3,171 research outputs found
Time dependent local potential in a Tomonaga-Luttinger liquid
We study the energy deposition in a one dimensional interacting quantum
system with a point like potential modulated in amplitude. The point like
potential at position has a constant part and a small oscillation in time
with a frequency . We use bosonization, renormalization group and
linear response theory to calculate the corresponding energy deposition. It
exhibits a power law behavior as a function of the frequency that reflects the
Tomonaga-Luttinger liquid (TLL) nature of the system. Depending on the
interactions in the system, characterized by the TLL parameter of the
system, a crossover between week and strong coupling for the backscattering due
to the potential is possible. We compute the frequency scale , at
which such crossover exists. We find that the energy deposition due to the
backscattering shows different exponent for and . We discuss
possible experimental consequences, in the context of cold atomic gases, of our
theoretical results.Comment: 13 pages, 3 figure
Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure
As machine learning systems move from computer-science laboratories into the
open world, their accountability becomes a high priority problem.
Accountability requires deep understanding of system behavior and its failures.
Current evaluation methods such as single-score error metrics and confusion
matrices provide aggregate views of system performance that hide important
shortcomings. Understanding details about failures is important for identifying
pathways for refinement, communicating the reliability of systems in different
settings, and for specifying appropriate human oversight and engagement.
Characterization of failures and shortcomings is particularly complex for
systems composed of multiple machine learned components. For such systems,
existing evaluation methods have limited expressiveness in describing and
explaining the relationship among input content, the internal states of system
components, and final output quality. We present Pandora, a set of hybrid
human-machine methods and tools for describing and explaining system failures.
Pandora leverages both human and system-generated observations to summarize
conditions of system malfunction with respect to the input content and system
architecture. We share results of a case study with a machine learning pipeline
for image captioning that show how detailed performance views can be beneficial
for analysis and debugging
Dynamics of a Mobile Impurity in a Two Leg Bosonic Ladder
We have analyzed the behavior of a mobile quantum impurity in a bath formed
by a two-leg bosonic ladder by a combination of field theory
(Tomonaga-Luttinger liquid) and numerical (Density Matrix Renormalization
Group) techniques. Computing the Green's function of the impurity as a function
of time at different momenta, we find a power law decay at zero momentum, which
signals the breakdown of any quasi-particle description of the impurity motion.
We compute the exponent both for the limits of weak and strong impurity-bath
interactions. At small impurity-bath interaction, we find that the impurity
experiences the ladder as a single channel one-dimensional bath, but effective
coupling is reduced by a factor of , thus the impurity is less mobile
in the ladder compared to a one dimensional bath. We compared the numerical
results for the exponent at zero momentum with a semi-analytical expression
that was initially established for the chain and find excellent agreement
without adjustable parameters. We analyze the dependence of the exponent in the
transverse hopping in the bath and find surprisingly an increase of the
exponent at variance with the naive extrapolation of the single channel regime.
We study the momentum dependence of the impurity Green's function and find
that, as for the single chain, two different regime of motion exist, one
dominated by infrared metatrophy and a more conventional polaronic behavior. We
compute the critical momentum between these two regimes and compare with
prediction based on the structure factor of the bath. In the polaronic regime
we also compute numerically the lifetime of the polaron. Finally we discuss how
our results could be measured in cold atomic experiments.Comment: 14 Pages, 13 figure
Stochastic Privacy
Online services such as web search and e-commerce applications typically rely
on the collection of data about users, including details of their activities on
the web. Such personal data is used to enhance the quality of service via
personalization of content and to maximize revenues via better targeting of
advertisements and deeper engagement of users on sites. To date, service
providers have largely followed the approach of either requiring or requesting
consent for opting-in to share their data. Users may be willing to share
private information in return for better quality of service or for incentives,
or in return for assurances about the nature and extend of the logging of data.
We introduce \emph{stochastic privacy}, a new approach to privacy centering on
a simple concept: A guarantee is provided to users about the upper-bound on the
probability that their personal data will be used. Such a probability, which we
refer to as \emph{privacy risk}, can be assessed by users as a preference or
communicated as a policy by a service provider. Service providers can work to
personalize and to optimize revenues in accordance with preferences about
privacy risk. We present procedures, proofs, and an overall system for
maximizing the quality of services, while respecting bounds on allowable or
communicated privacy risk. We demonstrate the methodology with a case study and
evaluation of the procedures applied to web search personalization. We show how
we can achieve near-optimal utility of accessing information with provable
guarantees on the probability of sharing data
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