BACKGROUND Antidepressant treatment efficacy is low, but might be improved by matching patients to interventions. At present, clinicians have no empirically validated mechanisms to assess whether a patient with depression will respond to a specific antidepressant. We aimed to develop an algorithm to assess whether patients will achieve symptomatic remission from a 12-week course of citalopram. ...

Aiming at the low accuracy of network intrusion detection (In-De) in traditional communication strategy new energy vehicles (NEVs), this paper proposes an electronic control (E-C) for NEVs based on cloud platform internet things (IoT) environment. First, and deep learning (D-L) algorithm, E-C system model including sensor, actuator, gateway, is constructed, basis, edge computing introduced to e...

the evaluation and control of the trains induced vibrations needs even more attention in the case of underground tracks which passes near to monuments and historical sites. the rail corrugations which occur due to the wheels’ impulse loads during the operation period of underground railway tracks, usually amplify the ground borne noise and vibration. in the current study, the mentioned phenomen...

a reliable quantitative structure retention relationship (qsrr) study has been evaluated to predict the retention indices (ris) of a broad spectrum of compounds, namely 118 non-linear, cyclic and heterocyclic terpenoids (both saturated and unsaturated), on an hp-5ms fused silica column. a principal component analysis showed that seven compounds lay outside of the main cluster. after elimination...

In this paper we review some of our recent results on discrete-state spiking neuron models. The discrete-state spiking neuron model is a wired system of shift registers and can generate various spike-trains by adjusting the pattern of the wirings. In this paper we show basic relations between the wiring pattern and characteristics of the spike-train. We also show a learning algorithm which util...

Neurons are sensitive to correlations among synaptic inputs. However, analytical models that explicitly include correlations are hard to solve analytically, so their influence on a neuron's response has been difficult to ascertain. To gain some intuition on this problem, we studied the firing times of two simple integrate-and-fire model neurons driven by a correlated binary variable that repres...

In this paper, a new method of 3D model automatic annotation is proposed based on a twodimensional Hidden Markov Model (2-D HMM). Growing importance in the last years Hidden Markov Models are a widely used methodology for sequential data modeling. Recent years, HMMs are applied to research of automatic annotation, such as images and models annotation. The three basic problems with HMM-liked mod...

Measuring agreement between a statistical model and a spike train data series, that is, evaluating goodness of fit, is crucial for establishing the model's validity prior to using it to make inferences about a particular neural system. Assessing goodness-of-fit is a challenging problem for point process neural spike train models, especially for histogram-based models such as perstimulus time hi...