Sex Differences in White Matter Structure in Attention Deficit Hyperactivity Disorder: MR Diffusion Fixel-Based Analysis.

Background And Purpose: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder prevalent among adolescents and exhibits notable sex dimorphism. Despite an increasing body of research, the impact of sex on ADHD remains underexplored. This study aimed to examine sex differences in white matter organization in children with ADHD using magnetic resonance (MR) diffusion data analyzed with fixel-based analysis (FBA), a novel technique that enables detailed assessment of both microstructural and macrostructural properties of white matter.

Spin-qubit control with a milli-kelvin CMOS chip.

A key virtue of spin qubits is their sub-micron footprint, enabling a single silicon chip to host the millions of qubits required to execute useful quantum algorithms with error correction. However, with each physical qubit needing multiple control lines, a fundamental barrier to scale is the extreme density of connections that bridge quantum devices to their external control and readout hardware. A promising solution is to co-locate the control system proximal to the qubit platform at milli-kelvin temperatures, wired up by miniaturized interconnects.

A 2 × 2 Quantum Dot Array in Silicon with Fully Tunable Pairwise Interdot Coupling.

Recent advances in semiconductor spin qubits have enabled linear arrays with more than 10 qubits. Scaling to two-dimensional (2D) arrays is essential for fault-tolerant implementations but introduces significant fabrication challenges due to the increased density of the gate electrodes. Moreover, implementing two-qubit entanglement control requires the addition of interstitial exchange gates between quantum dots in this dense gate structure.

Photoenhanced Field-Emission Nanoscale Air Channel Devices with High Temperature Tolerance for On-Chip Terahertz Generation.

Nanoscale air channel devices (NACDs), characterized by scattering-free ballistic electron transport in a quasi-vacuum channel, provide a transformative platform for vacuum electronics and nanoelectronics. However, enabling NACDs for high-frequency operation is still a great challenge due to the low field-emission current and high impedance. Herein, photoenhanced NACDs are demonstrated as high-temperature-resistant photomixers capable of generating coherent terahertz (THz) signals from 120 to 260 GHz, offering the successful experimental demonstration of NACDs operating beyond 100 GHz.

A new compression strategy to reduce the size of nanopore sequencing data.

Nanopore sequencing is an increasingly central tool for genomics. Despite rapid advances in the field, large data volumes and computational bottlenecks continue to pose major challenges. Here, we introduce ex-zd, a new data compression strategy that helps address the large size of raw signal data generated during nanopore experiments.

Bell inequality violation in gate-defined quantum dots.

Quantum computers leverage entanglement to achieve superior computational power. However, verifying that the entangled state does not follow the principle of local causality has proven difficult for spin qubits in gate-defined quantum dots, as it requires simultaneously high concurrence values and readout fidelities to break the classical bound imposed by Bell's inequality. While low error rates for state preparation, control, and measurement have been independently demonstrated, a simultaneous demonstration remained challenging.

Wavelet correlation noise analysis for qubit operation variable time series.

In quantum computing, characterizing the full noise profile of qubits can aid in increasing coherence times and fidelities by developing error-mitigating techniques specific to the noise present. This characterization also supports efforts in advancing device fabrication to remove sources of noise. Qubit properties can be subject to non-trivial correlations in space and time, for example, spin qubits in MOS quantum dots are exposed to noise originating from the complex glassy behavior of two-level fluctuator ensembles.

Stochastic reservoir computers.

Reservoir computing is a form of machine learning that utilizes nonlinear dynamical systems to perform complex tasks in a cost-effective manner when compared to typical neural networks. Recent advancements in reservoir computing, in particular quantum reservoir computing, use reservoirs that are inherently stochastic. In this paper, we investigate the universality of stochastic reservoir computers which use the probabilities of each stochastic reservoir state as the readout instead of the states themselves.

Optimization of Decision Support Technology for Offshore Oil Condition Monitoring with Carbon Neutrality as the Goal in the Enterprise Development Process.

This study aims to explore the integration of the Faster R-CNN (Region-based Convolutional Neural Network) algorithm from deep learning into the MobileNet v2 architecture, within the context of enterprises aiming for carbon neutrality in their development process. The experiment develops a marine oil condition monitoring and classification model based on the fusion of MobileNet v2 and Faster R-CNN algorithms. This model utilizes the MobileNet v2 network to extract rich feature information from input images and combines the Faster R-CNN algorithm to rapidly and accurately generate candidate regions for oil condition monitoring, followed by detailed feature fusion and classification of these regions.

Rationale and design of the TeleClinical Care Cardiac (TCC-Cardiac) trial: A pragmatic randomized trial of adjunctive virtual models of care in the secondary prevention of cardiovascular events.

Background: Digital health interventions have potential to improve outcomes in high risk cardiac patients through remote monitoring and patient education but introduce accessibility issues among patients who lack suitable smartphones. We will evaluate the effectiveness and scalability of the TeleClinical Care Cardiac (TCCCardiac) platform, that aims to reduce hospital readmissions and improve adherence to care.

Methods: A pragmatic, all-comers trial with nested randomization, where patients being discharged home following an admission with acute myocardial infarction (MI) or decompensated heart failure (HF) are divided into 3 cohorts pragmatically, based on their access to technology.

Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.

Background: Tuberculosis (TB) remains a significant health concern, contributing to the highest mortality among infectious diseases worldwide. However, none of the various TB diagnostic tools introduced is deemed sufficient on its own for the diagnostic pathway, so various artificial intelligence (AI)-based methods have been developed to address this issue.

Objective: We aimed to provide a comprehensive evaluation of AI-based algorithms for TB detection across various data modalities.

Glaucoma detection and staging from visual field images using machine learning techniques.

Purpose: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters.

Methods: A total of 265 PD plots and 265 numerical datasets of Humphrey 24-2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma.

Improving the performance of echo state networks through state feedback.

Reservoir computing, using nonlinear dynamical systems, offers a cost-effective alternative to neural networks for complex tasks involving processing of sequential data, time series modeling, and system identification. Echo state networks (ESNs), a type of reservoir computer, mirror neural networks but simplify training. They apply fixed, random linear transformations to the internal state, followed by nonlinear changes.

Contrastive Clustering-Based Patient Normalization to Improve Automated In Vivo Oral Cancer Diagnosis from Multispectral Autofluorescence Lifetime Images.

Multispectral autofluorescence lifetime imaging systems have recently been developed to quickly and non-invasively assess tissue properties for applications in oral cancer diagnosis. As a non-traditional imaging modality, the autofluorescence signal collected from the system cannot be directly visually assessed by a clinician and a model is needed to generate a diagnosis for each image. However, training a deep learning model from scratch on small multispectral autofluorescence datasets can fail due to inter-patient variability, poor initialization, and overfitting.

Efficient second-harmonic generation of quasi-bound states in the continuum in lithium niobate thin film enhanced by Bloch surface waves.

Nonlinear optics has generated a wide range of applications in the fields of optical communications, biomedicine, and materials science, with nonlinear conversion efficiency serving as a vital metric for its progress. However, the weak nonlinear response of materials, high optical loss, and inhomogeneous distribution of the light field hamper the improvement of the conversion efficiency. We present a composite grating waveguide structure integrated into a Bragg reflector platform.

Computer-assisted discrimination of cancerous and pre-cancerous from benign oral lesions based on multispectral autofluorescence imaging endoscopy.

Significance: Diagnosis of cancerous and pre-cancerous oral lesions at early stages is critical for the improvement of patient care, to increase survival rates and minimize the invasiveness of tumor resection surgery. Unfortunately, oral precancerous and early-stage cancerous lesions are often difficult to distinguish from oral benign lesions with the existing diagnostic tools used during standard clinical oral examination. In consequence, early diagnosis of oral cancer can be achieved in only about 30% of patients.

Tomography of entangling two-qubit logic operations in exchange-coupled donor electron spin qubits.

Scalable quantum processors require high-fidelity universal quantum logic operations in a manufacturable physical platform. Donors in silicon provide atomic size, excellent quantum coherence and compatibility with standard semiconductor processing, but no entanglement between donor-bound electron spins has been demonstrated to date. Here we present the experimental demonstration and tomography of universal one- and two-qubit gates in a system of two weakly exchange-coupled electrons, bound to single phosphorus donors introduced in silicon by ion implantation.

Entangling gates on degenerate spin qubits dressed by a global field.

Semiconductor spin qubits represent a promising platform for future large-scale quantum computers owing to their excellent qubit performance, as well as the ability to leverage the mature semiconductor manufacturing industry for scaling up. Individual qubit control, however, commonly relies on spectral selectivity, where individual microwave signals of distinct frequencies are used to address each qubit. As quantum processors scale up, this approach will suffer from frequency crowding, control signal interference and unfeasible bandwidth requirements.

Scalable Atomic Arrays for Spin-Based Quantum Computers in Silicon.

Semiconductor spin qubits combine excellent quantum performance with the prospect of manufacturing quantum devices using industry-standard metal-oxide-semiconductor (MOS) processes. This applies also to ion-implanted donor spins, which further afford exceptional coherence times and large Hilbert space dimension in their nuclear spin. Here multiple strategies are demonstrated and integrated to manufacture scale-up donor-based quantum computers.

Membrane capacitive deionization (MCDI): A flexible and tunable technology for customized water softening.

There is a growing demand for water treatment systems for which the quality of feedwater in and product water out are not necessarily fixed with "tunable" technologies essential in many instances to satisfy the unique requirements of particular end-users. For example, in household applications, the optimal water hardness differs for particular end uses of the supplied product (such as water for potable purposes, water for hydration, or water for coffee or tea brewing) with the inclusion of specific minerals enhancing the suitability of the product in each case. However, conventional softening technologies are not dynamically flexible or tunable and, typically, simply remove all hardness ions from the feedwater.

Bounds to electron spin qubit variability for scalable CMOS architectures.

Spins of electrons in silicon MOS quantum dots combine exquisite quantum properties and scalable fabrication. In the age of quantum technology, however, the metrics that crowned Si/SiO as the microelectronics standard need to be reassessed with respect to their impact upon qubit performance. We chart spin qubit variability due to the unavoidable atomic-scale roughness of the Si/SiO interface, compiling experiments across 12 devices, and develop theoretical tools to analyse these results.

Strong microwave squeezing above 1 Tesla and 1 Kelvin.

Squeezed states of light have been used extensively to increase the precision of measurements, from the detection of gravitational waves to the search for dark matter. In the optical domain, high levels of vacuum noise squeezing are possible due to the availability of low loss optical components and high-performance squeezers. At microwave frequencies, however, limitations of the squeezing devices and the high insertion loss of microwave components make squeezing vacuum noise an exceptionally difficult task.

Latched detection of zeptojoule spin echoes with a kinetic inductance parametric oscillator.

When strongly pumped at twice their resonant frequency, nonlinear resonators develop a high-amplitude intracavity field, a phenomenon known as parametric self-oscillations. The boundary over which this instability occurs can be extremely sharp and thereby presents an opportunity for realizing a detector. Here, we operate such a device based on a superconducting microwave resonator whose nonlinearity is engineered from kinetic inductance.