arXiv subjects

Li Chen

Publications and source records attributed to Li Chen.

At least 397 records · Page 22Linked to original sources

arXiv2023

Convex hypersurfaces of prescribed curvatures in hyperbolic space

For a smooth, closed and uniformly $h$-convex hypersurface $M$ in $\mathbb{H}^{n+1}$, the horospherical Gauss map $G: M \rightarrow \mathbb{S}^n$ is a diffeomorphism. We consider the problem of finding a smooth, closed and uniformly $h$-convex hypersurface $M\subset \mathbb{H}^{n+1}$ whose $k$-th shifted mean curvature $\widetilde{H}_{k}$ ($1\leq k\leq n$) is prescribed as a positive function $\tilde{f}(x)$ defined on $\mathbb{S}^n$, i.e. \begin{eqnarray*} \widetilde{H}_{k}(G^{-1}(x))=\tilde{f}(x). \end{eqnarray*} We can prove the existence of solution to this problem if the given function $\tilde{f}$ is even. The similar problem has been considered by Guan-Guan for convex hypersurfaces in Euclidean space two decades ago.

math.AP↗

arXiv2023

SwiftAvatar: Efficient Auto-Creation of Parameterized Stylized Character on Arbitrary Avatar Engines

The creation of a parameterized stylized character involves careful selection of numerous parameters, also known as the "avatar vectors" that can be interpreted by the avatar engine. Existing unsupervised avatar vector estimation methods that auto-create avatars for users, however, often fail to work because of the domain gap between realistic faces and stylized avatar images. To this end, we propose SwiftAvatar, a novel avatar auto-creation framework that is evidently superior to previous works. SwiftAvatar introduces dual-domain generators to create pairs of realistic faces and avatar images using shared latent codes. The latent codes can then be bridged with the avatar vectors as pairs, by performing GAN inversion on the avatar images rendered from the engine using avatar vectors. Through this way, we are able to synthesize paired data in high-quality as many as possible, consisting of avatar vectors and their corresponding realistic faces. We also propose semantic augmentation to improve the diversity of synthesis. Finally, a light-weight avatar vector estimator is trained on the synthetic pairs to implement efficient auto-creation. Our experiments demonstrate the effectiveness and efficiency of SwiftAvatar on two different avatar engines. The superiority and advantageous flexibility of SwiftAvatar are also verified in both subjective and objective evaluations.

cs.CV↗

arXiv2023

Meissner effect induced Majorana zero modes at small magnetic field

One fundamental difficulty in realizing Majorana zero modes (MZMs) is the required high magnetic field, which causes serious issues, e.g., shrinks the superconducting gap, reduces topological region, and weakens their robustness against disorder. In this work, we propose that the Meissner effect can bring the topological superconducting phase to a superconductor/topological-insulator/superconductor (SC/TI/SC) hybrid system. Remarkably, the required magnetic field strength ($<$10 mT) to support MZMs has been reduced by several orders of magnitude compared to that ($>$0.5 T) in the previous schemes. Tuning the phase difference between the top and bottom superconductors can control the number and position of the MZMs. In addition, we account for the electrostatic potential in the superconductor/topological-insulator (SC/TI) interface through the self-consistent Schrödinger-Poisson calculation, which shows the experimental accessibility of our proposal. Our proposal only needs a small magnetic eld of less than 10 mT and is robust against the chemical potential fluctuation, which makes SC/TI/SC hybrid an ideal Majorana platform.

cond-mat.mes-hall↗

arXiv2023

Mixed Near- and Far-Field Communications for Extremely Large-Scale Array: An Interference Perspective

Extremely large-scale array (XL-array) is envisioned to achieve super-high spectral efficiency in future wireless networks. Different from the existing works that mostly focus on the near-field communications, we consider in this paper a new and practical scenario, called mixed near- and far-field communications, where there exist both near- and far-field users in the network. For this scenario, we first obtain a closed-form expression for the inter-user interference at the near-field user caused by the far-field beam by using Fresnel functions, based on which the effects of the number of BS antennas, far-field user (FU) angle, near-field user (NU) angle and distance are analyzed. We show that the strong interference exists when the number of the BS antennas and the NU distance are relatively small, and/or the NU and FU angle-difference is small. Then, we further obtain the achievable rate of the NU as well as its rate loss caused by the FU interference. Last, numerical results are provided to corroborate our analytical results.

eess.SP↗

arXiv2023

Rigorous Derivation of the Degenerate Parabolic-Elliptic Keller-Segel System from a Moderately Interacting Stochastic Particle System. Part I Partial Differential Equation

The aim of this paper is to provide the analysis result for the partial differential equations arising from the rigorous derivation of the degenerate parabolic-elliptic Keller-Segel system from a moderately interacting stochastic particle system. The rigorous derivation is divided into two articles. In this paper, we establish the solution theory of the degenerate parabolic-elliptic Keller-Segel problem and its non-local version, which will be used in the second paper for the discussion of the mean-field limit. A parabolic regularized system is introduced to bridge the stochastic particle model and the degenerate Keller-Segel system. We derive the existence of the solution to this regularized system by constructing approximate solutions, giving uniform estimates and taking the limits, where a crucial step is to obtain the L infty Bernstein type estimate for the gradient of the approximate solution. Based on this, we obtain the well-posedness of the corresponding non-local equation through perturbation method. Finally, the weak solution of the degenerate Keller-Segel system is obtained by using a nonlinear version of Aubin-Lions lemma.

math.AP↗

arXiv2023

Personalized Prompt Learning for Explainable Recommendation

Providing user-understandable explanations to justify recommendations could help users better understand the recommended items, increase the system's ease of use, and gain users' trust. A typical approach to realize it is natural language generation. However, previous works mostly adopt recurrent neural networks to meet the ends, leaving the potentially more effective pre-trained Transformer models under-explored. In fact, user and item IDs, as important identifiers in recommender systems, are inherently in different semantic space as words that pre-trained models were already trained on. Thus, how to effectively fuse IDs into such models becomes a critical issue. Inspired by recent advancement in prompt learning, we come up with two solutions: find alternative words to represent IDs (called discrete prompt learning), and directly input ID vectors to a pre-trained model (termed continuous prompt learning). In the latter case, ID vectors are randomly initialized but the model is trained in advance on large corpora, so they are actually in different learning stages. To bridge the gap, we further propose two training strategies: sequential tuning and recommendation as regularization. Extensive experiments show that our continuous prompt learning approach equipped with the training strategies consistently outperforms strong baselines on three datasets of explainable recommendation.

cs.IR↗

arXiv2023

Emergence of cooperation in a population with bimodal response behaviors

We human beings show remarkable adaptability in response to complex surroundings, we adopt different behavioral modes at different occasions, such response multimodality is critical to our survival. Yet, how this behavioral multimodality affects the evolution of cooperation remains largely unknown. Here we build a toy model to address this issue by considering a population with bimodal response behaviors, or specifically, with the Fermi and Tit-for-tat updating rules. While the former rule tends to imitate the strategies of those neighbors who are doing well, the latter repeats what their neighbors did to them. In a structural mixing implementation, where the updating rule is fixed for each individual, we find that a moderate mode mixture unexpectedly boosts the overall cooperation level of the population. The boost is even more pronounced in the probabilistic mixing, where each individual randomly chooses one of the two modes at each step, and full cooperation is seen in a wide range. These findings are robust to the underlying topology of the population. Our mean-field treatment reveals that the cooperation prevalence within the players with the Fermi rule linearly increases with the fraction of TFT players and explains the non-monotonic dependence in the structural mixing. Our study shows that the diversity in response behaviors may help to explain the emergence of cooperation in realistic contexts.

physics.soc-ph↗

arXiv2023

Pinning control of social fairness in the Ultimatum game

Decent social fairness is highly desired both for socio-economic activities and individuals, as it is one of the cornerstones of our social welfare and sustainability. How to effectively promote the level of fairness thus becomes a significant issue to be addressed. Here, by adopting a pinning control procedure, we find that when a very small fraction of individuals are pinned to be fair players in the Ultimatum Game, the whole population unexpectedly evolves into the full fairness level. The basic observations are quite robust in homogeneous networks, but the converging time as a function of the pinning number shows different laws for different underlying topologies. For heterogeneous networks, this leverage effect is even more pronounced that one hub node is sufficient for the aim, and a periodic on-off control procedure can be applied to further save the control cost. Intermittent failures are seen when the pinning control is marginally strong, our statistical analysis indicates some sort of criticality. Our work suggests that the pinning control procedure could potentially be a good strategy to promote the social fairness for some real scenarios when necessary.

physics.soc-ph↗

arXiv2023

Reversible Attack based on Local Visual Adversarial Perturbation

Adding perturbations to images can mislead classification models to produce incorrect results. Recently, researchers exploited adversarial perturbations to protect image privacy from retrieval by intelligent models. However, adding adversarial perturbations to images destroys the original data, making images useless in digital forensics and other fields. To prevent illegal or unauthorized access to sensitive image data such as human faces without impeding legitimate users, the use of reversible adversarial attack techniques is increasing. The original image can be recovered from its reversible adversarial examples. However, existing reversible adversarial attack methods are designed for traditional imperceptible adversarial perturbations and ignore the local visible adversarial perturbation. In this paper, we propose a new method for generating reversible adversarial examples based on local visible adversarial perturbation. The information needed for image recovery is embedded into the area beyond the adversarial patch by the reversible data hiding technique. To reduce image distortion, lossless compression and the B-R-G (bluered-green) embedding principle are adopted. Experiments on CIFAR-10 and ImageNet datasets show that the proposed method can restore the original images error-free while ensuring good attack performance.

cs.CV↗

arXiv2022

Hunting for Neighboring Open Clusters with Gaia DR3: 101 New Open Clusters within 500 pc

We systematically searched for open clusters in the solar neighborhood within 500 pc using pyUPMASK and HDBSCAN clustering algorithms based on {\it Gaia} DR3. Taking into consideration that the physical size for most open clusters is less than 50 pc, we adopted a slicing approach for different distance shells and identified 324 neighboring open clusters, including 223 reported open clusters and 101 newly discovered open clusters (named as OCSN, Open Cluster of Solar Neighborhood). Our discovery has increased the number of open clusters in the solar neighborhood by about 45\%. In this work, larger spatial extents and more member stars were attained for our cluster sample. We provided the member stars and the membership probabilities through the pyUPMASK algorithm for each cluster and derived their astrophysical, age, and structural parameters.

astro-ph.SR↗

arXiv2022

You Only Need a Good Embeddings Extractor to Fix Spurious Correlations

Spurious correlations in training data often lead to robustness issues since models learn to use them as shortcuts. For example, when predicting whether an object is a cow, a model might learn to rely on its green background, so it would do poorly on a cow on a sandy background. A standard dataset for measuring state-of-the-art on methods mitigating this problem is Waterbirds. The best method (Group Distributionally Robust Optimization - GroupDRO) currently achieves 89\% worst group accuracy and standard training from scratch on raw images only gets 72\%. GroupDRO requires training a model in an end-to-end manner with subgroup labels. In this paper, we show that we can achieve up to 90\% accuracy without using any sub-group information in the training set by simply using embeddings from a large pre-trained vision model extractor and training a linear classifier on top of it. With experiments on a wide range of pre-trained models and pre-training datasets, we show that the capacity of the pre-training model and the size of the pre-training dataset matters. Our experiments reveal that high capacity vision transformers perform better compared to high capacity convolutional neural networks, and larger pre-training dataset leads to better worst-group accuracy on the spurious correlation dataset.

cs.CV↗

arXiv2022

Global weak solutions to the compressible Cucker-Smale-Navier-Stokes system in a bounded domain

A coupled kinetic-fluid model is investigated, which describes the dynamic behavior of an ensemble of Cucker-Smale flocking particles interacting with a viscous fluid in a three-dimensional bounded domain. This system consists of a kinetic Cucker-Smale equation and a compressible Navier-Stokes system with nonhomogeneous boundary conditions. The global existence of weak solutions to this system with adiabatic coefficient $γ> {3}/{2}$ is established.

math.AP↗

arXiv2022

iGniter: Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud

GPUs are essential to accelerating the latency-sensitive deep neural network (DNN) inference workloads in cloud datacenters. To fully utilize GPU resources, spatial sharing of GPUs among co-located DNN inference workloads becomes increasingly compelling. However, GPU sharing inevitably brings severe performance interference among co-located inference workloads, as motivated by an empirical measurement study of DNN inference on EC2 GPU instances. While existing works on guaranteeing inference performance service level objectives (SLOs) focus on either temporal sharing of GPUs or reactive GPU resource scaling and inference migration techniques, how to proactively mitigate such severe performance interference has received comparatively little attention. In this paper, we propose iGniter, an interference-aware GPU resource provisioning framework for cost-efficiently achieving predictable DNN inference in the cloud. iGniter is comprised of two key components: (1) a lightweight DNN inference performance model, which leverages the system and workload metrics that are practically accessible to capture the performance interference; (2) A cost-efficient GPU resource provisioning strategy that jointly optimizes the GPU resource allocation and adaptive batching based on our inference performance model, with the aim of achieving predictable performance of DNN inference workloads. We implement a prototype of iGniter based on the NVIDIA Triton inference server hosted on EC2 GPU instances. Extensive prototype experiments on four representative DNN models and datasets demonstrate that iGniter can guarantee the performance SLOs of DNN inference workloads with practically acceptable runtime overhead, while saving the monetary cost by up to 25% in comparison to the state-of-the-art GPU resource provisioning strategies.

cs.DC↗

arXiv2022

Hierarchical-Absolute Reciprocity Calibration for Millimeter-wave Hybrid Beamforming Systems

In time-division duplexing (TDD) millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, the reciprocity mismatch severely degrades the performance of the hybrid beamforming (HBF). In this work, to mitigate the detrimental effect of the reciprocity mismatch, we investigate reciprocity calibration for the mmWave-HBF system with a fully-connected phase shifter network. To reduce the overhead and computational complexity of reciprocity calibration, we first decouple digital radio frequency (RF) chains and analog RF chains with beamforming design. Then, the entire calibration problem of the HBF system is equivalently decomposed into two subproblems corresponding to the digital-chain calibration and analog-chain calibration. To solve the calibration problems efficiently, a closed-form solution to the digital-chain calibration problem is derived, while an iterative-alternating optimization algorithm for the analog-chain calibration problem is proposed. To measure the performance of the proposed algorithm, we derive the Cramér-Rao lower bound on the errors in estimating mismatch coefficients. The results reveal that the estimation errors of mismatch coefficients of digital and analog chains are uncorrelated, and that the mismatch coefficients of receive digital chains can be estimated perfectly. Simulation results are presented to validate the analytical results and to show the performance of the proposed calibration approach.

eess.SP↗

arXiv2022

Harmonics-assisted optical phase amplifier

The change in the relative phase between two light fields serves as a basic principle for the measurement of the physical quantity that guides this change. It would therefore be highly advantageous if the relative phase could be amplified to enhance the measurement resolution. One well-known method for phase amplification involves the use of the multi-photon number and path entangled state known as the NOON state; however, a high-number NOON state is very difficult to prepare and is highly sensitive to optical losses. Here we propose and experimentally demonstrate in principle a phase amplifier scheme with the assistance of a harmonic generation process. The relative phase difference between two polarization modes in a polarized interferometer is amplified coherently four times with cascaded second-harmonic generation processes. We demonstrate that these amplification processes can be recycled and therefore have the potential to realize much higher numbers of multiple amplification steps. The phase amplification method presented here shows considerable advantages over the method based on NOON states and will be highly promising for use in precision optical measurements.

physics.optics↗

arXiv2022

Deep Learning is Provably Robust to Symmetric Label Noise

Deep neural networks (DNNs) are capable of perfectly fitting the training data, including memorizing noisy data. It is commonly believed that memorization hurts generalization. Therefore, many recent works propose mitigation strategies to avoid noisy data or correct memorization. In this work, we step back and ask the question: Can deep learning be robust against massive label noise without any mitigation? We provide an affirmative answer for the case of symmetric label noise: We find that certain DNNs, including under-parameterized and over-parameterized models, can tolerate massive symmetric label noise up to the information-theoretic threshold. By appealing to classical statistical theory and universal consistency of DNNs, we prove that for multiclass classification, $L_1$-consistent DNN classifiers trained under symmetric label noise can achieve Bayes optimality asymptotically if the label noise probability is less than $\frac{K-1}{K}$, where $K \ge 2$ is the number of classes. Our results show that for symmetric label noise, no mitigation is necessary for $L_1$-consistent estimators. We conjecture that for general label noise, mitigation strategies that make use of the noisy data will outperform those that ignore the noisy data.

stat.ML↗

arXiv2022

On the Relationship between Explanation and Recommendation: Learning to Rank Explanations for Improved Performance

Explaining to users why some items are recommended is critical, as it can help users to make better decisions, increase their satisfaction, and gain their trust in recommender systems (RS). However, existing explainable RS usually consider explanation as a side output of the recommendation model, which has two problems: (1) it is difficult to evaluate the produced explanations because they are usually model-dependent, and (2) as a result, how the explanations impact the recommendation performance is less investigated. In this paper, explaining recommendations is formulated as a ranking task, and learned from data, similar to item ranking for recommendation. This makes it possible for standard evaluation of explanations via ranking metrics (e.g., NDCG). Furthermore, this paper extends traditional item ranking to an item-explanation joint-ranking formalization to study if purposely selecting explanations could reach certain learning goals, e.g., improving recommendation performance. A great challenge, however, is that the sparsity issue in the user-item-explanation data would be inevitably severer than that in traditional user-item interaction data, since not every user-item pair can be associated with all explanations. To mitigate this issue, this paper proposes to perform two sets of matrix factorization by considering the ternary relationship as two groups of binary relationships. Experiments on three large datasets verify the solution's effectiveness on both explanation ranking and item recommendation.

cs.IR↗

arXiv2022

Oscillatory cooperation prevalence emerges from misperception

Oscillatory behaviors are ubiquitous in nature and the human society. However, most previous works fail to reproduce them in the two-strategy game-theoretical models. Here we show that oscillatory behaviors naturally emerge if incomplete information is incorporated into the cooperation evolution of a non-Markov model. Specifically, we consider a population playing prisoner's dilemma game, where each individual can only probabilistically get access to their neighbors' payoff information and store them within their memory with a given length. They make their decisions based upon these memories. Interestingly, we find that the level of cooperation generally cannot stabilize but render quasi-periodic oscillation, and this observation is strengthened for a longer memory and a smaller information acquisition probability. The mechanism uncovered shows that there are misperceived payoffs about the player's neighborhood, facilitating the growth of cooperators and defectors at different stages that leads to oscillatory behaviors as a result. Our findings are robust to the underlying structure of the population. Given the omnipresence of incomplete information, our findings may provide a plausible explanation for the phenomenon of oscillatory behaviors in the real world.

q-bio.PE↗