Social Bots: Human-Like by Means of Human Control?

Social bots are currently regarded an influential but also somewhat mysterious factor in public discourse and opinion making. They are considered to be capable of massively distributing propaganda in social and online media and their application is even suspected to be partly responsible for recent election results. Astonishingly, the term `Social Bot' is not well defined and different scientific disciplines use divergent definitions. This work starts with a balanced definition attempt, before providing an overview of how social bots actually work (taking the example of Twitter) and what their current technical limitations are. Despite recent research progress in Deep Learning and Big Data, there are many activities bots cannot handle well. We then discuss how bot capabilities can be extended and controlled by integrating humans into the process and reason that this is currently the most promising way to go in order to realize effective interactions with other humans.Comment: 36 pages, 13 figure

The Extended Mind and Network-Enabled Cognition

In thinking about the transformative potential of network technologies with respect to human cognition, it is common to see network resources as playing a largely assistive or augmentative role. In this paper we propose a somewhat more radical vision. We suggest that the informational and technological elements of a network system can, at times, constitute part of the material supervenience base for a human agent’s mental states and processes. This thesis (called the thesis of network-enabled cognition) draws its inspiration from the notion of the extended mind that has been propounded in the philosophical and cognitive science literature. Our basic claim is that network systems can do more than just augment cognition; they can also constitute part of the physical machinery that makes mind and cognition mechanistically possible. In evaluating this hypothesis, we identify a number of issues that seem to undermine the extent to which contemporary network systems, most notably the World Wide Web, can legitimately feature as part of an environmentally-extended cognitive system. Specific problems include the reliability and resilience of network-enabled devices, the accessibility of online information content, and the extent to which network-derived information is treated in the same way as information retrieved from biological memory. We argue that these apparent shortfalls do not necessarily merit the wholesale rejection of the network-enabled cognition thesis; rather, they point to the limits of the current state-of-the-art and identify the targets of many ongoing research initiatives in the network and information sciences. In addition to highlighting the importance of current research and technology development efforts, the thesis of network-enabled cognition also suggests a number of areas for future research. These include the formation and maintenance of online trust relationships, the subjective assessment of information credibility and the long-term impact of network access on human psychological and cognitive functioning. The nascent discipline of web science is, we suggest, suitably placed to begin an exploration of these issues

Landau Theory of the Mott Transition in the Fully Frustrated Hubbard Model in Infinite Dimensions

We discuss the solution of the Mott transition problem in a fully frustrated lattice with a semicircular density of states in the limit of infinite dimensions from the point of view of a Landau free energy functional. This approach provides a simple relation between the free energy of the lattice model and that of its local description in terms of an impurity model. The character of the Mott transition in infinite dimensions, (as reviewed by Georges Kotliar Krauth and Rozenberg, RMP 68, 1996, 13) follows simply from the form of the free energy functional and the physics of quantum impurity models. At zero temperature, below a critical value of the interaction U, a Mott insulator with a finite gap in the one particle spectrum, becomes unstable to the formation of a narrow band near the Fermi energy. Using the insights provided by the Landau approach we answer questions raised about the dynamical mean field solution of the Mott transition problem, and comment on its applicability to three dimensional transition metal oxides

HER2 testing in breast cancer: Opportunities and challenges

Human epidermal growth factor receptor 2 (HER2) is overexpressed in 15-25% of breast cancers, usually as a result of HER2 gene amplification. Positive HER2 status is considered to be an adverse prognostic factor. Recognition of the role of HER2 in breast cancer growth has led to the development of anti-HER2 directed therapy, with the humanized monoclonal antibody trastuzumab (Herceptin (R)) having been approved for the therapy of HER2-positive metastatic breast cancer. Clinical studies have further suggested that HER2 status can provide important information regarding success or failure of certain hormonal therapies or chemotherapies. As a result of these developments, there has been increasing demand to perform HER2 testing on current and archived breast cancer specimens. This article reviews the molecular background of HER2 function, activation and inhibition as well as current opinions concerning its role in chemosensitivity and interaction with estrogen receptor biology. The different tissue-based assays used to detect HER2 amplification and overexpression are discussed with respect to their advantages and disadvantages, when to test (at initial diagnosis or pre-treatment), where to test (locally or centralized) and the need for quality assurance to ensure accurate and valid testing results

Anderson-Hubbard model with box disorder: Statistical dynamical mean-field theory investigation

Strongly correlated electrons with box disorder in high-dimensional lattices are investigated. We apply the statistical dynamical mean-field theory, which treats local correlations non-perturbatively. The incorporation of a finite lattice connectivity allows for the detection of disorder-induced localization via the probability distribution function of the local density of states. We obtain a complete paramagnetic ground state phase diagram and find correlation-induced as well as disorder-induced metal-insulator transitions. Our results qualitatively confirm predictions obtained by typical medium theory. Moreover, we find that the probability distribution function of the local density of states in the metallic phase strongly deviates from a log-normal distribution as found for the non-interacting case.Comment: 13 pages, 15 figures, published versio

Methods combination and ML-based re-ranking of multiple hypothesis for question-answering systems

International audienceQuestion answering systems answer correctly to different questions because they are based on different strategies. In order to increase the number of questions which can be answered by a single process, we propose solutions to combine two question answering systems, QAVAL and RITEL. QAVAL proceeds by selecting short passages, annotates them by question terms, and then extracts from them answers which are ordered by a machine learning validation process. RITEL develops a multi-level analysis of questions and documents. Answers are extracted and ordered according to two strategies: by exploiting the redundancy of candidates and a Bayesian model. In order to merge the system results, we developed different methods either by merging passages before answer ordering, or by merging end-results. The fusion of end-results is realized by voting, merging, and by a machine learning process on answer characteristics, which lead to an improvement of the best system results of 19 %