Visual Integration of Data and Model Space in Ensemble Learning

Ensembles of classifier models typically deliver superior performance and can outperform single classifier models given a dataset and classification task at hand. However, the gain in performance comes together with the lack in comprehensibility, posing a challenge to understand how each model affects the classification outputs and where the errors come from. We propose a tight visual integration of the data and the model space for exploring and combining classifier models. We introduce a workflow that builds upon the visual integration and enables the effective exploration of classification outputs and models. We then present a use case in which we start with an ensemble automatically selected by a standard ensemble selection algorithm, and show how we can manipulate models and alternative combinations.Comment: 8 pages, 7 picture

A Quantum Broadcasting Problem in Classical Low Power Signal Processing

We pose a problem called ``broadcasting Holevo-information'': given an unknown state taken from an ensemble, the task is to generate a bipartite state transfering as much Holevo-information to each copy as possible. We argue that upper bounds on the average information over both copies imply lower bounds on the quantum capacity required to send the ensemble without information loss. This is because a channel with zero quantum capacity has a unitary extension transfering at least as much information to its environment as it transfers to the output. For an ensemble being the time orbit of a pure state under a Hamiltonian evolution, we derive such a bound on the required quantum capacity in terms of properties of the input and output energy distribution. Moreover, we discuss relations between the broadcasting problem and entropy power inequalities. The broadcasting problem arises when a signal should be transmitted by a time-invariant device such that the outgoing signal has the same timing information as the incoming signal had. Based on previous results we argue that this establishes a link between quantum information theory and the theory of low power computing because the loss of timing information implies loss of free energy.Comment: 28 pages, late

The Importance of Biophysical and Biochemical Stimuli in Dynamic Skeletal Muscle Models

Classical approaches to engineer skeletal muscle tissue based on current regenerative and surgical procedures still do not meet the desired outcome for patient applications. Besides the evident need to create functional skeletal muscle tissue for the repair of volumetric muscle defects, there is also growing demand for platforms to study muscle-related diseases, such as muscular dystrophies or sarcopenia. Currently, numerous studies exist that have employed a variety of biomaterials, cell types and strategies for maturation of skeletal muscle tissue in 2D and 3D environments. However, researchers are just at the beginning of understanding the impact of different culture settings and their biochemical (growth factors and chemical changes) and biophysical cues (mechanical properties) on myogenesis. With this review we intend to emphasize the need for new in vitro skeletal muscle (disease) models to better recapitulate important structural and functional aspects of muscle development. We highlight the importance of choosing appropriate system components, e.g., cell and biomaterial type, structural and mechanical matrix properties or culture format, and how understanding their interplay will enable researchers to create optimized platforms to investigate myogenesis in healthy and diseased tissue. Thus, we aim to deliver guidelines for experimental designs to allow estimation of the potential influence of the selected skeletal muscle tissue engineering setup on the myogenic outcome prior to their implementation. Moreover, we offer a workflow to facilitate identifying and selecting different analytical tools to demonstrate the successful creation of functional skeletal muscle tissue. Ultimately, a refinement of existing strategies will lead to further progression in understanding important aspects of muscle diseases, muscle aging and muscle regeneration to improve quality of life of patients and enable the establishment of new treatment options

Splenic switch-off as a predictor for coronary adenosine response: validation against 13N-ammonia during co-injection myocardial perfusion imaging on a hybrid PET/CMR scanner

BACKGROUND Inadequate coronary adenosine response is a potential cause for false negative ischemia testing. Recently, the splenic switch-off (SSO) sign has been identified as a promising tool to ascertain the efficacy of adenosine during vasodilator stress cardiovascular magnetic resonance imaging (CMR). We assessed the value of SSO to predict adenosine response, defined as an increase in myocardial blood flow (MBF) during quantitative stress myocardial perfusion 13 N-ammonia positron emission tomography (PET). METHODS We prospectively enrolled 64 patients who underwent simultaneous CMR and PET myocardial perfusion imaging on a hybrid PET/CMR scanner with co-injection of gadolinium based contrast agent (GBCA) and 13N-ammonia during rest and adenosine-induced stress. A myocardial flow reserve (MFR) of  > 1.5 or ischemia as assessed by PET were defined as markers for adequate coronary adenosine response. The presence or absence of SSO was visually assessed. The stress-to-rest intensity ratio (SIR) was calculated as the ratio of stress over rest peak signal intensity for splenic tissue. Additionally, the spleen-to-myocardium ratio, defined as the relative change of spleen to myocardial signal, was calculated for stress (SMR$_{stress}$) and rest. RESULTS Sixty-one (95%) patients were coronary adenosine responders, but SSO was absent in 18 (28%) patients. SIR and SMR$_{stress}$ were significantly lower in patients with SSO (SIR: 0.56 ± 0.13 vs. 0.93 ± 0.23; p < 0.001 and SMR$_{stress}$: 1.09 ± 0.47 vs. 1.68 ± 0.62; p < 0.001). Mean hyperemic and rest MBF were 2.12 ± 0.68 ml/min/g and 0.78 ± 0.26 ml/min/g, respectively. MFR was significantly higher in patients with vs. patients without presence of SSO (3.07 ± 1.03 vs. 2.48 ± 0.96; p = 0.038), but there was only a weak inverse correlation between SMR$_{stress}$ and MFR (R = -0.378; p = 0.02) as well as between SIR and MFR (R = -0.356; p = 0.004). CONCLUSIONS The presence of SSO implies adequate coronary adenosine-induced MBF response. Its absence, however, is not a reliable indicator for failed adenosine-induced coronary vasodilatation