1,780 research outputs found
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 leads, with each at
a different chemical potential, there can be 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 . 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 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 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 QAOA parameters, in the infinite size
limit that can be evaluated on a computer with complexity. We
evaluate the formula up to , and find that the QAOA at outperforms
the standard semidefinite programming algorithm. Moreover, we show
concentration: With probability tending to one as , 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
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