Dimensional Expansion Research Articles

Three dimensional biatrial expansion index as an independent predictor of outcome in dilated cardiomyopathy

Abstract Background Left atrial expansion index (LAEI) is a marker of left ventricular (LV) diastolic dysfunction and an outcome predictor in heart failure (HF). However, the role of the right atrial expansion index (RAEI) in the setting of left-sided HF is incompletely understood. Purpose We sought to evaluate the left, right and combined bi-atrial expansion index (BAEI) with three-dimensional (3D) echocardiography in a cohort of patients with dilated cardiomyopathy (DCM) and to assess their prognostic role. Methods We enrolled 121 consecutive patients (mean age 59±14 years, 74% men) with DCM in sinus rhythm, who underwent 3D echocardiography and were prospectively followed for 19±11 months for an endpoint of death, nonfatal cardiac arrest and HF hospitalization. As previously described, for the left atrium (LA), LAEI was measured as (3D LA Vmax – 3D LA Vmin) x 100/3D LA Vmin, where Vmax was the maximal end-systolic atrial volume and Vmin was the minimal end-diastolic atrial volume. For the right atrium (RA), RAEI was calculated as (3D RA Vmax – 3D RA Vmin) x 100/3D RA Vmin. BAEI was defined as the sum of LAEI and RAEI. Results 55 patients (46%) reached the endpoint: there were 27 deaths, 5 nonfatal cardiac arrests and 23 readmissions for HF. Patients with adverse outcome had lower LAEI (52±25 vs. 63±36, p=0.047), lower RAEI (57±29 vs. 70±32, p=0.02), lower BAEI (108±46 vs. 133±55, p=0.008). In univariate Cox regression, all three expansion indices were predictors of adverse events: HR=3.23 [95% CI, 1.45-7.20], p=0.004 for LAEI, HR=2.12 [95% CI, 1.24-3.61], p=0.006 for RAEI, HR=3.99 [95% CI, 1.70-9.36], p=0.001 for BAEI. We constructed a multivariable model with well-established event predictors in DCM and one expansion index at a time. After adjustment for age, left ventricular ejection fraction, mitral regurgitation severity and pulmonary artery systolic pressure, all three expansion indices remained independent predictors of the composite endpoint: HR=2.75 [95% CI, 1.18-6.38], p=0.02 for LAEI, HR=1.86 [95% CI, 1.04-3.31], p=0.04 for RAEI, HR=3.53 [95% CI, 1.46-8.51], p=0.005 for BAEI. BAEI yielded the highest incremental prognostic power (likelihood ratio chi2 test=14, p=0.03) on top of the baseline model consisting of age, left ventricular ejection fraction, mitral regurgitation severity and pulmonary artery systolic pressure (likelihood ratio chi2 test=9). Conclusion In DCM, 3D atrial expansion indices are significantly lower in patients with major adverse events, reflecting a more impaired atrial reservoir dysfunction. Both LAEI, RAEI and BAEI were independent outcome predictors in patients with DCM. Combining the left and right atrial expansion indices might improve risk stratification, since BAEI had the highest predictive value after adjustment for confounders.

A transient high-dimensional geometry affords stable conjunctive subspaces for efficient action selection

Flexible action selection requires cognitive control mechanisms capable of mapping the same inputs to different output actions depending on the context. From a neural state-space perspective, this requires a control representation that separates similar input neural states by context. Additionally, for action selection to be robust and time-invariant, information must be stable in time, enabling efficient readout. Here, using EEG decoding methods, we investigate how the geometry and dynamics of control representations constrain flexible action selection in the human brain. Participants performed a context-dependent action selection task. A forced response procedure probed action selection different states in neural trajectories. The result shows that before successful responses, there is a transient expansion of representational dimensionality that separated conjunctive subspaces. Further, the dynamics stabilizes in the same time window, with entry into this stable, high-dimensional state predictive of individual trial performance. These results establish the neural geometry and dynamics the human brain needs for flexible control over behavior.

A Transient High-dimensional Geometry Affords Stable Conjunctive Subspaces for Efficient Action Selection.

Flexible action selection requires cognitive control mechanisms capable of mapping the same inputs to different output actions depending on the context. From a neural state-space perspective, this requires a control representation that separates similar input neural states by context. Additionally, for action selection to be robust and time-invariant, information must be stable in time, enabling efficient readout. Here, using EEG decoding methods, we investigate how the geometry and dynamics of control representations constrain flexible action selection in the human brain. Participants performed a context-dependent action selection task. A forced response procedure probed action selection different states in neural trajectories. The result shows that before successful responses, there is a transient expansion of representational dimensionality that separated conjunctive subspaces. Further, the dynamics stabilizes in the same time window, with entry into this stable, high-dimensional state predictive of individual trial performance. These results establish the neural geometry and dynamics the human brain needs for flexible control over behavior.

Painlevé integrability and multiple soliton solutions for the extensions of the (modified) Korteweg-de Vries-type equations with second-order time-derivative

This work introduces two (3+1)-dimensional expansions of the Korteweg–de Vries (KdV) and modified KdV (mKdV) equations. These extensions incorporate a second-order time-derivative term, similar to the Boussinesq equation. The Painlevé test is utilized to verify the integrability of each extended model. The bilinear form is employed to investigate the existence of multiple-soliton (MS) solutions for each system under consideration. Furthermore, we provide solutions in the form of lumps for the extended KdV equation. The multidimensional KdV-type equations surpass the standard KdV equation. However, they offer enhanced accuracy by representing a broader spectrum of nonlinear phenomena in plasma physics, fluid mechanics, tsunami phenomena, and other science disciplines. Furthermore, the aforementioned equations can be employed to analyze the characteristics of various acoustic waves (AW) in different plasma models, such as their amplitude, width, frequency, and dispersion, as well as the phase shifts after collisions.

Fast descriptor-based 2D and 3D microstructure reconstruction using the Portilla–Simoncelli algorithm

AbstractReconstructing microstructures from statistical descriptors is a key enabler of computer-based inverse materials design. In the Yeong–Torquato algorithm and other common methods, the problem is approached by formulating it as an optimization problem in the space of possible microstructures. In this case, the error between the desired microstructure and the current reconstruction is measured in terms of a descriptor. As an alternative, descriptors can be regarded as constraints defining subspaces or regions in the microstructure space. Given a set of descriptors, a valid microstructure can be obtained by sequentially projecting onto these subspaces. This is done in the Portilla–Simoncelli algorithm, which is well known in the field of texture synthesis. Noting the algorithm’s potential, the present work aims at introducing it to microstructure reconstruction. After exploring its capabilities and limitations in 2D, a dimensionality expansion is developed for reconstructing 3D volumes from 2D reference data. The resulting method is extremely efficient, as it allows for high-resolution reconstructions on conventional laptops. Various numerical experiments are conducted to demonstrate its versatility and scalability. Finally, the method is validated by comparing homogenized mechanical properties of original and reconstructed 3D microstructures.

Trajectory-based global sensitivity analysis in multiscale models

This research introduces a novel global sensitivity analysis (GSA) framework for agent-based models (ABMs) that explicitly handles their distinctive features, such as multi-level structure and temporal dynamics. The framework uses Grassmannian diffusion maps to reduce output data dimensionality and sparse polynomial chaos expansion (PCE) to compute sensitivity indices for stochastic input parameters. To demonstrate the versatility of the proposed GSA method, we applied it to a non-linear system dynamics model and epidemiological and economic ABMs, depicting different dynamics. Unlike traditional GSA approaches, the proposed method enables a more general estimation of parametric sensitivities spanning from the micro level (individual agents) to the macro level (entire population). The new framework encourages the use of manifold-based techniques in uncertainty quantification, enhances understanding of complex spatio-temporal processes, and equips ABM practitioners with robust tools for detailed model analysis. This empowers them to make more informed decisions when developing, fine-tuning, and verifying models, thereby advancing the field and improving routine practice for GSA in ABMs.

Multi-plane denoising diffusion-based dimensionality expansion for 2D-to-3D reconstruction of microstructures with harmonized sampling

Acquiring reliable microstructure datasets is a pivotal step toward the systematic design of materials with the aid of integrated computational materials engineering (ICME) approaches. However, obtaining three-dimensional (3D) microstructure datasets is often challenging due to high experimental costs or technical limitations, while acquiring two-dimensional (2D) micrographs is comparatively easier. To deal with this issue, this study proposes a novel framework called ‘Micro3Diff’ for 2D-to-3D reconstruction of microstructures using diffusion-based generative models (DGMs). Specifically, this approach solely requires pre-trained DGMs for the generation of 2D samples, and dimensionality expansion (2D-to-3D) takes place only during the generation process (i.e., reverse diffusion process). The proposed framework incorporates a concept referred to as ‘multi-plane denoising diffusion’, which transforms noisy samples (i.e., latent variables) from different planes into the data structure while maintaining spatial connectivity in 3D space. Furthermore, a harmonized sampling process is developed to address possible deviations from the reverse Markov chain of DGMs during the dimensionality expansion. Combined, we demonstrate the feasibility of Micro3Diff in reconstructing 3D samples with connected slices that maintain morphologically equivalence to the original 2D images. To validate the performance of Micro3Diff, various types of microstructures (synthetic or experimentally observed) are reconstructed, and the quality of the generated samples is assessed both qualitatively and quantitatively. The successful reconstruction outcomes inspire the potential utilization of Micro3Diff in upcoming ICME applications while achieving a breakthrough in comprehending and manipulating the latent space of DGMs.

An ensemble neural network model for predicting the energy utility in individual houses

With the rapid increase in the world's human population and the advancement of technology, energy utilization has substantially increased. To sustain a stable stream of energy, it is vital to anticipate power utilization beforehand. This issue makes predicting short-term electrical time series use difficult. In this research, the user proposed a neural network with a deep learning prototype that performs energy prediction by combining. Long Short-Term Memory (LSTM) and Feed Forward Neural Networks (FFNN). In order to enhance the performance, the ensemble FFNN-LSTM network and the Stationary Wavelet Transform (SWT) method are combined to create a hybrid deep learning algorithm. The SWT's reduction of instability and expansion of data dimensionality may improve the FFNN-LSTM forecasting accuracy. The suggested technique's predicting effectiveness is even further improved by the ensemble Long short-term neural network. On the basis of a real-world household energy usage database gathered by the "UK-DALE" project, validation studies were carried out. Researchers determined that the LSTM-FFNN based SWT produced a better result in terms of accuracy and energy utilization than other existing results after contrasting the experimental findings to the baseline, which is the conventional LSTM and Moving Average (MA) based upon the Root Mean Squared Error (RMSE) score.

A Quantum-Like Tensor Compression Sentence Representation Based on Constraint Functions for Semantics Analysis

To emphasize the semantic impact of local semantic and grammatical information among adjacent words in the input text, we establish a constraint functions-based quantum-like tensor compression sentence representation model by integrating the concept of extending the pure state-based density matrix to the mixed-state projection operator in quantum mechanics. The provided model highlights the semantic significance of mixed word associations in the input text, simultaneously reducing the reliance on information derived solely from dictionary statistics. We combine the correlation coefficient with the attention mechanism to establish the correlation coefficient between words. The quantum-like sentence representation based on pure state density matrix is extended to the projection operator of mixed states. Combining the acquisition of maximum in convex optimization, a constraint functions-based quantum-like text representation pruning model is established to reduce redundant information caused by dimensional expansion of tensor operations. The experimental results on SICK-2014, STS-benchmark, and STS-companion show that the provided model is more effective than the mainstream models in mining semantic information, especially more sensitive to the negative semantics of sentences.

Bifunctional oxygen reduction/evolution reaction electrocatalysts achieved by axial ligand modulation on two-dimensional porphyrin frameworks.

Exploring efficient and low-cost oxygen reduction and oxygen evolution reaction (ORR/OER) bifunctional catalysts is essential for the development of energy storage and conversion devices. Herein, enlightened by the experimentally synthesized cobalt(II) meso-tetraethynylporphyrins (Co-TEP) molecule, we designed a novel 2D covalent organic framework (COF), namely a 2D Co-TEP monolayer, by dimensional expansion. The 2D Co-TEP monolayer, with Co atoms distributed separately and stabilized by uniform pyrrolic-N coordination, features metal-nitrogen-carbon single-atom catalyst activity and shows tunable catalytic activity for the electrochemical ORR/OER by axial ligand (O, OH, Cl, CN, CH3, NO, F) modulation. By means of the state-of-the-art constant-potential first-principles computations and microkinetic simulations, we demonstrated that 2D Co-TEP-CN exhibits good ORR/OER performance in both acidic and alkaline conditions. The difference between the onset-potential for the OER and the half-wave potential for the ORR is only 0.85 V at pH = 1, smaller than that of Pt/IrO2 electrocatalysts. The good electrocatalytic performance is maintained by replacing the center metal atoms with Mn, Fe and/or Ni. Our investigation highlights the role of the pyrrolic-N coordination and the ligands in improving the catalytic activity of 2D COFs and provides new insights into the rational design of efficient bifunctional ORR/OER catalysts.

PCAIME: Principal Component Analysis-Enhanced Approximate Inverse Model Explanations Through Dimensional Decomposition and Expansion

PCAIME: Principal Component Analysis-Enhanced Approximate Inverse Model Explanations Through Dimensional Decomposition and Expansion

MAGSleepNet: Adaptively multi-scale temporal focused sleep staging model for multi-age groups

Deep learning-based automatic sleep staging methods have been widely applied for sleep scoring and sleep diagnosis. However, most methods consider only a single temporal scale when dealing with sleep signals. Furthermore, these methods are limited to target only a single-age group or single dataset. In this paper, we propose a multi-scale temporally focused sleep staging model, MAGSleepNet, which can be used for multi-age groups simultaneously. MAGSleepNet consists of (1) a group age classification (GAC) module that can offer a preliminary screening on epidemic estimation of multiple age, (2) a dimensional expansion module (DEM) that can expand the dimension of the input signals, (3) a sequential multi-scale convolutional neural network (SMCNN) that extracts multi-scale features and short-time temporal information, and (4) sequence temporal encoder (STE) that extracts sequential temporal information. In addition, two auxiliary tasks are used to complement the short-time temporal information and to reassign the probabilities of different age groups to enhance the model robustness, respectively. The MAGSleepNet is evaluated in adult, child and infant, tested on MASS, CHAT, and CHFU datasets with accruacy of 86.7%, 80.1% and 66.5%, outperforming the state-of-the-art methods. Based on the excellent performance of the proposed method, it is expected to pave the way for automatic sleep staging methods with strong generalizability for multiple age groups.

On analyzing two dimensional fractional order brain tumor model based on orthonormal Bernoulli polynomials and Newton's method

Recently, modeling problems in various field of sciences and engineering with the help of fractional calculus has been welcomed by researchers. One of these interesting models is a brain tumor model. In this framework, a two dimensional expansion of the diffusion equation and glioma growth is considered. The analytical solution of this model is not an easy task, so in this study, a numerical approach based on the operational matrix of conventional orthonormal Bernoulli polynomials (OBPs) has been used to estimate the solution of this model. As an important advantage of the proposed method is to obtain the fractional derivative in matrix form, which makes calculations easier. Also, by using this technique, the problem under the study is converted into a system of nonlinear algebraic equations. This system is solved via Newton's method and the error analysis is presented. At the end to show the accuracy of the work, we have examined two examples and compared the numerical results with other works.

CARMEL: Capturing spatio-temporal correlations via time-series sub-window imaging for home appliance classification

Energy management systems (EMS), as enablers of more efficient energy consumption, monitor and manage appliances to help residents be more energy efficient and thus more frugal. Recent appliance detection and identification techniques for such systems rely on machine learning. However, machine learning solutions for appliance classification on existing low-frequency household metering have not yet been thoroughly investigated. In this paper, we propose CARMEL, a new approach for identifying home appliances from load monitoring in building EMS based on a new data representation technique and a new model that leverages spatio-temporal correlations in the new representation. The proposed data representation technique performs dimensionality expansion of time series that scales linearly, rather than quadratically and, together with the proposed model, outperform the state of the art image transformation models by 5 percentage points. Evaluation on 5 different low-frequency household metering datasets, considering 29 appliances in total, shows that the proposed representation and the corresponding resource-aware deep learning architecture (1) achieve an average weighted F1 score of 0.92 and (2) require only 230 labeled samples and 3x fewer epochs to transfer to new households.

Exploring the behavior feature of complex trajectories of ships with Fourier transform processing: a case from fishing vessels

The significant uncertainty and complexity of vessels at sea poses challenges for regulatory bodies in the fishing industry. This paper presents a method for identifying fishing vessel trajectory characteristics involving the Fourier series transform. The model utilizes the Fourier series and Gaussian mixture clustering to address the complexity and uncertainty issues in fishing vessel trajectories. First, the vessel trajectories undergo a process of dimensionality expansion and projection along the temporal axis. The relationship between trajectories and complex plane projection was elucidated in this process. Second, a vessel trajectory identification model involving Fourier transformation was constructed. Subsequently, the phase spectrum was assigned binary values using differentiation, and the phase spectrum characteristics of the transformed trajectories through Fourier transformation were analyzed. Finally, six encoding formats for fishing vessel motion trajectories in phase spectrum encoding are introduced, along with the determination of uncertain vessel motion range through mixed Gaussian clustering. This method has been validated using a dataset comprising 7,000 fishing vessel trajectories collected from the Beidou satellite positioning system. The results demonstrate that the range of uncertain vessel motion was able to be obtained with the assistance of Gaussian mixture clustering, with an 80% probability position of approximately 1,000 m and a 50% probability position of around 2,000 m. Effective identification of fishing vessel operating and navigational states was achieved, leading to the determination of a safety distance for fishing vessels in the range of 1,000m–2,000 m. This research holds important reference value for fishery regulatory agencies in terms of supervising fishing vessels and maintaining a safe navigational distance.

How does low-carbon city construction drive enterprise green governance? A complete chain mediation model

Purpose In confronting the challenge of climate change and progressing towards dual carbon goals, China is actively implementing low-carbon city pilot policy. This paper aims to focus on the potential impact of this policy on enterprise green governance, aiming to promote the reduction and balance of carbon emissions. Design/methodology/approach Based on the panel data of China's large-scale industrial enterprises from 2007 to 2013, this paper uses the Difference-in-differences (DID) method to study the impact and path mechanism of the implementation of low-carbon city pilot policy on enterprise green governance. Heterogeneity analysis is used to compare the effects of low-carbon city pilot policy in different regions, different enterprises and different industries. Findings The low-carbon pilot can indeed effectively enhance corporate green governance, a conclusion that still holds after a series of robustness tests. The low-carbon city pilot policy mainly enhances enterprise green governance through two paths: an industrial structure upgrade and enterprise energy consumption, and it improves green governance by reducing enterprise energy consumption through industrial structure upgrade. The impact of low-carbon city pilot policy on enterprise green governance shows significant differences across different regions, different enterprises and different industries. Research limitations/implications This paper examines the impact of low-carbon city pilot policy on enterprise green governance. However, due to availability of data, there are still some limitations to be further tackled. The parallel trend test in this paper shows that the pilot policy has a significant positive effect on the green governance of enterprises. However, due to serious lack of data in some years, the authors only selected the enterprise data of a shorter period as our experimental data, which leads the results to still have certain deficiencies. For the verification of the impact mechanism, the conclusions obtained in this paper are relatively limited. Although all the mechanism tests are passed, the reliability of the results still needs to be further tested through future data samples. In addition, as the pilot policy of low-carbon cities is still in progress, the policy can be tracked and analysed in the future as more data are disclosed, and further research can be carried out through dimensional expansion. Practical implications Low-carbon city pilot policy plays an important role in inducing the green governance of enterprises. Therefore, policy makers can continue to strengthen the construction of low-carbon city pilots by refining pilot experience, building typical cases, actively promoting pilot policy experience, expanding pilot scope and enhancing the implementation efficiency of pilot policy nationwide, which will contribute to the optimization and upgrading of the regional industrial structure at the urban level and will provide experience and reference for the synergistic implementation plan of pollution reduction and carbon reduction. Social implications The impact of the low-carbon city pilot policy on enterprise green governance not only exists in two separate paths of urban industrial upgrading and enterprise energy consumption but also exists in a chain transmission path from macro to micro. The authors find that the effect value of each influence path is different, and there is an obvious leading influence path for the role of enterprise green governance. Therefore, in the process of implementing a low-carbon city pilot policy, policies should be designed specifically for different mechanisms. Moreover, complementing and coordinating several paths should be advocated to give full play to the green governance effect of enterprises brought by different paths and to further expand the scope of industries and enterprises where policies play a role. Originality/value To the best of the authors’ knowledge, for the first time, this paper connects macro mechanisms with micro mechanisms, discovering a macro-to-micro transmission mechanism in the process of low-carbon city pilot policy affecting enterprise green governance. That is, the low-carbon city pilot policy can facilitate industrial structure upgrading, resulting in reduced enterprise energy consumption, ultimately enhancing enterprise green governance.

Low-Latency Dimensional Expansion and Anomaly Detection Empowered Secure IoT Network

Low-Latency Dimensional Expansion and Anomaly Detection Empowered Secure IoT Network

Hyper-regular graphs and high dimensional expanders

Hyper-regular graphs and high dimensional expanders

Machine Learning-Assisted Battery Electrode Microstructure Characterization through Secondary-Ions Imaging

Electrodes are a key component of modern Li-ion and solid-state batteries, and their microstructure is recognized to affect their cycle life, safety, energy and power densities, underlying the importance of microstructure characterization.1–4 Among the different techniques used to this scope, Secondary Electrons Microscopy (SEM) and Focused-Ion Beam SEM (FIB-SEM) are widely used for acquiring 2D and 3D information, respectively.5,6 Despite the advantages of these techniques, one main limitation is that chemically different species having similar atomic components or weights are often not distinguishable, not allowing the characterization of, for instance, the solid electrolyte interfaces (SEI), i.e., degradation products formed at the active material surfaces and playing a key role in the electrode performance and lifetime.Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) is a surface technique based on the analysis of the secondary ions generated through the impact between an accelerated primary ion and the sample (Figure 1A). ToF-SIMS allows obtaining chemically-specific information and acquiring images of sizes in the order of hundreds of um, with a resolution down to 150 nm.7,8 However, the long acquisition time for imaging limits its applications to 2D microstructural characterization.In this work, we combined a devoted experimental set-up to image a statistically representative section of the electrode (its cross-section) through ToF-SIMS, and a machine learning procedure9 based on generative adversarial networks to reconstruct a realistic 3D electrode microstructure using, as input, the 2D ToF-SIMS image (Figure 1B). This approach showed to be applicable not only to characterize the spatial distribution of the electrode main components, but also to map the spatial distribution of degradation products forming the SEI, opening the door to more in-depth analyses of the relationships between electrode microstructure, spatial distribution of the degradation products, and electrochemical performance. This approach could be of use for both experimental and computational analyses, the latter through 3D electrochemical models using as input the so-obtained 3D electrode microstructure. References (1) Xu, H.; Zhu, J.; Finegan, D. P.; Zhao, H.; Lu, X.; Li, W.; Hoffman, N.; Bertei, A.; Shearing, P.; Bazant, M. Z. Guiding the Design of Heterogeneous Electrode Microstructures for Li‐Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning. Adv Energy Mater 2021, 2003908, 2003908. https://doi.org/10.1002/aenm.202003908.(2) Lu, X.; Bertei, A.; Finegan, D. P.; Tan, C.; Daemi, S. R.; Weaving, J. S.; Regan, K. B. O.; Heenan, T. M. M.; Hinds, G.; Kendrick, E.; Brett, D. J. L.; Shearing, P. R. 3D Microstructure Design of Lithium-Ion Battery Electrodes Assisted by X-Ray Nano-Computed Tomography and Modelling. Nat Commun 2020, 11 (2079), 1–13. https://doi.org/10.1038/s41467-020-15811-x.(3) Chouchane, M.; Franco, A. A. Deconvoluting the Impacts of the Active Material Skeleton and the Inactive Phase Morphology on the Performance of Lithium Ion Battery Electrodes. Energy Storage Mater 2022, 47, 649–655. https://doi.org/10.1016/j.ensm.2022.02.016.(4) Bielefeld, A.; Weber, D. A.; Janek, J. Microstructural Modeling of Composite Cathodes for All-Solid-State Batteries. Journal of Physical Chemistry C 2019, 123 (3), 1626–1634. https://doi.org/10.1021/acs.jpcc.8b11043.(5) Joos, J.; Buchele, A.; Schmidt, A.; Weber, A.; Ivers-Tiffée, E. Virtual Electrode Design for Lithium-Ion Battery Cathodes. Energy Technology 2021, 2000891. https://doi.org/10.1002/ente.202000891.(6) Zielke, L.; Hutzenlaub, T.; Wheeler, D. R.; Chao, C. W.; Manke, I.; Hilger, A.; Paust, N.; Zengerle, R.; Thiele, S. Three-Phase Multiscale Modeling of a LiCoO2 Cathode: Combining the Advantages of FIB-SEM Imaging and X-Ray Tomography. Adv Energy Mater 2015, 5 (5). https://doi.org/10.1002/aenm.201401612.(7) Walther, F.; Strauss, F.; Wu, X.; Mogwitz, B.; Hertle, J.; Sann, J.; Rohnke, M.; Brezesinski, T.; Janek, J. The Working Principle of a Li2CO3/LiNbO3Coating on NCM for Thiophosphate-Based All-Solid-State Batteries. Chemistry of Materials 2021, 33 (6), 2110–2125. https://doi.org/10.1021/acs.chemmater.0c04660.(8) Walther, F.; Koerver, R.; Fuchs, T.; Ohno, S.; Sann, J.; Rohnke, M.; Zeier, W. G.; Janek, J. Visualization of the Interfacial Decomposition of Composite Cathodes in Argyrodite-Based All-Solid-State Batteries Using Time-of-Flight Secondary-Ion Mass Spectrometry. Chemistry of Materials 2019. https://doi.org/10.1021/acs.chemmater.9b00770.(9) Kench, S.; Cooper, S. J. Generating Three-Dimensional Structures from a Two-Dimensional Slice with Generative Adversarial Network-Based Dimensionality Expansion. Nat Mach Intell 2021, 3 (4), 299–305. https://doi.org/10.1038/s42256-021-00322-1. Figure 1

Bounded degree cosystolic expanders of every dimension

In this work we present a new local to global criterion for proving a form of high dimensional expansion, which we term cosystolic expansion. Applying this criterion on Ramanujan complexes yields for every dimension an infinite family of bounded degree complexes with the topological overlap property. This answers an open question raised by Gromov.