Polarized Low-Density Parity-Check Codes on the BSC

The connections between variable nodes and check nodes have a great influence on the performance of low-density parity-check (LDPC) codes. Inspired by the unique structure of polar code's generator matrix, we proposed a new method of constructing LDPC codes that achieves a polarization effect. The new code, named as polarized LDPC codes, is shown to achieve lower or no error floor in the binary symmetric channel (BSC)Comment: 6 pages, 5 figures, Presented at WCSP, Xian 201

Role of Kupffer cells in liver injury induced by CpG oligodeoxynucleotide and flucloxacillin in mice

CpG oligodeoxynucleotide (CpG-ODN) is a Toll-like receptor 9 (TLR9) agonist that can induce innate immune responses. In a previous study, flucloxacillin (FLUX; 100 mg/kg, gavage)-induced liver injury in mice was enhanced by co-administration of CpG-ODN (40 μg/mouse, intraperitoneally). In this study, the mechanism of CpG-ODN sensitization to FLUX-induced liver injury was further investigated in mice inhibited of Kupffer cells (KCs) function by gadolinium chloride (GdCl3; 10 mg/kg, intravenously). GdCl3-treated mice administrated with CpG-ODN and FLUX showed lower liver injury than wild-type (WT) mice treated with CpG-ODN and FLUX. Upregulation of Fas and FasL by CpG-ODN was also inhibited in GdCl3-treated mice and mitochondrial swelling in response to FLUX failed to occur regardless of pre-treatment with CpG-ODN. When FasL-mutant gld/gld mice were treated with CpG-ODN, mitochondrial swelling in response to FLUX was also inhibited. These results suggest that KCs play an essential role in liver injury induced by CpG-ODN and FLUX. CpG-ODN may activate KCs, resulting in induction of Fas/FasL-mediated apoptosis of hepatocytes. The Fas/FasL pathway may also be an upstream regulator of CpG-ODN- and FLUX-induced changes in mitochondrial permeability transition. These results enhance our understanding of the mechanism of the adjuvant effect of CpG-ODN in this mouse model of liver injury

PREF: Phasorial Embedding Fields for Compact Neural Representations

We present an efficient frequency-based neural representation termed PREF: a shallow MLP augmented with a phasor volume that covers significant border spectra than previous Fourier feature mapping or Positional Encoding. At the core is our compact 3D phasor volume where frequencies distribute uniformly along a 2D plane and dilate along a 1D axis. To this end, we develop a tailored and efficient Fourier transform that combines both Fast Fourier transform and local interpolation to accelerate na\"ive Fourier mapping. We also introduce a Parsvel regularizer that stables frequency-based learning. In these ways, Our PREF reduces the costly MLP in the frequency-based representation, thereby significantly closing the efficiency gap between it and other hybrid representations, and improving its interpretability. Comprehensive experiments demonstrate that our PREF is able to capture high-frequency details while remaining compact and robust, including 2D image generalization, 3D signed distance function regression and 5D neural radiance field reconstruction

Development of Braking Force Distribution Strategy for Dual-Motor-Drive Electric Vehicle

In the development of the optimal braking force distribution strategy for a dual-motor-drive electric vehicle (DMDEV) with a series cooperative braking system, three key factors were taken into consideration, i.e. the regenerative force distribution coefficient between the front and the rear motor (β), the energy recovery coefficient at the wheels (α3), and the front-and-rear-axle braking force distribution coefficient (λ). First, the overall power loss model of the two surface-mounted permanent magnetic synchronous motors (SMPMSMs) was created based on the d-q axis equivalent circuit model. The optimal relationship of β and the overall efficiency of the dual-motor system were confirmed, where the latter was quite different from that obtained from the traditional look-up table method for the motors' efficiency. Then, four dimensionless evaluation coefficients were used to evaluate braking stability, regenerative energy transfer efficiency, and energy recovery at the wheels. Finally, based on several typical braking operations, the comprehensive effects of the four coefficients on braking stability and energy recovery were revealed. An optimal braking force distribution strategy balancing braking stability and energy recovery is suggested for a DMDEV with a series cooperative braking system

Development of Drive Control Strategy for Front-and-Rear-Motor-Drive Electric Vehicle (FRMDEV)

In order to achieve both high-efficiency drive and low-jerk mode switch in FRMDEVs, a drive control strategy is proposed, consisting of top-layer torque distribution aimed at optimal efficiency and low-layer coordination control improving mode-switch jerk. First, with the use of the off-line particle swarm optimization algorithm (PSOA), the optimal switching boundary between single-motor-drive mode (SMDM) and dual-motor drive mode (DMDM) was modelled and a real-time torque distribution model based on the radial basis function (RBF) was created to achieve the optimal torque distribution. Then, referring to the dynamic characteristics of mode switch tested on a dual-motor test bench, a torque coordination strategy by controlling the variation rate of the torque distribution coefficient during the mode-switch process was developed. Finally, based on a hardware-in-loop (HIL) test platform and an FRMDEV, the proposed drive control strategy was verified. The test results show that both drive economy and comfort were improved significantly by the use of the developed drive control strategy

A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation

Unsupervised domain adaptation (UDA) methods facilitate the transfer of models to target domains without labels. However, these methods necessitate a labeled target validation set for hyper-parameter tuning and model selection. In this paper, we aim to find an evaluation metric capable of assessing the quality of a transferred model without access to target validation labels. We begin with the metric based on mutual information of the model prediction. Through empirical analysis, we identify three prevalent issues with this metric: 1) It does not account for the source structure. 2) It can be easily attacked. 3) It fails to detect negative transfer caused by the over-alignment of source and target features. To address the first two issues, we incorporate source accuracy into the metric and employ a new MLP classifier that is held out during training, significantly improving the result. To tackle the final issue, we integrate this enhanced metric with data augmentation, resulting in a novel unsupervised UDA metric called the Augmentation Consistency Metric (ACM). Additionally, we empirically demonstrate the shortcomings of previous experiment settings and conduct large-scale experiments to validate the effectiveness of our proposed metric. Furthermore, we employ our metric to automatically search for the optimal hyper-parameter set, achieving superior performance compared to manually tuned sets across four common benchmarks. Codes will be available soon