Fundamental limitations to gain enhancement in periodic media and waveguides

A common strategy to compensate for losses in optical nanostructures is to add gain material in the system. By exploiting slow-light effects it is expected that the gain may be enhanced beyond its bulk value. Here we show that this route cannot be followed uncritically: inclusion of gain inevitably modifies the underlying dispersion law, and thereby may degrade the slow-light properties underlying the device operation and the anticipated gain enhancement itself. This degradation is generic; we demonstrate it for three different systems of current interest (coupled resonator optical waveguides, Bragg stacks, and photonic crystal waveguides). Nevertheless, a small amount of added gain may be beneficial

On Fairness, Diversity and Randomness in Algorithmic Decision Making

Consider a binary decision making process where a single machine learning classifier replaces a multitude of humans. We raise questions about the resulting loss of diversity in the decision making process. We study the potential benefits of using random classifier ensembles instead of a single classifier in the context of fairness-aware learning and demonstrate various attractive properties: (i) an ensemble of fair classifiers is guaranteed to be fair, for several different measures of fairness, (ii) an ensemble of unfair classifiers can still achieve fair outcomes, and (iii) an ensemble of classifiers can achieve better accuracy-fairness trade-offs than a single classifier. Finally, we introduce notions of distributional fairness to characterize further potential benefits of random classifier ensembles

Metal transfer to sediments, invertebrates and fish following waterborne exposure to silver nitrate or silver sulfide nanoparticles in an indoor stream mesocosm.

The fate of engineered nanomaterials in ecosystems is unclear. An aquatic stream mesocosm was explored the fate and bioaccumulation of silver sulfide nanoparticles (Ag2S NPs) compared to silver nitrate (AgNO3). The aims were to determine the total Ag in water, sediment and biota, and to evaluate the bioavailable fractions of silver in the sediment using a serial extraction method. The total Ag in the water column from a nominal daily dose of 10 μg L-1 of Ag for the AgNO3 or Ag2S NP treatments reached a plateau of around 13 and 12 μg L-1, respectively, by the end of the study. Similarly, the sediment of both Ag-treatments reached ~380 μg Ag kg-1, and with most of it being acid-extractable/labile. The biota accumulated 4-59 μg Ag g-1 dw, depending on the type of Ag-treatment and organism. The oligochaete worm, Lumbriculus variegatus, accumulated Ag from the Ag2S exposure over time, which was similar to the AgNO3 treatment by the end of the experiment. The planarian, Girardia tigrina, and the chironomid larva, Chironomus riparius, showed much higher Ag concentrations than the oligochaete worms; and with a clearer time-dependent statistically significant Ag accumulation relative to the untreated controls. For the pulmonated snail, Physa acuta, bioaccumulation of Ag from AgNO3 and Ag2S NP exposures was observed, but was lower from the nano treatment. The AgNO3 exposure caused appreciable Ag accumulation in the water flea, Daphnia magna, but accumulation was higher in the Ag2S NP treatment (reaching 59 μg g-1 dw). In the rainbow trout, Oncorhynchus mykiss, AgNO3, but not Ag2S NPs, caused total Ag concentrations to increase in the tissues. Overall, the study showed transfer of total Ag from the water column to the sediment, and Ag bioaccumulation in the biota, with Ag from Ag2S NP exposure generally being less bioavailable than that from AgNO3

Machine Advice with a Warning about Machine Limitations. Experimentally Testing the Solution Mandated by the Wisconsin Supreme Court

The Wisconsin Supreme Court allows machine advice in the courtroom only if accompanied by a series of warnings. We test 878 US lay participants with jury experience on fifty past cases where we know ground truth. The warnings affect their estimates of the likelihood of recidivism and their confidence, but not their decision whether to grant bail. Participants do not get better at identifying defendants who recidivated during the next two years. Results are essentially the same if participants are warned in easily accessible language, and if they are additionally informed about the low accuracy of machine predictions. The decision to grant bail is also unaffected by the warnings mandated by the Supreme Court if participants do not first decide without knowing the machine prediction. Oversampling cases where defendants committed violent crime does not change results either, whether coupled with machine predictions for general or for violent crime. Giving participants feedback and incentivizing them for finding ground truth has a small, weakly significant effect. The effect becomes significant at conventional levels when additionally using strong graphical warnings. Then participants are less likely to follow the advice. But the effect is counterproductive: they follow the advice less if it actually is closer to ground truth