Simultaneous Representation Research Articles

Time Series Components Separation Based on Singular Spectral Analysis Visualization: an HJ-biplot Method Application

The extraction of essential features of any real-valued time series is crucial for exploring, modeling and producing, for example, forecasts. Taking advantage of the representation of a time series data by its trajectory matrix of Hankel constructed using Singular Spectrum Analysis, as well as of its decomposition through Principal Component Analysis via Partial Least Squares, we implement a graphical display employing the biplot methodology. A diversity of types of biplots can be constructed depending on the two matrices considered in the factorization of the trajectory matrix. In this work, we discuss the called HJ-biplot which yields a simultaneous representation of both rows and columns of the matrix with maximum quality. Interpretation of this type of biplot on Hankel related trajectory matrices is discussed from a real-world data set.

Galois theoretic study on simultaneous representation of primes by binary quadratic forms

We study intrinsic Galois structure behind theorems by Kaplansky, Brink, and Mortenson on simultaneous representation of primes by binary quadratic forms with different discriminants. This study brings new theorems like theirs also in indefinite quadratic forms.

Under-Sampling and Feature Selection Algorithms for S2SMLP

Imbalance learning is a hot topic in the data mining and machine learning domains. Data-level, algorithm-level and ensemble solutions are the three main methods proposed thus far to address imbalance learning. To alleviate the issues of data explosion and feature selection in multilayer perceptron based on simultaneous two-sample representation(S2SMLP), in this paper, firstly, spectral clustering is exploited to select majority samples so as to construct a smaller training dataset for the classifier. We divide all majority samples into many clusters through spectral clustering, extract different numbers of representative samples from a cluster according to the size of each cluster, the average distance between the minority class and all samples of the cluster, then construct the training dataset of the classifier by combining these extracted samples from the majority class and all minority samples. Secondly, we propose a novel feature selection method based on the pairwise samples distance constraint, which considers the class labels of paired samples, select the features which push two similar samples closer together and pull two different samples farther apart. Finally, we conduct extensive experiments on 44 two-class imbalanced datasets and four high-dimensional DNA microarray datasets. The experimental results demonstrate that our proposed algorithms outperform some state-of-the-art algorithms in terms of $F\textrm {-measure},G\textrm {-mean}$ and AUC.

Efficient fusion of face and palmprint in Gabor filtered Wigner domain

In this paper, a new transform Gabor filtered Wigner transform (GFWT) has been proposed. In GFWT, Gabor filtering is performed on the Wigner transformed image. Wigner transform gives a simultaneous representation of an image in time and frequency domain which is further processed using Gabor filters. The proposed transform is then used to extract the features from the biometrics to develop different multimodal biometric systems. A detailed study has been carried out in which, different unimodal and multimodal systems such as feature level and score level fusion are analysed. In order to improve the performance of the system, an optimisation technique, particle swarm optimisation (PSO) is used to find the optimal parameters of the Gabor filter and to select the significant GFWT feature vector. The PSO technique not only improves the performance of the system but also able to reduce the dimension of the feature vectors. Numerical experiments are carried out on face and palmprint database to show the effectiveness of the proposed transform for different unimodal and multimodal systems.

Efficient fusion of face and palmprint in Gabor filtered Wigner domain

In this paper, a new transform Gabor filtered Wigner transform (GFWT) has been proposed. In GFWT, Gabor filtering is performed on the Wigner transformed image. Wigner transform gives a simultaneous representation of an image in time and frequency domain which is further processed using Gabor filters. The proposed transform is then used to extract the features from the biometrics to develop different multimodal biometric systems. A detailed study has been carried out in which, different unimodal and multimodal systems such as feature level and score level fusion are analysed. In order to improve the performance of the system, an optimisation technique, particle swarm optimisation (PSO) is used to find the optimal parameters of the Gabor filter and to select the significant GFWT feature vector. The PSO technique not only improves the performance of the system but also able to reduce the dimension of the feature vectors. Numerical experiments are carried out on face and palmprint database to show the effectiveness of the proposed transform for different unimodal and multimodal systems.

Scalable Person Re-Identification by Harmonious Attention

Existing person re-identification (re-id) deep learning methods rely heavily on the utilisation of large and computationally expensive convolutional neural networks. They are therefore not scalable to large scale re-id deployment scenarios with the need of processing a large amount of surveillance video data, due to the lengthy inference process with high computing costs. In this work, we address this limitation via jointly learning re-id attention selection. Specifically, we formulate a novel harmonious attention network (HAN) framework to jointly learn soft pixel attention and hard region attention alongside simultaneous deep feature representation learning, particularly enabling more discriminative re-id matching by efficient networks with more scalable model inference and feature matching. Extensive evaluations validate the cost-effectiveness superiority of the proposed HAN approach for person re-id against a wide variety of state-of-the-art methods on four large benchmark datasets: CUHK03, Market-1501, DukeMTMC, and MSMT17.

Semantic-enhanced discrete matrix factorization hashing for heterogeneous modal matching

The simultaneous hash representation of heterogeneous modalities has shown its excellent performance in the multi-modal learning community. However, the binary discrete constraint of hash brings a great challenge to the common hash representation of heterogeneous modal data. To resolve the issue, this paper proposes a novel discrete hash method, referred to as Semantic enhanced Discrete Matrix Factorization Hashing (SDMFH). SDMFH directly extracts the common discrete hash representation of all modalities from the reconstructed semantic intermodal similarity graph, which makes the hash codes more discriminative. Meanwhile, the semantic labels are regressed to the extracted discrete hash book, so as to further strengthen the discriminating ability of the learned discrete hash book. Moreover, a linear embedding from the kernel space of the original data to the Hamming space is explored to generate the common hash codes of each modality to ensure the consistency of the hash codes of heterogeneous modalities. More importantly, we develop an efficient discrete iterative algorithm based on Stiefel manifold that can directly learn the discrete hash book in a closed form, thus both avoiding quantization loss caused by discrete relaxation and reducing computational complexity. Experimental results on three benchmark data sets show that SDMFH performs better than several state-of-the-art methods for heterogeneous modal matching tasks.

Simultaneous numerical representation of soil microsite production and consumption of carbon dioxide, methane, and nitrous oxide using probability distribution functions.

Production and consumption of nitrous oxide (N2 O), methane (CH4 ), and carbon dioxide (CO2 ) are affected by complex interactions of temperature, moisture, and substrate supply, which are further complicated by spatial heterogeneity of the soil matrix. This microsite heterogeneity is often invoked to explain non-normal distributions of greenhouse gas (GHG) fluxes, also known as hot spots and hot moments. To advance numerical simulation of these belowground processes, we expanded the Dual Arrhenius and Michaelis-Menten model, to apply it consistently for all three GHGs with respect to the biophysical processes of production, consumption, and diffusion within the soil, including the contrasting effects of oxygen (O2 ) as substrate or inhibitor for each process. High-frequency chamber-based measurements of all three GHGs at the Howland Forest (ME, USA) were used to parameterize the model using a multiple constraint approach. The area under a soil chamber is partitioned according to a bivariate log-normal probability distribution function (PDF) of carbon and water content across a range of microsites, which leads to a PDF of heterotrophic respiration and O2 consumption among microsites. Linking microsite consumption of O2 with a diffusion model generates a broad range of microsite concentrations of O2 , which then determines the PDF of microsites that produce or consume CH4 and N2 O, such that a range of microsites occurs with both positive and negative signs for net CH4 and N2 O flux. Results demonstrate that it is numerically feasible for microsites of N2 O reduction and CH4 oxidation to co-occur under a single chamber, thus explaining occasional measurement of simultaneous uptake of both gases. Simultaneous simulation of all three GHGs in a parsimonious modeling framework is challenging, but it increases confidence that agreement between simulations and measurements is based on skillful numerical representation of processes across a heterogeneous environment.

The Ethno‐Graphic Sensibility

Ethnography is both a set of tools and a way of approaching the world, but ethnographic methods texts tend to reduce ethnography to its tools, minimizing the humanistic elements of our work and ignoring how these tools interact with the social world. Recent work on “ethnographic thinking” helps us shift from an instrumental focus on tools, but we believe that if “the medium we think in defines what we can see”, then textual, linear narratives limit our ability to see and learn about the sensory, embodied, aesthetic, and emotive dimensions of ethnography. We, therefore, ask: how might we reimagine how we teach and learn about ethnography and the ethnographic sensibility? How can we teach beyond the tools? Comics as a medium affords multiple possibilities for expressing the complex dimensions of ethnography. Comics, like ethnography, allows for the simultaneous representation of multiple ideas, perspectives, and experiences. It requires the reader to participate and grapple with the setting, the emotions of the process, the visual feel and mood of a place, and enables a “you are there” sense of place due to the juxtaposition of visual and textual forms. We believe comics can help aspiring ethnographers explore the dynamism inherent in this work.

The Dmipy Toolbox: Diffusion MRI Multi-Compartment Modeling and Microstructure Recovery Made Easy.

Non-invasive estimation of brain microstructure features using diffusion MRI (dMRI)—known as Microstructure Imaging—has become an increasingly diverse and complicated field over the last decades. Multi-compartment (MC)-models, representing the measured diffusion signal as a linear combination of signal models of distinct tissue types, have been developed in many forms to estimate these features. However, a generalized implementation of MC-modeling as a whole, providing deeper insights in its capabilities, remains missing. To address this fact, we present Diffusion Microstructure Imaging in Python (Dmipy), an open-source toolbox implementing PGSE-based MC-modeling in its most general form. Dmipy allows on-the-fly implementation, signal modeling, and optimization of any user-defined MC-model, for any PGSE acquisition scheme. Dmipy follows a “building block”-based philosophy to Microstructure Imaging, meaning MC-models are modularly constructed to include any number and type of tissue models, allowing simultaneous representation of a tissue's diffusivity, orientation, volume fractions, axon orientation dispersion, and axon diameter distribution. In particular, Dmipy is geared toward facilitating reproducible, reliable MC-modeling pipelines, often allowing the whole process from model construction to parameter map recovery in fewer than 10 lines of code. To demonstrate Dmipy's ease of use and potential, we implement a wide range of well-known MC-models, including IVIM, AxCaliber, NODDI(x), Bingham-NODDI, the spherical mean-based SMT and MC-MDI, and spherical convolution-based single- and multi-tissue CSD. By allowing parameter cascading between MC-models, Dmipy also facilitates implementation of advanced approaches like CSD with voxel-varying kernels and single-shell 3-tissue CSD. By providing a well-tested, user-friendly toolbox that simplifies the interaction with the otherwise complicated field of dMRI-based Microstructure Imaging, Dmipy contributes to more reproducible, high-quality research.

Nonfluorescent RNA In Situ Hybridization Combined with Antibody Staining to Visualize Multiple Gene Expression Patterns in the Embryonic Brain of Drosophila.

In Drosophila, the brain arises from about 100 neural stem cells (called neuroblasts) per hemisphere which originate from the neuroectoderm. Products of developmental control genes are expressed in spatially restricted domains in the neuroectoderm and provide positional cues that determine the formation and identity of neuroblasts. Here, we present a protocol for nonfluorescent double in situ hybridization combined with antibody staining which allows the simultaneous representation of gene expression patterns in Drosophila embryos in up to three different colors. Such visible multiple stainings are especially useful to analyze the expression and regulatory interactions of developmental control genes during early embryonic brain development. We also provide protocols for wholemount and flat preparations of Drosophila embryos, which allow a more detailed analysis of gene expression patterns in relation to the cellular context of the early brain (and facilitate the identification of individual brain neuroblasts) using conventional light microscopy.

The gist of Anne Treisman's revolution.

Anne Treisman investigated many aspects of perception, and in particular the roles of different forms of attention. Four aspects of her work are reviewed here, including visual search, set mean perception, perception in special populations, and binocular rivalry. The importance of the breakthrough in each case is demonstrated. Search is easy or slow depending on whether it depends on the application of global or focused attention. Mean perception depends on global attention and affords simultaneous representation of the means of at least two sets of elements, and then of comparing them. Deficits exhibited in Balint's or unilateral neglect patients identify basic sensory system mechanisms. And, the ability to integrate binocular information for stereopsis despite simultaneous binocular rivalry for color, demonstrates the division of labor underlying visual system computations. All these studies are related to an appreciation of the difference between perceiving the gist of a scene, its elements or objects, versus perceiving the details of the scene and its components. This relationship between Anne Treisman's revolutionary discoveries and the concept of gist perception is the core of the current review.

Graphical comparisons of relative disease burden across multiple risk factors

BackgroundPopulation attributable fractions (PAF) measure the proportion of disease prevalence that would be avoided in a hypothetical population, similar to the population of interest, but where a particular risk factor is eliminated. They are extensively used in epidemiology to quantify and compare disease burden due to various risk factors, and directly influence public policy regarding possible health interventions. In contrast to individual specific metrics such as relative risks and odds ratios, attributable fractions depend jointly on both risk factor prevalence and relative risk. The relative contributions of these two components is important, and usually needs to be presented in summary tables that are presented together with the attributable fraction calculation. However, representing PAF in an accessible graphical format, that captures both prevalence and relative risk, may assist interpretation.MethodsTaylor-series approximations to PAF in terms of risk factor prevalence and log-odds ratio are derived that facilitate simultaneous representation of PAF, risk factor prevalence and risk-factor/disease log-odds ratios on a single co-ordinate axis. Methods are developed for binary, multi-category and continuous exposure variables.ResultsThe methods are demonstrated using INTERSTROKE, a large international case control dataset focused on risk factors for stroke.ConclusionsThe described methods could be used as a complement to tables summarizing prevalence, odds ratios and PAF, and may convey the same information in a more intuitive and visually appealing manner. The suggested nomogram can also be used to visually estimate the effects of health interventions which only partially reduce risk factor prevalence. Finally, in the binary risk factor case, the approximations can also be used to quickly convert logistic regression coefficients for a risk factor into approximate PAFs.

Excess properties and isothermal vapour-liquid equilibrium data for the ((toluene + pyridine)) system from 343 K to 383 K

Isothermal vapour-liquid equilibrium (VLE) data for the ((toluene + pyridine)) system has been measured at four temperatures from approximately 343 K to 383 K, using a dynamic-analytic apparatus. The densities of ((toluene + pyridine)) mixtures were also measured from 298 K to 363 K using an Anton Paar DMA-5000 densitometer. The vapour-liquid phase equilibrium data were modelled by the γ-Φ approach and the Non-Random Two-Liquid and UNIQUAC activity coefficient models. The consistency of the experimental isothermal VLE data were checked by the area and point tests. The experimental density data were modelled using an 8-parameter empirical correlation which allowed for the simultaneous representation of temperature and liquid composition. The experimental data were used to determine the excess properties for volume, Gibbs energy, enthalpy and entropy. The excess volume was modelled using Redlich-Kister polynomials.

A snake who eats the devil’s tail: The recursivity of good and evil in the security state

The Islamic State and Mexican drug ‘cartels’ have been positioned as extreme menaces to the Western world by media and state actors despite their inability to pose existential threats to the US. These groups deftly facilitate such representations through barbaric violence which security and information sharing apparatuses uptake and amplify. The ‘good’ neoliberal security state combats and inflates these ‘evil’ threats which, in turn, empowers purveyors of security in a deregulated environment. The authors interrogate this problem through the lens of negative utopias presented in speculative fiction to understand the implications for state and society. Projected representations of evil as an existential threat present a conflicted vision of the future manipulated by political and media actors with dire consequences for democratic ideals. National security relies on a never-ending cast of foreign threats that legitimize counter-terror actions in the name of moral good. Security institutions persist primarily through the simultaneous representation of a never-ending battle of good and evil. They stand to gain from the existence of extremely violent groups, without legitimate progress towards their eradication. They also bring fantasy into reality through the recursive enactment of good versus evil.

Multi-Window 3D Interaction for Collaborative Virtual Reality.

We present a novel collaborative virtual reality system that offers multiple immersive 3D views at large 3D scenes. The physical setup consists of two synchronized multi-user 3D displays: a tabletop and a large vertical projection screen. These displays afford different presentations of the shared 3D scene. The wall display lends itself to the egocentric exploration at 1:1 scale, while the tabletop affords an allocentric overview. Additionally, handheld 3D portals facilitate the personal exploration of the scene, the comparison of views, and the exchange with others. Our developments enable seamless 3D interaction across these independent 3D views. This requires the simultaneous representation of user input in the different viewing contexts. However, the resulting interactions cannot be executed independently. The application must coordinate the interactions and resolve potential ambiguities to provide plausible effects. We analyze and document the challenges of seamless 3D interaction across multiple independent viewing windows, propose a high-level software design to realize the necessary functionality, and apply the design to a set of interaction tools. Our setup was tested in a formal user study, which revealed general advantages of collaborative 3D data exploration with multiple views in terms of user preference, comfort, and task performance.

Attitude and Learning Approaches in the Study of General Didactics. A Multivariate Analysis

The influence of different approaches and attitude towards learning in the General Didactics is studied in few investigations. The main objective of this work is to describe in a multivariate way the relationships between attitude towards General Didactics and learning approaches of students majoring in Social Learning of the University of Salamanca. The Measurement of Attitude towards General Didactics and the Revised Study Process Questionnaire two factor (R-SPQ-2F) questionnaires were used to gather the information. The analysis of the relationship between attitude and learning approaches is performed using the HJ-Biplot. This multivariate statistical technique allows the simultaneous representation of students, attitudes and learning approaches. This methodology, combined with hierarchical clustering method, reveals the existence of four types of students: C1, those characterised by high marks on interest, professional usefulness and deep study of the subject; C2, those that display high anxiety and high marks in superficial study; C3, students that show average interest, low anxiety, superficial study and believe in the professional usefulness of Didactics; and C4, students with high levels of anxiety that study the subject in depth. These results point the existence of a relationship between attitudes and learning approaches and can be used to improve the performance and offerings of educational teams, achieving more efficient strategies that lead to a better educated student community.

Actitud y enfoques de aprendizaje en el estudio de la Didáctica General. Una visión multivariante

The influence of different approaches and attitude towards learning in the General Didactics is studied in few investigations. The main objective of this work is to describe in a multivariate way the relationships between attitude towards General Didactics and learning approaches of students majoring in Social Learning of the University of Salamanca. The Measurement of Attitude towards General Didactics and the Revised Study Process Questionnaire two factor (R-SPQ-2F) questionnaires were used to gather the information. The analysis of the relationship between attitude and learning approaches is performed using the HJ-Biplot. This multivariate statistical technique allows the simultaneous representation of students, attitudes and learning approaches. This methodology, combined with hierarchical clustering method, reveals the existence of four types of students: C1, those characterized by high marks on interest, professional usefulness and deep study of the subject; C2, those that display high anxiety and high marks in superficial study; C3, students that show average interest, low anxiety, superficial study and believe in the professional usefulness of Didactics; and C4, students with high levels of anxiety that study the subject in depth. These results point the existence of a relationship between attitudes and learning approaches and can be used to improve the performance and offerings of educational teams, achieving more efficient strategies that lead to a better educated student community.

Thermodynamic Study of Four {Methylpiperidine + Water} Systems: New Experimental Data and Challenging Modeling for the Simultaneous Representation of Liquid–Liquid Equilibrium and Energetic Properties

In carbon capture processes based on chemical absorption, the solution used for absorbing carbon dioxide plays a central role and must be carefully selected. It is expected to have the potential to capture large amounts of carbon dioxide on the one hand, and to be fully regenerated at the lowest energy price on the other hand. As a continuation of a study initiated by Coulier et al. some years ago, the present work focuses on the measurement and modeling of thermodynamic properties of aqueous solvents containing methylpiperidines (a class of amines), potentially usable in carbon capture processes and leading to liquid–liquid phase split in process conditions. To complete liquid–liquid equilibrium (LLE), excess enthalpy and excess heat capacity data previously measured by Coulier et al. for the N-methylpiperidine + water and 2-methylpiperidine + water systems, LLE data, and excess-enthalpy data were measured for the 3-methylpiperidine + water and 4-methylpiperidine + water systems. To model all these data,...

The use of classroom assessment based on multi-representation ability in Mechanics concept

This study aimed to describe the use of classroom assessment based on multi-representation ability. This research used descriptive research method. Research participants were 20 prospective physics teacher at one university in Indonesia, who attended basic physics course. Research instruments used questionnaires and essays test based on multi-representations. The results showed that the learning process only use summative assessment which focuses at the end of material and learning units. Learning process and assessment in Basic Physics Course used one or two representational modes, so those did not facilitate simultaneous representation modes. The result described that only 42% prospective physics teachers representation ability was good category (75-84 attainment) on mathematical representation. The recommendation of this study is to develope learning program, formative, and diagnostic assessment based multi-representation in Basics Physics Course.