Distributionally Robust Optimization: A Review

The concepts of risk-aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. Statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these concepts. A modeling framework, called distributionally robust optimization (DRO), has recently received significant attention in both the operations research and statistical learning communities. This paper surveys main concepts and contributions to DRO, and its relationships with robust optimization, risk-aversion, chance-constrained optimization, and function regularization

VER: Learning Natural Language Representations for Verbalizing Entities and Relations

Entities and relationships between entities are vital in the real world. Essentially, we understand the world by understanding entities and relations. For instance, to understand a field, e.g., computer science, we need to understand the relevant concepts, e.g., machine learning, and the relationships between concepts, e.g., machine learning and artificial intelligence. To understand a person, we should first know who he/she is and how he/she is related to others. To understand entities and relations, humans may refer to natural language descriptions. For instance, when learning a new scientific term, people usually start by reading its definition in dictionaries or encyclopedias. To know the relationship between two entities, humans tend to create a sentence to connect them. In this paper, we propose VER: A Unified Model for Verbalizing Entities and Relations. Specifically, we attempt to build a system that takes any entity or entity set as input and generates a sentence to represent entities and relations, named ``natural language representation''. Extensive experiments demonstrate that our model can generate high-quality sentences describing entities and entity relationships and facilitate various tasks on entities and relations, including definition modeling, relation modeling, and generative commonsense reasoning

Incorporating engineering in high school biology

textThe purpose of this project was to create a series of lessons that incorporate both Biology and Engineering concepts. The three lessons were intended to increase in complexity as the students progress throughout the year. Using PyMol software allowed students to visually represent complex protein structures while introducing and providing an opportunity to practice programming. Each lesson was followed by a worksheet or activity to aid in students' comprehension and application of practice. These lessons were designed to maximize students' time learning to program and using PyMol software while enhancing the current curriculum. Lesson one introduced students to the PyMol software while building and representing the four main structures of proteins. With increased programming knowledge, lesson two focused on modeling the DNA double helix. The final lesson introduced students to evolutionary relationships based on a protein's amino acid sequence.Science, Technology, Engineering, and Mathematics Educatio

Characterizing learning environments capable of nurturing generic capabilities in higher education

There has been wide recognition that today's graduates need the type of generic capabilities necessary for lifelong learning. However, the mechanism by which universities can develop these generic skills is not clearly established. This study aimed to investigate the mechanism for their development. Structural equation modeling (SEM) was used to test a hypothesized model of capability development through a suitable learning environment with 1756 undergraduates at a university in Hong Kong. To triangulate against this model and more fully characterize the learning environment, focus group interviews were held with five to six students from three programs with good records of capability development. Analysis of the interview data resulted in a set of categories, describing a learning environment, which were consistent with the SEM model. The learning environment which seemed conducive to capability development aimed for understanding of key concepts through a variety of assessment methods and active engagement in learning activities. Teacher-student relationships were developed through interaction, feedback and assistance. The promotion of peer-student relationships led to a high degree of collaborative learning. © Springer Science+Business Media, LLC 2007.postprin

What students learn in problem-based learning: a process analysis.

This study aimed to provide an account of how learning takes place in problem-based learning (PBL), and to identify the relationships between the learning-oriented activities of students with their learning outcomes. First, the verbal interactions and computer resources studied by nine students for an entire PBL cycle were recorded. The relevant concepts articulated and studied individually while working on the problem-at-hand were identified as units of analysis and counted to demonstrate the growth in concepts acquired over the PBL cycle. We identified two distinct phases in the process-an initial concept articulation, and a later concept repetition phase. To overcome the sample-size limitations of the first study, we analyzed the verbal interactions of, and resources studied, by another 35 students in an entire PBL cycle using structural equation modeling. Results show that students' verbal contributions during the problem analysis phase strongly influenced their verbal contributions during self-directed learning and reporting phases. Verbal contributions and individual study influenced similarly the contributions during the reporting phase. Increased verbalizations of concepts during the reporting phase also led to higher achievement. We found that collaborative learning is significant in the PBL process, and may be more important than individual study in determining students' achievement

Enrichment of ontologies using machine learning and summarization

Biomedical ontologies are structured knowledge systems in biomedicine. They play a major role in enabling precise communications in support of healthcare applications, e.g., Electronic Healthcare Records (EHR) systems. Biomedical ontologies are used in many different contexts to facilitate information and knowledge management. The most widely used clinical ontology is the SNOMED CT. Placing a new concept into its proper position in an ontology is a fundamental task in its lifecycle of curation and enrichment. A large biomedical ontology, which typically consists of many tens of thousands of concepts and relationships, can be viewed as a complex network with concepts as nodes and relationships as links. This large-size node-link diagram can easily become overwhelming for humans to understand or work with. Adding concepts is a challenging and time-consuming task that requires domain knowledge and ontology skills. IS-A links (aka subclass links) are the most important relationships of an ontology, enabling the inheritance of other relationships. The position of a concept, represented by its IS-A links to other concepts, determines how accurately it is modeled. Therefore, considering as many parent candidate concepts as possible leads to better modeling of this concept. Traditionally, curators rely on classifiers to place concepts into ontologies. However, this assumes the accurate relationship modeling of the new concept as well as the existing concepts. Since many concepts in existing ontologies, are underspecified in terms of their relationships, the placement by classifiers may be wrong. In cases where the curator does not manually check the automatic placement by classifier programs, concepts may end up in wrong positions in the IS-A hierarchy. A user searching for a concept, without knowing its precise name, would not find it in its expected location. Automated or semi-automated techniques that can place a concept or narrow down the places where to insert it, are highly desirable. Hence, this dissertation is addressing the problem of concept placement by automatically identifying IS-A links and potential parent concepts correctly and effectively for new concepts, with the assistance of two powerful techniques, Machine Learning (ML) and Abstraction Networks (AbNs). Modern neural networks have revolutionized Machine Learning in vision and Natural Language Processing (NLP). They also show great promise for ontology-related tasks, including ontology enrichment, i.e., insertion of new concepts. This dissertation presents research using ML and AbNs to achieve knowledge enrichment of ontologies. Abstraction networks (AbNs), are compact summary networks that preserve a significant amount of the semantics and structure of the underlying ontologies. An Abstraction Network is automatically derived from the ontology itself. It consists of nodes, where each node represents a set of concepts that are similar in their structure and semantics. Various kinds of AbNs have been previously developed by the Structural Analysis of Biomedical Ontologies Center (SABOC) to support the summarization, visualization, and quality assurance (QA) of biomedical ontologies. Two basic kinds of AbNs are the Area Taxonomy and the Partial-area Taxonomy, which have been developed for various biomedical ontologies (e.g., SNOMED CT of SNOMED International and NCIt of the National Cancer Institute). This dissertation presents four enrichment studies of SNOMED CT, utilizing both ML and AbN-based techniques

Reading female learning in the mid-Victorian novel

text"Reading Female Learning in the mid-Victorian Novel" considers depictions of learning girls and learned women in English novels between 1848 and 1870 as dramatizing the varied relationships between femininity and learning during an era of great educational change. In analyzing novels by Charlotte Yonge, Charles Dickens, Charlotte Brontë, George Eliot, and Lewis Carroll in the context of their cultural-historical conditions, this project examines the significance of education to understandings and performances of Victorian femininity. Its readings identify a pervasive vision of middle-class femininity as incompatible with scholarly learning or educational ambition. "Reading Female Learning" surveys shifting contemporary perceptions and practices of education for girls and women, demonstrating that female education remained a central concern over the course of the nineteenth century in England. Close readings track how novels portray how education affects the female learner as well as how novels construct, consider, and resolve (or not) the perceived incompatibility between femininity and learning. This dissertation reads narratives of girls' progress to womanhood in novels by Yonge and Dickens as modeling the effects of learning on individual women and broader concepts of womanhood. It investigates how Brontë's Villette and Carroll's Alice books represent the impact of education and ambition for learning on the female body. It examines how Eliot's The Mill on the Floss represents the influence of learning on individual female identity in relation to society. As a whole, the project explores the relationships between individual women and society, paying particular attention to how novels implicitly or explicitly position the learning female character as a example for women inside and outside the text. Looking beyond the governess and the "New Woman" to the diverse concepts and experiences of female education in mid-Victorian England, "Reading Female Learning" presents the learning or learned woman as a valuable lens through which to investigate education's potentials for and effects on individual and gender development.Englis

Towards Interpretable Deep Learning Models for Knowledge Tracing

As an important technique for modeling the knowledge states of learners, the traditional knowledge tracing (KT) models have been widely used to support intelligent tutoring systems and MOOC platforms. Driven by the fast advancements of deep learning techniques, deep neural network has been recently adopted to design new KT models for achieving better prediction performance. However, the lack of interpretability of these models has painfully impeded their practical applications, as their outputs and working mechanisms suffer from the intransparent decision process and complex inner structures. We thus propose to adopt the post-hoc method to tackle the interpretability issue for deep learning based knowledge tracing (DLKT) models. Specifically, we focus on applying the layer-wise relevance propagation (LRP) method to interpret RNN-based DLKT model by backpropagating the relevance from the model's output layer to its input layer. The experiment results show the feasibility using the LRP method for interpreting the DLKT model's predictions, and partially validate the computed relevance scores from both question level and concept level. We believe it can be a solid step towards fully interpreting the DLKT models and promote their practical applications in the education domain