Flexible Queueing Architectures

We study a multi-server model with $n$ flexible servers and $n$ queues, connected through a bipartite graph, where the level of flexibility is captured by the graph's average degree, $d_n$. Applications in content replication in data centers, skill-based routing in call centers, and flexible supply chains are among our main motivations. We focus on the scaling regime where the system size $n$ tends to infinity, while the overall traffic intensity stays fixed. We show that a large capacity region and an asymptotically vanishing queueing delay are simultaneously achievable even under limited flexibility ($d_n \ll n$). Our main results demonstrate that, when $d_n\gg \ln n$, a family of expander-graph-based flexibility architectures has a capacity region that is within a constant factor of the maximum possible, while simultaneously ensuring a diminishing queueing delay for all arrival rate vectors in the capacity region. Our analysis is centered around a new class of virtual-queue-based scheduling policies that rely on dynamically constructed job-to-server assignments on the connectivity graph. For comparison, we also analyze a natural family of modular architectures, which is simpler but has provably weaker performance.Comment: Revised October 2016. A preliminary version of this paper appeared at the 2013 ACM Sigmetrics conference; the performance of the architectures proposed in the current paper is significantly better than the one in the conference versio

Efficient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing

Branching processes are a class of continuous-time Markov chains (CTMCs) with ubiquitous applications. A general difficulty in statistical inference under partially observed CTMC models arises in computing transition probabilities when the discrete state space is large or uncountable. Classical methods such as matrix exponentiation are infeasible for large or countably infinite state spaces, and sampling-based alternatives are computationally intensive, requiring a large integration step to impute over all possible hidden events. Recent work has successfully applied generating function techniques to computing transition probabilities for linear multitype branching processes. While these techniques often require significantly fewer computations than matrix exponentiation, they also become prohibitive in applications with large populations. We propose a compressed sensing framework that significantly accelerates the generating function method, decreasing computational cost up to a logarithmic factor by only assuming the probability mass of transitions is sparse. We demonstrate accurate and efficient transition probability computations in branching process models for hematopoiesis and transposable element evolution.Comment: 18 pages, 4 figures, 2 table

An Overview of STAR Experimental Results

With large acceptance and excellent particle identification, STAR is one of the best mid-rapidity collider experiments for studying high-energy nuclear collisions. The STAR experiment provides full information on initial conditions, properties of the hot and dense medium as well as the properties at freeze-out. In Au+Au collisions at $\sqrt{s_{NN}} = 200$ GeV, STAR's focus is on the nature of the sQGP produced at RHIC. In order to explore the properties of the QCD phase diagram, since 2010, the experiment has collected sizable data sets of Au+Au collisions at the lower collision energy region where the net-baryon density is large. At the 2014 Quark Matter Conference, the STAR experiment made 16 presentations that cover physics topics including {\it collective dynamics}, {\it electromagnetic probes}, {\it heavy flavor}, {\it initial state physics}, {\it jets}, {\it QCD phase diagram}, {\it thermodynamics and hadron chemistry}, and {\it future experimental facilities, upgrades, and instrumentation} [1-16]. In this overview we will highlight a few results from the STAR experiment, especially those from the recent measurements of the RHIC beam energy scan program. At the end, instead of a summary, we will discuss STAR's near future physics programs at RHIC.Comment: 8 pages, 8 figure

Designing Quantum Spin-Orbital Liquids in Artificial Mott Insulators

Quantum spin-orbital liquids are elusive strongly correlated states of matter that emerge from quantum frustration between spin and orbital degrees of freedom. A promising route towards the observation of those states is the creation of artificial Mott insulators where antiferromagnetic correlations between spins and orbitals can be designed. We show that Coulomb impurity lattices on the surface of gapped honeycomb substrates, such as graphene on SiC, can be used to simulate SU(4) symmetric spin-orbital lattice models. We exploit the property that massive Dirac fermions form mid-gap bound states with spin and valley degeneracies in the vicinity of a Coulomb impurity. Due to electronic repulsion, the antiferromagnetic correlations of the impurity lattice are driven by a super-exchange interaction with SU(4) symmetry, which emerges from the bound states degeneracy at quarter filling. We propose that quantum spin-orbital liquids can be engineered in artificially designed solid-state systems at vastly higher temperatures than achievable in optical lattices with cold atoms. We discuss the experimental setup and possible scenarios for candidate quantum spin-liquids in Coulomb impurity lattices of various geometries.Comment: 9 pages + supplementary materials, 4 figures; v2: published version, minor changes, references adde

Ab-initio calculations of spin tunneling through an indirect barrier

We use a fully relativistic layer Green's functions approach to investigate spin-dependent tunneling through a symmetric indirect band gap barrier like GaAs/AlAs/GaAs heterostructure along [100] direction. The method is based on Linear Muffin Tin Orbitals and it is within the Density Functional Theory (DFT) in the Local Density Approximation (LDA). We find that the results of our {\it ab-initio} calculations are in good agreement with the predictions of our previous empirical tight binding model [Phys. Rev. {\bf B}, 075313 (2006)]. In addition we show the $k_{||}$-dependence of the spin polarization which we did not previously include in the model. The {\it ab-initio} calculations indicate a strong $k_{||}$-dependence of the transmission and the spin polarization due to band non-parabolicity. A large window of 25-50 % spin polarization was found for a barrier of 8 AlAs monolayers at $k_{||}$ = 0.03 $2\pi/a$. Our calculations show clearly that the appearance of energy windows with significant spin polarization depends mostly on the location of transmission resonances and their corresponding zeros and not on the magnitude of the spin splitting in the barrier.Comment: 10 pages, 3 figure

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Liouville and Toda Solitons in M-theory

We study the general form of the equations for isotropic single-scalar, multi-scalar and dyonic $p$-branes in superstring theory and M-theory, and show that they can be cast into the form of Liouville, Toda (or Toda-like) equations. The general solutions describe non-extremal isotropic $p$-branes, reducing to the previously-known extremal solutions in limiting cases. In the non-extremal case, the dilatonic scalar fields are finite at the outer event horizon.Comment: Latex, 10 pages. Minor corrections to text and titl