Multidimensional news quality: A comparison of crowdsourcing and nichesourcing

In the age of fake news and of filter bubbles, assessing the quality of information is a compelling issue: it is important for users to understand the quality of the information they consume online. We report on our experiment aimed at understanding if workers from the crowd can be a suitable alternative to experts for information quality assessment. Results show that the data collected by crowdsourcing seem reliable. The agreement with the experts is not full, but in a task that is so complex and related to the assessor’s background, this is expected and, to some extent, positive

Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses

Breakthroughs in our understanding of physical phenomena have traditionally followed improvements in instrumentation. Studies of the magnetic field of the Sun, and its influence on the solar dynamo and space weather events, have benefited from improvements in resolution and measurement frequency of new instruments. However, in order to fully understand the solar cycle, high-quality data across time-scales longer than the typical lifespan of a solar instrument are required. At the moment, discrepancies between measurement surveys prevent the combined use of all available data. In this work, we show that machine learning can help bridge the gap between measurement surveys by learning to \textbf{super-resolve} low-resolution magnetic field images and \textbf{translate} between characteristics of contemporary instruments in orbit. We also introduce the notion of physics-based metrics and losses for super-resolution to preserve underlying physics and constrain the solution space of possible super-resolution outputs

New Notions and Constructions of Sparsification for Graphs and Hypergraphs

A sparsifier of a graph G (Benczu´r and Karger; Spielman and Teng) is a sparse weighted subgraph ˜ G that approximately retains the same cut structure of G. For general graphs, non-trivial sparsification is possible only by using weighted graphs in which different edges have different weights. Even for graphs that admit unweighted sparsifiers (that is, sparsifiers in which all the edge weights are equal to the same scaling factor), there are no known polynomial time algorithms that find such unweighted sparsifiers. We study a weaker notion of sparsification suggested by Oveis Gharan, in which the number of cut edges in each cut (S, ¯ S) is not approximated within a multiplicative factor (1 + ǫ), but is, instead, approximated up to an additive term bounded by ǫ times d · |S| + vol(S), where d is the average degree of the graph and vol(S) is the sum of the degrees of the vertices in S. We provide a probabilistic polynomial time construction of such sparsifiers for every graph, and our sparsifiers have a near-optimal number of edges O(ǫ−2npolylog(1/ǫ)). We also provide a deterministic polynomial time construction that constructs sparsifiers with a weaker property having the optimal number of edges O(ǫ−2n). Our constructions also satisfy a spectral version of the “additive sparsification” property. Notions of sparsification have also been studied for hypergraphs. Our construction of “additive sparsifiers” with Oǫ(n) edges also works for hypergraphs, and provides the first non-trivial notion of sparsification for hypergraphs achievable with O(n) hyperedges when ǫ and the rank r of the hyperedges are constant. Finally, we provide a new construction of spectral hypergraph sparsifiers, according to the standard definition, with poly(ǫ−1,r)·nlogn hyperedges, improving over the previous spectral construction (Soma and Yoshida) that used ˜ O(n3) hyperedges even for constant r and ǫ

MOOMIN - Mathematical explOration of 'Omics data on a MetabolIc Network.

MOTIVATION: Analysis of differential expression of genes is often performed to understand how the metabolic activity of an organism is impacted by a perturbation. However, because the system of metabolic regulation is complex and all changes are not directly reflected in the expression levels, interpreting these data can be difficult. RESULTS: In this work, we present a new algorithm and computational tool that uses a genome-scale metabolic reconstruction to infer metabolic changes from differential expression data. Using the framework of constraint-based analysis, our method produces a qualitative hypothesis of a change in metabolic activity. In other words, each reaction of the network is inferred to have increased, decreased, or remained unchanged in flux. In contrast to similar previous approaches, our method does not require a biological objective function and does not assign on/off activity states to genes. An implementation is provided and it is available online. We apply the method to three published datasets to show that it successfully accomplishes its two main goals: confirming or rejecting metabolic changes suggested by differentially expressed genes based on how well they fit in as parts of a coordinated metabolic change, as well as inferring changes in reactions whose genes did not undergo differential expression. AVAILABILITY AND IMPLEMENTATION: github.com/htpusa/moomin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online