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Large loss of CO2 in winter observed across the northern permafrost region

Recent warming in the Arctic, which has been amplified during the winter1–3, greatly enhances microbial decomposition of soil organic matter and subsequent release of carbon dioxide (CO2)4. However, the amount of CO2 released in winter is not known and has not been well represented by ecosystem models or empirically based estimates5,6. Here we synthesize regional in situ observations of CO2 flux f

Cavity Field Control for Linear Particle Accelerators

High-energy linear particle accelerators enable exploration of the microscopic structure of pharmaceuticals, solar cells, fuel cells, high-temperature superconductors, and the universe itself. These accelerators accelerate charged particles using oscillating magnetic fields that are confined in metal cavities. The amplitudes and phases of the electromagnetic fields need to be accurately controlled

Models for control of intravenous anesthesia

Modeling is fundamental to both feed-forward and feedback control. Within automated anesthesia the two paradigms are usually referred to as target-controlled infusion (TCI) and closed-loop drug delivery, respectively. In both cases, the objective is to control a system with anesthetic drug infusion rate as input, and (measured) clinical effect as output. The input is related to the output through

Self-adaptation of software using automatically generated control-theoretical solutions

Control theory has contributed a set of foundational techniques to handle "change" at runtime in software applications. These techniques however have fundamental limitations as well: (i) they require the development and understanding of mathematical models; (ii) synthesizing solutions is often done on a per-problem basis, discouraging flexibility and generality. Software engineering, as a discipli

Game-Theoretic Network Bandwidth Distribution for Self-Adaptive Cameras

Devices sharing a network compete for bandwidth, being able to transmit only a limited amount of data. This is for example the case with a network of cameras, that should record and transmit video streams to a monitor node for video surveillance. Adaptive cameras can reduce the quality of their video, thereby increasing the frame compression, to limit network congestion. In this paper, we exploit

A Decade Frequency Range CMOS Power Amplifier for Sub-6-GHz Cellular Terminals

A wideband 65-nm CMOS power amplifier (PA) is presented, with a decade frequency range from 600 MHz to 6.0 GHz. In this frequency range, the output power exceeds 26.6 dBm and the power gain and power-added efficiency (PAE) exceed 18.1 dB and 49%, respectively. For a 7.5-dB peak-to-average-power-ratio (PAPR) long term evolution (LTE) signal at 1.9 GHz, the circuit provides an average output power o

A 65-nm CMOS Low-Power Front-End for 3rd Generation DNA Sequencing

A continuous-time, 65-nm CMOS, front-end for processing DNA sequencing measurements from biological nanopore sensors is presented. The measured design has an input referred noise floor of 8.5\;{\text{fA}}/\sqrt {{\text{Hz}}} for 100 pA DC current while consuming 10X less power. The chip also consists of an integrated ADC.

Modeling of Request Cloning in Cloud Server Systems using Processor Sharing

The interest for studying server systems subject to cloned requests has recently increased. In this paper we present a model that allows us to equivalently represent a system of servers with cloned requests, as a single server. The model is very general, and we show that no assumptions on either inter-arrival or service time distributions are required, allowing for, e.g., both heterogeneity and de

Towards Performance Modeling of Speculative Execution for Cloud Applications

Interesting approaches to counteract performance variability within cloud datacenters include sending multiple request clones, either immediately or after a specified waiting time. In this paper we present a performance model of cloud applications that utilize the latter concept, known as speculative execution. We study the popular Join-Shortest-Queue load-balancing strategy under the processor sh

Minimizing Side-Channel Attack Vulnerability Via Schedule Randomization

Control systems can be vulnerable to security threats where an attacker gathers information about the execution of the system. In particular, side-channel attacks exploit the predictability of real-time control systems and of their schedules. To counteract their action, a scheduler can randomize the temporal execution of tasks and limit the amount of information the attacker can gather. Schedule r

Complexity revealed in the greening of the Arctic

As the Arctic warms, vegetation is responding, and satellite measures indicate widespread greening at high latitudes. This ‘greening of the Arctic’ is among the world’s most important large-scale ecological responses to global climate change. However, a consensus is emerging that the underlying causes and future dynamics of so-called Arctic greening and browning trends are more complex, variable a

Future projections of cyclone activity in the Arctic for the 21st century from regional climate models (Arctic-CORDEX)

Changes in the characteristics of cyclone activity (frequency, depth and size) in the Arctic are analyzed based on simulations with state-of-the-art regional climate models (RCMs) from the Arctic-CORDEX initiative and global climate models (GCMs) from CMIP5 under the Representative Concentration Pathway (RCP) 8.5 scenario. Most of RCMs show an increase of cyclone frequency in winter (DJF) and a de

Variational auto-encoders with Student’s t-prior

We propose a new structure for the variational auto-encoders (VAEs) prior, with the weakly informative multivariate Student’s t-distribution. In the proposed model all distribution parameters are trained, thereby allowing for a more robust approximation of the underlying data distribution. We used Fashion-MNIST data in two experiments to compare the proposed VAEs with the standard Gaussian priors.

Large-scale and distributed optimization : An introduction

The recent explosion in size and complexity of datasets and the increased availability of computational resources has led us to what is sometimes called the big data era. In many big data fields, mathematical optimization has over the last decade emerged as a vital tool in extracting information from the data sets and creating predictors for unseen data. The large dimension of these data sets and

An Accurate Analysis of Phase Noise in CMOS Ring Oscillators

We present an accurate analysis of phase noise in ring oscillators, where each stage in the ring is a CMOS inverter loaded by a capacitance. Closed-form phase noise expressions are obtained in both 1/f2 and 1/f3 offset frequency regions, displaying an excellent agreement with numerical simulations using either ideal Verilog-A MOS models (allowing theoretical predictions to be tested with exactly t

Machine Learning and System Identification for Estimation in Physical Systems

In this thesis, we draw inspiration from both classical system identification and modern machine learning in order to solve estimation problems for real-world, physical systems. The main approach to estimation and learning adopted is optimization based. Concepts such as regularization will be utilized for encoding of prior knowledge and basis-function expansions will be used to add nonlinear model

Robotic Seam Tracking for Friction Stir Welding under Large Contact Forces

Friction stir welding (FSW) is a solid-state welding process where metals are joined without melting. Heat is generated by friction between a rotating non-consumable tool and the work-piece material, and by mechanical deformation of the material. The process, invented in 1991, provides several benefits over arc welding and other fusion processes: No filler material has to be added, low energy cons

Self-aware computing systems : Open challenges and future research directions

In this chapter, we discuss the open challenges in building self-aware computing systems that are still being faced by the research and development community. The challenges can be theoretical, technical, computational, or even sociological. First, we highlight the challenges associated with each of the earlier parts of the book and summarize on respective future research directions. We then offer

A Radial Basis Function Method for Approximating the Optimal Event-Based Sampling Policy

In networked control systems it is desirable to have efficient wireless communication (saving energy and bandwidth) while still ensuring good control performance. By abandoning periodic sampling, communication can be made more efficient by sampling and updating the control signal only "when required" based on the system’s behaviour. This is the concept of event-based control. In this work we consi