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In pattern recognition, feature extraction techniques are widely employed to reduce the dimensionality of data and to enhance the discriminatory information. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two most popular linear dimensionality reduction methods. However, PCA is not very effective for the extraction of the most discriminant features and LDA is not stable due to the small sample size problem. In this paper, we propose some new (linear and nonlinear) feature extractors based on maximum margin criterion (MMC). Geometrically, feature extractors based on MMC maximize the (average) margin between classes after dimensionality reduction. It is shown that MMC can represent class separability better than PCA. As a connection to LDA, we may also derive LDA from MMC by incorporating some constraints. By using some other constraints, we establish a new linear feature extractor that does not suffer from the small sample size problem, which is known to cause serious stability problems for LDA. The kernelized (nonlinear) counterpart of this linear feature extractor is also established in the paper. Our extensive experiments demonstrate that the new feature extractors are effective, stable, and efficient.

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Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses proton-proton collision data corresponding to an integrated luminosity of 3.2 fb −1 at ffiffi ffi s p ¼ 13 TeV collected in 2015 with the ATLAS detector at the Large Hadron Collider. Events are required to have at least one jet with a transverse momentum above 250 GeV and no leptons. Several signal regions are considered with increasing missing-transverse-momentum requirements between E miss T > 250 GeV and E miss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model predictions. The results are translated into exclusion limits in models with large extra spatial dimensions, pair production of weakly interacting dark-matter candidates, and the production of supersymmetric particles in several compressed scenarios.

To satisfy the long-awaited need of new lithium-ion battery cathode materials with higher energy density, anionic redox chemistry has emerged as a new paradigm that is responsible for the high capacity in Li-rich layered oxides, for example, in Li1.2Ni0.13Mn0.54Co0.13O2 (Li-rich NMC). However, their marketimplementation has been plagued by certain bottlenecks originating intriguingly from the anionic redox activity itself. To fundamentally understand these bottlenecks (voltage fade, hysteresis and sluggish kinetics), we decided to target the ligand by switching to isostructural Li-rich layered sulfides. Herein, we designed new Li1.33-2y/3Ti0.67-y/3FeyS2 cathodes that enlist sustained reversible capacities of ~245 mAh•g-1 due to cumulated cationic (Fe 2+/3+) and anionic (S 2-/ S n-, n < 2) redox processes. In-depth electrochemical analysis revealed nearly zero irreversible capacity during the initial cycle, very small voltage fade upon long cycling, with low voltage hysteresis and fast kinetics, which contrasts positively with respect to their Li-rich NMC oxide analogues. Our study, further complemented with DFT calculations, demonstrates that moving from oxygen to sulfur as the ligand is an adequate strategy to partially mitigate the practical bottlenecks affecting anionic redox, although with an expected penalty in cell voltage. Altogether the present findings provide chemical clues on improving the holistic performance of anionic redox electrodes via ligand tuning, and hence strengthen the feasibility to ultimately capitalize on the energy benefits of oxygen redox.