arXiv subjects

Qi Zhang

Publications and source records attributed to Qi Zhang.

At least 649 records · Page 36Linked to original sources

arXiv2024

A Novel Coupled bES-FEM Formulation with SUPG stabilization for Thermo-Hydro-Mechanical Analysis in Saturated Porous Media

Two primary types of numerical instabilities often occur in low-order finite element method (FEM) analyses of thermo-hydro-mechanical (THM) phenomena: (1) pressure oscillations arising improper interpolation of pressure and displacement fields; and (2) spatial oscillations induced by nonlinear convection terms in convection-dominated scenarios. In response to these issues, this paper proposes a novel stabilized edge-based smoothed FEM with a bubble function (bES-FEM) for THM analysis within saturated porous media. In the proposed framework, a cubic bubble function is first incorporated into ES-FEM to efficiently mitigate pressure oscillations that breach the Inf-Sup condition, and then the Streamline Upwind Petrov-Galerkin (SUPG) scheme is adopted in bES-FEM to effectively reduce the spurious oscillations in convection-dominated heat transfer scenarios. The accuracy of the bES-FEM with SUPG formulation for THM coupled problems is validated through a series of five benchmark tests. Moreover, the simulations of open-loop ground source energy systems demonstrate the proposed method's exceptional capability in tackling complex THM challenges in real-world applications. All the obtained results showcase the superiority of proposed bES-FEM with SUPG in eliminating the spatial and pressure oscillations, marking it as a promising tool for the exploration of coupled THM issues.

physics.geo-ph↗

arXiv2024

Information Cascade Prediction under Public Emergencies: A Survey

With the advent of the era of big data, massive information, expert experience, and high-accuracy models bring great opportunities to the information cascade prediction of public emergencies. However, the involvement of specialist knowledge from various disciplines has resulted in a primarily application-specific focus (e.g., earthquakes, floods, infectious diseases) for information cascade prediction of public emergencies. The lack of a unified prediction framework poses a challenge for classifying intersectional prediction methods across different application fields. This survey paper offers a systematic classification and summary of information cascade modeling, prediction, and application. We aim to help researchers identify cutting-edge research and comprehend models and methods of information cascade prediction under public emergencies. By summarizing open issues and outlining future directions in this field, this paper has the potential to be a valuable resource for researchers conducting further studies on predicting information cascades.

cs.SI↗

arXiv2024

Nissist: An Incident Mitigation Copilot based on Troubleshooting Guides

Effective incident management is pivotal for the smooth operation of enterprises-level cloud services. In order to expedite incident mitigation, service teams compile troubleshooting knowledge into Troubleshooting Guides (TSGs) accessible to on-call engineers (OCEs). While automated pipelines are enabled to resolve the most frequent and easy incidents, there still exist complex incidents that require OCEs' intervention. However, TSGs are often unstructured and incomplete, which requires manual interpretation by OCEs, leading to on-call fatigue and decreased productivity, especially among new-hire OCEs. In this work, we propose Nissist which leverages TSGs and incident mitigation histories to provide proactive suggestions, reducing human intervention. Leveraging Large Language Models (LLM), Nissist extracts insights from unstructured TSGs and historical incident mitigation discussions, forming a comprehensive knowledge base. Its multi-agent system design enhances proficiency in precisely discerning user queries, retrieving relevant information, and delivering systematic plans consecutively. Through our user case and experiment, we demonstrate that Nissist significant reduce Time to Mitigate (TTM) in incident mitigation, alleviating operational burdens on OCEs and improving service reliability. Our demo is available at https://aka.ms/nissist_demo.

cs.SE↗

arXiv2024

Selective Focus: Investigating Semantics Sensitivity in Post-training Quantization for Lane Detection

Lane detection (LD) plays a crucial role in enhancing the L2+ capabilities of autonomous driving, capturing widespread attention. The Post-Processing Quantization (PTQ) could facilitate the practical application of LD models, enabling fast speeds and limited memories without labeled data. However, prior PTQ methods do not consider the complex LD outputs that contain physical semantics, such as offsets, locations, etc., and thus cannot be directly applied to LD models. In this paper, we pioneeringly investigate semantic sensitivity to post-processing for lane detection with a novel Lane Distortion Score. Moreover, we identify two main factors impacting the LD performance after quantization, namely intra-head sensitivity and inter-head sensitivity, where a small quantization error in specific semantics can cause significant lane distortion. Thus, we propose a Selective Focus framework deployed with Semantic Guided Focus and Sensitivity Aware Selection modules, to incorporate post-processing information into PTQ reconstruction. Based on the observed intra-head sensitivity, Semantic Guided Focus is introduced to prioritize foreground-related semantics using a practical proxy. For inter-head sensitivity, we present Sensitivity Aware Selection, efficiently recognizing influential prediction heads and refining the optimization objectives at runtime. Extensive experiments have been done on a wide variety of models including keypoint-, anchor-, curve-, and segmentation-based ones. Our method produces quantized models in minutes on a single GPU and can achieve 6.4% F1 Score improvement on the CULane dataset.

cs.CV↗

arXiv2024

Non-invasive magnetocardiography of living rat based on diamond quantum sensor

Magnetocardiography (MCG) has emerged as a sensitive and precise method to diagnose cardiovascular diseases, providing more diagnostic information than traditional technology. However, the sensor limitations of conventional MCG systems, such as large size and cryogenic requirement, have hindered the widespread application and in-depth understanding of this technology. In this study, we present a high-sensitivity, room-temperature MCG system based on the negatively charged Nitrogen-Vacancy (NV) centers in diamond. The magnetic cardiac signal of a living rat, characterized by an approximately 20 pT amplitude in the R-wave, is successfully captured through non-invasive measurement using this innovative solid-state spin sensor. To detect these extremely weak biomagnetic signals, we utilize sensitivity-enhancing techniques such as magnetic flux concentration. These approaches have enabled us to simultaneously achieve a magnetometry sensitivity of 9 $\text{pT}\cdot \text{Hz}^{-1/2}$ and a sensor scale of 5 $\text{mm}$. By extending the sensing scale of the NV centers from cellular and molecular level to macroscopic level of living creatures, we have opened the future of solid-state quantum sensing technologies in clinical environments.

physics.med-ph↗

arXiv2024

MetaRM: Shifted Distributions Alignment via Meta-Learning

The success of Reinforcement Learning from Human Feedback (RLHF) in language model alignment is critically dependent on the capability of the reward model (RM). However, as the training process progresses, the output distribution of the policy model shifts, leading to the RM's reduced ability to distinguish between responses. This issue is further compounded when the RM, trained on a specific data distribution, struggles to generalize to examples outside of that distribution. These two issues can be united as a challenge posed by the shifted distribution of the environment. To surmount this challenge, we introduce MetaRM, a method leveraging meta-learning to align the RM with the shifted environment distribution. MetaRM is designed to train the RM by minimizing data loss, particularly for data that can improve the differentiation ability to examples of the shifted target distribution. Extensive experiments demonstrate that MetaRM significantly improves the RM's distinguishing ability in iterative RLHF optimization, and also provides the capacity to identify subtle differences in out-of-distribution samples.

cs.LG↗

arXiv2024

Digital-analog quantum convolutional neural networks for image classification

We propose digital-analog quantum kernels for enhancing the detection of complex features in the classification of images. We consider multipartite-entangled analog blocks, stemming from native Ising interactions in neutral-atom quantum processors, and individual operations as digital steps to implement the protocol. To further improving the detection of complex features, we apply multiple quantum kernels by varying the qubit connectivity according to the hardware constraints. An architecture that combines non-trainable quantum kernels and standard convolutional neural networks is used to classify realistic medical images, from breast cancer and pneumonia diseases, with a significantly reduced number of parameters. Despite this fact, the model exhibits better performance than its classical counterparts and achieves comparable metrics according to public benchmarks. These findings demonstrate the relevance of digital-analog encoding, paving the way for surpassing classical models in image recognition approaching us to quantum-advantage regimes.

quant-ph↗

arXiv2024

Winning the Social Media Influence Battle: Uncertainty-Aware Opinions to Understand and Spread True Information via Competitive Influence Maximization

Competitive Influence Maximization (CIM) involves entities competing to maximize influence in online social networks (OSNs). Current Deep Reinforcement Learning (DRL) methods in CIM rely on simplistic binary opinion models (i.e., an opinion is represented by either 0 or 1) and often overlook the complexity of users' behavioral characteristics and their prior knowledge. We propose a novel DRL-based framework that enhances CIM analysis by integrating Subjective Logic (SL) to accommodate uncertain opinions, users' behaviors, and their preferences. This approach targets the mitigation of false information by effectively propagating true information. By modeling two competitive agents, one spreading true information and the other spreading false information, we capture the strategic interplay essential to CIM. Our framework utilizes an uncertainty-based opinion model (UOM) to assess the impact on information quality in OSNs, emphasizing the importance of user behavior alongside network topology in selecting influential seed nodes. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods, achieving faster and more influential results (i.e., outperforming over 20%) under realistic network conditions. Moreover, our method shows robust performance in partially observable networks, effectively doubling the performance when users are predisposed to disbelieve true information.

cs.SI↗

arXiv2024

Verco: Learning Coordinated Verbal Communication for Multi-agent Reinforcement Learning

In recent years, multi-agent reinforcement learning algorithms have made significant advancements in diverse gaming environments, leading to increased interest in the broader application of such techniques. To address the prevalent challenge of partial observability, communication-based algorithms have improved cooperative performance through the sharing of numerical embedding between agents. However, the understanding of the formation of collaborative mechanisms is still very limited, making designing a human-understandable communication mechanism a valuable problem to address. In this paper, we propose a novel multi-agent reinforcement learning algorithm that embeds large language models into agents, endowing them with the ability to generate human-understandable verbal communication. The entire framework has a message module and an action module. The message module is responsible for generating and sending verbal messages to other agents, effectively enhancing information sharing among agents. To further enhance the message module, we employ a teacher model to generate message labels from the global view and update the student model through Supervised Fine-Tuning (SFT). The action module receives messages from other agents and selects actions based on current local observations and received messages. Experiments conducted on the Overcooked game demonstrate our method significantly enhances the learning efficiency and performance of existing methods, while also providing an interpretable tool for humans to understand the process of multi-agent cooperation.

cs.MA↗

arXiv2024

Graph Signal Diffusion Model for Collaborative Filtering

Collaborative filtering is a critical technique in recommender systems. It has been increasingly viewed as a conditional generative task for user feedback data, where newly developed diffusion model shows great potential. However, existing studies on diffusion model lack effective solutions for modeling implicit feedback. Particularly, the standard isotropic diffusion process overlooks correlation between items, misaligned with the graphical structure of the interaction space. Meanwhile, Gaussian noise destroys personalized information in a user's interaction vector, causing difficulty in its reconstruction. In this paper, we adapt standard diffusion model and propose a novel Graph Signal Diffusion Model for Collaborative Filtering (named GiffCF). To better represent the correlated distribution of user-item interactions, we define a generalized diffusion process using heat equation on the item-item similarity graph. Our forward process smooths interaction signals with an advanced family of graph filters, introducing the graph adjacency as beneficial prior knowledge for recommendation. Our reverse process iteratively refines and sharpens latent signals in a noise-free manner, where the updates are conditioned on the user's history and computed from a carefully designed two-stage denoiser, leading to high-quality reconstruction. Finally, through extensive experiments, we show that GiffCF effectively leverages the advantages of both diffusion model and graph signal processing, and achieves state-of-the-art performance on three benchmark datasets.

cs.IR↗

arXiv2024

Dynamic fault detection and diagnosis for alkaline water electrolyzer with variational Bayesian Sparse principal component analysis

Electrolytic hydrogen production serves as not only a vital source of green hydrogen but also a key strategy for addressing renewable energy consumption challenges. For the safe production of hydrogen through alkaline water electrolyzer (AWE), dependable process monitoring technology is essential. However, random noise can easily contaminate the AWE process data collected in industrial settings, presenting new challenges for monitoring methods. In this study, we develop the variational Bayesian sparse principal component analysis (VBSPCA) method for process monitoring. VBSPCA methods based on Gaussian prior and Laplace prior are derived to obtain the sparsity of the projection matrix, which corresponds to $\ell_2$ regularization and $\ell_1$ regularization, respectively. The correlation of dynamic latent variables is then analyzed by sparse autoregression and fault variables are diagnosed by fault reconstruction. The effectiveness of the method is verified by an industrial hydrogen production process, and the test results demonstrated that both Gaussian prior and Laplace prior based VBSPCA can effectively detect and diagnose critical faults in AWEs.

eess.SY↗

arXiv2024

Non-negative Contrastive Learning

Deep representations have shown promising performance when transferred to downstream tasks in a black-box manner. Yet, their inherent lack of interpretability remains a significant challenge, as these features are often opaque to human understanding. In this paper, we propose Non-negative Contrastive Learning (NCL), a renaissance of Non-negative Matrix Factorization (NMF) aimed at deriving interpretable features. The power of NCL lies in its enforcement of non-negativity constraints on features, reminiscent of NMF's capability to extract features that align closely with sample clusters. NCL not only aligns mathematically well with an NMF objective but also preserves NMF's interpretability attributes, resulting in a more sparse and disentangled representation compared to standard contrastive learning (CL). Theoretically, we establish guarantees on the identifiability and downstream generalization of NCL. Empirically, we show that these advantages enable NCL to outperform CL significantly on feature disentanglement, feature selection, as well as downstream classification tasks. At last, we show that NCL can be easily extended to other learning scenarios and benefit supervised learning as well. Code is available at https://github.com/PKU-ML/non_neg.

cs.LG↗

arXiv2024

Self-Polish: Enhance Reasoning in Large Language Models via Problem Refinement

To enhance the multi-step reasoning capabilities of large language models, researchers have extensively explored prompting methods, notably the Chain-of-Thought (CoT) method which explicitly elicits human-like rationales. However, they have inadvertently overlooked the potential of enhancing model reasoning performance by formulating higher-quality problems. In this work, we start from the problem side and propose Self-Polish (SP), a novel method that facilitates the model's reasoning by guiding it to progressively refine the given problems to be more comprehensible and solvable. We also explore several automatic prompting varients and propose the Self-Polish prompt bank for the community. SP is orthogonal to all other prompting methods of answer/reasoning side like CoT, allowing for seamless integration with state-of-the-art techniques for further improvement. Thorough experiments show that the proposed method attains notable and consistent effectiveness on five reasoning benchmarks across different models. Furthermore, our method also showcases impressive performance on robustness evaluation. Codes and prompts are available at https://github.com/WooooDyy/Self-Polish.

cs.CL↗

arXiv2024

HyDiscGAN: A Hybrid Distributed cGAN for Audio-Visual Privacy Preservation in Multimodal Sentiment Analysis

Multimodal Sentiment Analysis (MSA) aims to identify speakers' sentiment tendencies in multimodal video content, raising serious concerns about privacy risks associated with multimodal data, such as voiceprints and facial images. Recent distributed collaborative learning has been verified as an effective paradigm for privacy preservation in multimodal tasks. However, they often overlook the privacy distinctions among different modalities, struggling to strike a balance between performance and privacy preservation. Consequently, it poses an intriguing question of maximizing multimodal utilization to improve performance while simultaneously protecting necessary modalities. This paper forms the first attempt at modality-specified (i.e., audio and visual) privacy preservation in MSA tasks. We propose a novel Hybrid Distributed cross-modality cGAN framework (HyDiscGAN), which learns multimodality alignment to generate fake audio and visual features conditioned on shareable de-identified textual data. The objective is to leverage the fake features to approximate real audio and visual content to guarantee privacy preservation while effectively enhancing performance. Extensive experiments show that compared with the state-of-the-art MSA model, HyDiscGAN can achieve superior or competitive performance while preserving privacy.

cs.MM↗

arXiv2024

Unveiling the Misuse Potential of Base Large Language Models via In-Context Learning

The open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress. This includes both base models, which are pre-trained on extensive datasets without alignment, and aligned models, deliberately designed to align with ethical standards and human values. Contrary to the prevalent assumption that the inherent instruction-following limitations of base LLMs serve as a safeguard against misuse, our investigation exposes a critical oversight in this belief. By deploying carefully designed demonstrations, our research demonstrates that base LLMs could effectively interpret and execute malicious instructions. To systematically assess these risks, we introduce a novel set of risk evaluation metrics. Empirical results reveal that the outputs from base LLMs can exhibit risk levels on par with those of models fine-tuned for malicious purposes. This vulnerability, requiring neither specialized knowledge nor training, can be manipulated by almost anyone, highlighting the substantial risk and the critical need for immediate attention to the base LLMs' security protocols.

cs.CL↗

arXiv2024

Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control

The accuracy of the underlying model predictions is crucial for the success of model predictive control (MPC) applications. If the model is unable to accurately analyze the dynamics of the controlled system, the performance and stability guarantees provided by MPC may not be achieved. Learning-based MPC can learn models from data, improving the applicability and reliability of MPC. This study develops a nonlinear sparse variational Bayesian learning based MPC (NSVB-MPC) for nonlinear systems, where the model is learned by the developed NSVB method. Variational inference is used by NSVB-MPC to assess the predictive accuracy and make the necessary corrections to quantify system uncertainty. The suggested approach ensures input-to-state (ISS) and the feasibility of recursive constraints in accordance with the concept of an invariant terminal region. Finally, a PEMFC temperature control model experiment confirms the effectiveness of the NSVB-MPC method.

cs.LG↗

arXiv2024

Dynamic fault detection and diagnosis of industrial alkaline water electrolyzer process with variational Bayesian dictionary learning

Alkaline Water Electrolysis (AWE) is one of the simplest green hydrogen production method using renewable energy. AWE system typically yields process variables that are serially correlated and contaminated by measurement uncertainty. A novel robust dynamic variational Bayesian dictionary learning (RDVDL) monitoring approach is proposed to improve the reliability and safety of AWE operation. RDVDL employs a sparse Bayesian dictionary learning to preserve the dynamic mechanism information of AWE process which allows the easy interpretation of fault detection results. To improve the robustness to measurement uncertainty, a low-rank vector autoregressive (VAR) method is derived to reliably extract the serial correlation from process variables. The effectiveness of the proposed approach is demonstrated with an industrial hydrogen production process, and RDVDL can efficiently detect and diagnose critical AWE faults.

cs.LG↗

arXiv2024

A Copula Graphical Model for Multi-Attribute Data using Optimal Transport

Motivated by modern data forms such as images and multi-view data, the multi-attribute graphical model aims to explore the conditional independence structure among vectors. Under the Gaussian assumption, the conditional independence between vectors is characterized by blockwise zeros in the precision matrix. To relax the restrictive Gaussian assumption, in this paper, we introduce a novel semiparametric multi-attribute graphical model based on a new copula named Cyclically Monotone Copula. This new copula treats the distribution of the node vectors as multivariate marginals and transforms them into Gaussian distributions based on the optimal transport theory. Since the model allows the node vectors to have arbitrary continuous distributions, it is more flexible than the classical Gaussian copula method that performs coordinatewise Gaussianization. We establish the concentration inequalities of the estimated covariance matrices and provide sufficient conditions for selection consistency of the group graphical lasso estimator. For the setting with high-dimensional attributes, a {Projected Cyclically Monotone Copula} model is proposed to address the curse of dimensionality issue that arises from solving high-dimensional optimal transport problems. Numerical results based on synthetic and real data show the efficiency and flexibility of our methods.

stat.ML↗