Jarosite versus Soluble Iron-Sulfate Formation and Their Role in Acid Mine Drainage Formation at the Pan de Azúcar Mine Tailings (Zn-Pb-Ag), NW Argentina

Secondary jarosite and water-soluble iron-sulfate minerals control the composition of acid mine waters formed by the oxidation of sulfide in tailings impoundments at the (Zn-Pb-Ag) Pan de Azúcar mine located in the Pozuelos Lagoon Basin (semi-arid climate) in Northwest (NW) Argentina. In the primary zone of the tailings (9.5 wt % pyrite-marcasite) precipitation of anglesite (PbSO4), wupatkite ((Co,Mg,Ni)Al2(SO4)4) and gypsum retain Pb, Co and Ca, while mainly Fe2+, Zn2+, Al3+, Mg2+, As3+/5+ and Cd2+ migrate downwards, forming a sulfate and metal-rich plume. In the oxidation zone, jarosite (MFe3(TO4)2(OH)6) is the main secondary Fe3+ phase; its most suitable composition is M = K+, Na+, and Pb2+and TO4 = SO42−; AsO42−. During the dry season, iron-sulfate salts precipitate by capillary transport on the tailings and at the foot of DC2 (tailings impoundment DC2) tailings dam where an acid, Fe2+ rich plume outcrops. The most abundant compounds in the acid mine drainage (AMD) are SO42−, Fe2+, Fe3+, Zn2+, Al3+, Mg2+, Cu2+, As3+/5+, Cd2+. These show peak concentrations at the beginning of the wet season, when the soluble salts and jarosite dissolve. The formation of soluble sulfate salts during the dry season and dilution during the wet season conform an annual cycle of rapid metals and acidity transference from the tailings to the downstream environment.Fil: Murray, Jesica María. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Bio y Geociencias del NOA. Universidad Nacional de Salta. Facultad de Ciencias Naturales. Museo de Ciencias Naturales. Instituto de Bio y Geociencias del NOA; ArgentinaFil: Kirschbaum, Alicia Matilde. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Bio y Geociencias del NOA. Universidad Nacional de Salta. Facultad de Ciencias Naturales. Museo de Ciencias Naturales. Instituto de Bio y Geociencias del NOA; ArgentinaFil: Dold, Bernhard. Sustainable Mining Research & Consult EIRL; ChileFil: Mendes Guimaraes, Edi. Universidade do Brasilia; BrasilFil: Pannunzio Miner, Elisa Victoria. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; Argentin

On the Complexity of Rule Discovery from Distributed Data

This paper analyses the complexity of rule selection for supervised learning in distributed scenarios. The selection of rules is usually guided by a utility measure such as predictive accuracy or weighted relative accuracy. Other examples are support and confidence, known from association rule mining. A common strategy to tackle rule selection from distributed data is to evaluate rules locally on each dataset. While this works well for homogeneously distributed data, this work proves limitations of this strategy if distributions are allowed to deviate. To identify those subsets for which local and global distributions deviate may be regarded as an interesting learning task of its own, explicitly taking the locality of data into account. This task can be shown to be basically as complex as discovering the globally best rules from local data. Based on the theoretical results some guidelines for algorithm design are derived. --

Comparing Knowledge-Based Sampling to Boosting

Boosting algorithms for classification are based on altering the ini- tial distribution assumed to underly a given example set. The idea of knowledge-based sampling (KBS) is to sample out prior knowledge and previously discovered patterns to achieve that subsequently ap- plied data mining algorithms automatically focus on novel patterns without any need to adjust the base algorithm. This sampling strat- egy anticipates a user's expectation based on a set of constraints how to adjust the distribution. In the classified case KBS is similar to boosting. This article shows that a specific, very simple KBS algo- rithm is able to boost weak base classifiers. It discusses differences to AdaBoost.M1 and LogitBoost, and it compares performances of these algorithms empirically in terms of predictive accuracy, the area under the ROC curve measure, and squared error. --

SeqScout: Using a Bandit Model to Discover Interesting Subgroups in Labeled Sequences

International audienceIt is extremely useful to exploit labeled datasets not only to learn models but also to improve our understanding of a domain and its available targeted classes. The so-called subgroup discovery task has been considered for a long time. It concerns the discovery of patterns or descriptions, the set of supporting objects of which have interesting properties, e.g., they characterize or discriminate a given target class. Though many subgroup discovery algorithms have been proposed for transactional data, discovering subgroups within labeled sequential data and thus searching for descriptions as sequential patterns has been much less studied. In that context, exhaustive exploration strategies can not be used for real-life applications and we have to look for heuristic approaches. We propose the algorithm SeqScout to discover interesting subgroups (w.r.t. a chosen quality measure) from labeled sequences of itemsets. This is a new sampling algorithm that mines discriminant sequential patterns using a multi-armed bandit model. It is an anytime algorithm that, for a given budget, finds a collection of local optima in the search space of descriptions and thus subgroups. It requires a light configuration and it is independent from the quality measure used for pattern scoring. Furthermore, it is fairly simple to implement. We provide qualitative and quantitative experiments on several datasets to illustrate its added-value

SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine

Traditional medicine typically applies one-size-fits-all treatment for the entire patient population whereas precision medicine develops tailored treatment schemes for different patient subgroups. The fact that some factors may be more significant for a specific patient subgroup motivates clinicians and medical researchers to develop new approaches to subgroup detection and analysis, which is an effective strategy to personalize treatment. In this study, we propose a novel patient subgroup detection method, called Supervised Biclustring (SUBIC) using convex optimization and apply our approach to detect patient subgroups and prioritize risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach not only finds patient subgroups with guidance of a clinically relevant target variable but also identifies and prioritizes risk factors by pursuing sparsity of the input variables and encouraging similarity among the input variables and between the input and target variable

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio