The Precious metals (Au, Ag, Pt, Pd, Rh) adsorption on the Silicon – organic sorbents

Sorption activity of two types of Silicon-organic sorbents for the previous metals has been studied. A polymer – poly (3- silsesquioxanilpropylthiocarbamate) - 3- silsesquioxanilpropylammonium which was obtained by the hydrolytic poly-condensation reaction and has been determined its physical, chemical characteristics and its sorption activity for the Ag(I), Au(III), Pt(IV), Pd(II), Rh(III). It has been found out that the sorbent shows high static sorption of Gold (III), Mercury (II) at acidic condition. The second a net structured silicon-organic copolymer {SiO2*2[O1.5Si(CH2)3NHC5H4N}n was synthesized by hydrolytic co-poly-condensation reaction. It likely to react as an anionit that adsorbs chloro-complex anion of the Au (III), Pt(IV), Pd(II), Rh(III).DOI: http://dx.doi.org/10.5564/mjc.v12i0.167 Mongolian Journal of Chemistry Vol.12 2011: 29-3

Towards Bottom-Up Analysis of Social Food

in ACM Digital Health Conference 201

Deep Memory Networks for Attitude Identification

We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral. Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.Comment: Accepted to WSDM'1

Dynamic Key-Value Memory Networks for Knowledge Tracing

Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities. One important purpose of KT is to personalize the practice sequence to help students learn knowledge concepts efficiently. However, existing methods such as Bayesian Knowledge Tracing and Deep Knowledge Tracing either model knowledge state for each predefined concept separately or fail to pinpoint exactly which concepts a student is good at or unfamiliar with. To solve these problems, this work introduces a new model called Dynamic Key-Value Memory Networks (DKVMN) that can exploit the relationships between underlying concepts and directly output a student's mastery level of each concept. Unlike standard memory-augmented neural networks that facilitate a single memory matrix or two static memory matrices, our model has one static matrix called key, which stores the knowledge concepts and the other dynamic matrix called value, which stores and updates the mastery levels of corresponding concepts. Experiments show that our model consistently outperforms the state-of-the-art model in a range of KT datasets. Moreover, the DKVMN model can automatically discover underlying concepts of exercises typically performed by human annotations and depict the changing knowledge state of a student.Comment: To appear in 26th International Conference on World Wide Web (WWW), 201

Review of Intrinsic Motivation in Simulation-based Game Testing

This paper presents a review of intrinsic motivation in player modeling, with a focus on simulation-based game testing. Modern AI agents can learn to win many games; from a game testing perspective, a remaining research problem is how to model the aspects of human player behavior not explained by purely rational and goal-driven decision making. A major piece of this puzzle is constituted by intrinsic motivations, i.e., psychological needs that drive behavior without extrinsic reinforcement such as game score. We first review the common intrinsic motivations discussed in player psychology research and artificial intelligence, and then proceed to systematically review how the various motivations have been implemented in simulated player agents. Our work reveals that although motivations such as competence and curiosity have been studied in AI, work on utilizing them in simulation-based game testing is sparse, and other motivations such as social relatedness, immersion, and domination appear particularly underexplored

Ionic liquids as diluents in solvent extraction: first evidence of supramolecular aggregation of a couple of extractant molecules

International audienc

Extraction of uranium from sulfuric acid media using amino-diamide extractants

International audienc