Interfaces Within Graphene Nanoribbons

We study the conductance through two types of graphene nanostructures: nanoribbon junctions in which the width changes from wide to narrow, and curved nanoribbons. In the wide-narrow structures, substantial reflection occurs from the wide-narrow interface, in contrast to the behavior of the much studied electron gas waveguides. In the curved nanoribbons, the conductance is very sensitive to details such as whether regions of a semiconducting armchair nanoribbon are included in the curved structure -- such regions strongly suppress the conductance. Surprisingly, this suppression is not due to the band gap of the semiconducting nanoribbon, but is linked to the valley degree of freedom. Though we study these effects in the simplest contexts, they can be expected to occur for more complicated structures, and we show results for rings as well. We conclude that experience from electron gas waveguides does not carry over to graphene nanostructures. The interior interfaces causing extra scattering result from the extra effective degrees of freedom of the graphene structure, namely the valley and sublattice pseudospins.Comment: 19 pages, published version, several references added, small changes to conclusion

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Boundary Terms in Supergravity and Supersymmetry

We begin with the simplest possible introduction to supergravity. Then we discuss its spin 3/2 stress tensor; these results are new. Next, we discuss boundary conditions on fields and boundary actions for N=1 supergravity. Finally, we discuss new boundary contributions to the mass and central charge of monopoles in N=4 super Yang-Mills theory. All models are in 3+1 dimensions.Comment: 15 pages. Talk given by P. van Nieuwenhuizen at the Einstein-celebration gravitational conference at Puri (India) in December 200

AC0(MOD2) lower bounds for the Boolean inner product

AC0 ◦MOD2 circuits are AC0 circuits augmented with a layer of parity gates just above the input layer. We study AC0 ◦ MOD2 circuit lower bounds for computing the Boolean Inner Product functions. Recent works by Servedio and Viola (ECCC TR12-144) and Akavia et al. (ITCS 2014) have highlighted this problem as a frontier problem in circuit complexity that arose both as a first step towards solving natural special cases of the matrix rigidity problem and as a candidate for constructing pseudorandom generators of minimal complexity. We give the first superlinear lower bound for the Boolean Inner Product function against AC0 ◦ MOD2 of depth four or greater. Specifically, we prove a superlinear lower bound for circuits of arbitrary constant depth, and an Ω( ˜ n 2 ) lower bound for the special case of depth-4 AC0 ◦ MOD2. Our proof of the depth-4 lower bound employs a new “moment-matching” inequality for bounded, nonnegative integer-valued random variables that may be of independent interest: we prove an optimal bound on the maximum difference between two discrete distributions’ values at 0, given that their first d moments match

Strengthening Deterministic Policies for POMDPs

The synthesis problem for partially observable Markov decision processes (POMDPs) is to compute a policy that satisfies a given specification. Such policies have to take the full execution history of a POMDP into account, rendering the problem undecidable in general. A common approach is to use a limited amount of memory and randomize over potential choices. Yet, this problem is still NP-hard and often computationally intractable in practice. A restricted problem is to use neither history nor randomization, yielding policies that are called stationary and deterministic. Previous approaches to compute such policies employ mixed-integer linear programming (MILP). We provide a novel MILP encoding that supports sophisticated specifications in the form of temporal logic constraints. It is able to handle an arbitrary number of such specifications. Yet, randomization and memory are often mandatory to achieve satisfactory policies. First, we extend our encoding to deliver a restricted class of randomized policies. Second, based on the results of the original MILP, we employ a preprocessing of the POMDP to encompass memory-based decisions. The advantages of our approach over state-of-the-art POMDP solvers lie (1) in the flexibility to strengthen simple deterministic policies without losing computational tractability and (2) in the ability to enforce the provable satisfaction of arbitrarily many specifications. The latter point allows taking trade-offs between performance and safety aspects of typical POMDP examples into account. We show the effectiveness of our method on a broad range of benchmarks