21,730 research outputs found
Nonlinear ac response of anisotropic composites
When a suspension consisting of dielectric particles having nonlinear
characteristics is subjected to a sinusoidal (ac) field, the electrical
response will in general consist of ac fields at frequencies of the
higher-order harmonics. These ac responses will also be anisotropic. In this
work, a self-consistent formalism has been employed to compute the induced
dipole moment for suspensions in which the suspended particles have nonlinear
characteristics, in an attempt to investigate the anisotropy in the ac
response. The results showed that the harmonics of the induced dipole moment
and the local electric field are both increased as the anisotropy increases for
the longitudinal field case, while the harmonics are decreased as the
anisotropy increases for the transverse field case. These results are
qualitatively understood with the spectral representation. Thus, by measuring
the ac responses both parallel and perpendicular to the uniaxial anisotropic
axis of the field-induced structures, it is possible to perform a real-time
monitoring of the field-induced aggregation process.Comment: 14 pages and 4 eps figure
L-functions of Symmetric Products of the Kloosterman Sheaf over Z
The classical -variable Kloosterman sums over the finite field
give rise to a lisse -sheaf on , which we call the Kloosterman
sheaf. Let be the
-function of the -fold symmetric product of . We
construct an explicit virtual scheme of finite type over such that the -Euler factor of the zeta function of coincides with
. We also prove
similar results for and .Comment: 16 page
Time-varying Learning and Content Analytics via Sparse Factor Analysis
We propose SPARFA-Trace, a new machine learning-based framework for
time-varying learning and content analytics for education applications. We
develop a novel message passing-based, blind, approximate Kalman filter for
sparse factor analysis (SPARFA), that jointly (i) traces learner concept
knowledge over time, (ii) analyzes learner concept knowledge state transitions
(induced by interacting with learning resources, such as textbook sections,
lecture videos, etc, or the forgetting effect), and (iii) estimates the content
organization and intrinsic difficulty of the assessment questions. These
quantities are estimated solely from binary-valued (correct/incorrect) graded
learner response data and a summary of the specific actions each learner
performs (e.g., answering a question or studying a learning resource) at each
time instance. Experimental results on two online course datasets demonstrate
that SPARFA-Trace is capable of tracing each learner's concept knowledge
evolution over time, as well as analyzing the quality and content organization
of learning resources, the question-concept associations, and the question
intrinsic difficulties. Moreover, we show that SPARFA-Trace achieves comparable
or better performance in predicting unobserved learner responses than existing
collaborative filtering and knowledge tracing approaches for personalized
education
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