A response bias explanation of conservative human inference

Response bias explanation of conservative human inferenc

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

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

Entanglement of separate nitrogen-vacancy centers coupled to a whispering-gallery mode cavity

We present a quantum electrodynamical model involving nitrogen-vacancy centers coupled to a whispering-gallery mode cavity. Two schemes are considered to create W state and Bell state, respectively. One of the schemes makes use of the Raman transition with the cavity field virtually excited; The other enables the Bell state preparation and quantum information transfer by virtue of dark state evolution and adiabatic passage, which is tolerant to ambient noise and experimental parameter fluctuations. We justify our schemes by considering the experimental feasibility and challenge using currently available technology.Comment: 8 pages and 5 figure

Situational reasoning for road driving in an urban environment

Robot navigation in urban environments requires situational reasoning. Given the complexity of the environment and the behavior specified by traffic rules, it is necessary to recognize the current situation to impose the correct traffic rules. In an attempt to manage the complexity of the situational reasoning subsystem, this paper describes a finite state machine model to govern the situational reasoning process. The logic state machine and its interaction with the planning system are discussed. The approach was implemented on Alice, Team Caltech’s entry into the 2007 DARPA Urban Challenge. Results from the qualifying rounds are discussed. The approach is validated and the shortcomings of the implementation are identified

Deep Learning Based Vehicle Make-Model Classification

This paper studies the problems of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is proposed to combine an SSD (Single Shot Multibox Detector) model with a CNN (Convolutional Neural Network) model to train on the database. In the pipeline, we first detect the vehicles by following an algorithm which reduces the time for annotation. Then, we feed them into the CNN model. It is reached approximately 4% better classification accuracy result than using a conventional CNN model. Next, we propose to use the detected vehicles as ground truth bounding box (GTBB) of the images and feed them into an SSD model in another pipeline. At this stage, it is reached reasonable classification accuracy result without using perfectly shaped GTBB. Lastly, an application is implemented in a use case by using our proposed pipelines. It detects the unauthorized vehicles by comparing their license plate numbers and make & models. It is assumed that license plates are readable.Comment: 10 pages, ICANN 2018: Artificial Neural Networks and Machine Learnin

Spatial oscillations in the spontaneous emission rate of an atom inside a metallic wedge

A method of images is applied to study the spontaneous emission of an atom inside a metallic wedge with an opening angle of $\pi/N$, where N is an arbitrary positive integer. We show the method of images gives a rate formula consistent with that from Quantum Electrodynamics. Using the method of images, we show the correspondence between the oscillations in the spontaneous emission rate and the closed-orbits of emitted photon going away and returning to the atom inside the wedge. The closed-orbits can be readily constructed using the method of images and they are also extracted from the spontaneous emission rate.Comment: 8 figure

Action Recognition Based on Joint Trajectory Maps Using Convolutional Neural Networks

Recently, Convolutional Neural Networks (ConvNets) have shown promising performances in many computer vision tasks, especially image-based recognition. How to effectively use ConvNets for video-based recognition is still an open problem. In this paper, we propose a compact, effective yet simple method to encode spatio-temporal information carried in $3D$ skeleton sequences into multiple $2D$ images, referred to as Joint Trajectory Maps (JTM), and ConvNets are adopted to exploit the discriminative features for real-time human action recognition. The proposed method has been evaluated on three public benchmarks, i.e., MSRC-12 Kinect gesture dataset (MSRC-12), G3D dataset and UTD multimodal human action dataset (UTD-MHAD) and achieved the state-of-the-art results

Factors affecting metal mobilisation during oxidation of sulphidic, sandy wetland substrates

Most metals accumulate as sulphides under anoxic conditions in wetland substrates, reducing their bioavailability due to the solubility of metal sulphides. However, upon oxidation of these sulphides when the substrate is occasionally oxidised, metals can be released from the solid phase to the pore water or overlaying surface water. This release can be affected by the presence of carbonates, organic matter and clay. We compared changes of Cd, Cu and Zn mobility (CaCl2 extraction) during oxidation of a carbonate-rich and a carbonate-poor sulphidic, sandy wetland substrate. In addition, we studied how clay with low and high cation sorption capacity (bentonite and kaolinite, respectively) and organic matter (peat) can counteract Cd, Cu and Zn release during oxidation of both carbonate-rich and carbonate-poor sulphidic sediments. CaCl2-extractability of Cu, a measure for its availability, is low in both carbonate-poor and carbonate-rich substrates, whereas its variability is high. The availability of Cd and Zn is much higher and increases when peat is supplied to carbonate-poor substrates. A strong reduction of Cd and Zn extractability is observed when clay is added to carbonate-poor substrates. This reduction depends on the clay type. Most observations could be explained taking into account pH differences between treatments, with kaolinite resulting in a lower pH in comparison to bentonite. These pH differences affect the presence and characteristics of dissolved organic carbon and the metal speciation, which in turns affects the interaction of metals with the solid soil phase. In carbonate-rich substrates, Cd and Zn availability is lower and the effects of peat and clay amendment are less clear. The latter can also be attributed to the high pH and lack of pH differences between treatments