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We describe here the recent results of a multidisciplinary effort to design a biomarker that can actively and continuously decode the progressive changes in neuronal organization leading to epilepsy, a process known as epileptogenesis. Using an animal model of acquired epilepsy, we chronically record hippocampal evoked potentials elicited by an auditory stimulus. Using a set of reduced coordinates,...
In many fields, superior gains have been obtained by leveraging the computational power of machine learning techniques to solve expert tasks. In this paper we present an application of machine learning to agriculture, solving a particular problem of diagnosis of crop disease based on plant images taken with a smartphone. Two pieces of information are important here, the disease incidence and disease...
Ontology, the shared formal conceptualization of domain information, has been shown to have multiple applications in modeling, processing and understanding natural language text. In this work, we use distributed word vectors out of various recent language models from Deep Learning for semi-automated domain ontology creation for closed domains. We cover all major aspects of Domain Ontology Induction...
Automatic summarization, a difficult but pressing problem in natural language processing, aims at shortening source documents while retaining main information. In recent years, more statistical machine learning methods have been applied to automatic summarization. In this paper, we propose a novel approach for summarization, based on hierarchical Bayesian model of topic-semantic indexing (TSI) and...
Topic models employ the Bag-of-Words (BOW) representation, which break terms into constituent words and treat words as surface strings without assuming predefined knowledge about word meaning. In this paper, we propose the Semantic Concept Latent Dirichlet Allocation (SCLDA) and Semantic Concept Hierarchical Dirichlet Process (SCHDP) based approaches by representing text as meaningful concepts rather...
Supervised methods for inferring gene regulatory networks (GRNs) perform well with good training data. However, when training data is absent, these methods are not applicable. Unsupervised methods do not need training data but their accuracy is low. In this paper, we combine supervised and unsupervised methods to infer GRNs using time-series gene expression data. Specifically, we use results obtained...
In this work, we apply the idea of conditional computation to the gated recurrent unit (GRU), a type of recurrent activation function. With slight modifications to the GRU, the number of floating point operations required to calculate the feed-forward pass through the network may be significantly reduced. This allows for more rapid computation, enabling a trade-off between model accuracy and model...
Induction of descriptive models is one of the most important technologies in data mining. The expressiveness of descriptive models are of paramount importance in applications that examine the causality of relationships between variables. Most of the work on descriptive models has concentrated on less expressive approaches such as clustering algorithms or rule-based approaches that are limited to a...
Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. Thus, tools for summarizing, clustering, visualizing and classifying high-dimensional data are essential. In this work, we show...
Contextual aware matrix factorization has been widely used in recommender systems by learning latent feature vectors of users and items along with contextual information. While most of them add identical bias for each type of side information to represent systematic tendencies in users' rating behaviors, they are not able to capture the preference unique to users or items. In this paper, we propose...
Comparable corpora contain significant quantities of useful data for Natural Language Processing tasks, especially in the area of Machine Translation. They are mainly the source of parallel text fragments. This paper investigates how to effectively extract bilingual texts from comparable corpora relying on a small-size parallel training corpus. We propose a new technique to filter non parallel articles...
Authorship identification is the task of identifying the author of a given text from a set of suspects. The main concern of this task is to define an appropriate characterization of texts that captures the writing style of authors. Although deep learning was recently used in different natural language processing tasks, it has not been used in author identification (to the best of our knowledge). In...
Travel time prediction is important for freight transportation companies. Accurate travel time prediction can help these companies make better planning and task scheduling. For several reasons, most companies are not able to obtain traffic flow data from traffic management authorities, but a large amount of trajectory data were collected everyday which has not been fully utilised. In this study, we...
Multi-Attribute Reverse Auctions (MARAs) are considered an excellent way to buy and sell efficiently. However, eliciting the buyer's requirements and preferences as well as determining the winner, are both challenging tasks. In this paper, we propose a multi-round and semi-sealed MARA auction system, capable of determining the winner given a set of user's preferences and requirements. This system...
Sudden weight gain in patients living with Congestive Heart Failure (CHF) is often an indication that the individual is retaining fluid, which often means that patient's heart has weakened leading to increased risk of kidney or cardiac failure. Clinical interventions can be made at this stage, leading to better outcomes, however it is essential that the interventions take place before the patient's...
In this paper, a study is conducted on combining analytical and holistic strategies for handwriting recognition. Even though the big majority of the recent high recognition rate systems adopts analytical strategies, physiological scientists suggest that the holistic strategy is the key for realizing near-human performance. In what we believe is a fresh perspective on handwriting recognition, combining...
The Arbitrary Lagrangian-Eulerian (ALE) method is used in a variety of engineering and scientific applications for enabling multi-physics simulations. Unfortunately, the ALE method can suffer from simulation failures that require users to adjust parameters iteratively in order to complete a simulation. In this paper, we present a supervised learning framework for predicting conditions leading to simulation...
A deep neural networks is proposed for the classification of premature ventricular contraction (PVC) beat, which is an irregular heartbeat initiated by Purkinje fibers rather than by sinoatrial node. Several machine learning approaches were proposed for the detection of PVC beats although they resulted in either achieving low accuracy of classification or using limited portion of data from existing...
Deep learning models have recently achieved the state-of-the-art results on a well-known pedestrian detection dataset. However, such images were obtained from open scenarios with fixed imaging geometry parameters, which may produce a network not suitable for detecting a person in more general settings, such as the ones found in surveillance systems. As gathering and annotating data is a highly expensive...
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