Results 91 to 100 of about 9,131,053 (251)

Control of gammaherpesvirus latency by latent antigen-specific CD8(+) T cells [PDF]

, 2000
The contribution of the latent antigen-specific CD8(+) T cell response to the control of gammaherpesvirus latency is currently obscure. Some latent antigens induce potent T cell responses, but little is known about their induction or the role they play ...
Blackman, M A, Dutia, B M, Woodland, D L, Ward, K, Roy, D J, Usherwood, E J, Surman, S L, Stewart, J P   +7 more
core  

Tiered-Latency DRAM: A Low Latency and Low Cost DRAM Architecture

, 2018
The capacity and cost-per-bit of DRAM have historically scaled to satisfy the needs of increasingly large and complex computer systems. However, DRAM latency has remained almost constant, making memory latency the performance bottleneck in today's ...
Onur Mutlu (5357288), Jamie Liu (5357303), Yoongu Kim (5357300), Vivek Seshadri (5357294), Lavanya Subramanian (5358338), Donghyuk Lee (5357297)   +5 more
core   +1 more source

Patients improve, patterns persist: longitudinal stability of RA joint involvement patterns

Arthritis Care &Research, Accepted Article.
Objective Rheumatoid arthritis is a heterogeneous disease. Data‐driven approaches, from synovial histology to joint involvement patterns (JIPs), have sought to define clinically meaningful subgroups. Whether these subgroups represent stable phenotypes or transient disease states remains unclear.
Tjardo Maarseveen, Yasuo Nagafuchi, Kristina Lend, Seyam Alemyar, Herman Kasper Glas, Gerdur Gröndal, Bjorn Gudbjornsson, Espen A. Haavardsholm, Kim Hørslev Petersen, Jon Lampa, Merete Lund Hetland, Dan Nordström, Mike Nurmohamed, Mikkel Østergaard, Anna Rudin, Marte Schrumpf Heiberg, Tuulikki Sokka‐Isler, Till Uhlig, Josien Veris‐van Dieren, Ronald van Vollenhoven, Tom Huizinga, Marcel JT Reinders, Sytske Anne Bergstra, Erik van den Akker, Rachel Knevel   +24 more
wiley   +1 more source

SHARP-AODV: An Intelligent Adaptive Routing Protocol for Highly Mobile Autonomous Aerial Vehicle (AAV) Networks

Sensors
In ad hoc networks employing Autonomous Aerial Vehicles (AAVs), the importance of real-time applications and edge computing is steadily increasing. However, existing routing protocols still fail to meet the strict performance requirements under the ...
Nguyen Duc Tu, Ammar Muthanna, Abdukodir Khakimov, Irina Kochetkova, Konstantin Samouylov, Abdelhamied A. Ateya, Andrey Koucheryavy   +6 more
doaj   +1 more source

Implementing Lower Latency, Fast Startup and Switching for low-latency low-delay (L3D) DASH

8490Low-latency video streaming is essential for interactive applications such as sports betting and live concerts. The sixth edition of the MPEG DASH specification introduces new features enabling the L3D-DASH (Low Latency, Low Delay DASH) approach ...
Güclü, Görkem, Silhavy, Daniel, Jerbi, Khaled, Pham, Stefan, Giladi, Alex, Balk, Alex, Nguyen, Minh   +6 more
core   +1 more source

Unsupervised Machine Learning Identifies Shared Biomechanical and Clinical Variation in Individuals with Knee Osteoarthritis and High Body Mass Index

Arthritis Care &Research, EarlyView.
Objective High body mass index (BMI ≥ 27 kg/m2) and aberrant gait biomechanics are primary risk factors for knee osteoarthritis (KOA), yet the specific loading patterns linked to clinical disease features remain unclear. Unsupervised machine learning (ML) was used to identify vertical ground reaction force (vGRF) waveform modes of variation and ...
Ashley N. Buck, J. Elyse Borgert, Hyeon Lee, Liubov Arbeeva, Yvonne M. Golightly, Richard F. Loeser, Leigh F. Callahan, Stephen P. Messier, Brian Pietrosimone, Amanda E. Nelson, James S. Marron   +10 more
wiley   +1 more source

Machine Learning for Low-Latency Communications

Machine Learning for Low-Latency Communications presents the principles and practice of various deep learning methodologies for mitigating three critical latency components: access latency, transmission latency, and processing latency. In particular, the
Wu, Youlong, Zhou, Yong, Zou, Yinan, Zhang, Jun, Shi, Yuanming   +4 more
core   +1 more source

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

DDR2 and Low Latency Variants [PDF]

, 2000
This paper describes a performance examination of the DDR2 DRAM architecture and the proposed cache-enhanced variants. These preliminary studies are based upon ongoing collaboration between the authors and the Joint Electronic Device Engineering Council (
Davis, Brian, Mudge, Trevor, Cuppu, Vinodh, Jacob, Bruce   +3 more
core   +1 more source

Time-Sensitive Networking in Low Latency Cyber-Physical Systems [PDF]

Oversikt over tilgjengelige algoritmer i TSN for å endre og justere profilen på kritiske nettverksstrømmer, særlig i forbindelse med lav-latency systemer hvor presisjon og deterministisk forsinkelse er viktig.Time-Sensitive Networking in Low Latency ...
Austad, Henrik, Mathisen, Geir
core   +1 more source