Unbound Protein-Protein Docking Selections by the DFIRE-based Statistical Pair Potential

A newly developed statistical pair potential based on Distance-scaled Finite Ideal-gas REference (DFIRE) state is applied to unbound protein-protein docking structure selections. The performance of the DFIRE energy function is compared to those of the well-established ZDOCK energy scores and RosettaDock energy function using the comprehensive decoy sets generated by ZDOCK and RosettaDock. Despite significant difference in the functional forms and complexities of the three energy scores, the differences in overall performance for docking structure selections are small between DFIRE and ZDOCK2.3 and between DFIRE and RosettaDock. This result is remarkable considering that a single-term DFIRE energy function was originally designed for monomer proteins while multiple-term energy functions of ZDOCK and RosettaDock were specifically optimized for docking. This provides hope that the accuracy of the existing energy functions for docking can be improved

Practical Design and Implementation of Metamaterial-Enhanced Magnetic Induction Communication

Although wireless communications in complex environments, such as underground, underwater, and indoor, can enable a large number of novel applications, their performances are constrained by lossy media and complicated structures. Magnetic Induction (MI) has been proved to be an efficient solution to achieve reliable communication in such environments. However, due to the small coil antenna's physical limitation, MI's communication range is still very limited if devices are required to be portable. To this end, Metamaterial-enhanced Magnetic Induction (M$^2$I) communication has been proposed and the theoretical results predict that it can significantly increase the communication performance, namely, data rate and communication range. Nevertheless, currently, the real implementation of M$^2$I is still a challenge and there is no guideline on design and fabrication of spherical metamaterials. In this paper, a practical design is proposed by leveraging a spherical coil array to realize M$^2$I. We prove that the effectively negative permeability can be achieved and there exists a resonance condition where the radiated magnetic field can be significantly amplified. The radiation and communication performances are evaluated and full-wave simulation is conducted to validate the design objectives. By using the spherical coil array-based M$^2$I, the communication range can be significantly extended, exactly as we predicted in the ideal M$^2$I model. Finally, the proposed M$^2$I antenna is implemented and tested in various environments.Comment: arXiv admin note: text overlap with arXiv:1510.0846

A statistical mechanical approach to restricted integer partition functions

The main aim of this paper is twofold: (1) Suggesting a statistical mechanical approach to the calculation of the generating function of restricted integer partition functions which count the number of partitions --- a way of writing an integer as a sum of other integers under certain restrictions. In this approach, the generating function of restricted integer partition functions is constructed from the canonical partition functions of various quantum gases. (2) Introducing a new type of restricted integer partition functions corresponding to general statistics which is a generalization of Gentile statistics in statistical mechanics; many kinds of restricted integer partition functions are special cases of this restricted integer partition function. Moreover, with statistical mechanics as a bridge, we reveals a mathematical fact: the generating function of restricted integer partition function is just the symmetric function which is a class of functions being invariant under the action of permutation groups. Using the approach, we provide some expressions of restricted integer partition functions as examples

Canonical partition functions: ideal quantum gases, interacting classical gases, and interacting quantum gases

In statistical mechanics, for a system with fixed number of particles, e.g., a finite-size system, strictly speaking, the thermodynamic quantity needs to be calculated in the canonical ensemble. Nevertheless, the calculation of the canonical partition function is difficult.\textbf{ }In this paper, based on the mathematical theory of the symmetric function, we suggest a method for the calculation of the canonical partition function of\ ideal quantum gases, including ideal Bose, Fermi, and Gentile gases. Moreover, we express the canonical partition functions of interacting classical and quantum gases given by the classical and quantum cluster expansion methods in terms of the Bell polynomial in mathematics. The virial coefficients of ideal Bose, Fermi, and Gentile gases is calculated from the exact canonical partition function. The virial coefficients of interacting classical and quantum gases is calculated from the canonical partition function by using the expansion of the Bell polynomial, rather than calculated from the grand canonical potential

Calculating eigenvalues of many-body systems from partition functions

A method for calculating the eigenvalue of a many-body system without solving the eigenfunction is suggested. In many cases, we only need the knowledge of eigenvalues rather than eigenfunctions, so we need a method solving only the eigenvalue, leaving alone the eigenfunction. In this paper, the method is established based on statistical mechanics. In statistical mechanics, calculating thermodynamic quantities needs only the knowledge of eigenvalues and then the information of eigenvalues is embodied in thermodynamic quantities. The method suggested in the present paper is indeed a method for extracting the eigenvalue from thermodynamic quantities. As applications, we calculate the eigenvalues for some many-body systems. Especially, the method is used to calculate the quantum exchange energies in quantum many-body systems. Using the method, we also\ calculate the influence of the topological effect on eigenvalues. Moreover, we improve the result of the relation between the counting function and the heat kernel in literature

Monetary Cost Optimizations for Hosting Workflow-as-a-Service in IaaS Clouds

Recently, we have witnessed workflows from science and other data-intensive applications emerging on Infrastructure-asa-Service (IaaS) clouds, and many workflow service providers offering workflow as a service (WaaS). The major concern of WaaS providers is to minimize the monetary cost of executing workflows in the IaaS cloud. While there have been previous studies on this concern, most of them assume static task execution time and static pricing scheme, and have the QoS notion of satisfying a deterministic deadline. However, cloud environment is dynamic, with performance dynamics caused by the interference from concurrent executions and price dynamics like spot prices offered by Amazon EC2. Therefore, we argue that WaaS providers should have the notion of offering probabilistic performance guarantees for individual workflows on IaaS clouds. We develop a probabilistic scheduling framework called Dyna to minimize the monetary cost while offering probabilistic deadline guarantees. The framework includes an A*-based instance configuration method for performance dynamics, and a hybrid instance configuration refinement for utilizing spot instances. Experimental results with three real-world scientific workflow applications on Amazon EC2 demonstrate (1) the accuracy of our framework on satisfying the probabilistic deadline guarantees required by the users; (2) the effectiveness of our framework on reducing monetary cost in comparison with the existing approaches

A Taxonomy and Survey on eScience as a Service in the Cloud

Cloud computing has recently evolved as a popular computing infrastructure for many applications. Scientific computing, which was mainly hosted in private clusters and grids, has started to migrate development and deployment to the public cloud environment. eScience as a service becomes an emerging and promising direction for science computing. We review recent efforts in developing and deploying scientific computing applications in the cloud. In particular, we introduce a taxonomy specifically designed for scientific computing in the cloud, and further review the taxonomy with four major kinds of science applications, including life sciences, physics sciences, social and humanities sciences, and climate and earth sciences. Our major finding is that, despite existing efforts in developing cloud-based eScience, eScience still has a long way to go to fully unlock the power of cloud computing paradigm. Therefore, we present the challenges and opportunities in the future development of cloud-based eScience services, and call for collaborations and innovations from both the scientific and computer system communities to address those challenges

Distance Majorization and Its Applications

The problem of minimizing a continuously differentiable convex function over an intersection of closed convex sets is ubiquitous in applied mathematics. It is particularly interesting when it is easy to project onto each separate set, but nontrivial to project onto their intersection. Algorithms based on Newton's method such as the interior point method are viable for small to medium-scale problems. However, modern applications in statistics, engineering, and machine learning are posing problems with potentially tens of thousands of parameters or more. We revisit this convex programming problem and propose an algorithm that scales well with dimensionality. Our proposal is an instance of a sequential unconstrained minimization technique and revolves around three ideas: the majorization-minimization (MM) principle, the classical penalty method for constrained optimization, and quasi-Newton acceleration of fixed-point algorithms. The performance of our distance majorization algorithms is illustrated in several applications.Comment: 29 pages, 6 figure

Reshaped Wirtinger Flow and Incremental Algorithm for Solving Quadratic System of Equations

We study the phase retrieval problem, which solves quadratic system of equations, i.e., recovers a vector $\boldsymbol{x}\in \mathbb{R}^n$ from its magnitude measurements $y_i=|\langle \boldsymbol{a}_i, \boldsymbol{x}\rangle|, i=1,..., m$. We develop a gradient-like algorithm (referred to as RWF representing reshaped Wirtinger flow) by minimizing a nonconvex nonsmooth loss function. In comparison with existing nonconvex Wirtinger flow (WF) algorithm \cite{candes2015phase}, although the loss function becomes nonsmooth, it involves only the second power of variable and hence reduces the complexity. We show that for random Gaussian measurements, RWF enjoys geometric convergence to a global optimal point as long as the number $m$ of measurements is on the order of $n$, the dimension of the unknown $\boldsymbol{x}$. This improves the sample complexity of WF, and achieves the same sample complexity as truncated Wirtinger flow (TWF) \cite{chen2015solving}, but without truncation in gradient loop. Furthermore, RWF costs less computationally than WF, and runs faster numerically than both WF and TWF. We further develop the incremental (stochastic) reshaped Wirtinger flow (IRWF) and show that IRWF converges linearly to the true signal. We further establish performance guarantee of an existing Kaczmarz method for the phase retrieval problem based on its connection to IRWF. We also empirically demonstrate that IRWF outperforms existing ITWF algorithm (stochastic version of TWF) as well as other batch algorithms.Comment: Part of this draft is accepted to NIPS 201

Acoustic scattering theory without large-distance asymptotics

In conventional acoustic scattering theory, a large-distance asymptotic approximation is employed. In this approximation, a far-field pattern, an asymptotic approximation of the exact result, is used to describe a scattering process. The information of the distance between the target and the observer, however, is lost in the large-distance asymptotic approximation. In this paper, we provide a rigorous theory of acoustic scattering without the large-distance asymptotic approximation. The acoustic scattering treatment developed in this paper provides an improved description for the acoustic wave outside the target. Moreover, as examples, we consider acoustic scattering on a rigid sphere and on a nonrigid sphere. We also illustrate the influence of the near target effect on the angular distribution of outgoing waves. It is shown that for long wavelength acoustic scattering, the near target effect must be reckoned in