ABC: Aggregation before Communication, a Communication Reduction Framework for Distributed Graph Neural Network Training and Effective Partition

Graph Neural Networks(GNNs) are a family of neural models tailored for graph-structure data and have shown superior performance in learning representations for graph-structured data. However, training GNNs on large graphs remains challenging and a promising direction is distributed GNN training, which is to partition the input graph and distribute the workload across multiple machines. The key bottleneck of the existing distributed GNNs training framework is the across-machine communication induced by the dependency on the graph data and aggregation operator of GNNs. In this paper, we study the communication complexity during distributed GNNs training and propose a simple lossless communication reduction method, termed the Aggregation before Communication (ABC) method. ABC method exploits the permutation-invariant property of the GNNs layer and leads to a paradigm where vertex-cut is proved to admit a superior communication performance than the currently popular paradigm (edge-cut). In addition, we show that the new partition paradigm is particularly ideal in the case of dynamic graphs where it is infeasible to control the edge placement due to the unknown stochastic of the graph-changing process

Decodable network coding in wireless network

Network coding is a network layer technique to improve transmission efficiency. Coding packets is especially beneficial in a wireless environment where the demand for radio spectrum is high. However, to fully realize the benefits of network coding two challenging issues that must be addressed are: (1) Guaranteeing separation of coded packets at the destination, and (2) Mitigating the extra coding/decoding delay. If the destination has all the needed packets to decode a coded packet, then separation failure can be averted. If the scheduling algorithm considers the arrival time of coding pairs, then the extra delay can be mitigated. In this paper, we develop a network coding method to address these (decoding and latency) issues for multi-source multi-destination unicast and multicast sessions. We use linear programming to find the most efficient coding design solution with guaranteed decodability. To reduce network delay, we develop a scheduling algorithm to minimize the extra coding/decoding delay. Our coding design method and scheduling algorithm are validated through experiments. Simulation results show improved transmission efficiency and reduced network delay --Abstract, page iii

Structure of Core-Periphery Communities

It has been experimentally shown that communities in social networks tend to have a core-periphery topology. However, there is still a limited understanding of the precise structure of core-periphery communities in social networks including the connectivity structure and interaction rates between agents. In this paper, we use a game-theoretic approach to derive a more precise characterization of the structure of core-periphery communities

Practical Deep Dispersed Watermarking with Synchronization and Fusion

Deep learning based blind watermarking works have gradually emerged and achieved impressive performance. However, previous deep watermarking studies mainly focus on fixed low-resolution images while paying less attention to arbitrary resolution images, especially widespread high-resolution images nowadays. Moreover, most works usually demonstrate robustness against typical non-geometric attacks (\textit{e.g.}, JPEG compression) but ignore common geometric attacks (\textit{e.g.}, Rotate) and more challenging combined attacks. To overcome the above limitations, we propose a practical deep \textbf{D}ispersed \textbf{W}atermarking with \textbf{S}ynchronization and \textbf{F}usion, called \textbf{\proposed}. Specifically, given an arbitrary-resolution cover image, we adopt a dispersed embedding scheme which sparsely and randomly selects several fixed small-size cover blocks to embed a consistent watermark message by a well-trained encoder. In the extraction stage, we first design a watermark synchronization module to locate and rectify the encoded blocks in the noised watermarked image. We then utilize a decoder to obtain messages embedded in these blocks, and propose a message fusion strategy based on similarity to make full use of the consistency among messages, thus determining a reliable message. Extensive experiments conducted on different datasets convincingly demonstrate the effectiveness of our proposed {\proposed}. Compared with state-of-the-art approaches, our blind watermarking can achieve better performance: averagely improve the bit accuracy by 5.28\% and 5.93\% against single and combined attacks, respectively, and show less file size increment and better visual quality. Our code is available at https://github.com/bytedance/DWSF.Comment: Accpeted by ACM MM 202

Metabonomic Evaluation of ZHENG Differentiation and Treatment by Fuzhenghuayu Tablet in Hepatitis-B-Caused Cirrhosis

In Traditional Chinese Medicine (TCM), treatment based on ZHENG (also called TCM syndrome and pattern) differentiation has been applied for about 3 thousand years, while there are some difficulties to communicate with western medicine. In the present work, metabonomic methods were utilized to differentiate ZHENG types and evaluate the therapeutic efficiency of Fuzhenghuayu (FZHY) tablet in hepatitis-B-caused cirrhosis (HBC). Urine samples of 12 healthy volunteers (control group, CG) and 31 HBC patients (HBCG) were analyzed by gas chromatography mass spectrometry (GC/MS) and multivariate statistical analysis. The significantly changed metabolites between CG and HBCG were selected by PLS-DA loading plot analysis. Moreover, 4 ZHENGs were differentiated mutually, suggesting that there was urine metabolic material basis in ZHENG differentiation. The efficiency of FZHY tablet on subjects with spleen deficiency with dampness encumbrance syndrome (SDDES) and liver-kidney yin deficiency syndrome (LKYDS) was better than that of other syndromes. The efficiency of FZHY treatment based on ZHENG differentiation indicated that accurately ZHENG differentiating could guide the appropriate TCM treatment in HBC

Pore Scale Modeling of Capillary Action in Determining Oil–Water Flowing by Water Flooding: A Case Study of Low-Permeability Sandstone in the Ordos Basin, Northern China

AbstractCapillary action plays an important role in oil recovery by water flooding. As the pore channel radius decreases, the capillary action increases, which seriously affects reservoir development, especially in a low-permeability sandstone reservoir. The Ordos Basin is a typical low-permeability sandstone reservoir in China. Studying how variations in the capillary force affect the remaining oil production on the pore scale helps in understanding how the capillary action improves the development of unconventional reservoirs. In this study, the core of the Chang 6 Formation in the Ansai Oilfield, Ordos Basin was scanned by computed tomography. Then, the digital core model was established. The oil–water two-phase flow in pores was described using the method based on the Navier–Stokes equation coupled with the method of the volume of fluid simulation. The water flooding process was simulated on the pore scale. The results show that in the process of pore scale water flooding, the oil–water interface stays at the position between the throat channel and the pore area, where the oil–water interface reverses and the capillary force presents resistance, forming the capillary barrier or capillary valve. Affected by the capillary barrier, the oil–water two-phase flow in the process of water flooding is described by a “step-by-step” model. The pore structure characteristics at the junction of the pore area and the throat channel control the movement of the oil–water interface and affect the water flooding production and the ultimate recovery factor. As the liquid injection rate increases, the oil on both sides of the main channel is produced. While the oil recovery rate reaches 66%, the remaining oil on the edges becomes increasingly difficult to be produced. This difficulty is closely related to the viscosity of the injection fluid, interfacial tension, injection rate, pore radius, and pore wall wettability

Mechanical alignment tolerance of a cruciate-retaining knee prosthesis under gait loading—A finite element analysis

Component alignment is one of the most crucial factors affecting total knee arthroplasty’s clinical outcome and survival. This study aimed to investigate how coronal, sagittal, and transverse malalignment affects the mechanical behavior of the tibial insert and to determine a suitable alignment tolerance on the coronal, sagittal, and transverse planes. A finite element model of a cruciate-retaining knee prosthesis was assembled with different joint alignments (−10°, −7°, −5°, −3°, 0°, 3°, 5°, 7°, 10°) to assess the effect of malalignment under gait loading. The results showed that varus or valgus, extension, internal rotation, and excessive external rotation malalignments increased the maximum Von Mises stress and contact pressure on the tibial insert. The mechanical alignment tolerance of the studied prosthesis on the coronal, sagittal, and transverse planes was 3° varus to 3° valgus, 0°–10° flexion, and 0°–5° external rotation, respectively. This study suggests that each prosthesis should include a tolerance range for the joint alignment angle on the three planes, which may be used during surgical planning

Comparison of navigation systems for total knee arthroplasty: A systematic review and meta-analysis

BackgroundComponent alignment is a crucial factor affecting the clinical outcome of total knee arthroplasty (TKA). Accelerometer-based navigation (ABN) systems were developed to improve the accuracy of alignment during surgery. This study aimed to compare differences in component alignment, clinical outcomes, and surgical duration when using conventional instrumentation (CONI), ABN, and computer navigation (CN) systems.MethodsA comprehensive literature search was carried out using the Web of Science, Embase, PubMed, and Cochrane databases. Articles that met the eligibility criteria were included in the study. Meta-analyses were performed using the Cochrane Collaboration Review Manager based on Cochrane Review Method. The variables used for the analyses were postoperative clinical outcome (PCO), surgical duration, and component alignment, including the hip-knee-ankle (HKA) angle, coronal femoral angle (CFA), coronal tibial angle (CTA), sagittal femoral angle (SFA), sagittal tibial angle (STA), and the outliers for the mentioned angles. The mean difference (MD) was calculated to determine the difference between the surgical techniques for continuous variables and the odds ratio (OR) was used for the dichotomous outcomes.ResultsThe meta-analysis of the CONI and ABN system included 18 studies involving 2,070 TKA procedures, while the comparison of the ABN and CN systems included 5 studies involving 478 TKA procedures. The results showed that the ABN system provided more accurate component alignment for HKA, CFA, CTA, and SFA and produced fewer outliers for HKA, CFA, CTA, and STA. However, while the ABN system also required a significantly longer surgical time than the CONI approach, there was no statistical difference in PCO for the two systems. For the ABN and CN systems, there was no statistical difference in all variables except for the ABN system having a significantly shorter surgical duration.ConclusionThere was no significant difference in the accuracy of component alignment between the ABN and CN systems, but the ABN approach had a shorter surgical duration and at lower cost. The ABN system also significantly improved the accuracy of component alignment when compared to the CONI approach, although the surgery was longer. However, there was no significant difference in PCO between the CONI, ABN, and CN systems