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Countless science and engineering applications in multi-objective optimization (MOO) necessitate that decision-makers (DMs) select a Pareto-optimal solution which aligns with their preferences. Evaluating individual solutions is often expensive, necessitating cost-sensitive optimization techniques. Due to competing objectives, the space of trade-offs is also expansive -- thus, examining the full Pareto frontier may prove overwhelming to a DM. Such real-world settings generally have loosely-defined and context-specific desirable regions for each objective function that can aid in constraining the search over the Pareto frontier. We introduce a novel conceptual framework that operationalizes these priors using soft-hard functions, SHFs, which allow for the DM to intuitively impose soft and hard bounds on each objective -- which has been lacking in previous MOO frameworks. Leveraging a novel minimax formulation for Pareto frontier sampling, we propose a two-step process for obtaining a compact set of Pareto-optimal points which respect the user-defined soft and hard bounds: (1) densely sample the Pareto frontier using Bayesian optimization, and (2) sparsify the selected set to surface to the user, using robust submodular function optimization. We prove that (2) obtains the optimal compact Pareto-optimal set of points from (1). We further show that many practical problems fit within the SHF framework and provide extensive empirical validation on diverse domains, including brachytherapy, engineering design, and large language model personalization. Specifically, for brachytherapy, our approach returns a compact set of points with over 3% greater SHF-defined utility than the next best approach. Among the other diverse experiments, our approach consistently leads in utility, allowing the DM to reach >99% of their maximum possible desired utility within validation of 5 points.

Preparing quantum states across many qubits is necessary to unlock the full potential of quantum computers. However, a key challenge is to realize efficient preparation protocols which are stable to noise and gate imperfections. Here, using a measurement-based protocol on a 127 superconducting qubit device, we study the generation of the simplest long-range order -- Ising order, familiar from Greenberger-Horne-Zeilinger (GHZ) states and the repetition code -- on 54 system qubits. Our efficient implementation of the constant-depth protocol and classical decoder shows higher fidelities for GHZ states compared to size-dependent, unitary protocols. By experimentally tuning coherent and incoherent error rates, we demonstrate stability of this decoded long-range order in two spatial dimensions, up to a critical point which corresponds to a transition belonging to the unusual Nishimori universality class. Although in classical systems Nishimori physics requires fine-tuning multiple parameters, here it arises as a direct result of the Born rule for measurement probabilities -- locking the effective temperature and disorder driving this transition. Our study exemplifies how measurement-based state preparation can be meaningfully explored on quantum processors beyond a hundred qubits., Comment: 16 pages, 18 figures

Quantum simulation traditionally relies on unitary dynamics, inherently imposing efficiency constraints on the generation of intricate entangled states. In principle, these limitations can be superseded by non-unitary, dynamic circuits. These circuits exploit measurements alongside conditional feed-forward operations, providing a promising approach for long-range entangling gates, higher effective connectivity of near-term hardware, and more efficient state preparations. Here, we explore the utility of shallow dynamic circuits for creating long-range entanglement on large-scale quantum devices. Specifically, we study two tasks: CNOT gate teleportation between up to 101 qubits by feeding forward 99 mid-circuit measurement outcomes, and the preparation of Greenberger-Horne-Zeilinger (GHZ) states with genuine entanglement. In the former, we observe that dynamic circuits can outperform their unitary counterparts. In the latter, by tallying instructions of compiled quantum circuits, we provide an error budget detailing the obstacles that must be addressed to unlock the full potential of dynamic circuits. Looking forward, we expect dynamic circuits to be useful for generating long-range entanglement in the near term on large-scale quantum devices., Comment: 7 pages, 3 figures (main text) + 11 pages, 6 figures (appendix)

In recent years, the field of data-driven neural network-based machine learning (ML) algorithms has grown significantly and spurred research in its applicability to instrumentation and control systems. While they are promising in operational contexts, the trustworthiness of such algorithms is not adequately assessed. Failures of ML-integrated systems are poorly understood; the lack of comprehensive risk modeling can degrade the trustworthiness of these systems. In recent reports by the National Institute for Standards and Technology, trustworthiness in ML is a critical barrier to adoption and will play a vital role in intelligent systems' safe and accountable operation. Thus, in this work, we demonstrate a real-time model-agnostic method to evaluate the relative reliability of ML predictions by incorporating out-of-distribution detection on the training dataset. It is well documented that ML algorithms excel at interpolation (or near-interpolation) tasks but significantly degrade at extrapolation. This occurs when new samples are "far" from training samples. The method, referred to as the Laplacian distributed decay for reliability (LADDR), determines the difference between the operational and training datasets, which is used to calculate a prediction's relative reliability. LADDR is demonstrated on a feedforward neural network-based model used to predict safety significant factors during different loss-of-flow transients. LADDR is intended as a "data supervisor" and determines the appropriateness of well-trained ML models in the context of operational conditions. Ultimately, LADDR illustrates how training data can be used as evidence to support the trustworthiness of ML predictions when utilized for conventional interpolation tasks., Comment: This paper was originally presented at the 13th Nuclear Plant Instrumentation, Control & Human-Machine Interface Technologies conference and was awarded best student paper

Resistive memory based on 2D WS2, MoS2, and h-BN materials has been studied, including experiments and simulations. The influences with different active layer thicknesses have been discussed, including experiments and simulations. The thickness with the best On/Off ratio is also found for the 2D RRAM. This work reveals fundamental differences between a 2D RRAM and a conventional oxide RRAM. Furthermore, from the physical parameters extracted with the KMC model, the 2D materials have a lower diffusion activation energy from the vertical direction, where a smaller bias voltage and a shorter switching time can be achieved. It was also found the diffusion activation energy from the CVD-grown sample is much lower than the mechanical exfoliated sample. The result shows MoS2 has the fastest switching speed among three 2D materials.

Two-dimensional (2D) semiconductors are a promising solution for the miniaturization of electronic devices and for the exploration of novel physics. However, practical applications and demonstrations of physical phenomena are hindered by high Schottky barriers at the contacts to 2D semiconductors. While the process of image-force barrier lowering (IFBL) can considerably decrease the Schottky barrier, IFBL is not fully understood for the majority of prevalent contact geometries. We introduce a novel technique to determine the IFBL potential energy with application spanning far beyond that of any existing method. We do so by solving Poisson's equation with the boundary conditions of two metal surfaces separated by an angle Omega. We then prove that our result can also be obtained with the method of images provided a non-Euclidean, cone-manifold space is used. The resulting IFBL is used to calculate the expected contact resistance of the most prevalent geometric contacts. Finally, we investigate contact resistance and show how the stronger IFBL counteracts the effect of larger depletion width with increasing contact angle. We find that top contacts experience lower contact resistance than edge contacts. Remarkably, our results identify tunable parameters for reducing Schottky barriers and likewise contact resistance to edge-contacted 2D materials, enhancing potential applications., Comment: 22 pages, 6 figures

Human-system interface is one of the key advanced design features applied to modern digital instrumentation and control systems of nuclear power plants. The conventional design is based on a compact workstation-based system within the control room. The compact workstation provides both a strategic operating environment while also a convenient display for plant status information necessary to the operator. The control environment is further enhanced through display panels, visual and auditory alarms, and procedure systems. However, just like the legacy control, the HSI should incorporate diversity to demonstrate sufficient defense-in-depth protection against common cause failures of the safety system. Furthermore, the vulnerability of the HSI is affected by a plethora of factors, such as human error, cyberattacks, software common cause failures, etc., that complicate the design and analysis. Therefore, this work aims to identify and evaluate existing system vulnerabilities to support the licensing, deployment and operation of HSI designs, especially the functions that are relevant to a reactor trip. We performed a systematic hazard analysis to investigate potential vulnerabilities within the HSI design using the novel redundancy-guided systems-theoretic hazard analysis. This method was developed and demonstrated by Idaho National Laboratory under a project initiated by the Risk-Informed Systems Analysis Pathway of the U.S. Department of Energy's Light Water Reactor Sustainability Program. The goal of the project is to develop a strong technical basis for risk assessment strategies to support effective, reliable, and licensable digital instrumentation and control technologies., Comment: 16 pages, 6 figures, 3 tables, presented at the American Nuclear Society 12th Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies Conference 2021

This paper presents an approach for modeling software common cause failures (CCFs) within digital instrumentation and control (I&C) systems. CCFs consist of a concurrent failure between two or more components due to a shared failure cause and coupling mechanism. This work emphasizes the importance of identifying software-centric attributes related to the coupling mechanisms necessary for simultaneous failures of redundant software components. The groups of components that share coupling mechanisms are called common cause component groups (CCCGs). Most CCF models rely on operational data as the basis for establishing CCCG parameters and predicting CCFs. This work is motivated by two primary concerns: (1) a lack of operational and CCF data for estimating software CCF model parameters; and (2) the need to model single components as part of multiple CCCGs simultaneously. A hybrid approach was developed to account for these concerns by leveraging existing techniques: a modified beta factor model allows single components to be placed within multiple CCCGs, while a second technique provides software-specific model parameters for each CCCG. This hybrid approach provides a means to overcome the limitations of conventional methods while offering support for design decisions under the limited data scenario., Comment: 12 pages, 3 Figures, 7 Tables, presented at the Probabilistic Safety Assessment & Management conference in 2022. arXiv admin note: text overlap with arXiv:2204.03717

The modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate reliability methodologies by both the United States (U.S.) Nuclear Regulatory Commission (NRC) and the industry. In this work, we develop an approach called Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to quantify probabilities of various software failure modes in a DI&C system. The method utilizes accepted industry methodologies for quality assurance that are verified by experimental evidence. In essence, the approach combines a semantic failure classification model with a reliability growth model to predict the probability of failure modes of a software system. A case study was conducted on a representative I&C platform (ChibiOS) running a smart sensor acquisition software developed by Virginia Commonwealth University (VCU). The testing and evidence collection guidance in ORCAS was applied, and defects were uncovered in the software. Qualitative evidence, such as modified condition decision coverage, was used to gauge the completeness and trustworthiness of the assessment while quantitative evidence was used to determine the software failure probabilities. The reliability of the software was then estimated and compared to existing operational data of the sensor device. It is demonstrated that by using ORCAS, a semantic reasoning framework can be developed to justify if the software is reliable (or unreliable) while still leveraging the strength of the existing methods., Comment: 12 pages, 3 figures, 4 tables, conference transaction presented at Probabilistic Safety Assessment and Management 2022

In recent years, there has been considerable effort to modernize existing and new nuclear power plants with digital instrumentation and control systems. However, there has also been considerable concern both by industry and regulatory bodies for the risk and consequence analysis of these systems. Of concern are digital common cause failures specifically due to software defects. These failures by the software can occur in both the control and monitoring of a system. While many methods have been proposed to identify software failure modes, such as Systems Theoretic Process Analysis, Hazard and Consequence Analysis for Digital Systems, etc., these methods are focused primarily on the control action pathway of a system. In contrast, the information feedback pathway lacks Unsafe Control Actions, which are typically related to software basic events; thus, assessment of software basic events in such systems is unclear. In this work, we present the idea of intermediate processors and Unsafe Information Flow (UIF) to help safety analysts trace failure mechanisms in the feedback pathway and how they can be integrated into a fault tree for improved assessment capability. The concepts presented are demonstrated in two comprehensive case studies, a smart sensor integrated platform for unmanned autonomous vehicles and another on a representative advanced human system interface for safety critical plant monitoring. The qualitative software basic events are identified, and a fault tree analysis is conducted based on a modified Redundancy guided Systems theoretic Hazard Analysis methodology. The case studies demonstrate the use of UIFs and intermediate processors in the fault tree to improve traceability of software failures in highly complex digital instrumentation feedback. The improved method clarifies fault tree construction when multiple component dependencies are present in the system., Comment: 13 pages, 7 figures, 4 tables, conference transaction presented at Probabilistic Safety Assessment and Management 2022

The use of machine learning (ML) model as digital-twins for reduced-order-modeling (ROM) in lieu of system codes has grown traction over the past few years. However, due to the complex and non-linear nature of nuclear reactor transients as well as the large range of tasks required, it is infeasible for a single ML model to generalize across all tasks. In this paper, we incorporate issue specific digital-twin ML models with ensembles to enhance the prediction outcome. The ensemble also utilizes an indirect probabilistic tracking method of surrogate state variables to produce accurate predictions of unobservable safety goals. The unique method named Ensemble Diagnostic Digital-twin Modeling (EDDM) can select not only the most appropriate predictions from the incorporated diagnostic digital-twin models but can also reduce generalization error associated with training as opposed to single models., Comment: 9 pages, 4 figures, 3 tables, presented in the American Nuclear Society Mathematics and Computation 2021 Annual Conference

Digital instrumentation and control (DIC) systems at nuclear power plants (NPPs) have many advantages over analog systems. They are proven to be more reliable, cheaper, and easier to maintain given obsolescence of analog components. However, they also pose new engineering and technical challenges, such as possibility of common cause failures (CCFs) unique to digital systems. This paper proposes a Platform for Risk Assessment of DIC (PRADIC) that is developed by Idaho National Laboratory (INL). A methodology for evaluation of software CCFs in high safety-significant safety-related DIC systems of NPPs was developed as part of the framework. The framework integrates three stages of a typical risk assessment, qualitative hazard analysis and quantitative reliability and consequence analyses. The quantified risks compared with respective acceptance criteria provide valuable insights for system architecture alternatives allowing design optimization in terms of risk reduction and cost savings. A comprehensive case study performed to demonstrate the framework capabilities is documented in this paper. Results show that the PRADIC is a powerful tool capable to identify potential digital-based CCFs, estimate their probabilities, and evaluate their impacts on system and plant safety., Comment: 41 pages, 25 figures, 13 tables. The manuscript has been submitted to Reliability Engineering & System Safety

Quantum error correction offers a promising path for performing quantum computations with low errors. Although a fully fault-tolerant execution of a quantum algorithm remains unrealized, recent experimental developments, along with improvements in control electronics, are enabling increasingly advanced demonstrations of the necessary operations for applying quantum error correction. Here, we perform quantum error correction on superconducting qubits connected in a heavy-hexagon lattice. The full processor can encode a logical qubit with distance three and perform several rounds of fault-tolerant syndrome measurements that allow the correction of any single fault in the circuitry. Furthermore, by using dynamic circuits and classical computation as part of our syndrome extraction protocols, we can exploit real-time feedback to reduce the impact of energy relaxation error in the syndrome and flag qubits. We show that the logical error varies depending on the use of a perfect matching decoder compared to a maximum likelihood decoder. We observe a logical error per syndrome measurement round as low as $\sim0.04$ for the matching decoder and as low as $\sim0.03$ for the maximum likelihood decoder. Our results suggest that more significant improvements to decoders are likely on the horizon as quantum hardware has reached a new stage of development towards fully fault-tolerant operations., Comment: 15 pages, 6 figures, 5 tables

In this paper, we study the significance of the pleura and adipose tissue in lung ultrasound AI analysis. We highlight their more prominent appearance when using high-frequency linear (HFL) instead of curvilinear ultrasound probes, showing HFL reveals better pleura detail. We compare the diagnostic utility of the pleura and adipose tissue using an HFL ultrasound probe. Masking the adipose tissue during training and inference (while retaining the pleural line and Merlin's space artifacts such as A-lines and B-lines) improved the AI model's diagnostic accuracy., Comment: Published in MICCAI 2021 workshop on Lessons Learned from the development and application of medical imaging-based AI technologies for combating COVID-19 (LL-COVID19). The first two authors contributed equally to this work

Arbitrarily long quantum computations require quantum memories that can be repeatedly measured without being corrupted. Here, we preserve the state of a quantum memory, notably with the additional use of flagged error events. All error events were extracted using fast, mid-circuit measurements and resets of the physical qubits. Among the error decoders we considered, we introduce a perfect matching decoder that was calibrated from measurements containing up to size-4 correlated events. To compare the decoders, we used a partial post-selection scheme shown to retain ten times more data than full post-selection. We observed logical errors per round of $2.2\pm0.1\times10^{-2}$ (decoded without post-selection) and $5.1\pm0.7\times10^{-4}$ (full post-selection), which was less than the physical measurement error of $7\times10^{-3}$ and therefore surpasses a pseudo-threshold for repeated logical measurements., Comment: 16 pages, 14 figures, 5 tables, for peer-review

We present quantum simulations of carbon nanotube field-effect transistors (CNT-FETs) based on top-gated architectures and compare to electrical characterization on devices with 15 nm channel lengths. A non-equilibrium Green's function (NEGF) quantum transport method coupled with a $\vec{k} \cdot \vec{p}$ description of the electronic structure is demonstrated to achieve excellent agreement with the reported experimental data. Factors influencing the electrostatic control of the channel are investigated and reveal that detailed modeling of the electrostatics and the electronic band structure of the CNT is required to achieve quantitative agreement with experiment.

Ultrasound image quality has continually been improving. However, when needles or other metallic objects are operating inside the tissue, the resulting reverberation artifacts can severely corrupt the surrounding image quality. Such effects are challenging for existing computer vision algorithms for medical image analysis. Needle reverberation artifacts can be hard to identify at times and affect various pixel values to different degrees. The boundaries of such artifacts are ambiguous, leading to disagreement among human experts labeling the artifacts. We propose a weakly- and semi-supervised, probabilistic needle-and-reverberation-artifact segmentation algorithm to separate the desired tissue-based pixel values from the superimposed artifacts. Our method models the intensity decay of artifact intensities and is designed to minimize the human labeling error. We demonstrate the applicability of the approach and compare it against other segmentation algorithms. Our method is capable of differentiating between the reverberations from artifact-free patches as well as of modeling the intensity fall-off in the artifacts. Our method matches state-of-the-art artifact segmentation performance and sets a new standard in estimating the per-pixel contributions of artifact vs underlying anatomy, especially in the immediately adjacent regions between reverberation lines. Our algorithm is also able to improve the performance downstream image analysis algorithms.

Identifying landmarks in the femoral area is crucial for ultrasound (US) -based robot-guided catheter insertion, and their presentation varies when imaged with different scanners. As such, the performance of past deep learning-based approaches is also narrowly limited to the training data distribution; this can be circumvented by fine-tuning all or part of the model, yet the effects of fine-tuning are seldom discussed. In this work, we study the US-based segmentation of multiple classes through transfer learning by fine-tuning different contiguous blocks within the model, and evaluating on a gamut of US data from different scanners and settings. We propose a simple method for predicting generalization on unseen datasets and observe statistically significant differences between the fine-tuning methods while working towards domain generalization., Comment: Machine Learning for Health (ML4H) at NeurIPS 2020 - Extended Abstract

Silicon quantum dot qubits must contend with low-lying valley excited states which are sensitive functions of the quantum well heterostructure and disorder; quantifying and maximizing the energies of these states are critical to improving device performance. We describe a spectroscopic method for probing excited states in isolated Si/SiGe double quantum dots using standard baseband pulsing techniques, easing the extraction of energy spectra in multiple-dot devices. We use this method to measure dozens of valley excited state energies spanning multiple wafers, quantum dots, and orbital states, crucial for evaluating the dependence of valley splitting on quantum well width and other epitaxial conditions. Our results suggest that narrower wells can be beneficial for improving valley splittings, but this effect can be confounded by variations in growth and fabrication conditions. These results underscore the importance of valley splitting measurements for guiding the development of Si qubits., Comment: 13 pages, 12 figures. accepted for publication by Physical Review Applied