Poor man's derivation of the Bethe-Ansatz equations for the Dicke model

We present an elementary derivation of the exact solution (Bethe-Ansatz equations) of the Dicke model, using only commutation relations and an informed Ansatz for the structure of its eigenstates.Comment: 2 page

Formation of long-lived, scarlike modes near avoided resonance crossings in optical microcavities

We study the formation of long-lived states near avoided resonance crossings in open systems. For three different optical microcavities (rectangle, ellipse, and semi-stadium) we provide numerical evidence that these states are localized along periodic rays, resembling scarred states in closed systems. Our results shed light on the morphology of long-lived states in open mesoscopic systems.Comment: 4 pages, 5 figures (in reduced quality), to appear in Phys. Rev. Let

Efficient simulation of infinite tree tensor network states on the Bethe lattice

We show that the simple update approach proposed by Jiang et. al. [H.C. Jiang, Z.Y. Weng, and T. Xiang, Phys. Rev. Lett. 101, 090603 (2008)] is an efficient and accurate method for determining the infinite tree tensor network states on the Bethe lattice. Ground state properties of the quantum transverse Ising model and the Heisenberg XXZ model on the Bethe lattice are studied. The transverse Ising model is found to undergo a second-order quantum phase transition with a diverging magnetic susceptibility but a finite correlation length which is upper-bounded by 1/ln(q-1) even at the transition point (q is the coordinate number of the Bethe lattice). An intuitive explanation on this peculiar "critical" phenomenon is given. The XXZ model on the Bethe lattice undergoes a first-order quantum phase transition at the isotropic point. Furthermore, the simple update scheme is found to be related with the Bethe approximation. Finally, by applying the simple update to various tree tensor clusters, we can obtain rather nice and scalable approximations for two-dimensional lattices.Comment: 9 pages, 10 figure

Variational matrix product state approach to quantum impurity models

We present a unified framework for renormalization group methods, including Wilson's numerical renormalization group (NRG) and White's density-matrix renormalization group (DMRG), within the language of matrix product states. This allows improvements over Wilson's NRG for quantum impurity models, as we illustrate for the one-channel Kondo model. Moreover, we use a variational method for evaluating Green's functions. The proposed method is more flexible in its description of spectral properties at finite frequencies, opening the way to time-dependent, out-of-equilibrium impurity problems. It also substantially improves computational efficiency for one-channel impurity problems, suggesting potentially \emph{linear} scaling of complexity for $n$-channel problems.Comment: revised version with application to Kondo model at large magnetic field (5 pages, 2 figures

THE POTENTIAL USE OF POLLUTION INSURANCE AS ENVIRONMENTAL POLICY: AN EMPIRICAL ANALYSIS

Market-based environmental policies have been forwarded as alternatives to current pollution control policies. Implementation of the "polluter pays" principle and governmental enforcement of pollution clean-up have led to astronomical environmental liabilities and clean-up costs, which may threaten the survival of many productive ventures, unless producers can spread pollution risk through insurance. An emission constrained target MOTAD LP (TMLP) model showed that pollution insurance for irrigation farmers can be a feasible and efficient solution to agricultural salinization problems in the Loskop Valley, and fairly low salinity standards with pollution insurance will still be reconcilable with profitable farming. Pollution insurance appears to hold promise for applying the "polluter pays" principles also to non-point pollution. Site specific studies are needed for pollution policy, and more research is needed on pollution standards.Environmental Economics and Policy,

Ohmic and step noise from a single trapping center hybridized with a Fermi sea

We show that single electron tunneling devices such as the Cooper-pair box or double quantum dot can be sensitive to the zero-point fluctuation of a single trapping center hybridized with a Fermi sea. If the trap energy level is close to the Fermi sea and has line-width \gamma > k_B T, its noise spectrum has an Ohmic Johnson-Nyquist form, whereas for \gamma < k_B T the noise has a Lorentzian form expected from the semiclassical limit. Trap levels above the Fermi level are shown to lead to steps in the noise spectrum that can be used to probe their energetics, allowing the identification of individual trapping centers coupled to the device.Comment: Revised version to appear in Phys. Rev. Let

Nonequilibrium excitations in Ferromagnetic Nanoparticles

In recent measurements of tunneling transport through individual ferromagnetic Co nanograins, Deshmukh, Gu\'eron, Ralph et al. \cite{mandar,gueron} (DGR) observed a tunneling spectrum with discrete resonances, whose spacing was much smaller than what one would expect from naive independent-electron estimates. In a previous publication, \cite{prl_kleff} we had suggested that this was a consequence of nonequilibrium excitations, and had proposed a ``minimal model'' for ferromagnetism in nanograins with a discrete excitation spectrum as a framework for analyzing the experimental data. In the present paper, we provide a detailed analysis of the properties of this model: We delineate which many-body electron states must be considered when constructing the tunneling spectrum, discuss various nonequilibrium scenarios and compare their results with the experimental data of Refs. \cite{mandar,gueron}. We show that a combination of nonequilibrium spin- and single-particle excitations can account for most of the observed features, in particular the abundance of resonances, the resonance spacing and the absence of Zeeman splitting.Comment: 13 pages, 10 figure

Synthetic Cell-Based Immunotherapies for Neurologic Diseases

The therapeutic success and widespread approval of genetically engineered T cells for a variety of hematologic malignancies spurred the development of synthetic cell-based immunotherapies for CNS lymphoma, primary brain tumors, and a growing spectrum of nononcologic disease conditions of the nervous system. Chimeric antigen receptor effector T cells bear the potential to deplete target cells with higher efficacy, better tissue penetration, and greater depth than antibody-based cell depletion therapies. In multiple sclerosis and other autoimmune disorders, engineered T-cell therapies are being designed and currently tested in clinical trials for their safety and efficacy to eliminate pathogenic B-lineage cells. Chimeric autoantibody receptor T cells expressing a disease-relevant autoantigen as cell surface domains are designed to selectively deplete autoreactive B cells. Alternative to cell depletion, synthetic antigen-specific regulatory T cells can be engineered to locally restrain inflammation, support immune tolerance, or efficiently deliver neuroprotective factors in brain diseases in which current therapeutic options are very limited. In this article, we illustrate prospects and bottlenecks for the clinical development and implementation of engineered cellular immunotherapies in neurologic diseases

Machine Learning of Free Energies in Chemical Compound Space Using Ensemble Representations: Reaching Experimental Uncertainty for Solvation

Free energies govern the behavior of soft and liquid matter, and improving their predictions could have a large impact on the development of drugs, electrolytes or homogeneous catalysts. Unfortunately, it is challenging to devise an accurate description of effects governing solvation such as hydrogen-bonding, van der Waals interactions, or conformational sampling. We present a Free energy Machine Learning (FML) model applicable throughout chemical compound space and based on a representation that employs Boltzmann averages to account for an approximated sampling of configurational space. Using the FreeSolv database, FML's out-of-sample prediction errors of experimental hydration free energies decay systematically with training set size, and experimental uncertainty (0.6 kcal/mol) is reached after training on 490 molecules (80\% of FreeSolv). Corresponding FML model errors are also on par with state-of-the art physics based approaches. To generate the input representation for a new query compound, FML requires approximate and short molecular dynamics runs. We showcase its usefulness through analysis of FML solvation free energies for 116k organic molecules (all force-field compatible molecules in QM9 database) identifying the most and least solvated systems, and rediscovering quasi-linear structure property relationships in terms of simple descriptors such as hydrogen-bond donors, number of NH or OH groups, number of oxygen atoms in hydrocarbons, and number of heavy atoms. FML's accuracy is maximal when the temperature used for the molecular dynamics simulation to generate averaged input representation samples in training is the same as for the query compounds. The sampling time for the representation converges rapidly with respect to the prediction error