This study presents a neural network-based method for static Bluetooth Low Energy (BLE) localization, using phase measurements and carrier frequency per data packet, captured from a single (or multiple) multi-antenna receiver(s). Deterministic phase-based techniques fail to accurately estimate the position of a tag using a single multi-antenna receiver with closely spaced antennas. This is due to the random and unknown carrier phase offset (CPO) at each antenna element, introduced during BLE’s inherent frequency hopping between consecutive packets. Our long short-term memory (LSTM) model learns to handle the unknown distribution of CPO and suppress phase noise, enabling robust localization with just one multi-antenna receiver. Among the tested architectures, the LSTM model showed notable resilience to phase noise and achieved higher accuracy during high-rate tag transmissions compared to the feedforward (FF) and convolutional neural network (CNN) models. All models were trained on simulated or real data and tested on real data from various environments, where they outperformed deterministic techniques—even in cases where those techniques either excelled or failed to estimate the tag’s position.

For many real-world optimization problems it is possible to perform partial evaluations, meaning that the impact of changing a few variables on a solution's fitness can be computed very efficiently. It has been shown that such partial evaluations can be excellently leveraged by the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (RV-GOMEA) that uses a linkage model to capture dependencies between problem variables. Recently, conditional linkage models were introduced for RV-GOMEA, expanding its state-of-the-art performance even to problems with overlapping dependencies. However, that work assumed that the dependency structure is known a priori. Fitness-based linkage learning techniques have previously been used to detect dependencies during optimization, but only for non-conditional linkage models. In this work, we combine fitness-based linkage learning and conditional linkage modelling in RV-GOMEA. In addition, we propose a new way to model overlapping dependencies in conditional linkage models to maximize the joint sampling of fully interdependent groups of variables. We compare the resulting novel variant of RV-GOMEA to other variants of RV-GOMEA and VkD-CMA on 12 problems with varying degree of overlapping dependencies. We find that the new RV-GOMEA not only performs best on most problems, also the overhead of learning the conditional linkage models during optimization is often negligible.

Finding a realistic deformation that transforms one image into another, in case large deformations are required, is considered a key challenge in medical image analysis. Having a proper image registration approach to achieve this could unleash a number of applications requiring information to be transferred between images. Clinical adoption is currently hampered by many existing methods requiring extensive configuration effort before each use, or not being able to (realistically) capture large deformations. A recent multi-objective approach that uses the Multi-Objective Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm (MO-RV-GOMEA) and a dual-dynamic mesh transformation model has shown promise, exposing the trade-offs inherent to image registration problems and modeling large deformations in 2D. This work builds on this promise and introduces MOREA: the first evolutionary algorithm-based multi-objective approach to deformable registration of 3D images capable of tackling large deformations. MOREA includes a 3D biomechanical mesh model for physical plausibility and is fully GPU-accelerated. We compare MOREA to two state-of-the-art approaches on abdominal CT scans of 4 cervical cancer patients, with the latter two approaches configured for the best results per patient. Without requiring per-patient configuration, MOREA significantly outperforms these approaches on 3 of the 4 patients that represent the most difficult cases.

Cloud datacenters provide a backbone to our digital society. Inaccurate capacity procurement for cloud datacenters can lead to significant performance degradation, denser targets for failure, and unsustainable energy consumption. Although this activity is core to improving cloud infrastructure, relatively few comprehensive approaches and support tools exist for mid-tier operators, leaving many planners with merely rule-of-thumb judgement. We derive requirements from a unique survey of experts in charge of diverse datacenters in several countries. We propose Capelin, a data-driven, scenario-based capacity planning system for mid-tier cloud datacenters. Capelin introduces the notion of portfolios of scenarios, which it leverages in its probing for alternative capacity-plans. At the core of the system, a trace-based, discrete-event simulator enables the exploration of different possible topologies, with support for scaling the volume, variety, and velocity of resources, and for horizontal (scale-out) and vertical (scale-up) scaling. Capelin compares alternative topologies and for each gives detailed quantitative operational information, which could facilitate human decisions of capacity planning. We implement and open-source Capelin, and show through comprehensive trace-based experiments it can aid practitioners. The results give evidence that reasonable choices can be worse by a factor of 1.5-2.0 than the best, in terms of performance degradation or energy consumption.

Cloud datacenters are important for the digital society, serving stakeholders across industry, government, and academia. Simulation is a critical part of exploring datacenter technologies, enabling scalable experimentation with millions of jobs and hundreds of thousands of machines, and what-if analysis in a matter of minutes to hours. Although the community has already developed powerful simulators, emerging technologies and applications in modern datacenters require new approaches. Addressing this requirement, in this work we propose OpenDC, a new platform for datacenter simulation. OpenDC includes novel models for emerging cloud-datacenter technologies and applications, such as serverless computing with FaaS deployment and TensorFlow-based machine learning. Our design also focuses on convenience, with a web-based interface for interactive experimentation, support for experiment automation, a library of prefabs for constructing and sharing datacenter designs, and support for diverse input formats and output metrics. We implement, validate, and open-source OpenDC 2.0, a significant redesign and release after a multi-year research and development process. We demonstrate the benefits of OpenDC for the field through a set of representative use-cases: serverless, machine learning, procurement of HPC-as-a-Service infrastructure, educational practices, and reproducibility studies. Overall, OpenDC helps understand how datacenters work, design datacenter infrastructure, and train the next generation of experts.

Our society is digital: industry, science, governance, and individuals depend, often transparently, on the inter-operation of large numbers of distributed computer systems. Although the society takes them almost for granted, these computer ecosystems are not available for all, may not be affordable for long, and raise numerous other research challenges. Inspired by these challenges and by our experience with distributed computer systems, we envision Massivizing Computer Systems, a domain of computer science focusing on understanding, controlling, and evolving successfully such ecosystems. Beyond establishing and growing a body of knowledge about computer ecosystems and their constituent systems, the community in this domain should also aim to educate many about design and engineering for this domain, and all people about its principles. This is a call to the entire community: there is much to discover and achieve.

This demo showcases some of the capabilities of Diligent, a platform for collecting and analysing data from Online Social Networks and is still under development. Diligent relies on microservices and reactive streams, which optimize the time spent (t), to the resources used (r), ratio (t/r). The proposed demo will present: - The vast hardware utilization margins produced by using both blocking and reactive I/O approaches. - The performance gap between using blocking I/O and Reactive I/O clients. Both experiments highlight the added benefits of using reactive approaches in online social network data processing systems.

In the new Digital Economy, massive computer systems, often grouped in datacenters, serve as factories "producing" cloud services with massive consumption. However, to afford cloud services globally, we must address new research challenges in designing, operating, and using modern datacenters. We must also address challenges in educating and training the next generation of datacenter engineers. Addressing such challenges, in this work we present our vision on OpenDC: we envision the exploration of various datacenter concepts and technologies, using existing and new scientific methods, enabling new education practices and topics, and leading to the creation of new software and data artifacts. We present the datacenter concepts and technologies we are currently planning to explore using OpenDC. We identify the scientific methods we want to use, and explain our vision of education practices. We present the architecture and open-source program underlying the OpenDC software, and the format and open-access data we use for datacenter experiments. We conclude with an open invitation for the community to join our effort.