Packing Steiner Trees

Let $T$ be a distinguished subset of vertices in a graph $G$. A $T$-\emph{Steiner tree} is a subgraph of $G$ that is a tree and that spans $T$. Kriesell conjectured that $G$ contains $k$ pairwise edge-disjoint $T$-Steiner trees provided that every edge-cut of $G$ that separates $T$ has size $\ge 2k$. When $T=V(G)$ a $T$-Steiner tree is a spanning tree and the conjecture is a consequence of a classic theorem due to Nash-Williams and Tutte. Lau proved that Kriesell's conjecture holds when $2k$ is replaced by $24k$, and recently West and Wu have lowered this value to $6.5k$. Our main result makes a further improvement to $5k+4$.Comment: 38 pages, 4 figure

An invitation to 2D TQFT and quantization of Hitchin spectral curves

This article consists of two parts. In Part 1, we present a formulation of two-dimensional topological quantum field theories in terms of a functor from a category of Ribbon graphs to the endofuntor category of a monoidal category. The key point is that the category of ribbon graphs produces all Frobenius objects. Necessary backgrounds from Frobenius algebras, topological quantum field theories, and cohomological field theories are reviewed. A result on Frobenius algebra twisted topological recursion is included at the end of Part 1. In Part 2, we explain a geometric theory of quantum curves. The focus is placed on the process of quantization as a passage from families of Hitchin spectral curves to families of opers. To make the presentation simpler, we unfold the story using SL_2(\mathbb{C})-opers and rank 2 Higgs bundles defined on a compact Riemann surface $C$ of genus greater than $1$. In this case, quantum curves, opers, and projective structures in $C$ all become the same notion. Background materials on projective coordinate systems, Higgs bundles, opers, and non-Abelian Hodge correspondence are explained.Comment: 53 pages, 6 figure

FPT Approximation of Generalised Hypertree Width for Bounded Intersection Hypergraphs

Generalised hypertree width ($ghw$) is a hypergraph parameter that is central to the tractability of many prominent problems with natural hypergraph structure. Computing $ghw$ of a hypergraph is notoriously hard. The decision version of the problem, checking whether $ghw(H) \leq k$, is paraNP-hard when parameterised by $k$. Furthermore, approximation of $ghw$ is at least as hard as approximation of Set-Cover, which is known to not admit any fpt approximation algorithms. Research in the computation of ghw so far has focused on identifying structural restrictions to hypergraphs -- such as bounds on the size of edge intersections -- that permit XP algorithms for $ghw$. Yet, even under these restrictions that problem has so far evaded any kind of fpt algorithm. In this paper we make the first step towards fpt algorithms for $ghw$ by showing that the parameter can be approximated in fpt time for graphs of bounded edge intersection size. In concrete terms we show that there exists an fpt algorithm, parameterised by $k$ and $d$, that for input hypergraph $H$ with maximal cardinality of edge intersections $d$ and integer $k$ either outputs a tree decomposition with $ghw(H) \leq 4k(k+d+1+)(2k-1)$, or rejects, in which case it is guaranteed that $ghw(H) > k$. Thus, in the special case, of hypergraphs of bounded edge intersection, we obtain an fpt $O(k^3)$-approximation algorithm for $ghw$

Hamilton cycles in large graphs and hypergraphs: existence and counting

The study of Hamilton cycles forms a central part of classical graph theory. In this thesis we present our contribution to the modern research on this topic. In Chapter 2, we prove that $k$-uniform hypergraphs satisfying a `Dirac-like' condition on the minimum $(k-1)$-degree contain many of a natural hypergraph analogue of a Hamilton cycle. In Chapter 3, we show that almost all optimal edge-colourings of $K_{n}$ admit a Hamilton path whose edges all have distinct colours; that is, a rainbow Hamilton path. If $n$ is odd, we show that one is further able to find a rainbow Hamilton cycle. Chapter 4 is given to the proof that almost all optimal colourings of a directed analogue of $K_{n}$ we call $\overleftrightarrow{K_{n}}$ admit many rainbow directed Hamilton cycles; equivalently, almost all $n\times n$ Latin squares contain many structures we call `Hamilton transversals'. Finally, in Chapter 5 we introduce an upcoming result which combines the R\"{o}dl Nibble with the Polynomial Method to substantially improve upon known results on the size of matchings in almost-regular hypergaphs. We also state an application of this result to the problem of finding rainbow almost-Hamilton directed cycles in any optimal colouring of $\overleftrightarrow{K_{n}}$

Generative models of brain connectivity for population studies

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 131-139).Connectivity analysis focuses on the interaction between brain regions. Such relationships inform us about patterns of neural communication and may enhance our understanding of neurological disorders. This thesis proposes a generative framework that uses anatomical and functional connectivity information to find impairments within a clinical population. Anatomical connectivity is measured via Diffusion Weighted Imaging (DWI), and functional connectivity is assessed using resting-state functional Magnetic Resonance Imaging (fMRI). We first develop a probabilistic model to merge information from DWI tractography and resting-state fMRI correlations. Our formulation captures the interaction between hidden templates of anatomical and functional connectivity within the brain. We also present an intuitive extension to population studies and demonstrate that our model learns predictive differences between a control and a schizophrenia population. Furthermore, combining the two modalities yields better results than considering each one in isolation. Although our joint model identifies widespread connectivity patterns influenced by a neurological disorder, the results are difficult to interpret and integrate with our regioncentric knowledge of the brain. To alleviate this problem, we present a novel approach to identify regions associated with the disorder based on connectivity information. Specifically, we assume that impairments of the disorder localize to a small subset of brain regions, which we call disease foci, and affect neural communication to/from these regions. This allows us to aggregate pairwise connectivity changes into a region-based representation of the disease. Once again, we use a probabilistic formulation: latent variables specify a template organization of the brain, which we indirectly observe through resting-state fMRI correlations and DWI tractography. Our inference algorithm simultaneously identifies both the afflicted regions and the network of aberrant functional connectivity. Finally, we extend the region-based model to include multiple collections of foci, which we call disease clusters. Preliminary results suggest that as the number of clusters increases, the refined model explains progressively more of the functional differences between the populations.by Archana Venkataraman.Ph.D

Quantum hypergraph homomorphisms and non-local games

Using the simulation paradigm in information theory, we define notions of quantum hypergraph homomorphisms and quantum hypergraph isomorphisms, and show that they constitute partial orders and equivalence relations, respectively. Specialising to the case where the underlying hypergraphs arise from non-local games, we define notions of quantum non-local game homomorphisms and quantum non-local game isomorphisms, and show that games, isomorphic with respect to a given correlation type, have equal values and asymptotic values relative to this type. We examine a new class of no-signalling correlations, which witness the existence of non-local game homomorphisms, and characterise them in terms of states on tensor products of canonical operator systems. We define jointly synchronous correlations and show that they correspond to traces on the tensor product of the canonical C*-algebras associated with the game parties

Algorithms for Routing Unmanned Vehicles with Motions, Resource, and Communication Constraints

Multiple small autonomous or unmanned aerial and ground vehicles are being used together with stationary sensing devices for a wide variety of data gathering, monitoring and surveillance applications in military, civilian, and agricultural applications, to name a few. Even though there are several advantages due to the small platforms for these vehicles, they pose a variety of challenges. This dissertation aims to address the following challenges to routing multiple small autonomous aerial or ground vehicles: (i) limited communication capabilities of the stationary sensing devices, (ii) dynamics of the vehicles, (iii) varying sensing capabilities of all the vehicles, and (iv) resource constraints in the form of fuel restrictions on each vehicle. The dissertation formulates four different routing problems for multiple unmanned vehicles, one for each of the aforementioned constraints, as mixed-integer linear programs and develops numerically efficient algorithms based on the branch-and-cut paradigm to compute optimal solutions for practically reasonable size of test instances

Approximation Algorithms for Independence Systems

In this thesis, we study three maximization problems over independence systems. • Chapter 2 – Weighted k-Set Packing is a fundamental combinatorial optimization problem that captures matching problems in graphs and hypergraphs. For over 20 years Berman’s algorithm stood as the state-of-the-art approximation algorithm for this problem, until Neuwohner’s recent improvements. Our focus is on the value k = 3 which is well motivated from theory and practice, and for which improvements are arguably the hardest. We largely improve upon her approximation, by giving an algorithm that yields state-of-the-art results. Our techniques are simple and naturally expand upon Berman’s analysis. Our analysis holds for any value of k with greater improvements over Berman’s result as k grows. • Chapter 3 – We continue the study of the weighted k-set packing problem. Building on Chapter 2, we reach the tightest approximation factor possible for k = 3, and k ≥ 7 using our techniques. As a consequence, we improve over all the results in Chapter 2. In particular, we obtain √3, and k/2 -approximation for k = 3 and k ≥ 7 respectively. Our result for k ≥ 7 is in fact analogous to that of Hurkens and Schrijver who obtained the same approximation factor for the unweighted problem. • Chapter 4 – We present improved multipass streaming algorithms for maximizing monotone and arbitrary submodular functions over independence systems. Our result demonstrates that the simple local-search algorithm for maximizing a monotone sub- modular function can be efficiently simulated using a few passes over the dataset. Our results improve the number of passes needed compared to the state-of-the-art. • Chapter 5 – We conclude the thesis by presenting improved approximation algorithms for Sparse Least-Square Estimation, Bayesian A-optimal Design, and Column Subset Selection over a matroid constraint. At the heart of this chapter is the demonstration of a new property that considered applications satisfy. We call it: β-weak submodularity. We leverage this property to derive new algorithms with strengthened guarantees. The notion of β-weak submodularity is of independent interest and we believe that it will have further use in machine learning and statistics