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This research article presents various design patterns for improving interoperability in Polkadot, a blockchain platform. These patterns include chain bridges, interoperability standards, common asset identifiers, governance agreements, oracle chains, and a hypothetical design pattern called Zero Trust Chain. Implementation of these design patterns can help improve security and confidence in transactions between different chains on the Polkadot network, allowing for faster and more efficient communication. The article also emphasizes the importance of interoperability in blockchain technology and highlights Polkadot's flexibility in creating customized specialized chains that can further improve interoperability on the network. Overall, this article highlights how design patterns can improve interoperability in Polkadot, which could lead to greater adoption of blockchain technology in various industries., Comment: 6 pages

The fabrication of laser-induced periodic surface structures (LIPSS) over extended areas at high processing speeds requires the use of high repetition rate femtosecond lasers. It is known that industrially relevant materials such as steel experience heat accumulation when irradiated at repetition rates above some hundreds of kHz, and significant debris redeposition can take place. However, there are few studies on how the laser repetition rate influences both the debris deposition and the final LIPSS morphology. In this work, we present a study of fs laser-induced fabrication of low spatial frequency LIPSS (LSFL), with pulse repetition rates ranging from 10 kHz to 2 MHz on commercially available steel. The morphology of the laser-structured areas as well as the redeposited debris was characterized by scanning electron microscopy (SEM) and {\mu}-Raman spectroscopy. To identify repetition rate ranges where heat accumulation is present during the irradiations, we developed a simple heat accumulation model that solves the heat equation in 1 dimension implementing a Forward differencing in Time and Central differencing in Space (FTCS) scheme. Contact angle measurements with water demonstrated the influence of heat accumulation and debris on the functional wetting behavior. The findings are directly relevant for the processing of metals using high repetition rate femtosecond lasers, enabling the identification of optimum conditions in terms of desired morphology, functionality, and throughput.

We study the decay $J/\psi\to\pi^{+}\pi^{-}\pi^{0}$ within the framework of the Khuri-Treiman equations. We find that the BESIII experimental di-pion mass distribution in the $\rho(770)$-region is well reproduced with a once-subtracted $P$-wave amplitude. Furthermore, we show that $F$-wave contributions to the amplitude improve the description of the data in the $\pi\pi$ mass region around 1.5 GeV. We also present predictions for the $J/\psi\to\pi^{0}\gamma^{*}$ transition form factor., Comment: 20 pages, 9 figures

Objective: Breast tissue is mainly a mixture of adipose and fibro-glandular tissue. Cancer risk and risk of undetected breast cancer increases with the amount of glandular tissue in the breast. Therefore, radiologists must report the total volume glandular fraction or a BI-RADS classification in screening and diagnostic mammography. A Maximum Likelihood algorithm is shown to estimate the pixel-wise glandular fraction from mammographic images. The pixel-wise glandular fraction provides information that helps localize dense tissue. The total volume glandular fraction can be calculated from pixel-wise glandular fraction. The algorithm was implemented for images acquired with an anti-scatter grid, and without using the anti-scatter grid but followed by software scatter removal. The work also studied if presenting the pixel-wise glandular fraction image alongside the usual mammographic image has the potential to improve the contrast-to-noise ratio on micro-calcifications in the breast. Results: For the TOPAS simulated images, the glandular fraction was estimated with a root mean squared error of 3.2% and 2.5% for the without and with anti-scatter grid cases. Average absolute errors were (3.7 +/- 2.4)% and (3.6 +/- 0.9)%, respectively. Results from DICOM clinical images (where the true glandular fraction is unknown) show that the algorithm gives a glandular fraction within the average range expected from the literature. For microcalcification detection, the contrast-to-noise ratio improved by 17.5-548% in DICOM images and 5.1-88% in TOPAS images. Conclusion: We show a method of accurate estimation of pixel-wise glandular fraction image, providing localization information of breast density. The glandular fraction images also showed an improvement in contrast to noise ratio for detecting microcalcifications, a risk factor in breast cancer., Comment: Pre-print

Considering pure quantum states, entanglement concentration is the procedure where from $N$ copies of a partially entangled state, a single state with higher entanglement can be obtained. Getting a maximally entangled state is possible for $N=1$. However, the associated success probability can be extremely low while increasing the system's dimensionality. In this work, we study two methods to achieve a probabilistic entanglement concentration for bipartite quantum systems with a large dimensionality for $N=1$, regarding a reasonably good probability of success at the expense of having a non-maximal entanglement. Firstly, we define an efficiency function $\mathcal{Q}$ considering a tradeoff between the amount of entanglement (quantified by the I-Concurrence) of the final state after the concentration procedure and its success probability, which leads to solving a quadratic optimization problem. We found an analytical solution, ensuring that an optimal scheme for entanglement concentration can always be found in terms of $\mathcal{Q}$. Finally, a second method was explored, which is based on fixing the success probability and searching for the maximum amount of entanglement attainable. Both ways resemble the Procrustean method applied to a subset of the most significant Schmidt coefficients but obtaining non-maximally entangled states., Comment: 11 pages, 4 figures

We introduce a mechanism that can both hold and amplify electromagnetic waves by rapidly changing the permittivity of the medium during the wave travel from a positive to a dispersionless (i.e. non-Foster) negative value and then back again. The underlying physics behind this phenomenon is theoretically explored by considering a plane wave in an unbounded medium. Interestingly, we show that a rapid positive-to-negative temporal change of {\epsilon}(t) causes the propagation of the wave to stop (observed by a frozen phase in time) while the amplitude of the frozen field exponentially grows. Stepping the permittivity back to the original (or a new) positive value will cause the wave to thaw and resume propagation with the original (or the new) frequency, respectively. We numerically study the case of dipole radiation in such time-varying non-Foster structures. As a possible implementation, we propose a parallel plate waveguide platform loaded with time-dependent media emulating parallel lumped non-Foster negative capacitors. Such non-Foster time-varying structures may open new venues in controlling and manipulating wave-matter interaction., Comment: 51 pages (20 pages of main text and 31 pages of supplementary materials), 21 figures (6 figures in the main text and 15 figures in the supplementary materials)

Under the U.S. High Luminosity LHC Accelerator Upgrade Project (HL-LHC AUP), the 150 mm bore, high-field Nb3Sn low-\b{eta} MQXFA quadrupole magnets are being fabricated, assembled and tested, in the context of the CERN Hi-Luminosity LHC (HL-LHC) upgrade. These magnets have 4.2 m magnetic length and 4.56 m long iron yoke. To date, eight MQXFA magnets have been tested. One of the magnets additionally underwent a successful endurance test with 40 triggered quenches, and two magnets did not perform as expected. This work summarizes for the first time the available strain gauge data from eight identical Nb3Sn MQXFA tested magnets, focusing on the endurance test, and on a possible cause of underperformance of the two magnets that did not pass the vertical test. We applied methods to prevent this from happening in future MQXFA magnets, which shown to be effective for last two tested magnets.

Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dynamics. As these random-effects substitution models often require many more parameters than their usual counterparts, inference can be both statistically and computationally challenging. Thus, we also propose an efficient approach to compute an approximation to the gradient of the data likelihood with respect to all unknown substitution model parameters. We demonstrate that this approximate gradient enables scaling of sampling-based inference, namely Bayesian inference via Hamiltonian Monte Carlo, under random-effects substitution models across large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY model with random-effects shows strong signals of nonreversibility in the substitution process, and posterior predictive model checks clearly show that it is a more adequate model than a reversible model. When analyzing the pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences between 14 regions, a random-effects phylogeographic substitution model infers that air travel volume adequately predicts almost all dispersal rates. A random-effects state-dependent substitution model reveals no evidence for an effect of arboreality on the swimming mode in the tree frog subfamily Hylinae. Simulations reveal that random-effects substitution models can accommodate both negligible and radical departures from the underlying base substitution model. We show that our gradient-based inference approach is over an order of magnitude more time efficient than conventional approaches.

The increase in luminosity, and consequent higher backgrounds, of the LHC upgrades require improved rejection of fake tracks in the forward region of the ATLAS Muon Spectrometer. The New Small Wheel upgrade of the Muon Spectrometer aims to reduce the large background of fake triggers from track segments that are not originated from the interaction point. The New Small Wheel employs two detector technologies, the resistive strip Micromegas detectors and the "small" Thin Gap Chambers, with a total of 2.45 Million electrodes to be sensed. The two technologies require the design of a complex electronics system given that it consists of two different detector technologies and is required to provide both precision readout and a fast trigger. It will operate in a high background radiation region up to about 20 kHz/cm$^{2}$ at the expected HL-LHC luminosity of $\mathcal{L}$=7.5$\times10^{34}$cm$^{-2}$s$^{-1}$. The architecture of the system is strongly defined by the GBTx data aggregation ASIC, the newly-introduced FELIX data router and the software based data handler of the ATLAS detector. The electronics complex of this new detector was designed and developed in the last ten years and consists of multiple radiation tolerant Application Specific Integrated Circuits, multiple front-end boards, dense boards with FPGA's and purpose-built Trigger Processor boards within the ATCA standard. The New Small Wheel has been installed in 2021 and is undergoing integration within ATLAS for LHC Run 3. It should operate through the end of Run 4 (December 2032). In this manuscript, the overall design of the New Small Wheel electronics is presented., Comment: 61 pages

The aim of Predictive Maintenance, within the field of Prognostics and Health Management (PHM), is to identify and anticipate potential issues in the equipment before these become critical. The main challenge to be addressed is to assess the amount of time a piece of equipment will function effectively before it fails, which is known as Remaining Useful Life (RUL). Deep Learning (DL) models, such as Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) networks, have been widely adopted to address the task, with great success. However, it is well known that this kind of black box models are opaque decision systems, and it may be hard to explain its outputs to stakeholders (experts in the industrial equipment). Due to the large number of parameters that determine the behavior of these complex models, understanding the reasoning behind the predictions is challenging. This work presents a critical and comparative revision on a number of XAI methods applied on time series regression model for PM. The aim is to explore XAI methods within time series regression, which have been less studied than those for time series classification. The model used during the experimentation is a DCNN trained to predict the RUL of an aircraft engine. The methods are reviewed and compared using a set of metrics that quantifies a number of desirable properties that any XAI method should fulfill. The results show that GRAD-CAM is the most robust method, and that the best layer is not the bottom one, as is commonly seen within the context of Image Processing.

In this work we introduce the notion of constant angle null hypersurface of a Lorentzian manifold with respect to a given ambient vector field. We analyze the case in which the vector field is closed and conformal, thus finding that such null hypersurfaces have a canonical principal direction. We further provide some classification results for constant angle null surfaces with vanishing null mean curvature., Comment: 23 pages, 2 figures

Ferroics based on transition-metal (TM) substituted SrTiO$_{3}$ have called much attention as magnetism and/or ferroelectricity can be tuned by using cations substitution and defects, strain and/or oxygen deficiency. C. A. Ross et al. [Phys. Rev. Applied 7, 024006 (2017)] demonstrated the SrTi$_{1-x}$Fe$_{x}$O$_{3-\delta}$ (STF) magnetization behavior for different deposition oxygen-pressures, substrates and magnetic fields. The relation between oxygen deficiency and ferroic orders is yet to be well understood, for which the full potential of oxygen-stoichiometry engineered materials remain an open question. Here, we use hybrid-DFT to calculate different oxygen vacancy ($v_{o}$) states in STF with a variety of TM distributions. The resulting cations' magnetic states and alignments associated to the $v_{o}$ ground-states for $x=\{0.125,0.25\}$ are used within a Monte Carlo scope for collinear magnetism to simulate the spontaneous magnetization. Our model captures several experimental STF features i.e., display a maximum of the magnetization at intermediate number of vacancies, a monotonous quenching from $\sim{0.35}\mu{_{B}}$ for small ${\delta}$, and a slower decreasing of such saturation for larger number of vacancies. Moreover, our approach gives a further insight into the relations between defects stabilization and magnetization, vacancy density and the oxygen pressure required to maximize such ferroic order, and sets guidelines for future Machine Learning based computational synthesis of multiferroic oxides.

In this work we introduce the taxicab and uniform products for Lorentzian pre-length spaces. We further use these concepts to endow the space $D(R\times_T X)$ of causal diamonds with a Lorentzian length space structure, closely relating its causal properties with its geometry as a metric space furnished with its associated Hausdorff distance. Among the general results, we show that this space is geodesic and globally hyperbolic for complete $X$., Comment: 29 pages, 1 figure

The interplay between topological defects, such as dislocations or disclinations, and the electronic degrees of freedom in graphene has been extensively studied. In the literature, for the study of this kind of problems, it is in general used either a gauge theory or a curved spatial Riemannian geometry approach, where, in the geometric case, the information about the defects is contained in the metric and the spin-connection. However, these topological defects can also be associated to a Riemann-Cartan geometry where curvature and torsion plays an important role. In this article we study the interplay between a wedge dislocations in a planar graphene sheet and the properties of its electronic degrees of freedom. Our approach relies in its relation with elasticity theory through the so called elastic-gauge, where their typical coefficients, as for example the Poisson's ratio, appear directly in the metric, and consequently also in the electronic spectrum., Comment: 6 pages, v2 minor modifications

The purpose of this document is to define the specifications for the structure fabrication and assembly of MQXFA series magnets to be used by the US High-Luminosity LHC Accelerator Upgrade Project (AUP). Magnets fabricated according to these specifications are expected to allow MQXFA magnets to meet the MQXFA Functional Requirements Specification. These specifications for the fabrication of MQXFA series magnets are based on the R&D performed by the US LHC Accelerator Research Program (LARP) and the development performed by AUP, in collaboration with CERN, through fabrication and test of pre-series coils and magnets.

We present an open-source software pipeline to create patient-specific left atrial (LA) models with fibre orientations and a fibrosis map, suitable for electrophysiology simulations. The semi-automatic pipeline takes as input a contrast enhanced magnetic resonance angiogram, and a late gadolinium enhanced (LGE) contrast magnetic resonance (CMR). Five operators were allocated 20 cases each from a set of 50 CMR datasets to create a total of 100 models to evaluate inter/intra-operator variability. Each output model consisted of (1) a labelled surface mesh open at the pulmonary veins (PV) and mitral valve (MV), (2) fibre orientations mapped from a diffusion tensor MRI human atlas, (3) fibrosis map from the LGE-CMR scan, and (4) simulation of local activation time (LAT) and phase singularity (PS) mapping. We evaluated reproducibility in our pipeline by comparing agreement in shape of the output meshes, fibrosis distribution in the LA body, and fibre orientations; simulations outputs were evaluated comparing total activation times of LAT maps, mean conduction velocity (CV), and structural similarity index measure (SSIM) of PS maps. Our workflow allows a single model to be created in 16.72 +/- 12.25 minutes. Results in this abstract are reported as inter/intra. Shape only differed noticeably with users' selection of the MV and the length of the PV from the ostia to the distal end; fibrosis agreement (0.91/0.99 ICC) and fibre orientation agreement (60.63/71.77 %) were high. LAT maps showed good agreement, the median of the absolute difference of the total activation times was 2.02ms/1.37ms. The average of the mean CV difference was -4.04mm/s / 2.1mm/s. PS maps showed a moderately good agreement with SSIM of 0.648/0.608. Although we found notable differences in the models due to user input, our tests show that operator variability was comparable to that of image resolution or fibre estimation., Comment: 17 pages, 7 figures, submitted for review at Journal of Computers in Biology and Medicine (in press)

Despite continued advancement in recent years, deep neural networks still rely on large amounts of training data to avoid overfitting. However, labeled training data for real-world applications such as healthcare is limited and difficult to access given longstanding privacy, and strict data sharing policies. By manipulating image datasets in the pixel or feature space, existing data augmentation techniques represent one of the effective ways to improve the quantity and diversity of training data. Here, we look to advance augmentation techniques by building upon the emerging success of text-to-image diffusion probabilistic models in augmenting the training samples of our macroscopic skin disease dataset. We do so by enabling fine-grained control of the image generation process via input text prompts. We demonstrate that this generative data augmentation approach successfully maintains a similar classification accuracy of the visual classifier even when trained on a fully synthetic skin disease dataset. Similar to recent applications of generative models, our study suggests that diffusion models are indeed effective in generating high-quality skin images that do not sacrifice the classifier performance, and can improve the augmentation of training datasets after curation.

Our Microbiome Network Alignment Algorithm (MiNAA) aligns two microbial networks using a combination of the GRAph ALigner (GRAAL) algorithm and the Hungarian algorithm. Network alignment algorithms find pairs of nodes (one node from the first network and the other node from the second network) that have the highest similarity. Traditionally, similarity has been defined as topological similarity such that the neighborhoods around the two nodes are similar. Recent implementations of network alignment methods such as NETAL and L-GRAAL also include measures of biological similarity, yet these methods are restricted to one specific type of biological similarity (e.g. sequence similarity in L-GRAAL). Our work extends existing network alignment implementations by allowing any type of biological similarity to be input by the user. This flexibility allows the user to choose whatever measure of biological similarity is suitable for the study at hand. In addition, unlike most existing network alignment methods that are tailored for protein or gene interaction networks, our work is the first one suited for microbiome networks.

The ALICE-USA collaboration presents its plans for the 2023 U.S. Long Range Plan for Nuclear Science., Comment: 26 pages. 1 figure