arXiv subjects

Gong Chen

Publications and source records attributed to Gong Chen.

At least 55 records · Page 3Linked to original sources

arXiv2025

Mini-Game Lifetime Value Prediction in WeChat

The LifeTime Value (LTV) prediction, which endeavors to forecast the cumulative purchase contribution of a user to a particular item, remains a vital challenge that advertisers are keen to resolve. A precise LTV prediction system enhances the alignment of user interests with meticulously designed advertisements, thereby generating substantial profits for advertisers. Nonetheless, this issue is complicated by the paucity of data typically observed in real-world advertising scenarios. The purchase rate among registered users is often as critically low as 0.1%, resulting in a dataset where the majority of users make only several purchases. Consequently, there is insufficient supervisory signal for effectively training the LTV prediction model. An additional challenge emerges from the interdependencies among tasks with high correlation. It is a common practice to estimate a user's contribution to a game over a specified temporal interval. Varying the lengths of these intervals corresponds to distinct predictive tasks, which are highly correlated. For instance, predictions over a 7-day period are heavily reliant on forecasts made over a 3-day period, where exceptional cases can adversely affect the accuracy of both tasks. In order to comprehensively address the aforementioned challenges, we introduce an innovative framework denoted as Graph-Represented Pareto-Optimal LifeTime Value prediction (GRePO-LTV). Graph representation learning is initially employed to address the issue of data scarcity. Subsequently, Pareto-Optimization is utilized to manage the interdependence of prediction tasks.

cs.LG↗

arXiv2025

On uniqueness of KP soliton structures

We consider the Kadomtsev-Petviashvili II (KP) model placed in $\mathbb R_t \times \mathbb R_{x,y}^2$, in the case of smooth data that are not necessarily in a Sobolev space. In this paper, the subclass of smooth solutions we study is of ``soliton type'', characterized by a phase $Θ=Θ(t,x,y)$ and a unidimensional profile $F$. In particular, every classical KP soliton and multi-soliton falls into this category with suitable $Θ$ and $F$. We establish concrete characterizations of KP solitons by means of a natural set of nonlinear differential equations and inclusions of functionals of Wronskian, Airy and Heat types, among others. These functional equations only depend on the new variables $Θ$ and $F$. A distinct characteristic of this set of functionals is its special and rigid structure tailored to the considered soliton. By analyzing $Θ$ and $F$, we establish the uniqueness of line-solitons, multi-solitons, and other degenerate solutions among a large class of KP solutions. Our results are also valid for other 2D dispersive models such as the quadratic and cubic Zakharov-Kuznetsov equations.

math.AP↗

arXiv2025

MT4DP: Data Poisoning Attack Detection for DL-based Code Search Models via Metamorphic Testing

Recently, several studies have indicated that data poisoning attacks pose a severe security threat to deep learning-based (DL-based) code search models. Attackers inject carefully crafted malicious patterns into the training data, misleading the code search model to learn these patterns during training. During the usage of the poisoned code search model for inference, once the malicious pattern is triggered, the model tends to rank the vulnerability code higher. However, existing detection methods for data poisoning attacks on DL-based code search models remain insufficiently effective. To address this critical security issue, we propose MT4DP, a Data Poisoning Attack Detection Framework for DL-based Code Search Models via Metamorphic Testing. MT4DP introduces a novel Semantically Equivalent Metamorphic Relation (SE-MR) designed to detect data poisoning attacks on DL-based code search models. Specifically, MT4DP first identifies the high-frequency words from search queries as potential poisoning targets and takes their corresponding queries as the source queries. For each source query, MT4DP generates two semantically equivalent follow-up queries and retrieves its source ranking list. Then, each source ranking list is re-ranked based on the semantic similarities between its code snippets and the follow-up queries. Finally, variances between the source and re-ranked lists are calculated to reveal violations of the SE-MR and warn the data poisoning attack. Experimental results demonstrate that MT4DP significantly enhances the detection of data poisoning attacks on DL-based code search models, outperforming the best baseline by 191% on average F1 score and 265% on average precision. Our work aims to promote further research into effective techniques for mitigating data poisoning threats on DL-based code search models.

cs.SE↗

arXiv2025

RollingQ: Reviving the Cooperation Dynamics in Multimodal Transformer

Multimodal learning faces challenges in effectively fusing information from diverse modalities, especially when modality quality varies across samples. Dynamic fusion strategies, such as attention mechanism in Transformers, aim to address such challenge by adaptively emphasizing modalities based on the characteristics of input data. However, through amounts of carefully designed experiments, we surprisingly observed that the dynamic adaptability of widely-used self-attention models diminishes. Model tends to prefer one modality regardless of data characteristics. This bias triggers a self-reinforcing cycle that progressively overemphasizes the favored modality, widening the distribution gap in attention keys across modalities and deactivating attention mechanism's dynamic properties. To revive adaptability, we propose a simple yet effective method Rolling Query (RollingQ), which balances attention allocation by rotating the query to break the self-reinforcing cycle and mitigate the key distribution gap. Extensive experiments on various multimodal scenarios validate the effectiveness of RollingQ and the restoration of cooperation dynamics is pivotal for enhancing the broader capabilities of widely deployed multimodal Transformers. The source code is available at https://github.com/GeWu-Lab/RollingQ_ICML2025.

cs.LG↗

arXiv2025

IssueCourier: Multi-Relational Heterogeneous Temporal Graph Neural Network for Open-Source Issue Assignment

Issue assignment plays a critical role in open-source software (OSS) maintenance, which involves recommending the most suitable developers to address the reported issues. Given the high volume of issue reports in large-scale projects, manually assigning issues is tedious and costly. Previous studies have proposed automated issue assignment approaches that primarily focus on modeling issue report textual information, developers' expertise, or interactions between issues and developers based on historical issue-fixing records. However, these approaches often suffer from performance limitations due to the presence of incorrect and missing labels in OSS datasets, as well as the long tail of developer contributions and the changes of developer activity as the project evolves. To address these challenges, we propose IssueCourier, a novel Multi-Relational Heterogeneous Temporal Graph Neural Network approach for issue assignment. Specifically, we formalize five key relationships among issues, developers, and source code files to construct a heterogeneous graph. Then, we further adopt a temporal slicing technique that partitions the graph into a sequence of time-based subgraphs to learn stage-specific patterns. Furthermore, we provide a benchmark dataset with relabeled ground truth to address the problem of incorrect and missing labels in existing OSS datasets. Finally, to evaluate the performance of IssueCourier, we conduct extensive experiments on our benchmark dataset. The results show that IssueCourier can improve over the best baseline up to 45.49% in top-1 and 31.97% in MRR.

cs.SE↗

arXiv2025

Long time asymptotics of a perturbed modified KdV equation

We derive full asymptotics of the modified KdV equation (mKdV) with a higher-order perturbative term. We make use of the perturbative theory of infinite-dimensional integrable systems developed by P. Deift and X. Zhou \cite{DZ-2}, and some new and simpler proofs of certain $L^\infty$ bounds and $L^p$ a priori estimates developed recently in \cite{CLT}. We show that the perturbed equation exhibits the same long-time behavior as the completely integrable mKdV.

math.AP↗

arXiv2025

Stability of nonlinear recovery from scattering and modified scattering maps

We prove stability estimates for the recovery of the nonlinearity from the scattering or modified scattering map for one-dimensional nonlinear Schrödinger equations. We consider nonlinearities of the form $a(x) |u|^p u$ for $p\in [2,4]$ and $[1+a(x)]|u|^2 u$, where $a$ is a localized function. In the first case, we show that for $p\in(2,4]$ we may obtain a Hölder-type stability estimate for recovery via the scattering map, while for $p=2$ we obtain a logarithmic stability estimate. In the second case, we show a logarithmic stability estimate for recovery via the modified scattering map.

math.AP↗

arXiv2025

Divide and Conquer: Heterogeneous Noise Integration for Diffusion-based Adversarial Purification

Existing diffusion-based purification methods aim to disrupt adversarial perturbations by introducing a certain amount of noise through a forward diffusion process, followed by a reverse process to recover clean examples. However, this approach is fundamentally flawed: the uniform operation of the forward process across all pixels compromises normal pixels while attempting to combat adversarial perturbations, resulting in the target model producing incorrect predictions. Simply relying on low-intensity noise is insufficient for effective defense. To address this critical issue, we implement a heterogeneous purification strategy grounded in the interpretability of neural networks. Our method decisively applies higher-intensity noise to specific pixels that the target model focuses on while the remaining pixels are subjected to only low-intensity noise. This requirement motivates us to redesign the sampling process of the diffusion model, allowing for the effective removal of varying noise levels. Furthermore, to evaluate our method against strong adaptative attack, our proposed method sharply reduces time cost and memory usage through a single-step resampling. The empirical evidence from extensive experiments across three datasets demonstrates that our method outperforms most current adversarial training and purification techniques by a substantial margin.

cs.CV↗

arXiv2025

Mixture of Knowledge Minigraph Agents for Literature Review Generation

Literature reviews play a crucial role in scientific research for understanding the current state of research, identifying gaps, and guiding future studies on specific topics. However, the process of conducting a comprehensive literature review is yet time-consuming. This paper proposes a novel framework, collaborative knowledge minigraph agents (CKMAs), to automate scholarly literature reviews. A novel prompt-based algorithm, the knowledge minigraph construction agent (KMCA), is designed to identify relations between concepts from academic literature and automatically constructs knowledge minigraphs. By leveraging the capabilities of large language models on constructed knowledge minigraphs, the multiple path summarization agent (MPSA) efficiently organizes concepts and relations from different viewpoints to generate literature review paragraphs. We evaluate CKMAs on three benchmark datasets. Experimental results show the effectiveness of the proposed method, further revealing promising applications of LLMs in scientific research.

cs.CL↗

arXiv2024

Magneto-Ionic Physical Reservoir Computing

Recent progresses in magnetoionics offer exciting potentials to leverage its non-linearity, short-term memory, and energy-efficiency to uniquely advance the field of physical reservoir computing. In this work, we experimentally demonstrate the classification of temporal data using a magneto-ionic (MI) heterostructure. The device was specifically engineered to induce non-linear ion migration dynamics, which in turn imparted non-linearity and short-term memory (STM) to the magnetization. These capabilities, key features for enabling reservoir computing, were investigated, and the role of the ion migration mechanism, along with its history-dependent influence on STM, was explained. These attributes were utilized to distinguish between sine and square waveforms within a randomly distributed set of pulses. Additionally, two important performance metrics, short-term memory and parity check capacity (PC), were quantified, yielding promising values of 1.44 and 2, respectively, comparable to those of other state-of-the-art reservoirs. Our work paves the way for exploiting the relaxation dynamics of solid-state magneto-ionic platforms and developing energy-efficient magneto-ionic reservoir computing devices.

cond-mat.mes-hall↗

arXiv2024

Nonexistence of minimal mass blow-up solution for the 2D cubic Zakharov-Kuznetsov equation

For the 2D cubic (mass-critical) Zakharov-Kuznetsov equation, \begin{equation*} \partial_tϕ+\partial_{x_1}(Δϕ+ϕ^3)=0,\quad (t,x)\in [0,\infty)\times \mathbb{R}^{2}, \end{equation*} we prove that there exist no finite/infinite time blow-up solution with minimal mass in the energy space. This nonexistence result is in contrast to the one obtained by Martel-Merle-Raphaël [17] for the mass-critical generalized Korteweg-de Vries (gKdV) equation. The proof relies on a refined ODE argument related to the modulation theory and a modified energy-virial Lyapunov functional with a monotonicity property.

math.AP↗

arXiv2024

Asymptotic stability of the sine-Gordon kink

We establish the full asymptotic stability of the sine-Gordon kink outside symmetry under small perturbations in weighted Sobolev norms. Our proof consists of a space-time resonances approach based on the distorted Fourier transform to capture modified scattering effects combined with modulation techniques to take into account the invariance under Lorentz transformations and under spatial translations. A major challenge is the slow local decay of the radiation term caused by the threshold resonances of the non-selfadjoint linearized matrix operator around the moving kink. Our analysis crucially relies on two remarkable null structures in the quadratic nonlinearities of the evolution equation for the radiation term and of the modulation equations. The entire framework of our proof, including the systematic development of the distorted Fourier theory, is not specific to the sine-Gordon model and extends to many other asymptotic stability problems for moving solitons in relativistic scalar field theories on the line.

math.AP↗

arXiv2024

Robot Guided Evacuation with Viewpoint Constraints

We present a viewpoint-based non-linear Model Predictive Control (MPC) for evacuation guiding robots. Specifically, the proposed MPC algorithm enables evacuation guiding robots to track and guide cooperative human targets in emergency scenarios. Our algorithm accounts for the environment layout as well as distances between the robot and human target and distance to the goal location. A key challenge for evacuation guiding robot is the trade-off between its planned motion for leading the target toward a goal position and staying in the target's viewpoint while maintaining line-of-sight for guiding. We illustrate the effectiveness of our proposed evacuation guiding algorithm in both simulated and real-world environments with an Unmanned Aerial Vehicle (UAV) guiding a human. Our results suggest that using the contextual information from the environment for motion planning, increases the visibility of the guiding UAV to the human while achieving faster total evacuation time.

cs.RO↗

arXiv2024

Upper-Limb Rehabilitation with a Dual-Mode Individualized Exoskeleton Robot: A Generative-Model-Based Solution

Several upper-limb exoskeleton robots have been developed for stroke rehabilitation, but their rather low level of individualized assistance typically limits their effectiveness and practicability. Individualized assistance involves an upper-limb exoskeleton robot continuously assessing feedback from a stroke patient and then meticulously adjusting interaction forces to suit specific conditions and online changes. This paper describes the development of a new upper-limb exoskeleton robot with a novel online generative capability that allows it to provide individualized assistance to support the rehabilitation training of stroke patients. Specifically, the upper-limb exoskeleton robot exploits generative models to customize the fine and fit trajectory for the patient, as medical conditions, responses, and comfort feedback during training generally differ between patients. This generative capability is integrated into the two working modes of the upper-limb exoskeleton robot: an active mirroring mode for patients who retain motor abilities on one side of the body and a passive following mode for patients who lack motor ability on both sides of the body. The performance of the upper-limb exoskeleton robot was illustrated in experiments involving healthy subjects and stroke patients.

cs.RO↗

arXiv2024

Cooperative Beam Selection for RIS-Aided Terahertz MIMO Networks via Multi-Task Learning

Reconfigurable intelligent surface (RIS) have been cast as a promising alternative to alleviate blockage vulnerability and enhance coverage capability for terahertz (THz) communications. Owing to large-scale array elements at transceivers and RIS, the codebook based beamforming can be utilized in a computationally efficient manner. However, the codeword selection for analog beamforming is an intractable combinatorial optimization (CO) problem. To this end, by taking the CO problem as a classification problem, a multi-task learning based analog beam selection (MTL-ABS) framework is developed to implement cooperative beam selection concurrently at transceivers and RIS. In addition, residual network and self-attention mechanism are used to combat the network degradation and mine intrinsic THz channel features. Finally, the network convergence is analyzed from a blockwise perspective, and numerical results demonstrate that the MTL-ABS framework greatly decreases the beam selection overhead and achieves near optimal sum-rate compared with heuristic search based counterparts.

cs.IT↗

arXiv2024

On the near soliton dynamics for the 2D cubic Zakharov-Kuznetsov equations

In this article, we consider the Cauchy problem for the cubic (mass-critical) Zakharov-Kuznetsov equations in dimension two: $$\partial_t u+\partial_{x_1}(Δu+u^3)=0,\quad (t,x)\in [0,\infty)\times \mathbb{R}^{2}.$$ For initial data in $H^1$ close to the soliton with a suitable space-decay property, we fully describe the asymptotic behavior of the corresponding solution. More precisely, for such initial data, we show that only three possible behaviors can occur: 1) The solution leaves a tube near soliton in finite time; 2) the solution blows up in finite time; 3) the solution is global and locally converges to a soliton. In addition, we show that for initial data near a soliton with non-positive energy and above the threshold mass, the corresponding solution will blow up as described in Case 2. Our proof is inspired by the techniques developed for mass-critical generalized Korteweg-de Vries equation (gKdV) equation in a similar context by Martel-Merle-Raphaël. More precisely, our proof relies on refined modulation estimates and a modified energy-virial Lyapunov functional. The primary challenge in our problem is the lack of coercivity of the Schrödinger operator which appears in the virial-type estimate. To overcome the difficulty, we apply a transform, which was first introduced in Kenig-Martel [13], to perform the virial computations after converting the original problem to the adjoint one. Th coercivity of the Schrödinger operator in the adjoint problem has been numerically verified by Farah-Holmer-Roudenko-Yang [9].

math.AP↗

arXiv2024

LLMs for Coding and Robotics Education

Large language models and multimodal large language models have revolutionized artificial intelligence recently. An increasing number of regions are now embracing these advanced technologies. Within this context, robot coding education is garnering increasing attention. To teach young children how to code and compete in robot challenges, large language models are being utilized for robot code explanation, generation, and modification. In this paper, we highlight an important trend in robot coding education. We test several mainstream large language models on both traditional coding tasks and the more challenging task of robot code generation, which includes block diagrams. Our results show that GPT-4V outperforms other models in all of our tests but struggles with generating block diagram images.

cs.RO↗

arXiv2023

Long-time dynamics of small solutions to 1$d$ cubic nonlinear Schrödinger equations with a trapping potential

In this paper, we analyze the long-time dynamics of small solutions to the $1d$ cubic nonlinear Schrödinger equation (NLS) with a trapping potential. We show that every small solution will decompose into a small solitary wave and a radiation term which exhibits the modified scattering. In particular, this result implies the asymptotic stability of small solitary waves. Our analysis also establishes the long-time behavior of solutions to a perturbation of the integrable cubic NLS with the appearance of solitons.

math.AP↗