To Write Code: The Cultural Fabrication of Programming Notation and Practice
Writing and its means have become detached. Unlike written and drawn practices developed prior to the 20th century, notation for programming… (see more) computers developed in concert and conflict with discretizing infrastructure such as the shift-key typewriter and data processing pipelines. In this paper, I recall the emergence of high-level notation for representing computation. I show how the earliest inventors of programming notations borrowed from various written cultural practices, some of which came into conflict with the constraints of digitizing machines, most prominently the typewriter. As such, I trace how practices of "writing code" were fabricated along social, cultural, and material lines at the time of their emergence. By juxtaposing early visions with the modern status quo, I question long-standing terminology, dichotomies, and epistemological tendencies in the field of computer programming. Finally, I argue that translation work is a fundamental property of the practice of writing code by advancing an intercultural lens on programming practice rooted in history.
Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus
Haojie Wei
Di Niu
Haolan Chen
Yancheng He
The ability to ask questions is important in both human and machine intelligence. Learning to ask questions helps knowledge acquisition, imp… (see more)roves question-answering and machine reading comprehension tasks, and helps a chatbot to keep the conversation flowing with a human. Existing question generation models are ineffective at generating a large amount of high-quality question-answer pairs from unstructured text, since given an answer and an input passage, question generation is inherently a one-to-many mapping. In this paper, we propose Answer-Clue-Style-aware Question Generation (ACS-QG), which aims at automatically generating high-quality and diverse question-answer pairs from unlabeled text corpus at scale by imitating the way a human asks questions. Our system consists of: i) an information extractor, which samples from the text multiple types of assistive information to guide question generation; ii) neural question generators, which generate diverse and controllable questions, leveraging the extracted assistive information; and iii) a neural quality controller, which removes low-quality generated data based on text entailment. We compare our question generation models with existing approaches and resort to voluntary human evaluation to assess the quality of the generated question-answer pairs. The evaluation results suggest that our system dramatically outperforms state-of-the-art neural question generation models in terms of the generation quality, while being scalable in the meantime. With models trained on a relatively smaller amount of data, we can generate 2.8 million quality-assured question-answer pairs from a million sentences found in Wikipedia.
A Unifying Framework for Fairness-Aware Influence Maximization
Behrouz Babaki
Michel Gendreau
The problem of selecting a subset of nodes with greatest influence in a graph, commonly known as influence maximization, has been well studi… (see more)ed over the past decade. This problem has real world applications which can potentially affect lives of individuals. Algorithmic decision making in such domains raises concerns about their societal implications. One of these concerns, which surprisingly has only received limited attention so far, is algorithmic bias and fairness. We propose a flexible framework that extends and unifies the existing works in fairness-aware influence maximization. This framework is based on an integer programming formulation of the influence maximization problem. The fairness requirements are enforced by adding linear constraints or modifying the objective function. Contrary to the previous work which designs specific algorithms for each variant, we develop a formalism which is general enough for specifying different notions of fairness. A problem defined in this formalism can be then solved using efficient mixed integer programming solvers. The experimental evaluation indicates that our framework not only is general but also is competitive with existing algorithms.
Uncertainty Evaluation Metric for Brain Tumour Segmentation
Raghav Mehta
Angelos Filos
Yarin Gal
In this paper, we develop a metric designed to assess and rank uncertainty measures for the task of brain tumour sub-tissue segmentation in … (see more)the BraTS 2019 sub-challenge on uncertainty quantification. The metric is designed to: (1) reward uncertainty measures where high confidence is assigned to correct assertions, and where incorrect assertions are assigned low confidence and (2) penalize measures that have higher percentages of under-confident correct assertions. Here, the workings of the components of the metric are explored based on a number of popular uncertainty measures evaluated on the BraTS 2019 dataset.
Systems consolidation impairs behavioral flexibility
Sankirthana Sathiyakumar
Sofia Skromne Carrasco
Lydia Saad
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims
Miles Brundage
Shahar Avin
Jasmine Wang
Haydn Belfield
Gretchen Krueger
Gillian K. Hadfield
Heidy Khlaaf
Jingying Yang
H. Toner
Ruth Catherine Fong
Pang Wei Koh
Sara Hooker
Jade Leung
Andrew John Trask
Emma Bluemke
Jonathan Lebensbold
Cullen C. O'keefe
Mark Koren
Th'eo Ryffel … (see 39 more)
JB Rubinovitz
Tamay Besiroglu
Federica Carugati
Jack Clark
Peter Eckersley
Sarah de Haas
Maritza L. Johnson
Ben Laurie
Alex Ingerman
Igor Krawczuk
Amanda Askell
Rosario Cammarota
A. Lohn
Charlotte Stix
Peter Mark Henderson
Logan Graham
Carina E. A. Prunkl
Bianca Martin
Elizabeth Seger
Noa Zilberman
Sean O hEigeartaigh
Frens Kroeger
Girish Sastry
R. Kagan
Adrian Weller
Brian Shek-kam Tse
Elizabeth Barnes
Allan Dafoe
Paul D. Scharre
Ariel Herbert-Voss
Martijn Rasser
Shagun Sodhani
Carrick Flynn
Thomas Krendl Gilbert
Lisa Dyer
Saif M. Khan
Markus Anderljung
On generalized surrogate duality in mixed-integer nonlinear programming
Benjamin Müller
Gonzalo Muñoz
Ambros Gleixner
Andrea Lodi
Felipe Serrano
Clustering for Continuous-Time Hidden Markov Models.
Yu Luo
David A. Stephens
We develop clustering procedures for longitudinal trajectories based on a continuous-time hidden Markov model (CTHMM) and a generalized line… (see more)ar observation model. Specifically in this paper, we carry out infinite mixture model-based clustering for CTHMM and achieve inference using Markov chain Monte Carlo (MCMC). Specifically, for Bayesian nonparametric inference using a Dirichlet process mixture model, we utilize restricted Gibbs sampling split-merge proposals to expedite the MCMC algorithm. We employ the proposed algorithm to the simulated data as well as a large real data example, and the results demonstrate the desired performance of the new sampler.
Establishing an evaluation metric to quantify climate change image realism
Sharon Zhou
Alexandra Luccioni
Gautier Cosne
Michael S. Bernstein
CNN Detection of New and Enlarging Multiple Sclerosis Lesions from Longitudinal Mri Using Subtraction Images
Nazanin Mohammadi Sepahvand
Douglas Arnold
Accurate detection and segmentation of new lesional activity in longitudinal Magnetic Resonance Images (MRIs) of patients with Multiple Scle… (see more)rosis (MS) is important for monitoring disease activity, as well as for assessing treatment effects. In this work, we present the first deep learning framework to automatically detect and segment new and enlarging (NE) T2w lesions from longitudinal brain MRIs acquired from relapsing-remitting MS (RRMS) patients. The proposed framework is an adapted 3D U-Net [1] which includes as inputs the reference multi-modal MRI and T2-weighted lesion maps, as well an attention mechanism based on the subtraction MRI (between the two timepoints) which serves to assist the network in learning to differentiate between real anatomical change and artifactual change, while constraining the search space for small lesions. Experiments on a large, proprietary, multi -center, multi-modal, clinical trial dataset consisting of 1677 multi-modal scans illustrate that network achieves high overall detection accuracy (detection AUC=.95), outperforming (1) a U-Net without an attention mechanism (de-tection AUC=.93), (2) a framework based on subtracting independent T2-weighted segmentations (detection AUC=.57), and (3) DeepMedic (detection AUC=.84) [2], particularly for small lesions. In addition, the method was able to accurately classify patients as active/inactive with (sensitivities of. 69 and specificities of. 97).
Combating False Negatives in Adversarial Imitation Learning (Student Abstract)
Konrad Żołna
Chitwan Saharia
Léonard Boussioux
David Y. T. Hui
Maxime Chevalier-Boisvert
Literature Mining for Incorporating Inductive Bias in Biomedical Prediction Tasks (Student Abstract)