The construction of meanings for trend in active graphing

The development of increased and accessible computing power has been a major agent in the current emphasis placed upon the presentation of data in graphical form as a means of informing or persuading. However research in Science and Mathematics Education has shown that skills in the interpretation and production of graphs are relatively difficult for Secondary school pupils. Exploratory studies have suggested that the use of spreadsheets might have the potential to change fundamentally how children learn graphing skills. We describe research using a pedagogic strategy developed during this exploratory work, which we call Active Graphing, in which access to spreadsheets allows graphs to be used as analytic tools within practical experiments. Through a study of pairs of 8 and 9 year old pupils working on such tasks, we have been able to identify aspects of their interaction with the experiment itself, the data collected and the graphs, and so trace the emergence of meanings for trend. © 2000 Kluwer Academic Publishers

Dose-finding study of a 90-day contraceptive vaginal ring releasing estradiol and segesterone acetate.

ObjectiveTo evaluate serum estradiol (E2) concentrations during use of 90-day contraceptive vaginal rings releasing E2 75, 100, or 200 mcg/day and segesterone acetate (SA) 200 mcg/day to identify a dose that avoids hypoestrogenism.Study designWe conducted a multicenter dose-finding study in healthy, reproductive-aged women with regular cycles with sequential enrollment to increasing E2 dose groups. We evaluated serum E2 concentrations twice weekly for the primary outcome of median E2 concentrations throughout initial 30-day use (target ≥40 pg/mL). In an optional 2-cycle extension substudy, we randomized participants to 2- or 4-day ring-free intervals per 30-day cycle to evaluate bleeding and spotting based on daily diary information.ResultsSixty-five participants enrolled in E2 75 (n = 22), 100 (n = 21), and 200 (n = 22) mcg/day groups; 35 participated in the substudy. Median serum E2 concentrations in 75 and 100 mcg/day groups were <40 pg/mL. In the 200 mcg/day group, median E2 concentrations peaked on days 4-5 of CVR use at 194 pg/mL (range 114-312 pg/mL) and remained >40 pg/mL throughout 30 days; E2 concentrations were 37 pg/mL (range 28-62 pg/mL) on days 88-90 (n = 11). Among the E2 200 mcg/day substudy participants, all had withdrawal bleeding following ring removal. The 2-day ring-free interval group reported zero median unscheduled bleeding and two (range 0-16) and three (range 0-19) unscheduled spotting days in extension cycles 1 and 2, respectively. The 4-day ring-free interval group reported zero median unscheduled bleeding or spotting days.ConclusionsEstradiol concentrations with rings releasing E2 200 mcg/day and SA 200 mcg/day avoid hypoestrogenism over 30-day use.ImplicationsA 90-day contraceptive vaginal ring releasing estradiol 200 mcg/day and segesterone acetate 200 mcg/day achieves estradiol concentrations that should avoid hypoestrogenism and effectively suppresses ovulation

Visually grounded learning of keyword prediction from untranscribed speech

During language acquisition, infants have the benefit of visual cues to ground spoken language. Robots similarly have access to audio and visual sensors. Recent work has shown that images and spoken captions can be mapped into a meaningful common space, allowing images to be retrieved using speech and vice versa. In this setting of images paired with untranscribed spoken captions, we consider whether computer vision systems can be used to obtain textual labels for the speech. Concretely, we use an image-to-words multi-label visual classifier to tag images with soft textual labels, and then train a neural network to map from the speech to these soft targets. We show that the resulting speech system is able to predict which words occur in an utterance---acting as a spoken bag-of-words classifier---without seeing any parallel speech and text. We find that the model often confuses semantically related words, e.g. "man" and "person", making it even more effective as a semantic keyword spotter.Comment: 5 pages, 3 figures, 5 tables; small updates, added link to code; accepted to Interspeech 201