We study the synchronizability of weighted aging scale-free networks with non-normalized and asymmetrical coupling matrices. We found that the synchronizability of such networks is improved when the couplings from older to younger nodes become dominant, where the out degrees of the nodes are heterogeneous and their in degrees are homogeneous, and that the synchronizabilty of the networks is seriously weakened or even lost when the couplings from younger to older nodes become dominant, where the out degrees of the nodes are homogeneous and their in degrees are heterogeneous. We also found that both the heterogeneity of nodes and a smaller average degree can improve the synchronizability of the networks with some appropriately chosen weighting parameter values. We finally show an example of the coupled Lorenz systems for illustration and verification of the theoretical analysis.

In order to improve the accuracy of cell selection in heterogeneous cellular systems, this paper proposes a fuzzy multiple-objective decision-based cell selection (FMDCS) strategy. Since heterogeneous cellular systems have different access technologies and multiple traffic classes, the strategy adopts cell type, data rate, coverage, transmission delay, and call arrival rate as evaluation indices, and uses different weight vectors according to the traffic classes of the mobile host. Then, a fuzzy multiple-objective decision algorithm is applied to select the optimal cell from all candidates. This paper also gives an instance analysis and simulation. The instance analysis shows FMDCS makes different selections for different traffic classes. Simulation results of the after-handoff quality-of-service (QoS) show the selected cell can provide MH optimal service.

A new area of interest in the search for biomarkers for schizophrenia is the study of the acoustic parameters of speech called 'speech signal features'. Several of these features have been shown to be related to emotional responsiveness, a characteristic that is notably restricted in patients with schizophrenia, particularly those with prominent negative symptoms. Assess the relationship of selected acoustic parameters of speech to the severity of clinical symptoms in patients with chronic schizophrenia and compare these characteristics between patients and matched healthy controls. Ten speech signal features-six prosody features, formant bandwidth and amplitude, and two spectral features-were assessed using 15-minute speech samples obtained by smartphone from 26 inpatients with chronic schizophrenia (at enrollment and 1 week later) and from 30 healthy controls (at enrollment only). Clinical symptoms of the patients were also assessed at baseline and 1 week later using the Positive and Negative Syndrome Scale, the Scale for the Assessment of Negative Symptoms, and the Clinical Global Impression-Schizophrenia scale. In the patient group the symptoms were stable over the 1-week interval and the 1-week test-retest reliability of the 10 speech features was good (intraclass correlation coefficients [ICC] ranging from 0.55 to 0.88). Comparison of the speech features between patients and controls found no significant differences in the six prosody features or in the formant bandwidth and amplitude features, but the two spectral features were different: the Mel-frequency cepstral coefficient (MFCC) scores were significantly lower in the patient group than in the control group, and the linear prediction coding (LPC) scores were significantly higher in the patient group than in the control group. Within the patient group, 10 of the 170 associations between the 10 speech features considered and the 17 clinical parameters considered were statistically significant at the p<0.05 level. This study provides some support for the potential value of speech signal features as indicators (i.e., biomarkers) of the severity of negative symptoms in schizophrenia, but more detailed studies using larger samples of more diverse patients that are followed over time will be needed before the potential utility of such acoustic parameters of speech can be fully assessed.

The application of deep learning to the detection of gravitational wave (GW) signals has attracted much attention in recent years, and nearly all of the existing deep learning-based GW detection algorithms are based on the convolutional neural network (CNN), which can be used to identify the GW merger moment and produce responding triggers. However, current CNN-based models are not best optimized tools in modeling the causal logic of sequential data in the time dimension to incorporate the information of the inspiral, merger and ringdown phases of the GW waveform. To address this issue we propose a deep learning model designed based on the bidirectional gated recurrent unit (BiGRU), a variant of the recurrent neural network (RNN) that is sensitive to the sequential logic of GW data. We train the neural network and implement the GW detection task by using the data obtained during the first, second and third observation runs (O1/O2/O3) of the LIGO-VIGRO scientific collaboration. We find that our BiGRU model achieves a false alarm probability (FAP) of $4.88\times {10}^{-4}$ (corresponding false alarm rate is about 1 per 18.2 hours) and maintains a detection ratio of about 89.6%. We also construct a model ensemble containing two independently trained BiGRU models and find it does not trigger any false alarm events on our testing dataset. Meanwhile, our BiGRU model not only recovers all the reported GW events of O1-O2 runs, including the unique (as of today) confident (with observed electromagnetic counterparts) GW event originating from a binary neutron star (BNS) merger (GW170817), but also achieves a detection ratio of about 80% of the O3 run. By calculating and comparing the receiver operator characteristic (ROC) curves we demonstrate the superiority of the BiGRU model over traditional CNN-based models, and show that the BiGRU model, being optimized to deal with GW waveform that sequentially contains the inspiral, merger and ringdown phases, is a potentially powerful tool to study the GW data in the future.