Nanjing University

About: Nanjing University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 85961 authors who have published 105504 publications receiving 2289036 citations. The organization is also known as: NJU & Nanking University.

A new inexact alternating directions method for monotone variational inequalities

Abstract: The alternating directions method (ADM) is an effective method for solving a class of variational inequalities (VI) when the proximal and penalty parameters in sub-VI problems are properly selected. In this paper, we propose a new ADM method which needs to solve two strongly monotone sub-VI problems in each iteration approximately and allows the parameters to vary from iteration to iteration. The convergence of the proposed ADM method is proved under quite mild assumptions and flexible parameter conditions.

Performance of the ATLAS Trigger System in 2010

Abstract: Proton-proton collisions at root s = 7 TeV and heavy ion collisions at root(NN)-N-s = 2.76 TeV were produced by the LHC and recorded using the ATLAS experiment's trigger system in 2010. The LHC is designed with a maximum bunch crossing rate of 40 MHz and the ATLAS trigger system is designed to record approximately 200 of these per second. The trigger system selects events by rapidly identifying signatures of muon, electron, photon, tau lepton, jet, and B meson candidates, as well as using global event signatures, such as missing transverse energy. An overview of the ATLAS trigger system, the evolution of the system during 2010 and the performance of the trigger system components and selections based on the 2010 collision data are shown. A brief outline of plans for the trigger system in 2011 is presented.

Palaeozoic tectonic evolution of the Tianshan belt, NW China

Abstract: The Chinese Tianshan belt is a major part of the southern Central Asian Orogenic Belt, extending westward to Kyrgyzstan and Kazakhstan. Its Paleozoic tectonic evolution, crucial for understanding the amalgamation of Central Asia, comprises two stages of subduction-collision. The first collisional stage built the Eo-Tianshan Mountains, before a Visean unconformity, in which all structures are verging north. It implied a southward subduction of the Central Tianshan Ocean beneath the Tarim ac-tive margin, that induced the Ordovician-Early Devonian Central Tianshan arc, to the south of which the South Tianshan back-arc basin opened. During the Late Devonian, the closure of this ocean led to a collision between Central Tianshan arc and the Kazakhstan-Yili-North Tianshan Block, and subsequently closure of the South Tianhan back-arc basin, producing two su-ture zones, namely the Central Tianshan and South Tianshan suture zones where ophiolitic melanges and HP metamorphic rocks were emplaced northward. The second stage included the Late Devonian-Carboniferous southward subduction of North Tianshan Ocean beneath the Eo-Tianshan active margin, underlined by the Yili-North Tianshan arc, leading to the collision between the Kazakhstan-Yili-NTS plate and an inferred Junggar Block at Late Carboniferous-Early Permian time. The North Tianshan Suture Zone underlines likely the last oceanic closure of Central Asia Orogenic Belt; all the oceanic domains were consumed before the Middle Permian. The amalgamated units were affected by a Permian major wrenching, dextral in the Tianshan. The correlation with the Kazakh and Kyrgyz Tianshan is clarified. The Kyrgyz South Tianshan is equivalent to the whole part of Chinese Tianshan (CTS and STS) located to the south of Narat Fault and Main Tianshan Shear Zone; the so-called Middle Tianshan thins out toward the east. The South Tianshan Suture of Kyrgyzstan correlates with the Central Tianshan Suture of Chinese Tianshan. The evolution of this southern domain remains similar from east (Gangou area) to west until the Talas-Ferghana Fault, which reflects the convergence history between the Kazakhstan and Tarim blocks.

A k-nearest neighbor based algorithm for multi-label classification

Abstract: In multi-label learning, each instance in the training set is associated with a set of labels, and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, a multi-label lazy learning approach named ML-kNN is presented, which is derived from the traditional k-nearest neighbor (kNN) algorithm. In detail, for each new instance, its k-nearest neighbors are firstly identified. After that, according to the label sets of these neighboring instances, maximum a posteriori (MAP) principle is utilized to determine the label set for the new instance. Experiments on a real-world multi-label bioinformatic data show that ML-kNN is highly comparable to existing multi-label learning algorithms.