scholarly journals Six dimensions describe action understanding: the ACT-FASTaxonomy

2019 ◽  
Author(s):  
Mark Allen Thornton ◽  
Diana Tamir
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Dimensional Structure ◽  
Representational Space ◽  
Socially Relevant ◽  
Low Dimensional ◽  
Motor Actions ◽  
The Mind ◽  
Psychological Dimensions ◽  
Optimal Set

Humans engage in a wide variety of different actions and activities. These range from simple motor actions like reaching for an object, to complex activities like governing a nation. Navigating everyday life requires people to make sense of this diversity of actions. We suggest that the mind simplifies this complex domain by attending primarily to the most essential features of actions. Using a parsimonious set of action dimensions, the mind can organize action knowledge in a low-dimensional representational space. In nine studies, we derive and validate such an action taxonomy. Studies 1-3 use large-scale text analyses to generate and test potential action dimensions. Study 4 validates interpretable labels for these dimensions. Studies 5-7 demonstrate that these dimensions can explain human judgments about actions. We perform model selection on data from Studies 5-7 to arrive at the optimal set of six psychological dimensions, together forming the Abstraction, Creation, Tradition, Food, Animacy, Spiritualism Taxonomy (ACT-FAST). Study 8 demonstrates that ACT-FAST can predict socially relevant qualities of actions, including how, when, where, why, and by whom they are performed. Finally, Study 9 shows that ACT-FAST can explain action-related patterns of brain activity using naturalistic fMRI. Together, these studies reveal the dimensional structure the mind applies to organize action concepts.

scholarly journals Neural Excursions from Low-Dimensional Manifold Structure Explain Intersubject Variation in Human Motor Learning

2021 ◽  
Author(s):  
Corson N Areshenkoff ◽  
Daniel J Gale ◽  
Joe Y Nashed ◽  
Dominic Standage ◽  
John Randall Flanagan ◽  
...  
Keyword(s):  
Motor Learning ◽  
Large Scale ◽  
Brain Activity ◽  
Dimensional Subspace ◽  
Learning Performance ◽  
Dimensional Manifold ◽  
Learning Abilities ◽  
Manifold Structure ◽  
Electrophysiological Studies ◽  
Low Dimensional

Humans vary greatly in their motor learning abilities, yet little is known about the neural mechanisms that underlie this variability. Recent neuroimaging and electrophysiological studies demonstrate that large-scale neural dynamics inhabit a low-dimensional subspace or manifold, and that learning is constrained by this intrinsic manifold architecture. Here we asked, using functional MRI, whether subject-level differences in neural excursion from manifold structure can explain differences in learning across participants. We had subjects perform a sensorimotor adaptation task in the MRI scanner on two consecutive days, allowing us to assess their learning performance across days, as well as continuously measure brain activity. We find that the overall neural excursion from manifold activity in both cognitive and sensorimotor brain networks is associated with differences in subjects' patterns of learning and relearning across days. These findings suggest that off-manifold activity provides an index of the relative engagement of different neural systems during learning, and that intersubject differences in patterns of learning and relearning across days are related to reconfiguration processes in cognitive and sensorimotor networks during learning.

scholarly journals Gaussian Information Bottleneck and the Non-Perturbative Renormalization Group

2021 ◽  
Author(s):  
Adam Gordon Kline ◽  
Stephanie Palmer
Keyword(s):  
Renormalization Group ◽  
Large Scale ◽  
Dimensional Structure ◽  
Many Body ◽  
Closed Form Solutions ◽  
Information Bottleneck ◽  
Perturbative Renormalization ◽  
Optimal Encoding ◽  
Many Body Systems ◽  
Low Dimensional

Abstract The renormalization group (RG) is a class of theoretical techniques used to explain the collective physics of interacting, many-body systems. It has been suggested that the RG formalism may be useful in finding and interpreting emergent low-dimensional structure in complex systems outside of the traditional physics context, such as in biology or computer science. In such contexts, one common dimensionality-reduction framework already in use is information bottleneck (IB), in which the goal is to compress an ``input'' signal X while maximizing its mutual information with some stochastic ``relevance'' variable Y. IB has been applied in the vertebrate and invertebrate processing systems to characterize optimal encoding of the future motion of the external world. Other recent work has shown that the RG scheme for the dimer model could be ``discovered'' by a neural network attempting to solve an IB-like problem. This manuscript explores whether IB and any existing formulation of RG are formally equivalent. A class of soft-cutoff non-perturbative RG techniques are defined by families of non-deterministic coarsening maps, and hence can be formally mapped onto IB, and vice versa. For concreteness, this discussion is limited entirely to Gaussian statistics (GIB), for which IB has exact, closed-form solutions. Under this constraint, GIB has a semigroup structure, in which successive transformations remain IB-optimal. Further, the RG cutoff scheme associated with GIB can be identified. Our results suggest that IB can be used to impose a notion of ``large scale'' structure, such as biological function, on an RG procedure.

scholarly journals Low-Dimensional Dynamics of Brain Activity Associated with Manual Acupuncture in Healthy Subjects

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7432
Author(s):  
Xinmeng Guo ◽  
Jiang Wang
Keyword(s):  
Latent Variables ◽  
Large Scale ◽  
Scientific Explanation ◽  
Brain Activity ◽  
Dimensional Subspace ◽  
Oscillatory Activity ◽  
Manual Acupuncture ◽  
Brain Response ◽  
Acupuncture Stimulation ◽  
Low Dimensional

Acupuncture is one of the oldest traditional medical treatments in Asian countries. However, the scientific explanation regarding the therapeutic effect of acupuncture is still unknown. The much-discussed hypothesis it that acupuncture’s effects are mediated via autonomic neural networks; nevertheless, dynamic brain activity involved in the acupuncture response has still not been elicited. In this work, we hypothesized that there exists a lower-dimensional subspace of dynamic brain activity across subjects, underpinning the brain’s response to manual acupuncture stimulation. To this end, we employed a variational auto-encoder to probe the latent variables from multichannel EEG signals associated with acupuncture stimulation at the ST36 acupoint. The experimental results demonstrate that manual acupuncture stimuli can reduce the dimensionality of brain activity, which results from the enhancement of oscillatory activity in the delta and alpha frequency bands induced by acupuncture. Moreover, it was found that large-scale brain activity could be constrained within a low-dimensional neural subspace, which is spanned by the “acupuncture mode”. In each neural subspace, the steady dynamics of the brain in response to acupuncture stimuli converge to topologically similar elliptic-shaped attractors across different subjects. The attractor morphology is closely related to the frequency of the acupuncture stimulation. These results shed light on probing the large-scale brain response to manual acupuncture stimuli.

scholarly journals A Rank-Deficient and Sparse Penalized Optimization Model for Compressive Indoor Radar Target Localization

2020 ◽  
Author(s):  
Van Ha Tang ◽  
Van-Giang Nguyen
Keyword(s):  
Large Scale ◽  
Optimization Method ◽  
Radar Data ◽  
Target Localization ◽  
Low Rank ◽  
Dimensional Structure ◽  
Optimization Approach ◽  
Gradient Technique ◽  
Low Dimensional ◽  
Penalized Optimization

This paper proposes a rank-deficient and sparse penalized optimization method for addressing the problem of through-wall radar imaging (TWRI) in the presence of structured wall clutter. Compressive TWRI enables fast data collection and accurate target localization, but faces with the challenges of incomplete data measurements and strong wall clutter. This paper handles these challenges by formulating the task of wall-clutter removal and target image reconstruction as a joint low-rank and sparse regularized minimization problem. In this problem,  the low-rank regularization is used to capture the low-dimensional structure of the wall signals and the sparse penalty is employed to represent the image of the indoor targets. We introduce an iterative algorithm based on the forward-backward proximal gradient technique to solve the large-scale optimization problem, which simultaneously removes unwanted wall clutter and reconstruct an image of indoor targets. Simulated and real radar data are used to validate the effectiveness of the proposed rank-deficient and sparse regularized optimization approach.

scholarly journals In your phase: neural phase synchronisation underlies visual imagery of faces

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrés Canales-Johnson ◽  
Renzo C. Lanfranco ◽  
Juan Pablo Morales ◽  
David Martínez-Pernía ◽  
Joaquín Valdés ◽  
...  
Keyword(s):  
Long Range ◽  
Visual Imagery ◽  
Brain Activity ◽  
Neural Connectivity ◽  
Frequency Range ◽  
Subjective Impression ◽  
Gamma Frequency ◽  
Gamma Range ◽  
Theta Frequency ◽  
Socially Relevant

AbstractMental imagery is the process through which we retrieve and recombine information from our memory to elicit the subjective impression of “seeing with the mind’s eye”. In the social domain, we imagine other individuals while recalling our encounters with them or modelling alternative social interactions in future. Many studies using imaging and neurophysiological techniques have shown several similarities in brain activity between visual imagery and visual perception, and have identified frontoparietal, occipital and temporal neural components of visual imagery. However, the neural connectivity between these regions during visual imagery of socially relevant stimuli has not been studied. Here we used electroencephalography to investigate neural connectivity and its dynamics between frontal, parietal, occipital and temporal electrodes during visual imagery of faces. We found that voluntary visual imagery of faces is associated with long-range phase synchronisation in the gamma frequency range between frontoparietal electrode pairs and between occipitoparietal electrode pairs. In contrast, no effect of imagery was observed in the connectivity between occipitotemporal electrode pairs. Gamma range synchronisation between occipitoparietal electrode pairs predicted subjective ratings of the contour definition of imagined faces. Furthermore, we found that visual imagery of faces is associated with an increase of short-range frontal synchronisation in the theta frequency range, which temporally preceded the long-range increase in the gamma synchronisation. We speculate that the local frontal synchrony in the theta frequency range might be associated with an effortful top-down mnemonic reactivation of faces. In contrast, the long-range connectivity in the gamma frequency range along the fronto-parieto-occipital axis might be related to the endogenous binding and subjective clarity of facial visual features.

scholarly journals A Short Review on Recent Advances of Hydrogel-Based Adsorbents for Heavy Metal Ions

Metals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 864
Author(s):  
Suguna Perumal ◽  
Raji Atchudan ◽  
Thomas Nesakumar Jebakumar Immanuel Edison ◽  
Rajendran Suresh Babu ◽  
Petchimuthu Karpagavinayagam ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Metal Ions ◽  
Heavy Metal Ions ◽  
Large Scale ◽  
Water Bodies ◽  
Dimensional Structure ◽  
High Porosity ◽  
Adsorption Method ◽  
Toxic Heavy Metal ◽  
Practical Applications

The growth of industry fulfills our necessity and promotes economic development. However, pollutants from such industries pollute water bodies which pose a high risk for living organisms. Thus, researchers have been urged to develop an efficient method to remove toxic heavy metal ions from water bodies. The adsorption method shows promising results for the removal of heavy metal ions and is easy to operate on a large scale, thus can be applied to practical applications. Numerous adsorbents were developed and reported, among them hydrogels, which attract great attention because of the reusability, ease of preparation, and handling. Hydrogels are generally prepared by the cross-linking of polymers that result in a three-dimensional structure, showing high porosity and high functionality. They are hydrophilic in nature because of the functional groups, and are non-toxic. Thus, this review provides various methods of hydrogel adsorbents preparation and summarizes recent progress in the use of hydrogel adsorbents for the removal of heavy metal ions. Further, the mechanism involved in the removal of heavy metal ions is briefly discussed. The most recent studies about the adsorption method for the treatment of heavy metal ions contaminated water are presented.

scholarly journals Biophysical and biochemical investigations of RNA catalysis in the hammerhead ribozyme

1999 ◽  
Vol 32 (3) ◽  
pp. 241-284 ◽  
Author(s):  
William G. Scott
Keyword(s):  
Metal Ions ◽  
Conformational Change ◽  
Large Scale ◽  
Enzyme Inhibitor ◽  
Hammerhead Ribozyme ◽  
Three Dimensional ◽  
Chemical Mechanism ◽  
Dimensional Structure ◽  
Three Dimensional Structure ◽  
Hammerhead Rna

1. How do ribozymes work? 2412. The hammerhead RNA as a prototype ribozyme 2422.1 RNA enzymes 2422.2 Satellite self-cleaving RNAs 2422.3 Hammerhead RNAs and hammerhead ribozymes 2443. The chemical mechanism of hammerhead RNA self-cleavage 2463.1 Phosphodiester isomerization via an SN2(P) reaction 2473.2 The canonical role of divalent metal ions in the hammerhead ribozyme reaction 2513.3 The hammerhead ribozyme does not actually require metal ions for catalysis 2543.4 Hammerhead RNA enzyme kinetics 2574. Sequence requirements for hammerhead RNA self-cleavage 2604.1 The conserved core, mutagenesis and functional group modifications 2604.2 Ground-state vs. transition-state effects 2615. The three-dimensional structure of the hammerhead ribozyme 2625.1 Enzyme–inhibitor complexes 2625.2 Enzyme–substrate complex in the initial state 2645.3 Hammerhead ribozyme self-cleavage in the crystal 2645.4 The requirement for a conformational change 2655.5 Capture of conformational intermediates using crystallographic freeze-trapping 2665.6 The structure of a hammerhead ribozyme ‘early’ conformational intermediate 2675.7 The structure of a hammerhead ribozyme ‘later’ conformational intermediate 2685.8 Is the conformational change pH dependent? 2695.9 Isolating the structure of the cleavage product 2715.10 Evidence for and against additional large-scale conformation changes 2745.11 NMR spectroscopic studies of the hammerhead ribozyme 2786. Concluding remarks 2807. Acknowledgements 2818. References 2811. How do ribozymes work? 241The discovery that RNA can be an enzyme (Guerrier-Takada et al. 1983; Zaug & Cech, 1986) has created the fundamental question of how RNA enzymes work. Before this discovery, it was generally assumed that proteins were the only biopolymers that had sufficient complexity and chemical heterogeneity to catalyze biochemical reactions. Clearly, RNA can adopt sufficiently complex tertiary structures that make catalysis possible. How does the three- dimensional structure of an RNA endow it with catalytic activity? What structural and functional principles are unique to RNA enzymes (or ribozymes), and what principles are so fundamental that they are shared with protein enzymes?

scholarly journals Crystal structure of a tankyrase 1–telomere repeat factor 1 complex

2016 ◽  
Vol 72 (4) ◽  
pp. 320-327 ◽  
Author(s):  
Bo Li ◽  
Ruihong Qiao ◽  
Zhizhi Wang ◽  
Weihong Zhou ◽  
Xin Li ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Large Scale ◽  
Sparse Matrix ◽  
Critical Role ◽  
Three Dimensional ◽  
Mitotic Cell ◽  
Ankyrin Repeat ◽  
Dimensional Structure ◽  
Telomere Repeat ◽  
Tankyrase 1

Telomere repeat factor 1 (TRF1) is a subunit of shelterin (also known as the telosome) and plays a critical role in inhibiting telomere elongation by telomerase. Tankyrase 1 (TNKS1) is a poly(ADP-ribose) polymerase that regulates the activity of TRF1 through poly(ADP-ribosyl)ation (PARylation). PARylation of TRF1 by TNKS1 leads to the release of TRF1 from telomeres and allows telomerase to access telomeres. The interaction between TRF1 and TNKS1 is thus important for telomere stability and the mitotic cell cycle. Here, the crystal structure of a complex between the N-terminal acidic domain of TRF1 (residues 1–55) and a fragment of TNKS1 covering the second and third ankyrin-repeat clusters (ARC2-3) is presented at 2.2 Å resolution. The TNKS1–TRF1 complex crystals were optimized using an `oriented rescreening' strategy, in which the initial crystallization condition was used as a guide for a second round of large-scale sparse-matrix screening. This crystallographic and biochemical analysis provides a better understanding of the TRF1–TNKS1 interaction and the three-dimensional structure of the ankyrin-repeat domain of TNKS.

scholarly journals Small-variance asymptotics for non-parametric online robot learning

2018 ◽  
Vol 38 (1) ◽  
pp. 3-22 ◽  
Author(s):  
Ajay Kumar Tanwani ◽  
Sylvain Calinon
Keyword(s):  
Mixture Models ◽  
Dirichlet Process ◽  
Large Scale ◽  
Principal Component ◽  
Small Variance ◽  
Remote Manipulation ◽  
State Duration ◽  
Duration Information ◽  
Low Dimensional ◽  
Non Parametric

Small-variance asymptotics is emerging as a useful technique for inference in large-scale Bayesian non-parametric mixture models. This paper analyzes the online learning of robot manipulation tasks with Bayesian non-parametric mixture models under small-variance asymptotics. The analysis yields a scalable online sequence clustering (SOSC) algorithm that is non-parametric in the number of clusters and the subspace dimension of each cluster. SOSC groups the new datapoint in low-dimensional subspaces by online inference in a non-parametric mixture of probabilistic principal component analyzers (MPPCA) based on a Dirichlet process, and captures the state transition and state duration information online in a hidden semi-Markov model (HSMM) based on a hierarchical Dirichlet process. A task-parameterized formulation of our approach autonomously adapts the model to changing environmental situations during manipulation. We apply the algorithm in a teleoperation setting to recognize the intention of the operator and remotely adjust the movement of the robot using the learned model. The generative model is used to synthesize both time-independent and time-dependent behaviors by relying on the principles of shared and autonomous control. Experiments with the Baxter robot yield parsimonious clusters that adapt online with new demonstrations and assist the operator in performing remote manipulation tasks.

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