Space Representation Research Articles

Classification of the difficulty of a climbing route using the transformation of representation spaces and cascading classifier ensemble (CCE)

Climbing has gained popularity in recent years and encompasses various disciplines, among which bouldering stands out as one of the most well-known. Determining the difficulty of a bouldering route is a challenging task due to the varying combinations of handholds. Accurate assessment of the difficulty of a route is important, as it allows a climber’s ability to be measured against that of other climbers. Furthermore, climbers must gradually progress from less to more difficult routes to improve their skills effectively. The difficulty rating of a route poses a complex problem, and previous research has explored machine learning techniques, including deep learning, to address it. We propose a novel approach based on a series of transformations in the representation space that incorporates expert knowledge to improve the classification of climbing route difficulty. We introduce a cascade classifier ensemble (CCE) model specifically designed for levelling classification. Compared to similar works, this model also incorporates explainable artificial intelligence (XAI) mechanisms. With the CEE model, the relevance of specific handholds in the transition from one level of difficulty to the next is obtained. Finally, we illustrate how our proposed model not only achieves good results but also empowers climbers to identify the most crucial sections in each level of difficulty.

What Is Faster than Where in Vocal Emotional Perception.

Voices carry a vast amount of information about speakers (e.g., emotional state; spatial location). Neuroimaging studies postulate that spatial ("where") and emotional ("what") cues are processed by partially independent processing streams. Although behavioral evidence reveals interactions between emotion and space, the temporal dynamics of these processes in the brain and its modulation by attention remain unknown. We investigated whether and how spatial and emotional features interact during voice processing as a function of attention focus. Spatialized nonverbal vocalizations differing in valence (neutral, amusement, anger) were presented at different locations around the head, whereas listeners discriminated either the spatial location or emotional quality of the voice. Neural activity was measured with ERPs of the EEG. Affective ratings were collected at the end of the EEG session. Emotional vocalizations elicited decreased N1 but increased P2 and late positive potential amplitudes. Interactions of space and emotion occurred at the salience detection stage: neutral vocalizations presented at right (vs. left) locations elicited increased P2 amplitudes, but no such differences were observed for emotional vocalizations. When task instructions involved emotion categorization, the P2 was increased for vocalizations presented at front (vs. back) locations. Behaviorally, only valence and arousal ratings showed emotion-space interactions. These findings suggest that emotional representations are activated earlier than spatial representations in voice processing. The perceptual prioritization of emotional cues occurred irrespective of task instructions but was not paralleled by an augmented stimulus representation in space. These findings support the differential responding to emotional information by auditory processing pathways.

Designing Connectivity-Guaranteed Porous Metamaterial Units Using Generative Graph Neural Networks

Abstract Designing 3D porous metamaterial units while ensuring complete connectivity of both solid and pore phases presents a significant challenge. This complete connectivity is crucial for manufacturability and structure-fluid interaction applications (e.g., fluid-filled lattices). In this study, we propose a generative graph neural network-based framework for designing the porous metamaterial units with the constraint of complete connectivity. First, we propose a graph-based metamaterial unit generation approach to generate porous metamaterial samples with complete connectivity in both solid and pore phases. Second, we establish and evaluate three distinct variational graph autoencoder (VGAE)-based generative models to assess their effectiveness in generating an accurate latent space representation of metamaterial structures. By choosing the model with the highest reconstruction accuracy, the property-driven design search is conducted to obtain novel metamaterial unit designs with the targeted properties. A case study on designing liquid-filled metamaterials for thermal conductivity properties is carried out. The effectiveness of the proposed graph neural network-based design framework is evaluated by comparing the performances of the obtained designs with those of known designs in the metamaterial database. Merits and shortcomings of the proposed framework are also discussed.

Denoising Alignment with Large Language Model for Recommendation

The mainstream approach of GNN-based recommendation aggregates high-order ID information associated with the node in the user-item graph. The aggregation pattern using ID as signal has two disadvantages: lack of textual semantics and the impact of interaction noise. These disadvantages pose a threat to effectively learn user preferences, especially in capturing intricate user-item semantic relationships. Although large language models (LLMs) allow the integration of rich textual information into recommenders and have had groundbreaking applications in recommender systems, current works need to bridge the gap between different representation spaces. This is because LLMs-based methods align the representations of GNN-based models only by using text embedding of LLM, leading to unsatisfactory results. To address this challenge, we propose a D enoising A lignment framework with L LMs for GNN-based R ecommenders (DALR), which aims to align structural representation with textual representation and mitigate the effects of noise. Specifically, We propose a modeling framework that integrates the representation of graph structure with textual information from LLMs to capture intricate user-item interactions. We also suggest an alignment paradigm to enhance representation performance by aligning semantic signals from LLMs and structural features from GNN models. Additionally, we introduce a contrastive learning scheme to relieve the impact of noise and improve model performance. Extensive experiments on public datasets demonstrate that our model consistently outperforms the state-of-the-art methods. DALR achieves improvements ranging from 2.82% to 12.20% in Recall@5 and from 1.04% to 3.48% in NDCG@5 compared to the strongest baseline model, using the Steam dataset as an example.

SemiGMMPoint: Semi-supervised point cloud segmentation based on Gaussian mixture models

Existing semi-supervised point cloud segmentation methods emphasize on discriminative learning, which overlooks the underlying class-conditional distributions and distribution similarities. In this paper, we propose SemiGMMPoint, the first generative framework for semi-supervised 3D point cloud segmentation in real-world and large-scale settings. Specifically, we propose a point dense generative classifier based on Gaussian mixture models (GMMs) to explicitly estimate class-conditional distributions. On top of it, we incorporate a novel similarity-minimization algorithm into the Expectation–Maximization (EM) based GMM parameter estimation, which minimizes the inter-class distribution similarity in the representation space. Moreover, we utilize the well-calibrated posterior to develop a modified point contrastive loss to mitigate sampling bias in semi-supervised settings. Extensive experiments show that SemiGMMPoint significantly boosts performance for semi-supervised point cloud segmentation on many state-of-the-art backbones without requiring architectural changes. Codes are available at https://github.com/jojodidli/SemiGMMPoint.

Navigating Memorability Landscapes: Hyperbolic Geometry Reveals Hierarchical Structures in Object Concept Memory.

Why are some object concepts (e.g., birds, cars, vegetables, etc.) more memorable than others? Prior studies have suggested that features (e.g., color, animacy, etc.) and typicality (e.g., robin vs. penguin) of object images influences the likelihood of being remembered. However, a complete understanding of object memorability remains elusive. In this study, we examine whether the geometric relationship between object concepts explains differences in their memorability. Specifically, we hypothesize that image concepts will be geometrically arranged in hierarchical structures and that memorability will be explained by a concept's depth in these hierarchical trees. To test this hypothesis, we construct a Hyperbolic representation space of object concepts (N=1,854) from the THINGS database (Hebart et al., 2019), which consists of naturalistic images of concrete objects, and a space of 49 feature dimensions derived from data-driven models. Using ALBATROSS (Stier, A. J., Giusti, C., & Berman, M. G., In prep), a stochastic topological data analysis technique that detects underlying structures of data, we demonstrate that Hyperbolic geometry efficiently captures the hierarchical organization of object concepts above and beyond a traditional Euclidean geometry and that hierarchical organization is related to memorability. We find that concepts closer to the center of the representational space are more prototypical and also more memorable. Importantly, Hyperbolic distances are more predictive of memorability and prototypicality than Euclidean distances, suggesting that concept memorability and typicality are organized hierarchically. Taken together, our work presents a novel hierarchical representational structure of object concepts that explains memorability and typicality.

Deep contrastive learning for predicting cancer prognosis using gene expression values.

Recent advancements in image classification have demonstrated that contrastive learning (CL) can aid in further learning tasks by acquiring good feature representation from a limited number of data samples. In this paper, we applied CL to tumor transcriptomes and clinical data to learn feature representations in a low-dimensional space. We then utilized these learned features to train a classifier to categorize tumors into a high- or low-risk group of recurrence. Using data from The Cancer Genome Atlas (TCGA), we demonstrated that CL can significantly improve classification accuracy. Specifically, our CL-based classifiers achieved an area under the receiver operating characteristic curve (AUC) greater than 0.8 for 14 types of cancer, and an AUC greater than 0.9 for 3 types of cancer. We also developed CL-based Cox (CLCox) models for predicting cancer prognosis. Our CLCox models trained with the TCGA data outperformed existing methods significantly in predicting the prognosis of 19 types of cancer under consideration. The performance of CLCox models and CL-based classifiers trained with TCGA lung and prostate cancer data were validated using the data from two independent cohorts. We also show that the CLCox model trained with the whole transcriptome significantly outperforms the Cox model trained with the 16 genes of Oncotype DX that is in clinical use for breast cancer patients. The trained models and the Python codes are publicly accessible and provide a valuable resource that will potentially find clinical applications for many types of cancer.

How to Help Older Adults Navigate Through VR? Effects in Outdoor–Indoor Transition Environments

In more complex outdoor–indoor transition scenarios, people were more prone to getting lost due to difficulties in effectively integrating proprioceptive cues and external navigation cues. Previous research had mostly focused on spatial perception driven by head or limb movements, with less discussion on locomotion methods that involved significant visual discrepancies. The challenge in designing navigation systems lies in balancing the enhancement of wayfinding performance with the potential negative impact on spatial cognition. We believe that navigation legibility might be a key optimization approach. Additionally, the differences in performance between different age groups were also worth investigating. Therefore, this study aimed to explore how locomotion methods and navigation legibility affected wayfinding performance, navigation interaction, navigation regression, and spatial cognition in younger and older adults during outdoor–indoor transition scenarios. Twenty-four younger (aged 19–27) and 24 older adults (aged 60–83) underwent VR wayfinding tasks. Participants initially followed virtual phone navigation from outdoor starting points to indoor endpoints, then drew cognitive route maps. Five main findings emerged: Continuous teleportation positively affected navigation performance and global representation of routes. Improving navigation legibility boosted navigation interaction, navigation regression, and global representation of routes. Older adults engaged more in navigation interaction but less in navigation regression, exhibiting poorer performance and route representation. Location-specific differences were observed in locomotion, legibility, and age effects. Furthermore, most older adults and over half of younger adults displayed a deflection effect based on outdoor–indoor (OI) space in route representation. These findings indicated that continuous teleportation and enhanced navigation legibility could improve user experience and spatial cognition, highlighting the importance of considering user age and the characteristics of outdoor–indoor transition scenarios when optimizing navigation methods.

The chemical diversity of the metal-poor Milky Way

ABSTRACT We present a detailed study of the chemical diversity of the metal-poor Milky Way using data from the GALAH DR3 survey. Considering 17 chemical abundances relative to iron ([X/Fe]) for 9923 stars, we employ principal component analysis (PCA) and extreme deconvolution (XD) to identify 10 distinct stellar groups. This approach, free from chemical or dynamical cuts, reveals known populations, including the accreted halo, thick disc, thin disc, and in situ halo. The thick disc is characterized by multiple substructures, suggesting it comprises stars formed in diverse environments. Our findings highlight the limited discriminatory power of magnesium in separating accreted and disc stars. Elements such as Ba, Al, Cu, and Sc are critical in distinguishing disc from accreted stars, while Ba, Y, Eu, and Zn differentiate disc and accreted stars from the in situ halo. This study demonstrates the potential power of combining a latent space representation of the data (PCA) with a clustering algorithm (XD) in Galactic archaeology, in providing new insights into the Galaxy’s assembly and evolutionary history.

UAV Autonomous Navigation Based on Deep Reinforcement Learning in Highly Dynamic and High-Density Environments

Autonomous navigation of Unmanned Aerial Vehicles (UAVs) based on deep reinforcement learning (DRL) has made great progress. However, most studies assume relatively simple task scenarios and do not consider the impact of complex task scenarios on UAV flight performance. This paper proposes a DRL-based autonomous navigation algorithm for UAVs, which enables autonomous path planning for UAVs in high-density and highly dynamic environments. This algorithm proposes a state space representation method that contains position information and angle information by analyzing the impact of UAV position changes and angle changes on navigation performance in complex environments. In addition, a dynamic reward function is constructed based on a non-sparse reward function to balance the agent’s conservative behavior and exploratory behavior during the model training process. The results of multiple comparative experiments show that the proposed algorithm not only has the best autonomous navigation performance but also has the optimal flight efficiency in complex environments.

Flagged observation analyses as a tool for scoping and communication in integrated ecosystem assessments.

Working groups for integrated ecosystem assessments are often challenged with understanding and assessing recent change in ecosystems. As a basis for this, the groups typically have at their disposal many time series and will often need to prioritize which ones to follow up for closer analyses and assessment. In this article we provide a procedure termed Flagged Observation analysis that can be applied to all the available time series to help identifying time series that should be prioritized. The statistical procedure first applies a structural time series model including a stochastic trend model to the data to estimate the long-term trend. The model adopts a state space representation, and the trend component is estimated by a Kalman filter algorithm. The algorithm obtains one- or more-years-ahead prediction values using all past information from the data. Thus, depending on the number of years the investigator wants to consider as "the most recent", the expected trend for these years is estimated through the statistical procedure by using only information from the years prior to them. Forecast bands are estimated around the predicted trends for the recent years, and in the final step, an assessment is made on the extent to which observations from the most recent years fall outside these forecast bands. Those that do, may be identified as flagged observations. A procedure is also presented for assessing whether the combined information from all the most recent observations form a pattern that deviates from the predicted trend and thus represents an unexpected tendency that may be flagged. In addition to form the basis for identifying time series that should be prioritized in an integrated ecosystem assessment, flagged observations can provide the basis for communicating with managers and stakeholders about recent ecosystem change. Applications of the framework are illustrated with two worked examples.

Robust estimation of hydrogeological parameters from wireline logs usingsemi-supervised deep neural networks assisted with global optimization-based regression methods

Understanding the distribution of hydrogeological properties of the aquifers is crucial for sustainable groundwater resource development. This research explores the application of deep autoencoder neural networks (AE-NN), assisted with global optimization methods for estimating hydrogeological parameters in the Quaternary aquifer system in the Debrecen area, Hungary. Traditional methods for estimating aquifer parameters typically depend on field experiments and laboratory analyses, which are both costly and time-consuming, and often fail to account for the heterogeneity of groundwater formations. In this study, deep AE-NN models are trained to extract latent space (LS) representations that capture key features from the available well logs, including spontaneous potential (SP), natural gamma ray (NGR), shallow resistivity (RS), and deep resistivity (RD). The LS log is then correlated with shale volume and hydraulic conductivity, as determined by the Larionov and Csókás methods, respectively. Regression analysis revealed a Gaussian relationship between the LS log and shale volume and a negative nonlinear relationship with hydraulic conductivity. Global optimization methods, including simulated annealing (SA) and particle swarm optimization (PSO), were used to refine the regression parameters, enhancing the predictive capabilities of the models. The results demonstrated that AE-NN assisted with global optimization methods can be effectively used to estimate shale volume and hydraulic conductivity, proposing a novel and independent approach for estimating hydrogeological parameters critical to groundwater flow and contaminant transport modeling.

Measuring standardized functional leaf traits of aquatic carnivorous plants – challenges and opportunities

Aquatic carnivorous plants (ACP) are an important component of humic nutrient-poor freshwater environments. However, these habitats are facing multiple impacts that ultimately lead to habitat degradation and declining ACP populations. Functional traits, particularly those within the leaf economics spectrum, are a valuable tool for studying plant adaptation strategies and plasticity. Given their unique morphological structure, ACP are essentially excluded from functional comparisons, which potentially limits our knowledge about the ecological roles of these species compared to non-carnivorous ones. In this study, we developed a protocol for measuring the leaf functional traits of ACP (leaf fresh and dry weight, leaf area, leaf dry matter content, specific leaf area, leaf pigment content, leaf phosphorus, nitrogen and carbon contents), and carnivory-related traits (number of traps and investment in carnivory). We measured 15 traits in seven ACP species (Aldrovanda vesiculosa, Utricularia australis, U. bremii, U. intermedia, U. ochroleuca, U. stygia, U. vulgaris), grown in the outdoor collection of aquatic and wetland plants of the Institute of Botany CAS at Třeboň, the Czech Republic. We used the functional traits of other macrophyte groups/species (lemnids, Nuphar lutea, Ceratophyllum demersum), collected with a similar methodology, to assess the comparability of ACP traits. We identified the “functional unit”, a modular structure, including one leaf node, plus an internode, which performs the function of a leaf in ACP, and selected its position along the stem to reflect species-specific growth rates. We collected 714 new trait records for the target ACP. Based on a multivariate trait space representation (PCA), ACP were distinct from the other macrophyte groups/species, which highlights these species’ structural and physiological peculiarities. Nonetheless, ACP entirely overlapped the comparison data along the first PCA axis, and most of the traits lay within the ranges observed for other macrophyte groups/species, which demonstrates the comparability of the ACP traits measured by the new protocol. Applying this protocol in ecological studies could shed light on the adaptations of ACP to environmental variability, with important conservation implications.

Cosmological correlators for Bogoliubov initial states

We consider late-time correlators in de Sitter (dS) space for initial states related to the Bunch-Davies vacuum by a Bogoliubov transformation. We propose to study such late-time correlators by reformulating them in the familiar language of Witten diagrams in Euclidean anti-de Sitter space (EAdS), showing that they can be perturbatively re-cast in terms of corresponding dS boundary correlators in the Bunch-Davies vacuum and in turn, Witten diagrams in EAdS. Unlike the standard relationship between late-time correlators in the Bunch-Davies vacuum and EAdS Witten diagrams, this involves points on the upper and lower sheet of the EAdS hyperboloid which account for antipodal singularities of the two-point functions. Such Bogoliubov states include an infinite one parameter family of de Sitter invariant vacua as a special case, where the late-time correlators are constrained by conformal Ward identities. In momentum space, it is well known that their late-time correlators exhibit singularities in collinear (“folded”) momentum configurations. We give a position space interpretation of such solutions to the conformal Ward identities, where in embedding space they can be generated from the solution without collinear singularities by application of the antipodal map. We also discuss the operator product expansion (OPE) limit of late-time correlators in a generic dS invariant vacuum. Many results are derived using the Mellin space representation of late-time correlators, which in this work we extend to accommodate generic dS invariant vacua.

A survey of deep causal models and their industrial applications

The notion of causality assumes a paramount position within the realm of human cognition. Over the past few decades, there has been significant advancement in the domain of causal effect estimation across various disciplines, including but not limited to computer science, medicine, economics, and industrial applications. Given the continous advancements in deep learning methodologies, there has been a notable surge in its utilization for the estimation of causal effects using counterfactual data. Typically, deep causal models map the characteristics of covariates to a representation space and then design various objective functions to estimate counterfactual data unbiasedly. Different from the existing surveys on causal models in machine learning, this review mainly focuses on the overview of the deep causal models based on neural networks, and its core contributions are as follows: (1) we cast insight on a comprehensive overview of deep causal models from both timeline of development and method classification perspectives; (2) we outline some typical applications of causal effect estimation to industry; (3) we also endeavor to present a detailed categorization and analysis on relevant datasets, source codes and experiments.

Young walls and graded dimension formulas for finite quiver Hecke algebras of types A2n−1(2), Dn(1) and Bn(1)

In this paper, we establish the graded dimension formulas of finite quiver Hecke algebras using level-[Formula: see text] Fock space representations of quantum affine algebras of types [Formula: see text], [Formula: see text] and [Formula: see text].

Riemannian optimization of photonic quantum circuits in phase and Fock space

We propose a framework to design and optimize generic photonic quantum circuits composed of Gaussian objects (pure and mixed Gaussian states, Gaussian unitaries, Gaussian channels, Gaussian measurements) as well as non-Gaussian effects such as photon-number-resolving measurements. In this framework, we parametrize a phase space representation of Gaussian objects using elements of the symplectic group (or the unitary or orthogonal group in special cases), and then we transform it into the Fock representation using a single linear recurrence relation that computes the Fock amplitudes of any Gaussian object recursively. We also compute the gradient of the Fock amplitudes with respect to phase space parameters by differentiating through the recurrence relation. We can then use Riemannian optimization on the symplectic group to optimize MM-mode Gaussian objects, avoiding the need to commit to particular realizations in terms of fundamental gates. This allows us to “mod out” all the different gate-level implementations of the same circuit, which now can be chosen after the optimization has completed. This can be especially useful when looking to answer general questions, such as bounding the value of a property over a class of states or transformations, or when one would like to worry about hardware constraints separately from the circuit optimization step. Finally, we make our framework extendable to non-Gaussian objects that can be written as linear combinations of Gaussian ones, by explicitly computing the change in global phase when states undergo Gaussian transformations. We implemented all of these methods in the freely available open-source library MrMustard, which we use in three examples to optimize the 216-mode interferometer in Borealis, and 2- and 3-modes circuits (with Fock measurements) to produce cat states and cubic phase states.

Of Deep Time and Slow Violence: Anthropo-Scenic Timespaces and the Chronotopes of Climate Theatre.

This article explores how contemporary climate theatre navigates the distorted scales of the Anthropocene and the climate crisis through its unique aesthetics – embodiment, spatiality, temporality, and reciprocity. Previous literature on climate theatre has largely discussed its anthropomorphism and anthropocentrism in relation to global warming. Building on ecodramaturgical frameworks, this article draws on ecocritical appropriations of Mikhail Bakhtin’s chronotope to investigate how theatrical time and space make the abstract nature of climate change tangible. The argument posits that chronotopes of climate theatre are crucial for understanding how contemporary theatre and performance tackle scalar derangement. By drawing on interdisciplinary approaches from environmental humanities, performance and theatre studies, and posthumanism, the study situates itself at the intersections of performance art, political theatre, aesthetic theatre, drama, and environmentalism, examining how different theatrical formats use theatrical ‘timespaces’ to address the spatio-temporal complexities of the Anthropocene. It discusses Ella Hickson’s Oil (2016); April De Angelis’s Extinct (2021); Extinction Rebellion’s “The Sea Is Rising and so Are We” (2019); and Chris Rapley and Duncan Macmillan’s 2071 (2014), showing how each uses theatrical chronotopes to transform deep time, slow violence, and the dispersed nature of climate change into the ‘hyper-present’ of the stage.

How do visual tasks alter the representational space of identical scenes? Insights from a brain-supervised convolutional neural network

How do visual tasks alter the representational space of identical scenes? Insights from a brain-supervised convolutional neural network

Joint consensus kernel learning and adaptive hypergraph regularization for graph-based clustering

Recent advancements in multiple kernel learning-based graph clustering methods have demonstrated significant promise in effectively learning a consensus kernel matrix from various candidate kernel matrices. This approach enables the creation of a low-dimensional representation in the kernel space of high-dimensional data through self-expressiveness. However, a key challenge remains in capturing the latent geometric properties embedded within different kernel matrices to enhance data representation. In this paper, we propose a novel method called joint consensus kernel learning and adaptive hypergraph regularization for graph-based clustering (JKHR). Our approach integrates an innovative adaptive hypergraph Laplacian regularizer, which is characterized by the fusion of multiple nearest neighbor kernel graphs, into the multiple kernel learning-based graph clustering framework. JKHR jointly and adaptively optimizes both the consensus kernel matrix and the hypergraph Laplacian regularizer to achieve a low-dimensional representation that effectively preserves the intrinsic geometry of the data. Experimental results on both synthetic and real benchmark datasets demonstrate that JKHR outperforms state-of-the-art self-expressiveness-based graph clustering methods as well as traditional clustering techniques.