Should surface science exploit more quantitative experiments?

In recent years two particular methods, scanning probe microscopy and theoretical total energy calculations (based, particularly, on density functional theory), have led to major advances in our understanding of surface science. However, performed to the exclusion of more ‘traditional’ experimental methods that provide quantitative information on the composition, vibrational properties, adsorption and desorption energies, and on the electronic and geometrical structure, the interpretation of the results can be unnecessarily speculative. Combined with these methods, on the other hand, they give considerable added power to the long-learnt lesson of the need to use a range of complementary techniques to unravel the complexities of surface phenomena

The role of reconstruction in self-assembly of alkylthiolate monolayers on coinage metal surfaces

Through a combination of standard laboratory-based surface science methods, together with synchrotron radiation-based normal incidence X-ray standing wave (NIXSW) experiments, the interface structure of simple alkylthiolate ‘self-assembled monolayers’ on Cu(1 1 1), Ag(1 1 1) and Au(1 1 1) has been investigated over the last not, vert, similar15 years. A key conclusion is that in all cases the adsorbate produces a substantial, density-lowering, reconstruction of the outermost metal layer, although the nature of these reconstructions is quite different on the three metals. The main results of these investigations are briefly reviewed and contrasted

The Query Complexity of Mastermind with l_p Distances

Consider a variant of the Mastermind game in which queries are l_p distances, rather than the usual Hamming distance. That is, a codemaker chooses a hidden vector y in {-k,-k+1,...,k-1,k}^n and answers to queries of the form ||y-x||_p where x in {-k,-k+1,...,k-1,k}^n. The goal is to minimize the number of queries made in order to correctly guess y. In this work, we show an upper bound of O(min{n,(n log k)/(log n)}) queries for any real 10. Thus, essentially any approximation of this problem is as hard as finding the hidden vector exactly, up to constant factors. Finally, we show that for the noisy version of the problem, i.e., the setting when the codemaker answers queries with any q = (1 +/- epsilon)||y-x||_p, there is no query efficient algorithm

Towards Optimal Moment Estimation in Streaming and Distributed Models

One of the oldest problems in the data stream model is to approximate the p-th moment ||X||_p^p = sum_{i=1}^n X_i^p of an underlying non-negative vector X in R^n, which is presented as a sequence of poly(n) updates to its coordinates. Of particular interest is when p in (0,2]. Although a tight space bound of Theta(epsilon^-2 log n) bits is known for this problem when both positive and negative updates are allowed, surprisingly there is still a gap in the space complexity of this problem when all updates are positive. Specifically, the upper bound is O(epsilon^-2 log n) bits, while the lower bound is only Omega(epsilon^-2 + log n) bits. Recently, an upper bound of O~(epsilon^-2 + log n) bits was obtained under the assumption that the updates arrive in a random order. We show that for p in (0, 1], the random order assumption is not needed. Namely, we give an upper bound for worst-case streams of O~(epsilon^-2 + log n) bits for estimating |X |_p^p. Our techniques also give new upper bounds for estimating the empirical entropy in a stream. On the other hand, we show that for p in (1,2], in the natural coordinator and blackboard distributed communication topologies, there is an O~(epsilon^-2) bit max-communication upper bound based on a randomized rounding scheme. Our protocols also give rise to protocols for heavy hitters and approximate matrix product. We generalize our results to arbitrary communication topologies G, obtaining an O~(epsilon^2 log d) max-communication upper bound, where d is the diameter of G. Interestingly, our upper bound rules out natural communication complexity-based approaches for proving an Omega(epsilon^-2 log n) bit lower bound for p in (1,2] for streaming algorithms. In particular, any such lower bound must come from a topology with large diameter