Solving Connectivity Problems Parameterized by Treedepth in Single-Exponential Time and Polynomial Space

A breakthrough result of Cygan et al. (FOCS 2011) showed that connectivity problems parameterized by treewidth can be solved much faster than the previously best known time ?^*(2^{?(twlog tw)}). Using their inspired Cut&Count technique, they obtained ?^*(?^tw) time algorithms for many such problems. Moreover, they proved these running times to be optimal assuming the Strong Exponential-Time Hypothesis. Unfortunately, like other dynamic programming algorithms on tree decompositions, these algorithms also require exponential space, and this is widely believed to be unavoidable. In contrast, for the slightly larger parameter called treedepth, there are already several examples of matching the time bounds obtained for treewidth, but using only polynomial space. Nevertheless, this has remained open for connectivity problems. In the present work, we close this knowledge gap by applying the Cut&Count technique to graphs of small treedepth. While the general idea is unchanged, we have to design novel procedures for counting consistently cut solution candidates using only polynomial space. Concretely, we obtain time ?^*(3^d) and polynomial space for Connected Vertex Cover, Feedback Vertex Set, and Steiner Tree on graphs of treedepth d. Similarly, we obtain time ?^*(4^d) and polynomial space for Connected Dominating Set and Connected Odd Cycle Transversal

Overlapping and Robust Edge-Colored Clustering in Hypergraphs

A recent trend in data mining has explored (hyper)graph clustering algorithms for data with categorical relationship types. Such algorithms have applications in the analysis of social, co-authorship, and protein interaction networks, to name a few. Many such applications naturally have some overlap between clusters, a nuance which is missing from current combinatorial models. Additionally, existing models lack a mechanism for handling noise in datasets. We address these concerns by generalizing Edge-Colored Clustering, a recent framework for categorical clustering of hypergraphs. Our generalizations allow for a budgeted number of either (a) overlapping cluster assignments or (b) node deletions. For each new model we present a greedy algorithm which approximately minimizes an edge mistake objective, as well as bicriteria approximations where the second approximation factor is on the budget. Additionally, we address the parameterized complexity of each problem, providing FPT algorithms and hardness results

A Systematic Study of Isomorphism Invariants of Finite Groups via the Weisfeiler-Leman Dimension

We investigate the relationship between various isomorphism invariants for finite groups. Specifically, we use the Weisfeiler-Leman dimension (WL) to characterize, compare and quantify the effectiveness and complexity of invariants for group isomorphism. It turns out that a surprising number of invariants and characteristic subgroups that are classic to group theory can be detected and identified by a low dimensional Weisfeiler-Leman algorithm. These include the center, the inner automorphism group, the commutator subgroup and the derived series, the abelian radical, the solvable radical, the Fitting group and ?-radicals. A low dimensional WL-algorithm additionally determines the isomorphism type of the socle as well as the factors in the derives series and the upper and lower central series. We also analyze the behavior of the WL-algorithm for group extensions and prove that a low dimensional WL-algorithm determines the isomorphism types of the composition factors of a group. Finally we develop a new tool to define a canonical maximal central decomposition for groups. This allows us to show that the Weisfeiler-Leman dimension of a group is at most one larger than the dimensions of its direct indecomposable factors. In other words the Weisfeiler-Leman dimension increases by at most 1 when taking direct products

A Systematic Study of Isomorphism Invariants of Finite Groups via the Weisfeiler-Leman Dimension

We investigate the relationship between various isomorphism invariants for finite groups. Specifically, we use the Weisfeiler-Leman dimension (WL) to characterize, compare and quantify the effectiveness and complexity of invariants for group isomorphism. It turns out that a surprising number of invariants and characteristic subgroups that are classic to group theory can be detected and identified by a low dimensional Weisfeiler-Leman algorithm. These include the center, the inner automorphism group, the commutator subgroup and the derived series, the abelian radical, the solvable radical, the Fitting group and ?-radicals. A low dimensional WL-algorithm additionally determines the isomorphism type of the socle as well as the factors in the derives series and the upper and lower central series. We also analyze the behavior of the WL-algorithm for group extensions and prove that a low dimensional WL-algorithm determines the isomorphism types of the composition factors of a group. Finally we develop a new tool to define a canonical maximal central decomposition for groups. This allows us to show that the Weisfeiler-Leman dimension of a group is at most one larger than the dimensions of its direct indecomposable factors. In other words the Weisfeiler-Leman dimension increases by at most 1 when taking direct products

Testing and Learning Quantum Juntas Nearly Optimally

We consider the problem of testing and learning quantum $k$-juntas: $n$-qubit unitary matrices which act non-trivially on just $k$ of the $n$ qubits and as the identity on the rest. As our main algorithmic results, we give (a) a $\widetilde{O}(\sqrt{k})$-query quantum algorithm that can distinguish quantum $k$-juntas from unitary matrices that are "far" from every quantum $k$-junta; and (b) a $O(4^k)$-query algorithm to learn quantum $k$-juntas. We complement our upper bounds for testing quantum $k$-juntas and learning quantum $k$-juntas with near-matching lower bounds of $\Omega(\sqrt{k})$ and $\Omega(\frac{4^k}{k})$, respectively. Our techniques are Fourier-analytic and make use of a notion of influence of qubits on unitaries

The Ambivalence of Power in the Twenty-First Century Economy

The Ambivalence of Power in the Twenty-First Century Economy contributes to the understanding of the ambivalent nature of power, oscillating between conflict and cooperation, public and private, global and local, formal and informal, and does so from an empirical perspective. It offers a collection of country-based cases, as well as critically assesses the existing conceptions of power from a cross-disciplinary perspective. The diverse analyses of power at the macro, meso or micro levels allow the volume to highlight the complexity of political economy in the twenty-first century. Each chapter addresses key elements of that political economy (from the ambivalence of the cases of former communist countries that do not conform with the grand narratives about democracy and markets, to the dual utility of new technologies such as face-recognition), thus providing mounting evidence for the centrality of an understanding of ambivalence in the analysis of power, especially in the modern state power-driven capitalism. Anchored in economic sociology and political economy, this volume aims to make ‘visible’ the dimensions of power embedded in economic practices. The chapters are predominantly based on post-communist practices, but this divergent experience is relevant to comparative studies of how power and economy are interrelated

The Ambivalence of Power in the Twenty-First Century Economy

The Ambivalence of Power in the Twenty-First Century Economy contributes to the understanding of the ambivalent nature of power, oscillating between conflict and cooperation, public and private, global and local, formal and informal, and does so from an empirical perspective. It offers a collection of country-based cases, as well as critically assesses the existing conceptions of power from a cross-disciplinary perspective. The diverse analyses of power at the macro, meso or micro levels allow the volume to highlight the complexity of political economy in the twenty-first century. Each chapter addresses key elements of that political economy (from the ambivalence of the cases of former communist countries that do not conform with the grand narratives about democracy and markets, to the dual utility of new technologies such as face-recognition), thus providing mounting evidence for the centrality of an understanding of ambivalence in the analysis of power, especially in the modern state power-driven capitalism. Anchored in economic sociology and political economy, this volume aims to make ‘visible’ the dimensions of power embedded in economic practices. The chapters are predominantly based on post-communist practices, but this divergent experience is relevant to comparative studies of how power and economy are interrelated