Exploring Intensity Invariance in Deep Neural Networks for Brain Image Registration

Image registration is a widely-used technique in analysing large scale datasets that are captured through various imaging modalities and techniques in biomedical imaging such as MRI, X-Rays, etc. These datasets are typically collected from various sites and under different imaging protocols using a variety of scanners… Such heterogeneity in the data collection process causes inhomogeneity or variation in intensity (brightness) and noise distribution. These variations play a detrimental role in the performance of image registration, segmentation and detection algorithms. Classical […]

September 25, 2020 Machine Learning

Tabling Optimization for Contextual Abduction

Tabling for contextual abduction in logic programming has been introduced as a means to store previously obtained abductive solutions in one context to be reused in another context. This paper identifies a number of issues in the existing implementations of tabling in contextual abduction and aims to mitigate the issues… We propose a new program transformation for integrity constraints to deal with their proper application for filtering solutions while also reducing the table memory usage. We further optimize the table […]

September 25, 2020 ANOMALY DETECTION, Machine Learning

Automating Outlier Detection via Meta-Learning

Given an unsupervised outlier detection (OD) task on a new dataset, how can we automatically select a good outlier detection method and its hyperparameter(s) (collectively called a model)? Thus far, model selection for OD has been a “black art”; as any model evaluation is infeasible due to the lack of (i) hold-out data with labels, and (ii) a universal objective function… In this work, we develop the first principled data-driven approach to model selection for OD, called MetaOD, based on […]

September 25, 2020 ASPECT-BASED SENTIMENT ANALYSIS, Machine Learning

GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis

In this paper, we focus on the imbalance issue, which is rarely studied in aspect term extraction and aspect sentiment classification when regarding them as sequence labeling tasks. Besides, previous works usually ignore the interaction between aspect terms when labeling polarities… We propose a GRadient hArmonized and CascadEd labeling model (GRACE) to solve these problems. Specifically, a cascaded labeling module is developed to enhance the interchange between aspect terms and improve the attention of sentiment tokens when labeling sentiment polarities. […]

September 25, 2020 DIMENSIONALITY REDUCTION, Machine Learning

Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey

Stochastic Neighbor Embedding (SNE) is a manifold learning and dimensionality reduction method with a probabilistic approach. In SNE, every point is consider to be the neighbor of all other points with some probability and this probability is tried to be preserved in the embedding space… SNE considers Gaussian distribution for the probability in both the input and embedding spaces. However, t-SNE uses the Student-t and Gaussian distributions in these spaces, respectively. In this tutorial and survey paper, we explain SNE, […]

September 25, 2020 Machine Learning

Conditional Sequential Modulation for Efficient Global Image Retouching

Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from photographic defects such as over/under exposure, poor contrast, inharmonious saturation. Practically, photo retouching can be accomplished by a series of image processing operations… In this paper, we investigate some commonly-used retouching operations and mathematically find that these pixel-independent operations can be approximated or formulated by multi-layer perceptrons (MLPs). Based on this analysis, we propose an extremely light-weight framework – Conditional Sequential Retouching Network (CSRNet) – for […]

September 25, 2020 Machine Learning, RELATION EXTRACTION

Global-to-Local Neural Networks for Document-Level Relation Extraction

Relation extraction (RE) aims to identify the semantic relations between named entities in text. Recent years have witnessed it raised to the document level, which requires complex reasoning with entities and mentions throughout an entire document… In this paper, we propose a novel model to document-level RE, by encoding the document information in terms of entity global and local representations as well as context relation representations. Entity global representations model the semantic information of all entities in the document, entity […]

September 25, 2020 Machine Learning

LP2PB: Translating Answer Set Programs into Pseudo-Boolean Theories

Answer set programming (ASP) is a well-established knowledge representation formalism. Most ASP solvers are based on (extensions of) technology from Boolean satisfiability solving… While these solvers have shown to be very successful in many practical applications, their strength is limited by their underlying proof system, resolution. In this paper, we present a new tool LP2PB that translates ASP programs into pseudo-Boolean theories, for which solvers based on the (stronger) cutting plane proof system exist. We evaluate our tool, and the […]

September 25, 2020 Data

Python Data Tools

Python is an increasingly popular object-oriented, interpreted and interactive programming language used for heavy-duty data analysis. Python is designed for ease-of-use, speed, readability and tailored for data-intensive applications. Python supports multiple programming paradigms, including object-oriented, imperative and functional programming styles. It features a fully dynamic type system and automatic memory management, similar to that of Scheme, Ruby, Perl and Tcl. You can create customized data tools using Python that can handle large data sets efficiently – it lets you work more quickly and […]

September 25, 2020 Analysis

An indispensable Python : Data sourcing to Data science.

Data analysis echo system has grown all the way from SQL’s to NoSQL and from Excel analysis to Visualization. Today, we are in scarceness of the resources to process ALL (You better understand what i mean by ALL) kind of data that is coming to enterprise. Data goes through profiling, formatting, munging or cleansing, pruning, transformation steps to analytics and predictive modeling. Interestingly, there is no one tool proved to be an effective solution to run all these operations { Don’t forget the […]

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