Space Representation Research Articles

Towards Sustainable Material Design: A Comparative Analysis of Latent Space Representations in AI Models

In this study, we employed machine learning techniques to improve sustainable materials design by examining how various latent space representations affect the AI performance in property predictions. We compared three fingerprinting methodologies: (a) neural networks trained on specific properties, (b) encoder–decoder architectures, and c) traditional Morgan fingerprints. Their encoding quality was quantitatively compared by using these fingerprints as inputs for a simple regression model (Random Forest) to predict glass transition temperatures (Tg), a critical parameter in determining material performance. We found that the task-specific neural networks achieved the highest accuracy, with a mean absolute percentage error (MAPE) of 10% and an R2 of 0.9, significantly outperforming encoder–decoder models (MAPE: 19%, R2: 0.76) and Morgan fingerprints (MAPE: 24%, R2: 0.6). In addition, we used dimensionality reduction techniques, such as principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE), to gain insights on the models’ abilities to learn relevant molecular features to Tg. By offering a more profound understanding of how chemical structures influence AI-based property predictions, this approach enables the efficient identification of high-performing materials in applications that range from water decontamination to polymer recyclability with minimum experimental effort, promoting a circular economy in materials science.

The transformation of sensory to perceptual braille letter representations in the visually deprived brain.

Experience-based plasticity of the human cortex mediates the influence of individual experience on cognition and behavior. The complete loss of a sensory modality is among the most extreme such experiences. Investigating such a selective, yet extreme change in experience allows for the characterization of experience-based plasticity at its boundaries. Here, we investigated information processing in individuals who lost vision at birth or early in life by probing the processing of braille letter information. We characterized the transformation of braille letter information from sensory representations depending on the reading hand to perceptual representations that are independent of the reading hand. Using a multivariate analysis framework in combination with functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and behavioral assessment, we tracked cortical braille representations in space and time, and probed their behavioral relevance. We located sensory representations in tactile processing areas and perceptual representations in sighted reading areas, with the lateral occipital complex as a connecting 'hinge' region. This elucidates the plasticity of the visually deprived brain in terms of information processing. Regarding information processing in time, we found that sensory representations emerge before perceptual representations. This indicates that even extreme cases of brain plasticity adhere to a common temporal scheme in the progression from sensory to perceptual transformations. Ascertaining behavioral relevance through perceived similarity ratings, we found that perceptual representations in sighted reading areas, but not sensory representations in tactile processing areas are suitably formatted to guide behavior. Together, our results reveal a nuanced picture of both the potentials and limits of experience-dependent plasticity in the visually deprived brain.

The transformation of sensory to perceptual braille letter representations in the visually deprived brain

Experience-based plasticity of the human cortex mediates the influence of individual experience on cognition and behavior. The complete loss of a sensory modality is among the most extreme such experiences. Investigating such a selective, yet extreme change in experience allows for the characterization of experience-based plasticity at its boundaries. Here, we investigated information processing in individuals who lost vision at birth or early in life by probing the processing of braille letter information. We characterized the transformation of braille letter information from sensory representations depending on the reading hand to perceptual representations that are independent of the reading hand. Using a multivariate analysis framework in combination with functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and behavioral assessment, we tracked cortical braille representations in space and time, and probed their behavioral relevance. We located sensory representations in tactile processing areas and perceptual representations in sighted reading areas, with the lateral occipital complex as a connecting ‘hinge’ region. This elucidates the plasticity of the visually deprived brain in terms of information processing. Regarding information processing in time, we found that sensory representations emerge before perceptual representations. This indicates that even extreme cases of brain plasticity adhere to a common temporal scheme in the progression from sensory to perceptual transformations. Ascertaining behavioral relevance through perceived similarity ratings, we found that perceptual representations in sighted reading areas, but not sensory representations in tactile processing areas are suitably formatted to guide behavior. Together, our results reveal a nuanced picture of both the potentials and limits of experience-dependent plasticity in the visually deprived brain.

Disturbance decoupling controller design of switched Boolean control networks in recursion

This paper studies the disturbance decoupling problem (DDP) of switched Boolean control networks (SBCNs) by the methodology of recursion. All the state nodes of the original SBCNs are split into two parts based on whether or not the state node influences the outputs. By driving the part of state nodes which influence the outputs to the largest control invariant set, the concept of DDP in recursion is proposed. Using the algebraic state space representation (ASSR) method, both mode-independent and mode-dependent state feedback controllers are constructed to solve the DDP in recursion of SBCNs. Furthermore, based on the obtained mode-independent state feedback DDP controllers, the mode-independent output feedback controllers are designed for the DDP in recursion of SBCNs.

“Empty Space” of the Civil War: Theatricalization of the theme on the Soviet Screen in 1960s

The article analyses three films: “Optimistic Tragedy” (1963) by Samson Samsonov, “The First Russians” (1967) by Evgeny Schiffers and Alexander Ivanov, and “Intervention” (1968) by Gennady Poloka. This combination is based not only on the special “picturesqueness” of the films and a certain type of eccentricity but also on an ample use of the conventional theatrical space that paradoxically leads to the reinterpretation of the very concept of the Civil War on the Soviet screen in the 1960s.

Unsupervised hyperspectral noise estimation and restoration via interband-invariant representation learning

Hyperspectral images (HSIs) acquired from different imaging platforms are inevitably contaminated by multiple types of noise. However, the existing supervised learning based denoising methods often show poor generalizability on data with complex degradation, due to the discrepancy between synthetic training data and real data. Although some unsupervised denoisers have been developed to learn priors on real data, the noise assumptions or image priors in these methods limit their performances. In this paper, an unsupervised noise estimation and restoration (UNER) framework is proposed based on disentangled representation learning, to create an interband representation space that is resistant to noise within a single HSI, i.e., an interband-invariant representation space. Real HSIs are categorized internally into noisy and clean bands by noise intensity estimation. Intraband and interband disentangled reconstruction are designed to train two encoder-decoder modules to learn the interband-invariant representation. Noise patterns are separated from HSIs by transforming noisy bands into the representation space without introducing hand-crafted priors. The estimated noise and clean bands are then combined to train the self-supervised interband information restoration module, thereby exploiting spectral correlation and restoring the latent noise-free data with high information fidelity. In this way, the UNER framework can learn specific priors from real HSIs without clean data and be applied to various scenarios with complicated noise patterns. Noise removal experiments conducted on three airborne hyperspectral datasets representing urban, agricultural, and forestry areas respectively demonstrate the superiority of the proposed UNER framework over the state-of-the-art hyperspectral denoising methods.

Organizing space through saccades and fixations between primate posterior parietal cortex and hippocampus

The primate posterior parietal cortex (PPC) withholds a unified representation of the visual space supporting visual exploration, while the hippocampus (HPC) provides a memory-based cognitive place map of the environment. To probe the interactions between these two representations, i.e. between view and place, we compared neural activity in the two regions of macaques navigating a virtual maze. We show that a large proportion of PPC neurons displayed spatial selectivity, along with the HPC. We hypothesized that such modulation by self-position might stem from visual cues processing through saccades and fixations. Accordingly, we found saccade-modulated neurons and cells driven by direct fixations on maze paths or landmarks in both brain regions. These populations of “path” and “landmark cells” gave rise to task-relevant maze segmentation, specific to each region. Finally, both regions anticipated landmarks before they appeared in the field of view, suggesting a shared knowledge of the spatial layout. Altogether, these findings highlight the neural processes that make up place, combining visual exploration of objects in space with memory-driven actions.

Imaging-genomic spatial-modality attentive fusion for studying neuropsychiatric disorders.

Multimodal learning has emerged as a powerful technique that leverages diverse data sources to enhance learning and decision-making processes. Adapting this approach to analyzing data collected from different biological domains is intuitive, especially for studying neuropsychiatric disorders. A complex neuropsychiatric disorder like schizophrenia (SZ) can affect multiple aspects of the brain and biologies. These biological sources each present distinct yet correlated expressions of subjects' underlying physiological processes. Joint learning from these data sources can improve our understanding of the disorder. However, combining these biological sources is challenging for several reasons: (i) observations are domain specific, leading to data being represented in dissimilar subspaces, and (ii) fused data are often noisy and high-dimensional, making it challenging to identify relevant information. To address these challenges, we propose a multimodal artificial intelligence model with a novel fusion module inspired by a bottleneck attention module. We use deep neural networks to learn latent space representations of the input streams. Next, we introduce a two-dimensional (spatio-modality) attention module to regulate the intermediate fusion for SZ classification. We implement spatial attention via a dilated convolutional neural network that creates large receptive fields for extracting significant contextual patterns. The resulting joint learning framework maximizes complementarity allowing us to explore the correspondence among the modalities. We test our model on a multimodal imaging-genetic dataset and achieve an SZ prediction accuracy of 94.10% (p < .0001), outperforming state-of-the-art unimodal and multimodal models for the task. Moreover, the model provides inherent interpretability that helps identify concepts significant for the neural network's decision and explains the underlying physiopathology of the disorder. Results also show that functional connectivity among subcortical, sensorimotor, and cognitive control domains plays an important role in characterizing SZ. Analysis of the spatio-modality attention scores suggests that structural components like the supplementary motor area, caudate, and insula play a significant role in SZ. Biclustering the attention scores discover a multimodal cluster that includes genes CSMD1, ATK3, MOB4, and HSPE1, all of which have been identified as relevant to SZ. In summary, feature attribution appears to be especially useful for probing the transient and confined but decisive patterns of complex disorders, and it shows promise for extensive applicability in future studies.

Towards a Flexible Semantic Guided Model for Single Image Enhancement and Restoration.

Low-light image enhancement (LLIE) investigates how to improve the brightness of an image captured in illumination-insufficient environments. The majority of existing methods enhance low-light images in a global and uniform manner, without taking into account the semantic information of different regions. Consequently, a network may easily deviate from the original color of local regions. To address this issue, we propose a semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that adaptively integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in the LLIE task. We further present a refined framework SKF++ with two new techniques: (a) Extra convolutional branch for intra-class illumination and color recovery through extracting local information and (b) Equalization-based histogram transformation for contrast enhancement and high dynamic range adjustment. Extensive experiments on various benchmarks of LLIE task and other image processing tasks show that models equipped with the SKF/SKF++ significantly outperform the baselines and our SKF/SKF++ generalizes to different models and scenes well. Besides, the potential benefits of our method in face detection and semantic segmentation in low-light conditions are discussed.

Five-Year-Olds’ Use of Geometric and Featural Information in Virtual Space Representation

Five-Year-Olds’ Use of Geometric and Featural Information in Virtual Space Representation

Tool Representations in Human Visual Cortex.

Tools such as pens, forks, and scissors play an important role in many daily-life activities, an importance underscored by the presence in visual cortex of a set of tool-selective brain regions. This review synthesizes decades of neuroimaging research that investigated the representational spaces in the visual ventral stream for objects, such as tools, that are specifically characterized by action-related properties. Overall, results reveal a dissociation between representational spaces in ventral and lateral occipito-temporal cortex (OTC). While lateral OTC encodes both visual (shape) and action-related properties of objects, distinguishing between objects acting as end-effectors (e.g., tools, hands) versus similar noneffector manipulable objects (e.g., a glass), ventral OTC primarily represents objects' visual features such as their surface properties (e.g., material and texture). These areas act in concert with regions outside of OTC to support object interaction and tool use. The parallel investigation of the dimensions underlying object representations in artificial neural networks reveals both the possibilities and the difficulties in capturing the action-related dimensions that distinguish tools from other objects. Although artificial neural networks offer promise as models of visual cortex computations, challenges persist in replicating the action-related dimensions that go beyond mere visual features. Taken together, we propose that regions in OTC support the representation of tools based on a behaviorally relevant action code and suggest future paths to generate a computational model of this object space.

The Sine–Gordon QFT in de Sitter spacetime

We consider the massless Sine–Gordon model in de Sitter spacetime, in the regime β2<4π\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta ^2 < 4 \\pi $$\\end{document} and using the framework of perturbative algebraic quantum field theory. We show that a Fock space representation exists for the free massless field, but that the natural one-parameter family of vacuum-like states breaks the de Sitter boost symmetries. We prove convergence of the perturbative series for the S matrix in this representation and construct the interacting Haag–Kastler net of local algebras from the relative S matrices. We show that the net fulfills isotony, locality and de Sitter covariance (in the algebraic adiabatic limit), even though the states that we consider are not invariant. We furthermore prove convergence of the perturbative series for the interacting field and the vertex operators, and verify that the interacting equation of motion holds.

SENNA: Unified Hardware/Software Space Exploration for Parametrizable Neural Network Accelerators

Parametrizable neural network accelerators enable the deployment of targeted hardware for specialized environments. Finding the best architecture configuration for a given specification, however, is challenging. A large number of hardware configurations have to be considered, and for each hardware instance, an efficient software execution plan needs to be found, leading to a vast search space. Prior work has tackled this problem by dividing the search into subproblems for individual layers of a network. There is no guarantee, however, that the overall best hardware configuration that delivers the desired end-to-end performance across the entire network is among the best individual layer configurations. This work presents SENNA, a unified hardware/software space exploration framework for parametrizable neural network accelerators. To guide the exploration towards the overall best configuration, SENNA employs a multi-objective genetic algorithm with a novel design space representation that encodes the configuration of hardware and software parameters in a single chromosome. Using the Parallel Island Model (PIM), each layer is represented by one or more individual islands each containing a separate population to simultaneously search for the best configuration across the entire network. A tailored gene migration technique enables the exchange of genes between the populations of different islands. SENNA is evaluated with three parametrizable architectures and four neural networks. The evaluation result demonstrates that SENNA achieves upto 1.92x EDP improvement compared to the State-of-the-Art. With equivalent evaluation budgets, SENNA shows 2.5x-9.3x speedup compared to an Oracle scheme and the State-of-the-Art.

Reconstruction of the past through artistic practices

Artists express their past experiences through artistic practices, bringing the personal and historical dynamics of the past into the present by using found objects. The relational nature of art practices created through the use of found objects has influenced how artists convey and represent their experiences, leading them to adopt different practices such as installation art. These artistic practices enable spectators to re-experience the past by transporting past experiences into the present. However, past experiences cannot be transferred to the present exactly as they were. The re-experiencing of past experiences should be viewed as a relational representational space that is reconstructed under the conditions of the present. This study examines how a past experience is transformed into a representational space in the practices of Michael Landy, Anny and Sibel Öztürk, Hale Tenger, and Ilya Kabakov. It focuses on the relationship between memory and space, emphasizing how past experiences are conveyed through representation with the relational impact of installation art, a contemporary art practice. The narrative potential and relationality of installation art bring together elements of objects, time, and representation to offer spatial experiences.

General-purpose machine-learned potential for 16 elemental metals and their alloys

Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a promising approach for constructing a unified general-purpose MLP for numerous elements, demonstrated through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve a complete representation of the chemical space, we show, via principal component analysis and diverse test datasets, that employing one-component and two-component systems suffices. Our unified UNEP-v1 model exhibits superior performance across various physical properties compared to a widely used embedded-atom method potential, while maintaining remarkable efficiency. We demonstrate our approach’s effectiveness through reproducing experimentally observed chemical order and stable phases, and large-scale simulations of plasticity and primary radiation damage in MoTaVW alloys.

Phraseological representation of the elemental space in English

Phraseological representation of the elemental space in English

The shaping of cityscape and imagination: Song Dynasty urban pavilions along the streets

ABSTRACT In response to the presence of numerous wine pavilions in the Song dynasty capital, this article reconsiders the role of architecture as urban space. Within the historical context of urban development, there were shifts in Song Dynasty official control and management of pavilion construction. Song Dynasty urban texts and images indicate that wine pavilions gradually became indispensable structures in cities, deeply integrated as spatial coordinates within the urban fabric. The wine pavilions also functioned as representational spaces, connecting individuals to the city through associated imagery. They served not only as containers of urban life and carriers of urban stories but also as a manifestation of spatial practice. Between mechanisms of power and daily life, wine pavilions shaped a distinctive cityscape of the Song Dynasty, blending reality with imagination.

Hebrew Dreams in the Berlin of Yesterday: German-Jewish Symbiosis Fantasy on the City’s Streets in Lifney Hamakom by Haim Be’er and Avedot by Lea Goldberg

Abstract This article examines Berlin’s urban space as represented in the novels Lifney Hamakom by Haim Be’er and Avedot by Lea Goldberg. Based on close reading as well as distant reading methods (mapping, annotation, and visualization), the article argues that, despite being written in different periods, both novels are similar in their representation of space: neither novel depicts Berlin as an actual living city or as a concrete setting for their plots. Rather, in both novels Berlin’s space is seen as a symbol for a German-Jewish fantasy of symbiosis: cosmopolitanism in the spirit of Bildung. Poetically, however, this impression takes two different forms: while Avedot freezes the city as an image of potential symbiosis, Lifney Hamakom is significantly affected by the mass of symbols of the city’s space.

Using Social Space Perspective to Explore SDGs Curricula Localisation: Case Study at Two Japanese National and Private Universities

ABSTRACTDrawing on the concept of the Production of Space this article examines how sustainable development agenda was localised—in design, operation and students' lived experiences—in two international education programmes at two universities in Japan. We analysed relevant programme documents, interviews with faculty members, students' reflective notes, as well as the authors' autoethnographic journals. The findings show that the local and institutional context influenced the SDGs curriculum design (representations of space or the conceived space). The virtual and physical learning spaces, the pedagogy and the operation of the programme (spatial practice or perceived space) shaped students' interaction and learning outcomes (representational space or the lived space). As instructors attempted to adopt SDGs in their teaching (through perceived space) and as students tried to gain interdisciplinary knowledge through SDGs (through lived space), both parties negotiated and challenged the programme design shifting the conceived spaces. Our study contributes to a deeper understanding of the localisation of SDGs within the internationalisation of higher education by employing Lefebvre's spatial analysis. This approach reveals the complex socio‐spatial dynamics at play, offering insights into how educational environments are constructed and experienced.

Quarkyonic equation of state with momentum-dependent interaction and neutron star structure

The structure and basic properties of dense nuclear matter still remain one of the open problems of Physics. In particular, the composition of the matter that composes neutron stars is under theoretical and experimental investigation. Among the theories that have been proposed, apart from the classical one where the composition is dominated by hadrons, the existence or coexistence of free quark matter is a dominant guess. An approach towards this solution is the phenomenological view according to which the existence of quarkyonic matter plays a dominant role in the construction of the equation of state (EOS). According to it the structure of the EOS is based on the existence of the quarkyonic particle which is a hybrid state of a particle that combines properties of hadronic and quark matter with a corresponding representation in momentum space. In this paper we propose a phenomenological model for quarkyonic matter, borrowed from corresponding applications in hadronic models, where the interaction in the quarkyonic matter depends not only on the position but also on the momentum of the quarkyonic particles. This consideration, as we demonstrate, can have a remarkable consequence on the shape of the EOS and thus on the properties of neutron stars, offering a sufficiently flexible model. Comparison with recent observational data can place constraints on the parameterization of the particular model and help improve its reliability.