Multi-Document Summarization using Distributed Bag-of-Words Model

As the number of documents on the web is growing exponentially, multi-document summarization is becoming more and more important since it can provide the main ideas in a document set in short time. In this paper, we present an unsupervised centroid-based document-level reconstruction framework using distributed bag of words model. Specifically, our approach selects summary sentences in order to minimize the reconstruction error between the summary and the documents. We apply sentence selection and beam search, to further improve the performance of our model. Experimental results on two different datasets show significant performance gains compared with the state-of-the-art baselines

DCT: Dual Channel Training of Action Embeddings for Reinforcement Learning with Large Discrete Action Spaces

The ability to learn robust policies while generalizing over large discrete action spaces is an open challenge for intelligent systems, especially in noisy environments that face the curse of dimensionality. In this paper, we present a novel framework to efficiently learn action embeddings that simultaneously allow us to reconstruct the original action as well as to predict the expected future state. We describe an encoder-decoder architecture for action embeddings with a dual channel loss that balances between action reconstruction and state prediction accuracy. We use the trained decoder in conjunction with a standard reinforcement learning algorithm that produces actions in the embedding space. Our architecture is able to outperform two competitive baselines in two diverse environments: a 2D maze environment with more than 4000 discrete noisy actions, and a product recommendation task that uses real-world e-commerce transaction data. Empirical results show that the model results in cleaner action embeddings, and the improved representations help learn better policies with earlier convergence.Comment: 17 page

Preliminary Evaluation of the Disease Surveillance System During Influenza Outbreaks of Pandemic Scale

In the United States it is currently unknown whether the influenza surveillance system is capable of producing timely and accurate data for case estimation during an outbreak of pandemic scale. This simulation provides a preliminary evaluation of the surveillance system’s ability to collect data and produce timely and accurate trends of cases confirmed with an influenza virus. For the evaluation, a computer-based simulation of the data-collection process was used, which was validated with real demographic and epidemiologic information. The results were analyzed to determine the most significant behavioral and operational factors influencing the data collection and to propose the exploration of more efficient data-collection policies for the generation of timely and accurate trends of confirmed cases

Multi-Agent Learning of Efficient Fulfilment and Routing Strategies in E-Commerce

This paper presents an integrated algorithmic framework for minimising product delivery costs in e-commerce (known as the cost-to-serve or C2S). One of the major challenges in e-commerce is the large volume of spatio-temporally diverse orders from multiple customers, each of which has to be fulfilled from one of several warehouses using a fleet of vehicles. This results in two levels of decision-making: (i) selection of a fulfillment node for each order (including the option of deferral to a future time), and then (ii) routing of vehicles (each of which can carry multiple orders originating from the same warehouse). We propose an approach that combines graph neural networks and reinforcement learning to train the node selection and vehicle routing agents. We include real-world constraints such as warehouse inventory capacity, vehicle characteristics such as travel times, service times, carrying capacity, and customer constraints including time windows for delivery. The complexity of this problem arises from the fact that outcomes (rewards) are driven both by the fulfillment node mapping as well as the routing algorithms, and are spatio-temporally distributed. Our experiments show that this algorithmic pipeline outperforms pure heuristic policies

The structure of human SULT1A1 crystallized with estradiol: An insight into active site plasticity and substrate inhibition with multi-ring substrates

Human SULT1A1 belongs to the supergene family of sulfotransferases (SULTs) involved in the sulfonation of xeno- and endo-biotics. The enzyme is also one of the SULTs responsible for metabolic activation of mutagenic and carcinogenic compounds and therefore is implicated in various cancer forms. Further, how substrate inhibition takes place with rigid fused multi-ring substrates such as E2 at high substrate concentrations when subcellular fractions or recombinant enzymes are used is not well understood. To investigate how estradiol binds to SULT1A1, we co-crystallized SULT1A1 with the cofactor product PAP (3’-phosphoadenosine 5’-phosphate) and sulfated estradiol (E2S). The crystal structure of SULT1A1 that we present here has PAP and one molecule of E2 bound in a non-productive mode in the active site. The structure reveals how the SULT1A1 binding site undergoes conformational changes to accept fused ring substrates such as steroids. In agreement with previous reports, the enzyme shows partial substrate inhibition at high concentrations of E2. A model to explain these kinetics is developed based on the formation of an enzyme:PAP:E2 dead-end complex during catalysis. This model provides a very good quantitative description of the rate versus [E2] curve. This dead-end complex is proposed to be that described by the observed structure, where E2 is bound in a non-productive mode