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arXiv 2024年

作者： Wang, Yuhe Wang, Yating National & Local Joint Engineering Laboratory for Big Data Analysis and Computing Technology Beijing100190 China Institute for Scientific Computation Texas A&M University College StationTX77843 United States School of Mathematics and Statistics Xi’an Jiaotong University Shaanxi Xi’an710049 China

Fluid flow through bimodal porous media, characterized by a distinct separation in pore size distribution, is critical in various scientific and engineering applications, including groundwater management, oil and gas production, and carbon sequestration. This note delves into the physical similarity of fluid flow within such media, bridging the gap between microscale phenomena and macroscale observations. We present a representative mathematical model that conceptualizes bimodal porous media as a double-continuum system, distinguishing between macroporous and microporous regions. The model captures the complex interactions between these regions, particularly focusing on the challenges of modeling fluid flow when there is significant disparity in pore sizes. By employing a heuristic approach grounded in pore-scale tomography, we derive governing equations that describe fluid flow and analyze the solution characteristics. The results reveal unique features of the fluid flow in bimodal systems, such as the occurrence of boundary discontinuities and the delayed transient response, which are not observed in conventional porous media. This work provides ground for further studies in bimodal porous media, offering insights that could enhance predictive modeling and optimization in various applications concerning porous media with similar bimodal pore size distributions. © 2024, CC BY.

关键词： Transient analysis

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LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding

arXiv

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arXiv 2023年

作者： Yang, Qu Zhang, Malu Wu, Jibin Tan, Kay Chen Li, Haizhou The Department of Electrical and Computer Engineering National University of Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen [CUHK- Shenzhen China University of Electronic Science and Technology of China China The Department of Computing The Hong Kong Polytechnic University Hong Kong

The biological neurons use precise spike times, in addition to the spike firing rate, to communicate with each other. The time-to-first-spike (TTFS) coding is inspired by such biological observation. However, there is a lack of effective solutions for training TTFS-based spiking neural network (SNN). In this paper, we put forward a simple yet effective network conversion algorithm, which is referred to as LC-TTFS, by addressing two main problems that hinder an effective conversion from a high-performance artificial neural network (ANN) to a TTFS-based SNN. We show that our algorithm can achieve a near-perfect mapping between the activation values of an ANN and the spike times of an SNN on a number of challenging AI tasks, including image classification, image reconstruction, and speech enhancement. With TTFS coding, we can achieve up to orders of magnitude saving in computation over ANN and other rate-based SNNs. The study, therefore, paves the way for deploying ultra-low-power TTFS-based SNNs on power-constrained edge computing platforms. © 2023, CC0.

关键词： Image classification

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An Efficient Benders Decomposition Approach for Optimal Large-Scale Network Slicing

arXiv

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arXiv 2023年

作者： Chen, Wei-Kun Wu, Zheyu Zhang, Rui-Jin Liu, Ya-Feng Dai, Yu-Hong Luo, Zhi-Quan The School of Mathematics and Statistics Beijing Key Laboratory on MCAACI Beijing Institute of Technology Beijing100081 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China

This paper considers the network slicing (NS) problem which attempts to map multiple customized virtual network requests to a common shared network infrastructure and allocate network resources to meet diverse service requirements. This paper proposes an efficient customized Benders decomposition algorithm for globally solving the large-scale NP-hard NS problem. The proposed algorithm decomposes the hard NS problem into two relatively easy function placement (FP) and traffic routing (TR) subproblems and iteratively solves them enabling the information feedback between each other, which makes it particularly suitable to solve large-scale problems. Specifically, the FP subproblem is to place service functions into cloud nodes in the network, and solving it can return a function placement strategy based on which the TR subproblem is defined;and the TR subproblem is to find paths connecting two nodes hosting two adjacent functions in the network, and solving it can either verify that the solution of the FP subproblem is an optimal solution of the original problem, or return a valid inequality to the FP subproblem that cuts off the current infeasible solution. The proposed algorithm is guaranteed to find the globally optimal solution of the NS problem. By taking the special structure of the NS problem into consideration, we successfully develop two families of valid inequalities that render the proposed algorithm converge much more quickly and thus much more efficient. Numerical results demonstrate that the proposed valid inequalities effectively accelerate the convergence of the decomposition algorithm, and the proposed algorithm significantly outperforms the existing algorithms in terms of both solution efficiency and quality. Copyright © 2023, The Authors. All rights reserved.

关键词： Resource allocation

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An autoencoder-like nonnegative matrix co-factorization for improved student cognitive modeling 24

An autoencoder-like nonnegative matrix co-factorization for ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Shenbao Yu Yinghui Pan Yifeng Zeng Prashant Doshi Guoquan Liu Kim-Leng Poh Mingwei Lin College of Computer and Cyber Security Fujian Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University UK Intelligent Thought and Action Lab School of Computing University of Georgia Financial Technology Research Institute Fudan University China College of Design and Engineering National University of Singapore Singapore

ISBN: (纸本)9798331314385

Student cognitive modeling (SCM) is a fundamental task in intelligent education, with applications ranging from personalized learning to educational resource allocation. By exploiting students' response logs, SCM aims to predict their exercise performance as well as estimate knowledge proficiency in a subject. data mining approaches such as matrix factorization can obtain high accuracy in predicting student performance on exercises, but the knowledge proficiency is unknown or poorly estimated. The situation is further exacerbated if only sparse interactions exist between exercises and students (or knowledge concepts). To solve this dilemma, we root monotonicity (a fundamental psychometric theory on educational assessments) in a co-factorization framework and present an autoencoder-like nonnegative matrix co-factorization (AE-NMCF), which improves the accuracy of estimating the student's knowledge proficiency via an encoder-decoder learning pipeline. The resulting estimation problem is nonconvex with nonnegative constraints. We introduce a projected gradient method based on block coordinate descent with Lipschitz constants and guarantee the method's theoretical convergence. Experiments on several real-world data sets demonstrate the efficacy of our approach in terms of both performance prediction accuracy and knowledge estimation ability, when compared with existing student cognitive models.

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Epl: Empirical Prototype Learning for Deep Face Recognition

SSRN

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SSRN 2024年

作者： Fan, Weijia Wen, Jiajun Jia, Xi Shen, Linlin Zhou, Jiancan Li, Qiufu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Computer Vision Institute Shenzhen University Shenzhen518060 China School of Computer Science University of Birmingham Birmingham United Kingdom Aqara Lumi United Technology Coopration Shenzhen China

Prototype learning is widely used in face recognition, which takes the row vectors of coefficient matrix in the last linear layer of the feature extraction model as the prototypes for each class. When the prototypes are updated using the facial sample feature gradients in the model training, they are prone to being pulled away from the class center by the hard samples, resulting in decreased overall model performance. this paper, we explicitly define prototypes as the expectations of sample features in each class and design the empirical prototypes using the existing samples in the dataset. We then devise a strategy to adaptively update these empirical prototypes during the model training based on the similarity between the sample features and the empirical prototypes. Furthermore, we propose an empirical prototype learning (EPL) method, which utilizes an adaptive margin parameter with respect to sample features. EPL assigns larger margins to the normal samples and smaller margins to the hard samples, allowing the learned empirical prototypes to better reflect the class center dominated by the normal samples and finally pull the hard samples towards the empirical prototypes through the learning. extensive experiments on MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace demonstrate the effectiveness of EPL. Our code is available at https://***/WakingHours-GitHub/EPL. © 2024, The Authors. All rights reserved.

关键词： Face recognition

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AUCSeg: AUC-oriented pixel-level long-tail semantic segmentation 24

AUCSeg: AUC-oriented pixel-level long-tail semantic segmenta...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Boyu Han Qianqian Xu Zhiyong Yang Shilong Bao Peisong Wen Yangbangyan Jiang Qingming Huang Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory School of Computer Science and Tech. University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm's generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg.

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CFT constraints on parity-odd interactions with axions and dilatons

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Physical Review D 2024年第12期110卷 125008-125008页

作者： Claudio Corianò Stefano Lionetti Dipartimento di Matematica e Fisica Università del Salento and INFN Sezione di Lecce Via Arnesano 73100 Lecce Italy National Center for HPC Big Data and Quantum Computing Via Magnanelli 2 40033 Casalecchio di Reno Italy Institute of Nanotechnology National Research Council (CNR-NANOTEC) Lecce 73100 Italy

We illustrate how the conformal Ward identities in momentum space completely determine the structure of a parity-odd three-point correlator involving currents, energy-momentum tensors, and at least one scalar operator in d=4. Conformal invariance fixes almost all such possible correlators to vanish. The only exceptions are given by the ⟨JJO⟩odd and the ⟨TTO⟩odd which in momentum space are protected by chiral and conformal anomalies. Specifically, one can obtain a nonvanishing solution by considering scalar operators such as O=∇·JA, O=gμνTμν, or their shadow transforms. We comment on the implications of these results that constrain the coupling of axions and dilatons in a conformal phase of the early universe.

关键词： Axions Conformal symmetry Parity

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Parity-violating CFT and the gravitational chiral anomaly

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Physical Review D 2024年第4期109卷 045004-045004页

作者： Claudio Corianò Stefano Lionetti Matteo Maria Maglio Dipartimento di Matematica e Fisica Università del Salento and INFN Sezione di Lecce Via Arnesano 73100 Lecce Italy and National Center for HPC Big Data and Quantum Computing Via Magnanelli 2 40033 Casalecchio di Reno Italy Institute for Theoretical Physics (ITP) University of Heidelberg Philosophenweg 16 69120 Heidelberg Germany

We illustrate how the conformal Ward identities (CWI) and the gravitational chiral anomaly completely determine the structure of the ⟨TTJ5⟩ (graviton-graviton-chiral gauge current) correlator in momentum space. This analysis extends our previous results on the anomaly vertices ⟨AVV⟩ and ⟨AAA⟩, as well as the ⟨TJJ⟩ parity-odd conformal anomaly vertex in general CFTs. The ⟨TTJ5⟩ plays a fundamental role in the analysis of the conformal backreaction in early Universe cosmology, affecting the particle content and the evolution of the primordial plasma. Our approach is nonperturbative and not Lagrangian based, requiring the inclusion of a single anomaly pole in the solution of the anomaly constraint. The pole and its residue, along with the CWIs, determine the entire correlator in all of its sectors (longitudinal/transverse), all of which are proportional to the same anomaly coefficient. The method does not rely on a specific expression of the CP-odd anomalous current, which in free field theory can be represented either by a bilinear fermion current or by a gauge-dependent Chern-Simons current; it relies solely on the symmetry constraints. We compute the correlator perturbatively at one loop in free field theory and verify its exact agreement with the nonperturbative result. A comparison with the perturbative analysis confirms the presence of a sum rule satisfied by the correlator, similar to the parity-even ⟨TJJ⟩ and the chiral ⟨AVV⟩.

关键词： Anomalies Conformal field theory Feynman diagrams Gravitation Particle interactions Perturbation theory Quantum field theory

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MMLmiRLocNet: miRNA Subcellular Localization Prediction based on Multi-view Multi-label Learning for Drug Design

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IEEE Journal of Biomedical and Health Informatics 2024年 PP卷 PP-PP页

作者： Bai, Tao Xie, Junxi Liu, Yumeng Liu, Bin Beijing Institute of Technology School of Computer Science and Technology Beijing100081 China Yan'an University School of Mathematics and Computer Science Yan'an716000 China Shenzhen Technology University College of Big Data and Internet Guangdong Shenzhen518118 China Shenzhen MSU-BIT University Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen518172 China

Identifying subcellular localization of microRNAs (miRNAs) is essential for comprehensive understanding of cellular function and has significant implications for drug design. In the past, several computational methods for miRNA subcellular localization is being used for uncovering multiple facets of RNA function to facilitate the biological applications. Unfortunately, most existing classification methods rely on a single sequencebased view, making the effective fusion of data from multiple heterogeneous networks a primary challenge. Inspired by multi-view multi-label learning strategy, we propose a computational method, named MMLmiRLocNet, for predicting the subcellular localizations of miRNAs. The MMLmiRLocNet predictor extracts multi-perspective sequence representations by analyzing lexical, syntactic, and semantic aspects of biological sequences. Specifically, it integrates lexical attributes derived from k-mer physicochemical profiles, syntactic characteristics obtained via word2vec embeddings, and semantic representations generated by pre-trained feature embeddings. Finally, module for extracting multi-view consensus-level features and specific-level features was constructed to capture consensus and specific features from various perspectives. The full connection networks are utilized as the output module to predict the miRNA subcellular localization. Experimental results suggest that MMLmiRLocNet outperforms existing methods in terms of F1, subACC, and Accuracy, and achieves best performance with the help of multi-view consensus features and specific features extract network. © 2013 IEEE.

关键词： Embeddings

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Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework

arXiv

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arXiv 2022年

作者： Lou, Wei Li, Haofeng Li, Guanbin Han, Xiaoguang Wan, Xiang Shenzhen Research Institute of Big Data Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong at Shenzhen Shenzhen518172 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Pazhou Lab Guangzhou510330 China

Recently deep neural networks, which require a large amount of annotated samples, have been widely applied in nuclei instance segmentation of H&E stained pathology images. However, it is inefficient and unnecessary to label all pixels for a dataset of nuclei images which usually contain similar and redundant patterns. Although unsupervised and semi-supervised learning methods have been studied for nuclei segmentation, very few works have delved into the selective labeling of samples to reduce the workload of annotation. Thus, in this paper, we propose a novel full nuclei segmentation framework that chooses only a few image patches to be annotated, augments the training set from the selected samples, and achieves nuclei segmentation in a semi-supervised manner. In the proposed framework, we first develop a novel consistency-based patch selection method to determine which image patches are the most beneficial to the training. Then we introduce a conditional single-image GAN with a component-wise discriminator, to synthesize more training samples. Lastly, our proposed framework trains an existing segmentation model with the above augmented samples. The experimental results show that our proposed method could obtain the same-level performance as a fully-supervised baseline by annotating less than 5% pixels on some benchmarks. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

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