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Felix Bock

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5 publications

preprint2026arXiv

Quantum-Inspired Robust and Scalable SAR Object Classification

SAR image classification naturally has to deal with huge noise and a high dynamic range particularly requiring robust classification models. Additionally, the deployment of these models on edge devices, such as drones and military aircraft, requires a careful balance between model size and classification accuracy. This study explores the potential of tensor networks to meet these robustness requirements, specifically evaluating their resilience to data poisoning. Unlike previous works that concentrated on conventional neural networks for SAR object detection, this research focuses on the robustness and model reduction capabilities of tensor networks in object classification. Our findings indicate that tensor networks are adept at addressing both the challenges of robustness and the need for model efficiency, thereby contributing valuable insights to the ongoing discourse in radar applications and deep learning methodologies in general.

preprint2022arXiv

A bound on the dissociation number

The dissociation number diss(G){\rm diss}(G) of a graph GG is the maximum order of a set of vertices of GG inducing a subgraph that is of maximum degree at most 11. Computing the dissociation number of a given graph is algorithmically hard even when restricted to subcubic bipartite graphs. For a graph GG with nn vertices, mm edges, kk components, and c1c_1 induced cycles of length 11 modulo 33, we show diss(G)≥n−13(m+k+c1){\rm diss}(G)\geq n-\frac{1}{3}\Big(m+k+c_1\Big). Furthermore, we characterize the extremal graphs in which every two cycles are vertex-disjoint.

preprint2022arXiv

Majority Edge-Colorings of Graphs

We propose the notion of a majority kk-edge-coloring of a graph GG, which is an edge-coloring of GG with kk colors such that, for every vertex uu of GG, at most half the edges of GG incident with uu have the same color. We show the best possible results that every graph of minimum degree at least 22 has a majority 44-edge-coloring, and that every graph of minimum degree at least 44 has a majority 33-edge-coloring. Furthermore, we discuss a natural variation of majority edge-colorings and some related open problems.

preprint2022arXiv

Relating dissociation, independence, and matchings

A dissociation set in a graph is a set of vertices inducing a subgraph of maximum degree at most 11. Computing the dissociation number diss(G){\rm diss}(G) of a given graph GG, defined as the order of a maximum dissociation set in GG, is algorithmically hard even when GG is restricted to be bipartite. Recently, Hosseinian and Butenko proposed a simple 43\frac{4}{3}-approximation algorithm for the dissociation number problem in bipartite graphs. Their result relies on the inequality diss(G)≤43α(G−M){\rm diss}(G)\leq\frac{4}{3}α(G-M) implicit in their work, where GG is a bipartite graph, MM is a maximum matching in GG, and α(G−M)α(G-M) denotes the independence number of G−MG-M. We show that the pairs (G,M)(G,M) for which this inequality holds with equality can be recognized efficiently, and that a maximum dissociation set can be determined for them efficiently. The dissociation number of a graph GG satisfies max⁡{α(G),2νs(G)}≤diss(G)≤α(G)+νs(G)≤2α(G)\max\{ α(G),2ν_s(G)\} \leq {\rm diss}(G)\leq α(G)+ν_s(G)\leq 2α(G), where νs(G)ν_s(G) denotes the induced matching number of GG. We show that deciding whether diss(G){\rm diss}(G) equals any of the four terms lower and upper bounding diss(G){\rm diss}(G) is NP-hard.

preprint2022arXiv

Relating the independence number and the dissociation number

The independence number α(G)α(G) and the dissociation number diss(G){\rm diss}(G) of a graph GG are the largest orders of induced subgraphs of GG of maximum degree at most 00 and at most 11, respectively. We consider possible improvements of the obvious inequality 2α(G)≥diss(G)2α(G)\geq {\rm diss}(G). For connected cubic graphs GG distinct from K4K_4, we show 5α(G)≥3diss(G)5α(G)\geq 3{\rm diss}(G), and describe the rich and interesting structure of the extremal graphs in detail. For bipartite graphs, and, more generally, triangle-free graphs, we also obtain improvements. For subcubic graphs though, the inequality cannot be improved in general, and we characterize all extremal subcubic graphs.

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