arXiv subjects

Hao Wang

Publications and source records attributed to Hao Wang.

At least 811 records · Page 45Linked to original sources

arXiv2025

A Universal Framework for Compressing Embeddings in CTR Prediction

Accurate click-through rate (CTR) prediction is vital for online advertising and recommendation systems. Recent deep learning advancements have improved the ability to capture feature interactions and understand user interests. However, optimizing the embedding layer often remains overlooked. Embedding tables, which represent categorical and sequential features, can become excessively large, surpassing GPU memory limits and necessitating storage in CPU memory. This results in high memory consumption and increased latency due to frequent GPU-CPU data transfers. To tackle these challenges, we introduce a Model-agnostic Embedding Compression (MEC) framework that compresses embedding tables by quantizing pre-trained embeddings, without sacrificing recommendation quality. Our approach consists of two stages: first, we apply popularity-weighted regularization to balance code distribution between high- and low-frequency features. Then, we integrate a contrastive learning mechanism to ensure a uniform distribution of quantized codes, enhancing the distinctiveness of embeddings. Experiments on three datasets reveal that our method reduces memory usage by over 50x while maintaining or improving recommendation performance compared to existing models. The implementation code is accessible in our project repository https://github.com/USTC-StarTeam/MEC.

cs.IR↗

arXiv2025

RGB-Only Gaussian Splatting SLAM for Unbounded Outdoor Scenes

3D Gaussian Splatting (3DGS) has become a popular solution in SLAM, as it can produce high-fidelity novel views. However, previous GS-based methods primarily target indoor scenes and rely on RGB-D sensors or pre-trained depth estimation models, hence underperforming in outdoor scenarios. To address this issue, we propose a RGB-only gaussian splatting SLAM method for unbounded outdoor scenes--OpenGS-SLAM. Technically, we first employ a pointmap regression network to generate consistent pointmaps between frames for pose estimation. Compared to commonly used depth maps, pointmaps include spatial relationships and scene geometry across multiple views, enabling robust camera pose estimation. Then, we propose integrating the estimated camera poses with 3DGS rendering as an end-to-end differentiable pipeline. Our method achieves simultaneous optimization of camera poses and 3DGS scene parameters, significantly enhancing system tracking accuracy. Specifically, we also design an adaptive scale mapper for the pointmap regression network, which provides more accurate pointmap mapping to the 3DGS map representation. Our experiments on the Waymo dataset demonstrate that OpenGS-SLAM reduces tracking error to 9.8\% of previous 3DGS methods, and achieves state-of-the-art results in novel view synthesis. Project Page: https://3dagentworld.github.io/opengs-slam/

cs.CV↗

arXiv2025

Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy

Scaling Laws have emerged as a powerful framework for understanding how model performance evolves as they increase in size, providing valuable insights for optimizing computational resources. In the realm of Sequential Recommendation (SR), which is pivotal for predicting users' sequential preferences, these laws offer a lens through which to address the challenges posed by the scalability of SR models. However, the presence of structural and collaborative issues in recommender systems prevents the direct application of the Scaling Law (SL) in these systems. In response, we introduce the Performance Law for SR models, which aims to theoretically investigate and model the relationship between model performance and data quality. Specifically, we first fit the HR and NDCG metrics to transformer-based SR models. Subsequently, we propose Approximate Entropy (ApEn) to assess data quality, presenting a more nuanced approach compared to traditional data quantity metrics. Our method enables accurate predictions across various dataset scales and model sizes, demonstrating a strong correlation in large SR models and offering insights into achieving optimal performance for any given model configuration.

cs.AI↗

arXiv2025

A Framework for Semantics-based Situational Awareness during Mobile Robot Deployments

Deployment of robots into hazardous environments typically involves a ``Human-Robot Teaming'' (HRT) paradigm, in which a human supervisor interacts with a remotely operating robot inside the hazardous zone. Situational Awareness (SA) is vital for enabling HRT, to support navigation, planning, and decision-making. This paper explores issues of higher-level ``semantic'' information and understanding in SA. In semi-autonomous, or variable-autonomy paradigms, different types of semantic information may be important, in different ways, for both the human operator and an autonomous agent controlling the robot. We propose a generalizable framework for acquiring and combining multiple modalities of semantic-level SA during remote deployments of mobile robots. We demonstrate the framework with an example application of search and rescue (SAR) in disaster response robotics. We propose a set of ``environment semantic indicators" that can reflect a variety of different types of semantic information, e.g. indicators of risk, or signs of human activity, as the robot encounters different scenes. Based on these indicators, we propose a metric to describe the overall situation of the environment called ``Situational Semantic Richness (SSR)". This metric combines multiple semantic indicators to summarise the overall situation. The SSR indicates if an information-rich and complex situation has been encountered, which may require advanced reasoning for robots and humans and hence the attention of the expert human operator. The framework is tested on a Jackal robot in a mock-up disaster response environment. Experimental results demonstrate that the proposed semantic indicators are sensitive to changes in different modalities of semantic information in different scenes, and the SSR metric reflects overall semantic changes in the situations encountered.

cs.RO↗

arXiv2025

Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation

In the era of information overload, recommendation systems play a pivotal role in filtering data and delivering personalized content. Recent advancements in feature interaction and user behavior modeling have significantly enhanced the recall and ranking processes of these systems. With the rise of large language models (LLMs), new opportunities have emerged to further improve recommendation systems. This tutorial explores two primary approaches for integrating LLMs: LLMs-enhanced recommendations, which leverage the reasoning capabilities of general LLMs, and generative large recommendation models, which focus on scaling and sophistication. While the former has been extensively covered in existing literature, the latter remains underexplored. This tutorial aims to fill this gap by providing a comprehensive overview of generative large recommendation models, including their recent advancements, challenges, and potential research directions. Key topics include data quality, scaling laws, user behavior mining, and efficiency in training and inference. By engaging with this tutorial, participants will gain insights into the latest developments and future opportunities in the field, aiding both academic research and practical applications. The timely nature of this exploration supports the rapid evolution of recommendation systems, offering valuable guidance for researchers and practitioners alike.

cs.IR↗

arXiv2025

Origin of the density wave instability in trilayer nickelate La$_{4}$Ni$_{3}$O$_{10}$ revealed by optical and ultrafast spectroscopy

In the intricate phase diagram of unconventional superconductors characterized by intertwined electronic orders and superconductivity, a key step in understanding the superconducting mechanism is to investigate the parent compounds from which superconductivity emerges through doping or pressure. In this study, we employed optical spectroscopy and ultrafast reflectivity measurements to examine the density wave instability in the trilayer nickelate La$_{4}$Ni$_{3}$O$_{10}$, which displays pressure-induced superconductivity up to 30 K. Our optical spectroscopy measurements reveal that La$_{4}$Ni$_{3}$O$_{10}$ behaves as a metal with a high plasma frequency. Upon cooling, we observed a distinct formation of a density wave energy gap in both optical conductivity and pump-probe measurements. The gap feature is more pronounced compared to the bilayer nickelate La$_{3}$Ni$_{2}$O$_{7}$. Through a comparison of the experimentally determined plasma frequency with first-principles calculations, we classify La$_{4}$Ni$_{3}$O$_{10}$ as a moderately electron-correlated material, resembling the parent compound of iron-based superconductors but exhibiting weaker correlation than the bilayer nickelate La$_{3}$Ni$_{2}$O$_{7}$. The enhanced gap feature and weaker electronic correlation in La$_{4}$Ni$_{3}$O$_{10}$ may explain its lower superconductivity transition temperature under high pressure. These findings significantly advance our comprehension of the density wave and superconductivity mechanisms in the trilayer nickelate La$_{4}$Ni$_{3}$O$_{10}$.

cond-mat.str-el↗

arXiv2025

Decoding the spatial spread of cyanobacterial blooms in an epilimnion

Cyanobacterial blooms (CBs) pose significant global challenges due to their harmful toxins and socio-economic impacts, with nutrient availability playing a key role in their growth, as described by ecological stoichiometry (ES). However, real-world ecosystems exhibit spatial heterogeneity, limiting the applicability of simpler, spatially uniform models. To address this, we develop a spatially explicit partial differential equation model based on ES to study cyanobacteria in the epilimnion of freshwater systems. We establish the well-posedness of the model and perform a stability analysis, showing that it admits two linearly stable steady states, leading to either extinction or saturation. We use the finite elements method to numerically solve our system on a real lake domain derived from Geographic Information System (GIS) data and realistic wind conditions extrapolated from ERA5-Land. Our numerical results highlight the importance of lake shape and size in CB monitoring, while global sensitivity analysis using Sobol Indices identifies light attenuation and intensity as primary drivers of bloom variation, with water movement influencing early bloom stages and nutrient input becoming critical over time. This model supports continuous water-quality monitoring, informing agricultural, recreational, economic, and public health strategies for mitigating CBs.

math.AP↗

arXiv2025

Multi-granularity Interest Retrieval and Refinement Network for Long-Term User Behavior Modeling in CTR Prediction

Click-through Rate (CTR) prediction is crucial for online personalization platforms. Recent advancements have shown that modeling rich user behaviors can significantly improve the performance of CTR prediction. Current long-term user behavior modeling algorithms predominantly follow two cascading stages. The first stage retrieves subsequence related to the target item from the long-term behavior sequence, while the second stage models the relationship between the subsequence and the target item. Despite significant progress, these methods have two critical flaws. First, the retrieval query typically includes only target item information, limiting the ability to capture the user's diverse interests. Second, relational information, such as sequential and interactive information within the subsequence, is frequently overlooked. Therefore, it requires to be further mined to more accurately model user interests. To this end, we propose Multi-granularity Interest Retrieval and Refinement Network (MIRRN). Specifically, we first construct queries based on behaviors observed at different time scales to obtain subsequences, each capturing users' interest at various granularities. We then introduce an noval multi-head Fourier transformer to efficiently learn sequential and interactive information within the subsequences, leading to more accurate modeling of user interests. Finally, we employ multi-head target attention to adaptively assess the impact of these multi-granularity interests on the target item. Extensive experiments have demonstrated that MIRRN significantly outperforms state-of-the-art baselines. Furthermore, an A/B test shows that MIRRN increases the average number of listening songs by 1.32% and the average time of listening songs by 0.55% on the Huawei Music App. The implementation code is publicly available at https://github.com/USTC-StarTeam/MIRRN.

cs.IR↗

arXiv2025

ShieldLearner: A New Paradigm for Jailbreak Attack Defense in LLMs

Large Language Models (LLMs) have achieved remarkable success in various domains but remain vulnerable to adversarial jailbreak attacks. Existing prompt-defense strategies, including parameter-modifying and parameter-free approaches, face limitations in adaptability, interpretability, and customization, constraining their effectiveness against evolving threats. To address these challenges, we propose ShieldLearner, a novel paradigm that mimics human learning in defense. Through trial and error, it autonomously distills attack signatures into a Pattern Atlas and synthesizes defense heuristics into a Meta-analysis Framework, enabling systematic and interpretable threat detection. Furthermore, we introduce Adaptive Adversarial Augmentation to generate adversarial variations of successfully defended prompts, enabling continuous self-improvement without model retraining. In addition to standard benchmarks, we create a hard test set by curating adversarial prompts from the Wildjailbreak dataset, emphasizing more concealed malicious intent. Experimental results show that ShieldLearner achieves a significantly higher defense success rate than existing baselines on both conventional and hard test sets, while also operating with lower computational overhead, making it a practical and efficient solution for real-world adversarial defense.

cs.CR↗

arXiv2025

The neutrino luminosity and energy spectrum of nova outburst

The nova outburst can produce a large number of neutrinos, whether it is the nuclear reaction process during the explosion or the shock wave acceleration proton process. We study the low-energy nuclear and thermal neutrino luminosity of novae with CO white dwarf (WD) mass ranging from 0.6 to 1.1 $\rm M_{\odot}$ with different accretion rates $\dot{M}$, core temperatures $(T_{\mathrm{C}})$, and mixing degrees. We find that during the accretion phase, low-energy neutrinos are mainly produced by pp chains and plasma decay, and photon luminosity is greater than low-energy nuclear and thermal neutrino luminosity. During the thermonuclear runaway (TNR) phase, low-energy neutrinos are mainly produced by the CNO cycle and photon-neutrino, and the low-energy nuclear and thermal neutrino luminosity far exceeds the photon luminosity. We find that the more massive the WD, the shorter the cycle time and the higher the low-energy nuclear neutrino luminosity. The higher the accretion rate, the lower the low-energy nuclear neutrino luminosity. If the accretion mixing effect is not taken into account, the outburst interval becomes longer, the low-energy nuclear neutrino luminosity will be increased. And for the cooler nova model $(T_{\mathrm{C}}=1\times10^{7}\rm K)$, the low-energy nuclear neutrino luminosity will be lower during the accretion phase and higher at the TNR. We also predict the neutrino luminosity and energy spectrum of the upcoming recurrent nova T Coronae Borealis (T CrB). We estimate that the next T CrB outburst has a low-energy nuclear neutrino peak luminosity of $2.7\times10^{8}\ \rm L_{ν,\odot}$ and a low-energy nuclear neutrino outburst duration of 88 days. In addition, we predict that the high-energy hadronic neutrino flux produced by T CrB nova can not be observed by the current-generation IceCube.

astro-ph.SR↗

arXiv2025

An Engorgio Prompt Makes Large Language Model Babble on

Auto-regressive large language models (LLMs) have yielded impressive performance in many real-world tasks. However, the new paradigm of these LLMs also exposes novel threats. In this paper, we explore their vulnerability to inference cost attacks, where a malicious user crafts Engorgio prompts to intentionally increase the computation cost and latency of the inference process. We design Engorgio, a novel methodology, to efficiently generate adversarial Engorgio prompts to affect the target LLM's service availability. Engorgio has the following two technical contributions. (1) We employ a parameterized distribution to track LLMs' prediction trajectory. (2) Targeting the auto-regressive nature of LLMs' inference process, we propose novel loss functions to stably suppress the appearance of the token, whose occurrence will interrupt the LLM's generation process. We conduct extensive experiments on 13 open-sourced LLMs with parameters ranging from 125M to 30B. The results show that Engorgio prompts can successfully induce LLMs to generate abnormally long outputs (i.e., roughly 2-13$\times$ longer to reach 90%+ of the output length limit) in a white-box scenario and our real-world experiment demonstrates Engergio's threat to LLM service with limited computing resources. The code is released at: https://github.com/jianshuod/Engorgio-prompt.

cs.CR↗

arXiv2025

Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) have shown significant promise in various applications, leading to broad interest from researchers and practitioners alike. However, a comprehensive evaluation of their long-context capabilities remains underexplored. To address these gaps, we introduce the MultiModal Needle-in-a-haystack (MMNeedle) benchmark, specifically designed to assess the long-context capabilities of MLLMs. Besides multi-image input, we employ image stitching to further increase the input context length, and develop a protocol to automatically generate labels for sub-image level retrieval. Essentially, MMNeedle evaluates MLLMs by stress-testing their capability to locate a target sub-image (needle) within a set of images (haystack) based on textual instructions and descriptions of image contents. This setup necessitates an advanced understanding of extensive visual contexts and effective information retrieval within long-context image inputs. With this benchmark, we evaluate state-of-the-art MLLMs, encompassing both API-based and open-source models. The findings reveal that GPT-4o consistently surpasses other models in long-context scenarios, but suffers from hallucination problems in negative samples, i.e., when needles are not in the haystacks. Our comprehensive long-context evaluation of MLLMs also sheds lights on the considerable performance gap between API-based and open-source models. All the code, data, and instructions required to reproduce the main results are available at https://github.com/Wang-ML-Lab/multimodal-needle-in-a-haystack.

cs.LG↗

arXiv2025

Position reconstruction and surface background model for the PandaX-4T detector

We report the position reconstruction methods and surface background model for the PandaX-4T dark matter direct search experiment. This work develops two position reconstruction algorithms: template matching (TM) method and photon acceptance function (PAF) method. Both methods determine the horizontal position of events based on the light pattern of secondary scintillation collected by the light sensors. After a comprehensive evaluation of resolution, uniformity, and robustness, the PAF method was selected for position reconstruction, while the TM method was employed for verification. The PAF method achieves a bulk event resolution of 1.0 mm and a surface event resolution of 4.4 mm for a typical $S2$ signal with a bottom charge of 1500 PE (about 14 keV). The uniformity is around 20\%. Robustness studies reveal average deviations of 5.1 mm and 8.8 mm for the commissioning run (Run0) and the first science run (Run1), respectively, due to the deactivation of certain PMTs. A data-driven surface background model is developed based on the PAF method. The surface background is estimated to be $0.09 \pm 0.06$ events for Run0 (0.54 tonne$\cdot$year) and $0.17 \pm 0.11$ events for Run1 (1.00 tonne$\cdot$year).

physics.ins-det↗

arXiv2025

A Novel Low-Background Photomultiplier Tube Developed for Xenon Based Detectors

Photomultiplier tubes (PMTs) are essential in xenon detectors like PandaX, LZ, and XENON experiments for dark matter searches and neutrino properties measurement. To minimize PMT-induced backgrounds, stringent requirements on PMT radioactivity are crucial. A novel 2-inch low-background R12699 PMT has been developed through a collaboration between the PandaX team and Hamamatsu Photonics K.K. corporation. Radioactivity measurements conducted with a high-purity germanium detector show levels of approximately 0.08 mBq/PMT for $\rm^{60}Co$ and 0.06~mBq/PMT for the $\rm^{238}U$ late chain, achieving a 15-fold reduction compared to R11410 PMT used in PandaX-4T. The radon emanation rate is below 3.2 $\rm μ$Bq/PMT (@90\% confidence level), while the surface $\rm^{210}Po$ activity is less than 18.4 $μ$Bq/cm$^2$. The electrical performance of these PMTs at cryogenic temperature was evaluated. With an optimized readout base, the gain was enhanced by 30\%, achieving an average gain of $4.23 \times 10^6$ at -1000~V and -100~$^{\circ}$C. The dark count rate averaged 2.5~Hz per channel. Compactness, low radioactivity, and robust electrical performance in the cryogenic temperature make the R12699 PMT ideal for next-generation liquid xenon detectors and other rare event searches.

physics.ins-det↗

arXiv2025

Director-layer dynamics in the antiferroelectric smectic-ZA phase of a ferroelectric nematic liquid crystal

A dynamic light scattering study of director-layer fluctuations in the antiferroelectric smectic-ZA phase of the ferroelectric nematic liquid crystal DIO is reported. The dynamics are consistent with the distinctive feature of the ZA phase that the smectic layers form parallel to the axis of molecular orientational order (director). A model is developed to describe quantitatively the dispersion of the fluctuation relaxation rates. The model is based on a specialization of the elastic free energy density of the smectic-C phase to the case of 90 degree director tilt, a "first-order" approximation of the viscous stresses by their form for an incompressible uniaxial fluid, and a treatment of the effect of chevron layer structure that develops in planar sample cells due to temperature-dependent layer shrinkage, as documented in previous studies on DIO. From the modeling, the layer compression elastic constant is estimated to be ~100 times lower in the smectic-ZA phase than in an ordinary smectic-A liquid crystal. Possible effects of the antiferroelectric layer polarization on the director splay elasticity and viscosity are described. The temperature dependencies of the splay, twist, and bend elastic constants and associated viscosities in the higher temperature nematic phase are also presented.

cond-mat.soft↗

arXiv2025

A non-zero-sum game with reinforcement learning under mean-variance framework

In this paper, we investigate a competitive market involving two agents who consider both their own wealth and the wealth gap with their opponent. Both agents can invest in a financial market consisting of a risk-free asset and a risky asset, under conditions where model parameters are partially or completely unknown. This setup gives rise to a non-zero-sum differential game within the framework of reinforcement learning (RL). Each agent aims to maximize his own Choquet-regularized, time-inconsistent mean-variance objective. Adopting the dynamic programming approach, we derive a time-consistent Nash equilibrium strategy in a general incomplete market setting. Under the additional assumption of a Gaussian mean return model, we obtain an explicit analytical solution, which facilitates the development of a practical RL algorithm. Notably, the proposed algorithm achieves uniform convergence, even though the conventional policy improvement theorem does not apply to the equilibrium policy. Numerical experiments demonstrate the robustness and effectiveness of the algorithm, underscoring its potential for practical implementation.

math.OC↗

arXiv2025

Dark Matter Search Results from 1.54 Tonne$\cdot$Year Exposure of PandaX-4T

In this letter, we report the dark matter search results from the commissioning run and the first science run of the PandaX-4T experiment. A blind analysis is carried out on the entire data set. The data processing is improved compared to previous work, unifying the low-level signal reconstruction in a wide energy range up to 120 keV. With a total exposure of 1.54 tonne$\cdot$year, no significant excess of nuclear recoil events is found. The lowest 90% confidence level exclusion on the spin-independent cross section is $1.6 \times 10^{-47}\,\mathrm{cm}^2$ at a dark matter mass of 40 GeV$/c^2$. Our results represent the most stringent constraint for a dark matter mass above 100 GeV$/c^2$.

hep-ex↗

arXiv2025

Exploring New Physics with PandaX-4T Low Energy Electronic Recoil Data

New particles beyond the standard model of particle physics, such as axions, can be effectively searched through their interactions with electrons. We use the large liquid xenon detector PandaX-4T to search for novel electronic recoil signals induced by solar axions, neutrinos with anomalous magnetic moment, axionlike particles, dark photons, and light fermionic dark matter. A detailed background model is established using the latest datasets with 1.54 $\rm ton \cdot yr$ exposure. No significant excess above the background has been observed, and we have obtained competitive constraints for axion couplings, neutrino magnetic moment, and fermionic dark matter interactions.

hep-ex↗