLRAreg, to develop both predictive and interpretable QSAR models. The algorithm determines a feature to best separate the data into regions and identifies linear equations to predict the outcome variable in each region. A regularisation term is introduced to prevent overfitting problems and implicitly selects the most informative features. As OPLRAreg is based on mathematical programming, a flexible and transparent representation for optimisation problems, the algorithm also permits customised constraints to be easily added to the model. The proposed algorithm is presented as a more interpretable alternative to other commonly used machine learning algorithms and has shown comparable predictive accuracy to Random Forest, Support Vector Machine and Random Generalised Linear Model on tests with five QSAR data sets compiled from the ChEMBL database.

Flexibility of categorical body representation following limb-loss and prosthesis usage in the occipitotemporal cortex

Flexibility of categorical body representation following limb-loss and prosthesis usage in the occipitotemporal cortex

Optimal multi-object segmentation with novel gradient vector flow based shape priors

Shape priors have been widely utilized in medical image segmentation to improve segmentation accuracy and robustness. A major way to encode such a prior shape model is to use a mesh representation, which is prone to causing self-intersection or mesh folding. Those problems require complex and expensive algorithms to mitigate. In this paper, we propose a novel shape prior directly embedded in the voxel grid space, based on gradient vector flows of a pre-segmentation. The flexible and powerful prior shape representation is ready to be extended to simultaneously segmenting multiple interacting objects with minimum separation distance constraint. The segmentation problem of multiple interacting objects with shape priors is formulated as a Markov Random Field problem, which seeks to optimize the label assignment (objects or background) for each voxel while keeping the label consistency between the neighboring voxels. The optimization problem can be efficiently solved with a single minimum s-t cut in an appropriately constructed graph.The proposed algorithm has been validated on two multi-object segmentation applications: the brain tissue segmentation in MRI images and the bladder/prostate segmentation in CT images. Both sets of experiments showed superior or competitive performance of the proposed method to the compared state-of-the-art methods.

Retrospective Cues Mitigate Information Loss in Human Cortex during Working Memory Storage.

Working memory (WM) enables the flexible representation of information over short intervals. It is well established that WM performance can be enhanced by a retrospective cue presented during storage, yet the neural mechanisms responsible for this benefit are unclear. Here, we tested several explanations for retrospective cue benefits by quantifying changes in spatial WM representations reconstructed from alpha-band (8-12 Hz) EEG activity recorded from human participants (both sexes) before and after the presentation of a retrospective cue. This allowed us to track cue-related changes in WM representations with high temporal resolution (tens of milliseconds). Participants encoded the locations of two colored discs for subsequent report. During neutral trials, an uninformative cue instructed participants to remember the locations of both discs across a blank delay, and we observed a monotonic decrease in the fidelity of reconstructed spatial WM representations with time. During valid trials, a 100% reliable cue indicated that the color of the disc participants would be probed to report. Critically, valid cues were presented immediately after the termination of the encoding display ["valid early" (VE) trials] or midway through the delay period ["valid late" (VL) trials]. During VE trials, the gradual loss of location-specific information observed during neutral trials was eliminated, while during VL trials it was partially reversed. Our findings suggest that retrospective cues engage several different mechanisms that together serve to mitigate information loss during WM storage.SIGNIFICANCE STATEMENT Working memory (WM) performance can be improved by a cue presented during storage. This effect, termed a retrospective cue benefit, has been used to explore the limitations of attentional prioritization in WM. However, the mechanisms responsible for retrospective cue benefits are unclear. Here we tested several explanations for retrospective cue benefits by examining how they influence WM representations reconstructed from human EEG activity. This approach allowed us to visualize, quantify, and track the effects of retrospective cues with high temporal resolution (on the order of tens of milliseconds). We show that under different circumstances retrospective cues can both eliminate and even partially reverse information loss during WM storage, suggesting that retrospective cue benefits have manifold origins.

Quantum Representation and Basic Operations of Digital Signals

This paper presented a quantum representation and some basic operations for one-dimensional finite length digital signal. For the representation, similar to the existing flexible representation of quantum audio (FRQA), two sets of qubits are used to represent the amplitudes and positions of the signal, respectively, wherein the amplitude is represented by an (n + 1)-bit signed number in twos complement, including one sign bit, m integer bits and n − m fractional bits. The basic operations include the addition, subtraction, multiplication, division, and circular convolution of two digital signals. We designed the quantum circuits that implements these basic operations, analyzed the computational complexity, and verified the correctness of these circuits by simulations on classical computers.

Evolution modeling with multi-scale smoothing for action recognition

The aim of this paper is to model long-term evolution of an action video with temporal multi-scale representation. This task is tough due to huge intra-class variations in motion speed. Most of the existing methods consider evolution modeling and multi-scale feature fusion in two separated phases, which generates sub-optimal representation. To address this issue, this paper proposes a novel method to integrate the evolution modeling and multi-scale representation into a unified framework. The core idea is to introduce a temporal multi-scale smoothing vector, which is used to define how the representations at different temporal scales are combined together for frame smoothing. By formulating the smoothing vector learning, evolution modeling and classifier training jointly, our method can learn a discriminative and flexible representation of multi-scale rather than a single scale or a fixed multi-scale smoothing. Experimental results on three datasets demonstrate the effectiveness of our method.

Object representations and the influence of learning: Insights from single neurons in the human brain

Objectives Learning associations between co-occurring events enables us to extract structure from our environment. However, the neuronal processes underlying the formation of new associations in the human brain are not yet well understood. Here I will present evidence for sparse representations of concepts in the human medial temporal lobe (MTL) and show how these representations can be modified by short-term learning. Methods and results Human participants, implanted with depth electrodes, learned arbitrary associations between images presented in an ordered, predictable sequence. During learning we recorded from MTL neurons and found that single MTL neurons showed asymmetric shifts in activity and started firing earlier in the sequence in anticipation of their preferred stimulus. In contrast, neurons in temporal cortex did not show anticipation effects as a result of this short-term learning. Conclusion These results demonstrate flexible representations in the MTL that could support rapid learning.

Audio inpainting: Evaluation of time-frequency representations and structured sparsity approaches

Abstract Audio signals such as music are known to exhibit distinct and sparse time-frequency patterns. In particular, the short-time Fourier/Gabor transform is widely used for sparsely representing audio signals. In this contribution, such sparsity patterns are exploited to reconstruct missing samples. The quality of reconstruction is evaluated for various kinds of proximal splitting algorithms, time-frequency discretizations and sparsity enforcing constraints. Furthermore, given a time-frequency representation, we investigate the performance of synthesis vs. analysis approaches to the reconstruction problem. The equidistant discretization scheme of Gabor transforms is non-adaptive. It is plausible, that more flexible time-frequency representations like wavelets or ERBlets improve reconstruction of missing audio samples. The numerical results presented in this contribution confirm this conjecture and lead to good reconstruction results even for gaps of contiguously missing samples, whose size is notably larger than reported for inpainting experiments carried out previously.

Functional cerebral asymmetry analyses reveal how the control system implements its flexibility.

The control system in human brain generally exerts the goal-directed regulation on a variety of mental processes. To deal with different control demands, these brain areas of the control system, especially the dorsolateral prefrontal cortex (DLPFC), may be flexibly recruited across different tasks. However, few studies have investigated how the flexibility of the control system is realized during cognitive control. Present study employed functional magnetic resonance imaging to examine the brain responses during two domain distinct conflict tasks (verbal color-word Stroop and visuospatial arrow flanker). The voxel-wise asymmetries in both functional activity and psychophysiological interaction (PPI) between these two tasks were compared. The results showed that the brain areas of control system were consistently activated in these two tasks. When considering functional cerebral asymmetries, the left DLPFC was dominantly activated during the Stroop task, while more symmetric DLPFC activation was found during the flanker task. The left DLPFC rather than the right DLPFC showed greater positive interaction with the visual areas V1 and V2 during the Stroop interference, but interactions of both the left and right DLPFC with the right visual area V5/MT were positively enhanced during the flanker interference. These results suggest that the flexible cognitive control is achieved by the control system's task-specific activity and its top-down interaction with domain-specific brain areas, in implementing flexible representation and modulation of control demands.

Analytical metadata modeling for next generation BI systems

Business Intelligence (BI) systems are extensively used as in-house solutions to support decision-making in organizations. Next generation BI 2.0 systems claim for expanding the use of BI solutions to external data sources and assisting the user in conducting data analysis. In this context, the Analytical Metadata (AM) framework defines the metadata artifacts (e.g., schema and queries) that are exploited for user assistance purposes. As such artifacts are typically handled in ad-hoc and system specific manners, BI 2.0 argues for a flexible solution supporting metadata exploration across different systems.In this paper, we focus on the AM modeling. We propose SM4AM, an RDF-based Semantic Metamodel for AM. On the one hand, we claim for ontological metamodeling as the proper solution, instead of a fixed universal model, due to (meta)data models heterogeneity in BI 2.0. On the other hand, RDF provides means for facilitating defining and sharing flexible metadata representations. Furthermore, we provide a method to instantiate our metamodel. Finally, we present a real-world case study and discuss how SM4AM, specially the schema and query artifacts, can help traversing different models instantiating our metamodel and enabling innovative means to explore external repositories in what we call metamodel-driven (meta)data exploration.

Cognitive Maps: Some People Make Them, Some People Struggle

The proposal that humans can develop cognitive maps of their environment has a long and controversial history. We suggest an individual-differences approach to this question instead of a normative one. Specifically, there is evidence that some people derive flexible maplike representations from information acquired during navigation, whereas others store much less accurate information. Our research uses a virtual-reality paradigm in which two routes are learned and must be related to each other. It defines three groups: integrators, nonintegrators, and imprecise navigators. These groups show distinctive patterns of spatial skills and working memory, as well as personality. We contrast our approach with research challenging the cognitive-map hypothesis and offer directions for rapprochement between the two views.

Modelación del perfil fustal y volumen total para Pinus ayacahuite Ehren

El perfil del fuste o ahusamiento describe el cambio del diámetro conforme aumenta la altura del árbol a partir del tocón. Para describir esta variación y cuantificar las secciones del fuste de árboles en pie, se han utilizado modelos matemáticos, los cuales constituyen una herramienta estadística flexible para procesar datos y determinar existencias y productos maderables. El objetivo de esta investigación fue modelar el ahusamiento de Pinus ayacahuite Ehren que se distribuye en rodales mezclados bajo manejo forestal, en la región Sierra Norte de Oaxaca. Con datos de 55 individuos de la especie estudiada se ensayaron cinco modelos de ahusamiento cuyos ajustes fueron por regresión lineal y no lineal. La evaluación y selección de los modelos más apropiados se hizo con base en indicadores estadísticos de bondad de ajuste, además de un análisis gráfico. Dada la capacidad predictiva del modelo Cielito 2, este resultó ser el más adecuado para describir el perfil del fuste y determinar la distribución de productos maderables a un diámetro de punta y altura deseados. Al evaluar la estimación del volumen a través de diferentes alternativas, la más adecuada resultó ser mediante una ecuación de volumen de doble entrada, debido a que presentó el menor sesgo en la estimación. Aunque la ecuación de volumen total definida en la integración del modelo Cielito 2 posee la capacidad de estimar el volumen entre dos alturas a lo largo del fuste, la utilidad mayor es para la distribución de productos de árboles en pie.

Bayesian Nonparametric Modeling of Categorical Data for Information Fusion and Causal Inference.

This paper presents a nonparametric regression model of categorical time series in the setting of conditional tensor factorization and Bayes network. The underlying algorithms are developed to provide a flexible and parsimonious representation for fusion of correlated information from heterogeneous sources, which can be used to improve the performance of prediction tasks and infer the causal relationship between key variables. The proposed method is first illustrated by numerical simulation and then validated with two real-world datasets: (1) experimental data, collected from a swirl-stabilized lean-premixed laboratory-scale combustor, for detection of thermoacoustic instabilities and (2) publicly available economics data for causal inference-making.