Outage Capacity and Optimal Transmission for Dying Channels

In wireless networks, communication links may be subject to random fatal impacts: for example, sensor networks under sudden power losses or cognitive radio networks with unpredictable primary user spectrum occupancy. Under such circumstances, it is critical to quantify how fast and reliably the information can be collected over attacked links. For a single point-to-point channel subject to a random attack, named as a \emph{dying channel}, we model it as a block-fading (BF) channel with a finite and random delay constraint. First, we define the outage capacity as the performance measure, followed by studying the optimal coding length $K$ such that the outage probability is minimized when uniform power allocation is assumed. For a given rate target and a coding length $K$, we then minimize the outage probability over the power allocation vector \mv{P}_{K}, and show that this optimization problem can be cast into a convex optimization problem under some conditions. The optimal solutions for several special cases are discussed. Furthermore, we extend the single point-to-point dying channel result to the parallel multi-channel case where each sub-channel is a dying channel, and investigate the corresponding asymptotic behavior of the overall outage probability with two different attack models: the independent-attack case and the $m$-dependent-attack case. It can be shown that the overall outage probability diminishes to zero for both cases as the number of sub-channels increases if the \emph{rate per unit cost} is less than a certain threshold. The outage exponents are also studied to reveal how fast the outage probability improves over the number of sub-channels.Comment: 31 pages, 9 figures, submitted to IEEE Transactions on Information Theor

On Design of Collaborative Beamforming for Two-Way Relay Networks

We consider a two-way relay network, where two source nodes, S1 and S2, exchange information through a cluster of relay nodes. The relay nodes receive the sum signal from S1 and S2 in the first time slot. In the second time slot, each relay node multiplies its received signal by a complex coefficient and retransmits the signal to the two source nodes, which leads to a collaborative two-way beamforming system. By applying the principle of analog network coding, each receiver at S1 and S2 cancels the "self-interference" in the received signal from the relay cluster and decodes the message. This paper studies the 2-dimensional achievable rate region for such a two-way relay network with collaborative beamforming. With different assumptions of channel reciprocity between the source-relay and relay-source channels, the achievable rate region is characterized under two setups. First, with reciprocal channels, we investigate the achievable rate regions when the relay cluster is subject to a sum-power constraint or individual-power constraints. We show that the optimal beamforming vectors obtained from solving the weighted sum inverse-SNR minimization (WSISMin) problems are sufficient to characterize the corresponding achievable rate region. Furthermore, we derive the closed form solutions for those optimal beamforming vectors and consequently propose the partially distributed algorithms to implement the optimal beamforming, where each relay node only needs the local channel information and one global parameter. Second, with the non-reciprocal channels, the achievable rate regions are also characterized for both the sum-power constraint case and the individual-power constraint case. Although no closed-form solutions are available under this setup, we present efficient numerical algorithms.Comment: new version of the previously posted, single column double spacing, 24 page

FacetClumps: A Facet-based Molecular Clump Detection Algorithm

A comprehensive understanding of molecular clumps is essential for investigating star formation. We present an algorithm for molecular clump detection, called FacetClumps. This algorithm uses a morphological approach to extract signal regions from the original data. The Gaussian Facet model is employed to fit the signal regions, which enhances the resistance to noise and the stability of the algorithm in diverse overlapping areas. The introduction of the extremum determination theorem of multivariate functions offers theoretical guidance for automatically locating clump centers. To guarantee that each clump is continuous, the signal regions are segmented into local regions based on gradient, and then the local regions are clustered into the clump centers based on connectivity and minimum distance to identify the regional information of each clump. Experiments conducted with both simulated and synthetic data demonstrate that FacetClumps exhibits great recall and precision rates, small location error and flux loss, a high consistency between the region of detected clump and that of simulated clump, and is generally stable in various environments. Notably, the recall rate of FacetClumps in the synthetic data, which comprises $^{13}CO$ ($J = 1-0$) emission line of the MWISP within $11.7^{\circ} \leq l \leq 13.4^{\circ}$, $0.22^{\circ} \leq b \leq 1.05^{\circ}$ and 5 km s$^{-1}$ $\leq v \leq$ 35 km s$^{-1}$ and simulated clumps, reaches 90.2\%. Additionally, FacetClumps demonstrates satisfactory performance when applied to observational data.Comment: 27pages,28figure

Synthesis and Application of Copper Nanowires and Silver Nanosheet-Coated Copper Nanowires as Nanofillers in Several Polymers

A large amount of copper (Cu) nanowires was synthesized through the reduction of Cu(OH)2 by hydrazine in an aqueous solution containing NaOH and ethylenediamine. Besides, Cu nanowires coated by silver nanosheet (denoted as Cu@Ag nanowires) were prepared with a facile transmetalation reaction method. In the meantime, the as‐prepared Cu and Cu@Ag nanowires were used as the nanofillers of polyvinyl chloride (PVC), ultra‐high molecular weight polyethylene (UHMWPE) and epoxy resin (EP), and their effects on the thermal properties and mechanical properties as well as friction and wear behavior of the polymer‐matrix composites nanocomposites were examined. Results indicate that the as‐prepared Cu@Ag nanowires consist of Cu nanowires core and Ag nanosheet shell. The Ag nanosheet shell can well inhibit the oxidation of the Cu nanowires core, thereby providing the as‐prepared Cu@Ag nanowires with good thermal stability even at an elevated temperature of 230°C. As compared with Cu nanowires, Cu@Ag nanowires could effectively increase the thermal stability of the PVC matrix composites. Moreover, due to the special morphology and microstructure, the as‐prepared Cu@Ag nanowires can effectively improve the mechanical properties and wear resistance of PVC, UHMWPE, and EP

Research on Correlation Analysis Method for Nuclear Power Operation Data Based on Multi-Scale Time Window

[Introduction] Nuclear power operation data is characterized by high dimension and large volume, and the complexity of the internal system of nuclear power plant makes it difficult to build a corresponding mechanism model. Therefore, it is very difficult to manually screen out relevant parameters from nuclear power data, and the introduction of non-relevant parameters will greatly affect the accuracy of the model. By means of improving the model accuracy, the purpose of accurate modeling can be reached. [Method] This paper proposed a correlation analysis method based on multi-scale time window. This method extracted state switch points for target parameters, classifies each sensor according to the characteristics of the data recorded by different sensors, and then designs detection windows for different kinds of sensors that meet their characteristics. The state switch detection was carried out in the corresponding time neighborhood of each sensor, and the correlation matching rate between each sensor and the target sensor was calculated to judge the correlation. [Result] Based on the actual historical operation data of nuclear power plant, the sensor parameters associated with the target sensor are selected successfully by the established correlation matching rate rule. [Conclusion] The experimental results show that the proposed method can screen out the correlation parameters more accurately. Compared with the commonly used Pearson correlation coefficient, the proposed method is more accurate

Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling

Conversion rate (CVR) prediction is one of the most critical tasks for digital display advertising. Commercial systems often require to update models in an online learning manner to catch up with the evolving data distribution. However, conversions usually do not happen immediately after a user click. This may result in inaccurate labeling, which is called delayed feedback problem. In previous studies, delayed feedback problem is handled either by waiting positive label for a long period of time, or by consuming the negative sample on its arrival and then insert a positive duplicate when a conversion happens later. Indeed, there is a trade-off between waiting for more accurate labels and utilizing fresh data, which is not considered in existing works. To strike a balance in this trade-off, we propose Elapsed-Time Sampling Delayed Feedback Model (ES-DFM), which models the relationship between the observed conversion distribution and the true conversion distribution. Then we optimize the expectation of true conversion distribution via importance sampling under the elapsed-time sampling distribution. We further estimate the importance weight for each instance, which is used as the weight of loss function in CVR prediction. To demonstrate the effectiveness of ES-DFM, we conduct extensive experiments on a public data and a private industrial dataset. Experimental results confirm that our method consistently outperforms the previous state-of-the-art results.Comment: This paper has been accepted by AAAI 202

A Tutorial on Environment-Aware Communications via Channel Knowledge Map for 6G

Sixth-generation (6G) mobile communication networks are expected to have dense infrastructures, large-dimensional channels, cost-effective hardware, diversified positioning methods, and enhanced intelligence. Such trends bring both new challenges and opportunities for the practical design of 6G. On one hand, acquiring channel state information (CSI) in real time for all wireless links becomes quite challenging in 6G. On the other hand, there would be numerous data sources in 6G containing high-quality location-tagged channel data, making it possible to better learn the local wireless environment. By exploiting such new opportunities and for tackling the CSI acquisition challenge, there is a promising paradigm shift from the conventional environment-unaware communications to the new environment-aware communications based on the novel approach of channel knowledge map (CKM). This article aims to provide a comprehensive tutorial overview on environment-aware communications enabled by CKM to fully harness its benefits for 6G. First, the basic concept of CKM is presented, and a comparison of CKM with various existing channel inference techniques is discussed. Next, the main techniques for CKM construction are discussed, including both the model-free and model-assisted approaches. Furthermore, a general framework is presented for the utilization of CKM to achieve environment-aware communications, followed by some typical CKM-aided communication scenarios. Finally, important open problems in CKM research are highlighted and potential solutions are discussed to inspire future work

An innovative pyroptosis-related long-noncoding-RNA signature predicts the prognosis of gastric cancer via affecting immune cell infiltration landscape

Background: Gastric cancer (GC) is a worldwide popular malignant tumor. However, the survival rate of advanced GC remains low. Pyroptosis and long non-coding RNAs (lncRNAs) are important in cancer progression. Thus, we aimed to find out a pyroptosis-related lncRNAs (PRLs) signature and use it to build a practical risk model with the purpose to predict the prognosis of patients with GC.Methods: Univariate Cox regression analysis was used to identify PRLs linked to GC patient’s prognosis. Subsequently, to construct a PRLs signature, the least absolute shrinkage and selection operator regression, and multivariate Cox regression analysis were used. Kaplan–Meier analysis, principal component analysis, and receiver operating characteristic curve analysis were performed to assess our novel lncRNA signature. The correlation between risk signature and clinicopathological features was also examined. Finally, the relationship of pyroptosis and immune cells were evaluated through the CIBERSORT tool and single-sample lncRNA set enrichment analysis (ssGSEA).Results: A PRLs signature comprising eight lncRNAs was discerned as a self-determining predictor of prognosis. GC patients were sub-divided into high-risk and low-risk groups via this risk-model. Stratified analysis of different clinical factors also displayed that the PRLs signature was a good prognosis factor. According to the risk score and clinical characteristics, a nomogram was established. Moreover, the difference between the groups is significance in immune cells and immune pathways.Conclusion: This study established an effective prognostic signature consist of eight PRLs in GC, and constructed an efficient nomogram model. Further, the PRLs correlated with immune cells and immune pathways