Ferric chloride leaching of the Delta sulfide ores and gold extraction from the leaching residue

Conventional differential and bulk flotation processes have difficulties in achieving high recoveries with acceptable grades far zinc, lead and copper from the complex sulfide ores found at Tok, Alaska. Furthermore, gold and silver, which account for a significant fraction of total value of the ores, are distributed evenly in the flotation tailings and concentrate. Therefore, processing both flotation tailings and concentrate would be necessary to obtain high recoveries of gold and silver. A mineralogical study revealed that the economic sulfide minerals are interstitially associated with a large preponderance of pyrite. The economic sulfide minerals are 10 to 40 microns in size. These mineralogical facts explain the difficulties encountered in the flotation process. A hydrometallurgical method involving ferric chloride leaching and subsequent steps to recover lead, zinc, silver and copper from the leach liquor has been studied at the Mineral Industry Research Laboratory, University of Alaska Fairbanks for the treatment of Delta ores. This alternative is attractive for processing complex sulfide ores which conventional flotation and smelting cannot handle. In addition, the liberation of sulfur in the environmentally acceptable elemental form, rather than as sulfur dioxide, may prove a major advantage of this hydrometallurgical method because of stringent environmental regulations

Multi-Perspective Relevance Matching with Hierarchical ConvNets for Social Media Search

Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have only been applied to standard ad hoc retrieval tasks over web pages and newswire documents. This paper proposes MP-HCNN (Multi-Perspective Hierarchical Convolutional Neural Network) a novel neural ranking model specifically designed for ranking short social media posts. We identify document length, informal language, and heterogeneous relevance signals as features that distinguish documents in our domain, and present a model specifically designed with these characteristics in mind. Our model uses hierarchical convolutional layers to learn latent semantic soft-match relevance signals at the character, word, and phrase levels. A pooling-based similarity measurement layer integrates evidence from multiple types of matches between the query, the social media post, as well as URLs contained in the post. Extensive experiments using Twitter data from the TREC Microblog Tracks 2011--2014 show that our model significantly outperforms prior feature-based as well and existing neural ranking models. To our best knowledge, this paper presents the first substantial work tackling search over social media posts using neural ranking models.Comment: AAAI 2019, 10 page

Modeling Relation Paths for Representation Learning of Knowledge Bases

Representation learning of knowledge bases (KBs) aims to embed both entities and relations into a low-dimensional space. Most existing methods only consider direct relations in representation learning. We argue that multiple-step relation paths also contain rich inference patterns between entities, and propose a path-based representation learning model. This model considers relation paths as translations between entities for representation learning, and addresses two key challenges: (1) Since not all relation paths are reliable, we design a path-constraint resource allocation algorithm to measure the reliability of relation paths. (2) We represent relation paths via semantic composition of relation embeddings. Experimental results on real-world datasets show that, as compared with baselines, our model achieves significant and consistent improvements on knowledge base completion and relation extraction from text.Comment: 10 page

Multivariate Distributions of Correlated Binary Variables Generated by Pair-Copulas

Correlated binary data are prevalent in a wide range of scientific disciplines, including healthcare and medicine. The generalized estimating equations (GEEs) and the multivariate probit (MP) model are two of the popular methods for analyzing such data. However, both methods have some significant drawbacks. The GEEs may not have an underlying likelihood and the MP model may fail to generate a multivariate binary distribution with specified marginals and bivariate correlations. In this paper, we study multivariate binary distributions that are based on D-vine pair-copula models as a superior alternative to these methods. We elucidate the construction of these binary distributions in two and three dimensions with numerical examples. For higher dimensions, we provide a method of constructing a multidimensional binary distribution with specified marginals and equicorrelated correlation matrix. We present a real-life data analysis to illustrate the application of our results

Models of risky choice: A state-trace and signed difference analysis

Models of risky choice fall into two broad classes; fixed utility models that satisfy the condition of simple scalability and everything else. While it is known that choice behavior can be observed that is inconsistent with all models, this has largely been based on the construction of special cases. We use state-trace analysis and signed difference analysis to test a set of models on a set of ecologically representative risky choices. An advantage of this approach is that there is no requirement to posit a particular form for the error function that links the difference in the utilities of two gambles, A and B, with the probability of choosing A over B. We presented groups of participants with 30 variable gambles (A), each paired with one of four fixed gambles (B). We use state-trace analysis to test the prediction of all fixed utility models that the probability of choosing each A has the same order for all B. The results show that this prediction is not confirmed and a more complex model is required. We then use signed difference analysis to test two more complex models — the random subjective expected utility model based on Decision Field Theory and a fixed utility mixture model. We derive a key prediction from the random subjective expected utility model and show that it is confirmed by the data. In contrast, the data are shown to be inconsistent with the fixed utility mixture model