Can Automated Gesture Recognition Support the Study of Child Language Development?

Children's prelinguistic gestures play a central role in their communicative development. Early gesture use has been shown to be predictive of both concurrent and later language ability, making the identification of gestures in video data at scale a potentially valuable tool for both theoretical and clinical purposes. We describe a new dataset consisting of videos of 72 infants interacting with their caregivers at 11&12 months, annotated for the appearance of 12 different gesture types. We propose a model based on deep convolutional neural networks to classify these. The model achieves 48.32% classification accuracy overall, but with significant variation between gesture types. Critically, we found strong (0.7 or above) rank order correlations between by-child gesture counts from human and machine coding for 7 of the 12 gestures (including the critical gestures of declarative pointing, hold outs and gives). Given the challenging nature of the data - recordings of many different dyads in different environments engaged in diverse activities - we consider these results a very encouraging first attempt at the task, and evidence that automatic or machine-assisted gesture identification could make a valuable contribution to the study of cognitive development

Evaluation of rapeseed-mustard cultivars under late sown condition in coastal ecosystem of West Bengal

In our present report, we evaluated seven rapeseed mustard cultivars at coastal saline zone of West Bengal, India under rice-mustard sequence in a triplicated randomized block design for 14 traits to study their performance under late sown (2nd December) condition. The cultivars were sown at 30 cm × 10 cm spacing during winter of 2013?14 and 2014?15. The soil was clay in texture and had the following key properties for the 0?30 cm layer: pH 5.84, electrical conductivity (EC) 1.55 dS/m, available nitrogen (N) 155.24 kg/ha, available phosphorus (P) 105.76 kg/ha, available potassium (K) 365.86 kg/ha and available B 2.63 kg/ha. Among the seven cultivars, Kranti produced significantly (p?0.05) higher seed yield (1.33 t/ha) closely followed by the hybrids PAC-409 (1.23 t/ha) and Pusa Bold (1.21 t/ha). Seed yield showed significant (p?0.05) positive correlation with all the independent variables (plant height, R2=0.88; dry matter, R2=0.42; days to 50 % flowering, R2=0.27; number of siliqua/plant, R2=0.38; seeds/siliqua, R2=0.48; except number of fertile plants/m2, R2=-0.06; number of secondary branches/plant, R2=-0.97 and length of siliqua, R2=-0.07). However, number of secondary branches/plant had significant (p?0.05) and negative correlation with seed yield of mustard (R2=-0.97). Plant height revealed the highest degree of correlation (R2=0.88) with seed yield followed by siliqua per main branch (R2=0.77), days to harvest (R2=0.75) and 1000-seed weight (R2=0.52). The results indicated that selection of suitable rapeseed mustard cultivars based on these traits would be more effective in improving seed yield in mustard

DeepGraphMol, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach

Abstract We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using graph convolution networks (GCNs). Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. We provide an example based on the binding potency of small molecules to dopamine transporters. We extend our method successfully to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine. Our method should be generally applicable to the generation in silico of molecules with desirable properties

The crosslinguistic acquisition of sentence structure: Computational modeling and grammaticality judgments from adult and child speakers of English, Japanese, Hindi, Hebrew and K'iche'

This preregistered study tested three theoretical proposals for how children form productive yet restricted linguistic generalizations, avoiding errors such as *The clown laughed the man, across three age groups (5–6 years, 9–10 years, adults) and five languages (English, Japanese, Hindi, Hebrew and K'iche'). Participants rated, on a five-point scale, correct and ungrammatical sentences describing events of causation (e.g., *Someone laughed the man; Someone made the man laugh; Someone broke the truck; ?Someone made the truck break). The verb-semantics hypothesis predicts that, for all languages, by-verb differences in acceptability ratings will be predicted by the extent to which the causing and caused event (e.g., amusing and laughing) merge conceptually into a single event (as rated by separate groups of adult participants). The entrenchment and preemption hypotheses predict, for all languages, that by-verb differences in acceptability ratings will be predicted by, respectively, the verb's relative overall frequency, and frequency in nearly-synonymous constructions (e.g., X made Y laugh for *Someone laughed the man). Analysis using mixed effects models revealed that entrenchment/preemption effects (which could not be distinguished due to collinearity) were observed for all age groups and all languages except K'iche', which suffered from a thin corpus and showed only preemption sporadically. All languages showed effects of event-merge semantics, except K'iche' which showed only effects of supplementary semantic predictors. We end by presenting a computational model which successfully simulates this pattern of results in a single discriminative-learning mechanism, achieving by-verb correlations of around r = 0.75 with human judgment data.Additional co-authors: Rukmini Bhaya Nair, Seth Campbell, Clifton Pye, Pedro Mateo Pedro, Sindy Fabiola Can Pixabaj, Mario Marroquín Pelíz, Margarita Julajuj Mendoz

Testing a computational model of causative overgeneralizations: Child judgment and production data from English, Hebrew, Hindi, Japanese and K'iche'.

How do language learners avoid the production of verb argument structure overgeneralization errors ( *The clown laughed the man c.f. The clown made the man laugh), while retaining the ability to apply such generalizations productively when appropriate? This question has long been seen as one that is both particularly central to acquisition research and particularly challenging. Focussing on causative overgeneralization errors of this type, a previous study reported a computational model that learns, on the basis of corpus data and human-derived verb-semantic-feature ratings, to predict adults' by-verb preferences for less- versus more-transparent causative forms (e.g., * The clown laughed the man vs The clown made the man laugh) across English, Hebrew, Hindi, Japanese and K'iche Mayan. Here, we tested the ability of this model (and an expanded version with multiple hidden layers) to explain binary grammaticality judgment data from children aged 4;0-5;0, and elicited-production data from children aged 4;0-5;0 and 5;6-6;6 ( N=48 per language). In general, the model successfully simulated both children's judgment and production data, with correlations of r=0.5-0.6 and r=0.75-0.85, respectively, and also generalized to unseen verbs. Importantly, learners of all five languages showed some evidence of making the types of overgeneralization errors - in both judgments and production - previously observed in naturalistic studies of English (e.g., *I'm dancing it). Together with previous findings, the present study demonstrates that a simple learning model can explain (a) adults' continuous judgment data, (b) children's binary judgment data and (c) children's production data (with no training of these datasets), and therefore constitutes a plausible mechanistic account of the acquisition of verbs' argument structure restrictions

DABCO- and DBU-promoted one-pot reaction of N-sulfonyl ketimines with Morita–Baylis–Hillman carbonates: a sequential approach to (2-hydroxyaryl)nicotinate derivatives

An intriguing DABCO-catalyzed and DBU-promoted one-pot synthesis of an important class of (2-hydroxyaryl)pyridine derivatives bearing a carboxylate or a nitrile group suitably placed at C3 position of the aza-ring has been achieved in acceptable chemical yields with a broad functional group tolerance. This sequential C–C/C–N bond making process proceeds through a regioselective allylic alkylation/aza-Michael reaction between MBH carbonates derived from an acrylate/acrylonitrile and N-sulfonyl ketimines as C,N-binucleophiles catalyzed by DABCO, followed by elimination of SO2 under the influence of base and subsequent aromatization in an open atmosphere