Rising early warning signals in affect associated with future changes in depression: a dynamical systems approach

Background Predicting future states of psychopathology such as depressive episodes has been a hallmark initiative in mental health research. Dynamical systems theory has proposed that rises in certain ‘early warning signals’ (EWSs) in time-series data (e.g. auto-correlation, temporal variance, network connectivity) may precede impending changes in disorder severity. The current study investigates whether rises in these EWSs over time are associated with future changes in disorder severity among a group of patients with major depressive disorder (MDD). Methods Thirty-one patients with MDD completed the study, which consisted of daily smartphone-delivered surveys over 8 weeks. Daily positive and negative affect were collected for the time-series analyses. A rolling window approach was used to determine whether rises in auto-correlation of total affect, temporal standard deviation of total affect, and overall network connectivity in individual affect items were predictive of increases in depression symptoms. Results Results suggested that rises in auto-correlation were significantly associated with worsening in depression symptoms (r = 0.41, p = 0.02). Results indicated that neither rises in temporal standard deviation (r = −0.23, p = 0.23) nor in network connectivity (r = −0.12, p = 0.59) were associated with changes in depression symptoms. Conclusions This study more rigorously examines whether rises in EWSs were associated with future depression symptoms in a larger group of patients with MDD. Results indicated that rises in auto-correlation were the only EWS that was associated with worsening future changes in depression.

Evaluation of micro rain radar-based precipitation classification algorithms to discriminate between stratiform and convective precipitation

In this paper, we present two micro rain radar-based approaches to discriminate between stratiform and convective precipitation. One is based on probability density functions (PDFs) in combination with a confidence function, and the other one is an artificial neural network (ANN) classification. Both methods use the maximum radar reflectivity per profile, the maximum of the observed mean Doppler velocity per profile and the maximum of the temporal standard deviation (±15 min) of the observed mean Doppler velocity per profile from a micro rain radar (MRR). Training and testing of the algorithms were performed using a 2-year data set from the Jülich Observatory for Cloud Evolution (JOYCE). Both methods agree well, giving similar results. However, the results of the ANN are more decisive since it is also able to distinguish an inconclusive class, in turn making the stratiform and convective classes more reliable.

Evaluation of micro rain radar-based precipitation classification algorithms to discriminate between stratiform and convective precipitation

In this paper, we present two micro rain radar-based approaches to discriminate between stratiform and convective precipitation. One is based on probability density functions (PDFs) in combination with a confidence function and the other one is an artificial neural network (ANN) classification. Both methods use the maximum radar reflectivity per profile, the maximum of the observed mean Doppler velocity per profile and the maximum of the temporal standard deviation (±15 min) of the observed 5 mean Doppler velocity per profile from a micro rain radar (MRR). Training and testing of the algorithms were performed using a two year data set from the Jülich Observatory for Cloud Evolution (JOYCE). Both methods agree well giving similar results. However, the results of the artificial neural network are more reasonable since it is also able to distinguish into an inconclusive class, in turn making the stratiform and convective classes more reliable.

Intraoperative motion correction in neurosurgery: a comparison of intensity- and feature-based methods

The intraoperative identification of normal and anomalous brain tissue can be disturbed by pulsatile brain motion and movements of the patient and surgery devices. The performance of four motion correction methods are compared in this paper: Two intensity-based, applying optical flow algorithms, and two feature-based, which take corner features into account to track brain motion. The target registration error with manually selected marking points and the temporal standard deviation of intensity were analyzed in the evaluation. The results reveal the potential of the two types of methods.

Real-Time Boundary Detection of Fast Moving Object in Dark Surrounds

Dark surrounds make detection of moving target more difficult based on traditional methods. A real time identification of fast moving object under weak illumination is critical for some special applications. Traditional blob, contour and kernel-based tracking methods either need high computational loads or require normal illumination which limit their application. In this paper, we propose a new method trying to settle such difficulty based on temporal standard deviation. The performance of new method was evaluated with simulation data and real video data recorded by a simple imaging system. Combining hardware acceleration, a real time detection and visualization of fast moving boundary in dark environment can be achieved.

FLUCTUATIONS OF QUANTUM RANDOM WALKS ON CIRCLES

Temporal fluctuations in the Hadamard walk on circles are studied. A temporal standard deviation of probability that a quantum random walker is positive at a given site is introduced to manifest striking differences between quantum and classical random walks. An analytical expression of the temporal standard deviation on a circle with odd sites is shown and its asymptotic behavior is considered for large system size. In contrast with classical random walks, the temporal fluctuation of quantum random walks depends on the position and initial conditions, since temporal standard deviation of the classical case is zero for any site. It indicates that the temporal fluctuation of the Hadamard walk can be controlled.

EVOLUTION OF CONTINUOUS-TIME QUANTUM RANDOM WALKS ON CIRCLES

This work deals with both instantaneous uniform mixing property and temporal standard deviation for continuous-time quantum random walks on circles in order to study their fluctuations comparing with discrete-time quantum random walks, and continuous- and discrete-time classical random walks.