Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

On robust estimation of the location parameter

The author's preface gives an outline: "This book is about weakconvergence methods in metric spaces, with applications sufficient to show their power and utility. The Introduction motivates the definitions and indicates how the theory will yield solutions to problems arising outside it. Chapter 1 sets out the basic general theorems, which are then specialized in Chapter 2 to the space C[0, l ] of continuous functions on the unit interval and in Chapter 3 to the space D [0, 1 ] of functions with discontinuities of the first kind. The results of the first three chapters are used in Chapter 4 to derive a variety of limit theorems for dependent sequences of random variables. " The book develops and expands on Donsker's 1951 and 1952 papers on the invariance principle and empirical distributions. The basic random variables remain real-valued although, of course, measures on C[0, l ] and D[0, l ] are vitally used. Within this framework, there are various possibilities for a different and apparently better treatment of the material. More of the general theory of weak convergence of probabilities on separable metric spaces would be useful. Metrizability of the convergence is not brought up until late in the Appendix. The close relation of the Prokhorov metric and a metric for convergence in probability is (hence) not mentioned (see V. Strassen, Ann. Math. Statist. 36 (1965), 423-439; the reviewer, ibid. 39 (1968), 1563-1572). This relation would illuminate and organize such results as Theorems 4.1, 4.2 and 4.4 which give isolated, ad hoc connections between weak convergence of measures and nearness in probability. In the middle of p. 16, it should be noted that C*(S) consists of signed measures which need only be finitely additive if 5 is not compact. On p. 239, where the author twice speaks of separable subsets having nonmeasurable cardinal, he means "discrete" rather than "separable." Theorem 1.4 is Ulam's theorem that a Borel probability on a complete separable metric space is tight. Theorem 1 of Appendix 3 weakens completeness to topological completeness. After mentioning that probabilities on the rationals are tight, the author says it is an

Convergence of probability measures

Introduction to linear and nonlinear programming

Polydopamine is one of the simplest and most versatile approaches to functionalizing material surfaces, having been inspired by the adhesive nature of catechols and amines in mussel adhesive proteins. Since its first report in 2007, a decade of studies on polydopamine molecular structure, deposition conditions, and physicochemical properties have ensued. During this time, potential uses of polydopamine coatings have expanded in many unforeseen directions, seemingly only limited by the creativity of researchers seeking simple solutions to manipulating surface chemistry. In this review, we describe the current state of the art in polydopamine coating methods, describe efforts underway to uncover and tailor the complex structure and chemical properties of polydopamine, and identify emerging trends and needs in polydopamine research, including the use of dopamine analogs, nitrogen-free polyphenolic precursors, and improvement of coating mechanical properties.

/pdf/polydopamine-surface-chemistry-a-decade-of-discovery-1zduh7yuyy.pdf

Polydopamine Surface Chemistry: A Decade of Discovery.

We show that several machine learning estimators, including square-root LASSO (Least Absolute Shrinkage and Selection) and regularized logistic regression can be represented as solutions to distributionally robust optimization (DRO) problems. The associated uncertainty regions are based on suitably defined Wasserstein distances. Hence, our representations allow us to view regularization as a result of introducing an artificial adversary that perturbs the empirical distribution to account for out-of-sample effects in loss estimation. In addition, we introduce RWPI (Robust Wasserstein Profile Inference), a novel inference methodology which extends the use of methods inspired by Empirical Likelihood to the setting of optimal transport costs (of which Wasserstein distances are a particular case). We use RWPI to show how to optimally select the size of uncertainty regions, and as a consequence, we are able to choose regularization parameters for these machine learning estimators without the use of cross validation. Numerical experiments are also given to validate our theoretical findings.

Robust Wasserstein Profile Inference and Applications to Machine Learning

Cyclodextrin-based host–guest supramolecular hydrogel and its application in biomedical fields

Light-, pH- and thermal-responsive hydrogels with the triple-shape memory effect

We show that several machine learning estimators, including square-root least absolute shrinkage and selection and regularized logistic regression, can be represented as solutions to distributionally robust optimization problems. The associated uncertainty regions are based on suitably defined Wasserstein distances. Hence, our representations allow us to view regularization as a result of introducing an artificial adversary that perturbs the empirical distribution to account for out-of-sample effects in loss estimation. In addition, we introduce RWPI (robust Wasserstein profile inference), a novel inference methodology which extends the use of methods inspired by empirical likelihood to the setting of optimal transport costs (of which Wasserstein distances are a particular case). We use RWPI to show how to optimally select the size of uncertainty regions, and as a consequence we are able to choose regularization parameters for these machine learning estimators without the use of cross validation. Numerical experiments are also given to validate our theoretical findings.

Shape memory polymers (SMPs), with the capability to change from one or more temporary shapes to predetermined shapes in response to an external stimulus, have attracted much interest from both academia and industries. When introducing supramolecular interactions that have been featured as dynamic and reversible into the design of novel SMPs, intriguing and unique functionalities have been engendered and thereby broaden the potential applications of the SMPs to new territories. In this review, we summarize recent progress made in SMPs based on supramolecular interactions, provide insight into the material design and shape memory mechanism, elucidate and evaluate their properties and performance, and point out opportunities and applications of SMPs.

Yang Kang

Papers

Robust Wasserstein Profile Inference and Applications to Machine Learning

Cyclodextrin-based host–guest supramolecular hydrogel and its application in biomedical fields

Light-, pH- and thermal-responsive hydrogels with the triple-shape memory effect

Robust Wasserstein Profile Inference and Applications to Machine Learning

Shape Memory Polymers Based on Supramolecular Interactions