arXiv subjects

Xiao Li

Publications and source records attributed to Xiao Li.

At least 379 records · Page 21Linked to original sources

arXiv2021

Asymmetrical Uplink and Downlink Transceivers in Massive MIMO Systems

Even if massive multiple-input multiple-output (MIMO) can theoretically bring huge benefits, it incurs substantial hardware complexity and expensive hardware costs. To address these issues while maintaining the system performance simultaneously, we develop an asymmetrical transceiver architecture for massive MIMO systems in this paper by releasing the shackles on radio frequency (RF) chains. Specifically, we first develop the architecture for the asymmetrical transceiver where the number of receive RF chains is different from that of the transmit RF chains. Due to this unequal number of RF chains, channel inconsistency appears. To overcome the channel inconsistency and thus fully harness the system's downlink data transmission capability, we propose two uplink-to-downlink channel transfer algorithms. The cost and power consumption models for the asymmetrical transceiver are also developed and the uplink signal-to-noise loss due to the channel inconsistency is investigated. Through analyzing system spectral, cost, and energy efficiency, we demonstrate that the proposed asymmetrical transceiver-based massive MIMO system can achieve excellent downlink spectral efficiency while maintaining a reasonable energy efficiency.

cs.IT↗

arXiv2021

Reversible modulation of metal-insulator transition in VO2 via chemically-induced oxygen migration

Metal-insulator transitions (MIT),an intriguing correlated phenomenon induced by the subtle competition of the electrons' repulsive Coulomb interaction and kinetic energy, is of great potential use for electronic applications due to the dramatic change in resistivity. Here, we demonstrate a reversible control of MIT in VO2 films via oxygen stoichiometry engineering. By facilely depositing and dissolving a water-soluble yet oxygen-active Sr3Al2O6 capping layer atop the VO2 at room temperature, oxygen ions can reversibly migrate between VO2 and Sr3Al2O6, resulting in a gradual suppression and a complete recovery of MIT in VO2. The migration of the oxygen ions is evidenced in a combination of transport measurement, structural characterization and first-principles calculations. This approach of chemically-induced oxygen migration using a water-dissolvable adjacent layer could be useful for advanced electronic and iontronic devices and studying oxygen stoichiometry effects on the MIT.

cond-mat.mtrl-sci↗

arXiv2021

Attention-Based 3D Seismic Fault Segmentation Training by a Few 2D Slice Labels

Detection faults in seismic data is a crucial step for seismic structural interpretation, reservoir characterization and well placement. Some recent works regard it as an image segmentation task. The task of image segmentation requires huge labels, especially 3D seismic data, which has a complex structure and lots of noise. Therefore, its annotation requires expert experience and a huge workload. In this study, we present lambda-BCE and lambda-smooth L1loss to effectively train 3D-CNN by some slices from 3D seismic data, so that the model can learn the segmentation of 3D seismic data from a few 2D slices. In order to fully extract information from limited data and suppress seismic noise, we propose an attention module that can be used for active supervision training and embedded in the network. The attention heatmap label is generated by the original label, and letting it supervise the attention module using the lambda-smooth L1loss. The experiment demonstrates the effectiveness of our loss function, the method can extract 3D seismic features from a few 2D slice labels. And it also shows the advanced performance of the attention module, which can significantly suppress the noise in the seismic data while increasing the model's sensitivity to the foreground. Finally, on the public test set, we only use the 2D slice labels training that accounts for 3.3% of the 3D volume label, and achieve similar performance to the 3D volume label training.

cs.CV↗

arXiv2021

High fidelity entanglement of neutral atoms via a Rydberg-mediated single-modulated-pulse controlled-PHASE gate

Neutral atom platform has become an attractive choice to study the science of quantum information and quantum simulation, where intense efforts have been devoted to the entangling processes between individual atoms. For the development of this area, two-qubit controlled-PHASE gate via Rydberg blockade is one of the most essential elements. Recent theoretical studies have suggested the advantages of introducing non-trivial waveform modulation into the gate protocol, which is anticipated to improve its performance towards the next stage. We report our recent experimental results in realizing a two-qubit controlled-PHASE($C_Z$) gate via off-resonant modulated driving(ORMD) embedded in two-photon transition for Rb atoms. It relies upon a single modulated driving pulse with a carefully calculated smooth waveform to gain the appropriate phase accumulations required by the two-qubit gate. Combining this $C_Z$ gate with global microwave pulses, two-atom entanglement is generated with the raw fidelity of 0.945(6). Accounting for state preparation and measurement (SPAM) errors, we extract the entanglement operation fidelity to be 0.980(7). Our work features completing the $C_Z$ gate operation within a single pulse to avoid shelved Rydberg population, thus demonstrate another promising route for realizing high-fidelity two-qubit gate for neutral atom platform.

quant-ph↗

arXiv2021

Experimental demonstration of the short bunch extraction by bunch rotation in a high-intensity rapid cycling proton synchrotron

Short bunch proton beams are of great significance for the applications of white neutron beams and muon beams. The accelerator complex of China Spallation Neutron Source (CSNS) was designed to support the applications mainly based on neutron scattering techniques where the proton pulse length is not very sensitive. Some theoretical and experimental studies have been performed to see if one can extract a short-bunch proton beam by bunch rotation from the rapid cycling synchrotron (RCS) at CSNS. The experimental results at RCS have evidently displayed the bunch lengthening and rotation process, which demonstrates the effectiveness of this method even with a very short available time for the RF gymnastic processes and a high-intensity beam. With a beam power of 50 kW and normal longitudinal emittance at the injection, the proton beam with a bunch length of about 53% with respect to the one in the normal operation mode was obtained and transported to the spallation target. With a reduced longitudinal emittance at injection and the beam power of 30 kW, the shortest extraction bunch length obtained is about 26% of the one in the normal operation mode. Different machine settings have also been tested to show the impact of the desynchronization between the RF and magnetic fields, the influence of the non-adiabatic risetime and the adiabatic decay time of the RF voltage on the extraction bunch length. The experimental results are well consistent with the theoretical and simulated ones. It is interesting to observe that space charge has a beneficial effect on the bunch lengthening which will result in a shorter bunch at the extraction with the later bunch rotation. The controlled desynchronization method between the RF and magnetic fields in an RCS was also proven successful.

physics.acc-ph↗

arXiv2021

Enabling Plug-and-Play and Crowdsourcing SLAM in Wireless Communication Systems

Simultaneous localization and mapping (SLAM) during communication is emerging. This technology promises to provide information on propagation environments and transceivers' location, thus creating several new services and applications for the Internet of Things and environment-aware communication. Using crowdsourcing data collected by multiple agents appears to be much potential for enhancing SLAM performance. However, the measurement uncertainties in practice and biased estimations from multiple agents may result in serious errors. This study develops a robust SLAM method with measurement plug-and-play and crowdsourcing mechanisms to address the above problems. First, we divide measurements into different categories according to their unknown biases and realize a measurement plug-and-play mechanism by extending the classic belief propagation (BP)-based SLAM method. The proposed mechanism can obtain the time-varying agent location, radio features, and corresponding measurement biases (such as clock bias, orientation bias, and received signal strength model parameters), with high accuracy and robustness in challenging scenarios without any prior information on anchors and agents. Next, we establish a probabilistic crowdsourcing-based SLAM mechanism, in which multiple agents cooperate to construct and refine the radio map in a decentralized manner. Our study presents the first BP-based crowdsourcing that resolves the "double count" and "data reliability" problems through the flexible application of probabilistic data association methods. Numerical results reveal that the crowdsourcing mechanism can further improve the accuracy of the mapping result, which, in turn, ensures the decimeter-level localization accuracy of each agent in a challenging propagation environment.

cs.IT↗

arXiv2021

Unconditionally stable exponential time differencing schemes for the mass-conserving Allen-Cahn equation with nonlocal and local effects

It is well known that the classic Allen-Cahn equation satisfies the maximum bound principle (MBP), that is, the absolute value of its solution is uniformly bounded for all time by certain constant under suitable initial and boundary conditions. In this paper, we consider numerical solutions of the modified Allen-Cahn equation with a Lagrange multiplier of nonlocal and local effects, which not only shares the same MBP as the original Allen-Cahn equation but also conserves the mass exactly. We reformulate the model equation with a linear stabilizing technique, then construct first- and second-order exponential time differencing schemes for its time integration. We prove the unconditional MBP preservation and mass conservation of the proposed schemes in the time discrete sense and derive their error estimates under some regularity assumptions. Various numerical experiments in two and three dimensions are also conducted to verify the theoretical results.

math.NA↗

arXiv2021

Whiteout: when do fixed-X knockoffs fail?

A core strength of knockoff methods is their virtually limitless customizability, allowing an analyst to exploit machine learning algorithms and domain knowledge without threatening the method's robust finite-sample false discovery rate control guarantee. While several previous works have investigated regimes where specific implementations of knockoffs are provably powerful, general negative results are more difficult to obtain for such a flexible method. In this work we recast the fixed-$X$ knockoff filter for the Gaussian linear model as a conditional post-selection inference method. It adds user-generated Gaussian noise to the ordinary least squares estimator $\hatβ$ to obtain a "whitened" estimator $\widetildeβ$ with uncorrelated entries, and performs inference using $\text{sgn}(\widetildeβ_j)$ as the test statistic for $H_j:\; β_j = 0$. We prove equivalence between our whitening formulation and the more standard formulation involving negative control predictor variables, showing how the fixed-$X$ knockoffs framework can be used for multiple testing on any problem with (asymptotically) multivariate Gaussian parameter estimates. Relying on this perspective, we obtain the first negative results that universally upper-bound the power of all fixed-$X$ knockoff methods, without regard to choices made by the analyst. Our results show roughly that, if the leading eigenvalues of $\text{Var}(\hatβ)$ are large with dense leading eigenvectors, then there is no way to whiten $\hatβ$ without irreparably erasing nearly all of the signal, rendering $\text{sgn}(\widetildeβ_j)$ too uninformative for accurate inference. We give conditions under which the true positive rate (TPR) for any fixed-$X$ knockoff method must converge to zero even while the TPR of Bonferroni-corrected multiple testing tends to one, and we explore several examples illustrating this phenomenon.

math.ST↗

arXiv2021

Out-of-Step Detection Based On an Improved Line Potential Energy Criterion

The line potential energy in the cutset is used as the criterion for monitoring the generator instability, but the criterion has the following two limitations due to narrowly defined conditions. The assumption of an ideal constant power load is difficult to satisfy, and the condition of the critical cutset is too narrow. The limitations are addressed by analyzing the relationship between the power of the load elements and the power of the cutset based on the two-machine equivalent. Modifications of the theoretical derivation steps of this criterion are presented to extend the applications of the criterion and provide theoretical support for its online use. Two simple strategies are proposed to ensure early detection of the generator instability.

eess.SY↗

arXiv2021

A new class of exact mobility edges in non-Hermitian quasiperiodic models

Quantum localization in 1D non-Hermitian systems, especially the search for exact single-particle mobility edges, has attracted considerable interest recently. While much progress has been made, the available methods to determine the ME of such models are still limited. In this work, we propose a new method to determine the exact mobility edge in a large class of 1D non-Hermitian quasiperiodic models with parity-time ($\mathcal{PT}$) symmetry. We illustrate our method by studying a specific model. We first use our method to determine the energy-dependent mobility edge as well as the spectrum for localized eigenstates in this model. We then demonstrate that the metal-insulator transition must occur simultaneously with the spontaneous $\mathcal{PT}$-symmetry breaking transition in this model. Finally, we propose an experimental protocol based on a 1D photonic lattice to distinguish the extended and localized single-particle states in our model.

cond-mat.dis-nn↗

arXiv2021

End-to-End Video Object Detection with Spatial-Temporal Transformers

Recently, DETR and Deformable DETR have been proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance as previous complex hand-crafted detectors. However, their performance on Video Object Detection (VOD) has not been well explored. In this paper, we present TransVOD, an end-to-end video object detection model based on a spatial-temporal Transformer architecture. The goal of this paper is to streamline the pipeline of VOD, effectively removing the need for many hand-crafted components for feature aggregation, e.g., optical flow, recurrent neural networks, relation networks. Besides, benefited from the object query design in DETR, our method does not need complicated post-processing methods such as Seq-NMS or Tubelet rescoring, which keeps the pipeline simple and clean. In particular, we present temporal Transformer to aggregate both the spatial object queries and the feature memories of each frame. Our temporal Transformer consists of three components: Temporal Deformable Transformer Encoder (TDTE) to encode the multiple frame spatial details, Temporal Query Encoder (TQE) to fuse object queries, and Temporal Deformable Transformer Decoder to obtain current frame detection results. These designs boost the strong baseline deformable DETR by a significant margin (3%-4% mAP) on the ImageNet VID dataset. TransVOD yields comparable results performance on the benchmark of ImageNet VID. We hope our TransVOD can provide a new perspective for video object detection. Code will be made publicly available at https://github.com/SJTU-LuHe/TransVOD.

cs.CV↗

arXiv2021

A Geometric Analysis of Neural Collapse with Unconstrained Features

We provide the first global optimization landscape analysis of $Neural\;Collapse$ -- an intriguing empirical phenomenon that arises in the last-layer classifiers and features of neural networks during the terminal phase of training. As recently reported by Papyan et al., this phenomenon implies that ($i$) the class means and the last-layer classifiers all collapse to the vertices of a Simplex Equiangular Tight Frame (ETF) up to scaling, and ($ii$) cross-example within-class variability of last-layer activations collapses to zero. We study the problem based on a simplified $unconstrained\;feature\;model$, which isolates the topmost layers from the classifier of the neural network. In this context, we show that the classical cross-entropy loss with weight decay has a benign global landscape, in the sense that the only global minimizers are the Simplex ETFs while all other critical points are strict saddles whose Hessian exhibit negative curvature directions. In contrast to existing landscape analysis for deep neural networks which is often disconnected from practice, our analysis of the simplified model not only does it explain what kind of features are learned in the last layer, but it also shows why they can be efficiently optimized in the simplified settings, matching the empirical observations in practical deep network architectures. These findings could have profound implications for optimization, generalization, and robustness of broad interests. For example, our experiments demonstrate that one may set the feature dimension equal to the number of classes and fix the last-layer classifier to be a Simplex ETF for network training, which reduces memory cost by over $20\%$ on ResNet18 without sacrificing the generalization performance.

cs.LG↗

arXiv2021

3-D Deployment of UAV Swarm for Massive MIMO Communications

We consider the uplink transmission between a multi-antenna ground station and an unmanned aerial vehicle (UAV) swarm. The UAVs are assumed as intelligent agents, which can explore their optimal three dimensional (3-D) deployment to maximize the channel capacity of the multiple input multiple output (MIMO) system. Specifically, considering the limitations of each UAV in accessing the global information of the network, we focus on a decentralized control strategy by noting that each UAV in the swarm can only utilize the local information to achieve the optimal 3-D deployment. In this case, the optimization problem can be divided into several optimization sub-problems with respect to the rank function. Due to the non-convex nature of the rank function and the fact that the optimization sub-problems are coupled, the original problem is NP-hard and, thus, cannot be solved with standard convex optimization solvers. Interestingly, we can relax the constraint condition of each sub-problem and solve the optimization problem by a formulated UAVs channel capacity maximization game. We analyze such game according to the designed reward function and the potential function. Then, we discuss the existence of the pure Nash equilibrium in the game. To achieve the best Nash equilibrium of the MIMO system, we develop a decentralized learning algorithm, namely decentralized UAVs channel capacity learning. The details of the algorithm are provided, and then, the convergence, the effectiveness and the computational complexity are analyzed, respectively. Moreover, we give some insightful remarks based on the proofs and the theoretical analysis. Also, extensive simulations illustrate that the developed learning algorithm can achieve a high MIMO channel capacity by optimizing the 3-D UAV swarm deployment with the local information.

cs.IT↗

arXiv2021

Enhanced metal-insulator transition in freestanding VO2 down to 5 nm thickness

Ultrathin freestanding membranes with a pronounced metal-insulator transition (MIT) provides huge potential in future flexible electronic applications as well as a unique aspect of the study of lattice-electron interplay. However, the reduction of the thickness to an ultrathin region (a few nm) is typically detrimental to the MIT in epitaxial films, and even catastrophic for their freestanding form. Here, we report an enhanced MIT in VO2-based freestanding membranes, with a lateral size up to millimetres and VO2 thickness down to 5 nm. The VO2-membranes were detached by dissolving a Sr3Al2O6 sacrificial layer between the VO2 thin film and c-Al2O3(0001) substrate, allowing a transfer onto arbitrary surfaces. Furthermore, the MIT in the VO2-membrane was greatly enhanced by inserting an intermediate Al2O3 buffer layer. In comparison to the best available ultrathin VO2-membranes, the enhancement of MIT is over 400% at 5 nm VO2 thickness and more than one order of magnitude for VO2 above 10 nm. Our study widens the spectrum of functionality in ultrathin and large-scale membranes, and enables the potential integration of MIT into flexible electronics and photonics.

cond-mat.mtrl-sci↗

arXiv2021

Stabilized integrating factor Runge-Kutta method and unconditional preservation of maximum bound principle

Maximum bound principle (MBP) is an important property for a large class of semilinear parabolic equations, in the sense that the time-dependent solution of the equation with appropriate initial and boundary conditions and nonlinear operator preserves for all time a uniform pointwise bound in absolute value. It has been a challenging problem on how to design unconditionally MBP-preserving high-order accurate time-stepping schemes for these equations. In this paper, we combine the integrating factor Runge-Kutta (IFRK) method with the linear stabilization technique to develop a stabilized IFRK (sIFRK) method, and successfully derive sufficient conditions for the proposed method to preserve MBP unconditionally in the discrete setting. We then elaborate some sIFRK schemes with up to the third-order accuracy, which are proven to be unconditionally MBP-preserving by verifying these conditions. In addition, it is shown that many classic strong stability-preserving sIFRK schemes do not satisfy these conditions except the first-order one. Extensive numerical experiments are also carried out to demonstrate the performance of the proposed method.

math.NA↗

arXiv2021

Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation

Tremendous efforts have been made on instance segmentation but the mask quality is still not satisfactory. The boundaries of predicted instance masks are usually imprecise due to the low spatial resolution of feature maps and the imbalance problem caused by the extremely low proportion of boundary pixels. To address these issues, we propose a conceptually simple yet effective post-processing refinement framework to improve the boundary quality based on the results of any instance segmentation model, termed BPR. Following the idea of looking closer to segment boundaries better, we extract and refine a series of small boundary patches along the predicted instance boundaries. The refinement is accomplished by a boundary patch refinement network at higher resolution. The proposed BPR framework yields significant improvements over the Mask R-CNN baseline on Cityscapes benchmark, especially on the boundary-aware metrics. Moreover, by applying the BPR framework to the PolyTransform + SegFix baseline, we reached 1st place on the Cityscapes leaderboard.

cs.CV↗

arXiv2021

Weakly Convex Optimization over Stiefel Manifold Using Riemannian Subgradient-Type Methods

We consider a class of nonsmooth optimization problems over the Stiefel manifold, in which the objective function is weakly convex in the ambient Euclidean space. Such problems are ubiquitous in engineering applications but still largely unexplored. We present a family of Riemannian subgradient-type methods -- namely Riemannain subgradient, incremental subgradient, and stochastic subgradient methods -- to solve these problems and show that they all have an iteration complexity of ${\cal O}(\varepsilon^{-4})$ for driving a natural stationarity measure below $\varepsilon$. In addition, we establish the local linear convergence of the Riemannian subgradient and incremental subgradient methods when the problem at hand further satisfies a sharpness property and the algorithms are properly initialized and use geometrically diminishing stepsizes. To the best of our knowledge, these are the first convergence guarantees for using Riemannian subgradient-type methods to optimize a class of nonconvex nonsmooth functions over the Stiefel manifold. The fundamental ingredient in the proof of the aforementioned convergence results is a new Riemannian subgradient inequality for restrictions of weakly convex functions on the Stiefel manifold, which could be of independent interest. We also show that our convergence results can be extended to handle a class of compact embedded submanifolds of the Euclidean space. Finally, we discuss the sharpness properties of various formulations of the robust subspace recovery and orthogonal dictionary learning problems and demonstrate the convergence performance of the algorithms on both problems via numerical simulations.

math.OC↗

arXiv2021

Sensing population distribution from satellite imagery via deep learning: model selection, neighboring effect, and systematic biases

The rapid development of remote sensing techniques provides rich, large-coverage, and high-temporal information of the ground, which can be coupled with the emerging deep learning approaches that enable latent features and hidden geographical patterns to be extracted. This study marks the first attempt to cross-compare performances of popular state-of-the-art deep learning models in estimating population distribution from remote sensing images, investigate the contribution of neighboring effect, and explore the potential systematic population estimation biases. We conduct an end-to-end training of four popular deep learning architectures, i.e., VGG, ResNet, Xception, and DenseNet, by establishing a mapping between Sentinel-2 image patches and their corresponding population count from the LandScan population grid. The results reveal that DenseNet outperforms the other three models, while VGG has the worst performances in all evaluating metrics under all selected neighboring scenarios. As for the neighboring effect, contradicting existing studies, our results suggest that the increase of neighboring sizes leads to reduced population estimation performance, which is found universal for all four selected models in all evaluating metrics. In addition, there exists a notable, universal bias that all selected deep learning models tend to overestimate sparsely populated image patches and underestimate densely populated image patches, regardless of neighboring sizes. The methodological, experimental, and contextual knowledge this study provides is expected to benefit a wide range of future studies that estimate population distribution via remote sensing imagery.

cs.CV↗