Dimensional Representation Research Articles

Scalable Cluster Tendency Assessment for Streaming Activity Data using Recurring Shapelets.

Automatic interpretation of cluster structure in rapidly arriving data streams is essential for timely detection of interesting events. Human activities often contain bursts of repeating patterns. In this paper, we propose a new relative of the Visual Assessment of Cluster Tendency (VAT) model, to interpret cluster evolution in streaming activity data where shapes of recurring patterns are important. Existing VAT algorithms are either suitable only for small batch data and unscalable to rapidly evolving streams, or cannot capture shape patterns. Our proposed incremental algorithm processes streaming data in chunks and identifies repeating patterns or shapelets from each chunk, creating a Dictionary-of-Shapes (DoS) that is updated on the fly. Each chunk is transformed into a lower dimensional representation based on it's distance from the shapelets in the current DoS. Then a small set of transformed chunks are sampled using an intelligent Maximin Random Sampling (MMRS) scheme, to create a scalable VAT image that is incrementally updated as the data stream progresses. Experiments on two upper limb activity datasets demonstrate that the proposed method can successfully and efficiently visualize clusters in long streams of data and can also identify anomalous movements.

Effect of increasing the ramification on pseudo-deformation rings

Given a continuous, odd, semi-simple $2$-dimensional representation of $G_{\mathbb{Q},Np}$ over a finite field of odd characteristic $p$ and a prime $\ell$ not dividing $Np$, we study the relation between the universal deformation rings of the corresponding pseudo-representation for the groups $G_{\mathbb{Q},N\ell p}$ and $G_{\mathbb{Q},Np}$. As a related problem, we investigate when the universal pseudo-representation arises from an actual representation over the universal deformation ring. Under some hypotheses, we prove analogues of theorems of Boston and Bockle for the reduced pseudo-deformation rings. We improve these results when the pseudo-representation is unobstructed and $p$ does not divide $\ell^2-1$. When the pseudo-representation is unobstructed and $p$ divides $\ell+1$, we prove that the universal deformation rings in characteristic $0$ and $p$ of the pseudo-representation for $G_{\mathbb{Q},N\ell p}$ are not local complete intersection rings. As an application of our main results, we prove a big $R=\mathbb{T}$ theorem.

An approach to p-adic qubits from irreducible representations of SO(3)p

We introduce the notion of a p-adic quantum bit (p-qubit) in the context of the p-adic quantum mechanics initiated and developed after the seminal paper of Volovich [Theor. Math. Phys. 71, 574 (1987)]. In this approach, physics takes place in a three-dimensional p-adic space rather than Euclidean space. Based on our prior work describing the p-adic special orthogonal group [Di Martino et al., arXiv:2104.06228 [math.NT] (2021)], we outline a program to classify its continuous unitary projective representations, which can be interpreted as a theory of p-adic angular momentum. The p-adic quantum bit arises from the irreducible representations of minimal nontrivial dimension two, of which we construct examples for all primes p.

On analogues of Mazur-Tate type conjectures in the Rankin-Selberg setting

We study the Fitting ideals over the finite layers of the cyclotomic $\mathbb{Z}_p$-extension of $\mathbb{Q}$ of Selmer groups attached to the Rankin--Selberg convolution of two modular forms $f$ and $g$. Inspired by the Theta elements for modular forms defined by Mazur and Tate in ``Refined conjectures of the Birch and Swinnerton-Dyer type'', we define new Theta elements for Rankin--Selberg convolutions of $f$ and $g$ using Loeffler--Zerbes' geometric $p$-adic $L$-functions attached to $f$ and $g$. Under certain technical hypotheses, we generalize a recent work of Kim--Kurihara on elliptic curves to prove a result very close to the \emph{weak main conjecture} of Mazur and Tate for Rankin--Selberg convolutions. Special emphasis is given to the case where $f$ corresponds to an elliptic curve $E$ and $g$ to a two dimensional odd irreducible Artin representation $\rho$ with splitting field $F$. As an application, we give an upper bound of the dimension of the $\rho$-isotypic component of the Mordell-Weil group of $E$ over the finite layers of the cyclotomic $\mathbb{Z}_p$-extension of $F$ in terms of the order of vanishing of our Theta elements.

Uncertainty Analysis and Sensitivity Estimation on an Artillery External Ballistic System

Abstract In the design of artillery external ballistics, sensitivity analysis can effectively quantify the influence of multi-source uncertain parameters on the dispersion of projectile landing points to improve the precise attack ability of artillery. However, for a complicated artillery external ballistic system containing multiple inputs and outputs, its mapping relationships are not definite under uncertainty and it is difficult to estimate a comprehensive sensitivity index due to involving the calculation of high dimensional integral. Therefore, a sensitivity analysis method based on the combination of variance and covariance decomposition with the approximate high dimensional model representation (AHDMR) is proposed to measure the influence of muzzle state parameters, projectile characteristic parameters, etc. on projectile landing points under uncertainty in this paper. First, we establish the numerical simulation model of artillery external ballistics by combing the external ballistic theory and Runge–Kutta algorithm to acquire the mapping relationships between the uncertain input parameters and the dispersion of projectile landing points and implement uncertainty analysis under different uncertainty levels (UL) and distributions. Then, with the use of a set of orthogonal polynomials for uniform and Gaussian distribution, respectively, the high dimensional model representation of the mapping relationship is approximately expressed and the compressive sensitivity indices can be effectively estimated based on the Monte Carlo simulation. Moreover, the comparison results of two numerical examples indicate the proposed sensitivity analysis method is accurate and practical. Finally, through the method, the importance rankings of multi-uncertain parameters on projectile landing points for two distributions are effectively quantified under the UL = [0.01, 0.02, 0.03, 0.04, 0.05].

Still Infantilizing Autism? An Update and Extension of Stevenson et al. (2011).

Stevenson et al. (2011) examined photographs and language used to represent autism on chapter websites for the Autism Society of America, autism charity websites, movies, television shows, fictional books, and U.S. new stories and found that they overwhelmingly used children to represent autism. Using Stevenson et al.'s methods, we tested the hypothesis that, a decade on, these same sources would now include more representations of autistic adults. We statistically compared our findings with theirs. On the chapter websites of the Autism Society of America and in fictional books, the hypothesis was supported in that there were more representations of adults (19%-20%) than in the original study (5%-9%), but there were still far more representations of children than of adults. In movies, television shows, and U.S. news stories, there were equal numbers of representations of autistic adults and autistic children. These findings suggest a move away from infantilizing autism in some domains, but they rely on a narrow construal of "infantilizing": the underrepresentation of autistic adults in media. However, even when autistic adults are represented, they may still be infantilized in various ways. Future research will need to examine the impact of infantilizing media on both autistic and non-autistic people, and other ways in which these representations are limited (e.g., gender and race/ethnicity).

Matrix Valued Concomitants of $\text {SL}_{2}(\mathbb {C})$

AbstractTo a finite dimensional representation of a complex Lie group G, an associative algebra of adjoint covariant polynomial maps from the direct sum of m copies of the Lie algebra $\mathfrak {g}$ g of G into an algebra of complex matrices is associated. When the tangent representation of the given representation is irreducible, the center of this algebra of concomitants can be identified with the algebra of adjoint invariant polynomial functions on m-tuples of elements of $\mathfrak {g}$ g . For irreducible finite dimensional representations of $\text {SL}_{2}(\mathbb {C})$ SL 2 ( ℂ ) minimal generating systems of the corresponding algebras of concomitants are determined, both as an algebra and as a module over its center.

Efficient Inverse Method for Structural Identification Considering Modeling and Response Uncertainties

The inverse problem analysis method provides an effective way for the structural parameter identification. However, uncertainties wildly exist in the practical engineering inverse problems. Due to the coupling of multi-source uncertainties in the measured responses and the modeling parameters, the traditional inverse method under the deterministic framework faces the challenges in solving mechanism and computing cost. In this paper, an uncertain inverse method based on convex model and dimension reduction decomposition is proposed to realize the interval identification of unknown structural parameters according to the uncertain measured responses and modeling parameters. Firstly, the polygonal convex set model is established to quantify the epistemic uncertainties of modeling parameters. Afterwards, a space collocation method based on dimension reduction decomposition is proposed to transform the inverse problem considering multi-source uncertainties into a few interval inverse problems considering response uncertainty. The transformed interval inverse problem involves the two-layer solving process including interval propagation and optimization updating. In order to solve the interval inverse problems considering response uncertainty, an efficient interval inverse method based on the high dimensional model representation and affine algorithm is further developed. Through the coupling of the above two strategies, the proposed uncertain inverse method avoids the time-consuming multi-layer nested calculation procedure, and then effectively realizes the uncertainty identification of unknown structural parameters. Finally, two engineering examples are provided to verify the effectiveness of the proposed uncertain inverse method.

Deep learning approach to nuclear masses and α -decay half-lives

Ab-initio calculations of nuclear masses, the binding energy and the $\alpha$ decay half-lives are intractable for heavy nucleus, because of the curse of dimensionality in many body quantum simulations as proton number($\mathrm{N}$) and neutron number($\mathrm{Z}$) grow. We take advantage of the powerful non-linear transformation and feature representation ability of deep neural network(DNN) to predict the nuclear masses and $\alpha$ decay half-lives. For nuclear binding energy prediction problem we achieve standard deviation $\sigma=0.263$ MeV on 10-fold cross validation on 2149 nuclei. Word-vectors which are high dimensional representation of nuclei from the hidden layers of mass-regression DNN help us to calculate $\alpha$ decay half-lives. For this task, we get $\sigma=0.797$ on 100 times 10-fold cross validation on 350 nuclei on $log_{10}T_{1/2}$ and $\sigma=0.731 $ on 486 nuclei. We also find physical a priori such as shell structure, magic numbers and augmented inputs inspired by Finite Range Droplet Model are important for this small data regression task.

A formula embedding approach for semantic similarity and relatedness between formulas

SummaryThe mathematical formula is one of the most vital components in a scientific document, which can explicitly describe various complex concepts and ideas. In addition to numerical calculations, they are also used to clarify definitions and disambiguate explanations transcribed in natural language. Nevertheless, the formulas have a noteworthy impact in the scientific documents, the existing information retrieval systems have limited access to scientific documents based on formulas‐based queries. To accomplish this, in this research, we have studied and implemented the formula embedding approach, which encodes the formula into the embedded vector. For encoding the formula, we have used pretrained sentence bidirectional encoder representations from transformers model. The proposed embedding model takes the latex formula as input and generates an upshot as a fixed dimensional embedding representation. In addition to this, the Siamese network is used to reform the semantic meaning of the formulas. Furthermore, the embedding of the formulas and the queried formula are compared, and cosine similarity is estimated. The performance of the suggested methodology is verified using a math stack exchange corpus of ARQMath 2020, and obtained results have shown a remarkable contribution in the task of formula retrieval.

Abstract 2708: Toward improved cancer classification using PCA + tSNE dimensionality reduction on bulk RNA-seq data

Abstract Intro: Minor variations in cancer type can have a major impact on therapeutic effectiveness and on the course of drug research and development. In order to improve upon existing -omic data classification methods, this study seeks to correctly classify previously unknown cancer samples and to determine -omic overlap between cancers by clustering patient RNA-seq data using PCA+tSNE for dimensionality reduction. Background: Although there have been major innovations in the use of bulk RNA-seq data for in silico modeling, RNA-seq analysis has limitations. RNA-seq data has over 20,000 dimensions, making it difficult to perform clustering and differential analysis important for in silico modeling. Thus, dimensionality reduction is a key for RNA-seq analysis. A novel method typically used in single-cell RNA-seq analysis combines PCA with tSNE. A common argument against the use of tSNE beyond data visualization is that the results are inconsistent and can vary depending on the hyperparameters of tSNE. This work demonstrates that combining PCA with tSNE creates a robust dimensional reduction of bulk RNA-seq data that allows for more accurate clustering of patient samples. Methods: Using the TCGA dataset with over 38 cancer subtypes and 10,351 total samples, the dimension was reduced to 50 principal components using PCA. TSNE was then applied with 1000-2000 iterations, a learning rate of 200, and varying perplexity of 5-50. Robustness was measured by how cancers of the same subtype are grouped in the lower dimension, in this case 2D. Agglomerative and k-means clustering algorithms were applied to the dimensional reduction results. This was done to track the accuracy of clustering on cancer subtypes and the movement of samples between these clusters. Results: For pure tSNE without PCA pre-reduction, clustering was not robust and showed less distinction between cancer subtypes and an increased number of outliers. Clustering accuracy was 50-60% for both k-means and agglomerative models and all varied hyperparameters. PCA+tSNE produced the most robust results with 60-70% accuracy for both k-means and agglomerative and all varied hyperparameters. It was observed that a low perplexity of 5-20, a learning rate of 100-300, and higher iterations (>1,000) produced the best results. Conclusions: PCA+tSNE is able to create accurate low dimensional representations of patient RNA-seq data that can be used to determine similarities and differences between patient samples based on gene expression data. The 10% variance in clustering accuracy suggests the method is robust and may help inform research and treatment by more robustly classifying cancer samples and by identifying similarities between cancers, especially among underserved, misdiagnosed, and rare cancers. Citation Format: Michael Bocker, Mikhail G. Grushko, Katherine E. Arline. Toward improved cancer classification using PCA + tSNE dimensionality reduction on bulk RNA-seq data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2708.

Abstract 1888: Identification and optimization of novel small molecule modulators of immune checkpoint resistance with a unified representation space for genomic and chemical perturbations

Abstract Immune checkpoint inhibitors have revolutionized cancer treatment, producing a durable response consistent with immunologic memory in a subset of patients. However, the majority of patients demonstrate innate or acquired resistance that must be characterized and overcome to induce successful treatment. Advancements in human reverse translation and scaled in vivo CRISPR screening have uncovered novel molecular and genomic correlates of resistance, and promising druggable mechanisms - driven by highly complex interactions between tumor cells and the immune system. It is this core biology that must be disentangled to build the treatment paradigms of the future. Here we demonstrate a technique for massively parallel prioritization of new immuno-oncology hypotheses using industrial-scale experimentation and machine learning. Leveraging high-content imaging data from whole-genome CRISPR knockout and a library of >250,000 compounds, a deep learning model was trained to construct a batch-invariant low dimensional representation of each perturbation. Millions of perturbations in multiple cell types were embedded in a unified representation space that was leveraged to increase the rate of discovery, accelerate reverse translation, yield novel biological insights, and guide the advancement of lead molecular series through SAR. Here we highlight multiple discovery programs driven by inferred relationships between small molecules and gene knockout with translation from inference to in vivo efficacy. Specifically, we prioritize molecules with activity in STK11-deficient tumors and additional immune checkpoint sensitizers. Citation Format: Ashish Bhandari, Michael F. Cuccarese, Kevin Fales, Kiran Nadella, Rebecca Sarto Basso, Daria Beshnova, Hayley Donnella, Bahar Shamloo, Jacob Cooper, Imran Haque, Ron Alfa, Jacob Rinaldi. Identification and optimization of novel small molecule modulators of immune checkpoint resistance with a unified representation space for genomic and chemical perturbations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1888.

Hidden Kac-Moody structures in the fermionic sector of five-dimensional supergravity

We study the supersymmetric quantum dynamics of the cosmological models obtained by reducing $D=5$ supergravity to one timelike dimension. This consistent truncation has fourteen bosonic degrees of freedom, while the quantization of the homogeneous gravitino field leads to a $2^{16}$--dimensional fermionic Hilbert space. We construct a consistent quantization of the model in which the wave function of the Universe is a $2^{16}$--component spinor %\textcolor{red}{of Spin(24,8)} depending on fourteen continuous coordinates, which satisfies eight Dirac-like wave equations (supersymmetry constraints) and one Klein-Gordon-like equation (Hamiltonian constraint). The fermionic part of the quantum Hamiltonian is built from operators that generate a $2^{16}$-dimensional representation of the (infinite-dimensional) maximally compact sub-algebra $K(G_2^{++})$ of the rank-4 hyperbolic Kac--Moody algebra $G_2^{++}$. The (quartic-in-fermions) squared-mass term $\widehat \mu^2$ entering the Klein-Gordon-like equation has several remarkable properties: (i) it commutes with the generators of $K(G_2^{++})$; and (ii) it is a quadratic polynomial in the fermion number $N_F \sim \overline\Psi \Psi$, and a symplectic fermion bilinear $C_F \sim \Psi C\Psi$. Some aspects of the structure of the solutions of our model are discussed, and notably the Kac-Moody meaning of the operators describing the reflection of the wave function on the fermion-dependent potential walls ("quantum fermionic Kac-Moody billiard").

Three-dimensional modeling and analysis of fractal characteristics of rupture source combined acoustic emission and fractal theory

Deep mining exploration and the sustainable use of enormous data via acoustic emission (AE) systems is a major experimental approach for fulfilling the perception, characterization, and forecasting for rock engineering catastrophes such mines, tunnels, and water conservation. To carry out the fine numerical characterization of the nonlinear and disordered time-space-energy information in the AE system, we use the fractal theory to analyzing. The key elements such as time, space, and energy of the fracturing source are comprehensively extracted. The fractal dimension statistical framework and its optimal dimension are designed, and the three-dimensional (3D) analysis model that can characterize the actual failure and instability process is constructed. The AE experimental data and numerical simulation are used to obtain the spatial clustering and energy dissipation fractal dimension characteristics of regional AE events in the time domain. The model is optimized and verified on the spot. The results show that the time-space-energy parameters of AE can be characterized by fractal dimension. The fractal dimension analysis model can transform and finely characterize the failure anomaly data. The distribution results of the fractal dimension value field can be consistent with the actual failure. Through iterative optimization and comprehensive verification of the scientificity and applicability of the fractal dimension analysis model, a comprehensive fractal dimension representation method of time-space-energy of fracture source in rock failure process is formed, which can provide guidance for the service and analysis of existing monitoring systems in mines, and provide theoretical support and technical conditions for fine judgment of rock mass failure in mines.

On the Formal Degree Conjecture for Non-Singular Supercuspidal Representations

Abstract We prove the formal degree conjecture for non-singular supercuspidal representations based on Schwein’s work [16] proving the formal degree conjecture for regular supercuspidal representations. The main difference between our work and Schwein’s work is that in non-singular case, the Deligne–Lusztig representations can be reducible, and the $S$-groups are not necessarily abelian. Therefore, we have to compare the dimensions of irreducible constituents of the Deligne–Lusztig representations and the dimensions of irreducible representations of $S$-groups.

Semi-supervised sparse neighbor constrained co-clustering with dissimilarity and similarity regularization

Nonnegative matrix factorization (NMF) is a very effective method for high dimensional data analysis, which has been widely used in computer vision. However, the conventional NMF is unsupervised, and thus, it cannot utilize the label information. To this end, semi-supervised NMF is proposed, which performs the NMF with the guidance of the supervisory information. However, semi-supervised NMF fails to make full use of label information, which limits the performance of clustering using low dimensional representation, and samples and features cannot be clustered in a mutually reinforcing manner. To solve the above shortcomings, we propose a semi-supervised sparse neighbor constrained co-clustering with dissimilarity and similarity regularization model (SSCCDS). First, co-clustering is introduced to cluster samples and features simultaneously. Secondly, the model imposes similarity and dissimilarity regularization constraints on samples by low-dimensional representations. Specifically, similarity and dissimilar regularization constraints are imposed on labeled samples, and similarity regularization constraints are imposed on unlabeled data. Thirdly, the model proposes sparse neighbor constraints for feature consistent learning. Then, a multiplicative alternating scheme is proposed for objective optimization. A large number of experiments on different data sets show that SSCCDS has good clustering ability.

HEALTH CONDITIONS AND TEMPORAL STABILITY OF THE INTERNAL STRUCTURE OF ILLNESS REPRESENTATION IN PATIENTS WITH MYOCARDIAL INFARCTION

The study aimed to examine the effects of health conditions – previous history of cardiovascular disease (high blood pressure, hypertension, angina pectoris), heredity, risk factors for MI (smoking, obesity, hyperlipidemia, physical inactivity), and acute/chronic stress in the previous year, and socio-demographic variables (gender, age, education, relationship, and material status) on the formation and change in the mental representation of MI. In addition, the temporal stability of the internal structure of the illness representation dimensions in MI patients, and the effect the health and socio-demographic conditions may have on it, were also examined. The results point to significant effects of health and socio-demographic factors on the formation and change of myocardial infarction representation. A significant change in the internal structure of the illness representation was already found 1.5-2 months following the event. This change can only be partially explained by the examined health and socio-demographic conditions, given that after partialization of these effects, differences in the construct's internal structure at the two measurement time points remain significant. The results are discussed according to the theoretical background and previous research findings. Future research should explore the effects of some other variables that could further explain the temporal changes in the MI representation, but also the initial representation formation.

Effective drug-target interaction prediction with mutual interaction neural network.

MotivationAccurately predicting drug–target interaction (DTI) is a crucial step to drug discovery. Recently, deep learning techniques have been widely used for DTI prediction and achieved significant performance improvement. One challenge in building deep learning models for DTI prediction is how to appropriately represent drugs and targets. Target distance map and molecular graph are low dimensional and informative representations, which however have not been jointly used in DTI prediction. Another challenge is how to effectively model the mutual impact between drugs and targets. Though attention mechanism has been used to capture the one-way impact of targets on drugs or vice versa, the mutual impact between drugs and targets has not yet been explored, which is very important in predicting their interactions.ResultsTherefore, in this article we propose MINN-DTI, a new model for DTI prediction. MINN-DTI combines an interacting-transformer module (called Interformer) with an improved Communicative Message Passing Neural Network (CMPNN) (called Inter-CMPNN) to better capture the two-way impact between drugs and targets, which are represented by molecular graph and distance map, respectively. The proposed method obtains better performance than the state-of-the-art methods on three benchmark datasets: DUD-E, human and BindingDB. MINN-DTI also provides good interpretability by assigning larger weights to the amino acids and atoms that contribute more to the interactions between drugs and targets.Availability and implementationThe data and code of this study are available at https://github.com/admislf/MINN-DTI.

Semi-parametric Bayes regression with network-valued covariates

Although there has been an explosive rise in network data in a variety of disciplines, there is very limited development of regression modeling approaches based on high-dimensional networks. The scarce literature in this area typically assume linear relationships between the outcome and the high-dimensional network edges that results in an inflated model plagued by the curse of dimensionality and these models are unable to accommodate non-linear relationships or higher order interactions. In order to overcome these limitations, we develop a novel two-stage Bayesian non-parametric regression modeling framework using high-dimensional networks as covariates, which first finds a lower dimensional node-specific representation for the networks, and then embeds these representations in a flexible Gaussian process regression framework along with supplemental covariates for modeling the continuous outcome variable. Moving from edge-level analysis to node-level model allows us to scale up to high-dimensional networks, and enables node selection via an extension of the Gaussian process framework that involves spike-and-slab priors on the lengthscale parameters. Extensive simulations show a distinct advantage of the proposed approach in terms of prediction, coverage, and node selection. The proposed model achieves considerable gains when predicting posttraumatic stress disorder (PTSD) resilience based on brain networks in our motivating neuroimaging applications, and also identifies important brain regions associated with PTSD. In contrast, existing non-linear approaches that employ the full-edge set or those that use other dimension reduction techniques on the network are not equipped for node selection and results in poor prediction and characterization of predictive uncertainty, while linear approaches using the edge-level features are overly inflated and typically result in poor performance.

Unique contributions of perceptual and conceptual humanness to object representations in the human brain

The human brain is able to quickly and accurately identify objects in a dynamic visual world. Objects evoke different patterns of neural activity in the visual system, which reflect object category memberships. However, the underlying dimensions of object representations in the brain remain unclear. Recent research suggests that objects similarity to humans is one of the main dimensions used by the brain to organise objects, but the nature of the human-similarity features driving this organisation are still unknown. Here, we investigate the relative contributions of perceptual and conceptual features of humanness to the representational organisation of objects in the human visual system. We collected behavioural judgements of human-similarity of various objects, which were compared with time-resolved neuroimaging responses to the same objects. The behavioural judgement tasks targeted either perceptual or conceptual humanness features to determine their respective contribution to perceived human-similarity. Behavioural and neuroimaging data revealed significant and unique contributions of both perceptual and conceptual features of humanness, each explaining unique variance in neuroimaging data. Furthermore, our results showed distinct spatio-temporal dynamics in the processing of conceptual and perceptual humanness features, with later and more lateralised brain responses to conceptual features. This study highlights the critical importance of social requirements in information processing and organisation in the human brain.