55 research outputs found
December 16th, 2016
The Heidelberg Laureate Forum (HLF) is an annual meeting bringing together the winners of the most prestigious international awards in mathematics (Abel Prize, Fields Medal and Nevanlinna Prize) and computer science (ACM Turing Award) with a select group of highly talented young researchers in computer science and mathematics. PhD students and postdoctoral researchers from all over the world can apply for one of 200 coveted spots to interact with their scientific role models during lectures, discussions and various social events. Over the course of a week, the up and coming scientists will have a unique opportunity to engage in inspiring and motivating conversations with the top researchers in their fields. Since its first meeting in 2013, five PhD students and researchers from the BSC attended the Heidelberg Laureate Forum. In this seminar, Fabrizio Gagliardi, former ACM Europe Council Chair and Senior Strategy Advisor at BSC will introduce the Heidelberg Laureate Forum, and BSC researchers Paul Carpenter and Claudia Rosas will share their experiences in attending HLF. The seminar aims to encourage BSC students and researchers to apply to HLF 2017 in order to take advantage of this unique opportunity
Downlink Coverage and Rate Analysis of Low Earth Orbit Satellite Constellations Using Stochastic Geometry
As low Earth orbit (LEO) satellite communication systems are gaining
increasing popularity, new theoretical methodologies are required to
investigate such networks' performance at large. This is because deterministic
and location-based models that have previously been applied to analyze
satellite systems are typically restricted to support simulations only. In this
paper, we derive analytical expressions for the downlink coverage probability
and average data rate of generic LEO networks, regardless of the actual
satellites' locality and their service area geometry. Our solution stems from
stochastic geometry, which abstracts the generic networks into uniform binomial
point processes. Applying the proposed model, we then study the performance of
the networks as a function of key constellation design parameters. Finally, to
fit the theoretical modeling more precisely to real deterministic
constellations, we introduce the effective number of satellites as a parameter
to compensate for the practical uneven distribution of satellites on different
latitudes. In addition to deriving exact network performance metrics, the study
reveals several guidelines for selecting the design parameters for future
massive LEO constellations, e.g., the number of frequency channels and
altitude.Comment: Accepted for publication in the IEEE Transactions on Communications
in April 202
FairComp: Workshop on Fairness and Robustness in Machine Learning for Ubiquitous Computing
How can we ensure that Ubiquitous Computing (UbiComp) research outcomes are
both ethical and fair? While fairness in machine learning (ML) has gained
traction in recent years, fairness in UbiComp remains unexplored. This workshop
aims to discuss fairness in UbiComp research and its social, technical, and
legal implications. From a social perspective, we will examine the relationship
between fairness and UbiComp research and identify pathways to ensure that
ubiquitous technologies do not cause harm or infringe on individual rights.
From a technical perspective, we will initiate a discussion on data practices
to develop bias mitigation approaches tailored to UbiComp research. From a
legal perspective, we will examine how new policies shape our community's work
and future research. We aim to foster a vibrant community centered around the
topic of responsible UbiComp, while also charting a clear path for future
research endeavours in this field
GANTouch: An Attack-Resilient Framework for Touch-based Continuous Authentication System
Previous studies have shown that commonly studied (vanilla) implementations
of touch-based continuous authentication systems (V-TCAS) are susceptible to
active adversarial attempts. This study presents a novel Generative Adversarial
Network assisted TCAS (G-TCAS) framework and compares it to the V-TCAS under
three active adversarial environments viz. Zero-effort, Population, and
Random-vector. The Zero-effort environment was implemented in two variations
viz. Zero-effort (same-dataset) and Zero-effort (cross-dataset). The first
involved a Zero-effort attack from the same dataset, while the second used
three different datasets. G-TCAS showed more resilience than V-TCAS under the
Population and Random-vector, the more damaging adversarial scenarios than the
Zero-effort. On average, the increase in the false accept rates (FARs) for
V-TCAS was much higher (27.5% and 21.5%) than for G-TCAS (14% and 12.5%) for
Population and Random-vector attacks, respectively. Moreover, we performed a
fairness analysis of TCAS for different genders and found TCAS to be fair
across genders. The findings suggest that we should evaluate TCAS under active
adversarial environments and affirm the usefulness of GANs in the TCAS
pipeline.Comment: 11 pages, 7 figures, 2 tables, 3 algorithms, in IEEE TBIOM 202
Corporate influence and the academic computer science discipline.
Prosopography of a major academic center for computer science
iRegNet: Non-rigid Registration of MRI to Interventional US for Brain-Shift Compensation using Convolutional Neural Networks
Accurate and safe neurosurgical intervention can be affected by intra-operative tissue deformation, known as brain-shift. In this study, we propose an automatic, fast, and accurate deformable method, called iRegNet, for registering pre-operative magnetic resonance images to intra-operative ultrasound volumes to compensate for brain-shift. iRegNet is a robust end-to-end deep learning approach for the non-linear registration of MRI-iUS images in the context of image-guided neurosurgery. Pre-operative MRI (as moving image) and iUS (as fixed image) are first appended to our convolutional neural network, after which a non-rigid transformation field is estimated. The MRI image is then transformed using the output displacement field to the iUS coordinate system. Extensive experiments have been conducted on two multi-location databases, which are the BITE and the RESECT. Quantitatively, iRegNet reduced the mean landmark errors from pre-registration value of (4.18 ± 1.84 and 5.35 ± 4.19 mm) to the lowest value of (1.47 ± 0.61 and 0.84 ± 0.16 mm) for the BITE and RESECT datasets, respectively. Additional qualitative validation of this study was conducted by two expert neurosurgeons through overlaying MRI-iUS pairs before and after the deformable registration. Experimental findings show that our proposed iRegNet is fast and achieves state-of-the-art accuracies outperforming state-of-the-art approaches. Furthermore, the proposed iRegNet can deliver competitive results, even in the case of non-trained images as proof of its generality and can therefore be valuable in intra-operative neurosurgical guidance
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