@inproceedings{15580,
  abstract     = {{This paper deals with aspect phrase extraction and classification in sentiment analysis. We summarize current approaches and datasets from the domain of aspect-based sentiment analysis. This domain detects sentiments expressed for individual aspects in unstructured text data. So far, mainly commercial user reviews for products or services such as restaurants were investigated. We here present our dataset consisting of German physician reviews, a sensitive and linguistically complex field. Furthermore, we describe the annotation process of a dataset for supervised learning with neural networks. Moreover, we introduce our model for extracting and classifying aspect phrases in one step, which obtains an F1-score of 80%. By applying it to a more complex domain, our approach and results outperform previous approaches.}},
  author       = {{Kersting, Joschka and Geierhos, Michaela}},
  booktitle    = {{Proceedings of the 12th International Conference on Agents and Artificial Intelligence (ICAART 2020) --  Special Session on Natural Language Processing in Artificial Intelligence (NLPinAI 2020)}},
  keywords     = {{Deep Learning, Natural Language Processing, Aspect-based Sentiment Analysis}},
  location     = {{Valetta, Malta}},
  pages        = {{391----400}},
  publisher    = {{SCITEPRESS}},
  title        = {{{Aspect Phrase Extraction in Sentiment Analysis with Deep Learning}}},
  year         = {{2020}},
}

@inproceedings{15256,
  abstract     = {{This paper deals with online customer reviews of local multi-service providers. While many studies investigate product reviews and online labour markets with service providers delivering intangible products “over the wire”, we focus on websites where providers offer multiple distinct services that can be booked, paid and reviewed online but are performed locally offline. This type of service providers has so far been neglected in the literature. This paper analyses reviews and applies sentiment analysis. It aims to gain new insights into local multi-service providers’ performance. There is a broad literature range presented with regard to the topics addressed. The results show, among other things, that providers with good ratings continue to perform well over time. We find that many positive reviews seem to encourage sales. On average, quantitative star ratings and qualitative ratings in the form of review texts match. Further results are also achieved in this study.}},
  author       = {{Kersting, Joschka and Geierhos, Michaela}},
  booktitle    = {{Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods}},
  keywords     = {{Customer Reviews, Sentiment Analysis, Online Labour Markets}},
  location     = {{Valetta, Malta}},
  pages        = {{263----272}},
  publisher    = {{SCITEPRESS}},
  title        = {{{What Reviews in Local Online Labour Markets Reveal about the Performance of Multi-Service Providers}}},
  year         = {{2020}},
}

@article{20678,
  author       = {{Bielak, Christian Roman and Böhnke, Max and Beck, Robert and Bobbert, Mathias and Meschut, Gerson}},
  journal      = {{Journal of Advanced Joining Processes. }},
  keywords     = {{Clinching, process simulation, FEM, pre-straining, sensitivity analysis}},
  publisher    = {{Elsevier}},
  title        = {{{Numerical analysis of the robustness of clinching process considering the pre-forming of the parts }}},
  doi          = {{https://doi.org/10.1016/j.jajp.2020.100038}},
  year         = {{2020}},
}

@article{33263,
  abstract     = {{Dynamical systems often admit geometric properties that must be taken into account when studying their behavior. We show that many such properties can be encoded by means of quiver representations. These properties include classical symmetry, hidden symmetry, and feedforward structure, as well as subnetwork and quotient relations in network dynamical systems. A quiver equivariant dynamical system consists of a collection of dynamical systems with maps between them that send solutions to solutions. We prove that such quiver structures are preserved under Lyapunov--Schmidt reduction, center manifold reduction, and normal form reduction.}},
  author       = {{Nijholt, Eddie and Rink, Bob W. and Schwenker, Sören}},
  issn         = {{1536-0040}},
  journal      = {{SIAM Journal on Applied Dynamical Systems}},
  keywords     = {{Modeling and Simulation, Analysis}},
  number       = {{4}},
  pages        = {{2428--2468}},
  publisher    = {{Society for Industrial & Applied Mathematics (SIAM)}},
  title        = {{{Quiver Representations and Dimension Reduction in Dynamical Systems}}},
  doi          = {{10.1137/20m1345670}},
  volume       = {{19}},
  year         = {{2020}},
}

@inproceedings{48847,
  abstract     = {{Dynamic optimization problems have gained significant attention in evolutionary computation as evolutionary algorithms (EAs) can easily adapt to changing environments. We show that EAs can solve the graph coloring problem for bipartite graphs more efficiently by using dynamic optimization. In our approach the graph instance is given incrementally such that the EA can reoptimize its coloring when a new edge introduces a conflict. We show that, when edges are inserted in a way that preserves graph connectivity, Randomized Local Search (RLS) efficiently finds a proper 2-coloring for all bipartite graphs. This includes graphs for which RLS and other EAs need exponential expected time in a static optimization scenario. We investigate different ways of building up the graph by popular graph traversals such as breadth-first-search and depth-first-search and analyse the resulting runtime behavior. We further show that offspring populations (e. g. a (1 + {$\lambda$}) RLS) lead to an exponential speedup in {$\lambda$}. Finally, an island model using 3 islands succeeds in an optimal time of {$\Theta$}(m) on every m-edge bipartite graph, outperforming offspring populations. This is the first example where an island model guarantees a speedup that is not bounded in the number of islands.}},
  author       = {{Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
  isbn         = {{978-1-4503-7128-5}},
  keywords     = {{dynamic optimization, evolutionary algorithms, running time analysis, theory}},
  pages        = {{1277–1285}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{More Effective Randomized Search Heuristics for Graph Coloring through Dynamic Optimization}}},
  doi          = {{10.1145/3377930.3390174}},
  year         = {{2020}},
}

@inproceedings{48851,
  abstract     = {{Several important optimization problems in the area of vehicle routing can be seen as variants of the classical Traveling Salesperson Problem (TSP). In the area of evolutionary computation, the Traveling Thief Problem (TTP) has gained increasing interest over the last 5 years. In this paper, we investigate the effect of weights on such problems, in the sense that the cost of traveling increases with respect to the weights of nodes already visited during a tour. This provides abstractions of important TSP variants such as the Traveling Thief Problem and time dependent TSP variants, and allows to study precisely the increase in difficulty caused by weight dependence. We provide a 3.59-approximation for this weight dependent version of TSP with metric distances and bounded positive weights. Furthermore, we conduct experimental investigations for simple randomized local search with classical mutation operators and two variants of the state-of-the-art evolutionary algorithm EAX adapted to the weighted TSP. Our results show the impact of the node weights on the position of the nodes in the resulting tour.}},
  author       = {{Bossek, Jakob and Casel, Katrin and Kerschke, Pascal and Neumann, Frank}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
  isbn         = {{978-1-4503-7128-5}},
  keywords     = {{dynamic optimization, evolutionary algorithms, running time analysis, theory}},
  pages        = {{1286–1294}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics}}},
  doi          = {{10.1145/3377930.3390243}},
  year         = {{2020}},
}

@inproceedings{48895,
  abstract     = {{Evolutionary algorithms (EAs) are general-purpose problem solvers that usually perform an unbiased search. This is reasonable and desirable in a black-box scenario. For combinatorial optimization problems, often more knowledge about the structure of optimal solutions is given, which can be leveraged by means of biased search operators. We consider the Minimum Spanning Tree (MST) problem in a single- and multi-objective version, and introduce a biased mutation, which puts more emphasis on the selection of edges of low rank in terms of low domination number. We present example graphs where the biased mutation can significantly speed up the expected runtime until (Pareto-)optimal solutions are found. On the other hand, we demonstrate that bias can lead to exponential runtime if "heavy" edges are necessarily part of an optimal solution. However, on general graphs in the single-objective setting, we show that a combined mutation operator which decides for unbiased or biased edge selection in each step with equal probability exhibits a polynomial upper bound - as unbiased mutation - in the worst case and benefits from bias if the circumstances are favorable.}},
  author       = {{Roostapour, Vahid and Bossek, Jakob and Neumann, Frank}},
  booktitle    = {{Proceedings of the 2020 Genetic and Evolutionary Computation Conference}},
  isbn         = {{978-1-4503-7128-5}},
  keywords     = {{biased mutation, evolutionary algorithms, minimum spanning tree problem, runtime analysis}},
  pages        = {{551–559}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Runtime Analysis of Evolutionary Algorithms with Biased Mutation for the Multi-Objective Minimum Spanning Tree Problem}}},
  doi          = {{10.1145/3377930.3390168}},
  year         = {{2020}},
}

@article{53322,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>The Cauchy problem in <jats:inline-formula><jats:alternatives><jats:tex-math>$${\mathbb {R}}^n$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
                  <mml:msup>
                    <mml:mrow>
                      <mml:mi>R</mml:mi>
                    </mml:mrow>
                    <mml:mi>n</mml:mi>
                  </mml:msup>
                </mml:math></jats:alternatives></jats:inline-formula>, <jats:inline-formula><jats:alternatives><jats:tex-math>$$n\ge 1$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
                  <mml:mrow>
                    <mml:mi>n</mml:mi>
                    <mml:mo>≥</mml:mo>
                    <mml:mn>1</mml:mn>
                  </mml:mrow>
                </mml:math></jats:alternatives></jats:inline-formula>, for the parabolic equation <jats:disp-formula><jats:alternatives><jats:tex-math>$$\begin{aligned} u_t=u^p \Delta u \qquad \qquad (\star ) \end{aligned}$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
                  <mml:mrow>
                    <mml:mtable>
                      <mml:mtr>
                        <mml:mtd>
                          <mml:mrow>
                            <mml:msub>
                              <mml:mi>u</mml:mi>
                              <mml:mi>t</mml:mi>
                            </mml:msub>
                            <mml:mo>=</mml:mo>
                            <mml:msup>
                              <mml:mi>u</mml:mi>
                              <mml:mi>p</mml:mi>
                            </mml:msup>
                            <mml:mi>Δ</mml:mi>
                            <mml:mi>u</mml:mi>
                            <mml:mspace />
                            <mml:mspace />
                            <mml:mrow>
                              <mml:mo>(</mml:mo>
                              <mml:mo>⋆</mml:mo>
                              <mml:mo>)</mml:mo>
                            </mml:mrow>
                          </mml:mrow>
                        </mml:mtd>
                      </mml:mtr>
                    </mml:mtable>
                  </mml:mrow>
                </mml:math></jats:alternatives></jats:disp-formula>is considered in the strongly degenerate regime <jats:inline-formula><jats:alternatives><jats:tex-math>$$p\ge 1$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
                  <mml:mrow>
                    <mml:mi>p</mml:mi>
                    <mml:mo>≥</mml:mo>
                    <mml:mn>1</mml:mn>
                  </mml:mrow>
                </mml:math></jats:alternatives></jats:inline-formula>. The focus is firstly on the case of positive continuous and bounded initial data, in which it is known that a minimal positive classical solution exists, and that this solution satisfies <jats:disp-formula><jats:alternatives><jats:tex-math>$$\begin{aligned} t^\frac{1}{p}\Vert u(\cdot ,t)\Vert _{L^\infty ({\mathbb {R}}^n)} \rightarrow \infty \quad \hbox {as } t\rightarrow \infty . \end{aligned}$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
                  <mml:mrow>
                    <mml:mtable>
                      <mml:mtr>
                        <mml:mtd>
                          <mml:mrow>
                            <mml:msup>
                              <mml:mi>t</mml:mi>
                              <mml:mfrac>
                                <mml:mn>1</mml:mn>
                                <mml:mi>p</mml:mi>
                              </mml:mfrac>
                            </mml:msup>
                            <mml:msub>
                              <mml:mrow>
                                <mml:mo>‖</mml:mo>
                                <mml:mi>u</mml:mi>
                                <mml:mrow>
                                  <mml:mo>(</mml:mo>
                                  <mml:mo>·</mml:mo>
                                  <mml:mo>,</mml:mo>
                                  <mml:mi>t</mml:mi>
                                  <mml:mo>)</mml:mo>
                                </mml:mrow>
                                <mml:mo>‖</mml:mo>
                              </mml:mrow>
                              <mml:mrow>
                                <mml:msup>
                                  <mml:mi>L</mml:mi>
                                  <mml:mi>∞</mml:mi>
                                </mml:msup>
                                <mml:mrow>
                                  <mml:mo>(</mml:mo>
                                  <mml:msup>
                                    <mml:mrow>
                                      <mml:mi>R</mml:mi>
                                    </mml:mrow>
                                    <mml:mi>n</mml:mi>
                                  </mml:msup>
                                  <mml:mo>)</mml:mo>
                                </mml:mrow>
                              </mml:mrow>
                            </mml:msub>
                            <mml:mo>→</mml:mo>
                            <mml:mi>∞</mml:mi>
                            <mml:mspace />
                            <mml:mtext>as</mml:mtext>
                            <mml:mspace />
                            <mml:mi>t</mml:mi>
                            <mml:mo>→</mml:mo>
                            <mml:mi>∞</mml:mi>
                            <mml:mo>.</mml:mo>
                          </mml:mrow>
                        </mml:mtd>
                      </mml:mtr>
                    </mml:mtable>
                  </mml:mrow>
                </mml:math></jats:alternatives></jats:disp-formula>The first result of this study complements this by asserting that given any positive <jats:inline-formula><jats:alternatives><jats:tex-math>$$f\in C^0([0,\infty ))$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
                  <mml:mrow>
                    <mml:mi>f</mml:mi>
                    <mml:mo>∈</mml:mo>
                    <mml:msup>
                      <mml:mi>C</mml:mi>
                      <mml:mn>0</mml:mn>
                    </mml:msup>
                    <mml:mrow>
                      <mml:mo>(</mml:mo>
                      <mml:mrow>
                        <mml:mo>[</mml:mo>
                        <mml:mn>0</mml:mn>
                        <mml:mo>,</mml:mo>
                        <mml:mi>∞</mml:mi>
                        <mml:mo>)</mml:mo>
                      </mml:mrow>
                      <mml:mo>)</mml:mo>
                    </mml:mrow>
                  </mml:mrow>
                </mml:math></jats:alternatives></jats:inline-formula> fulfilling <jats:inline-formula><jats:alternatives><jats:tex-math>$$f(t)\rightarrow +\infty $$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
                  <mml:mrow>
                    <mml:mi>f</mml:mi>
                    <mml:mo>(</mml:mo>
                    <mml:mi>t</mml:mi>
                    <mml:mo>)</mml:mo>
                    <mml:mo>→</mml:mo>
                    <mml:mo>+</mml:mo>
                    <mml:mi>∞</mml:mi>
                  </mml:mrow>
                </mml:math></jats:alternatives></jats:inline-formula> as <jats:inline-formula><jats:alternatives><jats:tex-math>$$t\rightarrow \infty $$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
                  <mml:mrow>
                    <mml:mi>t</mml:mi>
                    <mml:mo>→</mml:mo>
                    <mml:mi>∞</mml:mi>
                  </mml:mrow>
                </mml:math></jats:alternatives></jats:inline-formula> one can find a positive nondecreasing function <jats:inline-formula><jats:alternatives><jats:tex-math>$$\phi \in C^0([0,\infty ))$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
                  <mml:mrow>
                    <mml:mi>ϕ</mml:mi>
                    <mml:mo>∈</mml:mo>
                    <mml:msup>
                      <mml:mi>C</mml:mi>
                      <mml:mn>0</mml:mn>
                    </mml:msup>
                    <mml:mrow>
                      <mml:mo>(</mml:mo>
                      <mml:mrow>
                        <mml:mo>[</mml:mo>
                        <mml:mn>0</mml:mn>
                        <mml:mo>,</mml:mo>
                        <mml:mi>∞</mml:mi>
                        <mml:mo>)</mml:mo>
                      </mml:mrow>
                      <mml:mo>)</mml:mo>
                    </mml:mrow>
                  </mml:mrow>
                </mml:math></jats:alternatives></jats:inline-formula> such that whenever <jats:inline-formula><jats:alternatives><jats:tex-math>$$u_0\in C^0({\mathbb {R}}^n)$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
                  <mml:mrow>
                    <mml:msub>
                      <mml:mi>u</mml:mi>
                      <mml:mn>0</mml:mn>
                    </mml:msub>
                    <mml:mo>∈</mml:mo>
                    <mml:msup>
                      <mml:mi>C</mml:mi>
                      <mml:mn>0</mml:mn>
                    </mml:msup>
                    <mml:mrow>
                      <mml:mo>(</mml:mo>
                      <mml:msup>
                        <mml:mrow>
                          <mml:mi>R</mml:mi>
                        </mml:mrow>
                        <mml:mi>n</mml:mi>
                      </mml:msup>
                      <mml:mo>)</mml:mo>
                    </mml:mrow>
                  </mml:mrow>
                </mml:math></jats:alternatives></jats:inline-formula> is radially symmetric with <jats:inline-formula><jats:alternatives><jats:tex-math>$$0&lt; u_0 &lt; \phi (|\cdot |)$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
                  <mml:mrow>
                    <mml:mn>0</mml:mn>
                    <mml:mo>&lt;</mml:mo>
                    <mml:msub>
                      <mml:mi>u</mml:mi>
                      <mml:mn>0</mml:mn>
                    </mml:msub>
                    <mml:mrow>
                      <mml:mo>&lt;</mml:mo>
                      <mml:mi>ϕ</mml:mi>
                      <mml:mo>(</mml:mo>
                      <mml:mo>|</mml:mo>
                    </mml:mrow>
                    <mml:mo>·</mml:mo>
                    <mml:mrow>
                      <mml:mo>|</mml:mo>
                      <mml:mo>)</mml:mo>
                    </mml:mrow>
                  </mml:mrow>
                </mml:math></jats:alternatives></jats:inline-formula>, the corresponding minimal solution <jats:italic>u</jats:italic> satisfies <jats:disp-formula><jats:alternatives><jats:tex-math>$$\begin{aligned} \frac{t^\frac{1}{p}\Vert u(\cdot ,t)\Vert _{L^\infty ({\mathbb {R}}^n)}}{f(t)} \rightarrow 0 \quad \hbox {as } t\rightarrow \infty . \end{aligned}$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
                  <mml:mrow>
                    <mml:mtable>
                      <mml:mtr>
                        <mml:mtd>
                          <mml:mrow>
                            <mml:mfrac>
                              <mml:mrow>
                                <mml:msup>
                                  <mml:mi>t</mml:mi>
                                  <mml:mfrac>
                                    <mml:mn>1</mml:mn>
                                    <mml:mi>p</mml:mi>
                                  </mml:mfrac>
                                </mml:msup>
                                <mml:msub>
                                  <mml:mrow>
                                    <mml:mo>‖</mml:mo>
                                    <mml:mi>u</mml:mi>
                                    <mml:mrow>
                                      <mml:mo>(</mml:mo>
                                      <mml:mo>·</mml:mo>
                                      <mml:mo>,</mml:mo>
                                      <mml:mi>t</mml:mi>
                                      <mml:mo>)</mml:mo>
                                    </mml:mrow>
                                    <mml:mo>‖</mml:mo>
                                  </mml:mrow>
                                  <mml:mrow>
                                    <mml:msup>
                                      <mml:mi>L</mml:mi>
                                      <mml:mi>∞</mml:mi>
                                    </mml:msup>
                                    <mml:mrow>
                                      <mml:mo>(</mml:mo>
                                      <mml:msup>
                                        <mml:mrow>
                                          <mml:mi>R</mml:mi>
                                        </mml:mrow>
                                        <mml:mi>n</mml:mi>
                                      </mml:msup>
                                      <mml:mo>)</mml:mo>
                                    </mml:mrow>
                                  </mml:mrow>
                                </mml:msub>
                              </mml:mrow>
                              <mml:mrow>
                                <mml:mi>f</mml:mi>
                                <mml:mo>(</mml:mo>
                                <mml:mi>t</mml:mi>
                                <mml:mo>)</mml:mo>
                              </mml:mrow>
                            </mml:mfrac>
                            <mml:mo>→</mml:mo>
                            <mml:mn>0</mml:mn>
                            <mml:mspace />
                            <mml:mtext>as</mml:mtext>
                            <mml:mspace />
                            <mml:mi>t</mml:mi>
                            <mml:mo>→</mml:mo>
                            <mml:mi>∞</mml:mi>
                            <mml:mo>.</mml:mo>
                          </mml:mrow>
                        </mml:mtd>
                      </mml:mtr>
                    </mml:mtable>
                  </mml:mrow>
                </mml:math></jats:alternatives></jats:disp-formula>Secondly, (<jats:inline-formula><jats:alternatives><jats:tex-math>$$\star $$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
                  <mml:mo>⋆</mml:mo>
                </mml:math></jats:alternatives></jats:inline-formula>) is considered along with initial conditions involving nonnegative but not necessarily strictly positive bounded and continuous initial data <jats:inline-formula><jats:alternatives><jats:tex-math>$$u_0$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
                  <mml:msub>
                    <mml:mi>u</mml:mi>
                    <mml:mn>0</mml:mn>
                  </mml:msub>
                </mml:math></jats:alternatives></jats:inline-formula>. It is shown that if the connected components of <jats:inline-formula><jats:alternatives><jats:tex-math>$$\{u_0&gt;0\}$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
                  <mml:mrow>
                    <mml:mo>{</mml:mo>
                    <mml:msub>
                      <mml:mi>u</mml:mi>
                      <mml:mn>0</mml:mn>
                    </mml:msub>
                    <mml:mo>&gt;</mml:mo>
                    <mml:mn>0</mml:mn>
                    <mml:mo>}</mml:mo>
                  </mml:mrow>
                </mml:math></jats:alternatives></jats:inline-formula> comply with a condition reflecting some uniform boundedness property, then a corresponding uniquely determined continuous weak solution to (<jats:inline-formula><jats:alternatives><jats:tex-math>$$\star $$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
                  <mml:mo>⋆</mml:mo>
                </mml:math></jats:alternatives></jats:inline-formula>) satisfies <jats:disp-formula><jats:alternatives><jats:tex-math>$$\begin{aligned} 0&lt; \liminf _{t\rightarrow \infty } \Big \{ t^\frac{1}{p} \Vert u(\cdot ,t)\Vert _{L^\infty ({\mathbb {R}}^n)} \Big \} \le \limsup _{t\rightarrow \infty } \Big \{ t^\frac{1}{p} \Vert u(\cdot ,t)\Vert _{L^\infty ({\mathbb {R}}^n)} \Big \} &lt;\infty . \end{aligned}$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
                  <mml:mrow>
                    <mml:mtable>
                      <mml:mtr>
                        <mml:mtd>
                          <mml:mrow>
                            <mml:mn>0</mml:mn>
                            <mml:mo>&lt;</mml:mo>
                            <mml:munder>
                              <mml:mo>lim inf</mml:mo>
                              <mml:mrow>
                                <mml:mi>t</mml:mi>
                                <mml:mo>→</mml:mo>
                                <mml:mi>∞</mml:mi>
                              </mml:mrow>
                            </mml:munder>
                            <mml:mrow>
                              <mml:mo>{</mml:mo>
                            </mml:mrow>
                            <mml:msup>
                              <mml:mi>t</mml:mi>
                              <mml:mfrac>
                                <mml:mn>1</mml:mn>
                                <mml:mi>p</mml:mi>
                              </mml:mfrac>
                            </mml:msup>
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                </mml:math></jats:alternatives></jats:disp-formula>Under a somewhat complementary hypothesis, particularly fulfilled if <jats:inline-formula><jats:alternatives><jats:tex-math>$$\{u_0&gt;0\}$$</jats:tex-math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML">
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                </mml:math></jats:alternatives></jats:inline-formula> contains components with arbitrarily small principal eigenvalues of the associated Dirichlet Laplacian, it is finally seen that (0.1) continues to hold also for such not everywhere positive weak solutions.</jats:p>}},
  author       = {{Winkler, Michael}},
  issn         = {{1040-7294}},
  journal      = {{Journal of Dynamics and Differential Equations}},
  keywords     = {{Analysis}},
  number       = {{S1}},
  pages        = {{3--23}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Approaching Critical Decay in a Strongly Degenerate Parabolic Equation}}},
  doi          = {{10.1007/s10884-020-09892-x}},
  volume       = {{36}},
  year         = {{2020}},
}

@article{37660,
  author       = {{Rösler, Margit}},
  issn         = {{0022-1236}},
  journal      = {{Journal of Functional Analysis}},
  keywords     = {{Analysis}},
  number       = {{12}},
  publisher    = {{Elsevier BV}},
  title        = {{{Riesz distributions and Laplace transform in the Dunkl setting of type A}}},
  doi          = {{10.1016/j.jfa.2020.108506}},
  volume       = {{278}},
  year         = {{2020}},
}

@article{20533,
  author       = {{Krüger, Stefan and Späth, Johannes and Ali, Karim and Bodden, Eric and Mezini, Mira}},
  issn         = {{2326-3881}},
  journal      = {{IEEE Transactions on Software Engineering}},
  keywords     = {{Java, Encryption, Static analysis, Tools, Ciphers, Semantics, cryptography, domain-specific language, static analysis}},
  pages        = {{1--1}},
  title        = {{{CrySL: An Extensible Approach to Validating the Correct Usage of Cryptographic APIs}}},
  doi          = {{10.1109/TSE.2019.2948910}},
  year         = {{2019}},
}

@techreport{23389,
  abstract     = {{Background - Software companies increasingly rely on static analysis tools to detect potential bugs and security vulnerabilities in their software products. In the past decade, more and more commercial and open-source static analysis tools have been developed and are maintained. Each tool comes with its own reporting format, preventing an easy integration of multiple analysis tools in a single interface, such as the Static Analysis Server Protocol (SASP). In 2017, a collaborative effort in industry, including Microsoft and GrammaTech, has proposed the Static Analysis Results Interchange Format (SARIF) to address this issue. SARIF is a standardized format in which static analysis warnings can be encoded, to allow the import and export of analysis reports between different tools.
Purpose - This paper explains the SARIF format through examples and presents a proof of concept of the connector that allows the static analysis tool CogniCrypt to generate and export its results in SARIF format.
Design/Approach - We conduct a cross-sectional study between the SARIF format and CogniCrypt's output format before detailing the implementation of the connector. The study aims to find the components of interest in CogniCrypt that the SARIF export module can complete.
Originality/Value - The integration of SARIF into CogniCrypt described in this paper can be reused to integrate SARIF into other static analysis tools.
Conclusion - After detailing the SARIF format, we present an initial implementation to integrate SARIF into CogniCrypt. After taking advantage of all the features provided by SARIF, CogniCrypt will be able to support SASP.}},
  author       = {{Kummita, Sriteja and Piskachev, Goran}},
  keywords     = {{Static Analysis, Static Analysis Results Interchange Format, SARIF, Static Analysis Server Protocol, SASP}},
  title        = {{{Integration of the Static Analysis Results Interchange Format in CogniCrypt}}},
  year         = {{2019}},
}

@inproceedings{9613,
  abstract     = {{The ability to openly evaluate products, locations and services is an achievement of the Web 2.0. It has never been easier to inform oneself about the quality of products or services and possible alternatives. Forming one’s own opinion based on the impressions of other people can lead to better experiences. However, this presupposes trust in one’s fellows as well as in the quality of the review platforms. In previous work on physician reviews and the corresponding websites, it was observed that there occurs faulty behavior by some reviewers and there were noteworthy differences in the technical implementation of the portals and in the efforts of site operators to maintain high quality reviews. These experiences raise new questions regarding what trust means on review platforms, how trust arises and how easily it can be destroyed.}},
  author       = {{Kersting, Joschka and Bäumer, Frederik Simon and Geierhos, Michaela}},
  booktitle    = {{Proceedings of the 4th International Conference on Internet of Things, Big Data and Security}},
  editor       = {{Ramachandran, Muthu and Walters, Robert and Wills, Gary and Méndez Muñoz, Víctor and Chang, Victor}},
  isbn         = {{978-989-758-369-8}},
  keywords     = {{Trust, Physician Reviews, Network Analysis}},
  location     = {{Heraklion, Greece}},
  pages        = {{147--155}},
  publisher    = {{SCITEPRESS}},
  title        = {{{In Reviews We Trust: But Should We? Experiences with Physician Review Websites}}},
  year         = {{2019}},
}

@inproceedings{15838,
  abstract     = {{In the field of software analysis a trade-off between scalability and accuracy always exists. In this respect, Android app analysis is no exception, in particular, analyzing large or many apps can be challenging. Dealing with many small apps is a typical challenge when facing micro-benchmarks such as DROIDBENCH or ICC-BENCH. These particular benchmarks are not only used for the evaluation of novel tools but also in continuous integration pipelines of existing mature tools to maintain and guarantee a certain quality-level. Considering this latter usage it becomes very important to be able to achieve benchmark results as fast as possible. Hence, benchmarks have to be optimized for this purpose. One approach to do so is app merging. We implemented the Android Merge Tool (AMT) following this approach and show that its novel aspects can be used to produce scaled up and accurate benchmarks. For such benchmarks Android app analysis tools do not suffer from the scalability-accuracy trade-off anymore. We show this throughout detailed experiments on DROIDBENCH employing three different analysis tools (AMANDROID, ICCTA, FLOWDROID). Benchmark execution times are largely reduced without losing benchmark accuracy. Moreover, we argue why AMT is an advantageous successor of the state-of-the-art app merging tool (APKCOMBINER) in analysis lift-up scenarios.}},
  author       = {{Pauck, Felix and Zhang, Shikun}},
  booktitle    = {{2019 34th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW)}},
  isbn         = {{9781728141367}},
  keywords     = {{Program Analysis, Android App Analysis, Taint Analysis, App Merging, Benchmark}},
  title        = {{{Android App Merging for Benchmark Speed-Up and Analysis Lift-Up}}},
  doi          = {{10.1109/asew.2019.00019}},
  year         = {{2019}},
}

@article{34672,
  author       = {{Black, Tobias}},
  issn         = {{1937-1179}},
  journal      = {{Discrete &amp; Continuous Dynamical Systems - S}},
  keywords     = {{Applied Mathematics, Discrete Mathematics and Combinatorics, Analysis}},
  number       = {{2}},
  pages        = {{119--137}},
  publisher    = {{American Institute of Mathematical Sciences (AIMS)}},
  title        = {{{Global generalized solutions to a parabolic-elliptic Keller-Segel system with singular sensitivity}}},
  doi          = {{10.3934/dcdss.2020007}},
  volume       = {{13}},
  year         = {{2019}},
}

@article{34671,
  author       = {{Black, Tobias and Lankeit, Johannes and Mizukami, Masaaki}},
  issn         = {{0003-6811}},
  journal      = {{Applicable Analysis}},
  keywords     = {{Applied Mathematics, Analysis}},
  number       = {{16}},
  pages        = {{2877--2891}},
  publisher    = {{Informa UK Limited}},
  title        = {{{Stabilization in the Keller–Segel system with signal-dependent sensitivity}}},
  doi          = {{10.1080/00036811.2019.1585534}},
  volume       = {{99}},
  year         = {{2019}},
}

@article{28687,
  abstract     = {{Although there is considerable research on and knowledge about students’ conceptual-izations of learning or academic practices and skills, the variability of these conceptu-alizations has been consistently neglected.In the present study, we address this varia-bility in the field of academic readingwith the help of a novel approach. Drawing on qualitative metaphor analysis, we report a detailed system of students’ conceptual met-aphors of reading. Ourspecific methodologicalapproach to identify the structure of these conceptual metaphorsallowsto analyze subjective agency on a lexical as well as grammatical level.The conceptual metaphors we identified by this method are markedly variable, although they create an overall impression of medium to low agency, that is a reader who is only weakly active or potent. Interrater reliability of the coding system was very good. We also report and analyze the frequency of the conceptual metaphors ina sample of 143 texts written by bachelor students.}},
  author       = {{Scharlau, Ingrid and Körber, Miriam and Karsten, Andrea}},
  issn         = {{2295-3159}},
  journal      = {{Frontline Learning Research}},
  keywords     = {{metaphor, conceptual metaphor, metaphor analysis, academic reading, transitivity}},
  number       = {{4}},
  pages        = {{25 -- 57}},
  title        = {{{Plunging into a world? A novel approach to undergraduates’ metaphors of reading}}},
  doi          = {{10.14786/flr.v7i4.559}},
  volume       = {{9}},
  year         = {{2019}},
}

@article{35398,
  abstract     = {{The Haller relationship was applied to estimate the nematic order parameter S from 1H NMR spectra of fully protonated liquid crystals aligned in the magnetic field. The NMR line shapes were approximated as doublets of very broad peaks. Both the temperature-dependent doublet Splitting and the full width at half maximum of the whole spectra were used for Haller extrapolation. The order parameters obtained with the proposed approach for 4-cyano-4'-pentylbiphenyl (5CB) and the nematic mixture E7 were found to be in good agreement with previously reports.}},
  author       = {{Tang, Ming-xue and Schmidt, Claudia}},
  journal      = {{Chinese Journal of Magnetic Resonance}},
  keywords     = {{nematic liquid crystal, order parameter, Haller analysis, 1H NMR}},
  pages        = {{138--147}},
  title        = {{{Estimation of Nematic Order Parameters via Haller Analysis of 1H NMR Spectra of Liquid Crystals }}},
  doi          = {{10.11938/cjmr20182685 }},
  volume       = {{36}},
  year         = {{2019}},
}

@inproceedings{48843,
  abstract     = {{We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical graph coloring problem and investigate the dynamic setting where edges are added to the current graph. We then analyze the expected time for randomized search heuristics to recompute high quality solutions. This includes the (1+1) EA and RLS in a setting where the number of colors is bounded and we are minimizing the number of conflicts as well as iterated local search algorithms that use an unbounded color palette and aim to use the smallest colors and - as a consequence - the smallest number of colors. We identify classes of bipartite graphs where reoptimization is as hard as or even harder than optimization from scratch, i. e. starting with a random initialization. Even adding a single edge can lead to hard symmetry problems. However, graph classes that are hard for one algorithm turn out to be easy for others. In most cases our bounds show that reoptimization is faster than optimizing from scratch. Furthermore, we show how to speed up computations by using problem specific operators concentrating on parts of the graph where changes have occurred.}},
  author       = {{Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}},
  booktitle    = {{Proceedings of the Genetic and Evolutionary Computation Conference}},
  isbn         = {{978-1-4503-6111-8}},
  keywords     = {{dynamic optimization, evolutionary algorithms, running time analysis, theory}},
  pages        = {{1443–1451}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring}}},
  doi          = {{10.1145/3321707.3321792}},
  year         = {{2019}},
}

@inproceedings{48870,
  abstract     = {{The edge coloring problem asks for an assignment of colors to edges of a graph such that no two incident edges share the same color and the number of colors is minimized. It is known that all graphs with maximum degree {$\Delta$} can be colored with {$\Delta$} or {$\Delta$} + 1 colors, but it is NP-hard to determine whether {$\Delta$} colors are sufficient. We present the first runtime analysis of evolutionary algorithms (EAs) for the edge coloring problem. Simple EAs such as RLS and (1+1) EA efficiently find (2{$\Delta$} - 1)-colorings on arbitrary graphs and optimal colorings for even and odd cycles, paths, star graphs and arbitrary trees. A partial analysis for toroids also suggests efficient runtimes in bipartite graphs with many cycles. Experiments support these findings and investigate additional graph classes such as hypercubes, complete graphs and complete bipartite graphs. Theoretical and experimental results suggest that simple EAs find optimal colorings for all these graph classes in expected time O({$\Delta\mathscrl$}2m log m), where m is the number of edges and {$\mathscrl$} is the length of the longest simple path in the graph.}},
  author       = {{Bossek, Jakob and Sudholt, Dirk}},
  booktitle    = {{Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms}},
  isbn         = {{978-1-4503-6254-2}},
  keywords     = {{edge coloring problem, runtime analysis}},
  pages        = {{102–115}},
  publisher    = {{Association for Computing Machinery}},
  title        = {{{Time Complexity Analysis of RLS and (1 + 1) EA for the Edge Coloring Problem}}},
  doi          = {{10.1145/3299904.3340311}},
  year         = {{2019}},
}

@inproceedings{10108,
  abstract     = {{Recent years have seen the development of numerous tools for the analysis of taint flows in Android apps. Taint analyses aim at detecting data leaks, accidentally or by purpose programmed into apps. Often, such tools specialize in the treatment of specific features impeding precise taint analysis (like reflection or inter-app communication). This multitude of tools, their specific applicability and their various combination options complicate the selection of a tool (or multiple tools) when faced with an analysis instance, even for knowledgeable users, and hence hinders the successful adoption of taint analyses.

In this work, we thus present CoDiDroid, a framework for cooperative Android app analysis. CoDiDroid (1) allows users to ask questions about flows in apps in varying degrees of detail, (2) automatically generates subtasks for answering such questions, (3) distributes tasks onto analysis tools (currently DroidRA, FlowDroid, HornDroid, IC3 and two novel tools) and (4) at the end merges tool answers on subtasks into an overall answer. Thereby, users are freed from having to learn about the use and functionality of all these tools while still being able to leverage their capabilities. Moreover, we experimentally show that cooperation among tools pays off with respect to effectiveness, precision and scalability.}},
  author       = {{Pauck, Felix and Wehrheim, Heike}},
  booktitle    = {{Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering}},
  isbn         = {{978-1-4503-5572-8}},
  keywords     = {{Android Taint Analysis, Cooperation, Precision, Tools}},
  pages        = {{374--384}},
  title        = {{{Together Strong: Cooperative Android App Analysis}}},
  doi          = {{10.1145/3338906.3338915}},
  year         = {{2019}},
}

