Electrically conductive anodized aluminum coatings

A process for producing anodized aluminum with enhanced electrical conductivity, comprising anodic oxidation of aluminum alloy substrate, electrolytic deposition of a small amount of metal into the pores of the anodized aluminum, and electrolytic anodic deposition of an electrically conductive oxide, including manganese dioxide, into the pores containing the metal deposit; and the product produced by the process

Top quark associated production of the neutral top-pion at high energy e+e−e^{+}e^{-} colliders

In the context of topcolor-assisted technicolor (TC2) models, we calculate the associated production of the neutral top-pion $\pi_{t}^{0}$ with a pair of top quarks via the process $e^{+}e^{-}\longrightarrow t\bar{t}\pi_{t}^{0}$. We find that the production cross section is larger than that of the process $e^{+}e^{-}\longrightarrow t\bar{t}H$ both in the standard model (SM) and in the minimal supersymmetric SM. With reasonable values of the parameters in TC2 models, the cross section can reach $20fb$. The neutral top-pion $\pi_{t}^{0}$ may be direct observed via this process.Comment: Latex files, 10 pages and 3 figure

A SM-like Higgs near 125 GeV in low energy SUSY: a comparative study for MSSM and NMSSM

Motivated by the recent LHC hints of a Higgs boson around 125 GeV, we assume a SM-like Higgs with the mass 123-127 GeV and study its implication in low energy SUSY by comparing the MSSM and NMSSM. We consider various experimental constraints at 2-sigma level (including the muon g-2 and the dark matter relic density) and perform a comprehensive scan over the parameter space of each model. Then in the parameter space which is allowed by current experimental constraints and also predicts a SM-like Higgs in 123-127 GeV, we examine the properties of the sensitive parameters (like the top squark mass and the trilinear coupling A_t) and calculate the rates of the di-photon signal and the VV^* (V=W,Z) signals at the LHC. Our typical findings are: (i) In the MSSM the top squark and A_t must be large and thus incur some fine-tuning, which can be much ameliorated in the NMSSM; (ii) In the MSSM a light stau is needed to enhance the di-photon rate of the SM-like Higgs to exceed its SM prediction, while in the NMSSM the di-photon rate can be readily enhanced in several ways; (iii) In the MSSM the signal rates of pp -> h -> VV^* at the LHC are never enhanced compared with their SM predictions, while in the NMSSM they may get enhanced significantly; (iv) A large part of the parameter space so far survived will be soon covered by the expected XENON100(2012) sensitivity (especially for the NMSSM).Comment: Version in JHEP (refs added

Transparent dense sodium

Under pressure, metals exhibit increasingly shorter interatomic distances. Intuitively, this response is expected to be accompanied by an increase in the widths of the valence and conduction bands and hence a more pronounced free-electron-like behaviour. But at the densities that can now be achieved experimentally, compression can be so substantial that core electrons overlap. This effect dramatically alters electronic properties from those typically associated with simple free-electron metals such as lithium and sodium, leading in turn to structurally complex phases and superconductivity with a high critical temperature. But the most intriguing prediction - that the seemingly simple metals Li and Na will transform under pressure into insulating states, owing to pairing of alkali atoms - has yet to be experimentally confirmed. Here we report experimental observations of a pressure-induced transformation of Na into an optically transparent phase at 200 GPa (corresponding to 5.0-fold compression). Experimental and computational data identify the new phase as a wide bandgap dielectric with a six-coordinated, highly distorted double-hexagonal close-packed structure. We attribute the emergence of this dense insulating state not to atom pairing, but to p-d hybridizations of valence electrons and their repulsion by core electrons into the lattice interstices. We expect that such insulating states may also form in other elements and compounds when compression is sufficiently strong that atomic cores start to overlap strongly.Comment: Published in Nature 458, 182-185 (2009

Ionic high-pressure form of elemental boron

Boron is an element of fascinating chemical complexity. Controversies have shrouded this element since its discovery was announced in 1808: the new 'element' turned out to be a compound containing less than 60-70 percent of boron, and it was not until 1909 that 99-percent pure boron was obtained. And although we now know of at least 16 polymorphs, the stable phase of boron is not yet experimentally established even at ambient conditions. Boron's complexities arise from frustration: situated between metals and insulators in the periodic table, boron has only three valence electrons, which would favour metallicity, but they are sufficiently localized that insulating states emerge. However, this subtle balance between metallic and insulating states is easily shifted by pressure, temperature and impurities. Here we report the results of high-pressure experiments and ab initio evolutionary crystal structure predictions that explore the structural stability of boron under pressure and, strikingly, reveal a partially ionic high-pressure boron phase. This new phase is stable between 19 and 89 GPa, can be quenched to ambient conditions, and has a hitherto unknown structure (space group Pnnm, 28 atoms in the unit cell) consisting of icosahedral B12 clusters and B2 pairs in a NaCl-type arrangement. We find that the ionicity of the phase affects its electronic bandgap, infrared adsorption and dielectric constants, and that it arises from the different electronic properties of the B2 pairs and B12 clusters and the resultant charge transfer between them.Comment: Published in Nature 453, 863-867 (2009

Learning visual and textual representations for multimodal matching and classification

AbstractMultimodal learning has been an important and challenging problem for decades, which aims to bridge the modality gap between heterogeneous representations, such as vision and language. Unlike many current approaches which only focus on either multimodal matching or classification, we propose a unified network to jointly learn multimodal matching and classification (MMC-Net) between images and texts. The proposed MMC-Net model can seamlessly integrate the matching and classification components. It first learns visual and textual embedding features in the matching component, and then generates discriminative multimodal representations in the classification component. Combining the two components in a unified model can help in improving their performance. Moreover, we present a multi-stage training algorithm by minimizing both of the matching and classification loss functions. Experimental results on four well-known multimodal benchmarks demonstrate the effectiveness and efficiency of the proposed approach, which achieves competitive performance for multimodal matching and classification compared to state-of-the-art approaches.Abstract Multimodal learning has been an important and challenging problem for decades, which aims to bridge the modality gap between heterogeneous representations, such as vision and language. Unlike many current approaches which only focus on either multimodal matching or classification, we propose a unified network to jointly learn multimodal matching and classification (MMC-Net) between images and texts. The proposed MMC-Net model can seamlessly integrate the matching and classification components. It first learns visual and textual embedding features in the matching component, and then generates discriminative multimodal representations in the classification component. Combining the two components in a unified model can help in improving their performance. Moreover, we present a multi-stage training algorithm by minimizing both of the matching and classification loss functions. Experimental results on four well-known multimodal benchmarks demonstrate the effectiveness and efficiency of the proposed approach, which achieves competitive performance for multimodal matching and classification compared to state-of-the-art approaches

CGMP: cloud-assisted green multimedia processing

With continued advancements of mobile computing and communications, emerging novel multimedia services and applications have attracted lots of attention and been developed for mobile users, such as mobile social network, mobile cloud medical treatment, mobile cloud game. However, because of limited resources on mobile terminals, it is of great challenge to improve the energy efficiency of multimedia services. In this paper, we propose a cloud-assisted green multimedia processing architecture (CGMP) based on mobile cloud computing. Specifically, the tasks of multimedia processing with energy-extensive consumption can be offloaded to the cloud, and the face recognition algorithm with improved principal component analysis and nearest neighbor classifier is realized on CGMP based cloud platform. Experimental results show that the proposed scheme can effectively save the energy consumption of the equipment

Independent Prognostic Significance of Monosomy 17 and Impact of Karyotype Complexity in Monosomal Karyotype/Complex Karyotype Acute Myeloid Leukemia: Results from Four ECOG-ACRIN Prospective Therapeutic Trials

The presence of a monosomal karyotype (MK+) and/or a complex karyotype (CK+) identifies subcategories of AML with poor prognosis. The prognostic significance of the most common monosomies (monosomy 5, monosomy 7, and monosomy 17) within MK+/CK+ AML is not well defined. We analyzed data from 1,592 AML patients age 17–93 years enrolled on ECOG-ACRIN therapeutic trials. The majority of MK+ patients (182/195; 93%) were MK+/CK+ with 87% (158/182) having ≥5 clonal abnormalities (CK≥ 5). MK+ patients with karyotype complexity ≤4 had a median overall survival (OS) of 0.4y compared to 1.0y for MK- with complexity ≤4 (p < 0.001), whereas no OS difference was seen in MK+ vs. MK- patients with CK≥ 5 (p = 0.82). Monosomy 5 (93%; 50/54) typically occurred within a highly complex karyotype and had no impact on OS (0.4y; p = 0.95). Monosomy 7 demonstrated no impact on OS in patients with CK≥ 5 (p = 0.39) or CK ≤ 4 (p = 0.44). Monosomy 17 appeared in 43% (68/158) of CK≥ 5 patients and demonstrated statistically significant worse OS (0.4y) compared to CK≥ 5 patients without monosomy 17 (0.5y; p = 0.012). Our data suggest that the prognostic impact of MK+ is limited to those with less complex karyotypes and that monosomy 17 may independently predict for worse survival in patients with AML

CycleMatch : A cycle-consistent embedding network for image-text matching

AbstractIn numerous multimedia and multi-modal tasks from image and video retrieval to zero-shot recognition to multimedia question and answering, bridging image and text representations plays an important and in some cases an indispensable role. To narrow the modality gap between vision and language, prior approaches attempt to discover their correlated semantics in a common feature space. However, these approaches omit the intra-modal semantic consistency when learning the inter-modal correlations. To address this problem, we propose cycle-consistent embeddings in a deep neural network for matching visual and textual representations. Our approach named as CycleMatch can maintain both inter-modal correlations and intra-modal consistency by cascading dual mappings and reconstructed mappings in a cyclic fashion. Moreover, in order to achieve a robust inference, we propose to employ two late-fusion approaches: average fusion and adaptive fusion. Both of them can effectively integrate the matching scores of different embedding features, without increasing the network complexity and training time. In the experiments on cross-modal retrieval, we demonstrate comprehensive results to verify the effectiveness of the proposed approach. Our approach achieves state-of-the-art performance on two well-known multi-modal datasets, Flickr30K and MSCOCO.Abstract In numerous multimedia and multi-modal tasks from image and video retrieval to zero-shot recognition to multimedia question and answering, bridging image and text representations plays an important and in some cases an indispensable role. To narrow the modality gap between vision and language, prior approaches attempt to discover their correlated semantics in a common feature space. However, these approaches omit the intra-modal semantic consistency when learning the inter-modal correlations. To address this problem, we propose cycle-consistent embeddings in a deep neural network for matching visual and textual representations. Our approach named as CycleMatch can maintain both inter-modal correlations and intra-modal consistency by cascading dual mappings and reconstructed mappings in a cyclic fashion. Moreover, in order to achieve a robust inference, we propose to employ two late-fusion approaches: average fusion and adaptive fusion. Both of them can effectively integrate the matching scores of different embedding features, without increasing the network complexity and training time. In the experiments on cross-modal retrieval, we demonstrate comprehensive results to verify the effectiveness of the proposed approach. Our approach achieves state-of-the-art performance on two well-known multi-modal datasets, Flickr30K and MSCOCO