Topology Changes Research Articles

HVTR++: Image and Pose Driven Human Avatars Using Hybrid Volumetric-Textural Rendering.

Recent neural rendering methods have made great progress in generating photorealistic human avatars. However, these methods are generally conditioned only on low-dimensional driving signals (e.g., body poses), which are insufficient to encode the complete appearance of a clothed human. Hence they fail to generate faithful details. To address this problem, we exploit driving view images (e.g., in telepresence systems) as additional inputs. We propose a novel neural rendering pipeline, Hybrid Volumetric-Textural Rendering (HVTR++), which synthesizes 3D human avatars from arbitrary driving poses and views while staying faithful to appearance details efficiently and at high quality. First, we learn to encode the driving signals of pose and view image on a dense UV manifold of the human body surface and extract UV-aligned features, preserving the structure of a skeleton-based parametric model. To handle complicated motions (e.g., self-occlusions), we then leverage the UV-aligned features to construct a 3D volumetric representation based on a dynamic neural radiance field. While this allows us to represent 3D geometry with changing topology, volumetric rendering is computationally heavy. Hence we employ only a rough volumetric representation using a pose- and image-conditioned downsampled neural radiance field (PID-NeRF), which we can render efficiently at low resolutions. In addition, we learn 2D textural features that are fused with rendered volumetric features in image space. The key advantage of our approach is that we can then convert the fused features into a high-resolution, high-quality avatar by a fast GAN-based textural renderer. We demonstrate that hybrid rendering enables HVTR++ to handle complicated motions, render high-quality avatars under user-controlled poses/shapes, and most importantly, be efficient at inference time. Our experimental results also demonstrate state-of-the-art quantitative results.

A Sample Covariance Model-Based Method for Topology Change Detection and Location of Power Grids With High-Level Renewables

A Sample Covariance Model-Based Method for Topology Change Detection and Location of Power Grids With High-Level Renewables

Analyzing the topological properties of resting-state brain function network connectivity based on graph theoretical methods in patients with high myopia

AimRecent imaging studies have found significant abnormalities in the brain’s functional or structural connectivity among patients with high myopia (HM), indicating a heightened risk of cognitive impairment and other behavioral changes. However, there is a lack of research on the topological characteristics and connectivity changes of the functional networks in HM patients. In this study, we employed graph theoretical analysis to investigate the topological structure and regional connectivity of the brain function network in HM patients.MethodsWe conducted rs-fMRI scans on 82 individuals with HM and 59 healthy controls (HC), ensuring that the two groups were matched for age and education level. Through graph theoretical analysis, we studied the topological structure of whole-brain functional networks among participants, exploring the topological properties and differences between the two groups.ResultsIn the range of 0.05 to 0.50 of sparsity, both groups demonstrated a small-world architecture of the brain network. Compared to the control group, HM patients showed significantly lower values of normalized clustering coefficient (γ) (P = 0.0101) and small-worldness (σ) (P = 0.0168). Additionally, the HM group showed lower nodal centrality in the right Amygdala (P < 0.001, Bonferroni-corrected). Notably, there is an increase in functional connectivity (FC) between the saliency network (SN) and Sensorimotor Network (SMN) in the HM group, while the strength of FC between the basal ganglia is relatively weaker (P < 0.01).ConclusionHM Patients exhibit reduced small-world characteristics in their brain networks, with significant drops in γ and σ values indicating weakened global interregional information transfer ability. Not only that, the topological properties of the amygdala nodes in HM patients significantly decline, indicating dysfunction within the brain network. In addition, there are abnormalities in the FC between the SN, SMN, and basal ganglia networks in HM patients, which is related to attention regulation, motor impairment, emotions, and cognitive performance. These findings may provide a new mechanism for central pathology in HM patients.

Minimal specialization: Coevolution of network structure and dynamics

The changing topology of a network is driven by the need to maintain or optimize network function. As this function is often related to moving quantities such as traffic, information, etc., efficiently through the network, the structure of the network and the dynamics on the network directly depend on the other. To model this interplay of network structure and dynamics we use the dynamics on the network, or the dynamical processes the network models, to influence the dynamics of the network structure, i.e., to determine where and when to modify the network structure. We model the dynamics on the network using Jackson network dynamics and the dynamics of the network structure using minimal specialization, a variant of the more general network growth model known as specialization. The resulting model, which we refer to as the integrated specialization model, coevolves both the structure and the dynamics of the network. We show that by reducing dynamic bottlenecks, i.e. reducing the network’s maximal asymptotic load, this model simultaneously produces networks with real-world like properties. This includes right-skewed degree distributions, sparsity, the small-world property, and non-trivial equitable partitions. Additionally, when compared to other growth models, the integrated specialization model creates networks with small diameter, minimizing distances across the network. The result are networks that have both dynamic and structural features that allow quantities to more efficiently move through the network.

Brain Responses Difference between Sexes for Strong Desire to Void: A Functional Magnetic Resonance Imaging Study in Adults Based on Graph Theory.

Background: The alternations of brain responses to a strong desire to void were unclear, and the gender differences under the strong desire to void remain controversial. The present study aims to identify the functional brain network's topologic property changes evoked by a strong desire to void in healthy male and female adults with synchronous urodynamics using a graph theory analysis. Methods: The bladders of eleven healthy males and eleven females were filled via a catheter using a specific infusion and withdrawal pattern. A resting-state functional magnetic resonance imaging (fMRI) was performed on the enrolled subjects, scanning under both the empty bladder and strong desire to void states. An automated anatomical labeling (AAL) atlas was used to identify the ninety cortical and subcortical regions. Pearson's correlation calculations were performed to establish a brain connection matrix. A paired t-test (p < 0.05) and Bonferroni correction were applied to identify the significant statistical differences in topological properties between the two states, including small-world network property parameters [gamma (γ) and lambda (λ)], characteristic path length (Lp), clustering coefficient (Cp), global efficiency (Eglob), local efficiency (Eloc), and regional nodal efficiency (Enodal). Results: The final data suggested that females and males had different brain response patterns to a strong desire to void, compared with an empty bladder state. Conclusions: More brain regions involving emotion, cognition, and social work were active in females, and males might obtain a better urinary continence via a compensatory mechanism.

Targeting DNA junction sites by bis-intercalators induces topological changes with potent antitumor effects.

Targeting inter-duplex junctions in catenated DNA with bidirectional bis-intercalators is a potential strategy for enhancing anticancer effects. In this study, we used d(CGTATACG)2, which forms a tetraplex base-pair junction that resembles the DNA-DNA contact structure, as a model target for two alkyl-linked diaminoacridine bis-intercalators, DA4 and DA5. Cross-linking of the junction site by the bis-intercalators induced substantial structural changes in the DNA, transforming it from a B-form helical end-to-end junction to an over-wounded side-by-side inter-duplex conformation with A-DNA characteristics and curvature. These structural perturbations facilitated the angled intercalation of DA4 and DA5 with propeller geometry into two adjacent duplexes. The addition of a single carbon to the DA5 linker caused a bend that aligned its chromophores with CpG sites, enabling continuous stacking and specific water-mediated interactions at the inter-duplex contacts. Furthermore, we have shown that the different topological changes induced by DA4 and DA5 lead to the inhibition of topoisomerase 2 activities, which may account for their antitumor effects. Thus, this study lays the foundations for bis-intercalators targeting biologically relevant DNA-DNA contact structures for anticancer drug development.

Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks

Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity. These limitations cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic analysis pipeline combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to identify reprogramming features associated with high-producing clones and metabolic bottlenecks limiting product formation in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization.

A deep reinforcement learning-based intelligent QoS optimization algorithm for efficient routing in vehicular networks

With the rapid development of Telematics, Vehicle Self-Organizing Networks (VANETs) play an increasingly critical role in Intelligent Transportation Systems (ITS). Especially in the environment without roadside assistance units (RSUs), how to effectively manage inter-vehicle communication and improve the stability and communication efficiency of the network has become a hot topic of current research. In this paper, a Deep Reinforcement Learning-based Intelligent QoS-optimized efficient routing algorithm for vehicular networks (DRLIQ) is proposed for VANETs with/without RSU environments, and routing methods are proposed respectively. Among them, in RSU-free environment, the DRLIQ algorithm utilizes the powerful processing capability of deep reinforcement learning to intelligently select the optimal data transmission path by dynamically learning and adapting to the changes in the vehicular network, thus effectively reducing communication interruptions and delays, and improving the accuracy of data transmission. The performance of the DRLIQ algorithm under different vehicle densities is evaluated in simulation experiments and compared with current popular algorithms. The experimental results show that the DRLIQ algorithm outperforms the comparison algorithms in reducing the number of communication interruptions, BER and network delay, especially in vehicle-dense environments. In addition, the DRLIQ algorithm shows higher adaptability and stability in coping with network topology changes and vehicle dynamics.

Multi-Material Mesh-Based Surface Tracking with Implicit Topology Changes

We introduce a multi-material non-manifold mesh-based surface tracking algorithm that converts self-intersections into topological changes. Our algorithm generalizes prior work on manifold surface tracking with topological changes: it preserves surface features like mesh-based methods, and it robustly handles topological changes like level set methods. Our method also offers improved efficiency and robustness over the state of the art. We demonstrate the effectiveness of the approach on a range of examples, including complex soap film simulations with thousands of interacting bubbles, and boolean unions of non-manifold meshes consisting of millions of triangles.

Differentiable Voronoi Diagrams for Simulation of Cell-Based Mechanical Systems

Navigating topological transitions in cellular mechanical systems is a significant challenge for existing simulation methods. While abstract models lack predictive capabilities at the cellular level, explicit network representations struggle with topology changes, and per-cell representations are computationally too demanding for large-scale simulations. To address these challenges, we propose a novel cell-centered approach based on differentiable Voronoi diagrams. Representing each cell with a Voronoi site, our method defines shape and topology of the interface network implicitly. In this way, we substantially reduce the number of problem variables, eliminate the need for explicit contact handling, and ensure continuous geometry changes during topological transitions. Closed-form derivatives of network positions facilitate simulation with Newton-type methods for a wide range of per-cell energies. Finally, we extend our differentiable Voronoi diagrams to enable coupling with arbitrary rigid and deformable boundaries. We apply our approach to a diverse set of examples, highlighting splitting and merging of cells as well as neighborhood changes. We illustrate applications to inverse problems by matching soap foam simulations to real-world images. Comparative analysis with explicit cell models reveals that our method achieves qualitatively comparable results at significantly faster computation times.

Enhancing routing and quality of service using AI-driven technique for internet of vehicles contexts

This paper introduces an Enhancing Routing and Quality of Service (QoS) Using AI-driven Technique for Internet of Vehicles (IoV) Contexts. The proposed approach aims to enhance QoS and overall network performance in connected vehicle networks. Vehicles utilize Road-Side Units and the Internet to aggregate data from neighboring vehicles, optimizing routing and data management. The proposed approach utilizes a graph traversal algorithm, a widely adopted technique in Artificial Intelligence for graph search, which facilitates traversal in routing. The proposed approach integrates a mobility score based on metrics such as velocity, acceleration, and neighboring information, ensuring optimal routing for dynamic vehicular networks. Objectives include improving QoS and network performance by reducing overheads, optimizing load balancing, and extending network lifetime. Implemented in NS-3 and MOVE simulators, results demonstrate significant performance enhancements compared to existing approaches. This approach addresses the challenges of extensive vehicle mobility and frequent topology changes in connected environments. Overall, the paper presents a comprehensive solution for IoV networks, combining AI-driven routing with mobility-aware strategies to advance network efficiency and QoS.

Training a Graph Neural Network to solve URLLC and eMBB coexisting in 5G networks

Coexistence of enhanced mobile broadband and ultra-reliable low latency communication in 5G networks is a challenging problem due to the conflicting requirements. In this paper, we decompose the problem into eMBB and URLLC resource allocation phases. For the first phase we propose a heuristic algorithm with O(n) runtime and prove its efficiency and optimality under min–max fairness paradigm. For the URLLC resource allocation, the puncturing framework is adopted and a novel approach using the Graph Neural Networks is proposed to maximize eMBB data rates and fairness while minimizing URLLC outage probability. We show that the runtime of this GNN-based algorithm is also O(n). To train the GNN, an application-specific loss function is designed and empirically shown to be convergent. Our simulation results show that our proposed approach performs very well in terms of eMBB data rates, fairness, and URLLC outage probability in comparison to a number of thoughtfully chosen baselines. We also demonstrate that the proposed GNN is robust to changes in network topology and traffic volume. As we show our algorithm has O(n) runtime, it is fully practical for solving the resource allocation problem in the very short time spans that are required by 5G and future generation networks.

Space–time isogeometric analysis of tire aerodynamics with complex tread pattern, road contact, and tire deformation

AbstractThe space–time (ST) computational method “ST-SI-TC-IGA” and recently-introduced complex-geometry isogeometric analysis (IGA) mesh generation methods have enabled high-fidelity computational analysis of tire aerodynamics with near-actual tire geometry, road contact, tire deformation, and aerodynamic influence of the car body. The tire geometries used in the computations so far included the longitudinal and transverse grooves. Here, we bring the tire geometry much closer to an actual tire geometry by using a complex, asymmetric tread pattern. The complexity of the tread pattern required an updated version of the NURBS Surface-to-Volume Guided Mesh Generation (NSVGMG) method, which was introduced recently and is robust even in mesh generation for complex shapes with distorted boundaries. The core component of the ST-SI-TC-IGA is the ST Variational Multiscale (ST-VMS) method, and the other key components are the ST Slip Interface (ST-SI) and ST Topology Change (ST-TC) methods and the ST Isogeometric Analysis (ST-IGA). They all play a key role. The ST-TC, uniquely offered by the ST framework, enables moving-mesh computation even with the topology change created by the contact between the tire and the road. It deals with the contact while maintaining high-resolution flow representation near the tire.The computational analysis we present is the first of its kind and shows the effectiveness of the ST-SI-TC-IGA and NSVGMG in tire aerodynamic analysis with complex tread pattern, road contact, and tire deformation.

Spatial-Temporal Dynamic Hypergraph Information Bottleneck for Brain Network Classification.

Recently, Graph Neural Networks (GNNs) have gained widespread application in automatic brain network classification tasks, owing to their ability to directly capture crucial information in non-Euclidean structures. However, two primary challenges persist in this domain. First, within the realm of clinical neuro-medicine, signals from cerebral regions are inevitably contaminated with noise stemming from physiological or external factors. The construction of brain networks heavily relies on set thresholds and feature information within brain regions, making it susceptible to the incorporation of such noises into the brain topology. Additionally, the static nature of the artificially constructed brain network's adjacent structure restricts real-time changes in brain topology. Second, mainstream GNN-based approaches tend to focus solely on capturing information interactions of nearest neighbor nodes, overlooking high-order topology features. In response to these challenges, we propose an adaptive unsupervised Spatial-Temporal Dynamic Hypergraph Information Bottleneck (ST-DHIB) framework for dynamically optimizing brain networks. Specifically, adopting an information theory perspective, Graph Information Bottleneck (GIB) is employed for purifying graph structure, and dynamically updating the processed input brain signals. From a graph theory standpoint, we utilize the designed Hypergraph Neural Network (HGNN) and Bi-LSTM to capture higher-order spatial-temporal context associations among brain channels. Comprehensive patient-specific and cross-patient experiments have been conducted on two available datasets. The results demonstrate the advancement and generalization of the proposed framework.

Appraising the structural and magnetic properties in template assisted neodymium based metal organic frameworks

This study demonstrates synthesis of two novel lanthanide-based metal-organic frameworks, namely [{NdIII(5 N3-H2isa)2(H2O)2}(H2O)2}]∞ (1) and [{NdIII(5 N3-H2isa)2(H2O)2}(H2O)(1,2-PE)}]∞ (2), synthesized via solvothermal methods using 5-azidoisophthalic acid (5 N3-H2isa) and NdCl3. Both compounds were extensively characterized using single crystal X-ray diffraction, as well as other physicochemical techniques such as IR, TGA, PXRD, and elemental analysis. Structural differences were observed in both compounds depending on the nature of the template molecule used. Compound 1 exhibits an infinite 2D architecture with lattice-occluded water molecules within the 2D layers. In contrast, compound 2, where 1-(-2-pyridyl)-2-(4-pyridyl)ethylene (1,2-PE) was used as the template, shows replacement of interstitial water molecules by the template molecule, resulting in significant changes in overall pore topology which is further corroborated by BET analysis. These structural variations were further supported by magnetic behaviour analyses, indicating an antiferromagnetic behaviour for both compounds.

A topological drive for spacetime travel

We present a toy metric of spacetime travel from topological change. A bubble-like baby Universe is detached and re-attached from our Universe. Depending on where the bubble is re-attached, matter may travel superluminally or backwards-in-time through the bubble. Quasiregular singularities are formed at the detachment and re-attachment spacetime points. The spacetime is traversable and not covered by any horizons. Exotic matter violating energy conditions is required to realize such spacetimes.

Large-Scale Measurements Using Robotic Volumetric LPT With Shake-The-Box Inside A Vehicle Cabin: Influence Of Different Fan Speeds On The Flow Topology

In this work, the flow topology inside a passenger car cabin for different fan speeds of the heat, ventilation and air conditioning (HVAC) system is investigated using robotic volumetric Lagrangian Particle Tracking (LPT). The coaxial volumetric velocimeter (CVV), also called MiniShaker, in combination with a robotic arm enables the measurement of the complete passenger's side. In total 37 CVV perspectives (sub-volumes) were measured. At each position 3000 double frame images were recorded. The interior was painted black and the mannequin was wrapped with black aluminum foil in order to keep reflections at a low level. Experimental results for three different fan speeds using helium-filled soap bubbles (HFSB) as tracers and Shake-the-Box (StB) algorithm for the three-dimensional LPT data analyses show that the flow topology changes significantly with increasing fan speeds. At higher fan speeds the flow fields show the highest complexity with four vortices.

Interaction Effects of Vegetation and Soil Factors on Microbial Communities in Alpine Steppe Under Degradation

To clarify the regulating effect of vegetation and soil factors on microbial communities in the alpine steppe under degradation on the Qinghai-Xizang Plateau, the alpine steppe in the Sanjiangyuan area of the Qinghai-Tibet Plateau was chosen. We analyzed the differences in vegetation and soil factors in different stages of degradation （non-degradation, moderate degradation, and severe degradation） and detected the variations in microbial community characteristics in the alpine steppe under different degradation stages using high-throughput sequencing technology. Eventually, redundancy analysis （RDA） and multiple regression matrixes （MRM） based on the similarity or dissimilarity matrix were used to identify key environmental factors regulating microbial （bacterial and fungal） community changes under degradation. The results showed that the degradation of the alpine steppe significantly changed the community coverage, height, biomass, and important value of graminae； significantly reduced the contents of soil organic matter, total nitrogen, total phosphorus, and silt； and increased the soil bulk density and sand content. Degradation did not change the composition of bacteria and fungi, but their composition proportions changed and also resulted in the loss of microbial richness （Chao1 index and Richness index） but did not significantly change the microbial diversity （Shannon index）. With the occurrence of degradation, the vegetation characteristics, soil physicochemical properties, and microbial diversity showed a consistent change trend. Combined with the characteristics of the network topology changes （the number of nodes and clustering coefficient significantly decreased）, it was found that degradation of the alpine steppe led to the decline of interspecies interactions, decentralization of network, and homogenization of microorganisms, but the cooperation relations among the species were maintained （positive correlation connections accounted for more than 90% in all degradation stages）. Under the alpine steppe degradation, the vegetation-soil interaction had the greatest effect on soil bacterial community, whereas soil physicochemical properties had the greatest influence on soil fungal community. Specifically, vegetation community height, biomass, and soil bulk density were the mutual factors regulating soil microorganisms, whereas the vegetation Simpson index, important value of graminae, soil total phosphorus, total potassium, and silt content were the unique factors affecting the soil bacterial community, and soil pH and total nitrogen content were the particular factors affecting the soil fungal community.

Artificial intelligence detects awareness of functional relation with the environment in 3 month old babies

A recent experiment probed how purposeful action emerges in early life by manipulating infants’ functional connection to an object in the environment (i.e., tethering an infant’s foot to a colorful mobile). Vicon motion capture data from multiple infant joints were used here to create Histograms of Joint Displacements (HJDs) to generate pose-based descriptors for 3D infant spatial trajectories. Using HJDs as inputs, machine and deep learning systems were tasked with classifying the experimental state from which snippets of movement data were sampled. The architectures tested included k-Nearest Neighbour (kNN), Linear Discriminant Analysis (LDA), Fully connected network (FCNet), 1D-Convolutional Neural Network (1D-Conv), 1D-Capsule Network (1D-CapsNet), 2D-Conv and 2D-CapsNet. Sliding window scenarios were used for temporal analysis to search for topological changes in infant movement related to functional context. kNN and LDA achieved higher classification accuracy with single joint features, while deep learning approaches, particularly 2D-CapsNet, achieved higher accuracy on full-body features. For each AI architecture tested, measures of foot activity displayed the most distinct and coherent pattern alterations across different experimental stages (reflected in the highest classification accuracy rate), indicating that interaction with the world impacts the infant behaviour most at the site of organism~world connection.

Lifshitz transition enabling superconducting dome around a charge-order critical point.

Superconductivity often emerges as a dome around a quantum critical point (QCP) where long-range order is suppressed to zero temperature, mostly in magnetically ordered materials. However, the emergence of superconductivity at charge-order QCPs remains shrouded in mystery, despite its relevance to high-temperature superconductors and other exotic phases of matter. Here, we present resistance measurements proving that a dome of superconductivity surrounds the putative charge-density-wave QCP in pristine samples of titanium diselenide tuned with hydrostatic pressure. In addition, our quantum oscillation measurements combined with electronic structure calculations show that superconductivity sets in precisely when large electron and hole pockets suddenly appear through an abrupt change of the Fermi surface topology, also known as a Lifshitz transition. Combined with the known repulsive interaction, this suggests that unconventional s± superconductivity is mediated by charge-density-wave fluctuations in titanium diselenide. These results highlight the importance of the electronic ground state and charge fluctuations in enabling unconventional superconductivity.