Machine Learning Inspired Energy-Efficient Hybrid Precoding for MmWave Massive MIMO Systems

Hybrid precoding is a promising technique for mmWave massive MIMO systems, as it can considerably reduce the number of required radio-frequency (RF) chains without obvious performance loss. However, most of the existing hybrid precoding schemes require a complicated phase shifter network, which still involves high energy consumption. In this paper, we propose an energy-efficient hybrid precoding architecture, where the analog part is realized by a small number of switches and inverters instead of a large number of high-resolution phase shifters. Our analysis proves that the performance gap between the proposed hybrid precoding architecture and the traditional one is small and keeps constant when the number of antennas goes to infinity. Then, inspired by the cross-entropy (CE) optimization developed in machine learning, we propose an adaptive CE (ACE)-based hybrid precoding scheme for this new architecture. It aims to adaptively update the probability distributions of the elements in hybrid precoder by minimizing the CE, which can generate a solution close to the optimal one with a sufficiently high probability. Simulation results verify that our scheme can achieve the near-optimal sum-rate performance and much higher energy efficiency than traditional schemes.Comment: This paper has been accepted by IEEE ICC 2017. The simulation codes are provided to reproduce the results in this paper at: http://oa.ee.tsinghua.edu.cn/dailinglong/publications/publications.htm

Immunological Aspects of Fulminant Type 1 Diabetes in Chinese

Background. Fulminant type 1 diabetes (FT1D) is a novel subtype of type 1 diabetes characterized by extremely rapid onset and complete deficiency of insulin due to the destruction of pancreatic β cells. However, the precise mechanisms underlying the etiology of this disease remain unclear. Methods. A total of 22 patients with FT1D and 10 healthy subjects were recruited. Serum antibodies to GAD, IA2, and ZnT8 in patients were tested. And peripheral T cell responses to GAD65, insulin B9–23 peptide, or C peptide were determined in 10 FT1D patients and 10 healthy controls. The mRNA levels of several related cytokines and molecules, such as IFN-γ, IL-4, RORC, and IL-17 in PBMCs from FT1D patients were analyzed by qRT-PCR. Result. We found that a certain proportion of Chinese FT1D patients actually have developed islet-related autoantibodies after onset of the disease. The GAD, insulin, or C peptide-reactive T cells were found in some FT1D patients. We also detected a significant increase for IFN-γ expression in FT1D PBMCs as compared with that of healthy controls. Conclusion. Autoimmune responses might be involved in the pathogenesis of Chinese FT1D

Power of linkage analysis using traits generated from simulated longitudinal data of the Framingham Heart Study

The Framingham Heart Study is a very successful longitudinal research for cardiovascular diseases. The completion of a 10-cM genome scan in Framingham families provided an opportunity to evaluate linkage using longitudinal data. Several descriptive traits based on simulated longitudinal data from the Genetic Analysis Workshop 13 (GAW13) were generated, and linkage analyses were performed for these traits. We compared the power of detecting linkage for baseline and slope genes in the simulated data of GAW13 using these traits. We found that using longitudinal traits based on multiple follow-ups may not be more powerful than using cross-sectional traits for genetic linkage analysis

Proteomic analysis of rhein-induced cyt: ER stress mediates cell death in breast cancer cells

Rhein is a natural product purified from herbal plants such as Rheum palmatum, which has been shown to have anti-angiogenesis and anti-tumor metastasis properties. However, the biological effects of rhein on the behavior of breast cancers are not completely elucidated. To evaluate whether rhein might be useful in the treatment of breast cancer and its cytotoxic mechanism, we analyzed the impact of rhein treatment on differential protein expression as well as redox regulation in a non-invasive breast cancer cell line, MCF-7, and an invasive breast cancer cell line, MDA-MB-231, using lysine- and cysteine-labeling two-dimensional difference gel electrophoresis (2D-DIGE) combined with MALDI-TOF/TOF mass spectrometry. This proteomic study revealed that 73 proteins were significantly changed in protein expression; while 9 proteins were significantly altered in thiol reactivity in both MCF-7 and MDA-MB-231 cells. The results also demonstrated that rhein-induced cytotoxicity in breast cancer cells mostly involves dysregulation of cytoskeleton regulation, protein folding, the glycolysis pathway and transcription control. A further study also indicated that rhein promotes misfolding of cellular proteins as well as unbalancing of the cellular redox status leading to ER-stress. Our work shows that the current proteomic strategy offers a high-through-put platform to study the molecular mechanisms of rhein-induced cytotoxicity in breast cancer cells. The identified differentially expressed proteins might be further evaluated as potential targets in breast cancer therapy

Genome-wide linkage analysis using cross-sectional and longitudinal traits for body mass index in a subsample of the Framingham Heart Study

To evaluate linkage evidence for body mass index (BMI) using both cross-sectional and longitudinal data, we performed genome-wide multipoint linkage analyses on subjects who had complete data at four selected time points (initial, 8(th), 12(th), and 16(th )year following the initial visit) from the Framingham Heart Study. The cross-sectional measures included BMI at each of the four selected time points and the longitudinal measure was the within-subject mean of BMI at the above four time points. Using the variance components method, we consistently observed the maximum LOD score out of the genome scan using BMI at each time point and the mean of BMI between 049xd2 and GATA71H05 on chromosome 16. The highest LOD score (3.0) was at time point 1, while the lowest (1.9) was at time point 4. We also observed other suggestive linkages on chromosome 6, 10, and 18 at time point 1 only. The longitudinal measure we studied (mean of BMI) did not provide greater power to identify a positive linkage than some of the cross-sectional measures (e.g., time point 1). The changing of linkage evidence over time provided some insights on the variation of genetic effect on BMI with aging. There may be a QTL on chromosome 16 that contributes to BMI and this locus, and maybe others, is more likely to affect BMI during early adulthood

KAgF3: quasi-one-dimensional magnetism in three-dimensional magnetic ions sublattice

The electronic structure and magnetic properties of the Jahn-Teller-distorted perovskite KAgF3 have been investigated using the full-potential linerized aug- mented plane-wave method. It is found that KAgF3 exhibits significant quasi-one- dimensional antiferromagnetism with the ratio of exchange constant jJ?j (perpen- dicular to the z axis) and J (along the z axis) about 0.04, although the sublattice of magnetic ion is three-dimensional. The strong quasi-one-dimensional antiferromag- netism originates from the C-antiferro-distortive orbital ordering of the Ag2+ 4d9 ions. The orbital ordered antiferromagnetic insulating state in KAgF3 is determined by on-site Coulomb repulsion to a large extent.Comment: 15 pages, 6 figure

Trade-offs among cost, integration, and segregation in the human connectome

AbstractThe human brain structural network is thought to be shaped by the optimal trade-off between cost and efficiency. However, most studies on this problem have focused on only the trade-off between cost and global efficiency (i.e., integration) and have overlooked the efficiency of segregated processing (i.e., segregation), which is essential for specialized information processing. Direct evidence on how trade-offs among cost, integration, and segregation shape the human brain network remains lacking. Here, adopting local efficiency and modularity as segregation factors, we used a multiobjective evolutionary algorithm to investigate this problem. We defined three trade-off models, which represented trade-offs between cost and integration (Dual-factor model), and trade-offs among cost, integration, and segregation (local efficiency or modularity; Tri-factor model), respectively. Among these, synthetic networks with optimal trade-off among cost, integration, and modularity (Tri-factor model [Q]) showed the best performance. They had a high recovery rate of structural connections and optimal performance in most network features, especially in segregated processing capacity and network robustness. Morphospace of this trade-off model could further capture the variation of individual behavioral/demographic characteristics in a domain-specific manner. Overall, our results highlight the importance of modularity in the formation of the human brain structural network and provide new insights into the original cost-efficiency trade-off hypothesis