15,465 research outputs found
Non-Linear Editor for Text-Based Screencast
Screencasts, where computer screen is broadcast to a large audience on the
web, are becoming popular as an online educational tool. Among various types of
screencast content, popular are the contents that involve text editing,
including computer programming. There are emerging platforms that support such
text-based screencasts by recording every character insertion/deletion from the
creator and reconstructing its playback on the viewer's screen. However, these
platforms lack rich support for creating and editing the screencast itself,
mainly due to the difficulty of manipulating recorded text changes; the changes
are tightly coupled in sequence, thus modifying arbitrary part of the sequence
is not trivial. We present a non-linear editing tool for text-based
screencasts. With the proposed selective history rewrite process, our editor
allows users to substitute an arbitrary part of a text-based screencast while
preserving overall consistency of the rest of the text-based screencast.Comment: To appear in Adjunct Proceedings of the 30th Annual ACM Symposium on
User Interface Software & Technology (UIST 2017, Poster
Holographic Nucleons in the Nuclear Medium
We investigate the nucleon's rest mass and dispersion relation in the nuclear
medium which is holographically described by the thermal charged AdS geometry.
On this background, the chiral condensate plays an important role to determine
the nucleon's mass in both the vacuum and the nuclear medium. It also
significantly modifies the nucleon's dispersion relation. The nucleon's mass in
the high density regime increases with density as expected, while in the low
density regime it slightly decreases. We further study the splitting of the
nucleon's masses caused by the isospin interaction with the nuclear medium.Comment: 15 pages, 6 figure
Adversarial Sampling and Training for Semi-Supervised Information Retrieval
Ad-hoc retrieval models with implicit feedback often have problems, e.g., the
imbalanced classes in the data set. Too few clicked documents may hurt
generalization ability of the models, whereas too many non-clicked documents
may harm effectiveness of the models and efficiency of training. In addition,
recent neural network-based models are vulnerable to adversarial examples due
to the linear nature in them. To solve the problems at the same time, we
propose an adversarial sampling and training framework to learn ad-hoc
retrieval models with implicit feedback. Our key idea is (i) to augment clicked
examples by adversarial training for better generalization and (ii) to obtain
very informational non-clicked examples by adversarial sampling and training.
Experiments are performed on benchmark data sets for common ad-hoc retrieval
tasks such as Web search, item recommendation, and question answering.
Experimental results indicate that the proposed approaches significantly
outperform strong baselines especially for high-ranked documents, and they
outperform IRGAN in NDCG@5 using only 5% of labeled data for the Web search
task.Comment: Published in WWW 201
Lyapunov Exponent and the Solid-Fluid Phase Transition
We study changes in the chaotic properties of a many-body system undergoing a
solid-fluid phase transition. To do this, we compute the temperature dependence
of the largest Lyapunov exponents for both two- and
three-dimensional periodic systems of -particles for various densities. The
particles interact through a soft-core potential. The two-dimensional system
exhibits an apparent second-order phase transition as indicated by a
-shaped peak in the specific heat. The first derivative of
with respect to the temperature shows a peak at the same
temperature. The three-dimensional system shows jumps, in both system energy
and , at the same temperature, suggesting a first-order phase
transition. Relaxation phenomena in the phase-transition region are analyzed by
using the local time averages.Comment: 16 pages, REVTeX, 10 eps figures, epsfig.st
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