Recent Submissions

  • Cellular automata imbedded memristor-based recirculated logic in-memory computing

    Liu, Yanming; Tian, He; Wu, Fan; Liu, Anhan; Li, Yihao; Sun, Hao; Lanza, Mario; Ren, Tian-Ling (Research Square Platform LLC, 2022-08-05) [Preprint]
    Cellular automata is an important tool to study the emergent properties of complex systems based on its well-known parallel, bio-inspired, computational characteristics. However, running cellular automata on conventional chips suffer from low parallelism, and high hardware cost. Establish dedicated hardware for cellular automata remains elusive. Here, we propose a recirculate logic operations scheme (RLOS) based on memristive hardware combined with 2D transistors to realize cellular automata evolution. The scheme utilizes the storage and calculation characteristics of memristive devices, which greatly reduces hardware complexity. The versatility of the RLOS scheme allows implementing multiple different cellular automata algorithms on the same circuitry. The entire rule (rule 1-254) of elementary cellular automata and more complicated 1D CA model majority classification algorithm have been verified to be applicable to this circuitry. Further, the edge detection algorithm based on 2D cellular automata has been authenticated through RLOS. The experimental and evaluation results reveal that the scheme reduces the hardware cost up to 79 times comparing to the Field Programmable Gate Array (FPGA) approach. To our best knowledge, RLOS has the lowest hardware cost (6 components/per cell) among state-of-art hardware implementations. This work can pave the road towards high-efficiency and low-cost cellular automata hardware realization, and also facilitates the exploration of memristive applications.
  • Multilevel Importance Sampling for McKean-Vlasov Stochastic Differential Equation

    Rached, Nadhir Ben; Haji-Ali, Abdul-Lateef; Mohan, Shyam; Tempone, Raul (arXiv, 2022-08-05) [Preprint]
    This work combines multilevel Monte Carlo methods with importance sampling (IS) to estimate rare event quantities that can be expressed as the expectation of a Lipschitz observable of the solution to the McKean-Vlasov stochastic differential equation. We first extend the double loop Monte Carlo (DLMC) estimator, introduced in this context in our previous work [Ben Rached et al. 2022], to the multilevel setting. We formulate a novel multilevel DLMC (MLDLMC) estimator, and perform a comprehensive work-error analysis yielding new and improved complexity results. Crucially, we also devise an antithetic sampler to estimate level differences that guarantees reduced work complexity for the MLDLMC estimator compared with the single level DLMC estimator. To tackle rare events, we apply the same single level IS scheme, obtained via stochastic optimal control in [Ben Rached et al. 2022], over all levels of the MLDLMC estimator. Combining IS and MLDLMC not only reduces computational complexity by one order, but also drastically reduces the associated constant, ensuring feasible estimates for rare event quantities. We illustrate effectiveness of proposed MLDLMC estimator on the Kuramoto model from statistical physics with Lipschitz observables, confirming reduced complexity from O(TOL−4r) for the single level DLMC estimator to O(TOL−3r) while providing feasible estimation for rare event quantities up to the prescribed relative error tolerance TOLr.
  • A Fractional Image Inpainting Model Using a Variant of Mumford-Shah Model

    Halim, Abdul; Rohim, Abdur; Kumar, B. V. Rathish; Saha, Ripan (arXiv, 2022-08-05) [Preprint]
    In this paper, we propose a fourth order PDE model for image inpainting based on a variant of the famous Mumford-Shah (MS) image segmentation model. Convexity splitting is used to discrtised the time and we replace the Laplacian by its fractional counterpart in the time discretised scheme. Fourier spectral method is used for space discretization. Consistency, stability and convergence of the time discretised model has been carried out. The model is tested on some standard test images and compared them with the result of some models existed in the literature.
  • Time-Varying Dispersion Integer-Valued GARCH Models

    Barreto-Souza, Wagner; Piancastelli, Luiza S. C.; Fokianos, Konstantinos; Ombao, Hernando (arXiv, 2022-08-04) [Preprint]
    We propose a general class of INteger-valued Generalized AutoRegressive Conditionally Heteroskedastic (INGARCH) processes by allowing time-varying mean and dispersion parameters, which we call time-varying dispersion INGARCH (tv-DINGARCH) models. More specifically, we consider mixed Poisson INGARCH models and allow for a dynamic modeling of the dispersion parameter (as well as the mean), similarly to the spirit of the ordinary GARCH models. We derive conditions to obtain first and second order stationarity, and ergodicity as well. Estimation of the parameters is addressed and their associated asymptotic properties established as well. A restricted bootstrap procedure is proposed for testing constant dispersion against time-varying dispersion. Monte Carlo simulation studies are presented for checking point estimation, standard errors, and the performance of the restricted bootstrap approach. The inclusion of covariates is also addressed and applied to the daily number of deaths due to COVID-19 in Ireland. Insightful results were obtained in the data analysis, including a superior performance of the tv-DINGARCH processes over the ordinary INGARCH models.
  • Origin of Interfacial Charges of Al2o3/Si and Al2o3/Gan Heterogeneous Heterostructures

    Wang, Chuanju; AlQatari, Feras S.; Khandelwal, Vishal; Lin, Rongyu; Li, Xiaohang (Elsevier BV, 2022-08-04) [Preprint]
    Al2O3 is a broadly employed dielectric and significant interfacial charges occur at Al2O3/semiconductor interfaces. However, the charge origin is often unclear that severely impacts device engineering and design. Al2O3/Si and Al2O3/GaN are two of the most common heterogeneous heterostructures (H2s) for many crucial devices including GaN transistors and Si solar cells. While negative charges are extensively observed in Al2O3/Si, positive charges exist in Al2O3/GaN, both of which are not well understood. In this study, we performed in-depth interfacial studies of the Al2O3/Si and Al2O3/GaN H2s to clarify the origin of the interfacial charges. Stoichiometry deviations were found at the interfaces of the two H2s where Al surpasses O for Al2O3/GaN, whereas O dominates at the Al2O3/Si interface. Therefore, we propose that the different interfacial charges are caused by nonstoichiometry atomic ratios of Al2O3 at the interface. The study indicates the important role of the semiconductor surface on the device performance, provide a deep understanding on the origin of interfacial charges at the insulator-semiconductor interfaces.
  • Visually Evaluating Generative Adversarial Networks Using Itself under Multivariate Time Series

    Pan, Qilong (arXiv, 2022-08-04) [Preprint]
    Visually evaluating the goodness of generated Multivariate Time Series (MTS) are difficult to implement, especially in the case that the generative model is Generative Adversarial Networks (GANs). We present a general framework named Gaussian GANs to visually evaluate GANs using itself under the MTS generation task. Firstly, we attempt to find the transformation function in the multivariate Kolmogorov Smirnov (MKS) test by explicitly reconstructing the architecture of GANs. Secondly, we conduct the normality test of transformed MST where the Gaussian GANs serves as the transformation function in the MKS test. In order to simplify the normality test, an efficient visualization is proposed using the chi square distribution. In the experiment, we use the UniMiB dataset and provide empirical evidence showing that the normality test using Gaussian GANs and chi sqaure visualization is effective and credible.
  • Methane and n-hexane ignition in a newly developed diaphragmless shock tube

    Subburaj, Janardhanraj; Kashif, Touqeer Anwar; Farooq, Aamir (arXiv, 2022-08-03) [Preprint]
    Shock tubes have been routinely used to generate reliable chemical kinetic data for gas-phase chemistry. The conventional diaphragm-rupture mode for shock tube operation presents many challenges that may ultimately affect the quality of chemical kinetics data. Numerous diaphragmless concepts have been developed to overcome the drawbacks of using diaphragms. Most of these diaphragmless designs require significant alterations in the driver section of the shock tube and, in some cases, fail to match the performance of the diaphragm-mode of operation. In the present work, an existing diaphragm-type shock tube is retrofitted with a fast-acting valve, and the performance of the diaphragmless shock tube is evaluated for investigating the ignition of methane and n-hexane. The diaphragmless shock tube reported here presents many advantages, such as eliminating the use of diaphragms, avoiding substantial manual effort during experiments, automating the shock tube facility, having good control over driver conditions, and obtaining good repeatability for reliable gas-phase chemical kinetic studies. Ignition delay time measurements have been performed in the diaphragmless shock tube for three methane mixtures and two n-hexane mixtures at P5 = 10 - 20 bar and T5 = 738 - 1537 K. The results obtained for fuel-rich, fuel-lean, and oxygen-rich (undiluted) mixtures show very good agreement with previously reported experimental data and literature kinetic models (AramcoMech 3.0 [1] for methane and Zhang et al. mechanism [2] for n-hexane). The study presents an easy and simple method to upgrade conventional shock tubes to a diaphragmless mode of operation and opens new possibilities for reliable chemical kinetics investigations.
  • Negative Frames Matter in Egocentric Visual Query 2D Localization

    Xu, Mengmeng; Fu, Cheng-Yang; Li, Yanghao; Ghanem, Bernard; Perez-Rua, Juan-Manuel; Xiang, Tao (arXiv, 2022-08-03) [Preprint]
    The recently released Ego4D dataset and benchmark significantly scales and diversifies the first-person visual perception data. In Ego4D, the Visual Queries 2D Localization task aims to retrieve objects appeared in the past from the recording in the first-person view. This task requires a system to spatially and temporally localize the most recent appearance of a given object query, where query is registered by a single tight visual crop of the object in a different scene. Our study is based on the three-stage baseline introduced in the Episodic Memory benchmark. The baseline solves the problem by detection and tracking: detect the similar objects in all the frames, then run a tracker from the most confident detection result. In the VQ2D challenge, we identified two limitations of the current baseline. (1) The training configuration has redundant computation. Although the training set has millions of instances, most of them are repetitive and the number of unique object is only around 14.6k. The repeated gradient computation of the same object lead to an inefficient training; (2) The false positive rate is high on background frames. This is due to the distribution gap between training and evaluation. During training, the model is only able to see the clean, stable, and labeled frames, but the egocentric videos also have noisy, blurry, or unlabeled background frames. To this end, we developed a more efficient and effective solution. Concretely, we bring the training loop from ~15 days to less than 24 hours, and we achieve 0.17% spatial-temporal AP, which is 31% higher than the baseline. Our solution got the first ranking on the public leaderboard.
  • Approximating Hessian matrices using Bayesian inference: a new approach for quasi-Newton methods in stochastic optimization

    Carlon, Andre Gustavo; Espath, Luis; Tempone, Raul (arXiv, 2022-07-31) [Preprint]
    Using quasi-Newton methods in stochastic optimization is not a trivial task. In deterministic optimization, these methods are often a common choice due to their excellent performance regardless of the problem's condition number. However, standard quasi-Newton methods fail to extract curvature information from noisy gradients in stochastic optimization. Moreover, pre-conditioning noisy gradient observations tend to amplify the noise by a factor given by the largest eigenvalue of the pre-conditioning matrix. We propose a Bayesian approach to obtain a Hessian matrix approximation for stochastic optimization that minimizes the secant equations residue while retaining the smallest eigenvalue above a specified limit. Thus, the proposed approach assists stochastic gradient descent to converge to local minima without augmenting gradient noise. The prior distribution is modeled as the exponential of the negative squared distance to the previous Hessian approximation, in the Frobenius sense, with logarithmic barriers imposing extreme eigenvalues constraints. The likelihood distribution is derived from the secant equations, i.e., the probability of obtaining the observed gradient evaluations for a given candidate Hessian approximation. We propose maximizing the log-posterior using the Newton-CG method. Numerical results on a stochastic quadratic function and a ℓ2 regularized logistic regression problem are presented. In all the cases tested, our approach improves the convergence of stochastic gradient descent, compensating for the overhead of solving the log-posterior maximization. In particular, pre-conditioning the stochastic gradient with the inverse of our Hessian approximation becomes more advantageous the larger the condition number of the problem is.
  • Functional-Coefficient Models for Multivariate Time Series in Designed Experiments: with Applications to Brain Signals

    Redondo, Paolo Victor; Huser, Raphaël; Ombao, Hernando (arXiv, 2022-07-30) [Preprint]
    To study the neurophysiological basis of attention deficit hyperactivity disorder (ADHD), clinicians use electroencephalography (EEG) which record neuronal electrical activity on the cortex. The most commonly-used metric in ADHD is the theta-to-beta spectral power ratio (TBR) that is based on a single-channel analysis. However, initial findings for this measure have not been replicated in other studies. Thus, instead of focusing on single-channel spectral power, a novel model for investigating interactions (dependence) between channels in the entire network is proposed. Although dependence measures such as coherence and partial directed coherence (PDC) are well explored in studying brain connectivity, these measures only capture linear dependence. Moreover, in designed clinical experiments, these dependence measures are observed to vary across subjects even within a homogeneous group. To address these limitations, we propose the mixed-effects functional-coefficient autoregressive (MX-FAR) model which captures between-subject variation by incorporating subject-specific random effects. The advantages of MX-FAR are the following: (1.) it captures potential non-linear dependence between channels; (2.) it is nonparametric and hence flexible and robust to model mis-specification; (3.) it can capture differences between groups when they exist; (4.) it accounts for variation across subjects; (5.) the framework easily incorporates well-known inference methods from mixed-effects models; (6.) it can be generalized to accommodate various covariates and factors. Finally, we apply the proposed MX-FAR model to analyze multichannel EEG signals and report novel findings on altered brain functional networks in ADHD.
  • Max-Min Data Rate Optimization for RIS-aided Uplink Communications with Green Constraints

    Subhash, Athira; Kammoun, Abla; Elzanaty, Ahmed; Kalyani, Sheetal; Al-Badarneh, Yazan H.; Alouini, Mohamed-Slim (arXiv, 2022-07-30) [Preprint]
    Smart radio environments aided by reconfigurable intelligent reflecting surfaces (RIS) have attracted much research attention recently. We propose a joint optimization strategy for beamforming, RIS phases, and power allocation to maximize the minimum SINR of an uplink RIS-aided communication system. The users are subject to constraints on their transmit power. We derive a closed-form expression for the beam forming vectors and a geometric programming-based solution for power allocation. We also propose two solutions for optimizing the phase shifts at the RIS, one based on the matrix lifting method and one using an approximation for the minimum function. We also propose a heuristic algorithm for optimizing quantized phase shift values. The proposed algorithms are of practical interest for systems with constraints on the maximum allowable electromagnetic field exposure. For instance, considering 24-element RIS, 12-antenna BS, and 6 users, numerical results show that the proposed algorithm achieves close to 300% gain in terms of minimum SINR compared to a scheme with random RIS phases.
  • Improving Few-shot News Recommendation via Cross-lingual Transfer

    Guo, Taicheng; Yu, Lu; Zhang, Xiangliang (arXiv, 2022-07-28) [Preprint]
    The cold-start problem has been commonly recognized in recommendation systems and studied by following a general idea to leverage the abundant interaction records of warm users to infer the preference of cold users. However, the performance of these solutions is limited by the amount of records available from warm users to use. Thus, building a recommendation system based on few interaction records from a few users still remains a challenging problem for unpopular or early-stage recommendation platforms. This paper focuses on solving the few-shot recommendation problem for news recommendation based on two observations. First, news at different platforms (even in different languages) may share similar topics. Second, the user preference over these topics is transferable across different platforms. Therefore, we propose to solve the few-shot news recommendation problem by transferring the user-news preference from a rich source domain to a low-resource target domain. To bridge two domains in different languages without any overlapping users and news, we propose a novel unsupervised cross-lingual transfer model as the news encoder that aligns semantically similar news in two domains. A user encoder is constructed on top of the aligned news encoding and transfers the user preference from the source to the target domain. Experimental results on two real-world news recommendation datasets show the superior performance of our proposed method on addressing few-shot news recommendation, comparing to the state-of-the-art baselines.
  • Fabrication of Nio Dopped Cuo Nanosheets Decorated with Resorcinol Farmaldehyde Resin for Enhanced Photocatalytic Application

    Ullah, Sana; Shah, Muslim; Raziq, Fazal; Ali, Sharafat; Rehman, Anis Ur; Zarshad, Nighat; wadood, Abdul; Ullah, Ihsan; Hayat, Khizar; Li, Guigen; Ali, Asad (Elsevier BV, 2022-07-27) [Preprint]
    The quest for z-scheme efficient photo catalytic system has been proved to be an impressive approach to enhance reactivity and selectivity for carbon dioxide conversion into value-added energy dense molecules to cope with the increasing clean energy demand in future. Herein, we developed a versatile strategy for the Z-scheme heterojunction synthesis of NiO doped CuO well defined hexagonal nanosheets decorated with RF (Resorcinol-Formaldehyde resin). The Photo-generated electrons migrate to NiO doped CuO from the π-π stacking in benzenoid-quinoid conjugated system of RF resin, where these electrons are accommodated by carbonyl group of quinoid acceptor unit, and the positive holes leave in semiconductor heterojunction for water oxidation. The nanosheets morphology of NiO doped CuO has enhanced surface area, active sites, improved charge separation and elevated CO2 reduction potential of the heterojunction. The small HOMO-LUMO gap of RF resin facilitate migration of electrons from the CB of RF to VB of CuO. The photocatalytic degradation of the 2,4-DCP was investigated which is 95% per hour. We claim the highest activity achievement in terms of CO2 reduction (230 μ mol g-1 h-1) which is 6 folds greater than the pure CuO (39.5 μ mol g-1 h-1) photocatalyst and pollutant degradation of the heterojunction till date.
  • On a Dynamic Variant of the Iteratively Regularized Gauss-Newton Method with Sequential Data

    Chada, Neil Kumar; Iglesias, Marco A.; Lu, Shuai; Werner, Frank (arXiv, 2022-07-27) [Preprint]
    For numerous parameter and state estimation problems, assimilating new data as they become available can help produce accurate and fast inference of unknown quantities. While most existing algorithms for solving those kind of ill-posed inverse problems can only be used with a single instance of the observed data, in this work we propose a new framework that enables existing algorithms to invert multiple instances of data in a sequential fashion. Specifically we will work with the well-known iteratively regularized Gauss-Newton method (IRGNM), a variational methodology for solving nonlinear inverse problems. We develop a theory of convergence analysis for a proposed dynamic IRGNM algorithm in the presence of Gaussian white noise. We combine this algorithm with the classical IRGNM to deliver a practical (hybrid) algorithm that can invert data sequentially while producing fast estimates. Our work includes the proof of well-definedness of the proposed iterative scheme, as well as various error bounds that rely on standard assumptions for nonlinear inverse problems. We use several numerical experiments to verify our theoretical findings, and to highlight the benefits of incorporating sequential data. The context of the numerical experiments comprises various parameter identification problems including a Darcy flow elliptic PDE example, and that of electrical impedance tomography.
  • Multivariate Functional Outlier Detection using the FastMUOD Indices

    Ojo, Oluwasegun Taiwo; Anta, Antonio Fernández; Genton, Marc G.; Lillo, Rosa E. (arXiv, 2022-07-26) [Preprint]
    We present definitions and properties of the fast massive unsupervised outlier detection (FastMUOD) indices, used for outlier detection (OD) in functional data. FastMUOD detects outliers by computing, for each curve, an amplitude, magnitude and shape index meant to target the corresponding types of outliers. Some methods adapting FastMUOD to outlier detection in multivariate functional data are then proposed. These include applying FastMUOD on the components of the multivariate data and using random projections. Moreover, these techniques are tested on various simulated and real multivariate functional datasets. Compared with the state of the art in multivariate functional OD, the use of random projections showed the most effective results with similar, and in some cases improved, OD performance.
  • RandProx: Primal-Dual Optimization Algorithms with Randomized Proximal Updates

    Condat, Laurent Pierre; Richtarik, Peter (arXiv, 2022-07-26) [Preprint]
    Proximal splitting algorithms are well suited to solving large-scale nonsmooth optimization problems, in particular those arising in machine learning. We propose a new primal-dual algorithm, in which the dual update is randomized; equivalently, the proximity operator of one of the function in the problem is replaced by a stochastic oracle. For instance, some randomly chosen dual variables, instead of all, are updated at each iteration. Or, the proximity operator of a function is called with some small probability only. A nonsmooth variance-reduction technique is implemented so that the algorithm finds an exact minimizer of the general problem involving smooth and nonsmooth functions, possibly composed with linear operators. We derive linear convergence results in presence of strong convexity; these results are new even in the deterministic case, when our algorithms reverts to the recently proposed Primal-Dual Davis-Yin algorithm. Some randomized algorithms of the literature are also recovered as particular cases (e.g., Point-SAGA). But our randomization technique is general and encompasses many unbiased mechanisms beyond sampling and probabilistic updates, including compression. Since the convergence speed depends on the slowest among the primal and dual contraction mechanisms, the iteration complexity might remain the same when randomness is used. On the other hand, the computation complexity can be significantly reduced. Overall, randomness helps getting faster algorithms. This has long been known for stochastic-gradient-type algorithms, and our work shows that this fully applies in the more general primal-dual setting as well.
  • Large-Scale Low-Rank Gaussian Process Prediction with Support Points

    Song, Yan; Dai, Wenlin; Genton, Marc G. (arXiv, 2022-07-26) [Preprint]
    Low-rank approximation is a popular strategy to tackle the "big n problem" associated with large-scale Gaussian process regressions. Basis functions for developing low-rank structures are crucial and should be carefully specified. Predictive processes simplify the problem by inducing basis functions with a covariance function and a set of knots. The existing literature suggests certain practical implementations of knot selection and covariance estimation; however, theoretical foundations explaining the influence of these two factors on predictive processes are lacking. In this paper, the asymptotic prediction performance of the predictive process and Gaussian process predictions is derived and the impacts of the selected knots and estimated covariance are studied. We suggest the use of support points as knots, which best represent data locations. Extensive simulation studies demonstrate the superiority of support points and verify our theoretical results. Real data of precipitation and ozone are used as examples, and the efficiency of our method over other widely used low-rank approximation methods is verified.
  • Pdzn/Zro2 + Sapo-34 Bifunctional Catalyst for Co2 Conversion: Further Insights by Spectroscopic Characterization

    Ticali, Pierfrancesco; Morandi, Sara; Shterk, Genrikh; Ould-Chikh, Samy; Ramirez, Adrian; Gascon, Jorge; Chung, Sang-ho; Ruiz-Martinez, Javier; Bordiga, Silvia (Elsevier BV, 2022-07-26) [Preprint]
    The present work aims at further investigating a previously studied PdZn/ZrO2+SAPO-34 bifunctional catalyst for CO2 conversion. High activity and selectivity for propane was proved and the results obtained by NAP-XPS measurements and CO adsorption at liquid-nitrogen temperature (LNT) followed by FT-IR spectroscopy are shown. After reduction, we confirmed the formation of PdZn alloy. At LNT Pd carbonyl band shows a peculiar behavior linked to an intimate interaction between PdZn particles, ZnO and ZrO2.The combined system was characterized as fresh, used and regenerated. On the fresh PdZn/ZrO2+SAPO-34 the characteristic features of the two components do not appear perturbed by the mixing. As for the used system, the absence of Pd carbonyls and the decrease of CO on SAPO-34 Brønsted acid sites are correlated to organic species revealed by ssNMR. Regeneration in oxygen restores catalytic sites, although new Pd carbonyls appear due to Pd2+ ionic exchange into SAPO-34 framework.
  • Pdzn/Zro2 + Sapo-34 Bifunctional Catalyst for Co2 Conversion: Further Insights by Spectroscopic Characterization

    Ticali, Pierfrancesco; Morandi, Sara; Shterk, Genrikh; Ould-Chikh, Samy; Ramirez, Adrian; Gascon, Jorge; Chung, Sang-ho; Ruiz-Martinez, Javier; Bordiga, Silvia (Elsevier BV, 2022-07-26) [Preprint]
    The present work aims at further investigating a previously studied PdZn/ZrO2+SAPO-34 bifunctional catalyst for CO2 conversion. High activity and selectivity for propane was proved and the results obtained by NAP-XPS measurements and CO adsorption at liquid-nitrogen temperature (LNT) followed by FT-IR spectroscopy are shown. After reduction, we confirmed the formation of PdZn alloy. At LNT Pd carbonyl band shows a peculiar behavior linked to an intimate interaction between PdZn particles, ZnO and ZrO2.The combined system was characterized as fresh, used and regenerated. On the fresh PdZn/ZrO2+SAPO-34 the characteristic features of the two components do not appear perturbed by the mixing. As for the used system, the absence of Pd carbonyls and the decrease of CO on SAPO-34 Brønsted acid sites are correlated to organic species revealed by ssNMR. Regeneration in oxygen restores catalytic sites, although new Pd carbonyls appear due to Pd2+ ionic exchange into SAPO-34 framework.
  • Novel Tpms Contactors Designed with Imprinted Porosity: Numerical Evaluation of Momentum and Energy Transport

    Grande, Carlos; Asif, Mohammad (Elsevier BV, 2022-07-25) [Preprint]
    Structured packings in reactors and separation processes have an extensive trait for process intensification such as enhancement in mass and heat transport without having any substantial pressure drop and can now successfully be produced by using additive manufacturing methods such as 3D printing. Structured packings manufactured with Triply Periodical Minimum Surfaces (TPMS) have good mixing properties and enhanced thermal transport, but they do not have high surface areas.In this work, we report a new type of hybrid TPMS structures with high surface area while keeping good mixing properties. The new shapes are made by generating solids on the boundaries of a 2D tessellation of polygons over the TPMS surface. The new shapes have a higher surface area than a TPMS and at the same time, a higher porosity. We have evaluated the pressure drop and heat transfer properties of such structures for Reynolds numbers 1-200 in ten different solids. The results indicate that pressure drop is dominated by porosity. Heat transfer properties however depend also on available surface area and thus are improved in the porous structures.

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