Asymmetric development of Cotyledons of Tomato Embryo: Testing the prediction of Self-Organization

Developmental biologists have long strived to understand how organisms acquire shape and form. The architecture of the mature plant is established during embryogenesis. They have learned much about how gene expression controls the specification of cell type and about how cells interact with one another to coordinate such specific decisions. Far less is known about autocatalytic feedback flow of resource molecules regulating a plant and its parts, shape and form. Indeed, it has even been proposed that the development of shape is not under genetic control but rather is determined by physical forces. Asymmetric development of sinks that depend on common resource pool has been viewed as a consequence of autocatalytic feedback process of flow of resource units into them. The feedback process implies that the stronger a sink is relative to its competitors, the greater is its probability of getting further resources as a non-linear function of its resource drawing ability and sink size. We have shown that this model contrasts with that of sink strength dependent model in its prediction of the subsequent development of the initial asymmetry of growing cotyledons of the tomato embryo (_Lycopersicon esculentum L._), when their resource drawing ability is enhanced by exogenous application of the growth regulators (NAA, GA and BA), we test these prediction and show that the results are in conformity with the autocatalytic model proposed by Ganeshaiah and Uma Shaanker

Assessing the Genotypic Differences for Seed Set and Seed Abortion in Tomato Genotypes

Tomato (_Lycopersicon esculentum_ Mill.) is one of the most popular fruit vegetable around the world. Seed abortion where in only a small proportion of ovules in an ovary develops into matured seeds, is a wide spread phenomenon in multi-ovulated species. In agriculturally important crops such as chickpea, groundnut, Brassica, pigeon pea and field bean seed abortion substantially reduces their productivity. Tomato genotypes exhibited seed abortion where in only some proportion of ovules developed into matured seeds. Seed abortion in tomato cultivars would increase the cost of hybrid seed production. In this study, we have analyzed 19 genotypes for number of ovules, seed set and seed abortion. Tomato genotypes differed significantly for number of ovules per ovary, seed set per fruit and per cent seed abortion. The ovules, matured seeds and seed abortion ranged from 52 to 412 per ovary; 50.90 to 240.76 per fruit and 6.06 to 24.44 per cent respectively. Strong positive correlation was observed in genotypes with higher number of ovules showed higher percentage of seed abortion

An Investigation of Recurrent Neural Architectures for Drug Name Recognition

Drug name recognition (DNR) is an essential step in the Pharmacovigilance (PV) pipeline. DNR aims to find drug name mentions in unstructured biomedical texts and classify them into predefined categories. State-of-the-art DNR approaches heavily rely on hand crafted features and domain specific resources which are difficult to collect and tune. For this reason, this paper investigates the effectiveness of contemporary recurrent neural architectures - the Elman and Jordan networks and the bidirectional LSTM with CRF decoding - at performing DNR straight from the text. The experimental results achieved on the authoritative SemEval-2013 Task 9.1 benchmarks show that the bidirectional LSTM-CRF ranks closely to highly-dedicated, hand-crafted systems.Comment: Accepted for Oral Presentation at LOUHI 2016 : EMNLP 2016 Workshop - The Seventh International Workshop on Health Text Mining and Information Analysis (LOUHI 2016

Bidirectional LSTM-CRF for Clinical Concept Extraction

Automated extraction of concepts from patient clinical records is an essential facilitator of clinical research. For this reason, the 2010 i2b2/VA Natural Language Processing Challenges for Clinical Records introduced a concept extraction task aimed at identifying and classifying concepts into predefined categories (i.e., treatments, tests and problems). State-of-the-art concept extraction approaches heavily rely on handcrafted features and domain-specific resources which are hard to collect and define. For this reason, this paper proposes an alternative, streamlined approach: a recurrent neural network (the bidirectional LSTM with CRF decoding) initialized with general-purpose, off-the-shelf word embeddings. The experimental results achieved on the 2010 i2b2/VA reference corpora using the proposed framework outperform all recent methods and ranks closely to the best submission from the original 2010 i2b2/VA challenge.Comment: This paper "Bidirectional LSTM-CRF for Clinical Concept Extraction" is accepted for short paper presentation at Clinical Natural Language Processing Workshop at COLING 2016 Osaka, Japan. December 11, 201

Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features

One-class support vector machine (OC-SVM) for a long time has been one of the most effective anomaly detection methods and extensively adopted in both research as well as industrial applications. The biggest issue for OC-SVM is yet the capability to operate with large and high-dimensional datasets due to optimization complexity. Those problems might be mitigated via dimensionality reduction techniques such as manifold learning or autoencoder. However, previous work often treats representation learning and anomaly prediction separately. In this paper, we propose autoencoder based one-class support vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier features to approximate the radial basis kernel, into deep learning context by combining it with a representation learning architecture and jointly exploit stochastic gradient descent to obtain end-to-end training. Interestingly, this also opens up the possible use of gradient-based attribution methods to explain the decision making for anomaly detection, which has ever been challenging as a result of the implicit mappings between the input space and the kernel space. To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection. We evaluate our method on a wide range of unsupervised anomaly detection tasks in which our end-to-end training architecture achieves a performance significantly better than the previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201

Deep learning for pedestrian collective behavior analysis in smart cities: A model of group trajectory outlier detection

This paper introduces a new model to identify collective abnormal human behaviors from large pedestrian data in smart cities. To accurately solve the problem, several algorithms have been proposed in this paper. These can be split into two categories. First, algorithms based on data mining and knowledge discovery, which study the different correlation among human behavioral data, and identify the collective abnormal human behavior from knowledge extracted. Secondly, algorithms exploring convolution deep neural networks, which learn different features of historical data to determine the collective abnormal human behaviors. Experiments on an actual human behaviors database have been carried out to demonstrate the usefulness of the proposed algorithms. The results show that the deep learning solution outperforms both data mining as well as the state-of-the-art solutions in terms of runtime and accuracy performance. In particular, for large datasets, the accuracy of the deep learning solution reaches 88%, however other solutions do not exceed 81%. Additionally, the runtime of the deep learning solution is below 50 seconds, whereas other solutions need more than 80 seconds for analyzing the same database