Arbitrary Size Research Articles

Optimal hybrid multiplexed AC-DC-AC power converters

The flexibility of multi-terminal AC-DC-AC converters connected in distribution networks can be increased by changing the sizes of the individual AC-DC converter stages and connecting the AC side of those converters to electromechanical switches (multiplexers) to allow reconfiguration within the network. The combinations of real powers that can be transferred by such a design can be described using a capability chart. In this work, it is proposed that the area of these capability charts is a meaningful metric for describing the flexibility of such a device. These capability chart areas are calculated in closed form for a three-terminal AC-DC-AC device consisting of three AC-DC converters of arbitrary sizes, allowing the optimal AC-DC converter sizing to be determined to maximise this area. It is shown that this optimal design yields a capability chart area that is 64% larger than the equivalent area from a conventional equally-sized AC-DC-AC converter. Converters which are optimal in other senses are discussed, such as a design with 10% increased per-feeder real power transfer, albeit with an 8% area reduction. It is concluded that the capability chart area is an intuitive and informative approach for describing the increased flexibility of multiplexed AC-DC-AC converters.

Absence of operator growth for average equal-time observables in charge-conserved sectors of the Sachdev-Ye-Kitaev model

Quantum scrambling plays an important role in understanding thermalization in closed quantum systems. By this effect, quantum information spreads throughout the system and becomes hidden in the form of non-local correlations. Alternatively, it can be described in terms of the increase in complexity and spatial support of operators in the Heisenberg picture, a phenomenon known as operator growth. In this work, we study the disordered fully-connected Sachdev-Ye-Kitaev (SYK) model, and we demonstrate that scrambling is absent for disorder-averaged expectation values of observables. In detail, we adopt a formalism typical of open quantum systems to show that, on average and within charge-conserved sectors, operators evolve in a relatively simple way which is governed by their operator size. This feature only affects single-time correlation functions, and in particular it does not hold for out-of-time-order correlators, which are well-known to show scrambling behavior. Making use of these findings, we develop a cumulant expansion approach to approximate the evolution of equal-time observables. We employ this scheme to obtain analytic results that apply to arbitrary system size, and we benchmark its effectiveness by exact numerics. Our findings shed light on the structure of the dynamics of observables in the SYK model, and provide an approximate numerical description that overcomes the limitation to small systems of standard methods.

Robust bound states in the continuum in a dual waveguide system

Bound states in the continuum (BICs) provide a fascinating platform to route/manipulate waves with ultralow loss by patterning low-refractive-index materials on a high-refractive-index substrate. Principally, the phase of leaking channels can be manipulated via tuning the structural parameters to achieve destructive interference (i.e., the BIC condition), surprisingly leading to the total elimination of dissipation to the continuum of the substrate. Despite recent developments in BIC photonics, the BIC conditions can only be satisfied at specified geometric sizes for waveguides that dim their application prospects. Here, we propose a dual waveguide system that support BICs under arbitrary waveguide sizes by solely changing the intervals between the two waveguides. Our calculation results show that robust BICs in such architectures stem from the interaction (destructive interference) between leaking waves from the two waveguides. Furthermore, a cladding layer is introduced to improve the fabrication tolerance and reduce the sensitivity of the low-loss condition on the waveguide intervals of the presented dual waveguide system. The proposed approach offers an intriguing solution to establish a BIC concept and may be helpful to improve the potential of BIC photonic devices and circuits.

Porous Si loaded with Ag nanoparticles for ultra-broadband infrared absorption and detection

With the rapid development of semiconductor technology, silicon based infrared detector chips have been greatly optimized with the help of MEMS process. The infrared absorber, as the core part of thermal detector, requires infrared absorption characteristics such as wide spectrum and high absorption. In this paper, two kinds of porous silicon materials loaded with Ag nanoparticles (Si@AgNPs), honeycomb-like and forest-like, were constructed by chemical etching method. The experimental results and corresponding analyses illustrated that micro- and nano-porous structures of silicon with arbitrary sizes and random spatial positions can have a great guiding effect on the light waves, providing multiple reflections and scattering, allowing increased light propagation paths and generating light trapping effect, leading to enhanced infrared absorption in a wide spectral range. AgNPs are loaded, and the collective oscillation of free electrons resonates with incident photons, thereby exciting plasmon resonance effect, which can further improve the infrared absorption properties of the material. Benefiting from the synergistic effect of light trapping and plasmon resonance, the porous Si@AgNPs with honeycomb-like or forest-like structure both exhibited high absorption properties at 2.5–20 μm. Moreover, the honeycomb-like Si@AgNPs also possessed optical interference effect, which can further enhance the absorption, compared with the forest-like Si@AgNPs.

A Machine Learning Study of High Robustness Quantum Walk Search Algorithm with Qudit Householder Coins

In this work, the quantum random walk search algorithm with a walk coin constructed by generalized Householder reflection and phase multiplier has been studied. The coin register is one qudit with an arbitrary dimension. Monte Carlo simulations, in combination with supervised machine learning, are used to find walk coins that make the quantum algorithm more robust to deviations in the coin’s parameters. This is achieved by introducing functional dependence between these parameters. The functions that give the best performance of the algorithm are studied in detail by numerical statistical methods. A thorough comparison between our modification and an algorithm, with coins made using only Householder reflection, shows significant advantages of the former. By applying a deep neural network, we make a prediction for the parameters of an optimal coin with an arbitrary size and estimate the algorithm’s stability for such a coin.

A stochastic framework for computationally efficient fail-safe topology optimization

Fail-safe robustness is an important design philosophy for critical structural systems such as airframes. The basic idea of fail-safe design is that a structure should be designed to survive normal loading conditions when local damage occurs. In the context of topology optimization, fail-safe consideration innovates the morphogenesis for structural design in addition to the parametric safety margin. Existing fail-safe topology optimization methods usually enumerate all/predefined possible damages among the design domain and optimize the worst-damage scenario. However, virtually all damages can occur and cause the structural system to stop functioning. This work proposes the Stochastic Fail-Safe Topology Optimization (S-FSTO) for computationally viable fail-safe topology optimization which considers all possible damages according to the associated severity, including the worst-damage scenario. Critical damages for density value updates are sampled through the proposed two-level sampling strategy, which allows arbitrary size and location of damages. A design domain segmentation strategy is proposed to improve the sampling efficiency. The required number of FEA runs could be reduced by multiple orders of magnitudes. The proposed S-FSTO could be conveniently equivalent to a variety of existing schemes by customizing the proposal distributions of the two-level sampling strategy. The dependence on subjective decisions for selecting local damages could be minimized. The proposed framework has been evaluated for the design of cantilever beams and airplane bearing brackets. The generated conceptual designs are significantly more robust than the deterministic ones when local damage occurs. For the design of cantilever beams, S-FSTO produced designs with similar performance as that from the original FSTO and saved 99.4% FEA runs.

BSC: Belief Shift Clustering

It is still a challenging problem to characterize uncertainty and imprecision between specific (singleton) clusters with arbitrary shapes and sizes. In order to solve such a problem, we propose a belief shift clustering (BSC) method for dealing with object data. The BSC method is considered as the evidential version of mean shift or mode seeking under the theory of belief functions. First, a new notion, called belief shift, is provided to preliminarily assign each query object as the noise, precise, or imprecise one. Second, a new evidential clustering rule is designed to partial credal redistribution for each imprecise object. To avoid the “uniform effect” and useless calculations, a specific dynamic framework with simulated cluster centers is established to reassign each imprecise object to a singleton cluster or related meta-cluster. Once an object is assigned to a meta-cluster, this object may be in the overlapping or intermediate areas of different singleton clusters. Consequently, the BSC can reasonably characterize the uncertainty and imprecision between singleton clusters. The effectiveness has been verified on several artificial, natural, and image segmentation/classification datasets by comparison with other related methods.

RAPID: Approximate Pipelined Soft Multipliers and Dividers for High Throughput and Energy Efficiency

The rapid updates in error-resilient applications along with their quest for high throughput have motivated designing fast approximate functional units for Field-Programmable Gate Arrays (FPGAs). Studies that proposed imprecise functional techniques are posed with three shortcomings: first, most inexact multipliers and dividers are specialized for Application-Specific Integrated Circuit (ASIC) platforms. Second, state-of-the-art (SoA) approximate units are substituted, mostly in a single kernel of a multi-kernel application. Moreover, the end-to-end assessment is adopted on the Quality of Results (QoR), but not on the overall gained performance. Finally, existing imprecise components are not designed to support a pipelined approach, which could boost the operating frequency/throughput of, e.g., division-included applications. In this paper, we propose RAPID, the first pipelined approximate multiplier and divider architecture, customized for FPGAs. The proposed units efficiently utilize 6-input Look-up Tables (6-LUTs) and fast carry chains to implement Mitchell's approximate algorithms. Our novel error-refinement scheme not only has negligible overhead over the baseline Mitchell's approach but also boosts its accuracy to 99.4% for arbitrary size of multiplication and division. Experimental results demonstrate the efficiency of the proposed pipelined and non-pipelined RAPID multipliers and dividers over accurate counterparts. Moreover, the end-to-end evaluations of RAPID, deployed in three multi-kernel applications in the domains of bio-signal processing, image processing, and moving object tracking for Unmanned Air Vehicles (UAV) indicate up to 45% improvements in area, latency, and Area-Delay-Product (ADP), respectively, over accurate kernels, with negligible loss in QoR.

Exploiting graph neural networks to perform finite-difference time-domain based optical simulations

Having an artificial neural network that solves Maxwell’s equations in a general setting is an intellectual challenge and a great utility. Recently, there have been multiple successful attempts to use artificial neural networks to predict electromagnetic fields, given a specific source and interacting material distribution. However, many of these attempts are limited in domain size and restricted to object shapes similar to the learned ones. Here, we overcome these restrictions by using graph neural networks (GNNs) that adapt the propagation scheme of the finite-difference time-domain (FDTD) method to solve Maxwell’s equations for a distinct time step. GNNs yield a significant advantage, i.e., size invariance, over conventional neural network architectures, such as convolutional or linear neural networks. Once trained, a GNN can work on graphs of arbitrary size and connectivity. This allows us to train them on the propagation procedure of electromagnetic fields on small domain sizes and, finally, expand the domain to an arbitrary scale. Moreover, GNNs can adapt to any material shape and work not only on structured grids, such as FDTD, but also on arbitrary meshes. This work may be seen as the first benchmark for field predictions with graph networks and could be expanded to more complex mesh-based optical simulations, e.g., those based on finite elements.

Pair trajectories of uncharged conducting spheres in an electric field

In this paper, we study the role of electrostatic forces on pair trajectories of two uncharged conducting spheres subject to an external electric field. We consider the hydrodynamic interactions between the spheres as they move relative to one another. Previous studies have shown that electric-field-induced forces on a two-sphere system are always attractive, except for the configuration when the line joining the centers is perpendicular to the external electric field. In the current study, we derive the asymptotic form of the interparticle force induced by the electric field in the lubrication limit for arbitrary size ratios. The attractive electric force diverges as the separation approaches zero. Thus, our calculation shows that the electric-field-induced forces can overcome the continuum lubrication resistance and allow finite time contact between the surfaces of two spherical conductors. We calculate the asymptotic variation of interparticle separation using the near-field asymptotic expressions for the electric-field-induced forces, exploring the role of hydrodynamic interactions in interparticle motion parallel and perpendicular to the electric field.

Synchronization of phase oscillators on complex hypergraphs.

We study the effect of structured higher-order interactions on the collective behavior of coupled phase oscillators. By combining a hypergraph generative model with dimensionality reduction techniques, we obtain a reduced system of differential equations for the system's order parameters. We illustrate our framework with the example of a hypergraph with hyperedges of sizes 2 (links) and 3 (triangles). For this case, we obtain a set of two coupled nonlinear algebraic equations for the order parameters. For strong values of coupling via triangles, the system exhibits bistability and explosive synchronization transitions. We find conditions that lead to bistability in terms of hypergraph properties and validate our predictions with numerical simulations. Our results provide a general framework to study the synchronization of phase oscillators in hypergraphs, and they can be extended to hypergraphs with hyperedges of arbitrary sizes, dynamic-structural correlations, and other features.

Abstract P2-15-06: Light-based bioprinting of vascularized fatty tissue for the development of biological breast implants

Abstract Although multi-modal and ablative treatment strategies for breast cancer are becoming more popular, partial or full mastectomy is still a most common intervention. While proving to be effective in curing the cancer, it leaves the patient disfigured, which can pose a considerable psychological burden. Hence, reconstructive breast surgery is continuing to increase in importance as it offers the possibility to at least partially restore the original look of the breast. Autologous adipose tissue transfer techniques, amongst them the surgical gold standard flap surgery and liposuction-based fat grafting, present natural solutions with the patient’s own tissue, but either produce a considerable secondary wound or are insufficient to address larger defects. Synthetic solutions like silicone implants on the other hand can also be used to fill large defects and only require minimal surgical intervention, but suffer from foreign body-associated complications like fibrosis. Tissue engineering biological breast implants of arbitrary size with no or minor insult to the patient’s body and excellent biocompatibility, exclusively using biomaterials and patient-specific adipose cells, could open a new chapter for breast reconstruction. Such an approach has become an intriguing possibility through the rise of powerful biofabrication technologies in the last two decades, namely three-dimensional bioprinting. Bioprinting marries additive manufacturing with biomaterials and living cells with the aim to create life-mimicking tissues and organs, and while it is mainly focused on research today, current endeavors push for application in regenerative medicine. In our work we sought to fabricate adipose tissue constructs using light-based bioprinting to investigate the potential application of such technology for future reconstructive breast surgery. The engineered tissue blocks were designed to mimic native human adipose tissue by comprising fat, connective tissue and endothelial cells. Being specifically placed by the bioprinter within extracellular matrix-like hydrogels, the cells were cultured in vitro and differentiated within the cultivation time of up to 4 weeks to form an adipose-like tissue. Live imaging showed high viability of bioprinted tissue blocks over the whole cultivation period and pronounced cellular reorganization. Confocal and histological analyses revealed a dynamic tissue maturation process with increasing proportions of differentiated adipocytes, ECM remodeling and formation of microvascular structures. In conclusion, the obtained results gave proof to the concept of engineering adipose tissue through bioprinting and could lay the groundwork for the development of all-natural biological breast implants. Citation Format: Nina Hedemann, Alexander Thomas, Nils Tribian, Lutz Kloke, Dirk Bauerschlag, Marion van Mackelenbergh. Light-based bioprinting of vascularized fatty tissue for the development of biological breast implants [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-15-06.

Scheduling equal-length jobs with arbitrary sizes on uniform parallel batch machines

Abstract We consider the problem of scheduling jobs with equal lengths and arbitrary sizes on uniform parallel batch machines with different capacities. Each machine can only process the jobs whose sizes are not larger than its capacity. Several jobs can be processed as a batch simultaneously on a machine, as long as their total size does not exceed the machine’s capacity. The objective is to minimize makespan. Under a divisibility constraint, we obtain two efficient exact algorithms. For the general problem, we obtain an efficient 2-approximation algorithm. Previous work has shown that the problem cannot be approximated to within an approximation ratio better than 2, unless P = NP, even when all machines have identical speeds and capacities.

Inverse design of photonic crystal filters with arbitrary correlation and size for accurate spectrum reconstruction.

Spectroscopic technique based on nanophotonic filters can recover spectral information through compressive sensing theory. The spectral information is encoded by nanophotonic response functions and decoded by computational algorithms. They are generally ultracompact, low in cost, and offer single-shot operation with spectral resolution better than 1nm. Thus, they could be ideally suited for emerging wearable and portable sensing and imaging applications. Previous work has revealed that successful spectral reconstruction relies on well-designed filter response functions with sufficient randomness and low mutual correlation, but no thorough discussion has been performed on the filter array design. Here, instead of blind selection of filter structures, inverse design algorithms are proposed to obtain a photonic crystal filter array with predefined correlation coefficients and array size. Such rational spectrometer design can perform accurate reconstruction for a complex spectrum and maintain the performance under noise perturbation. We also discuss the impact of correlation coefficient and array size on the spectrum reconstruction accuracy. Our filter design method can be extended to different filter structures and suggests a better encoding component for reconstructive spectrometer applications.

Generalized image outpainting with U-transformer

In this paper, we develop a novel transformer-based generative adversarial neural network called U-Transformer for generalized image outpainting problems. Different from most present image outpainting methods conducting horizontal extrapolation, our generalized image outpainting could extrapolate visual context all-side around a given image with plausible structure and details even for complicated scenery, building, and art images. Specifically, we design a generator as an encoder-to-decoder structure embedded with the popular Swin Transformer blocks. As such, our novel neural network can better cope with image long-range dependencies which are crucially important for generalized image outpainting. We propose additionally a U-shaped structure and multi-view Temporal Spatial Predictor (TSP) module to reinforce image self-reconstruction as well as unknown-part prediction smoothly and realistically. By adjusting the predicting step in the TSP module in the testing stage, we can generate arbitrary outpainting size given the input sub-image. We experimentally demonstrate that our proposed method could produce visually appealing results for generalized image outpainting against the state-of-the-art image outpainting approaches.

Analysis of 2-D elastic solid with multiple V-notches by a fast multipole BEM with a novel singular element with multi-order asymptotic terms

A novel fast multipole boundary element method (BEM) is developed to analyze the stress fields of plane structures with multiple V-notches. To accurately evaluate the singular stresses in the vicinity of the V-notch tip, a novel singular element with multi-order asymptotic terms (SEMAT) is first proposed. The tractions and displacements on the SEMAT are expressed as the asymptotic expansions, where both the first singular term and other higher-order terms are considered. The SEMAT is then introduced into the fast multipole BEM, and several singularity problems of boundary integrals on the SEMAT are treated with caution. Finally, the fast multipole BEM with the SEMAT is used to obtain the whole stress and displacement fields for the plane V-notch/crack structures. Numerical examples show that the present method is accurate for analyzing the V-notch with opening angle of arbitrary size in both single and bonded materials. It is not necessary to use a dense mesh in modeling, and the computational efficiency of the present method is high for large-scale problems.

On the geometric priority set cover problem

We study the priority set cover problem for simple geometric set systems in the plane. For pseudo-halfspaces in the plane we obtain a PTAS via local search by showing that the corresponding set system admits a planar support. We show that the problem is APX-hard even for unit disks in the plane and argue that in this case the standard local search algorithm can output a solution that is arbitrarily bad compared to the optimal solution. We then present an LP-relative constant factor approximation algorithm (which also works in the weighted setting) for unit disks via quasi-uniform sampling. As a consequence we obtain a constant factor approximation for the capacitated set cover problem with unit disks. For arbitrary size disks, we show that the problem is at least as hard as the vertex cover problem in general graphs even when the disks have nearly equal sizes. We also present a few simple results for unit squares and orthants in the plane.

Polymer-dispersed liquid crystal elastomers as moldable shape-programmable material

The current development of soft shape-memory materials often results in materials that are typically limited to the synthesis of thin-walled specimens and usually rely on complex, low-yield manufacturing techniques to fabricate macro-sized, solid three-dimensional objects. However, such geometrical limitations and slow production rates can significantly hinder their practical implementation. In this work, we demonstrate a shape-memory composite material that can be effortlessly molded into arbitrary shapes or sizes. The composite material is made from main-chain liquid crystal elastomer (MC-LCE) microparticles dispersed in a silicone polymer matrix. Shape-programmability is achieved via low-temperature induced glassiness and hardening of MC-LCE inclusions, which effectively freezes-in any mechanically instilled deformations. Once thermally reset, the composite returns to its initial shape and can be shape-programmed again. Magnetically aligning MC-LCE microparticles prior to curing allows the shape-programmed artefacts to be additionally thermomechanically functionalized. Therefore, our material enables efficient morphing among the virgin, thermally-programmed, and thermomechanically-controlled shapes.

On the Role of Training Data for SVM-Based Microwave Brain Stroke Detection and Classification.

The aim of this work was to test microwave brain stroke detection and classification using support vector machines (SVMs). We tested how the nature and variability of training data and system parameters impact the achieved classification accuracy. Using experimentally verified numerical models, a large database of synthetic training and test data was created. The models consist of an antenna array surrounding reconfigurable geometrically and dielectrically realistic human head phantoms with virtually inserted strokes of arbitrary size, and different dielectric parameters in different positions. The generated synthetic data sets were used to test four different hypotheses, regarding the appropriate parameters of the training dataset, the appropriate frequency range and the number of frequency points, as well as the level of subject variability to reach the highest SVM classification accuracy. The results indicate that the SVM algorithm is able to detect the presence of the stroke and classify it (i.e., ischemic or hemorrhagic) even when trained with single-frequency data. Moreover, it is shown that data of subjects with smaller strokes appear to be the most suitable for training accurate SVM predictors with high generalization capabilities. Finally, the datasets created for this study are made available to the community for testing and developing their own algorithms.

Improvement of monodispersity of PbS quantum dots by filtration with organic solvent-resistant polyamide hollow fiber membranes

We report that membrane filtration can replace centrifugation as a highly efficient size classification process of colloidal quantum dots (QDs) after chemical synthesis. The production of colloidal QDs requires the separation of the targeted QDs dispersed in organic solvents from other by-products. The separation process has been conventionally performed by centrifugation. We investigated replacing the centrifugation with filtration using organic solvent-resistant polyamide hollow fiber membranes (HFMs). By choosing the pore size of HFMs, QDs of arbitrary size were classified. It was also demonstrated that not only large QDs but also small QDs could be separated simultaneously by using HFM with a dense layer of polyamide inside. Consequently, highly monodisperse QDs were easily obtained in a single filtration operation using HFMs.