Probing the QCD Critical Point with Higher Moments of Net-proton Multiplicity Distributions

Higher moments of event-by-event net-proton multiplicity distributions are applied to search for the QCD critical point in the heavy ion collisions. It has been demonstrated that higher moments as well as moment products are sensitive to the correlation length and directly connected to the thermodynamic susceptibilities computed in the Lattice QCD and Hadron Resonance Gas (HRG) model. In this paper, we will present measurements for kurtosis ($\kappa$), skewness ($S$) and variance ($\sigma^{2}$) of net-proton multiplicity distributions at the mid-rapidity ($|y|<0.5$) and $0.4<p_{T}<0.8$ GeV/$c$ for Au+Au collisions at $\sqrt{s_{NN}}$=19.6, 39, 62.4, 130 and 200 GeV, Cu+Cu collisions at $\sqrt{s_{NN}}$=22.4, 62.4 and 200 GeV, d+Au collisions at $\sqrt{s_{NN}}$=200 GeV and p+p collisions at $\sqrt{s_{NN}}$=62.4 and 200 GeV. The moment products $\kappa \sigma^{2}$ and $S \sigma$ of net-proton distributions, which are related to volume independent baryon number susceptibility ratio, are compared to the Lattice QCD and HRG model calculations. The $\kappa \sigma^{2}$ and $S \sigma$ of net-proton distributions are consistent with Lattice QCD and HRG model calculations at high energy, which support the thermalization of the colliding system. Deviations of $\kappa \sigma^{2}$ and $S \sigma$ for the Au+Au collisions at low energies from HRG model calculations are also observed.Comment: 10 pages, 8 figures, Proceedings of 27th Winter Workshon on Nuclear Dynamics. Feb. 6-13 (2011

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Nonlinear Realization of Spontaneously Broken N=1 Supersymmetry Revisited

This paper revisits the nonlinear realization of spontaneously broken N=1 supersymmetry. It is shown that the constrained superfield formalism can be reinterpreted in the language of standard realization of nonlinear supersymmetry via a new and simpler route. Explicit formulas of actions are presented for general renormalizable theories with or without gauge interactions. The nonlinear Wess-Zumino gauge is discussed and relations are pointed out for different definitions of gauge fields. In addition, a general procedure is provided to deal with theories of arbitrary Kahler potentials.Comment: 1+18 pages, LaTe

Matrix convex functions with applications to weighted centers for semidefinite programming

In this paper, we develop various calculus rules for general smooth matrix-valued functions and for the class of matrix convex (or concave) functions first introduced by Loewner and Kraus in 1930s. Then we use these calculus rules and the matrix convex function -log X to study a new notion of weighted convex centers for semidefinite programming (SDP) and show that, with this definition, some known properties of weighted centers for linear programming can be extended to SDP. We also show how the calculus rules for matrix convex functions can be used in the implementation of barrier methods for optimization problems involving nonlinear matrix functions.matrix convexity;matrix monotonicity;semidefinite programming

Harmonically trapped fermions in two dimensions: ground-state energy and contact of SU(2) and SU(4) systems via nonuniform lattice Monte Carlo

We study harmonically trapped, unpolarized fermion systems with attractive interactions in two spatial dimensions with spin degeneracies Nf = 2 and 4 and N/Nf = 1, 3, 5, and 7 particles per flavor. We carry out our calculations using our recently proposed quantum Monte Carlo method on a nonuniform lattice. We report on the ground-state energy and contact for a range of couplings, as determined by the binding energy of the two-body system, and show explicitly how the physics of the Nf-body sector dominates as the coupling is increased.Comment: 5 pages, 4 figure

Revisiting individual and group differences in thermal comfort based on ASHRAE database

Different thermal demands and preferences between individuals lead to a low occupant satisfaction rate, despite the high energy consumption by HVAC system. This study aims to quantify the difference in thermal demands, and to compare the influential factors which might lead to those differences. With the recently released ASHRAE Database, we quantitatively answered the following two research questions: which factors would lead to marked individual difference, and what the magnitude of this difference is. Linear regression has been applied to describe the macro-trend of how people feel thermally under different temperatures. Three types of factors which might lead to different thermal demands have been studied and compared in this study, i.e. individual factors, building characteristics and geographical factors. It was found that the local climate has the most marked impact on the neutral temperature, with an effect size of 3.5 °C; followed by country, HVAC operation mode and body built, which lead to a difference of more than 1 °C. In terms of the thermal sensitivity, building type and local climate are the most influential factors. Subjects in residential buildings or coming from Dry climate zone could accept 2.5 °C wider temperature range than those in office, education buildings or from Continental climate zone. The findings of this research could help thermal comfort researchers and designers to identify influential factors that might lead to individual difference, and could shed light on the feature selection for the development of personal comfort models

On defining partition entropy by inequalities

Partition entropy is the numerical metric of uncertainty within a partition of a finite set, while conditional entropy measures the degree of difficulty in predicting a decision partition when a condition partition is provided. Since two direct methods exist for defining conditional entropy based on its partition entropy, the inequality postulates of monotonicity, which conditional entropy satisfies, are actually additional constraints on its entropy. Thus, in this paper partition entropy is defined as a function of probability distribution, satisfying all the inequalities of not only partition entropy itself but also its conditional counterpart. These inequality postulates formalize the intuitive understandings of uncertainty contained in partitions of finite sets.We study the relationships between these inequalities, and reduce the redundancies among them. According to two different definitions of conditional entropy from its partition entropy, the convenient and unified checking conditions for any partition entropy are presented, respectively. These properties generalize and illuminate the common nature of all partition entropies

Effect of Dzyaloshinskii Moriya interaction on magnetic vortex

The effect of the Dzyaloshinskii Moriya interaction on the vortex in magnetic microdisk was investigated by micro magnetic simulation based on the Landau Lifshitz Gilbert equation. Our results show that the DM interaction modifies the size of the vortex core, and also induces an out of plane magnetization component at the edge and inside the disk. The DM interaction can destabilizes one vortex handedness, generate a bias field to the vortex core and couple the vortex polarity and chirality. This DM-interaction-induced coupling can therefore provide a new way to control vortex polarity and chirality

B→DsπB \to D_s \pi and the tree amplitude in B→π+π−B \to \pi^+ \pi^-

The recently-observed decay $B^0 \to D_s^+ \pi^-$ is expected to proceed mainly by means of a tree amplitude in the factorization limit: $B^0 \to \pi^- {(W^+)}^*$, ${(W^+)}^* \to D_s^+$. Under this assumption, we predict the corresponding contribution of the tree amplitude to $B^0 \to \pi^+ \pi^-$. We indicate the needed improvements in data that will allow a useful estimate of this amplitude with errors comparable to those accompanying other methods. Since the factorization hypothesis for this process goes beyond that proved in most approaches, we also discuss independent tests of this hypothesis.Comment: 7 pages, LaTeX, 1 figure, to be submitted to Phys. Rev. D (Brief Reports