A response bias explanation of conservative human inference

Response bias explanation of conservative human inferenc

An optimal linear solver for the Jacobian system of the extreme type-II Ginzburg--Landau problem

This paper considers the extreme type-II Ginzburg--Landau equations, a nonlinear PDE model for describing the states of a wide range of superconductors. Based on properties of the Jacobian operator and an AMG strategy, a preconditioned Newton--Krylov method is constructed. After a finite-volume-type discretization, numerical experiments are done for representative two- and three-dimensional domains. Strong numerical evidence is provided that the number of Krylov iterations is independent of the dimension $n$ of the solution space, yielding an overall solver complexity of O(n)

Strategic Vertical Pricing in the U.S. Butter Market

This article develops a methodology for empirically analyzing vertically strategic interactions in a multi-level supply channel. The model is used to analyze the vertical channel for U.S. butter manufacturing and retailing. Aggregating products to the firm level and using a nonlinear AIDS demand system under alternative strategic pricing assumptions is estimated using full information maximum likelihood (FIML) for seven geographic markets from 1998-2002. The market demand for butter was found to very price elastic. Furthermore, cross price elasticities between private labels and the two large national brands were also very elastic. The selected market structure was one indicating category profit maximization of national brands (separate from private label) at the retail level, Vertical Nash competition in the vertical channel, and Bretrand competition at the manufacturing level. Our results strongly suggest that the retail market for food products is impacted by the underlying vertical structure. The study provides useful measures of imperfect competition in the retail manufacturing sector.Vertical interaction, market structure, strategic pricing, market power, AIDS model, butter., Agribusiness, Agricultural and Food Policy, Consumer/Household Economics, Demand and Price Analysis, Industrial Organization, L13, L22, L66,

Robust H∞ feedback control for uncertain stochastic delayed genetic regulatory networks with additive and multiplicative noise

The official published version can found at the link below.Noises are ubiquitous in genetic regulatory networks (GRNs). Gene regulation is inherently a stochastic process because of intrinsic and extrinsic noises that cause kinetic parameter variations and basal rate disturbance. Time delays are usually inevitable due to different biochemical reactions in such GRNs. In this paper, a delayed stochastic model with additive and multiplicative noises is utilized to describe stochastic GRNs. A feedback gene controller design scheme is proposed to guarantee that the GRN is mean-square asymptotically stable with noise attenuation, where the structure of the controllers can be specified according to engineering requirements. By applying control theory and mathematical tools, the analytical solution to the control design problem is given, which helps to provide some insight into synthetic biology and systems biology. The control scheme is employed in a three-gene network to illustrate the applicability and usefulness of the design.This work was funded by Royal Society of the U.K.; Foundation for the Author of National Excellent Doctoral Dissertation of China. Grant Number: 2007E4; Heilongjiang Outstanding Youth Science Fund of China. Grant Number: JC200809; Fok Ying Tung Education Foundation. Grant Number: 111064; International Science and Technology Cooperation Project of China. Grant Number: 2009DFA32050; University of Science and Technology of China Graduate Innovative Foundation

Iris Recognition: The Consequences of Image Compression

Iris recognition for human identification is one of the most accurate biometrics, and its employment is expanding globally. The use of portable iris systems, particularly in law enforcement applications, is growing. In many of these applications, the portable device may be required to transmit an iris image or template over a narrow-bandwidth communication channel. Typically, a full resolution image (e.g., VGA) is desired to ensure sufficient pixels across the iris to be confident of accurate recognition results. To minimize the time to transmit a large amount of data over a narrow-bandwidth communication channel, image compression can be used to reduce the file size of the iris image. In other applications, such as the Registered Traveler program, an entire iris image is stored on a smart card, but only 4 kB is allowed for the iris image. For this type of application, image compression is also the solution. This paper investigates the effects of image compression on recognition system performance using a commercial version of the Daugman iris2pi algorithm along with JPEG-2000 compression, and links these to image quality. Using the ICE 2005 iris database, we find that even in the face of significant compression, recognition performance is minimally affected

Sketch-based Influence Maximization and Computation: Scaling up with Guarantees

Propagation of contagion through networks is a fundamental process. It is used to model the spread of information, influence, or a viral infection. Diffusion patterns can be specified by a probabilistic model, such as Independent Cascade (IC), or captured by a set of representative traces. Basic computational problems in the study of diffusion are influence queries (determining the potency of a specified seed set of nodes) and Influence Maximization (identifying the most influential seed set of a given size). Answering each influence query involves many edge traversals, and does not scale when there are many queries on very large graphs. The gold standard for Influence Maximization is the greedy algorithm, which iteratively adds to the seed set a node maximizing the marginal gain in influence. Greedy has a guaranteed approximation ratio of at least (1-1/e) and actually produces a sequence of nodes, with each prefix having approximation guarantee with respect to the same-size optimum. Since Greedy does not scale well beyond a few million edges, for larger inputs one must currently use either heuristics or alternative algorithms designed for a pre-specified small seed set size. We develop a novel sketch-based design for influence computation. Our greedy Sketch-based Influence Maximization (SKIM) algorithm scales to graphs with billions of edges, with one to two orders of magnitude speedup over the best greedy methods. It still has a guaranteed approximation ratio, and in practice its quality nearly matches that of exact greedy. We also present influence oracles, which use linear-time preprocessing to generate a small sketch for each node, allowing the influence of any seed set to be quickly answered from the sketches of its nodes.Comment: 10 pages, 5 figures. Appeared at the 23rd Conference on Information and Knowledge Management (CIKM 2014) in Shanghai, Chin

Simultaneous Measurements of Microwave Photoresistance and Cyclotron Reflection in the Multi-Photon Regime

We simultaneously measure photoresistance with electrical transport and plasmon-cyclotron resonance (PCR) using microwave reflection spectroscopy in high mobility GaAs/AlGaAs quantum wells under a perpendicular magnetic field. Multi-photon transitions are revealed as sharp peaks in the resistance and the cyclotron reflection on samples with various carrier densities. Our main finding is that plasmon coupling is relevant in the cyclotron reflection spectrum but has not been observed in the electrical conductivity signal. We discuss possible mechanisms relevant to reflection or dc conductivity signal to explain this discrepancy. We further confirm a trend that higher order multi-photon features can be observed using higher carrier density samples.Comment: 19 pages, 5 figure