Attention is All They Need: Exploring the Media Archaeology of the Computer Vision Research Paper

The success of deep learning has led to the rapid transformation and growth of many areas of computer science, including computer vision. In this work, we examine the effects of this growth through the computer vision research paper itself by analyzing the figures and tables in research papers from a media archaeology perspective. We ground our investigation both through interviews with veteran researchers spanning computer vision, graphics and visualization, and computational analysis of a decade of vision conference papers. Our analysis focuses on elements with roles in advertising, measuring and disseminating an increasingly commodified "contribution." We argue that each of these elements has shaped and been shaped by the climate of computer vision, ultimately contributing to that commodification. Through this work, we seek to motivate future discussion surrounding the design of the research paper and the broader socio-technical publishing system

HyperNP: Interactive Visual Exploration of Multidimensional Projection Hyperparameters

Projection algorithms such as t-SNE or UMAP are useful for the visualization of high dimensional data, but depend on hyperparameters which must be tuned carefully. Unfortunately, iteratively recomputing projections to find the optimal hyperparameter value is computationally intensive and unintuitive due to the stochastic nature of these methods. In this paper we propose HyperNP, a scalable method that allows for real-time interactive hyperparameter exploration of projection methods by training neural network approximations. HyperNP can be trained on a fraction of the total data instances and hyperparameter configurations and can compute projections for new data and hyperparameters at interactive speeds. HyperNP is compact in size and fast to compute, thus allowing it to be embedded in lightweight visualization systems such as web browsers. We evaluate the performance of the HyperNP across three datasets in terms of performance and speed. The results suggest that HyperNP is accurate, scalable, interactive, and appropriate for use in real-world settings

Towards a Relative-Pitch Neural Network System for Chorale Composition and Harmonization

Computational creativity researchers interested in machine learning approaches to computer composition often use the music of J.S. Bach to train their systems. Working with Bach, though, requires grappling with the conventions of tonal music, which can be difficult for computer systems to learn. In this paper, we propose and implement an alternate approach to composition and harmonization of chorales based on pitch-relative note encodings to avoid tonality altogether. We then evaluate our approach using a survey and expert analysis, and find that pitch-relative encodings do not significantly affect human-comparability, likability or creativity. However, an extension of this model that better addresses the criteria survey participants used to evaluate the music, such as instrument timbre and harmonic dissonance, still shows promise

Correct for Whom? Subjectivity and the Evaluation of Personalized Image Aesthetics Assessment Models

The problem of image aesthetic quality assessment is surprisingly difficult to define precisely. Most early work attempted to estimate the average aesthetic rating of a group of observers, while some recent work has shifted to an approach based on few-shot personalization. In this paper, we connect few-shot personalization, via Immanuel Kant's concept of disinterested judgment, to an argument from feminist aesthetics about the biased tendencies of objective standards for subjective pleasures. To empirically investigate this philosophical debate, we introduce PR-AADB, a relabeling of the existing AADB dataset with labels for pairs of images, and measure how well the existing groundtruth predicts our new pairwise labels. We find, consistent with the feminist critique, that both the existing groundtruth and few-shot personalized predictions represent some users' preferences significantly better than others, but that it is difficult to predict when and for whom the existing groundtruth will be correct. We thus advise against using benchmark datasets to evaluate models for personalized IAQA, and recommend caution when attempting to account for subjective difference using machine learning more generally

Argonne National Laboratory Reports

The abstracts are given of thirteen papers presented at a ''SQUID Symposium'' organized by the Division of Materials Sciences of the U.S. Department of Energy and held March 23-25, 1978, at the University of Virginia. Since SQUID systems have already been utilized in feasibility demonstration in geothermal reservoir exploration, it was recognized that these devices also hold great potential for many other important scientific measurements. Many of these are energy-related, and others include forefront investigations in a diverse group of scientific areas, from biomedical to earthquake monitoring. Research in SQUIDs has advanced so rapidly in recent years that it was felt that a symposium to review the current status and future prospects of the devices would be timely. The abstracts given present an overview of work in this area and hopefully provide an opportunity to increase awareness among basic and applied scientists of the inherent implications of the extreme measurement sensitivity in advanced SQUID systems