Quantum Transport and Integrability of the Anderson Model for a Quantum Dot with Multiple Leads

We show that an Anderson Hamiltonian describing a quantum dot connected to multiple leads is integrable. A general expression for the non-linear conductance is obtained by combining the Bethe ansatz exact solution with Landauer-B\"uttiker theory. In the Kondo regime, a closed form expression is given for the matrix conductance at zero temperature and when all the leads are close to the symmetric point. A bias-induced splitting of the Kondo resonance is possible for three or more leads. Specifically, for $N$ leads, with each at a different chemical potential, there can be $N-1$ Kondo peaks in the conductance.Comment: 5 pages, 2 figure

A Semiparametric Bayesian Model for Detecting Synchrony Among Multiple Neurons

We propose a scalable semiparametric Bayesian model to capture dependencies among multiple neurons by detecting their co-firing (possibly with some lag time) patterns over time. After discretizing time so there is at most one spike at each interval, the resulting sequence of 1's (spike) and 0's (silence) for each neuron is modeled using the logistic function of a continuous latent variable with a Gaussian process prior. For multiple neurons, the corresponding marginal distributions are coupled to their joint probability distribution using a parametric copula model. The advantages of our approach are as follows: the nonparametric component (i.e., the Gaussian process model) provides a flexible framework for modeling the underlying firing rates; the parametric component (i.e., the copula model) allows us to make inference regarding both contemporaneous and lagged relationships among neurons; using the copula model, we construct multivariate probabilistic models by separating the modeling of univariate marginal distributions from the modeling of dependence structure among variables; our method is easy to implement using a computationally efficient sampling algorithm that can be easily extended to high dimensional problems. Using simulated data, we show that our approach could correctly capture temporal dependencies in firing rates and identify synchronous neurons. We also apply our model to spike train data obtained from prefrontal cortical areas in rat's brain

Ground state fidelity in bond-alternative Ising chains with Dzyaloshinskii-Moriya interactions

A systematic analysis is performed for quantum phase transitions in a bond-alternative one-dimensional Ising model with a Dzyaloshinskii-Moriya (DM) interaction by using the fidelity of ground state wave functions based on the infinite matrix product states algorithm. For an antiferromagnetic phase, the fidelity per lattice site exhibits a bifurcation, which shows spontaneous symmetry breaking in the system. A critical DM interaction is inversely proportional to an alternating exchange coupling strength for a quantum phase transition. Further, a finite-entanglement scaling of von Neumann entropy with respect to truncation dimensions gives a central charge c = 0.5 at the critical point.Comment: 6 pages, 4 figure

Open Access Books: an International Collaboration to Explore the Practical Implications for Librarians of Increasing Access to Scholarly Research Outputs

Open access advocacy and partnership is an established role for libraries across the world: books continue to be a challenge. Books and book chapters remain a vital output for many research areas. Open access policies have focused primarily on journal articles and serial publications, potentially creating an imbalance in the research literature freely available, and possibly having a negative impact on book publications in terms of readership and citations. Publisher permissions for journal articles can usually be accessed from Sherpa RoMEO, but book contracts continue to be a mostly hidden agreement between publisher and researcher, inaccessible to librarians who are supporting and driving the open access agenda within an institution. What are the current challenges for librarians in making academics books openly available? To what extent will this limit the mediating role of librarians in scholarly communication? Is this role sustainable? A global perspective is provided with a comparison of distinctive experiences at two leading international universities: Swansea University; and the University of Nottingham Ningbo China. Swansea University is seeking to create more open access book content in line with the United Kingdom’s Higher Education Funding Council for Education Research Excellence Framework Open Access policy. The University of Nottingham Ningbo China is seeking to maximize the dissemination and visibility of research to a global audience through open access. This paper focusses on the issues and challenges for librarians who wish to increase the number of books and book chapters available open access, including: relationships with global publishing partners; the complexity of publisher policies for books; challenging existing researcher practices; and, reskilling librarians for advocacy and influencing roles in scholarly communication. A set of recommendations is drawn from this in order to improve the library and information service roles in supporting research, publishing process and improving open access to book content

ErAConD : Error Annotated Conversational Dialog Dataset for Grammatical Error Correction

Currently available grammatical error correction (GEC) datasets are compiled using well-formed written text, limiting the applicability of these datasets to other domains such as informal writing and dialog. In this paper, we present a novel parallel GEC dataset drawn from open-domain chatbot conversations; this dataset is, to our knowledge, the first GEC dataset targeted to a conversational setting. To demonstrate the utility of the dataset, we use our annotated data to fine-tune a state-of-the-art GEC model, resulting in a 16 point increase in model precision. This is of particular importance in a GEC model, as model precision is considered more important than recall in GEC tasks since false positives could lead to serious confusion in language learners. We also present a detailed annotation scheme which ranks errors by perceived impact on comprehensibility, making our dataset both reproducible and extensible. Experimental results show the effectiveness of our data in improving GEC model performance in conversational scenario

The Quantum Approximate Optimization Algorithm and the Sherrington-Kirkpatrick Model at Infinite Size

The Quantum Approximate Optimization Algorithm (QAOA) is a general-purpose algorithm for combinatorial optimization problems whose performance can only improve with the number of layers $p$. While QAOA holds promise as an algorithm that can be run on near-term quantum computers, its computational power has not been fully explored. In this work, we study the QAOA applied to the Sherrington-Kirkpatrick (SK) model, which can be understood as energy minimization of $n$ spins with all-to-all random signed couplings. There is a recent classical algorithm by Montanari that, assuming a widely believed conjecture, can be tailored to efficiently find an approximate solution for a typical instance of the SK model to within $(1-\epsilon)$ times the ground state energy. We hope to match its performance with the QAOA. Our main result is a novel technique that allows us to evaluate the typical-instance energy of the QAOA applied to the SK model. We produce a formula for the expected value of the energy, as a function of the $2p$ QAOA parameters, in the infinite size limit that can be evaluated on a computer with $O(16^p)$ complexity. We evaluate the formula up to $p=12$, and find that the QAOA at $p=11$ outperforms the standard semidefinite programming algorithm. Moreover, we show concentration: With probability tending to one as $n\to\infty$, measurements of the QAOA will produce strings whose energies concentrate at our calculated value. As an algorithm running on a quantum computer, there is no need to search for optimal parameters on an instance-by-instance basis since we can determine them in advance. What we have here is a new framework for analyzing the QAOA, and our techniques can be of broad interest for evaluating its performance on more general problems where classical algorithms may fail.Comment: 32 pages, 2 figures, 2 tables; improved presentation of figures and iterative formul