Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userToroidal graphs containing neither $K_5^{-}$ nor 6-cycles are 4-choosable
150
0
0.0
(
0
)
Authors
Ilkyoo Choi
Ask ChatGPT about the research
No Arabic abstract
The choosability $chi_ell(G)$ of a graph $G$ is the minimum $k$ such that having $k$ colors available at each vertex guarantees a proper coloring. Given a toroidal graph $G$, it is known that $chi_ell(G)leq 7$, and $chi_ell(G)=7$ if and only if $G$ contains $K_7$. Cai, Wang, and Zhu proved that a toroidal graph $G$ without 7-cycles is 6-choosable, and $chi_ell(G)=6$ if and only if $G$ contains $K_6$. They also prove that a toroidal graph $G$ without 6-cycles is 5-choosable, and conjecture that $chi_ell(G)=5$ if and only if $G$ contains $K_5$. We disprove this conjecture by constructing an infinite family of non-4-colorable toroidal graphs with neither $K_5$ nor cycles of length at least 6; moreover, this family of graphs is embeddable on every surface except the plane and the projective plane. Instead, we prove the following slightly weaker statement suggested by Zhu: toroidal graphs containing neither $K^-_5$ (a $K_5$ missing one edge) nor 6-cycles are 4-choosable. This is sharp in the sense that forbidding only one of the two structures does not ensure that the graph is 4-choosable.
rate research
Read More
A graph $G$ is total weight $(k,k)$-choosable if for any total list assignment $L$ which assigns to each vertex $v$ a set $L(v)$ of $k$ real numbers, and each edge $e$ a set $L(e)$ of $k$ real numbers, there is a proper total $L$-weighting, i.e., a mapping $f: V(G) cup E(G) to mathbb{R}$ such that for each $z in V(G) cup E(G)$, $f(z) in L(z)$, and for each edge $uv$ of $G$, $sum_{e in E(u)}f(e)+f(u) e sum_{e in E(v)}f(e) + f(v)$. This paper proves that if $G$ decomposes into complete graphs of odd order, then $G$ is total weight $(1,3)$-choosable. As a consequence, every Eulerian graph $G$ of large order and with minimum degree at least $0.91|V(G)|$ is total weight $(1,3)$-choosable. We also prove that any graph $G$ with minimum degree at least $0.999|V(G)|$ is total weight $(1,4)$-choosable.
In 1976, Steinberg conjectured that planar graphs without $4$-cycles and $5$-cycles are $3$-colorable. This conjecture attracted numerous researchers for about 40 years, until it was recently disproved by Cohen-Addad et al. (2017). However, coloring planar graphs with restrictions on cycle lengths is still an active area of research, and the interest in this particular graph class remains. Let $G$ be a planar graph without $4$-cycles and $5$-cycles. For integers $d_1$ and $d_2$ satisfying $d_1+d_2geq8$ and $d_2geq d_1geq 2$, it is known that $V(G)$ can be partitioned into two sets $V_1$ and $V_2$, where each $V_i$ induces a graph with maximum degree at most $d_i$. Since Steinbergs Conjecture is false, a partition of $V(G)$ into two sets, where one induces an empty graph and the other induces a forest is not guaranteed. Our main theorem is at the intersection of the two aforementioned research directions. We prove that $V(G)$ can be partitioned into two sets $V_1$ and $V_2$, where $V_1$ induces a forest with maximum degree at most $3$ and $V_2$ induces a forest with maximum degree at most $4$; this is both a relaxation of Steinbergs conjecture and a strengthening of results by Sittitrai and Nakprasit (2019) in a much stronger form.
A cycle $C$ in a graph $G$ is called a Tutte cycle if, after deleting $C$ from $G$, each component has at most three neighbors on $C$. Tutte cycles play an important role in the study of Hamiltonicity of planar graphs. Thomas and Yu and independently Sanders proved the existence of Tutte cycles containining three specified edges of a facial cycle in a 2-connected plane graph. We prove a quantitative version of this result, bounding the number of components of the graph obtained by deleting a Tutte cycle. As a corollary, we can find long cycles in essentially 4-connected plane graphs that also contain three prescribed edges of a facial cycle.
A textbook interpretation of quantum physics is that quantum objects can be described in a particle or a wave picture, depending on the operations and measurements performed. Beyond this widely held believe, we demonstrate in this contribution that neither the wave nor the particle description is sufficient to predict the outcomes of quantum-optical experiments. To show this, we derive correlation-based criteria that have to be satisfied when either particles or waves are fed into our interferometer. Using squeezed light, it is then confirmed that measured correlations are incompatible with either picture. Thus, within one single experiment, it is proven that neither a wave nor a particle model explains the observed phenomena. Moreover, we formulate a relation of wave and particle representations to two incompatible notions of quantum coherence, a recently discovered resource for quantum information processing.For such an information-theoretic interpretation of our method, we certify the nonclassicality of coherent states - the quantum counterpart to classical waves - in the particle picture, complementing the known fact that photon states are nonclassical in the typically applied wave picture.
When dealing with large scale gene expression studies, observations are commonly contaminated by unwanted variation factors such as platforms or batches. Not taking this unwanted variation into account when analyzing the data can lead to spurious associations and to missing important signals. When the analysis is unsupervised, e.g., when the goal is to cluster the samples or to build a corrected version of the dataset - as opposed to the study of an observed factor of interest - taking unwanted variation into account can become a difficult task. The unwanted variation factors may be correlated with the unobserved factor of interest, so that correcting for the former can remove the latter if not done carefully. We show how negative control genes and replicate samples can be used to estimate unwanted variation in gene expression, and discuss how this information can be used to correct the expression data or build estimators for unsupervised problems. The proposed methods are then evaluated on three gene expression datasets. They generally manage to remove unwanted variation without losing the signal of interest and compare favorably to state of the art corrections.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
