Abstract
The adoption of energy transition technologies for residential use is accelerated through incentive designs. The structure of such incentives affects technology adoption patterns, that is, the locations where new technologies are installed and used. These spatial adoption patterns influence network expansion costs and provide indication on potential cross-subsidization between population groups. While until today, most programs have been involuntarily favoring households with high-income and above-average educated population groups, incentive designs are currently under review. This paper presents a spatiotemporal technology adoption model that can predict adoption behavior of residential electric vehicle (EV) chargers and photovoltaic (PV) modules up to a predefined time horizon. A set of EV and PV adoption patterns for nine incentive design combinations are compared in order to assess potential synergies that may arise under orchestrated EV and PV adoption. Effects on adoption asymmetries are evaluated using an Information-Theoretic inequality metric. Results for Continental Portugal show that global network expansion costs can be reduced while minimizing technology adoption asymmetries, if specific incentive designs are combined.

Abstract
One common characteristic among current lock-free memory allocators is that they rely on the operating system to manage memory since they lack a lower-level mechanism capable of splitting and coalescing blocks of memory. In this paper, we discuss this problem and we propose a generic scheme for an efficient lock-free best-fit coalescing-capable mechanism that is able of satisfying memory allocation requests with desirable low fragmentation characteristics.

Abstract
We report on the development of numerical module for the HiLight simulation platform based on GPGPU supercomputing to solve a system of coupled fields governed by the Generalized Nonlinear Schrodinger Equation with local and/or nonlocal nonlinearities. This models plays an important role in describing a plethora of different phenomena in various areas of physics. In optics, this model was initially used to describe the propagation of light through local and/or nonlocal systems under the paraxial approximation, but more recently it has been extensively used as a support model to develop optical analogues. However, establishing the relation between the original system and the analogue, as well as, between their model and the actual experimental setup is not an easy task. First and foremost because in most cases the governing equations are nonintegrable, preventing from obtaining analytical solutions and hindering the optimization of the experiments. Alternatively, despite numerical methods not providing exact solutions, they allow to test different experimental scenarios and provide a better insight to what to expect in an actual experiment, while giving access to all the variables of the optical system being simulated. However, the numerical solution of a system of N-coupled Schrodinger fields in systems with two or three spatial dimensions requires massive computation resources, and must employ advanced supercomputing and parallelization techniques, such as GPGPU. This paper focuses on the numerical aspects behind this challenge, describing the structure of our simulation module, its performance and the tests performed.

Abstract
The Sternum is a human bone located in the anterior area of the thoracic cage. It is present in most of the axial cuts provided from the Magnetic Resonance Imaging (MRI) acquisitions. used in the medical field. Detecting the Sternum is relevant as it contains rigid key-points for 3D model reconstructions, assisting in the planning and evaluation of several surgical procedures, and for atlas development by segmenting structures in anatomical proximity. In the absence of applicable approaches for this specific problem. this paper focuses on two distinct automated methods for Sternum segmentation in MRI. The first. relies on K-Means (Clustering) to perform the segmentation, while the second encompasses the closed Minimum Path over the elliptical transformation of Gradient images. A dataset of 14 annotated acquisitions was used for evaluation. The results favored the Gradient approach over Clustering.

Abstract
This paper presents a new, heterogeneous CPU+GPU attacks against lattice-based (post-quantum) cryptosystems based on the Shortest Vector Problem (SVP), a central problem in lattice-based cryptanalysis. To the best of our knowledge, this is the first SVP-attack against lattice-based cryptosystems using CPUs and GPUs simultaneously. We show that Voronoi-cell based CPU+GPU attacks, algorithmically improved in previous work, are suitable for the proposed massively parallel platforms. Results show that 1) heterogeneous platforms are useful in this scenario, as they increment the overall memory available in the system (as GPU's memory can be used effectively), a typical bottleneck for Voronoi-cell algorithms, and we have also been able to increase the performance of the algorithm on such a platform, by successfully using the GPU as a co-processor, 2) this attack can be successfully accelerated using conventional GPUs and 3) we can take advantage of multiple GPUs to attack lattice-based cryptosystems. Experimental results show a speedup up to 7.6x for 2 GPUs hosted by an Intel Xeon E5-2695 v2 CPU (12 cores x2 sockets) using only 1 core and gains in the order of 20% for 2 GPUs hosted by the same machine using all 22 CPU threads (2 are reserved for orchestrating the GPUs), compared to single-CPU execution using the entire 24 threads available.

Abstract
Common natural walking techniques for navigating in virtual environments feature constraints that make it difficult to use those methods in cramped home environments. Indeed, natural walking requires unobstructed and open space, to allow users to roam around without fear of stumbling on obstacles while immersed in a virtual world. In this work, we propose a new virtual locomotion technique, CWIP-AVR, that allows people to take advantage of the available physical space and empowers them to use natural walking to navigate in the virtual world. To inform users about real world hazards our approach uses augmented virtual reality visual indicators. A user evaluation suggests that CWIP-AVR allows people to navigate safely, while switching between locomotion modes flexibly and maintaining a adequate degree of immersion. © 2019 Association for Computing Machinery.