arXiv subjects

Cong Wang

Publications and source records attributed to Cong Wang.

At least 235 records · Page 13Linked to original sources

arXiv2024

The existence of ground state solutions for critical Hénon equations in $\mathbb{R}^N$

In this paper we confirm that $2^*(γ)=\frac{2(N+γ)}{N-2}$ with $γ>0$ is exactly the critical exponent for the embedding from $H_r^1(\mathbb{R}^N)$ into $L^q(\mathbb{R}^N;|x|^γ)$($N\geqslant 3$) (see \cite{2007SWW-1,2007SWW-2}) and name it as the upper Hénon-Sobolev critical exponent. Based on this fact we study the ground state solutions of critical Hénon equations in $\mathbb{R}^N$ via the Nehari manifold methods and the great idea of Brezis-Nirenberg in \cite{1983BN}. We establish the existence of the positive radial ground state solutions for the problem with one single upper Hénon-Sobolev critical exponent. We also deal with the existence of the nonnegative radial ground state solutions for the problems with multiple critical exponents, including Hardy-Sobolev critical exponents or Sobolev critical exponents or the upper Hénon-Sobolev critical exponents.

math.AP↗

arXiv2024

Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion Models

Recent work has showcased the significant potential of diffusion models in pose-guided person image synthesis. However, owing to the inconsistency in pose between the source and target images, synthesizing an image with a distinct pose, relying exclusively on the source image and target pose information, remains a formidable challenge. This paper presents Progressive Conditional Diffusion Models (PCDMs) that incrementally bridge the gap between person images under the target and source poses through three stages. Specifically, in the first stage, we design a simple prior conditional diffusion model that predicts the global features of the target image by mining the global alignment relationship between pose coordinates and image appearance. Then, the second stage establishes a dense correspondence between the source and target images using the global features from the previous stage, and an inpainting conditional diffusion model is proposed to further align and enhance the contextual features, generating a coarse-grained person image. In the third stage, we propose a refining conditional diffusion model to utilize the coarsely generated image from the previous stage as a condition, achieving texture restoration and enhancing fine-detail consistency. The three-stage PCDMs work progressively to generate the final high-quality and high-fidelity synthesized image. Both qualitative and quantitative results demonstrate the consistency and photorealism of our proposed PCDMs under challenging scenarios.The code and model will be available at https://github.com/tencent-ailab/PCDMs.

cs.CV↗

arXiv2024

On the double critical Maxwell equations

In this paper, we focus on (no)existence and asymptotic behavior of solutions for the double critical Maxwell equation involving with the Hardy, Hardy-Sobolev, Sobolev critical exponents. The existence and noexistence of solutions completely depend on the power exponents and coefficients of equation. On one hand, based on the concentration-compactness ideas, applying the Nehari manifold and the mountain pass theorem, we prove the existence of the ground state solutions for the critical Maxwell equation for three different scenarios. On the other hand, for the case $λ<0$ and $0\leq s_2<s_1<2$, which is a type open problem raised by Li and Lin. Draw support from a changed version of Caffarelli-Kohn-Nirenberg inequality, we find that there exists a constant $λ^*$ which is a negative number having explicit expression, such that the problem has no nontrivial solution as the coefficient $λ<λ^*$. Moreover, there exists a constant $λ^*<λ^{**}<0$ such that, as $λ^{**}<λ<0$, the equation has a nontrivial solution using truncation methods. Furthermore, we establish the asymptotic behavior of solutions of equation as coefficient converges to zero for the all cases above.

math.AP↗

arXiv2024

Layered semiconducting electrides in p-block metal oxides

In conventional electrides, excess electrons are localized in crystal voids to serve as anions. Most of these electrides are metallic and the metal cations are primarily from the s-block, d-block, or rare-earth elements. Here, we report a class of p-block metal-based electrides found in bilayer SnO and PbO, which are semiconducting and feature electride states in both the valence band (VB) and conduction band (CB), as referred to 2D "bipolar" electrides. These bilayers are hybrid electrides where excess electrons are localized in the interlayer region and hybridize with the orbitals of Sn atoms in the VB, exhibiting strong covalent-like interactions with neighboring metal atoms. Compared to previously studied hybrid electrides, the higher electronegativity of Sn and Pb enhances these covalent-like interactions, leading to largely enhanced semiconducting bandgap of up to 2.5 eV. Moreover, the CBM primarily arises from the overlap between metal states and interstitial charges, denoting a potential electride and forming a free-electron-like (FEL) state with small effective mass. This state offers high carrier mobilities for both electron and hole in bilayer SnO, suggesting its potential as a promising p-type semiconductor material.

cond-mat.mtrl-sci↗

arXiv2024

FLMarket: Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning

Federated Learning (FL), as a mainstream privacy-preserving machine learning paradigm, offers promising solutions for privacy-critical domains such as healthcare and finance. Although extensive efforts have been dedicated from both academia and industry to improve the vanilla FL, little work focuses on the data pricing mechanism. In contrast to the straightforward in/post-training pricing techniques, we study a more difficult problem of pre-training pricing without direct information from the learning process. We propose FLMarket that integrates a two-stage, auction-based pricing mechanism with a security protocol to address the utility-privacy conflict. Through comprehensive experiments, we show that the client selection according to FLMarket can achieve more than 10% higher accuracy in subsequent FL training compared to state-of-the-art methods. In addition, it outperforms the in-training baseline with more than 2% accuracy increase and 3x run-time speedup.

cs.LG↗

arXiv2024

Ultra-High-Definition Image Restoration: New Benchmarks and A Dual Interaction Prior-Driven Solution

Ultra-High-Definition (UHD) image restoration has acquired remarkable attention due to its practical demand. In this paper, we construct UHD snow and rain benchmarks, named UHD-Snow and UHD-Rain, to remedy the deficiency in this field. The UHD-Snow/UHD-Rain is established by simulating the physics process of rain/snow into consideration and each benchmark contains 3200 degraded/clear image pairs of 4K resolution. Furthermore, we propose an effective UHD image restoration solution by considering gradient and normal priors in model design thanks to these priors' spatial and detail contributions. Specifically, our method contains two branches: (a) feature fusion and reconstruction branch in high-resolution space and (b) prior feature interaction branch in low-resolution space. The former learns high-resolution features and fuses prior-guided low-resolution features to reconstruct clear images, while the latter utilizes normal and gradient priors to mine useful spatial features and detail features to guide high-resolution recovery better. To better utilize these priors, we introduce single prior feature interaction and dual prior feature interaction, where the former respectively fuses normal and gradient priors with high-resolution features to enhance prior ones, while the latter calculates the similarity between enhanced prior ones and further exploits dual guided filtering to boost the feature interaction of dual priors. We conduct experiments on both new and existing public datasets and demonstrate the state-of-the-art performance of our method on UHD image low-light enhancement, dehazing, deblurring, desonwing, and deraining. The source codes and benchmarks are available at \url{https://github.com/wlydlut/UHDDIP}.

cs.CV↗

arXiv2024

AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion

Safety-critical scenarios are infrequent in natural driving environments but hold significant importance for the training and testing of autonomous driving systems. The prevailing approach involves generating safety-critical scenarios automatically in simulation by introducing adversarial adjustments to natural environments. These adjustments are often tailored to specific tested systems, thereby disregarding their transferability across different systems. In this paper, we propose AdvDiffuser, an adversarial framework for generating safety-critical driving scenarios through guided diffusion. By incorporating a diffusion model to capture plausible collective behaviors of background vehicles and a lightweight guide model to effectively handle adversarial scenarios, AdvDiffuser facilitates transferability. Experimental results on the nuScenes dataset demonstrate that AdvDiffuser, trained on offline driving logs, can be applied to various tested systems with minimal warm-up episode data and outperform other existing methods in terms of realism, diversity, and adversarial performance.

cs.LG↗

arXiv2024

Learning Adaptive Hydrodynamic Models Using Neural ODEs in Complex Conditions

Reinforcement learning-based quadruped robots excel across various terrains but still lack the ability to swim in water due to the complex underwater environment. This paper presents the development and evaluation of a data-driven hydrodynamic model for amphibious quadruped robots, aiming to enhance their adaptive capabilities in complex and dynamic underwater environments. The proposed model leverages Neural Ordinary Differential Equations (ODEs) combined with attention mechanisms to accurately process and interpret real-time sensor data. The model enables the quadruped robots to understand and predict complex environmental patterns, facilitating robust decision-making strategies. We harness real-time sensor data, capturing various environmental and internal state parameters to train and evaluate our model. A significant focus of our evaluation involves testing the quadruped robot's performance across different hydrodynamic conditions and assessing its capabilities at varying speeds and fluid dynamic conditions. The outcomes suggest that the model can effectively learn and adapt to varying conditions, enabling the prediction of force states and enhancing autonomous robotic behaviors in various practical scenarios.

cs.RO↗

arXiv2024

M$^{2}$M: Learning controllable Multi of experts and multi-scale operators are the Partial Differential Equations need

Learning the evolutionary dynamics of Partial Differential Equations (PDEs) is critical in understanding dynamic systems, yet current methods insufficiently learn their representations. This is largely due to the multi-scale nature of the solution, where certain regions exhibit rapid oscillations while others evolve more slowly. This paper introduces a framework of multi-scale and multi-expert (M$^2$M) neural operators designed to simulate and learn PDEs efficiently. We employ a divide-and-conquer strategy to train a multi-expert gated network for the dynamic router policy. Our method incorporates a controllable prior gating mechanism that determines the selection rights of experts, enhancing the model's efficiency. To optimize the learning process, we have implemented a PI (Proportional, Integral) control strategy to adjust the allocation rules precisely. This universal controllable approach allows the model to achieve greater accuracy. We test our approach on benchmark 2D Navier-Stokes equations and provide a custom multi-scale dataset. M$^2$M can achieve higher simulation accuracy and offer improved interpretability compared to baseline methods.

cs.LG↗

arXiv2024

MICSim: A Modular Simulator for Mixed-signal Compute-in-Memory based AI Accelerator

This work introduces MICSim, an open-source, pre-circuit simulator designed for early-stage evaluation of chip-level software performance and hardware overhead of mixed-signal compute-in-memory (CIM) accelerators. MICSim features a modular design, allowing easy multi-level co-design and design space exploration. Modularized from the state-of-the-art CIM simulator NeuroSim, MICSim provides a highly configurable simulation framework supporting multiple quantization algorithms, diverse circuit/architecture designs, and different memory devices. This modular approach also allows MICSim to be effectively extended to accommodate new designs. MICSim natively supports evaluating accelerators' software and hardware performance for CNNs and Transformers in Python, leveraging the popular PyTorch and HuggingFace Transformers frameworks. These capabilities make MICSim highly adaptive when simulating different networks and user-friendly. This work demonstrates that MICSim can easily be combined with optimization strategies to perform design space exploration and used for chip-level Transformers CIM accelerators evaluation. Also, MICSim can achieve a 9x - 32x speedup of NeuroSim through a statistic-based average mode proposed by this work.

cs.AI↗

arXiv2024

DreamVideo: High-Fidelity Image-to-Video Generation with Image Retention and Text Guidance

Image-to-video generation, which aims to generate a video starting from a given reference image, has drawn great attention. Existing methods try to extend pre-trained text-guided image diffusion models to image-guided video generation models. Nevertheless, these methods often result in either low fidelity or flickering over time due to their limitation to shallow image guidance and poor temporal consistency. To tackle these problems, we propose a high-fidelity image-to-video generation method by devising a frame retention branch based on a pre-trained video diffusion model, named DreamVideo. Instead of integrating the reference image into the diffusion process at a semantic level, our DreamVideo perceives the reference image via convolution layers and concatenates the features with the noisy latents as model input. By this means, the details of the reference image can be preserved to the greatest extent. In addition, by incorporating double-condition classifier-free guidance, a single image can be directed to videos of different actions by providing varying prompt texts. This has significant implications for controllable video generation and holds broad application prospects. We conduct comprehensive experiments on the public dataset, and both quantitative and qualitative results indicate that our method outperforms the state-of-the-art method. Especially for fidelity, our model has a powerful image retention ability and delivers the best results in UCF101 compared to other image-to-video models to our best knowledge. Also, precise control can be achieved by giving different text prompts. Further details and comprehensive results of our model will be presented in https://anonymous0769.github.io/DreamVideo/.

cs.CV↗

arXiv2024

EasyControl: Transfer ControlNet to Video Diffusion for Controllable Generation and Interpolation

Following the advancements in text-guided image generation technology exemplified by Stable Diffusion, video generation is gaining increased attention in the academic community. However, relying solely on text guidance for video generation has serious limitations, as videos contain much richer content than images, especially in terms of motion. This information can hardly be adequately described with plain text. Fortunately, in computer vision, various visual representations can serve as additional control signals to guide generation. With the help of these signals, video generation can be controlled in finer detail, allowing for greater flexibility for different applications. Integrating various controls, however, is nontrivial. In this paper, we propose a universal framework called EasyControl. By propagating and injecting condition features through condition adapters, our method enables users to control video generation with a single condition map. With our framework, various conditions including raw pixels, depth, HED, etc., can be integrated into different Unet-based pre-trained video diffusion models at a low practical cost. We conduct comprehensive experiments on public datasets, and both quantitative and qualitative results indicate that our method outperforms state-of-the-art methods. EasyControl significantly improves various evaluation metrics across multiple validation datasets compared to previous works. Specifically, for the sketch-to-video generation task, EasyControl achieves an improvement of 152.0 on FVD and 19.9 on IS, respectively, in UCF101 compared with VideoComposer. For fidelity, our model demonstrates powerful image retention ability, resulting in high FVD and IS in UCF101 and MSR-VTT compared to other image-to-video models.

cs.CV↗

arXiv2024

Counterfactual and Synthetic Control Method: Causal Inference with Instrumented Principal Component Analysis

In this paper, we propose a novel method for causal inference within the framework of counterfactual and synthetic control. Matching forward the generalized synthetic control method, our instrumented principal component analysis method instruments factor loadings with predictive covariates rather than including them as regressors. These instrumented factor loadings exhibit time-varying dynamics, offering a better economic interpretation. Covariates are instrumented through a transformation matrix, $Γ$, when we have a large number of covariates it can be easily reduced in accordance with a small number of latent factors helping us to effectively handle high-dimensional datasets and making the model parsimonious. Moreover, the novel way of handling covariates is less exposed to model misspecification and achieved better prediction accuracy. Our simulations show that this method is less biased in the presence of unobserved covariates compared to other mainstream approaches. In the empirical application, we use the proposed method to evaluate the effect of Brexit on foreign direct investment to the UK.

econ.EM↗

arXiv2024

On a class of coupled obstacle systems

In this paper, we explore cooperative and competitive coupled obstacle systems, which, up to now, are new type obstacle systems and formed by coupling two equations belonging to classical obstacle problem. On one hand, applying the constrained minimizer in variational methods we establish the existence of solutions for the systems. Moreover, the optimal regularity of solutions is obtained, which is the cornerstone for further research on so-called free boundary. Furthermore, as coefficient $λ\to0$, there exists a sequence of solutions converging to solutions of the single classical obstacle equation. On the other hand, motivated by the heartstirring ideas of single classical obstacle problem, based on the corresponding blowup methods, Weiss type monotonicity formula and Monneau type monotonicity formula of systems to be studied, we investigate the regularity of free boundary, and on the regular and singular points in particular, as it should be, which is more challenging but exceedingly meaningful in solving free boundary problems.

math.AP↗

arXiv2024

Exploring code portability solutions for HEP with a particle tracking test code

Traditionally, high energy physics (HEP) experiments have relied on x86 CPUs for the majority of their significant computing needs. As the field looks ahead to the next generation of experiments such as DUNE and the High-Luminosity LHC, the computing demands are expected to increase dramatically. To cope with this increase, it will be necessary to take advantage of all available computing resources, including GPUs from different vendors. A broad landscape of code portability tools -- including compiler pragma-based approaches, abstraction libraries, and other tools -- allow the same source code to run efficiently on multiple architectures. In this paper, we use a test code taken from a HEP tracking algorithm to compare the performance and experience of implementing different portability solutions.

hep-ex↗

arXiv2024

Fixed-time Disturbance Observer-Based MPC Robust Trajectory Tracking Control of Quadrotor

In this paper, a fixed-time disturbance observerbased model predictive control algorithm is proposed for trajectory tracking of quadrotor in the presence of disturbances. First, a novel multivariable fixed-time disturbance observer is proposed to estimate the lumped disturbances. The bi-limit homogeneity and Lyapunov techniques are employed to ensure the convergence of estimation error within a fixed convergence time, independent of the initial estimation error. Then, an observerbased model predictive control strategy is formulated to achieve robust trajectory tracking of quadrotor, attenuating the lumped disturbances and model uncertainties. Finally, simulations and real-world experiments are provided to illustrate the effectiveness of the proposed method.

eess.SY↗

arXiv2024

Kagome bands and magnetism in MoTe$_{2-x}$ kagome monolayers

Kagome lattices facilitate various quantum phases, yet in bulk materials, their kagome flat-bands often interact with bulk bands, suppressing kagome electronic characteristics for hosting these phases. Here, we use density-functional-theory calculations to predict the geometric and electronic structures, as well as the topological and magnetic properties, of a series of MoTe$_{2-x}$ kagome monolayers formed by mirror-twin-boundary (MTB) loops. We analyze ten MTB-loop configurations of varying sizes and arrangements to assess their impact on various properties. Within the intrinsic bandgap of MoTe$_{2}$, we identify two sets of kagome bands, primarily originating from in-plane and out-of-plane Mo d-orbitals at MTB-loop edges and -vertices, respectively. Three configurations exhibit superior stability, while three others show comparable stability. Among these, four display bandgaps and potentially non-zero Z$_{2}$ topological invariants, suggesting possible topological phases, while the remaining two are metallic and feature Stoner magnetization. These findings guide the design of kagome-based two-dimensional materials with tunable electronic, topological, and magnetic properties.

cond-mat.mtrl-sci↗

arXiv2024

Enhancing Modal Fusion by Alignment and Label Matching for Multimodal Emotion Recognition

To address the limitation in multimodal emotion recognition (MER) performance arising from inter-modal information fusion, we propose a novel MER framework based on multitask learning where fusion occurs after alignment, called Foal-Net. The framework is designed to enhance the effectiveness of modality fusion and includes two auxiliary tasks: audio-video emotion alignment (AVEL) and cross-modal emotion label matching (MEM). First, AVEL achieves alignment of emotional information in audio-video representations through contrastive learning. Then, a modal fusion network integrates the aligned features. Meanwhile, MEM assesses whether the emotions of the current sample pair are the same, providing assistance for modal information fusion and guiding the model to focus more on emotional information. The experimental results conducted on IEMOCAP corpus show that Foal-Net outperforms the state-of-the-art methods and emotion alignment is necessary before modal fusion.

cs.MM↗