A model-driven deep reinforcement learning heuristic algorithm for resource allocation in ultra-dense cellular networks

Resource allocation in ultra dense network (UDN) is an multi-objective optimization problem since it has to consider the tradeoff among spectrum efficiency (SE), energy efficiency (EE) and fairness. The existing methods can not effectively solve this NP-hard nonconvex problem, especially in the presence of limited channel state information (CSI). In this paper, we investigate a novel model-driven deep reinforcement learning assisted resource allocation method. We first design a novel deep neural network (DNN)-based optimization framework consisting of a series of Alternating Direction Method of Multipliers (ADMM) iterative procedures, which makes the CSI as the learned weights. Then a novel channel information absent Q-learning resource allocation (CIAQ) algorithm is proposed to train the DNN-based optimization framework without massive labeling data, where the SE, the EE, and the fairness can be jointly optimized by adjusting discount factor. Our simulation results show that, the proposed CIAQ with rapid convergence speed not only well characterizes the extent of optimization objective with partial CSI, but also significantly outperforms the current random initialization method of neural network and the other existing resource allocation algorithms in term of the tradeoff among the SE, EE and fairness

An optimized QoS scheme for IMS-NEMO in heterogeneous networks

The network mobility (NEMO) is proposed to support the mobility management when users move as a whole. In IP Multimedia Subsystem (IMS), the individual Quality of Service (QoS) control for NEMO results in excessive signaling cost. On the other hand, current QoS schemes have two drawbacks: unawareness of the heterogeneous wireless environment and inefficient utilization of the reserved bandwidth. To solve these problems, we present a novel heterogeneous bandwidth sharing (HBS) scheme for QoS provision under IMS-based NEMO (IMS-NEMO). The HBS scheme selects the most suitable access network for each session and enables the new coming non-real-time sessions to share bandwidth with the Variable Bit Rate (VBR) coded media flows. The modeling and simulation results demonstrate that the HBS can satisfy users' QoS requirement and obtain a more efficient use of the scarce wireless bandwidth

A scalable approach for content based image retrieval in cloud datacenter

The emergence of cloud datacenters enhances the capability of online data storage. Since massive data is stored in datacenters, it is necessary to effectively locate and access interest data in such a distributed system. However, traditional search techniques only allow users to search images over exact-match keywords through a centralized index. These techniques cannot satisfy the requirements of content based image retrieval (CBIR). In this paper, we propose a scalable image retrieval framework which can efficiently support content similarity search and semantic search in the distributed environment. Its key idea is to integrate image feature vectors into distributed hash tables (DHTs) by exploiting the property of locality sensitive hashing (LSH). Thus, images with similar content are most likely gathered into the same node without the knowledge of any global information. For searching semantically close images, the relevance feedback is adopted in our system to overcome the gap between low-level features and high-level features. We show that our approach yields high recall rate with good load balance and only requires a few number of hops

Nanoporous Structure of Sintered Metal Powder Heat Exchanger in Dilution Refrigeration: A Numerical Study

We use LAMMPS to randomly pack hard spheres to simulate the heat exchanger, where the hard spheres represent sintered metal particles in the heat exchanger. We simulated the heat exchanger under different sphere radii and different packing fractions of the metal particle and researched pore space. To improve the performance of the heat exchanger, we adopted this simulation method to explore when the packing fraction is 65%, the optimal sintering particle radius in the heat exchanger is 30~35nm.Comment: 5 pages,3 figures, one tabl

ARHI (DIRAS 3), an Imprinted Tumor Suppressor Gene, Binds to Importins, and Blocks Nuclear Translocation of Stat3

ARHI (DIRAS3) is an imprinted tumor suppressor gene whose expression is lost in the majority of breast and ovarian cancers. Unlike its homologs Ras and Rap, ARHI functions as a tumor suppressor. Our previous study showed that ARHI can interact with transcription activator Stat3 and inhibit its nuclear translocation in human breast and ovarian cancer cells. To identify proteins that interact with ARHI in nuclear translocation, we have performed proteomic analysis and identified several importins that can associate with ARHI. To further explore this novel finding, we have purified 10 GST-importin fusion proteins (importin 7, 8, 13, b1, a1, a3, a5, a6, a7 as well as mutant a1). Using a GST-pull down assay, we found that ARHI can bind strongly to most importins; however, its binding is significantly reduced with an importin a1 mutant which contains an altered nuclear localization signal (NLS) domain. In addition, an ARHI N-terminal deletion mutant (NTD) exhibits much less binding to all importins than does wild type ARHI ARHI and NTD proteins were purified and tested for their ability to inhibit nuclear importation of proteins in HeLa cells. ARHI protein inhibits interaction of Ran-importin complexes with GFP fusion proteins that contain an NLS domain and a beta-like import receptor binding domain, blocking their nuclear localization. Addition of ARHI also blocked nuclear localization of phosphorylated Stat3β. By GST-pull down assays, we found that ARHI could compete for Ran-importins binding. Thus, ARHI-induced disruption of importin binding to cargo proteins including Stat3 could serve as an important regulatory mechanism that contributes to the tumor suppressor function of ARHI

A distributed end-to-end overload control mechanism for networks of SIP servers.

The Session Initiation Protocol (SIP) is an application-layer control protocol standardized by the IETF for creating, modifying and terminating multimedia sessions. With the increasing use of SIP in large deployments, the current SIP design cannot handle overload effectively, which may cause SIP networks to suffer from congestion collapse under heavy offered load. This paper introduces a distributed end-to-end overload control (DEOC) mechanism, which is deployed at the edge servers of SIP networks and is easy to implement. By applying overload control closest to the source of traf?c, DEOC can keep high throughput for SIP networks even when the offered load exceeds the capacity of the network. Besides, it responds quickly to the sudden variations of the offered load and achieves good fairness. Theoretic analysis and extensive simulations verify that DEOC is effective in controlling overload of SIP networks

Association of the CHRNA3 Locus with Lung Cancer Risk and Prognosis in Chinese Han Population

IntroductionRecent genome-wide association studies in Caucasians revealed association with lung cancer risk of single nucleotide polymorphisms (SNPs) in the locus containing two nicotine acetylcholine receptor CHRNA genes. However, the reported risk SNPs are extremely rare in Asians. This study sought to identify other variants on CHRNA3 associated with lung cancer susceptibility and to explore whether SNPs of CHRNA3 are of prognostic factors in patients with non-small cell lung cancer (NSCLC) in Chinese Han population.MethodsA case-control study of 529 cases and 567 controls was performed to study the association of three SNPs (rs3743076, rs3743078, and rs3743073) in CHRNA3 with lung cancer risk in Chinese Han population using logistic regression models. The relationship between CHRNA3 polymorphisms with overall survival among 122 patients with advanced stage (stage IIIb and IV) NSCLC were evaluated using Cox multiple model based on the International Association for the Study of Lung Cancer recommended tumor, node, metastasis new staging.ResultsPatients with genotypes TG or GG for the novel SNP rs3743073 in CHRNA3 gene, compared with those with TT, showed an increased risk of lung cancer (adjusted odds ratio = 1.91; 95% confidence interval, 1.38–2.63; p = 9.67 × 10−5) and worst survival (adjusted hazard ratio = 2.35; 95% confidence interval, 1.05–5.26; p = 0.04) in patients with advanced stage NSCLC based on International Association for the Study of Lung Cancer recommended tumor, node, metastasis new staging.ConclusionsThese results suggest that the rs3743073 polymorphism in CHRNA3 is predictive for lung cancer risk and prognostic in advanced stage NSCLC in Chinese Han population