We report results of a search for light Dark Matter WIMPs with CDEX-1 experiment at the China Jinping Underground Laboratory, based on 53.9 kg-days of data from a p-type point-contact germanium detector enclosed by a NaI(Tl) crystal scintillator as anti-Compton detector. The event rate and spectrum above the analysis threshold of 475 eVee are consistent with the understood background model. Part of the allowed regions for WIMP-nucleus coherent elastic scattering at WIMP mass of 6-20 GeV are probed and excluded. Independent of interaction channels, this result contradicts the interpretation that the anomalous excesses of the CoGeNT experiment are induced by Dark Matter, since identical detector techniques are used in both experiments. PACS numbers: 95.35.+d, 98.70.Vc

The CDEX-1 experiment conducted a search of low-mass (< 10 GeV/c 2 ) Weakly Interacting Massive Particles (WIMPs) dark matter at the China Jinping Underground Laboratory using a ptype point-contact germanium detector with a fiducial mass of 915 g at a physics analysis threshold of 475 eVee. We report the hardware set-up, detector characterization, data acquisition and analysis procedures of this experiment. No excess of unidentified events are observed after subtraction of known background. Using 335.6 kg-days of data, exclusion constraints on the WIMP-nucleon spinindependent and spin-dependent couplings are derived.PACS numbers: 95.35.+d, 98.70.Vc

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The comparison with other detection methods shows the superior performance of this method, which indicates its potential for detecting seizure events in clinical practice. Additionally, much larger amounts of true continuous EEG data will be used to test the proposed method further in the future work.

We propose the SUð3Þ C × SUð3Þ L × Uð1Þ X model arising from SUð6Þ breaking. One family of the Standard Model (SM) fermions arises from two6 representations and one 15 representation of SUð6Þ gauge symmetry. To break the SUð3Þ C × SUð3Þ L × Uð1Þ X gauge symmetry down to the SM, we introduce three SUð3Þ L triplet Higgs fields, where two of them come from the6 representation while the other one from the 15 representation. We study the gauge boson masses and Higgs boson mass in detail, and find that the vacuum expectation value (VEV) of the Higgs field for SUð3Þ L × Uð1Þ X gauge symmetry breaking is around 10 TeV. The neutrino masses and mixing can be generated via the littlest inverse seesaw mechanism. In particular, we have normal hierarchy for neutrino masses and the lightest active neutrino is massless. Also, we consider constraints from the charged lepton flavor changing decays as well. Furthermore, introducing two SUð3Þ L adjoint fermions, one SUð3Þ C adjoint scalar, and one SUð3Þ L triplet scalar, we can achieve gauge coupling unification within 1%. These extra particles can provide a dark matter candidate as well.

The feature extraction and classification of brain signal is very significant in brain-computer interface (BCI). In this study, we describe an algorithm for motor imagery (MI) classification of electrocorticogram (ECoG)-based BCI. The proposed approach employs multi-resolution fractal measures and local binary pattern (LBP) operators to form a combined feature for characterizing an ECoG epoch recording from the right hemisphere of the brain. A classifier is trained by using the gradient boosting in conjunction with ordinary least squares (OLS) method. The fractal intercept, lacunarity and LBP features are extracted to classify imagined movements of either the left small finger or the tongue. Experimental results on dataset I of BCI competition III demonstrate the superior performance of our method. The cross-validation accuracy and accuracy is 90.6% and 95%, respectively. Furthermore, the low computational burden of this method makes it a promising candidate for real-time BCI systems.

Supersymmetry with hadronic R-parity violation in which the lightest neutralino decays into three quarks is still weakly constrained. This work aims to further improve the current search for this scenario by the boosted decision tree method with additional information from jet substructure. In particular, we find a deep neural network turns out to perform well in characterizing the neutralino jet substructure. We first construct a Convolutional Neutral Network (CNN) which is capable of tagging the neutralino jet in any signal process by using the idea of jet image. When applied to pure jet samples, such a CNN outperforms the N-subjettiness variable by a factor of a few in tagging efficiency. Moreover, we find the method, which combines the CNN output and jet invariant mass, can perform better and is applicable to a wider range of neutralino mass than the CNN alone. Finally, the ATLAS search for the signal of gluino pair production with subsequent decayg → qqχ 0 1 (→ qqq) is recasted as an application. In contrast to the pure sample, the heavy contamination among jets in this complex final state renders the discriminating powers of the CNN and N-subjettiness similar. By analyzing the jets substructure in events which pass the ATLAS cuts with our CNN method, the exclusion limit on gluino mass can be pushed up by ∼ 200 GeV for neutralino mass ∼ 100 GeV. * hustgj@itp.ac.cn