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GPS Code Collection (v1.2)
No full textThis repository provides a collection of codes for the analysis of GPS/RO observations
Multiscale modeling approach of solid oxide cell contacts with dimensional tolerances for estimating electrical contact resistance
No full textSoil moisture retrieval from Sentinel-1: Lessons learned after more than a decade in orbit
No full textDiagnosing crosstalk in large-scale QPUs using zero-entropy classical shadows
No full textAs quantum processing units (QPUs) scale toward hundreds of qubits, diagnosing noise-induced correlations (crosstalk) becomes critical for reliable quantum computation. In this work, we introduce Zero-Entropy Classical Shadows (ZECS), a diagnostic tool that uses information of a rank-one quantum state tomography reconstruction from classical shadow information to make a crosstalk diagnosis. We use ZECS on trapped ion and superconductive QPUs including ionq_forte (36 qubits), ibm_brisbane (127 qubits), and ibm_fez (156 qubits), using from 1000 to 6000 samples. With these samples, we use the ZECS to characterize crosstalk among disjoint qubit subsets across the full hardware. This information is then used to select low-crosstalk qubit subsets on ibm_fez for executing the quantum approximate optimization algorithm on a 20-qubit problem. Compared to the best qubit selection via Qiskit transpilation, our method improves solution quality by 10% and increases algorithmic coherence by 33%. ZECS offers a scalable and measurement-efficient approach to diagnosing crosstalk in large-scale QPUs
Complexity and Criticality in Neuro-Inspired Reservoirs
No full textUnderstanding information propagation and computational capabilities in large-scale brain models is crucial for advancing both neuroscience and neuro-inspired computing. It remains unknown how complexity affects the performance of simulated biological networks in predicting non-linear dynamics. Here, this issue is addressed by integrating reservoir computing, a recurrent neural network architecture, with The Virtual Brain, a whole-brain simulation platform, to create amore neurophysiologically-plausible machine learning framework. Metrics derived from nonlinear dynamics and complexity theory, including the largest Lyapunov exponent, which captures chaotic behavior, Detrended Fluctuation Analysis, which assesses temporal correlations, and the Perturbational Complexity Index, which evaluates the complexity of neural responses, provide a quantitative framework for characterizing cognitive dynamics. Deploying this framework on High Performance Computing enables a thorough exploration of the vast parameter space, utilizing a diverse evaluation framework that assesses simulations through these metrics, implemented on Graphics Processing Units (GPUs). This enables the identification of optimal parameter regimes, a comprehensive characterization of the complex dynamics exhibited by the system, and a deeper understanding of the underlying mechanisms governing the TVB-based reservoir’s computational capabilities. Ridge regression, accelerated also by GPUs, is used to extract the predictive capacity from the reservoir states. The results suggest that edge-of-chaosdynamics correspond to enhanced memory and prediction accuracy, supporting the potential of TVB-based reservoirs for brain-inspired machine learning
Apraxic deficits in Alzheimer’s disease are associated with altered dynamic connectivity in praxis-related networks
No full textApraxia is a common symptom in Alzheimer's disease (AD) that reduces autonomy and quality of life. However, the neural basis underlying apraxia in AD, for example, reflected by functional connectivity (FC) alterations, remains unexplored. We investigated static and dynamic FC using resting-state functional imaging in 14 patients with biomarker-confirmed AD pathology and 14 matched healthy participants. FC was estimated as average (static) and short-term (dynamic) connectivity strengths between motor- and praxis-related functional networks. Recurring connectivity patterns were clustered into dynamic states to compute temporal connectivity measures. Connectivity measures were used for correlations with apraxic deficits. In AD patients, static connectivity between visual and inferior parietal networks correlated with apraxic imitation (r = 0.762, PFDR = 0.043) and arm/hand gesture deficits (r = 0.848, PFDR = 0.020), while dynamic connectivity between these networks correlated with apraxic imitation deficits (r = 0.851, PFDR = 0.020). Dynamic FC analysis revealed a segregated and integrated state. AD patients spent more time overall (fraction time, PFDR < 0.001) and remained longer without switching (dwell time, PFDR = 0.004) in the segregated state. Both fraction (ρ = -0.858, PFDR = 0.015) and dwell time (ρ = -0.914, PFDR = 0.003) correlated with apraxic imitation deficits. Connectivity strengths between visual and inferior parietal networks and fraction time in the segregated state predicted apraxic imitation deficits (adjusted R2 = 0.782, P < 0.001). We conclude that apraxia in AD patients is associated with altered FC in praxis-related networks, suggesting FC as a potential clinical indicator for predicting motor-cognitive deficits.Keywords: Aging; Alzheimer’s disease; Cologne apraxia screening (KAS); Functional magnetic resonance imaging; Motor system; Praxis; Resting-state
AIRS Code Collection (v1.5)
No full textThis repository provides a collection of codes for the analysis of observations of NASA's Atmospheric InfraRed Sounder (AIRS)