Fast-Light in a Photorefractive Crystal for Gravitational Wave Detection

We demonstrate superluminal light propagation using two frequency multiplexed pump beams to produce a gain doublet in a photorefractive crystal of Ce:BaTiO3. The two gain lines are obtained by two-wave mixing between a probe field and two individual pump fields. The angular frequencies of the pumps are symmetrically tuned from the frequency of the probe. The frequency difference between the pumps corresponds to the separation of the two gain lines; as it increases, the crystal gradually converts from normal dispersion without detuning to an anomalously dispersive medium. The time advance is measured as 0.28 sec for a pulse propagating through a medium with a 2Hz gain separation, compared to the same pulse propagating through empty space. We also demonstrate directly anomalous dispersion profile using a modfied experimental configuration. Finally, we discuss how anomalous dispersion produced this way in a faster photorefractive crystal (such as SPS: Sn2P2S6) could be employed to enhance the sensitivity-bandwidth product of a LIGO type gravitational wave detector augmented by a White Light Cavity.Comment: 14 pages, 5 figure

Further SEASAT SAR coastal ocean wave analysis

Analysis techniques used to exploit SEASAT synthetic aperture radar (SAR) data of gravity waves are discussed and the SEASAT SAR's ability to monitor large scale variations in gravity wave fields in both deep and shallow water is evaluated. The SAR analysis techniques investigated included motion compensation adjustments and the semicausal model for spectral analysis of SAR wave data. It was determined that spectra generated from fast Fourier transform analysis (FFT) of SAR wave data were not significantly altered when either range telerotation adjustments or azimuth focus shifts were used during processing of the SAR signal histories, indicating that SEASAT imagery of gravity waves is not significantly improved or degraded by motion compensation adjustments. Evaluation of the semicausal (SC) model using SEASAT SAR data from Rev. 974 indicates that the SC spectral estimates were not significantly better than the FFT results

NbSe3: Effect of Uniaxial Stress on the Threshold Field and Fermiology

We have measured the effect of uniaxial stress on the threshold field ET for the motion of the upper CDW in NbSe3. ET exhibits a critical behavior, ET ~ (1 - e/ec)^g, wher e is the strain, and ec is about 2.6% and g ~ 1.2. This ecpression remains valid over more than two decades of ET, up to the highest fields of about 1.5keV/m. Neither g nor ec is very sensitive to the impurity concentraction. The CDW transition temperature Tp decreases linearly with e at a rate dTp/de = -10K/%, and it does not show any anomaly near ec. Shubnikov de-Haas measurements show that the extremal area of the Fermi surface decreases with increasing strain. The results suggest that there is an intimate relationship between pinning of the upper CDW and the Fermiology of NbSe3.Comment: 4 pages, 5 figure

Exact Hybrid Covariance Thresholding for Joint Graphical Lasso

This paper considers the problem of estimating multiple related Gaussian graphical models from a $p$-dimensional dataset consisting of different classes. Our work is based upon the formulation of this problem as group graphical lasso. This paper proposes a novel hybrid covariance thresholding algorithm that can effectively identify zero entries in the precision matrices and split a large joint graphical lasso problem into small subproblems. Our hybrid covariance thresholding method is superior to existing uniform thresholding methods in that our method can split the precision matrix of each individual class using different partition schemes and thus split group graphical lasso into much smaller subproblems, each of which can be solved very fast. In addition, this paper establishes necessary and sufficient conditions for our hybrid covariance thresholding algorithm. The superior performance of our thresholding method is thoroughly analyzed and illustrated by a few experiments on simulated data and real gene expression data

Discovering High-Utility Itemsets at Multiple Abstraction Levels

High-Utility Itemset Mining (HUIM) is a relevant data mining task. The goal is to discover recurrent combinations of items characterized by high prot from transactional datasets. HUIM has a wide range of applications among which market basket analysis and service proling. Based on the observation that items can be clustered into domain-specic categories, a parallel research issue is generalized itemset mining. It entails generating correlations among data items at multiple abstraction levels. The extraction of multiple-level patterns affords new insights into the analyzed data from dierent viewpoints. This paper aims at discovering a novel pattern that combines the expressiveness of generalized and High-Utility itemsets. According to a user-defined taxonomy items are rst aggregated into semantically related categories. Then, a new type of pattern,namely the Generalized High-utility Itemset (GHUI), is extracted. It represents a combinations of items at different granularity levels characterized by high prot (utility). While protable combinations of item categories provide interesting high-level information, GHUIs at lower abstraction levels represent more specic correlationsamong protable items. A single-phase algorithm is proposed to efficiently discover utility itemsets at multiple abstraction levels. The experiments, which were performed on both real and synthetic data, demonstrate the effectiveness and usefulness of the proposed approach

Quantum Simulations on a Quantum Computer

We present a general scheme for performing a simulation of the dynamics of one quantum system using another. This scheme is used to experimentally simulate the dynamics of truncated quantum harmonic and anharmonic oscillators using nuclear magnetic resonance. We believe this to be the first explicit physical realization of such a simulation.Comment: 4 pages, 2 figures (\documentstyle[prl,aps,epsfig,amscd]{revtex}); to appear in Phys. Rev. Let

Experimental evidence for the interplay between individual wealth and transaction network

We conduct a market experiment with human agents in order to explore the structure of transaction networks and to study the dynamics of wealth accumulation. The experiment is carried out on our platform for 97 days with 2,095 effective participants and 16,936 times of transactions. From these data, the hybrid distribution (log-normal bulk and power-law tail) in the wealth is observed and we demonstrate that the transaction networks in our market are always scale-free and disassortative even for those with the size of the order of few hundred. We further discover that the individual wealth is correlated with its degree by a power-law function which allows us to relate the exponent of the transaction network degree distribution to the Pareto index in wealth distribution.Comment: 6 pages, 7 figure

Network Topology of an Experimental Futures Exchange

Many systems of different nature exhibit scale free behaviors. Economic systems with power law distribution in the wealth is one of the examples. To better understand the working behind the complexity, we undertook an empirical study measuring the interactions between market participants. A Web server was setup to administer the exchange of futures contracts whose liquidation prices were coupled to event outcomes. After free registration, participants started trading to compete for the money prizes upon maturity of the futures contracts at the end of the experiment. The evolving `cash' flow network was reconstructed from the transactions between players. We show that the network topology is hierarchical, disassortative and scale-free with a power law exponent of 1.02+-0.09 in the degree distribution. The small-world property emerged early in the experiment while the number of participants was still small. We also show power law distributions of the net incomes and inter-transaction time intervals. Big winners and losers are associated with high degree, high betweenness centrality, low clustering coefficient and low degree-correlation. We identify communities in the network as groups of the like-minded. The distribution of the community sizes is shown to be power-law distributed with an exponent of 1.19+-0.16.Comment: 6 pages, 12 figure

Opportunistic Data Collection and Routing in Segmented Wireless Sensor Networks

International audienceIn this paper we address routing in the context of segmented wireless sensor networks in which a mobile entity, known as MULE, may collect data from the different subnetworks and forward it to a sink for processing. The chosen settings are inspired by the potential application of wireless sensor networks for airport surface monitoring. In such an environment, the subnetworks could take advantage of airport service vehicles, buses or even taxiing aircraft to transfer information to the sink (e.g., control tower), without interfering with the regular functioning of the airport. Generally, this kind of communication problem is addressed in the literature considering a single subsink in each subnetwork. We consider in this paper the multiple subsinks case and propose two strategies to decide when and where (to which subsink) sensor nodes should transmit their sensing data. Through a dedicated simulation model we have developed, we assess and compare the performance of both strategies in terms of packet delivery ratio, power consumption and workload balance among subsinks. This paper is an intermediate step in the research of this problem, which evidences the benefit of storing the information on the subsinks and distributing it among them before the arrival of the MULE. Based on results, we provide some information on further works