Resource Allocation in the RIS Assisted SCMA Cellular Network Coexisting with D2D Communications

The cellular network coexisting with device-to-device (D2D) communications has been studied extensively. Reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) are promising technologies for the evolution of 5G, 6G and beyond. Besides, sparse code multiple access (SCMA) is considered suitable for next-generation wireless network in code-domain NOMA. In this paper, we consider the RIS-aided uplink SCMA cellular network simultaneously with D2D users. We formulate the optimization problem which aims to maximize the cellular sum-rate by jointly designing D2D users resource block (RB) association, the transmitted power for both cellular users and D2D users, and the phase shifts at the RIS. The power limitation and users communication requirements are considered. The problem is non-convex, and it is challenging to solve it directly. To handle this optimization problem, we propose an efficient iterative algorithm based on block coordinate descent (BCD) method. The original problem is decoupled into three subproblems to solve separately. Simulation results demonstrate that the proposed scheme can significantly improve the sum-rate performance over various schemes.Comment: IEEE Acces

NAOMI: Non-Autoregressive Multiresolution Sequence Imputation

Missing value imputation is a fundamental problem in spatiotemporal modeling, from motion tracking to the dynamics of physical systems. Deep autoregressive models suffer from error propagation which becomes catastrophic for imputing long-range sequences. In this paper, we take a non-autoregressive approach and propose a novel deep generative model: Non-AutOregressive Multiresolution Imputation (NAOMI) to impute long-range sequences given arbitrary missing patterns. NAOMI exploits the multiresolution structure of spatiotemporal data and decodes recursively from coarse to fine-grained resolutions using a divide-and-conquer strategy. We further enhance our model with adversarial training. When evaluated extensively on benchmark datasets from systems of both deterministic and stochastic dynamics. NAOMI demonstrates significant improvement in imputation accuracy (reducing average prediction error by 60% compared to autoregressive counterparts) and generalization for long range sequences

Experimental Evidence for Partially Dehydrogenated epsilon-FeOOH

Hydrogen in hydrous minerals becomes highly mobile as it approaches the geotherm of the lower mantle. Its diffusion and transportation behaviors under high pressure are important in order to understand the crystallographic properties of hydrous minerals. However, they are difficult to characterize due to the limit of weak X-ray signals from hydrogen. In this study, we measured the volume changes of hydrous ε-FeOOH under quasi-hydrostatic and non-hydrostatic conditions. Its equation of states was set as the cap line to compare with ε-FeOOH reheated and decompression from the higher pressure pyrite-FeO2Hx phase with 0 < x < 1. We found the volumes of those re-crystallized ε-FeOOH were generally 2.2% to 2.7% lower than fully hydrogenated ε-FeOOH. Our observations indicated that ε-FeOOH transformed from pyrite-FeO2Hx may inherit the hydrogen loss that occurred at the pyrite-phase. Hydrous minerals with partial dehydrogenation like ε-FeOOHx may bring it to a shallower depth (e.g., < 1700 km) of the lower mantle

SUBP: Soft Uniform Block Pruning for 1xN Sparse CNNs Multithreading Acceleration

The study of sparsity in Convolutional Neural Networks (CNNs) has become widespread to compress and accelerate models in environments with limited resources. By constraining N consecutive weights along the output channel to be group-wise non-zero, the recent network with 1$\times$N sparsity has received tremendous popularity for its three outstanding advantages: 1) A large amount of storage space saving by a \emph{Block Sparse Row} matrix. 2) Excellent performance at a high sparsity. 3) Significant speedups on CPUs with Advanced Vector Extensions. Recent work requires selecting and fine-tuning 1$\times$N sparse weights based on dense pre-trained weights, leading to the problems such as expensive training cost and memory access, sub-optimal model quality, as well as unbalanced workload across threads (different sparsity across output channels). To overcome them, this paper proposes a novel \emph{\textbf{S}oft \textbf{U}niform \textbf{B}lock \textbf{P}runing} (SUBP) approach to train a uniform 1$\times$N sparse structured network from scratch. Specifically, our approach tends to repeatedly allow pruned blocks to regrow to the network based on block angular redundancy and importance sampling in a uniform manner throughout the training process. It not only makes the model less dependent on pre-training, reduces the model redundancy and the risk of pruning the important blocks permanently but also achieves balanced workload. Empirically, on ImageNet, comprehensive experiments across various CNN architectures show that our SUBP consistently outperforms existing 1$\times$N and structured sparsity methods based on pre-trained models or training from scratch. Source codes and models are available at \url{https://github.com/JingyangXiang/SUBP}.Comment: 14 pages, 4 figures, Accepted by 37th Conference on Neural Information Processing Systems (NeurIPS 2023

Uformer: A Unet based dilated complex & real dual-path conformer network for simultaneous speech enhancement and dereverberation

Complex spectrum and magnitude are considered as two major features of speech enhancement and dereverberation. Traditional approaches always treat these two features separately, ignoring their underlying relationship. In this paper, we propose Uformer, a Unet based dilated complex & real dual-path conformer network in both complex and magnitude domain for simultaneous speech enhancement and dereverberation. We exploit time attention (TA) and dilated convolution (DC) to leverage local and global contextual information and frequency attention (FA) to model dimensional information. These three sub-modules contained in the proposed dilated complex & real dual-path conformer module effectively improve the speech enhancement and dereverberation performance. Furthermore, hybrid encoder and decoder are adopted to simultaneously model the complex spectrum and magnitude and promote the information interaction between two domains. Encoder decoder attention is also applied to enhance the interaction between encoder and decoder. Our experimental results outperform all SOTA time and complex domain models objectively and subjectively. Specifically, Uformer reaches 3.6032 DNSMOS on the blind test set of Interspeech 2021 DNS Challenge, which outperforms all top-performed models. We also carry out ablation experiments to tease apart all proposed sub-modules that are most important.Comment: Accepted by ICASSP 202

The effect of nitrogen on the compressibility and conductivity of iron at high pressure

Although nitrogen in the Earth’s interior has attracted significant attention recently, it remains the most enigmatic of the light elements in the Earth’s core. In this work, synchrotron X-ray diffraction (XRD) and electrical conductivity experiments were conducted on iron nitrides (Fe2N and Fe4N) in diamond anvil cells (DACs) up to about 70 ​GPa ​at ambient temperature. These results show that iron nitrides are stable up to at least 70 ​GPa. From the equation of state (EOS) parameters, iron nitrides are more compressible than iron carbides. Moreover, using the van der Pauw method and Wiedemann-Franz law, the electrical and thermal conductivity of samples were determined to be much lower than that of iron carbides. The conductivities of Fe2N and Fe4N were similar at 20–70 ​GPa, suggesting no evident effects by varying the N stoichiometries in iron nitrides. Iron nitrides are less dense and conductive but more compressible than carbides at 0–70 ​GPa. This study indicates that less nitrogen than carbon can explain geophysical phenomena in the deep Earth, such as the density deficit

Association between Non-Suicidal Self-Injuries and Suicide Attempts in Chinese Adolescents and College Students: A Cross-Section Study

This study examined the association between non-suicidal self-injury (NSSI) and suicide attempts among Chinese adolescents and college students.A total sample of 2013 Chinese students were randomly selected from five schools in Wuhan, China, including 1101 boys and 912 girls with the age ranging between 10 and 24 years. NSSI, suicidal ideation, suicide attempts and depressive symptoms were measured by self-rated questionnaires. Self-reported suicide attempts were regressed on suicidal ideation and NSSI, controlling for participants' depressive symptoms, and demographic characteristics.The self-reported prevalence rates of NSSI, suicidal ideation, suicide attempts were 15.5%, 8.8%, and 3.5%, respectively. Logistic regression analyses indicated that NSSI was significantly associated with self-reported suicide attempts. Analyses examining the conditional association of NSSI and suicidal ideation with self-reported suicide attempts revealed that NSSI was significantly associated with greater risk of suicide attempts in those not reporting suicidal ideation than those reporting suicidal ideation in the past year.These findings highlight the importance of NSSI as a potentially independent risk factor for suicide attempts among Chinese/Han adolescents and college students