20 research outputs found
Automatic Document Image Binarization using Bayesian Optimization
Document image binarization is often a challenging task due to various forms
of degradation. Although there exist several binarization techniques in
literature, the binarized image is typically sensitive to control parameter
settings of the employed technique. This paper presents an automatic document
image binarization algorithm to segment the text from heavily degraded document
images. The proposed technique uses a two band-pass filtering approach for
background noise removal, and Bayesian optimization for automatic
hyperparameter selection for optimal results. The effectiveness of the proposed
binarization technique is empirically demonstrated on the Document Image
Binarization Competition (DIBCO) and the Handwritten Document Image
Binarization Competition (H-DIBCO) datasets
TwitterMancer: predicting interactions on Twitter accurately
This paper investigates the interplay between different types of user interactions on Twitter, with respect to predicting missing or unseen interactions. For example, given a set of retweet interactions between Twitter users, how accurately can we predict reply interactions? Is it more difficult to predict retweet or quote interactions between a pair of accounts? Also, how important is time locality, and which features of interaction patterns are most important to enable accurate prediction of specific Twitter interactions? Our empirical study of Twitter interactions contributes initial answers to these questions.We have crawled an extensive data set of Greek-speaking Twitter accounts and their follow, quote, retweet, reply interactions over a period of a month. We find we can accurately predict many interactions of Twitter users. Interestingly, the most predictive features vary with the user profiles, and are not the same across all users. For example, for a pair of users that interact with a large number of other Twitter users, we find that certain “higher-dimensional” triads, i.e., triads that involve multiple types of interactions, are very informative, whereas for less active Twitter users, certain in-degrees and out-degrees play a major role. Finally, we provide various other insights on Twitter user behavior. Our code and data are available at https://github.com/twittermancer/.Accepted manuscrip
TwitterMancer: Predicting Interactions on Twitter Accurately
This paper investigates the interplay between different types of user
interactions on Twitter, with respect to predicting missing or unseen
interactions. For example, given a set of retweet interactions between Twitter
users, how accurately can we predict reply interactions? Is it more difficult
to predict retweet or quote interactions between a pair of accounts? Also, how
important is time locality, and which features of interaction patterns are most
important to enable accurate prediction of specific Twitter interactions? Our
empirical study of Twitter interactions contributes initial answers to these
questions.
We have crawled an extensive dataset of Greek-speaking Twitter accounts and
their follow, quote, retweet, reply interactions over a period of a month.
We find we can accurately predict many interactions of Twitter users.
Interestingly, the most predictive features vary with the user profiles, and
are not the same across all users.
For example, for a pair of users that interact with a large number of other
Twitter users, we find that certain "higher-dimensional" triads, i.e., triads
that involve multiple types of interactions, are very informative, whereas for
less active Twitter users, certain in-degrees and out-degrees play a major
role. Finally, we provide various other insights on Twitter user behavior.
Our code and data are available at https://github.com/twittermancer/.
Keywords: Graph mining, machine learning, social media, social network
A phi layer in roots of Ceratonia siliqua L.
The central cylinder of the primary root of the carob tree (Ceratonia siliqua) is encircled by a layer of cells with wall thickenings, known as a phi (φ) cell layer. The development of the φ layer and the chemical composition of the cell wall thickenings have been studied in roots of C. siliqua. The results reveal the presence of condensed tannins in the mature phi thickenings and that the development of the φ layer is asynchronous: at 0-1 cm from the root tip φ thickenings appear before endodermis differentiation at the sites opposite phloem, at 1-4 cm new φ thickenings are developed at the sites opposite xylem, at 4-7 cm the φ layer consists of two layers of cells and it completely encloses the central cylinder
The Quest for Precision : A Layered Approach for Data Race Detection in Static Analysis
QC 20170105</p
Bi-Abductive Resource Invariant Synthesis
Abstract. We describe an algorithm for synthesizing resource invariants that are used in the verification of concurrent programs. This synthesis employs bi-abductive inference to identify the footprints of different parts of the program and decide what invariant each lock protects. We demonstrate our algorithm on several small (yet intricate) examples which are out of the reach of other automatic analyses in the literature.