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新论文 · v1arXiv:2611.06816

有限幂零群幂图的盒度

Boxicity of Power Graphs of Finite Nilpotent Groups

L. Sunil Chandran, Jinia Ghosh, Suraj Kumar Sahoo

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该论文确定了有限幂零群幂图的精确盒度,并部分回答了关于任意有限群幂图盒度的公开问题。

cs.DM · math.CO · math.GR

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The \textit{boxicity} of an undirected graph $Γ$, denoted as $\operatorname{box}(Γ)$, is the smallest non-negative integer $d$ such that $Γ$ can be represented as the intersection graph of axis-parallel rectangular boxes in $\mathbb{R}^d$. The \emph{power graph} of a group $G$, denoted as $\operatorname{Pow}(G)$, is an undirected graph whose vertex set is the group itself, with two elements being adjacent if one is a power of the other. We determine the exact boxicity of power graphs of finite nilpotent groups. For a cyclic group of order $n=\prod_{i=1}^{r}p_i^{e_i}>1$, where the $p_i$ are distinct primes, we prove that its power graph has boxicity $\sum_{i=1}^{r}e_i-\max_i e_i$. For a finite nilpotent group $G$, let the number of noncyclic odd Sylow subgroups be denoted by $t(G)$. If $G$ has odd order or if $G$ has a cyclic Sylow $2$-subgroup, then $\operatorname{box}(\operatorname{Pow}(G))=\operatorname{box}(\operatorname{Pow}(\mathbb{Z}_{\operatorname{exp}(G)}))+t(G)$, where $\operatorname{exp}(G)$ denotes the \emph{exponent} of a group $G$. When the Sylow $2$-subgroup is noncyclic, and the odd part is nontrivial, the boxicity equals this value or one more, and we characterise precisely when each of these values is attained. The boxicity of power graphs of arbitrary finite groups and the exact boxicity of the comparability graph of the Boolean lattice were posed as open questions in Discrete Mathematics, 349(7):115017, 2026. As a consequence of our results, we answer the latter question completely and the former question partially, by computing the exact boxicity of the power graphs of nilpotent groups.

版本更新 · v2arXiv:2512.17262

SHARP-QoS: Sparsely-gated Hierarchical Adaptive Routing for Joint Prediction of QoS

Suraj Kumar, Arvind Kumar, Soumi Chattopadhyay

原文摘要

Dependable service-oriented computing relies on multiple Quality-of-Service (QoS) parameters to assess service performance. However, real-world QoS data are often sparse, noisy, and shaped by hierarchical dependencies arising from QoS interactions, geographical constraints, and network-level factors, making accurate prediction challenging. Existing deep learning-based methods often predict each QoS parameter separately, requiring multiple similar models, which increases computational overhead and limits their suitability for applications requiring joint decisions across multiple QoS parameters. Although recent joint QoS prediction studies employ shared architectures, differences in numerical ranges across QoS parameters can lead to task imbalance and negative transfer, while inadequate representation learning and suboptimal joint optimization can degrade prediction accuracy. We present SHARP-QoS, a joint QoS prediction framework with three components. First, hyperbolic graph and hypergraph convolutions in the Poincaré ball extract hierarchical features from QoS invocation and contextual graphs. Second, sparsely gated sub-network routing selectively shares contextual and cross-QoS features, and a gated fusion module combines them with the structural features. Third, an EMA-based loss-balancing strategy smooths short-term fluctuations in task losses to provide less fluctuating task weights. Experiments on three multi-QoS datasets covering two, three, and four QoS parameters show that SHARP-QoS achieves lower prediction errors than existing joint QoS prediction methods. Because its contextual branch relies only on public region and AS attributes, it supports prediction for new users and services. Ablation, hyperparameter, outlier, and scalability analyses further assess its performance, highlighting the trade-off between prediction accuracy and computational cost.

cs.LG

新论文 · v1arXiv:2610.37199

A Bi-level Co-design Framework for Lunar Monoped with 2D Jump and Landing Control

Sanjay Ramkumar, Suraj Kumar

原文摘要

Legged robots for lunar exploration must be designed for low gravity, where reduced contact forces and long flight phases make leg morphology and controller choice interdependent. We study this for a planar monoped in lunar gravity ($1.625$ m/s$^2$) using a bi-level co-design framework: a differential evolution outer loop searches over link lengths, and each candidate is evaluated on a vertical jump by either a quadratic program based task-space controller (QP-TSC) or a direct-collocation based trajectory optimization. The two controllers yield different optimal link lengths, and trajectory optimization outperforms the QP-TSC across most of the design space, with 17% lower Cost of Transport (COT) at their respective optima. We then extend to a 2D jump, where trajectory optimization with centroidal angular momentum regulation produces stable in-place and forward jumps, and an LQR controller absorbs impact and stabilizes the robot after touchdown. This is demonstrated on a baseline morphology with both links 0.3 m long.

cs.RO · eess.SY