Preliminary list of bryophytes of Heishiding Nature Reserve, Guangdong Province, China

Thirty-seven species of hepatics and 66 species of mosses are reported from Heishiding Nature Reserve, including eight taxa new to China and one species new to mainland China. The new taxa for China are Ectropothecium aneitense Broth., Gammiella tonkinensis (Broth. & Par.) Tan, G. touwii Tan, Hypnum fauriei Card., Papillidiopsis complanata (Dix.) Buck & Tan, Syrrhopodon prolifer Schwaegr. var. papillosum (C.Müll.) Reese, Trichosteleum pseudo-mammosum Fleisch., and Trichostomum crispulum Bruch; and the species new to mainland China is Isocladiella surcularis (Dix.) Tan & Mohamed. The phytogeography of the area and the bryophytes are discussed

Kinetic Ballooning Mode Under Steep Gradient: High Order Eigenstates and Mode Structure Parity Transition

The existence of kinetic ballooning mode (KBM) high order (non-ground) eigenstates for tokamak plasmas with steep gradient is demonstrated via gyrokinetic electromagnetic eigenvalue solutions, which reveals that eigenmode parity transition is an intrinsic property of electromagnetic plasmas. The eigenstates with quantum number $l=0$ for ground state and $l=1,2,3\ldots$ for non-ground states are found to coexist and the most unstable one can be the high order states ($l\neq0$). The conventional KBM is the $l=0$ state. It is shown that the $l=1$ KBM has the same mode structure parity as the micro-tearing mode (MTM). In contrast to the MTM, the $l=1$ KBM can be driven by pressure gradient even without collisions and electron temperature gradient. The relevance between various eigenstates of KBM under steep gradient and edge plasma physics is discussed.Comment: 6 pages, 6 figure

Efficient Optimization of Performance Measures by Classifier Adaptation

In practical applications, machine learning algorithms are often needed to learn classifiers that optimize domain specific performance measures. Previously, the research has focused on learning the needed classifier in isolation, yet learning nonlinear classifier for nonlinear and nonsmooth performance measures is still hard. In this paper, rather than learning the needed classifier by optimizing specific performance measure directly, we circumvent this problem by proposing a novel two-step approach called as CAPO, namely to first train nonlinear auxiliary classifiers with existing learning methods, and then to adapt auxiliary classifiers for specific performance measures. In the first step, auxiliary classifiers can be obtained efficiently by taking off-the-shelf learning algorithms. For the second step, we show that the classifier adaptation problem can be reduced to a quadratic program problem, which is similar to linear SVMperf and can be efficiently solved. By exploiting nonlinear auxiliary classifiers, CAPO can generate nonlinear classifier which optimizes a large variety of performance measures including all the performance measure based on the contingency table and AUC, whilst keeping high computational efficiency. Empirical studies show that CAPO is effective and of high computational efficiency, and even it is more efficient than linear SVMperf.Comment: 30 pages, 5 figures, to appear in IEEE Transactions on Pattern Analysis and Machine Intelligence, 201