Comment on "Probing the equilibrium dynamics of colloidal hard spheres above the mode-coupling glass transition"

In the Letter [PRL 102, 085703 (2009)] Brambilla, et al. claimed to observe activated dynamics in colloidal hard spheres above the critical packing fraction of mode coupling theory (MCT). By performing microscopic MCT calculations, we show that polydispersity in their system shifts the critical packing fraction above the value determined by van Megen et al. for less polydisperse samples, and that the data agree with theory except for, possibly, the highest packing fraction.Comment: Comment in print in Phys. Rev. Lett.; for accompanying reply see arXiv Brambilla et al. (Monday 18.10.2010

Similarity based hierarchical clustering of physiological parameters for the identification of health states - a feasibility study

This paper introduces a new unsupervised method for the clustering of physiological data into health states based on their similarity. We propose an iterative hierarchical clustering approach that combines health states according to a similarity constraint to new arbitrary health states. We applied method to experimental data in which the physical strain of subjects was systematically varied. We derived health states based on parameters extracted from ECG data. The occurrence of health states shows a high temporal correlation to the experimental phases of the physical exercise. We compared our method to other clustering algorithms and found a significantly higher accuracy with respect to the identification of health states.Comment: 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC

ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking

Physical intuition is pivotal for intelligent agents to perform complex tasks. In this paper we investigate the passive acquisition of an intuitive understanding of physical principles as well as the active utilisation of this intuition in the context of generalised object stacking. To this end, we provide: a simulation-based dataset featuring 20,000 stack configurations composed of a variety of elementary geometric primitives richly annotated regarding semantics and structural stability. We train visual classifiers for binary stability prediction on the ShapeStacks data and scrutinise their learned physical intuition. Due to the richness of the training data our approach also generalises favourably to real-world scenarios achieving state-of-the-art stability prediction on a publicly available benchmark of block towers. We then leverage the physical intuition learned by our model to actively construct stable stacks and observe the emergence of an intuitive notion of stackability - an inherent object affordance - induced by the active stacking task. Our approach performs well even in challenging conditions where it considerably exceeds the stack height observed during training or in cases where initially unstable structures must be stabilised via counterbalancing.Comment: revised version to appear at ECCV 201

List Defective Colorings: Distributed Algorithms and Applications

The distributed coloring problem is at the core of the area of distributed graph algorithms and it is a problem that has seen tremendous progress over the last few years. Much of the remarkable recent progress on deterministic distributed coloring algorithms is based on two main tools: a) defective colorings in which every node of a given color can have a limited number of neighbors of the same color and b) list coloring, a natural generalization of the standard coloring problem that naturally appears when colorings are computed in different stages and one has to extend a previously computed partial coloring to a full coloring. In this paper, we introduce \emph{list defective colorings}, which can be seen as a generalization of these two coloring variants. Essentially, in a list defective coloring instance, each node $v$ is given a list of colors $x_{v,1},\dots,x_{v,p}$ together with a list of defects $d_{v,1},\dots,d_{v,p}$ such that if $v$ is colored with color $x_{v, i}$, it is allowed to have at most $d_{v, i}$ neighbors with color $x_{v, i}$. We highlight the important role of list defective colorings by showing that faster list defective coloring algorithms would directly lead to faster deterministic $(\Delta+1)$-coloring algorithms in the LOCAL model. Further, we extend a recent distributed list coloring algorithm by Maus and Tonoyan [DISC '20]. Slightly simplified, we show that if for each node $v$ it holds that $\sum_{i=1}^p \big(d_{v,i}+1)^2 > \mathrm{deg}_G^2(v)\cdot polylog\Delta$ then this list defective coloring instance can be solved in a communication-efficient way in only $O(\log\Delta)$ communication rounds. This leads to the first deterministic $(\Delta+1)$-coloring algorithm in the standard CONGEST model with a time complexity of $O(\sqrt{\Delta}\cdot polylog \Delta+\log^* n)$, matching the best time complexity in the LOCAL model up to a $polylog\Delta$ factor

Modeling WLAN Received Signal Strengths Using Gaussian Process Regression on the Sodindoorloc Dataset

While any wireless technology can be used for indoor localization purposes, WLANhas the advantage of having a huge existing infrastructure. A radio map that matches specific locations to received signal strength is needed, to enable most of these indoor localization methods. To create these radio maps, with enough detail to achieve sufficient localization accuracy, is expensive and time consuming. Therefore, methods to interpolate and extrapolate more detailed maps from sparse radio maps are being developed. One recent approach is to use Gaussian process regression. Even though some papers already studied Gaussian process regression, most studied only the basic model with zero mean and squared exponential kernel. In addition, when the model fit was evaluated in more detail, the experimental area was of limited complexity. Hence, this thesis evaluates the fit of Gaussian process regression, in a more complex indoor environment, based on adequate model metrics and analysis of the plots of the predicted mean and standard deviation functions. As a conclusion, the most suitable model is presented, as well as the reasoning why it was chosen

Distress and resilience of healthcare professionals during the COVID-19 pandemic (DARVID): study protocol for a mixed-methods research project

Introduction: The unprecedented COVID-19 pandemic has exposed healthcare professionals to exceptional situations that can lead to increased anxiety (i.e., infection anxiety, perceived vulnerability), traumatic stress and depression. We will investigate the development of these psychological disturbances in healthcare professionals at the treatment front line and second line during the COVID-19 pandemic over a 12-month period in different countries. Additionally, we will explore whether personal resilience factors and a work-related sense of coherence influence the development of mental health problems of healthcare professionals. Methods and analysis: We plan to carry out a sequential qualitative–quantitative mixed-methods-design study. The quantitative phase consists of a longitudinal online survey based on six validated questionnaires, to be completed at three points in time. A qualitative analysis will follow at the end of the pandemic, to comprise at least nine semi-structured interviews. The a-priori sample size for the survey will be a minimum of 160 participants, which we will extend to 400, to compensate for drop-out. Recruitment into the study will be through personal invitations and the ‘snowballing’ sampling technique. Hierarchical linear regression combined with qualitative data analysis will facilitate greater understanding of any associations between resilience and mental health issues in healthcare professionals during pandemics. Ethics and dissemination: The study participants will provide their electronic informed consent. All recorded data will be stored on a secured research server at the study site, which will only be accessible to the investigators. The Bern Cantonal Ethics Committee has waived the need for ethical approval (Req-2020-00355; 1 April, 2020). There are no ethical, legal or security issues regarding the data collection, processing, storage and dissemination in this project. Trial registration: ISRCTN13694948 (date of registration: 1 April, 2020