arXiv subjects

Yichao Wang

Publications and source records attributed to Yichao Wang.

At least 55 records · Page 3Linked to original sources

arXiv2025

Privacy-Preserving Distributed Defense Framework for DC Microgrids Against Exponentially Unbounded False Data Injection Attacks

This paper introduces a novel, fully distributed control framework for DC microgrids, enhancing resilience against exponentially unbounded false data injection (EU-FDI) attacks. Our framework features a consensus-based secondary control for each converter, effectively addressing these advanced threats. To further safeguard sensitive operational data, a privacy-preserving mechanism is incorporated into the control design, ensuring that critical information remains secure even under adversarial conditions. Rigorous Lyapunov stability analysis confirms the framework's ability to maintain critical DC microgrid operations like voltage regulation and load sharing under EU-FDI threats. The framework's practicality is validated through hardware-in-the-loop experiments, demonstrating its enhanced resilience and robust privacy protection against the complex challenges posed by quick variant FDI attacks.

eess.SY↗

arXiv2024

LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations

The lack of training data gives rise to the system cold-start problem in recommendation systems, making them struggle to provide effective recommendations. To address this problem, Large Language Models (LLMs) can model recommendation tasks as language analysis tasks and provide zero-shot results based on their vast open-world knowledge. However, the large scale of the item corpus poses a challenge to LLMs, leading to substantial token consumption that makes it impractical to deploy in real-world recommendation systems. To tackle this challenge, we introduce a tree-based LLM recommendation framework LLMTreeRec, which structures all items into an item tree to improve the efficiency of LLM's item retrieval. LLMTreeRec achieves state-of-the-art performance under the system cold-start setting in two widely used datasets, which is even competitive with conventional deep recommendation systems that use substantial training data. Furthermore, LLMTreeRec outperforms the baseline model in A/B testing on Huawei industrial systems. Consequently, LLMTreeRec demonstrates its effectiveness as an industry-friendly solution that has been successfully deployed online. Our code is available at: https://github.com/Applied-Machine-Learning-Lab/LLMTreeRec.

cs.IR↗

arXiv2024

SyNeg: LLM-Driven Synthetic Hard-Negatives for Dense Retrieval

The performance of Dense retrieval (DR) is significantly influenced by the quality of negative sampling. Traditional DR methods primarily depend on naive negative sampling techniques or on mining hard negatives through external retriever and meticulously crafted strategies. However, naive negative sampling often fails to adequately capture the accurate boundaries between positive and negative samples, whereas existing hard negative sampling methods are prone to false negatives, resulting in performance degradation and training instability. Recent advancements in large language models (LLMs) offer an innovative solution to these challenges by generating contextually rich and diverse negative samples. In this work, we present a framework that harnesses LLMs to synthesize high-quality hard negative samples. We first devise a \textit{multi-attribute self-reflection prompting strategy} to direct LLMs in hard negative sample generation. Then, we implement a \textit{hybrid sampling strategy} that integrates these synthetic negatives with traditionally retrieved negatives, thereby stabilizing the training process and improving retrieval performance. Extensive experiments on five benchmark datasets demonstrate the efficacy of our approach, and code is also publicly available.

cs.IR↗

arXiv2024

CELA: Cost-Efficient Language Model Alignment for CTR Prediction

Click-Through Rate (CTR) prediction holds a paramount position in recommender systems. The prevailing ID-based paradigm underperforms in cold-start scenarios due to the skewed distribution of feature frequency. Additionally, the utilization of a single modality fails to exploit the knowledge contained within textual features. Recent efforts have sought to mitigate these challenges by integrating Pre-trained Language Models (PLMs). They design hard prompts to structure raw features into text for each interaction and then apply PLMs for text processing. With external knowledge and reasoning capabilities, PLMs extract valuable information even in cases of sparse interactions. Nevertheless, compared to ID-based models, pure text modeling degrades the efficacy of collaborative filtering, as well as feature scalability and efficiency during both training and inference. To address these issues, we propose \textbf{C}ost-\textbf{E}fficient \textbf{L}anguage Model \textbf{A}lignment (\textbf{CELA}) for CTR prediction. CELA incorporates textual features and language models while preserving the collaborative filtering capabilities of ID-based models. This model-agnostic framework can be equipped with plug-and-play textual features, with item-level alignment enhancing the utilization of external information while maintaining training and inference efficiency. Through extensive offline experiments, CELA demonstrates superior performance compared to state-of-the-art methods. Furthermore, an online A/B test conducted on an industrial App recommender system showcases its practical effectiveness, solidifying the potential for real-world applications of CELA.

cs.IR↗

arXiv2024

Transient-Safe and Attack-Resilient Secondary Control in AC Microgrids Under Polynomially Unbounded FDI Attacks

This letter proposes a novel, fully distributed, transient-safe resilient secondary control strategies for AC microgrids, addressing unbounded false data injection (FDI) attacks on control input channels. Unlike existing methods that focus primarily on steady-state convergence, our approach guarantees transient safety, ensuring that system states remain within predefined safety bounds even during attack initiation a critical aspect overlooked in prior research. Given the reduction of network inertia by increasing the penetration of inverted-based renewables, large overshooting and intense fluctuations are more likely to occur during transients caused by disturbances and cyber-attacks. To mitigate these risks, the proposed control method enhances defense capabilities against polynomially unbounded FDI attacks, maintaining safe system trajectories for both frequency and voltage throughout the transient response. Through rigorous Lyapunov-based stability analysis, we formally certify the strategies to achieve uniformly ultimately bounded (UUB) convergence in frequency and voltage regulation, and active power sharing across multi-inverter-based AC microgrids. Numerical simulation studies verify the effectiveness of the proposed control protocols, demonstrating improved system reliability, safety and resilience under adverse conditions.

eess.SY↗

arXiv2024

A Unified Framework for Multi-Domain CTR Prediction via Large Language Models

Click-Through Rate (CTR) prediction is a crucial task in online recommendation platforms as it involves estimating the probability of user engagement with advertisements or items by clicking on them. Given the availability of various services like online shopping, ride-sharing, food delivery, and professional services on commercial platforms, recommendation systems in these platforms are required to make CTR predictions across multiple domains rather than just a single domain. However, multi-domain click-through rate (MDCTR) prediction remains a challenging task in online recommendation due to the complex mutual influence between domains. Traditional MDCTR models typically encode domains as discrete identifiers, ignoring rich semantic information underlying. Consequently, they can hardly generalize to new domains. Besides, existing models can be easily dominated by some specific domains, which results in significant performance drops in the other domains (i.e. the "seesaw phenomenon"). In this paper, we propose a novel solution Uni-CTR to address the above challenges. Uni-CTR leverages a backbone Large Language Model (LLM) to learn layer-wise semantic representations that capture commonalities between domains. Uni-CTR also uses several domain-specific networks to capture the characteristics of each domain. Note that we design a masked loss strategy so that these domain-specific networks are decoupled from backbone LLM. This allows domain-specific networks to remain unchanged when incorporating new or removing domains, thereby enhancing the flexibility and scalability of the system significantly. Experimental results on three public datasets show that Uni-CTR outperforms the state-of-the-art (SOTA) MDCTR models significantly. Furthermore, Uni-CTR demonstrates remarkable effectiveness in zero-shot prediction. We have applied Uni-CTR in industrial scenarios, confirming its efficiency.

cs.IR↗

arXiv2024

RBMD: A molecular dynamics package enabling to simulate 10 million all-atom particles in a single graphics processing unit

This paper introduces a random-batch molecular dynamics (RBMD) package for fast simulations of particle systems at the nano/micro scale. Different from existing packages, the RBMD uses random batch methods for nonbonded interactions of particle systems. The long-range part of Coulomb interactions is calculated in Fourier space by the random batch Ewald algorithm, which achieves linear complexity and superscalability, surpassing classical lattice-based Ewald methods. For the short-range part, the random batch list algorithm is used to construct neighbor lists, significantly reducing both computational and memory costs. The RBMD is implemented on GPU-CPU heterogeneous architectures, with classical force fields for all-atom systems. Benchmark systems are used to validate accuracy and performance of the package. Comparison with the particle-particle particle-mesh method and the Verlet list method in the LAMMPS package is performed on three different NVIDIA GPUs, demonstrating high efficiency of the RBMD on heterogeneous architectures. Our results also show that the RBMD enables simulations on a single GPU with a CPU core up to 10 million particles. Typically, for systems of one million particles, the RBMD allows simulating all-atom systems with a high efficiency of 8.20 ms per step, demonstrating the attractive feature for running large-scale simulations of practical applications on a desktop machine.

physics.comp-ph↗

arXiv2024

All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era

Recommender systems (RS) are vital for managing information overload and delivering personalized content, responding to users' diverse information needs. The emergence of large language models (LLMs) offers a new horizon for redefining recommender systems with vast general knowledge and reasoning capabilities. Standing across this LLM era, we aim to integrate recommender systems into a broader picture, and pave the way for more comprehensive solutions for future research. Therefore, we first offer a comprehensive overview of the technical progression of recommender systems, particularly focusing on language foundation models and their applications in recommendation. We identify two evolution paths of modern recommender systems -- via list-wise recommendation and conversational recommendation. These two paths finally converge at LLM agents with superior capabilities of long-term memory, reflection, and tool intelligence. Along these two paths, we point out that the information effectiveness of the recommendation is increased, while the user's acquisition cost is decreased. Technical features, research methodologies, and inherent challenges for each milestone along the path are carefully investigated -- from traditional list-wise recommendation to LLM-enhanced recommendation to recommendation with LLM agents. Finally, we highlight several unresolved challenges crucial for the development of future personalization technologies and interfaces and discuss the future prospects.

cs.IR↗

arXiv2024

ERASE: Benchmarking Feature Selection Methods for Deep Recommender Systems

Deep Recommender Systems (DRS) are increasingly dependent on a large number of feature fields for more precise recommendations. Effective feature selection methods are consequently becoming critical for further enhancing the accuracy and optimizing storage efficiencies to align with the deployment demands. This research area, particularly in the context of DRS, is nascent and faces three core challenges. Firstly, variant experimental setups across research papers often yield unfair comparisons, obscuring practical insights. Secondly, the existing literature's lack of detailed analysis on selection attributes, based on large-scale datasets and a thorough comparison among selection techniques and DRS backbones, restricts the generalizability of findings and impedes deployment on DRS. Lastly, research often focuses on comparing the peak performance achievable by feature selection methods, an approach that is typically computationally infeasible for identifying the optimal hyperparameters and overlooks evaluating the robustness and stability of these methods. To bridge these gaps, this paper presents ERASE, a comprehensive bEnchmaRk for feAture SElection for DRS. ERASE comprises a thorough evaluation of eleven feature selection methods, covering both traditional and deep learning approaches, across four public datasets, private industrial datasets, and a real-world commercial platform, achieving significant enhancement. Our code is available online for ease of reproduction.

cs.IR↗

arXiv2024

M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation

We primarily focus on the field of multi-scenario recommendation, which poses a significant challenge in effectively leveraging data from different scenarios to enhance predictions in scenarios with limited data. Current mainstream efforts mainly center around innovative model network architectures, with the aim of enabling the network to implicitly acquire knowledge from diverse scenarios. However, the uncertainty of implicit learning in networks arises from the absence of explicit modeling, leading to not only difficulty in training but also incomplete user representation and suboptimal performance. Furthermore, through causal graph analysis, we have discovered that the scenario itself directly influences click behavior, yet existing approaches directly incorporate data from other scenarios during the training of the current scenario, leading to prediction biases when they directly utilize click behaviors from other scenarios to train models. To address these problems, we propose the Multi-Scenario Causal-driven Adaptive Network M-scan). This model incorporates a Scenario-Aware Co-Attention mechanism that explicitly extracts user interests from other scenarios that align with the current scenario. Additionally, it employs a Scenario Bias Eliminator module utilizing causal counterfactual inference to mitigate biases introduced by data from other scenarios. Extensive experiments on two public datasets demonstrate the efficacy of our M-scan compared to the existing baseline models.

cs.IR↗

arXiv2024

Unfolding environmental $γ$ flux spectrum with portable CZT detector

Environmental $γ$-rays constitute a crucial source of background in various nuclear, particle and quantum physics experiments. To evaluate the flux rate and the spectrum of $γ$ background, we have developed a novel and straightforward approach to reconstruct the environmental $γ$ flux spectrum by applying a portable CZT $γ$ detector and iterative Bayesian unfolding, which possesses excellent transferability for broader applications. In this paper, the calibration and GEANT4 Monte-Carlo modeling of the CZT detector, the unfolding procedure as well as the uncertainty estimation are demonstrated in detail. The reconstructed spectrum reveals an environmental $γ$ flux intensity of $3.3\pm 0.9\times 10^{7}$~ (m$^2\cdot$sr$\cdot$hour)$^{-1}$ ranging from 73 to 3033~keV, along with characteristic peaks primarily arising from $^{232}$Th series, $^{238}$U series and $^{40}$K. We also give an instance of background rate evaluation with the unfolded spectrum for validation of the approach.

physics.ins-det↗

arXiv2023

Optimal Transport for Treatment Effect Estimation

Estimating conditional average treatment effect from observational data is highly challenging due to the existence of treatment selection bias. Prevalent methods mitigate this issue by aligning distributions of different treatment groups in the latent space. However, there are two critical problems that these methods fail to address: (1) mini-batch sampling effects (MSE), which causes misalignment in non-ideal mini-batches with outcome imbalance and outliers; (2) unobserved confounder effects (UCE), which results in inaccurate discrepancy calculation due to the neglect of unobserved confounders. To tackle these problems, we propose a principled approach named Entire Space CounterFactual Regression (ESCFR), which is a new take on optimal transport in the context of causality. Specifically, based on the framework of stochastic optimal transport, we propose a relaxed mass-preserving regularizer to address the MSE issue and design a proximal factual outcome regularizer to handle the UCE issue. Extensive experiments demonstrate that our proposed ESCFR can successfully tackle the treatment selection bias and achieve significantly better performance than state-of-the-art methods.

cs.LG↗

arXiv2023

Secondary Defense Strategies of AC Microgrids Against Generally Unbounded Attacks

This paper develops a fully distributed attack-resilient secondary defense strategies for AC microgrids, addressing more generally unbounded attacks on control input channels than those addressed in existing literature. The secondary control of local inverter includes consensus-based voltage and current regulators utilizing relative information from neighboring inverters. This distributed control approach relies on localized control and a sparse communication network, making it susceptible to malicious cyber-physical attacks that can impair consensus performance and potentially destabilize the overall microgrid. In contrast to existing solutions that are limited to addressing either bounded faults, noises or unbounded attacks with bounded first-order time derivatives, we aim to surpass these constraints and enhance the defense capabilities of counteracting cyber-physical attacks by enabling the AC microgrids adopting the proposed strategies to withstand a much wider range of unbounded cyber-attack signals. Fully distributed attack-resilient secondary defense strategies are developed for AC microgrids to counteract the detrimental effects of generally unbounded attacks on control input channels. Rigorous proofs using Lyapunov techniques demonstrate that the proposed defense strategies accomplish the uniformly ultimately bounded convergence on frequency regulation and achieve voltage containment and active power sharing simultaneously for multi-inverter-based AC microgrids in the face of generally unbounded attacks. The proposed defense strategies are validated on a modified IEEE 34-bus test feeder benchmark system incorporating four inverter-based DERs.

eess.SY↗

arXiv2023

Distributed Resilient Control of DC Microgrids Under Generally Unbounded FDI Attacks

Due to the nature of distributed secondary control paradigm, DC microgrids are prone to malicious cyber-physical attacks, which could be unbounded to maximize their damage. Existing resilient secondary control methods addressing unbounded attacks require that the first time derivatives of cyber-physical attack signals be bounded. The secondary defense strategy presented in this letter relax such a strict constraint by addressing more generally unbounded attack signals and hence, enhance the resilience of DC microgrids in adversarial environments. Rigorous proofs, based on Lyapunov techniques, show that the proposed method guarantees the uniformly ultimately bounded convergence for both voltage regulation and proportional load sharing under generally unbounded attacks. Comparative case studies further validate the enhanced resilience of the proposed attack-resilient control strategy.

eess.SY↗

arXiv2023

Resilient Model-Free Asymmetric Bipartite Consensus for Nonlinear Multi-Agent Systems against DoS Attacks

In this letter, we study an unified resilient asymmetric bipartite consensus (URABC) problem for nonlinear multi-agent systems with both cooperative and antagonistic interactions under denial-of-service (DoS) attacks. We first prove that the URABC problem is solved by stabilizing the neighborhood asymmetric bipartite consensus error. Then, we develop a distributed compact form dynamic linearization method to linearize the neighborhood asymmetric bipartite consensus error. By using an extended discrete state observer to enhance the robustness against unknown dynamics and an attack compensation mechanism to eliminate the adverse effects of DoS attacks, we finally propose a distributed resilient model-free adaptive control algorithm to solve the URABC problem. A numerical example validates the proposed results.

eess.SY↗

arXiv2023

Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendation

Click-Through Rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have shown that implementing multi-scenario recommendations contributes to strengthening information sharing and improving overall performance. However, existing multi-scenario models only consider coarse-grained explicit scenario modeling that depends on pre-defined scenario identification from manual prior rules, which is biased and sub-optimal. To address these limitations, we propose a Scenario-Aware Hierarchical Dynamic Network for Multi-Scenario Recommendations (HierRec), which perceives implicit patterns adaptively and conducts explicit and implicit scenario modeling jointly. In particular, HierRec designs a basic scenario-oriented module based on the dynamic weight to capture scenario-specific information. Then the hierarchical explicit and implicit scenario-aware modules are proposed to model hybrid-grained scenario information. The multi-head implicit modeling design contributes to perceiving distinctive patterns from different perspectives. Our experiments on two public datasets and real-world industrial applications on a mainstream online advertising platform demonstrate that our HierRec outperforms existing models significantly.

cs.IR↗

arXiv2023

Resilient Containment Control of Heterogeneous Multi-Agent Systems Against Unbounded Sensor and Actuator Attacks

Accurate local state measurement is important to ensure the reliable operation of distributed multi-agent systems (MAS). Existing fault-tolerant control strategies generally assume the sensor faults to be bounded and uncorrelated. In this paper, we study the ramifications of allowing the sensor attack injections to be unbounded and correlated. These malicious sensor attacks may bypass the conventional attack-detection methods and compromise the cooperative performance and even stability of the distributed networked MAS. Moreover, the attackers may gain access to the actuation computing channels and manipulate the control input commands. To this end, we consider the resilient containment control problem of general linear heterogeneous MAS in the face of correlated and unbounded sensor attacks, as well as general unbounded actuator attacks. We propose an attack-resilient control framework to guarantee the uniform ultimate boundedness of the closed-loop dynamical systems and preserve the bounded containment performance. Compared with existing literature addressing bounded faults and/or disturbances that are unintentionally caused in the sensor and actuator channels, the proposed control protocols are resilient against unknown unbounded attack signals simultaneously injected into sensor and actuator channels, and hence are more practical in the real-world security applications. A numerical example illustrates the efficacy of the proposed result, by highlighting the resilience improvement over the conventional cooperative control method.

eess.SY↗

arXiv2022

3D Human Mesh Construction Leveraging Wi-Fi

In this paper, we present, Wi-Mesh, a WiFi vision-based 3D human mesh construction system. Our system leverages the advances of WiFi to visualize the shape and deformations of the human body for 3D mesh construction. In particular, it leverages multiple transmitting and receiving antennas on WiFi devices to estimate the two-dimensional angle of arrival (2D AoA) of the WiFi signal reflections to enable WiFi devices to see the physical environment as we humans do. It then extracts only the images of the human body from the physical environment and leverages deep learning models to digitize the extracted human body into a 3D mesh representation. Experimental evaluation under various indoor environments shows that Wi-Mesh achieves an average vertices location error of 2.81cm and joint position error of 2.4cm, which is comparable to the systems that utilize specialized and dedicated hardware. The proposed system has the advantage of re-using the WiFi devices that already exist in the environment for potential mass adoption. It can also work in non-line of sight (NLoS), poor lighting conditions, and baggy clothes, where the camera-based systems do not work well.

cs.HC↗