Now showing items 241-260 of 389

    • Performance of data enhancements and training optimization for neural network: A polyp detection case study 

      Henriksen, Fredrik Lund; Jensen, Rune; Stensland, Håkon Kvale; Johansen, Dag; Riegler, Michael; Halvorsen, Pål (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-08-05)
      Deep learning using neural networks is becoming more and more popular. It is frequently used in areas like video analysis, image retrieval, traffic forecast and speech recognition. In this respect, the learning and training process usually requires a lot of data. However, in many areas, data is scarce which is definitely the case in our medical application scenario, i.e., polyp detection in the ...
    • Performance optimization and modeling of fine-grained irregular communication in UPC 

      Lagraviere, Jeremie Alexandre Emilien; Langguth, Johannes; Prugger, Martina; Einkemmer, Lukas; Ha, Hoai Phuong; Cai, Xing (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-03-03)
      The Unified Parallel C (UPC) programming language offers parallelism via logically partitioned shared memory, which typically spans physically disjoint memory subsystems. One convenient feature of UPC is its ability to automatically execute between-thread data movement, such that the entire content of a shared data array appears to be freely accessible by all the threads. The programmer friendliness, ...
    • Performance principles for trusted computing with intel SGX 

      Gjerdrum, Anders Tungeland; Pettersen, Robert; Johansen, Håvard D.; Johansen, Dag (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-07-14)
      Cloud providers offering Software-as-a-Service (SaaS) are increasingly being trusted by customers to store sensitive data. Companies often monetize such personal data through curation and analysis, providing customers with personalized application experiences and targeted advertisements. Personal data is often accompanied by strict privacy and security policies, requiring data processing to be ...
    • Pesto flavoured security 

      Dillema, Feike W.; Stabell-Kulø, Tage (Research report; Forskningsrapport, 2002-05)
      Pesto aims at providing highly available and secure storage for longlived data to mobile users roaming into untrusted environments. Security in Pesto encompasses the following three aspects: availability, safety, and privacy. A mechanism supporting one aspect may adversely affect another. For example, replication may increase availability but complicates supporting confidentiality, and simply ...
    • The Pesto project. Goals and motivation 

      Dillema, Feike W.; Stabell-Kulø, Tage (Research report; Forskningsrapport, 2001-06)
      Pesto is a storage system geared towards a computing model where private machines play a pivotal role. Sharing of data is crucial, both between partners, and between the many devices owned by individual users. Replication is the only sensible means to provide ubiquitous access to private data. However, without provisions, replication endangers privacy by enlarging the Trusted Computing Base. The ...
    • Photoperiod-dependent developmental reprogramming of the transcriptional response to seawater entry in Atlantic salmon (Salmo salar) 

      Iversen, Marianne; Mulugeta, Teshome Dagne; West, Alexander Christopher; Jørgensen, Even Hjalmar; Martin, Samuel A. M.; Sandve, Simen Rød; Hazlerigg, David (Journal article; Tidsskriftartikkel; Peer reviewed, 2021-03-12)
      The developmental transition of juvenile salmon from a freshwater resident morph (parr) to a seawater (SW) migratory morph (smolt), known as smoltification, entails a reorganization of gill function to cope with the altered water environment. Recently, we used RNAseq to characterize the breadth of transcriptional change which takes place in the gill in the FW phase of smoltification. This highlighted ...
    • Physicians Interrupted by Mobile Devices in Hospitals: Understanding the Interaction Between Devices, Roles, and Duties 

      Solvoll, Terje; Scholl, Jeremiah; Hartvigsen, Gunnar (Journal article; Tidsskriftartikkel; Peer reviewed, 2013)
      A common denominator of modern hospitals is a variety of communication problems. In particular, interruptions from mobile communication devices are a cause of great concern for many physicians. Objective: To characterize how interruptions from mobile devices disturb physicians in their daily work. The gathered knowledge will be subsequently used as input for the design and development of a ...
    • Physics-Guided Loss Functions Improve Deep Learning Performance in Inverse Scattering 

      Liu, Zicheng; Roy, Mayank; Prasad, Dilip K.; Agarwal, Krishna (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-03-15)
      Solving electromagnetic inverse scattering problems (ISPs) is challenging due to the intrinsic nonlinearity, ill-posedness, and expensive computational cost. Recently, deep neural network (DNN) techniques have been successfully applied on ISPs and shown potential of superior imaging over conventional methods. In this paper, we discuss techniques for effective incorporation of important physical ...
    • Polar Vantage and Oura Physical Activity and Sleep Trackers: Validation and Comparison Study 

      Henriksen, André; Grimsgaard, Sameline; Hartvigsen, Gunnar; Hopstock, Laila Arnesdatter (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-05-27)
      Background: Consumer-based activity trackers are increasingly used in research, as they have the potential to promote increased physical activity and can be used for estimating physical activity among participants. However, the accuracy of newer consumer-based devices is mostly unknown, and validation studies are needed.<p> <p>Objective: The objective of this study was to compare the Polar Vantage ...
    • Policies and metrics for fair resource sharing 

      Renesse, Robbert van; Kvalnes, Aage; Zagorodnov, Dmitrii; Johansen, Dag (Research report; Forskningsrapport, 2006)
      Performance isolation is essential to operating systems shared by dependable services. Unfortunately, most such systems, including real-time operating systems and VMMs, only fairly divide and account for CPU cycles. We submit that dependable services require specifying and enforcing policies for all resources, and that current metrics for evaluating fair sharing are insufficient. This paper proposes ...
    • Positional Differences in Peak- and Accumulated- Training Load Relative to Match Load in Elite Football 

      Baptista, Ivan; Johansen, Dag; Figueiredo, Pedro; Rebelo, Antonio; Pettersen, Svein Arne (Journal article; Tidsskriftartikkel; Peer reviewed, 2019-12-23)
      Quantification of training and match load is an important method to personalize the training stimulus’ prescription to players according to their match demands. The present study used time-motion analysis and triaxial-accelerometer to quantify and compare: a) The most demanding passages of play in training sessions and matches (5-min peaks); b) and the accumulated load of typical microcycles and ...
    • Power models, energy models and libraries for energy-efficient concurrent data structures and algorithms 

      Ha, Hoai Phuong; Tran, Vi Ngoc-Nha; Umar, Ibrahim; Atalar, Aras; Gidenstam, Anders; Renaud-Goud, Paul; Tsigas, Philippas; Walulya, Ivan (Research report; Forskningsrapport, 2016)
      This deliverable reports the results of the power models, energy models and librariesfor energy-efficient concurrent data structures and algorithms as available by projectmonth 30 of Work Package 2 (WP2). It reports i) the latest results of Task 2.2-2.4 onproviding programming abstractions and libraries for developing energy-efficient datastructures and algorithms and ii) the improved results of ...
    • Practical and low-overhead masking of failures of TCP-based servers 

      Marzullo, Keith; Zagorodnov, Dmitrii; Alvisi, Lorenzo; Bressoud, Thomas C. (Research report; Forskningsrapport, 2005-08-25)
      This article describes an architecture that allows a replicated service to survive crashes without breaking its TCP connections. Our approach does not require modifications to the TCP protocol, to the operating system on the server, or to any of the software running on the clients. Furthermore, it runs on commodity hardware. We compare two implementations of this architecture – one based on ...
    • A Pragmatic Machine Learning Approach to Quantify Tumor-Infiltrating Lymphocytes in Whole Slide Images 

      Shvetsov, Nikita; Grønnesby, Morten; Pedersen, Edvard; Møllersen, Kajsa; Rasmussen Busund, Lill-Tove; Schwienbacher, Ruth; Bongo, Lars Ailo; Kilvær, Thomas Karsten (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-06-16)
      Increased levels of tumor-infiltrating lymphocytes (TILs) indicate favorable outcomes in many types of cancer. The manual quantification of immune cells is inaccurate and time-consuming for pathologists. Our aim is to leverage a computational solution to automatically quantify TILs in standard diagnostic hematoxylin and eosin-stained sections (H&E slides) from lung cancer patients. Our approach ...
    • Predicting an unstable tear film through artificial intelligence 

      Fineide, Fredrik; Chen, Xiangjun; Magnø, Morten Schjerven; Yazidi, Anis; Riegler, Michael; Utheim, Tor Paaske; Storås, Andrea Marheim (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-12-10)
      Dry eye disease is one of the most common ophthalmological complaints and is defined by a loss of tear film homeostasis. Establishing a diagnosis can be time-consuming, resource demanding and unpleasant for the patient. In this pilot study, we retrospectively included clinical data from 431 patients with dry eye disease examined in the Norwegian Dry Eye Clinic to evaluate how artificial intelligence ...
    • Predicting breast cancer metastasis from whole-blood transcriptomic measurements 

      Holsbø, Einar; Perduca, Vittorio; Bongo, Lars Ailo; Lund, Eiliv; Birmelé, Etienne (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-05-20)
      <i>Objective</i> - In this exploratory work we investigate whether blood gene expression measurements predict breast cancer metastasis. Early detection of increased metastatic risk could potentially be life-saving. Our data comes from the Norwegian Women and Cancer epidemiological cohort study. The women who contributed to these data provided a blood sample up to a year before receiving a breast ...
    • Predicting in-hospital death from derived EHR trajectory features 

      Bopche, Rajeev; Gustad, Lise Tuset; Afset, Jan Egil; Damås, Jan Kristian; Nytrø, Øystein (Chapter; Bokkapittel, 2023)
      Medical histories of patients can provide insight into the immediate future of a patient. While most studies propose to predict survival from vital signs and hospital tests within one episode of care, we carry out selective feature engineering from longitudinal historical medical records in this study to develop a dataset with derived features. We then train multiple machine learning models for the ...
    • Predicting Meibomian Gland Dropout and Feature Importance Analysis with Explainable Artificial Intelligence 

      Fineide, Fredrik; Storås, Andrea; Riegler, Michael Alexander; Utheim, Tor Paaske (Journal article; Tidsskriftartikkel; Peer reviewed, 2023-07-17)
      Dry eye disease is a common and potentially debilitating medical condition. Meibum secreted from the meibomian glands is the largest contributor to the outermost, protective lipid layer of the tear film. Dysfunction of the meibomian glands is the most common cause of dry eye disease. As meibomian gland dysfunction progresses, gradual atrophy of the glands is observed. The meibomian glands are ...
    • Predicting peek readiness-to-train of soccer players using long short-term memory recurrent neural networks 

      Wiik, Theodor; Johansen, Håvard D.; Pettersen, Svein Arne; Matias Do Vale Baptista, Ivan Andre; Kupka, Tomas; Johansen, Dag; Riegler, Michael; Halvorsen, Pål (Conference object; Konferansebidrag, 2019-10-21)
      We are witnessing the emergence of a myriad of hardware and software systems that quantifies sport and physical activities. These are frequently touted as game changers and important for future sport developments. The vast amount of generated data is often visualized in graphs and dashboards, for use by coaches and other sports professionals to make decisions on training and match strategies. Modern ...
    • Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning 

      Storås, Andrea; Åsberg, Anders; Halvorsen, Pål; Riegler, Michael Alexander; Strumke, Inga (Journal article; Tidsskriftartikkel; Peer reviewed, 2022-08-31)
      Tacrolimus is one of the cornerstone immunosup-pressive drugs in most transplantation centers worldwide following solid organ transplantation. Therapeutic drug monitoring of tacrolimus is necessary in order to avoid rejection of the transplanted organ or severe side effects. However, finding the right dose for a given patient is challenging, even for experienced clinicians. Consequently, a tool that ...