Computing discriminating and generic words

International audienceWe study the following three problems of computing generic or discriminating words for a given collection of documents. Given a pattern P and a threshold d, we want to report (i) all longest extensions of P which occur in at least d documents, (ii) all shortest extensions of P which occur in less than d documents, and (iii) all shortest extensions of P which occur only in d selected documents. For these problems, we propose efficient algorithms based on suffix trees and using advanced data structure techniques. For problem (i), we propose an optimal solution with constant running time per output word

Green synthesis of copper oxide nanoparticles using Amaranthus dubius leaf extract for sensor and photocatalytic applications

Copper oxide nanoparticles (CuO NPs) were produced through an environmentally friendly green synthesis. The characteristics of these green synthesized CuO NPs, including their structural, optical, morphological, and electrochemical properties, were examined using various characterization techniques. X-ray diffraction analysis revealed that the CuO NPs have a monoclinic structure with a C2/c space group. Electrochemical detection of glucose was carried out using cyclic voltammetry. The green synthesized CuO NPs exhibited excellent catalytic properties for both electrochemical sensing and photocatalysis. Significantly, these CuO NPs exhibited excellent selectivity and sensitivity in glucose detection, with a sensitivity of 370 μA mM−1 cm−2 and a detection limit of 1.0 μM. Furthermore, the CuO NPs demonstrated a substantial 84 % degradation of dyes within 150 min. These results underscore the potential of the green synthesized CuO NPs as a promising material for applications in both sensing and dye degradation

Ultrafast spectroscopy and computational study of the photochemistry of diphenylphosphoryl azide: direct spectroscopic observation of a singlet phosphorylnitrene

The photochemistry of diphenylphosphoryl azide was studied by femtosecond transient absorption spectroscopy, by chemical analysis of light-induced reaction products, and by RI-CC2/TZVP and TD-B3LYP/TZVP computational methods. Theoretical methods predicted two possible mechanisms for singlet diphenylphosphorylnitrene formation from the photoexcited phosphoryl azide. (i) Energy transfer from the (π,π*) singlet excited state, localized on a phenyl ring, to the azide moiety, thereby leading to the formation of the singlet excited azide, which subsequently loses molecular nitrogen to form the singlet diphenylphosphorylnitrene. (ii) Direct irradiation of the azide moiety to form an excited singlet state of the azide, which in turn loses molecular nitrogen to form the singlet diphenylphosphorylnitrene. Two transient species were observed upon ultrafast photolysis (260 nm) of diphenylphosphoryl azide. The first transient absorption, centered at 430 nm (lifetime (τ) 28 ps), was assigned to a (π,π*) singlet S1 excited state localized on a phenyl ring, and the second transient observed at 525 nm (τ 480 ps) was assigned to singlet diphenylphosphorylnitrene. Experimental and computational results obtained from the study of diphenyl phosphoramidate, along with the results obtained with diphenylphosphoryl azide, supported the mechanism of energy transfer from the singlet excited phenyl ring to the azide moiety, followed by nitrogen extrusion to form the singlet phosphorylnitrene. Ultrafast time-resolved studies performed on diphenylphosphoryl azide with the singlet nitrene quencher, tris(trimethylsilyl)silane, confirmed the spectroscopic assignment of singlet diphenylphosphorylnitrene to the 525 nm absorption band

Clustering-Based Bidding Languages for Sponsored Search

Sponsored search auctions provide a marketplace where advertisers can bid for millions of advertising opportunities to promote their products. The main difficulty facing the advertisers in this market is the complexity of picking and evaluating keywords and phrases to bid on. This is due to the sheer number of possible keywords that the advertisers can bid on, and leads to inefficiencies in the market such as lack of coverage for “rare ” keywords. Approaches such as broad matching have been proposed to alleviate this problem. However, as we will observe in this paper, broad matching has undesirable economic properties (such as the non-existence of equilibria) that can make it hard for an advertiser to determine how much to bid for a broad-matched keyword. The main contribution of this paper is to introduce a bidding language for sponsored search auctions based on broad-matching keywords to non-overlapping clusters that greatly simplifies the bidding problem for the advertisers. We investigate the algorithmic problem of computing the optimal clustering given a set of estimated values and give an approximation algorithm for this problem. Furthermore, we present experimental results using real advertisers ’ data that show that it is possible to extract close to the optimal social welfare with a number of clusters considerably smaller than the number of keywords. This demonstrates the applicability of the clustering scheme and our algorithm in practice.