This paper presents multi-objective optimization algorithms for designing class-E switching-mode circuits. These methods expand the options for the passive elements incorporated into the circuit. As multi-objective optimization algorithms, we employ evolutionary computation methods such as MOEA/D and MOPSO. Additionally, we apply the Nelder — Mead method to the multi-objective optimization problem. A comparative analysis of these algorithms through an illustrative example provides valuable insights into the design of class-E amplifiers.

Service Function Chain (SFC) placement under service-level agreements (SLAs) is inherently constrained and multi-objective, requiring both feasibility and flexibility in placement decisions. While greedy heuristics can achieve high success rates in resource-rich settings, they often collapse to narrow solution patterns and provide limited alternatives under constraints. This paper proposes a topology-aware reinforcement learning framework based on Graph Attention Networks and Proximal Policy Optimization (GAT-PPO) for SLA-aware SFC placement. The proposed method integrates graph-based state encoding with penalty-based reward shaping and feasibility masking, enabling the agent to learn constraint-sensitive placement policies without relying on complex constrained optimization schemes. Simulation results on randomly generated substrate networks show that the proposed method maintains high feasibility in bandwidth-constrained regimes and, more importantly, learns a thicker Pareto set in the success-diversity plane compared with greedy and random baselines. Hypervolume analysis confirms improved Pareto robustness, while training dynamics demonstrate stable learning and reduced SLA violations. These results indicate that combining constraint-aware reinforcement learning with topology-sensitive representations enables more expressive and robust SFC placement under practical SLA constraints.

This paper advances the study of domination problems on generalized convex graphs, a hierarchical family of bipartite graphs defined via connectivity constraints with respect to a host graph. Building on our prior work that established the approximation hardness of five classical variants — domination, connected domination, total domination, paired domination, and independent domination — on star and comb convex graphs, we close several remaining gaps in the hierarchy. We prove that domination, connected domination, and independent domination remain hard to approximate on chordal bipartite graphs, completing the inapproximability landscape across major subclasses of generalized convex graphs. We also present constant-factor approximation algorithms for domination and connected domination on chordal bipartite graphs, all of which tightly match the APX-hardness of those problems. These results sharpen the approximability landscape within the generalized convex graph framework and clarify how the host graph structure influences the computational complexity of domination-type problems.

Code-based cryptosystems are among the leading candidates for post-quantum cryptography. In NIST’s standardization process, a lattice-based scheme was selected as the primary standard for key encapsulation, while the code-based HQC (Hamming Quasi-Cyclic) was chosen as a backup. Code-based approaches are also gaining attention in European post-quantum standardization efforts. Among code-based schemes, the McEliece cryptosystem is one of the oldest and most extensively studied. In particular, its QC-MDPC (quasi-cyclic moderate-density parity-check) variant, which underlies the BIKE (Bit Flipping Key Encapsulation) scheme, a Round-4 candidate in NIST’s standardization process, is noteworthy due to its small key length. The Guo-Johansson-Stankovski (GJS) attack against the QC-MDPC McEliece cryptosystem was proposed in 2016 and has intensively been studied. This attack reconstructs the secret key using information on decoding error rate (DER). However, in practice, obtaining complete DER information is presumed to be time-consuming. Although this imperfection has been discussed since the attack was originally proposed, explicit algorithms that work under the imperfection have been less studied. This paper proposes two algorithms to reconstruct the secret key under imperfection in the DER information by simply modifying previously known algorithms that work with complete DER information. We evaluate the relationship between the imperfection and efficiency of key reconstruction. This will help us to increase the efficacy of the GJS attack.

The stream version of asymmetric binary systems (ABS) developed by Duda is an entropy coder for information sources with a binary alphabet {0, 1}. It has the state parameter l of a positive integer and the probability parameter p with 0 < p < 1, where p is the probability for 1. The algorithm of stream encoding yields the edge shift X_G associated with the stream version of ABS while the algorithm produces the edge shift X_H associated with output blocks from the stream version of ABS. Previously, we have shown that X_G and X_H have the same topological entropy. In this research, we find that X_G and X_H are flow equivalent. We consider the case where p = 1/β with the golden mean β = (1 + √5)/2. For several l’s, we compute the Parry-Sullivan number D(A_G) and the Bowen-Franks group BF(A_G) since they form a complete set of flow equivalence invariants for irreducible shift of finite type. The results imply that for a fixed p, D(A_G) and BF(A_G) depend on l.

A k-bit delay decodable code-tuple is a lossless source code that can achieve a smaller average codeword length than Huffman codes by using a finite number of code tables and allowing at most k-bit delay for decoding. It is known that there exists a k-bit delay decodable code-tuple with at most 2^(2k)-2^(2k-1+1)+1 code tables that attains the optimal average codeword length among all the k-bit delay decodable code-tuples for any given i.i.d. source distribution. Namely, it suffices to consider only the code-tuples with at most 2^(2k)-2^(2k-1+1)+1 code tables to accomplish optimality. In this paper, we propose a method to significantly reduce the number of code tables considered in the theoretical analysis, code construction, and coding processes by focusing on symmetry among code tables.

The efficient organization of the metadata of artwork images necessitates an accurate image classification technique that can identify painting style. Thus, we propose a handcrafted algorithm to extract image features that evaluate brushstrokes and color usage in artwork. The proposed features are based on disparities in color hue between adjacent superpixels, which aim to represent a single brushstroke. Our experiment, which employed 22,314 images of Cubism, Romanticism, and Impressionism art, demonstrates that the proposed features significantly improve the macro-F1 score, which is the overall evaluation metric, by at most 2.96%, compared with the combination of conventional handcrafted features. In addition, both macro-precision and recall are improved by a maximum of 3.03% and 3.02%, respectively. Furthermore, the efficacy of the proposed features is demonstrated both by the increase in the F1-scores for all painting styles and the considerable improvement exhibited in the mean and median mutual information (at least 51.29%). Further comparisons and discussions are provided to verify the effectiveness of the proposed features.

Target tracking aims to estimate target states from noisy observations. Bayesian filtering methods often rely on an explicitly defined measurement model h(⋅) or accurate computation of its Jacobian matrix. However, such requirements are often violated in practical scenarios involving non-cooperative targets or sensors with partially unknown and uncalibrated parameters. To handle this problem, this paper proposes an Analytic-Jacobian-Free Neural Measurement Model Embedded Bayesian Filtering (AJF-NMBF). Specifically, the proposed method replaces the analytic measurement function with a neural operator termed the Differentiable Neural Measurement Model (DNMM), whose corresponding Jacobian is automatically computed through backpropagation. This design preserves the Bayesian inference structure while enabling the learning of complex and unknown nonlinear measurement relationships. Moreover, we develop a Space-Based Optical Angle Tracking Trajectory dataset (namely, SOATT). Experimental results on both our self-developed SOATT dataset and the publicly available ground-based radar dataset (LAST) demonstrate that the proposed DNMM achieves measurement accuracy comparable to analytic measurement functions. Meanwhile, AJF-NMBF achieves tracking performance comparable to model-based Bayesian filters with analytic measurement models across different noise levels and under both non-switching and switching-motion scenarios.

The complex dependencies and attack surface expansion of the microservice architecture in the cloud-edge collaborative network make it possible for attackers to horizontally penetrate through a single vulnerability. We propose CoQMIX, a microservice intrusion tolerant scheduling method based on Cooperative game and multi-agent reinforcement learning (MARL). The Cooperative game model based on Nash bargaining is used to coordinate the interest relationship between cloud and edge nodes, and the QMIX algorithm is used to optimize the intrusion tolerance strategy. Experiments show that CoQMIX reduces the security threat value by 50.44% compared with DSEOM, and reduces the cloud edge intrusion tolerance cost difference by 68.66% compared with SmartSCR, which can achieve the global optimal balance between resource consumption and security protection.

The rapid proliferation of LEO mega-constellations has intensified spectrum competition, necessitating decentralized allocation under Quality of Service (QoS) constraints. Existing methods often suffer from prohibitive complexity or a lack of formal stability. To tackle this, we model the resource competition as a non-cooperative game. While shown to lack the properties of an exact potential game, we propose the Service-Aware Regularized Potential Game (SARPG) framework. By integrating a strategy migration mechanism with stochastic search dynamics, we provide a formal convergence guarantee to a stable ε-Nash Equilibrium. Finally, experimental validation demonstrates its superior performance across multiple metrics in dynamic handover scenarios.