Synthesis of novel and stable g-C3N4/N-doped SrTiO3 hybrid nanocomposites with improved photocurrent and photocatalytic activity under visible light irradiation

Hybrid nanocomposites based on N-doped SrTiO3 nanoparticles wrapped in g-C3N4 nanosheets were successfully prepared by a facile and reproducible polymeric citrate and thermal exfoliation method. The results clearly indicated that the N-doped SrTiO3 nanoparticles are successfully wrapped in layers of the g-C3N4 nanosheets. The g-C3N4/N-doped SrTiO3 nanocomposites showed absorption edges at longer wavelengths compared with the pure g-C3N4 as well as N-doped SrTiO3. The hybrid nanocomposites exhibit an improved photocurrent response and photocatalytic activity under visible light irradiation. Interestingly, the hybrid nanocomposite possesses high photostability and reusability. Based on experimental results, the possible mechanism for prolonged lifetime of the photoinduced charge carrier was also discussed. The high performance of the g-C3N4/N-doped SrTiO3 photocatalysts is due to the synergic effect at the interface of g-C3N4 and N-doped SrTiO3 hetero/nanojunction including the high separation efficiency of the charge carrier, band energy matching and the suppressed recombination rate. Therefore, the hybrid photocatalyst could be of potential interest for water splitting and environmental remediation under natural sunlight.

Sums of a Random Number of Random Variables and their Approximations with ν- Accompanying Infinitely Divisible Laws

* Research supported by NATO GRANT CRG 900 798 and by Humboldt Award for U.S. Scientists.

On-off and multistate intermittencies in cascaded random distributed feedback fibre laser

A range of physical and engineering systems exhibit an irregular complex dynamics featuring alternation of quiet and burst time intervals called the intermittency. The intermittent dynamics most popular in laser science is the on-off intermittency [1]. The on-off intermittency can be understood as a conversion of the noise in a system close to an instability threshold into effective time-dependent fluctuations which result in the alternation of stable and unstable periods. The on-off intermittency has been recently demonstrated in semiconductor, Erbium doped and Raman lasers [2-5]. Recently demonstrated random distributed feedback (random DFB) fiber laser has an irregular dynamics near the generation threshold [6,7]. Here we show the intermittency in the cascaded random DFB fiber laser. We study intensity fluctuations in a random DFB fiber laser based on nitrogen doped fiber. The laser generates first and second Stokes components 1120 nm and 1180 nm respectively under an appropriate pumping. We study the intermittency in the radiation of the second Stokes wave. The typical time trace near the generation threshold of the second Stokes wave (Pth) is shown at Fig. 1a. From the number of long enough time-traces we calculate statistical distribution between major spikes in time dynamics, Fig. 1b. To eliminate contribution of high frequency components of spikes we use a low pass filter along with the reference value of the output power. Experimental data is fitted by power law, ~(P-Pth)y, where is a mean time between pikes. There are two different intermittency regimes. Just above Pth, the mean time is approximated by the -3/2 power law. The -3/2 power law is typical to the on-off intermittency with hopping between two states (first and second Stokes waves in our case) [7]. At higher power, the mean time is approximated by -4 power law, that indicates a change in intermittency type to multistate. Multistable dynamics is observed in erbium-doped fiber lasers [8]. The origin of multiples states in our system could be probably connected with polarization hopping or other reasons and should be further investigated. We have presented a first experimental statistical characterisation of the on-off and multistate intermittencies that occur in the generation of the second Stokes wave in nitrogen doped random DFB fiber laser. References [1] H. Fujisaka and T. Yamada, “A New Intermittency in Coupled Dynamical Systems,” Prog. Theor. Phys. 74, 918 (1985). [2] S. Osborne, A. Amann, D. Bitauld, and S. O’Brien, “On-off intermittency in an optically injected semiconductor laser,” Phys. Rev. E 85, 056204 (2012). [3] S. Sergeyev, K. O'Mahoney, S. Popov, and A. T. Friberg, “Coherence and anticoherence resonance in high-concentration erbium-doped fiber laser,” Opt. Lett. 35, 3736 (2010). [4] A.E. El-Taher, S.V. Sergeyev, E.G. Turitsyna, P. Harper, and S. K. Turitsyn, “Intermittent Self-Pulsing in a Fiber Raman Laser”, In proc. Conf. Nonlin. Photon., paper ID 1367139, Colorado Springs, USA, 2012 [5] S.K. Turitsyn, S.A. Babin, A.E. El-Taher, P. Harper, D.V. Churkin, S.I. Kablukov, J.D. Ania-Castañón, V. Karalekas, and E.V. Podivilov, “Random distributed feedback fibre laser”, Nat. Photon..4, 231 (2010). [6] I. D. Vatnik, D. V. Churkin, S. A. Babin, and S. K. Turitsyn, "Cascaded random distributed feedback Raman fiber laser operating at 1.2 μm," Opt. Express 19, 18486 (2011). [7] W. Feller, An introduction to probability theory and its applications, Vol. 1, 3rd ed. (Wiley, New-York, 1968). [8] G. Huerta-Cuellar, A.N. Pisarchik, and Y.O. Barmenkov, “Experimental characterization of hopping dynamics in a multistable fiber laser,” Phys. Rev. E 78, 035202(R) (2008).

A rule-based approach to implicit emotion detection in text

Most research in the area of emotion detection in written text focused on detecting explicit expressions of emotions in text. In this paper, we present a rule-based pipeline approach for detecting implicit emotions in written text without emotion-bearing words based on the OCC Model. We have evaluated our approach on three different datasets with five emotion categories. Our results show that the proposed approach outperforms the lexicon matching method consistently across all the three datasets by a large margin of 17–30% in F-measure and gives competitive performance compared to a supervised classifier. In particular, when dealing with formal text which follows grammatical rules strictly, our approach gives an average F-measure of 82.7% on “Happy”, “Angry-Disgust” and “Sad”, even outperforming the supervised baseline by nearly 17% in F-measure. Our preliminary results show the feasibility of the approach for the task of implicit emotion detection in written text.

Which rationing rule does a single consumer follow?

We will investigate the amount of residual demand in a market consisting of only one consumer and two producers. Since there is only one consumer, we cannot really speak about a rationing rule, but we can ask ourselves whether a known rationing rule reflects the consumer’s utility maximizing behavior. We will show that, if the consumer has a Cobb-Douglas utility function, then the amount purchased by the consumer from the high-price firm lies between the values determined according to the efficient rationing rule and the random rationing rule. We will show further, that if the consumer has a quasilinear utility function, then in the economically interesting case his residual demand function will be equal to the residual demand function under efficient rationing.

Irreducibility of Random Hilbert Schemes

We prove that a random Hilbert scheme that parametrizes the closed subschemes with a fixed Hilbert polynomial in some projective space is irreducible and nonsingular with probability greater than $0.5$. To consider the set of nonempty Hilbert schemes as a probability space, we transform this set into a disjoint union of infinite binary trees, reinterpreting Macaulay's classification of admissible Hilbert polynomials. Choosing discrete probability distributions with infinite support on the trees establishes our notion of random Hilbert schemes. To bound the probability that random Hilbert schemes are irreducible and nonsingular, we show that at least half of the vertices in the binary trees correspond to Hilbert schemes with unique Borel-fixed points.

Random Forest Based Approach for Concept-Drift Handling

Algorithms for concept drift handling are important for various applications including video analysis and smart grids. In this paper we present decision tree ensemble classication method based on the Random Forest algorithm for concept drift. The weighted majority voting ensemble aggregation rule is employed based on the ideas of Accuracy Weighted Ensemble (AWE) method. Base learner weight in our case is computed for each sample evaluation using base learners accuracy and intrinsic proximity measure of Random Forest. Our algorithm exploits both temporal weighting of samples and ensemble pruning as a forgetting strategy. We present results of empirical comparison of our method with îriginal random forest with incorporated replace-the-looser forgetting andother state-of-the-art concept-drift classiers like AWE2.

Stable carbon isotope ratios and accumulation rates of organic carbon and nutrients of DSDP Hole 79-545 (Appendix A)

Future warming is predicted to shift the Earth system into a mode with progressive increase and vigour of extreme climate events possibly stimulating other mechanisms that invigorate global warming. This study provides new data and modelling investigating climatic consequences and biogeochemical feedbacks that happened in a warmer world ~112 Myr ago. Our study focuses on the Cretaceous Oceanic Anoxic Event (OAE) 1b and explores how the Earth system responded to a moderate ~25,000 yr lasting climate perturbation that is modelled to be less than 1 °C in global average temperature. Using a new chronological model for OAE 1b we present high-resolution elemental and bulk carbon isotope records from DSDP Site 545 from Mazagan Plateau off NW Africa and combine this information with a coupled atmosphere-land-ocean model. The simulations suggest that a perturbation at the onset of OAE 1b caused almost instantaneous warming of the atmosphere on the order of 0.3 °C followed by a longer (~45,000 yr) period of ~0.8 °C cooling. The marine records from DSDP Site 545 support that these moderate swings in global climate had immediate consequences for African continental supply of mineral matter and nutrients (phosphorous), subsequent oxygen availability, and organic carbon burial in the eastern subtropical Atlantic, however, without turning the ocean anoxic. The match between modelling results and stratigraphic isotopic data support previous studies [summarized in Jenkyns 2003, doi:10.1098/rsta.2003.1240] in that methane emission from marine hydrates, albeit moderate in dimension, may have been the trigger for OAE 1b, though we can not finally rule out alternative mechanisms. Following the hydrate mechanism a total of 1.15 * 10**18 g methane carbon (delta13C=-60 ?), equivalent to about 10% to the total modern gas hydrate inventory, generated the delta13Ccarb profile recorded in the section. Modelling suggests a combination of moderate-scale methane pulses supplemented by continuous methane emission at elevated levels over ~25,000 yr. The proposed mechanism, though difficult to finally confirm in the geological past, is arguably more likely to occur in a warmer world and apparently perturbs global climate and ocean chemistry almost instantaneously. This study shows that, once set-off, this mechanism can maintain Earth's climate in a perturbed mode over geological time leading to pronounced changes in regional climate.

Random Access over Multi-Packet Reception Channels: A Mean-Field Approach

Thesis (Ph.D.)--University of Washington, 2016-06

Leveraging Multiple Features for Image Retrieval and Matching

The goal of image retrieval and matching is to find and locate object instances in images from a large-scale image database. While visual features are abundant, how to combine them to improve performance by individual features remains a challenging task. In this work, we focus on leveraging multiple features for accurate and efficient image retrieval and matching. We first propose two graph-based approaches to rerank initially retrieved images for generic image retrieval. In the graph, vertices are images while edges are similarities between image pairs. Our first approach employs a mixture Markov model based on a random walk model on multiple graphs to fuse graphs. We introduce a probabilistic model to compute the importance of each feature for graph fusion under a naive Bayesian formulation, which requires statistics of similarities from a manually labeled dataset containing irrelevant images. To reduce human labeling, we further propose a fully unsupervised reranking algorithm based on a submodular objective function that can be efficiently optimized by greedy algorithm. By maximizing an information gain term over the graph, our submodular function favors a subset of database images that are similar to query images and resemble each other. The function also exploits the rank relationships of images from multiple ranked lists obtained by different features. We then study a more well-defined application, person re-identification, where the database contains labeled images of human bodies captured by multiple cameras. Re-identifications from multiple cameras are regarded as related tasks to exploit shared information. We apply a novel multi-task learning algorithm using both low level features and attributes. A low rank attribute embedding is joint learned within the multi-task learning formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered. To locate objects in images, we design an object detector based on object proposals and deep convolutional neural networks (CNN) in view of the emergence of deep networks. We improve a Fast RCNN framework and investigate two new strategies to detect objects accurately and efficiently: scale-dependent pooling (SDP) and cascaded rejection classifiers (CRC). The SDP improves detection accuracy by exploiting appropriate convolutional features depending on the scale of input object proposals. The CRC effectively utilizes convolutional features and greatly eliminates negative proposals in a cascaded manner, while maintaining a high recall for true objects. The two strategies together improve the detection accuracy and reduce the computational cost.

Assigning Refugees to Landlords in Sweden: Stable Maximum Matchings

This version: August 15, 2017 (original version: December 7, 2016)

Assigning Refugees to Landlords in Sweden: Stable Maximum Matchings

The member states of the European Union received 1.2 million first time asylum applications in 2015 (a doubling compared to 2014). Even if asylum will be granted for many of the refugees that made the journey to Europe, several obstacles for successful integration remain. This paper focuses on one of these obstacles, namely the problem of finding housing for refugees once they have been granted asylum. In particular, the focus is restricted to the situation in Sweden during 2015–2016 and it is demonstrated that market design can play an important role in a partial solution to the problem. More specifically, because almost all accommodation options are exhausted in Sweden, the paper investigates a matching system, closely related to the system adopted by the European NGO “Refugees Welcome”, and proposes an easy-to-implement algorithm that finds a stable maximum matching. Such matching guarantees that housing is provided to a maximum number of refugees and that no refugee prefers some landlord to their current match when, at the same time, that specific landlord prefers that refugee to his current match.

Anomaly Detection in Random Graph

La presenti tesi ha come obiettivo lo studio di due algoritmi per il rilevamento di anomalie all' interno di grafi random. Per entrambi gli algoritmi sono stati creati dei modelli generativi di grafi dinamici in modo da eseguire dei test sintetici. La tesi si compone in una parte iniziale teorica e di una seconda parte sperimentale. Il secondo capitolo introduce la teoria dei grafi. Il terzo capitolo presenta il problema del rilevamento di comunità. Il quarto capitolo introduce possibili definizioni del concetto di anomalie dinamiche e il problema del loro rilevamento. Il quinto capitolo propone l' introduzione di un punteggio di outlierness associato ad ogni nodo sulla base del confronto tra la sua dinamica e quella della comunità a cui appartiene. L' ultimo capitolo si incentra sul problema della ricerca di una descrizione della rete in termini di gruppi o ruoli sulla base della quale incentrare la ricerca delle anomalie dinamiche.

Evaluation Of Fe Uptake And Translocation In Transgenic And Non-transgenic Soybean Plants Using Enriched Stable (57)fe As A Tracer.

A tracer experiment is carried out with transgenic T (variety M 7211 RR) and non-transgenic NT (variety MSOY 8200) soybean plants to evaluate if genetic modification can influence the uptake and translocation of Fe. A chelate of EDTA with enriched stable (57)Fe is applied to the plants cultivated in vermiculite plus substrate and the (57)Fe acts as a tracer. The exposure of plants to enriched (57)Fe causes the dilution of the natural previously existing Fe in the plant compartments and then the changed Fe isotopic ratio ((57)Fe/(56)Fe) is measured using a quadrupole-based inductively coupled plasma mass spectrometer equipped with a dynamic reaction cell (DRC). Mathematical calculations based on the isotope dilution methodology allow distinguishing the natural abundance Fe from the enriched Fe (incorporated during the experiment). The NT soybean plants acquire higher amounts of Fe from natural abundance (originally present in the soil) and from enriched Fe (coming from the (57)Fe-EDTA during the experiment) than T soybean ones, demonstrating that the NT soybean plants probably absorb higher amounts of Fe, independently of the source. The percentage of newly incorporated Fe (coming from the treatment) was approximately 2.0 and 1.1% for NT and T soybean plants, respectively. A higher fraction (90.1%) of enriched Fe is translocated to upper parts, and a slightly lower fraction (3.8%) is accumulated in the stems by NT plants than by T ones (85.1%; 5.1%). Moreover, in both plants, the Fe-EDTA facilitates the transport and translocation of Fe to the leaves. The genetic modification is probably responsible for differences observed between T and NT soybean plants.

Biased Random-key Genetic Algorithms For The Winner Determination Problem In Combinatorial Auctions.

Abstract In this paper, we address the problem of picking a subset of bids in a general combinatorial auction so as to maximize the overall profit using the first-price model. This winner determination problem assumes that a single bidding round is held to determine both the winners and prices to be paid. We introduce six variants of biased random-key genetic algorithms for this problem. Three of them use a novel initialization technique that makes use of solutions of intermediate linear programming relaxations of an exact mixed integer-linear programming model as initial chromosomes of the population. An experimental evaluation compares the effectiveness of the proposed algorithms with the standard mixed linear integer programming formulation, a specialized exact algorithm, and the best-performing heuristics proposed for this problem. The proposed algorithms are competitive and offer strong results, mainly for large-scale auctions.