Node embedding is a powerful approach for representing the structural role of each node in a graph. $textit{Node2vec}$ is a widely used method for node embedding that works by exploring the local neighborhoods via biased random walks on the graph. Ho
wever, $textit{node2vec}$ does not consider edge weights when computing walk biases. This intrinsic limitation prevents $textit{node2vec}$ from leveraging all the information in weighted graphs and, in turn, limits its application to many real-world networks that are weighted and dense. Here, we naturally extend $textit{node2vec}$ to $textit{node2vec+}$ in a way that accounts for edge weights when calculating walk biases, but which reduces to $textit{node2vec}$ in the cases of unweighted graphs or unbiased walks. We empirically show that $textit{node2vec+}$ is more robust to additive noise than $textit{node2vec}$ in weighted graphs using two synthetic datasets. We also demonstrate that $textit{node2vec+}$ significantly outperforms $textit{node2vec}$ on a commonly benchmarked multi-label dataset (Wikipedia). Furthermore, we test $textit{node2vec+}$ against GCN and GraphSAGE using various challenging gene classification tasks on two protein-protein interaction networks. Despite some clear advantages of GCN and GraphSAGE, they show comparable performance with $textit{node2vec+}$. Finally, $textit{node2vec+}$ can be used as a general approach for generating biased random walks, benefiting all existing methods built on top of $textit{node2vec}$. $textit{Node2vec+}$ is implemented as part of $texttt{PecanPy}$, which is available at https://github.com/krishnanlab/PecanPy .
In this work, we systematically study the two-proton($2p$) radioactivity half-lives using the two-potential approach while the nuclear potential is obtained by using Skyrme-Hartree-Fock approach with the Skyrme effective interaction of {SLy8}. For tr
ue $2p$ radioactivity($Q_{2p}$ $>$ 0 and $Q_p$ $< $0, where the $Q_p$ and $Q_{2p}$ are the released energy of the one-proton and two-proton radioactivity), the standard deviation between the experimental half-lives and our theoretical calculations is {0.701}. In addition, we extend this model to predict the half-lives of 15 possible $2p$ radioactivity candidates with $Q_{2p}$ $>$ 0 taken from the evaluated atomic mass table AME2016. The calculated results indicate that a clear linear relationship between the logarithmic $2p$ radioactivity half-lives $rm{log}_{10}T_{1/2}$ and coulomb parameters [ ($Z_{d}^{0.8}$+$l^{0.25}$)$Q_{2p}^{-1/2}$] considered the effect of orbital angular momentum proposed by Liu $et$ $al$ [Chin. Phys. C textbf{45}, 024108 (2021)] is also existed. For comparison, the generalized liquid drop model(GLDM), the effective liquid drop model(ELDM) and Gamow-like model are also used. Our predicted results are consistent with the ones obtained by the other models.
We consider the holographic QCD model with a planar horizon in the D dimensions with different consistent metric solutions. We investigate the black hole thermodynamics, phase diagram and equations of state (EoS) in different dimensions. The temperat
ure and chemical potential dependence of the drag force and diffusion coefficient also have been studied. From the results, the energy loss of heavy quark shows an enhancement near the phase transition temperature in D dimensions. This finding illustrates that the energy loss of heavy quark has a nontrivial and non-monotonic dependence on temperature. Furthermore, we find the heavy quark may lose less energy in higher dimension. The diffusion coefficient is larger in higher dimension.
Purpose: To introduce two novel learning-based motion artifact removal networks (LEARN) for the estimation of quantitative motion- and $B0$-inhomogeneity-corrected $R_2^ast$ maps from motion-corrupted multi-Gradient-Recalled Echo (mGRE) MRI data. M
ethods: We train two convolutional neural networks (CNNs) to correct motion artifacts for high-quality estimation of quantitative $B0$-inhomogeneity-corrected $R_2^ast$ maps from mGRE sequences. The first CNN, LEARN-IMG, performs motion correction on complex mGRE images, to enable the subsequent computation of high-quality motion-free quantitative $R_2^ast$ (and any other mGRE-enabled) maps using the standard voxel-wise analysis or machine-learning-based analysis. The second CNN, LEARN-BIO, is trained to directly generate motion- and $B0$-inhomogeneity-corrected quantitative $R_2^ast$ maps from motion-corrupted magnitude-only mGRE images by taking advantage of the biophysical model describing the mGRE signal decay. We show that both CNNs trained on synthetic MR images are capable of suppressing motion artifacts while preserving details in the predicted quantitative $R_2^ast$ maps. Significant reduction of motion artifacts on experimental in vivo motion-corrupted data has also been achieved by using our trained models. Conclusion: Both LEARN-IMG and LEARN-BIO can enable the computation of high-quality motion- and $B0$-inhomogeneity-corrected $R_2^ast$ maps. LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of $R_2^ast$ maps, while LEARN-BIO directly performs motion- and $B0$-inhomogeneity-corrected $R_2^ast$ estimation. Both LEARN-IMG and LEARN-BIO jointly process all the available gradient echoes, which enables them to exploit spatial patterns available in the data. The high computational speed of LEARN-BIO is an advantage that can lead to a broader clinical application.
Simulations of high-complexity quantum systems, which are intractable for classical computers, can be efficiently done with quantum computers. Similarly, the increasingly complex quantum electronic circuits themselves will also need efficient simulat
ions on quantum computers, which in turn will be important in quantum-aided design for next-generation quantum processors. Here, we implement variational quantum eigensolvers to simulate a Josephson-junction-array quantum circuit, which leads to the discovery of a new type of high-performance qubit, plasonium. We fabricate this new qubit and demonstrate that it exhibits not only long coherence time and high gate fidelity, but also a shrinking physical size and larger anharmonicity than the transmon, which can offer a number of advantages for scaling up multi-qubit devices. Our work opens the way to designing advanced quantum processors using existing quantum computing resources.
In this paper, we propose to align sentence representations from different languages into a unified embedding space, where semantic similarities (both cross-lingual and monolingual) can be computed with a simple dot product. Pre-trained language mode
ls are fine-tuned with the translation ranking task. Existing work (Feng et al., 2020) uses sentences within the same batch as negatives, which can suffer from the issue of easy negatives. We adapt MoCo (He et al., 2020) to further improve the quality of alignment. As the experimental results show, the sentence representations produced by our model achieve the new state-of-the-art on several tasks, including Tatoeba en-zh similarity search (Artetxe and Schwenk, 2019b), BUCC en-zh bitext mining, and semantic textual similarity on 7 datasets.
Continual learning (CL) refers to a machine learning paradigm that using only a small account of training samples and previously learned knowledge to enhance learning performance. CL models learn tasks from various domains in a sequential manner. The
major difficulty in CL is catastrophic forgetting of previously learned tasks, caused by shifts in data distributions. The existing CL models often employ a replay-based approach to diminish catastrophic forgetting. Most CL models stochastically select previously seen samples to retain learned knowledge. However, occupied memory size keeps enlarging along with accumulating learned tasks. Hereby, we propose a memory-efficient CL method. We devise a dynamic prototypes-guided memory replay module, incorporating it into an online meta-learning model. We conduct extensive experiments on text classification and additionally investigate the effect of training set orders on CL model performance. The experimental results testify the superiority of our method in alleviating catastrophic forgetting and enabling efficient knowledge transfer.
A distinctive feature of Doppler radar is the measurement of velocity in the radial direction for radar points. However, the missing tangential velocity component hampers object velocity estimation as well as temporal integration of radar sweeps in d
ynamic scenes. Recognizing that fusing camera with radar provides complementary information to radar, in this paper we present a closed-form solution for the point-wise, full-velocity estimate of Doppler returns using the corresponding optical flow from camera images. Additionally, we address the association problem between radar returns and camera images with a neural network that is trained to estimate radar-camera correspondences. Experimental results on the nuScenes dataset verify the validity of the method and show significant improvements over the state-of-the-art in velocity estimation and accumulation of radar points.
The existing active learning methods select the samples by evaluating the samples uncertainty or its effect on the diversity of labeled datasets based on different task-specific or model-specific criteria. In this paper, we propose the Influence Sele
ction for Active Learning(ISAL) which selects the unlabeled samples that can provide the most positive Influence on model performance. To obtain the Influence of the unlabeled sample in the active learning scenario, we design the Untrained Unlabeled sample Influence Calculation(UUIC) to estimate the unlabeled samples expected gradient with which we calculate its Influence. To prove the effectiveness of UUIC, we provide both theoretical and experimental analyses. Since the UUIC just depends on the model gradients, which can be obtained easily from any neural network, our active learning algorithm is task-agnostic and model-agnostic. ISAL achieves state-of-the-art performance in different active learning settings for different tasks with different datasets. Compared with previous methods, our method decreases the annotation cost at least by 12%, 13% and 16% on CIFAR10, VOC2012 and COCO, respectively.
Internet video delivery has undergone a tremendous explosion of growth over the past few years. However, the quality of video delivery system greatly depends on the Internet bandwidth. Deep Neural Networks (DNNs) are utilized to improve the quality o
f video delivery recently. These methods divide a video into chunks, and stream LR video chunks and corresponding content-aware models to the client. The client runs the inference of models to super-resolve the LR chunks. Consequently, a large number of models are streamed in order to deliver a video. In this paper, we first carefully study the relation between models of different chunks, then we tactfully design a joint training framework along with the Content-aware Feature Modulation (CaFM) layer to compress these models for neural video delivery. {bf With our method, each video chunk only requires less than $1% $ of original parameters to be streamed, achieving even better SR performance.} We conduct extensive experiments across various SR backbones, video time length, and scaling factors to demonstrate the advantages of our method. Besides, our method can be also viewed as a new approach of video coding. Our primary experiments achieve better video quality compared with the commercial H.264 and H.265 standard under the same storage cost, showing the great potential of the proposed method. Code is available at:url{https://github.com/Neural-video-delivery/CaFM-Pytorch-ICCV2021}
