Neural disjunctive normal form: Vertically integrating logic with deep learning for classification

Inspired by the limitations of pure deep learning and symbolic logic-based models, in this thesis we consider a specific type of neuro-symbolic integration called vertical integration to bridge logic reasoning and deep learning and address their limitations. The motivation of vertical integration is to combine perception and reasoning as two separate stages of computation, while still being able to utilize simple and efficient end-to-end learning. It uses a perceptive deep neural network (DNN) to learn abstract concepts from raw sensory data and uses a symbolic model that operates on these abstract concepts to make interpretable predictions. As a preliminary step towards this direction, we tackle the task of binary classification and propose the Neural Disjunctive Normal Form (Neural DNF). Specifically, we utilize a per- ceptive DNN module to extract features from data, then after binarization (0 or 1), feed them into a Disjunctive Normal Form (DNF) module to perform logical rule-based classi- fication. We introduce the BOAT algorithm to optimize these two normally-incompatible modules in an end-to-end manner. Compared to standard DNF, Neural DNF can handle prediction tasks from raw sensory data (such as images) thanks to the neurally-extracted concepts. Compared to standard DNN, Neural DNF offers improved interpretability via an explicit symbolic representation while being able to achieve comparable accuracy despite the reduction of model flexibility, and is particularly suited for certain classification tasks that require some logical composition. Our experiments show that BOAT can optimize Neural DNF in an end-to-end manner, i.e. jointly learn the logical rules and concepts from scratch, and that in certain cases the rules and the meanings of concepts are aligned with human understanding. We view Neural DNF as an important first step towards more sophisticated vertical inte- gration models, which use symbolic models of more powerful rule languages for advanced prediction and algorithmic tasks, beyond using DNF (propositional logic) for classification tasks. The BOAT algorithm introduced in this thesis can potentially be applied to such advanced hybrid models

Multi-Modal Answer Validation for Knowledge-Based VQA

The problem of knowledge-based visual question answering involves answering questions that require external knowledge in addition to the content of the image. Such knowledge typically comes in various forms, including visual, textual, and commonsense knowledge. Using more knowledge sources increases the chance of retrieving more irrelevant or noisy facts, making it challenging to comprehend the facts and find the answer. To address this challenge, we propose Multi-modal Answer Validation using External knowledge (MAVEx), where the idea is to validate a set of promising answer candidates based on answer-specific knowledge retrieval. Instead of searching for the answer in a vast collection of often irrelevant facts as most existing approaches do, MAVEx aims to learn how to extract relevant knowledge from noisy sources, which knowledge source to trust for each answer candidate, and how to validate the candidate using that source. Our multi-modal setting is the first to leverage external visual knowledge (images searched using Google), in addition to textual knowledge in the form of Wikipedia sentences and ConceptNet concepts. Our experiments with OK-VQA, a challenging knowledge-based VQA dataset, demonstrate that MAVEx achieves new state-of-the-art results. Our code is available at https://github.com/jialinwu17/MAVEXComment: AAAI 202

Subgroup Discovery in Unstructured Data

Subgroup discovery is a descriptive and exploratory data mining technique to identify subgroups in a population that exhibit interesting behavior with respect to a variable of interest. Subgroup discovery has numerous applications in knowledge discovery and hypothesis generation, yet it remains inapplicable for unstructured, high-dimensional data such as images. This is because subgroup discovery algorithms rely on defining descriptive rules based on (attribute, value) pairs, however, in unstructured data, an attribute is not well defined. Even in cases where the notion of attribute intuitively exists in the data, such as a pixel in an image, due to the high dimensionality of the data, these attributes are not informative enough to be used in a rule. In this paper, we introduce the subgroup-aware variational autoencoder, a novel variational autoencoder that learns a representation of unstructured data which leads to subgroups with higher quality. Our experimental results demonstrate the effectiveness of the method at learning subgroups with high quality while supporting the interpretability of the concepts

Human activities accelerated the degradation of saline seepweed red beaches by amplifying top‐down and bottom‐up forces

Salt marshes dominated by saline seepweed (Suaeda heteroptera) provide important ecosystem services such as sequestering carbon (blue carbon), maintaining healthy fisheries, and protecting shorelines. These salt marshes also constitute stunning red beach landscapes, and the resulting tourism significantly contributes to the local economy. However, land use change and degradation have led to a substantial loss of the red beach area. It remains unclear how human activities influence the top‐down and bottom‐up forces that regulate the distribution and succession of these salt marshes and lead to the degradation of the red beaches. We examined how bottom‐up forces influenced the germination, emergence, and colonization of saline seepweed with field measurements and a laboratory experiment. We also examined whether top‐down forces affected the red beach distribution by conducting a field survey for crab burrows and density, laboratory feeding trials, and waterbird investigations. The higher sediment accretion rate induced by human activities limited the establishment of new red beaches. The construction of tourism facilities and the frequent presence of tourists reduced the density of waterbirds, which in turn increased the density of crabs, intensifying the top‐down forces such as predators and herbivores that drive the degradation of the coastal red beaches. Our results show that sediment accretion and plant–herbivory changes induced by human activities were likely the two primary ecological processes leading to the degradation of the red beaches. Human activities significantly shaped the abundance and distribution of the red beaches by altering both top‐down and bottom‐up ecological processes. Our findings can help us better understand the dynamics of salt marshes and have implications for the management and restoration of coastal wetlands

A manometric feature descriptor with linear-SVM to distinguish esophageal contraction vigor

n clinical, if a patient presents with nonmechanical obstructive dysphagia, esophageal chest pain, and gastro esophageal reflux symptoms, the physician will usually assess the esophageal dynamic function. High-resolution manometry (HRM) is a clinically commonly used technique for detection of esophageal dynamic function comprehensively and objectively. However, after the results of HRM are obtained, doctors still need to evaluate by a variety of parameters. This work is burdensome, and the process is complex. We conducted image processing of HRM to predict the esophageal contraction vigor for assisting the evaluation of esophageal dynamic function. Firstly, we used Feature-Extraction and Histogram of Gradients (FE-HOG) to analyses feature of proposal of swallow (PoS) to further extract higher-order features. Then we determine the classification of esophageal contraction vigor normal, weak and failed by using linear-SVM according to these features. Our data set includes 3000 training sets, 500 validation sets and 411 test sets. After verification our accuracy reaches 86.83%, which is higher than other common machine learning methods