Large Scale Question Paraphrase Retrieval with Smoothed Deep Metric Learning

The goal of a Question Paraphrase Retrieval (QPR) system is to retrieve equivalent questions that result in the same answer as the original question. Such a system can be used to understand and answer rare and noisy reformulations of common questions by mapping them to a set of canonical forms. This has large-scale applications for community Question Answering (cQA) and open-domain spoken language question answering systems. In this paper we describe a new QPR system implemented as a Neural Information Retrieval (NIR) system consisting of a neural network sentence encoder and an approximate k-Nearest Neighbour index for efficient vector retrieval. We also describe our mechanism to generate an annotated dataset for question paraphrase retrieval experiments automatically from question-answer logs via distant supervision. We show that the standard loss function in NIR, triplet loss, does not perform well with noisy labels. We propose smoothed deep metric loss (SDML) and with our experiments on two QPR datasets we show that it significantly outperforms triplet loss in the noisy label setting

Learning When Not to Answer: A Ternary Reward Structure for Reinforcement Learning based Question Answering

In this paper, we investigate the challenges of using reinforcement learning agents for question-answering over knowledge graphs for real-world applications. We examine the performance metrics used by state-of-the-art systems and determine that they are inadequate for such settings. More specifically, they do not evaluate the systems correctly for situations when there is no answer available and thus agents optimized for these metrics are poor at modeling confidence. We introduce a simple new performance metric for evaluating question-answering agents that is more representative of practical usage conditions, and optimize for this metric by extending the binary reward structure used in prior work to a ternary reward structure which also rewards an agent for not answering a question rather than giving an incorrect answer. We show that this can drastically improve the precision of answered questions while only not answering a limited number of previously correctly answered questions. Employing a supervised learning strategy using depth-first-search paths to bootstrap the reinforcement learning algorithm further improves performance.Comment: Accepted at NAACL 2019. Version 1 was presented at NIPS 2018 workshop on Relational Representation Learnin

Influence of soil fabric on dynamic properties of sand: An experimental study

Natural field sites originate under the action of external sources such as rivers, wind, or marine environments. These sources are responsible for constituting a variety of soil fabrics, which ultimately modify the deformation characteristics. Additionally, the dynamic properties of a site present the characterization of a region and have been profusely utilized by field engineers and researchers. In the present study, the dynamic properties of soil specimens have been evaluated for specimen preparation techniques, namely, air pluviation (AP) and water sedimentation (WS). The cyclic triaxial tests were conducted on the strain-controlled consolidated undrained specimens at a frequency of 0.1 Hz. This frequency has been used in several studies to replicate earthquake or liquefaction scenarios. The calculation of shear modulus (G) and damping ratio (D) was performed using symmetric hysteresis loops generated through cyclic loadings. The outcomes suggest that the specimen prepared using the WS technique possesses a larger shear modulus value than AP ones. The reason behind this observation was the lower degradation characteristics of the WS-prepared specimens. Additionally, the liquefaction susceptibility of the specimens has been noticed for different specimens. © 2023 ISEC Press

Liquefaction proneness of stratified sand-silt layers based on cyclic triaxial tests

Most studies on liquefaction have addressed homogeneous soil strata using sand or sand with fine content without considering soil stratification. In this study, cyclic triaxial tests were conducted on the stratified sand specimens embedded with the silt layers to investigate the liquefaction failures and void-redistribution at confining stress of 100 kPa under stress-controlled mode. The loosening of underlying sand mass and hindrance to pore-water flow caused localized bulging at the sand-silt interface. It is observed that at a silt thickness of 0.2H (H is the height of the specimen), nearly 187 load cycles were required to attain liquefaction, which was the highest among all the silt thicknesses with a single silt layer. Therefore, 0.2H is assumed as the optimum silt thickness (topt). The silt was placed at the top, middle and bottom of the specimen to understand the effect of silt layer location. Due to the increase in depth of the silt layer from the top position (capped soil state) to the bottom, the cycles to reach liquefaction (Ncyc,L) increased 2.18 times. Also, when the number of silt layers increased from single to triple, there was an increase of about 880% in Ncyc,L. The micro-characterization analysis of the soil specimens indicated silty materials transported in upper sections of the specimen due to the dissipated pore pressure. The main parameters, including thickness (t), location (z), cyclic stress ratio (CSR), number of silt layers (n) and modified relative density (Dr,m), performed significantly in governing the liquefaction resistance. For this, a multilinear regression model is developed based on critical parameters for prediction of Ncyc,L. Furthermore, the developed constitutive model has been validated using the data from the present study and earlier findings

Using Pairwise Occurrence Information to Improve Knowledge Graph Completion on Large-Scale Datasets

Bilinear models such as DistMult and ComplEx are effective methods for knowledge graph (KG) completion. However, they require large batch sizes, which becomes a performance bottleneck when training on large scale datasets due to memory constraints. In this paper we use occurrences of entity-relation pairs in the dataset to construct a joint learning model and to increase the quality of sampled negatives during training. We show on three standard datasets that when these two techniques are combined, they give a significant improvement in performance, especially when the batch size and the number of generated negative examples are low relative to the size of the dataset. We then apply our techniques to a dataset containing 2 million entities and demonstrate that our model outperforms the baseline by 2.8% absolute on [email protected]: 8 pages, 3 figures, accepted at EMNLP 201

A HYBRID S N -P N FORMULATION FOR SOLUTION OF THE BOLTZMANN TRANSPORT EQUATION FOR PHONONS

ABSTRACT A generalized form of the Ballistic-Diffusive Equations (BDE) for approximate solution of the Boltzmann Transport Equation (BTE) for phonons is formulated. The formulation presented here is new and general in the sense that, unlike previously published formulations of the BDE, it does not require a priori knowledge of the specific heat capacity of the material. Furthermore, it does not introduce artifacts such as media and ballistic temperatures. As a consequence, the boundary conditions have clear physical meaning. In formulating the BDE, the phonon intensity is split into two components: ballistic and diffusive. The ballistic component is traditionally determined using a viewfactor formulation, while the diffusive component is solved by invoking spherical harmonics expansions. Use of the viewfactor approach for the ballistic component is prohibitive for complex large-scale geometries. Instead, in this work, the ballistic equation is solved using two different established methods that are appropriate for use in complex geometries, namely the discrete ordinates method (DOM), and the control angle discrete ordinates method (CADOM). Results of each method for solving the BDE are compared against benchmark Monte Carlo results, as well as solutions of the BTE using standalone DOM and CADOM for a two-dimensional transient heat conduction problem at various Knudsen numbers. It is found that standalone CADOM (for BTE) and hybrid CADOM-P 1 (for BDE) yield the best accuracy. The hybrid CADOM-P 1 is found to be the best method in terms of computational efficiency. NOMENCLATUR