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Multi-physics field simulations aim to predict and understand interactions between multiple physical phenomena, aiding in comprehending natural processes and guiding engineering design. Partial differential equations (PDEs) are crucial for representing these physical fields, and solving these PDEs is fundamental to such simulations. However, current methods primarily yield numerical outputs, limiting interpretability and generalizability.
We introduce T-NNGP, a hybrid genetic programming algorithm that integrates traditional numerical methods with deep learning to derive approximate symbolic expressions for multiple unknown functions within a system of PDEs. T-NNGP initially obtains numerical solutions using traditional methods, then generates candidate symbolic expressions via deep reinforcement learning, and finally optimizes these expressions using genetic programming. Additionally, a universal decoupling strategy guides the search direction and addresses coupling problems, thereby accelerating the search process.
Experimental results on three types of PDEs demonstrate that our method can reliably obtain human-understandable symbolic expressions that fit both the PDEs and the numerical solutions from traditional methods. This work advances multi-physics field simulations by enhancing our ability to derive and interpret symbolic expressions for PDEs, thereby improving our understanding of complex physical phenomena.

AAAI 2025

Interpretable Solutions for Multi-Physics PDEs Using T-NNGP

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-24 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. 

technical paper

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-25 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.



The task of one-shot face video re-enactment aims at generating target video of faces with the same identity of one source frame and facial deformation of the driving video. To achieve high quality generation, it is essential to precisely disentangle identity-related and  identity-independent characteristics, meanwhile build expressive features keeping high-frequency facial details, which still remain unaddressed for existing approaches. To deal with these two challenges, we propose a two-stage generation model based on StyleGAN, whose key novel techniques lie in better disentangling identity and deformation codes in the latent space through an identity-based modeling and manipulating intermediate StyleGAN features at the second stage for augmenting facial details of the generating targets. To further improve identity consistency, a data augmentation method is introduced during training for enhancing the key features affecting identity such as hair and wrinkles. Extensive experimental results demonstrate the superiority of our approach compared state-of-the-art approaches. Code is available at https://anonymous.4open.science/r/ViVFace-CBD3.

Enhancing Identity-Deformation Disentanglement in StyleGAN for One-Shot Face Video Re-Enactment

High Dynamic Range (HDR) video reconstruction is a technology designed to restore the real-world dynamic range as accurately as possible, and it is widely used in photography, video games, and virtual reality. Existing methods typically process one or a few adjacent frames together, which can lead to inconsistencies in brightness across frames because they do not treat the video as a whole. Additionally, the methods under supervised learning hold data bias from collected training data and degrade their effectiveness when the unseen test cases have a domain gap with the training ones. To address these issues, we propose an event-guided HDR video reconstruction method based on 3D Gaussian Splatting.  We use 3D Gaussians to represent the entire video, ensuring consistent brightness in the HDR output.  Since existing 3D Gaussians are limited to representing the 0-255 range, we have designed an HDR 3D Gaussian that can represent both HDR and LDR simultaneously.  Additionally, we employ learnable parameters to model the relationship between events and LDR images.  Our method is self-supervised, eliminating data bias present in existing event-guided HDR video reconstruction approaches. The proposed method demonstrates state-of-the-art performance both quantitatively and qualitatively for synthetic and real-world data.

EvHDR-GS: Event-guided HDR Video Reconstruction with 3D Gaussian Splatting

Domain adaptive object detection (DAOD) aims to generalize an object detector trained on labeled source-domain data to a target domain without annotations, the core principle of which is source-target feature alignment. Typically, existing approaches employ adversarial learning to align the distributions of the source and target domains as a whole, barely considering the varying significance of distinct regions, say instances under different circumstances and foreground vs background areas, during feature alignment. To overcome the shortcoming, this work investigates a differential feature alignment strategy. Specifically, a prediction-discrepancy feedback instance alignment module (dubbed PDFA) is designed to adaptively assign higher weights to instances of higher teacher-student detection discrepancy, effectively handling heavier domain-specific information. On the other hand, an uncertainty-based foreground-oriented image alignment module (UFOA) is proposed to explicitly guide the model to focus more on regions of interest. Extensive experiments on widely-used DAOD datasets together with ablation studies are conducted to demonstrate the efficacy of our proposed method and reveal its superiority over other SOTA alternatives. Our code will be made publicly available.

Differential Alignment for Domain Adaptive Object Detection

Deep hashing models have achieved great success in retrieval tasks due to their powerful representation ability and strong information compression capabilities. However, they inherit the vulnerability of deep neural networks to adversarial perturbations. Attackers can severely impact the retrieval capability of hashing models by adding small, carefully crafted adversarial perturbations to benign images, transforming them into adversarial images. Most existing adversarial attacks target image classification models, with few focusing on hashing retrieval models. We propose HUANG, a targeted adversarial attack algorithm for hashing retrieval in black-box scenarios. To the best of our knowledge, this is the first algorithm that leverages a diffusion model to achieve adversarial attacks on retrieval models. In our approach, adversarial denoising uses adversarial perturbations and residual images to guide the shift from benign to adversarial distribution. Extensive experiments demonstrate the superiority of HUANG across different datasets, achieving state-of-the-art performance in black-box targeted attacks. Additionally, the dynamic interplay between denoising and adding adversarial perturbations in adversarial denoising endows HUANG with exceptional robustness and transferability.

HUANG: A Robust Diffusion Model-based Targeted Adversarial Attack Against Deep Hashing Retrieval

Probability calibration transforms raw output of a classification model into empirically interpretable probability. When the model is purposed to detect rare event and only a small expensive data source has clean labels, it becomes extraordinarily challenging to obtain accurate probability calibration. Utilizing an additional large cheap data source is very helpful, however, such data sources oftentimes suffer from biased labels. To this end, we introduce an approximate expectation-maximization (EM) algorithm to extract useful information from the large data sources. For a family of calibration methods based on the logistic likelihood, we derive closed-form updates and call the resulting iterative algorithm CalEM. We shpw that CalEM inherits convergence guarantees from the approximate EM algorithm. We test the proposed model in simulation and on the marketing uplift dataset, where it shows significant performance increases.

Improving Model Probability Calibration by Integration of Large Data Sources with Biased Labels

A central problem in quantum mechanics involves solving the Electronic Schrodinger Equation for a molecule or material. The Variational Monte Carlo approach to this problem approximates a particular variational objective via sampling, and then optimizes this approximated objective over a chosen parameterized family of wavefunctions, known as the ansatz. Recently neural networks have been used as the ansatz, with accompanying success. However, sampling from such wavefunctions has required the use of a Markov Chain Monte Carlo approach, which is inherently inefficient. In this work, we propose a solution to this problem via an ansatz which is cheap to sample from, yet satisfies the requisite quantum mechanical properties. We prove that a normalizing flow using the following two essential ingredients satisfies our requirements: (a) a base distribution which is constructed from Determinantal Point Processes; (b) flow layers which are equivariant to a particular subgroup of the permutation group. We then show how to construct both continuous and discrete normalizing flows which satisfy the requisite equivariance. We further demonstrate the manner in which the non-smooth nature (``cusps'') of the wavefunction may be captured, and how the framework may be generalized to provide induction across multiple molecules. The resulting theoretical framework entails an efficient approach to solving the Electronic Schrodinger Equation.

A Theoretical Framework for an Efficient Normalizing Flow-Based Solution to the Electronic Schrödinger Equation

With the popularity of 3D volumetric video applications, such as Autonomous Driving, Virtual Reality, and Mixed Reality, current developers have turned to deep learning for compressing volumetric video frames, i.e., point clouds for video upstreaming. The latest deep learning-based solutions offer higher efficiency, lower distortion, and better hardware support compared to traditional ones like MPEG and JPEG. However, privacy threats arise, especially reconstruction attacks targeting to recover the original input point cloud from the intermediate results. In this paper, we design VVRec, to the best of our knowledge, which is the first targeting DL-based Volumetric Video Reconstruction attack scheme. VVRec demonstrates the ability to reconstruct high-quality point clouds from intercepted transmission intermediate results using four well-trained neural network modules we design. Leveraging the latest latent diffusion models with Gamma distribution and a refinement algorithm, VVRec excels in reconstruction quality, color recovery, and surpasses existing defenses. We evaluate VVRec using three volumetric video datasets. The results demonstrate that VVRec achieves 64.70dB reconstruction accuracy, with an impressive 46.39% reduction of distortion over baselines.

VVRec: Reconstruction Attacks on DL-based Volumetric Video Upstreaming via Latent Diffusion Model with Gamma Distribution

Hyperspectral image (HSI) reconstruction aims to restore the original 3D HSIs from the 2D hyperspectral snapshot compressive images (SCIs). The key to high-fidelity HSI reconstruction lies in designing refined spatial and spectral attention mechanisms, which are crucial for generating fine-grained representations of HSI based on the limited spatial and spectral information available in SCI. Recently, Mamba has demonstrated remarkable performance and efficiency in modeling spatial correlations. Its implicit attention mechanism generates three orders of magnitude more attention matrices than transformers, significantly raising the performance ceiling for HSI reconstruction. In this paper, we propose a novel joint SSM network named Sp3ctralMamba for HSI reconstruction. Sp3ctralMamba integrates frequency domain knowledge and physical priors to enhance reconstruction quality. Specifically, we first perform hierarchical decomposition of the 3D HSI embedding to mitigate the negative impact of distant bands on reconstruction. Next, we design a joint SSM block S3Mamba (S3MAB) to perform parallel scans of the embeddings from different bands. In addition to the conventional vanilla scan, S3MAB introduces a local scanning scheme to address the reconstruction challenges posed by the spatial sparsity of spectral information. Furthermore, a spiral scanning scheme in the frequency domain is incorporated to enhance the order correlation between different frequency signals. Finally, we introduce energy priors and structural priors to constrain the generation of spectral and spatial representations during the training process. Extensive experiments on both simulated and real datasets demonstrate that Sp3ctralMamba significantly elevates HSI reconstruction performance to a new level, surpassing SOTA methods in both quantitative and qualitative metrics.

Sp3ctralMamba: Physics-Driven Joint State Space Model for Hyperspectral Image Reconstruction

Rumor detection is critical as the spread of misinformation on social media threatens social stability. The propagation structure has gained attention for its ability to capture discriminative information, such as crowd stance, leading to improved detection methods using propagation trees. However, these detectors are vulnerable to attacks that can manipulate results and evade detection, potentially disrupting public order or influencing public opinion. While adversarial attacks on rumor detectors have been studied, the use of backdoor attacks—an evasive and powerful method—remains unexplored due to the challenges in applying them to propagation trees. In this paper, we introduce the first backdoor attack framework against propagation-based rumor detectors, designed to maintain overall detector performance while enabling targeted attacks on specific rumors. We propose an adaptive discrete trigger generator that injects trigger nodes into critical nodes, creating evasive, transferable attacks. Extensive experiments on three real-world rumor datasets demonstrate that our framework effectively undermines the performance of propagation-based rumor detectors and is transferable across different architectures.

Backdoor Attack on Propagation-based Rumor Detectors

Despite significant advancements in image and text conditional image editing, the exploration of using brain signals, which are more direct and personalized to reflect user intentions, remains limited. An intuitive method is to convert implicit brain signals into explicit representations such as images, which can then serve as prompts for editing. However, such two-stage method suffers from low inference efficiency, inaccurate brain interpretation, and unnatural editing results. In this paper, we apply brain signals of visual perception as prompts and propose a cross-modal self-supervised learning for natural image painting (MindPainter). This method achieves efficient and natural brain-conditioned image editing in a straightforward manner. MindPainter is trained for reconstruction from masked images directly with pseudo-brain signals, which is simulated by the proposed Pseudo Brain Generator. It facilitates efficient cross-modal integration. The proposed Brain Adapter further eliminates the gap in implicit space between modalities, ensuring accurate semantic interpretation of brain signals and coherent consolidation. Besides, the designed Multi-Mask Generation Policy enhances the generalization, realizing high-quality editing in various painting scenarios, including inpainting and outpainting. To the best of our knowledge, MindPainter is the first work to achieve efficient brain-conditioned image painting, providing potential for direct brain control in creative AI.

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Enhancing Identity-Deformation Disentanglement in StyleGAN for One-Shot Face Video Re-Enactment

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