Causal Strategic Learning with Competitive Selection

We study the problem of agent selection in causal strategic learning under multiple decision makers and address two key challenges that come with it. Firstly, while much of prior work focuses on studying a fixed pool of agents that remains static regardless of their evaluations, we consider the impact of selection procedure by which agents are not only evaluated, but also selected. When each decision maker unilaterally selects agents by maximising their own utility, we show that the optimal selection rule is a trade-off between selecting the best agents and providing incentives to maximise the agents' improvement. Furthermore, this optimal selection rule relies on incorrect predictions of agents' outcomes. Hence, we study the conditions under which a decision maker's optimal selection rule will not lead to deterioration of agents' outcome nor cause unjust reduction in agents' selection chance. To that end, we provide an analytical form of the optimal selection rule and a mechanism to retrieve the causal parameters from observational data, under certain assumptions on agents' behaviour. Secondly, when there are multiple decision makers, the interference between selection rules introduces another source of biases in estimating the underlying causal parameters. To address this problem, we provide a cooperative protocol which all decision makers must collectively adopt to recover the true causal parameters. Lastly, we complement our theoretical results with simulation studies. Our results highlight not only the importance of causal modeling as a strategy to mitigate the effect of gaming, as suggested by previous work, but also the need of a benevolent regulator to enable it.Comment: Corrected some in-text citation

Isolation and Characterization of Flavonoid Naringenin and Evaluation of Cytotoxic and Biological Efficacy of Water Lilly (Nymphaea mexicana Zucc.)

Despite its limited exploration, Nymphaea mexicana Zucc. can be beneficial if pharmacology, isolation, and biological evaluation are given attention. It is an aquatic species that belongs to the family Nymphaeaceae. The thrust area of the work was the extraction, isolation, and biological evaluation of different extracts of the N. mexicana Zucc. plant. The primary goal of this research was to assess the antimicrobial, antioxidant, and anticancer activities of the extracts and to isolate the target naringenin compound. Comparative FT IR analysis of different extracts of this plant revealed the presence of functional groups of plant secondary metabolites, including polyphenols, flavonoids, terpenoids, esters, amines, glycosides, alkanes, alkaloids, fatty acids, and alcohols. Moderate free radical scavenging potential has been achieved for the various extracts via reducing power and DPPH assays. While cytotoxic activity was evaluated by colorimetric and lactate dehydrogenase cell viability tests on potent cancer cell lines. Lung adenocarcinoma epithelial cells (A-549), and breast cells (MC-7) were treated with MeOH extract. The antimicrobial activity against bacterial strains was evaluated using Gram-positive and -negative cultures, where maximum and minimum inhibition zones were recorded for different strains, including 1.6–25.6 μg/mL for Streptococcus aureus, using the agar well diffusion method. In addition, the anti-inflammatory activity of different extracts of N. mexicana Zucc. was evaluated in a nitrite radical scavenging assay with high concentrations of secondary metabolites, which are important against human pathogens and other diseases

NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results

This paper reviews the 2nd NTIRE challenge on single image super-resolution (restoration of rich details in a low resolution image) with focus on proposed solutions and results. The challenge had 4 tracks. Track 1 employed the standard bicubic downscaling setup, while Tracks 2, 3 and 4 had realistic unknown downgrading operators simulating camera image acquisition pipeline. The operators were learnable through provided pairs of low and high resolution train images. The tracks had 145, 114, 101, and 113 registered participants, resp., and 31 teams competed in the final testing phase. They gauge the state-of-the-art in single image super-resolution