Range-Free Localization with the Radical Line

Due to hardware and computational constraints, wireless sensor networks (WSNs) normally do not take measurements of time-of-arrival or time-difference-of-arrival for rangebased localization. Instead, WSNs in some applications use rangefree localization for simple but less accurate determination of sensor positions. A well-known algorithm for this purpose is the centroid algorithm. This paper presents a range-free localization technique based on the radical line of intersecting circles. This technique provides greater accuracy than the centroid algorithm, at the expense of a slight increase in computational load. Simulation results show that for the scenarios studied, the radical line method can give an approximately 2 to 30% increase in accuracy over the centroid algorithm, depending on whether or not the anchors have identical ranges, and on the value of DOI.Comment: Proc. IEEE ICC'10, Cape Town, South Africa, May, 201

LW-ISP: A Lightweight Model with ISP and Deep Learning

The deep learning (DL)-based methods of low-level tasks have many advantages over the traditional camera in terms of hardware prospects, error accumulation and imaging effects. Recently, the application of deep learning to replace the image signal processing (ISP) pipeline has appeared one after another; however, there is still a long way to go towards real landing. In this paper, we show the possibility of learning-based method to achieve real-time high-performance processing in the ISP pipeline. We propose LW-ISP, a novel architecture designed to implicitly learn the image mapping from RAW data to RGB image. Based on U-Net architecture, we propose the fine-grained attention module and a plug-and-play upsampling block suitable for low-level tasks. In particular, we design a heterogeneous distillation algorithm to distill the implicit features and reconstruction information of the clean image, so as to guide the learning of the student model. Our experiments demonstrate that LW-ISP has achieved a 0.38 dB improvement in PSNR compared to the previous best method, while the model parameters and calculation have been reduced by 23 times and 81 times. The inference efficiency has been accelerated by at least 15 times. Without bells and whistles, LW-ISP has achieved quite competitive results in ISP subtasks including image denoising and enhancement.Comment: 16 PAGES, ACCEPTED AS A CONFERENCE PAPER AT: BMVC 202

Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning

It is well believed that video captioning is a fundamental but challenging task in both computer vision and artificial intelligence fields. The prevalent approach is to map an input video to a variable-length output sentence in a sequence to sequence manner via Recurrent Neural Network (RNN). Nevertheless, the training of RNN still suffers to some degree from vanishing/exploding gradient problem, making the optimization difficult. Moreover, the inherently recurrent dependency in RNN prevents parallelization within a sequence during training and therefore limits the computations. In this paper, we present a novel design --- Temporal Deformable Convolutional Encoder-Decoder Networks (dubbed as TDConvED) that fully employ convolutions in both encoder and decoder networks for video captioning. Technically, we exploit convolutional block structures that compute intermediate states of a fixed number of inputs and stack several blocks to capture long-term relationships. The structure in encoder is further equipped with temporal deformable convolution to enable free-form deformation of temporal sampling. Our model also capitalizes on temporal attention mechanism for sentence generation. Extensive experiments are conducted on both MSVD and MSR-VTT video captioning datasets, and superior results are reported when comparing to conventional RNN-based encoder-decoder techniques. More remarkably, TDConvED increases CIDEr-D performance from 58.8% to 67.2% on MSVD.Comment: AAAI 201

Non-line-of-sight Node Localization based on Semi-Definite Programming in Wireless Sensor Networks

An unknown-position sensor can be localized if there are three or more anchors making time-of-arrival (TOA) measurements of a signal from it. However, the location errors can be very large due to the fact that some of the measurements are from non-line-of-sight (NLOS) paths. In this paper, we propose a semi-definite programming (SDP) based node localization algorithm in NLOS environment for ultra-wideband (UWB) wireless sensor networks. The positions of sensors can be estimated using the distance estimates from location-aware anchors as well as other sensors. However, in the absence of LOS paths, e.g., in indoor networks, the NLOS range estimates can be significantly biased. As a result, the NLOS error can remarkably decrease the location accuracy. And it is not easy to efficiently distinguish LOS from NLOS measurements. In this paper, an algorithm is proposed that achieves high location accuracy without the need of identifying NLOS and LOS measurement.Comment: submitted to IEEE ICC'1

Gene deletion chemoselectivity: codeletion of the genes for p16(INK4), methylthioadenosine phosphorylase, and the alpha- and beta-interferons in human pancreatic cell carcinoma lines and its implications for chemotherapy

Pancreatic carcinoma cells lines are known to have a high incidence of homozygous deletion of the candidate tumor suppressor gene p16 (MTS1/CDKN2), which resides in the chromosome 9p21 region. Here we: (a)examined a series of these cell lines for the incidence of codeletion of genes located near p16, in particular, the gene for the enzyme 5\u27-deoxy-5\u27-methylthioadenosine phosphorylase (MTAP) and the genes of the IFN-alpha and -beta cluster (IFNs); and (b) investigated whether therapeutic strategies could be developed that target malignant cells that have undergone the codeletion of such genes. Five of the eight pancreatic carcinoma cell lines were p16(-), MTAP was codeleted in all five cases. Because MTAP phosphorolyzes 5\u27-deoxy-5\u27-methylthioadenosine (MTA), generated as a byproduct of polyamine synthesis, to the salvageable purine base adenine, loss of this pathway in p16(-), MTAP(-) cells might sensitize these cells to methotrexate (MTX), the mechanism of action of which involves, in part, an inhibition of purine de novo synthesis. MTAP(+) normal keratinocytes and pancreatic carcinoma lines had relatively poor sensitivity, in terms of efficacy, to the purine nucleotide-starving actions of MTX. This may be in part due to the MTAP-dependent salvage of adenine moieties from endogenously generated MTA, because the MTAP inhibitor 5\u27-chloro-5\u27-de- oxyformycin A potentiates the antipurine actions of MTX in some of these MTAP(+) lines. Also, exogenous MTA (10 microM) reverses the growth-inhibitory actions of MTX in these lines. In contrast, MTAP(-) cell lines, which cannot recycle purines from endogenous MTA, have a relatively high sensitivity to the antipurine actions of MTX, which is not modulated by 5\u27-chloro-5\u27-deoxyformycin A or exogenous MTA. Thus the MTAP loss in malignant cells may be an example of gene deletion chemoselectivity, in which genetic deletions that occur as part of the oncogenic process render these cells more sensitive to particular anticancer agents than normal cells, which have not undergone such deletions. We also examined whether the loss of IFN genes sensitize cells to the growth-inhibitory actions of these cytokines. Three of the five p16(-) cell lines bore homozygous deletions of IFNA1 and IFNB1 genes, representing each end of the IFN-alpha,-beta gene cluster; one cell line bore a codeletion of the IFNA1 gene but retained the IFNB1 locus. Whereas the cell lines that were most sensitive to the growth-inhibitory effects of IFN-beta or IFN-alpha(2b), tended to be those with IFN deletions, there were enough exceptions to this pattern to indicate that the IFN genotype does not reliably predict IFN responsiveness

An improved method for predicting CO2 minimum miscibility pressure based on artificial neural network

 The CO2 enhanced oil recovery (EOR) method is widely used in actual oilfields. It is extremely important to accurately predict the CO2 minimum miscibility pressure (MMP) for CO2-EOR. At present, many studies about MMP prediction are based on empirical, experimental, or numerical simulation methods, but these methods have limitations in accuracy or computation efficiency. Therefore, more work needs to be done. In this work, with the results of the slim-tube experiment and the data expansion of the multiple mixing cell methods, an improved artificial neural network (ANN) model that predicts CO2 MMP by the full composition of the crude oil and temperature is trained. To stabilize the neural network training process, L2 regularization and Dropout are used to address the issue of over-fitting in neural networks. Predicting results show that the ANN model with Dropout possesses higher prediction accuracy and stronger generalization ability. Then, based on the validation sample evaluation, the mean absolute percentage error and R-square of the ANN model are 6.99 and 0.948, respectively. Finally, the improved ANN model is tested by six samples obtained from slim-tube experiment results. The results indicate that the improved ANN model has extremely low time cost and high accuracy to predict CO2 MMP, which is of great significance for CO2-EOR.Cited as: Dong, P., Liao, X., Chen, Z., Chu, H. An improved method for predicting CO2 minimum miscibility pressure based on artificial neural network. Advances in Geo-Energy Research, 2019, 3(4): 355-364, doi: 10.26804/ager.2019.04.0

Circle Feature Graphormer: Can Circle Features Stimulate Graph Transformer?

In this paper, we introduce two local graph features for missing link prediction tasks on ogbl-citation2. We define the features as Circle Features, which are borrowed from the concept of circle of friends. We propose the detailed computing formulas for the above features. Firstly, we define the first circle feature as modified swing for common graph, which comes from bipartite graph. Secondly, we define the second circle feature as bridge, which indicates the importance of two nodes for different circle of friends. In addition, we firstly propose the above features as bias to enhance graph transformer neural network, such that graph self-attention mechanism can be improved. We implement a Circled Feature aware Graph transformer (CFG) model based on SIEG network, which utilizes a double tower structure to capture both global and local structure features. Experimental results show that CFG achieves the state-of-the-art performance on dataset ogbl-citation2.Comment: 3 pages, 2 figures, 1 table, 31 references, manuscript in preparatio