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Chong Zheng

Publications and source records attributed to Chong Zheng.

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On The Morse Ensemble Polynomial Of Simplicial Complexes

We introduce the \emph{Morse ensemble polynomial} $\ME_K(z_0,\ldots,z_d)$ of a finite simplicial complex $K$, defined as the generating function $\ME_K = \sum_M \prod_i z_i^{c_i(M)}$ over all acyclic matchings $M$ on the face poset of $K$, where $c_i(M)$ counts critical $i$-simplices. This polynomial records the complete critical-vector distribution over all acyclic matchings, equivalently over all discrete gradient vector fields arising from discrete Morse functions on $K$, and is an isomorphism invariant of simplicial complexes. In dimension one, this invariant recovers the Chari--Joswig graph formula for the $f$-vector of the discrete Morse complex in a two-variable Morse-vector normalization: $\ME_G=z_1^{m-n}\det(z_0z_1\,I_n+L_G)$ for a connected graph $G$. The main new contributions are higher-dimensional and structural. First, we prove a Top-Face Recursion for adding a top-dimensional simplex, with a non-liftable correction term $F(K,\sigma,\tau)$. The vanishing and leading obstruction of this correction term are controlled by the top incidence graph: an incidence-separation criterion detects exactly when $F=0$, a leading obstruction layer is governed by shortest obstruction paths, and a tree-like (incidence-forest) regime of the top incidence structure gives a correction-free higher-dimensional recursion, including stacked balls as a concrete class. Second, we introduce the independence ME polynomial $\Phi(G):=\ME_{\mathrm{Ind}(G)}$, a graph invariant which strictly refines the graph-level Morse ensemble, separates examples not distinguished by $T_G$ and $I(G;t)$, and records collapse-level information of $\mathrm{Ind}(G)$ through coefficients such as $[z_0]\Phi(G)$.

math.CO

Solvita: Enhancing Large Language Models for Competitive Programming via Agentic Evolution

Large language models (LLMs) still struggle with the rigorous reasoning demands of hard competitive programming. While recent multi-agent frameworks attempt to bridge this reliability gap, they remain fundamentally stateless: they rely on static retrieval and discard the valuable problem-solving and debugging experience gained from previous tasks. To address this, we present Solvita, an agentic evolution framework that enables continuous learning without requiring weight updates to the underlying LLM. Solvita reorganizes problem-solving into a closed-loop system of strategy selection, program synthesis, certified supervision, and targeted hacking, executed by four specialized agents: Planner, Solver, Oracle, and Hacker. Crucially, each agent is paired with a trainable, graph-structured knowledge network. As the system operates, outcome signals, such as pass/fail verdicts, test certification quality, and adversarial vulnerabilities discovered by the Hacker, are recast as reinforcement learning updates to these network weights. This allows the agents to dynamically route future queries based on past successes and failures, effectively accumulating transferable reasoning experience over time. Evaluated across CodeContests, APPS, AetherCode, and live Codeforces rounds, Solvita establishes a new state-of-the-art among code-generation agents, outperforming existing multi-agent pipelines and nearly doubling the accuracy of single-pass baselines.

cs.AI

Connecting Discrete Morse Functions via Birth-Death Transitions

We study transformations between discrete Morse functions on a finite simplicial complex via birth-death transitions--elementary chain maps between discrete Morse complexes that either create or cancel pairs of critical simplices. We prove that any two discrete Morse functions $f_1$, $f_2$ on a finite simplicial complex $K$ are linked by a finite sequence of such transitions.As applications, we present alternative proofs of several of Forman's fundamental results in discrete Morse theory and study the topology of the space of discrete Morse functions.

math.CO

Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks

Cross-layer resource allocation over mobile edge computing (MEC)-aided cell-free networks can sufficiently exploit the transmitting and computing resources to promote the data rate. However, the technical bottlenecks of traditional methods pose significant challenges to cross-layer optimization. In this paper, joint subcarrier allocation and beamforming optimization are investigated for the MEC-aided cell-free network from the perspective of deep learning to maximize the weighted sum rate. Specifically, we convert the underlying problem into a joint multi-task optimization problem and then propose a centralized multi-task self-supervised learning algorithm to solve the problem so as to avoid costly manual labeling. Therein, two novel and general loss functions, i.e., negative fraction linear loss and exponential linear loss whose advantages in robustness and target domain have been proved and discussed, are designed to enable self-supervised learning. Moreover, we further design a MEC-enabled distributed multi-task self-supervised learning (DMTSSL) algorithm, with low complexity and high scalability to address the challenge of dimensional disaster. Finally, we develop the distance-aware transfer learning algorithm based on the DMTSSL algorithm to handle the dynamic scenario with negligible computation cost. Simulation results under $3$rd generation partnership project 38.901 urban-macrocell scenario demonstrate the superiority of the proposed algorithms over the baseline algorithms.

cs.LG

The Connectedness Homomorphism between Discrete Morse Complexes

Given two discrete Morse functions on a simplicial complex, we introduce the {\em connectedness homomorphism} between the corresponding discrete Morse complexes. This concept leads to a novel framework for studying the connectedness in discrete Morse theory at the chain complex level. In particular, we apply it to describe a discrete analogy to `cusp-degeneration' of Morse complexes. A precise comparison between smooth case and our discrete cases is also given.

math.CO

Intelligent Hybrid Resource Allocation in MEC-assisted RAN Slicing Network

In this paper, we aim to maximize the SSR for heterogeneous service demands in the cooperative MEC-assisted RAN slicing system by jointly considering the multi-node computing resources cooperation and allocation, the transmission resource blocks (RBs) allocation, and the time-varying dynamicity of the system. To this end, we abstract the system into a weighted undirected topology graph and, then propose a recurrent graph reinforcement learning (RGRL) algorithm to intelligently learn the optimal hybrid RA policy. Therein, the graph neural network (GCN) and the deep deterministic policy gradient (DDPG) is combined to effectively extract spatial features from the equivalent topology graph. Furthermore, a novel time recurrent reinforcement learning framework is designed in the proposed RGRL algorithm by incorporating the action output of the policy network at the previous moment into the state input of the policy network at the subsequent moment, so as to cope with the time-varying and contextual network environment. In addition, we explore two use case scenarios to discuss the universal superiority of the proposed RGRL algorithm. Simulation results demonstrate the superiority of the proposed algorithm in terms of the average SSR, the performance stability, and the network complexity.

cs.NI

Persistent Pairs And Connectedness In Discrete Morse Functions On Simplicial Complex I

In this paper, we study some useful properties of persistent pairs in a discrete Morse function on a simplicial complex $K$. In case of $\dim K=1$ (i.e., a graph), by using the properties, we characterize strongly connectedness of critical simplices between two distinct discrete Morse functions, and relate the number of such pairs to the Euler characteristic of $K$.

math.CO

Weighted Tutte-Grothendieck polynomials of graphs

In this paper, we introduce the concept of the weighted (harmonic) chromatic polynomials of graphs and discuss some of its properties. We also present the notion of the weighted (harmonic) Tutte--Grothendieck polynomials of graphs and give a generalization of the recipe theorem between the harmonic Tutte--Grothendieck polynomials graphs and the harmonic Tutte polynomials of matroids. Moreover, we give some constructions of the weighted (harmonic) Tutte--Grothendieck invariants for graphs and the weighted (harmonic) Tutte invariants for matroids. Finally, we give a remark on the categorification of the harmonic chromatic polynomials of graphs and harmonic Tutte polynomials of matroids.

math.CO

Lithospheric loading model for large impact basin where mantle plug presents

Lithosphere is an outer rigid part of the terrestrial body, usually consisting of the crust and part of the mantle. Characterizing the physical properties of the lithosphere is critical in investigating its evolution. By modeling mass-related loads within the lithosphere, physical parameters such as the elastic thickness of the lithosphere can be inferred from gravity and topography data. In the impact basin region, however, the low topography-gravity correlation and the sharp change in admittance from negative to positive within a narrow spheric harmonic degree make this model inapplicable. In this work, we incorporated mantle uplift structures commonly formed in impact basin regions into the lithospheric loading model. The crustal-mantle boundary of this mantle uplift structure is inferred from the global crustal thickness model. The gravity anomaly of the deflected lithosphere is calculated at the surface and crustal-mantle boundary, then the theoretical gravity admittance and correlation can be compared with the observed data. We sampled parameters using this mantle loading model at Argyre and Isidis basin on Mars with a novel crustal thickness model from the InSight mission. Our work suggests that proper modelling of the impact-induced load is critical to understanding the physical properties of the planetary lithosphere in the basin region.

astro-ph.EP

Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile Edge Computing Networks

Nowadays wireless communication is rapidly reshaping entire industry sectors. In particular, mobile edge computing (MEC) as an enabling technology for industrial Internet of things (IIoT) brings powerful computing/storage infrastructure closer to the mobile terminals and, thereby, significant lowers the response latency. To reap the benefit of proactive caching at the network edge, precise knowledge on the popularity pattern among the end devices is essential. However, the complex and dynamic nature of the content popularity over space and time as well as the data-privacy requirements in many IIoT scenarios pose tough challenges to its acquisition. In this article, we propose an unsupervised and privacy-preserving popularity prediction framework for MEC-enabled IIoT. The concepts of local and global popularities are introduced and the time-varying popularity of each user is modelled as a model-free Markov chain. On this basis, a novel unsupervised recurrent federated learning (URFL) algorithm is proposed to predict the distributed popularity while achieve privacy preservation and unsupervised training. Simulations indicate that the proposed framework can enhance the prediction accuracy in terms of a reduced root-mean-squared error by up to $60.5\%-68.7\%$. Additionally, manual labeling and violation of users' data privacy are both avoided.

cs.MM

Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching

Mobile edge computing (MEC) is a prominent computing paradigm which expands the application fields of wireless communication. Due to the limitation of the capacities of user equipments and MEC servers, edge caching (EC) optimization is crucial to the effective utilization of the caching resources in MEC-enabled wireless networks. However, the dynamics and complexities of content popularities over space and time as well as the privacy preservation of users pose significant challenges to EC optimization. In this paper, a privacy-preserving distributed deep deterministic policy gradient (P2D3PG) algorithm is proposed to maximize the cache hit rates of devices in the MEC networks. Specifically, we consider the fact that content popularities are dynamic, complicated and unobservable, and formulate the maximization of cache hit rates on devices as distributed problems under the constraints of privacy preservation. In particular, we convert the distributed optimizations into distributed model-free Markov decision process problems and then introduce a privacy-preserving federated learning method for popularity prediction. Subsequently, a P2D3PG algorithm is developed based on distributed reinforcement learning to solve the distributed problems. Simulation results demonstrate the superiority of the proposed approach in improving EC hit rate over the baseline methods while preserving user privacy.

cs.LG

Hybrid Policy Learning for Energy-Latency Tradeoff in MEC-Assisted VR Video Service

Virtual reality (VR) is promising to fundamentally transform a broad spectrum of industry sectors and the way humans interact with virtual content. However, despite unprecedented progress, current networking and computing infrastructures are incompetent to unlock VR's full potential. In this paper, we consider delivering the wireless multi-tile VR video service over a mobile edge computing (MEC) network. The primary goal is to minimize the system latency/energy consumption and to arrive at a tradeoff thereof. To this end, we first cast the time-varying view popularity as a model-free Markov chain to effectively capture its dynamic characteristics. After jointly assessing the caching and computing capacities on both the MEC server and the VR playback device, a hybrid policy is then implemented to coordinate the dynamic caching replacement and the deterministic offloading, so as to fully utilize the system resources. The underlying multi-objective problem is reformulated as a partially observable Markov decision process, and a deep deterministic policy gradient algorithm is proposed to iteratively learn its solution, where a long short-term memory neural network is embedded to continuously predict the dynamics of the unobservable popularity. Simulation results demonstrate the superiority of the proposed scheme in achieving a trade-off between the energy efficiency and the latency reduction over the baseline methods.

cs.NI