A Robust Method for Speech Emotion Recognition Based on Infinite Student’s t

Speech emotion classification method, proposed in this paper, is based on Student’s t-mixture model with infinite component number (iSMM) and can directly conduct effective recognition for various kinds of speech emotion samples. Compared with the traditional GMM (Gaussian mixture model), speech emotion model based on Student’s t-mixture can effectively handle speech sample outliers that exist in the emotion feature space. Moreover, t-mixture model could keep robust to atypical emotion test data. In allusion to the high data complexity caused by high-dimensional space and the problem of insufficient training samples, a global latent space is joined to emotion model. Such an approach makes the number of components divided infinite and forms an iSMM emotion model, which can automatically determine the best number of components with lower complexity to complete various kinds of emotion characteristics data classification. Conducted over one spontaneous (FAU Aibo Emotion Corpus) and two acting (DES and EMO-DB) universal speech emotion databases which have high-dimensional feature samples and diversiform data distributions, the iSMM maintains better recognition performance than the comparisons. Thus, the effectiveness and generalization to the high-dimensional data and the outliers are verified. Hereby, the iSMM emotion model is verified as a robust method with the validity and generalization to outliers and high-dimensional emotion characters

Model Selection for Topic Models via Spectral Decomposition

Abstract Topic models have achieved significant successes in analyzing large-scale text corpus. In practical applications, we are always confronted with the challenge of model selection, i.e., how to appropriately set the number of topics. Following the recent advances in topic models via tensor decomposition, we make a first attempt to provide theoretical analysis on model selection in latent Dirichlet allocation. With mild conditions, we derive the upper bound and lower bound on the number of topics given a text collection of finite size. Experimental results demonstrate that our bounds are correct and tight. Furthermore, using Gaussian mixture model as an example, we show that our methodology can be easily generalized to model selection analysis in other latent models

5G embraces satellites for 6G ubiquitous IoT : basic models for integrated satellite terrestrial networks

Terrestrial communication networks mainly focus on users in urban areas but have poor coverage performance in harsh environments, such as mountains, deserts, and oceans. Satellites can be exploited to extend the coverage of terrestrial fifth-generation (5G) networks. However, satellites are restricted by their high latency and relatively low data rate. Consequently, the integration of terrestrial and satellite components has been widely studied, to take advantage of both sides and enable the seamless broadband coverage. Due to the significant differences between satellite communications (SatComs) and terrestrial communications (TerComs) in terms of channel fading, transmission delay, mobility, and coverage performance, the establishment of an efficient hybrid satellite-terrestrial network (HSTN) still faces many challenges. In general, it is difficult to decompose a HSTN into a sum of separate satellite and terrestrial links due to the complicated coupling relationships therein. To uncover the complete picture of HSTNs, we regard the HSTN as a combination of basic cooperative models that contain the main traits of satellite-terrestrial integration but are much simpler and thus more tractable than the large-scale heterogeneous HSTNs. In particular, we present three basic cooperative models, i.e., model X, model L, and model V, and provide a survey of the state-of-the-art technologies for each of them. We discuss future research directions towards establishing a cell-free, hierarchical, decoupled HSTN. We also outline open issues to envision an agile, smart, and secure HSTN for the sixth-generation (6G) ubiquitous Internet of Things (IoT)

Band Structure Engineering of Interfacial Semiconductors Based on Atomically Thin Lead Iodide Crystals

To explore new constituents in two-dimensional materials and to combine their best in van der Waals heterostructures, are in great demand as being unique platform to discover new physical phenomena and to design novel functionalities in interface-based devices. Herein, PbI2 crystals as thin as few-layers are first synthesized, particularly through a facile low-temperature solution approach with the crystals of large size, regular shape, different thicknesses and high-yields. As a prototypical demonstration of flexible band engineering of PbI2-based interfacial semiconductors, these PbI2 crystals are subsequently assembled with several transition metal dichalcogenide monolayers. The photoluminescence of MoS2 is strongly enhanced in MoS2/PbI2 stacks, while a dramatic photoluminescence quenching of WS2 and WSe2 is revealed in WS2/PbI2 and WSe2/PbI2 stacks. This is attributed to the effective heterojunction formation between PbI2 and these monolayers, but type I band alignment in MoS2/PbI2 stacks where fast-transferred charge carriers accumulate in MoS2 with high emission efficiency, and type II in WS2/PbI2 and WSe2/PbI2 stacks with separated electrons and holes suitable for light harvesting. Our results demonstrate that MoS2, WS2, WSe2 monolayers with very similar electronic structures themselves, show completely distinct light-matter interactions when interfacing with PbI2, providing unprecedent capabilities to engineer the device performance of two-dimensional heterostructures.Comment: 36 pages, 5 figure

Mesenchymal stromal cells and alpha-1 antitrypsin have a strong synergy in modulating inflammation and its resolution

Rationale: Trauma, surgery, and infection can cause severe inflammation. Both dysregulated inflammation intensity and duration can lead to significant tissue injuries, organ dysfunction, mortality, and morbidity. Anti-inflammatory drugs such as steroids and immunosuppressants can dampen inflammation intensity, but they derail inflammation resolution, compromise normal immunity, and have significant adverse effects. The natural inflammation regulator mesenchymal stromal cells (MSCs) have high therapeutic potential because of their unique capabilities to mitigate inflammation intensity, enhance normal immunity, and accelerate inflammation resolution and tissue healing. Furthermore, clinical studies have shown that MSCs are safe and effective. However, they are not potent enough, alone, to completely resolve severe inflammation and injuries. One approach to boost the potency of MSCs is to combine them with synergistic agents. We hypothesized that alpha-1 antitrypsin (A1AT), a plasma protein used clinically and has an excellent safety profile, was a promising candidate for synergism. Methods: This investigation examined the efficacy and synergy of MSCs and A1AT to mitigate inflammation and promote resolution, using in vitro inflammatory assay and in vivo mouse acute lung injury model. The in vitro assay measured cytokine releases, inflammatory pathways, reactive oxygen species (ROS), and neutrophil extracellular traps (NETs) production by neutrophils and phagocytosis in different immune cell lines. The in vivo model monitored inflammation resolution, tissue healing, and animal survival. Results: We found that the combination of MSCs and A1AT was much more effective than each component alone in i) modulating cytokine releases and inflammatory pathways, ii) inhibiting ROS and NETs production by neutrophils, iii) enhancing phagocytosis and, iv) promoting inflammation resolution, tissue healing, and animal survival. Conclusion: These results support the combined use of MSCs, and A1AT is a promising approach for managing severe, acute inflammation

The Effects of Icariin on Enhancing Motor Recovery Through Attenuating Pro-inflammatory Factors and Oxidative Stress via Mitochondrial Apoptotic Pathway in the Mice Model of Spinal Cord Injury

Spinal cord injury (SCI) is a severe medical problem leading to crucial life change. Icariin (ICA) is a natural flavonoid compound extracted from the Chinese herb Epimedium brevicornum which has neuroprotective effects. But little is known about the relationship between ICA and SCI. We hypothesized ICA may enhance motor recovery through attenuating inflammation, oxidative stress and mitochondrial dysfunction. Mice were randomly assigned to sham, SCI, ICA 20 μmol/kg (low dose) and ICA 50 μmol/kg (high dose) groups. And Behavioral, biochemical, molecular biological, immunofluorescent and histological assays were performed. First, ICA enhanced motor recovery greatly at 14, 28, and 42 days and protected spinal cord tissues especially in the high dose group. Meanwhile, ICA decreased the production of interleukin-1 beta, tumor necrosis factor-alpha and inducible nitric oxide synthase at 24 h and 3 days after SCI. The level of mitochondrial reduced glutathione, superoxide dismutase, adenosine triphosphate (ATP), Na+-K+-ATPase, mitochondrial membrane potential, state III respiration rate and the respiratory control ratio were also significantly increased, while malondialdehyde level and Ca2+ concentration were decreased by ICA. Furthermore, ICA decreased the expression of mitochondrial apoptotic proteins at 3 days after SCI. More importantly, transferase UTP nick end labeling (TUNEL) and Nissl staining implied that ICA at a high dose inhibited the neuronal apoptosis after SCI. Our research indicated that early and continuous treatment of ICA at a high dose significantly enhanced motor recovery after SCI through inhibiting pro-inflammatory factors, oxidative stress and neuronal apoptosis via mitochondrial apoptotic pathway

Refinement of pore size at sub-angstrom precision in robust metal-organic frameworks for separation of xylenes

The demand for xylenes is projected to increase over the coming decades. The separation of xylene isomers, particularly p- and m-xylenes, is vital for the production of numerous polymers and materials. However, current state-of-the-art separation is based upon fractional crystallisation at 220 K which is highly energy intensive. Here, we report the discrimination of xylene isomers via refinement of the pore size in a series of porous metal–organic frameworks, MFM-300, at sub-angstrom precision leading to the optimal kinetic separation of all three xylene isomers at room temperature. The exceptional performance of MFM-300 for xylene separation is confirmed by dynamic ternary breakthrough experiments. In-depth structural and vibrational investigations using synchrotron X-ray diffraction and terahertz spectroscopy define the underlying host–guest interactions that give rise to the observed selectivity (p-xylene < o-xylene < m-xylene) and separation factors of 4.6–18 for p- and m-xylenes