44 research outputs found
Topicality and Social Impact: Diverse Messages but Focused Messengers
Are users who comment on a variety of matters more likely to achieve high
influence than those who delve into one focused field? Do general Twitter
hashtags, such as #lol, tend to be more popular than novel ones, such as
#instantlyinlove? Questions like these demand a way to detect topics hidden
behind messages associated with an individual or a hashtag, and a gauge of
similarity among these topics. Here we develop such an approach to identify
clusters of similar hashtags by detecting communities in the hashtag
co-occurrence network. Then the topical diversity of a user's interests is
quantified by the entropy of her hashtags across different topic clusters. A
similar measure is applied to hashtags, based on co-occurring tags. We find
that high topical diversity of early adopters or co-occurring tags implies high
future popularity of hashtags. In contrast, low diversity helps an individual
accumulate social influence. In short, diverse messages and focused messengers
are more likely to gain impact.Comment: 9 pages, 7 figures, 6 table
Predicting Successful Memes using Network and Community Structure
We investigate the predictability of successful memes using their early
spreading patterns in the underlying social networks. We propose and analyze a
comprehensive set of features and develop an accurate model to predict future
popularity of a meme given its early spreading patterns. Our paper provides the
first comprehensive comparison of existing predictive frameworks. We categorize
our features into three groups: influence of early adopters, community
concentration, and characteristics of adoption time series. We find that
features based on community structure are the most powerful predictors of
future success. We also find that early popularity of a meme is not a good
predictor of its future popularity, contrary to common belief. Our methods
outperform other approaches, particularly in the task of detecting very popular
or unpopular memes.Comment: 10 pages, 6 figures, 2 tables. Proceedings of 8th AAAI Intl. Conf. on
Weblogs and social media (ICWSM 2014
Automatic Curriculum Learning For Deep RL: A Short Survey
Automatic Curriculum Learning (ACL) has become a cornerstone of recent
successes in Deep Reinforcement Learning (DRL).These methods shape the learning
trajectories of agents by challenging them with tasks adapted to their
capacities. In recent years, they have been used to improve sample efficiency
and asymptotic performance, to organize exploration, to encourage
generalization or to solve sparse reward problems, among others. The ambition
of this work is dual: 1) to present a compact and accessible introduction to
the Automatic Curriculum Learning literature and 2) to draw a bigger picture of
the current state of the art in ACL to encourage the cross-breeding of existing
concepts and the emergence of new ideas.Comment: Accepted at IJCAI202
Impact of DM direct searches and the LHC analyses on branon phenomenology
Dark Matter direct detection experiments are able to exclude interesting
parameter space regions of particle models which predict an important amount of
thermal relics. We use recent data to constrain the branon model and to compute
the region that is favored by CDMS measurements. Within this work, we also
update present colliders constraints with new studies coming from the LHC.
Despite the present low luminosity, it is remarkable that for heavy branons,
CMS and ATLAS measurements are already more constraining than previous analyses
performed with TEVATRON and LEP data.Comment: 17 pages, 2 figure
A Holistic Approach to Undesired Content Detection in the Real World
We present a holistic approach to building a robust and useful natural
language classification system for real-world content moderation. The success
of such a system relies on a chain of carefully designed and executed steps,
including the design of content taxonomies and labeling instructions, data
quality control, an active learning pipeline to capture rare events, and a
variety of methods to make the model robust and to avoid overfitting. Our
moderation system is trained to detect a broad set of categories of undesired
content, including sexual content, hateful content, violence, self-harm, and
harassment. This approach generalizes to a wide range of different content
taxonomies and can be used to create high-quality content classifiers that
outperform off-the-shelf models.Comment: Oral presentation at AAAI-2
Inhibition of lactate transport by MCT-1 blockade improves chimeric antigen receptor T-cell therapy against B-cell malignancies
BACKGROUND: Chimeric antigen receptor (CAR) T cells have shown remarkable results against B-cell malignancies, but only a minority of patients have long-term remission. The metabolic requirements of both tumor cells and activated T cells result in production of lactate. The export of lactate is facilitated by expression of monocarboxylate transporter (MCTs). CAR T cells express high levels of MCT-1 and MCT-4 on activation, while certain tumors predominantly express MCT-1. METHODS: Here, we studied the combination of CD19-specific CAR T-cell therapy with pharmacological blockade of MCT-1 against B-cell lymphoma. RESULTS: MCT-1 inhibition with small molecules AZD3965 or AR-C155858 induced CAR T-cell metabolic rewiring but their effector function and phenotype remained unchanged, suggesting CAR T cells are insensitive to MCT-1 inhibition. Moreover, improved cytotoxicity in vitro and antitumoral control on mouse models was found with the combination of CAR T cells and MCT-1 blockade. CONCLUSION: This work highlights the potential of selective targeting of lactate metabolism via MCT-1 in combination with CAR T cells therapies against B-cell malignancies