Noisy Settings Research Articles

A Novel Noise-Aware Classical Optimizer for Variational Quantum Algorithms

A key component of variational quantum algorithms (VQAs) is the choice of classical optimizer employed to update the parameterization of an ansatz. It is well recognized that quantum algorithms will, for the foreseeable future, necessarily be run on noisy devices with limited fidelities. Thus, the evaluation of an objective function (e.g., the guiding function in the quantum approximate optimization algorithm (QAOA) or the expectation of the electronic Hamiltonian in variational quantum eigensolver (VQE)) required by a classical optimizer is subject not only to stochastic error from estimating an expected value but also to error resulting from intermittent hardware noise. Model-based derivative-free optimization methods have emerged as popular choices of a classical optimizer in the noisy VQA setting, based on empirical studies. However, these optimization methods were not explicitly designed with the consideration of noise. In this work we adapt recent developments from the “noise-aware numerical optimization” literature to these commonly used derivative-free model-based methods. We introduce the key defining characteristics of these novel noise-aware derivative-free model-based methods that separate them from standard model-based methods. We study an implementation of such noise-aware derivative-free model-based methods and compare its performance on demonstrative VQA simulations to classical solvers packaged in scikit-quant. History: Accepted by Giacomo Nannicini, Area Editor for Quantum Computing and Operations Research. Accepted for Special Issue. Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers and the Office of Advanced Scientific Computing Research, Accelerated Research for Quantum Computing program under contract number DE-AC02-06CH11357. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0177 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0177 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Spectral Subtraction Based Weighted Function for Pitch Extraction in Noisy Speech

Many speech-related works employ the pitch period as a crucial component. Speech signals are typically collected in challenging noisy settings for real-world projects. Therefore, it is now more important than ever for the algorithm to be noise resistant in order to estimate pitch accurately. However, when dealing with noisy speech files at a low signal-to-noise ratio (SNR) value, many state-of-the-art algorithms are unable to produce satisfactory results. In this work, a new noise-resistant pitch estimation algorithm based on spectral subtraction is presented, which uses a weighted function to lessen the impact of the vocal tract effect. Furthermore, to enhance the correlation between the pitch estimates and smoothen the pitch contours, we employ a weighted function that combines the spectral subtraction-based technique as the numerator and the circular average magnitude difference function (CAMDF) as the denominator. We have utilized two noisy speech databases using seven different kinds of recorded ambient noise, and we evaluated our system against three cutting-edge methods. The suggested method lowers the Gross Pitch Error (GPE) rate at practically all SNRs in white noise and performs best on the NTT and KEELE databases.

Deceptively simple: An outsider's perspective on natural language processing

AbstractThis article highlights a collection of ideas with an underlying deceptive simplicity that addresses several practical challenges in computational social science and generative AI safety. These ideas lead to (1) an interpretable and quantifiable framework for political polarization; (2) a language identifier robust to noisy social media text settings; (3) a cross‐lingual semantic sampler that harnesses code‐switching; and (4) a bias audit framework that uncovers shocking racism, antisemitism, misogyny, and other biases in a wide suite of large language models.

Experiencing therapeutic soundscapes in rural tourism destinations

Rural tourism is perceived as an escape from noisy urban settings to tranquil rural environments to improve tourist well-being. However, the dynamics of how tourists engage with and benefit from rural soundscapes, particularly at night when visual stimuli diminish and auditory experiences become more pronounced, remain underexplored. This article investigates the effects of rural soundscapes on tourist well-being throughout different periods of the day. Using a mixed-methods approach, this study reveals that tourists’ experiences of therapeutic rural soundscapes are relational and contextual. Therapeutic rural soundscapes are primarily experienced during nighttime, facilitated by the absence of visual distractions and man-made sounds and the presence of familiar natural and rural sounds, alongside a prevailing sense of safety in the silence and darkness. Nevertheless, the majority of tourists are one-day visitors and thus largely excluded from these therapeutic soundscape experiences, which are overshadowed by visual attractions and the intrusion of man-made sounds. The findings advance the theory of therapeutic sensescapes by revealing the interplay between the senses of vision and hearing in shaping tourists’ safety perceptions and well-being experiences in rural destinations. This study also offers practical implications for enhancing the appeal of rural destinations by improving tourists’ auditory experiences across diverse spatio-temporal contexts.

Neural networks for quantum state tomography with constrained measurements

Quantum state tomography (QST) aiming at reconstructing the density matrix of a quantum state plays an important role in various emerging quantum technologies. Recognizing the challenges posed by imperfect measurement data, we develop a unified neural network (NN)-based approach for QST under constrained measurement scenarios, including limited measurement copies, incomplete measurements, and noisy measurements. Through comprehensive comparison with other estimation methods, we demonstrate that our method improves the estimation accuracy in scenarios with limited measurement resources, showcasing notable robustness in noisy measurement settings. These findings highlight the capability of NNs to enhance QST with constrained measurements.

Efficient and Robust Arabic Automotive Speech Command Recognition System

The automotive speech recognition field has become an active research topic as it enables drivers to activate various in-car functionalities without being distracted. However, research in Arabic remains nascent compared to English, French, and German. Therefore, this paper presents a Moroccan Arabic automotive speech recognition system. Our system aims to enhance the driving experience to make it comfortable and safe while assisting individuals with disabilities. We created a speech dataset comprising 20 commonly used car commands. It consists of 5600 instances collected from Moroccan contributors and recorded in clean and noisy environments to increase its representativity. We used MFCC, weighted MFCC, and Spectral Subband Centroids (SSC) for feature extraction, as they demonstrated promising results in noisy settings. For classifier construction, we proposed a hybrid architecture, consisting of Bidirectional Long Short-Term Memory (Bi-LSTM) and the Convolutional Neural Network (CNN). Training our proposed model with WMFCC and SSC features achieved an accuracy of 98.48%, outperforming all baseline models we trained and outperforming the existing solutions in the state-of-the-art literature. Moreover, it shows promising results in a clean and noisy environment and maintains resilience to additive Gaussian noise while using few computational resources.

Assessing the Effectiveness of Automatic Speech Recognition Technology in Emergency Medicine Settings: A Comparative Study of Four AI-powered Engines.

Cutting-edge automatic speech recognition (ASR) technology holds significant promise in transcribing and recognizing medical information during patient encounters, thereby enabling automatic and real-time clinical documentation, which could significantly alleviate care clinicians' burdens. Nevertheless, the performance of current-generation ASR technology in analyzing conversations in noisy and dynamic medical settings, such as prehospital or Emergency Medical Services (EMS), lacks sufficient validation. This study explores the current technological limitations and future potential of deploying ASR technology for clinical documentation in fastpaced and noisy medical settings such as EMS. In this study, we evaluated four ASR engines, including Google Speech-to-Text Clinical Conversation, OpenAI Speech-to-Text, Amazon Transcribe Medical, and Azure Speech-to-Text engine. The empirical data used for evaluation were 40 EMS simulation recordings. The transcribed texts were analyzed for accuracy against 23 Electronic Health Records (EHR) categories of EMS. The common types of errors in transcription were also analyzed. Among all four ASR engines, Google Speech-to-Text Clinical Conversation performed the best. Among all EHR categories, better performance was observed in categories "mental state" (F1 = 1.0), "allergies" (F1 = 0.917), "past medical history" (F1 = 0.804), "electrolytes" (F1 = 1.0), and "blood glucose level" (F1 = 0.813). However, all four ASR engines demonstrated low performance in transcribing certain critical categories, such as "treatment" (F1 = 0.650) and "medication" (F1 = 0.577). Current ASR solutions fall short in fully automating the clinical documentation in EMS setting. Our findings highlight the need for further improvement and development of automated clinical documentation technology to improve recognition accuracy in time-critical and dynamic medical settings.

Random coordinate descent: A simple alternative for optimizing parameterized quantum circuits

Variational quantum algorithms rely on the optimization of parameterized quantum circuits in noisy settings. The commonly used back-propagation procedure in classical machine learning is not directly applicable in this setting due to the collapse of quantum states after measurements. Thus, gradient estimations constitute a significant overhead in a gradient-based optimization of such quantum circuits. This paper introduces a random coordinate descent algorithm as a practical and easy-to-implement alternative to the full gradient descent algorithm. This algorithm only requires one partial derivative at each iteration. Motivated by the behavior of measurement noise in the practical optimization of parameterized quantum circuits, this paper presents an optimization problem setting that is amenable to analysis. Under this setting, the random coordinate descent algorithm exhibits the same level of stochastic stability as the full gradient approach, making it as resilient to noise. The complexity of the random coordinate descent method is generally no worse than that of the gradient descent and can be much better for various quantum optimization problems with anisotropic Lipschitz constants. Theoretical analysis and extensive numerical experiments validate our findings. Published by the American Physical Society 2024

Management of subjective tinnitus with conductive hearing loss through Ayurveda: An experience

Tinnitus (~Karnanada) is a type of perception of sound that people occasionally experience in the absence of an accompanying external sound. The noise can be intermittent or continuous and can vary in loudness. Tinnitus is frequently linked to hearing loss and impaired speech understanding in noisy settings. A 31-year-old female patient came to the outpatient department with complaints of annoying sounds in both ears, hearing impairment in the right ear, headache, and disturbed sleep for the past 3 years. The patient was examined using tuning fork test, pure-tone audiometry, and impedance audiometry, which led to a diagnosis of subjective tinnitus with conductive hearing loss in both ears. The difficulties faced by the patient in daily life due to tinnitus were assessed using the Tinnitus Handicap Inventory (THI) questionnaire and the THI score. The patient was treated with Nirgundyadi taila karnapurana (~instillation of medicated oil in the ear) along with Ashwagandhadi ghrita orally for 21 days. The patient showed relief in tinnitus along with an improvement in hearing loss. There was no recurrence during a follow-up period of 30 days. The treatment reveals that Ayurvedic interventions are effective in the management of tinnitus quite efficiently.

Variational Quantum Algorithms for Semidefinite Programming

A semidefinite program (SDP) is a particular kind of convex optimization problem with applications in operations research, combinatorial optimization, quantum information science, and beyond. In this work, we propose variational quantum algorithms for approximately solving SDPs. For one class of SDPs, we provide a rigorous analysis of their convergence to approximate locally optimal solutions, under the assumption that they are weakly constrained (i.e., N≫M, where N is the dimension of the input matrices and M is the number of constraints). We also provide algorithms for a more general class of SDPs that requires fewer assumptions. Finally, we numerically simulate our quantum algorithms for applications such as MaxCut, and the results of these simulations provide evidence that convergence still occurs in noisy settings.

On the optimality of the radical-pair quantum compass

Quantum sensing enables the ultimate precision attainable in parameter estimation. Circumstantial evidence suggests that certain organisms, most notably migratory songbirds, also harness quantum-enhanced magnetic field sensing via a radical-pair-based chemical compass for the precise detection of the weak geomagnetic field. However, what underpins the acuity of such a compass operating in a noisy biological setting, at physiological temperatures, remains an open question. Here, we address the fundamental limits of inferring geomagnetic field directions from radical-pair spin dynamics. Specifically, we compare the compass precision, as derived from the directional dependence of the radical-pair recombination yield, to the ultimate precision potentially realisable by a quantum measurement on the spin system under steady-state conditions. To this end, we probe the quantum Fisher information and associated Cramér–Rao bound in spin models of realistic complexity, accounting for complex inter-radical interactions, a multitude of hyperfine couplings, and asymmetric recombination kinetics, as characteristic for the magnetosensory protein cryptochrome. We compare several models implicated in cryptochrome magnetoreception and unveil their optimality through the precision of measurements ostensibly accessible to nature. Overall, the comparison provides insight into processes honed by nature to realise optimality whilst constrained to operating with mere reaction yields. Generally, the inference of compass orientation from recombination yields approaches optimality in the limits of complexity, yet levels off short of the theoretical optimal precision bounds by up to one or two orders of magnitude, thus underscoring the potential for improving on design principles inherent to natural systems.

Financial and prosocial incentives promote pro-environmental behavior in a consequential laboratory task

Incentives are a popular tool for fostering pro-environmental behavior. However, it is still largely unknown which types of incentives are most effective during incentive implementation and beyond incentive discontinuation. One likely reason for this is that incentives are often tested in noisy field settings where controlled comparisons and longitudinal assessments are difficult. In this study, we address this limitation by studying incentive effects on consequential pro-environmental behavior in the laboratory. In a preregistered experiment, N = 238 participants were repeatedly given the choice to save actual energy at a real waiting-time cost in the Pro-Environmental Behavior Task (PEBT). Incentivizing the energy-saving option with a 5-cent bonus payment led to a significant increase in pro-environmental behavior compared to a no-incentive control group (d = 0.68). Incentivizing the energy-saving option with a 5-cent donation to charity had a smaller, but still significant effect (d = 0.28). The difference between the incentive conditions was also significant (d = 0.41). Group differences disappeared in a follow-up experimental session that was completed without incentives. These data illustrate the (temporally limited) effectiveness of financial and prosocial incentives and the possibility of studying incentive effects on actual pro-environmental behavior in the laboratory.

Error-mitigated fermionic classical shadows on noisy quantum devices

Efficiently estimating fermionic Hamiltonian expectation values is vital for simulating various physical systems. Classical shadow (CS) algorithms offer a solution by reducing the number of quantum state copies needed, but noise in quantum devices poses challenges. We propose an error-mitigated CS algorithm assuming gate-independent, time-stationary, and Markovian (GTM) noise. For n-qubit systems, our algorithm, which employs the easily prepared initial state 0n0n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left\\vert {0}^{n}\\right\\rangle \\,\\left\\langle {0}^{n}\\right\\vert$$\\end{document} assumed to be noiseless, efficiently estimates k-RDMs with Õ(knk)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\widetilde{{{{\\mathcal{O}}}}}(k{n}^{k})$$\\end{document} state copies and Õ(n)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\widetilde{{{{\\mathcal{O}}}}}(\\sqrt{n})$$\\end{document} calibration measurements for GTM noise with constant fidelities. We show that our algorithm is robust against noise types like depolarizing, damping, and X-rotation noise with constant strengths, showing scalings akin to prior CS algorithms for fermions but with better noise resilience. Numerical simulations confirm our algorithm’s efficacy in noisy settings, suggesting its viability for near-term quantum devices.

Predicting Individual Well-Being in Teamwork Contexts Based on Speech Features

Current methods for assessing individual well-being in team collaboration at the workplace often rely on manually collected surveys. This limits continuous real-world data collection and proactive measures to improve team member workplace satisfaction. We propose a method to automatically derive social signals related to individual well-being in team collaboration from raw audio and video data collected in teamwork contexts. The goal was to develop computational methods and measurements to facilitate the mirroring of individuals’ well-being to themselves. We focus on how speech behavior is perceived by team members to improve their well-being. Our main contribution is the assembly of an integrated toolchain to perform multi-modal extraction of robust speech features in noisy field settings and to explore which features are predictors of self-reported satisfaction scores. We applied the toolchain to a case study, where we collected videos of 20 teams with 56 participants collaborating over a four-day period in a team project in an educational environment. Our audiovisual speaker diarization extracted individual speech features from a noisy environment. As the dependent variable, team members filled out a daily PERMA (positive emotion, engagement, relationships, meaning, and accomplishment) survey. These well-being scores were predicted using speech features extracted from the videos using machine learning. The results suggest that the proposed toolchain was able to automatically predict individual well-being in teams, leading to better teamwork and happier team members.

An Improved OMP Algorithm for Enhancing the Anti-Interference Performance of Array Antennas.

The demand for precise positioning in noisy environments has propelled the development of research on array antenna radar systems. Although the orthogonal matching pursuit (OMP) algorithm demonstrates superior performance in signal reconstruction, its application efficacy in noisy settings faces challenges. Consequently, this paper introduces an innovative OMP algorithm, DTM_OMP_ICA (a dual-threshold mask OMP algorithm based on independent component analysis), which optimizes the OMP signal reconstruction framework by utilizing two different observation bases in conjunction with independent component analysis (ICA). By implementing a mean mask strategy, it effectively denoises signals received by array antennas in noisy environments. Simulation results reveal that compared to traditional OMP algorithms, the DTM_OMP_ICA algorithm shows significant advantages in noise suppression capability and algorithm stability. Under optimal conditions, this algorithm achieves a noise suppression rate of up to 96.8%, with its stability also reaching as high as 99%. Furthermore, DTM_OMP_ICA surpasses traditional denoising algorithms in practical denoising applications, proving its effectiveness in reconstructing array antenna signals in noisy settings. This presents an efficient method for accurately reconstructing array antenna signals against a noisy backdrop.

Pediatric normative data for a novel and fast speech perception test in noise

ObjectivesCommunicating in noisy settings can be difficult due to interference and environmental noise, which can impact intelligibility for those with hearing impairments and those with normal hearing threshold. Speech intelligibility is commonly assessed in audiology through speech audiometry in quiet environments. Nevertheless, this test may not effectively assess hearing challenges in noisy environments, as total silence is rare in daily activities. A recently patented method, known as the SRT50 FAST, has been developed for conducting speech audiometry in noise. This new method enables the acceleration and simplification of free field speech audiometry tests involving competition noise.This study aims to establish normative scores and standardize the SRT50 FAST method as a test for evaluating speech perception in noise in pediatric patients. MethodsThe study included 30 participants with normal hearing, consisting of 11 females and 19 males, ranging in age from 6 to 11 years. A series of speech audiometry tests were conducted to determine the speech reception threshold 50% (SRT50) in competing conditions. This included testing both the fast mode (SRT50 FAST) currently being studied and the traditional method (SRT50 CLASSIC). The SRT50, or Signal to Noise Ratio (SNR) at which 50% of speech recognition occurred, was investigated for both methods. ResultsThe mean SRT50 FAST test score was −2.69 (SD = 3.15). The dataset exhibited a normal distribution with values ranging from 3.60 to −8.60. Since the scores are expressed in SRT, higher scores indicate poorer performance. We have established a threshold of 3.60 as the upper limit of the normal range, therefore, patients with scores above this threshold are considered to have abnormal results. ConclusionsThis study aimed to establish normative data for the evaluation of free field speech in noise recognition using the SRT50 FAST method in the pediatric population. This method accurately investigates the necessary signal-to-noise ratio for achieving 50% recognition scores with bisyllabic words in a quick manner. The ultimate objective is to employ this test to identify the optimal configuration of hearing rehabilitation devices, particularly for pediatric patients with hearing aids and/or cochlear implants. Additionally, it can be used to assess pediatric patients with unilateral hearing loss.

CAVEN: An Embodied Conversational Agent for Efficient Audio-Visual Navigation in Noisy Environments

Audio-visual navigation of an agent towards locating an audio goal is a challenging task especially when the audio is sporadic or the environment is noisy. In this paper, we present CAVEN, a Conversation-based Audio-Visual Embodied Navigation framework in which the agent may interact with a human/oracle for solving the task of navigating to an audio goal. Specifically, CAVEN is modeled as a budget-aware partially observable semi-Markov decision process that implicitly learns the uncertainty in the audio-based navigation policy to decide when and how the agent may interact with the oracle. Our CAVEN agent can engage in fully-bidirectional natural language conversations by producing relevant questions and interpret free-form, potentially noisy responses from the oracle based on the audio-visual context. To enable such a capability, CAVEN is equipped with: i) a trajectory forecasting network that is grounded in audio-visual cues to produce a potential trajectory to the estimated goal, and (ii) a natural language based question generation and reasoning network to pose an interactive question to the oracle or interpret the oracle's response to produce navigation instructions. To train the interactive modules, we present a large scale dataset: AVN-Instruct, based on the Landmark-RxR dataset. To substantiate the usefulness of conversations, we present experiments on the benchmark audio-goal task using the SoundSpaces simulator under various noisy settings. Our results reveal that our fully-conversational approach leads to nearly an order-of-magnitude improvement in success rate, especially in localizing new sound sources and against methods that use only uni-directional interaction.

A multi-function integrated PVDF transducer for fault detection and speed measurement of cylindrical roller bearings

Monitoring the condition of bearings in rotating machinery is crucial to ensure reliability, safety, and efficiency. Recently, integrated sensors have become more popular than traditional accelerometers in bearing condition monitoring because of their compact design, cost-effectiveness, and improved efficiency in fault detection through a short transmission path. This study presents a new approach to bearing condition monitoring by developing a bearing with an integrated Polyvinylidene Fluoride transducer. This polymer-based transducer, known as PVDF, has a piezoelectric effect which generates electric charge in response to mechanical excitation. The PVDF transducer was selected for its high sensitivity, flexibility, and thin structure. The developed bearing with the integrated PVDF transducer can detect faults under constant and variable speeds, and the damage size can be estimated with an accuracy of 10%. It outperformed a commercial accelerometer in noisy environments with artificial impacts from an air motor, making it a highly reliable option for bearing condition monitoring in noisy industrial settings. The PVDF transducer can also be used as a low-cost speed sensor with an accuracy of 5.2%, offering a versatile solution for machine monitoring. By simultaneously recording rotational speed and vibration, the developed integrated transducer provides a comprehensive view of machine health and performance as a multi-function transducer, enabling more accurate and efficient maintenance scheduling.

Listening-Related Fatigue in New and Experienced Adult Cochlear Implant Users.

Active listening in everyday settings is challenging and requires substantial mental effort, particularly in noisy settings. In some cases, effortful listening can lead to significant listening-related fatigue and negatively affect quality of life. However, our understanding of factors that affect the severity of fatigue is limited. Hearing aids and cochlear implants (CIs) can improve speech understanding and thus, potentially, reduce listening effort and fatigue. Some research supports this idea for adult hearing aid users with mild-to-moderate hearing loss, but similar work in CI users is very limited. This study examined (1) longitudinal changes in listening-related fatigue in new and established CI users, and (2) relationships between demographic and audiologic factors and preimplantation and postimplantation listening-related fatigue. Participants included an experimental group of 48 adult CI candidates receiving either a unilateral implant (n = 46) or simultaneous, bilateral implants (n = 2) and a control group of 96 experienced (>12 months experience) adult CI users (50 unilateral, 46 bilateral). Listening-related fatigue was evaluated using the 40-item version of the Vanderbilt Fatigue Scale for Adults. Experimental group ratings were obtained before implantation and again at 0.5-, 1-, 2-, 3-, 6-, and 12-month(s) postactivation. Control group participants completed the scale twice-upon study entry and approximately 3 months later. Additional measures, including a social isolation and disconnectedness questionnaire, hearing handicap inventory, and the Effort Assessment Scale, were also administered at multiple time points. The role of these measures and select demographic and audiologic factors on preimplant and postimplant fatigue ratings were examined. Adult CI candidates reported significantly more fatigue, greater self-perceived hearing handicap, greater listening effort, and more social isolation than experienced adult CI users. However, significant reductions in fatigue and effort were observed within 2 weeks postimplantation. By 3 months, there were no significant differences in fatigue, effort, hearing handicap, or social isolation between new CI recipients and experienced CI users. Secondary analyses revealed that age at onset of hearing loss (before or after 2 years of age) and subjective hearing handicap contributed significantly to the variance of preimplantation fatigue ratings (those with higher handicap reported higher fatigue). In contrast, variance in postimplantation fatigue ratings was not affected by age of hearing loss onset but was affected by gender (females reported more fatigue than males) and subjective ratings of effort, handicap, and isolation (those reporting more effort, handicap, and isolation reported more fatigue). Listening-related fatigue is a significant problem for many CI candidates, as well as for many experienced unilateral and bilateral CI users. Receipt of a CI significantly reduced listening-related fatigue (as well as listening effort, hearing handicap, and social isolation) as soon as 2 weeks post-CI activation. However, the magnitude of fatigue-related issues for both CI candidates and experienced CI users varies widely. Audiologic factors, such as hearing loss severity and aided speech recognition, were not predictive of individual differences in listening-related fatigue. In contrast, strong associations were observed between perceived hearing handicap and listening-related fatigue in all groups suggesting fatigue-related issues may be a component of perceived hearing handicap.

Virtual Quantum Resource Distillation.

Distillation, or purification, is central to the practical use of quantum resources in noisy settings often encountered in quantum communication and computation. Conventionally, distillation requires using some restricted "free" operations to convert a noisy state into one that approximates a desired pure state. Here, we propose to relax this setting by only requiring the approximation of the measurement statistics of a target pure state, which allows for additional classical postprocessing of the measurement outcomes. We show that this extended scenario, which we call "virtual resource distillation," provides considerable advantages over standard notions of distillation, allowing for the purification of noisy states from which no resources can be distilled conventionally. We show that general states can be virtually distilled with a cost (measurement overhead) that is inversely proportional to the amount of existing resource, and we develop methods to efficiently estimate such cost via convex and semidefinite programming, giving several computable bounds. We consider applications to coherence, entanglement, and magic distillation, and an explicit example in quantum teleportation (distributed quantum computing). This work opens a new avenue for investigating generalized ways to manipulate quantum resources.