Highlights d Cities possess a consistent ''core'' set of non-human microbes d Urban microbiomes echo important features of cities and city-life d Antimicrobial resistance genes are widespread in cities d Cities contain many novel bacterial and viral species

In the future, robotic surgical assistants may assist surgeons by performing specific subtasks such as retraction and suturing to reduce surgeon tedium and reduce the duration of some operations. We propose an apprenticeship learning approach that has potential to allow robotic surgical assistants to autonomously execute specific trajectories with superhuman performance in terms of speed and smoothness. In the first step, we record a set of trajectories using human-guided backdriven motions of the robot. These are then analyzed to extract a smooth reference trajectory, which we execute at gradually increasing speeds using a variant of iterative learning control. We evaluate this approach on two representative tasks using the Berkeley Surgical Robots: a figure eight trajectory and a two handed knot-tie, a tedious suturing sub-task required in many surgical procedures. Results suggest that the approach enables (i) rapid learning of trajectories, (ii) smoother trajectories than the human-guided trajectories, and (iii) trajectories that are 7 to 10 times faster than the best human-guided trajectories.

We give a "regularity lemma" for degree-d polynomial threshold functions (PTFs) over the Boolean cube {−1, 1} n . Roughly speaking, this result shows that every degree-d PTF can be decomposed into a constant number of subfunctions such that almost all of the subfunctions are close to being regular PTFs. Here a "regular" PTF is a PTF sign(p(x)) where the influence of each variable on the polynomial p(x) is a small fraction of the total influence of p.A conference version of this paper appeared in the Proc. As an application of this regularity lemma, we prove that for any constants d ≥ 1, ε > 0, every degree-d PTF over n variables can be approximated to accuracy ε by a constant-degree PTF that has integer weights of total magnitude O ε,d (n d ). This weight bound is shown to be optimal up to logarithmic factors.

We describe a general method for testing whether a function on n input variables has a concise representation. The approach combines ideas from the junta test of Fischer et al. [FKR + 04] with ideas from learning theory, and yields property testers that make poly(s/ǫ) queries (independent of n) for Boolean function classes such as s-term DNF formulas (answering a question posed by Parnas et al.[PRS02]), size-s decision trees, size-s Boolean formulas, and size-s Boolean circuits.The method can be applied to non-Boolean valued function classes as well. This is achieved via a generalization of the notion of variation from Fischer et al. to non-Boolean functions. Using this generalization we extend the original junta test of Fischer et al. to work for non-Boolean functions, and give poly(s/ǫ)-query testing algorithms for non-Boolean valued function classes such as size-s algebraic circuits and s-sparse polynomials over finite fields.We also prove aΩ( √ s) query lower bound for nonadaptively testing s-sparse polynomials over finite fields of constant size. This shows that in some instances, our general method yields a property tester with query complexity that is optimal (for nonadaptive algorithms) up to a polynomial factor.

BackgroundSearching for the longest common sequence (LCS) of multiple biosequences is one of the most fundamental tasks in bioinformatics. In this paper, we present a parallel algorithm named FAST_LCS to speedup the computation for finding LCS.ResultsA fast parallel algorithm for LCS is presented. The algorithm first constructs a novel successor table to obtain all the identical pairs and their levels. It then obtains the LCS by tracing back from the identical character pairs at the last level. Effective pruning techniques are developed to significantly reduce the computational complexity. Experimental results on gene sequences in the tigr database show that our algorithm is optimal and much more efficient than other leading LCS algorithms.ConclusionWe have developed one of the fastest parallel LCS algorithms on an MPP parallel computing model. For two sequences X and Y with lengths n and m, respectively, the memory required is max{4*(n+1)+4*(m+1), L}, where L is the number of identical character pairs. The time complexity is O(L) for sequential execution, and O(|LCS(X, Y)|) for parallel execution, where |LCS(X, Y)| is the length of the LCS of X and Y. For n sequences X1, X2, ..., Xn, the time complexity is O(L) for sequential execution, and O(|LCS(X1, X2, ..., Xn)|) for parallel execution. Experimental results support our analysis by showing significant improvement of the proposed method over other leading LCS algorithms.