^{63}Cu, ^{35}Cl, and ^{1}H NMR in the S=1/2 Kagom\'e Lattice ZnCu_{3}(OH)_{6}Cl_{2}

ZnCu$_{3}$(OH)$_{6}$Cl$_{2}$ ($S=1/2$) is a promising new candidate for an ideal Kagom\'e Heisenberg antiferromagnet, because there is no magnetic phase transition down to $\sim$50 mK. We investigated its local magnetic and lattice environments with NMR techniques. We demonstrate that the intrinsic local spin susceptibility {\it decreases} toward T=0, but that slow freezing of the lattice near $\sim$50 K, presumably associated with OH bonds, contributes to a large increase of local spin susceptibility and its distribution. Spin dynamics near T=0 obey a power-law behavior in high magnetic fields.Comment: Phys. Rev. Lett. (in press

Social interactions or business transactions? What customer reviews disclose about Airbnb marketplace

Airbnb is one of the most successful examples of sharing economy marketplaces. With rapid and global market penetration, understanding its attractiveness and evolving growth opportunities is key to plan business decision making. There is an ongoing debate, for example, about whether Airbnb is a hospitality service that fosters social exchanges between hosts and guests, as the sharing economy manifesto originally stated, or whether it is (or is evolving into being) a purely business transaction platform, the way hotels have traditionally operated. To answer these questions, we propose a novel market analysis approach that exploits customers’ reviews. Key to the approach is a method that combines thematic analysis and machine learning to inductively develop a custom dictionary for guests’ reviews. Based on this dictionary, we then use quantitative linguistic analysis on a corpus of 3.2 million reviews collected in 6 different cities, and illustrate how to answer a variety of market research questions, at fine levels of temporal, thematic, user and spatial granularity, such as (i) how the business vs social dichotomy is evolving over the years, (ii) what exact words within such top-level categories are evolving, (iii) whether such trends vary across different user segments and (iv) in different neighbourhoods

Spectral, optical and transport properties of the adiabatic anisotropic Holstein model: Application to slightly doped organic semiconductors

Spectral, optical and transport properties of an anisotropic three-dimensional Holstein model are studied within the adiabatic approximation. The parameter regime is appropriate for organic semiconductors used in single crystal based field effect transistors. Different approaches have been used to solve the model: self-consistent Born approximation valid for weak electron-phonon coupling, coherent potential approximation exact for infinite dimensions, and numerical diagonalization for finite lattices. With increasing temperature, the width of the spectral functions gets larger and larger making the approximation of quasi-particle less accurate. On the contrary, their peak positions are never strongly renormalized in comparison with the bare ones. As expected, the density of states is characterized by an exponential tail corresponding to localized states at low temperature. For weak electron-lattice coupling, the optical conductivity follows a Drude behavior, while, for intermediate electron-lattice coupling, a temperature dependent peak is present at low frequency. For high temperatures and low particle densities, the mobility always exhibits a power-law behavior as function of temperature. With decreasing the particle density, at low temperature, the mobility shows a transition from metallic to insulating behavior. Results are discussed in connection with available experimental data.Comment: 9 pages, 7 figures, submitted to Phys. Rev.

Charge-Transfer Dynamics at the α/β Subunit Interface of a Photochemical Ribonucleotide Reductase

United States. National Institutes of Health (GM 29595

Time-Dependent Probability of Exceeding a Target Level of Recovery

The resilience of a system is generally defined in terms of its ability to withstand external perturbations, adapt, and rapidly recover. This paper introduces a probabilistic formulation to predict the recovery process of a system given past recovery data and to estimate the probability of reaching or exceeding a target value of functionality at any time. A Bayesian inference is used to capture the changes over time of model parameters as recovery data become available during the work progress. The proposed formulation is general and can be applied to continuous recovery processes such as those of economic or natural systems, as well as to discrete recovery processes typical of engineering systems. As an illustration of the proposed formulation, two examples are provided. The paper models the recovery of a reinforced concrete bridge following seismic damage, as well as the population relocation after the occurrence of a seismic event when no data on the duration of the recovery are available a priori

Reversible, Long-Range Radical Transfer in E. coli Class Ia Ribonucleotide Reductase

Ribonucleotide reductases (RNRs) catalyze the conversion of nucleotides (NDPs or NTPs where N = C, U, G, or A) to 2′-deoxynucleotides (dNDPs or dNTPs)[superscript 1] and are responsible for controlling the relative ratios and absolute concentrations of cellular dNTP pools. For this reason, RNRs play a major role in ensuring the fidelity of DNA replication and repair. RNRs are found in all organisms and are classified based on the metallocofactor used to initiate catalysis,[superscript 1] with the class Ia RNRs requiring a diferric-tyrosyl radical (Y•) cofactor.National Institutes of Health (U.S.) (GM47274)National Institutes of Health (U.S.) (GM29595