Semi-supervised learning with self-supervision for closed and open sets

Semi-supervised learning (SSL) is a learning framework that enables the use of unlabeled data with labeled data. These methods play a crucial role in reducing the burden of human labeling in training deep learning models. Many methods for SSL learn from unlabeled data through confidence-based pseudo-labeling. This technique involves assigning artificial labels to unlabeled data based on model predictions, given that these predictions exceed a confidence threshold. A drawback of this approach is that large parts of data may be ignored. This work proposes a self-supervised component for these frameworks to enable learning from all unlabeled data. The proposed self-supervision involves aligning feature predictions across weak and strong data augmentations for each unlabeled sample. We show that this approach, DoubleMatch, leads to improved training speed and accuracy on many benchmark datasets.SSL is often studied in the closed-set scenario, where we assume that unlabeled data only contain classes present in the labeled data. More realistically, there is a risk that unlabeled data contain unseen classes, corrupted data, or outliers in other forms. This setting is referred to as open-set semi-supervised learning (OSSL). Many existing methods for OSSL use a procedure that involves selecting samples from unlabeled data that likely belong to the known classes, for inclusion in a traditional SSL objective. This work proposes an alternative approach, SeFOSS, that utilizes all unlabeled data through the inclusion of the self-supervised component proposed by DoubleMatch. Additionally, SeFOSS uses an energy-based method for classifying data as in-distribution (ID) or out-of-distribution (OOD). Experimental evaluation shows that SeFOSS achieves strong results for both closed-set accuracy and OOD detection in many open-set scenarios. Additionally, our results indicate that traditional methods for (closed-set) SSL may perform better in the open-set scenario than what has been previously suggested by other works.Furthermore, this work proposes another method for OSSL: the Beta-model. This method proposes a novel score for ID/OOD classification and introduces the use of the expectation-maximization algorithm in OSSL, for estimating conditional distributions of scores given ID or OOD data. This method demonstrates state-of-the-art results on many benchmark problems for OSSL

Effects of high energy photon emissions in laser generated ultra-relativistic plasmas: real-time synchrotron simulations

We model the emission of high energy photons due to relativistic charged particle motion in intense laser-plasma interactions. This is done within a particle-in-cell code, for which high frequency radiation normally cannot be resolved due to finite time steps and grid size. A simple expression for the synchrotron radiation spectra is used together with a Monte-Carlo method for the emittance. We extend previous work by allowing for arbitrary fields, considering the particles to be in instantaneous circular motion due to an effective magnetic field. Furthermore we implement noise reduction techniques and present validity estimates of the method. Finally, we perform a rigorous comparison to the mechanism of radiation reaction, and find the emitted energy to be in excellent agreement with the losses calculated using radiation reaction

Multi-log grasping using reinforcement learning and virtual visual servoing

We explore multi-log grasping using reinforcement learning and virtual visual servoing for automated forwarding. Automation of forest processes is a major challenge, and many techniques regarding robot control pose different challenges due to the unstructured and harsh outdoor environment. Grasping multiple logs involves problems of dynamics and path planning, where the interaction between the grapple, logs, terrain, and obstacles requires visual information. To address these challenges, we separate image segmentation from crane control and utilize a virtual camera to provide an image stream from 3D reconstructed data. We use Cartesian control to simplify domain transfer. Since log piles are static, visual servoing using a 3D reconstruction of the pile and its surroundings is equivalent to using real camera data until the point of grasping. This relaxes the limit on computational resources and time for the challenge of image segmentation, and allows for collecting data in situations where the log piles are not occluded. The disadvantage is the lack of information during grasping. We demonstrate that this problem is manageable and present an agent that is 95% successful in picking one or several logs from challenging piles of 2--5 logs.Comment: 8 pages, 10 figure

Недифференцированная дисплазия соединительной ткани и малые аномалии сердца как предиктор развития нарушений ритма у пациентов с ишемической болезнью сердца

ОНТОГЕНЕЗМОРФОГЕНЕЗИШЕМИЧЕСКАЯ БОЛЕЗНЬ СЕРДЦ

Структурно-цитохимические особенности развивающихся почек крысят при антенатальном воздействии инкорпорированных радионуклидов

ГЛИКОПРОТЕИНЫПОЧКИРАДИОАКТИВНЫЕ ИЗОТОПЫРАДИОНУКЛИД

Plasma concentration of galantamine - influence of dose and body mass index in Alzheimer’s disease.

Background/objectives: Patients with Alzheimer’s disease (AD) are at present treated with galantamine without actual knowledge of plasma concentration levels. The aim of this presentation is to analyse the relationship between galantamine plasma concentration, dose, demographics and body mass index (BMI). Methods: A total of 84 AD patients recruited at the Memory Clinic in Malmö, Sweden, treated with galantamine were included in this study. The patients were investigated at baseline, at 2 months and every 6 months for a period of three years. Blood samples were obtained at 180 of these investigations for the analysis of plasma galantamine concentration. Efficacy measures including cognitive tests (MMSE), functional ratings (IADL) and BMI were simultaneously evaluated. The dose as well as the time from drug intake to plasma extraction was investigated. Results: The mean galantamine plasma concentration demonstrated a strong positive linear association with dose (r=0.51, p<0.001). Moreover, patients with separate doses of galantamine (8, 16 and 24 mg daily) differed significantly in plasma concentration (p<0.001). No gender differences regarding dose were observed. There was no linear relationship between galantamine plasma concentration and BMI in the entire cohort. When investigating the impact of gender, a negative linear association (r=-0.45, p=0.001) between concentration and BMI was found in the male group but not in the female. Age did not influence the plasma concentration level. In a multivariate general linear model with concentration as the dependent variable, gender (p=0.010) and BMI (p=0.038) but not age (p=0.540) were predictive factors. Conclusion: Galantamine plasma concentration demonstrated a strong relationship with dose. The dose did not differ between genders, whereas the impact of body mass index on plasma concentration was important only among the males

Radiation in simulations of high intensity laser-matter interaction

We consider electromagnetic waves propagating in plasmas, with two main themes covered. First nonlinear plasma theory and wave-wave interaction. Here a wave-wave symmetry, the Manley-Rowe relations, is used as a method of determining the physicality of modified plasma fluid equations. Secondly, we consider radiation emission in simulations of laser-matter interaction where we develop a method of calculating high frequency radiation from relativistic particles, which is not included in particle-in-cell simulations. This is benchmarked against radiation reaction losses and also used in order to compare the radiation between cases where either classical or QED equations of motion are used