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Supervised machine learning (ML) is used extensively in biology and deserves closer scrutiny. The DOME recommendations aim to enhance the validation and reproducibility of ML research by establishing standards for key aspects such as data handling and processing, optimization, evaluation, and model interpretability. The recommendations help to ensure that key details are reported transparently by providing a structured set of questions. Here, we introduce the DOME Registry (URL: registry.dome-ml.org), a database that allows scientists to manage and access comprehensive DOME-related information on published ML studies. The registry uses external resources like ORCID, APICURON and the Data Stewardship Wizard to streamline the annotation process and ensure comprehensive documentation. By assigning unique identifiers and DOME scores to publications, the registry fosters a standardized evaluation of ML methods. Future plans include continuing to grow the registry through community curation, improving the DOME score definition and encouraging publishers to adopt DOME standards, promoting transparency and reproducibility of ML in the life sciences.

The progress in artificial intelligence and machine learning algorithms over the past decade has enabled the development of new methods for the objective measurement of eating, including both the measurement of eating episodes as well as the measurement of in-meal eating behavior. These allow the study of eating behavior outside the laboratory in free-living conditions, without the need for video recordings and laborious manual annotations. In this paper, we present a high-level overview of our recent work on intake monitoring using a smartwatch, as well as methods using an in-ear microphone. We also present evaluation results of these methods in challenging, real-world datasets. Furthermore, we discuss use-cases of such intake monitoring tools for advancing research in eating behavior, for improving dietary monitoring, as well as for developing evidence-based health policies. Our goal is to inform researchers and users of intake monitoring methods regarding (i) the development of new methods based on commercially available devices, (ii) what to expect in terms of effectiveness, and (iii) how these methods can be used in research as well as in practical applications.

In this treatise on the theory of the continuum of the surreal numbers of J.H. Conway, is proved ,that the three different techniques and hierarchies of the continuums of the transfinite real numbers of Glayzal A. (1937) defined through transfinite power series , of the surreal numbers of J.H. Conway (1976) defined by Dedekind cuts ,and of the ordinal real numbers of K. E. Kyritsis (1992) defined by fundamental Cauchy transfinite sequences, give by inductive limit or union the same class and continuum of infinite numbers. This is quite remarkable and is the analogue in the transfinite numbers, of that the real numbers can be constructed either as decimal power series, or by Dedekind cuts, or by Cauchy fundamental sequences., Comment: This text of 64 pages is actually 5 consecutive papers published in the same proceedings of the same conference

The consumption of tobacco has reached global epidemic proportions and is characterized as the leading cause of death and illness. Among the different ways of consuming tobacco (e.g., smokeless, cigars), smoking cigarettes is the most widespread. In this paper, we present a two-step, bottom-up algorithm towards the automatic and objective monitoring of cigarette-based, smoking behavior during the day, using the 3D acceleration and orientation velocity measurements from a commercial smartwatch. In the first step, our algorithm performs the detection of individual smoking gestures (i.e., puffs) using an artificial neural network with both convolutional and recurrent layers. In the second step, we make use of the detected puff density to achieve the temporal localization of smoking sessions that occur throughout the day. In the experimental section we provide extended evaluation regarding each step of the proposed algorithm, using our publicly available, realistic Smoking Event Detection (SED) and Free-living Smoking Event Detection (SED-FL) datasets recorded under semi-controlled and free-living conditions, respectively. In particular, leave-one-subject-out (LOSO) experiments reveal an F1-score of 0.863 for the detection of puffs and an F1-score/Jaccard index equal to 0.878/0.604 towards the temporal localization of smoking sessions during the day. Finally, to gain further insight, we also compare the puff detection part of our algorithm with a similar approach found in the recent literature., Comment: Manuscript accepted to be published in the 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2021. Acceptance date: 16-July-2021

The increased worldwide prevalence of obesity has sparked the interest of the scientific community towards tools that objectively and automatically monitor eating behavior. Despite the study of obesity being in the spotlight, such tools can also be used to study eating disorders (e.g. anorexia nervosa) or provide a personalized monitoring platform for patients or athletes. This paper presents a complete framework towards the automated i) modeling of in-meal eating behavior and ii) temporal localization of meals, from raw inertial data collected in-the-wild using commercially available smartwatches. Initially, we present an end-to-end Neural Network which detects food intake events (i.e. bites). The proposed network uses both convolutional and recurrent layers that are trained simultaneously. Subsequently, we show how the distribution of the detected bites throughout the day can be used to estimate the start and end points of meals, using signal processing algorithms. We perform extensive evaluation on each framework part individually. Leave-one-subject-out (LOSO) evaluation shows that our bite detection approach outperforms four state-of-the-art algorithms towards the detection of bites during the course of a meal (0.923 F1 score). Furthermore, LOSO and held-out set experiments regarding the estimation of meal start/end points reveal that the proposed approach outperforms a relevant approach found in the literature (Jaccard Index of 0.820 and 0.821 for the LOSO and heldout experiments, respectively). Experiments are performed using our publicly available FIC and the newly introduced FreeFIC datasets., Comment: Manuscript accepted to be published in IEEE Journal of Biomedical and Health Informatics. Acceptance date: 28-Mar-2020

Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old, causing symptoms that are subtle at first, but whose intensity increases as the disease progresses. Automated detection of these symptoms could offer clues as to the early onset of the disease, thus improving the expected clinical outcomes of the patients via appropriately targeted interventions. This potential has led many researchers to develop methods that use widely available sensors to measure and quantify the presence of PD symptoms such as tremor, rigidity and braykinesia. However, most of these approaches operate under controlled settings, such as in lab or at home, thus limiting their applicability under free-living conditions. In this work, we present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device. We propose a Multiple-Instance Learning approach, wherein a subject is represented as an unordered bag of accelerometer signal segments and a single, expert-provided, tremor annotation. Our method combines deep feature learning with a learnable pooling stage that is able to identify key instances within the subject bag, while still being trainable end-to-end. We validate our algorithm on a newly introduced dataset of 45 subjects, containing accelerometer signals collected entirely in-the-wild. The good classification performance obtained in the conducted experiments suggests that the proposed method can efficiently navigate the noisy environment of in-the-wild recordings.

In this treatise I present the solutions of the third Clay Millennium problem in the computational complexity and the fourth Clay Millennium problem in classical fluid dynamics., Comment: arXiv admin note: text overlap with arXiv:1108.1165 by other authors

In this paper, we extend our previous study of causality and local commutativity of string fields in the pp-wave lightcone string field theory to include interaction. Contrary to the flat space case result of Lowe, Polchinski, Susskind, Thorlacius and Uglum, we found that the pp-wave interaction does not affect the local commutativity condition. Our results show that the pp-wave lightcone string field theory is not continuously connected with the flat space one. We also discuss the relation between the condition of local commutativity and causality. While the two notions are closely related in a point particle theory, their relation is less clear in string theory. We suggest that string local commutativity may be relevant for an operational defintion of causality using strings as probes., Comment: Latex, JHEP3.cls, 18 pages, no figures. v2: add comments about the UV-IR mixing effect displayed in our result. version to appear in JHEP

In this letter, we determine the particle and the string light cone in the pp-wave background. The result is a deformed version of the flat one. We point out the light cone exhibits an intriguing periodicity in the light cone time direction x^+ with a period \sim 1/ \mu. Our results also suggest that a quantum theory in the pp-wave background can be formulated consistently only if the background is periodic in the light cone time x^+., Comment: 10 pages. v2: references and comments added