Forecast Combination Under Heavy-Tailed Errors

Forecast combination has been proven to be a very important technique to obtain accurate predictions. In many applications, forecast errors exhibit heavy tail behaviors for various reasons. Unfortunately, to our knowledge, little has been done to deal with forecast combination for such situations. The familiar forecast combination methods such as simple average, least squares regression, or those based on variance-covariance of the forecasts, may perform very poorly. In this paper, we propose two nonparametric forecast combination methods to address the problem. One is specially proposed for the situations that the forecast errors are strongly believed to have heavy tails that can be modeled by a scaled Student's t-distribution; the other is designed for relatively more general situations when there is a lack of strong or consistent evidence on the tail behaviors of the forecast errors due to shortage of data and/or evolving data generating process. Adaptive risk bounds of both methods are developed. Simulations and a real example show superior performance of the new methods

Sub-pixel change detection for urban land-cover analysis via multi-temporal remote sensing images

Conventional change detection approaches are mainly based on per-pixel processing, which ignore the sub-pixel spectral variation resulted from spectral mixture. Especially for medium-resolution remote sensing images used in urban land-cover change monitoring, land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial resolution. Thus, traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably, degrading the overall accuracy of change detection. In order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level, a novel change detection scheme is designed based on the spectral mixture analysis and decision-level fusion. Nonlinear spectral mixture model is selected for spectral unmixing, and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple compositi..

Denoising enabled channel estimation for underwater acoustic communications: A sparsity-aware model-driven learning approach

It has always been difficult to achieve accurate information of the channel for underwater acoustic communications because of the severe underwater propagation conditions, including frequency-selective property, high relative mobility, long propagation latency, and intensive ambient noise, etc. To this end, a deep unfolding neural network based approach is proposed, in which multiple layers of the network mimic the iterations of the classical iterative sparse approximation algorithm to extract the inherent sparse features of the channel by exploiting deep learning, and a scheme based on the Sparsity-Aware DNN (SA-DNN) for UAC estimation is proposed to improve the estimation accuracy. Moreover, we propose a Denoising Sparsity-Aware DNN (DeSA-DNN) based enhanced method that integrates a denoising CNN module in the sparsity-aware deep network, so that the degradation brought by intensive ambient noise could be eliminated and the estimation accuracy can be further improved. Simulation results demonstrate that the performance of the proposed schemes is superior to the state-of-the-art compressed sensing based and iterative sparse recovery schems in the aspects of channel recovery precision, pilot overhead, and robustness, particularly under unideal circumstances of intensive ambient noise or inadequate measurement pilots

The Logistic Regression from the Viewpoint of the Factor Space Theory

Logistic regression plays an important role in machine learning. People excitingly use it in conceptual matching yet with some details to be understood further. This paper aims to present a reasonable statement on logistic regression based on fuzzy sets and the factor space theory. An example about breast cancer diagnosis is displayed to show how the factor space theory can be incorporated into the understanding and use of logistic regression

Cell-Type Specific Distribution of T-Type Calcium Currents in Lamina II Neurons of the Rat Spinal Cord

Spinal lamina II (substantia gelatinosa, SG) neurons integrate nociceptive information from the primary afferents and are classified according to electrophysiological (tonic firing, delayed firing, single spike, initial burst, phasic firing, gap firing and reluctant firing) or morphological (islet, central, vertical, radial and unclassified) criteria. T-type calcium (Cav3) channels play an essential role in the central mechanism of pathological pain, but the electrophysiological properties and the cell-type specific distribution of T-type channels in SG neurons have not been fully elucidated. To investigate the electrophysiological and morphological features of T-type channel-expressing or -lacking neurons, voltage- and current-clamp recordings were performed on either transverse or parasagittal spinal cord slices. Recording made in transverse spinal cord slices showed that an inward current (IT) was observed in 44.5% of the SG neurons that was fully blocked by Ni2+ and TTA-A2. The amplitude of IT depended on the magnitude and the duration of hyperpolarization pre-pulse. The voltage for eliciting and maximizing IT were −70 mV and −35 mV, respectively. In addition, we found that most of the IT-expressing neurons are tonic firing neurons and exhibit more negative action potential (AP) threshold and smaller difference of AP threshold and resting membrane potential (RMP) than those neurons lacking IT. Consistently, a specific T-type calcium channel blocker TTA-P2 increased the AP threshold and enlarged the difference between AP threshold and membrane potential (Ihold = 0). Meanwhile, the morphological analysis indicated that most of the IT-expressing neurons are islet neurons. In conclusion, we identify a cell-type specific distribution and the function of T-type channels in SG neurons. These findings might provide new insights into the mechanisms underlying the contribution of T-type channels in sensory transmission

Acupuncture Modulates the Cerebello-Thalamo-Cortical Circuit and Cognitive Brain Regions in Patients of Parkinson's Disease With Tremor

Objective: To investigate the effect of acupuncture on Parkinson's disease (PD) patients with tremor and its potential neuromechanism by functional magnetic resonance imaging (fMRI).Methods: Forty-one PD patients with tremor were randomly assigned to true acupuncture group (TAG, n = 14), sham acupuncture group (SAG, n = 14) and waiting group (WG, n = 13). All patients received levodopa for 12 weeks. Patients in TAG were acupunctured on DU20, GB20, and the Chorea-Tremor Controlled Zone, and patients in SAG accepted sham acupuncture, while patients in WG received no acupuncture treatment until 12 weeks after the course was ended. The UPDRS II and III subscales, and fMRI scans of the patients' brains were obtained before and after the treatment course. UPDRS II and III scores were analyzed by SPSS, while the degree centrality (DC), regional homogeneity (ReHo) and amplitude low-frequency fluctuation (ALFF) were determined by REST.Results: Acupuncture improved the UPDRS II and III scores in PD patients with tremor without placebo effect, only in tremor score. Acupuncture had specific effects on the cerebrocerebellar pathways as shown by the decreased DC and ReHo and increased ALFF values, and nonspecific effects on the spinocerebellar pathways as shown by the increased ReHo and ALFF values (P < 0.05, AlphaSim corrected). Increased ReHo values were observed within the thalamus and motor cortex of the PD patients (P < 0.05, AlphaSim corrected). In addition, the default mode network (DMN), visual areas and insula were activated by the acupuncture with increased DC, ReHo and/or ALFF, while the prefrontal cortex (PFC) presented a significant decrease in ReHo and ALFF values after acupuncture (P < 0.05, AlphaSim corrected).Conclusions: The cerebellum, thalamus and motor cortex, which are connected to the cerebello-thalamo-cortical (CTC) circuit, were modulated by the acupuncture stimulation to alleviate the PD tremor. The regulation of neural activity within the cognitive brain regions (the DMN, visual areas, insula and PFC) together with CTC circuit may contributes to enhancing movement and improving patients' daily life activities