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Hyper-trabeculation or non-compaction in the left ventricle of the myocardium (LVNC) is a recently classified form of cardiomyopathy. Several methods have been proposed to quantify the trabeculae accurately in the left ventricle, but there is no general agreement in the medical community to use a particular approach. In previous work, we proposed DL-LVTQ, a deep learning approach for left ventricular trabecular quantification based on a U-Net CNN architecture. DL-LVTQ was an automatic diagnosis tool developed from a dataset of patients with the same cardiomyopathy (hypertrophic cardiomyopathy). In this work, we have extended and adapted DL-LVTQ to cope with patients with different cardiomyopathies. The dataset consists of up 379 patients in three groups with different particularities and cardiomyopathies. Patient images were taken from different scanners and hospitals. We have modified and adapted the U-Net convolutional neural network to account for the different particularities of a heterogeneous group of patients with various unclassifiable or mixed and inherited cardiomyopathies. The inclusion of new groups of patients has increased the accuracy, specificity and kappa values while maintaining the sensitivity of the automatic deep learning method proposed. Therefore, a better-prepared diagnosis tool is ready for various cardiomyopathies with different characteristics. Cardiologists have considered that 98.9% of the evaluated outputs are verified clinically for diagnosis. Therefore, the high precision to segment the different cardiac structures allows us to make a robust diagnostic system objective and faster, decreasing human error and time spent.

The transport of biomolecules, drugs, or reactants encapsulated inside stimuli-responsive polymer networks in aqueous media is fundamental for many material and environmental science applications, including drug delivery, biosensing, catalysis, nanofiltration, water purification, and desalination. The transport is particularly complex in dense polymer media, such as collapsed hydrogels, where the molecules strongly interact with the polymer network and diffuse via a hopping mechanism. In this study, we employ Dynamical Density Functional Theory (DDFT) to investigate the non-equilibrium release kinetics of non-ionic subnanometer-sized molecules initially uploaded inside collapsed microgel particles. The theory is consistent with previous molecular dynamics simulations of collapsed poly($N$-isopropylacrylamide) (PNIPAM) polymer matrices, accommodating molecules of varying shapes and sizes. We found that, despite the intricate physico-chemical properties involved in the released process, the kinetics is predominantly dictated by two material parameters: the diffusion coefficient of the molecules inside the microgel ($D^*$) and the interaction free energy of the molecules with the microgel ($\Delta G$). Our results reveal two distinct limiting regimes: For large, slowly diffusing molecules weakly attracted to the polymer network, the release is primarily driven by diffusion, with a release time that scales as $\tau_{1/2} \sim 1/D^*$. Conversely, for small molecules strongly attracted to the polymer network, the release time is dominated by the interaction, scaling as $\tau_{1/2} \sim \exp(-\Delta G/k_{\textrm{B}} T)$. Our DDFT calculations are directly compared with an analytical equation for the half-release time, demonstrating excellent quantitative agreement. This equation represents a valuable tool for predicting release kinetics of non-ionic molecules from collapsed microgels., Comment: 15 pages, 6 figures

Nowadays, the applications of hydraulic systems are present in a wide variety of devices in both industrial and everyday environments. The implementation and usage of hydraulic systems have been well documented; however, today, this still faces a challenge, the integration of tools that allow more accurate information about the functioning and operation of these systems for proactive decision-making. In industrial applications, many sensors and methods exist to measure and determine the status of process variables (e.g., flow, pressure, force). Nevertheless, little has been done to have systems that can provide users with device-health information related to hydraulic devices integrated into the machinery. Implementing artificial intelligence (AI) technologies and machine learning (ML) models in hydraulic system components has been identified as a solution to the challenge many industries currently face: optimizing processes and carrying them out more safely and efficiently. This paper presents a solution for the characterization and estimation of anomalies in one of the most versatile and used devices in hydraulic systems, cylinders. AI and ML models were implemented to determine the current operating status of these hydraulic components and whether they are working correctly or if a failure mode or abnormal condition is present., Comment: This article has been removed by arXiv administrators because the submitter did not have the authority to grant a license at the time of submission

Flux-tunable qubits are a useful resource for superconducting quantum processors. They can be used to perform cPhase gates, facilitate fast reset protocols, avoid qubit-frequency collisions in large processors, and enable certain fast readout schemes. However, flux-tunable qubits suffer from a trade-off between their tunability range and sensitivity to flux noise. Optimizing this trade-off is particularly important for enabling fast, high-fidelity, all-microwave cross-resonance gates in large, high-coherence processors. This is mainly because cross-resonance gates set stringent conditions on the frequency landscape of neighboring qubits, which are difficult to satisfy with non-tunable transmons due to their relatively large fabrication imprecision. To solve this problem, we realize a coherent, flux-tunable, transmon-like qubit, which exhibits a frequency tunability range as small as 43 MHz, and whose frequency, anharmonicity and tunability range are set by a few experimentally achievable design parameters. Such a weakly tunable qubit is useful for avoiding frequency collisions in a large lattice while limiting its susceptibility to flux noise.

Ocean waves provide a consistent, reliable source of clean energy making them a viable energy source for desalination. Ocean wave energy is useful to coastal communities, especially island nations. However, large capital costs render current wave-powered desalination technologies economically infeasible. This work presents a high efficiency configuration for ocean wave energy powering batch reverse osmosis. The proposed system uses seawater as the working fluid in a hydro-mechanical wave energy converter and replaces the reverse osmosis high-pressure pump with a hydraulic converter for direct-drive coupling. This allows for minimal intermediary power conversions, fewer components, and higher efficiencies. The concept was analyzed with MATLAB to model the transient energy dynamics of the wave energy converter, power take-off system, and desalination load. The fully hydro-mechanical coupling, incorporating energy recovery, could achieve an SEC and LCOW as low as 2.30 kWh/m3 and $1.96, respectively, for different sea states. The results were validated at the sub-system level against existing literature on wave energy models and previous work completed on batch reverse osmosis models, as this system was the first to combine these two technologies. SEC and LCOW values were validated by comparing to known and predicted values for various types of RO systems., Comment: 23 pages, 10 figures

Left ventricular non-compaction (LVNC) is a rare cardiomyopathy characterized by abnormal trabeculations in the left ventricle cavity. Although traditional computer vision approaches exist for LVNC diagnosis, deep learning-based tools could not be found in the literature. In this paper, a first approach using convolutional neural networks (CNNs) is presented. Four CNNs are trained to automatically segment the compacted and trabecular areas of the left ventricle for a population of patients diagnosed with Hypertrophic cardiomyopathy. Inference results confirm that deep learning-based approaches can achieve excellent results in the diagnosis and measurement of LVNC. The two best CNNs (U-Net and Efficient U-Net B1) perform image segmentation in less than 0.2 s on a CPU and in less than 0.01 s on a GPU. Additionally, a subjective evaluation of the output images with the identified zones is performed by expert cardiologists, with a perfect visual agreement for all the slices, outperforming already existing automatic tools.

We propose a 3D neural network with specific loss functions for quantitative computed tomography (QCT) noise reduction to compute micro-structural parameters such as tissue mineral density (TMD) and bone volume ratio (BV/TV) with significantly higher accuracy than using no or standard noise reduction filters. The vertebra-phantom study contained high resolution peripheral and clinical CT scans with simulated in vivo CT noise and nine repetitions of three different tube currents (100, 250 and 360 mAs). Five-fold cross validation was performed on 20466 purely spongy pairs of noisy and ground-truth patches. Comparison of training and test errors revealed high robustness against over-fitting. While not showing effects for the assessment of BMD and voxel-wise densities, the filter improved thoroughly the computation of TMD and BV/TV with respect to the unfiltered data. Root-mean-square and accuracy errors of low resolution TMD and BV/TV decreased to less than 17% of the initial values. Furthermore filtered low resolution scans revealed still more TMD- and BV/TV-relevant information than high resolution CT scans, either unfiltered or filtered with two state-of-the-art standard denoising methods. The proposed architecture is threshold and rotational invariant, applicable on a wide range of image resolutions at once, and likely serves for an accurate computation of further micro-structural parameters. Furthermore, it is less prone for over-fitting than neural networks that compute structural parameters directly. In conclusion, the method is potentially important for the diagnosis of osteoporosis and other bone diseases since it allows to assess relevant 3D micro-structural information from standard low exposure CT protocols such as 100 mAs and 120 kVp., Comment: A revised version of this manuscript was accepted for publication in Computerized Medical Imaging and Graphics

The integration of semiconductor Josephson junctions (JJs) in superconducting quantum circuits provides a versatile platform for hybrid qubits and offers a powerful way to probe exotic quasiparticle excitations. Recent proposals for using circuit quantum electrodynamics (cQED) to detect topological superconductivity motivate the integration of novel topological materials in such circuits. Here, we report on the realization of superconducting transmon qubits implemented with $(Bi_{0.06}Sb_{0.94})_{2}Te_{3}$ topological insulator (TI) JJs using ultra-high vacuum fabrication techniques. Microwave losses on our substrates with monolithically integrated hardmask, used for selective area growth of TI nanostructures, imply microsecond limits to relaxation times and thus their compatibility with strong-coupling cQED. We use the cavity-qubit interaction to show that the Josephson energy of TI-based transmons scales with their JJ dimensions and demonstrate qubit control as well as temporal quantum coherence. Our results pave the way for advanced investigations of topological materials in both novel Josephson and topological qubits., Comment: A second experimental run allowed for time-domain measurements of a TI-based transmon qubit. In the updated manuscript additional data on qubit control and coherence are included. Taking account of these new results, the focus and the title of the manuscript have been reworked, 14 pages, 10 figures

NELL is a system that continuously reads the Web to extract knowledge in form of entities and relations between them. It has been running since January 2010 and extracted over 50,000,000 candidate statements. NELL's generated data comprises all the candidate statements together with detailed information about how it was generated. This information includes how each component of the system contributed to the extraction of the statement, as well as when that happened and how confident the system is in the veracity of the statement. However, the data is only available in an ad hoc CSV format that makes it difficult to exploit out of the context of NELL. In order to make it more usable for other communities, we adopt Linked Data principles to publish a more standardized, self-describing dataset with rich provenance metadata.

We address the problem of providing contextual information about a logical formula (eg provenance, date of validity, or confidence) and representing it within a logical system. In this case, it is needed to rely on a higher order or non standard formalism, or some kind of reification mechanism. We explore the case of reification and formalize the concept of contextualizing logical statements in the case of Description Logics. Then, we define several properties of contextualization that are desirable. No previous approaches satisfy all of the them. Consequently, we define a new way of contextually annotating statements. It is inspired by NdFluents, which is itself an extension of the 4dFluents approach for annotating statements with temporal context. In NdFluents, instances that are involved in a contextual statement are sliced into contextual parts, such that only parts in the same context hold relations to one another, with the goal of better preserving inferences. We generalize this idea by defining contextual parts of relations and classes. This formal construction better satisfies the properties, although not entirely. We show that it is a particular case of a general mechanism that NdFluents also instantiates, and present other variations.

Annotating semantic data with metadata is becoming more and more important to provide information about the statements being asserted. While initial solutions proposed a data model to represent a specific dimension of meta-information (such as time or provenance), the need for a general annotation framework which allows representing different context dimensions is needed. In this paper, we extend the 4dFluents ontology by Welty and Fikes---on associating temporal validity to statements---to any dimension of context, and discuss possible issues that multidimensional context representations have to face and how we address them.

Josephson parametric converters (JPCs) are superconducting devices capable of performing nondegenerate, three-wave mixing in the microwave domain without losses. One drawback limiting their use in scalable quantum architectures is the large footprint of the auxiliary circuit needed for their operation, in particular, the use of off-chip, bulky, broadband hybrids and magnetic coils. Here, we realize a JPC which eliminates the need for these bulky components. The pump drive and flux bias are applied in the new device through an on-chip, lossless, three-port power divider and on-chip flux line, respectively. We show that the new design considerably simplifies the circuit and reduces the footprint of the device while maintaining a comparable performance to state-of-the-art JPCs. Furthermore, we exploit the tunable bandwidth property of the JPC and the added capability of applying alternating currents to the flux line in order to switch the resonance frequencies of the device, hence demonstrating time-multiplexed amplification of microwave tones that are separated by more than the dynamical bandwidth of the amplifier. Such a measurement technique can potentially serve to perform time-multiplexed, high-fidelity readout of superconducting qubits.