Learning Persistent Community Structures in Dynamic Networks via Topological Data Analysis

Dynamic community detection methods often lack effective mechanisms to ensure temporal consistency, hindering the analysis of network evolution. In this paper, we propose a novel deep graph clustering framework with temporal consistency regularization on inter-community structures, inspired by the concept of minimal network topological changes within short intervals. Specifically, to address the representation collapse problem, we first introduce MFC, a matrix factorization-based deep graph clustering algorithm that preserves node embedding. Based on static clustering results, we construct probabilistic community networks and compute their persistence homology, a robust topological measure, to assess structural similarity between them. Moreover, a novel neural network regularization TopoReg is introduced to ensure the preservation of topological similarity between inter-community structures over time intervals. Our approach enhances temporal consistency and clustering accuracy on real-world datasets with both fixed and varying numbers of communities. It is also a pioneer application of TDA in temporally persistent community detection, offering an insightful contribution to field of network analysis. Code and data are available at the public git repository: https://github.com/kundtx/MFC_TopoRegComment: AAAI 202

Beneficial and Technological Analysis for the Recycling of Solar Grade Silicon Wastes

In the current paper, different kinds of silicon wastes during the production of SoG-Si were summarized and the beneficial analyses, such as financial value, energy value, CO2 emissions, and efficiency and energy payback time, were briefly discussed for the recycling of SoG-Si wastes. Possible technologies to recycle and purify SoG-Si wastes were reviewed: such as filtration, sedimentation, solidification control, electromagnetic separation, plasma oxidation, centrifugation, and high temperature remelting process, et al. © 2011 TMS

Memristor Neural Network Design

Neural network, a powerful learning model, has archived amazing results. However, the current Von Neumann computing system–based implementations of neural networks are suffering from memory wall and communication bottleneck problems ascribing to the Complementary Metal Oxide Semiconductor (CMOS) technology scaling down and communication gap. Memristor, a two terminal nanosolid state nonvolatile resistive switching, can provide energy‐efficient neuromorphic computing with its synaptic behavior. Crossbar architecture can be used to perform neural computations because of its high density and parallel computation. Thus, neural networks based on memristor crossbar will perform better in real world applications. In this chapter, the design of different neural network architectures based on memristor is introduced, including spiking neural networks, multilayer neural networks, convolution neural networks, and recurrent neural networks. And the brief introduction, the architecture, the computing circuits, and the training algorithm of each kind of neural networks are presented by instances. The potential applications and the prospects of memristor‐based neural network system are discussed

Purification of Solar Grade Silicon using Electromagnetic Field

Non-metallic particles and the metallic impurity elements in the solar cell silicon have a strong detrimental effect on the conversion efficiency of the solar cell. Removing these impurities is one of the important tasks for silicon refining. the current paper proposed a new approach to purify silicon - electromagnetic (EM) separation. Since the non-metallic particles and the metallic impurity elements are non- or less conductive while the molten silicon is well conductive, under EM field, the Lorenz force will push the particles to the boundary layer, thus separate these inclusions. in the current study, a high frequency EM field was imposed on the silicon melt in laboratory scale experiments with a frequency of 60 kHz and 15.0 a current, the non-conductive SiC particles were successfully pushed to the boundary layer close to the crucible wall. © 2010 IEEE

Inverse Design with Dynamic Mode Decomposition

We introduce a computationally efficient method for the automation of inverse design in science and engineering. Based on simple least-square regression, the underlying dynamic mode decomposition algorithm can be used to construct a low-rank subspace spanning multiple experiments in parameter space. The proposed inverse design dynamic mode composition (ID-DMD) algorithm leverages the computed low-dimensional subspace to enable fast digital design and optimization on laptop-level computing, including the potential to prescribe the dynamics themselves. Moreover, the method is robust to noise, physically interpretable, and can provide uncertainty quantification metrics. The architecture can also efficiently scale to large-scale design problems using randomized algorithms in the ID-DMD. The simplicity of the method and its implementation are highly attractive in practice, and the ID-DMD has been demonstrated to be an order of magnitude more accurate than competing methods while simultaneously being 3-5 orders faster on challenging engineering design problems ranging from structural vibrations to fluid dynamics. Due to its speed, robustness, interpretability, and ease-of-use, ID-DMD in comparison with other leading machine learning methods represents a significant advancement in data-driven methods for inverse design and optimization, promising a paradigm shift in how to approach inverse design in practice

Study of FPGA-based Error-controllable Floating-point Operation Accelerators

Floating-point operations are fundamental operations in the field of High-Performance Computing(HPC). In the context of big data and cloud computing,the amount of data that HPC platforms need to process is continuously growing,and the round-off error of floating-point arithmetic numbers will accumulate in large-scale,long-term operations. Therefore,it is crucial to ensure the reliability of the calculation results while improving the performance of floating-point operations. In response to these issues,based on the programmable,low-power,and flexible characteristics of a Field Programmable Gate Array (FPGA),a floating-point polynomial accelerator is designed mainly for complex single item operations. Based on the idea of error free transformation,the round-off error value is calculated and compensated to the floating-point value,such that the error can be controlled. Asynchronous and parallel methods are adopted to accelerate computation,and a CPU-FPGA platform is constructed to maximize the utilization of computing resources and ensure the efficiency of computing task execution. The data test results demonstrate that the accelerator can achieve a peak performance of 91.85 MFLOPs at the main frequency of 200 MHz in the numerical relativity simulation without limiting the symmetry. Compared to the performance of Intel i7 6700K CPU running the maximum number of threads,this accelerator achieved an acceleration ratio of 50.54,and achieved an average accurate result percentage of 53.6% and lower relative error under these conditions,demonstrating high reliability

Perioperative immunotherapy for stage II-III non-small cell lung cancer: a meta-analysis base on randomized controlled trials

BackgroundIn recent years, we have observed the pivotal role of immunotherapy in improving survival for patients with non-small cell lung cancer (NSCLC). However, the effectiveness of immunotherapy in the perioperative (neoadjuvant + adjuvant) treatment of resectable NSCLC remains uncertain. We conducted a comprehensive analysis of its antitumor efficacy and adverse effects (AEs) by pooling data from the KEYNOTE-671, NADIM II, and AEGEAN clinical trials.MethodsFor eligible studies, we searched seven databases. The randomized controlled trials (RCTs) pertaining to the comparative analysis of combination neoadjuvant platinum-based chemotherapy plus perioperative immunotherapy (PIO) versus perioperative placebo (PP) were included. Primary endpoints were overall survival (OS) and event-free survival (EFS). Secondary endpoints encompassed drug responses, AEs, and surgical outcomes.ResultsThree RCTs (KEYNOTE-671, NADIM II, and AEGEAN) were included in the final analysis. PIO group (neoadjuvant platinum-based chemotherapy plus perioperative immunotherapy) exhibited superior efficacy in OS (hazard ratio [HR]: 0.63 [0.49-0.81]), EFS (HR: 0.61 [0.52, 0.72]), objective response rate (risk ratio [RR]: 2.21 [1.91, 2.54]), pathological complete response (RR: 4.36 [3.04, 6.25]), major pathological response (RR: 2.79 [2.25, 3.46]), R0 resection rate (RR: 1.13 [1.00, 1.26]) and rate of adjuvant treatment (RR: 1.08 [1.01, 1.15]) compared with PP group (neoadjuvant platinum-based chemotherapy plus perioperative placebo). In the subgroup analysis, EFS tended to favor the PIO group in almost all subgroups. BMI (>25), T stage (IV), N stage (N1-N2) and pathological response (with pathological complete response) were favorable factors in the PIO group. In the safety assessment, the PIO group exhibited higher rates of serious AEs (28.96% vs. 23.51%) and AEs leading to treatment discontinuation (12.84% vs. 5.81%). Meanwhile, although total adverse events, grade 3-5 adverse events, and fatal adverse events tended to favor the PP group, the differences were not statistically significant.ConclusionPIO appears to be superior to PP for resectable stage II-III NSCLC, demonstrating enhanced survival and pathological responses. However, its elevated adverse event (AE) rate warrants careful consideration.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/#recordDetails, identifier CRD42023487475