Precision and long-term stability of clumped-isotope analysis of CO2 using a small-sector isotope ratio mass spectrometer

RATIONALE The ratio of the measured abundance of 13C18O bonding CO2 to its stochastic abundance, prescribed by the delta 13C and delta 18O values from a carbonate mineral, is sensitive to its growth temperature. Recently, clumped-isotope thermometry, which uses this ratio, has been adopted as a new tool to elucidate paleotemperatures quantitatively. METHODS Clumped isotopes in CO2 were measured with a small-sector isotope ratio mass spectrometer. CO2 samples digested from several kinds of calcium carbonates by phosphoric acid at 25 degrees C were purified using both cryogenic and gas-chromatographic separations, and their isotopic composition (delta 13C, delta 18O, Delta 47, Delta 48 and Delta 49 values) were then determined using a dual-inlet Delta XP mass spectrometer. RESULTS The internal precisions of the single gas Delta 47 measurements were 0.005 and 0.02 parts per thousand (1 SE) for the optimum and the routine analytical conditions, respectively, which are comparable with those obtained using a MAT 253 mass spectrometer. The long-term variations in the Delta 47 values for the in-house working standard and the heated CO2 gases since 2007 were close to the routine, single gas uncertainty while showing seasonal-like periodicities with a decreasing trend. Unlike the MAT 253, the Delta XP did not show any significant relationship between the Delta 47 and delta 47 values. CONCLUSIONS The Delta XP gave results that were approximately as precise as those of the MAT 253 for clumped-isotope analysis. The temporal stability of the Delta XP seemed to be lower, although an advantage of the Delta XP was that no dependency of delta 47 on Delta 47 was found. Copyright (c) 2012 John Wiley & Sons, Ltd.

Active Sequential Hypothesis Testing with Application to a Visual Search Problem

We consider a visual search problem studied by Sripati and Olson where the objective is to identify an oddball image embedded among multiple distractor images as quickly as possible. We model this visual search task as an active sequential hypothesis testing problem (ASHT problem). Chernoff in 1959 proposed a policy in which the expected delay to decision is asymptotically optimal. The asymptotics is under vanishing error probabilities. We first prove a stronger property on the moments of the delay until a decision, under the same asymptotics. Applying the result to the visual search problem, we then propose a ``neuronal metric'' on the measured neuronal responses that captures the discriminability between images. From empirical study we obtain a remarkable correlation (r = 0.90) between the proposed neuronal metric and speed of discrimination between the images. Although this correlation is lower than with the L-1 metric used by Sripati and Olson, this metric has the advantage of being firmly grounded in formal decision theory.

Truthful multi-armed bandit mechanisms for multi-slot sponsored search auctions

In pay-per-click sponsored search auctions which are currently extensively used by search engines, the auction for a keyword involves a certain number of advertisers (say k) competing for available slots (say m) to display their advertisements (ads for short). A sponsored search auction for a keyword is typically conducted for a number of rounds (say T). There are click probabilities mu(ij) associated with each agent slot pair (agent i and slot j). The search engine would like to maximize the social welfare of the advertisers, that is, the sum of values of the advertisers for the keyword. However, the search engine does not know the true values advertisers have for a click to their respective advertisements and also does not know the click probabilities. A key problem for the search engine therefore is to learn these click probabilities during the initial rounds of the auction and also to ensure that the auction mechanism is truthful. Mechanisms for addressing such learning and incentives issues have recently been introduced. These mechanisms, due to their connection to the multi-armed bandit problem, are aptly referred to as multi-armed bandit (MAB) mechanisms. When m = 1, exact characterizations for truthful MAB mechanisms are available in the literature. Recent work has focused on the more realistic but non-trivial general case when m > 1 and a few promising results have started appearing. In this article, we consider this general case when m > 1 and prove several interesting results. Our contributions include: (1) When, mu(ij)s are unconstrained, we prove that any truthful mechanism must satisfy strong pointwise monotonicity and show that the regret will be Theta T7) for such mechanisms. (2) When the clicks on the ads follow a certain click precedence property, we show that weak pointwise monotonicity is necessary for MAB mechanisms to be truthful. (3) If the search engine has a certain coarse pre-estimate of mu(ij) values and wishes to update them during the course of the T rounds, we show that weak pointwise monotonicity and type-I separatedness are necessary while weak pointwise monotonicity and type-II separatedness are sufficient conditions for the MAB mechanisms to be truthful. (4) If the click probabilities are separable into agent-specific and slot-specific terms, we provide a characterization of MAB mechanisms that are truthful in expectation.

Similarity relations in visual search predict rapid visual categorization

How do we perform rapid visual categorization?It is widely thought that categorization involves evaluating the similarity of an object to other category items, but the underlying features and similarity relations remain unknown. Here, we hypothesized that categorization performance is based on perceived similarity relations between items within and outside the category. To this end, we measured the categorization performance of human subjects on three diverse visual categories (animals, vehicles, and tools) and across three hierarchical levels (superordinate, basic, and subordinate levels among animals). For the same subjects, we measured their perceived pair-wise similarities between objects using a visual search task. Regardless of category and hierarchical level, we found that the time taken to categorize an object could be predicted using its similarity to members within and outside its category. We were able to account for several classic categorization phenomena, such as (a) the longer times required to reject category membership; (b) the longer times to categorize atypical objects; and (c) differences in performance across tasks and across hierarchical levels. These categorization times were also accounted for by a model that extracts coarse structure from an image. The striking agreement observed between categorization and visual search suggests that these two disparate tasks depend on a shared coarse object representation.

Performance of a class of multi-robot deploy and search strategies based on centroidal voronoi configurations

This article considers a class of deploy and search strategies for multi-robot systems and evaluates their performance. The application framework used is deployment of a system of autonomous mobile robots equipped with required sensors in a search space to gather information. The lack of information about the search space is modelled as an uncertainty density distribution. The agents are deployed to maximise single-step search effectiveness. The centroidal Voronoi configuration, which achieves a locally optimal deployment, forms the basis for sequential deploy and search (SDS) and combined deploy and search (CDS) strategies. Completeness results are provided for both search strategies. The deployment strategy is analysed in the presence of constraints on robot speed and limit on sensor range for the convergence of trajectories with corresponding control laws responsible for the motion of robots. SDS and CDS strategies are compared with standard greedy and random search strategies on the basis of time taken to achieve reduction in the uncertainty density below a desired level. The simulation experiments reveal several important issues related to the dependence of the relative performances of the search strategies on parameters such as the number of robots, speed of robots and their sensor range limits.

Short- and long-term temporal variations in salinity and the oxygen, carbon and hydrogen isotopic compositions of the Hooghly Estuary water, India

The Hooghly River estuary provides a unique experimental site to understand the effect of monsoonal river discharge on freshwater and seawater mixing. Water samples collected bi-weekly for a duration of 17 months were analyzed for salinity, delta O-18,delta C-13(DIC), as well as delta D to investigate the differential mixing of freshwater and seawater. The differences in salinity and delta O-18 of samples collected during low and high tides on the same day are strongly correlated suggesting a well mixed water column at our sampling site. Low salinity and depleted delta O-18 during monsoon is consistent with increased river discharge as well as high rainfall. We identified different slopes in a delta O-18 versus salinity plot for the estuary water samples collected during monsoon and non-monsoon seasons. This is driven by composition of the freshwater source which is dominated by rainwater during monsoon and rivers during non-monsoon months. Selected delta D analyses of samples indicate that groundwater contributes significantly to the Hooghly Estuary during low rainfall times of the year. delta C-13(DIC) measured in the water recorded low values towards the end of monsoon indicating low productivity (i.e. increased organic respiration) while progressively increasing delta C-13(DIC) values from October till January as well as during some of the pre-monsoon months can be explained by increasing productivity. Very low delta C-13(DIC) (similar to-20%0) suggests involvement of carbon derived from anaerobic oxidation of organics and/or methane with potential contribution from increased anthropogenic water supply. An estimate of seawater incursion into the Hooghly Estuary at different times of the year is obtained by using salinity data in a two-component mixing model. Presence of seawater was found maximum (31-37%) during February till July and lowest (less than or equal to 6%) from September till November. We notice a temporal offset between Ganges River discharge farther upstream at Farakka and salinity variation at the Hooghly Estuary. We believe that this time lag is a result of the physical distance between Farakka and Kakdweep (our sampling location) and put constraints on the travel time of river water during early monsoon. (c) 2012 Published by Elsevier B.V.

Near-optimal large-MIMO detection using randomized MCMC and randomized search algorithms

Low-complexity near-optimal detection of signals in MIMO systems with large number (tens) of antennas is getting increased attention. In this paper, first, we propose a variant of Markov chain Monte Carlo (MCMC) algorithm which i) alleviates the stalling problem encountered in conventional MCMC algorithm at high SNRs, and ii) achieves near-optimal performance for large number of antennas (e.g., 16×16, 32×32, 64×64 MIMO) with 4-QAM. We call this proposed algorithm as randomized MCMC (R-MCMC) algorithm. Second, we propose an other algorithm based on a random selection approach to choose candidate vectors to be tested in a local neighborhood search. This algorithm, which we call as randomized search (RS) algorithm, also achieves near-optimal performance for large number of antennas with 4-QAM. The complexities of the proposed R-MCMC and RS algorithms are quadratic/sub-quadratic in number of transmit antennas, which are attractive for detection in large-MIMO systems. We also propose message passing aided R-MCMC and RS algorithms, which are shown to perform well for higher-order QAM.

Clustering using Levy Flight Cuckoo Search

In this paper, a comparative study is carried using three nature-inspired algorithms namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Cuckoo Search (CS) on clustering problem. Cuckoo search is used with levy flight. The heavy-tail property of levy flight is exploited here. These algorithms are used on three standard benchmark datasets and one real-time multi-spectral satellite dataset. The results are tabulated and analysed using various techniques. Finally we conclude that under the given set of parameters, cuckoo search works efficiently for majority of the dataset and levy flight plays an important role.

On Selection of Search Space Dimension in Compressive Sampling Matching Pursuit

Compressive Sampling Matching Pursuit (CoSaMP) is one of the popular greedy methods in the emerging field of Compressed Sensing (CS). In addition to the appealing empirical performance, CoSaMP has also splendid theoretical guarantees for convergence. In this paper, we propose a modification in CoSaMP to adaptively choose the dimension of search space in each iteration, using a threshold based approach. Using Monte Carlo simulations, we show that this modification improves the reconstruction capability of the CoSaMP algorithm in clean as well as noisy measurement cases. From empirical observations, we also propose an optimum value for the threshold to use in applications.

Tutorial: implementing a pedestrian tracker using inertial sensors

Shoe-mounted inertial sensors offer a convenient way to track pedestrians in situations where other localization systems fail. This tutorial outlines a simple yet effective approach for implementing a reasonably accurate tracker. This Web extra presents the Matlab implementation and a few sample recordings for implementing the pedestrian inertial tracking system using an error-state Kalman filter for zero-velocity updates (ZUPTs) and orientation estimation.

SC-FDMA for multiuser communication on the downlink

Single-carrier frequency division multiple access (SC-FDMA) has become a popular alternative to orthogonal frequency division multiple access (OFDMA) in multiuser communication on the uplink. This is mainly due to the low peak-to-average power ratio (PAPR) of SC-FDMA compared to that of OFDMA. Long-term evolution (LTE) uses SC-FDMA on the uplink to exploit this PAPR advantage to reduce transmit power amplifier backoff in user terminals. In this paper, we show that SC-FDMA can be beneficially used for multiuser communication on the downlink as well. We present SC-FDMA transmit and receive signaling architectures for multiuser communication on the downlink. The benefits of using SC-FDMA on the downlink are that SC-FDMA can achieve i) significantly better bit error rate (BER) performance at the user terminal compared to OFDMA, and ii) improved PAPR compared to OFDMA which reduces base station (BS) power amplifier backoff (making BSs more green). SC-FDMA receiver needs to do joint equalization, which can be carried out using low complexity equalization techniques. For this, we present a local neighborhood search based equalization algorithm for SC-FDMA. This algorithm is very attractive both in complexity as well as performance. We present simulation results that establish the PAPR and BER performance advantage of SC-FDMA over OFDMA in multiuser SISO/MIMO downlink as well as in large-scale multiuser MISO downlink with tens to hundreds of antennas at the BS.

Solvation dynamics of tryptophan in water-dimethyl sulfoxide binary mixture: In search of molecular origin of composition dependent multiple anomalies

Experimental and simulation studies have uncovered at least two anomalous concentration regimes in water-dimethyl sulfoxide (DMSO) binary mixture whose precise origin has remained a subject of debate. In order to facilitate time domain experimental investigation of the dynamics of such binary mixtures, we explore strength or extent of influence of these anomalies in dipolar solvation dynamics by carrying out long molecular dynamics simulations over a wide range of DMSO concentration. The solvation time correlation function so calculated indeed displays strong composition dependent anomalies, reflected in pronounced non-exponential kinetics and non-monotonous composition dependence of the average solvation time constant. In particular, we find remarkable slow-down in the solvation dynamics around 10%-20% and 35%-50% mole percentage. We investigate microscopic origin of these two anomalies. The population distribution analyses of different structural morphology elucidate that these two slowing down are reflections of intriguing structural transformations in water-DMSO mixture. The structural transformations themselves can be explained in terms of a change in the relative coordination number of DMSO and water molecules, from 1DMSO:2H(2)O to 1H(2)O:1DMSO and 1H(2)O:2DMSO complex formation. Thus, while the emergence of first slow down (at 15% DMSO mole percentage) is due to the percolation among DMSO molecules supported by the water molecules (whose percolating network remains largely unaffected), the 2nd anomaly (centered on 40%-50%) is due to the formation of the network structure where the unit of 1DMSO:1H(2)O and 2DMSO:1H(2)O dominates to give rise to rich dynamical features. Through an analysis of partial solvation dynamics an interesting negative cross-correlation between water and DMSO is observed that makes an important contribution to relaxation at intermediate to longer times.

Feature Search in the Grassmanian in Online Reinforcement Learning

We consider the problem of finding the best features for value function approximation in reinforcement learning and develop an online algorithm to optimize the mean square Bellman error objective. For any given feature value, our algorithm performs gradient search in the parameter space via a residual gradient scheme and, on a slower timescale, also performs gradient search in the Grassman manifold of features. We present a proof of convergence of our algorithm. We show empirical results using our algorithm as well as a similar algorithm that uses temporal difference learning in place of the residual gradient scheme for the faster timescale updates.

Group testing based spectrum hole search using a simple sub-nyquist sampling scheme

In this paper, we consider the problem of finding a spectrum hole of a specified bandwidth in a given wide band of interest. We propose a new, simple and easily implementable sub-Nyquist sampling scheme for signal acquisition and a spectrum hole search algorithm that exploits sparsity in the primary spectral occupancy in the frequency domain by testing a group of adjacent subbands in a single test. The sampling scheme deliberately introduces aliasing during signal acquisition, resulting in a signal that is the sum of signals from adjacent sub-bands. Energy-based hypothesis tests are used to provide an occupancy decision over the group of subbands, and this forms the basis of the proposed algorithm to find contiguous spectrum holes. We extend this framework to a multi-stage sensing algorithm that can be employed in a variety of spectrum sensing scenarios, including non-contiguous spectrum hole search. Further, we provide the analytical means to optimize the hypothesis tests with respect to the detection thresholds, number of samples and group size to minimize the detection delay under a given error rate constraint. Depending on the sparsity and SNR, the proposed algorithms can lead to significantly lower detection delays compared to a conventional bin-by-bin energy detection scheme; the latter is in fact a special case of the group test when the group size is set to 1. We validate our analytical results via Monte Carlo simulations.

Long-Term Environmental Correlates of Invasion by Lantana camara (Verbenaceae) in a Seasonally Dry Tropical Forest

Invasive species, local plant communities and invaded ecosystems change over space and time. Quantifying this change may lead to a better understanding of the ecology and the effective management of invasive species. We used data on density of the highly invasive shrub Lantana camara (lantana) for the period 1990-2008 from a 50 ha permanent plot in a seasonally dry tropical forest of Mudumalai in southern India. We used a cumulative link mixed-effects regression approach to model the transition of lantana from one qualitative density state to another as a function of biotic factors such as indicators of competition from local species (lantana itself, perennial grasses, invasive Chromolaena odorata, the native shrub Helicteres isora and basal area of native trees) and abiotic factors such as fire frequency, inter-annual variability of rainfall and relative soil moisture. The density of lantana increased substantially during the study period. Lantana density was negatively associated with the density of H. isora, positively associated with basal area of native trees, but not affected by the presence of grasses or other invasive species. In the absence of fire, lantana density increased with increasing rainfall. When fires occurred, transitions to higher densities occurred at low rainfall values. In drier regions, lantana changed from low to high density as rainfall increased while in wetter regions of the plot, lantana persisted in the dense category irrespective of rainfall. Lantana seems to effectively utilize resources distributed in space and time to its advantage, thus outcompeting local species and maintaining a population that is not yet self-limiting. High-risk areas and years could potentially be identified based on inferences from this study for facilitating management of lantana in tropical dry forests.