The monotonicity results and sharp inequalities for some power-type means of two arguments

For $a,b>0$ with $a\neq b$, we define M_{p}=M^{1/p}(a^{p},b^{p})\text{if}p\neq 0 \text{and} M_{0}=\sqrt{ab}, where $M=A,He,L,I,P,T,N,Z$ and $Y$ stand for the arithmetic mean, Heronian mean, logarithmic mean, identric (exponential) mean, the first Seiffert mean, the second Seiffert mean, Neuman-S\'{a}ndor mean, power-exponential mean and exponential-geometric mean, respectively. Generally, if $M$ is a mean of $a$ and $b$, then $M_{p}$ is also, and call "power-type mean". We prove the power-type means $P_{p}$, $T_{p}$, $N_{p}$, $Z_{p}$ are increasing in $p$ on $\mathbb{R}$ and establish sharp inequalities among power-type means $A_{p}$, $He_{p}$, $L_{p}$, $I_{p}$, $P_{p}$, $N_{p}$, $Z_{p}$, $Y_{p}$% . From this a very nice chain of inequalities for these means L_{2}<P<N_{1/2}<He<A_{2/3}<I<Z_{1/3}<Y_{1/2} follows. Lastly, a conjecture is proposed.Comment: 11 page

To Learn or Not to Learn Features for Deformable Registration?

Feature-based registration has been popular with a variety of features ranging from voxel intensity to Self-Similarity Context (SSC). In this paper, we examine the question on how features learnt using various Deep Learning (DL) frameworks can be used for deformable registration and whether this feature learning is necessary or not. We investigate the use of features learned by different DL methods in the current state-of-the-art discrete registration framework and analyze its performance on 2 publicly available datasets. We draw insights into the type of DL framework useful for feature learning and the impact, if any, of the complexity of different DL models and brain parcellation methods on the performance of discrete registration. Our results indicate that the registration performance with DL features and SSC are comparable and stable across datasets whereas this does not hold for low level features.Comment: 9 pages, 4 figure

Service Orientation and the Smart Grid state and trends

The energy market is undergoing major changes, the most notable of which is the transition from a hierarchical closed system toward a more open one highly based on a “smart” information-rich infrastructure. This transition calls for new information and communication technologies infrastructures and standards to support it. In this paper, we review the current state of affairs and the actual technologies with respect to such transition. Additionally, we highlight the contact points between the needs of the future grid and the advantages brought by service-oriented architectures.

Detecting and Responding to Concept Drift in Business Processes

Concept drift, which refers to changes in the underlying process structure or customer behaviour over time, is inevitable in business processes, causing challenges in ensuring that the learned model is a proper representation of the new data. Due to factors such as seasonal effects and policy updates, concept drifts can occur in customer transitions and time spent throughout the process, either suddenly or gradually. In a concept drift context, we can discard the old data and retrain the model using new observations (sudden drift) or combine the old data with the new data to update the model (gradual drift) or maintain the model as unchanged (no drift). In this paper, we model a response to concept drift as a sequential decision making problem by combing a hierarchical Markov model and a Markov decision process (MDP). The approach can detect concept drift, retrain the model and update customer profiles automatically. We validate the proposed approach on 68 artificial datasets and a real-world hospital billing dataset, with experimental results showing promising performance

Wilson Loops in N=2 Super-Yang-Mills from Matrix Model

We compute the expectation value of the circular Wilson loop in N=2 supersymmetric Yang-Mills theory with N_f=2N hypermultiplets. Our results indicate that the string tension in the dual string theory scales as the logarithm of the 't Hooft coupling.Comment: 37 pages, 9 figures; v2: Numerical factors corrected, simple derivation of Wilson loop and discussion of continuation to complex lambda added; v3: instanton partition function re-analyzed in order to take into account a contribution of the hypermultiplet

Room-Temperature Routes Toward the Creation of Zinc Oxide Films from Molecular Precursors

The fabrication of “flexible” electronics on plastic substrates with low melting points requires the development of thin-film deposition techniques that operate at low temperatures. This is easily achieved with vacuum- or solution-processed molecular or polymeric semiconductors, but oxide materials remain a significant challenge. Here, we show that zinc oxide (ZnO) can be prepared using only room-temperature processes, with the molecular thin-film precursor zinc phthalocyanine (ZnPc), followed by UV-light treatment in vacuum to elicit degradation of the organic components and transformation of the deposited film to the oxide material. The degradation mechanism was assessed by studying the influence of the atmosphere during the reaction: it was particularly sensitive to the oxygen pressure in the chamber and optimal degradation conditions were established as 3 mbar with 40% oxygen in nitrogen. The morphology of the film remained relatively unchanged during the reaction, but a detailed analysis of its composition using both scanning transmission electron microscopy and secondary ion mass spectrometry revealed that a 40 nm thick layer containing ZnO results from the 100 nm thick precursor after complete reaction. Our methodology represents a simple route for the fabrication of oxides and multilayer structures that can be easily integrated into current molecular thin-film growth setups, without the need for a high-temperature step

Field-induced polarisation of Dirac valleys in bismuth

Electrons are offered a valley degree of freedom in presence of particular lattice structures. Manipulating valley degeneracy is the subject matter of an emerging field of investigation, mostly focused on charge transport in graphene. In bulk bismuth, electrons are known to present a threefold valley degeneracy and a Dirac dispersion in each valley. Here we show that because of their huge in-plane mass anisotropy, a flow of Dirac electrons along the trigonal axis is extremely sensitive to the orientation of in-plane magnetic field. Thus, a rotatable magnetic field can be used as a valley valve to tune the contribution of each valley to the total conductivity. According to our measurements, charge conductivity by carriers of a single valley can exceed four-fifth of the total conductivity in a wide range of temperature and magnetic field. At high temperature and low magnetic field, the three valleys are interchangeable and the three-fold symmetry of the underlying lattice is respected. As the temperature lowers and/or the magnetic field increases, this symmetry is spontaneously lost. The latter may be an experimental manifestation of the recently proposed valley-nematic Fermi liquid state.Comment: 14 pages + 5 pages of supplementary information; a slightly modified version will appear as an article in Nature physic

Protective effects of hydrogen enriched saline on liver ischemia reperfusion injury by reducing oxidative stress and HMGB1 release

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