Output Fidelity Research Articles

Elastance-based nonlinear dynamical controller synthesis for the in-vitro cardiovascular circulatory system: A backstepping approach

This paper investigates the application of a nonlinear system-dependent and system-independent dynamical backstepping (DBST) controller on a cardiovascular circulatory simulator (CCS) with non-triangular affine dynamics for the first time in literature. That is, this study introduces several novel contributions to the field, including the enhancement of cardiac output and physiological fidelity while addressing the limitations of existing control methods. Additionally, we present a comparative performance evaluation of feedback linearization (FBL) and the developed DBST controller with respect to integral squared error (ISE), integral absolute error (IAE), and integral time absolute error (ITAE), offering new insights into each control technique’s error minimization capabilities and overall system performance effectiveness. Furthermore, we pioneer the use of Monte-Carlo (MC) simulations as a reliable in-vitro alternative to clinical trials for controller performance assessment under intra- and inter-patient parameter variations. This innovative approach overcomes the limitations of in-vivo evaluations, enabling the analysis of crucial factors such as signal energy and reference tracking error value. Our results, supported by both MC simulations, demonstrate the complete superiority of the DBST controller over FBL in terms of error reduction and adaptability due to patient-to-patient variability, thus validating its potential for practical applications in cardiovascular circulatory systems.

Quantum data centres: a simulation-based comparative noise analysis

Abstract Quantum Data Centres (QDCs) could overcome the scalability challenges of modern quantum computers. Single-processor monolithic quantum computers are affected by increased cross talk and difficulty of implementing gates when the number of qubits is increased. In a QDC, multiple quantum processing units (QPUs) are linked together over short distances, allowing the total number of computational qubits to be increased without increasing the number of qubits on any one processor. In doing so, the error incurred by operations at each QPU can be kept small, however additional noise will be added to the system due to the latency cost and errors incurred during inter-QPU entanglement distribution. We investigate the relative impact of these different types of noise using a classically simulated QDC with two QPUs and compare the robustness to noise of the two main ways of implementing remote gates, cat-comm and TP-comm. We find that considering the quantity of gates or inter-QPU entangled links is often inadequate to predict the output fidelity from a quantum circuit and infer that an improved understanding of error propagation during distributed quantum circuits may represent a significant optimisation opportunity for compilation.

Facial expression morphing: enhancing visual fidelity and preserving facial details in CycleGAN-based expression synthesis.

Recent advancements in facial expression synthesis using deep learning, particularly with Cycle-Consistent Adversarial Networks (CycleGAN), have led to impressive results. However, a critical challenge persists: the generated expressions often lack the sharpness and fine details of the original face, such as freckles, moles, or birthmarks. To address this issue, we introduce the Facial Expression Morphing (FEM) algorithm, a novel post-processing method designed to enhance the visual fidelity of CycleGAN-based outputs. The FEM method blends the input image with the generated expression, prioritizing the preservation of crucial facial details. We experimented with our method on the Radboud Faces Database (RafD) and evaluated employing the Fréchet Inception Distance (FID) standard benchmark for image-to-image translation and introducing a new metric, FSD (Facial Similarity Distance), to specifically measure the similarity between translated and real images. Our comprehensive analysis of CycleGAN, UNet Vision Transformer cycle-consistent GAN versions 1 (UVCGANv1) and 2 (UVCGANv2) reveals a substantial enhancement in image clarity and preservation of intricate details. The average FID score of 31.92 achieved by our models represents a remarkable 50% reduction compared to the previous state-of-the-art model's score of 63.82, showcasing the significant advancements made in this domain. This substantial enhancement in image quality is further supported by our proposed FSD metric, which shows a closer resemblance between FEM-processed images and the original faces.

Understanding and Addressing AI Hallucinations in Healthcare and Life Sciences

Purpose: This paper investigates the phenomenon of "AI hallucinations" in healthcare and life sciences, where large language models (LLMs) produce outputs that, while coherent, are factually incorrect, irrelevant, or misleading. Understanding and mitigating such errors is critical given the high stakes of accurate and reliable information in healthcare and life sciences. We classify hallucinations into three types input-conflicting, context-conflicting, and fact-conflicting and examine their implications through real-world cases. Methodology: Our methodology combines the Fact Score, Med-HALT, and adversarial testing to evaluate the fidelity of AI outputs. We propose several mitigation strategies, including Retrieval-Augmented Generation (RAG), Chain-of-Verification (CoVe), and Human-in-the-Loop (HITL) systems, to enhance model reliability. Findings: As artificial intelligence continues to permeate various sectors of society, the issue of hallucinations in AI-generated text poses significant challenges, especially in contexts where precision and reliability are paramount. This paper has delineated the types of hallucinations commonly observed in AI systems input-conflicting, context-conflicting, and fact-conflicting and highlighted their potential to undermine trust and efficacy in critical domains such as healthcare and legal proceedings. Unique contribution to theory, policy and practice: This study's unique contribution lies in its comprehensive analysis of AI hallucinations' types and impacts and the development of robust controls that advance theoretical understanding, practical application, and policy formulation in AI deployment. These efforts aim to foster safer, more effective AI integration across healthcare and life sciences sectors

EnColor: Improving Visual Accessibility with a Deep Encoder-Decoder Image Corrector for Color Vision Deficient Individuals

Individuals with color vision deficiencies (CVDs) often face significant challenges in accessing vital information for decision-making. In response, we introduce EnColor—a deep Encoder-decoder Color corrector for images, enabling individuals with CVDs to perceive the contents in originally intended colorization. Our network architecture is designed to effectively capture essential visual features for reconstructing standard images into color-corrected versions. In particular, our training pipeline is integrated with a CVD simulator so as to ensure the fidelity of output throughout the lens of individuals with impaired color vision. For evaluation, we focus primarily on tomato images, considering the profound impact of color vision deficiencies on practical domains like agri-food systems. Our quantitative results demonstrate that the EnColor model achieves over 16.8% improvement over previously introduced algorithms in terms of color retention, supporting our design choices. Furthermore, a survey with 43 participants provides subjective assessments with the highest scores on our method. Additionally, specific visual examples are presented to highlight accurately restored colors. We also publicly share all our codes of EnColor as well as the baseline methods to ensure reproducibility and facilitate more studies in CVD correction.

CaO:Tb<sup>3+ </sup> green-emitting phosphor for white light-emitted diode-phosphor applications: the improvement of light output intensity

The use of CaO:Tb3+ with light green emission on for improvement of both luminescent output and chromatic fidelity of the white light emitted from a light-emitting diode (LED). The CaO:Tb3+ is combined with the yellow-emitting phosphor of YAG:Ce3+ to provide sufficient colored spectral proportion for the white light generation, enhancing the color performance. The phosphor combination is utilized for the three most applied LED structures: conformal, in-cup, and remote phosphor structures. The changes in optical properties of these three LEDs are monitored with adjustments in the proportion of CaO:Tb3+. The higher proportion of the green phosphor results in higher scattering efficiency in all structures, offering better color coordination and stronger luminous flux. The color quality scale is somehow reduced when CaO:Tb3+ concentration is more than a certain level. Therefore, depending on the phosphor configuration of the white light-emitting diode (WLED), the concentration of CaO:Tb3+ should be modified to achieve a good color rendition with improved color consistency and luminous properties.

Performance comparison of conventional and striation-based beamformers for underwater bearing detection of pulse sources.

The multi-path and dispersion properties of shallow water waveguides make conventional beamforming (CBF) face issues such as beam shift, broadening, splitting, output distortion, and array gain reduction. In this paper, the striation-based beamforming (SBF) is investigated to address these issues. SBF differs from CBF by utilizing frequency-shift processing along interference striations. The performance difference between CBF and SBF is compared. It demonstrates that under ideal waveguide modeling with pulse sources, SBF can achieve a beam output response that is close to the plane wave condition. The speed term of SBF's response is approximately independent of modal indexes, which equips SBF to form a unique beam output and guarantee the beam resolution. The processing of consistent signals along the striation maintains the optimal signal correlation, which makes SBF ensure the output fidelity and array gain. To shift the mainlobe of SBF to the source azimuth, the time delay related to the waveform truncation point can be introduced to pre-compensate the array signals. There exist two theoretical accuracy limits to using the truncation. First, truncation time corresponds to the waveform point at r0/c (r0 is the source range), and the mainlobe of SBF is directed to the source azimuth. Second, truncation corresponds to the pulse peak point, and the azimuth estimation accuracy of SBF gets close to CBF. Simulations and experimental results are given as illustrations.

Data-Driven Reliability Models of Quantum Circuit: From Traditional ML to Graph Neural Network

The current advancement in quantum computers has been focusing on increasing the number of qubits and enhancing their fidelity. However, the available quantum devices, known as Intermediate Scale Quantum (NISQ) computers, still suffer from different sources of noise that impact their reliability. Thus, practical noise modeling is of great importance in the development of quantum error mitigation approaches. In this paper, we propose a Machine Learning (ML)-based scheme to predict the output fidelity of the quantum circuit executed on NISQ devices. We show the benefit of using Graph Neural Network (GNN)-based models compared to traditional ML-based models in capturing the quantum circuit structure in addition to its gates’ features, which enable characterizing unpredicted quantum circuit errors. We use different metrics to measure the fidelity of the quantum circuit output. Our experimental results using different quantum algorithms executed on IBM Q Guadalupe quantum computer show the high prediction accuracy of our ML reliability models. Our results also show that our models can guide the single-qubit gate rescheduling to improve the output fidelity of the quantum circuit without the need for prior execution of dedicated calibration circuits.

Reliable Extraction of Semantic Information and Rate of Innovation Estimation for Graph Signals

Semantic signal processing and communications are poised to play a central part in developing the next generation of sensor devices and networks. A crucial component of a semantic system is the extraction of semantic signals from the raw input signals, which has become increasingly tractable with the recent advances in machine learning (ML) and artificial intelligence (AI) techniques. The accurate extraction of semantic signals using the aforementioned ML and AI methods, and the detection of semantic innovation for scheduling transmission and/or storage events are critical tasks for reliable semantic signal processing and communications. In this work, we propose a reliable semantic information extraction framework based on our previous work on semantic signal representations in a hierarchical graph-based structure. The proposed framework includes a time integration method to increase fidelity of ML outputs in a class-aware manner, a graph-edit-distance based metric to detect innovation events at the graph-level and filter out sporadic errors, and a Hidden Markov Model (HMM) to produce smooth and reliable graph signals. The proposed methods within the framework are demonstrated individually and collectively through simulations and case studies based on real-world computer vision examples.

Calibrating the Classical Hardness of the Quantum Approximate Optimization Algorithm

Trading fidelity for scale enables approximate classical simulators such as matrix product states (MPS) to run quantum circuits beyond exact methods. A control parameter, the so-called bond dimension $\chi$ for MPS, governs the allocated computational resources and the output fidelity. Here, we characterize the fidelity for the quantum approximate optimization algorithm by the expectation value of the cost function it seeks to minimize and find that it follows a scaling law $F\bigl(\ln\chi\bigr/N\bigr)$ with $N$ the number of qubits. With $\ln\chi$ amounting to the entanglement that an MPS can encode, we show that the relevant variable for investigating the fidelity is the entanglement per qubit. Importantly, our results calibrate the classical computational power required to achieve the desired fidelity and benchmark the performance of quantum hardware in a realistic setup. For instance, we quantify the hardness of performing better classically than a noisy superconducting quantum processor by readily matching its output to the scaling function. Moreover, we relate the global fidelity to that of individual operations and establish its relationship with $\chi$ and $N$. We sharpen the requirements for noisy quantum computers to outperform classical techniques at running a quantum optimization algorithm in speed, size, and fidelity.

Controlling strokes in fast neural style transfer using content transforms

Fast style transfer methods have recently gained popularity in art-related applications as they make a generalized real-time stylization of images practicable. However, they are mostly limited to one-shot stylizations concerning the interactive adjustment of style elements. In particular, the expressive control over stroke sizes or stroke orientations remains an open challenge. To this end, we propose a novel stroke-adjustable fast style transfer network that enables simultaneous control over the stroke size and intensity, and allows a wider range of expressive editing than current approaches by utilizing the scale-variance of convolutional neural networks. Furthermore, we introduce a network-agnostic approach for style-element editing by applying reversible input transformations that can adjust strokes in the stylized output. At this, stroke orientations can be adjusted, and warping-based effects can be applied to stylistic elements, such as swirls or waves. To demonstrate the real-world applicability of our approach, we present StyleTune, a mobile app for interactive editing of neural style transfers at multiple levels of control. Our app allows stroke adjustments on a global and local level. It furthermore implements an on-device patch-based upsampling step that enables users to achieve results with high output fidelity and resolutions of more than 20 megapixels. Our approach allows users to art-direct their creations and achieve results that are not possible with current style transfer applications.

Protecting unknown qubit states from decoherence of qubit channels by weak measurement

The problem of combating de-coherence by weak measurements has already been studied for the amplitude damping channel and for specific input states. We generalize this to a large four-parameter family of qubit channels and for the average fidelity over all pure states. As a by-product we classify all the qubit channels which have one invariant pure state and show that the parameter manifold of these channels is isomorphic to S 2 × S 1 × S 1 and contains many interesting sub-classes of channels. By tuning the parameter of the weak measurement, we show that it is possible to increase the average input–output fidelity without blocking too many particles to pass through the channel. Quantitative analysis reveals that the increase of average fidelity can be up to 0.3, by blocking less than half of the particles.

Optimization and noise analysis of the quantum algorithm for solving one-dimensional Poisson equation

Solving differential equations is one of the most promising applications of quantum computing. Recently we proposed an efficient quantum algorithm for solving one-dimensional Poisson equation avoiding the need to perform quantum arithmetic or Hamiltonian simulation. In this paper, we further develop this algorithm to make it closer to the real application on the noisy intermediate-scale quantum (NISQ) devices. To this end, we first optimize the quantum 1D-Poisson solver by developing a new way of performing the sine transformation. The circuit depth for implementing the sine transform is reduced from n2 to n without increasing the total qubit cost of the whole circuit, which is achieved by neatly reusing the additional ancillary quits. Then, we analyse the effect of common noise existing in the real quantum devices on our algorithm using the IBM Qiskit toolkit. We find that the phase damping noise has little effect on our algorithm, while the bit flip noise has the greatest impact. In addition, threshold errors of the quantum gates are obtained to make the fidelity of the circuit output being greater than 90%. The results of noise analysis will provide a good guidance for the subsequent work of error mitigation and error correction for our algorithm. The noise-analysis method developed in this work can be used for other algorithms to be executed on the NISQ devices.

Storage of photonic time-bin qubits for up to 20\u2009ms in a rare-earth doped crystal

Long-duration quantum memories for photonic qubits are essential components for achieving long-distance quantum networks and repeaters. The mapping of optical states onto coherent spin-waves in rare earth ensembles is a particularly promising approach to quantum storage. However, it remains challenging to achieve long-duration storage at the quantum level due to read-out noise caused by the required spin-wave manipulation. In this work, we apply dynamical decoupling techniques and a small magnetic field to achieve the storage of six temporal modes for 20, 50, and 100 ms in a 151Eu3+:Y2SiO5 crystal, based on an atomic frequency comb memory, where each temporal mode contains around one photon on average. The quantum coherence of the memory is verified by storing two time-bin qubits for 20 ms, with an average memory output fidelity of F = (85 ± 2)% for an average number of photons per qubit of μin = 0.92 ± 0.04. The qubit analysis is done at the read-out of the memory, using a type of composite adiabatic read-out pulse we developed.

Physics-Informed Hyperspectral Remote Sensing Image Synthesis With Deep Conditional Generative Adversarial Networks

High-resolution hyperspectral remote sensing images are of great significance to agricultural, urban, and military applications. However, collecting and labeling hyperspectral images are time-consuming, expensive, and usually heavily rely on domain knowledge. In this article, we propose a new method for generating high-resolution hyperspectral images and subpixel ground-truth annotations from RGB images. Given a single high-resolution RGB image as its conditional input, unlike previous methods that directly predict spectral reflectance and ignores the physics behind it, we consider both imaging mechanism and spectral mixing, introduce a deep generative network that first recovers the spectral abundance for each pixel, and then generate the final spectral data cube with the standard USGS spectral library. In this way, our method not only synthesizes high-quality spectral data existing in the real world but also generates subpixel-level spectral abundance with well-defined spectral reflectance characteristics. We also introduce a spatial discriminative network and a spectral discriminative network to improve the fidelity of the synthetic output from both spatial and spectral perspectives. The whole framework can be trained end-to-end in an adversarial training paradigm. We refer to our method as “Physics-informed Deep Adversarial Spectral Synthesis (PDASS).” On the IEEE <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">grss_dfc_2018</i> dataset, our method achieves an MPSNR of 47.56 on spectral reconstruction accuracy and outperforms other state-of-the-art methods. As latent variables, the generated spectral abundance and the atmospheric absorption coefficients of sunlight also suggest the effectiveness of our method.

Multi-stage ensemble-learning-based model fusion for surface ozone simulations: A focus on CMIP6 models

Accurately simulating the geographical distribution and temporal variability of global surface ozone has long been one of the principal components of chemistry-climate modelling. However, the simulation outcomes have been reported to vary significantly as a result of the complex mixture of uncertain factors that control the tropospheric ozone budget. Settling the cross-model discrepancies to achieve higher accuracy predictions of surface ozone is thus a task of priority, and methods that overcome structural biases in models going beyond naïve averaging of model simulations are urgently required. Building on the Coupled Model Intercomparison Project Phase 6 (CMIP6), we have transplanted a conventional ensemble learning approach, and also constructed an innovative 2-stage enhanced space-time Bayesian neural network to fuse an ensemble of 57 simulations together with a prescribed ozone dataset, both of which have realised outstanding performances (R2 > 0.95, RMSE < 2.12 ppbv). The conventional ensemble learning approach is computationally cheaper and results in higher overall performance, but at the expense of oceanic ozone being overestimated and the learning process being uninterpretable. The Bayesian approach performs better in spatial generalisation and enables perceivable interpretability, but induces heavier computational burdens. Both of these multi-stage machine learning-based approaches provide frameworks for improving the fidelity of composition-climate model outputs for uses in future impact studies.

GluA4 facilitates cerebellar expansion coding and enables associative memory formation.

AMPA receptors (AMPARs) mediate excitatory neurotransmission in the central nervous system (CNS) and their subunit composition determines synaptic efficacy. Whereas AMPAR subunits GluA1-GluA3 have been linked to particular forms of synaptic plasticity and learning, the functional role of GluA4 remains elusive. Here, we demonstrate a crucial function of GluA4 for synaptic excitation and associative memory formation in the cerebellum. Notably, GluA4-knockout mice had ~80% reduced mossy fiber to granule cell synaptic transmission. The fidelity of granule cell spike output was markedly decreased despite attenuated tonic inhibition and increased NMDA receptor-mediated transmission. Computational network modeling incorporating these changes revealed that deletion of GluA4 impairs granule cell expansion coding, which is important for pattern separation and associative learning. On a behavioral level, while locomotor coordination was generally spared, GluA4-knockout mice failed to form associative memories during delay eyeblink conditioning. These results demonstrate an essential role for GluA4-containing AMPARs in cerebellar information processing and associative learning.

A multi-commodity network model for optimal quantum reversible circuit synthesis.

Quantum computing is a newly emerging computing environment that has recently attracted intense research interest in improving the output fidelity, fully utilizing its high computing power from both hardware and software perspectives. In particular, several attempts have been made to reduce the errors in quantum computing algorithms through the efficient synthesis of quantum circuits. In this study, we present an application of an optimization model for synthesizing quantum circuits with minimum implementation costs to lower the error rates by forming a simpler circuit. Our model has a unique structure that combines the arc-subset selection problem with a conventional multi-commodity network flow model. The model targets the circuit synthesis with multiple control Toffoli gates to implement Boolean reversible functions that are often used as a key component in many quantum algorithms. Compared to previous studies, the proposed model has a unifying yet straightforward structure for exploiting the operational characteristics of quantum gates. Our computational experiment shows the potential of the proposed model, obtaining quantum circuits with significantly lower quantum costs compared to prior studies. The proposed model is also applicable to various other fields where reversible logic is utilized, such as low-power computing, fault-tolerant designs, and DNA computing. In addition, our model can be applied to network-based problems, such as logistics distribution and time-stage network problems.

Performance characterization of Pauli channels assisted by indefinite causal order and post-measurement

Indefinite causal order has introduced disruptive procedures to improve the fidelity of quantum communication by introducing the superposition of { orders} on a set of quantum channels. It has been applied to several well characterized quantum channels as depolarizing, dephasing and teleportation. This work analyses the behavior of a parametric quantum channel for single qubits expressed in the form of Pauli channels. Combinatorics lets to obtain affordable formulas for the analysis of the output state of the channel when it goes through a certain imperfect quantum communication channel when it is deployed as a redundant application of it under indefinite causal order. In addition, the process exploits post-measurement on the associated control to select certain components of transmission. Then, the fidelity of such outputs is analysed to characterize the generic channel in terms of its parameters. As a result, we get notable enhancement in the transmission of information for well characterized channels due to the combined process: indefinite causal order plus post-measurement.

High-Quality Textured 3D Shape Reconstruction with Cascaded Fully Convolutional Networks

We present a learning-based approach to reconstructing high-resolution three-dimensional (3D) shapes with detailed geometry and high-fidelity textures. Albeit extensively studied, algorithms for 3D reconstruction from multi-view depth-and-color (RGB-D) scans are still prone to measurement noise and occlusions; limited scanning or capturing angles also often lead to incomplete reconstructions. Propelled by recent advances in 3D deep learning techniques, in this paper, we introduce a novel computation- and memory-efficient cascaded 3D convolutional network architecture, which learns to reconstruct implicit surface representations as well as the corresponding color information from noisy and imperfect RGB-D maps. The proposed 3D neural network performs reconstruction in a progressive and coarse-to-fine manner, achieving unprecedented output resolution and fidelity. Meanwhile, an algorithm for end-to-end training of the proposed cascaded structure is developed. We further introduce Human10, a newly created dataset containing both detailed and textured full-body reconstructions as well as corresponding raw RGB-D scans of 10 subjects. Qualitative and quantitative experimental results on both synthetic and real-world datasets demonstrate that the presented approach outperforms existing state-of-the-art work regarding visual quality and accuracy of reconstructed models.