Question Answering with Subgraph Embeddings

This paper presents a system which learns to answer questions on a broad range of topics from a knowledge base using few hand-crafted features. Our model learns low-dimensional embeddings of words and knowledge base constituents; these representations are used to score natural language questions against candidate answers. Training our system using pairs of questions and structured representations of their answers, and pairs of question paraphrases, yields competitive results on a competitive benchmark of the literature

Memory Networks

We describe a new class of learning models called memory networks. Memory networks reason with inference components combined with a long-term memory component; they learn how to use these jointly. The long-term memory can be read and written to, with the goal of using it for prediction. We investigate these models in the context of question answering (QA) where the long-term memory effectively acts as a (dynamic) knowledge base, and the output is a textual response. We evaluate them on a large-scale QA task, and a smaller, but more complex, toy task generated from a simulated world. In the latter, we show the reasoning power of such models by chaining multiple supporting sentences to answer questions that require understanding the intension of verbs

A Neural Attention Model for Abstractive Sentence Summarization

Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. Our method utilizes a local attention-based model that generates each word of the summary conditioned on the input sentence. While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data. The model shows significant performance gains on the DUC-2004 shared task compared with several strong baselines.Comment: Proceedings of EMNLP 201

A Comprehensive in Depth Study of Domestic Refrigerating Systems

Refrigeration systems are essential in everyday life, especially when it comes to food storage and preparation, as well as safety and convenience. The primary function of a home refrigerator is to preserve the quality of perishable goods. The efficiency of the refrigerator, which is largely determined by temperature distribution and air flow in the compartments, is critical to this quality. Thus, in this paper, we are attempting to shed light on the work of investigators by experimenting on various geometries of the refrigerating system in order to improve their efficiency. In addition, we have attempted to provide an overview of the DAR cycle as well as the components of the refrigeration system, which will aid scholars in understanding the fundamental operation of cooling systems

Thermal Analysis of Small Refrigerator Compartment by using CFD

Refrigeration systems are extremely important in daily life, especially in terms of preserving food, health, and comfort. The objective of this project work is to make some effective changes in the design of a conventional refrigerating system so that performance of the evaporator can be optimized.  The effects of the normal and perforated fin on the velocity and temperature distribution at different levels. To make a comparative analysis between various cases of with and without the fin refrigerating system. The analysis and modeling through CFD for refrigerators based on diffusion-absorption is presented as a feasible tool for the purpose of evaluating proposals in the internal design of the refrigerator. The present study considers that significant improvements can be achieved on the thermal profiles, by researching an optimal geometric plate-evaporator, in which the airflow is included as a parameter of great importance in the operability of the refrigerator and therefore, in the preservation of food supplies