1,720,962 research outputs found
Image embedding for denoising generative models
Full text linkDenoising Diffusion models are gaining increasing popularity in the field of generative modeling for several reasons, including the simple and stable training, the excellent generative quality, and the solid probabilistic foundation. In this article, we address the problem of embedding an image into the latent space of Denoising Diffusion Models, that is finding a suitable “noisy” image whose denoising results in the original image. We particularly focus on Denoising Diffusion Implicit Models due to the deterministic nature of their reverse diffusion process. As a side result of our investigation, we gain a deeper insight into the structure of the latent space of diffusion models, opening interesting perspectives on its exploration, the definition of semantic trajectories, and the manipulation/conditioning of encodings for editing purposes. A particularly interesting property highlighted by our research, which is also characteristic of this class of generative models, is the independence of the latent representation from the networks implementing the reverse diffusion process. In other words, a common seed passed to different networks (each trained on the same dataset), eventually results in identical images
Precipitation nowcasting with generative diffusion models
No full textIn recent years traditional numerical methods for accurate weather prediction have been increasingly challenged by deep learning methods. Numerous historical datasets used for short and medium-range weather forecasts are typically organized into a regular spatial grid structure. This arrangement closely resembles images: each weather variable can be visualized as a map or, when considering the temporal axis, as a video. Several classes of generative models, comprising Generative Adversarial Networks, Variational Autoencoders, or the recent Denoising Diffusion Models have largely proved their applicability to the next-frame prediction problem, and is thus natural to test their performance on the weather prediction benchmarks. Diffusion models are particularly appealing in this context, due to the intrinsically probabilistic nature of weather forecasting: what we are really interested to model is the probability distribution of weather indicators, whose expected value is the most likely prediction. In our study, we focus on a specific subset of the ERA-5 dataset, which includes hourly data pertaining to Central Europe from the years 2016 to 2021. Within this context, we examine the efficacy of diffusion models in handling the task of precipitation nowcasting, with a lead time of 1 to 3 hours. Our work is conducted in comparison to the performance of well-established U-Net models, as documented in the existing literature. An additional comparative analysis has been done with the forecasting capabilities of the CERRA system, part of the Copernicus Climate Change Service. The novelty of our approach, Generative Ensemble Diffusion (GED), lies in its innovative use of a diffusion model to generate a diverse set of possible weather scenarios. These scenarios are then amalgamated into a single prediction in a post-processing phase. This approach mimics the usual weather forecasting technique consisting in running an ensemble of numerical simulations under slightly different initial conditions by exploiting instead the intrinsic stochasticity of the generative model. In comparison to recent deep learning models addressing the same problem, our approach results in approximately a 25% reduction in the mean squared error. Reverse diffusion is a core concept in our GED approach, is particularly relevant to weather forecasting. In the context of diffusion models, reverse diffusion refers to the process of iteratively refining a noisy initial prediction into a coherent and realistic forecast. By leveraging reverse diffusion, our model effectively simulates the complex temporal dynamics of weather systems, mirroring the inherent uncertainty and variability in weather patterns
A Sentiment and Emotion Annotated Dataset for Bitcoin Price Forecasting Based on Reddit Posts
Full text linkCryptocurrencies have gained enormous momentum in finance and are nowadays commonly adopted as a medium of exchange for online payments. After recent events during which GameStop’s stocks were believed to be influenced by WallStreetBets subReddit, Reddit has become a very hot topic on the cryptocurrency market. The influence of public opinions on cryptocurrency price trends has inspired researchers on exploring solutions that integrate such information in crypto price change forecasting. A popular integration technique regards representing social media opinions via sentiment features. However, this research direction is still in its infancy, where a limited number of publicly available datasets with sentiment annotations exists. We propose a novel Bitcoin Reddit Sentiment Dataset, a ready-to-use dataset annotated with state-of-the-art sentiment and emotion recognition. The dataset contains pre-processed Reddit posts and comments about Bitcoin from several domain-related subReddits along with Bitcoin’s financial data. We evaluate several widely adopted neural architectures for crypto price change forecasting. Our results show controversial benefits of sentiment and emotion features advocating for more sophisticated social media integration techniques. We make our dataset publicly available for research
Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics
Full text linkIn shaping the Internet of Money, the application of blockchain and
distributed ledger technologies (DLTs) to the financial sector triggered
regulatory concerns. Notably, while the user anonymity enabled in this field
may safeguard privacy and data protection, the lack of identifiability hinders
accountability and challenges the fight against money laundering and the
financing of terrorism and proliferation (AML/CFT). As law enforcement agencies
and the private sector apply forensics to track crypto transfers across
ecosystems that are socio-technical in nature, this paper focuses on the
growing relevance of these techniques in a domain where their deployment
impacts the traits and evolution of the sphere. In particular, this work offers
contextualized insights into the application of methods of machine learning and
transaction graph analysis. Namely, it analyzes a real-world dataset of Bitcoin
transactions represented as a directed graph network through various
techniques. The modeling of blockchain transactions as a complex network
suggests that the use of graph-based data analysis methods can help classify
transactions and identify illicit ones. Indeed, this work shows that the neural
network types known as Graph Convolutional Networks (GCN) and Graph Attention
Networks (GAT) are a promising AML/CFT solution. Notably, in this scenario GCN
outperform other classic approaches and GAT are applied for the first time to
detect anomalies in Bitcoin. Ultimately, the paper upholds the value of
public-private synergies to devise forensic strategies conscious of the spirit
of explainability and data openness
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Procedural terrain generation with style transfer
Full text linkIn this study we introduce a new technique for the generation of terrain
maps, exploiting a combination of procedural generation and Neural Style
Transfer. We consider our approach to be a viable alternative to competing
generative models, with our technique achieving greater versatility, lower
hardware requirements and greater integration in the creative process of
designers and developers. Our method involves generating procedural noise maps
using either multi-layered smoothed Gaussian noise or the Perlin algorithm. We
then employ an enhanced Neural Style transfer technique, drawing style from
real-world height maps. This fusion of algorithmic generation and neural
processing holds the potential to produce terrains that are not only diverse
but also closely aligned with the morphological characteristics of real-world
landscapes, with our process yielding consistent terrain structures with low
computational cost and offering the capability to create customized maps.
Numerical evaluations further validate our model's enhanced ability to
accurately replicate terrain morphology, surpassing traditional procedural
methods
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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