arXiv subjects

Simin Wang

Publications and source records attributed to Simin Wang.

6 recordsLinked to original sources

arXiv2025

Double pole $S$-matrix singularity in the continuum of $^7$Be

The double pole singularity of the $S$-matrix, the so-called exceptional point, associated with the $5/2^-$ doublet of resonances in the spectrum of $^{7}$Be has been identified in the framework of the Gamow shell model. The exceptional point singularity is demonstrated by the coalescence of wave functions and spectral functions of the two resonances, as well as by the singular behavior of spectroscopic factors and electromagnetic transitions.

nucl-th↗

arXiv2025

Green Wireless Network Scaling for Joint Deployment: Multi-BSs or Multi-RISs?

The imminent emergence of sixth-generation (6G) networks faces critical challenges from spatially heterogeneous traffic and escalating energy consumption, necessitating sustainable scaling strategies for network infrastructure such as base stations (BSs) and reconfigurable intelligent surfaces (RISs). This paper presents a systematic scaling analysis of the Integrated Relative Energy Efficiency (IREE) metric under joint multi-BS and multi-RIS deployment in traffic-mismatched scenarios. Specifically, we propose an Alternating Directional Dual Radial Basis Function (ADD-RBF) framework that models the spatial capacity contributions of BSs and RISs through two separately parameterized RBF-type branches and maximizes IREE through accepted alternating optimization, with established representation expressiveness and stage-wise convergence properties. Theoretical analysis reveals distinct scaling behaviors: BS proliferation drives logarithmic capacity growth $\mathcal{O}(\log N^{BS})$ and polynomial large-scale mismatch reduction $\mathcal{O}\big((N^{BS})^{-t_g/2}\big)$, whereas RIS deployment provides a bounded passive capacity-gain correction and residual-structure-dependent mismatch reduction. Specifically, the RIS-side mismatch decreases polynomially as $\mathcal{O}\big((N^R)^{-t_\ell/2}\big)$ for spatially diffuse residuals and follows the stretched-exponential order $\mathcal{O}\big(\exp[-c_R\sqrt{N^R}]\big)$ for hotspot-dominated residuals. Simulation results show that RISs are effective in refining spatial traffic-capacity mismatch and alleviating hotspots, making them particularly attractive when mismatch dominates, while BSs are generally preferable under capacity shortages. These findings offer practical guidelines for green 6G network design.

eess.SY↗

arXiv2025

A Model-Data Dual-Driven Resource Allocation Scheme for IREE Oriented 6G Networks

The rapid and substantial fluctuations in wireless network capacity and traffic demand, driven by the emergence of 6G technologies, have exacerbated the issue of traffic-capacity mismatch, raising concerns about wireless network energy consumption. To address this challenge, we propose a model-data dual-driven resource allocation (MDDRA) algorithm aimed at maximizing the integrated relative energy efficiency (IREE) metric under dynamic traffic conditions. Unlike conventional model-driven or data-driven schemes, the proposed MDDRA framework employs a model-driven Lyapunov queue to accumulate long-term historical mismatch information and a data-driven Graph Radial bAsis Fourier (GRAF) network to predict the traffic variations under incomplete data, and hence eliminates the reliance on high-precision models and complete spatial-temporal traffic data. We establish the universal approximation property of the proposed GRAF network and provide convergence and complexity analysis for the MDDRA algorithm. Numerical experiments validate the performance gains achieved through the data-driven and model-driven components. By analyzing IREE and EE curves under diverse traffic conditions, we recommend that network operators shall spend more efforts to balance the traffic demand and the network capacity distribution to ensure the network performance, particularly in scenarios with large speed limits and higher driving visibility.

cs.NI↗

arXiv2025

A Sentence-Level Approach to Understanding Software Vulnerability Fixes

Understanding software vulnerabilities and their resolutions is crucial for securing modern software systems. This study presents a novel traceability model that links a pair of sentences describing at least one of the three types of semantics (triggers, crash phenomenon and fix action) for a vulnerability in natural language (NL) vulnerability artifacts, to their corresponding pair of code statements. Different from the traditional traceability models, our tracing links between a pair of related NL sentences and a pair of code statements can recover the semantic relationship between code statements so that the specific role played by each code statement in a vulnerability can be automatically identified. Our end-to-end approach is implemented in two key steps: VulnExtract and VulnTrace. VulnExtract automatically extracts sentences describing triggers, crash phenomenon and/or fix action for a vulnerability using 37 discourse patterns derived from NL artifacts (CVE summary, bug reports and commit messages). VulnTrace employs pre-trained code search models to trace these sentences to the corresponding code statements. Our empirical study, based on 341 CVEs and their associated code snippets, demonstrates the effectiveness of our approach, with recall exceeding 90% in most cases for NL sentence extraction. VulnTrace achieves a Top5 accuracy of over 68.2% for mapping a pair of related NL sentences to the corresponding pair of code statements. The end-to-end combined VulnExtract+VulnTrace achieves a Top5 accuracy of 59.6% and 53.1% for mapping two pairs of NL sentences to code statements. These results highlight the potential of our method in automating vulnerability comprehension and reducing manual effort.

cs.SE↗

arXiv2023

Two-proton emission and related phenomena

One of characteristic phenomena for nuclei beyond the proton dripline is the simultaneous emission of two protons (2\emph{p}). The current status of our knowledge of this most recently observed and the least known decay mode is presented. First, different approaches to theoretical description of this process, ranging from effective approximations to advanced three-body models are overviewed. Then, after a brief survey of main experimental methods to produce 2\emph{p}-emitting nuclei and techniques to study their decays, experimental findings in this research field are presented and discussed. This review covers decays of short-lived resonances and excited states of unbound nuclei as well as longer-lived, ground-state radioactive decays. In addition, more exotic decays like three- and four-proton emission are addressed. Finally, related few-body topics, like two-neutron and four-neutron radioactivity, and the problem of the tetraneutron are shortly discussed.

nucl-ex↗

arXiv2020

Synergy between Machine/Deep Learning and Software Engineering: How Far Are We?

Since 2009, the deep learning revolution, which was triggered by the introduction of ImageNet, has stimulated the synergy between Machine Learning (ML)/Deep Learning (DL) and Software Engineering (SE). Meanwhile, critical reviews have emerged that suggest that ML/DL should be used cautiously. To improve the quality (especially the applicability and generalizability) of ML/DL-related SE studies, and to stimulate and enhance future collaborations between SE/AI researchers and industry practitioners, we conducted a 10-year Systematic Literature Review (SLR) on 906 ML/DL-related SE papers published between 2009 and 2018. Our trend analysis demonstrated the mutual impacts that ML/DL and SE have had on each other. At the same time, however, we also observed a paucity of replicable and reproducible ML/DL-related SE studies and identified five factors that influence their replicability and reproducibility. To improve the applicability and generalizability of research results, we analyzed what ingredients in a study would facilitate an understanding of why a ML/DL technique was selected for a specific SE problem. In addition, we identified the unique trends of impacts of DL models on SE tasks, as well as five unique challenges that needed to be met in order to better leverage DL to improve the productivity of SE tasks. Finally, we outlined a road-map that we believe can facilitate the transfer of ML/DL-based SE research results into real-world industry practices.

cs.SE↗