Laboratory Evaluation of Mefluidide Effects on Elongation of Hydrilla and Eurasian Watermilfoil

The potential of mefluidide (N-(2,4-dimethyl-5[[trifluromethyl) sulfonyl] amino] phenol) acetamide) to act as a submersed aquatic plant growth regulator was evaluated using a laboratory bioassay system. Main stem elongation of hydrilla (Hydrilla verticillata (L.f.) Royle) and Eurasian watermilfoil (Myriophyllum spicatum L.) was effectively reduced by mefluidide at low concentrations. The lowest effective concentration of mefluidide that reduced stem length in Eurasian watermilfoil (100 yg a.i./L) was 5 times lower than that for hydrilla (500 yg a.i./L). Short-term net photosynthetic rates of these plants were not affected by mefluidide at concentrations as high as 1000 yg a.i./L. The minimum exposure time required to maintain an inhibitory effect for at least 28 days at a concentration of 500 yg ai.i./L was 3 to 7 days for Eurasian watermilfoil and 7 to 14 days for hydrilla. The results suggest that mefluidide is a more effective growth regulator for Eurasian watermilfoil than hydrilla. Exogenously applied gibberellic acid (GA) did not completely overcome the inhibitory effect of mefluidide even when GA was added at a high concentration (10-5 M). In addition, the internodal lengths of stems treated with mefluidide were not reduced as they were when treated with gibberellin synthesis inhibitors. The reduction of main stem elongation by mefluidide appeared to be due to the inhibition of new cell and tissue development at the stem tip rather than from inhibition of GA biosynthesis

Cognitive Deficit of Deep Learning in Numerosity

Subitizing, or the sense of small natural numbers, is an innate cognitive function of humans and primates; it responds to visual stimuli prior to the development of any symbolic skills, language or arithmetic. Given successes of deep learning (DL) in tasks of visual intelligence and given the primitivity of number sense, a tantalizing question is whether DL can comprehend numbers and perform subitizing. But somewhat disappointingly, extensive experiments of the type of cognitive psychology demonstrate that the examples-driven black box DL cannot see through superficial variations in visual representations and distill the abstract notion of natural number, a task that children perform with high accuracy and confidence. The failure is apparently due to the learning method not the CNN computational machinery itself. A recurrent neural network capable of subitizing does exist, which we construct by encoding a mechanism of mathematical morphology into the CNN convolutional kernels. Also, we investigate, using subitizing as a test bed, the ways to aid the black box DL by cognitive priors derived from human insight. Our findings are mixed and interesting, pointing to both cognitive deficit of pure DL, and some measured successes of boosting DL by predetermined cognitive implements. This case study of DL in cognitive computing is meaningful for visual numerosity represents a minimum level of human intelligence.Comment: Accepted for presentation at the AAAI-1