tsdownsample: high-performance time series downsampling for scalable visualization

Interactive line chart visualizations greatly enhance the effective exploration of large time series. Although downsampling has emerged as a well-established approach to enable efficient interactive visualization of large datasets, it is not an inherent feature in most visualization tools. Furthermore, there is no library offering a convenient interface for high-performance implementations of prominent downsampling algorithms. To address these shortcomings, we present tsdownsample, an open-source Python package specifically designed for CPU-based, in-memory time series downsampling. Our library focuses on performance and convenient integration, offering optimized implementations of leading downsampling algorithms. We achieve this optimization by leveraging low-level SIMD instructions and multithreading capabilities in Rust. In particular, SIMD instructions were employed to optimize the argmin and argmax operations. This SIMD optimization, along with some algorithmic tricks, proved crucial in enhancing the performance of various downsampling algorithms. We evaluate the performance of tsdownsample and demonstrate its interoperability with an established visualization framework. Our performance benchmarks indicate that the algorithmic runtime of tsdownsample approximates the CPU's memory bandwidth. This work marks a significant advancement in bringing high-performance time series downsampling to the Python ecosystem, enabling scalable visualization. The open-source code can be found at https://github.com/predict-idlab/tsdownsampleComment: Submitted to Software

Plotly-Resampler: Effective Visual Analytics for Large Time Series

Visual analytics is arguably the most important step in getting acquainted with your data. This is especially the case for time series, as this data type is hard to describe and cannot be fully understood when using for example summary statistics. To realize effective time series visualization, four requirements have to be met; a tool should be (1) interactive, (2) scalable to millions of data points, (3) integrable in conventional data science environments, and (4) highly configurable. We observe that open source Python visualization toolkits empower data scientists in most visual analytics tasks, but lack the combination of scalability and interactivity to realize effective time series visualization. As a means to facilitate these requirements, we created Plotly-Resampler, an open source Python library. Plotly-Resampler is an add-on for Plotly's Python bindings, enhancing line chart scalability on top of an interactive toolkit by aggregating the underlying data depending on the current graph view. Plotly-Resampler is built to be snappy, as the reactivity of a tool qualitatively affects how analysts visually explore and analyze data. A benchmark task highlights how our toolkit scales better than alternatives in terms of number of samples and time series. Additionally, Plotly-Resampler's flexible data aggregation functionality paves the path towards researching novel aggregation techniques. Plotly-Resampler's integrability, together with its configurability, convenience, and high scalability, allows to effectively analyze high-frequency data in your day-to-day Python environment.Comment: The first two authors contributed equally. Accepted at IEEE VIS 202

Do Not Sleep on Linear Models: Simple and Interpretable Techniques Outperform Deep Learning for Sleep Scoring

Over the last few years, research in automatic sleep scoring has mainly focused on developing increasingly complex deep learning architectures. However, recently these approaches achieved only marginal improvements, often at the expense of requiring more data and more expensive training procedures. Despite all these efforts and their satisfactory performance, automatic sleep staging solutions are not widely adopted in a clinical context yet. We argue that most deep learning solutions for sleep scoring are limited in their real-world applicability as they are hard to train, deploy, and reproduce. Moreover, these solutions lack interpretability and transparency, which are often key to increase adoption rates. In this work, we revisit the problem of sleep stage classification using classical machine learning. Results show that state-of-the-art performance can be achieved with a conventional machine learning pipeline consisting of preprocessing, feature extraction, and a simple machine learning model. In particular, we analyze the performance of a linear model and a non-linear (gradient boosting) model. Our approach surpasses state-of-the-art (that uses the same data) on two public datasets: Sleep-EDF SC-20 (MF1 0.810) and Sleep-EDF ST (MF1 0.795), while achieving competitive results on Sleep-EDF SC-78 (MF1 0.775) and MASS SS3 (MF1 0.817). We show that, for the sleep stage scoring task, the expressiveness of an engineered feature vector is on par with the internally learned representations of deep learning models. This observation opens the door to clinical adoption, as a representative feature vector allows to leverage both the interpretability and successful track record of traditional machine learning models.Comment: The first two authors contributed equally. Submitted to Biomedical Signal Processing and Contro

Prospectives

Tiré de: Prospectives, vol. 4, no 2 (avril 1968)Titre de l'écran-titre (visionné le 24 janv. 2013

Addressing Data Quality Challenges in Observational Ambulatory Studies: Analysis, Methodologies and Practical Solutions for Wrist-worn Wearable Monitoring

Chronic disease management and follow-up are vital for realizing sustained patient well-being and optimal health outcomes. Recent advancements in wearable sensing technologies, particularly wrist-worn devices, offer promising solutions for longitudinal patient follow-up by shifting from subjective, intermittent self-reporting to objective, continuous monitoring. However, collecting and analyzing wearable data presents unique challenges, such as data entry errors, non-wear periods, missing wearable data, and wearable artifacts. We therefore present an in-depth exploration of data analysis challenges tied to wrist-worn wearables and ambulatory label acquisition, using two real-world datasets (i.e., mBrain21 and ETRI lifelog2020). We introduce novel practical countermeasures, including participant compliance visualizations, interaction-triggered questionnaires to assess personal bias, and an optimized wearable non-wear detection pipeline. Further, we propose a visual analytics approach to validate processing pipelines using scalable tools such as tsflex and Plotly-Resampler. Lastly, we investigate the impact of missing wearable data on "window-of-interest" analysis methodologies. Prioritizing transparency and reproducibility, we offer open access to our detailed code examples, facilitating adaptation in future wearable research. In conclusion, our contributions provide actionable approaches for wearable data collection and analysis in chronic disease management.Comment: 29 pages, 16 figure

Ce que la polémique fait aux œuvres : une étude en trois temps de controverses dans l'art contemporain

Cette thèse a été réalisée avec l'appui financier du Fonds de recherche du Québec – Société et culture (FRQSC).Souvent reléguées aux marges de l’histoire de l’art, les controverses n’en ont pas moins un effet éminemment transformateur : elles font quelque chose aux œuvres. Consacrée à l’étude de polémiques récentes touchant des œuvres d’art contemporain, cette thèse met en lumière ce qui naît de ces conflits, à savoir de nouveaux objets, de nouveaux acteurs et de nouveaux modes opératoires. L’analyse s’articule autour de trois cas : la censure du film « A Fire in My Belly » (1986-1987) de David Wojnarowicz à la National Portrait Gallery de Washington, D.C. en 2010, le blanchiment d’une murale du street artist Blu réalisée au Museum of Contemporary Art de Los Angeles la même année et, enfin, la destruction de « Dialogue avec l’histoire » (1987) en 2015, œuvre d’art public conçue par Jean Pierre Raynaud pour la Place de Paris à Québec. Ces controverses signalent la mise en place d’une dynamique inédite quant au rôle exercé par la circulation d’images dans le développement des polémiques : ces reproductions, disséminées de manière soutenue dans le web, et plus généralement à travers différentes déclinaisons de l’espace public, infléchissent les trajectoires des œuvres. Ceci leur procure non seulement une visibilité accrue, mais leurs modes d’existence s’en trouvent par conséquent modifiés, les œuvres vivant pour ainsi dire par l’entremise d’images fixes ou animées qui prolifèrent en ligne. Prenant appui sur la conception pragmatiste des publics (Dewey), de même que sur la sociologie de l’acteur-réseau et les théories de la médiation, nous étudions par ailleurs la formation de collectifs nés conjointement à cette dissémination d’images « virales ». Des acteurs impliqués dans les controverses, qu’il s’agisse d’artistes ou d’activistes, entreprennent notamment de faire réapparaître les œuvres censurées ou disparues sous diverses formes, incluant des répliques, des œuvres dérivées ou encore des mèmes Internet. Nous démontrons que ce phénomène de réappropriation, inexistant lors de polémiques artistiques antérieures aux années 2010, est lié aux dynamiques de partage apparues avec le « web 2.0 ».While they are often relegated to the margins of art history, controversies nevertheless have an eminently transformative effect : they do something to artworks. Devoted to the study of recent debates surrounding contemporary works, this thesis brings into light what arises from these conflicts, namely new objects, new actors, and new modi operandi. The analysis is structured around three case studies: the censorship of the film “A Fire in My Belly” (1986-1987) by David Wojnarowicz at the National Portrait Gallery in Washington, D.C. in 2010, the whitewash of a mural by street artist Blu at the Museum of Contemporary Art, Los Angeles that took place the same year, and the destruction of “Dialogue avec l’histoire” (1987) in 2015, a work of public art created by Jean Pierre Raynaud for the Place de Paris in Québec City. These controversies reveal that there is a new dynamic taking place regarding the role of circulating images in the unfolding of such debates: these reproductions, extensively disseminated in the web, and more generally in public spaces and the public sphere, shape the artworks’ trajectories. Not only does this confers them a higher visibility, but their very modes of existence are modified in this process, works get to live, in a manner of speaking, through fixed or animated images that proliferate online. Drawing upon a pragmatist approach to publics (Dewey), actor-network theory, and sociology of mediation, we also study the forming of collectives born jointly with this dissemination of “viral” images. Actors involved in the controversies, either artists or activists, seek to make the censored or destroyed works reappear in various forms, including replicas, derivative works or even Internet memes. We demonstrate that this phenomenon of appropriation, inexistent during controversies prior to the 2010s, is linked to sharing dynamics that appeared with the “web 2.0”

Sportsman's hernia? An ambiguous term.

Groin pain is common in athletes. Yet, there is disagreement on aetiology, pathomechanics and terminology. A plethora of terms have been employed to explain inguinal-related groin pain in athletes. Recently, at the British Hernia Society in Manchester 2012, a consensus was reached to use the term inguinal disruption based on the pathophysiology while lately the Doha agreement in 2014 defined it as inguinal-related groin pain, a clinically based taxonomy. This review article emphasizes the anatomy, pathogenesis, standard clinical assessment and imaging, and highlights the treatment options for inguinal disruption

Orthopedic surgery increases atherosclerotic lesions and necrotic core area in ApoE-/- mice

Background and aims Observational studies show a peak incidence of cardiovascular events after major surgery. For example, the risk of myocardial infarction increases 25-fold early after hip replacement. The acuteness of this increased risk suggests abrupt enhancement in plaque vulnerability, which may be related to intra-plaque inflammation, thinner fibrous cap and/or necrotic core expansion. We hypothesized that acute systemic inflammation following major orthopedic surgery induces such changes. Methods ApoE−/− mice were fed a western diet for 10 weeks. Thereafter, half the mice underwent mid-shaft femur osteotomy followed by realignment with an intramedullary K-wire, to mimic major orthopedic surgery. Mice were sacrificed 5 or 15 days post-surgery (n = 22) or post-saline injection (n = 13). Serum amyloid A (SAA) was measured as a marker of systemic inflammation. Paraffin embedded slides of the aortic root were stained to measure total plaque area and to quantify fibrosis, calcification, necrotic core, and inflammatory cells. Results Surgery mice showed a pronounced elevation of serum amyloid A (SAA) and developed increased plaque and necrotic core area already at 5 days, which reached significance at 15 days (p = 0.019; p = 0.004 for plaque and necrotic core, respectively). Macrophage and lymphocyte density significantly decreased in the surgery group compared to the control group at 15 days (p = 0.037; p = 0.024, respectively). The density of neutrophils and mast cells remained unchanged. Conclusions Major orthopedic surgery in ApoE−/− mice triggers a systemic inflammatory response. Atherosclerotic plaque area is enlarged after surgery mainly due to an increase of the necrotic core. The role of intra-plaque inflammation in this response to surgical injury remains to be fully elucidated. © 2016 Elsevier Ireland Lt