The Competition for Attention and the Evolution of Science

Whenever the amount of information produced exceeds the amount of attention available to consume it, a competition for attention is born. The competition is increasingly fierce in science where the exponential growth of information has forced its producers, consumers and gatekeepers to become increasingly selective in what they attend to and what they ignore. Paradoxically, as the criteria of selection among authors, editors and readers of scientific journal articles co-evolve, they show signs of becoming increasingly unscientific. The present article suggests how the paradox can be addressed with computer simulation, and what its implications for the future of science might be.Attention, Competition, Evolution, Information, Production, Consumption

Generating Adversarial Examples with Adversarial Networks

Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires more research efforts. In this paper, we propose AdvGAN to generate adversarial examples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances. For AdvGAN, once the generator is trained, it can generate adversarial perturbations efficiently for any instance, so as to potentially accelerate adversarial training as defenses. We apply AdvGAN in both semi-whitebox and black-box attack settings. In semi-whitebox attacks, there is no need to access the original target model after the generator is trained, in contrast to traditional white-box attacks. In black-box attacks, we dynamically train a distilled model for the black-box model and optimize the generator accordingly. Adversarial examples generated by AdvGAN on different target models have high attack success rate under state-of-the-art defenses compared to other attacks. Our attack has placed the first with 92.76% accuracy on a public MNIST black-box attack challenge.Comment: Accepted to IJCAI201

Coarse-Grained Lattice Monte Carlo Simulations with Continuous Interaction Potentials

A coarse-grained lattice Metropolis Monte Carlo (CG-MMC) method is presented for simulating fluid systems described by standard molecular force fields. First, a thermodynamically consistent coarse-grained interaction potential is obtained numerically and automatically from a continuous force field such as Lennard-Jones. The coarse-grained potential then is used to driveCG-MMC simulations of vapor-liquid equilibrium in Lennard-Jones, square-well, and simple point chargewater systems. The CG-MMC predicts vapor-liquid phase envelopes, as well as the particle density distributions in both the liquid and vapor phases, in excellent agreement with full-resolution Monte Carlo simulations, at a fraction of the computational cost

Two Extraordinary Substellar Binaries at the T/Y Transition and the Y-Band Fluxes of the Coolest Brown Dwarfs

Using Keck laser guide star adaptive optics imaging, we have found that the T9 dwarf WISE J1217+1626 and T8 dwarf WISE J1711+3500 are exceptional binaries, with unusually wide separations (~0.8 arcsec, 8-15 AU), large near-IR flux ratios (~2-3 mags), and small mass ratios (~0.5) compared to previously known field ultracool binaries. Keck/NIRSPEC H-band spectra give a spectral type of Y0 for WISE J1217+1626B, and photometric estimates suggest T9.5 for WISE J1711+3500B. The WISE J1217+1626AB system is very similar to the T9+Y0 binary CFBDSIR J1458+1013AB; these two systems are the coldest known substellar multiples, having secondary components of ~400 K and being planetary-mass binaries if their ages are <~1 Gyr. Both WISE J1217+1626B and CFBDSIR J1458+1013B have strikingly blue Y-J colors compared to previously known T dwarfs, including their T9 primaries. Combining all available data, we find that Y-J color drops precipitously between the very latest T dwarfs and the Y dwarfs. The fact that this is seen in (coeval, mono-metallicity) binaries demonstrates that the color drop arises from a change in temperature, not surface gravity or metallicity variations among the field population. Thus, the T/Y transition established by near-IR spectra coincides with a significant change in the ~1 micron fluxes of ultracool photospheres. One explanation is the depletion of potassium, whose broad absorption wings dominate the far-red optical spectra of T dwarfs. This large color change suggests that far-red data may be valuable for classifying objects of <~500 K.Comment: ApJ, in press (accepted Aug 1, 2012). Small cosmetic changes in version 2 to match final publicatio

A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality

Microtask crowdsourcing is increasingly critical to the creation of extremely large datasets. As a result, crowd workers spend weeks or months repeating the exact same tasks, making it necessary to understand their behavior over these long periods of time. We utilize three large, longitudinal datasets of nine million annotations collected from Amazon Mechanical Turk to examine claims that workers fatigue or satisfice over these long periods, producing lower quality work. We find that, contrary to these claims, workers are extremely stable in their quality over the entire period. To understand whether workers set their quality based on the task's requirements for acceptance, we then perform an experiment where we vary the required quality for a large crowdsourcing task. Workers did not adjust their quality based on the acceptance threshold: workers who were above the threshold continued working at their usual quality level, and workers below the threshold self-selected themselves out of the task. Capitalizing on this consistency, we demonstrate that it is possible to predict workers' long-term quality using just a glimpse of their quality on the first five tasks.Comment: 10 pages, 11 figures, accepted CSCW 201