6,490 research outputs found
Dynamic quantum clustering: a method for visual exploration of structures in data
A given set of data-points in some feature space may be associated with a
Schrodinger equation whose potential is determined by the data. This is known
to lead to good clustering solutions. Here we extend this approach into a
full-fledged dynamical scheme using a time-dependent Schrodinger equation.
Moreover, we approximate this Hamiltonian formalism by a truncated calculation
within a set of Gaussian wave functions (coherent states) centered around the
original points. This allows for analytic evaluation of the time evolution of
all such states, opening up the possibility of exploration of relationships
among data-points through observation of varying dynamical-distances among
points and convergence of points into clusters. This formalism may be further
supplemented by preprocessing, such as dimensional reduction through singular
value decomposition or feature filtering.Comment: 15 pages, 9 figure
Identification of network modules by optimization of ratio association
We introduce a novel method for identifying the modular structures of a
network based on the maximization of an objective function: the ratio
association. This cost function arises when the communities detection problem
is described in the probabilistic autoencoder frame. An analogy with kernel
k-means methods allows to develop an efficient optimization algorithm, based on
the deterministic annealing scheme. The performance of the proposed method is
shown on a real data set and on simulated networks
Large magnetic circular dichroism in resonant inelastic x-ray scattering at the Mn L-edge of Mn-Zn ferrite
We report resonant inelastic x-ray scattering (RIXS) excited by circularly
polarized x-rays on Mn-Zn ferrite at the Mn L2,3-resonances. We demonstrate
that crystal field excitations, as expected for localized systems, dominate the
RIXS spectra and thus their dichroic asymmetry cannot be interpreted in terms
of spin-resolved partial density of states, which has been the standard
approach for RIXS dichroism. We observe large dichroic RIXS at the L2-resonance
which we attribute to the absence of metallic core hole screening in the
insulating Mn-ferrite. On the other hand, reduced L3-RIXS dichroism is
interpreted as an effect of longer scattering time that enables spin-lattice
core hole relaxation via magnons and phonons occurring on a femtosecond time
scale.Comment: 7 pages, 2 figures,
http://link.aps.org/doi/10.1103/PhysRevB.74.17240
A Non-Sequential Representation of Sequential Data for Churn Prediction
We investigate the length of event sequence giving best predictions
when using a continuous HMM approach to churn prediction from sequential
data. Motivated by observations that predictions based on only the few most recent
events seem to be the most accurate, a non-sequential dataset is constructed
from customer event histories by averaging features of the last few events. A simple
K-nearest neighbor algorithm on this dataset is found to give significantly
improved performance. It is quite intuitive to think that most people will react
only to events in the fairly recent past. Events related to telecommunications occurring
months or years ago are unlikely to have a large impact on a customerâs
future behaviour, and these results bear this out. Methods that deal with sequential
data also tend to be much more complex than those dealing with simple nontemporal
data, giving an added benefit to expressing the recent information in a
non-sequential manner
Magnetism in purple bronze LiMoO
Muon spin relaxation measurements around the 25 K metal-insulator transition
in LiMoO elucidate a profound role of disorder as a possible
mechanism for this transition. The relaxation rate and the muon Knight
shift are incompatible with the transition to a SDW state and thus exclude it.Comment: pages 2, fig 2, The conf. on strongly correlated electron systems,
SCES 2004, German
Blogging in the physics classroom: A research-based approach to shaping students' attitudes towards physics
Even though there has been a tremendous amount of research done in how to
help students learn physics, students are still coming away missing a crucial
piece of the puzzle: why bother with physics? Students learn fundamental laws
and how to calculate, but come out of a general physics course without a deep
understanding of how physics has transformed the world around them. In other
words, they get the "how" but not the "why". Studies have shown that students
leave introductory physics courses almost universally with decreased
expectations and with a more negative attitude. This paper will detail an
experiment to address this problem: a course weblog or "blog" which discusses
real-world applications of physics and engages students in discussion and
thinking outside of class. Specifically, students' attitudes towards the value
of physics and its applicability to the real-world were probed using a
26-question Likert scale survey over the course of four semesters in an
introductory physics course at a comprehensive Jesuit university. We found that
students who did not participate in the blog study generally exhibited a
deterioration in attitude towards physics as seen previously. However, students
who read, commented, and were involved with the blog maintained their initially
positive attitudes towards physics. Student response to the blog was
overwhelmingly positive, with students claiming that the blog made the things
we studied in the classroom come alive for them and seem much more relevant.Comment: 20 pages, 6 figure
Using patterns position distribution for software failure detection
Pattern-based software failure detection is an important topic of research in recent years. In this method, a set of patterns from program execution traces are extracted, and represented as features, while their occurrence frequencies are treated as the corresponding feature values. But this conventional method has its limitation due to ignore the patternâs position information, which is important for the classification of program traces. Patterns occurs in the different positions of the trace are likely to represent different meanings. In this paper, we present a novel approach for using patternâs position distribution as features to detect software failure. The comparative experiments in both artificial and real datasets show the effectiveness of this method
QL-CONST1: an expert system for quality level prediction in concrete structures
Current trends in the fields of artifical intelligence and expert systems are moving towards the exciting possibility of reproducing and simulating human expertise and expert behaviour into a knowledge base, coupled with an appropriate, partially âintelligentâ, computer code. This paper deals with the quality level prediction in concrete structures using the helpful assistance of an expert system, QL-CONST1, which is able to reason about this specific field of structural engineering. Evidence, hypotheses and factors related to this human knowledge field have been codified into a knowledge base. This knowledge base has been prepared in terms of probabilities of the presence of either hypotheses or evidence and the conditional presence of both. Human experts in the fields of structural engineering and the safety of structures gave their invaluable knowledge and assistance to the construction of the knowledge base. Some illustrative examples for, the validation of the expert system behaviour are included
Open-Ended Evolutionary Robotics: an Information Theoretic Approach
This paper is concerned with designing self-driven fitness functions for
Embedded Evolutionary Robotics. The proposed approach considers the entropy of
the sensori-motor stream generated by the robot controller. This entropy is
computed using unsupervised learning; its maximization, achieved by an on-board
evolutionary algorithm, implements a "curiosity instinct", favouring
controllers visiting many diverse sensori-motor states (sms). Further, the set
of sms discovered by an individual can be transmitted to its offspring, making
a cultural evolution mode possible. Cumulative entropy (computed from ancestors
and current individual visits to the sms) defines another self-driven fitness;
its optimization implements a "discovery instinct", as it favours controllers
visiting new or rare sensori-motor states. Empirical results on the benchmark
problems proposed by Lehman and Stanley (2008) comparatively demonstrate the
merits of the approach
- âŠ