Call for Presentations: Disability Studies

It’s time to share your most innovative ideas, professional practices, and theoretical knowledge of Disability Studies at the 2019 Pacific Rim International Conference on Disability & Diversity in Honolulu, Hawaii. We are seeking presenters who offer interdisciplinary insight in the following five topic areas: Disability Studies and Early Education - Do you know of or have experience with how labeling children at an early age stigmatize them in the education system? Do you know how we can identify and support young children with disabilities without burdening them with the label of being “different” or “defective”? Disability Studies and Education, K-12. - Do you know of or have experience with strategies and models that are effective for fully including children and youth with disabilities in the general curriculum without losing sight of the need to address individual differences? Disability Studies and Postsecondary Education - What role does Disability Studies play in the academy? How can Disability Studies in the academy transform the way that disability is perceived in higher education and professional practice? Disability Studies and Employment - Do you know what strategies are effective in changing negative perceptions about the value of disabled workers with employers and fellow employees? How can we “raise the bar” of expectations for disabled workers from “getting a job” to “having a career”? Disability Studies and Health and Wellbeing - Do you know or have you experience with how misperceptions about the relationship between illness and disability impact health care and personal happiness for individuals with disabilities? Do you know o have you experience with how social justice issues within indigenous communities intersect with the identification and treatment of individuals with disabilities? Disability Studies and Accessibility and Visitability - How do accessibility and visitability standards and practices reduce the marginalization of people with disabilities? In what ways does accessibility and visitability intersect with poverty, race and language? 34th Annual Pacific Rim International Conference on Disability & Diversity March 4 & 5, 2019, Honolulu, Hawaii, USA Submit proposal by November 30, 2018 at https://www.pacrim.hawaii.edu For more information about Disability Studies topics, contact topic chair, Megan Conway, [email protected]. For general information on the conference or registration, please contact [email protected], (808) 956-8816, fax (808) 956-4437 or email

Unpacking Large Language Models with Conceptual Consistency

If a Large Language Model (LLM) answers "yes" to the question "Are mountains tall?" then does it know what a mountain is? Can you rely on it responding correctly or incorrectly to other questions about mountains? The success of Large Language Models (LLMs) indicates they are increasingly able to answer queries like these accurately, but that ability does not necessarily imply a general understanding of concepts relevant to the anchor query. We propose conceptual consistency to measure a LLM's understanding of relevant concepts. This novel metric measures how well a model can be characterized by finding out how consistent its responses to queries about conceptually relevant background knowledge are. To compute it we extract background knowledge by traversing paths between concepts in a knowledge base and then try to predict the model's response to the anchor query from the background knowledge. We investigate the performance of current LLMs in a commonsense reasoning setting using the CSQA dataset and the ConceptNet knowledge base. While conceptual consistency, like other metrics, does increase with the scale of the LLM used, we find that popular models do not necessarily have high conceptual consistency. Our analysis also shows significant variation in conceptual consistency across different kinds of relations, concepts, and prompts. This serves as a step toward building models that humans can apply a theory of mind to, and thus interact with intuitively

Dimensionality and Structure in Cancer Genomics: A Statistical Learning Perspective

Computational analysis of genomic data has transformed research and clinical practice in oncology. Machine learning and AI advancements hold promise for answering theoretical and practical questions. While the modern researcher has access to a catalogue of tools from disciplines such as natural language processing and image recognition, before browsing for our favourite off-the-shelf technique it is worth asking a sequence of questions. What sort of data are we dealing with in cancer genomics? Do we have enough of it to be successful without designing into our models what we already know about its structure? If our methods do work, will we understand why? Are our tools robust enough to be applied in clinical practice? If so, are the technologies upon which they rely economically viable? While we will not answer all of these questions, we will provide language with which to discuss them. Understanding how much information we can expect to extract from data is a statistical question

Space is the machine, part four: theoretical syntheses

Part IV of the book, ‘Theoretical Syntheses’, begins to draw together some of the questions raised in Part I, the regularities shown in Part II and the laws proposed in Part III, to suggest how the two central problems in architectural theory, namely the form-function problem and the form-meaning problem, can be reconceptualised. Chapter 10, ‘Space is the machine’, reviews the form-function theory in architecture and attempts to establish a pathology of its formulation: how it came to be set up in such a way that it could not be solved. It then proposes how the configuration paradigm permits a reformulation, through which we can not only make sense of the relation between form and function in buildings, but also we can make sense of how and why buildings, in a powerful sense are ‘social objects’ and in fact play a powerful role in the realisation and sustaining of human society. Finally, in Chapter 11, ‘The reasoning art’, the notion of configuration is applied to the study of what architects do, that is, design. Previous models of the design process are reviewed, and it is shown that without knowledge of configuration and the concept of the non-discursive, we cannot understand the internalities of the design process. A new knowledge-based model of design is proposed, with configuration at its centre. It is argued from this that because design is a configurational process, and because it is the characteristic of configuration that local changes make global differences, design is necessarily a top down process. This does not mean that it cannot be analysed, or supported by research. It shows however that only configurationally biased knowledge can really support the design Introduction Space is the machine | Bill Hillier Space Syntax Introduction process, and this, essentially, is theoretical knowledge. It follows from this that attempts to support designers by building methods and systems for bottom up construction of designs must eventually fail as explanatory systems. They can serve to create specific architectural identities, but not to advance general architectural understanding. In pursuing an analytic rather than a normative theory of architecture, the book might be thought by some to have pretensions to make the art of architecture into a science. This is not what is intended. One effect of a better scientific understanding of architecture is to show that although architecture as a phenomenon is capable of considerable scientific understanding, this does not mean that as a practice architecture is not an art. On the contrary, it shows quite clearly why it is an art and what the nature and limits of that art are. Architecture is an art because, although in key respects its forms can be analysed and understood by scientific means, its forms can only be prescribed by scientific means in a very restricted sense. Architecture is law governed but it is not determinate. What is governed by the laws is not the form of individual buildings but the field of possibility within which the choice of form is made. This means that the impact of these laws on the passage from problem statement to solution is not direct but indirect. It lies deep in the spatial and physical forms of buildings, in their genotypes, not their phenotypes. Architecture is therefore not part art, and part science, in the sense that it has both technical and aesthetic aspects, but is both art and science in the sense that it requires both the processes of abstraction by which we know science and the processes of concretion by which we know art. The architect as scientist and as theorist seeks to establish the laws of the spatial and formal materials with which the architect as artist then composes. The greater scientific content of architecture over art is simply a function of the far greater complexity of the raw materials of space and form, and their far greater reverberations for other aspects of life, than any materials that an artist uses. It is the fact that the architect designs with the spatial stuff of living that builds the science of architecture into the art of architecture. It may seem curious to argue that the quest for a scientific understanding of architecture does not lead to the conclusion that architecture is a science, but nevertheless it is the case. In the last analysis, architectural theory is a matter of understanding architecture as a system of possibilities, and how these are restricted by laws which link this system of possibilities to the spatial potentialities of human life. At this level, and perhaps only at this level, architecture is analogous to language. Language is often naïvely conceptualised as a set of words and meanings, set out in a dictionary, and syntactic rules by which they may be combined into meaningful sentences, set out in grammars. This is not what language is, and the laws that govern language are not of this kind. This can be seen from the simple fact that if we take the words of the dictionary and combine them in grammatically correct sentences, virtually all are utterly meaningless and do not count as legitimate sentences. The structures of language are the laws which restrict the combinatorial possibilities of words, and through these restrictions construct the sayable and the meaningful. The laws of language do not therefore tell us what to say, but prescribe the structure and limits of the sayable. It is within these limits that we use language as the prime means to our individuality and creativity. In this sense architecture does resemble language. The laws of the field of architecture do not tell designers what to do. By restricting and structuring the field of combinatorial possibility, they prescribe the limits within which architecture is possible. As with language, what is left from this restrictive structuring is rich beyond imagination. Even so, without these laws buildings would not be human products, any more than meaningless but syntactically correct concatenations of words are human sentences. The case for a theoretical understanding of architecture then rests eventually not on aspiration to philosophical or scientific status, but on the nature of architecture itself. The foundational proposition of the book is that architecture is an inherently theoretical subject. The very act of building raises issues about the relations of the form of the material world and the way in which we live in it which (as any archaeologist knows who has tried to puzzle out a culture from material remains) are unavoidably both philosophical and scientific. Architecture is the most everyday, the most enveloping, the largest and the most culturally determined human artefact. The act of building implies the transmission of cultural conventions answering these questions through custom and habit. Architecture is their rendering explicit, and their transmutation into a realm of innovation and, at its best, of art. In a sense, architecture is abstract thought applied to building, even therefore in a sense theory applied to building. This is why, in the end, architecture must have analytic theories

A SENSORIMOTOR CHARACTERISATION OF SYNTAX, AND ITS IMPLICATIONS FOR MODELS OF LANGUAGE EVOLUTION

In this paper I consider the possibility that language is more strongly grounded in sensorimotor cognition than is normally assumed—a scenario which would be providential for language evolution theorists. I argue that the syntactic theory most compatible with this scenario, perhaps surprisingly, is generative grammar. I suggest that there may be a way of interpreting the syntactic structures posited in one theory of generative grammar (Minimalism) as descriptions of sensorimotor processing, and discuss the implications of this for models of language evolution. 1. An optimistic idea about how to study language evolution One way of studying language evolution is to investigate the interface between language and sensorimotor representations in modern humans. We know that there is an interface, of course, because we can talk about what we see and do in the world. But opinions vary about how much work is involved in converting sensorimotor signals into an utterance. If language is a Fodorian module, then a lot of work is involved, because there is no overlap between the sensorimotor mechanisms which create an episode representation and the syntactic mechanisms which express it as an utterance. But many cognitive scientists now argue that syntacti

Sifting the Signal from the Noise

Signalling games are useful for understanding how language emerges. In the standard models the dynamics in some sense already knows what the signals are, even if they do not yet have meaning. In this paper we relax this assumption, and develop a simple model we call an `attention game' in which agents have to learn which feature in their environment is the signal. We demonstrate that simple reinforcement learning agents can still learn to coordinate in contexts in which (i) the agents do not already know what the signal is and (ii) the other features in the agents' environment are uncorrelated with the signal. Furthermore, we show that, in cases in which other features are correlated with the signal, there is a surprising trade-off between learning what the signal is, and success in action. We show that the mutual information between a signal and a feature plays a key role in governing the accuracy and attention of the agent

Large Language Models are Few-Shot Summarizers: Multi-Intent Comment Generation via In-Context Learning

Code comment generation aims at generating natural language descriptions for a code snippet to facilitate developers' program comprehension activities. Despite being studied for a long time, a bottleneck for existing approaches is that given a code snippet, they can only generate one comment while developers usually need to know information from diverse perspectives such as what is the functionality of this code snippet and how to use it. To tackle this limitation, this study empirically investigates the feasibility of utilizing large language models (LLMs) to generate comments that can fulfill developers' diverse intents. Our intuition is based on the facts that (1) the code and its pairwise comment are used during the pre-training process of LLMs to build the semantic connection between the natural language and programming language, and (2) comments in the real-world projects, which are collected for the pre-training, usually contain different developers' intents. We thus postulate that the LLMs can already understand the code from different perspectives after the pre-training. Indeed, experiments on two large-scale datasets demonstrate the rationale of our insights: by adopting the in-context learning paradigm and giving adequate prompts to the LLM (e.g., providing it with ten or more examples), the LLM can significantly outperform a state-of-the-art supervised learning approach on generating comments with multiple intents. Results also show that customized strategies for constructing the prompts and post-processing strategies for reranking the results can both boost the LLM's performances, which shed light on future research directions for using LLMs to achieve comment generation.Comment: Accepted by the 46th International Conference on Software Engineering (ICSE 2024

Developing with data: Mapping engagement for greater understanding of library audiences

Poster was presented at ACRL 2019: Recasting the Narrative on Thursday, April 11, 2019 in Cleveland, OH.Who hears the stories we tell and how do they respond? In order to build a strong supporter base, a library must know who their supporters are and what motivates them to engage. A data mapping project was conducted to determine patterns in rates of engagement with the library’s email communication. Email engagement data was combined with donor profiles to develop a greater sense of audience and their values in order to develop a targeted and effective communication strategy and identify and address engagement gaps. Email engagement data was collected for over 23,000 email subscribers between January 2017 and January 2018. This data was then compared against the university’s donor database to gain insights on audience demographics and engagement both with the library and with the larger campus community. The demographic information collected gave us a better idea of who had expressed interest in hearing from the library. We were able to determine numbers of alumni, faculty, students, and parents as well as which departments they may be associated with. Through email engagement statistics, we could tell what types of stories our supporters were most interested in and how language, layout, and timing impacted their levels of engagement with that content. The donor data further enhanced our understanding of our audience’s values by giving us information on which university departments our supporters donated to aside from the library. This data told us a powerful story about what our audience cares about and how the library can tailor our messages to their values and interests. A set of guidelines and recommendations was developed from the analyzed data, which will continue to inform and guide the development of email content and sending strategies in the future. This project models a way in which libraries can better understand their unique supporter base and develop more effective communication strategies designed to increase supporter involvement and engage potential donors.UT Librarie