A joint multi user detection scheme for UWB sensor networks using waveform division multiple access

A joint multiuser detection (MUD) scheme for wireless sensor networks (WSNs) is proposed to suppress multiple access interference (MAI) caused by a large number of sensor nodes. In WSNs, waveform division multiple access ultra-wideband (WDMA-UWB) technology is well-suited for robust communications. Multiple sensor nodes are allowed to transmit modulated signals by sharing the same time periods and frequency bands using orthogonal pulse waveforms. This paper employs a mapping function based on the optimal multiuser detection (OMD) to map the received bits into the mapping space where error bits can be distinguished. In order to revise error bits caused by MAI, the proposed joint MUD scheme combines the mapping function with suboptimal algorithms. Numerical results demonstrate that the proposed MUD scheme provides good performances in terms of suppressing MAI and resisting near-far effect with low computational complexity

IA-OPD : an optimized orthogonal pulse design scheme for waveform division multiple access UWB systems

A new design scheme of orthogonal pulses is proposed for waveform division multiple access ultra-wideband (WDMA-UWB) systems. In order to achieve WDMA and to improve user capacity, the proposed method, termed as interference alignment based orthogonal pulse design (IA-OPD), employs combined orthogonal wavelet functions in the pulse design. The combination coefficients are optimized by using interference alignment. Due to the reciprocity between transmitted and local template signals, the iterative process based on maximum signal to interference plus noise ratio (Max-SINR) criterion can be used to solve the optimization problem in interference alignment. Numerical results demonstrate that the optimized orthogonal pulses provide excellent performances in terms of multiple access interference (MAI) suppression, user capacity and near-far resistance without using any multiuser detection (MUD) techniques. Thus, the IA-OPD scheme can be used to efficiently design a large number of orthogonal pulses for multiuser WDMA-UWB systems with low computational complexity and simple transceiver structure

Phonemic Adversarial Attack against Audio Recognition in Real World

Recently, adversarial attacks for audio recognition have attracted much attention. However, most of the existing studies mainly rely on the coarse-grain audio features at the instance level to generate adversarial noises, which leads to expensive generation time costs and weak universal attacking ability. Motivated by the observations that all audio speech consists of fundamental phonemes, this paper proposes a phonemic adversarial tack (PAT) paradigm, which attacks the fine-grain audio features at the phoneme level commonly shared across audio instances, to generate phonemic adversarial noises, enjoying the more general attacking ability with fast generation speed. Specifically, for accelerating the generation, a phoneme density balanced sampling strategy is introduced to sample quantity less but phonemic features abundant audio instances as the training data via estimating the phoneme density, which substantially alleviates the heavy dependency on the large training dataset. Moreover, for promoting universal attacking ability, the phonemic noise is optimized in an asynchronous way with a sliding window, which enhances the phoneme diversity and thus well captures the critical fundamental phonemic patterns. By conducting extensive experiments, we comprehensively investigate the proposed PAT framework and demonstrate that it outperforms the SOTA baselines by large margins (i.e., at least 11X speed up and 78% attacking ability improvement)

A robust modulation classification method using convolutional neural networks

Automatic modulation classification (AMC) is a core technique in noncooperative communication systems. In particular, feature-based (FB) AMC algorithms have been widely studied. Current FB AMC methods are commonly designed for a limited set of modulation and lack of generalization ability; to tackle this challenge, a robust AMC method using convolutional neural networks (CNN) is proposed in this paper. In total, 15 different modulation types are considered. The proposed method can classify the received signal directly without feature extracion, and it can automatically learn features from the received signals. The features learned by the CNN are presented and analyzed. The robust features of the received signals in a specific SNR range are studied. The accuracy of classification using CNN is shown to be remarkable, particularly for low SNRs. The generalization ability of robust features is also proven to be excellent using the support vector machine (SVM). Finally, to help us better understand the process of feature learning, some outputs of intermediate layers of the CNN are visualized

Tax Streams, Land Rents, and Urban Land Allocation

This paper examines the fiscal motives behind municipal governments\u27 decisions to allocate commercial and residential land when two categories of land use are subject to different fiscal revenue alternatives: business-related tax and/or land rent. We use urban parcel-level land transfers during China’s peak period of urbanization, match commercial parcels with residential parcels, and find significant price discounts on commercial parcels relative to adjacent residential parcels. The observed discounts arise from the future tax flows from commercial use, i.e., expected taxes from developed commercial land reduce its transfer price. We conduct a structural estimation to examine the implications on land use structure of future taxes lowering land transfer prices. Results show that while prospective taxes increase commercial land supply, a significant portion of the favorable treatment impact is mitigated by market price responses, suggesting that the land market counters commercial land favoritism when local revenues include both business-related taxes and land value-based charges. The results have implications for the design of urban public revenue systems

In situ Raman quantitative monitoring of methanogenesis: Culture experiments of a deep-sea cold seep methanogenic archaeon

Gas production from several metabolic pathways is a necessary process that accompanies the growth and central metabolism of some microorganisms. However, accurate and rapid nondestructive detection of gas production is still challenging. To this end, gas chromatography (GC) is primarily used, which requires sampling and sample preparation. Furthermore, GC is expensive and difficult to operate. Several researchers working on microbial gases are looking forward to a new method to accurately capture the gas trends within a closed system in real-time. In this study, we developed a precise quantitative analysis for headspace gas in Hungate tubes using Raman spectroscopy. This method requires only a controlled focus on the gas portion inside Hungate tubes, enabling nondestructive, real-time, continuous monitoring without the need for sampling. The peak area ratio was selected to establish a calibration curve with nine different CH4–N2 gaseous mixtures and a linear relationship was observed between the peak area ratio of methane to nitrogen and their molar ratios (A(CH4)/A(N2) = 6.0739 × n(CH4)/n(N2)). The results of in situ quantitative analysis using Raman spectroscopy showed good agreement with those of GC in the continuous monitoring of culture experiments of a deep-sea cold seep methanogenic archaeon. This method significantly improves the detection efficiency and shows great potential for in situ quantitative gas detection in microbiology. It can be a powerful complementary tool to GC

Molecular subtype identification and signature construction based on Golgi apparatus-related genes for better prediction prognosis and immunotherapy response in hepatocellular carcinoma

IntroductionThe Golgi apparatus (GA) is the center of protein and lipid synthesis and modification in normal cells and is involved in regulating various cellular process as a signaling hub, the dysfunction of which can lead to the development of various pathological conditions, including tumors. Mutations in Golgi apparatus-related genes (GARGs) are prevalent in most tumors, and their mutations can make them pro-tumor metastatic. The aim of this study was to analyze the predictive role of GARGs in the prognosis and immunotherapeutic outcome of hepatocellular carcinoma.MethodsWe used TCGA, GEO and ICGC databases to classify hepatocellular carcinoma samples into two molecular subtypes based on the expression of GARGs. Signature construction was then performed using GARGs, and signature genes were selected for expression validation and tumor phenotype experiments to determine the role of GARGs in the prognosis of hepatocellular carcinoma.ResultsUsing the TCGA, GEO and ICGC databases, two major subtypes of molecular heterogeneity among hepatocellular carcinoma tumors were identified based on the expression of GARGs, C1 as a high-risk subtype (low survival) and C2 as a low-risk subtype (high survival). The high-risk subtype had lower StromalScore, ImmuneScore, ESTIMATEScore and higher TumorPurity, indicating poorer treatment outcome for ICI. Meanwhile, we constructed a new risk assessment profile for hepatocellular carcinoma based on GARGs, and we found that the high-risk group had a worse prognosis, a higher risk of immune escape, and a higher TP53 mutation rate. Meanwhile, TME analysis showed higher tumor purity TumorPurity and lower ESTIMATEScore, ImmuneScore and StromalScore in the high-risk group. We also found that the high-risk group responded more strongly to a variety of anticancer drugs, which is useful for guiding clinical drug use. Meanwhile, the expression of BSG was experimentally found to be associated with poor prognosis of HCC. After interfering with the expression of BSG in HCC cells SMMC-7721, the proliferation and migration ability of HCC cells were significantly restricted.DiscussionThe signature we constructed using GARGs can well predict the prognosis and immunotherapy effect of hepatocellular carcinoma, providing new ideas and strategies for the treatment of hepatocellular carcinoma

HIF-1α Affects the Neural Stem Cell Differentiation of Human Induced Pluripotent Stem Cells via MFN2-Mediated Wnt/β-Catenin Signaling

Hypoxia-inducible factor 1α (HIF-1α) plays pivotal roles in maintaining pluripotency, and the developmental potential of pluripotent stem cells (PSCs). However, the mechanisms underlying HIF-1α regulation of neural stem cell (NSC) differentiation of human induced pluripotent stem cells (hiPSCs) remains unclear. In this study, we demonstrated that HIF-1α knockdown significantly inhibits the pluripotency and self-renewal potential of hiPSCs. We further uncovered that the disruption of HIF-1α promotes the NSC differentiation and development potential in vitro and in vivo. Mechanistically, HIF-1α knockdown significantly enhances mitofusin2 (MFN2)-mediated Wnt/β-catenin signaling, and excessive mitochondrial fusion could also promote the NSC differentiation potential of hiPSCs via activating the β-catenin signaling. Additionally, MFN2 significantly reverses the effects of HIF-1α overexpression on the NSC differentiation potential and β-catenin activity of hiPSCs. Furthermore, Wnt/β-catenin signaling inhibition could also reverse the effects of HIF-1α knockdown on the NSC differentiation potential of hiPSCs. This study provided a novel strategy for improving the directed differentiation efficiency of functional NSCs. These findings are important for the development of potential clinical interventions for neurological diseases caused by metabolic disorders

Cyclophilin A Restricts Influenza A Virus Replication through Degradation of the M1 Protein

Cyclophilin A (CypA) is a typical member of the cyclophilin family of peptidyl-prolyl isomerases and is involved in the replication of several viruses. Previous studies indicate that CypA interacts with influenza virus M1 protein and impairs the early stage of the viral replication. To further understand the molecular mechanism by which CypA impairs influenza virus replication, a 293T cell line depleted for endogenous CypA was established. The results indicated that CypA inhibited the initiation of virus replication. In addition, the infectivity of influenza virus increased in the absence of CypA. Further studies indicated that CypA had no effect on the stages of virus genome replication or transcription and also did not impair the nuclear export of the viral mRNA. However, CypA decreased the viral protein level. Additional studies indicated that CypA enhanced the degradation of M1 through the ubiquitin/proteasome-dependent pathway. Our results suggest that CypA restricts influenza virus replication through accelerating degradation of the M1 protein