Gate Connection Research Articles

A deep learning-based image analysis for assessing the extent of abduction in abducens nerve palsy patients before and after strabismus surgery

PurposeThis study aimed to propose a novel deep learning-based approach to assess the extent of abduction in patients with abducens nerve palsy before and after strabismus surgery. MethodsThis study included 13 patients who were diagnosed with abducens nerve palsy and underwent strabismus surgery in a tertiary hospital. Photographs of primary, dextroversion and levoversion position were collected before and after strabismus surgery. The eye location and eye segmentation network were trained via recurrent residual convolutional neural networks with attention gate connection based on U-Net (R2AU-Net). Facial images of abducens nerve palsy patients were used as the test set and parameters were measured automatically based on the masked images. Absolute abduction also was measured manually, and relative abduction was calculated. Agreements between manual and automatic measurements, as well as repeated automatic measurements were analyzed. Preoperative and postoperative results were compared. ResultsThe intraclass correlation coefficients (ICCs) between manual and automatic measurements of absolute abduction ranged from 0.985 to 0.992 (P＜0.001), and the bias ranged from −0.25 ​mm to −0.05 ​mm. The ICCs between two repeated automatic measurements ranged from 0.994 to 0.997 (P＜0.001), and the bias ranged from −0.11 ​mm to 0.05 ​mm. After strabismus surgery, absolute abduction of affected eye increased from 2.18 ​± ​1.40 ​mm to 3.36 ​± ​1.93 ​mm (P＜0.05). The relative abduction was improved in 76.9% patients (10/13) after surgery (P＜0.01). ConclusionsThis image analysis technique demonstrated excellent accuracy and repeatability for automatic measurements of ocular abduction, which has promising application prospects in objectively assessing surgical outcomes in patients with abducens nerve palsy.

Keep on moving: Optimized placement of moving walkways in airport terminals

Since its inception in 1955, when Delta Airlines pioneered the hub-and-spoke system at their Atlanta hub, airports have been engaged in fierce competition to ensure efficient and dependable transfers for passengers and airlines between connecting flights. In line with this overarching objective, we present a long-term layout design problem that focuses on optimizing the placement of moving walkways within the corridors of an airport terminal. Our primary goal is to facilitate faster transfers on these walkways, thereby improving gate connectivity. To achieve this, we propose the following budget-constrained optimization task: Maximize the weighted number of gate pairs that passengers can transfer between, without exceeding the specified minimum connecting time for each gate pair. We mathematically formulate the resulting optimization problem and provide a comprehensive analysis of its computational complexity. Additionally, we introduce suitable solution methods to tackle this challenging optimization task. Through a comprehensive computational study, using real-world terminal data, we evaluate the performance of our solution approaches. Our results demonstrate that optimized walkway layouts significantly contribute to reliable connecting processes during daily airport operations.

Shallow unitary decompositions of quantum Fredkin and Toffoli gates for connectivity-aware equivalent circuit averaging

The controlled-swap and controlled-controlled-not gates are at the heart of the original proposal of reversible classical computation by Fredkin and Toffoli. Their widespread use in quantum computation, both in the implementation of classical logic subroutines of quantum algorithms and in quantum schemes with no direct classical counterparts, has made it imperative early on to pursue their efficient decomposition in terms of the lower-level gate sets native to different physical platforms. Here, we add to this body of literature by providing several logically equivalent circuits for the Toffoli and Fredkin gates under all-to-all and linear qubit connectivity, the latter with two different routings for control and target qubits. Besides achieving the lowest cnot counts in the literature for all these configurations, we also demonstrate the remarkable effectiveness of the obtained decompositions at mitigating coherent errors on near-term quantum computers via equivalent circuit averaging. We first quantify the performance of the method in silico with a coherent-noise model before validating it experimentally on a superconducting quantum processor. In addition, we consider the case where the three qubits on which the Toffoli or Fredkin gates act nontrivially are not adjacent, proposing a novel scheme to reorder them that saves one cnot for every swap. This scheme also finds use in the shallow implementation of long-range cnots. Our results highlight the importance of considering different entangling gate structures and connectivity constraints when designing efficient quantum circuits.

Computationally efficient approach for risk-informed decision making

Probabilistic risk assessment (PRA) is used as an essential tool for risk-informed decision-making in the nuclear industry. The fault and event trees play a crucial role in PRA to estimate the probability of system failure based on the failure probabilities of components. The fault trees or event trees for an actual power plant unit can be fairly large in size with several different types of logic gates, interconnected events, dependent events, etc. A large fault tree can include hundreds of gates, basic events (BEs), multiple occurring events (MOEs), and dependent events. Complex connectivities can give rise to excessive computational demand and storage requirements for the analysis. Fault and event trees can be solved using the minimal cut-set approaches, or advanced quantification techniques such as Binary decision diagrams or Bayesian networks. However, these techniques can be computationally inefficient for larger fault trees and can run out of memory/storage space. This study focuses on developing and proposing a new approach for accurate estimation of the system-level risk while improving the computational efficiency significantly. More specifically, an attempt is made to reduce the complexity of the analysis of MOEs and dependent events in fault trees. The proposed algorithms in this study present a significant improvement over traditional approaches which makes it highly promising for additional development. The computational efficiency of the proposed approach over the traditional approach is illustrated for fault trees with a varying number of events and different types of logic gate connections.

High-fidelity parallel entangling gates on a neutral-atom quantum computer

The ability to perform entangling quantum operations with low error rates in a scalable fashion is a central element of useful quantum information processing1. Neutral-atom arrays have recently emerged as a promising quantum computing platform, featuring coherent control over hundreds of qubits2,3 and any-to-any gate connectivity in a flexible, dynamically reconfigurable architecture4. The main outstanding challenge has been to reduce errors in entangling operations mediated through Rydberg interactions5. Here we report the realization of two-qubit entangling gates with 99.5% fidelity on up to 60 atoms in parallel, surpassing the surface-code threshold for error correction6,7. Our method uses fast, single-pulse gates based on optimal control8, atomic dark states to reduce scattering9 and improvements to Rydberg excitation and atom cooling. We benchmark fidelity using several methods based on repeated gate applications10,11, characterize the physical error sources and outline future improvements. Finally, we generalize our method to design entangling gates involving a higher number of qubits, which we demonstrate by realizing low-error three-qubit gates12,13. By enabling high-fidelity operation in a scalable, highly connected system, these advances lay the groundwork for large-scale implementation of quantum algorithms14, error-corrected circuits7 and digital simulations15.

FCAE-AD: Full Convolutional Autoencoder Based on Attention Gate for Hyperspectral Anomaly Detection

Recently, the methods based on the autoencoder reconstruction background have been applied to the area of hyperspectral image (HSI) anomaly detection (HSI-AD). However, the encoding mechanism of the autoencoder (AE) makes it possible to treat the anomaly and the background indistinguishably during reconstruction, which can result in a small number of anomalous pixels still being included in the acquired reconstruction background. In addition, the problem of redundant information in HSIs also exists in reconstruction errors. To this end, a fully convolutional AE hyperspectral anomaly detection (AD) network with an attention gate (AG) connection is proposed. First, the low-dimensional feature map as a product of the encoder and the fine feature map as a product of the corresponding decoding stage are simultaneously input into the AG module. The network context information is used to suppress the irrelevant regions in the input image and obtain the significant feature map. Then, the features from the AG and the deep features from upsampling are efficiently combined in the decoder stage based on the skip connection to gradually estimate the reconstructed background image. Finally, post-processing optimization based on guided filtering (GF) is carried out on the reconstruction error to eliminate the wrong anomalous pixels in the reconstruction error image and amplify the contrast between the anomaly and the background.

High performance nanoscale SOI MOSFET with enhanced gate control

A nanoscale SOI MOSFET structure with enhanced gate control is provided in this article. The gate is positioned in the silicon region's center and surrounded by insulating HfO2 and SiO2. Using this structure, we attempt to enhance the gate control over the channel. Furthermore, the source connection is positioned horizontally within the silicon region to generate an electric field caused by the potential difference between the gate and the source junction in the channel region. In fact, in addition to enhanced control of the gate over the channel, the idea of utilizing gate and source connections within the silicon region has led to the maximum electron temperature shifting from the channel to the drain region. Also, generating an undoped silicon region has been feasible to lower the lattice temperature. In addition, the electrical characteristics of the proposed MOSFET in both DC and AC states will be improved. The electrical features such as effective mobility, hot electron effect, electric field, and self-heating effect have been improved. The proposed structure is designed for RF applications and is studied in the 1 MHz to 500 GHz range. The frequency characteristics of the proposed structure, like the gate-source and gate-drain capacitors, S parameters, maximum unilateral power gain, transducer power gain, minimum noise figure, and noise conductance have also been enhanced.

Modified UNet with attention gate and dense skip connection for flow field information prediction with porous media

Porous media flow phenomena are commonly found in nature and industry. Understanding the patterns can improve industrial production and efficiency. Traditional numerical methods can predict flow field information within porous media exactly, even on pore scale. However, once the pore structures of porous media are changed, the traditional numerical methods have to start over again, which implies poor extension and low efficiency for realistic application. To address the challenge, we propose a new deep learning method for image segmentation based on convolutional neural networks that can quickly predict the saturation variation and flow velocity profile of multiphase flow directly from the structure of porous media. In the present method, the UNet network structure is improved by replacing parallel splicing with different levels of dense skip connection to extract flow information at different depths. When processing flow topological information, the introduction of attention mechanism allows effective focus on adjusting the flow field edges and front shapes to improve accuracy. Depending on the pore structures information, the saturation of the porous media also can be evaluated. This result shows that the flow field information (saturation and velocity) of porous media can be quickly predicted by deep learning methods. Compared with the computational fluid dynamics (CFD) method, this approach has a better prediction efficiency for porous media. The machine learning method is a good prediction tool for porous media with different porosities and pore structures.

Quantum computational advantage attested by nonlocal games with the cyclic cluster state

We propose a set of Bell-type nonlocal games that can be used to prove an unconditional quantum advantage in an objective and hardware-agnostic manner. In these games, the circuit depth needed to prepare a cyclic cluster state and measure a subset of its Pauli stabilizers on a quantum computer is compared to that of classical Boolean circuits with the same, nearest-neighboring gate connectivity. Using a circuit-based trapped-ion quantum computer, we prepare and measure a six-qubit cyclic cluster state with an overall fidelity of 60.6% and 66.4%, before and after correcting for measurement-readout errors, respectively. Our experimental results indicate that while this fidelity readily passes conventional (or depth-0) Bell bounds for local hidden-variable models, it is on the cusp of demonstrating a higher probability of success than what is possible by depth-1 classical circuits. Our games offer a practical and scalable set of quantitative benchmarks for quantum computers in the pre-fault-tolerant regime as the number of qubits available increases.

Best Approximate Quantum Compiling Problems

We study the problem of finding the best approximate circuit that is the closest (in some pertinent metric) to a target circuit, and which satisfies a number of hardware constraints, like gate alphabet and connectivity. We look at the problem in the CNOT+rotation gate set from a mathematical programming standpoint, offering contributions both in terms of understanding the mathematics of the problem and its efficient solution. Among the results that we present, we are able to derive a 14-CNOT 4-qubit Toffoli decomposition from scratch, and show that the Quantum Shannon Decomposition can be compressed by a factor of two without practical loss of fidelity.

R2AU-Net: Attention Recurrent Residual Convolutional Neural Network for Multimodal Medical Image Segmentation

In recent years, semantic segmentation method based on deep learning provides advanced performance in medical image segmentation. As one of the typical segmentation networks, U-Net is successfully applied to multimodal medical image segmentation. A recurrent residual convolutional neural network with attention gate connection (R2AU-Net) based on U-Net is proposed in this paper. It enhances the capability of integrating contextual information by replacing basic convolutional units in U-Net by recurrent residual convolutional units. Furthermore, R2AU-Net adopts attention gates instead of the original skip connection. In this paper, the experiments are performed on three multimodal datasets: ISIC 2018, DRIVE, and public dataset used in LUNA and the Kaggle Data Science Bowl 2017. Experimental results show that R2AU-Net achieves much better performance than other improved U-Net algorithms for multimodal medical image segmentation.

Nongalvanic Calibration and Operation of a Quantum Dot Thermometer

A cryogenic quantum dot thermometer is calibrated and operated using only a single non-galvanic gate connection. The thermometer is probed with radio-frequency reflectometry and calibrated by fitting a physical model to the phase of the reflected radio-frequency signal taken at temperatures across a small range. Thermometry of the source and drain reservoirs of the dot is then performed by fitting the calibrated physical model to new phase data. The thermometer can operate at the transition between thermally broadened and lifetime broadened regimes, and outside the temperatures used in calibration. Electron thermometry was performed at temperatures between $3.0\,\mathrm{K}$ and $1.0\,\mathrm{K}$, in both a $1\,\mathrm{K}$ cryostat and a dilution refrigerator. The experimental setup allows fast electron temperature readout with a sensitivity of $4.0\pm0.3 \, \mathrm{mK}/\sqrt{\mathrm{Hz}}$, at Kelvin temperatures. The non-galvanic calibration process gives a readout of physical parameters, such as the quantum dot lever arm. The demodulator used for reflectometry readout is readily available and very affordable.

A hybrid self-aligned MIS-MESFET architecture for improved diamond-based transistors

Diamond is a promising electronic semiconductor candidate that has recently attracted intense interest in the implementation of its superior physical properties in electronic devices. In particular, attention has been focused on the surface transfer doping of diamond, in which the hydrogen-terminated diamond (diamond:H) benefices of a unique conductive two-dimensional hole gas (2DHG) layer at the diamond's sub-surface upon coverage with a suitable surface acceptor. Several diamond:H transistors have been developed. However, their inherent architecture dependence on the diamond:H conductive surface sensitivity to harsh processing environments has been a major barrier to the realization of high-performance devices. Here, we report on a diamond:H transistor structure that incorporates a mutual diamond:H active channel into the hybrid model of a metal-semiconductor field-effect transistor (MESFET) and a self-aligned metal-insulator-semiconductor FET (MISFET) with a common gate connection. The resulting diamond:H hybrid transistor exhibits a beneficial symbiosis that includes the advantages of both the MISFET (a high ON current of 0.8 μA/μm and a low OFF current of ∼10−9 μA/μm) and MESFET (almost an ideal subthreshold swing of 67 mV/dec) performance operations in the same multilayered device.

Analysis of Heterogeneous Settlement of Connecting Sections of Embankment on Deep Soft Ground Based on PLAXIS

The article analyzes the uneven settlement of the dam gate connection section of a reclamation project along the coast of eastern Zhejiang by PLAXIS finite element software, and compares and analyzes the in-situ monitoring results to understand the settlement development law of the dam gate connection section during operation and the location of the largest uneven settlement. Through analysis, after the sluice and the seawall are connected, the sluice settlement in the first year of the operation period is 5.51 mm, the settlement at the seawall is 105.30 mm, and the settlement difference is 99.79 mm; the cumulative settlement of the sluice is 9.01 mm, and the cumulative settlement at the seawall is 214.00 mm and the settlement difference is 204.99 mm. The uneven settlement of the connecting section of the dyke is mainly manifested at the junction of the empty box of the sluice and the riprap area of the seawall. When the uneven settlement is large, it will cause road cracks.

Expressibility and Entangling Capability of Parameterized Quantum Circuits for Hybrid Quantum‐Classical Algorithms

AbstractParameterized quantum circuits (PQCs) play an essential role in the performance of many variational quantum algorithms. One challenge in implementing such algorithms is choosing an effective circuit that well represents the solution space while maintaining a low circuit depth and parameter count. To characterize and identify expressible, yet compact, circuits, several descriptors are proposed, including expressibility and entangling capability, that are statistically estimated from classical simulations. These descriptors are computed for different circuit structures, varying the qubit connectivity and selection of gates. From these simulations, circuit fragments that perform well with respect to the descriptors are identified. In particular, a substantial improvement in performance of two‐qubit gates in a ring or all‐to‐all connected arrangement, compared to that of those on a line, is observed. Furthermore, improvement in both descriptors is achieved by sequences of controlled X‐rotation gates compared to sequences of controlled Z‐rotation gates. In addition, it is investigated how expressibility “saturates” with increased circuit depth, finding that the rate and saturated value appear to be distinguishing features of a PQC. While the correlation between each descriptor and algorithm performance remains to be investigated, methods and results from this study can be useful for algorithm development and design of experiments.

Mechanism and Novel Structure for di/dt Controllability in U-Shaped Channel Silicon-on-Insulator Lateral IGBTs

A planar gate U-shaped (PGU) channel silicon-on-insulator (SOI) lateral insulated gate bipolar transistor (LIGBT) featuring divided gates (G1 and G2) is proposed, which aims to suppress the gate voltage (VG) overshoot and improve the di/dt controllability. In the conventional PGU structure, the hole accumulating and the electric potential rising in the JFET region leads to a displacement current to overcharge the gate during the turn-on transient. Two gate connection modes (Mode A and B) for the proposed PGU structure are discussed. Mode A with a grounded G2 can effectively enhance the VG stability, however, degenerating the on-state voltage (VON). Mode B shields the displacement current by a pre-charged G2. G2 is driven with a separate gate resistor to the main gate G1, which results in an improved di/dt controllability without deteriorating VON. When adjusted for the same turn-on loss of 5.28mJ/cm2, the di/dt of the proposed PGU structure with mode B is 74% lower than that of the conventional PGU structure.

Three-Qubit Randomized Benchmarking.

As quantum circuits increase in size, it is critical to establish scalable multiqubit fidelity metrics. Here we investigate, for the first time, three-qubit randomized benchmarking (RB) on a quantum device consisting of three fixed-frequency transmon qubits with pairwise microwave-activated interactions (cross-resonance). We measure a three-qubit error per Clifford of 0.106 for all-to-all gate connectivity and 0.207 for linear gate connectivity. Furthermore, by introducing mixed dimensionality simultaneous RB-simultaneous one- and two-qubit RB-we show that the three-qubit errors can be predicted from the one- and two-qubit errors. However, by introducing certain coherent errors to the gates, we can increase the three-qubit error to 0.302, an increase that is not predicted by a proportionate increase in the one- and two-qubit errors from simultaneous RB. This demonstrates the importance of multiqubit metrics, such as three-qubit RB, on evaluating overall device performance.

Fault Diagnosis of Gearbox Based on Improved DUCG With Combination Weighting Method

To reduce the influence of uncertain factors on the results of gearbox operation condition evaluation and fault diagnosis, and to improve the reliability and stability of gearbox operation, an improved dynamic uncertain causality graph (DUCG) fault diagnosis method is proposed by combining the qualitative and quantitative information obtained. In addition, to address the lack of objectivity of correlation variables in the dynamic uncertainty causal graph, the combination weighting method is used to reassign correlation variables. The sub-DUCGs of gear, bearing, shaft, and box are established and connected with a logic gate and conditional connection variables. The DUCG is used to diagnose the faults in the gearbox, and the effectiveness and rationality of the method are verified by comparing the probabilities of the maximum pre-selected events before and after the improvement. Because the combination weighting method only makes moderate modifications for different weights, the limitations of the diagnosis accuracy and the calculation of variable weights are discussed by choosing faults with different numbers of weights. The results show that the improved DUCG can more accurately identify root faults, and the growth rate of the probability of maximum pre-selected event increases with an increase in the number of weights.

Multigate-Cell Stacked FET Design for Millimeter-Wave CMOS Power Amplifiers

To increase the voltage handling capability of scaled CMOS-based circuits, series connection (stacking) of transistors has been demonstrated in recently reported mm-wave power amplifiers. This paper discusses the implementation of stacked CMOS circuits employing a compact, multigate layout technique, rather than the conventional series connection of individual transistors. A unit multigate FET is composed of a single transistor with one source and drain and multiple (four) gate connections. Capacitances are implemented in a distributed manner allowing close proximity to the individual gate fingers using metal layers available within the CMOS back-end-of-line (BEOL) stack. The multigate structure is demonstrated to decrease parasitic resistances and capacitances, and has better layout for heat-sinking. The unit cell is replicated through tiling to implement larger effective gate widths. Millimeter-wave power amplifiers using the multigate-cell are presented which operate over the 25–35 GHz band and achieve 300 mW of saturated output power and peak power-added efficiency (PAE) of 30% in 45 nm CMOS SOI technology. To the authors’ knowledge, the output power is the best reported for a single stage CMOS power amplifier that does not use power-combining for this frequency range.

Differing roles for parvalbumin neurons after nerve injury.

Neuropathic pain is abnormal, persistent pain that is caused by injury to the somatosensory system. It commonly involves damage to the peripheral nerves as a result of physical trauma, metabolic diseases such as diabetes, infections such as HIV or shingles, or toxicity induced neuropathies as a result of cancer treatments. Neuropathic pain is common, with epidemiological studies suggesting that around 18–30% of American, European and Australian adults suffer from chronic pain. This leads to significant disabilities and suffering. People with chronic pain complain of spontaneous pains that may be electric-shock like or continuous. Often coexisting with these are abnormal, evoked sensations such as tactile allodynia (pain in response to innocuous stimuli e.g., light brush or pressure) and cold allodynia (pain in response to innocuous cooling), or hyperalgesia (increase pain in response to an already noxious stimulus), as well as ataxia and proprioceptive deficits. The management of these conditions is often suboptimal for two main reasons: firstly, an incomplete knowledge of the mechanisms that are involved (and which of them are the most important). Secondly, patients often have compliance issues with pharmacological medications (Attal, 2012). Both peripheral and central neuronal mechanisms contribute to the pathophysiology and since the turn of the century particular emphasis has focussed on the role of glial cells in pain (West et al., 2015).