105 research outputs found
Русская религиозная гносеология XIX - начала XX вв.: актуальность и методы изучения
Проводится исследование гносеологических идей русских религиозных мыслителей в контексте проблемы понимания. Автор полагает, что сравнительное изучение русской и западной философии открывает перспективы не только для истолкования самой русской мысли, но также и для углубленного осмысления проблем западной философии, развития всей мировой цивилизации в целом, исходя именно из контекста русской культуры
Gradient-free quantum optimization on NISQ devices
Variational Quantum Eigensolvers (VQEs) have recently attracted considerable
attention. Yet, in practice, they still suffer from the efforts for estimating
cost function gradients for large parameter sets or resource-demanding
reinforcement strategies. Here, we therefore consider recent advances in
weight-agnostic learning and propose a strategy that addresses the trade-off
between finding appropriate circuit architectures and parameter tuning. We
investigate the use of NEAT-inspired algorithms which evaluate circuits via
genetic competition and thus circumvent issues due to exceeding numbers of
parameters. Our methods are tested both via simulation and on real quantum
hardware and are used to solve the transverse Ising Hamiltonian and the
Sherrington-Kirkpatrick spin model.Comment: 13 pages, 6 figures, comments welcome
k is the Magic Number -- Inferring the Number of Clusters Through Nonparametric Concentration Inequalities
Most convex and nonconvex clustering algorithms come with one crucial
parameter: the in -means. To this day, there is not one generally
accepted way to accurately determine this parameter. Popular methods are simple
yet theoretically unfounded, such as searching for an elbow in the curve of a
given cost measure. In contrast, statistically founded methods often make
strict assumptions over the data distribution or come with their own
optimization scheme for the clustering objective. This limits either the set of
applicable datasets or clustering algorithms. In this paper, we strive to
determine the number of clusters by answering a simple question: given two
clusters, is it likely that they jointly stem from a single distribution? To
this end, we propose a bound on the probability that two clusters originate
from the distribution of the unified cluster, specified only by the sample mean
and variance. Our method is applicable as a simple wrapper to the result of any
clustering method minimizing the objective of -means, which includes
Gaussian mixtures and Spectral Clustering. We focus in our experimental
evaluation on an application for nonconvex clustering and demonstrate the
suitability of our theoretical results. Our \textsc{SpecialK} clustering
algorithm automatically determines the appropriate value for , without
requiring any data transformation or projection, and without assumptions on the
data distribution. Additionally, it is capable to decide that the data consists
of only a single cluster, which many existing algorithms cannot
Deep Archetypal Analysis
"Deep Archetypal Analysis" generates latent representations of
high-dimensional datasets in terms of fractions of intuitively understandable
basic entities called archetypes. The proposed method is an extension of linear
"Archetypal Analysis" (AA), an unsupervised method to represent multivariate
data points as sparse convex combinations of extremal elements of the dataset.
Unlike the original formulation of AA, "Deep AA" can also handle side
information and provides the ability for data-driven representation learning
which reduces the dependence on expert knowledge. Our method is motivated by
studies of evolutionary trade-offs in biology where archetypes are species
highly adapted to a single task. Along these lines, we demonstrate that "Deep
AA" also lends itself to the supervised exploration of chemical space, marking
a distinct starting point for de novo molecular design. In the unsupervised
setting we show how "Deep AA" is used on CelebA to identify archetypal faces.
These can then be superimposed in order to generate new faces which inherit
dominant traits of the archetypes they are based on.Comment: Published at the German Conference on Pattern Recognition 2019 (GCPR
On the Origins of Memes by Means of Fringe Web Communities
Internet memes are increasingly used to sway and manipulate public opinion.
This prompts the need to study their propagation, evolution, and influence
across the Web. In this paper, we detect and measure the propagation of memes
across multiple Web communities, using a processing pipeline based on
perceptual hashing and clustering techniques, and a dataset of 160M images from
2.6B posts gathered from Twitter, Reddit, 4chan's Politically Incorrect board
(/pol/), and Gab, over the course of 13 months. We group the images posted on
fringe Web communities (/pol/, Gab, and The_Donald subreddit) into clusters,
annotate them using meme metadata obtained from Know Your Meme, and also map
images from mainstream communities (Twitter and Reddit) to the clusters.
Our analysis provides an assessment of the popularity and diversity of memes
in the context of each community, showing, e.g., that racist memes are
extremely common in fringe Web communities. We also find a substantial number
of politics-related memes on both mainstream and fringe Web communities,
supporting media reports that memes might be used to enhance or harm
politicians. Finally, we use Hawkes processes to model the interplay between
Web communities and quantify their reciprocal influence, finding that /pol/
substantially influences the meme ecosystem with the number of memes it
produces, while \td has a higher success rate in pushing them to other
communities.Comment: A shorter version of this paper appears in the Proceedings of 18th
ACM Internet Measurement Conference (IMC 2018). This is the full versio
Forecasting Player Behavioral Data and Simulating in-Game Events
Understanding player behavior is fundamental in game data science. Video
games evolve as players interact with the game, so being able to foresee player
experience would help to ensure a successful game development. In particular,
game developers need to evaluate beforehand the impact of in-game events.
Simulation optimization of these events is crucial to increase player
engagement and maximize monetization. We present an experimental analysis of
several methods to forecast game-related variables, with two main aims: to
obtain accurate predictions of in-app purchases and playtime in an operational
production environment, and to perform simulations of in-game events in order
to maximize sales and playtime. Our ultimate purpose is to take a step towards
the data-driven development of games. The results suggest that, even though the
performance of traditional approaches such as ARIMA is still better, the
outcomes of state-of-the-art techniques like deep learning are promising. Deep
learning comes up as a well-suited general model that could be used to forecast
a variety of time series with different dynamic behaviors
Digital Propaganda: The Tyranny of Ignorance
© The Author(s) 2018. The existence of propaganda is inexorably bound to the nature of communication and communications technology. Mass communication by citizens in the digital age has been heralded as a means to counter elite propaganda; however, it also provides a forum for misinformation, aggression and hostility. The extremist group Britain First has used Facebook as a way to propagate hostility towards Muslims, immigrants and social security claimants in the form of memes, leading to a backlash from sites antithetical to their message. This article provides a memetic analysis, which addresses persuasion, organisation, political echo chambers and self-correcting online narratives; arguing that propaganda can be best understood as an evolving set of techniques and mechanisms which facilitate the propagation of ideas and actions. This allows the concept to be adapted to fit a changing political and technological landscape and to encompass both propaganda and counter-propaganda in the context of horizontal communications networks
Constructing the digitalized sporting body: black and white masculinity in NBA/NHL internet memes
In this article, I examine the ways sport fans construct and circulate discourses of race and masculinity in cyberspace. I do this through an examination of a set of Internet memes that juxtapose the bodies of National Hockey League players with National Basketball Association players in one single image. I argue these memes celebrate White masculinity, while at the same time constructing African American athletes as individualistic, selfish, and unwilling to sacrifice their bodies for the greater good of the team. More so, I argue that these memes construct a form of racial ideology that is representative of White backlash politics
K-Means clustering via the Frank-Wolfe algorithm
We show that k-means clustering is a matrix factorization problem. Seen from this point of view, k-means clustering can be computed using alternating least squares techniques and we show how the constrained optimization steps involved in this procedure can be solved efficiently using the Frank-Wolfe algorithm
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