Predilection for Perplexion: Preoperative microstructural damage is linked to postoperative delirium.

Postoperative delirium is the most common postsurgical complication in older adults and is associated with an increased risk of long-term cognitive decline and Alzheimer's disease (AD) and related dementias (ADRD). However, the neurological basis of this increased risk- whether postoperative delirium unmasks latent preoperative pathology or leads to AD-relevant pathology after perioperative brain injury-remains unclear. Recent advancements in neuroimaging techniques now enable the detection of subtle brain features or damage that may underlie clinical symptoms.

Algorithms on ensemble quantum computers.

In ensemble (or bulk) quantum computation, all computations are performed on an ensemble of computers rather than on a single computer. Measurements of qubits in an individual computer cannot be performed; instead, only expectation values (over the complete ensemble of computers) can be measured. As a result of this limitation on the model of computation, many algorithms cannot be processed directly on such computers, and must be modified, as the common strategy of delaying the measurements usually does not resolve this ensemble-measurement problem.

Non-Poisson Fluctuation Statistics In Neuronal Inter-Spike Intervals (ISI): Hurst parameter Estimates of Mouse Retinal Ganglion Signals.

There is considerable recent interest in both (i) modelling the retinal ganglion cells, so that the models can generate output that approximates the actual response of the retina (such models will help design retinal prosthetics); and (ii) understanding how relevant information is encoded in the spike patterns generated by the ganglion cells (these neuronal codes will help understand how the brain analyzes visual scenes). Since the signals (as captured by ISI) are fundamentally stochastic, any modelling or analysis tool will have to track, and make assumptions about, the fluctuations or noise inherently present in these signals. Even though there have been recent work claiming that the fluctuations are fractal in nature, showing long-range dependencies, almost all modelling and analysis work continue to assume Poisson fluctuations.

A filter based encoding model for mouse retinal ganglion cells.

We adopt a system theoretic approach and explore the model of retinal ganglion cells as linear filters followed by a maximum-likelihood Bayesian predictor. We evaluate the model by using cross-validation, i.e.

Statistical mechanical load balancer for the web.

The maximum entropy principle from statistical mechanics states that a closed system attains an equilibrium distribution that maximizes its entropy. We first show that for graphs with fixed number of edges one can define a stochastic edge dynamic that can serve as an effective thermalization scheme, and hence, the underlying graphs are expected to attain their maximum-entropy states, which turn out to be Erdös-Rényi (ER) random graphs. We next show that (i) a rate-equation-based analysis of node degree distribution does indeed confirm the maximum-entropy principle, and (ii) the edge dynamic can be effectively implemented using short random walks on the underlying graphs, leading to a local algorithm for the generation of ER random graphs.