The Mechanics and Statistics of Active Matter

Active particles contain internal degrees of freedom with the ability to take in and dissipate energy and, in the process, execute systematic movement. Examples include all living organisms and their motile constituents such as molecular motors. This article reviews recent progress in applying the principles of nonequilibrium statistical mechanics and hydrodynamics to form a systematic theory of the behavior of collections of active particles-active matter-with only minimal regard to microscopic details. A unified view of the many kinds of active matter is presented, encompassing not only living systems but inanimate analogs. Theory and experiment are discussed side by side.

Revision of the scolopendrid centipede digitipes Attems, 1930, from India (Chilopoda: Scolopendromorpha): reconciling molecular and morphological estimates of species diversity

Recent work on molecular phylogenetics of Scolopendridae from the Western Ghats, Peninsular India, has suggested the presence of six cryptic species of the otostigmine Digitipes Attems, 1930, together with three species described in previous taxonomic work by Jangi and Dass (1984). Digitipes is the correct generic attribution for a monophyletic group of Indian species, these being united with three species from tropical Africa (including the type) that share a distomedial process on the ultimate leg femur of males that is otherwise unknown in Otostigminae. Second maxillary characters previously used in the diagnosis of Digitipes are dismissed because Indian species do not possess the putatively diagnostic character states. Two new species from the Western Ghats that correspond to groupings identified based on monophyly, sequence divergence and coalescent analysis using molecular data are diagnosed based on distinct morphological characters. They are D. jangii and D. periyarensis n. spp. Three species named by Jangi and Dass (Digitipes barnabasi, D. coonoorensis and D. indicus) are revised based on new collections; D. indicus is a junior subjective synonym of Arthrorhabdus jonesii Verhoeff, 1938, the combination becoming Digitipes jonesii (Verhoeff, 1938) n. comb. The presence of Arthrorhabdus in India is accordingly refuted. Three putative species delimited by molecular and ecological data remain cryptic from the perspective of diagnostic morphological characters and are presently retained in D. barnabasi, D. jangii and D. jonesii. A molecularly-delimited species that resolved as sister group to a well-supported clade of Indian Digitipes is identified as Otostigmus ruficeps Pocock, 1890, originally described from a single specimen and revised herein. One Indian species originally assigned to Digitipes, D. gravelyi, deviates from confidently-assigned Digitipes with respect to several characters and is reassigned to Otostigmus, as O. gravelyi (Jangi and Dass, 1984) n. comb.

Mining Unit Tests for Discovery and Migration of Math APIs

Today's programming languages are supported by powerful third-party APIs. For a given application domain, it is common to have many competing APIs that provide similar functionality. Programmer productivity therefore depends heavily on the programmer's ability to discover suitable APIs both during an initial coding phase, as well as during software maintenance. The aim of this work is to support the discovery and migration of math APIs. Math APIs are at the heart of many application domains ranging from machine learning to scientific computations. Our approach, called MATHFINDER, combines executable specifications of mathematical computations with unit tests (operational specifications) of API methods. Given a math expression, MATHFINDER synthesizes pseudo-code comprised of API methods to compute the expression by mining unit tests of the API methods. We present a sequential version of our unit test mining algorithm and also design a more scalable data-parallel version. We perform extensive evaluation of MATHFINDER (1) for API discovery, where math algorithms are to be implemented from scratch and (2) for API migration, where client programs utilizing a math API are to be migrated to another API. We evaluated the precision and recall of MATHFINDER on a diverse collection of math expressions, culled from algorithms used in a wide range of application areas such as control systems and structural dynamics. In a user study to evaluate the productivity gains obtained by using MATHFINDER for API discovery, the programmers who used MATHFINDER finished their programming tasks twice as fast as their counterparts who used the usual techniques like web and code search, IDE code completion, and manual inspection of library documentation. For the problem of API migration, as a case study, we used MATHFINDER to migrate Weka, a popular machine learning library. Overall, our evaluation shows that MATHFINDER is easy to use, provides highly precise results across several math APIs and application domains even with a small number of unit tests per method, and scales to large collections of unit tests.