Following the 2016 U.S. presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: (i) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their “most important” source; (ii) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; (iii) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and (iv) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.

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Social Media and Fake News in the 2016 Election

Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity – which includes omitted variables, omitted selection, simultaneity, common-method variance, and measurement error – renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation could be confounded; these methods include fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66% and up to 90% of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.

On making causal claims : A review and recommendations

A stellar spectral flux library of wide spectral coverage and an example of its application are presented. The new library consists of 131 flux-calibrated spectra, encompassing all normal spectral types and luminosity classes at solar abundance, and metal-weak and metal-rich F-K dwarf and G-K giant components. Each library spectrum was formed by combining data from several sources overlapping in wavelength coverage. The SIMBAD database, measured colors, and line strengths were used to check that each input component has closely similar stellar type. The library has complete spectral coverage from 1150 to 10620 Afor all components and to 25000 Afor about half of them, mainly later types of solar abundance. Missing spectral coverage in the infrared currently consists of a smooth energy distribution formed from standard colors for the relevant types. The library is designed to permit inclusion of additional digital spectra, particularly of non-solar abundance stars in the infrared, as they become available. The library spectra are each given as Fl versus l, from 1150 to 25000 Ain steps of 5 A ˚. A program to combine the library spectra in the ratios appropriate to a selected isochrone is described and an example of a spectral component signature of a composite population of solar age and metallicity is illustrated. The library spectra and associated tables are available as text files by remote electronic access.

VizieR Online Data Catalog: A Stellar Spectral Flux Library: 1150 - 25000 A (Pickles 1998)

A super-peer is a node in a peer-to-peer network that operates both as a server to a set of clients, and as an equal in a network of super-peers. Super-peer networks strike a balance between the efficiency of centralized search, and the autonomy, load balancing and robustness to attacks provided by distributed search. Furthermore, they take advantage of the heterogeneity of capabilities (e.g., bandwidth, processing power) across peers, which recent studies have shown to be enormous. Hence, new and old P2P systems like KaZaA and Gnutella are adopting super-peers in their design. Despite their growing popularity, the behavior of super-peer networks is not well understood. For example, what are the potential drawbacks of super-peer networks? How can super-peers be made more reliable? How many clients should a super-peer take on to maximize efficiency? we examine super-peer networks in detail, gaming an understanding of their fundamental characteristics and performance tradeoffs. We also present practical guidelines and a general procedure for the design of an efficient super-peer network.

Designing an Super-Peer Network

In his seminal book, Shewhart (1931) makes no demand on the distribution of the characteristic to be plotted on a control chart. How then can we explain the idea that normality is, if not required, at least highly desirable? I believe that it has come about through the many statistical studies of control-chart behavior. If one is to study how a control chart behaves, it is necessary to relate it to some distribution. The obvious choice is the normal distribution because of its ubiquity as a satisfactory model. This is bolstered by the existence of the Central Limit Theorem.

Sampling: Design and Analysis

Distributed Computing Systems are a viable and less expensive alternative to parallel computers. However, a serious difficulty in concurrent programming of a distributed system is how to deal with scheduling and load balancing of such a system which may consist of heterogeneous computers. Distributed scheduling schemes suitable for parallel loops with independent iterations on heterogeneous computer clusters have been designed in the past. In this work we consider a class of Self-Scheduling schemes for parallel loops with independent iterations which have been applied to multiprocessor systems. We extend this type of schemes to heterogeneous distributed systems. We present tests that the distributed versions of these schemes maintain load balanced execution on heterogeneous systems.

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A class of loop self-scheduling for heterogeneous clusters

Neural networks have been applied successfully in many fields. However, satisfactory results can only be found under large sample conditions. When it comes to small training sets, the performance may not be so good, or the learning task can even not be accomplished. This deficiency limits the applications of neural network severely. The main reason why small datasets cannot provide enough information is that there exist gaps between samples, even the domain of samples cannot be ensured. Several computational intelligence techniques have been proposed to overcome the limits of learning from small datasets. We have the following goals: i. To discuss the meaning of "small" in the context of inferring from small datasets. ii. To overview computational intelligence solutions for this problem. iii. To illustrate the introduced concepts with a real-life application.

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Extreme Data Mining: Inference from Small Datasets

While the training parameters of machine learning models are adapted during the training phase, the values of the hyperparameters (or meta-parameters) have to be specified before the learning phase. The goal is to find a set of hyperparameter values which gives us the best model for our data in a reasonable amount of time. We present an integrated view of methods used in hyperparameter optimization of learning systems, with an emphasis on computational complexity aspects. Our thesis is that we should solve a hyperparameter optimization problem using a combination of techniques for: optimization, search space and training time reduction. Case studies from real-world applications illustrate the practical aspects. We create the framework for a future separation between parameters and hyperparameters in adaptive P systems.

Hyperparameter optimization in learning systems

The introduction of Transformer neural networks has changed the landscape of Natural Language Processing during the last three years. While models inspired by it have managed to lead the boards for a variety of tasks, some of the mechanisms through which these performances were achieved are not necessarily well-understood. Our survey is focused mostly on explaining Transformer architectures through visualizations. Since visualization enables some degree of explainability, we have examined the various Transformer facets that can be explored through visual analytics. The field is still at a nascent stage and is expected to witness dynamic growth in the near future, since the results are already interesting and promising. Currently, some of the visualizations are relatively close to their original models, whereas others are model-agnostic. The visualizations designed to explore the Transformer architectures enable some additional features, like exploration of all neuronal cells or attention maps, therefore providing an advantage for this particular task. We conclude by proposing a set of requirements for future Transformer visualization frameworks.

Visualizing Transformers for NLP: A Brief Survey

We introduce a new fuzzy ARTMAP (FAM) neural network: Fuzzy ARTMAP with relevance factor (FAMR) The FAMR architecture is able to incrementally "grow" and to sequentially accommodate input-output sample pairs Each training pair has a relevance factor assigned to it, proportional to the importance of that pair during the learning phase The relevance factors are user-defined or computed The FAMR can be trained as a classifier and, at the same time, as a nonparametric estimator of the probability that an input belongs to a given class The FAMR probability estimation converges almost surely and in the mean square to the posterior probability Our theoretical results also characterize the convergence rate of the approximation Using a relevance factor adds more flexibility to the training phase, allowing ranking of sample pairs according to the confidence we have in the information source We analyze the FAMR capability for mapping noisy functions when training data originates from multiple sources with known levels of noise

Razvan Andonie

Papers

A class of loop self-scheduling for heterogeneous clusters

Extreme Data Mining: Inference from Small Datasets

Hyperparameter optimization in learning systems

Visualizing Transformers for NLP: A Brief Survey

Fuzzy ARTMAP with input relevances