Advanced CMOS manufacturing of superconducting qubits on 300 mm wafers.

The development of superconducting qubit technology has shown great potential for the construction of practical quantum computers. As the complexity of quantum processors continues to grow, the need for stringent fabrication tolerances becomes increasingly critical. Utilizing advanced industrial fabrication processes could facilitate the necessary level of fabrication control to support the continued scaling of quantum processors.

Deep Photonic Reservoir Computer for Speech Recognition.

Speech recognition is a critical task in the field of artificial intelligence (AI) and has witnessed remarkable advancements thanks to large and complex neural networks, whose training process typically requires massive amounts of labeled data and computationally intensive operations. An alternative paradigm, reservoir computing (RC), is energy efficient and is well adapted to implementation in physical substrates, but exhibits limitations in performance when compared with more resource-intensive machine learning algorithms. In this work, we address this challenge by investigating different architectures of interconnected reservoirs, all falling under the umbrella of deep RC (DRC).

Assessing 'readiness' by tracking fluctuations in daily sleep duration and their effects on daily mood, motivation, and sleepiness.

Study Objectives: Consumer sleep trackers issue daily guidance on 'readiness' without clear empirical basis. We investigated how self-rated mood, motivation, and sleepiness (MMS) levels are affected by daily fluctuations in sleep duration, timing, and efficiency and overall sleep regularity. We also determined how temporally specific these associations are.

Integrated programmable spectral filter for frequency-multiplexed neuromorphic computers.

Artificial neural networks (ANN) are a groundbreaking technology massively employed in a plethora of fields. Currently, ANNs are mostly implemented through electronic digital computers, but analog photonic implementations are very interesting mainly because of low power consumption and high bandwidth. We recently demonstrated a photonic neuromorphic computing system based on frequency multiplexing that executes ANNs algorithms as reservoir computing and Extreme Learning Machines.

High speed human action recognition using a photonic reservoir computer.

The recognition of human actions in videos is one of the most active research fields in computer vision. The canonical approach consists in a more or less complex preprocessing stages of the raw video data, followed by a relatively simple classification algorithm. Here we address recognition of human actions using the reservoir computing algorithm, which allows us to focus on the classifier stage.

Working-from-home persistently influences sleep and physical activity 2 years after the Covid-19 pandemic onset: a longitudinal sleep tracker and electronic diary-based study.

Objective: Working from home (WFH) has become common place since the Covid-19 pandemic. Early studies observed population-level shifts in sleep patterns (later and longer sleep) and physical activity (reduced PA), during home confinement. Other studies found these changes to depend on the proportion of days that individuals WFH (vs.

Minimum number of nights for reliable estimation of habitual sleep using a consumer sleep tracker.

Study Objectives: To determine the minimum number of nights required to reliably estimate weekly and monthly mean sleep duration and sleep variability measures from a consumer sleep technology (CST) device (Fitbit).

Methods: Data comprised 107 144 nights from 1041 working adults aged 21-40 years. Intraclass correlation (ICC) analyses were conducted on both weekly and monthly time windows to determine the number of nights required to achieve ICC values of 0.

A randomized-controlled trial of a digital, small incentive-based intervention for working adults with short sleep.

Study Objectives: We evaluated the efficacy of a digitally delivered, small and scalable incentive-based intervention program on sleep and wellbeing in short-sleeping, working adults.

Methods: A 22-week, parallel-group, randomized-controlled trial was conducted on 21-40 y participants gifted with FitbitTM devices to measure sleep for ≥2 years, as part of a broader healthy lifestyle study. About 225 short sleepers (141 males; average time-in-bed, TIB < 7h) were randomly assigned in a 2:1 ratio to Goal-Setting or Control groups.

Bedtime procrastination and chronotype differentially predict adolescent sleep on school nights and non-school nights.

Objectives: Bedtime procrastination (BTP) refers to the tendency to delay sleep beyond an intended bedtime, in favor of continuing evening activities. BTP has been associated with negative sleep outcomes (later timing, shorter duration, poorer quality), and is viewed as a problem of exercising self-control. BTP could be particularly challenging in adolescents, given the combined effects of increasing bedtime autonomy, later chronotype, and a still developing self-control capacity.

Limited evidence for enhanced working memory performance and effort allocation in the face of loss versus gain incentives: A preregistered (non) replication.

The motivation to avoid losses is often considered a strong drive of human behavior, affecting decisions in the context of risk, temporal delay, and effort provision. However, studies measuring cognitive performance under loss and gain incentives have yielded mixed findings. In a recent study, we found evidence that losses motivated better working memory performance than gains.

Photonic reservoir computer based on frequency multiplexing.

Reservoir computing is a brain-inspired approach for information processing, well suited to analog implementations. We report a photonic implementation of a reservoir computer that exploits frequency domain multiplexing to encode neuron states. The system processes 25 comb lines simultaneously (i.

Inter-relationships between changes in stress, mindfulness, and dynamic functional connectivity in response to a social stressor.

We conducted a study to understand how dynamic functional brain connectivity contributes to the moderating effect of trait mindfulness on the stress response. 40 male participants provided subjective reports of stress, cortisol assays, and functional MRI before and after undergoing a social stressor. Self-reported trait mindfulness was also collected.

Reopening after lockdown: the influence of working-from-home and digital device use on sleep, physical activity, and wellbeing following COVID-19 lockdown and reopening.

Study Objectives: COVID-19 lockdowns drastically affected sleep, physical activity, and wellbeing. We studied how these behaviors evolved during reopening the possible contributions of continued working from home and smartphone usage.

Methods: Participants (N = 198) were studied through the lockdown and subsequent reopening period, using a wearable sleep/activity tracker, smartphone-delivered ecological momentary assessment (EMA), and passive smartphone usage tracking.

Photonic extreme learning machine based on frequency multiplexing.

The optical domain is a promising field for the physical implementation of neural networks, due to the speed and parallelism of optics. Extreme learning machines (ELMs) are feed-forward neural networks in which only output weights are trained, while internal connections are randomly selected and left untrained. Here we report on a photonic ELM based on a frequency-multiplexed fiber setup.

Photonic Reservoir Computer with Output Expansion for Unsupervized Parameter Drift Compensation.

We present a method to improve the performance of a reservoir computer by keeping the reservoir fixed and increasing the number of output neurons. The additional neurons are nonlinear functions, typically chosen randomly, of the reservoir neurons. We demonstrate the interest of this expanded output layer on an experimental opto-electronic system subject to slow parameter drift which results in loss of performance.

Resource efficient single photon source based on active frequency multiplexing.

We propose a new, to the best of our knowledge, single photon source based on the principle of active multiplexing of heralded single photons, which, unlike previously reported architecture, requires a limited amount of physical resources. We discuss both its feasibility and the purity and indistinguishability of single photons as a function of the key parameters of a possible implementation.

Trait-like nocturnal sleep behavior identified by combining wearable, phone-use, and self-report data.

Using polysomnography over multiple weeks to characterize an individual's habitual sleep behavior while accurate, is difficult to upscale. As an alternative, we integrated sleep measurements from a consumer sleep-tracker, smartphone-based ecological momentary assessment, and user-phone interactions in 198 participants for 2 months. User retention averaged >80% for all three modalities.

COVID-19-related mobility reduction: heterogenous effects on sleep and physical activity rhythms.

Study Objectives: Mobility restrictions imposed to suppress transmission of COVID-19 can alter physical activity (PA) and sleep patterns that are important for health and well-being. Characterization of response heterogeneity and their underlying associations may assist in stratifying the health impact of the pandemic.

Methods: We obtained wearable data covering baseline, incremental mobility restriction, and lockdown periods from 1,824 city-dwelling, working adults aged 21-40 years, incorporating 206,381 nights of sleep and 334,038 days of PA.

Losses Motivate Cognitive Effort More Than Gains in Effort-Based Decision Making and Performance.

Human behavior is more strongly driven by the motivation to avoid losses than to pursue gains (loss aversion). However, there is little research on how losses influence the motivation to exert effort. We compared the effects of loss and gain incentives on cognitive task performance and effort-based decision making.

Dissociable influences of implicit temporal expectation on attentional performance and mind wandering.

Mind wandering at critical moments during a cognitive task degrades performance. At other moments, mind wandering could serve to conserve task-relevant resources, allowing a brief mental respite. Recent research has shown that, if target timing is predictable, mind wandering episodes coincide with moments of low target likelihood.

Pace yourself: Neural activation and connectivity changes over time vary by task type and pacing.

Performance deterioration over time, or time-on-task (TOT) effects, can be observed across a variety of tasks, but little attention has been paid to how TOT-related brain activity may differ based on task pacing and cognitive demands. Here, we employ a set of three closely related tasks to investigate the effect of these variables on fMRI activation and connectivity. When participants dictated the pace of their own responses, activation and network connectivity within the dorsal attention network (DAN) increased over short time scales (~2-3 min), a phenomenon that was not observed when participants had no control over their pace of work.

Sleep deprivation, effort allocation and performance.

Sleep deprivation causes physiological alterations (e.g., decreased arousal, intrusion of micro-sleeps), that negatively affect performance on a wide range of cognitive domains.