Deep Learning Based Channel Covariance Matrix Estimation with User Location and Scene Images

Channel covariance matrix (CCM) is one critical parameter for designing the communications systems. In this paper, a novel framework of the deep learning (DL) based CCM estimation is proposed that exploits the perception of the transmission environment without any channel sample or the pilot signals. Specifically, as CCM is affected by the user's movement, we design a deep neural network (DNN) to predict CCM from user location and user speed, and the corresponding estimation method is named as ULCCME. A location denoising method is further developed to reduce the positioning error and improve the robustness of ULCCME. For cases when user location information is not available, we propose an interesting way that uses the environmental 3D images to predict the CCM, and the corresponding estimation method is named as SICCME. Simulation results show that both the proposed methods are effective and will benefit the subsequent channel estimation.Comment: 30 pages, 18 figure

Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with Uncertainty

Knowledge distillation is an effective paradigm for boosting the performance of pocket-size model, especially when multiple teacher models are available, the student would break the upper limit again. However, it is not economical to train diverse teacher models for the disposable distillation. In this paper, we introduce a new concept dubbed Avatars for distillation, which are the inference ensemble models derived from the teacher. Concretely, (1) For each iteration of distillation training, various Avatars are generated by a perturbation transformation. We validate that Avatars own higher upper limit of working capacity and teaching ability, aiding the student model in learning diverse and receptive knowledge perspectives from the teacher model. (2) During the distillation, we propose an uncertainty-aware factor from the variance of statistical differences between the vanilla teacher and Avatars, to adjust Avatars' contribution on knowledge transfer adaptively. Avatar Knowledge Distillation AKD is fundamentally different from existing methods and refines with the innovative view of unequal training. Comprehensive experiments demonstrate the effectiveness of our Avatars mechanism, which polishes up the state-of-the-art distillation methods for dense prediction without more extra computational cost. The AKD brings at most 0.7 AP gains on COCO 2017 for Object Detection and 1.83 mIoU gains on Cityscapes for Semantic Segmentation, respectively.Comment: Accepted by ACM MM 202

Alteration of Innate Immunity by Donor IL-6 Deficiency in a Presensitized Heart Transplant Model

Engraftment of IL-6 deficient donor into wild-type recipient could significantly improve allograft survival through T cell lineage particularly regulatory T cells (Tregs) in non-sensitized transplant host. However, its effect on innate immune responses remains uncertain. Our data revealed that donor IL-6 deficiency significantly increased infiltration of two subsets of MDSCs (CD11b+Gr1+myeloid-derived suppressor cells), CD11b+Gr1-low and CD11b+Gr1-int with strong immunosuppression activity in the transplanted graft. It resulted in a dramatic increase of CD11b+Gr1-low frequency and a significant decrease of the frequency of CD11b+Gr1-high and CD4-CD8-NK1.1+ cells in the recipient’s spleen. Unexpectedly, donor IL-6 deficiency could not significantly reduce macrophage frequency irrespective of in the host’s spleen or graft. Taken together, suppression of innate immune effector cells and enhanced activity of regulatory MDSCs contributed to tolerance induction by blockade of IL-6 signaling pathway. The unveiled novel mechanism of targeting IL-6 might shed light on clinical therapeutic application in preventing accelerated allograft rejection for those pre-sensitized transplant recipients

Lipin1 Regulates Skeletal Muscle Differentiation through Extracellular Signal-regulated Kinase (ERK) Activation and Cyclin D Complex-Regulated Cell Cycle Withdrawal

Lipin1, an intracellular protein, plays critical roles in controlling lipid synthesis and energy metabolism through its enzymatic activity and nuclear transcriptional functions. Several mouse models of skeletal muscle wasting are associated with lipin1 mutation or altered expression. Recent human studies have suggested that children with homozygous null mutations in the LPIN1 gene suffer from rhabdomyolysis. However, the underlying pathophysiologic mechanism is still poorly understood. In the present study we examined whether lipin1 contributes to regulating muscle regeneration. We characterized the time course of skeletal muscle regeneration in lipin1-deficient fld mice after injury. We found that fld mice exhibited smaller regenerated muscle fiber cross-sectional areas compared with wild-type mice in response to injury. Our results from a series of in vitro experiments suggest that lipin1 is up-regulated and translocated to the nucleus during myoblast differentiation and plays a key role in myogenesis by regulating the cytosolic activation of ERK1/2 to form a complex and a downstream effector cyclin D3-mediated cell cycle withdrawal. Overall, our study reveals a previously unknown role of lipin1 in skeletal muscle regeneration and expands our understanding of the cellular and molecular mechanisms underlying skeletal muscle regeneration

Zinc inhibits TRPV1 to alleviate chemotherapy-induced neuropathic pain

Zinc is a transition metal that has a long history of use as an anti-inflammatory agent. It also soothes pain sensations in a number of animal models. However, the effects and mechanisms of zinc on chemotherapy-induced peripheral neuropathy remain unknown. Here we show that locally injected zinc markedly reduces neuropathic pain in male and female mice induced by paclitaxel, a chemotherapy drug, in a TRPV1-dependent manner. Extracellularly applied zinc also inhibits the function of TRPV1 expressed in HEK293 cells and mouse DRG neurons, which requires the presence of zinc-permeable TRPA1 to mediate entry of zinc into the cytoplasm. Moreover, TRPA1 is required for zinc-induced inhibition of TRPV1-mediated acute nociception. Unexpectedly, zinc transporters, but not TRPA1, are required for zinc-induced inhibition of TRPV1-dependent chronic neuropathic pain produced by paclitaxel. Together, our study demonstrates a novel mechanism underlying the analgesic effect of zinc on paclitaxel-induced neuropathic pain that relies on the function of TRPV1

Application of CCG Sensors to a High-Temperature Structure Subjected to Thermo-Mechanical Load

This paper presents a simple methodology to perform a high temperature coupled thermo-mechanical test using ultra-high temperature ceramic material specimens (UHTCs), which are equipped with chemical composition gratings sensors (CCGs). The methodology also considers the presence of coupled loading within the response provided by the CCG sensors. The theoretical strain of the UHTCs specimens calculated with this technique shows a maximum relative error of 2.15% between the analytical and experimental data. To further verify the validity of the results from the tests, a Finite Element (FE) model has been developed to simulate the temperature, stress and strain fields within the UHTC structure equipped with the CCG. The results show that the compressive stress exceeds the material strength at the bonding area, and this originates a failure by fracture of the supporting structure in the hot environment. The results related to the strain fields show that the relative error with the experimental data decrease with an increase of temperature. The relative error is less than 15% when the temperature is higher than 200 °C, and only 6.71% at 695 °C

An inverse dynamics method for railway vehicle systems

The wheel–rail action will obviously be increased during the vehicles in high-speed operation state. However, in many practical cases, direct measurement of the wheel–rail contact forces cannot be performed with traditional procedures and transducers. An inverse mathematical dynamic model for the estimation of wheel–rail contact forces from measured accelerations was developed. The inverse model is a non-iteration recurrence method to identify the time history of input excitation based on the dynamic programming equation. Furthermore, the method overcomes the weakness of large fluctuations which exist in current inverse techniques. Based on the inverse dynamic model, a high-speed vehicle multibody model with twenty-seven Degree of Freedoms (DOFs) is established. With the measured responses as input, the inverse vehicle model can not only identify the responses in other parts of vehicle, but also identify the vertical and lateral wheel–rail forces respectively. Results from the inverse model were compared with experiment data. In a more complex operating condition, the inverse model was also compared with results from simulations calculated by SIMPACK. First published online: 22 May 201