arXiv subjects

Meng Li

Publications and source records attributed to Meng Li.

At least 307 records · Page 17Linked to original sources

arXiv2022

CEPC Cost Model Study and Circumference Optimization

The CEPC is a proposed high luminosity Higgs/Z factory, with the potential to be upgraded to top factory at center-of-mass energy of 360GeV. We perform an optimization study on the circumference of CEPC. We calculate the instant luminosity, the construction and operation cost for different circumferences. With respect to the total cost and average cost per particle, we conclude that the optimal circumference for the CEPC Higgs operation is 80 km. Taking into account of the Z pole operation, the potential high-energy upgrade of CEPC (top factory), the optimal circumference increased to 100 km. The long future proton-proton upgrade of CEPC (SPPC) also favors a larger circumference, and we conclude that 100 km is the global optimized circumference for this facility.

physics.acc-ph↗

arXiv2022

Ordered and tunable Majorana-zero-mode lattice in naturally strained LiFeAs

Majorana zero modes (MZMs) obey non-Abelian statistics and are considered building blocks for constructing topological qubits. Iron-based superconductors with topological band structures have emerged as promising hosting materials, since isolated candidate MZMs in the quantum limit have been observed inside the topological vortex cores. However, these materials suffer from issues related to alloying-induced disorder, uncontrolled vortex lattices and a low yield of topological vortices. Here, we report the formation of an ordered and tunable MZM lattice in naturally-strained stoichiometric LiFeAs by scanning tunneling microscopy/spectroscopy (STM/S). We observe biaxial charge density wave (CDW) stripes along the Fe-Fe and As-As directions in the strained regions. The vortices are pinned on the CDW stripes in the As-As direction and form an ordered lattice. We detect more than 90 percent of the vortices to be topological and possess the characteristics of isolated MZMs at the vortex center, forming an ordered MZM lattice with the density and the geometry tunable by an external magnetic field. Remarkably, with decreasing the spacing of neighboring vortices, the MZMs start to couple with each other. Our findings provide a new pathway towards tunable and ordered MZM lattices as a platform for future topological quantum computation.

cond-mat.supr-con↗

arXiv2022

PPA: Principal Parcellation Analysis for Brain Connectomes and Multiple Traits

Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring connectivity between pairs of ROIs. Statistical analyses are then heavily driven by the (largely arbitrary) choice of ROIs. In this article, we propose a novel tractography-based representation of brain connectomes, which clusters fiber endpoints to define a data adaptive parcellation targeted to explain variation among individuals and predict human traits. This representation leads to Principal Parcellation Analysis (PPA), representing individual brain connectomes by compositional vectors building on a basis system of fiber bundles that captures the connectivity at the population level. PPA reduces subjectivity and facilitates statistical analyses. We illustrate the proposed approach through applications to data from the Human Connectome Project (HCP) and show that PPA connectomes improve power in predicting human traits over state-of-the-art methods based on classical connectomes, while dramatically improving parsimony and maintaining interpretability. Our PPA package is publicly available on GitHub, and can be implemented routinely for diffusion image data.

stat.AP↗

arXiv2022

Decentralized Unsupervised Learning of Visual Representations

Collaborative learning enables distributed clients to learn a shared model for prediction while keeping the training data local on each client. However, existing collaborative learning methods require fully-labeled data for training, which is inconvenient or sometimes infeasible to obtain due to the high labeling cost and the requirement of expertise. The lack of labels makes collaborative learning impractical in many realistic settings. Self-supervised learning can address this challenge by learning from unlabeled data. Contrastive learning (CL), a self-supervised learning approach, can effectively learn visual representations from unlabeled image data. However, the distributed data collected on clients are usually not independent and identically distributed (non-IID) among clients, and each client may only have few classes of data, which degrades the performance of CL and learned representations. To tackle this problem, we propose a collaborative contrastive learning framework consisting of two approaches: feature fusion and neighborhood matching, by which a unified feature space among clients is learned for better data representations. Feature fusion provides remote features as accurate contrastive information to each client for better local learning. Neighborhood matching further aligns each client's local features to the remote features such that well-clustered features among clients can be learned. Extensive experiments show the effectiveness of the proposed framework. It outperforms other methods by 11% on IID data and matches the performance of centralized learning.

cs.LG↗

arXiv2022

SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systems

We design deep neural networks (DNNs) and corresponding networks' splittings to distribute DNNs' workload to camera sensors and a centralized aggregator on head mounted devices to meet system performance targets in inference accuracy and latency under the given hardware resource constraints. To achieve an optimal balance among computation, communication, and performance, a split-aware neural architecture search framework, SplitNets, is introduced to conduct model designing, splitting, and communication reduction simultaneously. We further extend the framework to multi-view systems for learning to fuse inputs from multiple camera sensors with optimal performance and systemic efficiency. We validate SplitNets for single-view system on ImageNet as well as multi-view system on 3D classification, and show that the SplitNets framework achieves state-of-the-art (SOTA) performance and system latency compared with existing approaches.

cs.LG↗

arXiv2022

Resilience-Motivated Distribution System Restoration Considering Electricity-Water-Gas Interdependency

A major outage in the electricity distribution system may affect the operation of water and natural gas supply systems, leading to an interruption of multiple services to critical customers. Therefore, enhancing resilience of critical infrastructures requires joint efforts of multiple sectors. In this paper, a distribution system service restoration method considering the electricity-water-gas interdependency is proposed. The objective is to provide electricity, water, and natural gas supplies to critical customers in the desired ratio according to their needs after an extreme event. The operational constraints of electricity, water, and natural gas networks are considered. The characteristics of electricity-driven coupling components, including water pumps and gas compressors, are also modeled. Relaxation techniques are applied to nonconvex constraints posed by physical laws of those networks. Consequently, the restoration problem is formulated as a mixed-integer second-order cone program, which can readily be solved by the off-the-shelf solvers. The proposed method is validated by numerical simulations on electricity-water-gas integrated systems, developed based on benchmark models of the subsystems. The results indicate that considering the interdependency refines the allocation of limited generation resources and demonstrate the exactness of the proposed convex relaxation.

eess.SY↗

arXiv2022

New Penalized Stochastic Gradient Methods for Linearly Constrained Strongly Convex Optimization

For minimizing a strongly convex objective function subject to linear inequality constraints, we consider a penalty approach that allows one to utilize stochastic methods for problems with a large number of constraints and/or objective function terms. We provide upper bounds on the distance between the solutions to the original constrained problem and the penalty reformulations, guaranteeing the convergence of the proposed approach. We give a nested accelerated stochastic gradient method and propose a novel way for updating the smoothness parameter of the penalty function and the step-size. The proposed algorithm requires at most $\tilde O(1/\sqrtε)$ expected stochastic gradient iterations to produce a solution within an expected distance of $ε$ to the optimal solution of the original problem, which is the best complexity for this problem class to the best of our knowledge. We also show how to query an approximate dual solution after stochastically solving the penalty reformulations, leading to results on the convergence of the duality gap. Moreover, the nested structure of the algorithm and upper bounds on the distance to the optimal solutions allows one to safely eliminate constraints that are inactive at an optimal solution throughout the algorithm, which leads to improved complexity results. Finally, we present computational results that demonstrate the effectiveness and robustness of our algorithm.

math.OC↗

arXiv2022

MHTTS: Fast multi-head text-to-speech for spontaneous speech with imperfect transcription

Neural network based end-to-end Text-to-Speech (TTS) has greatly improved the quality of synthesized speech. While how to use massive spontaneous speech without transcription efficiently still remains an open problem. In this paper, we propose MHTTS, a fast multi-speaker TTS system that is robust to transcription errors and speaking style speech data. Specifically, we introduce a multi-head model and transfer text information from high-quality corpus with manual transcription to spontaneous speech with imperfectly recognized transcription by jointly training them. MHTTS has three advantages: 1) Our system synthesizes better quality multi-speaker voice with faster inference speed. 2) Our system is capable of transferring correct text information to data with imperfect transcription, simulated using corruption, or provided by an Automatic Speech Recogniser (ASR). 3) Our system can utilize massive real spontaneous speech with imperfect transcription and synthesize expressive voice.

cs.SD↗

arXiv2021

Attributed Graph Neural Networks for Recommendation Systems on Large-Scale and Sparse Graph

Link prediction in structured-data is an important problem for many applications, especially for recommendation systems. Existing methods focus on how to learn the node representation based on graph-based structure. High-dimensional sparse edge features are not fully exploited. Because balancing precision and computation efficiency is significant for recommendation systems in real world, multiple-level feature representation in large-scale sparse graph still lacks effective and efficient solution. In this paper, we propose a practical solution about graph neural networks called Attributed Graph Convolutional Networks(AGCN) to incorporate edge attributes when apply graph neural networks in large-scale sparse networks. We formulate the link prediction problem as a subgraph classification problem. We firstly propose an efficient two-level projection to decompose topological structures to node-edge pairs and project them into the same interaction feature space. Then we apply multi-layer GCN to combine the projected node-edge pairs to capture the topological structures. Finally, the pooling representation of two units is treated as the input of classifier to predict the probability. We conduct offline experiments on two industrial datasets and one public dataset and demonstrate that AGCN outperforms other excellent baselines. Moreover, we also deploy AGCN method to important scenarios on Xianyu and AliExpress. In online systems, AGCN achieves over 5% improvement on online metrics.

cs.SI↗

arXiv2021

Protonation-induced discrete superconducting phases in bulk FeSe single crystals

The superconducting transition temperature, $T_{\rm{c}}$, of FeSe can be significantly enhanced several-fold by applying pressure, electron doping, intercalating spacing layer, and reducing dimensionality. Various ordered electronic phases, such as nematicity and spin density waves, have also been observed accompanying high-$T_{\rm{c}}$ superconductivity. Investigation on the evolution of the electronic structure with $T_{\rm{c}}$ is essential to understanding electronic behavior and high-$T_{\rm{c}}$ superconductivity in FeSe and its derived superconductors. In this report, we have found a series of discrete superconducting phases, with a maximum $T_{\rm{c}}$ up to 44 K, in H$^+$-intercalated FeSe single crystals using an ionic liquid gating method. Accompanied with the increase of $T_{\rm{c}}$, suppression of the nematic phase and evolution from non-Fermi-liquid to Fermi-liquid behavior was observed. An abrupt change in the Fermi surface topology was proposed to explain the discrete superconducting phases. A band structure that favors the high-$T_{\rm{c}}$ superconducting phase was also revealed.

cond-mat.supr-con↗

arXiv2021

Demonstration of magnetic and light-controlled actuation of a photomagnetically actuated deformable mirror for wavefront control

Deformable Mirrors (DMs) have wide applications ranging from astronomical imaging to laser communications and vision science. However, they often require bulky multi-channel cables for delivering high power to their drive actuators. A low powered DM which is driven in a contactless fashion could provide a possible alternative to this problem.Here, we present a photo-magnetically actuated deformable mirror (PMADM) concept which is actuated in a contactless fashion by a permanent magnet and low power laser heating source. This paper presents the laboratory demonstration of prototype optical surface quality, magnetic control of focus, and COMSOL simulations of its precise photo-control. The PMADM prototype is made of a magnetic composite (polydimethylsiloxane [PDMS] + ferromagnetic $\text{CrO}_\text{2}$) and an optical-quality substrate layer and is 30.48 mm $\times$ 30.48 mm $\times$ 175 $μ$ m in dimension with an optical pupil diameter of 8 mm. It deforms to 5.76 $μ$ m when subjected to a 0.12 T magnetic flux density and relaxes to 3.76 $μ$ m when illuminated by a 50 mW laser. A maximum stroke of 8.78 $μ$ m before failure is also estimated considering a 3x safety factor. This works also includes simulation of astigmatism generation with the PMADM, a first step in demonstrating control of higher order modes. A fully developed PMADM can have potential application for wavefront corrections in vacuum and space environments.

astro-ph.IM↗

arXiv2021

Bayesian Inference for Stationary Points in Gaussian Process Regression Models for Event-Related Potentials Analysis

Stationary points embedded in the derivatives are often critical for a model to be interpretable and may be considered as key features of interest in many applications. We propose a semiparametric Bayesian model to efficiently infer the locations of stationary points of a nonparametric function, while treating the function itself as a nuisance parameter. We use Gaussian processes as a flexible prior for the underlying function and impose derivative constraints to control the function's shape via conditioning. We develop an inferential strategy that intentionally restricts estimation to the case of at least one stationary point, bypassing possible mis-specifications in the number of stationary points and avoiding the varying dimension problem that often brings in computational complexity. We illustrate the proposed methods using simulations and then apply the method to the estimation of event-related potentials (ERP) derived from electroencephalography (EEG) signals. We show how the proposed method automatically identifies characteristic components and their latencies at the individual level, which avoids the excessive averaging across subjects which is routinely done in the field to obtain smooth curves. By applying this approach to EEG data collected from younger and older adults during a speech perception task, we are able to demonstrate how the time course of speech perception processes changes with age.

stat.ME↗

arXiv2021

Thermodynamic properties and Hilbert space of the human brain

Any macrosystem consists of many microparticles. According to statistical physics, the macroproperties of a system are realized as the statistical average of the corresponding microproperties. In our study, a model based on ensemble theory from statistical physics is proposed. Specifically, the functional connectivity (FC) patterns confirmed by Leading Eigenvector Dynamics Analysis (LEiDA) are taken as the microstates of a system, and static functional connectivity (SFC) is seen as the macrostate. When SFC can be written as the linear combination of these FC patterns, it is realized that these FC patterns are valid microstates for which the statistical results of relevant behaviors can describe the corresponding properties of SFC. In this case, the thermodynamic functions in ensemble theory are expressed in terms of these microstates. We apply the model to study the biological effect of ketamine on the brain and prove by maximum work principle that compared to that in the control group, the capability of work done by the brain that has been injected with ketamine declines significantly. Moreover, the quantum mechanical operator of the brain is further studied, and a Hilbert space spanned by the eigenvectors of the operator is obtained. The confirmation of the mechanical operator and Hilbert space opens great possibilities of using quantum mechanics to study brain systems, which would herald a new era in relevant neuroscience studies.

physics.bio-ph↗

arXiv2021

A Rigid Registration Method in TEVAR

Since the mapping relationship between definitized intra-interventional X-ray and undefined pre-interventional Computed Tomography(CT) is uncertain, auxiliary positioning devices or body markers, such as medical implants, are commonly used to determine this relationship. However, such approaches can not be widely used in clinical due to the complex realities. To determine the mapping relationship, and achieve a initializtion post estimation of human body without auxiliary equipment or markers, proposed method applies image segmentation and deep feature matching to directly match the X-ray and CT images. As a result, the well-trained network can directly predict the spatial correspondence between arbitrary X-ray and CT. The experimental results show that when combining our approach with the conventional approach, the achieved accuracy and speed can meet the basic clinical intervention needs, and it provides a new direction for intra-interventional registration.

eess.IV↗

arXiv2021

Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation

Vision Transformers (ViTs) have emerged with superior performance on computer vision tasks compared to convolutional neural network (CNN)-based models. However, ViTs are mainly designed for image classification that generate single-scale low-resolution representations, which makes dense prediction tasks such as semantic segmentation challenging for ViTs. Therefore, we propose HRViT, which enhances ViTs to learn semantically-rich and spatially-precise multi-scale representations by integrating high-resolution multi-branch architectures with ViTs. We balance the model performance and efficiency of HRViT by various branch-block co-optimization techniques. Specifically, we explore heterogeneous branch designs, reduce the redundancy in linear layers, and augment the attention block with enhanced expressiveness. Those approaches enabled HRViT to push the Pareto frontier of performance and efficiency on semantic segmentation to a new level, as our evaluation results on ADE20K and Cityscapes show. HRViT achieves 50.20% mIoU on ADE20K and 83.16% mIoU on Cityscapes, surpassing state-of-the-art MiT and CSWin backbones with an average of +1.78 mIoU improvement, 28% parameter saving, and 21% FLOPs reduction, demonstrating the potential of HRViT as a strong vision backbone for semantic segmentation.

cs.CV↗

arXiv2021

PyTorchVideo: A Deep Learning Library for Video Understanding

We introduce PyTorchVideo, an open-source deep-learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tasks, including classification, detection, self-supervised learning, and low-level processing. The library covers a full stack of video understanding tools including multimodal data loading, transformations, and models that reproduce state-of-the-art performance. PyTorchVideo further supports hardware acceleration that enables real-time inference on mobile devices. The library is based on PyTorch and can be used by any training framework; for example, PyTorchLightning, PySlowFast, or Classy Vision. PyTorchVideo is available at https://pytorchvideo.org/

cs.CV↗

arXiv2021

Improving the performance of reputation evaluating by combining the structure of network and nonlinear recovery

Characterizing the reputation of an evaluator is particularly significant for consumer to obtain useful information from online rating systems. Furthermore, to overcome the difficulties with spam attacks on the rating system and to get the reliable on reputation of evaluators is an important topic in the research. We have noticed that most of the existing evaluator reputation evaluation methods only rely on the evaluator's rating information and abnormal behavior to establish a reputation system, which miss the systematic aspects of the rating systems including the structure of the evaluator-object bipartite network and the effects of nonlinear effects. This study we propose an improved reputation evaluation method by combining the structure of the evaluator-object bipartite network with rating information and introducing penalty and reward factors. This novel method has been empirically analyzed on a large-scale artificial data set and two real data sets. The results show that the proposed method is more accurate and robust in the presence of spam attacks. This fresh idea contributes a new way for building reputation evaluation models in sparse bipartite rating network.

cs.SI↗

arXiv2021

Functional Group Bridge for Simultaneous Regression and Support Estimation

This article is motivated by studying multisensory effects on brain activities in intracranial electroencephalography (iEEG) experiments. Differential brain activities to multisensory stimulus presentations are zero in most regions and non-zero in some local regions, yielding locally sparse functions. Such studies are essentially a function-on-scalar regression problem, with interest being focused not only on estimating nonparametric functions but also on recovering the function supports. We propose a weighted group bridge approach for simultaneous function estimation and support recovery in function-on-scalar mixed effect models, while accounting for heterogeneity present in functional data. We use B-splines to transform sparsity of functions to its sparse vector counterpart of increasing dimension, and propose a fast non-convex optimization algorithm using nested alternative direction method of multipliers (ADMM) for estimation. Large sample properties are established. In particular, we show that the estimated coefficient functions are rate optimal in the minimax sense under the $L_2$ norm and resemble a phase transition phenomenon. For support estimation, we derive a convergence rate under the $L_{\infty}$ norm that leads to a sparsistency property under $δ$-sparsity, and provide a simple sufficient regularity condition under which a strict sparsistency property is established. An adjusted extended Bayesian information criterion is proposed for parameter tuning. The developed method is illustrated through simulation and an application to a novel iEEG dataset to study multisensory integration. We integrate the proposed method into RAVE, an R package that gains increasing popularity in the iEEG community.

stat.ME↗