Hexapod Coloron at the LHC

Instead of the usual dijet decay, the coloron may mainly decay into its own "Higgs bosons", which subsequently decay into many jets. This is a general feature of the renormalizable coloron model, where the corresponding "Higgs bosons" are a color-octet $\Theta$ and a color-singlet $\phi_I$. In this paper, we perform a detailed collider study for the signature of $pp \rightarrow G' \rightarrow (\Theta \rightarrow gg) (\phi_I \rightarrow gg q\bar{q})$ with the coloron $G'$ as a six-jet resonance. For a light $\phi_I$ below around 0.5 TeV, it may be boosted and behave as a four-prong fat jet. We also develop a jet-substructure-based search strategy to cover this boosted $\phi_I$ case. Independent of whether $\phi_I$ is boosted or not, the 13 TeV LHC with 100 fb$^{-1}$ has great discovery potential for a coloron with the mass sensitivity up to 5 TeV.Comment: 18 pages, 10 figure

Sketch-a-Net that Beats Humans

We propose a multi-scale multi-channel deep neural network framework that, for the first time, yields sketch recognition performance surpassing that of humans. Our superior performance is a result of explicitly embedding the unique characteristics of sketches in our model: (i) a network architecture designed for sketch rather than natural photo statistics, (ii) a multi-channel generalisation that encodes sequential ordering in the sketching process, and (iii) a multi-scale network ensemble with joint Bayesian fusion that accounts for the different levels of abstraction exhibited in free-hand sketches. We show that state-of-the-art deep networks specifically engineered for photos of natural objects fail to perform well on sketch recognition, regardless whether they are trained using photo or sketch. Our network on the other hand not only delivers the best performance on the largest human sketch dataset to date, but also is small in size making efficient training possible using just CPUs.Comment: Accepted to BMVC 2015 (oral

Efficient self protection algorithms for static wireless sensor networks

Abstract—Wireless sensor networks have been widely used in many surveillance applications. Due to the importance of sensor nodes in such applications, certain level of protection needs to be provided to them. We study the self protection problem for static wireless sensor networks in this paper. Self protection problem focuses on using sensor nodes to provide protection to themselves instead of the target objects or certain target area, so that the sensor nodes can resist the attacks targeting on them directly. A wireless sensor network is p-self-protected, if for any wireless sensor there are at least p active sensors that can monitor it. The problem finding minimum p-self-protection is NP-complete and no efficient self protection algorithms have been proposed. In this paper, we provide efficient centralized and distributed algorithms with constant approximation ratio for minimum p-self-protection problem. In addition, we design efficient distributed algorithms to not only achieve p-self-protection but also maintain the connectivity of all active sensors. Our simulation confirms the performances of proposed algorithms. I

Machine learning reconstruction of depth-dependent thermal conductivity profile from frequency-domain thermoreflectance signals

Characterizing materials with spatially varying thermal conductivities is significant to unveil the structure-property relationship for a wide range of functional materials, such as chemical-vapor-deposited diamonds, ion-irradiated materials, nuclear materials under radiation, and battery electrode materials. Although the development of thermal conductivity microscopy based on time/frequency-domain thermoreflectance (TDTR/FDTR) enabled in-plane scanning of thermal conductivity profile, measuring depth-dependent thermal conductivity remains challenging. This work proposed a machine-learning-based reconstruction method for extracting depth-dependent thermal conductivity K(z) directly from frequency-domain phase signals. We demonstrated that the simple supervised-learning algorithm kernel ridge regression (KRR) can reconstruct K(z) without requiring pre-knowledge about the functional form of the profile. The reconstruction method can not only accurately reproduce typical K(z) distributions such as the pre-assumed exponential profile of chemical-vapor-deposited (CVD) diamonds and Gaussian profile of ion-irradiated materials, but also complex profiles artificially constructed by superimposing Gaussian, exponential, polynomial, and logarithmic functions. In addition, the method also shows excellent performances of reconstructing K(z) of ion-irradiated semiconductors from Fourier-transformed TDTR signals. This work demonstrates that combining machine learning with pump-probe thermoreflectance is an effective way for depth-dependent thermal property mapping

Relativistic symmetry breaking in light kaonic nuclei

As the experimental data from kaonic atoms and $K^{-}N$ scatterings imply that the $K^{-}$-nucleon interaction is strongly attractive at saturation density, there is a possibility to form $K^{-}$-nuclear bound states or kaonic nuclei. In this work, we investigate the ground-state properties of the light kaonic nuclei with the relativistic mean field theory. It is found that the strong attraction between $K^{-}$ and nucleons reshapes the scalar and vector meson fields, leading to the remarkable enhancement of the nuclear density in the interior of light kaonic nuclei and the manifest shift of the single-nucleon energy spectra and magic numbers therein. As a consequence, the pseudospin symmetry is shown to be violated together with enlarged spin-orbit splittings in these kaonic nuclei.Comment: 15 pages, 7 figure